Merging imagery and models for river current prediction
Blain, Cheryl Ann; Linzell, Robert S.; McKay, Paul
2011-06-01
To meet the challenge of operating in river environments with denied access and to improve the riverine intelligence available to the warfighter, advanced high resolution river circulation models are combined with remote sensing feature extraction algorithms to produce a predictive capability for currents and water levels in rivers where a priori knowledge of the river environment is limited. A River Simulation Tool (RST) is developed to facilitate the rapid configuration of a river model. River geometry is extracted from the automated processing of available imagery while minimal user input is collected to complete the parameter and forcing specifications necessary to configure a river model. Contingencies within the RST accommodate missing data such as a lack of water depth information and allow for ensemble computations. Successful application of the RST to river environments is demonstrated for the Snohomish River, WA. Modeled currents compare favorably to in-situ currents reinforcing the value of the developed approach.
Tsunami-HySEA model validation for tsunami current predictions
Macías, Jorge; Castro, Manuel J.; González-Vida, José Manuel; Ortega, Sergio
2016-04-01
Model ability to compute and predict tsunami flow velocities is of importance in risk assessment and hazard mitigation. Substantial damage can be produced by high velocity flows, particularly in harbors and bays, even when the wave height is small. Besides, an accurate simulation of tsunami flow velocities and accelerations is fundamental for advancing in the study of tsunami sediment transport. These considerations made the National Tsunami Hazard Mitigation Program (NTHMP) proposing a benchmark exercise focussed on modeling and simulating tsunami currents. Until recently, few direct measurements of tsunami velocities were available to compare and to validate model results. After Tohoku 2011 many current meters measurement were made, mainly in harbors and channels. In this work we present a part of the contribution made by the EDANYA group from the University of Malaga to the NTHMP workshop organized at Portland (USA), 9-10 of February 2015. We have selected three out of the five proposed benchmark problems. Two of them consist in real observed data from the Tohoku 2011 event, one at Hilo Habour (Hawaii) and the other at Tauranga Bay (New Zealand). The third one consists in laboratory experimental data for the inundation of Seaside City in Oregon. Acknowledgements: This research has been partially supported by the Junta de Andalucía research project TESELA (P11-RNM7069) and the Spanish Government Research project DAIFLUID (MTM2012-38383-C02-01) and Universidad de Málaga, Campus de Excelencia Andalucía TECH. The GPU and multi-GPU computations were performed at the Unit of Numerical Methods (UNM) of the Research Support Central Services (SCAI) of the University of Malaga.
Modelling Monsoons: Understanding and Predicting Current and Future Behaviour
Energy Technology Data Exchange (ETDEWEB)
Turner, A; Sperber, K R; Slingo, J M; Meehl, G A; Mechoso, C R; Kimoto, M; Giannini, A
2008-09-16
including, but not limited to, the Mei-Yu/Baiu sudden onset and withdrawal, low-level jet orientation and variability, and orographic forced rainfall. Under anthropogenic climate change many competing factors complicate making robust projections of monsoon changes. Without aerosol effects, increased land-sea temperature contrast suggests strengthened monsoon circulation due to climate change. However, increased aerosol emissions will reflect more solar radiation back to space, which may temper or even reduce the strength of monsoon circulations compared to the present day. A more comprehensive assessment is needed of the impact of black carbon aerosols, which may modulate that of other anthropogenic greenhouse gases. Precipitation may behave independently from the circulation under warming conditions in which an increased atmospheric moisture loading, based purely on thermodynamic considerations, could result in increased monsoon rainfall under climate change. The challenge to improve model parameterizations and include more complex processes and feedbacks pushes computing resources to their limit, thus requiring continuous upgrades of computational infrastructure to ensure progress in understanding and predicting the current and future behavior of monsoons.
The Tsushima Warm Current from a High Resolution Ocean Prediction Model, HYCOM
Directory of Open Access Journals (Sweden)
Seongbong Seo
2013-06-01
Full Text Available This study investigates the characteristic of the Tsushima Warm Current from an assimilated high resolution global ocean prediction model, 1/12o Global HYbrid Coordiate Ocean Model (HYCOM. The model results were verified through a comparison with current measurements obtained by acoustic Doppler current profiler (ADCP mounted on the passenger ferryboat between Busan, Korea, and Hakata, Japan. The annual mean transport of the Tsushima Warm Current was 2.56 Sverdrup (Sv (1 Sv = 106 m3s−1, which is similar to those from previous studies (Takikawa et al. 1999; Teague et al. 2002. The volume transport time series of the Tsushima Warm Current from HYCOM correlates to a high degree with that from the ADCP observation (the correlation coefficient between the two is 0.82. The spatiotemporal structures of the currents as well as temperature and salinity from HYCOM are comparable to the observed ones.
Sembiring, L.; Van Ormondt, M.; Van Dongeren, A. R.; Roelvink, J. A.
2017-07-01
Rip currents are one of the most dangerous coastal hazards for swimmers. In order to minimize the risk, a coastal operational-process based-model system can be utilized in order to provide forecast of nearshore waves and currents that may endanger beach goers. In this paper, an operational model for rip current prediction by utilizing nearshore bathymetry obtained from video image technique is demonstrated. For the nearshore scale model, XBeach1 is used with which tidal currents, wave induced currents (including the effect of the wave groups) can be simulated simultaneously. Up-to-date bathymetry will be obtained using video images technique, cBathy 2. The system will be tested for the Egmond aan Zee beach, located in the northern part of the Dutch coastline. This paper will test the applicability of bathymetry obtained from video technique to be used as input for the numerical modelling system by comparing simulation results using surveyed bathymetry and model results using video bathymetry. Results show that the video technique is able to produce bathymetry converging towards the ground truth observations. This bathymetry validation will be followed by an example of operational forecasting type of simulation on predicting rip currents. Rip currents flow fields simulated over measured and modeled bathymetries are compared in order to assess the performance of the proposed forecast system.
Jin, N.; Yang, F.; Shang, S. Y.; Tao, T.; Liu, J. S.
2016-08-01
According to the limitations of the LVRT technology of traditional photovoltaic inverter existed, this paper proposes a low voltage ride through (LVRT) control method based on model current predictive control (MCPC). This method can effectively improve the photovoltaic inverter output characteristics and response speed. The MCPC method of photovoltaic grid-connected inverter designed, the sum of the absolute value of the predictive current and the given current error is adopted as the cost function with the model predictive control method. According to the MCPC, the optimal space voltage vector is selected. Photovoltaic inverter has achieved automatically switches of priority active or reactive power control of two control modes according to the different operating states, which effectively improve the inverter capability of LVRT. The simulation and experimental results proves that the proposed method is correct and effective.
Harmonic current prediction by impedance modeling of grid-tied inverters
DEFF Research Database (Denmark)
Pereira, Heverton A.; Freijedo, Francisco D.; Silva, M. M.
2017-01-01
impedance models when used in harmonic integration studies. It is aimed to estimate the harmonic current contribution as a function of the background harmonic voltages components. Time domain simulations based on detailed and average models are compared with the impedance model developed in frequency domain....... In grids with harmonic voltages, impedance models can predict the current distortion for all active power injection scenarios. Furthermore, measurements in a 1.4 MW PV plant connected in a distributed grid are used to validate the simulation based on impedance models during different power injections...... and harmonic voltage profiles. Results reinforce that impedance models can represent with relatively accuracy the harmonic current emitted by the PV plants at the point of common coupling (PCC). Lastly, a stress test is performed to show how a variation in the harmonic voltage phase angle impacts the PV plant...
Indahlastari, Aprinda; Chauhan, Munish; Schwartz, Benjamin; Sadleir, Rosalind J.
2016-12-01
Objective. In this study, we determined efficient head model sizes relative to predicted current densities in transcranial direct current stimulation (tDCS). Approach. Efficiency measures were defined based on a finite element (FE) simulations performed using nine human head models derived from a single MRI data set, having extents varying from 60%-100% of the original axial range. Eleven tissue types, including anisotropic white matter, and three electrode montages (T7-T8, F3-right supraorbital, Cz-Oz) were used in the models. Main results. Reducing head volume extent from 100% to 60%, that is, varying the model’s axial range from between the apex and C3 vertebra to one encompassing only apex to the superior cerebellum, was found to decrease the total modeling time by up to half. Differences between current density predictions in each model were quantified by using a relative difference measure (RDM). Our simulation results showed that {RDM} was the least affected (a maximum of 10% error) for head volumes modeled from the apex to the base of the skull (60%-75% volume). Significance. This finding suggested that the bone could act as a bioelectricity boundary and thus performing FE simulations of tDCS on the human head with models extending beyond the inferior skull may not be necessary in most cases to obtain reasonable precision in current density results.
Li, T.; Hasegawa, T.; Yin, X.; Zhu, Y.; Boote, K.; Adam, M.; Bregaglio, S.; Buis, S.; Confalonieri, R.; Fumoto, T.; Gaydon, D.; Marcaida III, M.; Nakagawa, H.; Oriol, P.; Ruane, A.C.; Ruget, F.; Singh, B.; Singh, U.; Tang, L.; Yoshida, H.; Zhang, Z.; Bouman, B.
2015-01-01
Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluat
Model Predictive Control with Integral Action for Current Density Profile Tracking in NSTX-U
Ilhan, Z. O.; Wehner, W. P.; Schuster, E.; Boyer, M. D.
2016-10-01
Active control of the toroidal current density profile may play a critical role in non-inductively sustained long-pulse, high-beta scenarios in a spherical torus (ST) configuration, which is among the missions of the NSTX-U facility. In this work, a previously developed physics-based control-oriented model is embedded in a feedback control scheme based on a model predictive control (MPC) strategy to track a desired current density profile evolution specified indirectly by a desired rotational transform profile. An integrator is embedded into the standard MPC formulation to reject various modeling uncertainties and external disturbances. Neutral beam powers, electron density, and total plasma current are used as actuators. The proposed MPC strategy incorporates various state and actuator constraints directly into the control design process by solving a constrained optimization problem in real-time to determine the optimal actuator requests. The effectiveness of the proposed controller in regulating the current density profile in NSTX-U is demonstrated in closed-loop nonlinear simulations. Supported by the US DOE under DE-AC02-09CH11466.
Model Predictive Current Control for High-Power Grid-Connected Converters with Output LCL Filter
DEFF Research Database (Denmark)
Delpino, Hernan Anres Miranda; Teodorescu, Remus; Rodriguez, Pedro
2009-01-01
A model predictive control strategy for a highpower, grid connected 3-level neutral clamped point converter is presented. Power losses constraints set a limit on commutation losses so reduced switching frequency is required, thus producing low frequency current harmonics. To reduce these harmonics...... an LCL filter is used. The proposed control strategy allows control of the active and reactive power fed into the grid, reduce the switching frequency within acceptable operational margins and keep balance of the DC-link capacitor voltages while avoiding excitation of the filter resonance frequencies....
Directory of Open Access Journals (Sweden)
T. Aly Saandy
2015-08-01
Full Text Available Abstract This article presents to an analytical calculation methodology of the Steinmetz coefficient applied to the prediction of Eddy current loss in a single-phase transformer. Based on the electrical circuit theory the active power consumed by the core is expressed analytically in function of the electrical parameters as resistivity and the geometrical dimensions of the core. The proposed modeling approach is established with the duality parallel series. The required coefficient is identified from the empirical Steinmetz data based on the experimented active power expression. To verify the relevance of the model validations both by simulations with two in two different frequencies and measurements were carried out. The obtained results are in good agreement with the theoretical approach and the practical results.
Directory of Open Access Journals (Sweden)
Martin R Langer
Full Text Available Species-range expansions are a predicted and realized consequence of global climate change. Climate warming and the poleward widening of the tropical belt have induced range shifts in a variety of marine and terrestrial species. Range expansions may have broad implications on native biota and ecosystem functioning as shifting species may perturb recipient communities. Larger symbiont-bearing foraminifera constitute ubiquitous and prominent components of shallow water ecosystems, and range shifts of these important protists are likely to trigger changes in ecosystem functioning. We have used historical and newly acquired occurrence records to compute current range shifts of Amphistegina spp., a larger symbiont-bearing foraminifera, along the eastern coastline of Africa and compare them to analogous range shifts currently observed in the Mediterranean Sea. The study provides new evidence that amphisteginid foraminifera are rapidly progressing southwestward, closely approaching Port Edward (South Africa at 31°S. To project future species distributions, we applied a species distribution model (SDM based on ecological niche constraints of current distribution ranges. Our model indicates that further warming is likely to cause a continued range extension, and predicts dispersal along nearly the entire southeastern coast of Africa. The average rates of amphisteginid range shift were computed between 8 and 2.7 km year(-1, and are projected to lead to a total southward range expansion of 267 km, or 2.4° latitude, in the year 2100. Our results corroborate findings from the fossil record that some larger symbiont-bearing foraminifera cope well with rising water temperatures and are beneficiaries of global climate change.
Langer, Martin R; Weinmann, Anna E; Lötters, Stefan; Bernhard, Joan M; Rödder, Dennis
2013-01-01
Species-range expansions are a predicted and realized consequence of global climate change. Climate warming and the poleward widening of the tropical belt have induced range shifts in a variety of marine and terrestrial species. Range expansions may have broad implications on native biota and ecosystem functioning as shifting species may perturb recipient communities. Larger symbiont-bearing foraminifera constitute ubiquitous and prominent components of shallow water ecosystems, and range shifts of these important protists are likely to trigger changes in ecosystem functioning. We have used historical and newly acquired occurrence records to compute current range shifts of Amphistegina spp., a larger symbiont-bearing foraminifera, along the eastern coastline of Africa and compare them to analogous range shifts currently observed in the Mediterranean Sea. The study provides new evidence that amphisteginid foraminifera are rapidly progressing southwestward, closely approaching Port Edward (South Africa) at 31°S. To project future species distributions, we applied a species distribution model (SDM) based on ecological niche constraints of current distribution ranges. Our model indicates that further warming is likely to cause a continued range extension, and predicts dispersal along nearly the entire southeastern coast of Africa. The average rates of amphisteginid range shift were computed between 8 and 2.7 km year(-1), and are projected to lead to a total southward range expansion of 267 km, or 2.4° latitude, in the year 2100. Our results corroborate findings from the fossil record that some larger symbiont-bearing foraminifera cope well with rising water temperatures and are beneficiaries of global climate change.
Datta, Abhishek; Baker, Julie M; Bikson, Marom; Fridriksson, Julius
2011-07-01
Although numerous published reports have demonstrated the beneficial effects of transcranial direct-current stimulation (tDCS) on task performance, fundamental questions remain regarding the optimal electrode configuration on the scalp. Moreover, it is expected that lesioned brain tissue will influence current flow and should therefore be considered (and perhaps leveraged) in the design of individualized tDCS therapies for stroke. The current report demonstrates how different electrode configurations influence the flow of electrical current through brain tissue in a patient who responded positively to a tDCS treatment targeting aphasia. The patient, a 60-year-old man, sustained a left hemisphere ischemic stroke (lesion size = 87.42 mL) 64 months before his participation. In this study, we present results from the first high-resolution (1 mm(3)) model of tDCS in a brain with considerable stroke-related damage; the model was individualized for the patient who received anodal tDCS to his left frontal cortex with the reference cathode electrode placed on his right shoulder. We modeled the resulting brain current flow and also considered three additional reference electrode positions: right mastoid, right orbitofrontal cortex, and a "mirror" configuration with the anode over the undamaged right cortex. Our results demonstrate the profound effect of lesioned tissue on resulting current flow and the ability to modulate current pattern through the brain, including perilesional regions, through electrode montage design. The complexity of brain current flow modulation by detailed normal and pathologic anatomy suggest: (1) That computational models are critical for the rational interpretation and design of individualized tDCS stroke-therapy; and (2) These models must accurately reproduce head anatomy as shown here.
Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon; Gaydon, Donald; Marcaida, Manuel, III; Nakagawa, Hiroshi; Oriol, Philippe; Ruane, Alex C.; Ruget, Francoise; Singh, Balwinder; Singh, Upendra; Tang, Liang; Tao, Fulu; Wilkens, Paul; Yoshida, Hiroe; Zhang, Zhao; Bouman, Bas
2014-01-01
Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.
Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review.
Directory of Open Access Journals (Sweden)
Eugene Y H Tang
Full Text Available Accurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance.Our previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science. Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC or c-statistic were included.In total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model.There is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular
A coarse-grained DNA model for the prediction of current signals in DNA translocation experiments
Weik, Florian; Kesselheim, Stefan; Holm, Christian
2016-11-01
We present an implicit solvent coarse-grained double-stranded DNA (dsDNA) model confined to an infinite cylindrical pore that reproduces the experimentally observed current modulations of a KaCl solution at various concentrations. Our model extends previous coarse-grained and mean-field approaches by incorporating a position dependent friction term on the ions, which Kesselheim et al. [Phys. Rev. Lett. 112, 018101 (2014)] identified as an essential ingredient to correctly reproduce the experimental data of Smeets et al. [Nano Lett. 6, 89 (2006)]. Our approach reduces the computational effort by orders of magnitude compared with all-atom simulations and serves as a promising starting point for modeling the entire translocation process of dsDNA. We achieve a consistent description of the system's electrokinetics by using explicitly parameterized ions, a friction term between the DNA beads and the ions, and a lattice-Boltzmann model for the solvent.
A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction
Energy Technology Data Exchange (ETDEWEB)
Hemphill, Geralyn M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-09-27
Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type has become a necessity in cancer research. A major challenge in cancer management is the classification of patients into appropriate risk groups for better treatment and follow-up. Such risk assessment is critically important in order to optimize the patient’s health and the use of medical resources, as well as to avoid cancer recurrence. This paper focuses on the application of machine learning methods for predicting the likelihood of a recurrence of cancer. It is not meant to be an extensive review of the literature on the subject of machine learning techniques for cancer recurrence modeling. Other recent papers have performed such a review, and I will rely heavily on the results and outcomes from these papers. The electronic databases that were used for this review include PubMed, Google, and Google Scholar. Query terms used include “cancer recurrence modeling”, “cancer recurrence and machine learning”, “cancer recurrence modeling and machine learning”, and “machine learning for cancer recurrence and prediction”. The most recent and most applicable papers to the topic of this review have been included in the references. It also includes a list of modeling and classification methods to predict cancer recurrence.
Normand, Frédéric; Lauri, Pierre-Éric
2012-03-01
Accurate and reliable predictive models are necessary to estimate nondestructively key variables for plant growth studies such as leaf area and leaf, stem, and total biomass. Predictive models are lacking at the current-year branch scale despite the importance of this scale in plant science. We calibrated allometric models to estimate leaf area and stem and branch (leaves + stem) mass of current-year branches, i.e., branches several months old studied at the end of the vegetative growth season, of four mango cultivars on the basis of their basal cross-sectional area. The effects of year, site, and cultivar were tested. Models were validated with independent data and prediction accuracy was evaluated with the appropriate statistics. Models revealed a positive allometry between dependent and independent variables, whose y-intercept but not the slope, was affected by the cultivar. The effects of year and site were negligible. For each branch characteristic, cultivar-specific models were more accurate than common models built with pooled data from the four cultivars. Prediction quality was satisfactory but with data dispersion around the models, particularly for large values. Leaf area and stem and branch mass of mango current-year branches could be satisfactorily estimated on the basis of branch basal cross-sectional area with cultivar-specific allometric models. The results suggested that, in addition to the heteroscedastic behavior of the variables studied, model accuracy was probably related to the functional plasticity of branches in relation to the light environment and/or to the number of growth units composing the branches.
Mathewson, Paul D; Moyer-Horner, Lucas; Beever, Erik A; Briscoe, Natalie J; Kearney, Michael; Yahn, Jeremiah M; Porter, Warren P
2017-03-01
How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect
Mathewson, Paul; Moyer-Horner, Lucas; Beever, Erik; Briscoe, Natalie; Kearney, Michael T; Yahn, Jeremiah; Porter, Warren P.
2017-01-01
How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect
Directory of Open Access Journals (Sweden)
Hayley Patricia Ellis
2015-11-01
Full Text Available Glioblastoma (GB is the most common malignant primary brain tumour, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumour recurrence. Technological advances are continually improving our understanding of the disease and in particular our knowledge of clonal evolution, intratumour heterogeneity and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modelling (including neural networks, and strategies such as multiple-sampling during tumour resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB.
Bronuzzi, J.; Mapelli, A.; Sallese, J. M.
2016-12-01
A silicon wafer bonding technique has been recently proposed for the fabrication of monolithic silicon radiation detectors. This new process would enable direct bonding of a read-out electronic chip wafer on a highly resistive silicon substrate wafer. Therefore, monolithic silicon detectors could be fabricated in this way which would allow the free choice of electronic chips and high resistive silicon bulk, even from different providers. Moreover, a monolithic detector with a high resistive bulk would also be available. Electrical properties of the bonded interface are then critical for this application. Indeed, mobile charges generated by radiation inside the bonded bulk are expected to transit through the interface to be collected by the read-out electronics. In order to characterize this interface, the concept of Transient Current Technique (TCT) has been explored by means of numerical simulations combined with a physics based analytical model. In this work, the analytical model giving insight into the physics behind the TCT dependence upon interface traps is validated using both TCAD simulations and experimental measurements.
Easton, Dexter M
2005-10-01
Gompertz kinetics is a simple, realistic, accurate, and computationally parsimonious alternative for prediction of macroscopic changes in Na+ conductance during voltage clamp. Conductance delay and time course depend on initial amplitudes and decay rates of surrogates for the macroscopic gating currents. The model is tested by the fit to published data of other authors. The proposed physical basis for the model is that membrane potential perturbation triggers motion of charged "gating" components of the axon membrane at rapid (activating) and at slow (inactivating) rates. The resulting distortion increases and more slowly diminishes the probability that conduction channels will be open.
Directory of Open Access Journals (Sweden)
Toralf eNeuling
2012-09-01
Full Text Available Transcranial direct current stimulation (tDCS has been applied in numerous scientiﬁc studies over the past decade. However, the possibility to apply tDCS in therapy of neuropsychiatric disorders is still debated. While transcranial magnetic stimulation (TMS has been approved for treatment of major depression in the United States by the Food and Drug Administration (FDA, tDCS is not as widely accepted. One of the criticisms against tDCS is the lack of spatial speciﬁcity. Focality is limited by the electrode size (35 cm2 are commonly used and the bipolar arrangement. However, a current ﬂow through the head directly from anode to cathode is an outdated view. Finite element (FE models have recently been used to predict the exact current ﬂow during tDCS. These simulations have demonstrated that the current ﬂow depends on tissue shape and conductivity. Toface the challenge to predict the location, magnitude and direction of the current ﬂow induced by tDCS and transcranial alternating current stimulation (tACS, we used a reﬁned realistic FE modeling approach. With respect to the literature on clinical tDCS and tACS, we analyzed two common setups for the location of the stimulation electrodes which target the frontal lobe and the occipital lobe, respectively. We compared lateral and medial electrode conﬁguration with regard to theirusability. We were able to demonstrate that the lateral conﬁgurations yielded more focused stimulation areas as well as higher current intensities in the target areas. The high resolution of our simulation allows one to combine the modeled current ﬂow with the knowledge of neuronal orientation to predict the consequences of tDCS and tACS. Our results not only oﬀer a basis for a deeper understanding of the stimulation sites currently in use for clinical applications but also oﬀer a better interpretation of observed eﬀects.
Slater, Hannah; Michael, Edwin
2012-01-01
Modelling the spatial distributions of human parasite species is crucial to understanding the environmental determinants of infection as well as for guiding the planning of control programmes. Here, we use ecological niche modelling to map the current potential distribution of the macroparasitic disease, lymphatic filariasis (LF), in Africa, and to estimate how future changes in climate and population could affect its spread and burden across the continent. We used 508 community-specific infection presence data collated from the published literature in conjunction with five predictive environmental/climatic and demographic variables, and a maximum entropy niche modelling method to construct the first ecological niche maps describing potential distribution and burden of LF in Africa. We also ran the best-fit model against climate projections made by the HADCM3 and CCCMA models for 2050 under A2a and B2a scenarios to simulate the likely distribution of LF under future climate and population changes. We predict a broad geographic distribution of LF in Africa extending from the west to the east across the middle region of the continent, with high probabilities of occurrence in the Western Africa compared to large areas of medium probability interspersed with smaller areas of high probability in Central and Eastern Africa and in Madagascar. We uncovered complex relationships between predictor ecological niche variables and the probability of LF occurrence. We show for the first time that predicted climate change and population growth will expand both the range and risk of LF infection (and ultimately disease) in an endemic region. We estimate that populations at risk to LF may range from 543 and 804 million currently, and that this could rise to between 1.65 to 1.86 billion in the future depending on the climate scenario used and thresholds applied to signify infection presence.
Directory of Open Access Journals (Sweden)
Hannah Slater
Full Text Available Modelling the spatial distributions of human parasite species is crucial to understanding the environmental determinants of infection as well as for guiding the planning of control programmes. Here, we use ecological niche modelling to map the current potential distribution of the macroparasitic disease, lymphatic filariasis (LF, in Africa, and to estimate how future changes in climate and population could affect its spread and burden across the continent. We used 508 community-specific infection presence data collated from the published literature in conjunction with five predictive environmental/climatic and demographic variables, and a maximum entropy niche modelling method to construct the first ecological niche maps describing potential distribution and burden of LF in Africa. We also ran the best-fit model against climate projections made by the HADCM3 and CCCMA models for 2050 under A2a and B2a scenarios to simulate the likely distribution of LF under future climate and population changes. We predict a broad geographic distribution of LF in Africa extending from the west to the east across the middle region of the continent, with high probabilities of occurrence in the Western Africa compared to large areas of medium probability interspersed with smaller areas of high probability in Central and Eastern Africa and in Madagascar. We uncovered complex relationships between predictor ecological niche variables and the probability of LF occurrence. We show for the first time that predicted climate change and population growth will expand both the range and risk of LF infection (and ultimately disease in an endemic region. We estimate that populations at risk to LF may range from 543 and 804 million currently, and that this could rise to between 1.65 to 1.86 billion in the future depending on the climate scenario used and thresholds applied to signify infection presence.
Lázaro y De Mercado, Pablo; Blasco Bravo, Antonio Javier; Lázaro y De Mercado, Ignacio; Castañeda, Santos; López Robledillo, Juan Carlos
2013-01-01
To: 1) describe the distribution of the public sector rheumatologists; 2) identify variables on which the workload in Rheumatology depends; and 3) build a predictive model on the need of rheumatologists for the next 10 years, in the Community of Madrid (CM). The information was obtained through structured questionnaires sent to all services/units of Rheumatology of public hospitals in the CM. The population figures, current and forecasted, were obtained from the National Statistics Institute. A predictive model was built based on information about the current and foreseeable supply, current and foreseeable demand, and the assumptions and criteria used to match supply with demand. The underlying uncertainty in the model was assessed by sensitivity analysis. In the CM in 2011 there were 150 staff rheumatologists and 49 residents in 27 centers, which is equivalent to one rheumatologist for every 33,280 inhabitants in the general population, and one for every 4,996 inhabitants over 65 years. To keep the level of assistance of 2011 in 2021 in the general population, it would be necessary to train more residents or hire more rheumatologists in scenarios of demand higher than 15%. However, to keep the level of assistance in the population over 65 years of age it would be necessary to train more residents or hire more specialists even without increased demand. The model developed may be very useful for planning, with the CM policy makers, the needs of human resources in Rheumatology in the coming years. Copyright © 2012 Elsevier España, S.L. All rights reserved.
Wave Current Interactions and Wave-blocking Predictions Using NHWAVE Model
2013-03-01
release; distribution is unlimited 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) Wave blocking in river inlets is examined using the...14. SUBJECT TERMS wave blocking, wave-current interactions, SWASH, NHWAVE 15. NUMBER OF PAGES 61 16. PRICE CODE 17. SECURITY CLASSIFICATION...Renold’s Averaged Navier-Stokes Equations VOF Volume of Fluid MAC Marker and Cell SPH Smoothed Partical Hydrodynamics SWASH Simulating Waves
Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems
DEFF Research Database (Denmark)
Preindl, Matthias; Schaltz, Erik
2010-01-01
behaviour. It compensates the load torque influence on the speed control setting a feed forward torque value, i.e. current reference value. The benefits are twice. The speed controller reaches immediately the speed reference value avoiding offsets which must be compensated by the weak integrator. Moreover......, a better response to load torque variations which are detected and compensated leading to small speed variations is obtained....
Directory of Open Access Journals (Sweden)
S. W. H. Cowley
2008-12-01
Full Text Available We develop two related models of magnetosphere-ionosphere coupling in the jovian system by combining previous models defined at ionospheric heights with magnetospheric magnetic models that allow system parameters to be extended appropriately into the magnetosphere. The key feature of the combined models is thus that they allow direct connection to be made between observations in the magnetosphere, particularly of the azimuthal field produced by the magnetosphere-ionosphere coupling currents and the plasma angular velocity, and the auroral response in the ionosphere. The two models are intended to reflect typical steady-state sub-corotation conditions in the jovian magnetosphere, and transient super-corotation produced by sudden major solar wind-induced compressions, respectively. The key simplification of the models is that of axi-symmetry of the field, flow, and currents about the magnetic axis, limiting their validity to radial distances within ~30 R_{J} of the planet, though the magnetic axis is appropriately tilted relative to the planetary spin axis and rotates with the planet. The first exploration of the jovian polar magnetosphere is planned to be undertaken in 2016–2017 during the NASA New Frontiers Juno mission, with observations of the polar field, plasma, and UV emissions as a major goal. Evaluation of the models along Juno planning orbits thus produces predictive results that may aid in science mission planning. It is shown in particular that the low-altitude near-periapsis polar passes will generally occur underneath the corresponding auroral acceleration regions, thus allowing brief examination of the auroral primaries over intervals of ~1–3 min for the main oval and ~10 s for narrower polar arc structures, while the "lagging" field deflections produced by the auroral current systems on these passes will be ~0.1°, associated with azimuthal fields above the ionosphere of a few hundred nT.
An Evaluation of Four Current Models to Predict the Creep-Fatigue Interaction in Rene 95
1979-06-01
life of AISI 304 stainless steel, whose life was degraded by waveshapes in which more time was spent under a tensile strain hold than under a...Air Force Flight Dynamics Laboratory, Wright-Patterson AFB, OH, 1970), pp. 301 -311. 13. H. L. Bernstein, "An Evaluation of Four Models for the Creep
Gonzalez Vida, J. M., Sr.; Macias Sanchez, J.; Castro, M. J.; Ortega, S.
2015-12-01
Model ability to compute and predict tsunami flow velocities is of importance in risk assessment and hazard mitigation. Substantial damage can be produced by high velocity flows, particularly in harbors and bays, even when the wave height is small. Besides, an accurate simulation of tsunami flow velocities and accelerations is fundamental for advancing in the study of tsunami sediment transport. These considerations made the National Tsunami Hazard Mitigation Program (NTHMP) proposing a benchmark exercise focused on modeling and simulating tsunami currents. Until recently, few direct measurements of tsunami velocities were available to compare and to validate model results. After Tohoku 2011 many current meters measurement were made, mainly in harbors and channels. In this work we present a part of the contribution made by the EDANYA group from the University of Malaga to the NTHMP workshop organized at Portland (USA), 9-10 of February 2015. We have selected three out of the five proposed benchmark problems. Two of them consist in real observed data from the Tohoku 2011 event, one at Hilo Habour (Hawaii) and the other at Tauranga Bay (New Zealand). The third one consists in laboratory experimental data for the inundation of Seaside City in Oregon. For this model validation the Tsunami-HySEA model, developed by EDANYA group, was used. The overall conclusion that we could extract from this validation exercise was that the Tsunami-HySEA model performed well in all benchmark problems proposed. The greater spatial variability in tsunami velocity than wave height makes it more difficult its precise numerical representation. The larger variability in velocities is likely a result of the behaviour of the flow as it is channelized and as it flows around bathymetric highs and structures. In the other hand wave height do not respond as strongly to chanelized flow as current velocity.
Current theoretical models fail to predict the topological complexity of the human genome.
Arsuaga, Javier; Jayasinghe, Reyka G; Scharein, Robert G; Segal, Mark R; Stolz, Robert H; Vazquez, Mariel
2015-01-01
Understanding the folding of the human genome is a key challenge of modern structural biology. The emergence of chromatin conformation capture assays (e.g., Hi-C) has revolutionized chromosome biology and provided new insights into the three dimensional structure of the genome. The experimental data are highly complex and need to be analyzed with quantitative tools. It has been argued that the data obtained from Hi-C assays are consistent with a fractal organization of the genome. A key characteristic of the fractal globule is the lack of topological complexity (knotting or inter-linking). However, the absence of topological complexity contradicts results from polymer physics showing that the entanglement of long linear polymers in a confined volume increases rapidly with the length and with decreasing volume. In vivo and in vitro assays support this claim in some biological systems. We simulate knotted lattice polygons confined inside a sphere and demonstrate that their contact frequencies agree with the human Hi-C data. We conclude that the topological complexity of the human genome cannot be inferred from current Hi-C data.
Directory of Open Access Journals (Sweden)
Izaskun Garrido
2016-08-01
Full Text Available Plasma stability is one of the obstacles in the path to the successful operation of fusion devices. Numerical control-oriented codes as it is the case of the widely accepted RZIp may be used within Tokamak simulations. The novelty of this article relies in the hierarchical development of a dynamic control loop. It is based on a current profile Model Predictive Control (MPC algorithm within a multiloop structure, where a MPC is developed at each step so as to improve the Proportional Integral Derivative (PID global scheme. The inner control loop is composed of a PID-based controller that acts over the Multiple Input Multiple Output (MIMO system resulting from the RZIp plasma model of the Tokamak à Configuration Variable (TCV. The coefficients of this PID controller are initially tuned using an eigenmode reduction over the passive structure model. The control action corresponding to the state of interest is then optimized in the outer MPC loop. For the sake of comparison, both the traditionally used PID global controller as well as the multiloop enhanced MPC are applied to the same TCV shot. The results show that the proposed control algorithm presents a superior performance over the conventional PID algorithm in terms of convergence. Furthermore, this enhanced MPC algorithm contributes to extend the discharge length and to overcome the limited power availability restrictions that hinder the performance of advanced tokamaks.
Cestari, Andrea
2013-01-01
Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.
Directory of Open Access Journals (Sweden)
Tucker James R
2009-06-01
Full Text Available Abstract Background Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance. Results Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948 and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras. Conclusion Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources
Holt, Ashley C; Salkeld, Daniel J; Fritz, Curtis L; Tucker, James R; Gong, Peng
2009-06-28
Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance. Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras. Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent model results were significantly
2012-12-01
FFACTR” which is a part of the Engineers Refractive Index Prediction System (EREPS) Tactical Decision Aid ( TDA ) developed by what is now SPAWARS SSC San...currently used in JSAF is “FFACTR” which is a part of the Engineers Refractive Index Prediction System (EREPS) Tactical Decision Aid ( TDA ) developed by what...Decision Aid ( TDA ) developed by what is now SPAWARS SSC San Diego in 1988. This model is no longer supported by SPAWARS or any other group and has been
Ren, Da-Bing; Yang, Zhao-Hui; Liang, Yi-Zeng; Ding, Qiong; Chen, Chen; Ouyang, Mei-Lan
2013-08-02
Selection of a suitable solvent system is the first and foremost step for a successful counter-current chromatography (CCC) separation. In this paper, a thermodynamic model, nonrandom two-liquid segment activity coefficient model (NRTL-SAC) which uses four types of conceptual segments to describe the effective surface interactions for each solvent and solute molecule, was employed to correlate and predict the partition coefficients (K) of a given compound in a specific solvent system. Then a suitable solvent system was selected according to the predicted partition coefficients. Three solvent system families, heptane/methanol/water, heptane/ethyl acetate/methanol/water (Arizona) and hexane/ethyl acetate/methanol/water, and several solutes were selected to investigate the effectiveness of the NRTL-SAC model for predicting the partition coefficients. Comparison between experimental results and predicted results showed that the NRTL-SAC model is of potential for estimating the K value of a given compound. Also a practical separation case on magnolol and honokiol suggests the NRTL-SAC model is effective, reliable and practical for the purpose of predicting partition coefficients and selecting a suitable solvent system for CCC separation. Copyright © 2013. Published by Elsevier B.V.
Angst, Sebastian; Engelke, Lukas; Winterer, Markus; Wolf, Dietrich E.
2017-06-01
Densification of (semi-)conducting particle agglomerates with the help of an electrical current is much faster and more energy efficient than traditional thermal sintering or powder compression. Therefore, this method becomes more and more common among experimentalists, engineers, and in industry. The mechanisms at work at the particle scale are highly complex because of the mutual feedback between current and pore structure. This paper extends previous modelling approaches in order to study mixtures of particles of two different materials. In addition to the delivery of Joule heat throughout the sample, especially in current bottlenecks, thermoelectric effects must be taken into account. They lead to segregation or spatial correlations in the particle arrangement. Various model extensions are possible and will be discussed.
Feng, Jinian
2017-01-01
Climate change will markedly impact biology, population ecology, and spatial distribution patterns of insect pests because of the influence of future greenhouse effects on insect development and population dynamics. Onion maggot, Delia antiqua, larvae are subterranean pests with limited mobility, that directly feed on bulbs of Allium sp. and render them completely unmarketable. Modeling the spatial distribution of such a widespread and damaging pest is crucial not only to identify current potentially suitable climactic areas but also to predict where the pest is likely to spread in the future so that appropriate monitoring and management programs can be developed. In this study, Maximum Entropy Niche Modeling was used to estimate the current potential distribution of D. antiqua and to predict the future distribution of this species in 2030, 2050, 2070 and 2080 by using emission scenario (A2) with 7 climate variables. The results of this study show that currently highly suitable habitats for D.antiqua occur throughout most of East Asia, some regions of North America, Western Europe, and Western Asian countries near the Caspian sea and Black Sea. In the future, we predict an even broader distribution of this pest spread more extensively throughout Asia, North America and Europe, particularly in most of European countries, Central regions of United States and much of East Asia. Our present day and future predictions can enhance strategic planning of agricultural organizations by identifying regions that will need to develop Integrated Pest Management programs to manage the onion maggot. The distribution forecasts will also help governments to optimize economic investments in management programs for this pest by identifying regions that are or will become less suitable for current and future infestations. PMID:28158259
Directory of Open Access Journals (Sweden)
Kyoungwon Jung
2016-08-01
Full Text Available Background: Injury severity scoring systems that quantify and predict trauma outcomes have not been established in Korea. This study was designed to determine the best system for use in the Korean trauma population. Methods: We collected and analyzed the data from trauma patients admitted to our institution from January 2010 to December 2014. Injury Severity Score (ISS, Revised Trauma Score (RTS, and Trauma and Injury Severity Score (TRISS were calculated based on the data from the enrolled patients. Area under the receiver operating characteristic (ROC curve (AUC for the prediction ability of each scoring system was obtained, and a pairwise comparison of ROC curves was performed. Additionally, the cut-off values were estimated to predict mortality, and the corresponding accuracy, positive predictive value, and negative predictive value were obtained. Results: A total of 7,120 trauma patients (6,668 blunt and 452 penetrating injuries were enrolled in this study. The AUCs of ISS, RTS, and TRISS were 0.866, 0.894, and 0.942, respectively, and the prediction ability of the TRISS was significantly better than the others (p < 0.001, respectively. The cut-off value of the TRISS was 0.9082, with a sensitivity of 81.9% and specificity of 92.0%; mortality was predicted with an accuracy of 91.2%; its positive predictive value was the highest at 46.8%. Conclusions: The results of our study were based on the data from one institution and suggest that the TRISS is the best prediction model of trauma outcomes in the current Korean population. Further study is needed with more data from multiple centers in Korea.
Numerical modeling of transformer inrush currents
Cardelli, E.; Faba, A.
2014-02-01
This paper presents an application of a vector hysteresis model to the prediction of the inrush current due the arbitrary initial excitation of a transformer after a fault. The approach proposed seems promising in order to predict the transient overshoot in current and the optimal time to close the circuit after the fault.
Numerical modeling of transformer inrush currents
Energy Technology Data Exchange (ETDEWEB)
Cardelli, E. [Department of Industrial Engineering, University of Perugia, I-06125 Perugia (Italy); Center for Electric and Magnetic Applied Research (Italy); Faba, A., E-mail: faba@unipg.it [Department of Industrial Engineering, University of Perugia, I-06125 Perugia (Italy); Center for Electric and Magnetic Applied Research (Italy)
2014-02-15
This paper presents an application of a vector hysteresis model to the prediction of the inrush current due the arbitrary initial excitation of a transformer after a fault. The approach proposed seems promising in order to predict the transient overshoot in current and the optimal time to close the circuit after the fault.
1987-08-01
and Rocky Geyer. Paul Dragos was a very patient tutor on the computer. Vincent Lyne had good insight into the model when I was first learning how it...Schwab for being mostly not sane; to David Rudd for the cartoons; to Paul Thogersen for the key to his house in Boston, no advance notice needed; to...can be increased by biological adhesion of sediment grains (Nowell, Jumars, and Eckman , 1981; Grant, Boyer and Sanford, 1982) or electrochemical
Levy, R.; Mcginness, H.
1976-01-01
Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.
Estimates of current debris from flux models
Energy Technology Data Exchange (ETDEWEB)
Canavan, G.H.
1997-01-01
Flux models that balance accuracy and simplicity are used to predict the growth of space debris to the present. Known and projected launch rates, decay models, and numerical integrations are used to predict distributions that closely resemble the current catalog-particularly in the regions containing most of the debris.
PREDICT : model for prediction of survival in localized prostate cancer
Kerkmeijer, Linda G W; Monninkhof, Evelyn M.; van Oort, Inge M.; van der Poel, Henk G.; de Meerleer, Gert; van Vulpen, Marco
2016-01-01
Purpose: Current models for prediction of prostate cancer-specific survival do not incorporate all present-day interventions. In the present study, a pre-treatment prediction model for patients with localized prostate cancer was developed.Methods: From 1989 to 2008, 3383 patients were treated with I
MODEL PREDICTIVE CONTROL FUNDAMENTALS
African Journals Online (AJOL)
2012-07-02
Jul 2, 2012 ... paper, we will present an introduction to the theory and application of MPC with Matlab codes written to ... model predictive control, linear systems, discrete-time systems, ... and then compute very rapidly for this open-loop con-.
Climate-induced boreal forest change: Predictions versus current observations
Soja, Amber J.; Tchebakova, Nadezda M.; French, Nancy H. F.; Flannigan, Michael D.; Shugart, Herman H.; Stocks, Brian J.; Sukhinin, Anatoly I.; Parfenova, E. I.; Chapin, F. Stuart; Stackhouse, Paul W.
2007-04-01
For about three decades, there have been many predictions of the potential ecological response in boreal regions to the currently warmer conditions. In essence, a widespread, naturally occurring experiment has been conducted over time. In this paper, we describe previously modeled predictions of ecological change in boreal Alaska, Canada and Russia, and then we investigate potential evidence of current climate-induced change. For instance, ecological models have suggested that warming will induce the northern and upslope migration of the treeline and an alteration in the current mosaic structure of boreal forests. We present evidence of the migration of keystone ecosystems in the upland and lowland treeline of mountainous regions across southern Siberia. Ecological models have also predicted a moisture-stress-related dieback in white spruce trees in Alaska, and current investigations show that as temperatures increase, white spruce tree growth is declining. Additionally, it was suggested that increases in infestation and wildfire disturbance would be catalysts that precipitate the alteration of the current mosaic forest composition. In Siberia, 7 of the last 9 yr have resulted in extreme fire seasons, and extreme fire years have also been more frequent in both Alaska and Canada. In addition, Alaska has experienced extreme and geographically expansive multi-year outbreaks of the spruce beetle, which had been previously limited by the cold, moist environment. We suggest that there is substantial evidence throughout the circumboreal region to conclude that the biosphere within the boreal terrestrial environment has already responded to the transient effects of climate change. Additionally, temperature increases and warming-induced change are progressing faster than had been predicted in some regions, suggesting a potential non-linear rapid response to changes in climate, as opposed to the predicted slow linear response to climate change.
Climate-Induced Boreal Forest Change: Predictions versus Current Observations
Soja, Amber J.; Tchebakova, Nadezda M.; French, Nancy H. F.; Flannigan, Michael D.; Shugart, Herman H.; Stocks, Brian J.; Sukhinin, Anatoly I.; Parfenova, E. I.; Chapin, F. Stuart, III; Stackhouse, Paul W., Jr.
2007-01-01
For about three decades, there have been many predictions of the potential ecological response in boreal regions to the currently warmer conditions. In essence, a widespread, naturally occurring experiment has been conducted over time. In this paper, we describe previously modeled predictions of ecological change in boreal Alaska, Canada and Russia, and then we investigate potential evidence of current climate-induced change. For instance, ecological models have suggested that warming will induce the northern and upslope migration of the treeline and an alteration in the current mosaic structure of boreal forests. We present evidence of the migration of keystone ecosystems in the upland and lowland treeline of mountainous regions across southern Siberia. Ecological models have also predicted a moisture-stress-related dieback in white spruce trees in Alaska, and current investigations show that as temperatures increase, white spruce tree growth is declining. Additionally, it was suggested that increases in infestation and wildfire disturbance would be catalysts that precipitate the alteration of the current mosaic forest composition. In Siberia, five of the last seven years have resulted in extreme fire seasons, and extreme fire years have also been more frequent in both Alaska and Canada. In addition, Alaska has experienced extreme and geographically expansive multi-year outbreaks of the spruce beetle, which had been previously limited by the cold, moist environment. We suggest that there is substantial evidence throughout the circumboreal region to conclude that the biosphere within the boreal terrestrial environment has already responded to the transient effects of climate change. Additionally, temperature increases and warming-induced change are progressing faster than had been predicted in some regions, suggesting a potential non-linear rapid response to changes in climate, as opposed to the predicted slow linear response to climate change.
Nominal model predictive control
Grüne, Lars
2013-01-01
5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...
Nominal Model Predictive Control
Grüne, Lars
2014-01-01
5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...
Numerical prediction of magnetising inrush current in transformers
Ling, P. C. Y.; Basak, A.
1989-08-01
A computational technique of prediction of magnetising inrush current at various switching conditions is described. An improved modelling of B/H curve of electrical steel is presented. The effects of varying switching angles on the voltage wave, the energising circuit impedance and the remanent flux density are discussed. The effects of other parameters, such as the winding space factor and energising winding length, which have not been previously taken into consideration are also presented in this paper.
Candidate Prediction Models and Methods
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik
2005-01-01
This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....
Predictive Surface Complexation Modeling
Energy Technology Data Exchange (ETDEWEB)
Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences
2016-11-29
Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO_{2} and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.
Directory of Open Access Journals (Sweden)
Michael eKattan
2014-10-01
Full Text Available Purpose: To develop a nomogram predictive of current bone scan positivity in patients receiving androgen-deprivation therapy (ADT for advanced prostate cancer; to augment clinical judgment and highlight patients in need of additional imaging investigations.Materials and Methods: A retrospective chart review of bone scan records (conventional 99mTc-scintigraphy of 1,293 patients who received ADT at the Memorial Sloan-Kettering Cancer Center from 2000 to 2011. Multivariable logistic regression analysis was used to identify variables suitable for inclusion in the nomogram. The probability of current bone scan positivity was determined using these variables and the predictive accuracy of the nomogram was quantified by concordance index.Results: In total, 2,681 bone scan records were analyzed and 636 patients had a positive result. Overall, the median pre-scan prostate-specific antigen (PSA level was 2.4 ng/ml; median PSA doubling time (PSADT was 5.8 months. At the time of a positive scan, median PSA level was 8.2 ng/ml; 53% of patients had PSA <10 ng/ml; median PSADT was 4.0 months. Five variables were included in the nomogram: number of previous negative bone scans after initiating ADT, PSA level, Gleason grade sum, and history of radical prostatectomy and radiotherapy. A concordance index value of 0.721 was calculated for the nomogram. This was a retrospective study based on limited data in patients treated in a large cancer centre who underwent conventional 99mTc bone scans, which themselves have inherent limitations. Conclusions: This is the first nomogram to predict current bone scan positivity in ADT-treated prostate cancer patients, providing high predictive accuracy.
Research on Predicting Drive Current of Shipborne Satcom Antenna
Directory of Open Access Journals (Sweden)
Kong Jinping
2015-01-01
Full Text Available Predicting the effect of antenna wind load on servo system precisely is meaningful to ensure the safety of satcom antenna on operation, which can avoid overload operation. In this paper, the computational fluid dynamics is used to proceed numerical computation on the pressure distribution of the reflector and torque of drive shaft under different wind speed, windward angle and angle of pitch of the antenna. The simulation model is built under MATLAB/Simulink simulation environment, and the drive current of the antenna servo system is analyzed under wind load effect and ship swing. Then, a method of predicting drive current of antenna servo system according to the wind speed, wind direction and attitude of the antenna is concluded. And this method is verified by simulation at last.
Candidate Prediction Models and Methods
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik
2005-01-01
This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...
Balanis, Constantine A.; Polka, Lesley A.; Polycarpou, Anastasis C.
1993-01-01
Part 1 of this report continues the investigation, initiated in previous reports, of scattering from rectangular plates coated with lossy dielectrics. The hard polarization coefficients given in the last report are incorporated into a model, which includes second- and third-order diffractions, for the coated plate. Computed results from this model are examined and compared to measured data. A breakdown of the contribution of each of the higher-order terms to the total radar cross section (RCS) is given. The effectiveness of the uniform theory of diffraction (UTD) model in accounting for the coating effect is investigated by examining a Physical Optics (PO) model which incorporates the equivalent surface impedance approximation used in the UTD model. The PO, UTD, and experimental results are compared. Part 2 of this report presents a RCS model, based on PO and the Method of Equivalent Currents (MEC), for a trihedral corner reflector. PO is used to account for the reflected fields, while MEC is used for the diffracted fields. Single, double, and triple reflections and first-order diffractions are included in the model. A detailed derivation of the E(sub theta)-polarization, monostatic RCS is included. Computed results are compared with finite-difference time-domain (FDTD) results for validation. The PO/MEC model of this report compares very well with the FDTD model, and it is a much faster model in terms of computational speed.
Melanoma risk prediction models
Directory of Open Access Journals (Sweden)
Nikolić Jelena
2014-01-01
Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were
Wang, Tiening; Takayasu, Makoto; Bordini, Bernardo
2014-01-01
Superconducting Nb3Sn Powder-In-Tube (PIT) strands could be used for the superconducting magnets of the next generation Large Hadron Collider. The strands are cabled into the typical flat Rutherford cable configuration. During the assembly of a magnet and its operation the strands experience not only longitudinal but also transverse load due to the pre-compression applied during the assembly and the Lorentz load felt when the magnets are energized. To properly design the magnets and guarantee their safe operation, mechanical load effects on the strand superconducting properties are studied extensively; particularly, many scaling laws based on tensile load experiments have been established to predict the critical current dependence on strain. However, the dependence of the superconducting properties on transverse load has not been extensively studied so far. One of the reasons is that transverse loading experiments are difficult to conduct due to the small diameter of the strand (about 1 mm) and the data curre...
Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current
Directory of Open Access Journals (Sweden)
Wenbiao Li
2016-04-01
Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.
Numerical weather prediction model tuning via ensemble prediction system
Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.
2011-12-01
This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.
A comparison of current prediction imaging programs
Smith, J. Dempsey; Thomas, Paul M.; Proffit, William R.
2013-01-01
To investigate perceived differences in the ability of current software to simulate the actual outcome of orthognathic surgery, we chose 10 difficult test cases with vertical discrepancies and “retreated” them using the actual surgical changes. Five programs—Dentofacial Planner Plus, Dolphin Imaging, Orthoplan, Quick Ceph Image, and Vistadent—were evaluated, by using both the default result and a refined result created with each program’s enhancement tools. Three panels (orthodontists, oral-maxillofacial surgeons, and laypersons) judged the default images and the retouched simulations by ranking the simulations in side-by-side comparisons and by rating each simulation relative to the actual outcome on a 6-point scale. For the default and retouched images, Dentofacial Planner Plus was judged the best default simulation 79% and 59% of the time, respectively, and its default images received the best (lowest) mean score (2.46) on the 6-point scale. It also scored best (2.26) when the retouched images were compared, but the scores for Dolphin Imaging (2.83) and Quick Ceph (3.03) improved. Retouching had little impact on the scores for the other programs. Although the results show differences in simulation ability, selecting a software package depends on many factors. Performance and ease of use, cost, compatibility, and other features such as image and practice management tools are all important considerations. Users concerned with operating system compatibility and practice management integration might want to consider Dolphin Imaging and Quick Ceph, the programs comprising the second tier. PMID:15127020
Worley, Rachel Rogers; Yang, Xiaoxia; Fisher, Jeffrey
2017-09-01
Manufacturing of perfluorooctanoic acid (PFOA), a synthetic chemical with a long half-life in humans, peaked between 1970 and 2002, and has since diminished. In the United States, PFOA is detected in the blood of >99% of people tested, but serum concentrations have decreased since 1999. Much is known about exposure to PFOA in drinking water; however, the impact of non-drinking water PFOA exposure on serum PFOA concentrations is not well characterized. The objective of this research is to apply physiologically based pharmacokinetic (PBPK) modeling and Monte Carlo analysis to evaluate the impact of historic non-drinking water PFOA exposure on serum PFOA concentrations. In vitro to in vivo extrapolation was utilized to inform descriptions of PFOA transport in the kidney. Monte Carlo simulations were incorporated to evaluate factors that account for the large inter-individual variability of serum PFOA concentrations measured in individuals from North Alabama in 2010 and 2016, and the Mid-Ohio River Valley between 2005 and 2008. Predicted serum PFOA concentrations were within two-fold of experimental data. With incorporation of Monte Carlo simulations, the model successfully tracked the large variability of serum PFOA concentrations measured in populations from the Mid-Ohio River Valley. Simulation of exposure in a population of 45 adults from North Alabama successfully predicted 98% of individual serum PFOA concentrations measured in 2010 and 2016, respectively, when non-drinking water ingestion of PFOA exposure was included. Variation in serum PFOA concentrations may be due to inter-individual variability in the disposition of PFOA and potentially elevated historical non-drinking water exposures. Published by Elsevier Inc.
Paiement, Jean-François; Grandvalet, Yves; Bengio, Samy
2008-01-01
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce generative models for melodies. We decompose melodic modeling into two subtasks. We first propose a rhythm model based on the distributions of distances between subsequences. Then, we define a generative model for melodies given chords and rhythms based on modeling sequences of Narmour featur...
Extended Range Prediction of Indian Summer Monsoon: Current status
Sahai, A. K.; Abhilash, S.; Borah, N.; Joseph, S.; Chattopadhyay, R.; S, S.; Rajeevan, M.; Mandal, R.; Dey, A.
2014-12-01
The main focus of this study is to develop forecast consensus in the extended range prediction (ERP) of monsoon Intraseasonal oscillations using a suit of different variants of Climate Forecast system (CFS) model. In this CFS based Grand MME prediction system (CGMME), the ensemble members are generated by perturbing the initial condition and using different configurations of CFSv2. This is to address the role of different physical mechanisms known to have control on the error growth in the ERP in the 15-20 day time scale. The final formulation of CGMME is based on 21 ensembles of the standalone Global Forecast System (GFS) forced with bias corrected forecasted SST from CFS, 11 low resolution CFST126 and 11 high resolution CFST382. Thus, we develop the multi-model consensus forecast for the ERP of Indian summer monsoon (ISM) using a suite of different variants of CFS model. This coordinated international effort lead towards the development of specific tailor made regional forecast products over Indian region. Skill of deterministic and probabilistic categorical rainfall forecast as well the verification of large-scale low frequency monsoon intraseasonal oscillations has been carried out using hindcast from 2001-2012 during the monsoon season in which all models are initialized at every five days starting from 16May to 28 September. The skill of deterministic forecast from CGMME is better than the best participating single model ensemble configuration (SME). The CGMME approach is believed to quantify the uncertainty in both initial conditions and model formulation. Main improvement is attained in probabilistic forecast which is because of an increase in the ensemble spread, thereby reducing the error due to over-confident ensembles in a single model configuration. For probabilistic forecast, three tercile ranges are determined by ranking method based on the percentage of ensemble members from all the participating models falls in those three categories. CGMME further
Zephyr - the prediction models
DEFF Research Database (Denmark)
Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg
2001-01-01
This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Dani...
Balanis, Constantine A.; Polka, Lesley A.; Polycarpou, Anastasis C.
Formulations for scattering from the coated plate and the coated dihedral corner reflector are included. A coated plate model based upon the Uniform Theory of Diffraction (UTD) for impedance wedges was presented in the last report. In order to resolve inaccuracies and discontinuities in the predicted patterns using the UTD-based model, an improved model that uses more accurate diffraction coefficients is presented. A Physical Optics (PO) model for the coated dihedral corner reflector is presented as an intermediary step in developing a high-frequency model for this structure. The PO model is based upon the reflection coefficients for a metal-backed lossy material. Preliminary PO results for the dihedral corner reflector suggest that, in addition to being much faster computationally, this model may be more accurate than existing moment method (MM) models. An improved Physical Optics (PO)/Equivalent Currents model for modeling the Radar Cross Section (RCS) of both square and triangular, perfectly conducting, trihedral corner reflectors is presented. The new model uses the PO approximation at each reflection for the first- and second-order reflection terms. For the third-order reflection terms, a Geometrical Optics (GO) approximation is used for the first reflection; and PO approximations are used for the remaining reflections. The previously reported model used GO for all reflections except the terminating reflection. Using PO for most of the reflections results in a computationally slower model because many integrations must be performed numerically, but the advantage is that the predicted RCS using the new model is much more accurate. Comparisons between the two PO models, Finite-Difference Time-Domain (FDTD) and experimental data are presented for validation of the new model.
Confidence scores for prediction models
DEFF Research Database (Denmark)
Gerds, Thomas Alexander; van de Wiel, MA
2011-01-01
modelling strategy is applied to different training sets. For each modelling strategy we estimate a confidence score based on the same repeated bootstraps. A new decomposition of the expected Brier score is obtained, as well as the estimates of population average confidence scores. The latter can be used...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...
Modelling, controlling, predicting blackouts
Wang, Chengwei; Baptista, Murilo S
2016-01-01
The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids, and another one for smart grids. The control strategie...
Melanoma Risk Prediction Models
Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Prediction models in complex terrain
DEFF Research Database (Denmark)
Marti, I.; Nielsen, Torben Skov; Madsen, Henrik
2001-01-01
are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production......The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... and HIRLAM predictions. The statistical models belong to the class of conditional parametric models. The models are estimated using local polynomial regression, but the estimation method is here extended to be adaptive in order to allow for slow changes in the system e.g. caused by the annual variations...
National Oceanic and Atmospheric Administration, Department of Commerce — Current meter data were collected using moored current meter casts in the Northeast Pacific Ocean from September 26, 1984 to July 16, 1985. Data were submitted by...
Predictability of surface currents and fronts off the Mississippi Delta
Energy Technology Data Exchange (ETDEWEB)
Walker, N.D.; Rouse, L.J.; Wiseman, W.J. [Louisiana State Univ., Baton Rouge, LA (United States)
2001-07-01
The dynamic coastal region of the lower Mississippi River was examined under varying conditions of wind, river discharge and circulation patterns of the Gulf of Mexico. Nearly 7,000 deep-sea merchant vessels enter the port complex each year and the area boasts the highest concentration of offshore drilling rigs, rendering the Mississippi delta and adjacent coastal areas vulnerable to risk from oil spills. Satellite imagery has been useful in tracking movements of the Mississippi river plume as recognizable turbidity and temperature fronts are formed where river waters encounter ambient shelf waters. Oil spill modelers often base their predictions of oil movement on the surface wind field and surface currents, but past studies have indicated that this can be overly simplistic in regions affected by river flow because river fronts have significant control over the movement of oil in opposition to prevailing winds. Frontal zones, such as those found where river waters meet oceanic waters, are characterized by strong convergence of surface flow. These frontal zones can provide large and efficient traps or natural booms for spilled oil. In an effort to facilitate cleanup operations, this study made use of the National Ocean and Atmospheric Administration (NOAA) AVHRR satellite imagery of temperature and reflectance to study front locations and their variability in space and time. The main objectives were to quantify surface temperature structure and locations of fronts throughout the year using satellite image data, to map the structure of the Mississippi sediment plume and to assess the forcing factors responsible for its variability over space and time. The final objective was to use in-situ measurements of surface currents together with satellite image data to better understand surface flow in this region of strong and variable currents. It was concluded that the main factors controlling circulation in the Mississippi River outflow region are river discharge and
Prediction models in complex terrain
DEFF Research Database (Denmark)
Marti, I.; Nielsen, Torben Skov; Madsen, Henrik
2001-01-01
The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...
Predictive Modeling of the CDRA 4BMS
Coker, Robert; Knox, James
2016-01-01
Fully predictive models of the Four Bed Molecular Sieve of the Carbon Dioxide Removal Assembly on the International Space Station are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.
Directory of Open Access Journals (Sweden)
E. Joetzjer
2014-08-01
Full Text Available While a majority of Global Climate Models project dryer and longer dry seasons over the Amazon under higher CO2 levels, large uncertainties surround the response of vegetation to persistent droughts in both present-day and future climates. We propose a detailed evaluation of the ability of the ISBACC Land Surface Model to capture drought effects on both water and carbon budgets, comparing fluxes and stocks at two recent ThroughFall Exclusion (TFE experiments performed in the Amazon. We also explore the model sensitivity to different Water Stress Function (WSF and to an idealized increase in CO2 concentration and/or temperature. In spite of a reasonable soil moisture simulation, ISBACC struggles to correctly simulate the vegetation response to TFE whose amplitude and timing is highly sensitive to the WSF. Under higher CO2 concentration, the increased Water Use Efficiency (WUE mitigates the ISBACC's sensitivity to drought. While one of the proposed WSF formulation improves the response of most ISBACC fluxes, except respiration, a parameterization of drought-induced tree mortality is missing for an accurate estimate of the vegetation response. Also, a better mechanistic understanding of the forest responses to drought under a warmer climate and higher CO2 concentration is clearly needed.
Predictive models of forest dynamics.
Purves, Drew; Pacala, Stephen
2008-06-13
Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.
Current filamentation model for the Weibel/Filamentation instabilities
Ryu, Chang-Mo; Huynh, Cong Tuan; Kim, Chul Min
2016-10-01
A current filamentaion model for a nonrelativistic plasma with e +/e- beam has been presented together with PIC simulations, which can explain the mangetic field enhancement during the Weibel/ Filamentation instabilities. This filament model assumes the Hammer-Rostoker equilibrium. In addition, this model predicts preferential acceleration/deceleration for electron-ion plasmas depending on the injected beam to be e +/e-.
An optimal current observer for predictive current controlled buck DC-DC converters.
Min, Run; Chen, Chen; Zhang, Xiaodong; Zou, Xuecheng; Tong, Qiaoling; Zhang, Qiao
2014-05-19
In digital current mode controlled DC-DC converters, conventional current sensors might not provide isolation at a minimized price, power loss and size. Therefore, a current observer which can be realized based on the digital circuit itself, is a possible substitute. However, the observed current may diverge due to the parasitic resistors and the forward conduction voltage of the diode. Moreover, the divergence of the observed current will cause steady state errors in the output voltage. In this paper, an optimal current observer is proposed. It achieves the highest observation accuracy by compensating for all the known parasitic parameters. By employing the optimal current observer-based predictive current controller, a buck converter is implemented. The converter has a convergently and accurately observed inductor current, and shows preferable transient response than the conventional voltage mode controlled converter. Besides, costs, power loss and size are minimized since the strategy requires no additional hardware for current sensing. The effectiveness of the proposed optimal current observer is demonstrated experimentally.
Towards an Airframe Noise Prediction Methodology: Survey of Current Approaches
Farassat, Fereidoun; Casper, Jay H.
2006-01-01
In this paper, we present a critical survey of the current airframe noise (AFN) prediction methodologies. Four methodologies are recognized. These are the fully analytic method, CFD combined with the acoustic analogy, the semi-empirical method and fully numerical method. It is argued that for the immediate need of the aircraft industry, the semi-empirical method based on recent high quality acoustic database is the best available method. The method based on CFD and the Ffowcs William- Hawkings (FW-H) equation with penetrable data surface (FW-Hpds ) has advanced considerably and much experience has been gained in its use. However, more research is needed in the near future particularly in the area of turbulence simulation. The fully numerical method will take longer to reach maturity. Based on the current trends, it is predicted that this method will eventually develop into the method of choice. Both the turbulence simulation and propagation methods need to develop more for this method to become useful. Nonetheless, the authors propose that the method based on a combination of numerical and analytical techniques, e.g., CFD combined with FW-H equation, should also be worked on. In this effort, the current symbolic algebra software will allow more analytical approaches to be incorporated into AFN prediction methods.
Institute of Scientific and Technical Information of China (English)
阮学云; 李志远; 魏浩征; 黄莹
2011-01-01
换流变压器作为高压直流换流站主要噪声源,其噪声预测精度和控制方法的选择将直接影响换流站整体噪声预测水平及治理效果.通过对换流变压器噪声的产生机理、噪声频谱及常见治理方案等方面进行系统研究,重点推出了换流变压器噪声控制方法BOX-IN技术,并就BOX-IN装置的噪声预测模型进行了简化和对比验证.通过对降噪量进行现场测试,结果表明,BOX-IN装置降噪量达到20 dB(A)左右,与理论计算值近似,为进一步提高高压直流换流站噪声预测精度提供了理论依据.%As a main noise source of High-Voltage Direct Current (HVDC), converter transformer's noise prediction precision and the choice of control methods will directly influence the level of noise prediction and management effect for converter station. Through the systematic studies on the noise-generation mechanism, frequency spectrum, noise consol measurements and so on, the paper highlighted the BOX-IN technology, simplifies and validates noise prediction model for BOX-IN equipment. According to the field test, the result indicates that the noise reduction quantities of BOX-IN equipment achieves about 20dB(A) corresponding to the theoretical calculations, which provides a theoretical basis for improving the noise prediction precision of HVDC furtherly.
Evaluation of Fast-Time Wake Vortex Prediction Models
Proctor, Fred H.; Hamilton, David W.
2009-01-01
Current fast-time wake models are reviewed and three basic types are defined. Predictions from several of the fast-time models are compared. Previous statistical evaluations of the APA-Sarpkaya and D2P fast-time models are discussed. Root Mean Square errors between fast-time model predictions and Lidar wake measurements are examined for a 24 hr period at Denver International Airport. Shortcomings in current methodology for evaluating wake errors are also discussed.
Current Challenges in Dynamo Modeling
Glatzmaier, G. A.
2001-12-01
Three-dimensional, dynamically self-consistent, numerical simulations have been used for two decades to study the generation of global magnetic fields in the deep fluid interiors of planets and stars. In particular, the number of geodynamo models has increased significantly within the last five years. These simulations of magnetic field generation by laminar convection have provided considerable insight to the dynamo process and have produced large-scale fields similar to those observed. However, no global convective dynamo simulation has yet been able to afford the spatial resolution required to simulate turbulent convection, which surely must exist in these low-viscosity fluids. They have all employed greatly enhanced eddy diffusivities to stabilize the low resolution numerical solutions and crudely account for the transport and mixing by the unresolved turbulence. A grand challenge for the next generation of geodynamo models is to produce a simulation with the thermal and viscous (eddy) diffusivities set no larger than the actual magnetic diffusivity of the Earth's fluid core (2 m2/s), while using the core's dimensions, mass, rotation rate and heat flow. This would correspond to the Ekman and magnetic Ekman numbers both set to 10-9 and the Rayleigh number being many orders of magnitude greater than critical. Dynamo models for stars and planets present an additional complication: the large variation of density with radius. A grand challenge for the next generation of these models is to reach similarly low Ekman numbers and high Rayleigh numbers with a density that decreases by at least three orders of magnitude from the base of the convection zone to the model's outer boundary. The advances in numerical methods and massively parallel computing needed to meet these challenges will be discussed.
Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm.
Sadowski, Lukasz; Nikoo, Mehdi
2014-01-01
This study attempted to predict corrosion current density in concrete using artificial neural networks (ANN) combined with imperialist competitive algorithm (ICA) used to optimize weights of ANN. For that reason, temperature, AC resistivity over the steel bar, AC resistivity remote from the steel bar, and the DC resistivity over the steel bar are considered as input parameters and corrosion current density as output parameter. The ICA-ANN model has been compared with the genetic algorithm to evaluate its accuracy in three phases of training, testing, and prediction. The results showed that the ICA-ANN model enjoys more ability, flexibility, and accuracy.
Comparing model predictions for ecosystem-based management
DEFF Research Database (Denmark)
Jacobsen, Nis Sand; Essington, Timothy E.; Andersen, Ken Haste
2016-01-01
Ecosystem modeling is becoming an integral part of fisheries management, but there is a need to identify differences between predictions derived from models employed for scientific and management purposes. Here, we compared two models: a biomass-based food-web model (Ecopath with Ecosim (Ew......E)) and a size-structured fish community model. The models were compared with respect to predicted ecological consequences of fishing to identify commonalities and differences in model predictions for the California Current fish community. We compared the models regarding direct and indirect responses to fishing...... on one or more species. The size-based model predicted a higher fishing mortality needed to reach maximum sustainable yield than EwE for most species. The size-based model also predicted stronger top-down effects of predator removals than EwE. In contrast, EwE predicted stronger bottom-up effects...
Research on Predicting Drive Current of Shipborne Satcom Antenna
Kong Jinping; Xu Zhengfeng; Wu Botao
2015-01-01
Predicting the effect of antenna wind load on servo system precisely is meaningful to ensure the safety of satcom antenna on operation, which can avoid overload operation. In this paper, the computational fluid dynamics is used to proceed numerical computation on the pressure distribution of the reflector and torque of drive shaft under different wind speed, windward angle and angle of pitch of the antenna. The simulation model is built under MATLAB/Simulink simulation environment, and the dr...
A Comparison of Prediction Algorithms for Prefetching in the Current Web
Josep Domenech; Sahuquillo Borrás, Julio; Gil Salinas, José Antonio; Pont Sanjuan, Ana
2012-01-01
This paper reviews a representative subset of the prediction algorithms used for Web prefetching classifying them according to the information gathered. Then, the DDG algorithm is described. The main novelty of this algorithm lies in the fact that, unlike previous algorithms, it creates a prediction model according to the structure of the current web. To this end, the algorithm distinguishes between container objects and embedded objects. Its performance is compared against important existing...
Nur, Nadav; Jahncke, Jaime; Herzog, Mark P; Howar, Julie; Hyrenbach, K David; Zamon, Jeannette E; Ainley, David G; Wiens, John A; Morgan, Ken; Ballance, Lisa T; Stralberg, Diana
2011-09-01
Marine protected areas (MPAs) provide an important tool for conservation of marine ecosystems. To be most effective, these areas should be strategically located in a manner that supports ecosystem function. To inform marine spatial planning and support strategic establishment of MPAs within the California Current System, we identified areas predicted to support multispecies aggregations of seabirds ("hotspots"). We developed habitat-association models for 16 species using information from at-sea observations collected over an 11-year period (1997-2008), bathymetric data, and remotely sensed oceanographic data for an area from north of Vancouver Island, Canada, to the USA/Mexico border and seaward 600 km from the coast. This approach enabled us to predict distribution and abundance of seabirds even in areas of few or no surveys. We developed single-species predictive models using a machine-learning algorithm: bagged decision trees. Single-species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas ("core areas") to individual species, and (3) predicted persistence of hotspots across years. Model predictions were applied to the entire California Current for four seasons (represented by February, May, July, and October) in each of 11 years. Overall, bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Predicted hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank), Southern California (adjacent to the Channel Islands), and adjacent to Vancouver Island, British Columbia, that are not currently included in protected areas. Prioritization and identification of multispecies hotspots will
Modeling of Current Transformers Under Saturation Conditions
Directory of Open Access Journals (Sweden)
Martin Prochazka
2006-01-01
Full Text Available During a short circuit the input signal of the relay can be distort by the magnetic core saturation of the current transformer. It is useful to verify the behavior of CT by a mathematical model. The paper describes one phase and three phase models and it presents some methods of how to analyze and classify a deformed secondary current
Schubert, Siegfried
2011-01-01
Drought is fundamentally the result of an extended period of reduced precipitation lasting anywhere from a few weeks to decades and even longer. As such, addressing drought predictability and prediction in a changing climate requires foremost that we make progress on the ability to predict precipitation anomalies on subseasonal and longer time scales. From the perspective of the users of drought forecasts and information, drought is however most directly viewed through its impacts (e.g., on soil moisture, streamflow, crop yields). As such, the question of the predictability of drought must extend to those quantities as well. In order to make progress on these issues, the WCRP drought information group (DIG), with the support of WCRP, the Catalan Institute of Climate Sciences, the La Caixa Foundation, the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and the National Science Foundation, has organized a workshop to focus on: 1. User requirements for drought prediction information on sub-seasonal to centennial time scales 2. Current understanding of the mechanisms and predictability of drought on sub-seasonal to centennial time scales 3. Current drought prediction/projection capabilities on sub-seasonal to centennial time scales 4. Advancing regional drought prediction capabilities for variables and scales most relevant to user needs on sub-seasonal to centennial time scales. This introductory talk provides an overview of these goals, and outlines the occurrence and mechanisms of drought world-wide.
Bridging prediction and attention in current research on perception and action.
Schröger, Erich; Kotz, Sonja A; SanMiguel, Iria
2015-11-11
Prediction and attention are fundamental brain functions in the service of perception and action. Theories on prediction relate to neural (mental) models inferring about (present or future) sensory or action-related information, whereas theories of attention are about the control of information flow underlying perception and action. Both concepts are related and not always clearly distinguishable. The special issue includes current research on prediction and attention in various subfields of perception and action. It especially considers interactions between predictive and attentive processes, which constitute a newly emerging and highly interesting field of research. As outlined in this editorial, the contributions in this special issue allow specifying as well as bridging concepts on prediction and attention. The joint consideration of prediction and attention also reveals common functional principles of perception and action.
Gas explosion prediction using CFD models
Energy Technology Data Exchange (ETDEWEB)
Niemann-Delius, C.; Okafor, E. [RWTH Aachen Univ. (Germany); Buhrow, C. [TU Bergakademie Freiberg Univ. (Germany)
2006-07-15
A number of CFD models are currently available to model gaseous explosions in complex geometries. Some of these tools allow the representation of complex environments within hydrocarbon production plants. In certain explosion scenarios, a correction is usually made for the presence of buildings and other complexities by using crude approximations to obtain realistic estimates of explosion behaviour as can be found when predicting the strength of blast waves resulting from initial explosions. With the advance of computational technology, and greater availability of computing power, computational fluid dynamics (CFD) tools are becoming increasingly available for solving such a wide range of explosion problems. A CFD-based explosion code - FLACS can, for instance, be confidently used to understand the impact of blast overpressures in a plant environment consisting of obstacles such as buildings, structures, and pipes. With its porosity concept representing geometry details smaller than the grid, FLACS can represent geometry well, even when using coarse grid resolutions. The performance of FLACS has been evaluated using a wide range of field data. In the present paper, the concept of computational fluid dynamics (CFD) and its application to gas explosion prediction is presented. Furthermore, the predictive capabilities of CFD-based gaseous explosion simulators are demonstrated using FLACS. Details about the FLACS-code, some extensions made to FLACS, model validation exercises, application, and some results from blast load prediction within an industrial facility are presented. (orig.)
Share, P.; Jones, A. G.; Muller, M. R.; Miensopust, M. P.; Khoza, D. T.; Fourie, S.; Webb, S. J.; Thunehed, H.
2009-12-01
hypothesized that the return path of DC current, flowing along the path of least resistance between the two electrodes, is most likely to lie somewhere within, or in the vicinity of, the DMB. To obtain a better understanding of the current flow we propose using geological information, previous results of studies of the conductivity of the DMB and surrounding regions and 2D and 3D inversion results from the AMT and MT data recorded during SAMTEX in northern Botswana and Namibia, as input to a 3D DC resistivity forward modelling code, and to try to predict the return path that the DC current will follow.
Predicting the influence of plate geometry on the eddy-current pendulum
Weigel, Catherine; Wachter, Jeremy M.; Wagoner, Paul; Atherton, Timothy J.
2016-09-01
We quantitatively analyze a familiar classroom demonstration, Van Waltenhofen's eddy current pendulum, to predict the damping effect for a variety of plate geometries from first principles. Results from conformal mapping, finite element simulations, and a simplified model suitable for introductory classes are compared with experiments.
Predicting the Influence of Plate Geometry on the Eddy Current Pendulum
Weigel, Catherine; Wagoner, Paul; Atherton, Timothy J
2015-01-01
We quantitatively analyze a familiar classroom demonstration, Van Waltenhofen's eddy current pendulum, to predict the damping effect for a variety of plate geometries from first principles. Results from conformal mapping, finite element simulations and a simplified model suitable for introductory classes are compared with experiments.
Trapped electron effects on ICRF Current Drive Predictions in TFTR
Wright, John C.; Phillips, Cynthia K.; Bonoli, Paul T.
1996-11-01
Most 2D RF modeling codes use a parameterization^1 of current drive efficiencies to calculate fast wave driven currents. Because this parameterization is derived from a ray--tracing model, there are difficulties in applying it to a spectrum of waves. In addition, one cannot account for multiple resonances and coherency effects between the electrons and the waves. These difficulties may be avoided by a direct calculation of the quasilinear diffusion coefficient in an inhomogenous geometry coupled with a full wave code for the field polarizations. Current profiles are then calculated using the adjoint formulation^2, with the magnetic equilibrium specified consistently in both the adjoint routine and the full wave code. This approach has been implemented in the FISIC code^3. Results are benchmarked by comparing a power deposition calculation from conductivity to one from the quasilinear expression. It is shown that the two expressions agree. We quantify differences seen based upon aspect ratio and elongation. The largest discrepancies are seen in the regime of small aspect ratio, and little loss in accuracy for moderate aspect ratios ~>3. This work supported by DoE contract No. DE--AC02--76--CH03073. ^1 D. A. Ehst and C. F. F. Karney, Nucl. Fusion 31, 1933 (1991). ^2 C. F. F. Karney, Computer Physics Reports 4, 183 (1986). ^3 M. Brambilla and T. Krücken, Nucl. Fusion 28, 1813 (1988).
Prediction of severe acute pancreatitis: Current knowledge and novel insights
Institute of Scientific and Technical Information of China (English)
Georgios I Papachristou
2008-01-01
Acute pancreatitis (AP) is a common and potentially lethal acute inflammatory process with a highly variable clinical course. It is still unclear why some patients progress to organ failure and others do not. Physicians, ability to predict which patients will develop severe disease is limited. Routine clinical and laboratory data and multi-factorial clinical scores measured on admission and during the first 48 h of hospitalization are currently the standards of care used to estimate the magnitude of the inflammatory response to injury. Current literature highlights several common environmental, metabolic and genetic factors that increase the risk of AP development and subsequent adverse sequelae. Several cytokines have been found to play a critical role in the pathogenesis of AP by driving the subsequent inflammatory response, to include tumor necrosis factor-α (TNF-α), Interleukin-1 (IL-1), IL-6 and monocyte chemotactic protein-1 (HCP-1). Large, prospective studies are still needed to address these questions by identifying AP risk factors and serum biomarkers of severe disease.
Hong, Geun; Lee, Kwang-Woong; Suh, Sukwon; Yoo, Tae; Kim, Hyeyoung; Park, Min-Su; Choi, Youngrok; Yi, Nam-Joon; Suh, Kyung-Suk
2013-08-01
To adopt the model for end-stage liver disease (MELD) score-based system in Korea, the feasibility should be evaluated by analysis of Korean database. The aim of this study was to investigate the feasibility of the MELD score-based system compared with the current Child-Turcotte-Pugh (CTP) based-system and to suggest adequate cut-off to stratify waiting list mortality among Korean population. We included 788 adult patients listed in waiting list in Seoul National University Hospital from January 2008 to May 2011. The short-term survival until 6 months after registration was evaluated. Two hundred forty six (31.2%) patients underwent live donor liver transplantation and 353 (44.8%) patients were still waiting and 121 (15.4%) patients were dropped out due to death. Significant difference was observed when MELD score 24 and 31 were used as cut-off. Three-months survival of Status 2A was 70.2%. However, in Status 2A patients whose MELD score less than 24 (n=82), 86.6% of patients survived until 6 month. Furthermore, patients with high MELD score (≥31) among Status 2B group showed poorer survival rate (45.8%, 3-month) than Status 2A group. In conclusion, MELD score-based system can predict short term mortality better and select more number of high risk patients in Korean population.
Predictive Modeling of Cardiac Ischemia
Anderson, Gary T.
1996-01-01
The goal of the Contextual Alarms Management System (CALMS) project is to develop sophisticated models to predict the onset of clinical cardiac ischemia before it occurs. The system will continuously monitor cardiac patients and set off an alarm when they appear about to suffer an ischemic episode. The models take as inputs information from patient history and combine it with continuously updated information extracted from blood pressure, oxygen saturation and ECG lines. Expert system, statistical, neural network and rough set methodologies are then used to forecast the onset of clinical ischemia before it transpires, thus allowing early intervention aimed at preventing morbid complications from occurring. The models will differ from previous attempts by including combinations of continuous and discrete inputs. A commercial medical instrumentation and software company has invested funds in the project with a goal of commercialization of the technology. The end product will be a system that analyzes physiologic parameters and produces an alarm when myocardial ischemia is present. If proven feasible, a CALMS-based system will be added to existing heart monitoring hardware.
Tidal Current Short-Term Prediction Based on Support Vector Regression
Guozhen, Yang; Haifeng, Wang; Hui, Qian; Jianming, Fang
2017-05-01
The traditional method of short-term tidal current prediction, harmonic method, typically needs more than 18 years of history records. The method in the article uses univariate feature selection and F-test to reduce the dimension of the data fed to support vector regressor, which reduces the need of history records to less than a year. Model parameters are selected by grid searching and cross-validation. History records from two datasets are used to build prediction models, spanning 3 months and 1 year respectively. Mean average errors of both datasets after normalizing are less than 0.05.
Analysis of Leakage Current to Predict Insulator Flashover Using Artificial Neural Network
Directory of Open Access Journals (Sweden)
N. Narmadhai
2011-01-01
Full Text Available Problem statement: The phenomenon of flashover in polluted insulators has been continued by the study of the characteristics of contaminating layers deposited on the surface of insulators in high voltage laboratories. In the literature, Experimental investigations have been carried out on a real insulator or a flat plate model of insulators under high voltage application. This study proposed the Equivalent insulator flat plate model for studying the flashover phenomena due to pollution under wet conditions even at low voltage. Laboratory based tests were carried out on the model under AC voltage at different pollution levels. Different concentrations of salt solution has been prepared using sodium chloride, Kaolin and distilled water representing the various contaminations. Leakage current during the experimental studies were measured for various polluted conditions. Approach: A new model of Vc = f (V, Iinitial, Iem, Iemax and Iσ based on artificial neural network has been developed to predict flashover from the analysis of leakage current. The input variable to the artificial neural network are mean (Imean, Maximum(Imax and standard deviation(Iσ of leakage current extracted along with the initial value of leakage current Iinitial and the input voltage(V.The target obtained was used to evaluate the performance of the neural network model. Results: The optimum process has been carried out based on the training accuracy measured by RMSE, the network converged to a threshold of 0.0001.The trained model prediction is in good agreement with the actual results and the R2 value of the developed model is 0.99996. Conclusion: The developed ANN model is well-suited for the analysis of leakage current to predict flashover on the insulator surface with high accuracy.
Active power filter for medium voltage networks with predictive current control
Energy Technology Data Exchange (ETDEWEB)
Verne, Santiago A.; Valla, Maria I. [Laboratorio de Electronica Industrial, Control e Instrumentacion (LEICI), Facultad de Ingenieria, Universidad Nacional de La Plata and CONICET, La Plata (Argentina)
2010-12-15
A transformer less Shunt Active Power Filter (SAPF) for medium voltage distribution networks based on Multilevel Diode Clamped Inverter is presented in this paper. Converter current control is based on a Model Predictive strategy, which gives very fast current response. Also, the algorithm includes voltage balancing capability which is essential for proper converter operation. The presented current control algorithm is naturally applicable to converters with an arbitrary number of levels with reduced computational effort by virtue of the incorporation of switching restrictions which are necessary for reliable converter operation. The performance of the proposed algorithm is evaluated by means of computer simulations. (author)
Predictive In Vivo Models for Oncology.
Behrens, Diana; Rolff, Jana; Hoffmann, Jens
2016-01-01
Experimental oncology research and preclinical drug development both substantially require specific, clinically relevant in vitro and in vivo tumor models. The increasing knowledge about the heterogeneity of cancer requested a substantial restructuring of the test systems for the different stages of development. To be able to cope with the complexity of the disease, larger panels of patient-derived tumor models have to be implemented and extensively characterized. Together with individual genetically engineered tumor models and supported by core functions for expression profiling and data analysis, an integrated discovery process has been generated for predictive and personalized drug development.Improved “humanized” mouse models should help to overcome current limitations given by xenogeneic barrier between humans and mice. Establishment of a functional human immune system and a corresponding human microenvironment in laboratory animals will strongly support further research.Drug discovery, systems biology, and translational research are moving closer together to address all the new hallmarks of cancer, increase the success rate of drug development, and increase the predictive value of preclinical models.
Return Predictability, Model Uncertainty, and Robust Investment
DEFF Research Database (Denmark)
Lukas, Manuel
Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...
Behavioral modeling of Digitally Adjustable Current Amplifier
Josef Polak; Lukas Langhammer; Jan Jerabek
2015-01-01
This article presents the digitally adjustable current amplifier (DACA) and its analog behavioral model (ABM), which is suitable for both ideal and advanced analyses of the function block using DACA as active element. There are four levels of this model, each being suitable for simulation of a certain degree of electronic circuits design (e.g. filters, oscillators, generators). Each model is presented through a schematic wiring in the simulation program OrCAD, including a description of equat...
Li, Kunpeng
2017-01-01
The compatibility problem between rapidity and overshooting in the traditional predictive current control structure is inevitable and difficult to solve by reason of using PI controller. A novel predictive current control (PCC) algorithm for permanent magnet synchronous motor (PMSM) based on linear active disturbance rejection control (LADRC) is presented in this paper. In order to displace PI controller, the LADRC strategy which consisted of linear state error feedback (LSEF) control algorithm and linear extended state observer (LESO), is designed based on the mathematic model of PMSM. The purpose of LSEF is to make sure fast response to load mutation and system uncertainties, and LESO is designed to estimate the uncertain disturbances. The principal structures of the proposed system are speed outer loop based on LADRC and current inner loop based on predictive current control. Especially, the instruction value of qaxis current in inner loop is derived from the control quantity which is designed in speed outer loop. The simulation is carried out in Matlab/Simulink software, and the results illustrate that the dynamic and static performances of proposed system are satisfied. Moreover the robust against model parameters mismatch is enhanced obviously.
A prediction model for Clostridium difficile recurrence
Directory of Open Access Journals (Sweden)
Francis D. LaBarbera
2015-02-01
Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.
Directory of Open Access Journals (Sweden)
J.O. Adepitan
2012-05-01
Full Text Available The study is aimed at determining the dependence of the current along a channel on the model used, assuming the same base current. We compared three transmission-line-type models, namely: Transmission Line (TL, Modified Transmission Line with Linear decay, Modified Transmission Line with Exponential decay and two traveling-current-source-type models: Bruce-Golde (BG and Traveling Current Source (TCS models. The current profiles along the channel at different heights predicted by these models are presented and discussed. Comparison is based on the assumption that all the models have the same base current. It was found that at low heights and within a time window frame of 15 :s, the currents of the transmission-line-type models predict a zero value at one time or the other with a maximum turning point following some 1:s after. A linear relationship is predicted between the current peak and the channel height. A discontinuity of current peak was observed at high heights. No zero value of current was recorded in case of TCS both at low and high channel heights.
Simulation of Gravity Currents Using VOF Model
Institute of Scientific and Technical Information of China (English)
邹建锋; 黄钰期; 应新亚; 任安禄
2002-01-01
By the Volume of Fluid (VOF) multiphase flow model two-dimensional gravity currents with three phases including air are numerically simulated in this article. The necessity of consideration of turbulence effect for high Reynolds numbers is demonstrated quantitatively by LES (the Large Eddy Simulation) turbulence model. The gravity currents are simulated for h ≠ H as well as h = H, where h is the depth of the gravity current before the release and H is the depth of the intruded fluid. Uprising of swell occurs when a current flows horizontally into another lighter one for h ≠ H. The problems under what condition the uprising of swell occurs and how long it takes are considered in this article. All the simulated results are in reasonable agreement with the experimental results available.
Predictive Model Assessment for Count Data
2007-09-05
critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts...the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. We consider a recent suggestion by Baker and...Figure 5. Boxplots for various scores for patent data count regressions. 11 Table 1 Four predictive models for larynx cancer counts in Germany, 1998–2002
Neural Network based Modeling and Simulation of Transformer Inrush Current
Directory of Open Access Journals (Sweden)
Puneet Kumar Singh
2012-05-01
Full Text Available Inrush current is a very important phenomenon which occurs during energization of transformer at no load due to temporary over fluxing. It depends on several factors like magnetization curve, resistant and inductance of primary winding, supply frequency, switching angle of circuit breaker etc. Magnetizing characteristics of core represents nonlinearity which requires improved nonlinearity solving technique to know the practical behavior of inrush current. Since several techniques still working on modeling of transformer inrush current but neural network ensures exact modeling with experimental data. Therefore, the objective of this study was to develop an Artificial Neural Network (ANN model based on data of switching angle and remanent flux for predicting peak of inrush current. Back Propagation with Levenberg-Marquardt (LM algorithm was used to train the ANN architecture and same was tested for the various data sets. This research work demonstrates that the developed ANN model exhibits good performance in prediction of inrush current’s peak with an average of percentage error of -0.00168 and for modeling of inrush current with an average of percentage error of -0.52913.
Community Benchmarking of Tsunami-Induced Nearshore Current Models
Lynett, P. J.; Wilson, R. I.; Gately, K.
2015-12-01
To help produce accurate and consistent maritime hazard products, the National Tsunami Hazard Mitigation Program (NTHMP) Strategic Plan includes a requirement to develop and run a benchmarking workshop to evaluate the numerical tsunami modeling of currents. For this workshop, five different benchmarking datasets were organized. These datasets were selected based on characteristics such as geometric complexity, currents that are shear/separation driven (and thus are de-coupled from the incident wave forcing), tidal coupling, and interaction with the built environment. While tsunami simulation models have generally been well validated against wave height and runup, comparisons with speed data are much less common. As model results are increasingly being used to estimate or indicate damage to coastal infrastructure, understanding the accuracy and precision of speed predictions becomes of important. As a result of this 2-day workshop held in early 2015, modelers now have a better awareness of their ability to accurately capture the physics of tsunami currents, and therefore a better understanding of how to use these simulation tools for hazard assessment and mitigation efforts. In this presentation, the model results - from 14 different modelers - will be presented and summarized, with a focus on statistical and ensemble properties of the current predictions.
Flavour-changing neutral currents in models with extra ' boson
Indian Academy of Sciences (India)
S Sahoo; L Maharana
2004-09-01
New neutral gauge bosons ' are the features of many models addressing the physics beyond the standard model. Together with the existence of new neutral gauge bosons, models based on extended gauge groups (rank > 4) often predict new charged fermions also. A mixing of the known fermions with new states, with exotic weak-isospin assignments (left-handed singlets and right-handed doublets) will induce tree-level flavour-changing neutral interactions mediated by exchange, while if the mixing is only with new states with ordinary weak-isospin assignments, the flavour-changing neutral currents are mainly due to the exchange of the new neutral gauge boson '. We review flavour-changing neutral currents in models with extra ' boson. Then we discuss some flavour-changing processes forbidden in the standard model and new contributions to standard model processes.
The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics
Directory of Open Access Journals (Sweden)
Ronald de Vlaming
2015-01-01
Full Text Available In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i the theoretical foundations of ridge regression, (ii its link to commonly used methods in animal breeding, (iii the computational feasibility, and (iv the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis. Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N<10,000 the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP. However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.
The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics.
de Vlaming, Ronald; Groenen, Patrick J F
2015-01-01
In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i) the theoretical foundations of ridge regression, (ii) its link to commonly used methods in animal breeding, (iii) the computational feasibility, and (iv) the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis). Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N < 10,000) the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP). However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.
Nonlinear chaotic model for predicting storm surges
Directory of Open Access Journals (Sweden)
M. Siek
2010-09-01
Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.
Nonlinear chaotic model for predicting storm surges
Siek, M.; Solomatine, D.P.
This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables.
Energy Technology Data Exchange (ETDEWEB)
Hong Junjie, E-mail: hongjjie@mail.sysu.edu.cn [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China); Li Liyi, E-mail: liliyi@hit.edu.cn [Dept. Electrical Engineering, Harbin Institute of Technology, Harbin 150000 (China); Zong Zhijian; Liu Zhongtu [School of Engineering, Sun Yat-Sen University, Guangzhou 510006 (China)
2011-10-15
Highlights: {yields} The structure of the permanent magnet linear synchronous motor (SW-PMLSM) is new. {yields} A new current control method CEVPC is employed in this motor. {yields} The sectional power supply method is different to the others and effective. {yields} The performance gets worse with voltage and current limitations. - Abstract: To include features such as greater thrust density, higher efficiency without reducing the thrust stability, this paper proposes a section winding permanent magnet linear synchronous motor (SW-PMLSM), whose iron core is continuous, whereas winding is divided. The discrete system model of the motor is derived. With the definition of the current error vector and selection of the value function, the theory of the current error vector based prediction control (CEVPC) for the motor currents is explained clearly. According to the winding section feature, the motion region of the mover is divided into five zones, in which the implementation of the current predictive control method is proposed. Finally, the experimental platform is constructed and experiments are carried out. The results show: the current control effect has good dynamic response, and the thrust on the mover remains constant basically.
EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH
Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.
2014-01-01
The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain,...
Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?
Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander
2016-01-01
Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.
How to Establish Clinical Prediction Models
Directory of Open Access Journals (Sweden)
Yong-ho Lee
2016-03-01
Full Text Available A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.
One-Dimensional Modelling of Marine Current Turbine Runaway Behaviour
Directory of Open Access Journals (Sweden)
Staffan Lundin
2016-04-01
Full Text Available If a turbine loses its electrical load, it will rotate freely and increase speed, eventually achieving that rotational speed which produces zero net torque. This is known as a runaway situation. Unlike many other types of turbine, a marine current turbine will typically overshoot the final runaway speed before slowing down and settling at the runaway speed. Since the hydrodynamic forces acting on the turbine are dependent on rotational speed and acceleration, turbine behaviour during runaway becomes important for load analyses during turbine design. In this article, we consider analytical and numerical models of marine current turbine runaway behaviour in one dimension. The analytical model is found not to capture the overshoot phenomenon, while still providing useful estimates of acceleration at the onset of runaway. The numerical model incorporates turbine wake build-up and predicts a rotational speed overshoot. The predictions of the models are compared against measurements of runaway of a marine current turbine. The models are also used to recreate previously-published results for a tidal turbine and applied to a wind turbine. It is found that both models provide reasonable estimates of maximum accelerations. The numerical model is found to capture the speed overshoot well.
Behavioral modeling of Digitally Adjustable Current Amplifier
Directory of Open Access Journals (Sweden)
Josef Polak
2015-03-01
Full Text Available This article presents the digitally adjustable current amplifier (DACA and its analog behavioral model (ABM, which is suitable for both ideal and advanced analyses of the function block using DACA as active element. There are four levels of this model, each being suitable for simulation of a certain degree of electronic circuits design (e.g. filters, oscillators, generators. Each model is presented through a schematic wiring in the simulation program OrCAD, including a description of equations representing specific functions in the given level of the simulation model. The design of individual levels is always verified using PSpice simulations. The ABM model has been developed based on practically measured values of a number of DACA amplifier samples. The simulation results for proposed levels of the ABM model are shown and compared with the results of the real easurements of the active element DACA.
Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant
Institute of Scientific and Technical Information of China (English)
CHEN Yue-hua; CAO Guang-yi; ZHU Xin-jian
2007-01-01
This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.
Gaussian mixture models as flux prediction method for central receivers
Grobler, Annemarie; Gauché, Paul; Smit, Willie
2016-05-01
Flux prediction methods are crucial to the design and operation of central receiver systems. Current methods such as the circular and elliptical (bivariate) Gaussian prediction methods are often used in field layout design and aiming strategies. For experimental or small central receiver systems, the flux profile of a single heliostat often deviates significantly from the circular and elliptical Gaussian models. Therefore a novel method of flux prediction was developed by incorporating the fitting of Gaussian mixture models onto flux profiles produced by flux measurement or ray tracing. A method was also developed to predict the Gaussian mixture model parameters of a single heliostat for a given time using image processing. Recording the predicted parameters in a database ensures that more accurate predictions are made in a shorter time frame.
Comparison of Prediction-Error-Modelling Criteria
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
is a realization of a continuous-discrete multivariate stochastic transfer function model. The proposed prediction error-methods are demonstrated for a SISO system parameterized by the transfer functions with time delays of a continuous-discrete-time linear stochastic system. The simulations for this case suggest......Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which...... computational resources. The identification method is suitable for predictive control....
Current Status and Prediction on Development of PE Market
Institute of Scientific and Technical Information of China (English)
Yu Jiao
2003-01-01
This article comprehensively analyzes the status of market demand/supply and import/export volumes of PE in the world and in China, and predicts the future development trends in the fields of PE production and consumption.
Lee, Jong-Chul; Lee, Won-Ho; Kim, Woun-Jea
2015-09-01
The design and development procedures of SF6 gas circuit breakers are still largely based on trial and error through testing although the development costs go higher every year. The computation cannot cover the testing satisfactorily because all the real processes arc not taken into account. But the knowledge of the arc behavior and the prediction of the thermal-flow inside the interrupters by numerical simulations are more useful than those by experiments due to the difficulties to obtain physical quantities experimentally and the reduction of computational costs in recent years. In this paper, in order to get further information into the interruption process of a SF6 self-blast interrupter, which is based on a combination of thermal expansion and the arc rotation principle, gas flow simulations with a CFD-arc modeling are performed during the whole switching process such as high-current period, pre-current zero period, and current-zero period. Through the complete work, the pressure-rise and the ramp of the pressure inside the chamber before current zero as well as the post-arc current after current zero should be a good criterion to predict the short-line fault interruption performance of interrupters.
Validation of a multi-objective, predictive urban traffic model
Wilmink, I.R.; Haak, P. van den; Woldeab, Z.; Vreeswijk, J.
2013-01-01
This paper describes the results of the verification and validation of the ecoStrategic Model, which was developed, implemented and tested in the eCoMove project. The model uses real-time and historical traffic information to determine the current, predicted and desired state of traffic in a network
Case studies in archaeological predictive modelling
Verhagen, Jacobus Wilhelmus Hermanus Philippus
2007-01-01
In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing p
Childhood asthma prediction models: a systematic review.
Smit, Henriette A; Pinart, Mariona; Antó, Josep M; Keil, Thomas; Bousquet, Jean; Carlsen, Kai H; Moons, Karel G M; Hooft, Lotty; Carlsen, Karin C Lødrup
2015-12-01
Early identification of children at risk of developing asthma at school age is crucial, but the usefulness of childhood asthma prediction models in clinical practice is still unclear. We systematically reviewed all existing prediction models to identify preschool children with asthma-like symptoms at risk of developing asthma at school age. Studies were included if they developed a new prediction model or updated an existing model in children aged 4 years or younger with asthma-like symptoms, with assessment of asthma done between 6 and 12 years of age. 12 prediction models were identified in four types of cohorts of preschool children: those with health-care visits, those with parent-reported symptoms, those at high risk of asthma, or children in the general population. Four basic models included non-invasive, easy-to-obtain predictors only, notably family history, allergic disease comorbidities or precursors of asthma, and severity of early symptoms. Eight extended models included additional clinical tests, mostly specific IgE determination. Some models could better predict asthma development and other models could better rule out asthma development, but the predictive performance of no single model stood out in both aspects simultaneously. This finding suggests that there is a large proportion of preschool children with wheeze for which prediction of asthma development is difficult.
Model for Predicting Passage of Invasive Fish Species Through Culverts
Neary, V.
2010-12-01
Conservation efforts to promote or inhibit fish passage include the application of simple fish passage models to determine whether an open channel flow allows passage of a given fish species. Derivations of simple fish passage models for uniform and nonuniform flow conditions are presented. For uniform flow conditions, a model equation is developed that predicts the mean-current velocity threshold in a fishway, or velocity barrier, which causes exhaustion at a given maximum distance of ascent. The derivation of a simple expression for this exhaustion-threshold (ET) passage model is presented using kinematic principles coupled with fatigue curves for threatened and endangered fish species. Mean current velocities at or above the threshold predict failure to pass. Mean current velocities below the threshold predict successful passage. The model is therefore intuitive and easily applied to predict passage or exclusion. The ET model’s simplicity comes with limitations, however, including its application only to uniform flow, which is rarely found in the field. This limitation is addressed by deriving a model that accounts for nonuniform conditions, including backwater profiles and drawdown curves. Comparison of these models with experimental data from volitional swimming studies of fish indicates reasonable performance, but limitations are still present due to the difficulty in predicting fish behavior and passage strategies that can vary among individuals and different fish species.
Current fluctuations in a two dimensional model of heat conduction
Pérez-Espigares, Carlos; Garrido, Pedro L.; Hurtado, Pablo I.
2011-03-01
In this work we study numerically and analytically current fluctuations in the two-dimensional Kipnis-Marchioro-Presutti (KMP) model of heat conduction. For that purpose, we use a recently introduced algorithm which allows the direct evaluation of large deviations functions. We compare our results with predictions based on the Hydrodynamic Fluctuation Theory (HFT) of Bertini and coworkers, finding very good agreement in a wide interval of current fluctuations. We also verify the existence of a well-defined temperature profile associated to a given current fluctuation which depends exclusively on the magnitude of the current vector, not on its orientation. This confirms the recently introduced Isometric Fluctuation Relation (IFR), which results from the time-reversibility of the dynamics, and includes as a particular instance the Gallavotti-Cohen fluctuation theorem in this context but adds a completely new perspective on the high level of symmetry imposed by timereversibility on the statistics of nonequilibrium fluctuations.
Probability of detection models for eddy current NDE methods
Energy Technology Data Exchange (ETDEWEB)
Rajesh, S.N.
1993-04-30
The development of probability of detection (POD) models for a variety of nondestructive evaluation (NDE) methods is motivated by a desire to quantify the variability introduced during the process of testing. Sources of variability involved in eddy current methods of NDE include those caused by variations in liftoff, material properties, probe canting angle, scan format, surface roughness and measurement noise. This thesis presents a comprehensive POD model for eddy current NDE. Eddy current methods of nondestructive testing are used widely in industry to inspect a variety of nonferromagnetic and ferromagnetic materials. The development of a comprehensive POD model is therefore of significant importance. The model incorporates several sources of variability characterized by a multivariate Gaussian distribution and employs finite element analysis to predict the signal distribution. The method of mixtures is then used for estimating optimal threshold values. The research demonstrates the use of a finite element model within a probabilistic framework to the spread in the measured signal for eddy current nondestructive methods. Using the signal distributions for various flaw sizes the POD curves for varying defect parameters have been computed. In contrast to experimental POD models, the cost of generating such curves is very low and complex defect shapes can be handled very easily. The results are also operator independent.
Prediction of Pollution Flashover Voltage Based on Leakage Current Under AC Operating Voltage
Institute of Scientific and Technical Information of China (English)
MEI Hongwei; WANG Liming; GUAN Zhicheng; MAO Yingke
2012-01-01
This paper presented a model to predict the AC flashover voltage of contaminated suspension insulators.The prediction method is based on the maximum leakage current under AC operating voltage.Three kinds of widely used suspension insulators were tested in various contamination states such as pollution layers with different equivalent salt deposit density（ESDD）,different composition of the conductive components,different non-soluble deposit density（NSDD） and different pollution distribution states to simulate the contamination states in nature.The effective ESDD was proposed and calculated.Influences of contamination states to maximum leakage current and flashover voltage were studied.Then,the relationships between flashover voltage and leakage current in these states were presented.Finally,considering the difference of insulator profiles,a new parameter is defined and a model to estimate the flashover voltage based on this parameter is developed.The model could be used in all kinds of suspension insulators in different contamination states and was validated by the test results.
Model predictive control classical, robust and stochastic
Kouvaritakis, Basil
2016-01-01
For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...
Predicting current and future peatmoss drought stress: Impact of hydrological complexity
Nijp, Jelmer; Metselaar, Klaas; Limpens, Juul; Teutschbein, Claudia; Peichl, Matthias; Nilsson, Mats; Berendse, Frank; van der Zee, Sjoerd
2016-04-01
Northern peatlands sequester enormous amounts of carbon and therefore represent a carbon store of global importance. The vegetation in northern peatlands is dominated by peat-forming bryophytes of the genus Sphagnum. The growth of this carbon fixer, and hence its carbon uptake, strongly depends on the moisture availability in the living moss layer, which is a function of both water table and rewetting by rain. Peatland hydrology models are used to predict how changes in climate may modify the future water balance of peatmoss carpets and influence associated carbon and energy balances. These models, however, differ considerably in the number and type of processes included, which will have yet unknown consequences for peatland drought predictions in a future climate. Here, we assessed the importance of rainwater storage and peat volume change for predicting peatmoss drought projections in northern peatlands using an ensemble of downscaled, bias-corrected climate scenarios for current (1991 - 2020) and future (2061 - 2090) climate. Peatmoss drought projections were compared among four model variants with or without rainwater storage in the peatmoss carpet and peat volume change, which are considered as two important hydrological feedbacks controlling moss moisture availability. The performance of the four model variants was assessed using field data from a site in northern Sweden (Degerö Stormyr, 64°N 19°E). Our results show that adding rainwater storage in the moss layer as well as peat volume change significantly improved model performance; the most complex model had best model performance. Compared to the reference model, including both model extensions reduced the predicted drought frequency experienced by peatmoss with around 50%. Moreover, projected climate change is expected to reduce predicted peatmoss drought stress with about 20% for the studied site. In conclusion, this study shows that including rainwater storage in the peatmoss layer and/or peat volume
Predicting the behavioural impact of transcranial direct current stimulation: issues and limitations
Directory of Open Access Journals (Sweden)
Archy Otto De Berker
2013-10-01
Full Text Available The transcranial application of weak currents to the human brain has enjoyed a decade of success, providing a simple and powerful tool for non-invasively altering human brain function. However, our understanding of current delivery and its impact upon neural circuitry leaves much to be desired. We argue that the credibility of conclusions drawn with tDCS is contingent upon realistic explanations of how tDCS works, and that our present understanding of tDCS limits the technique’s use to localize function in the human brain. We outline two central issues where progress is required: the localization of currents, and predicting their functional consequence. We encourage experimenters to eschew simplistic explanations of mechanisms of transcranial current stimulation. We suggest the use of individualized current modelling, together with computational neurostimulation to inform mechanistic frameworks in which to interpret the physiological impact of tDCS. We hope that through mechanistically richer descriptions of current flow and action, insight into the biological processes by which transcranial currents influence behaviour can be gained, leading to more effective stimulation protocols and empowering conclusions drawn with tDCS.
Benchmarking an Unstructured-Grid Model for Tsunami Current Modeling
Zhang, Yinglong J.; Priest, George; Allan, Jonathan; Stimely, Laura
2016-12-01
We present model results derived from a tsunami current benchmarking workshop held by the NTHMP (National Tsunami Hazard Mitigation Program) in February 2015. Modeling was undertaken using our own 3D unstructured-grid model that has been previously certified by the NTHMP for tsunami inundation. Results for two benchmark tests are described here, including: (1) vortex structure in the wake of a submerged shoal and (2) impact of tsunami waves on Hilo Harbor in the 2011 Tohoku event. The modeled current velocities are compared with available lab and field data. We demonstrate that the model is able to accurately capture the velocity field in the two benchmark tests; in particular, the 3D model gives a much more accurate wake structure than the 2D model for the first test, with the root-mean-square error and mean bias no more than 2 cm s-1 and 8 mm s-1, respectively, for the modeled velocity.
Grade and Recovery Prediction for Eddy Current Separation Processes
Rem, P.C.; Beunder, E.M.; Kuilman, W.
1998-01-01
Grade and recovery of eddy current separation can be estimated on the basis of trajectory simulations for particles of simple shapes. In order to do so, the feed is characterized in terms of a small set of test-particles, each test-particle representing a fraction of the feed of a given size, shape
Energy based prediction models for building acoustics
DEFF Research Database (Denmark)
Brunskog, Jonas
2012-01-01
In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...
Effective variable switching point predictive current control for ac low-voltage drives
Stolze, Peter; Karamanakos, Petros; Kennel, Ralph; Manias, Stefanos; Endisch, Christian
2015-07-01
This paper presents an effective model predictive current control scheme for induction machines driven by a three-level neutral point clamped inverter, called variable switching point predictive current control. Despite the fact that direct, enumeration-based model predictive control (MPC) strategies are very popular in the field of power electronics due to their numerous advantages such as design simplicity and straightforward implementation procedure, they carry two major drawbacks. These are the increased computational effort and the high ripples on the controlled variables, resulting in a limited applicability of such methods. The high ripples occur because in direct MPC algorithms the actuating variable can only be changed at the beginning of a sampling interval. A possible remedy for this would be to change the applied control input within the sampling interval, and thus to apply it for a shorter time than one sample. However, since such a solution would lead to an additional overhead which is crucial especially for multilevel inverters, a heuristic preselection of the optimal control action is adopted to keep the computational complexity at bay. Experimental results are provided to verify the potential advantages of the proposed strategy.
Massive Predictive Modeling using Oracle R Enterprise
CERN. Geneva
2014-01-01
R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...
Development and application of chronic disease risk prediction models.
Oh, Sun Min; Stefani, Katherine M; Kim, Hyeon Chang
2014-07-01
Currently, non-communicable chronic diseases are a major cause of morbidity and mortality worldwide, and a large proportion of chronic diseases are preventable through risk factor management. However, the prevention efficacy at the individual level is not yet satisfactory. Chronic disease prediction models have been developed to assist physicians and individuals in clinical decision-making. A chronic disease prediction model assesses multiple risk factors together and estimates an absolute disease risk for the individual. Accurate prediction of an individual's future risk for a certain disease enables the comparison of benefits and risks of treatment, the costs of alternative prevention strategies, and selection of the most efficient strategy for the individual. A large number of chronic disease prediction models, especially targeting cardiovascular diseases and cancers, have been suggested, and some of them have been adopted in the clinical practice guidelines and recommendations of many countries. Although few chronic disease prediction tools have been suggested in the Korean population, their clinical utility is not as high as expected. This article reviews methodologies that are commonly used for developing and evaluating a chronic disease prediction model and discusses the current status of chronic disease prediction in Korea.
Liver Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Colorectal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Cervical Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Prostate Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Pancreatic Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Colorectal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Bladder Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Esophageal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Lung Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Breast Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Ovarian Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Testicular Cancer Risk Prediction Models
Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Homology Modeling a Fast Tool for Drug Discovery: Current Perspectives
Vyas, V. K.; Ukawala, R. D.; Ghate, M.; Chintha, C.
2012-01-01
Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery. PMID:23204616
Posterior Predictive Model Checking in Bayesian Networks
Crawford, Aaron
2014-01-01
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…
A Course in... Model Predictive Control.
Arkun, Yaman; And Others
1988-01-01
Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)
A study of model bivalve siphonal currents
Monismith, Stephen G.; Koseff, Jeffrey R.; Thompson, Janet K.; O'Riordan, Catherine A.; Nepf, Heidi M.
1990-01-01
We carried out experiments studying the hydrodynamics of bivalve siphonal currents in a laboratory flume. Rather than use living animals, we devised a simple, model siphon pair connected to a pump. Fluorescence-based flow visualization was used to characterize siphon-jet flows for several geometric configurations and flow speeds. These measurements show that the boundary-layer velocity profile, siphon height, siphon pair orientation, and size of siphon structure all affect the vertical distribution of the excurrent flow downstream of the siphon pair and the fraction of excurrent that is refiltered. The observed flows may effect both the clearance rate of an entire population of siphonate bivalves as well as the efficiency of feeding of any individual. Our results imply that field conditions are properly represented in laboratory flume studies of phytoplankton biomass losses to benthic bivalves when the shear velocity and bottom roughness are matched to values found in the field. Numerical models of feeding by a bivalve population should include an effective sink distribution which is created by the combined incurrent-excurrent flow field. Near-bed flows need to be accounted for to properly represent these benthic-pelagic exchanges. We also present velocity measurements made with a laser-Doppler anemometer (LDA) for a single configuration (siphons flush with bed, inlet downstream) that show that the siphonal currents have a significant local effect on the properties of a turbulent boundary layer.
Signature prediction for model-based automatic target recognition
Keydel, Eric R.; Lee, Shung W.
1996-06-01
The moving and stationary target recognition (MSTAR) model- based automatic target recognition (ATR) system utilizes a paradigm which matches features extracted form an unknown SAR target signature against predictions of those features generated from models of the sensing process and candidate target geometries. The candidate target geometry yielding the best match between predicted and extracted features defines the identify of the unknown target. MSTAR will extend the current model-based ATR state-of-the-art in a number of significant directions. These include: use of Bayesian techniques for evidence accrual, reasoning over target subparts, coarse-to-fine hypothesis search strategies, and explicit reasoning over target articulation, configuration, occlusion, and lay-over. These advances also imply significant technical challenges, particularly for the MSTAR feature prediction module (MPM). In addition to accurate electromagnetics, the MPM must provide traceback between input target geometry and output features, on-line target geometry manipulation, target subpart feature prediction, explicit models for local scene effects, and generation of sensitivity and uncertainty measures for the predicted features. This paper describes the MPM design which is being developed to satisfy these requirements. The overall module structure is presented, along with the specific deign elements focused on MSTAR requirements. Particular attention is paid to design elements that enable on-line prediction of features within the time constraints mandated by model-driven ATR. Finally, the current status, development schedule, and further extensions in the module design are described.
Model predictive torque control with an extended prediction horizon for electrical drive systems
Wang, Fengxiang; Zhang, Zhenbin; Kennel, Ralph; Rodríguez, José
2015-07-01
This paper presents a model predictive torque control method for electrical drive systems. A two-step prediction horizon is achieved by considering the reduction of the torque ripples. The electromagnetic torque and the stator flux error between predicted values and the references, and an over-current protection are considered in the cost function design. The best voltage vector is selected by minimising the value of the cost function, which aims to achieve a low torque ripple in two intervals. The study is carried out experimentally. The results show that the proposed method achieves good performance in both steady and transient states.
Galeazzi fractures: Is DRUJ instability predicted by current guidelines?
Tsismenakis, Tony; Tornetta, Paul
2016-07-01
Clinically significant distal radioulnar joint (DRUJ) injuries can occur with radial shaft fractures. Several radiographic methods of diagnosis, such as radial shortening of >5mm or fracture line within 7.5cm from the lunate facet, have been proposed but not clinically validated. The purpose of this study was to compare radiographic measurements of radial shaft fractures associated with and without clinically significant DRUJ injury (i.e., true Galeazzi fracture-dislocation) in order to evaluate the predictive value of reported parameters of DRUJ injury. A retrospective record and radiographic review was performed of 66 consecutive skeletally mature patients with isolated radial shaft fractures from 2004 to 2014 treated at one level 1 academic trauma center. Intraoperatively determined DRUJ instability after radial shaft fixation was used as the gold standard for diagnosis of a Galeazzi fracture-dislocation. Average age was 34 years old (range: 18-90). By thirds, there were 10 proximal (15%), 27 middle (41%), and 29 distal (44%) fractures. 13 (20%) had an associated ulnar styloid fracture. 7 (11%) patients had DRUJ instability after radial fixation. Radial shortening averaged 4.4±5.2mm (-2.6-22), and 21 had shortening of >5mm. Twenty-six (39%) fractures were within 7.5cm of the wrist joint. Previous guidelines were only moderately accurate. Even greater shortening did not predict instability (3/7 patients with >10mm shortening had a true injury). Four out of 7 cases with instability had ulnar styloid fractures (p=0.02). Using a larger data set than has historically been evaluated, previously reported radiographic guidelines are only moderately accurate. The presence of an ulnar styloid fracture can be helpful. Surgeons should be aware of these associations but rely primarily on intraoperative assessment of the DRUJ after radial fixation to determine treatment. Copyright © 2016 Elsevier Ltd. All rights reserved.
Equivalency and unbiasedness of grey prediction models
Institute of Scientific and Technical Information of China (English)
Bo Zeng; Chuan Li; Guo Chen; Xianjun Long
2015-01-01
In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction mo-dels, the equivalence and unbiasedness of grey prediction mo-dels are analyzed and verified. The results show that al the grey prediction models that are strictly derived from x(0)(k) +az(1)(k) = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homoge-neous exponential sequence can be accomplished. However, the models derived from dx(1)/dt+ax(1) =b are only close to those derived from x(0)(k)+az(1)(k)=b provided that|a|has to satisfy|a| < 0.1; neither could the unbiased simulation for the homoge-neous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.
Predictability of extreme values in geophysical models
Directory of Open Access Journals (Sweden)
A. E. Sterk
2012-09-01
Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.
Gompertz kinetics model of fast chemical neurotransmission currents.
Easton, Dexter M
2005-10-01
At a chemical synapse, transmitter molecules ejected from presynaptic terminal(s) bind reversibly with postsynaptic receptors and trigger an increase in channel conductance to specific ions. This paper describes a simple but accurate predictive model for the time course of the synaptic conductance transient, based on Gompertz kinetics. In the model, two simple exponential decay terms set the rates of development and decline of transmitter action. The first, r, triggering conductance activation, is surrogate for the decelerated rate of growth of conductance, G. The second, r', responsible for Y, deactivation of the conductance, is surrogate for the decelerated rate of decline of transmitter action. Therefore, the differential equation for the net conductance change, g, triggered by the transmitter is dg/dt=g(r-r'). The solution of that equation yields the product of G(t), representing activation, and Y(t), which defines the proportional decline (deactivation) of the current. The model fits, over their full-time course, published records of macroscopic ionic current associated with fast chemical transmission. The Gompertz model is a convenient and accurate method for routine analysis and comparison of records of synaptic current and putative transmitter time course. A Gompertz fit requiring only three independent rate constants plus initial current appears indistinguishable from a Markov fit using seven rate constants.
Hybrid modeling and prediction of dynamical systems
Lloyd, Alun L.; Flores, Kevin B.
2017-01-01
Scientific analysis often relies on the ability to make accurate predictions of a system’s dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often used for this purpose. Accurate estimation of the model state and parameters prior to prediction is necessary, but may be complicated by issues such as noisy data and uncertainty in parameters and initial conditions. At the other end of the spectrum exist nonparametric methods, which rely solely on data to build their predictions. While these nonparametric methods do not require a model of the system, their performance is strongly influenced by the amount and noisiness of the data. In this article, we consider a hybrid approach to modeling and prediction which merges recent advancements in nonparametric analysis with standard parametric methods. The general idea is to replace a subset of a mechanistic model’s equations with their corresponding nonparametric representations, resulting in a hybrid modeling and prediction scheme. Overall, we find that this hybrid approach allows for more robust parameter estimation and improved short-term prediction in situations where there is a large uncertainty in model parameters. We demonstrate these advantages in the classical Lorenz-63 chaotic system and in networks of Hindmarsh-Rose neurons before application to experimentally collected structured population data. PMID:28692642
Risk terrain modeling predicts child maltreatment.
Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye
2016-12-01
As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.
Property predictions using microstructural modeling
Energy Technology Data Exchange (ETDEWEB)
Wang, K.G. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)]. E-mail: wangk2@rpi.edu; Guo, Z. [Sente Software Ltd., Surrey Technology Centre, 40 Occam Road, Guildford GU2 7YG (United Kingdom); Sha, W. [Metals Research Group, School of Civil Engineering, Architecture and Planning, The Queen' s University of Belfast, Belfast BT7 1NN (United Kingdom); Glicksman, M.E. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States); Rajan, K. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)
2005-07-15
Precipitation hardening in an Fe-12Ni-6Mn maraging steel during overaging is quantified. First, applying our recent kinetic model of coarsening [Phys. Rev. E, 69 (2004) 061507], and incorporating the Ashby-Orowan relationship, we link quantifiable aspects of the microstructures of these steels to their mechanical properties, including especially the hardness. Specifically, hardness measurements allow calculation of the precipitate size as a function of time and temperature through the Ashby-Orowan relationship. Second, calculated precipitate sizes and thermodynamic data determined with Thermo-Calc[copyright] are used with our recent kinetic coarsening model to extract diffusion coefficients during overaging from hardness measurements. Finally, employing more accurate diffusion parameters, we determined the hardness of these alloys independently from theory, and found agreement with experimental hardness data. Diffusion coefficients determined during overaging of these steels are notably higher than those found during the aging - an observation suggesting that precipitate growth during aging and precipitate coarsening during overaging are not controlled by the same diffusion mechanism.
Directory of Open Access Journals (Sweden)
Milić Dubravka M.
2013-01-01
Full Text Available Predicting species distribution in different climates is most often made by climate models (“climate envelope models” - CEM which are using the current geographical distribution of species and climate characteristics of the area. Hoverflies (Insecta: Diptera: Syrphidae can act as bioindicators and monitors of climate change and habitat quality. Cheilosia Meigen, 1822 is one of the largest hoverflies genera, with about 450 described species. The aim of this study was to model the current potential distribution of six species from Cheilosia proxima group on the Balkan Peninsula (Cheilosia aerea Dufour, 1848, C. balkana Vujić, 1994, C. gigantea Zetterstedt, 1838, C. pascuorum Becker, 1894, C. proxima Zetterstedt, 1843 and C. rufimana Becker, 1894 using maximum entropy modeling (Maxent. It is observed that parameters with highest influence on the analyzed species are Altitude and BIO 15 (Precipitation Seasonality for all species, except C. rufimana. Parameter that also substantially influenced for all species, except C. pascuorum, is BIO 18 (Precipitation of Warmest Quarter. The models of current distribution have shown that the most important area of the Balkan Peninsula, for species from Cheilosia proxima group, is Dinaric mountains. Information obtained in this paper can help in future monitoring of species, as well as for the conservation measures, especially for endemics and rare species. [Projekat Ministarstva nauke Republike Srbije, br. 173002 i br. 43002
Spatial Economics Model Predicting Transport Volume
Directory of Open Access Journals (Sweden)
Lu Bo
2016-10-01
Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.
Prediction of survival with alternative modeling techniques using pseudo values
T. van der Ploeg (Tjeerd); F.R. Datema (Frank); R.J. Baatenburg de Jong (Robert Jan); E.W. Steyerberg (Ewout)
2014-01-01
textabstractBackground: The use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo
Modeling and Prediction Using Stochastic Differential Equations
DEFF Research Database (Denmark)
Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp
2016-01-01
Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...
Precision Plate Plan View Pattern Predictive Model
Institute of Scientific and Technical Information of China (English)
ZHAO Yang; YANG Quan; HE An-rui; WANG Xiao-chen; ZHANG Yun
2011-01-01
According to the rolling features of plate mill, a 3D elastic-plastic FEM （finite element model） based on full restart method of ANSYS/LS-DYNA was established to study the inhomogeneous plastic deformation of multipass plate rolling. By analyzing the simulation results, the difference of head and tail ends predictive models was found and modified. According to the numerical simulation results of 120 different kinds of conditions, precision plate plan view pattern predictive model was established. Based on these models, the sizing MAS （mizushima automatic plan view pattern control system） method was designed and used on a 2 800 mm plate mill. Comparing the rolled plates with and without PVPP （plan view pattern predictive） model, the reduced width deviation indicates that the olate !olan view Dattern predictive model is preeise.
A Real-Time Nearshore Wave and Current Prediction System
2008-01-01
The MRFA04 Trial provided an opportunity to test (DIAS), developed by the Argonne National Laboratory, and evaluate a beach environmental...this component of the The Dclfl3D system, developed by Delft Hydraulics nearshorc modeling system was tailored specifically tbr ( htp :,’www.wldelft.nl...and 0.96. study, we performed three hindcasts using the following Scatter indices for all three test cases were consistently meteorological
A Fusion Model for CPU Load Prediction in Cloud Computing
Directory of Open Access Journals (Sweden)
Dayu Xu
2013-11-01
Full Text Available Load prediction plays a key role in cost-optimal resource allocation and datacenter energy saving. In this paper, we use real-world traces from Cloud platform and propose a fusion model to forecast the future CPU loads. First, long CPU load time series data are divided into short sequences with same length from the historical data on the basis of cloud control cycle. Then we use kernel fuzzy c-means clustering algorithm to put the subsequences into different clusters. For each cluster, with current load sequence, a genetic algorithm optimized wavelet Elman neural network prediction model is exploited to predict the CPU load in next time interval. Finally, we obtain the optimal cloud computing CPU load prediction results from the cluster and its corresponding predictor with minimum forecasting error. Experimental results show that our algorithm performs better than other models reported in previous works.
Ovarian reserve tests in the prediction of the fertile lifespan and current fertility
Depmann, M
2016-01-01
Widely used ovarian reserve tests do not predict age at menopause or current fertility This thesis assesssed the capacity of ovarian reserve tests in the prediction of the fertile life span and current fertility. Ovarian reserve tests of interest were anti-Müllerian hormone (AMH), the antral follicl
Ovarian reserve tests in the prediction of the fertile lifespan and current fertility
Depmann, M
2016-01-01
Widely used ovarian reserve tests do not predict age at menopause or current fertility This thesis assesssed the capacity of ovarian reserve tests in the prediction of the fertile life span and current fertility. Ovarian reserve tests of interest were anti-Müllerian hormone (AMH), the antral
NBC Hazard Prediction Model Capability Analysis
1999-09-01
Puff( SCIPUFF ) Model Verification and Evaluation Study, Air Resources Laboratory, NOAA, May 1998. Based on the NOAA review, the VLSTRACK developers...TO SUBSTANTIAL DIFFERENCES IN PREDICTIONS HPAC uses a transport and dispersion (T&D) model called SCIPUFF and an associated mean wind field model... SCIPUFF is a model for atmospheric dispersion that uses the Gaussian puff method - an arbitrary time-dependent concentration field is represented
Using Pareto points for model identification in predictive toxicology.
Palczewska, Anna; Neagu, Daniel; Ridley, Mick
2013-03-22
: Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology.
Desseilles, Martin
2012-01-01
In general, it appears that the suicidal act is highly unpredictable with the current scientific means available. In this article, the author submits the hypothesis that predicting suicide is complex because it results in predicting a choice, in itself unpredictable. The article proposes a Reinforcement learning model-based analysis. In this model, we integrate on the one hand, four ascending modulatory neurotransmitter systems (acetylcholine, noradrenalin, serotonin, and dopamine) with their regions of respective projections and afferences, and on the other hand, various observations of brain imaging identified until now in the suicidal process.
Neural Network Based Model for Predicting Housing Market Performance
Institute of Scientific and Technical Information of China (English)
Ahmed Khalafallah
2008-01-01
The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home develop-ers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the short-term in order to make appropriate financial decisions. This paper presents the development of artificial neu-ral network based models to support real estate investors and home developers in this critical task. The pa-per describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to pre-dict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between -2% and +2%.
Charge transport model to predict intrinsic reliability for dielectric materials
Energy Technology Data Exchange (ETDEWEB)
Ogden, Sean P. [Howard P. Isermann Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180 (United States); GLOBALFOUNDRIES, 400 Stonebreak Rd. Ext., Malta, New York 12020 (United States); Borja, Juan; Plawsky, Joel L., E-mail: plawsky@rpi.edu; Gill, William N. [Howard P. Isermann Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180 (United States); Lu, T.-M. [Department of Physics, Rensselaer Polytechnic Institute, Troy, New York 12180 (United States); Yeap, Kong Boon [GLOBALFOUNDRIES, 400 Stonebreak Rd. Ext., Malta, New York 12020 (United States)
2015-09-28
Several lifetime models, mostly empirical in nature, are used to predict reliability for low-k dielectrics used in integrated circuits. There is a dispute over which model provides the most accurate prediction for device lifetime at operating conditions. As a result, there is a need to transition from the use of these largely empirical models to one built entirely on theory. Therefore, a charge transport model was developed to predict the device lifetime of low-k interconnect systems. The model is based on electron transport and donor-type defect formation. Breakdown occurs when a critical defect concentration accumulates, resulting in electron tunneling and the emptying of positively charged traps. The enhanced local electric field lowers the barrier for electron injection into the dielectric, causing a positive feedforward failure. The charge transport model is able to replicate experimental I-V and I-t curves, capturing the current decay at early stress times and the rapid current increase at failure. The model is based on field-driven and current-driven failure mechanisms and uses a minimal number of parameters. All the parameters have some theoretical basis or have been measured experimentally and are not directly used to fit the slope of the time-to-failure versus applied field curve. Despite this simplicity, the model is able to accurately predict device lifetime for three different sources of experimental data. The simulation's predictions at low fields and very long lifetimes show that the use of a single empirical model can lead to inaccuracies in device reliability.
Corporate prediction models, ratios or regression analysis?
Bijnen, E.J.; Wijn, M.F.C.M.
1994-01-01
The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in
Modelling Chemical Reasoning to Predict Reactions
Segler, Marwin H S
2016-01-01
The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180,000 randomly selected binary reactions. We show that our data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-) discovering novel transformations (even including transition-metal catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph, and because each single reaction prediction is typically ac...
Precise methods for conducted EMI modeling,analysis,and prediction
Institute of Scientific and Technical Information of China (English)
2008-01-01
Focusing on the state-of-the-art conducted EMI prediction, this paper presents a noise source lumped circuit modeling and identification method, an EMI modeling method based on multiple slope approximation of switching transitions, and dou-ble Fourier integral method modeling PWM conversion units to achieve an accurate modeling of EMI noise source. Meanwhile, a new sensitivity analysis method, a general coupling model for steel ground loops, and a partial element equivalent circuit method are proposed to identify and characterize conducted EMI coupling paths. The EMI noise and propagation modeling provide an accurate prediction of conducted EMI in the entire frequency range (0―10 MHz) with good practicability and generality. Finally a new measurement approach is presented to identify the surface current of large dimensional metal shell. The proposed analytical modeling methodology is verified by experimental results.
Precise methods for conducted EMI modeling,analysis, and prediction
Institute of Scientific and Technical Information of China (English)
MA WeiMing; ZHAO ZhiHua; MENG Jin; PAN QiJun; ZHANG Lei
2008-01-01
Focusing on the state-of-the-art conducted EMI prediction, this paper presents a noise source lumped circuit modeling and identification method, an EMI modeling method based on multiple slope approximation of switching transitions, and dou-ble Fourier integral method modeling PWM conversion units to achieve an accurate modeling of EMI noise source. Meanwhile, a new sensitivity analysis method, a general coupling model for steel ground loops, and a partial element equivalent circuit method are proposed to identify and characterize conducted EMI coupling paths. The EMI noise and propagation modeling provide an accurate prediction of conducted EMI in the entire frequency range (0-10 MHz) with good practicability and generality. Finally a new measurement approach is presented to identify the surface current of large dimensional metal shell. The proposed analytical modeling methodology is verified by experimental results.
Evaluation of CASP8 model quality predictions
Cozzetto, Domenico
2009-01-01
The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.
Genetic models of homosexuality: generating testable predictions
Gavrilets, Sergey; Rice, William R.
2006-01-01
Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality inclu...
Wind farm production prediction - The Zephyr model
Energy Technology Data Exchange (ETDEWEB)
Landberg, L. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Giebel, G. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Madsen, H. [IMM (DTU), Kgs. Lyngby (Denmark); Nielsen, T.S. [IMM (DTU), Kgs. Lyngby (Denmark); Joergensen, J.U. [Danish Meteorologisk Inst., Copenhagen (Denmark); Lauersen, L. [Danish Meteorologisk Inst., Copenhagen (Denmark); Toefting, J. [Elsam, Fredericia (DK); Christensen, H.S. [Eltra, Fredericia (Denmark); Bjerge, C. [SEAS, Haslev (Denmark)
2002-06-01
This report describes a project - funded by the Danish Ministry of Energy and the Environment - which developed a next generation prediction system called Zephyr. The Zephyr system is a merging between two state-of-the-art prediction systems: Prediktor of Risoe National Laboratory and WPPT of IMM at the Danish Technical University. The numerical weather predictions were generated by DMI's HIRLAM model. Due to technical difficulties programming the system, only the computational core and a very simple version of the originally very complex system were developed. The project partners were: Risoe, DMU, DMI, Elsam, Eltra, Elkraft System, SEAS and E2. (au)
Comparison of empirical magnetic field models and global MHD simulations: The near-tail currents
Pulkkinen, T. I.; Baker, D. N.; Walker, R. J.; Raeder, J.; Ashour-Abdalla, M.
1995-01-01
The tail currents predicted by empirical magnetic field models and global MHD simulations are compared. It is shown that the near-Earth currents obtained from the MHD simulations are much weaker than the currents predicted by the Tsyganenko models, primarily because the ring current is not properly represented in the simulations. On the other hand, in the mid-tail and distant tail the lobe field strength predicted by the simulations is comparable to what is observed at about 50 R(sub E) distance, significantly larger than the very low lobe field values predicted by the Tsyganenko models at that distance. Ways to improve these complementary approaches to model the actual magnetospheric configuration are discussed.
Predictive model for segmented poly(urea
Directory of Open Access Journals (Sweden)
Frankl P.
2012-08-01
Full Text Available Segmented poly(urea has been shown to be of significant benefit in protecting vehicles from blast and impact and there have been several experimental studies to determine the mechanisms by which this protective function might occur. One suggested route is by mechanical activation of the glass transition. In order to enable design of protective structures using this material a constitutive model and equation of state are needed for numerical simulation hydrocodes. Determination of such a predictive model may also help elucidate the beneficial mechanisms that occur in polyurea during high rate loading. The tool deployed to do this has been Group Interaction Modelling (GIM – a mean field technique that has been shown to predict the mechanical and physical properties of polymers from their structure alone. The structure of polyurea has been used to characterise the parameters in the GIM scheme without recourse to experimental data and the equation of state and constitutive model predicts response over a wide range of temperatures and strain rates. The shock Hugoniot has been predicted and validated against existing data. Mechanical response in tensile tests has also been predicted and validated.
Support vector machine-based multi-model predictive control
Institute of Scientific and Technical Information of China (English)
Zhejing BA; Youxian SUN
2008-01-01
In this paper,a support vector machine-based multi-model predictive control is proposed,in which SVM classification combines well with SVM regression.At first,each working environment is modeled by SVM regression and the support vector machine network-based model predictive control(SVMN-MPC)algorithm corresponding to each environment is developed,and then a multi-class SVM model is established to recognize multiple operating conditions.As for control,the current environment is identified by the multi-class SVM model and then the corresponding SVMN.MPCcontroller is activated at each sampling instant.The proposed modeling,switching and controller design is demonstrated in simulation results.
PREDICTIVE CAPACITY OF ARCH FAMILY MODELS
Directory of Open Access Journals (Sweden)
Raphael Silveira Amaro
2016-03-01
Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.
Predictive QSAR modeling of phosphodiesterase 4 inhibitors.
Kovalishyn, Vasyl; Tanchuk, Vsevolod; Charochkina, Larisa; Semenuta, Ivan; Prokopenko, Volodymyr
2012-02-01
A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development. Copyright © 2011 Elsevier Inc. All rights reserved.
Model predictive control for Z-source power converter
DEFF Research Database (Denmark)
Mo, W.; Loh, P.C.; Blaabjerg, Frede
2011-01-01
This paper presents Model Predictive Control (MPC) of impedance-source (commonly known as Z-source) power converter. Output voltage control and current control for Z-source inverter are analyzed and simulated. With MPC's ability of multi- system variables regulation, load current and voltage...... of variable switching frequency as well as robustness of transient response can be obtained at the same time with a formulated Z-source network model. Operating steady state and transient state simulation of MPC are going to be presented, which shows good reference tracking ability of this control method....
Modelling the predictive performance of credit scoring
Directory of Open Access Journals (Sweden)
Shi-Wei Shen
2013-02-01
Full Text Available Orientation: The article discussed the importance of rigour in credit risk assessment.Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan.Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities.Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems.Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk.Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product.Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.
Calibrated predictions for multivariate competing risks models.
Gorfine, Malka; Hsu, Li; Zucker, David M; Parmigiani, Giovanni
2014-04-01
Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.
Improving Saliency Models by Predicting Human Fixation Patches
Dubey, Rachit
2015-04-16
There is growing interest in studying the Human Visual System (HVS) to supplement and improve the performance of computer vision tasks. A major challenge for current visual saliency models is predicting saliency in cluttered scenes (i.e. high false positive rate). In this paper, we propose a fixation patch detector that predicts image patches that contain human fixations with high probability. Our proposed model detects sparse fixation patches with an accuracy of 84 % and eliminates non-fixation patches with an accuracy of 84 % demonstrating that low-level image features can indeed be used to short-list and identify human fixation patches. We then show how these detected fixation patches can be used as saliency priors for popular saliency models, thus, reducing false positives while maintaining true positives. Extensive experimental results show that our proposed approach allows state-of-the-art saliency methods to achieve better prediction performance on benchmark datasets.
Modelling language evolution: Examples and predictions.
Gong, Tao; Shuai, Lan; Zhang, Menghan
2014-06-01
We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.
Modelling language evolution: Examples and predictions
Gong, Tao; Shuai, Lan; Zhang, Menghan
2014-06-01
We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.
Global Solar Dynamo Models: Simulations and Predictions
Indian Academy of Sciences (India)
Mausumi Dikpati; Peter A. Gilman
2008-03-01
Flux-transport type solar dynamos have achieved considerable success in correctly simulating many solar cycle features, and are now being used for prediction of solar cycle timing and amplitude.We first define flux-transport dynamos and demonstrate how they work. The essential added ingredient in this class of models is meridional circulation, which governs the dynamo period and also plays a crucial role in determining the Sun’s memory about its past magnetic fields.We show that flux-transport dynamo models can explain many key features of solar cycles. Then we show that a predictive tool can be built from this class of dynamo that can be used to predict mean solar cycle features by assimilating magnetic field data from previous cycles.
Model Predictive Control of Sewer Networks
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.
2017-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.
DKIST Polarization Modeling and Performance Predictions
Harrington, David
2016-05-01
Calibrating the Mueller matrices of large aperture telescopes and associated coude instrumentation requires astronomical sources and several modeling assumptions to predict the behavior of the system polarization with field of view, altitude, azimuth and wavelength. The Daniel K Inouye Solar Telescope (DKIST) polarimetric instrumentation requires very high accuracy calibration of a complex coude path with an off-axis f/2 primary mirror, time dependent optical configurations and substantial field of view. Polarization predictions across a diversity of optical configurations, tracking scenarios, slit geometries and vendor coating formulations are critical to both construction and contined operations efforts. Recent daytime sky based polarization calibrations of the 4m AEOS telescope and HiVIS spectropolarimeter on Haleakala have provided system Mueller matrices over full telescope articulation for a 15-reflection coude system. AEOS and HiVIS are a DKIST analog with a many-fold coude optical feed and similar mirror coatings creating 100% polarization cross-talk with altitude, azimuth and wavelength. Polarization modeling predictions using Zemax have successfully matched the altitude-azimuth-wavelength dependence on HiVIS with the few percent amplitude limitations of several instrument artifacts. Polarization predictions for coude beam paths depend greatly on modeling the angle-of-incidence dependences in powered optics and the mirror coating formulations. A 6 month HiVIS daytime sky calibration plan has been analyzed for accuracy under a wide range of sky conditions and data analysis algorithms. Predictions of polarimetric performance for the DKIST first-light instrumentation suite have been created under a range of configurations. These new modeling tools and polarization predictions have substantial impact for the design, fabrication and calibration process in the presence of manufacturing issues, science use-case requirements and ultimate system calibration
Modelling Chemical Reasoning to Predict Reactions
Segler, Marwin H. S.; Waller, Mark P.
2016-01-01
The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outpe...
Raman Model Predicting Hardness of Covalent Crystals
Zhou, Xiang-Feng; Qian, Quang-Rui; Sun, Jian; Tian, Yongjun; Wang, Hui-Tian
2009-01-01
Based on the fact that both hardness and vibrational Raman spectrum depend on the intrinsic property of chemical bonds, we propose a new theoretical model for predicting hardness of a covalent crystal. The quantitative relationship between hardness and vibrational Raman frequencies deduced from the typical zincblende covalent crystals is validated to be also applicable for the complex multicomponent crystals. This model enables us to nondestructively and indirectly characterize the hardness o...
Predicting Solar Cycle 25 using Surface Flux Transport Model
Imada, Shinsuke; Iijima, Haruhisa; Hotta, Hideyuki; Shiota, Daiko; Kusano, Kanya
2017-08-01
It is thought that the longer-term variations of the solar activity may affect the Earth’s climate. Therefore, predicting the next solar cycle is crucial for the forecast of the “solar-terrestrial environment”. To build prediction schemes for the next solar cycle is a key for the long-term space weather study. Recently, the relationship between polar magnetic field at the solar minimum and next solar activity is intensively discussed. Because we can determine the polar magnetic field at the solar minimum roughly 3 years before the next solar maximum, we may discuss the next solar cycle 3years before. Further, the longer term (~5 years) prediction might be achieved by estimating the polar magnetic field with the Surface Flux Transport (SFT) model. Now, we are developing a prediction scheme by SFT model as a part of the PSTEP (Project for Solar-Terrestrial Environment Prediction) and adapting to the Cycle 25 prediction. The predicted polar field strength of Cycle 24/25 minimum is several tens of percent smaller than Cycle 23/24 minimum. The result suggests that the amplitude of Cycle 25 is weaker than the current cycle. We also try to obtain the meridional flow, differential rotation, and turbulent diffusivity from recent modern observations (Hinode and Solar Dynamics Observatory). These parameters will be used in the SFT models to predict the polar magnetic fields strength at the solar minimum. In this presentation, we will explain the outline of our strategy to predict the next solar cycle and discuss the initial results for Cycle 25 prediction.
Predictive Modelling of Mycotoxins in Cereals
Fels, van der H.J.; Liu, C.
2015-01-01
In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts
Unreachable Setpoints in Model Predictive Control
DEFF Research Database (Denmark)
Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp
2008-01-01
steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...
Predictive Modelling of Mycotoxins in Cereals
Fels, van der H.J.; Liu, C.
2015-01-01
In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts produ
Prediction modelling for population conviction data
Tollenaar, N.
2017-01-01
In this thesis, the possibilities of using prediction models for judicial penal case data are investigated. The development and refinement of a risk taxation scale based on these data is discussed. When false positives are weighted equally severe as false negatives, 70% can be classified correctly.
A Predictive Model for MSSW Student Success
Napier, Angela Michele
2011-01-01
This study tested a hypothetical model for predicting both graduate GPA and graduation of University of Louisville Kent School of Social Work Master of Science in Social Work (MSSW) students entering the program during the 2001-2005 school years. The preexisting characteristics of demographics, academic preparedness and culture shock along with…
Predictability of extreme values in geophysical models
Sterk, A.E.; Holland, M.P.; Rabassa, P.; Broer, H.W.; Vitolo, R.
2012-01-01
Extreme value theory in deterministic systems is concerned with unlikely large (or small) values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical model
A revised prediction model for natural conception
Bensdorp, A.J.; Steeg, J.W. van der; Steures, P.; Habbema, J.D.; Hompes, P.G.; Bossuyt, P.M.; Veen, F. van der; Mol, B.W.; Eijkemans, M.J.; Kremer, J.A.M.; et al.,
2017-01-01
One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis
Distributed Model Predictive Control via Dual Decomposition
DEFF Research Database (Denmark)
Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle
2014-01-01
This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...
Predictive Modelling of Mycotoxins in Cereals
Fels, van der H.J.; Liu, C.
2015-01-01
In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts produ
Leptogenesis in minimal predictive seesaw models
Björkeroth, Fredrik; Varzielas, Ivo de Medeiros; King, Stephen F
2015-01-01
We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the "atmospheric" and "solar" neutrino masses with Yukawa couplings to $(\
Specialized Language Models using Dialogue Predictions
Popovici, C; Popovici, Cosmin; Baggia, Paolo
1996-01-01
This paper analyses language modeling in spoken dialogue systems for accessing a database. The use of several language models obtained by exploiting dialogue predictions gives better results than the use of a single model for the whole dialogue interaction. For this reason several models have been created, each one for a specific system question, such as the request or the confirmation of a parameter. The use of dialogue-dependent language models increases the performance both at the recognition and at the understanding level, especially on answers to system requests. Moreover other methods to increase performance, like automatic clustering of vocabulary words or the use of better acoustic models during recognition, does not affect the improvements given by dialogue-dependent language models. The system used in our experiments is Dialogos, the Italian spoken dialogue system used for accessing railway timetable information over the telephone. The experiments were carried out on a large corpus of dialogues coll...
Caries risk assessment models in caries prediction
Directory of Open Access Journals (Sweden)
Amila Zukanović
2013-11-01
Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.
Disease prediction models and operational readiness.
Directory of Open Access Journals (Sweden)
Courtney D Corley
Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology
Model Predictive Control based on Finite Impulse Response Models
DEFF Research Database (Denmark)
Prasath, Guru; Jørgensen, John Bagterp
2008-01-01
We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...
Mathematical modelling methodologies in predictive food microbiology: a SWOT analysis.
Ferrer, Jordi; Prats, Clara; López, Daniel; Vives-Rego, Josep
2009-08-31
Predictive microbiology is the area of food microbiology that attempts to forecast the quantitative evolution of microbial populations over time. This is achieved to a great extent through models that include the mechanisms governing population dynamics. Traditionally, the models used in predictive microbiology are whole-system continuous models that describe population dynamics by means of equations applied to extensive or averaged variables of the whole system. Many existing models can be classified by specific criteria. We can distinguish between survival and growth models by seeing whether they tackle mortality or cell duplication. We can distinguish between empirical (phenomenological) models, which mathematically describe specific behaviour, and theoretical (mechanistic) models with a biological basis, which search for the underlying mechanisms driving already observed phenomena. We can also distinguish between primary, secondary and tertiary models, by examining their treatment of the effects of external factors and constraints on the microbial community. Recently, the use of spatially explicit Individual-based Models (IbMs) has spread through predictive microbiology, due to the current technological capacity of performing measurements on single individual cells and thanks to the consolidation of computational modelling. Spatially explicit IbMs are bottom-up approaches to microbial communities that build bridges between the description of micro-organisms at the cell level and macroscopic observations at the population level. They provide greater insight into the mesoscale phenomena that link unicellular and population levels. Every model is built in response to a particular question and with different aims. Even so, in this research we conducted a SWOT (Strength, Weaknesses, Opportunities and Threats) analysis of the different approaches (population continuous modelling and Individual-based Modelling), which we hope will be helpful for current and future
Should we believe model predictions of future climate change? (Invited)
Knutti, R.
2009-12-01
As computers get faster and our understanding of the climate system improves, climate models to predict the future are getting more complex by including more and more processes, and they are run at higher and higher resolution to resolve more of the small scale processes. As a result, some of the simulated features and structures, e.g. ocean eddies or tropical cyclones look surprisingly real. But are these deceptive? A pattern can look perfectly real but be in the wrong place. So can the current global models really provide the kind of information on local scales and on the quantities (e.g. extreme events) that the decision maker would need to know to invest for example in adaptation? A closer look indicates that evaluating skill of climate models and quantifying uncertainties in predictions is very difficult. This presentation shows that while models are improving in simulating the climate features we observe (e.g. the present day mean state, or the El Nino Southern Oscillation), the spread from multiple models in predicting future changes is often not decreasing. The main problem is that (unlike with weather forecasts for example) we cannot evaluate the model on a prediction (for example for the year 2100) and we have to use the present, or past changes as metrics of skills. But there are infinite ways of testing a model, and many metrics used to test models do not clearly relate to the prediction. Therefore there is little agreement in the community on metrics to separate ‘good’ and ‘bad’ models, and there is a concern that model development, evaluation and posterior weighting or ranking of models are all using the same datasets. While models are continuously improving in representing what we believe to be the key processes, many models also share ideas, parameterizations or even pieces of model code. The current models can therefore not be considered independent. Robustness of a model simulated result is often interpreted as increasing the confidence
Current approaches to gene regulatory network modelling
Directory of Open Access Journals (Sweden)
Brazma Alvis
2007-09-01
Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.
ENSO Prediction using Vector Autoregressive Models
Chapman, D. R.; Cane, M. A.; Henderson, N.; Lee, D.; Chen, C.
2013-12-01
A recent comparison (Barnston et al, 2012 BAMS) shows the ENSO forecasting skill of dynamical models now exceeds that of statistical models, but the best statistical models are comparable to all but the very best dynamical models. In this comparison the leading statistical model is the one based on the Empirical Model Reduction (EMR) method. Here we report on experiments with multilevel Vector Autoregressive models using only sea surface temperatures (SSTs) as predictors. VAR(L) models generalizes Linear Inverse Models (LIM), which are a VAR(1) method, as well as multilevel univariate autoregressive models. Optimal forecast skill is achieved using 12 to 14 months of prior state information (i.e 12-14 levels), which allows SSTs alone to capture the effects of other variables such as heat content as well as seasonality. The use of multiple levels allows the model advancing one month at a time to perform at least as well for a 6 month forecast as a model constructed to explicitly forecast 6 months ahead. We infer that the multilevel model has fully captured the linear dynamics (cf. Penland and Magorian, 1993 J. Climate). Finally, while VAR(L) is equivalent to L-level EMR, we show in a 150 year cross validated assessment that we can increase forecast skill by improving on the EMR initialization procedure. The greatest benefit of this change is in allowing the prediction to make effective use of information over many more months.
Electrostatic ion thrusters - towards predictive modeling
Energy Technology Data Exchange (ETDEWEB)
Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)
2014-02-15
The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
Genetic models of homosexuality: generating testable predictions.
Gavrilets, Sergey; Rice, William R
2006-12-22
Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism.
Characterizing Attention with Predictive Network Models.
Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M
2017-04-01
Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.
A Study On Distributed Model Predictive Consensus
Keviczky, Tamas
2008-01-01
We investigate convergence properties of a proposed distributed model predictive control (DMPC) scheme, where agents negotiate to compute an optimal consensus point using an incremental subgradient method based on primal decomposition as described in Johansson et al. [2006, 2007]. The objective of the distributed control strategy is to agree upon and achieve an optimal common output value for a group of agents in the presence of constraints on the agent dynamics using local predictive controllers. Stability analysis using a receding horizon implementation of the distributed optimal consensus scheme is performed. Conditions are given under which convergence can be obtained even if the negotiations do not reach full consensus.
Directory of Open Access Journals (Sweden)
Timothy Heazlewood
2006-12-01
Full Text Available A number of studies have attempted to predict future Olympic performances in athletics and swimming based on trends displayed in previous Olympic Games. Some have utilised linear models to plot and predict change, whereas others have utilised multiple curve estimation methods based on inverse, sigmoidal, quadratic, cubic, compound, logistic, growth and exponential functions. The non linear models displayed closer fits to the actual data and were used to predict performance changes 10's, 100's and 1000's of years into the future. Some models predicted that in some events male and female times and distances would crossover and females would eventually display superior performance to males. Predictions using mathematical models based on pre-1996 athletics and pre-1998 swimming performances were evaluated based on how closely they predicted sprints and jumps, and freestyle swimming performances for both male and females at the 2000 and 2004 Olympic Games. The analyses revealed predictions were closer for the shorter swimming events where men's 50m and women's 50m and 100m actual times were almost identical to predicted times. For both men and women, as the swim distances increased the accuracy of the predictive model decreased, where predicted times were 4.5-7% faster than actual times achieved. The real trends in some events currently displaying performance declines were not foreseen by the mathematical models, which predicted consistent improvements across all athletic and swimming events selected for in this study
NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES
Directory of Open Access Journals (Sweden)
R. G. SILVA
1999-03-01
Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.
Directory of Open Access Journals (Sweden)
Qiaoling Tong
2015-04-01
Full Text Available To realize accurate current control for a boost converter, a precise measurement of the inductor current is required to achieve high resolution current regulating. Current sensors are widely used to measure the inductor current. However, the current sensors and their processing circuits significantly contribute extra hardware cost, delay and noise to the system. They can also harm the system reliability. Therefore, current sensorless control techniques can bring cost effective and reliable solutions for various boost converter applications. According to the derived accurate model, which contains a number of parasitics, the boost converter is a nonlinear system. An Extended Kalman Filter (EKF is proposed for inductor current estimation and output voltage filtering. With this approach, the system can have the same advantages as sensored current control mode. To implement EKF, the load value is necessary. However, the load may vary from time to time. This can lead to errors of current estimation and filtered output voltage. To solve this issue, a load variation elimination effect elimination (LVEE module is added. In addition, a predictive average current controller is used to regulate the current. Compared with conventional voltage controlled system, the transient response is greatly improved since it only takes two switching cycles for the current to reach its reference. Finally, experimental results are presented to verify the stable operation and output tracking capability for large-signal transients of the proposed algorithm.
Interpolation techniques in robust constrained model predictive control
Kheawhom, Soorathep; Bumroongsri, Pornchai
2017-05-01
This work investigates interpolation techniques that can be employed on off-line robust constrained model predictive control for a discrete time-varying system. A sequence of feedback gains is determined by solving off-line a series of optimal control optimization problems. A sequence of nested corresponding robustly positive invariant set, which is either ellipsoidal or polyhedral set, is then constructed. At each sampling time, the smallest invariant set containing the current state is determined. If the current invariant set is the innermost set, the pre-computed gain associated with the innermost set is applied. If otherwise, a feedback gain is variable and determined by a linear interpolation of the pre-computed gains. The proposed algorithms are illustrated with case studies of a two-tank system. The simulation results showed that the proposed interpolation techniques significantly improve control performance of off-line robust model predictive control without much sacrificing on-line computational performance.
Performance model to predict overall defect density
Directory of Open Access Journals (Sweden)
J Venkatesh
2012-08-01
Full Text Available Management by metrics is the expectation from the IT service providers to stay as a differentiator. Given a project, the associated parameters and dynamics, the behaviour and outcome need to be predicted. There is lot of focus on the end state and in minimizing defect leakage as much as possible. In most of the cases, the actions taken are re-active. It is too late in the life cycle. Root cause analysis and corrective actions can be implemented only to the benefit of the next project. The focus has to shift left, towards the execution phase than waiting for lessons to be learnt post the implementation. How do we pro-actively predict defect metrics and have a preventive action plan in place. This paper illustrates the process performance model to predict overall defect density based on data from projects in an organization.
Neuro-fuzzy modeling in bankruptcy prediction
Directory of Open Access Journals (Sweden)
Vlachos D.
2003-01-01
Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.
MULTIVARIATE MODEL FOR CORPORATE BANKRUPTCY PREDICTION IN ROMANIA
Directory of Open Access Journals (Sweden)
Daniel BRÎNDESCU – OLARIU
2016-06-01
Full Text Available The current paper proposes a methodology for bankruptcy prediction applicable for Romanian companies. Low bankruptcy frequencies registered in the past have limited the importance of bankruptcy prediction in Romania. The changes in the economic environment brought by the economic crisis, as well as by the entrance in the European Union, make the availability of performing bankruptcy assessment tools more important than ever before. The proposed methodology is centred on a multivariate model, developed through discriminant analysis. Financial ratios are employed as explanatory variables within the model. The study has included 53,252 yearly financial statements from the period 2007 – 2010, with the state of the companies being monitored until the end of 2012. It thus employs the largest sample ever used in Romanian research in the field of bankruptcy prediction, not targeting high levels of accuracy over isolated samples, but reliability and ease of use over the entire population.
Testing the Predictions of the Universal Structured GRB Jet Model
Nakar, E; Guetta, D; Nakar, Ehud; Granot, Jonathan; Guetta, Dafne
2004-01-01
The two leading models for the structure of GRB jets are (1) the uniform jet model, where the energy per solid angle, $\\epsilon$, is roughly constant within some finite half-opening angle, $\\theta_j$, and sharply drops outside of $\\theta_j$, and (2) the universal structured jet (USJ) model, where all GRB jets are intrinsically identical, and $\\epsilon$ drops as the inverse square of the angle from the jet axis. The simplicity of the USJ model gives it a strong predictive power, including a specific prediction for the observed GRB distribution as a function of both the redshift $z$ and the viewing angle $\\theta$ from the jet axis. We show that the current sample of GRBs with known $z$ and estimated $\\theta$ does not agree with the predictions of the USJ model. This can be best seen for a relatively narrow range in $z$, in which the USJ model predicts that most GRBs should be near the upper end of the observed range in $\\theta$, while in the observed sample most GRBs are near the lower end of that range. Since ...
Energy Technology Data Exchange (ETDEWEB)
Binder, J.O. (ANT Nachrichtentechnik GmbH, Abt. E331, D-7150 Backnang (DE)); Cormack, G.D. (Alberta Telecommunications Research Centre, Alberta T6E 5Y4 (CA))
1990-07-01
The gain versus current characteristic of individual semiconductor laser traveling wave amplifiers is shown to be predictable from data obtained during the anti-reflection coating procedure, namely light versus current curves and the residual reflectivity of the first-coated facet.
Pressure prediction model for compression garment design.
Leung, W Y; Yuen, D W; Ng, Sun Pui; Shi, S Q
2010-01-01
Based on the application of Laplace's law to compression garments, an equation for predicting garment pressure, incorporating the body circumference, the cross-sectional area of fabric, applied strain (as a function of reduction factor), and its corresponding Young's modulus, is developed. Design procedures are presented to predict garment pressure using the aforementioned parameters for clinical applications. Compression garments have been widely used in treating burning scars. Fabricating a compression garment with a required pressure is important in the healing process. A systematic and scientific design method can enable the occupational therapist and compression garments' manufacturer to custom-make a compression garment with a specific pressure. The objectives of this study are 1) to develop a pressure prediction model incorporating different design factors to estimate the pressure exerted by the compression garments before fabrication; and 2) to propose more design procedures in clinical applications. Three kinds of fabrics cut at different bias angles were tested under uniaxial tension, as were samples made in a double-layered structure. Sets of nonlinear force-extension data were obtained for calculating the predicted pressure. Using the value at 0° bias angle as reference, the Young's modulus can vary by as much as 29% for fabric type P11117, 43% for fabric type PN2170, and even 360% for fabric type AP85120 at a reduction factor of 20%. When comparing the predicted pressure calculated from the single-layered and double-layered fabrics, the double-layered construction provides a larger range of target pressure at a particular strain. The anisotropic and nonlinear behaviors of the fabrics have thus been determined. Compression garments can be methodically designed by the proposed analytical pressure prediction model.
The SIR Epidemiology Model in Predicting Herd Immunity
Directory of Open Access Journals (Sweden)
Joanna Nicho
2010-01-01
Full Text Available The Simple Epidemic Model uses three states to describe the spread of an infection: the susceptible (S, the infected (I, and the recovered (R. This model follows the trend of an infection over time and can predict whether an infection will spread. Using this model, epidemiologists may calculate the percentage of the population that needs to be vaccinated in order to provide a population immunity from a disease. This study will compare the vaccination percentage required for herd immunity to measles, mumps, and rubella against the current percentage of vaccinated individuals.
Ac Synchronous Servo Based On The Armature Voltage Prediction Model
Hoshino, Akihiro; Kuromaru, Hiroshi; Kobayashi, Shinichi
1987-10-01
A new control method of the AC synchro-nous servo-system (Brushless DC servo-system) is discussed. The new system is based on the armature voltage prediction model. Without a resolver-digital-conver-ter nor a tachometer-generator, the resolver provides following three signals to the system immediately, they are the current command, the induced voltage, and the rotor speed. The new method realizes a simple hardware configuration. Experimental results show a good performance of the system.
Statistical assessment of predictive modeling uncertainty
Barzaghi, Riccardo; Marotta, Anna Maria
2017-04-01
When the results of geophysical models are compared with data, the uncertainties of the model are typically disregarded. We propose a method for defining the uncertainty of a geophysical model based on a numerical procedure that estimates the empirical auto and cross-covariances of model-estimated quantities. These empirical values are then fitted by proper covariance functions and used to compute the covariance matrix associated with the model predictions. The method is tested using a geophysical finite element model in the Mediterranean region. Using a novel χ2 analysis in which both data and model uncertainties are taken into account, the model's estimated tectonic strain pattern due to the Africa-Eurasia convergence in the area that extends from the Calabrian Arc to the Alpine domain is compared with that estimated from GPS velocities while taking into account the model uncertainty through its covariance structure and the covariance of the GPS estimates. The results indicate that including the estimated model covariance in the testing procedure leads to lower observed χ2 values that have better statistical significance and might help a sharper identification of the best-fitting geophysical models.
Seasonal Predictability in a Model Atmosphere.
Lin, Hai
2001-07-01
The predictability of atmospheric mean-seasonal conditions in the absence of externally varying forcing is examined. A perfect-model approach is adopted, in which a global T21 three-level quasigeostrophic atmospheric model is integrated over 21 000 days to obtain a reference atmospheric orbit. The model is driven by a time-independent forcing, so that the only source of time variability is the internal dynamics. The forcing is set to perpetual winter conditions in the Northern Hemisphere (NH) and perpetual summer in the Southern Hemisphere.A significant temporal variability in the NH 90-day mean states is observed. The component of that variability associated with the higher-frequency motions, or climate noise, is estimated using a method developed by Madden. In the polar region, and to a lesser extent in the midlatitudes, the temporal variance of the winter means is significantly greater than the climate noise, suggesting some potential predictability in those regions.Forecast experiments are performed to see whether the presence of variance in the 90-day mean states that is in excess of the climate noise leads to some skill in the prediction of these states. Ensemble forecast experiments with nine members starting from slightly different initial conditions are performed for 200 different 90-day means along the reference atmospheric orbit. The serial correlation between the ensemble means and the reference orbit shows that there is skill in the 90-day mean predictions. The skill is concentrated in those regions of the NH that have the largest variance in excess of the climate noise. An EOF analysis shows that nearly all the predictive skill in the seasonal means is associated with one mode of variability with a strong axisymmetric component.
Current state of genome-scale modeling in filamentous fungi.
Brandl, Julian; Andersen, Mikael R
2015-06-01
The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full capacity. One of the major bottlenecks in the development of new strains into viable industrial hosts is the alteration of the metabolism towards optimal production. Genome-scale models promise a reduction in the time needed for metabolic engineering by predicting the most potent targets in silico before testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi.
Large eddy simulation subgrid model for soot prediction
El-Asrag, Hossam Abd El-Raouf Mostafa
Soot prediction in realistic systems is one of the most challenging problems in theoretical and applied combustion. Soot formation as a chemical process is very complicated and not fully understood. The major difficulty stems from the chemical complexity of the soot formation process as well as its strong coupling with the other thermochemical and fluid processes that occur simultaneously. Soot is a major byproduct of incomplete combustion, having a strong impact on the environment as well as the combustion efficiency. Therefore, innovative methods is needed to predict soot in realistic configurations in an accurate and yet computationally efficient way. In the current study, a new soot formation subgrid model is developed and reported here. The new model is designed to be used within the context of the Large Eddy Simulation (LES) framework, combined with Linear Eddy Mixing (LEM) as a subgrid combustion model. The final model can be applied equally to premixed and non-premixed flames over any required geometry and flow conditions in the free, the transition, and the continuum regimes. The soot dynamics is predicted using a Method of Moments approach with Lagrangian Interpolative Closure (MOMIC) for the fractional moments. Since no prior knowledge of the particles distribution is required, the model is generally applicable. The current model accounts for the basic soot transport phenomena as transport by molecular diffusion and Thermophoretic forces. The model is first validated against experimental results for non-sooting swirling non-premixed and partially premixed flames. Next, a set of canonical premixed sooting flames are simulated, where the effect of turbulence, binary diffusivity and C/O ratio on soot formation are studied. Finally, the model is validated against a non-premixed jet sooting flame. The effect of the flame structure on the different soot formation stages as well as the particle size distribution is described. Good results are predicted with
An efficient numerical target strength prediction model: Validation against analysis solutions
Fillinger, L.; Nijhof, M.J.J.; Jong, C.A.F. de
2014-01-01
A decade ago, TNO developed RASP (Rapid Acoustic Signature Prediction), a numerical model for the prediction of the target strength of immersed underwater objects. The model is based on Kirchhoff diffraction theory. It is currently being improved to model refraction, angle dependent reflection and t
A kinetic model for predicting biodegradation.
Dimitrov, S; Pavlov, T; Nedelcheva, D; Reuschenbach, P; Silvani, M; Bias, R; Comber, M; Low, L; Lee, C; Parkerton, T; Mekenyan, O
2007-01-01
Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a '10 day window' criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the '10-day window' criterion.
Institute of Scientific and Technical Information of China (English)
Ali Mohammad Rashidi
2012-01-01
The correlation between the grain size of electrodeposited coatings and the current densities was modeled by considering galvanostatic conditions. In order to test the model by experimental results, nanocrystalline (NC) nickel samples were deposited at different current densities using a Watts bath. The grain size of the deposits was evaluated by X-ray diffraction (XRD) technique. Model predictions were validated by finding a curve being the best-fit to the experimental results which were gathered from literature for different NC coatings in addition to those data measured in this research for NC nickel coatings. According to our model, the variation of grain size with the reciprocal of the current density follows a power law. A good agreement between the experimental results and model predictions was observed which indicated that the derived analytical model is applicable for producting the nanocrystalline electrodeposits with the desired grain size by controling current density.
Disease Prediction Models and Operational Readiness
Energy Technology Data Exchange (ETDEWEB)
Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.
2014-03-19
INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the
Nonlinear model predictive control theory and algorithms
Grüne, Lars
2017-01-01
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...
Predictive Modeling in Actinide Chemistry and Catalysis
Energy Technology Data Exchange (ETDEWEB)
Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-05-16
These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.
PEEX Modelling Platform for Seamless Environmental Prediction
Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku
2017-04-01
The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.
Predictive RANS simulations via Bayesian Model-Scenario Averaging
Edeling, W. N.; Cinnella, P.; Dwight, R. P.
2014-10-01
The turbulence closure model is the dominant source of error in most Reynolds-Averaged Navier-Stokes simulations, yet no reliable estimators for this error component currently exist. Here we develop a stochastic, a posteriori error estimate, calibrated to specific classes of flow. It is based on variability in model closure coefficients across multiple flow scenarios, for multiple closure models. The variability is estimated using Bayesian calibration against experimental data for each scenario, and Bayesian Model-Scenario Averaging (BMSA) is used to collate the resulting posteriors, to obtain a stochastic estimate of a Quantity of Interest (QoI) in an unmeasured (prediction) scenario. The scenario probabilities in BMSA are chosen using a sensor which automatically weights those scenarios in the calibration set which are similar to the prediction scenario. The methodology is applied to the class of turbulent boundary-layers subject to various pressure gradients. For all considered prediction scenarios the standard-deviation of the stochastic estimate is consistent with the measurement ground truth. Furthermore, the mean of the estimate is more consistently accurate than the individual model predictions.
Predictive RANS simulations via Bayesian Model-Scenario Averaging
Energy Technology Data Exchange (ETDEWEB)
Edeling, W.N., E-mail: W.N.Edeling@tudelft.nl [Arts et Métiers ParisTech, DynFluid laboratory, 151 Boulevard de l' Hospital, 75013 Paris (France); Delft University of Technology, Faculty of Aerospace Engineering, Kluyverweg 2, Delft (Netherlands); Cinnella, P., E-mail: P.Cinnella@ensam.eu [Arts et Métiers ParisTech, DynFluid laboratory, 151 Boulevard de l' Hospital, 75013 Paris (France); Dwight, R.P., E-mail: R.P.Dwight@tudelft.nl [Delft University of Technology, Faculty of Aerospace Engineering, Kluyverweg 2, Delft (Netherlands)
2014-10-15
The turbulence closure model is the dominant source of error in most Reynolds-Averaged Navier–Stokes simulations, yet no reliable estimators for this error component currently exist. Here we develop a stochastic, a posteriori error estimate, calibrated to specific classes of flow. It is based on variability in model closure coefficients across multiple flow scenarios, for multiple closure models. The variability is estimated using Bayesian calibration against experimental data for each scenario, and Bayesian Model-Scenario Averaging (BMSA) is used to collate the resulting posteriors, to obtain a stochastic estimate of a Quantity of Interest (QoI) in an unmeasured (prediction) scenario. The scenario probabilities in BMSA are chosen using a sensor which automatically weights those scenarios in the calibration set which are similar to the prediction scenario. The methodology is applied to the class of turbulent boundary-layers subject to various pressure gradients. For all considered prediction scenarios the standard-deviation of the stochastic estimate is consistent with the measurement ground truth. Furthermore, the mean of the estimate is more consistently accurate than the individual model predictions.
Probabilistic prediction models for aggregate quarry siting
Robinson, G.R.; Larkins, P.M.
2007-01-01
Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood (prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology, transportation network, and population density variables, were defined using quarry location and time of development data for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas, were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area, and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations. Both the aggregate industry and the land management community can use the model approach to target areas for more detailed site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation and population features. ?? International Association for Mathematical Geology 2007.
Operational advances in ring current modeling using RAM-SCB
Energy Technology Data Exchange (ETDEWEB)
Welling, Daniel T [Los Alamos National Laboratory; Jordanova, Vania K [Los Alamos National Laboratory; Zaharia, Sorin G [Los Alamos National Laboratory; Morley, Steven K [Los Alamos National Laboratory
2010-12-03
The Ring current Atmosphere interaction Model with Self-Consistently calculated 3D Magnetic field (RAM-SCB) combines a kinetic model of the ring current with a force-balanced model of the magnetospheric magnetic field to create an inner magnetospheric model that is magnetically self consistent. RAM-SCB produces a wealth of outputs that are valuable to space weather applications. For example, the anisotropic particle distribution of the KeV-energy population calculated by the code is key for predicting surface charging on spacecraft. Furthermore, radiation belt codes stand to benefit substantially from RAM-SCB calculated magnetic field values and plasma wave growth rates - both important for determining the evolution of relativistic electron populations. RAM-SCB is undergoing development to bring these benefits to the space weather community. Data-model validation efforts are underway to assess the performance of the system. 'Virtual Satellite' capability has been added to yield satellite-specific particle distribution and magnetic field output. The code's outer boundary is being expanded to 10 Earth Radii to encompass previously neglected geosynchronous orbits and allow the code to be driven completely by either empirical or first-principles based inputs. These advances are culminating towards a new, real-time version of the code, rtRAM-SCB, that can monitor the inner magnetosphere conditions on both a global and spacecraft-specific level. This paper summarizes these new features as well as the benefits they provide the space weather community.
Predicting Footbridge Response using Stochastic Load Models
DEFF Research Database (Denmark)
Pedersen, Lars; Frier, Christian
2013-01-01
Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing s...... as it pinpoints which decisions to be concerned about when the goal is to predict footbridge response. The studies involve estimating footbridge responses using Monte-Carlo simulations and focus is on estimating vertical structural response to single person loading....
Nonconvex Model Predictive Control for Commercial Refrigeration
DEFF Research Database (Denmark)
Hovgaard, Tobias Gybel; Larsen, Lars F.S.; Jørgensen, John Bagterp
2013-01-01
is to minimize the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost...... the iterations, which is more than fast enough to run in real-time. We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost...
Charged-current inclusive neutrino cross sections in the SuperScaling model
Energy Technology Data Exchange (ETDEWEB)
Ivanov, M. V., E-mail: martin.inrne@gmail.com [Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia 1784 (Bulgaria); Grupo de Física Nuclear, Departamento de Física Atómica, Molecular y Nuclear, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid E-28040 (Spain); Megias, G. D.; Caballero, J. A. [Departamento de Física Atómica, Molecular y Nuclear, Universidad de Sevilla, 41080 Sevilla (Spain); González-Jiménez, R. [Department of Physics and Astronomy, Ghent University, Proeftuinstraat 86, B-9000 Gent (Belgium); Moreno, O.; Donnelly, T. W. [Center for Theoretical Physics, Laboratory for Nuclear Science and Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Barbaro, M. B. [Dipartimento di Fisica, Università di Torino and INFN, Sezione di Torino, Via P. Giuria 1, 10125 Torino (Italy); Antonov, A. N. [Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia 1784 (Bulgaria); Moya de Guerra, E.; Udías, J. M. [Grupo de Física Nuclear, Departamento de Física Atómica, Molecular y Nuclear, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid E-28040 (Spain)
2016-03-25
SuperScaling model (SuSA) predictions to neutrino-induced charged-current π{sup +} production in the Δ-resonance region are explored under MiniBooNE experimental conditions. The SuSA charged-current π{sup +} results are in good agreement with data on neutrino flux-averaged double-differential cross sections. The SuSA model for quasielastic scattering and its extension to the pion production region are used for predictions of charged-current inclusive neutrino-nucleus cross sections. Results are also compared with the T2K experimental data for inclusive scattering.
Constructing predictive models of human running.
Maus, Horst-Moritz; Revzen, Shai; Guckenheimer, John; Ludwig, Christian; Reger, Johann; Seyfarth, Andre
2015-02-06
Running is an essential mode of human locomotion, during which ballistic aerial phases alternate with phases when a single foot contacts the ground. The spring-loaded inverted pendulum (SLIP) provides a starting point for modelling running, and generates ground reaction forces that resemble those of the centre of mass (CoM) of a human runner. Here, we show that while SLIP reproduces within-step kinematics of the CoM in three dimensions, it fails to reproduce stability and predict future motions. We construct SLIP control models using data-driven Floquet analysis, and show how these models may be used to obtain predictive models of human running with six additional states comprising the position and velocity of the swing-leg ankle. Our methods are general, and may be applied to any rhythmic physical system. We provide an approach for identifying an event-driven linear controller that approximates an observed stabilization strategy, and for producing a reduced-state model which closely recovers the observed dynamics. © 2014 The Author(s) Published by the Royal Society. All rights reserved.
Statistical Seasonal Sea Surface based Prediction Model
Suarez, Roberto; Rodriguez-Fonseca, Belen; Diouf, Ibrahima
2014-05-01
The interannual variability of the sea surface temperature (SST) plays a key role in the strongly seasonal rainfall regime on the West African region. The predictability of the seasonal cycle of rainfall is a field widely discussed by the scientific community, with results that fail to be satisfactory due to the difficulty of dynamical models to reproduce the behavior of the Inter Tropical Convergence Zone (ITCZ). To tackle this problem, a statistical model based on oceanic predictors has been developed at the Universidad Complutense of Madrid (UCM) with the aim to complement and enhance the predictability of the West African Monsoon (WAM) as an alternative to the coupled models. The model, called S4CAST (SST-based Statistical Seasonal Forecast) is based on discriminant analysis techniques, specifically the Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA). Beyond the application of the model to the prediciton of rainfall in West Africa, its use extends to a range of different oceanic, atmospheric and helth related parameters influenced by the temperature of the sea surface as a defining factor of variability.
Current status: Animal models of nausea
Fox, Robert A.
1991-01-01
The advantages, and possible benefits of a valid, reliable animal model for nausea are discussed, and difficulties inherent to the development of a model are considered. A principle problem for developing models arises because nausea is a subjective sensation that can be identified only in humans. Several putative measures of nausea in animals are considered, with more detailed consideration directed to variation in cardiac rate, levels of vasopressin, and conditioned taste aversion. Demonstration that putative measures are associated with reported nausea in humans is proposed as a requirement for validating measures to be used in animal models. The necessity for a 'real-time' measure of nausea is proposed as an important factor for future research; and the need for improved understanding of the neuroanatomy underlying the emetic syndrome is discussed.
Current status of cosmological MDM model
Mikheeva, E V; Arkhipova, N A; Malinovsky, A M
2000-01-01
An analysis of cosmological models in spatially flat Friedmann Universe with cosmic gravitational wave background and zero $\\Lambda$-term is presented. The number of free parameters is equal to 5, they are $\\sigma_8$, $n$, $\\Omega_\
Surface CUrrents from a Diagnostic model (SCUD): Pacific
National Oceanic and Atmospheric Administration, Department of Commerce — The SCUD data product is an estimate of upper-ocean velocities computed from a diagnostic model (Surface CUrrents from a Diagnostic model). This model makes daily...
Modeling of the Feed-Motor Transient Current in End Milling by Using Varying-Coefficient Model
Directory of Open Access Journals (Sweden)
Mi Xiao
2015-01-01
Full Text Available In order to ensure the stability of the machining process, it is vital to control the machining condition during the milling process. While the feed-motor current is related to many physical variables, such as the cutting force and tool wear, we can indicate it as the key variables to monitoring the conditions of the milling process. A predictive model of the feed-motor current amplitude is established in this paper. The change regulation of the transient current amplitude during the milling process is investigated, and the effect of the spindle speed on the transient current amplitude is studied as well. Since the transient current amplitude is time-varying, the predictive model is a typical panel data type. In this case, the varying-coefficient model (VCM, a potential soft computing method, is applied to solve this predictive model. Then several experiments are conducted to evaluate the performance of VCM method. Results show that the predicted values match the experimental value well, and the correctness of the predictive model for transient current amplitude is also validated.
Model predictive control for Z-source power converter
DEFF Research Database (Denmark)
Mo, W.; Loh, P.C.; Blaabjerg, Frede
2011-01-01
This paper presents Model Predictive Control (MPC) of impedance-source (commonly known as Z-source) power converter. Output voltage control and current control for Z-source inverter are analyzed and simulated. With MPC's ability of multi- system variables regulation, load current and voltage...... regulations, impedance network inductor current, capacitor voltage as well as switching frequency fixation, transient reservation and null state penalization are all regulated as subjecting to constraints of this control method. The quality of output waveform, stability of impedance-network, level constraint...... of variable switching frequency as well as robustness of transient response can be obtained at the same time with a formulated Z-source network model. Operating steady state and transient state simulation of MPC are going to be presented, which shows good reference tracking ability of this control method....
Advances in modeling of lower hybrid current drive
Peysson, Y.; Decker, J.; Nilsson, E.; Artaud, J.-F.; Ekedahl, A.; Goniche, M.; Hillairet, J.; Ding, B.; Li, M.; Bonoli, P. T.; Shiraiwa, S.; Madi, M.
2016-04-01
that this model is valid up to a line-averaged density of {{\\overline{n}}e}≃ 1.0× {{10}+20} m-3, a statement that is confirmed by simulations of the HXR scaling law with density. While simulations with GENRAY/CQL3D codes have ascribed the fast decrease of the HXR emission with density to parasitic absorption in the scrape-off layer by collisional damping, an alternative interpretetation based on an enhanced refraction as the LH wave propagates in the vicinity of the X-point is provided by C3PO/LUKE codes. The consequences for the predictions of LH current in ITER are discussed.
Zhu, Mu; Chen, Wenhong; Hirdes, John P; Stolee, Paul
2007-10-01
There may be great potential for using computer-modeling techniques and machine-learning algorithms in clinical decision making, if these can be shown to produce results superior to clinical protocols currently in use. We aim to explore the potential to use an automatic, data-driven, machine-learning algorithm in clinical decision making. Using a database containing comprehensive health assessment information (the interRAI-HC) on home care clients (N=24,724) from eight community-care regions in Ontario, Canada, we compare the performance of the K-nearest neighbor (KNN) algorithm and a Clinical Assessment Protocol (the "ADLCAP") currently used to predict rehabilitation potential. For our purposes, we define a patient as having rehabilitation potential if the patient had functional improvement or remained at home over a follow-up period of approximately 1 year. The KNN algorithm has a lower false positive rate in all but one of the eight regions in the sample, and lower false negative rates in all regions. Compared using likelihood ratio statistics, KNN is uniformly more informative than the ADLCAP. This article illustrates the potential for a machine-learning algorithm to enhance clinical decision making.
Predictive modeling by the cerebellum improves proprioception.
Bhanpuri, Nasir H; Okamura, Allison M; Bastian, Amy J
2013-09-04
Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance.
Meteorological Drought Prediction Using a Multi-Model Ensemble Approach
Chen, L.; Mo, K. C.; Zhang, Q.; Huang, J.
2013-12-01
In the United States, drought is among the costliest natural hazards, with an annual average of 6 billion dollars in damage. Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Started in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the National Multi-Model Ensemble (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the meteorological drought predictability using the retrospective NMME forecasts for the period from 1982 to 2010. Before predicting SPI, monthly-mean precipitation (P) forecasts from each model were bias corrected and spatially downscaled (BCSD) to regional grids of 0.5-degree resolution over the contiguous United States based on the probability distribution functions derived from the hindcasts. The corrected P forecasts were then appended to the CPC Unified Precipitation Analysis to form a P time series for computing 3-month and 6-month SPIs. The ensemble SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation and root-mean-square errors against the observations, are used to evaluate forecast skill. For P forecasts, errors vary among models and skill generally is low after the second month. All model P forecasts have higher skill in winter and lower skill in summer. In wintertime, BCSD improves both P and SPI forecast skill. Most improvements are over the western mountainous regions and along the Great Lake. Overall, SPI predictive skill is regionally and seasonally dependent. The six-month SPI forecasts are skillful out to four months. For
NASA AVOSS Fast-Time Wake Prediction Models: User's Guide
Ahmad, Nash'at N.; VanValkenburg, Randal L.; Pruis, Matthew
2014-01-01
The National Aeronautics and Space Administration (NASA) is developing and testing fast-time wake transport and decay models to safely enhance the capacity of the National Airspace System (NAS). The fast-time wake models are empirical algorithms used for real-time predictions of wake transport and decay based on aircraft parameters and ambient weather conditions. The aircraft dependent parameters include the initial vortex descent velocity and the vortex pair separation distance. The atmospheric initial conditions include vertical profiles of temperature or potential temperature, eddy dissipation rate, and crosswind. The current distribution includes the latest versions of the APA (3.4) and the TDP (2.1) models. This User's Guide provides detailed information on the model inputs, file formats, and the model output. An example of a model run and a brief description of the Memphis 1995 Wake Vortex Dataset is also provided.
Gamma-Ray Pulsars Models and Predictions
Harding, A K
2001-01-01
Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...
Artificial Neural Network Model for Predicting Compressive
Directory of Open Access Journals (Sweden)
Salim T. Yousif
2013-05-01
Full Text Available Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.
Ground Motion Prediction Models for Caucasus Region
Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino
2016-04-01
Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.
Modeling and Prediction of Krueger Device Noise
Guo, Yueping; Burley, Casey L.; Thomas, Russell H.
2016-01-01
This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.
A generative model for predicting terrorist incidents
Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger
2017-05-01
A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations
Modeling the Current and Future Distribution of Treeline Species in the Nepal Himalaya
Chhetri, P. K.; Cairns, D. M.
2015-12-01
Knowledge of the current distribution of treeline species is important for predicting their future distribution in the landscape. Many studies have indicated that treeline will advance with climate change. Treeline advance will result in a loss of alpine biodiversity because the advancing treeline will fragment alpine ecosystems. A species distribution modeling approach using predicted climate data can increase our understanding of how treeline species will expand their range in the future. We used the Maxent model to predict the current and future distributions of three dominant treeline species, Abies spectabilis, Betula utilis, and Pinus wallichiana, of the Nepal Himalaya. The Maxent model predicted that the distribution of treeline species will change significantly under future climate change scenarios. The range of these treeline species will expand northward or upslope in response to future climate change. The model also indicated that temperature-related climatic variables are the most important determinants of the distribution of treeline species.
Current Density and Continuity in Discretized Models
Boykin, Timothy B.; Luisier, Mathieu; Klimeck, Gerhard
2010-01-01
Discrete approaches have long been used in numerical modelling of physical systems in both research and teaching. Discrete versions of the Schrodinger equation employing either one or several basis functions per mesh point are often used by senior undergraduates and beginning graduate students in computational physics projects. In studying…
Hidden Semi-Markov Models for Predictive Maintenance
Directory of Open Access Journals (Sweden)
Francesco Cartella
2015-01-01
Full Text Available Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs with (i no constraints on the state duration density function and (ii being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL of the machine is calculated.
Optimal feedback scheduling of model predictive controllers
Institute of Scientific and Technical Information of China (English)
Pingfang ZHOU; Jianying XIE; Xiaolong DENG
2006-01-01
Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.
Objective calibration of numerical weather prediction models
Voudouri, A.; Khain, P.; Carmona, I.; Bellprat, O.; Grazzini, F.; Avgoustoglou, E.; Bettems, J. M.; Kaufmann, P.
2017-07-01
Numerical weather prediction (NWP) and climate models use parameterization schemes for physical processes, which often include free or poorly confined parameters. Model developers normally calibrate the values of these parameters subjectively to improve the agreement of forecasts with available observations, a procedure referred as expert tuning. A practicable objective multi-variate calibration method build on a quadratic meta-model (MM), that has been applied for a regional climate model (RCM) has shown to be at least as good as expert tuning. Based on these results, an approach to implement the methodology to an NWP model is presented in this study. Challenges in transferring the methodology from RCM to NWP are not only restricted to the use of higher resolution and different time scales. The sensitivity of the NWP model quality with respect to the model parameter space has to be clarified, as well as optimize the overall procedure, in terms of required amount of computing resources for the calibration of an NWP model. Three free model parameters affecting mainly turbulence parameterization schemes were originally selected with respect to their influence on the variables associated to daily forecasts such as daily minimum and maximum 2 m temperature as well as 24 h accumulated precipitation. Preliminary results indicate that it is both affordable in terms of computer resources and meaningful in terms of improved forecast quality. In addition, the proposed methodology has the advantage of being a replicable procedure that can be applied when an updated model version is launched and/or customize the same model implementation over different climatological areas.
Geological modelling of mineral deposits for prediction in mining
Sides, E. J.
Accurate prediction of the shape, location, size and properties of the solid rock materials to be extracted during mining is essential for reliable technical and financial planning. This is achieved through geological modelling of the three-dimensional (3D) shape and properties of the materials present in mineral deposits, and the presentation of results in a form which is accessible to mine planning engineers. In recent years the application of interactive graphics software, offering 3D database handling, modelling and visualisation, has greatly enhanced the options available for predicting the subsurface limits and characteristics of mineral deposits. A review of conventional 3D geological interpretation methods, and the model struc- tures and modelling methods used in reserve estimation and mine planning software packages, illustrates the importance of such approaches in the modern mining industry. Despite the widespread introduction and acceptance of computer hardware and software in mining applications, in recent years, there has been little fundamental change in the way in which geology is used in orebody modelling for predictive purposes. Selected areas of current research, aimed at tackling issues such as the use of orientation data, quantification of morphological differences, incorporation of geological age relationships, multi-resolution models and the application of virtual reality hardware and software, are discussed.
Prediction models from CAD models of 3D objects
Camps, Octavia I.
1992-11-01
In this paper we present a probabilistic prediction based approach for CAD-based object recognition. Given a CAD model of an object, the PREMIO system combines techniques of analytic graphics and physical models of lights and sensors to predict how features of the object will appear in images. In nearly 4,000 experiments on analytically-generated and real images, we show that in a semi-controlled environment, predicting the detectability of features of the image can successfully guide a search procedure to make informed choices of model and image features in its search for correspondences that can be used to hypothesize the pose of the object. Furthermore, we provide a rigorous experimental protocol that can be used to determine the optimal number of correspondences to seek so that the probability of failing to find a pose and of finding an inaccurate pose are minimized.
Introducing Model Predictive Control for Improving Power Plant Portfolio Performance
DEFF Research Database (Denmark)
Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon
2008-01-01
This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the `1-norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current...
Model predictive control of MSMPR crystallizers
Moldoványi, Nóra; Lakatos, Béla G.; Szeifert, Ferenc
2005-02-01
A multi-input-multi-output (MIMO) control problem of isothermal continuous crystallizers is addressed in order to create an adequate model-based control system. The moment equation model of mixed suspension, mixed product removal (MSMPR) crystallizers that forms a dynamical system is used, the state of which is represented by the vector of six variables: the first four leading moments of the crystal size, solute concentration and solvent concentration. Hence, the time evolution of the system occurs in a bounded region of the six-dimensional phase space. The controlled variables are the mean size of the grain; the crystal size-distribution and the manipulated variables are the input concentration of the solute and the flow rate. The controllability and observability as well as the coupling between the inputs and the outputs was analyzed by simulation using the linearized model. It is shown that the crystallizer is a nonlinear MIMO system with strong coupling between the state variables. Considering the possibilities of the model reduction, a third-order model was found quite adequate for the model estimation in model predictive control (MPC). The mean crystal size and the variance of the size distribution can be nearly separately controlled by the residence time and the inlet solute concentration, respectively. By seeding, the controllability of the crystallizer increases significantly, and the overshoots and the oscillations become smaller. The results of the controlling study have shown that the linear MPC is an adaptable and feasible controller of continuous crystallizers.
An Anisotropic Hardening Model for Springback Prediction
Zeng, Danielle; Xia, Z. Cedric
2005-08-01
As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.
Models of Solar Irradiance Variations: Current Status
Indian Academy of Sciences (India)
Natalie A. Krivova; Sami K. Solanki
2008-03-01
Regular monitoring of solar irradiance has been carried out since 1978 to show that solar total and spectral irradiance varies at different time scales. Whereas variations on time scales of minutes to hours are due to solar oscillations and granulation, variations on longer time scales are driven by the evolution of the solar surface magnetic field. Here the most recent advances in modelling of solar irradiance variations on time scales longer than a day are briefly reviewed.
High performance predictive current control of a three phase VSI: An experimental assessment
Indian Academy of Sciences (India)
Vikas Kumar; Prerna Gaur; A P Mittal
2014-12-01
Delay has a significant role to play in the implementation of the predictive current control scheme as large amount of calculations are involved. Compensating delay in the predictive current controller design can lead to an improved load current total harmonic distortion (THD) and also an increased switching frequency. Minimization of switching frequency while maintaining the lower value of load current THD requires multiple objective optimization which is achieved by optimizing a single objective function, constructed using weighting factors as a linear combination of individual objective function. The effect of weighting factor on the switching frequency minimization and the current tracking error with delay compensation for the two level voltage source inverter (VSI) are investigated in this paper. The outcomes of the predictive current control using an optimized weighting factor which is calculated using branch and bound algorithm with the delay compensation are compared with the PWM based current control scheme. The experimental tests are conducted on a 2.2 kW VSI to verify the simulation observations.
Estimating Predictive Variance for Statistical Gas Distribution Modelling
Lilienthal, Achim J.; Asadi, Sahar; Reggente, Matteo
2009-05-01
Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.
Predictive modelling of contagious deforestation in the Brazilian Amazon.
Directory of Open Access Journals (Sweden)
Isabel M D Rosa
Full Text Available Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1 it is probabilistic and quantifies uncertainty around predictions and parameters; (2 the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3 deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia"-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy, annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is
Predictive modelling of contagious deforestation in the Brazilian Amazon.
Rosa, Isabel M D; Purves, Drew; Souza, Carlos; Ewers, Robert M
2013-01-01
Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia")-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently
Predictive modelling of ferroelectric tunnel junctions
Velev, Julian P.; Burton, John D.; Zhuravlev, Mikhail Ye; Tsymbal, Evgeny Y.
2016-05-01
Ferroelectric tunnel junctions combine the phenomena of quantum-mechanical tunnelling and switchable spontaneous polarisation of a nanometre-thick ferroelectric film into novel device functionality. Switching the ferroelectric barrier polarisation direction produces a sizable change in resistance of the junction—a phenomenon known as the tunnelling electroresistance effect. From a fundamental perspective, ferroelectric tunnel junctions and their version with ferromagnetic electrodes, i.e., multiferroic tunnel junctions, are testbeds for studying the underlying mechanisms of tunnelling electroresistance as well as the interplay between electric and magnetic degrees of freedom and their effect on transport. From a practical perspective, ferroelectric tunnel junctions hold promise for disruptive device applications. In a very short time, they have traversed the path from basic model predictions to prototypes for novel non-volatile ferroelectric random access memories with non-destructive readout. This remarkable progress is to a large extent driven by a productive cycle of predictive modelling and innovative experimental effort. In this review article, we outline the development of the ferroelectric tunnel junction concept and the role of theoretical modelling in guiding experimental work. We discuss a wide range of physical phenomena that control the functional properties of ferroelectric tunnel junctions and summarise the state-of-the-art achievements in the field.
Simple predictions from multifield inflationary models.
Easther, Richard; Frazer, Jonathan; Peiris, Hiranya V; Price, Layne C
2014-04-25
We explore whether multifield inflationary models make unambiguous predictions for fundamental cosmological observables. Focusing on N-quadratic inflation, we numerically evaluate the full perturbation equations for models with 2, 3, and O(100) fields, using several distinct methods for specifying the initial values of the background fields. All scenarios are highly predictive, with the probability distribution functions of the cosmological observables becoming more sharply peaked as N increases. For N=100 fields, 95% of our Monte Carlo samples fall in the ranges ns∈(0.9455,0.9534), α∈(-9.741,-7.047)×10-4, r∈(0.1445,0.1449), and riso∈(0.02137,3.510)×10-3 for the spectral index, running, tensor-to-scalar ratio, and isocurvature-to-adiabatic ratio, respectively. The expected amplitude of isocurvature perturbations grows with N, raising the possibility that many-field models may be sensitive to postinflationary physics and suggesting new avenues for testing these scenarios.
J.S. Vermaat (Joost); I. van der Tweel (Ingeborg); N. Mehra (Niven); S. Sleijfer (Stefan); J.B. Haanen (John); J.M. Roodhart (Jeanine); J.Y. Engwegen (Judith); C.M. Korse (Catharina); M.H. Langenberg (Marlies); W.H.J. Kruit (Wim); G. Groenewegen (Gino); R.H. Giles
2009-01-01
textabstractBackground: In metastatic renal cell cancer (mRCC), the Memorial Sloan-Kettering Cancer Center (MSKCC) risk model is widely used for clinical trial design and patient management. To improve prognostication, we applied proteomics to identify novel serological proteins associated with over
Multinucleon Ejection Model for Two Body Current Neutrino Interactions
Energy Technology Data Exchange (ETDEWEB)
Sobczyk, Jan T.; /Fermilab
2012-06-01
A model is proposed to describe nucleons ejected from a nucleus as a result of two-body-current neutrino interactions. The model can be easily implemented in Monte Carlo neutrino event generators. Various possibilities to measure the two-body-current contribution are discussed. The model can help identify genuine charge current quasielastic events and allow for a better determination of the systematic error on neutrino energy reconstruction in neutrino oscillation experiments.
Formalization of the model of the enterprise insolvency risk prediction
Directory of Open Access Journals (Sweden)
Elena V. Shirinkina
2015-12-01
Full Text Available Objective to improve the conceptual apparatus and analytical procedures of insolvency risk identification. Methods general scientific methods of systemic and comparative analysis economicstatistical and dynamic analysis of economic processes and phenomena. Results nowadays managing the insolvency risk is relevant for any company regardless of the economy sector. Instability manifests itself through the uncertainty of the directions of the external environment changes and their high frequency. Analysis of the economic literature showed that currently there is no single approach to systematization of methods for insolvency risk prediction which means that there is no objective view on tools that can be used to monitor the insolvency risk. In this respect scientific and practical search of representative indicators for the formalization of the models predicting the insolvency is very important. Therefore the study has solved the following tasks defined the nature of the insolvency risk and its identification in the process of financial relations in management system proved the representativeness of the indicators in the insolvency risk prediction and formed the model of the risk insolvency prediction. Scientific novelty grounding the model of risk insolvency prediction. Practical significance development of a theoretical framework to address issues arising in the diagnosis of insolvent enterprises and application of the results obtained in the practice of the bankruptcy institution bodies. The presented model allows to predict the insolvency risk of the enterprise through the general development trend and the fluctuation boundaries of bankruptcy risk to determine the significance of each indicatorfactor its quantitative impact and therefore to avoid the risk of the enterprise insolvency. nbsp
Predictions of models for environmental radiological assessment
Energy Technology Data Exchange (ETDEWEB)
Peres, Sueli da Silva; Lauria, Dejanira da Costa, E-mail: suelip@ird.gov.br, E-mail: dejanira@irg.gov.br [Instituto de Radioprotecao e Dosimetria (IRD/CNEN-RJ), Servico de Avaliacao de Impacto Ambiental, Rio de Janeiro, RJ (Brazil); Mahler, Claudio Fernando [Coppe. Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia, Universidade Federal do Rio de Janeiro (UFRJ) - Programa de Engenharia Civil, RJ (Brazil)
2011-07-01
In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for {sup 137}Cs and {sup 60}Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)
Predicting Protein Secondary Structure with Markov Models
DEFF Research Database (Denmark)
Fischer, Paul; Larsen, Simon; Thomsen, Claus
2004-01-01
we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained......The primary structure of a protein is the sequence of its amino acids. The secondary structure describes structural properties of the molecule such as which parts of it form sheets, helices or coils. Spacial and other properties are described by the higher order structures. The classification task...
A Modified Model Predictive Control Scheme
Institute of Scientific and Technical Information of China (English)
Xiao-Bing Hu; Wen-Hua Chen
2005-01-01
In implementations of MPC (Model Predictive Control) schemes, two issues need to be addressed. One is how to enlarge the stability region as much as possible. The other is how to guarantee stability when a computational time limitation exists. In this paper, a modified MPC scheme for constrained linear systems is described. An offline LMI-based iteration process is introduced to expand the stability region. At the same time, a database of feasible control sequences is generated offline so that stability can still be guaranteed in the case of computational time limitations. Simulation results illustrate the effectiveness of this new approach.
Hierarchical Model Predictive Control for Resource Distribution
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob
2010-01-01
This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... facilitates plug-and-play addition of subsystems without redesign of any controllers. The method is supported by a number of simulations featuring a three-level smart-grid power control system for a small isolated power grid....
Explicit model predictive control accuracy analysis
Knyazev, Andrew; Zhu, Peizhen; Di Cairano, Stefano
2015-01-01
Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be explicitly computed off-line, which results in an off-line partition of the state space into non-overlapped convex regions, with affine control laws associated to each region of the partition. An actual implementation of this explicit MPC in low cost micro-controllers requires the data to be "quantized", i.e. repre...
Critical conceptualism in environmental modeling and prediction.
Christakos, G
2003-10-15
Many important problems in environmental science and engineering are of a conceptual nature. Research and development, however, often becomes so preoccupied with technical issues, which are themselves fascinating, that it neglects essential methodological elements of conceptual reasoning and theoretical inquiry. This work suggests that valuable insight into environmental modeling can be gained by means of critical conceptualism which focuses on the software of human reason and, in practical terms, leads to a powerful methodological framework of space-time modeling and prediction. A knowledge synthesis system develops the rational means for the epistemic integration of various physical knowledge bases relevant to the natural system of interest in order to obtain a realistic representation of the system, provide a rigorous assessment of the uncertainty sources, generate meaningful predictions of environmental processes in space-time, and produce science-based decisions. No restriction is imposed on the shape of the distribution model or the form of the predictor (non-Gaussian distributions, multiple-point statistics, and nonlinear models are automatically incorporated). The scientific reasoning structure underlying knowledge synthesis involves teleologic criteria and stochastic logic principles which have important advantages over the reasoning method of conventional space-time techniques. Insight is gained in terms of real world applications, including the following: the study of global ozone patterns in the atmosphere using data sets generated by instruments on board the Nimbus 7 satellite and secondary information in terms of total ozone-tropopause pressure models; the mapping of arsenic concentrations in the Bangladesh drinking water by assimilating hard and soft data from an extensive network of monitoring wells; and the dynamic imaging of probability distributions of pollutants across the Kalamazoo river.
Using Topic Modeling and Text Embeddings to Predict Deleted Tweets
Energy Technology Data Exchange (ETDEWEB)
Potash, Peter J.; Bell, Eric B.; Harrison, Joshua J.
2016-02-29
Predictive models for tweet deletion have been a relatively unexplored area of Twitter-related computational research. We first approach the deletion of tweets as a spam detection problem, applying a small set of handcrafted features to improve upon the current state-of-the- art in predicting deleted tweets. Next, we apply our approach to a dataset of deleted tweets that better reflects the current deletion rate. Since tweets are deleted for reasons beyond just the presence of spam, we apply topic modeling and text embeddings in order to capture the semantic content of tweets that can lead to tweet deletion. Our goal is to create an effective model that has a low-dimensional feature space and is also language-independent. A lean model would be computationally advantageous processing high-volumes of Twitter data, which can reach 9,885 tweets per second. Our results show that a small set of spam-related features combined with word topics and character-level text embeddings provide the best f1 when trained with a random forest model. The highest precision of the deleted tweet class is achieved by a modification of paragraph2vec to capture author identity.
Wall conditioning for ITER: Current experimental and modeling activities
Energy Technology Data Exchange (ETDEWEB)
Douai, D., E-mail: david.douai@cea.fr [CEA, IRFM, Association Euratom-CEA, 13108 St. Paul lez Durance (France); Kogut, D. [CEA, IRFM, Association Euratom-CEA, 13108 St. Paul lez Durance (France); Wauters, T. [LPP-ERM/KMS, Association Belgian State, 1000 Brussels (Belgium); Brezinsek, S. [FZJ, Institut für Energie- und Klimaforschung Plasmaphysik, 52441 Jülich (Germany); Hagelaar, G.J.M. [Laboratoire Plasma et Conversion d’Energie, UMR5213, Toulouse (France); Hong, S.H. [National Fusion Research Institute, Daejeon 305-806 (Korea, Republic of); Lomas, P.J. [CCFE, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Lyssoivan, A. [LPP-ERM/KMS, Association Belgian State, 1000 Brussels (Belgium); Nunes, I. [Associação EURATOM-IST, Instituto de Plasmas e Fusão Nuclear, 1049-001 Lisboa (Portugal); Pitts, R.A. [ITER International Organization, F-13067 St. Paul lez Durance (France); Rohde, V. [Max-Planck-Institut für Plasmaphysik, 85748 Garching (Germany); Vries, P.C. de [ITER International Organization, F-13067 St. Paul lez Durance (France)
2015-08-15
Wall conditioning will be required in ITER to control fuel and impurity recycling, as well as tritium (T) inventory. Analysis of conditioning cycle on the JET, with its ITER-Like Wall is presented, evidencing reduced need for wall cleaning in ITER compared to JET–CFC. Using a novel 2D multi-fluid model, current density during Glow Discharge Conditioning (GDC) on the in-vessel plasma-facing components (PFC) of ITER is predicted to approach the simple expectation of total anode current divided by wall surface area. Baking of the divertor to 350 °C should desorb the majority of the co-deposited T. ITER foresees the use of low temperature plasma based techniques compatible with the permanent toroidal magnetic field, such as Ion (ICWC) or Electron Cyclotron Wall Conditioning (ECWC), for tritium removal between ITER plasma pulses. Extrapolation of JET ICWC results to ITER indicates removal comparable to estimated T-retention in nominal ITER D:T shots, whereas GDC may be unattractive for that purpose.
Modeling and Prediction of Hot Deformation Flow Curves
Mirzadeh, Hamed; Cabrera, Jose Maria; Najafizadeh, Abbas
2012-01-01
The modeling of hot flow stress and prediction of flow curves for unseen deformation conditions are important in metal-forming processes because any feasible mathematical simulation needs accurate flow description. In the current work, in an attempt to summarize, generalize, and introduce efficient methods, the dynamic recrystallization (DRX) flow curves of a 17-4 PH martensitic precipitation hardening stainless steel, a medium carbon microalloyed steel, and a 304 H austenitic stainless steel were modeled and predicted using (1) a hyperbolic sine equation with strain dependent constants, (2) a developed constitutive equation in a simple normalized stress-normalized strain form and its modified version, and (3) a feed-forward artificial neural network (ANN). These methods were critically discussed, and the ANN technique was found to be the best for the modeling available flow curves; however, the developed constitutive equation showed slightly better performance than that of ANN and significantly better predicted values than those of the hyperbolic sine equation in prediction of flow curves for unseen deformation conditions.
Multivariate Models for Prediction of Human Skin Sensitization ...
One of the lnteragency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens TM assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches , logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine
Predictive Capability Maturity Model for computational modeling and simulation.
Energy Technology Data Exchange (ETDEWEB)
Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.
2007-10-01
The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.
A Predictive Maintenance Model for Railway Tracks
DEFF Research Database (Denmark)
Li, Rui; Wen, Min; Salling, Kim Bang
2015-01-01
For the modern railways, maintenance is critical for ensuring safety, train punctuality and overall capacity utilization. The cost of railway maintenance in Europe is high, on average between 30,000 – 100,000 Euro per km per year [1]. Aiming to reduce such maintenance expenditure, this paper...... presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time...... recovery on the track quality after tamping operation and (5) Tamping machine operation factors. A Danish railway track between Odense and Fredericia with 57.2 km of length is applied for a time period of two to four years in the proposed maintenance model. The total cost can be reduced with up to 50...
A predictive fitness model for influenza
Łuksza, Marta; Lässig, Michael
2014-03-01
The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage.
Predictive Model of Radiative Neutrino Masses
Babu, K S
2013-01-01
We present a simple and predictive model of radiative neutrino masses. It is a special case of the Zee model which introduces two Higgs doublets and a charged singlet. We impose a family-dependent Z_4 symmetry acting on the leptons, which reduces the number of parameters describing neutrino oscillations to four. A variety of predictions follow: The hierarchy of neutrino masses must be inverted; the lightest neutrino mass is extremely small and calculable; one of the neutrino mixing angles is determined in terms of the other two; the phase parameters take CP-conserving values with \\delta_{CP} = \\pi; and the effective mass in neutrinoless double beta decay lies in a narrow range, m_{\\beta \\beta} = (17.6 - 18.5) meV. The ratio of vacuum expectation values of the two Higgs doublets, tan\\beta, is determined to be either 1.9 or 0.19 from neutrino oscillation data. Flavor-conserving and flavor-changing couplings of the Higgs doublets are also determined from neutrino data. The non-standard neutral Higgs bosons, if t...
A predictive model for dimensional errors in fused deposition modeling
DEFF Research Database (Denmark)
Stolfi, A.
2015-01-01
This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...
Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.
Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F
2013-04-01
In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model......A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...
To predict the niche, model colonization and extinction
Yackulic, Charles B.; Nichols, James D.; Reid, Janice; Der, Ricky
2015-01-01
Ecologists frequently try to predict the future geographic distributions of species. Most studies assume that the current distribution of a species reflects its environmental requirements (i.e., the species' niche). However, the current distributions of many species are unlikely to be at equilibrium with the current distribution of environmental conditions, both because of ongoing invasions and because the distribution of suitable environmental conditions is always changing. This mismatch between the equilibrium assumptions inherent in many analyses and the disequilibrium conditions in the real world leads to inaccurate predictions of species' geographic distributions and suggests the need for theory and analytical tools that avoid equilibrium assumptions. Here, we develop a general theory of environmental associations during periods of transient dynamics. We show that time-invariant relationships between environmental conditions and rates of local colonization and extinction can produce substantial temporal variation in occupancy–environment relationships. We then estimate occupancy–environment relationships during three avian invasions. Changes in occupancy–environment relationships over time differ among species but are predicted by dynamic occupancy models. Since estimates of the occupancy–environment relationships themselves are frequently poor predictors of future occupancy patterns, research should increasingly focus on characterizing how rates of local colonization and extinction vary with environmental conditions.
Computational neurorehabilitation: modeling plasticity and learning to predict recovery.
Reinkensmeyer, David J; Burdet, Etienne; Casadio, Maura; Krakauer, John W; Kwakkel, Gert; Lang, Catherine E; Swinnen, Stephan P; Ward, Nick S; Schweighofer, Nicolas
2016-01-01
Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.
Current-voltage model of LED light sources
DEFF Research Database (Denmark)
Beczkowski, Szymon; Munk-Nielsen, Stig
2012-01-01
Amplitude modulation is rarely used for dimming light-emitting diodes in polychromatic luminaires due to big color shifts caused by varying magnitude of LED driving current and nonlinear relationship between intensity of a diode and driving current. Current-voltage empirical model of light...
Forced versus coupled dynamics in Earth system modelling and prediction
Directory of Open Access Journals (Sweden)
B. Knopf
2005-01-01
Full Text Available We compare coupled nonlinear climate models and their simplified forced counterparts with respect to predictability and phase space topology. Various types of uncertainty plague climate change simulation, which is, in turn, a crucial element of Earth System modelling. Since the currently preferred strategy for simulating the climate system, or the Earth System at large, is the coupling of sub-system modules (representing, e.g. atmosphere, oceans, global vegetation, this paper explicitly addresses the errors and indeterminacies generated by the coupling procedure. The focus is on a comparison of forced dynamics as opposed to fully, i.e. intrinsically, coupled dynamics. The former represents a particular type of simulation, where the time behaviour of one complex systems component is prescribed by data or some other external information source. Such a simplifying technique is often employed in Earth System models in order to save computing resources, in particular when massive model inter-comparisons need to be carried out. Our contribution to the debate is based on the investigation of two representative model examples, namely (i a low-dimensional coupled atmosphere-ocean simulator, and (ii a replica-like simulator embracing corresponding components.Whereas in general the forced version (ii is able to mimic its fully coupled counterpart (i, we show in this paper that for a considerable fraction of parameter- and state-space, the two approaches qualitatively differ. Here we take up a phenomenon concerning the predictability of coupled versus forced models that was reported earlier in this journal: the observation that the time series of the forced version display artificial predictive skill. We present an explanation in terms of nonlinear dynamical theory. In particular we observe an intermittent version of artificial predictive skill, which we call on-off synchronization, and trace it back to the appearance of unstable periodic orbits. We also
Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Sajjad Ali Haider
2017-03-01
Full Text Available Studying critical currents, critical temperatures, and critical fields carries substantial importance in the field of superconductivity. In this work, we study critical currents in the current–voltage characteristics of a diluted-square lattice on an Nb film. Our measurements are based on a commercially available Physical Properties Measurement System, which may prove time consuming and costly for repeated measurements for a wide range of parameters. We therefore propose a technique based on artificial neural networks to facilitate extrapolation of these curves for unforeseen values of temperature and magnetic fields. We demonstrate that our proposed algorithm predicts the curves with an immaculate precision and minimal overhead, which may as well be adopted for prediction in other types of regular and diluted lattices. In addition, we present a detailed comparison between three artificial neural networks architectures with respect to their prediction efficiency, computation time, and number of iterations to converge to an optimal solution.
A geomagnetically induced current warning system: model development and validation
McKay, A.; Clarke, E.; Reay, S.; Thomson, A.
Geomagnetically Induced Currents (GIC), which can flow in technological systems at the Earth's surface, are a consequence of magnetic storms and Space Weather. A well-documented practical problem for the power transmission industry is that GIC can affect the lifetime and performance of transformers within the power grid. Operational mitigation is widely considered to be one of the best strategies to manage the Space Weather and GIC risk. Therefore in the UK a magnetic storm warning and GIC monitoring and analysis programme has been under development by the British Geological Survey and Scottish Power plc (the power grid operator for Central Scotland) since 1999. Under the auspices of the European Space Agency's service development activities BGS is developing the capability to meet two key user needs that have been identified. These needs are, firstly, the development of a near real-time solar wind shock/ geomagnetic storm warning, based on L1 solar wind data and, secondly, the development of an integrated surface geo-electric field and power grid network model that should allow prediction of GIC throughout the power grid in near real time. While the final goal is a `seamless package', the components of the package utilise diverse scientific techniques. We review progress to date with particular regard to the validation of the individual components of the package. The Scottish power grid response to the October 2003 magnetic storms is also discussed and model and validation data are presented.
Dynamic Model Predicting Overweight, Obesity, and Extreme Obesity Prevalence Trends
Thomas, Diana M.; Weedermann, Marion; Fuemmeler, Bernard F.; Martin, Corby K.; Dhurandhar, Nikhil V.; Bredlau, Carl; Heymsfield, Steven B.; Ravussin, Eric; Bouchard, Claude
2013-01-01
Objective Obesity prevalence in the United States (US) appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau. Design and Methods A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and non-social influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth. Results The dynamic model predicts that: obesity prevalence is a function of birth rate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of obesity, overweight, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9%, respectively. Conclusions The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence. PMID:23804487
Two criteria for evaluating risk prediction models.
Pfeiffer, R M; Gail, M H
2011-09-01
We propose and study two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF (q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF (p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF (q) assesses the effectiveness of a program that follows 100q% of the population at highest risk. PNF (p) assess the feasibility of covering 100p% of cases by indicating how much of the population at highest risk must be followed. We show the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates of PCF and PNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data.
Modeling the current distribution in HTS tapes with transport current and applied magnetic field
Yazawa, Takashi; Rabbers, Jan-Jaap; Shevchenko, Oleg A.; Haken, ten Bennie; Kate, ten Herman H.J.; Maeda, Hideaki
1999-01-01
A numerical model is developed for the current distribution in a high temperature superconducting (HTS) tape, (Bi,Pb)2Sr2 Ca2Cu3Ox-Ag, subjected to a combination of a transport current and an applied magnetic field. This analysis is based on a two-dimensional formulation of Maxwell's equations in te
Methods for Handling Missing Variables in Risk Prediction Models
Held, Ulrike; Kessels, Alfons; Aymerich, Judith Garcia; Basagana, Xavier; ter Riet, Gerben; Moons, Karel G. M.; Puhan, Milo A.
2016-01-01
Prediction models should be externally validated before being used in clinical practice. Many published prediction models have never been validated. Uncollected predictor variables in otherwise suitable validation cohorts are the main factor precluding external validation.We used individual patient
Social network models predict movement and connectivity in ecological landscapes
Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.
2011-01-01
Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.
Predictive study of the poloidal field coil insert behaviour under pulsed current tests
Lacroix, B.; Ciazynski, D.; Duchateau, J. L.; Nicollet, S.; Pauty, N.
2008-02-01
Within the ITER Poloidal Field conductor design validation, the Poloidal Field Conductor Insert (PFCI) has been manufactured and will be tested in the Central Solenoid Model Coil (CSMC) facility at JAEA Naka (Japan). In this test facility, the PFCI can be tested under ITER-relevant operating conditions, the field produced by the CSMC being varied to simulate the real situation of the PF coils in ITER. Predictive analyses have been performed in order to study the electromagnetic and thermal-hydraulic behaviour of the PFCI, under two scenarios proposed for pulsed current tests. During these scenarios, simulations have been performed with the THEA code, in which classical formulas for the AC losses in a cable have been introduced. The study focuses on the lower part of the winding, which is a 44 m long conductor including a joint. It covers the sample thermal-hydraulic behaviour with particular emphasis on the losses. Due to the overcompaction in the joint area, the total energy dissipated during a scenario can be equivalent in the joint and in the conductor, in spite of the reduced length of the joint (0.45 m). This particular point is discussed and has led to the analysis of the temperature margin in the joint.
Estimating the magnitude of prediction uncertainties for the APLE model
Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, we conduct an uncertainty analysis for the Annual P ...
Dyer, Joseph J.; Brewer, Shannon K.; Worthington, Thomas A.; Bergey, Elizabeth A.
2013-01-01
1.A major limitation to effective management of narrow-range crayfish populations is the paucity of information on the spatial distribution of crayfish species and a general understanding of the interacting environmental variables that drive current and future potential distributional patterns. 2.Maximum Entropy Species Distribution Modeling Software (MaxEnt) was used to predict the current and future potential distributions of four endemic crayfish species in the Ouachita Mountains. Current distributions were modelled using climate, geology, soils, land use, landform and flow variables thought to be important to lotic crayfish. Potential changes in the distribution were forecast by using models trained on current conditions and projecting onto the landscape predicted under climate-change scenarios. 3.The modelled distribution of the four species closely resembled the perceived distribution of each species but also predicted populations in streams and catchments where they had not previously been collected. Soils, elevation and winter precipitation and temperature most strongly related to current distributions and represented 6587% of the predictive power of the models. Model accuracy was high for all models, and model predictions of new populations were verified through additional field sampling. 4.Current models created using two spatial resolutions (1 and 4.5km2) showed that fine-resolution data more accurately represented current distributions. For three of the four species, the 1-km2 resolution models resulted in more conservative predictions. However, the modelled distributional extent of Orconectes leptogonopodus was similar regardless of data resolution. Field validations indicated 1-km2 resolution models were more accurate than 4.5-km2 resolution models. 5.Future projected (4.5-km2 resolution models) model distributions indicated three of the four endemic species would have truncated ranges with low occurrence probabilities under the low-emission scenario
Prediction of Catastrophes: an experimental model
Peters, Randall D; Pomeau, Yves
2012-01-01
Catastrophes of all kinds can be roughly defined as short duration-large amplitude events following and followed by long periods of "ripening". Major earthquakes surely belong to the class of 'catastrophic' events. Because of the space-time scales involved, an experimental approach is often difficult, not to say impossible, however desirable it could be. Described in this article is a "laboratory" setup that yields data of a type that is amenable to theoretical methods of prediction. Observations are made of a critical slowing down in the noisy signal of a solder wire creeping under constant stress. This effect is shown to be a fair signal of the forthcoming catastrophe in both of two dynamical models. The first is an "abstract" model in which a time dependent quantity drifts slowly but makes quick jumps from time to time. The second is a realistic physical model for the collective motion of dislocations (the Ananthakrishna set of equations for creep). Hope thus exists that similar changes in the response to ...
A Computationally Efficient Aggregation Optimization Strategy of Model Predictive Control
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Model Predictive Control (MPC) is a popular technique and has been successfully used in various industrial applications. However, the big drawback of MPC involved in the formidable on-line computational effort limits its applicability to relatively slow and/or small processes with a moderate number of inputs. This paper develops an aggregation optimization strategy for MPC that can improve the computational efficiency of MPC. For the regulation problem, an input decaying aggregation optimization algorithm is presented by aggregating all the original optimized variables on control horizon with the decaying sequence in respect of the current control action.
Predictive modeling of low solubility semiconductor alloys
Rodriguez, Garrett V.; Millunchick, Joanna M.
2016-09-01
GaAsBi is of great interest for applications in high efficiency optoelectronic devices due to its highly tunable bandgap. However, the experimental growth of high Bi content films has proven difficult. Here, we model GaAsBi film growth using a kinetic Monte Carlo simulation that explicitly takes cation and anion reactions into account. The unique behavior of Bi droplets is explored, and a sharp decrease in Bi content upon Bi droplet formation is demonstrated. The high mobility of simulated Bi droplets on GaAsBi surfaces is shown to produce phase separated Ga-Bi droplets as well as depressions on the film surface. A phase diagram for a range of growth rates that predicts both Bi content and droplet formation is presented to guide the experimental growth of high Bi content GaAsBi films.
Distributed model predictive control made easy
Negenborn, Rudy
2014-01-01
The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...
Leptogenesis in minimal predictive seesaw models
Björkeroth, Fredrik; de Anda, Francisco J.; de Medeiros Varzielas, Ivo; King, Stephen F.
2015-10-01
We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the "atmospheric" and "solar" neutrino masses with Yukawa couplings to ( ν e , ν μ , ν τ ) proportional to (0, 1, 1) and (1, n, n - 2), respectively, where n is a positive integer. The neutrino Yukawa matrix is therefore characterised by two proportionality constants with their relative phase providing a leptogenesis-PMNS link, enabling the lightest right-handed neutrino mass to be determined from neutrino data and the observed BAU. We discuss an SU(5) SUSY GUT example, where A 4 vacuum alignment provides the required Yukawa structures with n = 3, while a {{Z}}_9 symmetry fixes the relatives phase to be a ninth root of unity.
Decadal prediction skill using a high-resolution climate model
Monerie, Paul-Arthur; Coquart, Laure; Maisonnave, Éric; Moine, Marie-Pierre; Terray, Laurent; Valcke, Sophie
2017-02-01
The ability of a high-resolution coupled atmosphere-ocean general circulation model (with a horizontal resolution of a quarter of a degree in the ocean and of about 0.5° in the atmosphere) to predict the annual means of temperature, precipitation, sea-ice volume and extent is assessed based on initialized hindcasts over the 1993-2009 period. Significant skill in predicting sea surface temperatures is obtained, especially over the North Atlantic, the tropical Atlantic and the Indian Ocean. The Sea Ice Extent and volume are also reasonably predicted in winter (March) and summer (September). The model skill is mainly due to the external forcing associated with well-mixed greenhouse gases. A decrease in the global warming rate associated with a negative phase of the Pacific Decadal Oscillation is simulated by the model over a suite of 10-year periods when initialized from starting dates between 1999 and 2003. The model ability to predict regional change is investigated by focusing on the mid-90's Atlantic Ocean subpolar gyre warming. The model simulates the North Atlantic warming associated with a meridional heat transport increase, a strengthening of the North Atlantic current and a deepening of the mixed layer over the Labrador Sea. The atmosphere plays a role in the warming through a modulation of the North Atlantic Oscillation: a negative sea level pressure anomaly, located south of the subpolar gyre is associated with a wind speed decrease over the subpolar gyre. This leads to a reduced oceanic heat-loss and favors a northward displacement of anomalously warm and salty subtropical water that both concur to the subpolar gyre warming. We finally conclude that the subpolar gyre warming is mainly triggered by ocean dynamics with a possible contribution of atmospheric circulation favoring its persistence.
Dinucleotide controlled null models for comparative RNA gene prediction
Directory of Open Access Journals (Sweden)
Gesell Tanja
2008-05-01
Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require
Prediction of survival with alternative modeling techniques using pseudo values.
Directory of Open Access Journals (Sweden)
Tjeerd van der Ploeg
Full Text Available BACKGROUND: The use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo values enable statistically appropriate analyses of survival outcomes when used in seven alternative modeling techniques. METHODS: In this case study, we analyzed survival of 1282 Dutch patients with newly diagnosed Head and Neck Squamous Cell Carcinoma (HNSCC with conventional Kaplan-Meier and Cox regression analysis. We subsequently calculated pseudo values to reflect the individual survival patterns. We used these pseudo values to compare recursive partitioning (RPART, neural nets (NNET, logistic regression (LR general linear models (GLM and three variants of support vector machines (SVM with respect to dichotomous 60-month survival, and continuous pseudo values at 60 months or estimated survival time. We used the area under the ROC curve (AUC and the root of the mean squared error (RMSE to compare the performance of these models using bootstrap validation. RESULTS: Of a total of 1282 patients, 986 patients died during a median follow-up of 66 months (60-month survival: 52% [95% CI: 50%-55%]. The LR model had the highest optimism corrected AUC (0.791 to predict 60-month survival, followed by the SVM model with a linear kernel (AUC 0.787. The GLM model had the smallest optimism corrected RMSE when continuous pseudo values were considered for 60-month survival or the estimated survival time followed by SVM models with a linear kernel. The estimated importance of predictors varied substantially by the specific aspect of survival studied and modeling technique used. CONCLUSIONS: The use of pseudo values makes it readily possible to apply alternative modeling techniques to survival problems, to compare their performance and to search further for promising
Usability Prediction & Ranking of SDLC Models Using Fuzzy Hierarchical Usability Model
Gupta, Deepak; Ahlawat, Anil K.; Sagar, Kalpna
2017-06-01
Evaluation of software quality is an important aspect for controlling and managing the software. By such evaluation, improvements in software process can be made. The software quality is significantly dependent on software usability. Many researchers have proposed numbers of usability models. Each model considers a set of usability factors but do not cover all the usability aspects. Practical implementation of these models is still missing, as there is a lack of precise definition of usability. Also, it is very difficult to integrate these models into current software engineering practices. In order to overcome these challenges, this paper aims to define the term `usability' using the proposed hierarchical usability model with its detailed taxonomy. The taxonomy considers generic evaluation criteria for identifying the quality components, which brings together factors, attributes and characteristics defined in various HCI and software models. For the first time, the usability model is also implemented to predict more accurate usability values. The proposed system is named as fuzzy hierarchical usability model that can be easily integrated into the current software engineering practices. In order to validate the work, a dataset of six software development life cycle models is created and employed. These models are ranked according to their predicted usability values. This research also focuses on the detailed comparison of proposed model with the existing usability models.
Learning Predictive Movement Models From Fabric-Mounted Wearable Sensors.
Michael, Brendan; Howard, Matthew
2016-12-01
The measurement and analysis of human movement for applications in clinical diagnostics or rehabilitation is often performed in a laboratory setting using static motion capture devices. A growing interest in analyzing movement in everyday environments (such as the home) has prompted the development of "wearable sensors", with the most current wearable sensors being those embedded into clothing. A major issue however with the use of these fabric-embedded sensors is the undesired effect of fabric motion artefacts corrupting movement signals. In this paper, a nonparametric method is presented for learning body movements, viewing the undesired motion as stochastic perturbations to the sensed motion, and using orthogonal regression techniques to form predictive models of the wearer's motion that eliminate these errors in the learning process. Experiments in this paper show that standard nonparametric learning techniques underperform in this fabric motion context and that improved prediction accuracy can be made by using orthogonal regression techniques. Modelling this motion artefact problem as a stochastic learning problem shows an average 77% decrease in prediction error in a body pose task using fabric-embedded sensors, compared to a kinematic model.
Model Predictive Control-Based Fast Charging for Vehicular Batteries
Directory of Open Access Journals (Sweden)
Zhibin Song
2011-08-01
Full Text Available Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs. In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC. A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV charge method.
Dust Composition in Climate Models: Current Status and Prospects
Pérez García-Pando, C.; Miller, R. L.; Perlwitz, J. P.; Kok, J. F.; Scanza, R.; Mahowald, N. M.
2015-12-01
Mineral dust created by wind erosion of soil particles is the dominant aerosol by mass in the atmosphere. It exerts significant effects on radiative fluxes, clouds, ocean biogeochemistry, and human health. Models that predict the lifecycle of mineral dust aerosols generally assume a globally uniform mineral composition. However, this simplification limits our understanding of the role of dust in the Earth system, since the effects of dust strongly depend on the particles' physical and chemical properties, which vary with their mineral composition. Hence, not only a detailed understanding of the processes determining the dust emission flux is needed, but also information about its size dependent mineral composition. Determining the mineral composition of dust aerosols is complicated. The largest uncertainty derives from the current atlases of soil mineral composition. These atlases provide global estimates of soil mineral fractions, but they are based upon massive extrapolation of a limited number of soil samples assuming that mineral composition is related to soil type. This disregards the potentially large variability of soil properties within each defined soil type. In addition, the analysis of these soil samples is based on wet sieving, a technique that breaks the aggregates found in the undisturbed parent soil. During wind erosion, these aggregates are subject to partial fragmentation, which generates differences on the size distribution and composition between the undisturbed parent soil and the emitted dust aerosols. We review recent progress on the representation of the mineral and chemical composition of dust in climate models. We discuss extensions of brittle fragmentation theory to prescribe the emitted size-resolved dust composition, and we identify key processes and uncertainties based upon model simulations and an unprecedented compilation of observations.
Directory of Open Access Journals (Sweden)
Yu-Ri Kim
2016-03-01
Full Text Available Recently, the Korean government has enforced disclosure of building energy performance, so that such information can help owners and prospective buyers to make suitable investment plans. Such a building energy performance policy of the government makes it mandatory for the building owners to obtain engineering audits and thereby evaluate the energy performance levels of their buildings. However, to calculate energy performance levels (i.e., asset rating methodology, a qualified expert needs to have access to at least the full project documentation and/or conduct an on-site inspection of the buildings. Energy performance certification costs a lot of time and money. Moreover, the database of certified buildings is still actually quite small. A need, therefore, is increasing for a simplified and user-friendly energy performance prediction tool for non-specialists. Also, a database which allows building owners and users to compare best practices is required. In this regard, the current study developed a simplified performance prediction model through experimental design, energy simulations and ANOVA (analysis of variance. Furthermore, using the new prediction model, a related mobile application was also developed.
Current and Predicted Fertility using Poisson Regression Model ...
African Journals Online (AJOL)
AJRH Managing Editor
Nigeria with persistent high growth rate is among top ten most populous countries. Monitoring key .... child and maternal health and overall family well- being1,2,22-26. .... large while the expectation remains stable, i.e., the probability of ...
Development and current status of the "Cambridge" loudness models.
Moore, Brian C J
2014-10-13
This article reviews the evolution of a series of models of loudness developed in Cambridge, UK. The first model, applicable to stationary sounds, was based on modifications of the model developed by Zwicker, including the introduction of a filter to allow for the effects of transfer of sound through the outer and middle ear prior to the calculation of an excitation pattern, and changes in the way that the excitation pattern was calculated. Later, modifications were introduced to the assumed middle-ear transfer function and to the way that specific loudness was calculated from excitation level. These modifications led to a finite calculated loudness at absolute threshold, which made it possible to predict accurately the absolute thresholds of broadband and narrowband sounds, based on the assumption that the absolute threshold corresponds to a fixed small loudness. The model was also modified to give predictions of partial loudness-the loudness of one sound in the presence of another. This allowed predictions of masked thresholds based on the assumption that the masked threshold corresponds to a fixed small partial loudness. Versions of the model for time-varying sounds were developed, which allowed prediction of the masked threshold of any sound in a background of any other sound. More recent extensions incorporate binaural processing to account for the summation of loudness across ears. In parallel, versions of the model for predicting loudness for hearing-impaired ears have been developed and have been applied to the development of methods for fitting multichannel compression hearing aids.
Current focusing and steering: modeling, physiology, and psychophysics.
Bonham, Ben H; Litvak, Leonid M
2008-08-01
Current steering and current focusing are stimulation techniques designed to increase the number of distinct perceptual channels available to cochlear implant (CI) users by adjusting currents applied simultaneously to multiple CI electrodes. Previous studies exploring current steering and current focusing stimulation strategies are reviewed, including results of research using computational models, animal neurophysiology, and human psychophysics. Preliminary results of additional neurophysiological and human psychophysical studies are presented that demonstrate the success of current steering strategies in stimulating auditory nerve regions lying between physical CI electrodes, as well as current focusing strategies that excite regions narrower than those stimulated using monopolar configurations. These results are interpreted in the context of perception and speech reception by CI users. Disparities between results of physiological and psychophysical studies are discussed. The differences in stimulation used for physiological and psychophysical studies are hypothesized to contribute to these disparities. Finally, application of current steering and focusing strategies to other types of auditory prostheses is also discussed.
Remaining Useful Lifetime (RUL - Probabilistic Predictive Model
Directory of Open Access Journals (Sweden)
Ephraim Suhir
2011-01-01
Full Text Available Reliability evaluations and assurances cannot be delayed until the device (system is fabricated and put into operation. Reliability of an electronic product should be conceived at the early stages of its design; implemented during manufacturing; evaluated (considering customer requirements and the existing specifications, by electrical, optical and mechanical measurements and testing; checked (screened during manufacturing (fabrication; and, if necessary and appropriate, maintained in the field during the product’s operation Simple and physically meaningful probabilistic predictive model is suggested for the evaluation of the remaining useful lifetime (RUL of an electronic device (system after an appreciable deviation from its normal operation conditions has been detected, and the increase in the failure rate and the change in the configuration of the wear-out portion of the bathtub has been assessed. The general concepts are illustrated by numerical examples. The model can be employed, along with other PHM forecasting and interfering tools and means, to evaluate and to maintain the high level of the reliability (probability of non-failure of a device (system at the operation stage of its lifetime.
A Predictive Model of Geosynchronous Magnetopause Crossings
Dmitriev, A; Chao, J -K
2013-01-01
We have developed a model predicting whether or not the magnetopause crosses geosynchronous orbit at given location for given solar wind pressure Psw, Bz component of interplanetary magnetic field (IMF) and geomagnetic conditions characterized by 1-min SYM-H index. The model is based on more than 300 geosynchronous magnetopause crossings (GMCs) and about 6000 minutes when geosynchronous satellites of GOES and LANL series are located in the magnetosheath (so-called MSh intervals) in 1994 to 2001. Minimizing of the Psw required for GMCs and MSh intervals at various locations, Bz and SYM-H allows describing both an effect of magnetopause dawn-dusk asymmetry and saturation of Bz influence for very large southward IMF. The asymmetry is strong for large negative Bz and almost disappears when Bz is positive. We found that the larger amplitude of negative SYM-H the lower solar wind pressure is required for GMCs. We attribute this effect to a depletion of the dayside magnetic field by a storm-time intensification of t...
Predictive modeling for EBPC in EBDW
Zimmermann, Rainer; Schulz, Martin; Hoppe, Wolfgang; Stock, Hans-Jürgen; Demmerle, Wolfgang; Zepka, Alex; Isoyan, Artak; Bomholt, Lars; Manakli, Serdar; Pain, Laurent
2009-10-01
We demonstrate a flow for e-beam proximity correction (EBPC) to e-beam direct write (EBDW) wafer manufacturing processes, demonstrating a solution that covers all steps from the generation of a test pattern for (experimental or virtual) measurement data creation, over e-beam model fitting, proximity effect correction (PEC), and verification of the results. We base our approach on a predictive, physical e-beam simulation tool, with the possibility to complement this with experimental data, and the goal of preparing the EBPC methods for the advent of high-volume EBDW tools. As an example, we apply and compare dose correction and geometric correction for low and high electron energies on 1D and 2D test patterns. In particular, we show some results of model-based geometric correction as it is typical for the optical case, but enhanced for the particularities of e-beam technology. The results are used to discuss PEC strategies, with respect to short and long range effects.
Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding
Xiao, Rui; Gao, Junbin; Bossomaier, Terry
2016-01-01
A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times the data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing “original pixel intensity”-based coding approaches using traditional image coders (e.g., JPEG2000) to the “residual”-based approaches using a video coder for better compression performance. A modified video coder is required to exploit spatial-spectral redundancy using pixel-level reflectance modelling due to the different characteristics of HS images in their spectral and shape domain of panchromatic imagery compared to traditional videos. In this paper a novel coding framework using Reflectance Prediction Modelling (RPM) in the latest video coding standard High Efficiency Video Coding (HEVC) for HS images is proposed. An HS image presents a wealth of data where every pixel is considered a vector for different spectral bands. By quantitative comparison and analysis of pixel vector distribution along spectral bands, we conclude that modelling can predict the distribution and correlation of the pixel vectors for different bands. To exploit distribution of the known pixel vector, we estimate a predicted current spectral band from the previous bands using Gaussian mixture-based modelling. The predicted band is used as the additional reference band together with the immediate previous band when we apply the HEVC. Every spectral band of an HS image is treated like it is an individual frame of a video. In this paper, we compare the proposed method with mainstream encoders. The experimental results are fully justified by three types of HS dataset with different wavelength ranges. The proposed method outperforms the existing mainstream HS encoders in terms of rate-distortion performance of HS image compression. PMID:27695102
García-Fernández, Amaya; Roldán, Vanessa; Rivera-Caravaca, José Miguel; Hernández-Romero, Diana; Valdés, Mariano; Vicente, Vicente; Lip, Gregory Y. H.; Marín, Francisco
2017-01-01
Von Willebrand factor (vWF) is a biomarker of endothelial dysfunction. We investigated its role on prognosis in anticoagulated atrial fibrillation (AF) patients and determined whether its addition to clinical risk stratification schemes improved event-risk prediction. Consecutive outpatients with non-valvular AF were recruited and rates of thrombotic/cardiovascular events, major bleeding and mortality were recorded. The effect of vWF on prognosis was calculated using a Cox regression model. Improvements in predictive accuracy over current scores were determined by calculating the integrated discrimination improvement (IDI), net reclassification improvement (NRI), comparison of receiver-operator characteristic (ROC) curves and Decision Curve Analysis (DCA). 1215 patients (49% males, age 76 (71–81) years) were included. Follow-up was almost 7 years. Significant associations were found between vWF and cardiovascular events, stroke, mortality and bleeding. Based on IDI and NRI, addition of vWF to CHA2DS2-VASc statistically improved its predictive value, but c-indexes were not significantly different. For major bleeding, the addition of vWF to HAS-BLED improved the c-index but not IDI or NRI. DCA showed minimal net benefit. vWF acts as a simple prognostic biomarker in AF and, whilst its addition to current scores statistically improves prediction for some endpoints, absolute changes and impact on clinical decision-making are marginal. PMID:28134282
Predicting future glacial lakes in Austria using different modelling approaches
Otto, Jan-Christoph; Helfricht, Kay; Prasicek, Günther; Buckel, Johannes; Keuschnig, Markus
2017-04-01
Glacier retreat is one of the most apparent consequences of temperature rise in the 20th and 21th centuries in the European Alps. In Austria, more than 240 new lakes have formed in glacier forefields since the Little Ice Age. A similar signal is reported from many mountain areas worldwide. Glacial lakes can constitute important environmental and socio-economic impacts on high mountain systems including water resource management, sediment delivery, natural hazards, energy production and tourism. Their development significantly modifies the landscape configuration and visual appearance of high mountain areas. Knowledge on the location, number and extent of these future lakes can be used to assess potential impacts on high mountain geo-ecosystems and upland-lowland interactions. Information on new lakes is critical to appraise emerging threads and potentials for society. The recent development of regional ice thickness models and their combination with high resolution glacier surface data allows predicting the topography below current glaciers by subtracting ice thickness from glacier surface. Analyzing these modelled glacier bed surfaces reveals overdeepenings that represent potential locations for future lakes. In order to predict the location of future glacial lakes below recent glaciers in the Austrian Alps we apply different ice thickness models using high resolution terrain data and glacier outlines. The results are compared and validated with ice thickness data from geophysical surveys. Additionally, we run the models on three different glacier extents provided by the Austrian Glacier Inventories from 1969, 1998 and 2006. Results of this historical glacier extent modelling are compared to existing glacier lakes and discussed focusing on geomorphological impacts on lake evolution. We discuss model performance and observed differences in the results in order to assess the approach for a realistic prediction of future lake locations. The presentation delivers
Model for predicting mountain wave field uncertainties
Damiens, Florentin; Lott, François; Millet, Christophe; Plougonven, Riwal
2017-04-01
Studying the propagation of acoustic waves throughout troposphere requires knowledge of wind speed and temperature gradients from the ground up to about 10-20 km. Typical planetary boundary layers flows are known to present vertical low level shears that can interact with mountain waves, thereby triggering small-scale disturbances. Resolving these fluctuations for long-range propagation problems is, however, not feasible because of computer memory/time restrictions and thus, they need to be parameterized. When the disturbances are small enough, these fluctuations can be described by linear equations. Previous works by co-authors have shown that the critical layer dynamics that occur near the ground produces large horizontal flows and buoyancy disturbances that result in intense downslope winds and gravity wave breaking. While these phenomena manifest almost systematically for high Richardson numbers and when the boundary layer depth is relatively small compare to the mountain height, the process by which static stability affects downslope winds remains unclear. In the present work, new linear mountain gravity wave solutions are tested against numerical predictions obtained with the Weather Research and Forecasting (WRF) model. For Richardson numbers typically larger than unity, the mesoscale model is used to quantify the effect of neglected nonlinear terms on downslope winds and mountain wave patterns. At these regimes, the large downslope winds transport warm air, a so called "Foehn" effect than can impact sound propagation properties. The sensitivity of small-scale disturbances to Richardson number is quantified using two-dimensional spectral analysis. It is shown through a pilot study of subgrid scale fluctuations of boundary layer flows over realistic mountains that the cross-spectrum of mountain wave field is made up of the same components found in WRF simulations. The impact of each individual component on acoustic wave propagation is discussed in terms of
Numerical modeling capabilities to predict repository performance
Energy Technology Data Exchange (ETDEWEB)
1979-09-01
This report presents a summary of current numerical modeling capabilities that are applicable to the design and performance evaluation of underground repositories for the storage of nuclear waste. The report includes codes that are available in-house, within Golder Associates and Lawrence Livermore Laboratories; as well as those that are generally available within the industry and universities. The first listing of programs are in-house codes in the subject areas of hydrology, solute transport, thermal and mechanical stress analysis, and structural geology. The second listing of programs are divided by subject into the following categories: site selection, structural geology, mine structural design, mine ventilation, hydrology, and mine design/construction/operation. These programs are not specifically designed for use in the design and evaluation of an underground repository for nuclear waste; but several or most of them may be so used.
Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics
Directory of Open Access Journals (Sweden)
Cecilia Noecker
2015-03-01
Full Text Available Upon infection of a new host, human immunodeficiency virus (HIV replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV. First, we found that the mode of virus production by infected cells (budding vs. bursting has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral
Transistor roadmap projection using predictive full-band atomistic modeling
Energy Technology Data Exchange (ETDEWEB)
Salmani-Jelodar, M., E-mail: m.salmani@gmail.com; Klimeck, G. [Network for Computational Nanotechnology and School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907 (United States); Kim, S. [Intel Corporation, 2501 Northwest 229th Avenue, Hillsboro, Oregon 97124 (United States); Ng, K. [Semiconductor Research Corporation (SRC), 1101 Slater Rd, Durham, North Carolina 27703 (United States)
2014-08-25
In this letter, a full band atomistic quantum transport tool is used to predict the performance of double gate metal-oxide-semiconductor field-effect transistors (MOSFETs) over the next 15 years for International Technology Roadmap for Semiconductors (ITRS). As MOSFET channel lengths scale below 20 nm, the number of atoms in the device cross-sections becomes finite. At this scale, quantum mechanical effects play an important role in determining the device characteristics. These quantum effects can be captured with the quantum transport tool. Critical results show the ON-current degradation as a result of geometry scaling, which is in contrast to previous ITRS compact model calculations. Geometric scaling has significant effects on the ON-current by increasing source-to-drain (S/D) tunneling and altering the electronic band structure. By shortening the device gate length from 20 nm to 5.1 nm, the ratio of S/D tunneling current to the overall subthreshold OFF-current increases from 18% to 98%. Despite this ON-current degradation by scaling, the intrinsic device speed is projected to increase at a rate of at least 8% per year as a result of the reduction of the quantum capacitance.
Models to predict intestinal absorption of therapeutic peptides and proteins.
Antunes, Filipa; Andrade, Fernanda; Ferreira, Domingos; Nielsen, Hanne Morck; Sarmento, Bruno
2013-01-01
Prediction of human intestinal absorption is a major goal in the design, optimization, and selection of drugs intended for oral delivery, in particular proteins, which possess intrinsic poor transport across intestinal epithelium. There are various techniques currently employed to evaluate the extension of protein absorption in the different phases of drug discovery and development. Screening protocols to evaluate protein absorption include a range of preclinical methodologies like in silico, in vitro, in situ, ex vivo and in vivo. It is the careful and critical use of these techniques that can help to identify drug candidates, which most probably will be well absorbed from the human intestinal tract. It is well recognized that the human intestinal permeability cannot be accurately predicted based on a single preclinical method. However, the present social and scientific concerns about the animal well care as well as the pharmaceutical industries need for rapid, cheap and reliable models predicting bioavailability give reasons for using methods providing an appropriate correlation between results of in vivo and in vitro drug absorption. The aim of this review is to describe and compare in silico, in vitro, in situ, ex vivo and in vivo methods used to predict human intestinal absorption, giving a special attention to the intestinal absorption of therapeutic peptides and proteins.
Directory of Open Access Journals (Sweden)
Jing Lu
2014-11-01
Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.
RFI modeling and prediction approach for SATOP applications: RFI prediction models
Nguyen, Tien M.; Tran, Hien T.; Wang, Zhonghai; Coons, Amanda; Nguyen, Charles C.; Lane, Steven A.; Pham, Khanh D.; Chen, Genshe; Wang, Gang
2016-05-01
This paper describes a technical approach for the development of RFI prediction models using carrier synchronization loop when calculating Bit or Carrier SNR degradation due to interferences for (i) detecting narrow-band and wideband RFI signals, and (ii) estimating and predicting the behavior of the RFI signals. The paper presents analytical and simulation models and provides both analytical and simulation results on the performance of USB (Unified S-Band) waveforms in the presence of narrow-band and wideband RFI signals. The models presented in this paper will allow the future USB command systems to detect the RFI presence, estimate the RFI characteristics and predict the RFI behavior in real-time for accurate assessment of the impacts of RFI on the command Bit Error Rate (BER) performance. The command BER degradation model presented in this paper also allows the ground system operator to estimate the optimum transmitted SNR to maintain a required command BER level in the presence of both friendly and un-friendly RFI sources.
Ding, J; Ding, Jintai; Feigin, Boris
1996-01-01
We construct a commutative current operator $\\bar x^+(z)$ inside $U_q(\\hat{\\frak sl}(2))$. With this operator and the condition of quantum integrability on the quantum current of $U_q(\\hat{\\frak sl}(2))$, we derive the quantization of the semi-infinite construction of integrable modules of The quantization of the functional models for $\\hat{\\frak sl}(2)$ are also given.
Predicting and understanding forest dynamics using a simple tractable model.
Purves, Drew W; Lichstein, Jeremy W; Strigul, Nikolay; Pacala, Stephen W
2008-11-04
The perfect-plasticity approximation (PPA) is an analytically tractable model of forest dynamics, defined in terms of parameters for individual trees, including allometry, growth, and mortality. We estimated these parameters for the eight most common species on each of four soil types in the US Lake states (Michigan, Wisconsin, and Minnesota) by using short-term (predictions to chronosequences of stand development. Predictions for the timing and magnitude of basal area dynamics and ecological succession on each soil were accurate, and predictions for the diameter distribution of 100-year-old stands were correct in form and slope. For a given species, the PPA provides analytical metrics for early-successional performance (H(20), height of a 20-year-old open-grown tree) and late-successional performance (Z*, equilibrium canopy height in monoculture). These metrics predicted which species were early or late successional on each soil type. Decomposing Z* showed that (i) succession is driven both by superior understory performance and superior canopy performance of late-successional species, and (ii) performance differences primarily reflect differences in mortality rather than growth. The predicted late-successional dominants matched chronosequences on xeromesic (Quercus rubra) and mesic (codominance by Acer rubrum and Acer saccharum) soil. On hydromesic and hydric soils, the literature reports that the current dominant species in old stands (Thuja occidentalis) is now failing to regenerate. Consistent with this, the PPA predicted that, on these soils, stands are now succeeding to dominance by other late-successional species (e.g., Fraxinus nigra, A. rubrum).
Prediction models : the right tool for the right problem
Kappen, Teus H.; Peelen, Linda M.
2016-01-01
PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to unders
A continuum model for current distribution in Rutherford cables
Akhmedov, A I; Breschi, M
2001-01-01
An analysis of eddy currents induced in flat Rutherford-type cables by external time dependent magnetic fields has been performed. The induced currents generate in turn a secondary magnetic field which has a longitudinal periodicity (periodic pattern). The dependence of the amplitude of the pattern on the history of the cable excitation has been investigated. The study has been carried out with two different models for the simulation of current distribution in Rutherford cables, namely a network model, based on a lumped parameters circuit and a "continuum" model, based on a distributed parameters circuit. We show the results of simulations of the current distribution in the inner cable of a short LHC dipole model in different powering conditions and compare them to experimental data. (12 refs).
Cardiac magnetic source imaging based on current multipole model
Institute of Scientific and Technical Information of China (English)
Tang Fa-Kuan; Wang Qian; Hua Ning; Lu Hong; Tang Xue-Zheng; Ma Ping
2011-01-01
It is widely accepted that the heart current source can be reduced into a current multipole. By adopting three linear inverse methods, the cardiac magnetic imaging is achieved in this article based on the current multipole model expanded to the first order terms. This magnetic imaging is realized in a reconstruction plane in the centre of human heart, where the current dipole array is employed to represent realistic cardiac current distribution. The current multipole as testing source generates magnetic fields in the measuring plane, serving as inputs of cardiac magnetic inverse problem. In the heart-torso model constructed by boundary element method, the current multipole magnetic field distribution is compared with that in the homogeneous infinite space, and also with the single current dipole magnetic field distribution.Then the minimum-norm least-squares (MNLS) method, the optimal weighted pseuDOInverse method (OWPIM), and the optimal constrained linear inverse method (OCLIM) are selected as the algorithms for inverse computation based on current multipole model innovatively, and the imaging effects of these three inverse methods are compared. Besides,two reconstructing parameters, residual and mean residual, are also discussed, and their trends under MNLS, OWPIM and OCLIM each as a function of SNR are obtained and compared.
Modelling and analysis of current-programmed ac/dc converters
Tymerski, R. P. E.; Daly, K. C.
1985-03-01
Current-programmed dc/dc converters operating at a fixed switching frequency are analyzed using state-space averaged modeling. For converters operating in the continuous conduction mode, general closed form expressions that describe the dynamic ac small signal characteristics of the converter are obtained. A reduced order model is used to derive the control current input-to-output voltage, audio susceptibility and output impedance small signal ac transfer functions for the ideal buck, boost, and buck-boost converters operating in the continuous conduction model. It is shown that state-space averaging can correctly predict instability on the buck converter. Current-programmed converters operating in the discontinuous conduction mode are addressed, showing that the transfer functions are represented by a finite pole and a zero at infinity. Instability is predicted for the buck converter when an external ramp is omitted and the output-to-input voltage ratio is greater than or equal to two-thirds.
Foundation Settlement Prediction Based on a Novel NGM Model
Directory of Open Access Journals (Sweden)
Peng-Yu Chen
2014-01-01
Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.
Predictability of the Indian Ocean Dipole in the coupled models
Liu, Huafeng; Tang, Youmin; Chen, Dake; Lian, Tao
2017-03-01
In this study, the Indian Ocean Dipole (IOD) predictability, measured by the Indian Dipole Mode Index (DMI), is comprehensively examined at the seasonal time scale, including its actual prediction skill and potential predictability, using the ENSEMBLES multiple model ensembles and the recently developed information-based theoretical framework of predictability. It was found that all model predictions have useful skill, which is normally defined by the anomaly correlation coefficient larger than 0.5, only at around 2-3 month leads. This is mainly because there are more false alarms in predictions as leading time increases. The DMI predictability has significant seasonal variation, and the predictions whose target seasons are boreal summer (JJA) and autumn (SON) are more reliable than that for other seasons. All of models fail to predict the IOD onset before May and suffer from the winter (DJF) predictability barrier. The potential predictability study indicates that, with the model development and initialization improvement, the prediction of IOD onset is likely to be improved but the winter barrier cannot be overcome. The IOD predictability also has decadal variation, with a high skill during the 1960s and the early 1990s, and a low skill during the early 1970s and early 1980s, which is very consistent with the potential predictability. The main factors controlling the IOD predictability, including its seasonal and decadal variations, are also analyzed in this study.
Modelling of the ring current in Saturn's magnetosphere
Giampieri, G.; Dougherty, M.
2004-02-01
. The existence of a ring current inside Saturn's magnetosphere was first suggested by smith80 and ness81,ness82, in order to explain various features in the magnetic field observations from the Pioneer 11 and Voyager 1 and 2 spacecraft. connerney83 formalized the equatorial current model, based on previous modelling work of Jupiter's current sheet and estimated its parameters from the two Voyager data sets. Here, we investigate the model further, by reconsidering the data from the two Voyager spacecraft, as well as including the Pioneer 11 flyby data set. First, we obtain, in closed form, an analytic expression for the magnetic field produced by the ring current. We then fit the model to the external field, that is the difference between the observed field and the internal magnetic field, considering all the available data. In general, through our global fit we obtain more accurate parameters, compared to previous models. We point out differences between the model's parameters for the three flybys, and also investigate possible deviations from the axial and planar symmetries assumed in the model. We conclude that an accurate modelling of the Saturnian disk current will require taking into account both of the temporal variations related to the condition of the magnetosphere, as well as non-axisymmetric contributions due to local time effects.
Nonconvex model predictive control for commercial refrigeration
Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John
2013-08-01
We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.
Leptogenesis in minimal predictive seesaw models
Energy Technology Data Exchange (ETDEWEB)
Björkeroth, Fredrik [School of Physics and Astronomy, University of Southampton,Southampton, SO17 1BJ (United Kingdom); Anda, Francisco J. de [Departamento de Física, CUCEI, Universidad de Guadalajara,Guadalajara (Mexico); Varzielas, Ivo de Medeiros; King, Stephen F. [School of Physics and Astronomy, University of Southampton,Southampton, SO17 1BJ (United Kingdom)
2015-10-15
We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the “atmospheric” and “solar” neutrino masses with Yukawa couplings to (ν{sub e},ν{sub μ},ν{sub τ}) proportional to (0,1,1) and (1,n,n−2), respectively, where n is a positive integer. The neutrino Yukawa matrix is therefore characterised by two proportionality constants with their relative phase providing a leptogenesis-PMNS link, enabling the lightest right-handed neutrino mass to be determined from neutrino data and the observed BAU. We discuss an SU(5) SUSY GUT example, where A{sub 4} vacuum alignment provides the required Yukawa structures with n=3, while a ℤ{sub 9} symmetry fixes the relatives phase to be a ninth root of unity.
QSPR Models for Octane Number Prediction
Directory of Open Access Journals (Sweden)
Jabir H. Al-Fahemi
2014-01-01
Full Text Available Quantitative structure-property relationship (QSPR is performed as a means to predict octane number of hydrocarbons via correlating properties to parameters calculated from molecular structure; such parameters are molecular mass M, hydration energy EH, boiling point BP, octanol/water distribution coefficient logP, molar refractivity MR, critical pressure CP, critical volume CV, and critical temperature CT. Principal component analysis (PCA and multiple linear regression technique (MLR were performed to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The results of PCA explain the interrelationships between octane number and different variables. Correlation coefficients were calculated using M.S. Excel to examine the relationship between multiple variables of the above parameters and the octane number of hydrocarbons. The data set was split into training of 40 hydrocarbons and validation set of 25 hydrocarbons. The linear relationship between the selected descriptors and the octane number has coefficient of determination (R2=0.932, statistical significance (F=53.21, and standard errors (s =7.7. The obtained QSPR model was applied on the validation set of octane number for hydrocarbons giving RCV2=0.942 and s=6.328.
Directory of Open Access Journals (Sweden)
Kennedy Curtis E
2011-10-01
Full Text Available Abstract Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1 selecting candidate variables; 2 specifying measurement parameters; 3 defining data format; 4 defining time window duration and resolution; 5 calculating latent variables for candidate variables not directly measured; 6 calculating time series features as latent variables; 7 creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8
DEFF Research Database (Denmark)
Davari, Pooya; Yang, Yongheng; Zare, Firuz
2016-01-01
of them can lead to the cancellation of specific harmonics. This paper proposes a new cost-effective harmonic mitigation solution for multi-drive systems using a predictive pulse pattern current modulation control strategy. The proposed technique applies suitable interaction among parallel drive units......The majority of the industrial motor drive systems are equipped with the conventional line-commutated front-end rectifiers, and being one of the main sources of harmonics in the power line. While a parallel combination of these drive units elevates current quality issues, a proper arrangement...... at the rectification stage to synthesize sinusoidal input currents. The input voltage sensing is avoided in order to minimize the number of required sensors, and the grid synchronization also has been implemented based on a common Phase-Locked-Loop (PLL) using the DC-link capacitor voltage ripple. Experimental results...
Directory of Open Access Journals (Sweden)
Wanjie Xu
2015-01-01
Full Text Available A physically based subthreshold current model for silicon nanowire transistors working in the ballistic regime is developed. Based on the electric potential distribution obtained from a 2D Poisson equation and by performing some perturbation approximations for subband energy levels, an analytical model for the subthreshold drain current is obtained. The model is further used for predicting the subthreshold slopes and threshold voltages of the transistors. Our results agree well with TCAD simulation with different geometries and under different biasing conditions.
Predictive modelling of complex agronomic and biological systems.
Keurentjes, Joost J B; Molenaar, Jaap; Zwaan, Bas J
2013-09-01
Biological systems are tremendously complex in their functioning and regulation. Studying the multifaceted behaviour and describing the performance of such complexity has challenged the scientific community for years. The reduction of real-world intricacy into simple descriptive models has therefore convinced many researchers of the usefulness of introducing mathematics into biological sciences. Predictive modelling takes such an approach another step further in that it takes advantage of existing knowledge to project the performance of a system in alternating scenarios. The ever growing amounts of available data generated by assessing biological systems at increasingly higher detail provide unique opportunities for future modelling and experiment design. Here we aim to provide an overview of the progress made in modelling over time and the currently prevalent approaches for iterative modelling cycles in modern biology. We will further argue for the importance of versatility in modelling approaches, including parameter estimation, model reduction and network reconstruction. Finally, we will discuss the difficulties in overcoming the mathematical interpretation of in vivo complexity and address some of the future challenges lying ahead. © 2013 John Wiley & Sons Ltd.
Inter-model analysis of tsunami-induced coastal currents
Lynett, Patrick J.; Gately, Kara; Wilson, Rick; Montoya, Luis; Arcas, Diego; Aytore, Betul; Bai, Yefei; Bricker, Jeremy D.; Castro, Manuel J.; Cheung, Kwok Fai; David, C. Gabriel; Dogan, Gozde Guney; Escalante, Cipriano; González-Vida, José Manuel; Grilli, Stephan T.; Heitmann, Troy W.; Horrillo, Juan; Kânoğlu, Utku; Kian, Rozita; Kirby, James T.; Li, Wenwen; Macías, Jorge; Nicolsky, Dmitry J.; Ortega, Sergio; Pampell-Manis, Alyssa; Park, Yong Sung; Roeber, Volker; Sharghivand, Naeimeh; Shelby, Michael; Shi, Fengyan; Tehranirad, Babak; Tolkova, Elena; Thio, Hong Kie; Velioğlu, Deniz; Yalçıner, Ahmet Cevdet; Yamazaki, Yoshiki; Zaytsev, Andrey; Zhang, Y. J.
2017-06-01
To help produce accurate and consistent maritime hazard products, the National Tsunami Hazard Mitigation Program organized a benchmarking workshop to evaluate the numerical modeling of tsunami currents. Thirteen teams of international researchers, using a set of tsunami models currently utilized for hazard mitigation studies, presented results for a series of benchmarking problems; these results are summarized in this paper. Comparisons focus on physical situations where the currents are shear and separation driven, and are thus de-coupled from the incident tsunami waveform. In general, we find that models of increasing physical complexity provide better accuracy, and that low-order three-dimensional models are superior to high-order two-dimensional models. Inside separation zones and in areas strongly affected by eddies, the magnitude of both model-data errors and inter-model differences can be the same as the magnitude of the mean flow. Thus, we make arguments for the need of an ensemble modeling approach for areas affected by large-scale turbulent eddies, where deterministic simulation may be misleading. As a result of the analyses presented herein, we expect that tsunami modelers now have a better awareness of their ability to accurately capture the physics of tsunami currents, and therefore a better understanding of how to use these simulation tools for hazard assessment and mitigation efforts.
Climate modelling, uncertainty and responses to predictions of change
Energy Technology Data Exchange (ETDEWEB)
Henderson-Sellers, A. [Climatic Impacts Centre, Macquarie University, Sydney (Australia)
1996-12-31
Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can`t yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes.
Nonlinear Model Predictive Control for Oil Reservoirs Management
DEFF Research Database (Denmark)
Capolei, Andrea
. With this objective function we link the optimization problem in production optimization to the Markowitz portfolio optimization problem in finance or to the the robust design problem in topology optimization. In this study we focus on open-loop configuration, i.e. without measurement feedback. We demonstrate......, the research community is working on improving current feedback model-based optimal control technologies. The topic of this thesis is production optimization for water flooding in the secondary phase of oil recovery. We developed numerical methods for nonlinear model predictive control (NMPC) of an oil field....... Further, we studied the use of robust control strategies in both open-loop, i.e. without measurement feedback, and closed-loop, i.e. with measurement feedback, configurations. This thesis has three main original contributions: The first contribution in this thesis is to improve the computationally...
Modelling of helical current filaments induced by LHW on EAST
Energy Technology Data Exchange (ETDEWEB)
Rack, Michael; Denner, Peter; Liang, Yunfeng [Institute of Energy and Climate Research - Plasma Physics, Forschungszentrum Juelich GmbH, Association EURATOM-FZJ, Partner in the Trilateral Euregio Cluster, D-52425 Juelich (Germany); Zeng, Long [Institute of Energy and Climate Research - Plasma Physics, Forschungszentrum Juelich GmbH, Association EURATOM-FZJ, Partner in the Trilateral Euregio Cluster, D-52425 Juelich (Germany); Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Gong, Xianzu; Gan, Kaifu; Wang, Liang; Liu, Fukun; Qian, Jinping; Shen, Biao; Li, Jiangang [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Gauthier, Eric [Association EURATOM-CEA, IRFM, F-13108 Saint-Paul-lez-Durance (France); Collaboration: the EAST Team
2013-07-01
Helical radiation belts have been observed in the scrape-off layer (SOL) of the plasma during the application of lower hybrid wave (LHW) heating at the superconducting tokamak EAST. Modelled SOL field lines, starting in-front of the LHW antennas, show agreement in position and pitch angle to the experimental observed radiation belts. A splitting of the strike-line can be observed on the outer divertor plates during the application of LHW heating. Agreement in the comparison of the Mirnov coil signals and a modelled electric current flow along these SOL field lines was found. A lower hybrid current drive can induce such an electric current flow near the plasma edge. This electric current flow causes a change of the plasma topology which could result in the splitting of the strike-line as known from the application of resonant magnetic perturbation fields. Comparisons of modelled footprint structures and experimental observed heat load patterns in the divertor region are discussed.
Predictability in models of the atmospheric circulation.
Houtekamer, P.L.
1992-01-01
It will be clear from the above discussions that skill forecasts are still in their infancy. Operational skill predictions do not exist. One is still struggling to prove that skill predictions, at any range, have any quality at all. It is not clear what the statistics of the analysis error are. The
Predictions of Radionuclide Dose Rates from Sellafield Discharges using a Compartmental Model
Energy Technology Data Exchange (ETDEWEB)
McCubbin, D.; Leonard, K.S.; Gurbutt, P.A.; Round, G.D
1998-07-01
A multi-compartmental model (MIRMAID) of the Irish Sea has been used to predict radionuclide dose rates to the public, via seafood consumption pathways. Radionuclides originate from the authorised discharge of low level liquid effluent from the BNF plc nuclear reprocessing plant at Sellafield. The model has been used to predict combined annual doses, the contribution of dose from individual radionuclides and to discriminate dose between present day and historic discharges. An assessment has been carried out to determine the sensitivity of the predictions to changes in various model parameters. The predicted dose to the critical group from seafood consumption in 1995 ranged from 37-96 {mu}Sv of which the majority originated from current discharges. The contribution from {sup 99}Tc was predicted to have increased from 0.2% in 1993 up to 20% in 1995. The predicted contribution of Pu and Am from historic discharges is underestimated in the model. (author)
Nicolas, B.; Gilbert, M. E.; Paw U, K. T.
2015-12-01
Soil-Vegetation-Atmosphere Transfer (SVAT) models are based upon well understood steady state photosynthetic physiology - the Farquhar-von Caemmerer-Berry model (FvCB). However, representations of physiological stress and damage have not been successfully integrated into SVAT models. Generally, it has been assumed that plants will strive to conserve water at higher temperatures by reducing stomatal conductance or adjusting osmotic balance, until potentially damaging temperatures and the need for evaporative cooling become more important than water conservation. A key point is that damage is the result of combined stresses: drought leads to stomatal closure, less evaporative cooling, high leaf temperature, less photosynthetic dissipation of absorbed energy, all coupled with high light (photosynthetic photon flux density; PPFD). This leads to excess absorbed energy by Photosystem II (PSII) and results in photoinhibition and damage, neither are included in SVAT models. Current representations of photoinhibition are treated as a function of PPFD, not as a function of constrained photosynthesis under heat or water. Thus, it seems unlikely that current models can predict responses of vegetation to climate variability and change. We propose a dynamic model of damage to Rubisco and RuBP-regeneration that accounts, mechanistically, for the interactions between high temperature, light, and constrained photosynthesis under drought. Further, these predictions are illustrated by key experiments allowing model validation. We also integrated this new framework within the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA). Preliminary results show that our approach can be used to predict reasonable photosynthetic dynamics. For instances, a leaf undergoing one day of drought stress will quickly decrease its maximum quantum yield of PSII (Fv/Fm), but it won't recover to unstressed levels for several days. Consequently, cumulative effect of photoinhibition on photosynthesis can cause
An assessment of a multi-model ensemble of decadal climate predictions
Bellucci, A.; Haarsma, R.; Gualdi, S.; Athanasiadis, P. J.; Caian, M.; Cassou, C.; Fernandez, E.; Germe, A.; Jungclaus, J.; Kröger, J.; Matei, D.; Müller, W.; Pohlmann, H.; Salas y Melia, D.; Sanchez, E.; Smith, D.; Terray, L.; Wyser, K.; Yang, S.
2015-05-01
A multi-model ensemble of decadal prediction experiments, performed in the framework of the EU-funded COMBINE (Comprehensive Modelling of the Earth System for Better Climate Prediction and Projection) Project following the 5th Coupled Model Intercomparison Project protocol is examined. The ensemble combines a variety of dynamical models, initialization and perturbation strategies, as well as data assimilation products employed to constrain the initial state of the system. Taking advantage of the multi-model approach, several aspects of decadal climate predictions are assessed, including predictive skill, impact of the initialization strategy and the level of uncertainty characterizing the predicted fluctuations of key climate variables. The present analysis adds to the growing evidence that the current generation of climate models adequately initialized have significant skill in predicting years ahead not only the anthropogenic warming but also part of the internal variability of the climate system. An important finding is that the multi-model ensemble mean does generally outperform the individual forecasts, a well-documented result for seasonal forecasting, supporting the need to extend the multi-model framework to real-time decadal predictions in order to maximize the predictive capabilities of currently available decadal forecast systems. The multi-model perspective did also allow a more robust assessment of the impact of the initialization strategy on the quality of decadal predictions, providing hints of an improved forecast skill under full-value (with respect to anomaly) initialization in the near-term range, over the Indo-Pacific equatorial region. Finally, the consistency across the different model predictions was assessed. Specifically, different systems reveal a general agreement in predicting the near-term evolution of surface temperatures, displaying positive correlations between different decadal hindcasts over most of the global domain.
Allostasis: a model of predictive regulation.
Sterling, Peter
2012-04-12
The premise of the standard regulatory model, "homeostasis", is flawed: the goal of regulation is not to preserve constancy of the internal milieu. Rather, it is to continually adjust the milieu to promote survival and reproduction. Regulatory mechanisms need to be efficient, but homeostasis (error-correction by feedback) is inherently inefficient. Thus, although feedbacks are certainly ubiquitous, they could not possibly serve as the primary regulatory mechanism. A newer model, "allostasis", proposes that efficient regulation requires anticipating needs and preparing to satisfy them before they arise. The advantages: (i) errors are reduced in magnitude and frequency; (ii) response capacities of different components are matched -- to prevent bottlenecks and reduce safety factors; (iii) resources are shared between systems to minimize reserve capacities; (iv) errors are remembered and used to reduce future errors. This regulatory strategy requires a dedicated organ, the brain. The brain tracks multitudinous variables and integrates their values with prior knowledge to predict needs and set priorities. The brain coordinates effectors to mobilize resources from modest bodily stores and enforces a system of flexible trade-offs: from each organ according to its ability, to each organ according to its need. The brain also helps regulate the internal milieu by governing anticipatory behavior. Thus, an animal conserves energy by moving to a warmer place - before it cools, and it conserves salt and water by moving to a cooler one before it sweats. The behavioral strategy requires continuously updating a set of specific "shopping lists" that document the growing need for each key component (warmth, food, salt, water). These appetites funnel into a common pathway that employs a "stick" to drive the organism toward filling the need, plus a "carrot" to relax the organism when the need is satisfied. The stick corresponds broadly to the sense of anxiety, and the carrot broadly to
A Neuron Model Based Ultralow Current Sensor System for Bioapplications
Directory of Open Access Journals (Sweden)
A. K. M. Arifuzzman
2016-01-01
Full Text Available An ultralow current sensor system based on the Izhikevich neuron model is presented in this paper. The Izhikevich neuron model has been used for its superior computational efficiency and greater biological plausibility over other well-known neuron spiking models. Of the many biological neuron spiking features, regular spiking, chattering, and neostriatal spiny projection spiking have been reproduced by adjusting the parameters associated with the model at hand. This paper also presents a modified interpretation of the regular spiking feature in which the firing pattern is similar to that of the regular spiking but with improved dynamic range offering. The sensor current ranges between 2 pA and 8 nA and exhibits linearity in the range of 0.9665 to 0.9989 for different spiking features. The efficacy of the sensor system in detecting low amount of current along with its high linearity attribute makes it very suitable for biomedical applications.
Current biomarkers for hepatocellular carcinoma：Surveillance, diagnosis and prediction of prognosis
Institute of Scientific and Technical Information of China (English)
Kerstin Schütte; Christian Schulz; Alexander Link; Peter Malfertheiner
2015-01-01
Biomarkers for surveillance, diagnosis and predictionof prognosis in patients with hepatocellular carcinoma（HCC） are currently not ready for introduction intoclinical practice because of limited sensitivity andspecificity. Especially for the early detection of smallHCC novel biomarkers are needed to improve thecurrent effectiveness of screening performed byultrasound. The use of high-throughput technologiesin hepatocellular research allows to identify moleculesinvolved in the complex pathways in hepatocarcinogenesis.Several invasive and non-invasive biomarkershave been identified already and have been evaluatedin different clinical settings. Gene signatures withprognostic potential have been identified by geneexpression profiling from tumor tissue. However, asingle ＂all-in-one＂ biomarker that fits all-surveillance,diagnosis, prediction of prognosis-has not been foundso far. The future of biomarkers most probably lies in acombination of non-invasive biomarkers, imaging andclinical parameters in a surveillance setting. Molecularprofiling of tumorous and non-tumorous liver tissuemay allow a prediction of prognosis for the individualpatient and hopefully clear the way for individualtreatment approaches. This article gives an overviewon current developments in biomarker research inHCC with a focus on currently available and novelbiomarkers, in particular on microRNA.
MODEL STUDY OF THE DOUBLE FED MACHINE WITH CURRENT CONTROL
Directory of Open Access Journals (Sweden)
A. S. Lyapin
2016-07-01
Full Text Available The paper deals with modeling results of the double fed induction machine with current control in the rotor circuit. We show the most promising applications of electric drives on the basis of the double fed induction machine and their advantages. We present and consider functional scheme of the electric drive on the basis of the double fed induction machine with current control. Equations are obtained for creation of such machine mathematical model. Expressions for vector projections of rotor current are given. According to the obtained results, the change of the vector projections of rotor current ensures operation of the double fed induction machine with the specified values of active and reactive stator power throughout the variation range of sliding motion. We consider static characteristics of double fed machine with current control. Energy processes proceeding in the machine are analyzed. We confirm the operationpossibility of double fed induction machine with current controlin the rotor circuit with given values of active and reactive stator power. The presented results can be used for creation of mathematical models and static characteristics of double fed machines with current control of various capacities.
Required Collaborative Work in Online Courses: A Predictive Modeling Approach
Smith, Marlene A.; Kellogg, Deborah L.
2015-01-01
This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…
A prediction model for assessing residential radon concentration in Switzerland
Hauri, D.D.; Huss, A.; Zimmermann, F.; Kuehni, C.E.; Roosli, M.
2012-01-01
Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the
Distributional Analysis for Model Predictive Deferrable Load Control
Chen, Niangjun; Gan, Lingwen; Low, Steven H.; Wierman, Adam
2014-01-01
Deferrable load control is essential for handling the uncertainties associated with the increasing penetration of renewable generation. Model predictive control has emerged as an effective approach for deferrable load control, and has received considerable attention. In particular, previous work has analyzed the average-case performance of model predictive deferrable load control. However, to this point, distributional analysis of model predictive deferrable load control has been elusive. In ...
Modeling the Inner Magnetosphere: Radiation Belts, Ring Current, and Composition
Glocer, Alex
2011-01-01
The space environment is a complex system defined by regions of differing length scales, characteristic energies, and physical processes. It is often difficult, or impossible, to treat all aspects of the space environment relative to a particular problem with a single model. In our studies, we utilize several models working in tandem to examine this highly interconnected system. The methodology and results will be presented for three focused topics: 1) Rapid radiation belt electron enhancements, 2) Ring current study of Energetic Neutral Atoms (ENAs), Dst, and plasma composition, and 3) Examination of the outflow of ionospheric ions. In the first study, we use a coupled MHD magnetosphere - kinetic radiation belt model to explain recent Akebono/RDM observations of greater than 2.5 MeV radiation belt electron enhancements occurring on timescales of less than a few hours. In the second study, we present initial results of a ring current study using a newly coupled kinetic ring current model with an MHD magnetosphere model. Results of a dst study for four geomagnetic events are shown. Moreover, direct comparison with TWINS ENA images are used to infer the role that composition plays in the ring current. In the final study, we directly model the transport of plasma from the ionosphere to the magnetosphere. We especially focus on the role of photoelectrons and and wave-particle interactions. The modeling methodology for each of these studies will be detailed along with the results.
Prediction for Major Adverse Outcomes in Cardiac Surgery: Comparison of Three Prediction Models
Directory of Open Access Journals (Sweden)
Cheng-Hung Hsieh
2007-09-01
Conclusion: The Parsonnet score performed as well as the logistic regression models in predicting major adverse outcomes. The Parsonnet score appears to be a very suitable model for clinicians to use in risk stratification of cardiac surgery.
Survey of current situation in radiation belt modeling
Fung, Shing F.
2004-01-01
The study of Earth's radiation belts is one of the oldest subjects in space physics. Despite the tremendous progress made in the last four decades, we still lack a complete understanding of the radiation belts in terms of their configurations, dynamics, and detailed physical accounts of their sources and sinks. The static nature of early empirical trapped radiation models, for examples, the NASA AP-8 and AE-8 models, renders those models inappropriate for predicting short-term radiation belt behaviors associated with geomagnetic storms and substorms. Due to incomplete data coverage, these models are also inaccurate at low altitudes (e.g., radiation data from modern space missions and advancement in physical modeling and data management techniques have now allowed the development of new empirical and physical radiation belt models. In this paper, we will review the status of modern radiation belt modeling. Published by Elsevier Ltd on behalf of COSPAR.
On hydrological model complexity, its geometrical interpretations and prediction uncertainty
Arkesteijn, E.C.M.M.; Pande, S.
2013-01-01
Knowledge of hydrological model complexity can aid selection of an optimal prediction model out of a set of available models. Optimal model selection is formalized as selection of the least complex model out of a subset of models that have lower empirical risk. This may be considered equivalent to
Application of Model Predictive Control to BESS for Microgrid Control
Directory of Open Access Journals (Sweden)
Thai-Thanh Nguyen
2015-08-01
Full Text Available Battery energy storage systems (BESSs have been widely used for microgrid control. Generally, BESS control systems are based on proportional-integral (PI control techniques with the outer and inner control loops based on PI regulators. Recently, model predictive control (MPC has attracted attention for application to future energy processing and control systems because it can easily deal with multivariable cases, system constraints, and nonlinearities. This study considers the application of MPC-based BESSs to microgrid control. Two types of MPC are presented in this study: MPC based on predictive power control (PPC and MPC based on PI control in the outer and predictive current control (PCC in the inner control loops. In particular, the effective application of MPC for microgrids with multiple BESSs should be considered because of the differences in their control performance. In this study, microgrids with two BESSs based on two MPC techniques are considered as an example. The control performance of the MPC used for the control microgrid is compared to that of the PI control. The proposed control strategy is investigated through simulations using MATLAB/Simulink software. The simulation results show that the response time, power and voltage ripples, and frequency spectrum could be improved significantly by using MPC.
Predictive modeling of reactive wetting and metal joining.
Energy Technology Data Exchange (ETDEWEB)
van Swol, Frank B.
2013-09-01
The performance, reproducibility and reliability of metal joints are complex functions of the detailed history of physical processes involved in their creation. Prediction and control of these processes constitutes an intrinsically challenging multi-physics problem involving heating and melting a metal alloy and reactive wetting. Understanding this process requires coupling strong molecularscale chemistry at the interface with microscopic (diffusion) and macroscopic mass transport (flow) inside the liquid followed by subsequent cooling and solidification of the new metal mixture. The final joint displays compositional heterogeneity and its resulting microstructure largely determines the success or failure of the entire component. At present there exists no computational tool at Sandia that can predict the formation and success of a braze joint, as current capabilities lack the ability to capture surface/interface reactions and their effect on interface properties. This situation precludes us from implementing a proactive strategy to deal with joining problems. Here, we describe what is needed to arrive at a predictive modeling and simulation capability for multicomponent metals with complicated phase diagrams for melting and solidification, incorporating dissolutive and composition-dependent wetting.
Cross-validation of component models: a critical look at current methods.
Bro, R; Kjeldahl, K; Smilde, A K; Kiers, H A L
2008-03-01
In regression, cross-validation is an effective and popular approach that is used to decide, for example, the number of underlying features, and to estimate the average prediction error. The basic principle of cross-validation is to leave out part of the data, build a model, and then predict the left-out samples. While such an approach can also be envisioned for component models such as principal component analysis (PCA), most current implementations do not comply with the essential requirement that the predictions should be independent of the entity being predicted. Further, these methods have not been properly reviewed in the literature. In this paper, we review the most commonly used generic PCA cross-validation schemes and assess how well they work in various scenarios.
Modeling Current Transfer from PV Modules Based on Meteorological Data
Energy Technology Data Exchange (ETDEWEB)
Hacke, Peter; Smith, Ryan; Kurtz, Sarah; Jordan, Dirk; Wohlgemuth, John
2016-11-21
Current transferred from the active cell circuit to ground in modules undergoing potential-induced degradation (PID) stress is analyzed with respect to meteorological data. Duration and coulombs transferred as a function of whether the module is wet (from dew or rain) or the extent of uncondensed surface humidity are quantified based on meteorological indicators. With this, functions predicting the mode and rate of coulomb transfer are developed for use in estimating the relative PID stress associated with temperature, moisture, and system voltage in any climate. Current transfer in a framed crystalline silicon module is relatively high when there is no condensed water on the module, whereas current transfer in a thin-film module held by edge clips is not, and displays a greater fraction of coulombs transferred when wet compared to the framed module in the natural environment.
Probabilistic Modeling and Visualization for Bankruptcy Prediction
DEFF Research Database (Denmark)
Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara
2017-01-01
In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...
Tanaka, Keiko; Sekijima, Yoshiki; Yoshida, Kunihiro; Mizuuchi, Asako; Yamashita, Hiromi; Tamai, Mariko; Ikeda, Shu-ichi; Fukushima, Yoshimitsu
2013-01-01
The current status of predictive genetic testing for late-onset hereditary neurological diseases in Japan is largely unknown. In this study, we analyzed data from 73 clients who visited the Division of Clinical and Molecular Genetics, Shinshu University Hospital, for the purpose of predictive genetic testing. The clients consisted of individuals with family histories of familial amyloid polyneuropathy (FAP; n=30), Huntington's disease (HD; n=16), spinocerebellar degeneration (SCD; n=14), myotonic dystrophy type 1 (DM1; n=9), familial amyotrophic lateral sclerosis type 1 (ALS1; n=3), and Alzheimer's disease (AD; n=1). Forty-nine of the 73 (67.1%) clients were in their twenties or thirties. Twenty-seven of the 73 (37.0%) clients visited a medical institution within 3 months after becoming aware of predictive genetic testing. The most common reason for requesting predictive genetic testing was a need for certainty or to reduce uncertainty and anxiety. The decision-making about marriage and having a child was also a main reason in clients in the twenties and thirties. The numbers of clients who actually underwent predictive genetic testing was 22 of 30 (73.3%) in FAP, 3 of 16 (18.8%) in HD, 6 of 10 (60.0%) in SCD, 7 of 9 (77.8%) in DM1, and 0 of 3 (0%) in ALS1 (responsible gene of the disease was unknown in 4 SCD patients and an AD patient). The percentage of test usage was lower in untreatable diseases such as HD and SCD than that in FAP, suggesting that many clients changed their way of thinking on the significance of testing through multiple genetic counseling sessions. In addition, it was obvious that existence of disease-modifying therapy promoted usage of predictive genetic testing in FAP. Improvement of genetic counseling system to manage predictive genetic testing is necessary, as consultation concerning predictive genetic testing is the main motivation to visit genetic counseling clinic in many at-risk clients.
Predictive modeling of dental pain using neural network.
Kim, Eun Yeob; Lim, Kun Ok; Rhee, Hyun Sill
2009-01-01
The mouth is a part of the body for ingesting food that is the most basic foundation and important part. The dental pain predicted by the neural network model. As a result of making a predictive modeling, the fitness of the predictive modeling of dental pain factors was 80.0%. As for the people who are likely to experience dental pain predicted by the neural network model, preventive measures including proper eating habits, education on oral hygiene, and stress release must precede any dental treatment.
Meson Exchange Current (MEC) Models in Neutrino Interaction Generators
Katori, Teppei
2013-01-01
Understanding of the so-called 2 particle-2 hole (2p-2h) effect is an urgent program in neutrino interaction physics for current and future oscillation experiments. Such processes are believed to be responsible for the event excesses observed by recent neutrino experiments. The 2p-2h effect is dominated by the meson exchange current (MEC), and is accompanied by a 2-nucleon emission from the primary vertex, instead of a single nucleon emission from the charged-current quasi-elastic (CCQE) interaction. Current and future high resolution experiments can potentially nail down this effect. For this reason, there are world wide efforts to model and implement this process in neutrino interaction simulations. In these proceedings, I would like to describe how this channel is modeled in neutrino interaction generators.
Genetic and physiological bases for phenological responses to current and predicted climates
Wilczek, A. M.; Burghardt, L. T.; Cobb, A. R.; Cooper, M D; Welch, S. M.; Schmitt, J
2010-01-01
We are now reaching the stage at which specific genetic factors with known physiological effects can be tied directly and quantitatively to variation in phenology. With such a mechanistic understanding, scientists can better predict phenological responses to novel seasonal climates. Using the widespread model species Arabidopsis thaliana, we explore how variation in different genetic pathways can be linked to phenology and life-history variation across geographical regions and seasons. We sho...
Predictive Trailing-Edge Modulation Average Current Control in DC-DC Converters
Directory of Open Access Journals (Sweden)
LASCU, D.
2013-11-01
Full Text Available The paper investigates predictive digital average current control (PDACC in dc/dc converters using trailing-edge modulation (TEM. The study is focused on the recurrence duty cycle equation and then stability analysis is performed. It is demonstrated that average current control using trailing-edge modulation is stable on the whole range of the duty cycle and thus design problems are highly reduced. The analysis is carried out in a general manner, independent of converter topology and therefore the results can then be easily applied for a certain converter (buck, boost, buck-boost, etc.. The theoretical considerations are confirmed for a boost converter first using the MATLAB program based on state-space equations and finally with the CASPOC circuit simulation package.
Temporal variations analyses and predictive modeling of microbiological seawater quality.
Lušić, Darija Vukić; Kranjčević, Lado; Maćešić, Senka; Lušić, Dražen; Jozić, Slaven; Linšak, Željko; Bilajac, Lovorka; Grbčić, Luka; Bilajac, Neiro
2017-08-01
Bathing water quality is a major public health issue, especially for tourism-oriented regions. Currently used methods within EU allow at least a 2.2 day period for obtaining the analytical results, making outdated the information forwarded to the public. Obtained results and beach assessment are influenced by the temporal and spatial characteristics of sample collection, and numerous environmental parameters, as well as by differences of official water standards. This paper examines the temporal variation of microbiological parameters during the day, as well as the influence of the sampling hour, on decision processes in the management of the beach. Apart from the fecal indicators stipulated by the EU Bathing Water Directive (E. coli and enterococci), additional fecal (C. perfringens) and non-fecal (S. aureus and P. aeriginosa) parameters were analyzed. Moreover, the effects of applying different evaluation criteria (national, EU and U.S. EPA) to beach ranking were studied, and the most common reasons for exceeding water-quality standards were investigated. In order to upgrade routine monitoring, a predictive statistical model was developed. The highest concentrations of fecal indicators were recorded early in the morning (6 AM) due to the lack of solar radiation during the night period. When compared to enterococci, E. coli criteria appears to be more stringent for the detection of fecal pollution. In comparison to EU and U.S. EPA criteria, Croatian national evaluation criteria provide stricter public health standards. Solar radiation and precipitation were the predominant environmental parameters affecting beach water quality, and these parameters were included in the predictive model setup. Predictive models revealed great potential for the monitoring of recreational water bodies, and with further development can become a useful tool for the improvement of public health protection. Copyright © 2017 Elsevier Ltd. All rights reserved.
Prediction of peptide bonding affinity: kernel methods for nonlinear modeling
Bergeron, Charles; Sundling, C Matthew; Krein, Michael; Katt, Bill; Sukumar, Nagamani; Breneman, Curt M; Bennett, Kristin P
2011-01-01
This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear partial least squares (PLS) algorithm, outperforms PLS, and that the incorporation of transferable atom equivalent features improves predictive capability.
Triedman, John K.; Jolley, Matthew; Stinstra, Jeroen; Brooks, Dana H.; MacLeod, Rob
2008-01-01
ICD implants may be complicated by body size and anatomy. One approach to this problem has been the adoption of creative, extracardiac implant strategies using standard ICD components. Because data on safety or efficacy of such ad hoc implant strategies is lacking, we have developed image-based finite element models (FEMs) to compare electric fields and expected defibrillation thresholds (DFTs) using standard and novel electrode locations. In this paper, we review recently published studies by our group using such models, and progress in meshing strategies to improve efficiency and visualization. Our preliminary observations predict that they may be large changes in DFTs with clinically relevant variations of electrode placement. Extracardiac ICDs of various lead configurations are predicted to be effective in both children and adults. This approach may aid both ICD development and patient-specific optimization of electrode placement, but the simplified nature of current models dictates further development and validation prior to clinical or industrial utilization. PMID:18817926
Integrated hydro-bacterial modelling for predicting bathing water quality
Huang, Guoxian; Falconer, Roger A.; Lin, Binliang
2017-03-01
In recent years health risks associated with the non-compliance of bathing water quality have received increasing worldwide attention. However, it is particularly challenging to establish the source of any non-compliance, due to the complex nature of the source of faecal indicator organisms, and the fate and delivery processes and scarcity of field measured data in many catchments and estuaries. In the current study an integrated hydro-bacterial model, linking a catchment, 1-D model and 2-D model were integrated to simulate the adsorption-desorption processes of faecal bacteria to and from sediment particles in river, estuarine and coastal waters, respectively. The model was then validated using hydrodynamic, sediment and faecal bacteria concentration data, measured in 2012, in the Ribble river and estuary, and along the Fylde coast, UK. Particular emphasis has been placed on the mechanism of faecal bacteria transport and decay through the deposition and resuspension of suspended sediments. The results showed that by coupling the E.coli concentration with the sediment transport processes, the accuracy of the predicted E.coli levels was improved. A series of scenario runs were then carried out to investigate the impacts of different management scenarios on the E.coli concentration levels in the coastal bathing water sites around Liverpool Bay, UK. The model results show that the level of compliance with the new EU bathing water standards can be improved significantly by extending outfalls and/or reducing urban sources by typically 50%.
Comparisons of Faulting-Based Pavement Performance Prediction Models
Directory of Open Access Journals (Sweden)
Weina Wang
2017-01-01
Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.
Seagrass Health Modeling and Prediction with NASA Science Data
Robinson, Harold D.; Easson, Greg; Slattery, Marc; Anderson, Daniel; Blonski, Slawomir; DeCurtins, Robert; Underwood, Lauren
2010-01-01
Previous research has demonstrated that MODIS data products can be used as inputs into the seagrass productivity model developed by Fong and Harwell (1994). To further explore this use to predict seagrass productivity, Moderate Resolution Imaging Spectroradiometer (MODIS) custom data products, including Sea Surface Temperature, Light Attenuation, and Chlorophyll-a have been created for use as model parameter inputs. Coastal researchers can use these MODIS data products and model results in conjunction with historical and daily assessment of seagrass conditions to assess variables that affect the productivity of the seagrass beds. Current monitoring practices involve manual data collection (typically on a quarterly basis) and the data is often insufficient for evaluating the dynamic events that influence seagrass beds. As part of a NASA-funded research grant, the University of Mississippi, is working with researchers at NASA and Radiance Technologies to develop methods to deliver MODIS derived model output for the northern Gulf of Mexico (GOM) to coastal and environmental managers. The result of the project will be a data portal that provides access to MODIS data products and model results from the past 5 years, that includes an automated process to incorporate new data as it becomes available. All model parameters and final output will be available through the use National Oceanic and Atmospheric Administration?s (NOAA) Environmental Research Divisions Data Access Program (ERDDAP) tools as well as viewable using Thematic Realtime Environmental Distributed Data Services (THREDDS) and the Integrated Data Viewer (IDV). These tools provide the ability to create raster-based time sequences of model output and parameters as well as create graphs of model parameters versus time. This tool will provide researchers and coastal managers the ability to analyze the model inputs so that the factors influencing a change in seagrass productivity can be determined over time.
Uric acid and the prediction models of tumor lysis syndrome in AML.
Directory of Open Access Journals (Sweden)
A Ahsan Ejaz
Full Text Available We investigated the ability of serum uric acid (SUA to predict laboratory tumor lysis syndrome (LTLS and compared it to common laboratory variables, cytogenetic profiles, tumor markers and prediction models in acute myeloid leukemia patients. In this retrospective study patients were risk-stratified for LTLS based on SUA cut-off values and the discrimination ability was compared to current prediction models. The incidences of LTLS were 17.8%, 21% and 62.5% in the low, intermediate and high-risk groups, respectively. SUA was an independent predictor of LTLS (adjusted OR 1.12, CI95% 1.0-1.3, p = 0.048. The discriminatory ability of SUA, per ROC curves, to predict LTLS was superior to LDH, cytogenetic profile, tumor markers and the combined model but not to WBC (AUCWBC 0.679. However, in comparisons between high-risk SUA and high-risk WBC, SUA had superior discriminatory capability than WBC (AUCSUA 0.664 vs. AUCWBC 0.520; p <0.001. SUA also demonstrated better performance than the prediction models (high-risk SUAAUC 0.695, p<0.001. In direct comparison of high-risk groups, SUA again demonstrated superior performance than the prediction models (high-risk SUAAUC 0.668, p = 0.001 in predicting LTLS, approaching that of the combined model (AUC 0.685, p<0.001. In conclusion, SUA alone is comparable and highly predictive for LTLS than other prediction models.
An employer brand predictive model for talent attraction and retention
Directory of Open Access Journals (Sweden)
Annelize Botha
2011-02-01
Full Text Available Orientation: In an ever shrinking global talent pool organisations use employer brand to attract and retain talent, however, in the absence of theoretical pointers, many organisations are losing out on a powerful business tool by not developing or maintaining their employer brand correctly. Research purpose: This study explores the current state of knowledge about employer brand and identifies the various employer brand building blocks which are conceptually integrated in a predictive model.Motivation for the study: The need for scientific progress though the accurate representation of a set of employer brand phenomena and propositions, which can be empirically tested, motivated this study.Research design, approach and method: This study was nonempirical in approach and searched for linkages between theoretical concepts by making use of relevant contextual data. Theoretical propositions which explain the identified linkages were developed for purpose of further empirical research.Main findings: Key findings suggested that employer brand is influenced by target group needs, a differentiated Employer Value Proposition (EVP, the people strategy, brand consistency, communication of the employer brand and measurement of Human Resources (HR employer branding efforts.Practical/managerial implications: The predictive model provides corporate leaders and their human resource functionaries a theoretical pointer relative to employer brand which could guide more effective talent attraction and retention decisions.Contribution/value add: This study adds to the small base of research available on employer brand and contributes to both scientific progress as well as an improved practical understanding of factors which influence employer brand.
Flavor Changing Neutral Currents in a Realistic Composite Technicolor Model
Carone, C D; Carone, Christopher D.; Hamilton, Rowan T.
1993-01-01
We consider the phenomenology of a composite technicolor model proposed recently by Georgi. Composite technicolor interactions produce four-quark operators in the low energy theory that contribute to flavor changing neutral current processes. While we expect operators of this type to be induced at the compositeness scale by the flavor-symmetry breaking effects of the preon mass matrices, the Georgi model also includes operators from higher scales that are not GIM-suppressed. Since these operators are potentially large, we study their impact on flavor changing neutral currents and CP violation in the neutral $B$, $D$, and $K$ meson systems.
Prediction using patient comparison vs. modeling: a case study for mortality prediction.
Hoogendoorn, Mark; El Hassouni, Ali; Mok, Kwongyen; Ghassemi, Marzyeh; Szolovits, Peter
2016-08-01
Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in the past: (1) extracting high-level (temporal) features from EMRs and building a predictive model, and (2) defining a patient similarity metric and predicting based on the outcome observed for similar patients. We analyze and compare both approaches on the MIMIC-II ICU dataset to predict patient mortality and find that the patient similarity approach does not scale well and results in a less accurate model (AUC of 0.68) compared to the modeling approach (0.84). We also show that mortality can be predicted within a median of 72 hours.
Fuzzy predictive filtering in nonlinear economic model predictive control for demand response
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.;
2016-01-01
The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...... problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...
Abellán-Nebot, J. V.; Liu, J.; Romero, F.
2009-11-01
The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.
Predictive modeling and reducing cyclic variability in autoignition engines
Energy Technology Data Exchange (ETDEWEB)
Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob
2016-08-30
Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.
Comparison of analytical eddy current models using principal components analysis
Contant, S.; Luloff, M.; Morelli, J.; Krause, T. W.
2017-02-01
Monitoring the gap between the pressure tube (PT) and the calandria tube (CT) in CANDU® fuel channels is essential, as contact between the two tubes can lead to delayed hydride cracking of the pressure tube. Multifrequency transmit-receive eddy current non-destructive evaluation is used to determine this gap, as this method has different depths of penetration and variable sensitivity to noise, unlike single frequency eddy current non-destructive evaluation. An Analytical model based on the Dodd and Deeds solutions, and a second model that accounts for normal and lossy self-inductances, and a non-coaxial pickup coil, are examined for representing the response of an eddy current transmit-receive probe when considering factors that affect the gap response, such as pressure tube wall thickness and pressure tube resistivity. The multifrequency model data was analyzed using principal components analysis (PCA), a statistical method used to reduce the data set into a data set of fewer variables. The results of the PCA of the analytical models were then compared to PCA performed on a previously obtained experimental data set. The models gave similar results under variable PT wall thickness conditions, but the non-coaxial coil model, which accounts for self-inductive losses, performed significantly better than the Dodd and Deeds model under variable resistivity conditions.
Current measures of metabolic heterogeneity within cervical cancer do not predict disease outcome
Directory of Open Access Journals (Sweden)
Brooks Frank J
2011-06-01
Full Text Available Abstract Background A previous study evaluated the intra-tumoral heterogeneity observed in the uptake of F-18 fluorodeoxyglucose (FDG in pre-treatment positron emission tomography (PET scans of cancers of the uterine cervix as an indicator of disease outcome. This was done via a novel statistic which ostensibly measured the spatial variations in intra-tumoral metabolic activity. In this work, we argue that statistic is intrinsically non-spatial, and that the apparent delineation between unsuccessfully- and successfully-treated patient groups via that statistic is spurious. Methods We first offer a straightforward mathematical demonstration of our argument. Next, we recapitulate an assiduous re-analysis of the originally published data which was derived from FDG-PET imagery. Finally, we present the results of a principal component analysis of FDG-PET images similar to those previously analyzed. Results We find that the previously published measure of intra-tumoral heterogeneity is intrinsically non-spatial, and actually is only a surrogate for tumor volume. We also find that an optimized linear combination of more canonical heterogeneity quantifiers does not predict disease outcome. Conclusions Current measures of intra-tumoral metabolic activity are not predictive of disease outcome as has been claimed previously. The implications of this finding are: clinical categorization of patients based upon these statistics is invalid; more sophisticated, and perhaps innately-geometric, quantifications of metabolic activity are required for predicting disease outcome.
Current gaps in understanding and predicting space weather: An operations perspective
Murtagh, W. J.
2016-12-01
The NOAA Space Weather Prediction Center (SWPC), one of the nine National Weather Service (NWS) National Centers for Environmental Prediction, is the Nation's official source for space weather alerts and warnings. Space weather effects the technology that forms the backbone of global economic vitality and national security, including satellite and airline operations, communications networks, and the electric power grid. Many of SWPC's over 48,000 subscribers rely on space weather forecasts for critical decision making. But extraordinary gaps still exist in our ability to meet customer needs for accurate and timely space weather forecasts and warnings. The 2015 National Space Weather Strategy recognizes that it is imperative that we improve the fundamental understanding of space weather and increase the accuracy, reliability, and timeliness of space-weather observations and forecasts in support of the growing demands. In this talk we provide a broad perspective of the key challenges that currently limit the forecaster's ability to better understand and predict space weather. We also examine the impact of these limitations on the end-user community.
Addressing Confounding in Predictive Models with an Application to Neuroimaging.
Linn, Kristin A; Gaonkar, Bilwaj; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T
2016-05-01
Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease efxcfects across the brain. We discuss several important issues that must be considered when analyzing data from neuroimaging studies using MVPA. In particular, we focus on the consequences of confounding by non-imaging variables such as age and sex on the results of MVPA. After reviewing current practice to address confounding in neuroimaging studies, we propose an alternative approach based on inverse probability weighting. Although the proposed method is motivated by neuroimaging applications, it is broadly applicable to many problems in machine learning and predictive modeling. We demonstrate the advantages of our approach on simulated and real data examples.
Intelligent predictive model of ventilating capacity of imperial smelt furnace
Institute of Scientific and Technical Information of China (English)
唐朝晖; 胡燕瑜; 桂卫华; 吴敏
2003-01-01
In order to know the ventilating capacity of imperial smelt furnace (ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.
A Prediction Model of the Capillary Pressure J-Function
Xu, W. S.; Luo, P. Y.; Sun, L.; Lin, N.
2016-01-01
The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative. PMID:27603701
Adaptation of Predictive Models to PDA Hand-Held Devices
Directory of Open Access Journals (Sweden)
Lin, Edward J
2008-01-01
Full Text Available Prediction models using multiple logistic regression are appearing with increasing frequency in the medical literature. Problems associated with these models include the complexity of computations when applied in their pure form, and lack of availability at the bedside. Personal digital assistant (PDA hand-held devices equipped with spreadsheet software offer the clinician a readily available and easily applied means of applying predictive models at the bedside. The purposes of this article are to briefly review regression as a means of creating predictive models and to describe a method of choosing and adapting logistic regression models to emergency department (ED clinical practice.
A model to predict the power output from wind farms
Energy Technology Data Exchange (ETDEWEB)
Landberg, L. [Riso National Lab., Roskilde (Denmark)
1997-12-31
This paper will describe a model that can predict the power output from wind farms. To give examples of input the model is applied to a wind farm in Texas. The predictions are generated from forecasts from the NGM model of NCEP. These predictions are made valid at individual sites (wind farms) by applying a matrix calculated by the sub-models of WASP (Wind Atlas Application and Analysis Program). The actual wind farm production is calculated using the Riso PARK model. Because of the preliminary nature of the results, they will not be given. However, similar results from Europe will be given.
Modelling microbial interactions and food structure in predictive microbiology
Malakar, P.K.
2002-01-01
Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.
Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of
Modelling microbial interactions and food structure in predictive microbiology
Malakar, P.K.
2002-01-01
Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology. Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of new technologies
Flavor changing neutral currents in a realistic composite technicolor model
Carone, Christopher D.; Hamilton, Rowan T.
1993-03-01
We consider the phenomenology of a composite technicolor model proposed recently by Georgi. Composite technicolor interactions produce four-quark operators in the low energy theory that contribute to flavor changing neutral current processes. While we expect operators of this type to be induced at the compositeness scale by the flavor-symmetry breaking effects of the preon mass matrices, the Georgi model also includes operators from higher scales that are not GIM-suppressed. Since these operators are potentially large, we study their impact on flavor changing neutral currents and CP violation in the neutral K, B, and D meson systems. Notably, we find that this model gives rise to a typical value for {ɛ‧}/{ɛ} that is much smaller than most standard model estimates.
Current Concepts: Mouse Models of Sjögren's Syndrome
Directory of Open Access Journals (Sweden)
Tegan N. Lavoie
2011-01-01
Full Text Available Sjögren's syndrome (SjS is a complex chronic autoimmune disease of unknown etiology which primarily targets the exocrine glands, resulting in eventual loss of secretory function. The disease can present as either primary SjS or secondary SjS, the latter of which occurs concomitantly with another autoimmune disease such as rheumatoid arthritis, systemic lupus erythematosus, scleroderma, or primary biliary cirrhosis. Current advancements in therapeutic prevention and treatment for SjS are impeded by lack of understanding in the pathophysiological and clinical progression of the disease. Development of appropriate mouse models for both primary and secondary SjS is needed in order to advance knowledge of this disease. This paper details important features, advantages, and pitfalls of current animal models of SjS, including spontaneous, transgenic, knockout, immunization, and transplantation chimera mouse models, and emphasizes the need for a better model in representing the human SjS phenotype.
Analogue Behavioral Modeling of Switched-Current Building Block Circuits
Institute of Scientific and Technical Information of China (English)
ZENG Xuan; WANG Wei; SHI Jianlei; TANG Pushan; D.ZHOU
2001-01-01
This paper proposes a behavioral modeling technique for the second-generation switched-current building block circuits. The proposed models are capable of capturing the non-ideal behavior of switched-current circuits, which includes the charge injection effects and device mismatch effects. As a result, system performance degradations due to the building block imperfections can be detected at the early design stage by fast behavioral simulations. To evaluate the accuracy of the proposed models, we developed a time-domain behavioral simulator. Experimental results have shown that compared with SPICE, the behavioral modeling error is less than 2.15%, while behavioral simulation speed up is 4 orders in time-domain.
Animal models of frailty: current applications in clinical research.
Kane, Alice E; Hilmer, Sarah N; Mach, John; Mitchell, Sarah J; de Cabo, Rafael; Howlett, Susan E
2016-01-01
The ethical, logistical, and biological complications of working with an older population of people inherently limits clinical studies of frailty. The recent development of animal models of frailty, and tools for assessing frailty in animal models provides an invaluable opportunity for frailty research. This review summarizes currently published animal models of frailty including the interleukin-10 knock-out mouse, the mouse frailty phenotype assessment tool, and the mouse clinical frailty index. It discusses both current and potential roles of these models in research into mechanisms of frailty, interventions to prevent/delay frailty, and the effect of frailty on outcomes. Finally, this review discusses some of the challenges and opportunities of translating research findings from animals to humans.
Numeral eddy current sensor modelling based on genetic neural network
Institute of Scientific and Technical Information of China (English)
Yu A-Long
2008-01-01
This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness,on-line modelling and high precision.The maximum nonlinearity error can be reduced to 0.037% by using GNN.However, the maximum nonlinearity error is 0.075% using the least square method.
Directory of Open Access Journals (Sweden)
Zhanfeng Song
2016-01-01
Full Text Available A predictive current control strategy can realize flexible regulation of three-phase grid-tied converters based on system behaviour prediction and cost function minimization. However, when the predictive current control strategy with conventional switching patterns is adopted, the predicted duration time for voltage vectors turns out to be negative in some cases, especially under the conditions of bidirectional power flows and transient situations, leading to system performance deteriorations. This paper aims to clarify the real reason for this phenomenon under bidirectional power flows, i.e., rectifier mode and inverter mode, and, furthermore, seeks to propose effective solutions. A detailed analysis of instantaneous current variations under different conditions was conducted. An enhanced predictive current control strategy with improved switching patterns was then proposed. An experimental platform was built based on a commercial converter produced by Danfoss, and moreover, relative experiments were carried out, confirming the superiority of the proposed scheme.
Predicting Career Advancement with Structural Equation Modelling
Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia
2012-01-01
Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…
Predicting Career Advancement with Structural Equation Modelling
Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia
2012-01-01
Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…
Modeling and prediction of surgical procedure times
P.S. Stepaniak (Pieter); C. Heij (Christiaan); G. de Vries (Guus)
2009-01-01
textabstractAccurate prediction of medical operation times is of crucial importance for cost efficient operation room planning in hospitals. This paper investigates the possible dependence of procedure times on surgeon factors like age, experience, gender, and team composition. The effect of these f
A turbidity current model for real world applications
Macías, Jorge; Castro, Manuel J.; Morales, Tomás
2016-04-01
Traditional turbidity current models suffer from several drawbacks. Among them not preserving freshwater mass, a missing pressure term, or not including terms related to deposition, erosion and entrainment in the momentum equation. In Morales et al.(2009) a new turbidity current model was proposed trying to overcome all these drawbacks. This model takes into account the interaction between the turbidity current and the bottom, considering deposition and erosion effects as well as solid bedload transport of particles at the bed due to the current. Moreover, this model includes the effects of the deposition, erosion and water entrainment into the momentum equation,commonly neglected in this type of models and, finally, in the absence of water entrainment, freshwater mass in the turbidity current is preserved. Despite these improvements, the numerical results obtained by this model when applied to real river systems were not satisfactory due to the simple form of the friction term that was considered. In the present work we propose a different parameterization of this term, where bottom and interface fluid frictions are separately parameterized with more complex expressions. Moreover, the discretization of the deposition/erosion terms is now performed semi-implicitly which guarantees the positivity of the volumetric concentration of sediments in suspension and in the erodible sediment layer at the bed. The numerical simulations obtained with this new turbidity current model (component of HySEA numerical computing platform) greatly improve previous numerical results for simplified geometries as well as for real river systems. Acknowledgements: This research has been partially supported by the Junta de Andalucía research project TESELA (P11-RNM7069) and the Spanish Government Research project DAIFLUID (MTM2012-38383-C02-01) and Universidad de Málaga, Campus de Excelencia Andalucía TECH. References: T. Morales, M. Castro, C. Parés, and E. Fernández-Nieto (2009). On
Prediction Model of Sewing Technical Condition by Grey Neural Network
Institute of Scientific and Technical Information of China (English)
DONG Ying; FANG Fang; ZHANG Wei-yuan
2007-01-01
The grey system theory and the artificial neural network technology were applied to predict the sewing technical condition. The representative parameters, such as needle, stitch, were selected. Prediction model was established based on the different fabrics' mechanical properties that measured by KES instrument. Grey relevant degree analysis was applied to choose the input parameters of the neural network. The result showed that prediction model has good precision. The average relative error was 4.08% for needle and 4.25% for stitch.
Active diagnosis of hybrid systems - A model predictive approach
2009-01-01
A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and faulty outputs constrained by tolerable performance requirements. As in standard model predictive control, the first element of the optimal input is applied to the system and the whole procedure is repeate...
Hu, Li-Le; Zhang, Hai-Ying; Qin, Ling; Yan, Bo-Qian
2012-09-01
With integration of literature data, specimens records, and field surveys, the current distribution map of Schisandra chinensis in China was drawn, and, based on this map and considering 21 environmental factors, the future distribution of S. chinensis in China in the 2050s and 2080s under the IPCC A2 and A1B climate change scenarios was predicted by using Maxent software. Currently, the S. chinensis in China occurred in 15 provinces, involving 151 counties, and its distribution area decreased with decreasing latitude and longitude. The main distribution area included Heilongjiang, Liaoning, Inner Mongolia, and Jilin. The potential distribution area of S. chinensis in China was 145.12 x 10(4) km2, 48.6% of which were the favorable habitat area, mainly distributed in Changbai Mountains, Xiaoxing'anling Mountains, Daxing'anling Mountains, and the regions between Hebei and Liaoning provinces. The most favorable habitat area only accounted for 0.3%, and was mainly in the Kuandian Manchu Autonomous County, Benxi Manchu Autonomous County, and Huanren Manchu Autonomous County of Liaoning Province, the Antu County and Helong County of Jilin Province, and the Yakeshi City of Inner Mongolia. Under the two climate change scenarios, the potential future distribution area of S. chinensis in China would have a gradual decrease, and the decrement would be larger under A2 than under A1B scenario. By 2050, the distribution area of the S. chinensis under A1B and A2 scenarios would be moderately decreased to 84.0% and 81.5% of the current distribution area, respectively; by 2080, the distribution of S. chinensis under A2 scenario would be dramatically decreased to only 0.5% of the current range, and that under A1B scenario would be decreased to 1/2 of the current range.
Comparison of Simple Versus Performance-Based Fall Prediction Models
Directory of Open Access Journals (Sweden)
Shekhar K. Gadkaree BS
2015-05-01
Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “any fall” and “recurrent falls.” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.
Testing and analysis of internal hardwood log defect prediction models
R. Edward. Thomas
2011-01-01
The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...
Comparison of Simple Versus Performance-Based Fall Prediction Models
Directory of Open Access Journals (Sweden)
Shekhar K. Gadkaree BS
2015-05-01
Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.
Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling
Kayastha, N.
2014-01-01
Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode
Refining the committee approach and uncertainty prediction in hydrological modelling
Kayastha, N.
2014-01-01
Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode
Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling
Kayastha, N.
2014-01-01
Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode
Refining the committee approach and uncertainty prediction in hydrological modelling
Kayastha, N.
2014-01-01
Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of mode
Adding propensity scores to pure prediction models fails to improve predictive performance
Directory of Open Access Journals (Sweden)
Amy S. Nowacki
2013-08-01
Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.
Modeling and strain gauging of eddy current repulsion deicing systems
Smith, Samuel O.
1993-01-01
Work described in this paper confirms and extends work done by Zumwalt, et al., on a variety of in-flight deicing systems that use eddy current repulsion for repelling ice. Two such systems are known as electro-impulse deicing (EIDI) and the eddy current repulsion deicing strip (EDS). Mathematical models for these systems are discussed for their capabilities and limitations. The author duplicates a particular model of the EDS. Theoretical voltage, current, and force results are compared directly to experimental results. Dynamic strain measurements results are presented for the EDS system. Dynamic strain measurements near EDS or EIDI coils are complicated by the high magnetic fields in the vicinity of the coils. High magnetic fields induce false voltage signals out of the gages.
Impact of modellers' decisions on hydrological a priori predictions
Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.
2014-06-01
In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of
Econometric models for predicting confusion crop ratios
Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)
1979-01-01
Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.
Current Models and Innovative Strategies in Management Education in China.
Wang, Zhong-Ming
1999-01-01
Current models of management education in China include national training, on-the-job technical training, and the national master's of business administration supervisory committee. Effective strategies being used include teamwork, process skills, action learning, cross-cultural management learning, and competency-based management development. (SK)
Hypersonic Vehicle Tracking Based on Improved Current Statistical Model
Directory of Open Access Journals (Sweden)
He Guangjun
2013-11-01
Full Text Available A new method of tracking the near space hypersonic vehicle is put forward. According to hypersonic vehicles’ characteristics, we improved current statistical model through online identification of the maneuvering frequency. A Monte Carlo simulation is used to analyze the performance of the method. The results show that the improved method exhibits very good tracking performance in comparison with the old method.
[Neither Descartes nor Freud? current pain models in psychosomatic medicine].
Egloff, N; Egle, U T; von Känel, R
2008-05-14
Models explaining chronic pain based on the mere presence or absence of peripheral somatic findings or which view pain of psychological origin when there is no somatic explanation, have their shortcomings. Current scientific knowledge calls for distinct pain concepts, which integrate neurobiological and neuropsychological aspects of pain processing.
Klapilová, Kateřina; Cobey, Kelly D; Wells, Timothy; Roberts, S Craig; Weiss, Petr; Havlíček, Jan
2014-01-10
Data from 1155 Czech women (493 using oral contraception, 662 non-users), obtained from the Czech National Survey of Sexual Behavior, were used to investigate evolutionary-based hypotheses concerning the predictive value of current oral contraceptive (OC) use on extra-pair and dyadic (in-pair) sexual behavior of coupled women. Specifically, the aim was to determine whether current OC use was associated with lower extra-pair and higher in-pair sexual interest and behavior, because OC use suppresses cyclical shifts in mating psychology that occur in normally cycling women. Zero-inflated Poisson (ZIP) regression and negative binomial models were used to test associations between OC use and these sexual measures, controlling for other relevant predictors (e.g., age, parity, in-pair sexual satisfaction, relationship length). The overall incidence of having had an extra-pair partner or one-night stand in the previous year was not related to current OC use (the majority of the sample had not). However, among the women who had engaged in extra-pair sexual behavior, OC users had fewer one-night stands than non-users, and tended to have fewer partners, than non-users. OC users also had more frequent dyadic intercourse than non-users, potentially indicating higher commitment to their current relationship. These results suggest that suppression of fertility through OC use may alter important aspects of female sexual behavior, with potential implications for relationship functioning and stability.
Directory of Open Access Journals (Sweden)
Kateřina Klapilová
2014-01-01
Full Text Available Data from 1155 Czech women (493 using oral contraception, 662 non-users, obtained from the Czech National Survey of Sexual Behavior, were used to investigate evolutionary-based hypotheses concerning the predictive value of current oral contraceptive (OC use on extra-pair and dyadic (in-pair sexual behavior of coupled women. Specifically, the aim was to determine whether current OC use was associated with lower extra-pair and higher in-pair sexual interest and behavior, because OC use suppresses cyclical shifts in mating psychology that occur in normally cycling women. Zero-inflated Poisson (ZIP regression and negative binomial models were used to test associations between OC use and these sexual measures, controlling for other relevant predictors (e.g., age, parity, in-pair sexual satisfaction, relationship length. The overall incidence of having had an extra-pair partner or one-night stand in the previous year was not related to current OC use (the majority of the sample had not. However, among the women who had engaged in extra-pair sexual behavior, OC users had fewer one-night stands than non-users, and tended to have fewer partners, than non-users. OC users also had more frequent dyadic intercourse than non-users, potentially indicating higher commitment to their current relationship. These results suggest that suppression of fertility through OC use may alter important aspects of female sexual behavior, with potential implications for relationship functioning and stability.
A Bingham-Plastic Model for Fluid Mud Transport Under Waves and Currents
Institute of Scientific and Technical Information of China (English)
刘春嵘; 吴博; 呼和敖德
2014-01-01
Simplified equations of fluid mud motion, which is described as Bingham-Plastic model under waves and currents, are presented by order analysis. The simplified equations are non-linear ordinary differential equations which are solved by hybrid numerical-analytical technique. As the computational cost is very low, the effects of wave current parameters and fluid mud properties on the transportation velocity of the fluid mud are studied systematically. It is found that the fluid mud can move toward one direction even if the shear stress acting on the fluid mud bed is much smaller than the fluid mud yield stress under the condition of wave and current coexistence. Experiments of the fluid mud motion under current with fluctuation water surface are carried out. The fluid mud transportation velocity predicted by the presented mathematical model can roughly match that measured in experiments.
Current advancements and challenges in soil-root interactions modelling
Schnepf, Andrea; Huber, Katrin; Abesha, Betiglu; Meunier, Felicien; Leitner, Daniel; Roose, Tiina; Javaux, Mathieu; Vanderborght, Jan; Vereecken, Harry
2015-04-01
Roots change their surrounding soil chemically, physically and biologically. This includes changes in soil moisture and solute concentration, the exudation of organic substances into the rhizosphere, increased growth of soil microorganisms, or changes in soil structure. The fate of water and solutes in the root zone is highly determined by these root-soil interactions. Mathematical models of soil-root systems in combination with non-invasive techniques able to characterize root systems are a promising tool to understand and predict the behaviour of water and solutes in the root zone. With respect to different fields of applications, predictive mathematical models can contribute to the solution of optimal control problems in plant recourse efficiency. This may result in significant gains in productivity, efficiency and environmental sustainability in various land use activities. Major challenges include the coupling of model parameters of the relevant processes with the surrounding environment such as temperature, nutrient concentration or soil water content. A further challenge is the mathematical description of the different spatial and temporal scales involved. This includes in particular the branched structures formed by root systems or the external mycelium of mycorrhizal fungi. Here, reducing complexity as well as bridging between spatial scales is required. Furthermore, the combination of experimental and mathematical techniques may advance the field enormously. Here, the use of root system, soil and rhizosphere models is presented through a number of modelling case studies, including image based modelling of phosphate uptake by a root with hairs, model-based optimization of root architecture for phosphate uptake from soil, upscaling of rhizosphere models, modelling root growth in structured soil, and the effect of root hydraulic architecture on plant water uptake efficiency and drought resistance.
Models Predicting Success of Infertility Treatment: A Systematic Review
Zarinara, Alireza; Zeraati, Hojjat; Kamali, Koorosh; Mohammad, Kazem; Shahnazari, Parisa; Akhondi, Mohammad Mehdi
2016-01-01
Background: Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn’t simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models. Methods: This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated. Results: Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems. Conclusion: Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples’ needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable. PMID:27141461
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTION
Directory of Open Access Journals (Sweden)
Priyanka H U
2016-09-01
Full Text Available Developing predictive modelling solutions for risk estimation is extremely challenging in health-care informatics. Risk estimation involves integration of heterogeneous clinical sources having different representation from different health-care provider making the task increasingly complex. Such sources are typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel computing tools collectively termed big data tools are in need which can synthesize and assist the physician to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel approach for combining the predictive ability of multiple models for better prediction accuracy. We demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study. Results show that the proposed multi-model predictive architecture is able to provide better accuracy than best model approach. By modelling the error of predictive models we are able to choose sub set of models which yields accurate results. More information was modelled into system by multi-level mining which has resulted in enhanced predictive accuracy.
The regional prediction model of PM10 concentrations for Turkey
Güler, Nevin; Güneri İşçi, Öznur
2016-11-01
This study is aimed to predict a regional model for weekly PM10 concentrations measured air pollution monitoring stations in Turkey. There are seven geographical regions in Turkey and numerous monitoring stations at each region. Predicting a model conventionally for each monitoring station requires a lot of labor and time and it may lead to degradation in quality of prediction when the number of measurements obtained from any õmonitoring station is small. Besides, prediction models obtained by this way only reflect the air pollutant behavior of a small area. This study uses Fuzzy C-Auto Regressive Model (FCARM) in order to find a prediction model to be reflected the regional behavior of weekly PM10 concentrations. The superiority of FCARM is to have the ability of considering simultaneously PM10 concentrations measured monitoring stations in the specified region. Besides, it also works even if the number of measurements obtained from the monitoring stations is different or small. In order to evaluate the performance of FCARM, FCARM is executed for all regions in Turkey and prediction results are compared to statistical Autoregressive (AR) Models predicted for each station separately. According to Mean Absolute Percentage Error (MAPE) criteria, it is observed that FCARM provides the better predictions with a less number of models.
Directory of Open Access Journals (Sweden)
S. Vicca
2014-01-01
Full Text Available As a key component of the carbon cycle, soil CO2 efflux (SCE is being increasingly studied to improve our mechanistic understanding of this important carbon flux. Predicting ecosystem responses to climate change often depends on extrapolation of current relationships between ecosystem processes and their climatic drivers to conditions not yet experienced by the ecosystem. This raises the question to what extent these relationships remain unaltered beyond the current climatic window for which observations are available to constrain the relationships. Here, we evaluate whether current responses of SCE to fluctuations in soil temperature and soil water content can be used to predict SCE under altered rainfall patterns. Of the 58 experiments for which we gathered SCE data, 20 were discarded because either too few data were available, or inconsistencies precluded their incorporation in the analyses. The 38 remaining experiments were used to test the hypothesis that a model parameterized with data from the control plots (using soil temperature and water content as predictor variables could adequately predict SCE measured in the manipulated treatment. Only for seven of these 38 experiments, this hypothesis was rejected. Importantly, these were the experiments with the most reliable datasets, i.e., those providing high-frequency measurements of SCE. Accordingly, regression tree analysis demonstrated that measurement frequency was crucial; our hypothesis could be rejected only for experiments with measurement intervals of less than 11 days, and was not rejected for any of the 24 experiments with larger measurement intervals. This highlights the importance of high-frequency measurements when studying effects of altered precipitation on SCE, probably because infrequent measurement schemes have insufficient capacity to detect shifts in the climate-dependencies of SCE. We strongly recommend that future experiments focus more strongly on establishing response
Electric current in flares ribbons: from the standard model in 3D to observations
Janvier, Miho; Bommier, V; Schmieder, B; Démoulin, P; Pariat, E
2014-01-01
The paper presents for the first time a quantification of the photospheric electric current ribbon evolutions during an eruptive flare, accurately predicted by the standard 3D flare model. The standard flare model in 3D has been developed with the MHD code OHM, which models the evolution of an unstable flux rope. Through a series of paper, the model has been successful in explaining observational characteristics of eruptive flares, as well as the intrinsic 3D reconnection mechanism. Such a model also explains the increase of the photospheric currents as a consequence of the evolution of the coronal current layer where reconnection takes place. The photospheric footprints of the 3D current layer reveal a ribbon shape structure. In the present paper, the evolution of the current density is analyzed for the X-class flare that occurred on 15/02/2011 in AR 11158. We first describe the structural evolution of the high vertical current density regions derived with the UNNOFIT inversion code from magnetograms (HMI, e...
Analytical Modeling for the Grating Eddy Current Displacement Sensors
Directory of Open Access Journals (Sweden)
Lv Chunfeng
2015-02-01
Full Text Available As a new type of displacement sensor, grating eddy current displacement sensor (GECDS combines traditional eddy current sensors and grating structure in one. The GECDS performs a wide range displacement measurement without precision reduction. This paper proposes an analytical modeling approach for the GECDS. The solution model is established in the Cartesian coordinate system, and the solving domain is limited to finite extents by using the truncated region eigenfunction expansion method. Based on the second order vector potential, expressions for the electromagnetic field as well as coil impedance related to the displacement can be expressed in closed-form. Theoretical results are then confirmed by experiments, which prove the suitability and effectiveness of the analytical modeling approach.
Numerical Model for Conduction-Cooled Current Lead Heat Loads
White, M J; Brueck, H D; 10.1063/1.4706965
2012-01-01
Current leads are utilized to deliver electrical power from a room temperature junction mounted on the vacuum vessel to a superconducting magnet located within the vacuum space of a cryostat. There are many types of current leads used at laboratories throughout the world, however, conduction-cooled current leads are often chosen for their simplicity and reliability. Conduction-cooled leads have the advantage of using common materials, have no superconducting/normal state transition, and have no boil-off vapor to collect. The XFEL (X-Ray Free Electron Laser) magnets are operated at 2 K, which makes vapor-cooled current leads impractical due to the sub-atmospheric bath pressure. This paper presents a numerical model for conduction-cooled current lead heat loads. This model takes into account varying material and fluid thermal properties, varying thicknesses along the length of the lead, heat transfer in the circumferential and longitudinal directions, electrical power dissipation, and the effect of thermal inte...
Modeling the current-voltage characteristics of bilayer polymer photovoltaic devices
Barker, J. A.; Ramsdale, C. M.; Greenham, N. C.
2003-02-01
We have developed a numerical model to predict the current-voltage curves of bilayer conjugated polymer photovoltaic devices. The model accounts for charge photogeneration, injection, drift, diffusion, and recombination, and includes the effect of space charge on the electric field within the device. Charge separation at the polymer-polymer interface leads to the formation of bound polaron pairs which may either recombine monomolecularly or be dissociated into free charges, and we develop expressions for the field dependence of the dissociation rate. We find that the short-circuit quantum efficiency is determined by the competition between polaron pair dissociation and recombination. The model shows a logarithmic dependence of the open-circuit voltage on the incident intensity, as seen experimentally. This additional intensity-dependent voltage arises from the field required to produce a drift current that balances the current due to diffusion of carriers away from the interface.
Nonlinear model predictive control of a packed distillation column
Energy Technology Data Exchange (ETDEWEB)
Patwardhan, A.A.; Edgar, T.F. (Univ. of Texas, Austin, TX (United States). Dept. of Chemical Engineering)
1993-10-01
A rigorous dynamic model based on fundamental chemical engineering principles was formulated for a packed distillation column separating a mixture of cyclohexane and n-heptane. This model was simplified to a form suitable for use in on-line model predictive control calculations. A packed distillation column was operated at several operating conditions to estimate two unknown model parameters in the rigorous and simplified models. The actual column response to step changes in the feed rate, distillate rate, and reboiler duty agreed well with dynamic model predictions. One unusual characteristic observed was that the packed column exhibited gain-sign changes, which are very difficult to treat using conventional linear feedback control. Nonlinear model predictive control was used to control the distillation column at an operating condition where the process gain changed sign. An on-line, nonlinear model-based scheme was used to estimate unknown/time-varying model parameters.
Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models
Plant, Nathaniel G.; Holland, K. Todd
2011-01-01
Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.
Singer, Christian F; Köstler, Wolfgang J; Hudelist, Gernot
2008-12-01
Breast cancer is the most common female malignancy in many industrialized countries. Approximately one fourth of all women diagnosed with early breast cancer present with tumors that are characterized by erbB2 amplification. While the associated Her-2/neu receptor overexpression results in a high risk of relapse and poor prognosis, these tumors also represent a target for a selective monoclonal antibody therapy with trastuzumab (Herceptin). The combination of trastuzumab with chemotherapy has led to a considerable reduction of recurrences and to a significant reduction in breast cancer mortality both in the adjuvant and metastatic setting. Unfortunately, despite Her-2/neu overexpression, not all patients equally benefit from trastuzumab treatment, and almost all women with metastatic breast cancer eventually progress during antibody therapy. Moreover, trastuzumab is burdened with cardiotoxicity, thus increasing the risk of symptomatic congestive heart failure. In addition, the marginal costs for a 1 year therapy of trastuzumab-based therapy, which is currently considered to be the most effective treatment regimen in the adjuvant setting, may amount for up to US$ 40.000. Testing for erbB2 oncogene amplification by fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH), respectively, and staining for Her-2/neu receptor overexpression by immunohistochemistry (IHC) represent the current standard for determining patient eligibility for trastuzumab-based therapy. However, while the negative predictive value of these assays for predicting the absence of benefit from trastuzumab-based therapy is sufficiently high, their positive predictive value remains insufficient, i.e. only a proportion of patients selected by these tests substantially benefit from trastuzumab-containing regimen. Accordingly, over the last years a number of biomarkers have been evaluated in their potential to predict response to trastuzumab-based therapies. These include
Combination of Maximin and Kriging Prediction Methods for Eddy-Current Testing Database Generation
Energy Technology Data Exchange (ETDEWEB)
Bilicz, Sandor; Lambert, Marc; Vazquez, Emmanuel; Gyimothy, Szabolcs, E-mail: sandor.bilicz@lss.supelec.fr
2010-11-01
Eddy-current testing (ECT) is a widely used nondestructive evaluation technique. The numerical simulation of ECT methods involves high complexity and computational load. However, one needs reliable solutions (within a reasonable CPU time) for these problems to be able to solve the related inverse problem. One possible approach is to build a configuration-specific database, consisting of well-chosen samples (corresponding input data - output signal pairs). Once the database has been constructed, the sought information can be retrieved practically in no time. However, the optimal choice of samples raises complex optimization problems. This paper presents a sampling method which aims to achieve databases being optimal in a certain sense. The goal of our approach is to spread out the output samples in the whole conceivable output domain. The method is formalized as a maximin problem which is solved step-by-step using the kriging prediction.
Unity power factor converter based on a fuzzy controller and predictive input current.
Bouafassa, Amar; Rahmani, Lazhar; Kessal, Abdelhalim; Babes, Badreddine
2014-11-01
This paper proposes analysis and control of a single-phase power factor corrector (PFC). The proposed control is capable of achieving a unity power factor for each DC link voltage or load fluctuation. The method under study is composed of two intelligent approaches, a fuzzy logic controller to ensure an output voltage at a suitable value and predictive current control. The fuzzy controller is used with minimum rules to attain a low cost. The method is verified and discussed through simulation on the MATLAB/Simulink platform. It presents high dynamic performance under various parameter changes. Moreover, in order to examine and evaluate the method in real-time, a test bench is built using dSPACE 1104. The implantation of the proposed method is very easy and flexible and allows for operation under parameter variations. Additionally, the obtained results are very significant.
A burnout prediction model based around char morphology
Energy Technology Data Exchange (ETDEWEB)
T. Wu; E. Lester; M. Cloke [University of Nottingham, Nottingham (United Kingdom). Nottingham Energy and Fuel Centre
2005-07-01
Poor burnout in a coal-fired power plant has marked penalties in the form of reduced energy efficiency and elevated waste material that can not be utilized. The prediction of coal combustion behaviour in a furnace is of great significance in providing valuable information not only for process optimization but also for coal buyers in the international market. Coal combustion models have been developed that can make predictions about burnout behaviour and burnout potential. Most of these kinetic models require standard parameters such as volatile content, particle size and assumed char porosity in order to make a burnout prediction. This paper presents a new model called the Char Burnout Model (ChB) that also uses detailed information about char morphology in its prediction. The model can use data input from one of two sources. Both sources are derived from image analysis techniques. The first from individual analysis and characterization of real char types using an automated program. The second from predicted char types based on data collected during the automated image analysis of coal particles. Modelling results were compared with a different carbon burnout kinetic model and burnout data from re-firing the chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen across several residence times. An improved agreement between ChB model and DTF experimental data proved that the inclusion of char morphology in combustion models can improve model predictions. 27 refs., 4 figs., 4 tabs.
Model-based uncertainty in species range prediction
DEFF Research Database (Denmark)
Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel;
2006-01-01
Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...... day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis). Results Our analyses show significant differences between predictions from different models......, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence-only approaches) and assumptions made by each...
A new ensemble model for short term wind power prediction
DEFF Research Database (Denmark)
Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan;
2012-01-01
As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re......-search of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset....... The conferred results show that the prediction errors can be decreased, while the computation time is reduced....
Energy Technology Data Exchange (ETDEWEB)
Edelen, J. P. [Fermilab; Sun, Y. [Argonne; Harris, J. R. [AFRL, NM; Lewellen, J. W. [Los Alamos Natl. Lab.
2016-09-28
In this paper we derive analytical expressions for the output current of an un-gated thermionic cathode RF gun in the presence of back-bombardment heating. We provide a brief overview of back-bombardment theory and discuss comparisons between the analytical back-bombardment predictions and simulation models. We then derive an expression for the output current as a function of the RF repetition rate and discuss relationships between back-bombardment, fieldenhancement, and output current. We discuss in detail the relevant approximations and then provide predictions about how the output current should vary as a function of repetition rate for some given system configurations.
Improving Environmental Model Calibration and Prediction
2011-01-18
groundwater model calibration. Adv. Water Resour., 29(4):605–623, 2006. [9] B.E. Skahill, J.S. Baggett, S. Frankenstein , and C.W. Downer. More efficient...of Hydrology, Environmental Modelling & Software, or Water Resources Research). Skahill, B., Baggett, J., Frankenstein , S., and Downer, C.W. (2009
Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus
load shifting capabilities of the units that adapts to the given price predictions. We furthermore evaluated control performance in terms of economic savings for different control strategies and forecasts. Chapter 5 describes and compares the proposed large-scale Aggregator control strategies....... Aggregators are assumed to play an important role in the future Smart Grid and coordinate a large portfolio of units. The developed economic MPC controllers interfaces each unit directly to an Aggregator. We developed several MPC-based aggregation strategies that coordinates the global behavior of a portfolio...
Combining logistic regression and neural networks to create predictive models.
Spackman, K. A.
1992-01-01
Neural networks are being used widely in medicine and other areas to create predictive models from data. The statistical method that most closely parallels neural networks is logistic regression. This paper outlines some ways in which neural networks and logistic regression are similar, shows how a small modification of logistic regression can be used in the training of neural network models, and illustrates the use of this modification for variable selection and predictive model building wit...
Assessment of performance of survival prediction models for cancer prognosis
Directory of Open Access Journals (Sweden)
Chen Hung-Chia
2012-07-01
Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in
3D mathematical modelling of scour around a circular pile in current
DEFF Research Database (Denmark)
Roulund, Andreas; Sumer, B. Mutlu; Fredsøe, Jørgen;
1999-01-01
This paper deals with scour around a circular pile exposed to a steady current. A 3D numerical model incorporated with the k-w,SST closure coupled with the sediment-continuity equation and a bedload sediment transport formula has been used to predict the scour. 3D calculations have also been...... to scour depth of 0.6-0.7 times the pile diameter. Calculations that describe the entire scour process (including the equilibrium stage) are underway....
Changing currents: a strategy for understanding and predicting the changing ocean circulation.
Bryden, Harry L; Robinson, Carol; Griffiths, Gwyn
2012-12-13
Within the context of UK marine science, we project a strategy for ocean circulation research over the next 20 years. We recommend a focus on three types of research: (i) sustained observations of the varying and evolving ocean circulation, (ii) careful analysis and interpretation of the observed climate changes for comparison with climate model projections, and (iii) the design and execution of focused field experiments to understand ocean processes that are not resolved in coupled climate models so as to be able to embed these processes realistically in the models. Within UK-sustained observations, we emphasize smart, cost-effective design of the observational network to extract maximum information from limited field resources. We encourage the incorporation of new sensors and new energy sources within the operational environment of UK-sustained observational programmes to bridge the gap that normally separates laboratory prototype from operational instrument. For interpreting the climate-change records obtained through a variety of national and international sustained observational programmes, creative and dedicated UK scientists should lead efforts to extract the meaningful signals and patterns of climate change and to interpret them so as to project future changes. For the process studies, individual scientists will need to work together in team environments to combine observational and process modelling results into effective improvements in the coupled climate models that will lead to more accurate climate predictions.
A thermodynamic model to predict wax formation in petroleum fluids
Energy Technology Data Exchange (ETDEWEB)
Coutinho, J.A.P. [Universidade de Aveiro (Portugal). Dept. de Quimica. Centro de Investigacao em Quimica]. E-mail: jcoutinho@dq.ua.pt; Pauly, J.; Daridon, J.L. [Universite de Pau et des Pays de l' Adour, Pau (France). Lab. des Fluides Complexes
2001-12-01
Some years ago the authors proposed a model for the non-ideality of the solid phase, based on the Predictive Local Composition concept. This was first applied to the Wilson equation and latter extended to NRTL and UNIQUAC models. Predictive UNIQUAC proved to be extraordinarily successful in predicting the behaviour of both model and real hydrocarbon fluids at low temperatures. This work illustrates the ability of Predictive UNIQUAC in the description of the low temperature behaviour of petroleum fluids. It will be shown that using Predictive UNIQUAC in the description of the solid phase non-ideality a complete prediction of the low temperature behaviour of synthetic paraffin solutions, fuels and crude oils is achieved. The composition of both liquid and solid phases, the amount of crystals formed and the cloud points are predicted within the accuracy of the experimental data. The extension of Predictive UNIQUAC to high pressures, by coupling it with an EOS/G{sup E} model based on the SRK EOS used with the LCVM mixing rule, is proposed and predictions of phase envelopes for live oils are compared with experimental data. (author)
A THERMODYNAMIC MODEL TO PREDICT WAX FORMATION IN PETROLEUM FLUIDS
Directory of Open Access Journals (Sweden)
J.A.P. Coutinho
2001-12-01
Full Text Available Some years ago the authors proposed a model for the non-ideality of the solid phase, based on the Predictive Local Composition concept. This was first applied to the Wilson equation and latter extended to NRTL and UNIQUAC models. Predictive UNIQUAC proved to be extraordinarily successful in predicting the behaviour of both model and real hydrocarbon fluids at low temperatures. This work illustrates the ability of Predictive UNIQUAC in the description of the low temperature behaviour of petroleum fluids. It will be shown that using Predictive UNIQUAC in the description of the solid phase non-ideality a complete prediction of the low temperature behaviour of synthetic paraffin solutions, fuels and crude oils is achieved. The composition of both liquid and solid phases, the amount of crystals formed and the cloud points are predicted within the accuracy of the experimental data. The extension of Predictive UNIQUAC to high pressures, by coupling it with an EOS/G E model based on the SRK EOS used with the LCVM mixing rule, is proposed and predictions of phase envelopes for live oils are compared with experimental data.
Kc, M.
2015-12-01
Ecosystem services and products are the foundation of sustainability for regional and global economy since we are directly or indirectly dependent on the ecosystem services like food, livestock, water, air, wildlife etc. It has been increasingly recognized that for sustainability concerns, the conservation problems need to be addressed in the context of entire ecosystems. This approach known as the ecosystem approach is fundamental to managing earth's finite resources since it addresses the interactions that link biotic systems, of which human, flora and fauna are integral parts, with the physical systems on which they depend. This approach is even more vital in the 21st century with formidable increasing human population and rapid changes in global environment. This study is being conducted to find the state of the science of ecosystem models in the South-Central region of US. The propose of the project is to conduct a systematic review and synthesize relevant information on the current state of the science of ecosystem modeling in the South-Central region of US toward coupling these models with climate, agronomic, hydrologic, economic or management models to better represent ecosystem dynamics as affected by climate change and human activities; and hence gain more reliable predictions of future ecosystem functions and service in the region. Better understandings of such processes will increase our ability to predict the ecosystem responses and feedbacks to environmental and human induced change in the region so that decision makers can make an informed management decisions of the ecosystem.
Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems
Kovalenko, Andriy
2014-08-01
Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology
Predictivity of models with spontaneously broken non-Abelian discrete flavor symmetries
Chen, Mu-Chun; Fallbacher, Maximilian; Omura, Yuji; Ratz, Michael; Staudt, Christian
2013-08-01
In a class of supersymmetric flavor models predictions are based on residual symmetries of some subsectors of the theory such as those of the charged leptons and neutrinos. However, the vacuum expectation values of the so-called flavon fields generally modify the Kähler potential of the setting, thus changing the predictions. We derive simple analytic formulae that allow us to understand the impact of these corrections on the predictions for the masses and mixing parameters. Furthermore, we discuss the effects on the vacuum alignment and on flavor changing neutral currents. Our results can also be applied to non-supersymmetric flavor models.
Predictivity of models with spontaneously broken non-Abelian discrete flavor symmetries
Chen, Mu-Chun; Omura, Yuji; Ratz, Michael; Staudt, Christian
2013-01-01
In a class of supersymmetric flavor models predictions are based on residual symmetries of some subsectors of the theory such as those of the charged leptons and neutrinos. However, the vacuum expectation values of the so-called flavon fields generally modify the K\\"ahler potential of the setting, thus changing the predictions. We derive simple analytic formulae that allow us to understand the impact of these corrections on the predictions for the masses and mixing parameters. Furthermore, we discuss the effects on the vacuum alignment and on flavor changing neutral currents. Our results can also be applied to non--supersymmetric flavor models.
A systematic review of predictive modeling for bronchiolitis.
Luo, Gang; Nkoy, Flory L; Gesteland, Per H; Glasgow, Tiffany S; Stone, Bryan L
2014-10-01
Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease course to help determine a proper management plan. This paper reviews the existing state of the art of predictive modeling for bronchiolitis. Predictive modeling for respiratory syncytial virus (RSV) infection is covered whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases. A systematic review was conducted through a PubMed search up to April 25, 2014. The literature on predictive modeling for bronchiolitis was retrieved using a comprehensive search query, which was developed through an iterative process. Search results were limited to human subjects, the English language, and children (birth to 18 years). The literature search returned 2312 references in total. After manual review, 168 of these references were determined to be relevant and are discussed in this paper. We identify several limitations and open problems in predictive modeling for bronchiolitis, and provide some preliminary thoughts on how to address them, with the hope to stimulate future research in this domain. Many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Osman, Marisol; Vera, C. S.
2016-11-01
This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to
Hengl, Tomislav; Heuvelink, Gerard B M; Kempen, Bas; Leenaars, Johan G B; Walsh, Markus G; Shepherd, Keith D; Sila, Andrew; MacMillan, Robert A; Mendes de Jesus, Jorge; Tamene, Lulseged; Tondoh, Jérôme E
2015-01-01
80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological
Current limitations and future opportunities for prediction of DILI from in vitro.
Funk, Christoph; Roth, Adrian
2017-01-01
Drug-induced liver injury (DILI) is a major concern for drug developers, regulators and clinicians. It is triggered by drug and xenobiotic insults leading to liver impairment or damage, in the worst-case liver failure. In contrast to acute "intrinsic" hepatotoxicity, DILI typically manifests in a very small subset of the population under treatment with no clear dose relationship and inconsistent temporal patterns and is therefore termed an idiosyncratic event. Involved are multifactorial, compound-dependent mechanisms and host-specific factors, making the prediction in preclinical test systems very challenging. While preclinical safety studies in animals usually are able to capture direct, acute liver toxicities, they are less predictive for human DILI, where specific, human-derived in vitro models can potentially close the gap. On one hand, mechanistic approaches addressing key mechanisms involved in DILI in well-characterized and standardized in vitro test systems have been developed. On the other hand, co-cultures of different cell types, including patient- and/or stem cell-derived cells, in a three-dimensional setup allow for prolonged incubations and multiplexed readouts. Such complex setups might better reflect multifactorial human DILI. One major challenge is that for many compounds with human DILI the underlying mechanisms are not yet fully understood, complicating establishment and validation of predictive cellular tools. A tiered approach including rapid mechanism-based in vitro screens followed by confirmatory tests in more physiologically relevant models might allow minimizing DILI risk early on in vitro. Such complex, integrated approaches will gain from larger collaborations in multidisciplinary groups bringing existing knowledge and state-of-the-art technology together.
Predicting and Modelling of Survival Data when Cox's Regression Model does not hold
DEFF Research Database (Denmark)
Scheike, Thomas H.; Zhang, Mei-Jie
2002-01-01
Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...
Accurate Holdup Calculations with Predictive Modeling & Data Integration
Energy Technology Data Exchange (ETDEWEB)
Azmy, Yousry [North Carolina State Univ., Raleigh, NC (United States). Dept. of Nuclear Engineering; Cacuci, Dan [Univ. of South Carolina, Columbia, SC (United States). Dept. of Mechanical Engineering
2017-04-03
In facilities that process special nuclear material (SNM) it is important to account accurately for the fissile material that enters and leaves the plant. Although there are many stages and processes through which materials must be traced and measured, the focus of this project is material that is “held-up” in equipment, pipes, and ducts during normal operation and that can accumulate over time into significant quantities. Accurately estimating the holdup is essential for proper SNM accounting (vis-à-vis nuclear non-proliferation), criticality and radiation safety, waste management, and efficient plant operation. Usually it is not possible to directly measure the holdup quantity and location, so these must be inferred from measured radiation fields, primarily gamma and less frequently neutrons. Current methods to quantify holdup, i.e. Generalized Geometry Holdup (GGH), primarily rely on simple source configurations and crude radiation transport models aided by ad hoc correction factors. This project seeks an alternate method of performing measurement-based holdup calculations using a predictive model that employs state-of-the-art radiation transport codes capable of accurately simulating such situations. Inverse and data assimilation methods use the forward transport model to search for a source configuration that best matches the measured data and simultaneously provide an estimate of the level of confidence in the correctness of such configuration. In this work the holdup problem is re-interpreted as an inverse problem that is under-determined, hence may permit multiple solutions. A probabilistic approach is applied to solving the resulting inverse problem. This approach rates possible solutions according to their plausibility given the measurements and initial information. This is accomplished through the use of Bayes’ Theorem that resolves the issue of multiple solutions by giving an estimate of the probability of observing each possible solution. To use
Megias, G D; Moreno, O; Williamson, C F; Caballero, J A; Gonzalez-Jimenez, R; De Pace, A; Barbaro, M B; Alberico, W M; Nardi, M; Amaro, J E
2014-01-01
We evaluate and discuss the impact of meson-exchange currents (MEC) on charged-current quasielastic (QE) neutrino cross sections. We consider the nuclear transverse response arising from 2p-2h states excited by the action of electromagnetic, purely isovector meson-exchange currents in a fully relativistic framework, based on the work by the Torino collaboration [1]. An accurate parametrization of this MEC response as a function of the momentum and energy transfers involved is presented. Results of neutrino-nucleus cross sections using this MEC parametrization together with a recent scaling approach for the 1p-1h contributions (SuSAv2) are compared with experimental data (MiniBooNE, MINERvA, NOMAD and T2K Collaborations).
A fluid mechanical model for current-generating-feeding jellyfish
Peng, Jifeng; Dabiri, John
2008-11-01
Many jellyfish species, e.g. moon jellyfish Aurelia aurita, use body motion to generate fluid currents which carry their prey to the vicinity of their capture appendages. In this study, a model was developed to understand the fluid mechanics for this current-generating-feeding mode of jellyfish. The flow generated by free-swimming Aurelia aurita was measured using digital particle image velocimetry. The dynamics of prey (e.g., brine shrimp Artemia) in the flow field were described by a modified Maxey-Riley equation which takes into consideration the inertia of prey and the escape forces, which prey exert in the presence of predator. A Lagrangian analysis was used to identify the region of the flow in which prey can be captured by the jellyfish and the clearance rate was quantified. The study provides a new methodology to study biological current-generating-feeding and the transport and mixing of particles in fluid flow in general.
3-dimensional current collection model. [of Tethered Satellite System 1
Hwang, Kai-Shen; Shiah, A.; Wu, S. T.; Stone, N.
1992-01-01
A three-dimensional, time dependent current collection model of a satellite has been developed for the TSS-1 system. The system has been simulated particularly for the Research of Plasma Electrodynamics (ROPE) experiment. The Maxwellian distributed particles with the geomagnetic field effects are applied in this numerical simulation. The preliminary results indicate that a ring current is observed surrounding the satellite in the equatorial plane. This ring current is found between the plasma sheath and the satellite surface and is oscillating with a time scale of approximately 1 microsec. This is equivalent to the electron plasma frequency. An hour glass shape of electron distribution was observed when the viewing direction is perpendicular to the equatorial plane. This result is consistent with previous findings from Linson (1969) and Antoniades et al. (1990). Electrons that are absorbed by the satellite are limited from the background ionosphere as indicated by Parker and Murphy (1967).
FOREST ECOSYSTEM DYNAMICS ASSESSMENT AND PREDICTIVE MODELLING IN EASTERN HIMALAYA
Directory of Open Access Journals (Sweden)
S. P. S. Kushwaha
2012-09-01
Full Text Available This study focused on the forest ecosystem dynamics assessment and predictive modelling deforestation and forest cover prediction in a part of north-eastern India i.e. forest areas along West Bengal, Bhutan, Arunachal Pradesh and Assam border in Eastern Himalaya using temporal satellite imagery of 1975, 1990 and 2009 and predicted forest cover for the period 2028 using Cellular Automata Markov Modedel (CAMM. The exercise highlighted large-scale deforestation in the study area during 1975–1990 as well as 1990–2009 forest cover vectors. A net loss of 2,334.28 km2 forest cover was noticed between 1975 and 2009, and with current rate of deforestation, a forest area of 4,563.34 km2 will be lost by 2028. The annual rate of deforestation worked out to be 0.35 and 0.78% during 1975–1990 and 1990–2009 respectively. Bamboo forest increased by 24.98% between 1975 and 2009 due to opening up of the forests. Forests in Kokrajhar, Barpeta, Darrang, Sonitpur, and Dhemaji districts in Assam were noticed to be worst-affected while Lower Subansiri, West and East Siang, Dibang Valley, Lohit and Changlang in Arunachal Pradesh were severely affected. Among different forest types, the maximum loss was seen in case of sal forest (37.97% between 1975 and 2009 and is expected to deplete further to 60.39% by 2028. The tropical moist deciduous forest was the next category, which decreased from 5,208.11 km2 to 3,447.28 (33.81% during same period with further chances of depletion to 2,288.81 km2 (56.05% by 2028. It noted progressive loss of forests in the study area between 1975 and 2009 through 1990 and predicted that, unless checked, the area is in for further depletion of the invaluable climax forests in the region, especially sal and moist deciduous forests. The exercise demonstrated high potential of remote sensing and geographic information system for forest ecosystem dynamics assessment and the efficacy of CAMM to predict the forest cover change.
Predictive error analysis for a water resource management model
Gallagher, Mark; Doherty, John
2007-02-01
SummaryIn calibrating a model, a set of parameters is assigned to the model which will be employed for the making of all future predictions. If these parameters are estimated through solution of an inverse problem, formulated to be properly posed through either pre-calibration or mathematical regularisation, then solution of this inverse problem will, of necessity, lead to a simplified parameter set that omits the details of reality, while still fitting historical data acceptably well. Furthermore, estimates of parameters so obtained will be contaminated by measurement noise. Both of these phenomena will lead to errors in predictions made by the model, with the potential for error increasing with the hydraulic property detail on which the prediction depends. Integrity of model usage demands that model predictions be accompanied by some estimate of the possible errors associated with them. The present paper applies theory developed in a previous work to the analysis of predictive error associated with a real world, water resource management model. The analysis offers many challenges, including the fact that the model is a complex one that was partly calibrated by hand. Nevertheless, it is typical of models which are commonly employed as the basis for the making of important decisions, and for which such an analysis must be made. The potential errors associated with point-based and averaged water level and creek inflow predictions are examined, together with the dependence of these errors on the amount of averaging involved. Error variances associated with predictions made by the existing model are compared with "optimized error variances" that could have been obtained had calibration been undertaken in such a way as to minimize predictive error variance. The contributions by different parameter types to the overall error variance of selected predictions are also examined.