Monthly streamflow forecasting with auto-regressive integrated moving average
Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani
2017-09-01
Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.
Permanasari, Adhistya Erna; Dominic, Dhanapal Durai
2009-01-01
Zoonosis refers to the transmission of infectious diseases from animal to human. The increasing number of zoonosis incidence makes the great losses to lives, including humans and animals, and also the impact in social economic. It motivates development of a system that can predict the future number of zoonosis occurrences in human. This paper analyses and presents the use of Seasonal Autoregressive Integrated Moving Average (SARIMA) method for developing a forecasting model that able to support and provide prediction number of zoonosis human incidence. The dataset for model development was collected on a time series data of human tuberculosis occurrences in United States which comprises of fourteen years of monthly data obtained from a study published by Centers for Disease Control and Prevention (CDC). Several trial models of SARIMA were compared to obtain the most appropriate model. Then, diagnostic tests were used to determine model validity. The result showed that the SARIMA(9,0,14)(12,1,24)12 is the fitt...
Lijing Yu; Lingling Zhou; Li Tan; Hongbo Jiang; Ying Wang; Sheng Wei; Shaofa Nie
2014-01-01
BACKGROUND: Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic. METHOD: In this paper, a hybrid model combining seasonal auto-regressive integrated moving average...
The Prediction of Exchange Rates with the Use of Auto-Regressive Integrated Moving-Average Models
Daniela Spiesová
2014-10-01
Full Text Available Currency market is recently the largest world market during the existence of which there have been many theories regarding the prediction of the development of exchange rates based on macroeconomic, microeconomic, statistic and other models. The aim of this paper is to identify the adequate model for the prediction of non-stationary time series of exchange rates and then use this model to predict the trend of the development of European currencies against Euro. The uniqueness of this paper is in the fact that there are many expert studies dealing with the prediction of the currency pairs rates of the American dollar with other currency but there is only a limited number of scientific studies concerned with the long-term prediction of European currencies with the help of the integrated ARMA models even though the development of exchange rates has a crucial impact on all levels of economy and its prediction is an important indicator for individual countries, banks, companies and businessmen as well as for investors. The results of this study confirm that to predict the conditional variance and then to estimate the future values of exchange rates, it is adequate to use the ARIMA (1,1,1 model without constant, or ARIMA [(1,7,1,(1,7] model, where in the long-term, the square root of the conditional variance inclines towards stable value.
Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina
2013-01-01
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Razana Alwee
2013-01-01
Full Text Available Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR and autoregressive integrated moving average (ARIMA to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Tani, Yuji; Ogasawara, Katsuhiko
2012-01-01
This study aimed to contribute to the management of a healthcare organization by providing management information using time-series analysis of business data accumulated in the hospital information system, which has not been utilized thus far. In this study, we examined the performance of the prediction method using the auto-regressive integrated moving-average (ARIMA) model, using the business data obtained at the Radiology Department. We made the model using the data used for analysis, which was the number of radiological examinations in the past 9 years, and we predicted the number of radiological examinations in the last 1 year. Then, we compared the actual value with the forecast value. We were able to establish that the performance prediction method was simple and cost-effective by using free software. In addition, we were able to build the simple model by pre-processing the removal of trend components using the data. The difference between predicted values and actual values was 10%; however, it was more important to understand the chronological change rather than the individual time-series values. Furthermore, our method was highly versatile and adaptable compared to the general time-series data. Therefore, different healthcare organizations can use our method for the analysis and forecasting of their business data.
Yu, Lijing; Zhou, Lingling; Tan, Li; Jiang, Hongbo; Wang, Ying; Wei, Sheng; Nie, Shaofa
2014-01-01
Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic. In this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012. The best-fitted hybrid model was combined with seasonal ARIMA [Formula: see text] and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively -965.03, -1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend. The model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.
Liang, Hao; Gao, Lian; Liang, Bingyu; Huang, Jiegang; Zang, Ning; Liao, Yanyan; Yu, Jun; Lai, Jingzhen; Qin, Fengxiang; Su, Jinming; Ye, Li; Chen, Hui
2016-01-01
Background Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease. Methods The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models. Results The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models. Conclusions The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County. PMID:27258555
A Study of Wind Statistics Through Auto-Regressive and Moving-Average (ARMA) Modeling
尹彰; 周宗仁
2001-01-01
Statistical properties of winds near the Taichung Harbour are investigated. The 26 years′incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.
A Simple Introduction to Moving Least Squares and Local Regression Estimation
Garimella, Rao Veerabhadra [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-06-22
In this brief note, a highly simpli ed introduction to esimating functions over a set of particles is presented. The note starts from Global Least Squares tting, going on to Moving Least Squares estimation (MLS) and nally, Local Regression Estimation (LRE).
Probing turbulence intermittency via Auto-Regressive Moving-Average models
Faranda, Davide; Dubrulle, Berengere; Daviaud, Francois
2014-01-01
We suggest a new approach to probing intermittency corrections to the Kolmogorov law in turbulent flows based on the Auto-Regressive Moving-Average modeling of turbulent time series. We introduce a new index $\\Upsilon$ that measures the distance from a Kolmogorov-Obukhov model in the Auto-Regressive Moving-Average models space. Applying our analysis to Particle Image Velocimetry and Laser Doppler Velocimetry measurements in a von K\\'arm\\'an swirling flow, we show that $\\Upsilon$ is proportional to the traditional intermittency correction computed from the structure function. Therefore it provides the same information, using much shorter time series. We conclude that $\\Upsilon$ is a suitable index to reconstruct the spatial intermittency of the dissipation in both numerical and experimental turbulent fields.
A self-organizing power system stabilizer using Fuzzy Auto-Regressive Moving Average (FARMA) model
Park, Y.M.; Moon, U.C. [Seoul National Univ. (Korea, Republic of). Electrical Engineering Dept.; Lee, K.Y. [Pennsylvania State Univ., University Park, PA (United States). Electrical Engineering Dept.
1996-06-01
This paper presents a self-organizing power system stabilizer (SOPSS) which use the Fuzzy Auto-Regressive Moving Average (FARMA) model. The control rules and the membership functions of the proposed logic controller are generated automatically without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. To show the effectiveness of the proposed controller, comparison with a conventional controller for one-machine infinite-bus system is presented.
Statistical early-warning indicators based on Auto-Regressive Moving-Average processes
Faranda, Davide; Dubrulle, Bérengère
2014-01-01
We address the problem of defining early warning indicators of critical transition. To this purpose, we fit the relevant time series through a class of linear models, known as Auto-Regressive Moving-Average (ARMA(p,q)) models. We define two indicators representing the total order and the total persistence of the process, linked, respectively, to the shape and to the characteristic decay time of the autocorrelation function of the process. We successfully test the method to detect transitions in a Langevin model and a 2D Ising model with nearest-neighbour interaction. We then apply the method to complex systems, namely for dynamo thresholds and financial crisis detection.
Integrating Naturopathy: Can We Move Forward?
Elder, Charles R
2013-01-01
Although acupuncture and chiropractic care have achieved some measure of acceptance within mainstream medicine, the integrative role for naturopathy has yet to be well specified. This essay provides a discussion of the potential benefits of naturopathic medicine, as well as an overview of current obstacles to its integration. Action steps toward improving communication between allopathic and naturopathic physicians are suggested.
Integrating naturopathy: can we move forward?
Elder, Charles R
2013-01-01
Although acupuncture and chiropractic care have achieved some measure of acceptance within mainstream medicine, the integrative role for naturopathy has yet to be well specified. This essay provides a discussion of the potential benefits of naturopathic medicine, as well as an overview of current obstacles to its integration. Action steps toward improving communication between allopathic and naturopathic physicians are suggested.
Integrating Naturopathy: Can We Move Forward?
Elder, Charles R
2013-01-01
Although acupuncture and chiropractic care have achieved some measure of acceptance within mainstream medicine, the integrative role for naturopathy has yet to be well specified. This essay provides a discussion of the potential benefits of naturopathic medicine, as well as an overview of current obstacles to its integration. Action steps toward improving communication between allopathic and naturopathic physicians are suggested.
Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection
Heng Liu
2017-01-01
Full Text Available In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.
European psychiatry: moving towards integration and harmony.
Cox, John L
2007-02-01
This paper summarizes political and social changes in Europe that directly affect the training of psychiatrists and the provision of mental health services. In Western Europe the demands of service users have led to a closer integration of social and health services, and a closer working and training of health professionals. The European psychiatrist of the future will be an internationalist, familiar with the impact of culture on mental disorders and able to work in a multi-professional team.
Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models
Pappas, S.S. [Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, 83 200 Samos (Greece); Ekonomou, L.; Chatzarakis, G.E. [Department of Electrical Engineering Educators, ASPETE - School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24100 Kalamata (Greece); Katsikas, S.K. [Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Srt., 18 532 Piraeus (Greece); Liatsis, P. [Division of Electrical Electronic and Information Engineering, School of Engineering and Mathematical Sciences, Information and Biomedical Engineering Centre, City University, Northampton Square, London EC1V 0HB (United Kingdom)
2008-09-15
This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts. (author)
Integrated Multiscale Latent Variable Regression and Application to Distillation Columns
Muddu Madakyaru
2013-01-01
Full Text Available Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions, which are usually estimated using inferential models. Commonly used inferential models include latent variable regression (LVR techniques, such as principal component regression (PCR, partial least squares (PLS, and regularized canonical correlation analysis (RCCA. Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of these models. Multiscale filtering has been shown to be a powerful feature extraction tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR modeling algorithm that integrates modeling and feature extraction. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods.
Stone, Wesley W.; Gilliom, Robert J.; Crawford, Charles G.
2008-01-01
Regression models were developed for predicting annual maximum and selected annual maximum moving-average concentrations of atrazine in streams using the Watershed Regressions for Pesticides (WARP) methodology developed by the National Water-Quality Assessment Program (NAWQA) of the U.S. Geological Survey (USGS). The current effort builds on the original WARP models, which were based on the annual mean and selected percentiles of the annual frequency distribution of atrazine concentrations. Estimates of annual maximum and annual maximum moving-average concentrations for selected durations are needed to characterize the levels of atrazine and other pesticides for comparison to specific water-quality benchmarks for evaluation of potential concerns regarding human health or aquatic life. Separate regression models were derived for the annual maximum and annual maximum 21-day, 60-day, and 90-day moving-average concentrations. Development of the regression models used the same explanatory variables, transformations, model development data, model validation data, and regression methods as those used in the original development of WARP. The models accounted for 72 to 75 percent of the variability in the concentration statistics among the 112 sampling sites used for model development. Predicted concentration statistics from the four models were within a factor of 10 of the observed concentration statistics for most of the model development and validation sites. Overall, performance of the models for the development and validation sites supports the application of the WARP models for predicting annual maximum and selected annual maximum moving-average atrazine concentration in streams and provides a framework to interpret the predictions in terms of uncertainty. For streams with inadequate direct measurements of atrazine concentrations, the WARP model predictions for the annual maximum and the annual maximum moving-average atrazine concentrations can be used to characterize
LI Chunxiang; ZHOU Dai
2004-01-01
The polynomial matrix using the block coefficient matrix representation auto-regressive moving average (referred to as the PM-ARMA) model is constructed in this paper for actively controlled multi-degree-of-freedom (MDOF) structures with time-delay through equivalently transforming the preliminary state space realization into the new state space realization. The PM-ARMA model is a more general formulation with respect to the polynomial using the coefficient representation auto-regressive moving average (ARMA) model due to its capability to cope with actively controlled structures with any given structural degrees of freedom and any chosen number of sensors and actuators. (The sensors and actuators are required to maintain the identical number.) under any dimensional stationary stochastic excitation.
Making the Move: A Mixed Research Integrative Review
Sarah Gilbert
2015-08-01
Full Text Available The purpose of this mixed research integrative review is to determine factors that influence relocation transitions for older adults who are considering a move from independent living to supervised housing, such as assisted living, using the Theory of Planned Behavior as a conceptual guide. PubMED, CINAHL, and PsychInfo databases were queried using key words: relocation, transition, older adults, and, elderly and time limited from 1992 to 2014. Sixteen articles were retained for review. The majority of articles, qualitative in design, reveal that older adults who comprehend the need to move and participate in the decision-making process of a relocation adjust to new living environments with fewer negative outcomes than older adults who experience a forced relocation. The few quantitative articles examined the elements of impending relocation using a variety of instruments but support the necessity for older adults to recognize the possibility of a future move and contribute to the relocation process. Additionally, the influence of family, friends, and health care providers provides the older adult with support and guidance throughout the process.
Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E
2016-07-19
Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning.
Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models
Jiefang Dong
2016-12-01
Full Text Available With the rapid economic development of the Xinjiang Uygur Autonomous Region, the area’s transport sector has witnessed significant growth, which in turn has led to a large increase in carbon dioxide emissions. As such, calculating of the carbon footprint of Xinjiang’s transportation sector and probing the driving factors of carbon dioxide emissions are of great significance to the region’s energy conservation and environmental protection. This paper provides an account of the growth in the carbon emissions of Xinjiang’s transportation sector during the period from 1989 to 2012. We also analyze the transportation sector’s trends and historical evolution. Combined with the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology model and ridge regression, this study further quantitatively analyzes the factors that influence the carbon emissions of Xinjiang’s transportation sector. The results indicate the following: (1 the total carbon emissions and per capita carbon emissions of Xinjiang’s transportation sector both continued to rise rapidly during this period; their average annual growth rates were 10.8% and 9.1%, respectively; (2 the carbon emissions of the transportation sector come mainly from the consumption of diesel and gasoline, which accounted for an average of 36.2% and 2.6% of carbon emissions, respectively; in addition, the overall carbon emission intensity of the transportation sector showed an “S”-pattern trend within the study period; (3 population density plays a dominant role in increasing carbon dioxide emissions. Population is then followed by per capita GDP and, finally, energy intensity. Cargo turnover has a more significant potential impact on and role in emission reduction than do private vehicles. This is because road freight is the primary form of transportation used across Xinjiang, and this form of transportation has low energy efficiency. These findings have important
Zhu, Zhen; Vana, Sudha; Bhattacharya, Sumit; Uijt de Haag, Maarten
2009-05-01
This paper discusses the integration of Forward-looking Infrared (FLIR) and traffic information from, for example, the Automatic Dependent Surveillance - Broadcast (ADS-B) or the Traffic Information Service-Broadcast (TIS-B). The goal of this integration method is to obtain an improved state estimate of a moving obstacle within the Field-of-View of the FLIR with added integrity. The focus of the paper will be on the approach phase of the flight. The paper will address methods to extract moving objects from the FLIR imagery and geo-reference these objects using outputs of both the onboard Global Positioning System (GPS) and the Inertial Navigation System (INS). The proposed extraction method uses a priori airport information and terrain databases. Furthermore, state information from the traffic information sources will be extracted and integrated with the state estimates from the FLIR. Finally, a method will be addressed that performs a consistency check between both sources of traffic information. The methods discussed in this paper will be evaluated using flight test data collected with a Gulfstream V in Reno, NV (GVSITE) and simulated ADS-B.
Integrating Research and Practice: Distractions, Controversies, and Options for Moving Forward
Gambrill, Eileen
2015-01-01
Integrating practice and research is vital in all helping professions in order to offer the most ethical, evidence-informed interventions to clients. This article describes some avoidable distractions that hinder integration, discusses controversies related to integration, and describes options for moving forward, including making wasted resources…
Effect of contact angle hysteresis on moving liquid film integrity.
Simon, F. F.; Hsu, Y. Y.
1972-01-01
A study was made of the formation and breakdown of a water film moving over solid surfaces (teflon, lucite, stainless steel, and copper). The flow rate associated with film formation was found to be higher than the flow rate at which film breakdown occurred. The difference in the flow rates for film formation and film breakdown was attributed to contact angle hysteresis. Analysis and experiment, which are in good agreement, indicated that film formation and film breakdown are functions of the advancing and receding angles, respectively.
Christensen, Bent Jesper; Kruse, Robinson; Sibbertsen, Philipp
We consider hypothesis testing in a general linear time series regression framework when the possibly fractional order of integration of the error term is unknown. We show that the approach suggested by Vogelsang (1998a) for the case of integer integration does not apply to the case of fractional...
Spinning particles moving around black holes: integrability and chaos
Lukes-Gerakopoulos, Georgios
2016-01-01
The motion of a stellar compact object around a supermassive black hole can be approximated by the motion of a spinning test particle. The equations of motion describing such systems are in general non-integrable, and therefore, chaotic motion should be expected. This article discusses the integrability issue of the spinning particle for the cases of Schwarzschild and Kerr spacetime, and then it focuses on a canonical Hamiltonian formalism where the spin of the particle is included only up to the linear order.
Connecticut School Integration: Moving Forward as the Northeast Retreats
Orfield, Gary
2015-01-01
This report analyzes the data on changes in patterns of racial segregation and their education consequences over a quarter century, from l987 to 2012. It examines a major transition in the racial and ethnic composition of Connecticut and the changes in integration and segregation in the schools of the state and its urban communities and it…
Study on an Integrated Sintered Metal Screen Moving Granular Bed Filter
吴晋沪; 王洋
2004-01-01
A new gas clean-up process called "integrated sintered metal screen moving granular bed" (ISMSMGB) for the integrated gasification combined cycle (IGCC) and pressured fluidized bed combustion (PFBC) was developed on the basis of a sintered metal candle filter and a cross-flow moving granular bed filter. This is a combination of the surface and deep bed filtering processes. A set of facilities was established and a series of cold model tests were carried out. The dust removal efficiency and the pressure drop of the filter were measured and analyzed. The results show that this process features the advantages of the moving bed for high capacity as well as high inlet dust load and the surface filter for high efficiency. Meanwhile, the granules moving downward cleans the cake on the screen surface, so that the system is operated at steady state.
Novel Frame Shift and Integral Technique for Enhancing Low-Light-Level Moving Images
无
2006-01-01
A novel frame shift and integral technique for the enhancement of low light level moving image sequence is introduced. According to the technique, motion parameters of target are measured by algorithm based on difference processing. To obtain spatial relativity, images are shifted according to the motion parameters. As a result, the processing of integral and average can be applied to images that have been shifted. The technique of frame shift and integral that includes the algorithm of motion parameter determination is discussed, experiments with low light level moving image sequences are also described. The experiment results show the effectiveness and the robustness of the parameter determination algorithm, and the improvement in the signal-to-noise ratio (SNR) of low light level moving images.
An integrated approach for visual analysis of a multisource moving objects knowledge base
Willems, N.; van Hage, W.R.; de Vries, G.; Janssens, J.H.M.; Malaisé, V.
2010-01-01
We present an integrated and multidisciplinary approach for analyzing the behavior of moving objects. The results originate from an ongoing research of four different partners from the Dutch Poseidon project (Embedded Systems Institute (2007)), which aims to develop new methods for Maritime Safety a
Way Forward in the Twenty-First Century in Content-Based Instruction: Moving towards Integration
Ruiz de Zarobe, Yolanda; Cenoz, Jasone
2015-01-01
The aim of this paper is to reflect on the theoretical and methodological underpinnings that provide the basis for an understanding of Content-Based Instruction/Content and Language Integrated Learning (CBI/CLIL) in the field and its relevance in education in the twenty-first century. It is argued that the agenda of CBI/CLIL needs to move towards…
Way Forward in the Twenty-First Century in Content-Based Instruction: Moving towards Integration
Ruiz de Zarobe, Yolanda; Cenoz, Jasone
2015-01-01
The aim of this paper is to reflect on the theoretical and methodological underpinnings that provide the basis for an understanding of Content-Based Instruction/Content and Language Integrated Learning (CBI/CLIL) in the field and its relevance in education in the twenty-first century. It is argued that the agenda of CBI/CLIL needs to move towards…
Messier, Kyle P.; Akita, Yasuyuki; Serre, Marc L.
2012-01-01
Geographic Information Systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques t...
Leite, Argentina; Paula Rocha, Ana; Eduarda Silva, Maria
2013-06-01
Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.
Messier, Kyle P; Akita, Yasuyuki; Serre, Marc L
2012-03-06
Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.
Dynamic response of axially moving Timoshenko beams：integral transform solution
安晨; 苏健
2014-01-01
The generalized integral transform technique (GITT) is used to find a semi-analytical numerical solution for dynamic response of an axially moving Timoshenko beam with clamped-clamped and simply-supported boundary conditions, respectively. The im-plementation of GITT approach for analyzing the forced vibration equation eliminates the space variable and leads to systems of second-order ordinary differential equations (ODEs) in time. The MATHEMATICA built-in function, NDSolve, is used to numeri-cally solve the resulting transformed ODE system. The good convergence behavior of the suggested eigenfunction expansions is demonstrated for calculating the transverse deflec-tion and the angle of rotation of the beam cross-section. Moreover, parametric studies are performed to analyze the effects of the axially moving speed, the axial tension, and the amplitude of external distributed force on the vibration amplitude of axially moving Timoshenko beams.
Superresolution of 3-D computational integral imaging based on moving least square method.
Kim, Hyein; Lee, Sukho; Ryu, Taekyung; Yoon, Jungho
2014-11-17
In this paper, we propose an edge directive moving least square (ED-MLS) based superresolution method for computational integral imaging reconstruction(CIIR). Due to the low resolution of the elemental images and the alignment error of the microlenses, it is not easy to obtain an accurate registration result in integral imaging, which makes it difficult to apply superresolution to the CIIR application. To overcome this problem, we propose the edge directive moving least square (ED-MLS) based superresolution method which utilizes the properties of the moving least square. The proposed ED-MLS based superresolution takes the direction of the edge into account in the moving least square reconstruction to deal with the abrupt brightness changes in the edge regions, and is less sensitive to the registration error. Furthermore, we propose a framework which shows how the data have to be collected for the superresolution problem in the CIIR application. Experimental results verify that the resolution of the elemental images is enhanced, and that a high resolution reconstructed 3-D image can be obtained with the proposed method.
Hunt, Thomas P; Issadore, David; Westervelt, R M
2008-01-01
We present an integrated circuit/microfluidic chip that traps and moves individual living biological cells and chemical droplets along programmable paths using dielectrophoresis (DEP). Our chip combines the biocompatibility of microfluidics with the programmability and complexity of integrated circuits (ICs). The chip is capable of simultaneously and independently controlling the location of thousands of dielectric objects, such as cells and chemical droplets. The chip consists of an array of 128 x 256 pixels, 11 x 11 microm(2) in size, controlled by built-in SRAM memory; each pixel can be energized by a radio frequency (RF) voltage of up to 5 V(pp). The IC was built in a commercial foundry and the microfluidic chamber was fabricated on its top surface at Harvard. Using this hybrid chip, we have moved yeast and mammalian cells through a microfluidic chamber at speeds up to 30 microm sec(-1). Thousands of cells can be individually trapped and simultaneously positioned in controlled patterns. The chip can trap and move pL droplets of water in oil, split one droplet into two, and mix two droplets into one. Our IC/microfluidic chip provides a versatile platform to trap and move large numbers of cells and fluid droplets individually for lab-on-a-chip applications.
XU Jing; YANG Chi; ZHANG Guoping
2007-01-01
Information model is adopted to integrate factors of various geosciences to estimate the susceptibility of geological hazards. Further combining the dynamic rainfall observations, Logistic regression is used for modeling the probabilities of geological hazard occurrences, upon which hierarchical warnings for rainfall-induced geological hazards are produced. The forecasting and warning model takes numerical precipitation forecasts on grid points as its dynamic input, forecasts the probabilities of geological hazard occurrences on the same grid, and translates the results into likelihoods in the form of a 5-level hierarchy. Validation of the model with observational data for the year 2004 shows that 80% of the geological hazards of the year have been identified as "likely enough to release warning messages". The model can satisfy the requirements of an operational warning system, thus is an effective way to improve the meteorological warnings for geological hazards.
Pineda, Silvia; Real, Francisco X; Kogevinas, Manolis; Carrato, Alfredo; Chanock, Stephen J; Malats, Núria; Van Steen, Kristel
2015-12-01
Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease
Boltz, Joshua P; Johnson, Bruce R; Daigger, Glen T; Sandino, Julian; Elenter, Deborah
2009-06-01
A steady-state model presented by Boltz, Johnson, Daigger, and Sandino (2009) describing integrated fixed-film activated sludge (IFAS) and moving-bed biofilm reactor (MBBR) systems has been demonstrated to simulate, with reasonable accuracy, four wastewater treatment configurations with published operational data. Conditions simulated include combined carbon oxidation and nitrification (both IFAS and MBBR), tertiary nitrification MBBR, and post denitrification IFAS with methanol addition as the external carbon source. Simulation results illustrate that the IFAS/MBBR model is sufficiently accurate for describing ammonia-nitrogen reduction, nitrate/nitrite-nitrogen reduction and production, biofilm and suspended biomass distribution, and sludge production.
Fearn, T; Hill, D C; Darby, S C
2008-05-30
In epidemiology, one approach to investigating the dependence of disease risk on an explanatory variable in the presence of several confounding variables is by fitting a binary regression using a conditional likelihood, thus eliminating the nuisance parameters. When the explanatory variable is measured with error, the estimated regression coefficient is biased usually towards zero. Motivated by the need to correct for this bias in analyses that combine data from a number of case-control studies of lung cancer risk associated with exposure to residential radon, two approaches are investigated. Both employ the conditional distribution of the true explanatory variable given the measured one. The method of regression calibration uses the expected value of the true given measured variable as the covariate. The second approach integrates the conditional likelihood numerically by sampling from the distribution of the true given measured explanatory variable. The two approaches give very similar point estimates and confidence intervals not only for the motivating example but also for an artificial data set with known properties. These results and some further simulations that demonstrate correct coverage for the confidence intervals suggest that for studies of residential radon and lung cancer the regression calibration approach will perform very well, so that nothing more sophisticated is needed to correct for measurement error.
Integrative analysis of multiple diverse omics datasets by sparse group multitask regression
Dongdong eLin
2014-10-01
Full Text Available A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: 1 treat the biomarker identification in each single study as a task and then combine them by multitask learning; 2 group variables from all studies for identifying significant genes; 3 enforce sparse constraint on groups of variables to overcome the ‘small sample, but large variables’ problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed
Zhang Xiaohua
2003-11-01
Full Text Available Abstract In the search for genetic determinants of complex disease, two approaches to association analysis are most often employed, testing single loci or testing a small group of loci jointly via haplotypes for their relationship to disease status. It is still debatable which of these approaches is more favourable, and under what conditions. The former has the advantage of simplicity but suffers severely when alleles at the tested loci are not in linkage disequilibrium (LD with liability alleles; the latter should capture more of the signal encoded in LD, but is far from simple. The complexity of haplotype analysis could be especially troublesome for association scans over large genomic regions, which, in fact, is becoming the standard design. For these reasons, the authors have been evaluating statistical methods that bridge the gap between single-locus and haplotype-based tests. In this article, they present one such method, which uses non-parametric regression techniques embodied by Bayesian adaptive regression splines (BARS. For a set of markers falling within a common genomic region and a corresponding set of single-locus association statistics, the BARS procedure integrates these results into a single test by examining the class of smooth curves consistent with the data. The non-parametric BARS procedure generally finds no signal when no liability allele exists in the tested region (ie it achieves the specified size of the test and it is sensitive enough to pick up signals when a liability allele is present. The BARS procedure provides a robust and potentially powerful alternative to classical tests of association, diminishes the multiple testing problem inherent in those tests and can be applied to a wide range of data types, including genotype frequencies estimated from pooled samples.
A Heterogeneous Access Remote Integrating Surveillance Heuristic Model for a Moving Train in Tunnel
Tanuja.P.Patgar
2016-03-01
Full Text Available Many number of real time applications are available for train monitoring using satellite based navigation system with high level of speed and precision. But these systems have faced lot of issues such as multipath loss and line of sight which results in lesser accuracy measurements. When the train is moving in low satellite visible areas such as tunnels, mountains, forest etc, then no position information is available. The service failure in tunnel made big challenge to demonstrate a self supporting innovative platform for navigation of train. This paper is focused on designing a novel approach by integrating Wireless Sensor Network (WSN and Radio Frequency Identification (RFID system for continuous monitoring of train moving in tunnel. The wireless tracking controller based on quadratic optimal control theory is considering for analysis. Overall performance of the control design is based on Liapunov approach, where quadratic performance index is directly related to Liapunov functions. By minimizing and maximizing the performance index value corresponding to control inputs will trace the tracking error inaccuracies. As maximizing the performance index, the tracking error produces 0.04% inaccuracy. The data loss is 0.06% when minimizing the performance value. Simulation is carried out using Mat lab.
Rahul Tripathi
2014-01-01
Full Text Available Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA models and was compared with the forecasted all Indian data. The autoregressive (p and moving average (q parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF and autocorrelation function (ACF of the different time series. ARIMA (2, 1, 0 model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1 was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC and Schwarz-Bayesian information criteria (SBC. The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE, which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan [Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor (Malaysia)
2014-09-12
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan
2014-09-01
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.
Furukawa, Hideaki; Miyazawa, Takaya; Wada, Naoya; Harai, Hiroaki
2014-01-13
Optical packet and circuit integrated (OPCI) networks provide both optical packet switching (OPS) and optical circuit switching (OCS) links on the same physical infrastructure using a wavelength multiplexing technique in order to deal with best-effort services and quality-guaranteed services. To immediately respond to changes in user demand for OPS and OCS links, OPCI networks should dynamically adjust the amount of wavelength resources for each link. We propose a resource-adjustable hybrid optical packet/circuit switch and transponder. We also verify that distributed control of resource adjustments can be applied to the OPCI ring network testbed we developed. In cooperation with the resource adjustment mechanism and the hybrid switch and transponder, we demonstrate that automatically allocating a shared resource and moving the wavelength resource boundary between OPS and OCS links can be successfully executed, depending on the number of optical paths in use.
Winkler, Richelle L; Johnson, Kenneth M
2016-08-01
This study analyzes the impact of migration on ethnoracial segregation among U.S. counties. Using county-level net migration estimates by age, race, and Hispanic origin from 1990-2000 and 2000-2010, we estimate migration's impact on segregation by age and across space. Overall, migration served to integrate ethnoracial groups in both decades, whereas differences in natural population change (increase/decrease) would have increased segregation. Age differences, however, are stark. Net migration of the population under age 40 reduced segregation, while net migration of people over age 60 further segregated people. Migration up and down the rural-urban continuum (including suburbanization among people of color) did most to decrease segregation, while interregional migration had only a small impact. People of color tended to move toward more predominantly white counties and regions at all ages. Migration among white young adults (aged 20-39) also decreased segregation. Whites aged 40 and older, however, showed tendencies toward white flight. Moderate spatial variation suggests that segregation is diminishing the most in suburban and fringe areas of several metropolitan areas in the Northeast and Midwest, while parts of the South, Southwest, and Appalachia show little evidence of integration.
Sakaeta, Kuniyuki; Nonaka, Kenichiro; Sekiguchi, Kazuma
2016-09-01
Localization is an important function for the robots to complete various tasks. For localization, both internal and external sensors are used generally. The odometry is widely used as the method based on the internal sensors, but it suffers from cumulative errors. In the method using the laser range sensor (LRS) which is a kind of external sensor, the estimation accuracy is affected by the number of available measurement data. In our previous study, we applied moving horizon estimation (MHE) to the vehicle localization for integrating the LRS measurement data and the odometry information where the weightings of them are balanced relatively adapting to the number of the available LRS measurement data. In this paper, the effectiveness of the proposed localization method is verified through both numerical simulations and experiments using a 1/10 scale vehicle. The verification is conducted in the situations where the vehicle position cannot be localized uniquely on a certain direction using the LRS measurement data only. We achieve accurate localization even in such a situation by integrating the odometry and LRS based on MHE. We also show the superiority of the method through comparisons with a method using extended Kalman filter (EKF).
Near real-time, on-the-move multisensor integration and computing framework
Burnette, Chris; Schneider, Matt; Agarwal, Sanjeev; Deterline, Diane; Geyer, Chris; Phan, Chung D.; Lydic, Richard M.; Green, Kevin; Swett, Bruce
2015-05-01
Implanted mines and improvised devices are a persistent threat to Warfighters. Current Army countermine missions for route clearance need on-the-move standoff detection to improve the rate of advance. Vehicle-based forward looking sensors such as electro-optical and infrared (EO/IR) devices can be used to identify potential threats in near real-time (NRT) at safe standoff distance to support route clearance missions. The MOVERS (Micro-Cloud for Operational, Vehicle-Based EO-IR Reconnaissance System) is a vehicle-based multi-sensor integration and exploitation system that ingests and processes video and imagery data captured from forward-looking EO/IR and thermal sensors, and also generates target/feature alerts, using the Video Processing and Exploitation Framework (VPEF) "plug and play" video processing toolset. The MOVERS Framework provides an extensible, flexible, and scalable computing and multi-sensor integration GOTS framework that enables the capability to add more vehicles, sensors, processors or displays, and a service architecture that provides low-latency raw video and metadata streams as well as a command and control interface. Functionality in the framework is exposed through the MOVERS SDK which decouples the implementation of the service and client from the specific communication protocols.
Laura German
2006-01-01
Most research in support of agricultural development and natural resource management in densely settled mountain ecosystems continues to emphasize component over system-level goals.Research by plant breeders, foresters and animal scientists is generally designed to maximize the yield of products within their particular area of expertise(edible plant parts, tree products and livestock products, respectively), while soil scientists aim largely to increase soil nutrient stocks. At landscape level the same dynamic holds within the agronomic sciences, while water engineers work independently to conserve water through its isolation from broader landscape dynamics, and other common property resources remain largely ignored. Opportunities to foster positive synergies between system components,and to integrate livelihood with conservation goals,are generally missed.This paper presents experiences of the African Highlands Initiative, an ecoregional program of the Consultative Group for International Agricultural Research (CGIAR) and a network of the Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA), in operationalizing integrated research at farm and landscape scale.Following a discussion of the shortcomings of the conventional research paradigm that beg for stronger integration and a review of the contributions of extant research paradigms that help us move in the right direction, the paper lays a conceptual foundation for integrated research. System components at farm and landscape level are delineated, and this somewhat arbitrary conceptual partitioning of agroecological systems shown to influence the current research paradigm as well as the partitioning of institutional mandates. Diverse meanings of systems integration are then discussed to illustrate the synergies that might be built into agricultural and natural resource research programs. The distinction between the logic of maximization and optimization is then utilized to
Multi-locus genome-wide association studies has become the state-of-the-art procedure to identify quantitative trait loci (QTL) associated with traits simultaneously. However, implementation of multi-locus model is still difficult. In this study, we integrated least angle regression with empirical B...
National Aeronautics and Space Administration — The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to...
Lo, Ching F.
1999-01-01
The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.
Zhang, Xujun; Pang, Yuanyuan; Cui, Mengjing; Stallones, Lorann; Xiang, Huiyun
2015-02-01
Road traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China. A seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung-Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012. The seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were -483.679, -475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung-Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012. This study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China. Copyright © 2015 Elsevier Inc. All rights reserved.
Looi, Chee-Kit; Chen, Wenli; Chen, Fang-Hao
2014-01-01
In this article, we studied the developmental trajectories of three teachers as they integrated GroupScribbles (GS) technology in their classroom lessons over a semester period of about 5 months. Coherency diagrams were used to capture the complex interplay of a teacher's knowledge (K), goals (G) and beliefs (B) in leveraging technology…
Rolling Regressions with Stata
Kit Baum
2004-01-01
This talk will describe some work underway to add a "rolling regression" capability to Stata's suite of time series features. Although commands such as "statsby" permit analysis of non-overlapping subsamples in the time domain, they are not suited to the analysis of overlapping (e.g. "moving window") samples. Both moving-window and widening-window techniques are often used to judge the stability of time series regression relationships. We will present an implementation of a rolling regression...
Lombards on the move--an integrative study of the migration period cemetery at Szolad, Hungary.
Kurt W Alt
Full Text Available In 2005 to 2007 45 skeletons of adults and subadults were excavated at the Lombard period cemetery at Szólád (6th century A.D., Hungary. Embedded into the well-recorded historical context, the article presents the results obtained by an integrative investigation including anthropological, molecular genetic and isotopic (δ(15N, δ(13C, (87Sr/(86Sr analyses. Skeletal stress markers as well as traces of interpersonal violence were found to occur frequently. The mitochondrial DNA profiles revealed a heterogeneous spectrum of lineages that belong to the haplogroups H, U, J, HV, T2, I, and K, which are common in present-day Europe and in the Near East, while N1a and N1b are today quite rare. Evidence of possible direct maternal kinship was identified in only three pairs of individuals. According to enamel strontium isotope ratios, at least 31% of the individuals died at a location other than their birthplace and/or had moved during childhood. Based on the peculiar 87 Sr/86 Sr ratio distribution between females, males, and subadults in comparison to local vegetation and soil samples, we propose a three-phase model of group movement. An initial patrilocal group with narrower male but wider female Sr isotope distribution settled at Szólád, whilst the majority of subadults represented in the cemetery yielded a distinct Sr isotope signature. Owing to the virtual absence of Szólád-born adults in the cemetery, we may conclude that the settlement was abandoned after approx. one generation. Population heterogeneity is furthermore supported by the carbon and nitrogen isotope data. They indicate that a group of high-ranking men had access to larger shares of animal-derived food whilst a few individuals consumed remarkable amounts of millet. The inferred dynamics of the burial community are in agreement with hypotheses of a highly mobile lifestyle during the Migration Period and a short-term occupation of Pannonia by Lombard settlers as conveyed by written
Poullis, Michael
2014-11-01
EuroSCORE II, despite improving on the original EuroSCORE system, has not solved all the calibration and predictability issues. Recursive, non-linear and mixed recursive and non-linear regression analysis were assessed with regard to sensitivity, specificity and predictability of the original EuroSCORE and EuroSCORE II systems. The original logistic EuroSCORE, EuroSCORE II and recursive, non-linear and mixed recursive and non-linear regression analyses of these risk models were assessed via receiver operator characteristic curves (ROC) and Hosmer-Lemeshow statistic analysis with regard to the accuracy of predicting in-hospital mortality. Analysis was performed for isolated coronary artery bypass grafts (CABGs) (n = 2913), aortic valve replacement (AVR) (n = 814), mitral valve surgery (n = 340), combined AVR and CABG (n = 517), aortic (n = 350), miscellaneous cases (n = 642), and combinations of the above cases (n = 5576). The original EuroSCORE had an ROC below 0.7 for isolated AVR and combined AVR and CABG. None of the methods described increased the ROC above 0.7. The EuroSCORE II risk model had an ROC below 0.7 for isolated AVR only. Recursive regression, non-linear regression, and mixed recursive and non-linear regression all increased the ROC above 0.7 for isolated AVR. The original EuroSCORE had a Hosmer-Lemeshow statistic that was above 0.05 for all patients and the subgroups analysed. All of the techniques markedly increased the Hosmer-Lemeshow statistic. The EuroSCORE II risk model had a Hosmer-Lemeshow statistic that was significant for all patients (P linear regression failed to improve on the original Hosmer-Lemeshow statistic. The mixed recursive and non-linear regression using the EuroSCORE II risk model was the only model that produced an ROC of 0.7 or above for all patients and procedures and had a Hosmer-Lemeshow statistic that was highly non-significant. The original EuroSCORE and the EuroSCORE II risk models do not have adequate ROC and Hosmer
Herrera Fernanda G
2013-01-01
Full Text Available Abstract Background Whole pelvis intensity modulated radiotherapy (IMRT is increasingly being used to treat cervical cancer aiming to reduce side effects. Encouraged by this, some groups have proposed the use of simultaneous integrated boost (SIB to target the tumor, either to get a higher tumoricidal effect or to replace brachytherapy. Nevertheless, physiological organ movement and rapid tumor regression throughout treatment might substantially reduce any benefit of this approach. Purpose To evaluate the clinical target volume - simultaneous integrated boost (CTV-SIB regression and motion during chemo-radiotherapy (CRT for cervical cancer, and to monitor treatment progress dosimetrically and volumetrically to ensure treatment goals are met. Methods and materials Ten patients treated with standard doses of CRT and brachytherapy were retrospectively re-planned using a helical Tomotherapy - SIB technique for the hypothetical scenario of this feasibility study. Target and organs at risk (OAR were contoured on deformable fused planning-computed tomography and megavoltage computed tomography images. The CTV-SIB volume regression was determined. The center of mass (CM was used to evaluate the degree of motion. The Dice’s similarity coefficient (DSC was used to assess the spatial overlap of CTV-SIBs between scans. A cumulative dose-volume histogram modeled estimated delivered doses. Results The CTV-SIB relative reduction was between 31 and 70%. The mean maximum CM change was 12.5, 9, and 3 mm in the superior-inferior, antero-posterior, and right-left dimensions, respectively. The CTV-SIB-DSC approached 1 in the first week of treatment, indicating almost perfect overlap. CTV-SIB-DSC regressed linearly during therapy, and by the end of treatment was 0.5, indicating 50% discordance. Two patients received less than 95% of the prescribed dose. Much higher doses to the OAR were observed. A multiple regression analysis showed a significant interaction
Aye, S. A.; Heyns, P. S.
2017-02-01
This paper proposes an optimal Gaussian process regression (GPR) for the prediction of remaining useful life (RUL) of slow speed bearings based on a novel degradation assessment index obtained from acoustic emission signal. The optimal GPR is obtained from an integration or combination of existing simple mean and covariance functions in order to capture the observed trend of the bearing degradation as well the irregularities in the data. The resulting integrated GPR model provides an excellent fit to the data and improves over the simple GPR models that are based on simple mean and covariance functions. In addition, it achieves a low percentage error prediction of the remaining useful life of slow speed bearings. These findings are robust under varying operating conditions such as loading and speed and can be applied to nonlinear and nonstationary machine response signals useful for effective preventive machine maintenance purposes.
Johnson, C. R., Jr.; Balas, M. J.
1980-01-01
A novel interconnection of distributed parameter system (DPS) identification and adaptive filtering is presented, which culminates in a common statement of coupled autoregressive, moving-average expansion or parallel infinite impulse response configuration adaptive parameterization. The common restricted complexity filter objectives are seen as similar to the reduced-order requirements of the DPS expansion description. The interconnection presents the possibility of an exchange of problem formulations and solution approaches not yet easily addressed in the common finite dimensional lumped-parameter system context. It is concluded that the shared problems raised are nevertheless many and difficult.
Shokri Saeid
2015-01-01
Full Text Available An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR was developed and the effects of integrating Vector Quantization (VQ with Principle Component Analysis (PCA were studied on the assessment of this soft sensor. First, in pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR was better than (PCA-SVR in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE= 0.0668 and R2= 0.995 in comparison with investigated models.
Pierce, Allan D.
1989-01-01
Transient sound propagation in an inhomogeneous moving medium is considered. For circumstances in which the medium is slowly varying over distances of a wavelength but possibly varying substantially over the propagation distance, a derivation is given of a new wave equation, which implicitly allows for diffraction and scattering and which also is consistent with earlier geometrical acoustics formulations. This wave equation is used as a starting point to derive a version of the Helmholtz-Kirchhoff integral relation that applies to inhomogeneous moving medium. It is suggested that a good approximation to the Green's function that appears in this relation is that derived from geometrical acoustics, the approximation becoming progressively better the shorter the distance between surfaces in the computation. It should also be at least as good as conventional ray acoustics, but can account for diffraction effects, such as at caustics.
Jones, Steven R.; Lim, YaeRi; Chandler, Katie R.
2017-01-01
Past research in calculus education has shown that Riemann sum-based conceptions of the definite integral, such as the multiplicatively based summation (MBS) conception, can have important value in interpreting and making sense of certain types of definite integral expressions. However, additional research has shown that students tend to not draw…
U.S. Geological Survey, Department of the Interior — Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large...
Young, S.L.; Pitla, S.K.; Evert, Van F.K.; Schueller, J.K.; Pierce, F.J.; Liebman, Matt
2017-01-01
Integrated weed management (IWM) is one of the most commonly referred to approaches for sustainable and effective weed control in agriculture, yet it is not widely practiced, likely because current IWM systems fail to meet performance expectations of growers. The effectiveness and value of IWM syste
Moving On: Farmer Education in Integrated Insect Pest and Disease Management
Jiggins, J.L.S.; Mancini, F.
2009-01-01
This chapter explores intensive hands-on occupational education for farmers in selected European, African, Latin American countries and in south India. An Indian case study of Farmer Field Schools for Integrated Pest and Production Management (IPPM) to ensure food security and livelihood improvement
Moving On: Farmer Education in Integrated Insect Pest and Disease Management
Jiggins, J.L.S.; Mancini, F.
2009-01-01
This chapter explores intensive hands-on occupational education for farmers in selected European, African, Latin American countries and in south India. An Indian case study of Farmer Field Schools for Integrated Pest and Production Management (IPPM) to ensure food security and livelihood improvement
Moving NASA Beyond Low Earth Orbit: Future Human-Automation-Robotic Integration Challenges
Marquez, Jessica
2016-01-01
This presentation will provide an overview of current human spaceflight operations. It will also describe how future exploration missions will have to adapt and evolve in order to deal with more complex missions and communication latencies. Additionally, there are many implications regarding advanced automation and robotics, and this presentation will outline future human-automation-robotic integration challenges.
Wadsworth, W Duncan; Argiento, Raffaele; Guindani, Michele; Galloway-Pena, Jessica; Shelbourne, Samuel A; Vannucci, Marina
2017-02-08
The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development. In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available. Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.
Moving from silos to teamwork: integration of interprofessional trainees into a medical home model.
Long, Theodore; Dann, Sarah; Wolff, Marissa Lynn; Brienza, Rebecca S
2014-09-01
As the United States faces an impending shortage in the primary care workforce, interprofessional teamwork training to improve clinic efficiency and health outcomes is becoming increasingly important. Currently there is limited integration of interprofessional training in educational models for health professionals. The implementation of Patient Aligned Care Teams at the Department of Veterans Affairs (VA) has provided an opportunity for interprofessional collaboration among trainee and faculty providers within the VA system. However, integration of interprofessional education is also necessary to train future providers in order to provide effective team-based care. We describe a transportable educational model for health professional collaboration from our experience as a VA Center of Excellence in Primary Care Education, including a complementary novel one-year post-Master's adult nurse practitioner interprofessional clinical fellowship. With growing recognition that interprofessional care can improve efficiency and outcomes, there is an increasing need for programs that train future providers in collaboration and team-based care.
Integrating yoga into psychotherapy: The ethics of moving from the mind to the mat.
Kamradt, Jaclyn M
2017-05-01
Given the rise in attention to client preferences in medical treatment and the shift in focus toward health promotion, it is not surprising that the use of complementary health approaches have increased in the past several years. Yoga is among the most prominent complementary health approaches. Recently, both qualitative and quantitative work has emerged supporting its use for a variety of medical and psychological disorders. However, there is a critical gap in knowledge regarding how to most optimally and ethically integrate complementary therapies (i.e., yoga) into current psychology practices. Moreover, it remains unclear which clients are the best candidates for receiving such complementary treatments and which therapists should provide them. The purpose of this paper is to provide an overview of the history of yoga, the scientific evidence in support of its use for mental health issues, and an ethical framework to guide psychologists interested in integrating yoga into psychotherapy. Copyright © 2017 Elsevier Ltd. All rights reserved.
Naranjo, Steven E; Ellsworth, Peter C
2009-12-01
Fifty years ago, Stern, Smith, van den Bosch and Hagen outlined a simple but sophisticated idea of pest control predicated on the complementary action of chemical and biological control. This integrated control concept has since been a driving force and conceptual foundation for all integrated pest management (IPM) programs. The four basic elements include thresholds for determining the need for control, sampling to determine critical densities, understanding and conserving the biological control capacity in the system and the use of selective insecticides or selective application methods, when needed, to augment biological control. Here we detail the development, evolution, validation and implementation of an integrated control (IC) program for whitefly, Bemisia tabaci (Genn.), in the Arizona cotton system that provides a rare example of the vision of Stern and his colleagues. Economic thresholds derived from research-based economic injury levels were developed and integrated with rapid and accurate sampling plans into validated decision tools widely adopted by consultants and growers. Extensive research that measured the interplay among pest population dynamics, biological control by indigenous natural enemies and selective insecticides using community ordination methods, predator:prey ratios, predator exclusion and demography validated the critical complementary roles played by chemical and biological control. The term 'bioresidual' was coined to describe the extended environmental resistance from biological control and other forces possible when selective insecticides are deployed. The tangible benefits have been a 70% reduction in foliar insecticides, a >$200 million saving in control costs and yield, along with enhanced utilization of ecosystem services over the last 14 years.
Catherine Grant; Giovanni Lo lacono; Vupenyu Dzingirai; Bernard Bett; Thomas R.A.Winnebah; Peter M.Atkinson
2016-01-01
This review outlines the benefits of using multiple approaches to improve model design and facilitate multidisciplinary research into infectious diseases,as well as showing and proposing practical examples of effective integration.It looks particularly at the benefits of using participatory research in conjunction with traditional modelling methods to potentially improve disease research,control and management.Integrated approaches can lead to more realistic mathematical models which in turn can assist with making policy decisions that reduce disease and benefit local people.The emergence,risk,spread and control of diseases are affected by many complex bio-physical,environmental and socio-economic factors.These include climate and environmental change,land-use variation,changes in population and people's behaviour.The evidence base for this scoping review comes from the work of a consortium,with the aim of integrating modelling approaches traditionally used in epidemiological,ecological and development research.A total of five examples of the impacts of participatory research on the choice of model structure are presented.Example 1 focused on using participatory research as a tool to structure a model.Example 2 looks at identifying the most relevant parameters of the system.Example 3 concentrates on identifying the most relevant regime of the system (e.g.,temporal stability or otherwise),Example 4 examines the feedbacks from mathematical models to guide participatory research and Example 5 goes beyond the so-far described two-way interplay between participatory and mathematical approaches to look at the integration of multiple methods and frameworks.This scoping review describes examples of best practice in the use of participatory methods,illustrating their potential to overcome disciplinary hurdles and promote multidisciplinary collaboration,with the aim of making models and their predictions more useful for decision-making and policy formulation.
Vaidyanathan, Uma; Vrieze, Scott I; Iacono, William G.
2015-01-01
While the past few decades have seen much work in psychopathology research that has yielded provocative insights, relatively little progress has been made in understanding the etiology of mental disorders. We contend that this is due to an overreliance on statistics and technology with insufficient attention to adequacy of experimental design, a lack of integration of data across various domains of research, and testing of theoretical models using relatively weak study designs. We provide a conceptual discussion of these issues and follow with a concrete demonstration of our proposed solution. Using two different disorders – depression and substance use – as examples, we illustrate how we can evaluate competing theories regarding their etiology by integrating information from various domains including latent variable models, neurobiology, and quasi-experimental data such as twin and adoption studies, rather than relying on any single methodology alone. More broadly, we discuss the extent to which such integrative thinking allows for inferences about the etiology of mental disorders, rather than focusing on descriptive correlates alone. Greater scientific insight will require stringent tests of competing theories and a deeper conceptual understanding of the advantages and pitfalls of methodologies and criteria we use in our studies. PMID:27030789
Claudia Erika Hernández Patiño
2013-01-01
Full Text Available One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: 1 the integration of data from high-throughput technologies, 2 the assessment of how metabolic activity is related to phenotype in cancer cell lines and 3 the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.
Hernández Patiño, Claudia E; Jaime-Muñoz, Gustavo; Resendis-Antonio, Osbaldo
2012-01-01
One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.
Jiang, Z; Piao, D; Bartels, K E; Holyoak, G R; Ritchey, J W; Ownby, C L; Rock, K; Slobodov, G
2011-12-01
The objective of this study was to evaluate if transrectal optical tomography implemented at three wavelength bands for spectral detection could monitor changes of the hemoglobin oxygen saturation (StO2) in addition to those of the total hemoglobin concentration ([HbT]) in lesions of a canine prostate, including an induced tumor modeling canine prostate cancer. Near-infrared (NIR) optical tomography was integrated with ultrasound (US) for transrectal imaging. Multi-spectral detection at 705_nm, 785_nm and 808_nm rendered measurements of [HbT] and StO2. Canine transmissible venereal tumor (TVT) cells were injected into the right lobe of a dog's prostate gland, which had a pre-existing cyst in the left lobe. Longitudinal assessments of the prostate were performed weekly over a 63-day duration by NIR imaging concurrent with grey-scale and Doppler US. Ultrasonography revealed a bi-lobular tumor-mass regressing from day-49 to day-63. At day-49 this tumor-mass developed a hypoxic core that became larger and more intense by day-56 and expanded further by day-63. The tumor-mass presented a strong hyper-[HbT] feature on day-56 that was inconsistent with US-visualized blood flow. Histology confirmed two necrotic TVT foci within this tumor-mass. The cyst appeared to have a large anoxic-like interior that was greater in size than its ultrasonographically delineated lesion, and a weak lesional elevation of [HbT]. On day-56, the cyst presented a strong hyper-[HbT] feature consistent with US-resolved blood flow. Histology revealed acute and chronic hemorrhage in the periphery of the cyst. The NIR imaging features of two other TVT nodules and a metastatic lymph node were evaluated retrospectively. Transrectal US-integrated spectral optical tomography seems to enable longitudinal monitoring of intra-lesional oxygenation dynamics in addition to the hemoglobin content of lesions in the canine prostate.
Revilla, Marta; Galán, Berta; Viguri, Javier R
2016-07-01
An integrated mathematical model is proposed for modelling a moving bed biofilm reactor (MBBR) for removal of chemical oxygen demand (COD) under aerobic conditions. The composite model combines the following: (i) a one-dimensional biofilm model, (ii) a bulk liquid model, and (iii) biological processes in the bulk liquid and biofilm considering the interactions among autotrophic, heterotrophic and predator microorganisms. Depending on the values for the soluble biodegradable COD loading rate (SCLR), the model takes into account a) the hydrolysis of slowly biodegradable compounds in the bulk liquid, and b) the growth of predator microorganisms in the bulk liquid and in the biofilm. The integration of the model and the SCLR allows a general description of the behaviour of COD removal by the MBBR under various conditions. The model is applied for two in-series MBBR wastewater plant from an integrated cellulose and viscose production and accurately describes the experimental concentrations of COD, total suspended solids (TSS), nitrogen and phosphorous obtained during 14 months working at different SCLRs and nutrient dosages. The representation of the microorganism group distribution in the biofilm and in the bulk liquid allow for verification of the presence of predator microorganisms in the second reactor under some operational conditions.
Gennadiy Burlak
2012-12-01
Full Text Available The time-frequency integrals and the two-dimensional stationary phase method are applied to study the electromagnetic waves radiated by moving modulated sources in dispersive media. We show that such unified approach leads to explicit expressions for the field amplitudes and simple relations for the field eigenfrequencies and the retardation time that become the coupled variables. The main features of the technique are illustrated by examples of the moving source fields in the plasma and the Cherenkov radiation. It is emphasized that the deeper insight to the wave effects in dispersive case already requires the explicit formulation of the dispersive material model. As the advanced application we have considered the Doppler frequency shift in a complex single-resonant dispersive metamaterial (Lorenz model where in some frequency ranges the negativity of the real part of the refraction index can be reached. We have demonstrated that in dispersive case the Doppler frequency shift acquires a nonlinear dependence on the modulating frequency of the radiated particle. The detailed frequency dependence of such a shift and spectral behavior of phase and group velocities (that have the opposite directions are studied numerically.
Moving Towards Integrated Policy Formulation and Evaluation: The Green Economy Model
Bassi Andrea M.
2015-12-01
Full Text Available The mainstreaming of concepts related to the Green Economy, an action-oriented approach to reach sustainable development, has increased demands for integrated models that can shed light on the complex relations existing across social, economic and environmental indicators. A gap exists, whereby our thinking is rapidly evolving, but the tools available are still in the vast majority of cases sectorial, leading to planning processes taking place in silos. To avoid the emergence of side effects, and anticipate future threats and opportunities, a more systemic approach is needed. The Green Economy Model (GEM was created taking into account four main capitals and their interconnections: physical capital, human capital, social capital and natural capital. The application of GEM in 10 countries has shown its capability to coherently represent reality and generate results that can more effectively inform decision making.
Moving Towards Integrated Policy Formulation and Evaluation: The Green Economy Model
Bassi, Andrea M.
2015-12-01
The mainstreaming of concepts related to the Green Economy, an action-oriented approach to reach sustainable development, has increased demands for integrated models that can shed light on the complex relations existing across social, economic and environmental indicators. A gap exists, whereby our thinking is rapidly evolving, but the tools available are still in the vast majority of cases sectorial, leading to planning processes taking place in silos. To avoid the emergence of side effects, and anticipate future threats and opportunities, a more systemic approach is needed. The Green Economy Model (GEM) was created taking into account four main capitals and their interconnections: physical capital, human capital, social capital and natural capital. The application of GEM in 10 countries has shown its capability to coherently represent reality and generate results that can more effectively inform decision making.
Integration of a free-piston Stirling engine and a moving grate incinerator
Hsieh, Y.C.; Hsu, T.C.; Chiou, J.S. [Department of Mechanical Engineering, National Cheng Kung University, Tainan 70101 (China)
2008-01-15
The feasibility of recovering the waste heat from a small-scale incinerator (designed by Industrial Technology Research Institute) and generating electric power by a linear free-piston Stirling engine is investigated in this study. A heat-transfer model is used to simulate the integration system of the Stirling engine and the incinerator. In this model, the external irreversibility is modeled by the finite temperature difference and by the actual heat transfer area, while the internal irreversibility is considered by an internal heat leakage. At a fixed source temperature and a fixed sink temperature, the optimal engine performance can be obtained by the method of Lagrange multipliers. From the energy and mass balances for the interesting incinerator with the feeding rate at 16 t/d, there is enough otherwise wasted energy for powering the Stirling engine and generate more than 50 kW of electricity. (author)
Tang, Bing; Yu, Chunfei; Bin, Liying; Zhao, Yiliang; Feng, Xianfeng; Huang, Shaosong; Fu, Fenglian; Ding, Jiewei; Chen, Cuiqun; Li, Ping; Chen, Qianyu
2016-07-01
This work aims at revealing the adhesion characteristics and microbial community of the biofilm in an integrated moving bed biofilm reactor-membrane bioreactor, and further evaluating their variations over time. With multiple methods, the adhesion characteristics and microbial community of the biofilm on the carriers were comprehensively illuminated, which showed their dynamic variation along with the operational time. Results indicated that: (1) the roughness of biofilm on the carriers increased very quickly to a maximum value at the start-up stage, then, decreased to become a flat curve, which indicated a layer of smooth biofilm formed on the surface; (2) the tightly-bound protein and polysaccharide was the most important factor influencing the stability of biofilm; (3) the development of biofilm could be divided into three stages, and Gammaproteobacteria were the most dominant microbial species in class level at the last stage, which occupied the largest ratio (51.48%) among all microbes.
Fung, Lawrence K; Reiss, Allan L
2016-07-15
The field of psychiatry is approaching a major inflection point. The basic science behind cognition, emotion, behavior, and social processes has been advancing rapidly in the past 20 years. However, clinical research supporting the classification system in psychiatry has not kept up with these scientific advances. To begin organizing the basic science of psychiatry in a comprehensive manner, we begin by selecting fragile X syndrome, a neurogenetic disease with cognitive-behavioral manifestations, to illustrate key concepts in an integrative, multidimensional model. Specifically, we describe key genetic and molecular mechanisms (e.g., gamma-aminobutyric acidergic dysfunction and metabotropic glutamate receptor 5-associated long-term depression) relevant to the pathophysiology of fragile X syndrome as well as neural correlates of cognitive-behavioral symptoms. We then describe what we have learned from fragile X syndrome that may be applicable to other psychiatric disorders. We conclude this review by discussing current and future opportunities in diagnosing and treating psychiatric diseases. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
Fuzhong Li
2014-03-01
Full Text Available Tai Ji Quan, developed as a martial art, has traditionally served multiple purposes, including self-defense, competition/performance, and health promotion. With respect to health, the benefits historically and anecdotally associated with Tai Ji Quan are now being supported by scientific and clinical research, with mounting evidence indicating its potential value in preventing and managing various diseases and improving well-being and quality of life in middle-aged and older adults. The research findings produced to date have both public health significance and clinical relevance. However, because of its roots in the martial arts, transforming traditional Tai Ji Quan movements and training approaches into contemporary therapeutic programs and functional applications is needed to maximize its ultimate utility. This paper addresses this issue by introducing Tai Ji Quan: Moving for Better Balance, a functional therapy that involves the use of Tai Ji Quan principles and Yang-style-based movements to form an innovative, contemporary therapeutic approach that integrates motor, sensory, and cognitive components to improve postural control, gait, and mobility for older adults and those who have neurodegenerative movement impairments. It provides a synergy of traditional and contemporary Tai Ji Quan practice with the ultimate goal of improving balance and gait, enhancing performance of daily functional tasks, and reducing incidence of falls among older adults.
Chen, Chieh-Fan; Ho, Wen-Hsien; Chou, Huei-Yin; Yang, Shu-Mei; Chen, I-Te; Shi, Hon-Yi
2011-01-01
This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.
Chieh-Fan Chen
2011-01-01
Full Text Available This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.
LongShuyao; HuDe'an
2003-01-01
The meshless method is a new numerical technique presented in recent years .It uses the moving least square (MLS) approximation as a shape function . The smoothness of the MLS approximation is determined by that of the basic function and of the weight function, and is mainly determined by that of the weight function. Therefore, the weight function greatly affects the accuracy of results obtained. Different kinds of weight functions, such as the spline function, the Gauss function and so on, are proposed recently by many researchers. In the present work, the features of various weight functions are illustrated through solving elasto-static problems using the local boundary integral equation method. The effect of various weight functions on the accuracy, convergence and stability of results obtained is also discussed. Examples show that the weight function proposed by Zhou Weiyuan and Gauss and the quartic spline weight function are better than the others if parameters c and a in Gauss and exponential weight functions are in the range of reasonable values, respectively, and the higher the smoothness of the weight function, the better the features of the solutions.
Li, Fuzhong
2014-03-01
Tai Ji Quan, developed as a martial art, has traditionally served multiple purposes, including self-defense, competition/performance, and health promotion. With respect to health, the benefits historically and anecdotally associated with Tai Ji Quan are now being supported by scientific and clinical research, with mounting evidence indicating its potential value in preventing and managing various diseases and improving well-being and quality of life in middle-aged and older adults. The research findings produced to date have both public health significance and clinical relevance. However, because of its roots in the martial arts, transforming traditional Tai Ji Quan movements and training approaches into contemporary therapeutic programs and functional applications is needed to maximize its ultimate utility. This paper addresses this issue by introducing Tai Ji Quan: Moving for Better Balance, a functional therapy that involves the use of Tai Ji Quan principles and Yang-style-based movements to form an innovative, contemporary therapeutic approach that integrates motor, sensory, and cognitive components to improve postural control, gait, and mobility for older adults and those who have neurodegenerative movement impairments. It provides a synergy of traditional and contemporary Tai Ji Quan practice with the ultimate goal of improving balance and gait, enhancing performance of daily functional tasks, and reducing incidence of falls among older adults.
Larsen, Klaus; Merlo, Juan
2005-01-01
The logistic regression model is frequently used in epidemiologic studies, yielding odds ratio or relative risk interpretations. Inspired by the theory of linear normal models, the logistic regression model has been extended to allow for correlated responses by introducing random effects. However......, the model does not inherit the interpretational features of the normal model. In this paper, the authors argue that the existing measures are unsatisfactory (and some of them are even improper) when quantifying results from multilevel logistic regression analyses. The authors suggest a measure...... of heterogeneity, the median odds ratio, that quantifies cluster heterogeneity and facilitates a direct comparison between covariate effects and the magnitude of heterogeneity in terms of well-known odds ratios. Quantifying cluster-level covariates in a meaningful way is a challenge in multilevel logistic...
Hao, Lingxin
2007-01-01
Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao
Selmane, Schehrazad; L'Hadj, Mohamed
2016-01-01
The aims of this study were to highlight some epidemiological aspects of scorpion envenomations, to analyse and interpret the available data for Biskra province, Algeria, and to develop a forecasting model for scorpion sting cases in Biskra province, which records the highest number of scorpion stings in Algeria. In addition to analysing the epidemiological profile of scorpion stings that occurred throughout the year 2013, we used the Box-Jenkins approach to fit a seasonal autoregressive integrated moving average (SARIMA) model to the monthly recorded scorpion sting cases in Biskra from 2000 to 2012. The epidemiological analysis revealed that scorpion stings were reported continuously throughout the year, with peaks in the summer months. The most affected age group was 15 to 49 years old, with a male predominance. The most prone human body areas were the upper and lower limbs. The majority of cases (95.9%) were classified as mild envenomations. The time series analysis showed that a (5,1,0)×(0,1,1)12 SARIMA model offered the best fit to the scorpion sting surveillance data. This model was used to predict scorpion sting cases for the year 2013, and the fitted data showed considerable agreement with the actual data. SARIMA models are useful for monitoring scorpion sting cases, and provide an estimate of the variability to be expected in future scorpion sting cases. This knowledge is helpful in predicting whether an unusual situation is developing or not, and could therefore assist decision-makers in strengthening the province's prevention and control measures and in initiating rapid response measures.
Yu, Hye-Kyung; Kim, Na-Young; Kim, Sung Soon; Chu, Chaeshin; Kee, Mee-Kyung
2013-12-01
From the introduction of HIV into the Republic of Korea in 1985 through 2012, 9,410 HIV-infected Koreans have been identified. Since 2000, there has been a sharp increase in newly diagnosed HIV-infected Koreans. It is necessary to estimate the changes in HIV infection to plan budgets and to modify HIV/AIDS prevention policy. We constructed autoregressive integrated moving average (ARIMA) models to forecast the number of HIV infections from 2013 to 2017. HIV infection data from 1985 to 2012 were used to fit ARIMA models. Akaike Information Criterion and Schwartz Bayesian Criterion statistics were used to evaluate the constructed models. Estimation was via the maximum likelihood method. To assess the validity of the proposed models, the mean absolute percentage error (MAPE) between the number of observed and fitted HIV infections from 1985 to 2012 was calculated. Finally, the fitted ARIMA models were used to forecast the number of HIV infections from 2013 to 2017. The fitted number of HIV infections was calculated by optimum ARIMA (2,2,1) model from 1985-2012. The fitted number was similar to the observed number of HIV infections, with a MAPE of 13.7%. The forecasted number of new HIV infections in 2013 was 962 (95% confidence interval (CI): 889-1,036) and in 2017 was 1,111 (95% CI: 805-1,418). The forecasted cumulative number of HIV infections in 2013 was 10,372 (95% CI: 10,308-10,437) and in 2017 was14,724 (95% CI: 13,893-15,555) by ARIMA (1,2,3). Based on the forecast of the number of newly diagnosed HIV infections and the current cumulative number of HIV infections, the cumulative number of HIV-infected Koreans in 2017 would reach about 15,000.
Margaret A. Cook; Carey W. King; F. Todd Davidson; Michael E. Webber
2015-01-01
Recent droughts and heat waves have revealed the vulnerability of some power plants to effects from higher temperature intake water for cooling. In this evaluation, we develop a methodology for predicting whether power plants are at risk of violating thermal pollution limits. We begin by developing a regression model of average monthly intake temperatures for open loop and recirculating cooling pond systems. We then integrate that information into a thermodynamic model of energy flows within ...
Margaret A. Cook; Carey W. King; F. Todd Davidson; Michael E. Webber
2015-01-01
Recent droughts and heat waves have revealed the vulnerability of some power plants to effects from higher temperature intake water for cooling. In this evaluation, we develop a methodology for predicting whether power plants are at risk of violating thermal pollution limits. We begin by developing a regression model of average monthly intake temperatures for open loop and recirculating cooling pond systems. We then integrate that information into a thermodynamic model of energy flows within ...
S.K. Lahiri
2009-09-01
Full Text Available Soft sensors have been widely used in the industrial process control to improve the quality of the product and assure safety in the production. The core of a soft sensor is to construct a soft sensing model. This paper introduces support vector regression (SVR, a new powerful machine learning methodbased on a statistical learning theory (SLT into soft sensor modeling and proposes a new soft sensing modeling method based on SVR. This paper presents an artificial intelligence based hybrid soft sensormodeling and optimization strategies, namely support vector regression – genetic algorithm (SVR-GA for modeling and optimization of mono ethylene glycol (MEG quality variable in a commercial glycol plant. In the SVR-GA approach, a support vector regression model is constructed for correlating the process data comprising values of operating and performance variables. Next, model inputs describing the process operating variables are optimized using genetic algorithm with a view to maximize the process performance. The SVR-GA is a new strategy for soft sensor modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics etc. is not required. Using SVR-GA strategy, a number of sets of optimized operating conditions were found. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the quality.
Pellegrini, Yves-Patrick
2015-01-01
The two-dimensional elastodynamic Green tensor is the primary building block of solutions of linear elasticity problems dealing with nonuniformly moving rectilinear line sources, such as dislocations. Elastodynamic solutions for these problems involve derivatives of this Green tensor, which stand as hypersingular kernels. These objects, well defined as distributions, prove cumbersome to handle in practice. This paper, restricted to isotropic media, examines some of their representations in the framework of distribution theory. A particularly convenient regularization of the Green tensor is introduced, that amounts to considering line sources of finite width. Technically, it is implemented by an analytic continuation of the Green tensor to complex times. It is applied to the computation of regularized forms of certain integrals of tensor character that involve the gradient of the Green tensor. These integrals are fundamental to the computation of the elastodynamic fields in the problem of nonuniformly moving d...
Kahane, Leo H
2007-01-01
Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition Offers greater coverage of simple panel-data estimation:
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Nick, Todd G; Campbell, Kathleen M
2007-01-01
The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as "statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable." Logistic regression models are used to study effects of predictor variables on categorical outcomes and normally the outcome is binary, such as presence or absence of disease (e.g., non-Hodgkin's lymphoma), in which case the model is called a binary logistic model. When there are multiple predictors (e.g., risk factors and treatments) the model is referred to as a multiple or multivariable logistic regression model and is one of the most frequently used statistical model in medical journals. In this chapter, we examine both simple and multiple binary logistic regression models and present related issues, including interaction, categorical predictor variables, continuous predictor variables, and goodness of fit.
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
Fassò, Francesco; Sansonetto, Nicola
2016-04-01
Energy is in general not conserved for mechanical nonholonomic systems with affine constraints. In this article we point out that, nevertheless, in certain cases, there is a modification of the energy that is conserved. Such a function is the pull-back of the energy of the system written in a system of time-dependent coordinates in which the constraint is linear, and for this reason will be called a `moving' energy. After giving sufficient conditions for the existence of a conserved, time-independent moving energy, we point out the role of symmetry in this mechanism. Lastly, we apply these ideas to prove that the motions of a heavy homogeneous solid sphere that rolls inside a convex surface of revolution in uniform rotation about its vertical figure axis, are (at least for certain parameter values and in open regions of the phase space) quasi-periodic on tori of dimension up to three.
Park, Chae-Hee; Chodzko-Zajko, Wojtek
2014-06-01
The purpose of the study was to assess the feasibility of implementing simple, safe, non-equipment evidence-based movements (Healthy Moves for Aging Well program) using an affordable and sustainable homecare-aide based delivery model that reaches the maximum possible number of frail older adults living at home in Illinois. Two local agencies were asked to identify two experienced home care aides and two inexperienced home care aides (n= 8). Each home care aides delivered the Healthy Moves to four clients (n= 16). Eight home care aides visited the client in the home and were asked to deliver the Healthy Moves program on a regular basis for a four-month time period. Outcome measures included a pre-and post- survey, a functional fitness test (older adults), and interviews. Evaluation procedures focused on older adult participants, homecare aids, and sites. The results showed that both interview and survey data revealed that most participants including older adults, home care aides, and site directors had a positive perception and high satisfaction with the program. Specially, 100% of older adult participants reported that they would recommend the program to others. Additionally, seniors and home care aides reported that they enjoyed working with each other on the program and both site directors reported that dissemination of the program in the State of Illinois employing home care aides was feasible and acceptable. Our study results indicate that Healthy Moves for Aging Well could be safely and successfully be disseminated to frail older adults in the State of Illinois.
Ibeanusi, V.M.; Henneman, T.; Cash, D. [Spelman College, Atlanta, GA (United States)
1995-12-31
We have developed an integrated ecosystem that supports the on-site speciation, detoxification, and mobilization of conglomerate toxic heavy metals of As(III), CR(VI), Pb, and Se from contaminated soil and water. The observed ecosystem dynamics of laboratory simulated ponds generated a pH profile (3-8.5) and a redox condition (0.25 V) that supported the oxidation of H{sub 3}AsO{sub 3} to H{sub 2}AsO{sub 4}. The infrared analysis of spent media identified an arsonic acid. The Cr(VI), Pb{sup 2} and Se (as in selenite) were reduced to Cr(III), PbS, and Se{sub 0}. In addition, molecular studies have identified unique protein molecules (< 10 KD) that are associated in the bioremediation processes. In these studies, the presence of a resistant bacterium (MRS-1), and cyanobacteria were highly significant in the bioremediation and sequestering of the metal ions to the pond surface. These results may have implications in the treatment of mixed wastes often encountered in mining areas.
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
McGlynn, Claire; Niens, Ulrike; Cairns, Ed; Hewstone, Miles
2004-01-01
As the integrated education movement in Northern Ireland passes its twenty-first anniversary, it is pertinent to explore the legacy of mixed Catholic and Protestant schooling. This paper summarises the findings of different studies regarding the impact of integrated education in Northern Ireland on social identity, intergroup attitudes and…
Ryabenkii, V. S.; Turchaninov, V. I.; Tsynkov, S. V.
1999-01-01
We propose a family of algorithms for solving numerically a Cauchy problem for the three-dimensional wave equation. The sources that drive the equation (i.e., the right-hand side) are compactly supported in space for any given time; they, however, may actually move in space with a subsonic speed. The solution is calculated inside a finite domain (e.g., sphere) that also moves with a subsonic speed and always contains the support of the right-hand side. The algorithms employ a standard consistent and stable explicit finite-difference scheme for the wave equation. They allow one to calculate tile solution for arbitrarily long time intervals without error accumulation and with the fixed non-growing amount of tile CPU time and memory required for advancing one time step. The algorithms are inherently three-dimensional; they rely on the presence of lacunae in the solutions of the wave equation in oddly dimensional spaces. The methodology presented in the paper is, in fact, a building block for constructing the nonlocal highly accurate unsteady artificial boundary conditions to be used for the numerical simulation of waves propagating with finite speed over unbounded domains.
Kanittha Yimnak
2014-01-01
Full Text Available The meshless local Pretrov-Galerkin method (MLPG with the test function in view of the Heaviside step function is introduced to solve the system of coupled nonlinear reaction-diffusion equations in two-dimensional spaces subjected to Dirichlet and Neumann boundary conditions on a square domain. Two-field velocities are approximated by moving Kriging (MK interpolation method for constructing nodal shape function which holds the Kronecker delta property, thereby enhancing the arrangement nodal shape construction accuracy, while the Crank-Nicolson method is chosen for temporal discretization. The nonlinear terms are treated iteratively within each time step. The developed formulation is verified in two numerical examples with investigating the convergence and the accuracy of numerical results. The numerical experiments revealing the solutions by the developed formulation are stable and more precise.
Keough, Brian; Wolfe, Mark
2012-01-01
This article discusses integrated approaches to the management and preservation of born digital photography. It examines the changing practices among photographers, and the needed relationships between the photographers using digital technology and the archivists responsible for acquiring their born digital images. Special consideration is given…
Stonesifer, R. B.; Atluri, S. N.
1982-01-01
The physical meaning of (Delta T)c and its applicability to creep crack growth are reviewed. Numerical evaluation of (Delta T)c and C(asterisk) is discussed with results being given for compact specimen and strip geometries. A moving crack-tip singularity, creep crack growth simulation procedure is described and demonstrated. The results of several crack growth simulation analyses indicate that creep crack growth in 304 stainless steel occurs under essentially steady-state conditions. Based on this result, a simple methodology for predicting creep crack growth behavior is summarized.
Radatz, Dana L; Wright, Emily M
2016-01-01
The majority of batterer intervention program (BIP) evaluations have indicated they are marginally effective in reducing domestic violence recidivism. Meanwhile, correctional programs used to treat a variety of offenders (e.g., substance users, violent offenders, and so forth) that adhere to the "principles of effective intervention" (PEI) have reported significant reductions in recidivism. This article introduces the PEI-the principles on which evidence-based practices in correctional rehabilitation are based-and identifies the degree to which they are currently integrated into BIPs. The case is made that batterer programs could be more effective if they incorporate the PEI. Recommendations for further integration of the principles into BIPs are also provided.
Guo Yan
2007-01-01
@@ China has already become the world's largest manufacturer of cement,copper and steel.Chinese producers have moved onto the world stage and dominated the global consumer market from textiles to electronics with amazing speed and efficiency.
Wellhoefer, Martin; Sprinzl, Wolfgang; Hahn, Rainer; Jungbauer, Alois
2013-12-06
An integrated process which combines continuous inclusion body dissolution with NaOH and continuous matrix-assisted refolding based on closed-loop simulated moving bed size exclusion chromatography was designed and experimentally evaluated at laboratory scale. Inclusion bodies from N(pro) fusion pep6His and N(pro) fusion MCP1 from high cell density fermentation were continuously dissolved with NaOH, filtered and mixed with concentrated refolding buffer prior to refolding by size exclusion chromatography (SEC). This process enabled an isocratic operation of the simulated moving bed (SMB) system with a closed-loop set-up with refolding buffer as the desorbent buffer and buffer recycling by concentrating the raffinate using tangential flow filtration. With this continuous refolding process, we increased the refolding and cleavage yield of both model proteins by 10% compared to batch dilution refolding. Furthermore, more than 99% of the refolding buffer of the raffinate could be recycled which reduced the buffer consumption significantly. Based on the actual refolding data, we compared throughput, productivity, and buffer consumption between two batch dilution refolding processes - one using urea for IB dissolution, the other one using NaOH for IB dissolution - and our continuous refolding process. The higher complexity of the continuous refolding process was rewarded with higher throughput and productivity as well as significantly lower buffer consumption compared to the batch dilution refolding processes. Copyright © 2013 Elsevier B.V. All rights reserved.
Yi-hung Chiou
2010-04-01
Full Text Available The goal of this article is to investigate the conditions under which ASEAN states are more likely to pursue regional economic integration, namely, a series of ASEAN Free Trade Area (AFTA agreements/ protocols. Adopting Putnam’s two-level-games model, this article examines the influences of domestic politics, political elites’ preferences, economic performance, and external impacts. Through the construction of a set of hypotheses, this article investigates five AFTA agreements/ protocols and the conditions of ASEAN states during the 1992–2003 period. The findings indicate that political leaders’ preferences have played a pivotal role in the development of the AFTA. Economic performance and domestic support in individual states has also affected the AFTA. The close link between AFTA agreements and external impacts reveals that the AFTA’s inherent nature is defensive.
Li, Yu-Ting; Wickens, Jeffery R.; Huang, Yi-Ling; Pan, Wynn H. T.; Chen, Fu-Yu Beverly; Chen, Jia-Jin Jason
2013-08-01
Objective. Fast-scan cyclic voltammetry (FSCV) is commonly used to monitor phasic dopamine release, which is usually performed using tethered recording and for limited types of animal behavior. It is necessary to design a wireless dopamine sensing system for animal behavior experiments. Approach. This study integrates a wireless FSCV system for monitoring the dopamine signal in the ventral striatum with an electrical stimulator that induces biphasic current to excite dopaminergic neurons in awake freely moving rats. The measured dopamine signals are unidirectionally transmitted from the wireless FSCV module to the host unit. To reduce electrical artifacts, an optocoupler and a separate power are applied to isolate the FSCV system and electrical stimulator, which can be activated by an infrared controller. Main results. In the validation test, the wireless backpack system has similar performance in comparison with a conventional wired system and it does not significantly affect the locomotor activity of the rat. In the cocaine administration test, the maximum electrically elicited dopamine signals increased to around 230% of the initial value 20 min after the injection of 10 mg kg-1 cocaine. In a classical conditioning test, the dopamine signal in response to a cue increased to around 60 nM over 50 successive trials while the electrically evoked dopamine concentration decreased from about 90 to 50 nM in the maintenance phase. In contrast, the cue-evoked dopamine concentration progressively decreased and the electrically evoked dopamine was eliminated during the extinction phase. In the histological evaluation, there was little damage to brain tissue after five months chronic implantation of the stimulating electrode. Significance. We have developed an integrated wireless voltammetry system for measuring dopamine concentration and providing electrical stimulation. The developed wireless FSCV system is proven to be a useful experimental tool for the continuous
Qian Sun
2016-11-01
Full Text Available Although the past few decades have witnessed the great development of Synthetic Aperture Radar Interferometry (InSAR technology in the monitoring of landslides, such applications are limited by geometric distortions and ambiguity of 1D Line-Of-Sight (LOS measurements, both of which are the fundamental weakness of InSAR. Integration of multi-sensor InSAR datasets has recently shown its great potential in breaking through the two limits. In this study, 16 ascending images from the Advanced Land Observing Satellite (ALOS and 18 descending images from the Environmental Satellite (ENVISAT have been integrated to characterize and to detect the slow-moving landslides in Zhouqu, China between 2008 and 2010. Geometric distortions are first mapped by using the imaging geometric parameters of the used SAR data and public Digital Elevation Model (DEM data of Zhouqu, which allow the determination of the most appropriate data assembly for a particular slope. Subsequently, deformation rates along respective LOS directions of ALOS ascending and ENVISAT descending tracks are estimated by conducting InSAR time series analysis with a Temporarily Coherent Point (TCP-InSAR algorithm. As indicated by the geometric distortion results, 3D deformation rates of the Xieliupo slope at the east bank of the Pai-lung River are finally reconstructed by joint exploiting of the LOS deformation rates from cross-heading datasets based on the surface–parallel flow assumption. It is revealed that the synergistic results of ALOS and ENVISAT datasets provide a more comprehensive understanding and monitoring of the slow-moving landslides in Zhouqu.
Li, Yu-Ting; Wickens, Jeffery R; Huang, Yi-Ling; Pan, Wynn H T; Chen, Fu-Yu Beverly; Chen, Jia-Jin Jason
2013-08-01
Fast-scan cyclic voltammetry (FSCV) is commonly used to monitor phasic dopamine release, which is usually performed using tethered recording and for limited types of animal behavior. It is necessary to design a wireless dopamine sensing system for animal behavior experiments. This study integrates a wireless FSCV system for monitoring the dopamine signal in the ventral striatum with an electrical stimulator that induces biphasic current to excite dopaminergic neurons in awake freely moving rats. The measured dopamine signals are unidirectionally transmitted from the wireless FSCV module to the host unit. To reduce electrical artifacts, an optocoupler and a separate power are applied to isolate the FSCV system and electrical stimulator, which can be activated by an infrared controller. In the validation test, the wireless backpack system has similar performance in comparison with a conventional wired system and it does not significantly affect the locomotor activity of the rat. In the cocaine administration test, the maximum electrically elicited dopamine signals increased to around 230% of the initial value 20 min after the injection of 10 mg kg(-1) cocaine. In a classical conditioning test, the dopamine signal in response to a cue increased to around 60 nM over 50 successive trials while the electrically evoked dopamine concentration decreased from about 90 to 50 nM in the maintenance phase. In contrast, the cue-evoked dopamine concentration progressively decreased and the electrically evoked dopamine was eliminated during the extinction phase. In the histological evaluation, there was little damage to brain tissue after five months chronic implantation of the stimulating electrode. We have developed an integrated wireless voltammetry system for measuring dopamine concentration and providing electrical stimulation. The developed wireless FSCV system is proven to be a useful experimental tool for the continuous monitoring of dopamine levels during animal learning
Margaret A. Cook
2015-11-01
Full Text Available Recent droughts and heat waves have revealed the vulnerability of some power plants to effects from higher temperature intake water for cooling. In this evaluation, we develop a methodology for predicting whether power plants are at risk of violating thermal pollution limits. We begin by developing a regression model of average monthly intake temperatures for open loop and recirculating cooling pond systems. We then integrate that information into a thermodynamic model of energy flows within each power plant to determine the change in cooling water temperature that occurs at each plant and the relationship of that water temperature to other plants in the river system. We use these models together with climate change models to estimate the monthly effluent temperature at twenty-six power plants in the Upper Mississippi River Basin and Texas between 2015 and 2035 to predict which ones are at risk of reaching thermal pollution limits. The intake model shows that two plants could face elevated intake temperatures between 2015 and 2035 compared to the 2010–2013 baseline. In general, a rise in ambient cooling water temperature of 1 °C could cause a drop in power output of 0.15%–0.5%. The energy balance shows that twelve plants might exceed state summer effluent limits.
Study on an integrated sintered metal screen moving granular bed filter%烧结复合式烧结金属丝网颗粒移动床过滤器研究
吴晋沪; 王洋
2004-01-01
A new gas clean-up process called "integrated sintered metal screen moving granular bed"(ISMSMGB)for the integrated gasification combined cycle(IGCC)and pressured fluidized bed combustion(PFBC)was developed on the basis of a sintered metal candle filter and a cross-flow moving granular bed filter.This is a combination of the surface and deep bed filtering processes.A set of facilities was established and a series of cold model tests were carried out.The dust removal efficiency and the pressure drop of the filter were measured and analyzed.The results show that this process features the advantages of the moving bed for high capacity as well as high inlet dust load and the surface filter for high efficiency.Meanwhile,the granules moving downward cleans the cake on the screen surface,so that the system is operated at steady state.
Nurull Qurraisha Nadiyya Md-Khair
2017-09-01
Full Text Available In this paper, a hybrid time series forecasting approach is proposed consisting of wavelet transform as the data decomposition method with Autoregressive Integrated Moving Average (ARIMA andLeast Square Support Vector Machine (LSSVM combination as the forecasting method to enhance the accuracy in forecasting the crude oil spot prices (COSP series. In brief, the original COSP is divided into a more stable constitutive series using discrete wavelet transform (DWT. These respective sub-series are then forecasted using ARIMA and LSSVM combination method and lastly, all forecasted components are combined back togetherto acquire the original forecasted series. The datasets consist of monthly COSP series from West Texas Intermediate (WTI and Brent North Sea (Brent. To evaluate the effectiveness of the proposed approach, several comparisons are made with the single forecasting approaches, a hybrid forecasting approach and also some existing forecasting approaches that utilize COSP series as the dataset by comparing the Mean Absolute Error (MAE and Root Mean Square Error (RMSE acquired. From the results, the proposed approach has managed to outperform the other approaches with smaller MAE and RMSE values which signify better forecasting accuracy. Ultimately, the study proves that the integration of data decomposition with forecasting combination method could increase the accuracy of COSP series forecasting.
Moving a House by Moved Participants
Axel, Erik
? The participant observer believed it was a matter of changing coordinates, but the engineers immediately saw it was an issue of pipes in the ground, could they be moved and still function as planned? To decide the possibility of this suggestion the engineer was given the task of investigating the consequences......The author performed an investigation of how a house was designed. He participated mainly in meetings, in which the house was engineered. The meetings proceeded in an agreeable atmosphere. While the architect was mostly concerned with integrating the functionality of the house, the engineer engaged...... of moving the house with respect to its servicing pipes. It was immediately underlined that this task was a surplus task and would therefore trigger an extra payment. When I interviewed the participants and asked them how they understood the function of the engineer, they revealed some irritation, since...
Lin, Zhiyue; Kahrilas, P J; Roman, S; Boris, L; Carlson, D; Pandolfino, J E
2012-08-01
The Integrated Relaxation Pressure (IRP) is the esophageal pressure topography (EPT) metric used for assessing the adequacy of esophagogastric junction (EGJ) relaxation in the Chicago Classification of motility disorders. However, because the IRP value is also influenced by distal esophageal contractility, we hypothesized that its normal limits should vary with different patterns of contractility. Five hundred and twenty two selected EPT studies were used to compare the accuracy of alternative analysis paradigms to that of a motility expert (the 'gold standard'). Chicago Classification metrics were scored manually and used as inputs for MATLAB™ programs that utilized either strict algorithm-based interpretation (fixed abnormal IRP threshold of 15 mmHg) or a classification and regression tree (CART) model that selected variable IRP thresholds depending on the associated esophageal contractility. The sensitivity of the CART model for achalasia (93%) was better than that of the algorithm-based approach (85%) on account of using variable IRP thresholds that ranged from a low value of >10 mmHg to distinguish type I achalasia from absent peristalsis to a high value of >17 mmHg to distinguish type III achalasia from distal esophageal spasm. Additionally, type II achalasia was diagnosed solely by panesophageal pressurization without the IRP entering the algorithm. Automated interpretation of EPT studies more closely mimics that of a motility expert when IRP thresholds for impaired EGJ relaxation are adjusted depending on the pattern of associated esophageal contractility. The range of IRP cutoffs suggested by the CART model ranged from 10 to 17 mmHg. © 2012 Blackwell Publishing Ltd.
Wavelet regression model in forecasting crude oil price
Hamid, Mohd Helmie; Shabri, Ani
2017-05-01
This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.
Wellhoefer, Martin; Sprinzl, Wolfgang; Hahn, Rainer; Jungbauer, Alois
2014-04-11
Continuous processing of recombinant proteins was accomplished by combining continuous matrix-assisted refolding and purification by tandem simulated moving bed (SMB) size-exclusion chromatography (SEC). Recombinant proteins, N(pro) fusion proteins from inclusion bodies were dissolved with NaOH and refolded in the SMB system with a closed-loop set-up with refolding buffer as the desorbent buffer and buffer recycling of the refolding buffer of the raffinate by tangential flow filtration. For further purification of the refolded proteins, a second SMB operation also based on SEC was added. The whole system could be operated isocratically with refolding buffer as the desorbent buffer, and buffer recycling could also be applied in the purification step. Thus, a significant reduction in buffer consumption was achieved. The system was evaluated with two proteins, the N(pro) fusion pep6His and N(pro) fusion MCP-1. Refolding solution, which contained residual N(pro) fusion peptide, the cleaved autoprotease N(pro), and the cleaved target peptide was used as feed solution. Full separation of the cleaved target peptide from residual proteins was achieved at a purity and recovery in the raffinate and extract, respectively, of approximately 100%. In addition, more than 99% of the refolding buffer of the raffinate was recycled. A comparison of throughput, productivity, and buffer consumption of the integrated continuous process with two batch processes demonstrated that up to 60-fold higher throughput, up to 180-fold higher productivity, and at least 28-fold lower buffer consumption can be obtained by the integrated continuous process, which compensates for the higher complexity. Copyright © 2014 Elsevier B.V. All rights reserved.
Albizuri, J; Grau, P; Christensson, M; Larrea, L
2014-01-01
The paper presents a systematic study of simulations, using a previously calibrated Colloid model, from which it was found that: (i) for pure moving-bed biofilm reactor (MBBR) processes with tertiary nitrification conditions (no influent chemical oxygen demand (COD)), dissolved oxygen = 5 mg/L and residual NH4-N > 4 mgN/L, a nitrification rate of 1.2 gN/(m(2)d) was obtained at 10 °C. This rate decreases sharply when residual NH4-N is lower than 2 mgN/L, (ii) for MBBR systems with predenitrification-nitrification zones and COD in the influent (soluble and particulate), the nitrification rate (0.6 gN/(m(2)d)) is half of that in tertiary nitrification due to the effect of influent colloidal XS (particulate slowly biodegradable COD) and (iii) for integrated fixed-film activated sludge (IFAS) processes the nitrification rate in the biofilm (0.72 gN/(m(2)d)) is 20% higher than for the pure MBBR due to the lower effect of influent XS since it is adsorbed onto flocs. However, it is still 40% lower than the tertiary nitrification rate. In the IFAS, the fraction of the nitrification rate in suspension ranges from 10 to 70% when the aerobic solids retention time varies from 1.4 to 6 days.
Friedrich, Maik; Rußwinkel, Nele; Möhlenbrink, Christoph
2016-06-10
Today, capturing the behavior of a human eye is considered a standard method for measuring the information-gathering process and thereby gaining insights into cognitive processes. Due to the dynamic character of most task environments there is still a lack of a structured and automated approach for analyzing eye movement in combination with moving objects. In this article, we present a guideline for advanced gaze analysis, called IGDAI (Integration Guideline for Dynamic Areas of Interest). The application of IGDAI allows gathering dynamic areas of interest and simplifies its combination with eye movement. The first step of IGDAI defines the basic requirements for the experimental setup including the embedding of an eye tracker. The second step covers the issue of storing the information of task environments for the dynamic AOI analysis. Implementation examples in XML are presented fulfilling the requirements for most dynamic task environments. The last step includes algorithms to combine the captured eye movement and the dynamic areas of interest. A verification study was conducted, presenting an air traffic controller environment to participants. The participants had to distinguish between different types of dynamic objects. The results show that in comparison to static areas of interest, IGDAI allows a faster and more detailed view on the distribution of eye movement.
Jia, Song; Xu, Tian-he; Sun, Zhang-zhen; Li, Jia-jing
2017-02-01
UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth's zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth's zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR.
Saeed Akhtar; Shafquat Rozi
2009-01-01
AIM: To identify the stochastic autoregressive integrated moving average (ARIMA) model for short term forecasting of hepatitis C virus (HCV) seropositivity among volunteer blood donors in Karachi, Pakistan. METHODS: Ninety-six months (1998-2005) data on volunteer blood donors tested at four major blood banks in Karachi, Pakistan were subjected to ARIMA modeling. Subsequently, a fitted ARIMA model was used to forecast HCV seropositive donors for 91-96 mo to contrast with observed series of the same months. To assess the forecast accuracy, the mean absolute error rate (%) between the observed and predicted HCV seroprevalence was calculated. Finally, a fitted ARIMA model was used for short-term forecasts beyond the observed series. RESULTS: The goodness-of-fit test of the optimum ARIMA (2,1,7) model showed non- s igni f icant autocorrelations in the residuals of the model. The forecasts by ARIMA for 91-96 mo closely followed the pattern of observed series for the same months, with mean monthly absolute forecast errors (%) over 6 mo of 6.5%. The short-term forecasts beyond the observed series adequately captured the pattern in the data and showed increasing tendency of HCV seropositivity with CONCLUSION: To curtail HCV spread, public health authorities need to educate communities and health care providers about HCV transmission routes based on known HCV epidemiology in Pakistan and its neighboring countries. Future research may focus on factors associated with hyperendemic levels of HCV infection.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Sen, Dipankar; Randall, Clifford W
2008-05-01
Research was undertaken to develop a model for activated sludge, integrated fixed-film activated sludge (IFAS), and moving-bed biofilm reactor (MBBR) systems. The model can operate with up to 12 cells (reactors) in series, with biofilm media incorporated to one or more cells, except the anaerobic cells. The process configuration can be any combination of anaerobic, anoxic, aerobic, post-anoxic with or without supplemental carbon, and reaeration; it can also include any combination of step feed and recycles, including recycles for mixed liquor, return activated sludge, nitrates, and membrane bioreactors. This paper presents the structure of the model. The model embeds a biofilm model into a multicell activated sludge model. The biofilm flux rates for organics, nutrients, and biomass can be computed by two methods--a semi-empirical model of the biofilm that is relatively simpler, or a diffusional model that is computationally intensive. The values of the kinetic parameters for the model were measured using pilot-scale activated sludge, IFAS, and MBBR systems. For the semiempirical version, a series of Monod equations were developed for chemical oxygen demand, ammonium-nitrogen, and oxidized-nitrogen fluxes to the biofilm. Within the equations, a second Monod expression is used to simulate the effect of changes in biofilm thickness and fraction nitrifiers in the biofilm. The biofilm flux model is then linked to the activated sludge model. The diffusional model and the verification of the models are presented in subsequent papers (Sen and Randall, 2008a, 2008b). The model can be used to quantify the amount of media and surface area required to achieve nitrification, identify the best locations for the media, and optimize the dissolved oxygen levels and nitrate recycle rates. Some of the advanced features include the ability to apply different media types and fill fractions in cells; quantify nitrification, denitrification, and biomass production in the biofilm and
2006-01-01
[figure removed for brevity, see original site] Context image for PIA03289 Moving Downhill This narrow canyon is part of Coprates Chasma. On the east side of the canyon a landslide is visible. The southern wall of the canyon is marked by bright and dark streaks where dust has slid down the cliff face. Image information: VIS instrument. Latitude -10.5N, Longitude 264.8E. 17 meter/pixel resolution. Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time. NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.
Regression analysis by example
Chatterjee, Samprit; Hadi, Ali S
2012-01-01
.... The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression...
Lombards on the Move – An Integrative Study of the Migration Period Cemetery at Szólád, Hungary
Peters, Daniel; Müller, Wolfgang; Maurer, Anne-France; Kollig, Isabelle; Nicklisch, Nicole; Müller, Christiane; Karimnia, Sarah; Brandt, Guido; Roth, Christina; Rosner, Martin; Mende, Balász; Schöne, Bernd R.; Vida, Tivadar; von Freeden, Uta
2014-01-01
In 2005 to 2007 45 skeletons of adults and subadults were excavated at the Lombard period cemetery at Szólád (6th century A.D.), Hungary. Embedded into the well-recorded historical context, the article presents the results obtained by an integrative investigation including anthropological, molecular genetic and isotopic (δ15N, δ13C, 87Sr/86Sr) analyses. Skeletal stress markers as well as traces of interpersonal violence were found to occur frequently. The mitochondrial DNA profiles revealed a heterogeneous spectrum of lineages that belong to the haplogroups H, U, J, HV, T2, I, and K, which are common in present-day Europe and in the Near East, while N1a and N1b are today quite rare. Evidence of possible direct maternal kinship was identified in only three pairs of individuals. According to enamel strontium isotope ratios, at least 31% of the individuals died at a location other than their birthplace and/or had moved during childhood. Based on the peculiar 87Sr/86Sr ratio distribution between females, males, and subadults in comparison to local vegetation and soil samples, we propose a three-phase model of group movement. An initial patrilocal group with narrower male but wider female Sr isotope distribution settled at Szólád, whilst the majority of subadults represented in the cemetery yielded a distinct Sr isotope signature. Owing to the virtual absence of Szólád-born adults in the cemetery, we may conclude that the settlement was abandoned after approx. one generation. Population heterogeneity is furthermore supported by the carbon and nitrogen isotope data. They indicate that a group of high-ranking men had access to larger shares of animal-derived food whilst a few individuals consumed remarkable amounts of millet. The inferred dynamics of the burial community are in agreement with hypotheses of a highly mobile lifestyle during the Migration Period and a short-term occupation of Pannonia by Lombard settlers as conveyed by written sources. PMID:25369022
Semiparametric regression during 2003–2007
Ruppert, David
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating e...
关于切换回归的集成模糊聚类算法 GFC%An Integrated Fuzzy Clustering Algorithm GFC for Switching Regressions
王士同; 江海峰; 陆宏钧
2002-01-01
已经有多个方法可用于解决切换回归问题.根据所提出的基于Newton引力定理的引力聚类算法GC,结合模糊聚类算法,进一步提出了新的集成模糊聚类算法 GFC.理论分析表明GFC 能收敛到局部最小.实验结果表明GFC在解决切换回归问题时,比标准模糊聚类算法更有效,特别在收敛速度方面.%In order to solve switching regression problems, many approaches have been investigated. In this paper, anintegrated fuzzy clustering algorithm GFC that combines gravity-based clustering algorithm GC with fuzzy clustering is presented. GC, as a new hard clustering algorithm presented here, is based on the well-known Newton's Gravity Law. The theoretic analysis shows that GFC can conve rge to a local minimum of the object function. Experimental results show that GFC for switching regression problems has better performance than standard fuzzy clustering algorithms, especially in terms of convergence speed.
Morris, Tim; Manley, David; Northstone, Kate;
2016-01-01
A large literature exists suggesting that residential mobility leads to increased participation in risky health behaviours such as cannabis use amongst youth. However, much of this work fails to account for the impact that underlying differences between mobile and non-mobile youth have on this re......A large literature exists suggesting that residential mobility leads to increased participation in risky health behaviours such as cannabis use amongst youth. However, much of this work fails to account for the impact that underlying differences between mobile and non-mobile youth have...... regression models (log odds: 0.94, standard error: 0.42), indicating that children who move houses are more likely to use cannabis than those who remain residentially stable. However, decomposing this relationship into within- and between-child components reveals that the conventional model is underspecified...
Superquantile Regression: Theory, Algorithms, and Applications
2014-12-01
Isabel. I love having you in my arms, and although you are still too young to understand what a hug is, your warmth has given me the strength and...squares and the quantile regression models adjust to changes in the data set, denoted by the red dots. Notice that the observa- tions are moved upwards...model hardly changes. If we change this observation in red even further upwards, we would notice no more changes in the quantile regression function
Nonparametric regression with filtered data
Linton, Oliver; Nielsen, Jens Perch; Van Keilegom, Ingrid; 10.3150/10-BEJ260
2011-01-01
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
Park, Chae-Hee; Chodzko-Zajko, Wojtek
2014-01-01
The purpose of the study was to assess the feasibility of implementing simple, safe, non-equipment evidence-based movements (Healthy Moves for Aging Well program) using an affordable and sustainable homecare-aide based delivery model that reaches the maximum possible number of frail older adults living at home in Illinois. Two local agencies were asked to identify two experienced home care aides and two inexperienced home care aides (n= 8). Each home care aides delivered the Healthy Moves to four clients (n= 16). Eight home care aides visited the client in the home and were asked to deliver the Healthy Moves program on a regular basis for a four-month time period. Outcome measures included a pre-and post- survey, a functional fitness test (older adults), and interviews. Evaluation procedures focused on older adult participants, homecare aids, and sites. The results showed that both interview and survey data revealed that most participants including older adults, home care aides, and site directors had a positive perception and high satisfaction with the program. Specially, 100% of older adult participants reported that they would recommend the program to others. Additionally, seniors and home care aides reported that they enjoyed working with each other on the program and both site directors reported that dissemination of the program in the State of Illinois employing home care aides was feasible and acceptable. Our study results indicate that Healthy Moves for Aging Well could be safely and successfully be disseminated to frail older adults in the State of Illinois. PMID:25061600
2016-07-21
the vector of commands, it is necessary to calculate all the values used in the dynamic model (2.42). Given the values for the ground speed g V...producing highly detailed digital elevation models , which can be applied, for example, to study the mass balance of Greenland. Meteorological data can...develop a model -based approach for detecting the location of a moving gaseous source and the concentration via the use of a Sensing Aerial Vehicle (SAV
Unitary Response Regression Models
Lipovetsky, S.
2007-01-01
The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…
Flexible survival regression modelling
Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben
2009-01-01
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by m...... treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work....
基于回归森林的车载组合导航系统研究%Research on Vehicle Integrated Navigation System Based on Forest Regression
张树君; 马永强
2014-01-01
Vehicle navigation system based GPS has been widely used. But GPS has a major defect that is it can navigate depending on at least 4 satellites’ signal. To overcome this shortage of GPS, a combined system consisting of GPS and INS is utilized. INS subsystem may produce accumulated errors. In order to improve the accuracy, Kalman filter and artificial intelligence approaches have been put forward. In this study, a combined system based on forest regression is proposed. The result of this experiment illustrates that this method has a significant optimized in the positional error.%基于 GPS 的车载导航系统被广泛应用，但是 GPS 存在不可避免的缺陷，需要4颗以上的卫星信号才可以实现导航，INS/GPS组合导航可以有效的弥补这一缺陷，实现全时全地形导航。INS子系统易产生累计误差，人们已经就如何减少累计误差做了很多研究，卡尔曼滤波及其改进算法，以及近年来提出的基于人工智能的多种方法。本文提出一种基于回归森林的组合导航算法，实验结果也证明此方法在减少导航误差方面有了大幅度的优化。
Naghshpour, Shahdad
2012-01-01
Regression analysis is the most commonly used statistical method in the world. Although few would characterize this technique as simple, regression is in fact both simple and elegant. The complexity that many attribute to regression analysis is often a reflection of their lack of familiarity with the language of mathematics. But regression analysis can be understood even without a mastery of sophisticated mathematical concepts. This book provides the foundation and will help demystify regression analysis using examples from economics and with real data to show the applications of the method. T
Zhang, Ying; Bi, Peng; Hiller, Janet
2008-01-01
This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990-2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.
CHRIS; DEVONSHIRE-ELLIS
2008-01-01
Central China, initially overlooked by many foreign investors as being too far from the ports in Tianjin, Shanghai and Shenzhen, is emerging as an essential destination for multinationals in China.The Future of Central China: A Provincial Roadmap There is a huge manufacturing drive in central China. Businesses are moving inland to set up projects, investments and operations here, primarily due to lower labor and land costs as well as preferential policies for manufacturers. Multinational companies also are starting to view the region as the next step toward an integrated China strategy, and the consumer population in the second-and third-tier cities in central China represents a growing, largely untapped domestic market for foreign products and services.
Autistic epileptiform regression.
Canitano, Roberto; Zappella, Michele
2006-01-01
Autistic regression is a well known condition that occurs in one third of children with pervasive developmental disorders, who, after normal development in the first year of life, undergo a global regression during the second year that encompasses language, social skills and play. In a portion of these subjects, epileptiform abnormalities are present with or without seizures, resembling, in some respects, other epileptiform regressions of language and behaviour such as Landau-Kleffner syndrome. In these cases, for a more accurate definition of the clinical entity, the term autistic epileptifom regression has been suggested. As in other epileptic syndromes with regression, the relationships between EEG abnormalities, language and behaviour, in autism, are still unclear. We describe two cases of autistic epileptiform regression selected from a larger group of children with autistic spectrum disorders, with the aim of discussing the clinical features of the condition, the therapeutic approach and the outcome.
Scaled Sparse Linear Regression
Sun, Tingni
2011-01-01
Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual squares and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs nearly nothing beyond the computation of a path of the sparse regression estimator for penalty levels above a threshold. For the scaled Lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression coefficients and noise level. Under mild regularity conditions, we prove that the method yields simultaneously an estimator for the noise level and an estimated coefficient vector in the Lasso path satisfying certain oracle inequalities for the estimation of the noise level, prediction, and the estimation of regression coefficients. These oracle inequalities provide sufficient conditions for the consistency and asymptotic...
SEEDS Moving Group Status Update
McElwain, Michael
2011-01-01
I will summarize the current status of the SEEDS Moving Group category and describe the importance of this sub-sample for the entire SEEDS survey. This presentation will include analysis of the sensitivity for the Moving Groups with general a comparison to other the other sub-categories. I will discuss the future impact of the Subaru SCExAO system for these targets and the advantage of using a specialized integral field spectrograph. Finally, I will present the impact of a pupil grid mask in order to produce fiducial spots in the focal plane that can be used for both photometry and astrometry.
Guijun YANG; Lu LIN; Runchu ZHANG
2007-01-01
Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Weisberg, Sanford
2005-01-01
Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: ""I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."" -Technometrics, February 1987 ""Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."" -American Scientist, May-June 1987
Moving Shadows, Moving Sun. Early Modem Sundials Restaging Miracles.
Mersmann, Jasmin
2015-01-01
Irrespective of geo- or heliocentric presuppositions, the functioning of sundials is based on the observation of moving shadows or light spots. Even though the cast shadow was often simply used to indicate the time, it could also remind the users of the ephemerality of earthly things or function as an index of planetary movements. This article examines the various ways in which early modem sundials visually interpret the moving shadow or light spot. The instruments address the shadow in inscriptions, integrate it into their design (e.g., in cruciform dials) or even manipulate its course (as in the so-called Horologium Ahaz). Both the crucifix and the Ahaz dials not only refer to astronomical miracles but actually restage them. Even though by means of the horologium it was not possible to explain the Old Testament miracle of the shadow moving backward, adepts were able to recreate it on a terrestrial scale.
Functional linear regression via canonical analysis
He, Guozhong; Wang, Jane-Ling; Yang, Wenjing; 10.3150/09-BEJ228
2011-01-01
We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and existence of the corresponding functional linear regression models and some basic properties are explored for this situation. We derive a representation of the regression parameter function in terms of the canonical components of the processes involved. This representation establishes a connection between functional regression and functional canonical analysis and suggests alternative approaches for the implementation of functional linear regression analysis. A specific procedure for the estimation of the regression parameter function using canonical expansions is proposed and compared with an established functional principal component regression approach. As an example of an application, we present an analysis of mortality data for cohorts of medflies, obtained in experimental studies of aging and longevity.
Gerber, Samuel [Univ. of Utah, Salt Lake City, UT (United States); Rubel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Whitaker, Ross T. [Univ. of Utah, Salt Lake City, UT (United States)
2012-01-19
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...
Tong, Xuming; Chen, Jinghang; Miao, Hongyu; Li, Tingting; Zhang, Le
2015-01-01
Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.
Time series regression model for infectious disease and weather.
Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro
2015-10-01
Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Wind speed prediction using statistical regression and neural network
Makarand A Kulkarni; Sunil Patil; G V Rama; P N Sen
2008-08-01
Prediction of wind speed in the atmospheric boundary layer is important for wind energy assess- ment,satellite launching and aviation,etc.There are a few techniques available for wind speed prediction,which require a minimum number of input parameters.Four different statistical techniques,viz.,curve ﬁtting,Auto Regressive Integrated Moving Average Model (ARIMA),extrapolation with periodic function and Artiﬁcial Neural Networks (ANN)are employed to predict wind speed.These methods require wind speeds of previous hours as input.It has been found that wind speed can be predicted with a reasonable degree of accuracy using two methods,viz.,extrapolation using periodic curve ﬁtting and ANN and the other two methods are not very useful.
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Least Squares Moving-Window Spectral Analysis.
Lee, Young Jong
2017-01-01
Least squares regression is proposed as a moving-windows method for analysis of a series of spectra acquired as a function of external perturbation. The least squares moving-window (LSMW) method can be considered an extended form of the Savitzky-Golay differentiation for nonuniform perturbation spacing. LSMW is characterized in terms of moving-window size, perturbation spacing type, and intensity noise. Simulation results from LSMW are compared with results from other numerical differentiation methods, such as single-interval differentiation, autocorrelation moving-window, and perturbation correlation moving-window methods. It is demonstrated that this simple LSMW method can be useful for quantitative analysis of nonuniformly spaced spectral data with high frequency noise.
... Happens in the Operating Room? Are Your Bowels Moving? KidsHealth > For Kids > Are Your Bowels Moving? A A A What's in this article? What's ... to Know? en español ¿Se mueven tus intestinos? Moving your bowels means to poop. If you said " ...
Transductive Ordinal Regression
Seah, Chun-Wei; Ong, Yew-Soon
2011-01-01
Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, are often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely Transductive Ordinal Regression (TOR). The key challenge of the present study lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive setting...
动基座条件下组合导航初始对准研究%Research on Initial Alignment of Integrated Navigation System on Moving Base
朱让剑; 战兴群; 秦峰
2012-01-01
初始对准是影响惯性导航系统的导航精度的重要因素,通过对惯性导航(INS)/全球定位系统(GPS)组合导航系统初始对准进行研究,利用GPS信息来辅助INS进行初始对准,来提升初始对准的精度和对准时间.在粗对准阶段,利用载机主惯导对子惯导进行装订,完成子惯导的粗对准；在精对准阶段,有效地利用GPS信息,采用速度匹配方式,并设计了组合对准滤波器,进行仿真验证.仿真结果表明:组合导航系统的初始对准能在15 s内完成对准,且能够达到7mrad的精度.%Initial alignment of inertial navigation system is an important factor that can affects the inertial navigation accuracy. The GPS/INS integrated navigation system is used to study initial alignment in order to improve the accuracy and shorten the time. In the coarse phase of alignment, the master inertial system carried on board is used to transmit its information to slave inertial system. In precise phase of alignment, information of GPS is used effectively and use the mean of speed match, then the Kalman filter to fulfill the simulation is designed. The simulation results show that integrated navigation system can complete the alignment in 15 s, and also can reach accuracy of 7 mrad.
[Understanding logistic regression].
El Sanharawi, M; Naudet, F
2013-10-01
Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge.
Constrained Sparse Galerkin Regression
Loiseau, Jean-Christophe
2016-01-01
In this work, we demonstrate the use of sparse regression techniques from machine learning to identify nonlinear low-order models of a fluid system purely from measurement data. In particular, we extend the sparse identification of nonlinear dynamics (SINDy) algorithm to enforce physical constraints in the regression, leading to energy conservation. The resulting models are closely related to Galerkin projection models, but the present method does not require the use of a full-order or high-fidelity Navier-Stokes solver to project onto basis modes. Instead, the most parsimonious nonlinear model is determined that is consistent with observed measurement data and satisfies necessary constraints. The constrained Galerkin regression algorithm is implemented on the fluid flow past a circular cylinder, demonstrating the ability to accurately construct models from data.
Practical Session: Logistic Regression
Clausel, M.; Grégoire, G.
2014-12-01
An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
Emerek, Ruth
2004-01-01
Bidraget diskuterer de forskellige intergrationsopfattelse i Danmark - og hvad der kan forstås ved vellykket integration......Bidraget diskuterer de forskellige intergrationsopfattelse i Danmark - og hvad der kan forstås ved vellykket integration...
Theurich, Melissa Ann; McCool, Megan Elizabeth
2016-08-01
In 2011, the Surgeon General's Call to Action to Support Breastfeeding called on all health professional organizations, medical schools, and credentialing boards to establish and incorporate minimum lactation education and training requirements into their credentialing, licensing, and certification processes and to include breastfeeding education in undergraduate and graduate education and training programs. Given the commonalities between the fields of nutrition and breastfeeding, it has been proposed that nutrition professionals are an underutilized resource in the field of lactation management. Considering the lack of breastfeeding knowledge and skills among health professionals, nutrition professionals should be afforded opportunities to learn lactation management during their studies. The United States Breastfeeding Committee published Core Competencies in Breastfeeding Care and Services for All Health Professionals in 2010. However, professional nutrition and lactation credentialing boards should cooperate to integrate mandatory minimum standards of lactation education for nutrition professionals. Undergraduate and graduate programs in nutrition and dietetics should incorporate lactation content into their core curricula to comply with such standards. In addition, dietetics programs should offer optional clinical lactation experiences for students who aspire to become an International Board Certified Lactation Consultant. © The Author(s) 2016.
Jajodia, Sushil; Swarup, Vipin; Wang, Cliff; Wang, X Sean
2011-01-01
Moving Target Defense: Creating Asymmetric Uncertainty for Cyber Threats was developed by a group of leading researchers. It describes the fundamental challenges facing the research community and identifies new promising solution paths. Moving Target Defense which is motivated by the asymmetric costs borne by cyber defenders takes an advantage afforded to attackers and reverses it to advantage defenders. Moving Target Defense is enabled by technical trends in recent years, including virtualization and workload migration on commodity systems, widespread and redundant network connectivity, instr
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Adaptive metric kernel regression
Goutte, Cyril; Larsen, Jan
2000-01-01
regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Software Regression Verification
2013-12-11
of recursive procedures. Acta Informatica , 45(6):403 – 439, 2008. [GS11] Benny Godlin and Ofer Strichman. Regression verifica- tion. Technical Report...functions. Therefore, we need to rede - fine m-term. – Mutual termination. If either function f or function f ′ (or both) is non- deterministic, then their
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
The moving mesh code Shadowfax
Vandenbroucke, Bert
2016-01-01
We introduce the moving mesh code Shadowfax, which can be used to evolve a mixture of gas, subject to the laws of hydrodynamics and gravity, and any collisionless fluid only subject to gravity, such as cold dark matter or stars. The code is written in C++ and its source code is made available to the scientific community under the GNU Affero General Public License. We outline the algorithm and the design of our implementation, and demonstrate its validity through the results of a set of basic test problems, which are also part of the public version. We also compare Shadowfax with a number of other publicly available codes using different hydrodynamical integration schemes, illustrating the advantages and disadvantages of the moving mesh technique.
The moving mesh code SHADOWFAX
Vandenbroucke, B.; De Rijcke, S.
2016-07-01
We introduce the moving mesh code SHADOWFAX, which can be used to evolve a mixture of gas, subject to the laws of hydrodynamics and gravity, and any collisionless fluid only subject to gravity, such as cold dark matter or stars. The code is written in C++ and its source code is made available to the scientific community under the GNU Affero General Public Licence. We outline the algorithm and the design of our implementation, and demonstrate its validity through the results of a set of basic test problems, which are also part of the public version. We also compare SHADOWFAX with a number of other publicly available codes using different hydrodynamical integration schemes, illustrating the advantages and disadvantages of the moving mesh technique.
Gómez Rodríguez, Rafael Ángel
2014-01-01
To say that someone possesses integrity is to claim that that person is almost predictable about responses to specific situations, that he or she can prudentially judge and to act correctly. There is a closed interrelationship between integrity and autonomy, and the autonomy rests on the deeper moral claim of all humans to integrity of the person. Integrity has two senses of significance for medical ethic: one sense refers to the integrity of the person in the bodily, psychosocial and intellectual elements; and in the second sense, the integrity is the virtue. Another facet of integrity of the person is la integrity of values we cherish and espouse. The physician must be a person of integrity if the integrity of the patient is to be safeguarded. The autonomy has reduced the violations in the past, but the character and virtues of the physician are the ultimate safeguard of autonomy of patient. A field very important in medicine is the scientific research. It is the character of the investigator that determines the moral quality of research. The problem arises when legitimate self-interests are replaced by selfish, particularly when human subjects are involved. The final safeguard of moral quality of research is the character and conscience of the investigator. Teaching must be relevant in the scientific field, but the most effective way to teach virtue ethics is through the example of the a respected scientist.
Low rank Multivariate regression
Giraud, Christophe
2010-01-01
We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting estimator. We also investigate the easier case where the variance of the noise is known and outline that the penalties appearing in our criterions are minimal (in some sense). These penalties involve the expected value of the Ky-Fan quasi-norm of some random matrices. These quantities can be evaluated easily in practice and upper-bounds can be derived from recent results in random matrix theory.
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Hansen, Henrik; Tarp, Finn
2001-01-01
. There are, however, decreasing returns to aid, and the estimated effectiveness of aid is highly sensitive to the choice of estimator and the set of control variables. When investment and human capital are controlled for, no positive effect of aid is found. Yet, aid continues to impact on growth via...... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes....
Robust Nonstationary Regression
1993-01-01
This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed which allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities and they belong to the same ...
TWO REGRESSION CREDIBILITY MODELS
Constanţa-Nicoleta BODEA
2010-03-01
Full Text Available In this communication we will discuss two regression credibility models from Non – Life Insurance Mathematics that can be solved by means of matrix theory. In the first regression credibility model, starting from a well-known representation formula of the inverse for a special class of matrices a risk premium will be calculated for a contract with risk parameter θ. In the next regression credibility model, we will obtain a credibility solution in the form of a linear combination of the individual estimate (based on the data of a particular state and the collective estimate (based on aggregate USA data. To illustrate the solution with the properties mentioned above, we shall need the well-known representation theorem for a special class of matrices, the properties of the trace for a square matrix, the scalar product of two vectors, the norm with respect to a positive definite matrix given in advance and the complicated mathematical properties of conditional expectations and of conditional covariances.
Pelowski, Matthew; Markey, Patrick S.; Forster, Michael; Gerger, Gernot; Leder, Helmut
2017-07-01
This paper has a rather audacious purpose: to present a comprehensive theory explaining, and further providing hypotheses for the empirical study of, the multiple ways by which people respond to art. Despite common agreement that interaction with art can be based on a compelling, and occasionally profound, psychological experience, the nature of these interactions is still under debate. We propose a model, The Vienna Integrated Model of Art Perception (VIMAP), with the goal of resolving the multifarious processes that can occur when we perceive and interact with visual art. Specifically, we focus on the need to integrate bottom-up, artwork-derived processes, which have formed the bulk of previous theoretical and empirical assessments, with top-down mechanisms which can describe how individuals adapt or change within their processing experience, and thus how individuals may come to particularly moving, disturbing, transformative, as well as mundane, results. This is achieved by combining several recent lines of theoretical research into a new integrated approach built around three processing checks, which we argue can be used to systematically delineate the possible outcomes in art experience. We also connect our model's processing stages to specific hypotheses for emotional, evaluative, and physiological factors, and address main topics in psychological aesthetics including provocative reactions-chills, awe, thrills, sublime-and difference between ;aesthetic; and ;everyday; emotional response. Finally, we take the needed step of connecting stages to functional regions in the brain, as well as broader core networks that may coincide with the proposed cognitive checks, and which taken together can serve as a basis for future empirical and theoretical art research.
Embodied affectivity: On moving and being moved
Thomas eFuchs
2014-06-01
Full Text Available There is a growing body of research indicating that bodily sensation and behaviour strongly influences one’s emotional reaction towards certain situations or objects. On this background, a framework model of embodied affectivity is suggested: we regard emotions as resulting from the circular interaction between affective qualities or affordances in the environment and the subject’s bodily resonance, be it in the form of sensations, postures, expressive movements or movement tendencies. Motion and emotion are thus intrinsically connected: one is moved by movement (perception; impression; affection and moved to move (action; expression; e-motion. Through its resonance, the body functions as a medium of emotional perception: it colours or charges self-experience and the environment with affective valences while it remains itself in the background of one’s own awareness. This model is then applied to emotional social understanding or interaffectivity which is regarded as an intertwinement of two cycles of embodied affectivity, thus continuously modifying each partner’s affective affordances and bodily resonance. We conclude with considerations of how embodied affectivity is altered in psychopathology and can be addressed in psychotherapy of the embodied self.
DIFFUSION BACKGROUND MODEL FOR MOVING OBJECTS DETECTION
B. V. Vishnyakov
2015-05-01
Full Text Available In this paper, we propose a new approach for moving objects detection in video surveillance systems. It is based on construction of the regression diffusion maps for the image sequence. This approach is completely different from the state of the art approaches. We show that the motion analysis method, based on diffusion maps, allows objects that move with different speed or even stop for a short while to be uniformly detected. We show that proposed model is comparable to the most popular modern background models. We also show several ways of speeding up diffusion maps algorithm itself.
PARALLEL MOVING MECHANICAL SYSTEMS
Florian Ion Tiberius Petrescu
2014-09-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 Moving mechanical systems parallel structures are solid, fast, and accurate. Between parallel systems it is to be noticed Stewart platforms, as the oldest systems, fast, solid and precise. The work outlines a few main elements of Stewart platforms. Begin with the geometry platform, kinematic elements of it, and presented then and a few items of dynamics. Dynamic primary element on it means the determination mechanism kinetic energy of the entire Stewart platforms. It is then in a record tail cinematic mobile by a method dot matrix of rotation. If a structural mottoelement consists of two moving elements which translates relative, drive train and especially dynamic it is more convenient to represent the mottoelement as a single moving components. We have thus seven moving parts (the six motoelements or feet to which is added mobile platform 7 and one fixed.
Ayala, R.E.; Gupta, R.P.; Chuck, T.
1995-12-01
The objective of this program is to develop mixed-metal oxide sorbent formulations that are suitable for moving-bed, high-temperature, desulfurization of coal gas. Work continues on zinc titanates formulations and Z-sorb III sorbent.
Hansen, Lennard Højbjerg
2014-01-01
Every day we are presented with bodily expressions in audiovisual media – by anchors, journalists and characters in films for instance. This article explores how body language in the moving image has been and can be approached in a scholarly manner.......Every day we are presented with bodily expressions in audiovisual media – by anchors, journalists and characters in films for instance. This article explores how body language in the moving image has been and can be approached in a scholarly manner....
Hansen, Lennard Højbjerg
2014-01-01
Every day we are presented with bodily expressions in audiovisual media – by anchors, journalists and characters in films for instance. This article explores how body language in the moving image has been and can be approached in a scholarly manner.......Every day we are presented with bodily expressions in audiovisual media – by anchors, journalists and characters in films for instance. This article explores how body language in the moving image has been and can be approached in a scholarly manner....
Minimal sets of Reidemeister moves
Polyak, Michael
2009-01-01
It is well known that any two diagrams representing the same oriented link are related by a finite sequence of Reidemeister moves O1, O2 and O3. Depending on orientations of fragments involved in the moves, one may distinguish 4 different versions of each of the O1 and O2 moves, and 8 versions of the O3 move. We introduce a minimal generating set of oriented Reidemeister moves, which includes two moves of types O1 and O2, and only one move of type O3. We then consider other sets of moves and show that only few of them generate all Reidemeister moves.
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Karim Hardani*
2012-05-01
Full Text Available A 10-month-old baby presented with developmental delay. He had flaccid paralysis on physical examination.An MRI of the spine revealed malformation of the ninth and tenth thoracic vertebral bodies with complete agenesis of the rest of the spine down that level. The thoracic spinal cord ends at the level of the fifth thoracic vertebra with agenesis of the posterior arches of the eighth, ninth and tenth thoracic vertebral bodies. The roots of the cauda equina appear tightened down and backward and ended into a subdermal fibrous fatty tissue at the level of the ninth and tenth thoracic vertebral bodies (closed meningocele. These findings are consistent with caudal regression syndrome.
A moving-barber-pole illusion.
Sun, Peng; Chubb, Charles; Sperling, George
2014-05-01
In the barber-pole illusion (BPI), a diagonally moving grating is perceived as moving vertically because of the shape of the vertically oriented window through which it is viewed-a strong shape-motion interaction. We introduce a novel stimulus-the moving barber pole-in which a diagonal, drifting sinusoidal carrier is windowed by a raised, vertical, drifting sinusoidal modulator that moves independently of the carrier. In foveal vision, the moving-barber-pole stimulus can be perceived as several active barber poles drifting horizontally but also as other complex dynamic patterns. In peripheral vision, pure vertical motion (the moving-barber-pole illusion [MBPI]) is perceived for a wide range of conditions. In foveal vision, the MBPI is observed, but only when the higher-order modulator motion is masked. Theories to explain the BPI make indiscriminable predictions in a standard barber-pole display. But, in moving-barber-pole stimuli, the motion directions of features (e.g., end stops) of the first-order carrier and of the higher-order modulator are all different from the MBPI. High temporal frequency stimuli viewed peripherally greatly reduce the effectiveness of higher-order motion mechanisms and, ideally, isolate a single mechanism responsible for the MBPI. A three-stage motion-path integration mechanism that (a) computes local motion energies, (b) integrates them for a limited time period along various spatial paths, and (c) selects the path with the greatest motion energy, quantitatively accounts for these high-frequency data. The MBPI model also accounts for the perceived motion-direction in peripherally viewed moving-barber-pole stimuli that do and do not exhibit the MBPI over the entire range of modulator (0-10 Hz) and carrier (2.5-10 Hz) temporal frequencies tested.
Moving Spatial Keyword Queries
Wu, Dingming; Yiu, Man Lung; Jensen, Christian S.
2013-01-01
Web users and content are increasingly being geo-positioned. This development gives prominence to spatial keyword queries, which involve both the locations and textual descriptions of content. We study the efficient processing of continuously moving top-k spatial keyword (MkSK) queries over spatial...... text data. State-of-the-art solutions for moving queries employ safe zones that guarantee the validity of reported results as long as the user remains within the safe zone associated with a result. However, existing safe-zone methods focus solely on spatial locations and ignore text relevancy. We...
BOYLE, PAUL J.; KULU, HILL; COOKE, THOMAS; GAYLE, VERNON; MULDER, CLARAH.
2008-01-01
This paper examines the effect of migration and residential mobility on union dissolution among married and cohabiting couples. Moving is a stressful life event, and a large, multidisciplinary literature has shown that family migration often benefits one partner (usually the man) more than the other. Even so, no study to date has examined the possible impact of within-nation geographical mobility on union dissolution. We base our longitudinal analysis on retrospective event-history data from Austria. Our results show that couples who move frequently have a significantly higher risk of union dissolution, and we suggest a variety of mechanisms that may explain this. PMID:18390300
Bardram, Jakob Eyvind; Bossen, Claus
2003-01-01
actors move about continuously in order to accomplish their work. They do so because they need to get access to knowledge, resources, persons and/or places. We analyze the integral nature of mobility to this kind of work practice from the ethnographic description of a hospital department...
Performance of the Analog Moving Window Detector
Hansen, V. Gregers
1970-01-01
A type of analog integrating moving window detector for use with a scanning pulse radar is examined. A performance analysis is carried out, which takes into account both the radiation pattern of the antenna and the dynamic character of the detection process due to the angular scanning...
David-Tabibi, Omid
2008-01-01
In this article we review standard null-move pruning and introduce our extended version of it, which we call verified null-move pruning. In verified null-move pruning, whenever the shallow null-move search indicates a fail-high, instead of cutting off the search from the current node, the search is continued with reduced depth. Our experiments with verified null-move pruning show that on average, it constructs a smaller search tree with greater tactical strength in comparison to standard null-move pruning. Moreover, unlike standard null-move pruning, which fails badly in zugzwang positions, verified null-move pruning manages to detect most zugzwangs and in such cases conducts a re-search to obtain the correct result. In addition, verified null-move pruning is very easy to implement, and any standard null-move pruning program can use verified null-move pruning by modifying only a few lines of code.
Developmental regression, depression, and psychosocial stress in an adolescent with Down syndrome.
Stein, David S; Munir, Kerim M; Karweck, Andrea J; Davidson, Emily J; Stein, Martin T
2013-04-01
Kristen is a 13-year-old girl with Down syndrome (DS) who was seen urgently with concerns of cognitive and developmental regression including loss of language, social, and toileting skills. The evaluation in the DS clinic focused on potential medical diagnoses including atlantoaxial joint instability, vitamin deficiency, obstructive sleep apnea (OSA), and seizures. A comprehensive medical evaluation yielded only a finding of moderate OSA. A reactive depression was considered in association with several psychosocial factors including moving homes, entering puberty/onset of menses, and classroom change from an integrated setting to a self-contained classroom comprising unfamiliar peers with behavior challenges.Urgent referrals for psychological and psychiatric evaluations were initiated. Neuropsychological testing did not suggest true regression in cognitive, language, and academic skills, although decreases in motivation and performance were noted with a reaction to stress and multiple environmental changes as a potential causative factor. Psychiatry consultation supported this finding in that psychosocial stress temporally correlated with Kristen's regression in skills.Working collaboratively, the team determined that Kristen's presentation was consistent with a reactive form of depression (DSM-IV-TR: depressive disorder, not otherwise specified). Kristen's presentation was exacerbated by salient environmental stress and sleep apnea, rather than a cognitive regression associated with a medical cause. Treatment consisted of an antidepressant medication, continuous positive airway pressure for OSA, and increased psychosocial supports. Her school initiated a change in classroom placement. With this multimodal approach to evaluation and intervention, Kristen steadily improved and she returned to her baseline function.
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
Rennie, Richard
2015-01-01
The history of the moving image (the cinema) is well documented in books and on the Internet. This article offers a number of activities that can easily be carried out in a science class. They make use of the phenomenon of "Persistence of Vision." The activities presented herein demonstrate the functionality of the phenakistoscope, the…
Maximilien Brice
2002-01-01
DELPHI (DEtector for Lepton, Photon and Hadron Identification) was one of four experiments at CERN's Large Electron-Positron collider (LEP). Following LEP's decommissioning, the DELPHI detector has been moved within the cavern at Point 8, and now awaits permanent display.
Leitheiser, Erin
two, fell short of increased expectations. This is significant because it demonstrates how external factors alone can change notions and attributions of responsibility (move the goalpost) as well as the effort needed to meet these new expectations. Overall, this shift illustrates the further...
Mortensen, Kristian; Hazel, Spencer
2014-01-01
Opening an interaction is a crucial step in establishing and maintaining social relationships. In this paper we describe how participants in an institutional setting, a help desk counter for exchange students at an international university, literally move into interaction. This is accomplished...
Christensen, Mark Schram; Grünbaum, Thor
2017-01-01
In this chapter, we assume the existence of a sense of “movement activity” that arises when a person actively moves a body part. This sense is usually supposed to be part of sense of agency (SoA). The purpose of the chapter is to determine whether the already existing experimental paradigms can...
Geertzen, Jan H. B.
2008-01-01
Moving beyond Disability was the theme of the 12th World Congress of the International Society for Prosthetics and Orthotics. This paper is a reflection of one of the keynote lectures discussing the International Classification of Functioning, Disability and Health (ICF). Multicultural aspects in di
Simonsen, Gunvor
2008-01-01
as characterized by cultural diversity and variation. Moreover, the field has been haunted by a tendency of moving to easily from descriptive evidence to conclusions about African identity in the Americas. A promising way to overcome these problems, it is suggested, is to develop research that focuses on single...
Ainscow, Mel; Hopkins, David
1992-01-01
In many countries, education legislation embodies contradictory pressures for centralization and decentralization. In the United Kingdom, there is growing government control over policy and direction of schools; schools are also being given more responsibility for resource management. "Moving" schools within Improving the Quality of Education for…
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
Combining Alphas via Bounded Regression
Zura Kakushadze
2015-11-01
Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK
Ge Guangying; Chen Lili; Xu Jianjian
2005-01-01
Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.
Olwig, Karen Fog
2011-01-01
After a long history dominated by out-migration, Denmark, Norway and Sweden have, in the past 50 years, become immigration societies. This article compares how these Scandinavian welfare societies have sought to incorporate immigrants and refugees into their national communities. It suggests that......, while the countries have adopted disparate policies and ideologies, differences in the actual treatment and attitudes towards immigrants and refugees in everyday life are less clear, due to parallel integration programmes based on strong similarities in the welfare systems and in cultural notions...... of equality in the three societies. Finally, it shows that family relations play a central role in immigrants’ and refugees’ establishment of a new life in the receiving societies, even though the welfare society takes on many of the social and economic functions of the family....
Boyle, P.J.; Kulu, H.; Cooke, T.; Gayle, V.; Mulder, C.H.
2008-01-01
This paper examines the effect of migration and residential mobility on union dissolution among married and cohabiting couples. Moving is a stressful life event, and a large, multidisciplinary literature has shown that family migration often benefits one partner (usually the man) more than the other. Even so, no study to date has examined the possible impact of within-nation geographical mobility on union dissolution. We base our longitudinal analysis on retrospective event-history data from ...
Ana M. González Ramos
2013-07-01
Full Text Available The special issue Women on the Move that the reader holds in their hands is the result of the hard work of very creative specialists in gender and mobility. Research on mobility and gender has progressively advanced from traditional, non-gender-specific literature on migration or mobility of highly skilled people. And, as these authors prove, the topic is already quantitatively and qualitatively relevant.
2012-01-01
As part of the "Move! Eat better" campaign, Novae’s nutrition adviser, Irène Rolfo, will give a talk on the subject of everyday good nutrition. This will be held in the main building auditorium at 12:30 on Thursday, 20 September 2012. Don’t miss this informative event. For more information, go to http://cern.ch/bpmm
2005-01-01
The Transport Service pulled out all the stops and, more specifically, its fleet of moving and lifting equipment for the Discovery Monday on 6 June - a truly moving experience for all the visitors who took part ! Visitors could play at being machine operator, twiddling the controls of a lift truck fitted with a jib to lift a dummy magnet into a wooden mock-up of a beam-line.They had to show even greater dexterity for this game of lucky dip...CERN-style.Those with a head for heights took to the skies 20 m above ground in a telescopic boom lift.Children were allowed to climb up into the operator's cabin - this is one of the cranes used to move the LHC magnets around. Warm thanks to all members of the Transport Service for their participation, especially B. Goicoechea, T. Ilkei, R. Bihery, S. Prodon, S. Pelletier, Y. Bernard, A. Sallot, B. Pigeard, S. Guinchard, B. Bulot, J. Berrez, Y. Grandjean, A. Bouakkaz, M. Bois, F. Stach, T. Mazzarino and S. Fumey.
Time-adaptive quantile regression
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Polynomial Regression on Riemannian Manifolds
Hinkle, Jacob; Fletcher, P Thomas; Joshi, Sarang
2012-01-01
In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.
Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models
Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung
2015-01-01
Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Business applications of multiple regression
Richardson, Ronny
2015-01-01
This second edition of Business Applications of Multiple Regression describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in deta
Moving related to separation : who moves and to what distance
Mulder, Clara H.; Malmberg, Gunnar
2011-01-01
We address the issue of moving from the joint home on the occasion of separation. Our research question is: To what extent can the occurrence of moves related to separation, and the distance moved, be explained by ties to the location, resources, and other factors influencing the likelihood of movin
1999-01-01
Many people live away from their homes and communities. Worldwide, about 125 million people are migrant workers, immigrants, or refugees in search of education, employment, or safety, making them vulnerable to sexually transmitted infections (STIs), including HIV. Some practical approaches to HIV prevention with people on the move are delineated. These include: 1) the project in Niger describing its work with migrant peer educators; 2) a national program improving health services; 3) a program in India providing STI treatment and health information for truck drivers; 4) a South African HIV program, which includes activities within communities; and 5) HIV prevention programs for refugees in Tanzania and Mozambique.
Ghosh, Debarchana; Manson, Steven M
2008-01-01
In this paper, we present a hybrid approach, robust principal component geographically weighted regression (RPCGWR), in examining urbanization as a function of both extant urban land use and the effect of social and environmental factors in the Twin Cities Metropolitan Area (TCMA) of Minnesota. We used remotely sensed data to treat urbanization via the proxy of impervious surface. We then integrated two different methods, robust principal component analysis (RPCA) and geographically weighted regression (GWR) to create an innovative approach to model urbanization. The RPCGWR results show significant spatial heterogeneity in the relationships between proportion of impervious surface and the explanatory factors in the TCMA. We link this heterogeneity to the "sprawling" nature of urban land use that has moved outward from the core Twin Cities through to their suburbs and exurbs.
Building Excellence in Project Execution: Integrated Project Management
2015-04-30
the final moves of our personnel. This planning ensures no surprises and a smooth transition . Finally, we must close the books on the project...maybe regression test? These all must be considered when developing an integrated project schedule. We can deduce that sequencing is an important first...system or Information Assurance (third functional area) must be accounted for, as well as the logistics aspects, which include deployment
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Moving KML geometry elements within Google Earth
Zhu, Liang-feng; Wang, Xi-feng; Pan, Xin
2014-11-01
During the process of modeling and visualizing geospatial information on the Google Earth virtual globe, there is an increasing demand to carry out such operations as moving geospatial objects defined by KML geometry elements horizontally or vertically. Due to the absence of the functionality and user interface for performing the moving transformation, it is either hard or impossible to interactively move multiple geospatial objects only using the existing Google Earth desktop application, especially when the data sets are in large volume. In this paper, we present a general framework and associated implementation methods for moving multiple KML geometry elements within Google Earth. In our proposed framework, we first load KML objects into the Google Earth plug-in, and then extract KML geometry elements from the imported KML objects. Subsequently, we interactively control the movement distance along a specified orientation by employing a custom user interface, calculate the transformed geographic location for each KML geometry element, and adjust geographic coordinates of the points in each KML objects. And finally, transformed KML geometry elements can be displayed in Google Earth for 3D visualization and spatial analysis. A key advantage of the proposed framework is that it provides a simple, uniform and efficient user interface for moving multiple KML geometry elements within Google Earth. More importantly, the proposed framework and associated implementations can be conveniently integrated into other customizable Google Earth applications to support interactively visualizing and analyzing geospatial objects defined by KML geometry elements.
Multi-features Based Approach for Moving Shadow Detection
ZHOU Ning; ZHOU Man-li; XU Yi-ping; FANG Bao-hong
2004-01-01
In the video-based surveillance application, moving shadows can affect the correct localization and detection of moving objects. This paper aims to present a method for shadow detection and suppression used for moving visual object detection. The major novelty of the shadow suppression is the integration of several features including photometric invariant color feature, motion edge feature, and spatial feature etc. By modifying process for false shadow detected, the averaging detection rate of moving object reaches above 90% in the test of Hall-Monitor sequence.
Agarwal, Pankaj K.; Arge, Lars Allan; Erickson, Jeff
2003-01-01
also describe an indexing scheme in which the number of I/Os required to answer a query depends monotonically on the difference between the query time stamp t and the current time. Finally, we develop an efficient indexing scheme to answer approximate nearest-neighbor queries among moving points.......We propose three indexing schemes for storing a set S of N points in the plane, each moving along a linear trajectory, so that any query of the following form can be answered quickly: Given a rectangle R and a real value t, report all K points of S that lie inside R at time t. We first present...... an indexing structure that, for any given constant >0, uses O(N/B) disk blocks and answers a query in O((N/B)1/2+ +K/B) I/Os, where B is the block size. It can also report all the points of S that lie inside R during a given time interval. A point can be inserted or deleted, or the trajectory of a point can...
Anaïs Schaeffer
2012-01-01
This year, the CERN Medical Service is launching a new public health campaign. Advertised by the catchphrase “Move! & Eat Better”, the particular aim of the campaign is to encourage people at CERN to take more regular exercise, of whatever kind. The CERN annual relay race is scheduled on 24 May this year. The CERN Medical Service will officially launch its “Move! & Eat Better” campaign at this popular sporting event. “We shall be on hand on the day of the race to strongly advocate regular physical activity,” explains Rachid Belkheir, one of the Medical Service doctors. "We really want to pitch our campaign and answer any questions people may have. Above all we want to set an example. So we are going to walk the same circuit as the runners to underline to people that they can easily incorporate movement into their daily routine.” An underlying concern has prompted this campaign: during their first few year...
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record
Regression Testing Cost Reduction Suite
Mohamed Alaa El-Din
2014-08-01
Full Text Available The estimated cost of software maintenance exceeds 70 percent of total software costs [1], and large portion of this maintenance expenses is devoted to regression testing. Regression testing is an expensive and frequently executed maintenance activity used to revalidate the modified software. Any reduction in the cost of regression testing would help to reduce the software maintenance cost. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant when the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. This paper focuses on proposing an effective approach for reducing the cost of regression testing process. The proposed approach is applied on real-time case study. It was found that the reduction in cost of regression testing for each regression testing cycle is ranging highly improved in the case of programs containing high number of selected statements which in turn maximize the benefits of using it in regression testing of complex software systems. The reduction in the regression test suite size will reduce the effort and time required by the testing teams to execute the regression test suite. Since regression testing is done more frequently in software maintenance phase, the overall software maintenance cost can be reduced considerably by applying the proposed approach.
Move Ordering using Neural Networks
Kocsis, L.; Uiterwijk, J.; Van Den Herik, J.
2001-01-01
© Springer-Verlag Berlin Heidelberg 2001. The efficiency of alpha-beta search algorithms heavily depends on the order in which the moves are examined. This paper focuses on using neural networks to estimate the likelihood of a move being the best in a certain position. The moves considered more like
A Stepwise Time Series Regression Procedure for Water Demand Model Identification
Miaou, Shaw-Pin
1990-09-01
Annual time series water demand has traditionally been studied through multiple linear regression analysis. Four associated model specification problems have long been recognized: (1) the length of the available time series data is relatively short, (2) a large set of candidate explanatory or "input" variables needs to be considered, (3) input variables can be highly correlated with each other (multicollinearity problem), and (4) model error series are often highly autocorrelated or even nonstationary. A step wise time series regression identification procedure is proposed to alleviate these problems. The proposed procedure adopts the sequential input variable selection concept of stepwise regression and the "three-step" time series model building strategy of Box and Jenkins. Autocorrelated model error is assumed to follow an autoregressive integrated moving average (ARIMA) process. The stepwise selection procedure begins with a univariate time series demand model with no input variables. Subsequently, input variables are selected and inserted into the equation one at a time until the last entered variable is found to be statistically insignificant. The order of insertion is determined by a statistical measure called between-variable partial correlation. This correlation measure is free from the contamination of serial autocorrelation. Three data sets from previous studies are employed to illustrate the proposed procedure. The results are then compared with those from their original studies.
Rank regression: an alternative regression approach for data with outliers.
Chen, Tian; Tang, Wan; Lu, Ying; Tu, Xin
2014-10-01
Linear regression models are widely used in mental health and related health services research. However, the classic linear regression analysis assumes that the data are normally distributed, an assumption that is not met by the data obtained in many studies. One method of dealing with this problem is to use semi-parametric models, which do not require that the data be normally distributed. But semi-parametric models are quite sensitive to outlying observations, so the generated estimates are unreliable when study data includes outliers. In this situation, some researchers trim the extreme values prior to conducting the analysis, but the ad-hoc rules used for data trimming are based on subjective criteria so different methods of adjustment can yield different results. Rank regression provides a more objective approach to dealing with non-normal data that includes outliers. This paper uses simulated and real data to illustrate this useful regression approach for dealing with outliers and compares it to the results generated using classical regression models and semi-parametric regression models.
Banichuk, Nikolay; Neittaanmäki, Pekka; Saksa, Tytti; Tuovinen, Tero
2014-01-01
This book deals with theoretical aspects of modelling the mechanical behaviour of manufacturing, processing, transportation or other systems in which the processed or supporting material is travelling through the system. Examples of such applications include paper making, transmission cables, band saws, printing presses, manufacturing of plastic films and sheets, and extrusion of aluminium foil, textiles and other materials. The work focuses on out-of-plane dynamics and stability analysis for isotropic and orthotropic travelling elastic and viscoelastic materials, with and without fluid-structure interaction, using analytical and semi-analytical approaches. Also topics such as fracturing and fatigue are discussed in the context of moving materials. The last part of the book deals with optimization problems involving physical constraints arising from the stability and fatigue analyses, including uncertainties in the parameters. The book is intended for researchers and specialists in the field, providin...
2013-01-01
Are you curious to know whether you’re doing enough daily exercise…? Test yourself with a pedometer! Through the Move! Eat better campaign, launched in May 2012, the CERN medical service is aiming to improve the health of members of the personnel by encouraging them to prioritise physical activity in conjunction with a balanced diet. Various successful activities have already taken place: relay race/Nordic walk, Bike2work, Zumba and fitness workshops, two conferences (“Physical activity for health” and “Good nutrition every day”), events in the restaurants, as well as posters and a website. Although everyone has got the message from our various communications that physical activity is good for your health, there is still a relevant question being asked: “What is the minimum amount of exercise recommended?” 10,000 steps per day is the ideal figure, which has been demonstrated as beneficial by scientific studies ...
Rai, Sudhanshu
In this article I discuss the Indian outsourcing phenomena and ask the question now what? Using data from the Euro-India project I demonstrate that a small but significant part of the Indian IT entities are moving beyond outsourcing, to co-creation where Innovation and the desire to create new...... markets is the key driver. This does not imply that outsourcing will disappear but it does mean that firms will engage in globalization using innovative mix of business models and technical platforms. We discuss the implication of this slow transformation to co-creation of innovation for the global...... outsourcing industry. The key thesis of this paper is to discuss co-creation as a form of innovation and how such a form of innovation is likely to bring Indian companies rich dividends....
王舟强; 吴小琴
2015-01-01
目的：了解医院住院量的变动趋势，对医院出院人数进行预测分析，为科学决策提供依据。方法应用乘积季节ARIMA模型对某院2003年1月-2013年12月出院人数进行模型拟合，预测2014年各月出院人数，用2014年1月-6月份实际资料评估模型的预测效果。结果该院出院人数呈明显的季节效应，且出院人数逐年小幅递增；乘积季节ARIMA(1,1,1)×(0,1,1)12(不含常数项)模型为最优模型，标准化的BIC(标准化贝叶斯信息量)和平均绝对误差百分比(MAPE)值最小，BIC 值为11.98，MAPE 值为5.43。Ljung-Box检验无统计学意义（Q18=10.575,P=0.782）。结论乘积季节ARIMA模型可以较好地拟合出院人数的变化趋势，是一种短期预测精度较高的预测模型。%Objective To investigate inpatient quantity trend, forecast the number of discharged patients ,in order to provide basis for scientific decision.Methods ARIMA model was used to fit the number of discharged patients from January 2003 to December 2013 in the hospital by multiple seasonal autoregressive integrated moving average model,to predict the number of discharged patients from January to December 2014. The model was evaluated by actual data from January to June 2014. Results The seasonal effect in the number of discharged patients was observed in the hospital, and the incidence was slightly increased over time. Multiple seasonal1,1,1, 0,1,1ARIMA 12(have no constant) model has been found as the most suitable mode with least Normalized Bayesian Information Criteria(BIC)of 11.98 and Mean Absolute Percent Error(MAPE)of 5.43. The mode was further validated by LjungBox test(Q18=10.575,P=0.782). Conclusion Multiple seasonal ARIMAmodel can be used to fit the changes of the number of discharged and it is a predicted model of high precision for short time forecast.
ORDINAL REGRESSION FOR INFORMATION RETRIEVAL
无
2008-01-01
This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effectiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM significantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.
Multiple Regression and Its Discontents
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
Multiple Regression and Its Discontents
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
Boyte, Stephen P.; Wylie, Bruce K.; Major, Donald J.; Brown, Jesslyn F.
2015-01-01
Cheatgrass exhibits spatial and temporal phenological variability across the Great Basin as described by ecological models formed using remote sensing and other spatial data-sets. We developed a rule-based, piecewise regression-tree model trained on 99 points that used three data-sets – latitude, elevation, and start of season time based on remote sensing input data – to estimate cheatgrass beginning of spring growth (BOSG) in the northern Great Basin. The model was then applied to map the location and timing of cheatgrass spring growth for the entire area. The model was strong (R2 = 0.85) and predicted an average cheatgrass BOSG across the study area of 29 March–4 April. Of early cheatgrass BOSG areas, 65% occurred at elevations below 1452 m. The highest proportion of cheatgrass BOSG occurred between mid-April and late May. Predicted cheatgrass BOSG in this study matched well with previous Great Basin cheatgrass green-up studies.
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Wrong Signs in Regression Coefficients
McGee, Holly
1999-01-01
When using parametric cost estimation, it is important to note the possibility of the regression coefficients having the wrong sign. A wrong sign is defined as a sign on the regression coefficient opposite to the researcher's intuition and experience. Some possible causes for the wrong sign discussed in this paper are a small range of x's, leverage points, missing variables, multicollinearity, and computational error. Additionally, techniques for determining the cause of the wrong sign are given.
From Rasch scores to regression
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
Role of moving planes and moving spheres following Dupin cyclides
Jia, Xiaohong
2014-03-01
We provide explicit representations of three moving planes that form a μ-basis for a standard Dupin cyclide. We also show how to compute μ-bases for Dupin cyclides in general position and orientation from their implicit equations. In addition, we describe the role of moving planes and moving spheres in bridging between the implicit and rational parametric representations of these cyclides. © 2014 Elsevier B.V.
Enterprise Architecture Integration in E-government
Janssen, M.F.W.H.A.; Cresswell, A.
2005-01-01
Achieving goals of better integrated and responsive government services requires moving away from stand alone applications toward more comprehensive, integrated architectures. As a result there is mounting pressure to move from disparate systems operating in parallel toward a shared architecture
Della Mussia, S
2004-01-01
The first large active detector component was lowered into the ATLAS cavern on 1st March. It consisted of the 8 modules forming the lower part of the central barrel of the tile hadronic calorimeter. The work of assembling the barrel, which comprises 64 modules, started the following day. Two road trailers each with 64 wheels, positioned side by side. This was the solution chosen to transport the lower part of the central barrel of ATLAS' tile hadronic calorimeter from Building 185 to the PX16 shaft at Point 1 (see Figure 1). The transportation, and then the installation of the component in the experimental cavern, which took place over three days were, to say the least, rather spectacular. On 25 February, the component, consisting of eight 6-metre modules, was loaded on to the trailers. The segment of the barrel was transported on a steel support so that it wouldn't move an inch during the journey. On 26 February, once all the necessary safety checks had been carried out, the convoy was able to leave Buildi...
撒哈拉
2005-01-01
还怀念当年跳舞毯风靡的时光吗？左脚、右脚向前．向后，在不经意间展示你炫目的舞步。而最近PS2平台上的《EyeToy》让我们又接触到了一种需要游戏者舞动身体来参与的摄像头游戏．既玩了游戏又锻炼了身体．而昂达Eyebob摄像头让PC用户也能参与其中。想要玩玩摄像头游戏．现在不用转投Ps2的阵营．也不用改造你的硬件．只要你拥有一款摄像头．你就可以和家人与朋友轻松享受游戏的乐趣。来吧！Come on! Move your body now!
2012-01-01
CERN has many traditions, but in a week that’s seen the launch of the Medical Service’s ‘Move & eat better’ campaign, it’s refreshing to note that among the oldest is a sporting one. The CERN relay race dates back to 15 October 1971 when 21 pioneering teams set off to pound the pavements of CERN. Back then, the Focus users group came in first with a time of 12 minutes and 42 seconds. Today’s route is slightly different, and the number of teams has risen to over 100, with a new category of Nordic Walking introduced, as part of the campaign, for the first time. The relay has provided some memorable events, and perhaps one of the longest-standing records in the history of sport, with the UA1 strollers’ 10 minutes and 13 seconds unbeaten for thirty years. In the women’s category, the UN Gazelles set the fastest time of 13 minutes and 16 seconds in 1996, while in the veterans category, you wi...
A Matlab program for stepwise regression
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Dynamic Regression Intervention Modeling for the Malaysian Daily Load
Fadhilah Abdrazak
2014-05-01
Full Text Available Malaysia is a unique country due to having both fixed and moving holidays. These moving holidays may overlap with other fixed holidays and therefore, increase the complexity of the load forecasting activities. The errors due to holidays’ effects in the load forecasting are known to be higher than other factors. If these effects can be estimated and removed, the behavior of the series could be better viewed. Thus, the aim of this paper is to improve the forecasting errors by using a dynamic regression model with intervention analysis. Based on the linear transfer function method, a daily load model consists of either peak or average is developed. The developed model outperformed the seasonal ARIMA model in estimating the fixed and moving holidays’ effects and achieved a smaller Mean Absolute Percentage Error (MAPE in load forecast.
XRA image segmentation using regression
Jin, Jesse S.
1996-04-01
Segmentation is an important step in image analysis. Thresholding is one of the most important approaches. There are several difficulties in segmentation, such as automatic selecting threshold, dealing with intensity distortion and noise removal. We have developed an adaptive segmentation scheme by applying the Central Limit Theorem in regression. A Gaussian regression is used to separate the distribution of background from foreground in a single peak histogram. The separation will help to automatically determine the threshold. A small 3 by 3 widow is applied and the modal of the local histogram is used to overcome noise. Thresholding is based on local weighting, where regression is used again for parameter estimation. A connectivity test is applied to the final results to remove impulse noise. We have applied the algorithm to x-ray angiogram images to extract brain arteries. The algorithm works well for single peak distribution where there is no valley in the histogram. The regression provides a method to apply knowledge in clustering. Extending regression for multiple-level segmentation needs further investigation.
Biplots in Reduced-Rank Regression
Braak, ter C.J.F.; Looman, C.W.N.
1994-01-01
Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal c
Interpretation of Standardized Regression Coefficients in Multiple Regression.
Thayer, Jerome D.
The extent to which standardized regression coefficients (beta values) can be used to determine the importance of a variable in an equation was explored. The beta value and the part correlation coefficient--also called the semi-partial correlation coefficient and reported in squared form as the incremental "r squared"--were compared for…
Ghosh, Pranoy
2017-01-01
``A problem with defining force as rate of change of linear momentum'': Let us consider a body of mass m, moving with velocity u initially, in the next time interval it is acted by a force in the direction of motion, and at instant t + its mass is M and velocity v. F .t =Mv-mu or,v = m/M.u +F/M.t or,v =B.u +A.t where A =F/M,B =m/M. So other eqn of motion are: dS =vdt or dS =(B.u +A.t)dt or S =B.u.t +A/2.t2 Andv2 =B2 u2 +2A .B .u .t +A2 t2 or,v2 =B2 u2 +2A.S However, defining acceleration as rate of change of velocity, we have established an identity v =u +a.t which is independent of choice of v, u. M>>m, B is very small, product B.u or its higher power always tend to be negligible, even in cases when u is finitely large.In cases v ->c,F,M -> ∞ , thus A becomes indeterminate.There is inconvenience as A, B are not predetermined and are functions of u, v and thus the definition goes in circle. Hence we conclude, our hypothesis that force =rate of change of linear momentum is not sufficient; we would now find trial solutions to define force in most convenient way.
Inferential Models for Linear Regression
Zuoyi Zhang
2011-09-01
Full Text Available Linear regression is arguably one of the most widely used statistical methods in applications. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs in the linear regression context. In general, an IM is able to produce meaningful probabilistic summaries of the statistical evidence for and against assertions about the unknown parameter of interest and, moreover, these summaries are shown to be properly calibrated in a frequentist sense. Here we demonstrate, using simple examples, that the IM framework is promising for linear regression analysis --- including model checking, variable selection, and prediction --- and for uncertain inference in general.
[Is regression of atherosclerosis possible?].
Thomas, D; Richard, J L; Emmerich, J; Bruckert, E; Delahaye, F
1992-10-01
Experimental studies have shown the regression of atherosclerosis in animals given a cholesterol-rich diet and then given a normal diet or hypolipidemic therapy. Despite favourable results of clinical trials of primary prevention modifying the lipid profile, the concept of atherosclerosis regression in man remains very controversial. The methodological approach is difficult: this is based on angiographic data and requires strict standardisation of angiographic views and reliable quantitative techniques of analysis which are available with image processing. Several methodologically acceptable clinical coronary studies have shown not only stabilisation but also regression of atherosclerotic lesions with reductions of about 25% in total cholesterol levels and of about 40% in LDL cholesterol levels. These reductions were obtained either by drugs as in CLAS (Cholesterol Lowering Atherosclerosis Study), FATS (Familial Atherosclerosis Treatment Study) and SCOR (Specialized Center of Research Intervention Trial), by profound modifications in dietary habits as in the Lifestyle Heart Trial, or by surgery (ileo-caecal bypass) as in POSCH (Program On the Surgical Control of the Hyperlipidemias). On the other hand, trials with non-lipid lowering drugs such as the calcium antagonists (INTACT, MHIS) have not shown significant regression of existing atherosclerotic lesions but only a decrease on the number of new lesions. The clinical benefits of these regression studies are difficult to demonstrate given the limited period of observation, relatively small population numbers and the fact that in some cases the subjects were asymptomatic. The decrease in the number of cardiovascular events therefore seems relatively modest and concerns essentially subjects who were symptomatic initially. The clinical repercussion of studies of prevention involving a single lipid factor is probably partially due to the reduction in progression and anatomical regression of the atherosclerotic plaque
Satellite rainfall retrieval by logistic regression
Chiu, Long S.
1986-01-01
The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.
Logistic regression for circular data
Al-Daffaie, Kadhem; Khan, Shahjahan
2017-05-01
This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.
Quasi-least squares regression
Shults, Justine
2014-01-01
Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitu
Regression of lumbar disk herniation
G. Yu Evzikov
2015-01-01
Full Text Available Compression of the spinal nerve root, giving rise to pain and sensory and motor disorders in the area of its innervation is the most vivid manifestation of herniated intervertebral disk. Different treatment modalities, including neurosurgery, for evolving these conditions are discussed. There has been recent evidence that spontaneous regression of disk herniation can regress. The paper describes a female patient with large lateralized disc extrusion that has caused compression of the nerve root S1, leading to obvious myotonic and radicular syndrome. Magnetic resonance imaging has shown that the clinical manifestations of discogenic radiculopathy, as well myotonic syndrome and morphological changes completely regressed 8 months later. The likely mechanism is inflammation-induced resorption of a large herniated disk fragment, which agrees with the data available in the literature. A decision to perform neurosurgery for which the patient had indications was made during her first consultation. After regression of discogenic radiculopathy, there was only moderate pain caused by musculoskeletal diseases (facet syndrome, piriformis syndrome that were successfully eliminated by minimally invasive techniques.
Heteroscedasticity checks for regression models
无
2001-01-01
For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in parametric and nonparametric regression models. For nonparametric regression, the test is not affected sensitively by the choice of smoothing parameters which are involved in estimation of the nonparametric regression function. The limiting null distribution of the test statistic remains the same in a wide range of the smoothing parameters. When the covariate is one-dimensional, the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap approximations is investigated. It is shown that a variant of the wild bootstrap is consistent while the classical bootstrap is not in the general case, but is applicable if some extra assumption on conditional variance of the squared error is imposed. A simulation study is performed to provide evidence of how the tests work and compare with tests that have appeared in the literature. The approach may readily be extended to handle partial linear, and linear autoregressive models.
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Growth Regression and Economic Theory
Elbers, Chris; Gunning, Jan Willem
2002-01-01
In this note we show that the standard, loglinear growth regression specificationis consistent with one and only one model in the class of stochastic Ramsey models. Thismodel is highly restrictive: it requires a Cobb-Douglas technology and a 100% depreciationrate and it implies that risk does not af
Correlation Weights in Multiple Regression
Waller, Niels G.; Jones, Jeff A.
2010-01-01
A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…
Ridge Regression for Interactive Models.
Tate, Richard L.
1988-01-01
An exploratory study of the value of ridge regression for interactive models is reported. Assuming that the linear terms in a simple interactive model are centered to eliminate non-essential multicollinearity, a variety of common models, representing both ordinal and disordinal interactions, are shown to have "orientations" that are favorable to…
Repetition priming from moving faces.
Lander, Karen; Bruce, Vicki
2004-06-01
Recent experiments have suggested that seeing a familiar face move provides additional dynamic information to the viewer, useful in the recognition of identity. In four experiments, repetition priming was used to investigate whether dynamic information is intrinsic to the underlying face representations. The results suggest that a moving image primes more effectively than a static image, even when the same static image is shown in the prime and the test phases (Experiment 1). Furthermore, when moving images are presented in the test phase (Experiment 2), there is an advantage for moving prime images. The most priming advantage is found with naturally moving faces, rather than with those shown in slow motion (Experiment 3). Finally, showing the same moving sequence at prime and test produced more priming than that found when different moving sequences were shown (Experiment 4). The results suggest that dynamic information is intrinsic to the face representations and that there is an advantage to viewing the same moving sequence at prime and test.
Congestion and residential moving behaviour
Larsen, Morten Marott; Pilegaard, Ninette; Van Ommeren, Jos
2008-01-01
we study how congestion and residential moving behaviour are interrelated, using a two-region job search model. Workers choose between interregional commuting and residential moving, in order to live closer to their place of work. This choice affects the external costs of commuting, due...
Moving sound source localization based on triangulation method
Miao, Feng; Yang, Diange; Wen, Junjie; Lian, Xiaomin
2016-12-01
This study develops a sound source localization method that extends traditional triangulation to moving sources. First, the possible sound source locating plane is scanned. Secondly, for each hypothetical source location in this possible plane, the Doppler effect is removed through the integration of sound pressure. Taking advantage of the de-Dopplerized signals, the moving time difference of arrival (MTDOA) is calculated, and the sound source is located based on triangulation. Thirdly, the estimated sound source location is compared to the original hypothetical location and the deviations are recorded. Because the real sound source location leads to zero deviation, the sound source can be finally located by minimizing the deviation matrix. Simulations have shown the superiority of MTDOA method over traditional triangulation in case of moving sound sources. The MTDOA method can be used to locate moving sound sources with as high resolution as DAMAS beamforming, as shown in the experiments, offering thus a new method for locating moving sound sources.
Effectively Indexing Uncertain Moving Objects for Predictive Queries
Zhang, Meihui; Chen, Su; Jensen, Christian Søndergaard
2009-01-01
Moving object indexing and query processing is a well studied research topic, with applications in areas such as intelligent transport systems and location-based services. While much existing work explicitly or implicitly assumes a deterministic object movement model, real-world objects often move...... in more complex and stochastic ways. This paper investigates the possibility of a marriage between moving-object indexing and probabilistic object modelling. Given the distributions of the current locations and velocities of moving objects, we devise an efficient inference method for the prediction...... of future locations. We demonstrate that such prediction can be seamlessly integrated into existing index structures designed for moving objects, thus improving the meaningfulness of range and nearest neighbor query results in highly dynamic and uncertain environments. The paper reports on extensive...
Logistic regression: a brief primer.
Stoltzfus, Jill C
2011-10-01
Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable's unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum "rules of thumb" ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model's internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model
2007-01-01
The offices of the Social Service are now on the 1st floor of Building 33 (Reception), exactly one floor above the old location. We remind you that the team, consisting of two social workers, a psychologist (external consultant, 1 day/week) and an administrative assistant, is at the disposal of all members of the personnel, whatever their status, as well as to their family members. Advice and support in the following areas are offered : · information on integration in the local area; · assistance in dealing with the relevant authorities/services; · consultations with a view to resolving problems of a personal, family or professional nature, such as problems of dependency (alcohol, drugs) relationship or behavioral problems (stress, depression, eating disorders), etc.; · support in facing new situations (maternity, divorce, bereavement, job change, separation from family/familiar surroundings); · assistance with decision making relating to family, personal or profes...
Zabala, Francisco; Polidoro, Peter; Robie, Alice; Branson, Kristin; Perona, Pietro; Dickinson, Michael H
2012-07-24
An important role of visual systems is to detect nearby predators, prey, and potential mates, which may be distinguished in part by their motion. When an animal is at rest, an object moving in any direction may easily be detected by motion-sensitive visual circuits. During locomotion, however, this strategy is compromised because the observer must detect a moving object within the pattern of optic flow created by its own motion through the stationary background. However, objects that move creating back-to-front (regressive) motion may be unambiguously distinguished from stationary objects because forward locomotion creates only front-to-back (progressive) optic flow. Thus, moving animals should exhibit an enhanced sensitivity to regressively moving objects. We explicitly tested this hypothesis by constructing a simple fly-sized robot that was programmed to interact with a real fly. Our measurements indicate that whereas walking female flies freeze in response to a regressively moving object, they ignore a progressively moving one. Regressive motion salience also explains observations of behaviors exhibited by pairs of walking flies. Because the assumptions underlying the regressive motion salience hypothesis are general, we suspect that the behavior we have observed in Drosophila may be widespread among eyed, motile organisms.
Huang, Lei
2015-09-30
To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required.
Geloni, Gianluca [European XFEL GmbH, Hamburg (Germany); Kocharyan, Vitali; Saldin, Evgeni [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
2017-04-15
It is generally accepted that in order to describe the dynamics of relativistic particles in the laboratory (lab) frame it is sufficient to take into account the relativistic dependence of the particle momenta on the velocity. This solution of the dynamics problem in the lab frame makes no reference to Lorentz transformations. For this reason they are not discussed in particle tracking calculations in accelerator and plasma physics. It is generally believed that the electrodynamics problem can be treated within the same ''single inertial frame'' description without reference to Lorentz transformations. In particular, in order to evaluate radiation fields arising from charged particles in motion we need to know their velocities and positions as a function of the lab frame time t. The relativistic motion of a particle in the lab frame is described by Newton's second law ''corrected'' for the relativistic dependence of momentum on velocity. It is assumed in all standard derivations that one can perform identification of the trajectories in the source part of the usual Maxwell's equations with the trajectories vector x(t) measured (or calculated by using the corrected Newton's second law) in the lab frame. This way of coupling fields and particles is considered since more than a century as the relativistically correct procedure.We argue that this procedure needs to be changed, and we demonstrate the following, completely counterintuitive statement: the results of conventional theory of radiation by relativistically moving charges are not consistent with the principle of relativity. In order to find the trajectory of a particle in the lab frame consistent with the usual Maxwell's equations, one needs to solve the dynamic equation inmanifestly covariant form by using the coordinate-independent proper time τ to parameterize the particle world-line in space-time. We show that there is a difference between &apos
Regression Verification Using Impact Summaries
Backes, John; Person, Suzette J.; Rungta, Neha; Thachuk, Oksana
2013-01-01
Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program
FBH1 Catalyzes Regression of Stalled Replication Forks
Fugger, Kasper; Mistrik, Martin; Neelsen, Kai J
2015-01-01
DNA replication fork perturbation is a major challenge to the maintenance of genome integrity. It has been suggested that processing of stalled forks might involve fork regression, in which the fork reverses and the two nascent DNA strands anneal. Here, we show that FBH1 catalyzes regression...... a model whereby FBH1 promotes early checkpoint signaling by remodeling of stalled DNA replication forks....... of a model replication fork in vitro and promotes fork regression in vivo in response to replication perturbation. Cells respond to fork stalling by activating checkpoint responses requiring signaling through stress-activated protein kinases. Importantly, we show that FBH1, through its helicase activity...
R B Magar; V Jothiprakash
2011-12-01
In this study, multi-linear regression (MLR) approach is used to construct intermittent reservoir daily inflow forecasting system. To illustrate the applicability and effect of using lumped and distributed input data in MLR approach, Koyna river watershed in Maharashtra, India is chosen as a case study. The results are also compared with autoregressive integrated moving average (ARIMA) models. MLR attempts to model the relationship between two or more independent variables over a dependent variable by fitting a linear regression equation. The main aim of the present study is to see the consequences of development and applicability of simple models, when sufficient data length is available. Out of 47 years of daily historical rainfall and reservoir inflow data, 33 years of data is used for building the model and 14 years of data is used for validating the model. Based on the observed daily rainfall and reservoir inflow, various types of time-series, cause-effect and combined models are developed using lumped and distributed input data. Model performance was evaluated using various performance criteria and it was found that as in the present case, of well correlated input data, both lumped and distributed MLR models perform equally well. For the present case study considered, both MLR and ARIMA models performed equally sound due to availability of large dataset.
Polynomial Regressions and Nonsense Inference
Daniel Ventosa-Santaulària
2013-11-01
Full Text Available Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340. by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions.
Producing The New Regressive Left
Crone, Christine
to be a committed artist, and how that translates into supporting al-Assad’s rule in Syria; the Ramadan programme Harrir Aqlak’s attempt to relaunch an intellectual renaissance and to promote religious pluralism; and finally, al-Mayadeen’s cooperation with the pan-Latin American TV station TeleSur and its ambitions...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...... coalition (Iran, Hizbollah, Syria), capitalises on a series of factors that bring them together in spite of their otherwise diverse worldviews and agendas. The New Regressive Left is united by resistance against the growing influence of Saudi Arabia in the religious, cultural, political, economic...
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Heteroscedasticity checks for regression models
ZHU; Lixing
2001-01-01
［1］Carroll, R. J., Ruppert, D., Transformation and Weighting in Regression, New York: Chapman and Hall, 1988.［2］Cook, R. D., Weisberg, S., Diagnostics for heteroscedasticity in regression, Biometrika, 1988, 70: 1—10.［3］Davidian, M., Carroll, R. J., Variance function estimation, J. Amer. Statist. Assoc., 1987, 82: 1079—1091.［4］Bickel, P., Using residuals robustly I: Tests for heteroscedasticity, Ann. Statist., 1978, 6: 266—291.［5］Carroll, R. J., Ruppert, D., On robust tests for heteroscedasticity, Ann. Statist., 1981, 9: 205—209.［6］Eubank, R. L., Thomas, W., Detecting heteroscedasticity in nonparametric regression, J. Roy. Statist. Soc., Ser. B, 1993, 55: 145—155.［7］Diblasi, A., Bowman, A., Testing for constant variance in a linear model, Statist. and Probab. Letters, 1997, 33: 95—103.［8］Dette, H., Munk, A., Testing heteoscedasticity in nonparametric regression, J. R. Statist. Soc. B, 1998, 60: 693—708.［9］Müller, H. G., Zhao, P. L., On a semi-parametric variance function model and a test for heteroscedasticity, Ann. Statist., 1995, 23: 946—967.［10］Stute, W., Manteiga, G., Quindimil, M. P., Bootstrap approximations in model checks for regression, J. Amer. Statist. Asso., 1998, 93: 141—149.［11］Stute, W., Thies, G., Zhu, L. X., Model checks for regression: An innovation approach, Ann. Statist., 1998, 26: 1916—1939.［12］Shorack, G. R., Wellner, J. A., Empirical Processes with Applications to Statistics, New York: Wiley, 1986.［13］Efron, B., Bootstrap methods: Another look at the jackknife, Ann. Statist., 1979, 7: 1—26.［14］Wu, C. F. J., Jackknife, bootstrap and other re-sampling methods in regression analysis, Ann. Statist., 1986, 14: 1261—1295.［15］H rdle, W., Mammen, E., Comparing non-parametric versus parametric regression fits, Ann. Statist., 1993, 21: 1926—1947.［16］Liu, R. Y., Bootstrap procedures under some non-i.i.d. models, Ann. Statist., 1988, 16: 1696—1708.［17
Clustered regression with unknown clusters
Barman, Kishor
2011-01-01
We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion relationships are unknown. The regression relation- ships define the experiment clusters, and in general, the predictor and response variables may not exhibit any clus- tering. We call this prediction problem clustered regres- sion with unknown clusters (CRUC) and in this paper we focus on linear regression. We study and compare several methods for CRUC, demonstrate their applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in- vestigate an associated mathematical model. CRUC is at the crossroads of many prior works and we study several prediction algorithms with diverse origins: an adaptation of the expectation-maximization algorithm, an approach in- spired by K-means clustering, the singular value threshold- ing approach to matrix rank minimization u...
Robust nonlinear regression in applications
Lim, Changwon; Sen, Pranab K.; Peddada, Shyamal D.
2013-01-01
Robust statistical methods, such as M-estimators, are needed for nonlinear regression models because of the presence of outliers/influential observations and heteroscedasticity. Outliers and influential observations are commonly observed in many applications, especially in toxicology and agricultural experiments. For example, dose response studies, which are routinely conducted in toxicology and agriculture, sometimes result in potential outliers, especially in the high dose gr...
Risk assessment of dengue fever in Zhongshan, China: a time-series regression tree analysis.
Liu, K-K; Wang, T; Huang, X-D; Wang, G-L; Xia, Y; Zhang, Y-T; Jing, Q-L; Huang, J-W; Liu, X-X; Lu, J-H; Hu, W-B
2017-02-01
Dengue fever (DF) is the most prevalent and rapidly spreading mosquito-borne disease globally. Control of DF is limited by barriers to vector control and integrated management approaches. This study aimed to explore the potential risk factors for autochthonous DF transmission and to estimate the threshold effects of high-order interactions among risk factors. A time-series regression tree model was applied to estimate the hierarchical relationship between reported autochthonous DF cases and the potential risk factors including the timeliness of DF surveillance systems (median time interval between symptom onset date and diagnosis date, MTIOD), mosquito density, imported cases and meteorological factors in Zhongshan, China from 2001 to 2013. We found that MTIOD was the most influential factor in autochthonous DF transmission. Monthly autochthonous DF incidence rate increased by 36·02-fold [relative risk (RR) 36·02, 95% confidence interval (CI) 25·26-46·78, compared to the average DF incidence rate during the study period] when the 2-month lagged moving average of MTIOD was >4·15 days and the 3-month lagged moving average of the mean Breteau Index (BI) was ⩾16·57. If the 2-month lagged moving average MTIOD was between 1·11 and 4·15 days and the monthly maximum diurnal temperature range at a lag of 1 month was <9·6 °C, the monthly mean autochthonous DF incidence rate increased by 14·67-fold (RR 14·67, 95% CI 8·84-20·51, compared to the average DF incidence rate during the study period). This study demonstrates that the timeliness of DF surveillance systems, mosquito density and diurnal temperature range play critical roles in the autochthonous DF transmission in Zhongshan. Better assessment and prediction of the risk of DF transmission is beneficial for establishing scientific strategies for DF early warning surveillance and control.
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Analysis of moving bottlenecks considering a triangular fundamental diagram
Karim Fadhloun
2016-10-01
Full Text Available A significant number of research efforts have studied and analyzed the case in which a vehicle is moving slower than the traffic stream. This phenomenon, known as a moving bottleneck, results in a disruption of traffic flow and may significantly impact the traffic stream behavior upstream, downstream and abreast the slow moving vehicle. In this paper, a macroscopic approach for modeling moving bottlenecks is developed using microscopically derived data considering a triangular fundamental diagram. The passing flow rates of different moving bottleneck scenarios are determined using a previously developed microscopic model based on simulated data derived from the INTEGRATION software. Using the simulation results, an explicit expression of the bottleneck diagram, a flow-density relationship that defines the phenomenon macroscopically is proposed and the behavior of the traffic stream downstream and abreast the moving obstruction is depicted. It is demonstrated that the behavior of the traffic stream downstream of the slow vehicle as well as the acceleration behavior while passing is governed by the demand level. Such a result is coherent and consistent, to a significant extent, with two decades of research related to modeling moving bottlenecks and constitutes a potential feasible and more detailed description of the phenomenon in the case of a triangular fundamental diagram. Finally, it is noteworthy that the research subject of this paper could be considered as a first step in developing a numerical and practitioner-friendly framework for the analysis of moving bottlenecks that does not involve approaching the problem from its theoretical perspective.
Gaussian moving averages and semimartingales
Basse-O'Connor, Andreas
2008-01-01
In the present paper we study moving averages (also known as stochastic convolutions) driven by a Wiener process and with a deterministic kernel. Necessary and sufficient conditions on the kernel are provided for the moving average to be a semimartingale in its natural filtration. Our results...... are constructive - meaning that they provide a simple method to obtain kernels for which the moving average is a semimartingale or a Wiener process. Several examples are considered. In the last part of the paper we study general Gaussian processes with stationary increments. We provide necessary and sufficient...
Genetics Home Reference: caudal regression syndrome
... Twitter Home Health Conditions caudal regression syndrome caudal regression syndrome Enable Javascript to view the expand/collapse ... Download PDF Open All Close All Description Caudal regression syndrome is a disorder that impairs the development ...
Brady, Lois Jean; Gonzalez, America X.; Zawadzki, Maciej; Presley, Corinda
2012-01-01
This practical resource is brimming with ideas and guidance for using simple ideas from speech and language pathology and occupational therapy to boost communication, sensory integration, and coordination skills in children on the autism spectrum. Suitable for use in the classroom, at home, and in community settings, it is packed with…
Brady, Lois Jean; Gonzalez, America X.; Zawadzki, Maciej; Presley, Corinda
2012-01-01
This practical resource is brimming with ideas and guidance for using simple ideas from speech and language pathology and occupational therapy to boost communication, sensory integration, and coordination skills in children on the autism spectrum. Suitable for use in the classroom, at home, and in community settings, it is packed with…
Towards Accurate Modeling of Moving Contact Lines
Holmgren, Hanna
2015-01-01
A main challenge in numerical simulations of moving contact line problems is that the adherence, or no-slip boundary condition leads to a non-integrable stress singularity at the contact line. In this report we perform the first steps in developing the macroscopic part of an accurate multiscale model for a moving contact line problem in two space dimensions. We assume that a micro model has been used to determine a relation between the contact angle and the contact line velocity. An intermediate region is introduced where an analytical expression for the velocity exists. This expression is used to implement boundary conditions for the moving contact line at a macroscopic scale, along a fictitious boundary located a small distance away from the physical boundary. Model problems where the shape of the interface is constant thought the simulation are introduced. For these problems, experiments show that the errors in the resulting contact line velocities converge with the grid size $h$ at a rate of convergence $...
Rehabilitation Counselor Certification: Moving Forward
Saunders, Jodi L.; Barros-Bailey, Mary; Chapman, Cindy; Nunez, Patricia
2009-01-01
This article provides a brief history of the Commission on Rehabilitation Counselor Certification and presents recent changes and strategic goals for moving forward. Challenges and opportunities for the profession in relation to certification are also discussed. (Contains 3 tables.)
Minimum Delay Moving Object Detection
Lao, Dong
2017-01-08
We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames, more than two frames are needed to reliably detect the object. Our method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.
Transient heating of moving objects
E.I. Baida
2014-06-01
Full Text Available A mathematical model of transient and quasistatic heating of moving objects by various heat sources is considered. The mathematical formulation of the problem is described, examples of thermal calculation given.
Autowaves in moving excitable media
V.A.Davydov
2004-01-01
Full Text Available Within the framework of kinematic theory of autowaves we suggest a method for analytic description of stationary autowave structures appearing at the boundary between the moving and fixed excitable media. The front breakdown phenomenon is predicted for such structures. Autowave refraction and, particulary, one-side "total reflection" at the boundary is considered. The obtained analytical results are confirmed by computer simulations. Prospects of the proposed method for further studies of autowave dynamics in the moving excitable media are discussed.
On Weighted Support Vector Regression
Han, Xixuan; Clemmensen, Line Katrine Harder
2014-01-01
We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...
1992-09-30
NAFTA That Fails to Integrate Internationally Binding Health, Safety and Environmental Safeguards With GATT Principles of Free Trade By Ann M...Under the GATT .......................... 73 C. NAFTA Negotiations .................................................................. 82 IV...to Fuel Trade-Pact Debate, N.Y. Times, May 8, 1991, at D2, col. 5. 19Note, supra note 3, at 891. 5 North American Free Trade Agreement ( NAFTA ).20 The
Kleibergen, F.R.
2004-01-01
We obtain the prior and posterior probability of a nested regression model as the Hausdorff-integral of the prior and posterior on the parameters of an encompassing linear regression model over a lower-dimensional set that represents the nested model. The Hausdorff-integral is invariant and
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
FBH1 Catalyzes Regression of Stalled Replication Forks
Kasper Fugger
2015-03-01
Full Text Available DNA replication fork perturbation is a major challenge to the maintenance of genome integrity. It has been suggested that processing of stalled forks might involve fork regression, in which the fork reverses and the two nascent DNA strands anneal. Here, we show that FBH1 catalyzes regression of a model replication fork in vitro and promotes fork regression in vivo in response to replication perturbation. Cells respond to fork stalling by activating checkpoint responses requiring signaling through stress-activated protein kinases. Importantly, we show that FBH1, through its helicase activity, is required for early phosphorylation of ATM substrates such as CHK2 and CtIP as well as hyperphosphorylation of RPA. These phosphorylations occur prior to apparent DNA double-strand break formation. Furthermore, FBH1-dependent signaling promotes checkpoint control and preserves genome integrity. We propose a model whereby FBH1 promotes early checkpoint signaling by remodeling of stalled DNA replication forks.
Regression model for tuning the PID controller with fractional order time delay system
S.P. Agnihotri; Laxman Madhavrao Waghmare
2014-01-01
In this paper a regression model based for tuning proportional integral derivative (PID) controller with fractional order time delay system is proposed. The novelty of this paper is that tuning parameters of the fractional order time delay system are optimally predicted using the regression model. In the proposed method, the output parameters of the fractional order system are used to derive the regression function. Here, the regression model depends on the weights of the exponential function...
Minimum Delay Moving Object Detection
Lao, Dong
2017-05-14
This thesis presents a general framework and method for detection of an object in a video based on apparent motion. The object moves, at some unknown time, differently than the “background” motion, which can be induced from camera motion. The goal of proposed method is to detect and segment the object as soon it moves in an online manner. Since motion estimation can be unreliable between frames, more than two frames are needed to reliably detect the object. Observing more frames before declaring a detection may lead to a more accurate detection and segmentation, since more motion may be observed leading to a stronger motion cue. However, this leads to greater delay. The proposed method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms, defined as declarations of detection before the object moves or incorrect or inaccurate segmentation at the detection time. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.
Moving target imaging using ultrawideband synthetic aperture radar
Guo, Hanwei; Liang, Diannong; Wan, Yan; Huang, Xiaotao; Dong, Zhen
2003-09-01
Moving Target High Resolution Imaging of Foliage Penetrate Ultra-Wide Band Synthetic Aperture Radar (FOPEN UWB SAR) is of great significance for battlefield awareness of concealed target. Great range migration and strong clutter make moving target detection and imaging difficult, especially the Signal to Clutter Ration(SCR) some times is so low that the moving targets is invisible in FOPEN UWB SAR imagery. To improve SCR, the clean technique is used in range compressed data domain. The clean technique and data reconstruction help single channel of FOPEN UWB SAR suppress strong tree clutter and stationary target signal from region of interest. A new definition called General Key-Stone Transform is given, which can correct any order of range migration. FOPEN UWB SAR has long integrated time. The plane and target moving in long time lead to complex range migration. To obtain high resolution imagery of moving target, General Key-Stone transform are applied to remove the range migration and realize multiple moving target data segment. Both General Key-Stone Transform and Clean Technique are applied in real data processing of FOPEN UWB SAR. The result shows that multiple moving targets in the trees are clearly detected and high resolution imagery is formed.
A MOVING CRACK IN A NONHOMOGENEOUS MATERIAL STRIP
Wang Baolin; Han Jiecai
2006-01-01
This paper considers an anti-plane moving crack in a nonhomogencous material strip of finite thickness. The shear modulus and the mass density of the strip are considered for a class of functional forms for which the equilibrium equation has analytical solutions. The problem is solved by means of the singular integral equation technique. The stress field near the crack tip is obtained. The results are plotted to show the effect of the material non-homogeneity and crack moving velocity on the crack tip field. Crack bifurcation behaviour is also discussed. The paper points out that use of an appropriate fracture criterion is essential for studying the stability of a moving crack in nonhomogeneous materials. The prediction whether the unstable crack growth will be enhanced or retarded is strongly dependent on the type of the fracture criterion used. is a suitable failure criterion for moving cracks in nonhomogeneous materials.
Prediction, Regression and Critical Realism
Næss, Petter
2004-01-01
This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... of prediction necessary and possible in spatial planning of urban development. Finally, the political implications of positions within theory of science rejecting the possibility of predictions about social phenomena are addressed....... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...
Nonparametric Regression with Common Shocks
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Practical Session: Multiple Linear Regression
Clausel, M.; Grégoire, G.
2014-12-01
Three exercises are proposed to illustrate the simple linear regression. In the first one investigates the influence of several factors on atmospheric pollution. It has been proposed by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr33.pdf) and is based on data coming from 20 cities of U.S. Exercise 2 is an introduction to model selection whereas Exercise 3 provides a first example of analysis of variance. Exercises 2 and 3 have been proposed by A. Dalalyan at ENPC (see Exercises 2 and 3 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_5.pdf).
Lumbar herniated disc: spontaneous regression
Yüksel, Kasım Zafer
2017-01-01
Background Low back pain is a frequent condition that results in substantial disability and causes admission of patients to neurosurgery clinics. To evaluate and present the therapeutic outcomes in lumbar disc hernia (LDH) patients treated by means of a conservative approach, consisting of bed rest and medical therapy. Methods This retrospective cohort was carried out in the neurosurgery departments of hospitals in Kahramanmaraş city and 23 patients diagnosed with LDH at the levels of L3−L4, L4−L5 or L5−S1 were enrolled. Results The average age was 38.4 ± 8.0 and the chief complaint was low back pain and sciatica radiating to one or both lower extremities. Conservative treatment was administered. Neurological examination findings, durations of treatment and intervals until symptomatic recovery were recorded. Laségue tests and neurosensory examination revealed that mild neurological deficits existed in 16 of our patients. Previously, 5 patients had received physiotherapy and 7 patients had been on medical treatment. The number of patients with LDH at the level of L3−L4, L4−L5, and L5−S1 were 1, 13, and 9, respectively. All patients reported that they had benefit from medical treatment and bed rest, and radiologic improvement was observed simultaneously on MRI scans. The average duration until symptomatic recovery and/or regression of LDH symptoms was 13.6 ± 5.4 months (range: 5−22). Conclusions It should be kept in mind that lumbar disc hernias could regress with medical treatment and rest without surgery, and there should be an awareness that these patients could recover radiologically. This condition must be taken into account during decision making for surgical intervention in LDH patients devoid of indications for emergent surgery. PMID:28119770
Credit Scoring Problem Based on Regression Analysis
Khassawneh, Bashar Suhil Jad Allah
2014-01-01
ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....
Multivariate Adaptive Regression Splines (Preprint)
1990-08-01
characteristics of olive oils as a function of production year by multivariate methods. La Revista Italiana delle Sostanze Grasse, 60, Oct. . Friedman, J...Projection pursuit 76, 817-823. Friedman, J. H. and Wright, M. J. (1981). A nested partitioni: integration. ACM Trans. Math. Software, March. simonious...data. Proc. 1964 ACM Nat. Conf., 517-524. Shumaker, L. L. (1976). Fitting surfaces to scattered data. In Approximation Theory III, G. G. Lorentz, C
on the move. In seven chapters, Nordic researchers from various disciplines, by means of ethnographic methods, attempt to comprehend the phenomenon of Nordic seniors who move to leisure areas in their own or in other countries. The number of people involved in this kind of migratory movement has grown...... for identities, traditions, feelings of belonging, family and friends, health, images of old age, societal planning and policies, and even for religious attachment. The book presents a joint statement, intended for international scholars in the field, but also for Nordic policymakers and practitioners involved...
Explaining immigrants’ moves into homeownership
Andersen, Hans Skifter
, employment and family situation, and actual changes, but the importance of these factors differ from Danes. Different immigrant groups have a somewhat lower propensity to move into homeownership than Danes, which only to some extent can be explained by differences in income, education and employment. Living...... in social housing and in multi-ethnic neighbourhoods reduces the probability of moving into homeownership. But there are still some unexplained reasons for lower homeownership rate among immigrants. A probable hypothesis is that immigrants are more uncertain about their future employment and income. Some...
Tactical Edge Command and Control On-The-Move - A New Paradigm
2011-06-01
and Decision Making Mr. Ken D. Teske And Mr. Michael D. Tisdel Command and Control On-The-Move Focused Integration Team 1562 Mitscher...Avenue Norfolk, Virginia 23551 Point of Contact Ken D. Teske Command and Control On-The-Move Focused Integration Team 1562 Mitscher Avenue...Paradigm” UNCLASSIFIED Mr. Ken Teske and Mr. Mike Tisdel FGM, Inc. C2OTM Focused Integration Team (FIT) 16th ICCRTS 22 June 2011 Paper ID 149
Illustrating Bayesian evaluation of informative hypotheses for regression models
Anouck eKluytmans
2012-01-01
Full Text Available In the present paper we illustrate the Bayesian evaluation of informative hypotheses for regression models. This approach allows psychologists to more directly test their theories than they would using conventional statis- tical analyses. Throughout this paper, both real-world data and simulated datasets will be introduced and evaluated to investigate the pragmatical as well as the theoretical qualities of the approach. We will pave the way from forming informative hypotheses in the context of regression models to interpreting the Bayes factors that express the support for the hypotheses being evaluated. In doing so, the present approach goes beyond p-values and uninformative null hypothesis testing, moving on to informative testing and quantification of model support in a way that is accessible to everyday psychologists.
孙玉香; 曹会彬; 冯勇; 葛运建
2012-01-01
针对矿井灾难环境特点,采用三维建模软件设计了一种轮腿一体化机器人.该机器人采用轮腿一体式结构,具备了腿式机器人和轮式机器人的运动优点.分析了在不同环境下机器人采用的行进方式(即机器人步态),增强了机器人的环境适应能力,并且设计了基于多传感器信息的运动控制系统.该系统能够完成灾难矿井下的环境探测、信息获取以及机器人步态控制等功能,为矿难救援工作提供了重要的信息.%According to the environmental characteristics of mine disaster, this paper designs a leg-wheeled integration robot by using 3D modeling software. The robot takes the structure of leg-wheeled integration, which has the movement advantages of leg robot and wheel robot, analyzes the walking method of robot in various environmental states, enhances the environmental adaptability of robot, and also designs the motion control system based on multi-sensor information. This system can fulfill various functions, such as environment detection, information acquisition , and robot walking control, etc, providing significant information for the rescue work when mine disaster happens.
Varying-coefficient functional linear regression
Wu, Yichao; Müller, Hans-Georg; 10.3150/09-BEJ231
2011-01-01
Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study a varying-coefficient approach where the scalar covariates are modeled as additional arguments of the regression parameter function. This extension of the functional linear regression model is analogous to the extension of conventional linear regression models to varying-coefficient models and shares its advantages, such as increased flexibility; however, the details of this extension are more challenging in the functional case. Our methodology combines smoothing methods with regularization by truncation at a finite numb...
Cherenkov radiation in moving medium
2010-01-01
Cherenkov radiation in uniformly moving homogenous isotropic medium without dispersion is studied. Formula for the spectrum of Cherenkov radiation of fermion was derived for the case when the speed of the medium is less than the speed of light in this medium at rest. The properties of Cherenkov spectrum are investigated.
Information security : the moving target
Dlamini, MT
2009-01-01
Full Text Available it is today and the direction in which it is moving. It is argued that information security is not about looking at the past in anger of an attack once faced; neither is it about looking at the present in fear of being attacked; nor about looking at the future...
An Improved Moving Mesh Algorithm
无
2001-01-01
we consider an iterative algorithm of mesh optimization for finite element solution, and give an improved moving mesh strategy that reduces rapidly the complexity and cost of solving variational problems.A numerical result is presented for a 2-dimensional problem by the improved algorithm.
Coalition Hakes an Explosive Move
Ni Yanshuo
2011-01-01
@@ WHEN Rafale, Mirage 2000 and other fighter-bombers from Western coalition forces circled the Mediterranean region bound for Libya and Tomahawk cruise missiles whistled into the North African country, the world held its breath.Domestic street protests had moved to civil conflicts and foreign military operations in little over a month.
Carlson Wagonlit Travel is moving
2013-01-01
The renovation of the Main Building continues! Because of this, Carlson Wagonlit Travel will move from building 62 to building 510 on 4 October and the agency will be closed in the afternoon. An emergency service will be organised for official travels only. Phone: 022 799 75 73 & 022 799 75 78 / e-mail: cern@carlsonwagonlit.ch
Power Prediction in Smart Grids with Evolutionary Local Kernel Regression
Kramer, Oliver; Satzger, Benjamin; Lässig, Jörg
Electric grids are moving from a centralized single supply chain towards a decentralized bidirectional grid of suppliers and consumers in an uncertain and dynamic scenario. Soon, the growing smart meter infrastructure will allow the collection of terabytes of detailed data about the grid condition, e.g., the state of renewable electric energy producers or the power consumption of millions of private customers, in very short time steps. For reliable prediction strong and fast regression methods are necessary that are able to cope with these challenges. In this paper we introduce a novel regression technique, i.e., evolutionary local kernel regression, a kernel regression variant based on local Nadaraya-Watson estimators with independent bandwidths distributed in data space. The model is regularized with the CMA-ES, a stochastic non-convex optimization method. We experimentally analyze the load forecast behavior on real power consumption data. The proposed method is easily parallelizable, and therefore well appropriate for large-scale scenarios in smart grids.
Functional Regression for Quasar Spectra
Ciollaro, Mattia; Freeman, Peter; Genovese, Christopher; Lei, Jing; O'Connell, Ross; Wasserman, Larry
2014-01-01
The Lyman-alpha forest is a portion of the observed light spectrum of distant galactic nuclei which allows us to probe remote regions of the Universe that are otherwise inaccessible. The observed Lyman-alpha forest of a quasar light spectrum can be modeled as a noisy realization of a smooth curve that is affected by a `damping effect' which occurs whenever the light emitted by the quasar travels through regions of the Universe with higher matter concentration. To decode the information conveyed by the Lyman-alpha forest about the matter distribution, we must be able to separate the smooth `continuum' from the noise and the contribution of the damping effect in the quasar light spectra. To predict the continuum in the Lyman-alpha forest, we use a nonparametric functional regression model in which both the response and the predictor variable (the smooth part of the damping-free portion of the spectrum) are function-valued random variables. We demonstrate that the proposed method accurately predicts the unobserv...
Knowledge and Awareness: Linear Regression
Monika Raghuvanshi
2016-12-01
Full Text Available Knowledge and awareness are factors guiding development of an individual. These may seem simple and practicable, but in reality a proper combination of these is a complex task. Economically driven state of development in younger generations is an impediment to the correct manner of development. As youths are at the learning phase, they can be molded to follow a correct lifestyle. Awareness and knowledge are important components of any formal or informal environmental education. The purpose of this study is to evaluate the relationship of these components among students of secondary/ senior secondary schools who have undergone a formal study of environment in their curricula. A suitable instrument is developed in order to measure the elements of Awareness and Knowledge among the participants of the study. Data was collected from various secondary and senior secondary school students in the age group 14 to 20 years using cluster sampling technique from the city of Bikaner, India. Linear regression analysis was performed using IBM SPSS 23 statistical tool. There exists a weak relation between knowledge and awareness about environmental issues, caused due to routine practices mishandling; hence one component can be complemented by other for improvement in both. Knowledge and awareness are crucial factors and can provide huge opportunities in any field. Resource utilization for economic solutions may pave the way for eco-friendly products and practices. If green practices are inculcated at the learning phase, they may become normal routine. This will also help in repletion of the environment.
Streamflow forecasting using functional regression
Masselot, Pierre; Dabo-Niang, Sophie; Chebana, Fateh; Ouarda, Taha B. M. J.
2016-07-01
Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Translating Cuba: Diasporic writing between moving cultures and moving media
Andrea Gremels
2015-04-01
Full Text Available This article discusses the interrelation between transculturality and transmediality with an emphasis on processes of translation. It focuses on two examples of transcultural and transmedial writing taken from contemporary Cuban literature in Paris: Miguel Sales's recontextualization of Cuban popular music in Paris and William Navarrete's ekphrastic reinscription of his island into the realm of French romantic painting. The case studies are significant in this context because they show how cultural borders are simultaneously set and transgressed at medial crossings—between music and poetry, text, and image. Thus, cultural translations go hand in hand with medial transpositions that include forms of rewriting, recomposition, and revisualization. The connection between moving cultures and moving media also points to the question of “travelling memory” in diaspora.
Moving in a moving medium: new perspectives on flight.
Shepard, Emily L C; Ross, Andrew N; Portugal, Steven J
2016-09-26
One of the defining features of the aerial environment is its variability; air is almost never still. This has profound consequences for flying animals, affecting their flight stability, speed selection, energy expenditure and choice of flight path. All these factors have important implications for the ecology of flying animals, and the ecosystems they interact with, as well as providing bio-inspiration for the development of unmanned aerial vehicles. In this introduction, we touch on the factors that drive the variability in airflows, the scales of variability and the degree to which given airflows may be predictable. We then summarize how papers in this volume advance our understanding of the sensory, biomechanical, physiological and behavioural responses of animals to air flows. Overall, this provides insight into how flying animals can be so successful in this most fickle of environments.This article is part of the themed issue 'Moving in a moving medium: new perspectives on flight'.
Moving in a moving medium: new perspectives on flight
Shepard, Emily L. C.; Portugal, Steven J.
2016-01-01
One of the defining features of the aerial environment is its variability; air is almost never still. This has profound consequences for flying animals, affecting their flight stability, speed selection, energy expenditure and choice of flight path. All these factors have important implications for the ecology of flying animals, and the ecosystems they interact with, as well as providing bio-inspiration for the development of unmanned aerial vehicles. In this introduction, we touch on the factors that drive the variability in airflows, the scales of variability and the degree to which given airflows may be predictable. We then summarize how papers in this volume advance our understanding of the sensory, biomechanical, physiological and behavioural responses of animals to air flows. Overall, this provides insight into how flying animals can be so successful in this most fickle of environments. This article is part of the themed issue ‘Moving in a moving medium: new perspectives on flight’. PMID:27528772
Time series analysis using semiparametric regression on oil palm production
Yundari, Pasaribu, U. S.; Mukhaiyar, U.
2016-04-01
This paper presents semiparametric kernel regression method which has shown its flexibility and easiness in mathematical calculation, especially in estimating density and regression function. Kernel function is continuous and it produces a smooth estimation. The classical kernel density estimator is constructed by completely nonparametric analysis and it is well reasonable working for all form of function. Here, we discuss about parameter estimation in time series analysis. First, we consider the parameters are exist, then we use nonparametrical estimation which is called semiparametrical. The selection of optimum bandwidth is obtained by considering the approximation of Mean Integrated Square Root Error (MISE).
Maximal workload capacity on moving platforms
Heus, R.; Wertheim, A.H.
1996-01-01
Physical tasks on a moving platform required more energy than the same tasks on a non-moving platform. In this study the maximum aerobic performance (defined as V_O2max) of people working on a moving floor was established compared to the maximal aerobic performance on a non-moving floor. The main
Maximal workload capacity on moving platforms
Heus, R.; Wertheim, A.H.
1996-01-01
Physical tasks on a moving platform required more energy than the same tasks on a non-moving platform. In this study the maximum aerobic performance (defined as V_O2max) of people working on a moving floor was established compared to the maximal aerobic performance on a non-moving floor. The main qu
A Framework for Representing Moving Objects
Becker, Ludger; Blunck, Henrik; Hinrichs, Klaus
2004-01-01
We present a framework for representing the trajectories of moving objects and the time-varying results of operations on moving objects. This framework supports the realization of discrete data models of moving objects databases, which incorporate representations of moving objects based on non-li...
Research on Maneuverability of Moving Centroid Warhead
LIN Peng; ZHOU Feng-qi; ZHOU Jun
2007-01-01
The dynamic equations of the moving centroid warhead are derived, simplified and linearized, and a transfer function from moving mass displacement to normal overload of the warhead is obtained. Thus, the normal overload can be calculated when the mass moves to any different places. The research results provide a necessary theoretic reference for general design of control system of the moving centroid warhead.
Learning a Nonnegative Sparse Graph for Linear Regression.
Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung
2015-09-01
Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.
2015-01-01
Based on the observed weather data and phonological information during the period 1986—2011 at Baoding city, we studied the responses of first flowering date of eight woody species to climate change with integral regressive method, and established integral regressive forecasting model of first flowering date. Results showed that: the first flowering dates of the eight species were jointly affected by three climate factors:air temperature, precipitation and sunlight, and the degree of their effect was ranked as air temperature > precipitation > sunlight. The patterns of the effect and driving force of first flowering date by each climate factor changed with time. The effect of climate factors on the first flowering date for each species could be both positive and negative several months before first flowering. Air temperature had negative effects on the first flowering date of six out of the eight species ( except for Ulmus pumila and Ziziphus jujuba) a few days before first flowering. The multi-variable model established with integral regression had a high precision of predicting first flowering date of plants. The simulation showed that, in mid-March, when the temperature changes by 1℃, the precipitation changes by 1 mm and the sunshine duration changes by 1 h, the first flowing date of the eight woody species would change by about 0.1-1.6 d.%基于1986—2011年保定市气象和物候观测资料,运用积分回归法研究常见的8种乔灌木开花始期对气候变化的响应,并建立开花始期积分回归预测模型。结果显示：保定市8种乔灌木的开花始期受气温、降水量和日照等3种气候要素的共同影响,但各气候要素影响作用的大小不同,总体表现为气温>降水>日照。不同气候要素对各树种开花始期的影响方式、“驱动冶力度与时俱变。在开花始期前几个月,各气候要素对物候影响的正负效应同时出现；除榆树和栆外,其他6个树种开花始期前几
Spontaneous Regression of an Incidental Spinal Meningioma
Yilmaz, Ali; Kizilay, Zahir; Sair, Ahmet; Avcil, Mucahit; Ozkul, Ayca
2015-01-01
AIM: The regression of meningioma has been reported in literature before. In spite of the fact that the regression may be involved by hemorrhage, calcification or some drugs withdrawal, it is rarely observed spontaneously. CASE REPORT...
Common pitfalls in statistical analysis: Logistic regression.
Ranganathan, Priya; Pramesh, C S; Aggarwal, Rakesh
2017-01-01
Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.
GSMNet: A Hierarchical Graph Model for Moving Objects in Networks
Hengcai Zhang
2017-03-01
Full Text Available Existing data models for moving objects in networks are often limited by flexibly controlling the granularity of representing networks and the cost of location updates and do not encompass semantic information, such as traffic states, traffic restrictions and social relationships. In this paper, we aim to fill the gap of traditional network-constrained models and propose a hierarchical graph model called the Geo-Social-Moving model for moving objects in Networks (GSMNet that adopts four graph structures, RouteGraph, SegmentGraph, ObjectGraph and MoveGraph, to represent the underlying networks, trajectories and semantic information in an integrated manner. The bulk of user-defined data types and corresponding operators is proposed to handle moving objects and answer a new class of queries supporting three kinds of conditions: spatial, temporal and semantic information. Then, we develop a prototype system with the native graph database system Neo4Jto implement the proposed GSMNet model. In the experiment, we conduct the performance evaluation using simulated trajectories generated from the BerlinMOD (Berlin Moving Objects Database benchmark and compare with the mature MOD system Secondo. The results of 17 benchmark queries demonstrate that our proposed GSMNet model has strong potential to reduce time-consuming table join operations an d shows remarkable advantages with regard to representing semantic information and controlling the cost of location updates.
Unbalanced Regressions and the Predictive Equation
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
Synthesizing Regression Results: A Factored Likelihood Method
Wu, Meng-Jia; Becker, Betsy Jane
2013-01-01
Regression methods are widely used by researchers in many fields, yet methods for synthesizing regression results are scarce. This study proposes using a factored likelihood method, originally developed to handle missing data, to appropriately synthesize regression models involving different predictors. This method uses the correlations reported…
Regression Analysis by Example. 5th Edition
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Regression with Sparse Approximations of Data
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected by...
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
Moving Manifolds in Electromagnetic Fields
David V. Svintradze
2017-08-01
Full Text Available We propose dynamic non-linear equations for moving surfaces in an electromagnetic field. The field is induced by a material body with a boundary of the surface. Correspondingly the potential energy, set by the field at the boundary can be written as an addition of four-potential times four-current to a contraction of the electromagnetic tensor. Proper application of the minimal action principle to the system Lagrangian yields dynamic non-linear equations for moving three dimensional manifolds in electromagnetic fields. The equations in different conditions simplify to Maxwell equations for massless three surfaces, to Euler equations for a dynamic fluid, to magneto-hydrodynamic equations and to the Poisson-Boltzmann equation.
Mintken, Paul E; Derosa, Carl; Little, Tamara; Smith, Britt
2010-05-01
Medical care historically has had a strong association with magic, illusion, and secrecy. Although we profess to be modern healthcare practitioners, utilizing manual therapy techniques, and strive for evidence-based practice, the reality is that one of the most ubiquitous of all manual therapy techniques, manipulation, is obscured by illusive and ill-defined terminology. As a first step in moving from magician to modern clinician, we recently proposed a nomenclature intended to standardize and clarify the terminology used in describing specific manual therapy techniques, recommending the use of 6 key characteristics. The persistent obfuscations appear to be aimed at obscuring the differentiation of manipulation from mobilization. The time has come for a more precise delineation between manipulation and mobilization and to move beyond seeing these valuable interventions simply as some sleight-of-hand technique.J Orthop Sports Phys Ther 2010;40(5):253-255. doi:10.2519/jospt.2010.0105.
Moving Horizon Estimation and Control
Jørgensen, John Bagterp
problems. Chapter 1 motivates moving horizon estimation and control as a paradigm for control of industrial processes. It introduces the extended linear quadratic control problem and discusses its central role in moving horizon estimation and control. Introduction, application and efficient solution...... control problem is motivated and justified. Chapter 3. A primal active set, a dual active set, and an interior point algorithm for solution of the constrained linear quadratic optimal control problem are outlined. The major computational effort in all these algorithms reduces to solution of certain...... programs arise in sequential quadratic programming algorithms. Appendix B uses a control vector parameterization approach to express various extended constrained linear quadratic optimal control problems as standard quadratic programs. Appendix C discuss construction of maximal output admissible sets...
The nearest young moving groups
López-Santiago, J; Fernández-Figueroa, M J; Montes, D
2006-01-01
The latest results in the research of forming planetary systems have led several authors to compile a sample of candidates for searching for planets in the vicinity of the sun. Young stellar associations are indeed excellent laboratories for this study, but some of them are not close enough to allow the detection of planets through adaptive optics techniques. However, the existence of very close young moving groups can solve this problem. Here we have compiled the members of the nearest young moving groups, as well as a list of new candidates from our catalogue of late-type stars possible members of young stellar kinematic groups, studying their membership through spectroscopic and photometric criteria.
The moving plate capacitor paradox
Davis, B. R.; Abbott, D.; Parrondo, J. M. R.
2000-03-01
For the first time we describe an apparent paradox concerning a moving plate capacitor driven by thermal noise from a resistor. A demon restores the plates of the capacitor to their original position, only when the voltage across the capacitor is small—hence only small forces are present for the demon to work against. The demon has to work harder than this to avoid the situation of perpetual motion, but the question is how? We explore the concept of a moving plate capacitor, driven by noise, a step further by examining the case where the restoring force on the capacitor plates is provided by a simple spring, rather than some unknown demon. We display simulation results with interesting behavior, particularly where the capacitor plates collide with each other.
1979-01-01
The measurements of hadron elastic scattering on protons at small angle (WA9 experiment) were extended at higher energies (NA8 experiment by the Clermont Ferrand-Leningrad-Lyon-Uppsala Collaboration). To this purpose the set-up was moved to the beam H8 in the EHN1 Hall of the SPS North Area. The photo shows the ionization chamber measuring the recoil energy (centre). Pierre Sahuc stands on the left.
Transverse contractions of moving bodies
Bramanti, D.
1978-05-11
One of the most important theoretical consequences of the principle of relativity, i.e. the absence of transverse Lorentz-Fitzgerald contractions in moving bodies, has never been subjected to direct experimental tests. The existing indirect evidence of this absence is discussed, and a simple experiment for testing it directly and with high accuracy is proposed. Some implications of a possible nonnull result of this experiment are also pointed out.
Boosted Regression Tree Models to Explain Watershed ...
Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite-nitrate (NO2-NO3), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO2-NO3 concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed-use and heavily impacted watershed
Regression with Sparse Approximations of Data
Noorzad, Pardis; Sturm, Bob L.
2012-01-01
We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...
Nonparametric instrumental regression with non-convex constraints
Grasmair, M.; Scherzer, O.; Vanhems, A.
2013-03-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.
Assumptions of Multiple Regression: Correcting Two Misconceptions
Matt N. Williams
2013-09-01
Full Text Available In 2002, an article entitled - Four assumptions of multiple regression that researchers should always test- by.Osborne and Waters was published in PARE. This article has gone on to be viewed more than 275,000 times.(as of August 2013, and it is one of the first results displayed in a Google search for - regression.assumptions- . While Osborne and Waters' efforts in raising awareness of the need to check assumptions.when using regression are laudable, we note that the original article contained at least two fairly important.misconceptions about the assumptions of multiple regression: Firstly, that multiple regression requires the.assumption of normally distributed variables; and secondly, that measurement errors necessarily cause.underestimation of simple regression coefficients. In this article, we clarify that multiple regression models.estimated using ordinary least squares require the assumption of normally distributed errors in order for.trustworthy inferences, at least in small samples, but not the assumption of normally distributed response or.predictor variables. Secondly, we point out that regression coefficients in simple regression models will be.biased (toward zero estimates of the relationships between variables of interest when measurement error is.uncorrelated across those variables, but that when correlated measurement error is present, regression.coefficients may be either upwardly or downwardly biased. We conclude with a brief corrected summary of.the assumptions of multiple regression when using ordinary least squares.
Regression in children with autism spectrum disorders.
Malhi, Prahbhjot; Singhi, Pratibha
2012-10-01
To understand the characteristics of autistic regression and to compare the clinical and developmental profile of children with autism spectrum disorders (ASD) in whom parents report developmental regression with age matched ASD children in whom no regression is reported. Participants were 35 (Mean age = 3.57 y, SD = 1.09) children with ASD in whom parents reported developmental regression before age 3 y and a group of age and IQ matched 35 ASD children in whom parents did not report regression. All children were recruited from the outpatient Child Psychology Clinic of the Department of Pediatrics of a tertiary care teaching hospital in North India. Multi-disciplinary evaluations including neurological, diagnostic, cognitive, and behavioral assessments were done. Parents were asked in detail about the age at onset of regression, type of regression, milestones lost, and event, if any, related to the regression. In addition, the Childhood Autism Rating Scale (CARS) was administered to assess symptom severity. The mean age at regression was 22.43 mo (SD = 6.57) and large majority (66.7%) of the parents reported regression between 12 and 24 mo. Most (75%) of the parents of the regression-autistic group reported regression in the language domain, particularly in the expressive language sector, usually between 18 and 24 mo of age. Regression of language was not an isolated phenomenon and regression in other domains was also reported including social skills (75%), cognition (31.25%). In majority of the cases (75%) the regression reported was slow and subtle. There were no significant differences in the motor, social, self help, and communication functioning between the two groups as measured by the DP II.There were also no significant differences between the two groups on the total CARS score and total number of DSM IV symptoms endorsed. However, the regressed children had significantly (t = 2.36, P = .021) more social deficits as per the DSM IV as
Coulomb's Law in a Moving Medium--A Review Exercise in Advanced Undergraduate Electromagnetism
Sastry, G. P.
1978-01-01
The electromagnetic field of a static charge in a moving medium is evaluated using elements of special relativity, residue calculus, and Fourier integration. Some of the concepts in electrodynamics that are of current research value are discussed. (BB)
Viscous dissipation effects on heat transfer in flow past a continuous moving plate
Soundalgekar, V.M.; Murty, T.V.R.
The study of thermal boundary layer on taking into account the viscous dissipative heat, on a continuously moving semi-infinite flat plate is presented here.Similarity solutions are derived and the resulting equations are integrated numerically...
Heat transfer in flow past a continuously moving porous flat plate with heat flux
Murty, T.V.R.; Sarma, Y.V.B.
The analysis of the heat transfer in flow past a continuously moving semi-infinite plate in the presence of suction/ injection with heat flux has been presented. Similarity solutions have been derived and the resulting equations are integrated...
Improving ISD Agility in Fast-moving Software Organizations
Persson, John Stouby; Nørbjerg, Jacob; Nielsen, Peter Axel
2016-01-01
Fast-moving software organizations must respond quickly to changing technological options and mar-ket trends while delivering high-quality services at competitive prices. Improving agility of infor-mation systems development (ISD) may reconcile these inherent tensions, but previous research...... study on how to improve ISD agility in a fast-moving software organization. The study maps central problems in the ISD management to direct improvements of agility. Our following intervention ad-dressed method improvements in defining types of ISD by customer relations and integrating the method...... with the task management tool used by the organization. The paper discusses how the action research contributes to our understanding of ISD agility in fast-moving software organizations with a framework for mapping and evaluating improvements of agility. The action research specifically points out that project...
Improving ISD Agility in Fast-Moving Software Organizations
Persson, John Stouby; Nørbjerg, Jacob; Nielsen, Peter Axel
2016-01-01
Fast-moving software organizations must respond quickly to changing technological options and market trends while delivering high-quality services at competitive prices. Improving agility of information systems development (ISD) may reconcile these inherent tensions, but previous research of agil...... managers need to attend to the company’s agility in relating to customers, that company agility links to project agility, and that this requires light method and tool support....... of agility predominantly focused separately on managing either the individual project or the organization. Limited research has investigated the management that ties the agility of individual projects with the company agility characterizing fast-moving organizations. This paper reports an action research...... study on how to improve ISD agility in a fast-moving software organization. The study maps central problems in the ISD management to direct improvements of agility. Our following intervention addressed method improvements in defining types of ISD by customer relations and integrating the method...
Using Regression Mixture Analysis in Educational Research
Cody S. Ding
2006-11-01
Full Text Available Conventional regression analysis is typically used in educational research. Usually such an analysis implicitly assumes that a common set of regression parameter estimates captures the population characteristics represented in the sample. In some situations, however, this implicit assumption may not be realistic, and the sample may contain several subpopulations such as high math achievers and low math achievers. In these cases, conventional regression models may provide biased estimates since the parameter estimates are constrained to be the same across subpopulations. This paper advocates the applications of regression mixture models, also known as latent class regression analysis, in educational research. Regression mixture analysis is more flexible than conventional regression analysis in that latent classes in the data can be identified and regression parameter estimates can vary within each latent class. An illustration of regression mixture analysis is provided based on a dataset of authentic data. The strengths and limitations of the regression mixture models are discussed in the context of educational research.
Automation of Flight Software Regression Testing
Tashakkor, Scott B.
2016-01-01
NASA is developing the Space Launch System (SLS) to be a heavy lift launch vehicle supporting human and scientific exploration beyond earth orbit. SLS will have a common core stage, an upper stage, and different permutations of boosters and fairings to perform various crewed or cargo missions. Marshall Space Flight Center (MSFC) is writing the Flight Software (FSW) that will operate the SLS launch vehicle. The FSW is developed in an incremental manner based on "Agile" software techniques. As the FSW is incrementally developed, testing the functionality of the code needs to be performed continually to ensure that the integrity of the software is maintained. Manually testing the functionality on an ever-growing set of requirements and features is not an efficient solution and therefore needs to be done automatically to ensure testing is comprehensive. To support test automation, a framework for a regression test harness has been developed and used on SLS FSW. The test harness provides a modular design approach that can compile or read in the required information specified by the developer of the test. The modularity provides independence between groups of tests and the ability to add and remove tests without disturbing others. This provides the SLS FSW team a time saving feature that is essential to meeting SLS Program technical and programmatic requirements. During development of SLS FSW, this technique has proved to be a useful tool to ensure all requirements have been tested, and that desired functionality is maintained, as changes occur. It also provides a mechanism for developers to check functionality of the code that they have developed. With this system, automation of regression testing is accomplished through a scheduling tool and/or commit hooks. Key advantages of this test harness capability includes execution support for multiple independent test cases, the ability for developers to specify precisely what they are testing and how, the ability to add
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Imperl, Bojan; Jeřabek, Boro; Šoštarič, Andrej
2003-01-01
In this article we would like to show how an arbitrary home and building electronic system based on the home automation standards such as Xl0 might be addressed and controlled by an appropriate mobile technology. Urge for mobility of users, which may be at the same time either the inhabitants of these homes or even administrators and supporters, is growing. The possibility to control and observe the status of home appliances while being on the move away from home using our mobile phones is be...
”I believe that all people need to move about. Actually, some have difficulties in doing so. They stay in their home neighbourhoods where they’ve grown up and feel safe. I can understand that, but my wife and I, we didn’t want that. We are more open to new ideas.” This anthology is about seniors...... for identities, traditions, feelings of belonging, family and friends, health, images of old age, societal planning and policies, and even for religious attachment. The book presents a joint statement, intended for international scholars in the field, but also for Nordic policymakers and practitioners involved...
Redondo, Javier [Muenchen Univ. (Germany). Arnold Sommerfeld Center; Max-Planck-Institut fuer Physik, Muenchen (Germany); Doebrich, Babette [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
2013-11-15
This proceedings contribution reports from the workshop Dark Matter - a light move, held at DESY in Hamburg in June 2013. Dark Matter particle candidates span a huge parameter range. In particular, well motivated candidates exist also in the sub-eV mass region, for example the axion. Whilst a plethora of searches for rather heavy Dark Matter particles exists, there are only very few experiments aimed at direct detection of sub-eV Dark Matter to this date. The aim of our workshop was to discuss if and how this could be changed in the near future.
Moving Tourism Social Entrepreneurship Forward
Dredge, Dianne
2017-01-01
This chapter concludes the book by considering the role that research and education can play to move the TSE agenda forward. In addition to consolidating the chapter authors’ thoughts about the future of SE and tourism, it also lays out some directions for research tracks in the future....... It considers the changes needed in research approaches, in our universities, our curricula, our learners, and ourselves as academics. These changes we hope will stimulate the dialog on how TSE can mobilize the energy, vision and social spirit of those who seek to change the world for the better through tourism....
Moving Walkways, Escalators, and Elevators
Cardinal, J; Hurtado, F; Langerman, S; Palop, B
2007-01-01
We study a simple geometric model of transportation facility that consists of two points between which the travel speed is high. This elementary definition can model shuttle services, tunnels, bridges, teleportation devices, escalators or moving walkways. The travel time between a pair of points is defined as a time distance, in such a way that a customer uses the transportation facility only if it is helpful. We give algorithms for finding the optimal location of such a transportation facility, where optimality is defined with respect to the maximum travel time between two points in a given set.
Song, Chao; Kwan, Mei-Po; Zhu, Jiping
2017-04-08
An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.
Beta blockers & left ventricular hypertrophy regression.
George, Thomas; Ajit, Mullasari S; Abraham, Georgi
2010-01-01
Left ventricular hypertrophy (LVH) particularly in hypertensive patients is a strong predictor of adverse cardiovascular events. Identifying LVH not only helps in the prognostication but also in the choice of therapeutic drugs. The prevalence of LVH is age linked and has a direct correlation to the severity of hypertension. Adequate control of blood pressure, most importantly central aortic pressure and blocking the effects of cardiomyocyte stimulatory growth factors like Angiotensin II helps in regression of LVH. Among the various antihypertensives ACE-inhibitors and angiotensin receptor blockers are more potent than other drugs in regressing LVH. Beta blockers especially the newer cardio selective ones do still have a role in regressing LVH albeit a minor one. A meta-analysis of various studies on LVH regression shows many lacunae. There have been no consistent criteria for defining LVH and documenting LVH regression. This article reviews current evidence on the role of Beta Blockers in LVH regression.
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
High-dimensional regression with unknown variance
Giraud, Christophe; Verzelen, Nicolas
2011-01-01
We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation-sparsity. The emphasize is put on non-asymptotic analyses and feasible procedures. In addition, a small numerical study compares the practical performance of three schemes for tuning the Lasso esti- mator and some references are collected for some more general models, including multivariate regression and nonparametric regression.
Senior Living: Staying Positive and Moving Forward
... Past Issues Feature: Senior Living Staying Positive and Moving Forward Past Issues / Summer 2009 Table of Contents ... page please turn Javascript on. For Juanita Kuhn, moving to an independent living facility is just the ...
Microscopic features of moving traffic jams
Kerner, B S; Klenov, S L; Rehborn, H; Hiller, Andreas; Kerner, Boris S.; Klenov, Sergey L.; Rehborn, Hubert
2005-01-01
Empirical and numerical microscopic features of moving traffic jams are presented. Based on a single vehicle data analysis, it is found that within wide moving jams, i.e., between the upstream and downstream jam fronts there is a complex microscopic spatiotemporal structure. This jam structure consists of alternations of regions in which traffic flow is interrupted and flow states of low speeds associated with "moving blanks" within the jam. Empirical features of the moving blanks are found. Based on microscopic models in the context of three-phase traffic theory, physical reasons for moving blanks emergence within wide moving jams are disclosed. Structure of moving jam fronts is studied based in microscopic traffic simulations. Non-linear effects associated with moving jam propagation are numerically investigated and compared with empirical results.
Towards Database Support for Moving Object Data
Meratnia, Nirvana
2005-01-01
To narrow down moving object challenges, the focus of this thesis is on four issues, namely, uncertainty handling for moving object data, faithful trajectory representation, trajectory compression techniques, and similarity measures for trajectories.
Housing tenure, residential moves and children's educational ...
Housing tenure, residential moves and children's educational performance in Accra, Ghana. ... Journal of Science and Technology (Ghana) ... a positive home ownership effect and a negative residential moves effect remain with significant ...
An Optimal Moving Horizon Estimation for Aerial Vehicular Navigation Application
Ubaid Gul, Haris; Kai, Yang Dong
2017-03-01
In this article, an optimal state is estimated using the moving horizon estimation technique (MHE), based on the minimizing the deterministic cost function defined for moving window with a finite number of samples at specific time interval. The optimal moving horizon observer was designed and implemented for the non-linear dynamic problem of aerial vehicle integrated navigation. The low grade commercial inertial measuring instrument (IMU) equipped with accelerometers and gyros sensors instrumented on-board in the strapdown configuration, is employed for collection of the real time experimental data. The data fusion algorithm of moving horizon estimation is realized and the results are collected from the offline algorithm testing on the Matlab software platform. Essential data processing and cleaning of data processing was conducted before algorithm application i.e. solving the multi rate sensors data synching and removing high frequency unwanted contents. Finally, the aerial vehicle dead reckoning integrated navigation was performed with recursive observer using IMU/GPS avionics. Contrary to the widely practiced extended Kalman filter results, recursive observer of MHE exhibited performance enhancement in the response and precision aspect, regardless of environmental noise and failure scenarios.
Privacy Preserving Moving KNN Queries
Hashem, Tanzima; Zhang, Rui
2011-01-01
We present a novel approach that protects trajectory privacy of users who access location-based services through a moving k nearest neighbor (MkNN) query. An MkNN query continuously returns the k nearest data objects for a moving user (query point). Simply updating a user's imprecise location such as a region instead of the exact position to a location-based service provider (LSP) cannot ensure privacy of the user for an MkNN query: continuous disclosure of regions enables the LSP to follow a user's trajectory. We identify the problem of trajectory privacy that arises from the overlap of consecutive regions while requesting an MkNN query and provide the first solution to this problem. Our approach allows a user to specify the confidence level that represents a bound of how much more the user may need to travel than the actual kth nearest data object. By hiding a user's required confidence level and the required number of nearest data objects from an LSP, we develop a technique to prevent the LSP from tracking...
Autonomous Landing on Moving Platforms
Mendoza Chavez, Gilberto
2016-08-01
This thesis investigates autonomous landing of a micro air vehicle (MAV) on a nonstationary ground platform. Unmanned aerial vehicles (UAVs) and micro air vehicles (MAVs) are becoming every day more ubiquitous. Nonetheless, many applications still require specialized human pilots or supervisors. Current research is focusing on augmenting the scope of tasks that these vehicles are able to accomplish autonomously. Precise autonomous landing on moving platforms is essential for self-deployment and recovery of MAVs, but it remains a challenging task for both autonomous and piloted vehicles. Model Predictive Control (MPC) is a widely used and effective scheme to control constrained systems. One of its variants, output-feedback tube-based MPC, ensures robust stability for systems with bounded disturbances under system state reconstruction. This thesis proposes a MAV control strategy based on this variant of MPC to perform rapid and precise autonomous landing on moving targets whose nominal (uncommitted) trajectory and velocity are slowly varying. The proposed approach is demonstrated on an experimental setup.
Regression calibration with heteroscedastic error variance.
Spiegelman, Donna; Logan, Roger; Grove, Douglas
2011-01-01
The problem of covariate measurement error with heteroscedastic measurement error variance is considered. Standard regression calibration assumes that the measurement error has a homoscedastic measurement error variance. An estimator is proposed to correct regression coefficients for covariate measurement error with heteroscedastic variance. Point and interval estimates are derived. Validation data containing the gold standard must be available. This estimator is a closed-form correction of the uncorrected primary regression coefficients, which may be of logistic or Cox proportional hazards model form, and is closely related to the version of regression calibration developed by Rosner et al. (1990). The primary regression model can include multiple covariates measured without error. The use of these estimators is illustrated in two data sets, one taken from occupational epidemiology (the ACE study) and one taken from nutritional epidemiology (the Nurses' Health Study). In both cases, although there was evidence of moderate heteroscedasticity, there was little difference in estimation or inference using this new procedure compared to standard regression calibration. It is shown theoretically that unless the relative risk is large or measurement error severe, standard regression calibration approximations will typically be adequate, even with moderate heteroscedasticity in the measurement error model variance. In a detailed simulation study, standard regression calibration performed either as well as or better than the new estimator. When the disease is rare and the errors normally distributed, or when measurement error is moderate, standard regression calibration remains the method of choice.
Enhanced piecewise regression based on deterministic annealing
ZHANG JiangShe; YANG YuQian; CHEN XiaoWen; ZHOU ChengHu
2008-01-01
Regression is one of the important problems in statistical learning theory. This paper proves the global convergence of the piecewise regression algorithm based on deterministic annealing and continuity of global minimum of free energy w.r.t temperature, and derives a new simplified formula to compute the initial critical temperature. A new enhanced piecewise regression algorithm by using "migration of prototypes" is proposed to eliminate "empty cell" in the annealing process. Numerical experiments on several benchmark datasets show that the new algo-rithm can remove redundancy and improve generalization of the piecewise regres-sion model.
Geodesic least squares regression on information manifolds
Verdoolaege, Geert, E-mail: geert.verdoolaege@ugent.be [Department of Applied Physics, Ghent University, Ghent, Belgium and Laboratory for Plasma Physics, Royal Military Academy, Brussels (Belgium)
2014-12-05
We present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models. Unlike the classic regression model, the conditional distribution of the response variable suggested by the data need not be the same as the modeled distribution. Instead they are matched by minimizing the Rao geodesic distance between them. This yields a more flexible regression method that is less constrained by the assumptions imposed through the regression model. As an example, we demonstrate the improved resistance of our method against some flawed model assumptions and we apply this to scaling laws in magnetic confinement fusion.
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R(2)) indicates the importance of independent variables in the outcome.
Logistic Regression for Evolving Data Streams Classification
YIN Zhi-wu; HUANG Shang-teng; XUE Gui-rong
2007-01-01
Logistic regression is a fast classifier and can achieve higher accuracy on small training data. Moreover,it can work on both discrete and continuous attributes with nonlinear patterns. Based on these properties of logistic regression, this paper proposed an algorithm, called evolutionary logistical regression classifier (ELRClass), to solve the classification of evolving data streams. This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier, to keep this classifier if its performance is deteriorated by the reason of bursting noise, or to construct a new classifier if a major concept drift is detected. The intensive experimental results demonstrate the effectiveness of this algorithm.
New ridge parameters for ridge regression
A.V. Dorugade
2014-04-01
Full Text Available Hoerl and Kennard (1970a introduced the ridge regression estimator as an alternative to the ordinary least squares (OLS estimator in the presence of multicollinearity. In ridge regression, ridge parameter plays an important role in parameter estimation. In this article, a new method for estimating ridge parameters in both situations of ordinary ridge regression (ORR and generalized ridge regression (GRR is proposed. The simulation study evaluates the performance of the proposed estimator based on the mean squared error (MSE criterion and indicates that under certain conditions the proposed estimators perform well compared to OLS and other well-known estimators reviewed in this article.
Kleibergen, F.
2003-01-01
We obtain the prior and posterior probability of a nested regression model as the Hausdorff-integral of the prior and posterior on the parameters of an encompassing linear regression model over a lower dimensional set that represents the nested model. The invariant expression of the
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
A Move towards the Integration between Education and Technology
Fuyin Xu; James A.Pershing
2010-01-01
@@ We are already well into the Information Age. The development of ICT has progressed rapidly causing a huge transformation away from traditional ways of teaching and towards creating a new ICT-based teaching environment. China is in the early stages of establishing such a foundation: we have seen significant progress in amassing a library of teaching materials for both vocational and higher education, the numbers of libraries and museums in universities, networks for China's education and scientific research and ICT facilities for all kinds and levels of schools.
Moving Toward an Integrated Transdisciplinary Approach to Solving Environmental Problems
Since the creation of the U.S. Environmental Protection Agency (EPA) four decades ago, much progress has been made in reducing emissions from the electric utility and mobile source sectors in the United States. These efforts have helped in improving human health and the environme...
An advanced integrated framework for moving ob ject tracking
Gwang-Min CHOE; Tian-jiang WANG; Fang LIU; Chun-Hwa CHOE; Hyo-Son SO; Chol-Ung PAK
2014-01-01
This paper first introduces the concept of a geogram that captures richer features to represent the objects. The spatiogram contains some moments upon the coordinates of the pixels corresponding to each bin, while the geogram contains information about the perimeter of grouped regions in addition to features in the spatiogram. Then we consider that a convergence process of mean shift is divided into obvious dynamic and steady states, and introduce a hybrid technique of feature description, to control the convergence process. Also, we propose a spline resampling to control the balance between computational cost and accuracy of particle filtering. Finally, we propose a boosting-refining approach, which is boosting the particles positioned in the ill-posed condition instead of eliminating the ill-posed particles, to refine the particles. It enables the estimation of the object state to obtain high accuracy. Experimental results show that our approach has promising discriminative capability in comparison with the state-of-the-art approaches.
Microscopic features of moving traffic jams.
Kerner, Boris S; Klenov, Sergey L; Hiller, Andreas; Rehborn, Hubert
2006-04-01
Empirical and numerical microscopic features of moving traffic jams are presented. Based on a single vehicle data analysis, it is found that within wide moving jams, i.e., between the upstream and downstream jam fronts there is a complex microscopic spatiotemporal structure. This jam structure consists of alternations of regions in which traffic flow is interrupted and flow states of low speeds associated with "moving blanks" within the jam. Moving blanks within a wide moving jam resemble electron holes in the valence band of semiconductors: As the moving blanks that propagate upstream appear due to downstream vehicle motion within the jam, so appearance of electron holes moving with the electric field results from electron motion against the electric field in the valence band of semiconductors. Empirical features of moving blanks are found. Based on microscopic models in the context of the Kerner's three-phase traffic theory, physical reasons for moving blanks emergence within wide moving jams are disclosed. Microscopic nonlinear effects of moving jam emergence, propagation, and dissolution as well as a diverse variety of hysteresis effects in freeway traffic associated with phase transitions and congested traffic propagation are numerically investigated. Microscopic structure of moving jam fronts is numerically studied and compared with empirical results.
Electromagnetic Radiation in a Uniformly Moving, Homogeneous Medium
Johannsen, Günther
1972-01-01
A new method of treating radiation problems in a uniformly moving, homogeneous medium is presented. A certain transformation technique in connection with the four-dimensional Green's function method makes it possible to elaborate the Green's functions of the governing differential equations in th...... in the rest system of the medium, whereas the final integrals determining the field may be calculated in the rest system of the source. ©1972 The American Institute of Physics...
Moving Us Toward a Theory of Individual Quality of Life.
Schalock, Robert L; Verdugo, Miguel A; Gomez, Laura E; Reinders, Hans S
2016-01-01
This article discusses three steps involved in moving us toward a theory of individual quality of life: developing a conceptual model, integrating theory components, and applying and evaluating the theory. Each of the proposed steps is guided by established standards regarding theory development and use. The article concludes with a discussion of criteria that can be used to evaluate the theory and the contribution that a theory of individual quality of life would make to the field of disability.
Incremental Net Effects in Multiple Regression
Lipovetsky, Stan; Conklin, Michael
2005-01-01
A regular problem in regression analysis is estimating the comparative importance of the predictors in the model. This work considers the 'net effects', or shares of the predictors in the coefficient of the multiple determination, which is a widely used characteristic of the quality of a regression model. Estimation of the net effects can be a…
Regression Analysis and the Sociological Imagination
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Dealing with Outliers: Robust, Resistant Regression
Glasser, Leslie
2007-01-01
Least-squares linear regression is the best of statistics and it is the worst of statistics. The reasons for this paradoxical claim, arising from possible inapplicability of the method and the excessive influence of "outliers", are discussed and substitute regression methods based on median selection, which is both robust and resistant, are…
Competing Risks Quantile Regression at Work
Dlugosz, Stephan; Lo, Simon M. S.; Wilke, Ralf
2017-01-01
Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We use...
Implementing Variable Selection Techniques in Regression.
Thayer, Jerome D.
Variable selection techniques in stepwise regression analysis are discussed. In stepwise regression, variables are added or deleted from a model in sequence to produce a final "good" or "best" predictive model. Stepwise computer programs are discussed and four different variable selection strategies are described. These…
Regression Model With Elliptically Contoured Errors
Arashi, M; Tabatabaey, S M M
2012-01-01
For the regression model where the errors follow the elliptically contoured distribution (ECD), we consider the least squares (LS), restricted LS (RLS), preliminary test (PT), Stein-type shrinkage (S) and positive-rule shrinkage (PRS) estimators for the regression parameters. We compare the quadratic risks of the estimators to determine the relative dominance properties of the five estimators.
Regression Analysis and the Sociological Imagination
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
A Simulation Investigation of Principal Component Regression.
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
Michela Moretti
2015-12-01
Full Text Available The paper proposes a reading of the Expo 2015 landscape project through the essay "Moving Forest "by Franco Zagari and Benedetto Selleri; in which the authors trace the design process of the exposition site. It describes the design features of the green spaces that surround and mark the Exposition City. The green project is the connection between innovation, technology and rural landscape, like that surrounds the site. The Expo map represents one of the largest landscape projects in the last years in Europe, with its 300,000 square meters, organized in a sequence of different landscape that improve a gradual transition from the rural and natural landscape outside, to the urban landscape inside the exposition city.
Rolling motion in moving droplets
Sumesh P Thampi; Rama Govindarajan
2015-03-01
Drops moving on a substrate under the action of gravity display both rolling and sliding motions. The two limits of a thin sheet-like drop in sliding motion on a surface, and a spherical drop in roll, have been extensively studied. We are interested in intermediate shapes. We quantify the contribution of rolling motion for any intermediate shape, and recently obtained a universal curve for the amount of roll as a function of a shape parameter using hybrid lattice Boltzmann simulations. In this paper, we discuss the linear relationship which is expected between the Capillary and Bond numbers, and provide detailed confirmation by simulations. We also show that the viscosity of the surrounding medium can qualitatively affect dynamics. Our results provide an answer to a natural question of whether drops roll or slide on a surface and carry implications for various applications where rolling motion may or may not be preferred.
Should metacognition be measured by logistic regression?
Rausch, Manuel; Zehetleitner, Michael
2017-03-01
Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.
Atherosclerotic plaque regression: fact or fiction?
Shanmugam, Nesan; Román-Rego, Ana; Ong, Peter; Kaski, Juan Carlos
2010-08-01
Coronary artery disease is the major cause of death in the western world. The formation and rapid progression of atheromatous plaques can lead to serious cardiovascular events in patients with atherosclerosis. The better understanding, in recent years, of the mechanisms leading to atheromatous plaque growth and disruption and the availability of powerful HMG CoA-reductase inhibitors (statins) has permitted the consideration of plaque regression as a realistic therapeutic goal. This article reviews the existing evidence underpinning current therapeutic strategies aimed at achieving atherosclerotic plaque regression. In this review we also discuss imaging modalities for the assessment of plaque regression, predictors of regression and whether plaque regression is associated with a survival benefit.
Pathological assessment of liver fibrosis regression
WANG Bingqiong
2017-03-01
Full Text Available Hepatic fibrosis is the common pathological outcome of chronic hepatic diseases. An accurate assessment of fibrosis degree provides an important reference for a definite diagnosis of diseases, treatment decision-making, treatment outcome monitoring, and prognostic evaluation. At present, many clinical studies have proven that regression of hepatic fibrosis and early-stage liver cirrhosis can be achieved by effective treatment, and a correct evaluation of fibrosis regression has become a hot topic in clinical research. Liver biopsy has long been regarded as the gold standard for the assessment of hepatic fibrosis, and thus it plays an important role in the evaluation of fibrosis regression. This article reviews the clinical application of current pathological staging systems in the evaluation of fibrosis regression from the perspectives of semi-quantitative scoring system, quantitative approach, and qualitative approach, in order to propose a better pathological evaluation system for the assessment of fibrosis regression.
Zhou, Qingping; Jiang, Haiyan; Wang, Jianzhou; Zhou, Jianling
2014-10-15
Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems.
Data-driven facial animation based on manifold Bayesian regression
无
2006-01-01
Driving facial animation based on tens of tracked markers is a challenging task due to the complex topology and to the non-rigid nature of human faces. We propose a solution named manifold Bayesian regression. First a novel distance metric, the geodesic manifold distance, is introduced to replace the Euclidean distance. The problem of facial animation can be formulated as a sparse warping kernels regression problem, in which the geodesic manifold distance is used for modelling the topology and discontinuities of the face models. The geodesic manifold distance can be adopted in traditional regression methods, e.g. radial basis functions without much tuning. We put facial animation into the framework of Bayesian regression. Bayesian approaches provide an elegant way of dealing with noise and uncertainty. After the covariance matrix is properly modulated, Hybrid Monte Carlo is used to approximate the integration of probabilities and get deformation results. The experimental results showed that our algorithm can robustly produce facial animation with large motions and complex face models.
An adaptive regression method for infrared blind-pixel compensation
Chen, Suting; Meng, Hao; Pei, Tao; Zhang, Yanyan
2017-09-01
Blind pixel compensation is an ill-posed inverse problem of infrared imaging systems and image restoration. The performance of a blind pixel compensation algorithm depends on the accuracy of estimation for the underlying true infrared images. We propose an adaptive regression method (ARM) for blind pixel compensation that integrates the multi-scale framework with a regression model. A blind-pixel is restored by exploiting the intra-scale properties through the nonparametric regressive estimation and the inter-scale characteristics via parametric regression for continuous learning. Combining the respective strengths of a parametric model and a nonparametric model, ARM establishes a set of multi-scale blind-pixel compensation method to correct the non-uniformity based on key frame extraction. Therefore, it is essentially different from the traditional frameworks for blind pixel compensation which are based on filtering and interpolation. Experimental results on some challenging cases of blind compensation show that the proposed algorithm outperforms existing methods by a significant margin in both isolated blind restoration and clustered blind restoration.
Quantile regression applied to spectral distance decay
Rocchini, D.; Cade, B.S.
2008-01-01
Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.
Hypotheses testing for fuzzy robust regression parameters
Kula, Kamile Sanli [Ahi Evran University, Department of Mathematics, 40200 Kirsehir (Turkey)], E-mail: sanli2004@hotmail.com; Apaydin, Aysen [Ankara University, Department of Statistics, 06100 Ankara (Turkey)], E-mail: apaydin@science.ankara.edu.tr
2009-11-30
The classical least squares (LS) method is widely used in regression analysis because computing its estimate is easy and traditional. However, LS estimators are very sensitive to outliers and to other deviations from basic assumptions of normal theory [Huynh H. A comparison of four approaches to robust regression. Psychol Bull 1982;92:505-12; Stephenson D. 2000. Available from: (http://folk.uib.no/ngbnk/kurs/notes/node38.html); Xu R, Li C. Multidimensional least-squares fitting with a fuzzy model. Fuzzy Sets and Systems 2001;119:215-23.]. If there exists outliers in the data set, robust methods are preferred to estimate parameters values. We proposed a fuzzy robust regression method by using fuzzy numbers when x is crisp and Y is a triangular fuzzy number and in case of outliers in the data set, a weight matrix was defined by the membership function of the residuals. In the fuzzy robust regression, fuzzy sets and fuzzy regression analysis was used in ranking of residuals and in estimation of regression parameters, respectively [Sanli K, Apaydin A. Fuzzy robust regression analysis based on the ranking of fuzzy sets. Inernat. J. Uncertainty Fuzziness and Knowledge-Based Syst 2008;16:663-81.]. In this study, standard deviation estimations are obtained for the parameters by the defined weight matrix. Moreover, we propose another point of view in hypotheses testing for parameters.
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
Relative risk regression analysis of epidemiologic data.
Prentice, R L
1985-11-01
Relative risk regression methods are described. These methods provide a unified approach to a range of data analysis problems in environmental risk assessment and in the study of disease risk factors more generally. Relative risk regression methods are most readily viewed as an outgrowth of Cox's regression and life model. They can also be viewed as a regression generalization of more classical epidemiologic procedures, such as that due to Mantel and Haenszel. In the context of an epidemiologic cohort study, relative risk regression methods extend conventional survival data methods and binary response (e.g., logistic) regression models by taking explicit account of the time to disease occurrence while allowing arbitrary baseline disease rates, general censorship, and time-varying risk factors. This latter feature is particularly relevant to many environmental risk assessment problems wherein one wishes to relate disease rates at a particular point in time to aspects of a preceding risk factor history. Relative risk regression methods also adapt readily to time-matched case-control studies and to certain less standard designs. The uses of relative risk regression methods are illustrated and the state of development of these procedures is discussed. It is argued that asymptotic partial likelihood estimation techniques are now well developed in the important special case in which the disease rates of interest have interpretations as counting process intensity functions. Estimation of relative risks processes corresponding to disease rates falling outside this class has, however, received limited attention. The general area of relative risk regression model criticism has, as yet, not been thoroughly studied, though a number of statistical groups are studying such features as tests of fit, residuals, diagnostics and graphical procedures. Most such studies have been restricted to exponential form relative risks as have simulation studies of relative risk estimation
Variable and subset selection in PLS regression
Høskuldsson, Agnar
2001-01-01
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Regressive language in severe head injury.
Thomsen, I V; Skinhoj, E
1976-09-01
In a follow-up study of 50 patients with severe head injuries three patients had echolalia. One patient with initially global aphasia had echolalia for some weeks when he started talking. Another patient with severe diffuse brain damage, dementia, and emotional regression had echolalia. The dysfunction was considered a detour performance. In the third patient echolalia and palilalia were details in a total pattern of regression lasting for months. The patient, who had extensive frontal atrophy secondary to a very severe head trauma, presented an extreme state of regression returning to a foetal-body pattern and behaving like a baby.
Regression of altitude-produced cardiac hypertrophy.
Sizemore, D. A.; Mcintyre, T. W.; Van Liere, E. J.; Wilson , M. F.
1973-01-01
The rate of regression of cardiac hypertrophy with time has been determined in adult male albino rats. The hypertrophy was induced by intermittent exposure to simulated high altitude. The percentage hypertrophy was much greater (46%) in the right ventricle than in the left (16%). The regression could be adequately fitted to a single exponential function with a half-time of 6.73 plus or minus 0.71 days (90% CI). There was no significant difference in the rates of regression for the two ventricles.
Regression of altitude-produced cardiac hypertrophy.
Sizemore, D. A.; Mcintyre, T. W.; Van Liere, E. J.; Wilson , M. F.
1973-01-01
The rate of regression of cardiac hypertrophy with time has been determined in adult male albino rats. The hypertrophy was induced by intermittent exposure to simulated high altitude. The percentage hypertrophy was much greater (46%) in the right ventricle than in the left (16%). The regression could be adequately fitted to a single exponential function with a half-time of 6.73 plus or minus 0.71 days (90% CI). There was no significant difference in the rates of regression for the two ventricles.
Lieshout, M.N.M. van
2008-01-01
We advocate the use of Markov sequential object processes for tracking a variable number of moving objects through video frames with a view towards depth calculation. A regression model based on a sequential object process quantifies goodness of fit; regularization terms are incorporated to control
SEEDS Moving Groups and CHARIS Status Updates
McElwain, Michael
2012-01-01
We present the status update for the SEEDS Moving Groups category. To date, we have observed 59 targets and currently have more than 20 candidates. We also present the expected scientific capabilities of CHARIS, the Coronagraphic High Angular Resolution Imaging Spectrograph, which is being built for the Subaru 8.2 m telescope of the National Astronomical Observatory of Japan. CHARIS will be implemented behind the new extreme adaptive optics system at Subaru, SCExAO, and the existing 188-actuator system AO188. CHARIS will offer three observing modes over nearinfrared wavelengths from 0.9 to 2.4 microns (the y-, J-, H-, and K-bands), including a low-spectral-resolution mode covering this entire wavelength range and a high-resolution mode within a single band. With these capabilities, CHARIS will offer exceptional sensitivity for discovering giant exoplanets, and will enable detailed characterization of their atmospheres, CHARIS, the only planned high-contrast integral field spectrograph on an 8m-class telescope in the Northern Hemisphere, will complement the similar instruments such as Project 1640 at Palomar, and GPI and SPHERE in Chile.
In precision agriculture regression has been used widely to quality the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually makes the regression model suboptimal. In this study, a regression-kriging method was attemp...
Taruffi, Liila; Koelsch, Stefan
2017-07-01
Pelowski et al. present a holistic framework within which the multiple processes underlying art viewing can be systematically organized [1]. The proposed model integrates a broad range of dynamic mechanisms, which can effectively account for empirical as well as humanistic perspectives on art perception. Particularly challenging is the final section of the article, where the authors draw a correspondence between behavioral and cognitive components and brain structures (as well as networks). Here, we comment on the implications of the Vienna Integrated Model of Art Perception for art therapy in clinical populations, particularly focusing on (1) expanding Pelowski et al.'s considerations of the Default Mode Network (DMN) into discussion of its relevance to mental diseases, and (2) elaborating on empathic resonance in aesthetic contexts and the capacity of art to build up empathic skills.
无
2006-01-01
China, the fast-growing and largest developing country in the world, and the Association of Southeast Asian Nations (ASEAN), a dynamic regional organization for economic, political and security integration, are celebrating the 15th anniversary of the launch of a bilateral dialogue mechanism through the second half of this year. Back in 1991, China and ASEAN initiated bilateral dialogues to further develop their ties, right after the former established or resumed Ml diplomatic relations with members of th...
Cosmology with moving bimetric fluids
García-García, Carlos; Martín-Moruno, Prado
2016-01-01
We study cosmological implications of bigravity and massive gravity solutions with non-simultaneously diagonal metrics by considering the generalized Gordon and Kerr-Schild ansatzes. The scenario that we obtain is equivalent to that of General Relativity with additional non-comoving perfect fluids. We show that the most general ghost-free bimetric theory generates three kinds of effective fluids whose equations of state are fixed by a function of the ansatz. Different choices of such function allow to reproduce the behaviour of different dark fluids. In particular, the Gordon ansatz is suitable for the description of various kinds of slowly-moving fluids, whereas the Kerr-Schild one is shown to describe a null dark energy component. The motion of those dark fluids with respect to the CMB is shown to generate, in turn, a relative motion of baryonic matter with respect to radition which contributes to the CMB anisotropies. CMB dipole observations are able to set stringent limits on the dark sector described by ...
Euphotic Zone Study moves forward
Denman, Kenneth
The Global Ocean Euphotic Zone Study (GOEZS), a potential core program of the International Geosphere-Biosphere Programme (IGBP) being planned jointly with the Scientific Committee on Oceanic Research (SCOR), was recently given the go-ahead by IGBP's Scientific Committee to move on to the next level of developing its scientific program.The GOEZS program will focus on the coupled physical, biological, and chemical processes operating in the euphotic zone, which is the ocean surface layer where sufficient light penetrates for photosynthesis by phytoplankton to exceed their metabolic energy losses. The upper ocean is extremely important to understanding the atmosphereocean system because it mediates exchanges of heat, momentum, carbon dioxide, sulphur, and nitrogen between the atmosphere and the ocean interior. For the major greenhouse gas carbon dioxide for example, there is more carbon in the upper ocean than in the whole atmosphere. Essentially all carbon dioxide from the atmosphere that passes from the upper ocean to the ocean interior has been transformed chemically or biologically in the upper ocean. Moreover, the upper ocean is the site of all marine shipping and most recreation and industrial activity and contains the planktonic food chain and most fish stocks.
Leadership in moving human groups.
Margarete Boos
2014-04-01
Full Text Available How is movement of individuals coordinated as a group? This is a fundamental question of social behaviour, encompassing phenomena such as bird flocking, fish schooling, and the innumerable activities in human groups that require people to synchronise their actions. We have developed an experimental paradigm, the HoneyComb computer-based multi-client game, to empirically investigate human movement coordination and leadership. Using economic games as a model, we set monetary incentives to motivate players on a virtual playfield to reach goals via players' movements. We asked whether (I humans coordinate their movements when information is limited to an individual group member's observation of adjacent group member motion, (II whether an informed group minority can lead an uninformed group majority to the minority's goal, and if so, (III how this minority exerts its influence. We showed that in a human group--on the basis of movement alone--a minority can successfully lead a majority. Minorities lead successfully when (a their members choose similar initial steps towards their goal field and (b they are among the first in the whole group to make a move. Using our approach, we empirically demonstrate that the rules of swarming behaviour apply to humans. Even complex human behaviour, such as leadership and directed group movement, follow simple rules that are based on visual perception of local movement.
IT Department
2009-01-01
As of 2nd March 2009, the Telecom Lab will move to Building 58 R-017. The Telecom Lab is the central point for all support questions regarding CERN mobile phone services (provision of SIM cards, requests for modifications of subscriptions, diagnostics for mobile phone problems, etc.). The opening hours as well as the contact details for the Telecom Lab remain unchanged: New location: Building 58 R-017 Opening hours: Every week day, from 11 a.m. to 12 a.m. Phone number: 72480 Email address: labo.telecom@cern.ch This change has no impact on support requests for mobile services. Users can still submit their requests concerning mobile phone subscriptions using the usual EDH form (https://edh.cern.ch/Document/GSM). The automatic message sent to inform users of their SIM card availability will be updated to indicate the new Telecom Lab location. You can find all information related to CERN mobile phone services at the following link: http://cern.ch/gsm CS Section - IT/CS group
Multiple Instance Regression with Structured Data
Wagstaff, Kiri L.; Lane, Terran; Roper, Alex
2008-01-01
This slide presentation reviews the use of multiple instance regression with structured data from multiple and related data sets. It applies the concept to a practical problem, that of estimating crop yield using remote sensed country wide weekly observations.
Prediction of Dynamical Systems by Symbolic Regression
Quade, Markus; Shafi, Kamran; Niven, Robert K; Noack, Bernd R
2016-01-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting a...
Some Simple Computational Formulas for Multiple Regression
Aiken, Lewis R., Jr.
1974-01-01
Short-cut formulas are presented for direct computation of the beta weights, the standard errors of the beta weights, and the multiple correlation coefficient for multiple regression problems involving three independent variables and one dependent variable. (Author)
Spontaneous Regression of an Incidental Spinal Meningioma
Ali Yilmaz
2015-12-01
Full Text Available AIM: The regression of meningioma has been reported in literature before. In spite of the fact that the regression may be involved by hemorrhage, calcification or some drugs withdrawal, it is rarely observed spontaneously. CASE REPORT: We report a 17 year old man with a cervical meningioma which was incidentally detected. In his cervical MRI an extradural, cranio-caudal contrast enchanced lesion at C2-C3 levels of the cervical spinal cord was detected. Despite the slight compression towards the spinal cord, he had no symptoms and refused any kind of surgical approach. The meningioma was followed by control MRI and it spontaneously regressed within six months. There were no signs of hemorrhage or calcification. CONCLUSION: Although it is a rare condition, the clinicians should consider that meningiomas especially incidentally diagnosed may be regressed spontaneously.
Spontaneous Regression of an Incidental Spinal Meningioma.
Yilmaz, Ali; Kizilay, Zahir; Sair, Ahmet; Avcil, Mucahit; Ozkul, Ayca
2016-03-15
The regression of meningioma has been reported in literature before. In spite of the fact that the regression may be involved by hemorrhage, calcification or some drugs withdrawal, it is rarely observed spontaneously. We report a 17 year old man with a cervical meningioma which was incidentally detected. In his cervical MRI an extradural, cranio-caudal contrast enchanced lesion at C2-C3 levels of the cervical spinal cord was detected. Despite the slight compression towards the spinal cord, he had no symptoms and refused any kind of surgical approach. The meningioma was followed by control MRI and it spontaneously regressed within six months. There were no signs of hemorrhage or calcification. Although it is a rare condition, the clinicians should consider that meningiomas especially incidentally diagnosed may be regressed spontaneously.
Vectors, a tool in statistical regression theory
Corsten, L.C.A.
1958-01-01
Using linear algebra this thesis developed linear regression analysis including analysis of variance, covariance analysis, special experimental designs, linear and fertility adjustments, analysis of experiments at different places and times. The determination of the orthogonal projection, yielding e
Patterns of Regression in Rett Syndrome
J Gordon Millichap
2002-10-01
Full Text Available Patterns and features of regression in a case series of 53 girls and women with Rett syndrome were studied at the Institute of Child Health and Great Ormond Street Children’s Hospital, London, UK.
A new bivariate negative binomial regression model
Faroughi, Pouya; Ismail, Noriszura
2014-12-01
This paper introduces a new form of bivariate negative binomial (BNB-1) regression which can be fitted to bivariate and correlated count data with covariates. The BNB regression discussed in this study can be fitted to bivariate and overdispersed count data with positive, zero or negative correlations. The joint p.m.f. of the BNB1 distribution is derived from the product of two negative binomial marginals with a multiplicative factor parameter. Several testing methods were used to check overdispersion and goodness-of-fit of the model. Application of BNB-1 regression is illustrated on Malaysian motor insurance dataset. The results indicated that BNB-1 regression has better fit than bivariate Poisson and BNB-2 models with regards to Akaike information criterion.
Heteroscedastic regression analysis method for mixed data
FU Hui-min; YUE Xiao-rui
2011-01-01
The heteroscedastic regression model was established and the heteroscedastic regression analysis method was presented for mixed data composed of complete data, type- I censored data and type- Ⅱ censored data from the location-scale distribution. The best unbiased estimations of regression coefficients, as well as the confidence limits of the location parameter and scale parameter were given. Furthermore, the point estimations and confidence limits of percentiles were obtained. Thus, the traditional multiple regression analysis method which is only suitable to the complete data from normal distribution can be extended to the cases of heteroscedastic mixed data and the location-scale distribution. So the presented method has a broad range of promising applications.
黎健; 吴寰宇; 李燕婷; 金汇明; 顾宝柯; 袁政安
2010-01-01
ObjectiveTo explore the feasibility of establishing and applying of autoregressive integrated moving average(ARIMA) model to predict the incidence rate of dysentery in Shanghai,so as to provide the theoretical basis for prevention and control of dysentery. MethodsARIMA model was established based on the monthly incidence rate of dysentery of Shanghai from 1990 to 2007. The parameters of model were estimated through unconditional least squares method, the structure was determined according to criteria of residual un-correlation and concision, and the model goodness-of-fit was determined through Akaike information criterion ( AIC ) and Schwarz Bayesian criterion (SBC). The constructed optimal model was applied to predict the incidence rate of dysentery of Shanghai in 2008 and evaluate the validity of model through comparing the difference of predicted incidence rate and actual one. The incidence rate of dysentery in 2010 was predicted by ARIMA model based on the incidence rate from January 1990 to June 2009. Results The model ARIMA ( 1,1,1 ) (0,1,2) _(12) had a good fitness to the incidence rate with both autoregressive coefficient (AR1= 0. 443 ) during the past time series, moving average coefficient ( MA1 =0. 806) and seasonal moving average coefficient ( SMA1 = 0. 543, SMA2 = 0. 321 ) being statistically significant( P < 0. 01 ). AIC and SBC were 2. 878 and 16. 131 respectively and predicting error was white noise. The mathematic function was ( 1 - 0. 443B) ( 1 - B) ( 1 - B~(12) ) Z_t = ( 1 - 0. 806B) ( 1 - 0. 543B~(12))(1-0. 321B~(2×12) )μ_t,. The predicted incidence rate in 2008 was consistent with the actual one, with the relative error of 6. 78%. The predicted incidence rate of dysentery in 2010 based on the incidence rate from January 1990 to June 2009 would be 9. 390 per 100 thousand. ConclusionARIMA model can be used to fit the changes of incidence rate of dysentery and to forecast the future incidence rate in Shanghai. It is a predicted model of high
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Boosted regression tree, table, and figure data
Spreadsheets are included here to support the manuscript Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition. This dataset is associated with the following publication:Golden , H., C. Lane , A. Prues, and E. D'Amico. Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition. JAWRA. American Water Resources Association, Middleburg, VA, USA, 52(5): 1251-1274, (2016).
Fuzzy multiple linear regression: A computational approach
Juang, C. H.; Huang, X. H.; Fleming, J. W.
1992-01-01
This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.
Discriminative Elastic-Net Regularized Linear Regression.
Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen
2017-03-01
In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.
Spontaneous regression of metastatic Merkel cell carcinoma.
Hassan, S J
2010-01-01
Merkel cell carcinoma is a rare aggressive neuroendocrine carcinoma of the skin predominantly affecting elderly Caucasians. It has a high rate of local recurrence and regional lymph node metastases. It is associated with a poor prognosis. Complete spontaneous regression of Merkel cell carcinoma has been reported but is a poorly understood phenomenon. Here we present a case of complete spontaneous regression of metastatic Merkel cell carcinoma demonstrating a markedly different pattern of events from those previously published.
The Infinite Hierarchical Factor Regression Model
Rai, Piyush
2009-01-01
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We apply this model to two problems (factor analysis and factor regression) in gene-expression data analysis.
Marginal longitudinal semiparametric regression via penalized splines.
Kadiri, M Al; Carroll, R J; Wand, M P
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Moving object detection and tracking in videos through turbulent medium
Halder, Kalyan Kumar; Tahtali, Murat; Anavatti, Sreenatha G.
2016-06-01
This paper addresses the problem of identifying and tracking moving objects in a video sequence having a time-varying background. This is a fundamental task in many computer vision applications, though a very challenging one because of turbulence that causes blurring and spatiotemporal movements of the background images. Our proposed approach involves two major steps. First, a moving object detection algorithm that deals with the detection of real motions by separating the turbulence-induced motions using a two-level thresholding technique is used. In the second step, a feature-based generalized regression neural network is applied to track the detected objects throughout the frames in the video sequence. The proposed approach uses the centroid and area features of the moving objects and creates the reference regions instantly by selecting the objects within a circle. Simulation experiments are carried out on several turbulence-degraded video sequences and comparisons with an earlier method confirms that the proposed approach provides a more effective tracking of the targets.
Multiple-Instance Regression with Structured Data
Wagstaff, Kiri L.; Lane, Terran; Roper, Alex
2008-01-01
We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.
[Iris movement mediates pupillary membrane regression].
Morizane, Yuki
2007-11-01
In the course of mammalian lens development, a transient capillary meshwork called as the pupillary membrane (PM) forms. It is located in the pupil area to nourish the anterior surface of the lens, and then regresses to clear the optical path. Although the involvement of the apoptotic process has been reported in PM regression, the initiating factor remains unknown. We initially found that regression of the PM coincided with the development of iris motility, and that iris movement caused cessation and resumption of blood flow within the PM. Therefore, we investigated whether the development of the capacity of the iris to constrict and dilate can function as an essential signal that induces apoptosis in the PM. Continuous inhibition of iris movement with mydriatic agents suppressed apoptosis of the PM and resulted in the persistence of PM in rats. The distribution of apoptotic cells in the regressing PM was diffuse and showed no apparent localization. These results indicated that iris movement induced regression of the PM by changing the blood flow within it. This study suggests the importance of the physiological interactions between tissues-in this case, the iris and the PM-as a signal to advance vascular regression during organ development.
Post-processing through linear regression
B. Van Schaeybroeck
2011-03-01
Full Text Available Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS method, a new time-dependent Tikhonov regularization (TDTR method, the total least-square method, a new geometric-mean regression (GM, a recently introduced error-in-variables (EVMOS method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified.
These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise. At long lead times the regression schemes (EVMOS, TDTR which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Post-processing through linear regression
van Schaeybroeck, B.; Vannitsem, S.
2011-03-01
Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
李军宏; 李艺星; 尹遵栋; 宁桂军; 罗会明; 梁晓峰
2011-01-01
Objective To establish a model of Autoregressive Integrated Moving Average (ARIMA) product season (p,d,q) (P,D,Q) on time serial data of meningococcal meningitis cases, predict the possible cases in 2010, and evaluate the model predictive effect.Methods Using the data from National Notifiable Diseases Registry System (NNDRS)during 2000-2009, the method of Box-Jenkins was adopted to establish ARIMA product season model, the order of model was confirmed through Akaike Information Criterion ( AIC ) and Bayesian Information Criterion (BIC), Statistics of Box-Ljung was used to evaluate the degree of fitness of ARIMA model, and data analyzed by Spssl3.0.Results The model of product season ARIMA( 1,1,1 )(0,1,1 )12 was established, average relative error of predict was 3.09％, model can appropriately fit the time series of meningococcal meningitis.Total 419 cases of meningococcal meningitis were predicted in 2010, Lower the 95％ CI was 244-719, and the peak at March.Conclusion The product season ARIMA model can be used to fit the time series trend of meningococcal meningitis, and to predict the meningococcal meningitis cases with high prediction precision of short term time series.%目的 应用自回归移动平均(Autoregressive Integrated Moving Average,ARIMA)乘积季节模型(p,d,q)(P,D,Q)s,对流行性脑脊髓膜炎(流脑)发病数据的时间序列资料建模,并预测2010年流脑发病趋势,考察ARIMA乘积季节模型应用于流脑发病的预测效果.方法 利用法定传染病报告系统的数据资料,采用Box-Jenkins 方法建模,依据赤池信息量准则(Akaike's Information Criterion)和施瓦茨信息量准则(Schwarz's Information Criterion)结果确定模型阶数,用Box-Ljung统计量评价ARIMA模型的拟合效果,用所得模型对2010年流脑发病数进行预测.使用社会科学统计软件包时间序列分析模块对资料进行分析.结果 对流脑的季节性时间序列建立了ARIMA(1,1,1)(0,1,1)(12)乘积模型,
Liquid repellency by a moving plate
Bouillant, Ambre; Anais Gauthier Team; David Quere Team; Christophe Clanet Team
2016-11-01
Moving solids can repel impacting drops, owing to their motion. Provided the solid velocity is larger than a threshold value, air entrained at the vicinity of the moving plate prevents the drop from wetting, and makes it bounce. In addition, the rebound is oblique, which enhances the evacuation of liquid. We discuss experiments and models on this theme, and extend them to case of small droplets (such as formed in a spray) found to be even more efficiently repelled by the moving plate.
Kepler AutoRegressive Planet Search: Motivation & Methodology
Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian
2015-08-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal
Wagemans, Johan
2017-07-01
Matthew Pelowski and his colleagues from the Helmut Leder lab [17] have made a remarkable contribution to the field of art perception by reviewing the extensive and varied literature (+300 references) on all the factors involved, from a coherent, synthetic perspective-The Vienna Integrated Model of top-down and bottom-up processes in Art Perception (VIMAP). VIMAP builds on earlier attempts from the same group to provide a comprehensive theoretical framework, but it is much wider in scope and richer in the number of levels and topics covered under its umbrella. It is particularly strong in its discussion of the different psychological processes that lead to a wide range of possible responses to art-from mundane, superficial reactions to more profound responses characterized as moving, disturbing, and transformative. By including physiological, emotional, and evaluative factors, the model is able to address truly unique, even intimate responses to art such as awe, chills, thrills, and the experience of the sublime. The unique way in which this rich set of possible responses to art is achieved is through a series of five mandatory consecutive processing steps (each with their own typical duration), followed by two conditional additional steps (which take more time). Three processing checks along this cascade lead to three more or less spontaneous outcomes (consuming ones (see their Fig. 1 for an excellent overview). I have no doubt that VIMAP will inspire a whole generation of scientists investigating perception and appreciation of art, testing specific hypotheses derived from this framework for decades to come.
Range Imaging without Moving Parts
Blair, J. Bryan; Scott, V. Stanley, III; Ramos-Izquierdo, Luis
2008-01-01
Range-imaging instruments of a type now under development are intended to generate the equivalent of three-dimensional images from measurements of the round-trip times of flight of laser pulses along known directions. These instruments could also provide information on characteristics of targets, including roughnesses and reflectivities of surfaces and optical densities of such semi-solid objects as trees and clouds. Unlike in prior range-imaging instruments based on times of flight along known directions, there would be no moving parts; aiming of the laser beams along the known directions would not be accomplished by mechanical scanning of mirrors, prisms, or other optical components. Instead, aiming would be accomplished by using solid-state devices to switch input and output beams along different fiber-optic paths. Because of the lack of moving parts, these instruments could be extraordinarily reliable, rugged, and long-lasting. An instrument of this type would include an optical transmitter that would send out a laser pulse along a chosen direction to a target. An optical receiver coaligned with the transmitter would measure the temporally varying intensity of laser light reflected from the target to determine the distance and surface characteristics of the target. The transmitter would be a combination of devices for generating precise directional laser illumination. It would include a pulsed laser, the output of which would be coupled into a fiber-optic cable with a fan-out and solid-state optical switches that would enable switching of the laser beam onto one or more optical fibers terminated at known locations in an array on a face at the focal plane of a telescope. The array would be imaged by the telescope onto the target space. The receiver optical system could share the aforementioned telescope with the transmitter or could include a separate telescope aimed in the same direction as that of the transmitting telescope. In either case, light reflected
Efficient robust nonparametric estimation in a semimartingale regression model
Konev, Victor
2010-01-01
The paper considers the problem of robust estimating a periodic function in a continuous time regression model with dependent disturbances given by a general square integrable semimartingale with unknown distribution. An example of such a noise is non-gaussian Ornstein-Uhlenbeck process with the L\\'evy process subordinator, which is used to model the financial Black-Scholes type markets with jumps. An adaptive model selection procedure, based on the weighted least square estimates, is proposed. Under general moment conditions on the noise distribution, sharp non-asymptotic oracle inequalities for the robust risks have been derived and the robust efficiency of the model selection procedure has been shown.
Regression Test Selection for C# Programs
Nashat Mansour
2009-01-01
Full Text Available We present a regression test selection technique for C# programs. C# is fairly new and is often used within the Microsoft .Net framework to give programmers a solid base to develop a variety of applications. Regression testing is done after modifying a program. Regression test selection refers to selecting a suitable subset of test cases from the original test suite in order to be rerun. It aims to provide confidence that the modifications are correct and did not affect other unmodified parts of the program. The regression test selection technique presented in this paper accounts for C#.Net specific features. Our technique is based on three phases; the first phase builds an Affected Class Diagram consisting of classes that are affected by the change in the source code. The second phase builds a C# Interclass Graph (CIG from the affected class diagram based on C# specific features. In this phase, we reduce the number of selected test cases. The third phase involves further reduction and a new metric for assigning weights to test cases for prioritizing the selected test cases. We have empirically validated the proposed technique by using case studies. The empirical results show the usefulness of the proposed regression testing technique for C#.Net programs.
Regression analysis using dependent Polya trees.
Schörgendorfer, Angela; Branscum, Adam J
2013-11-30
Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.
Hyperglycemia impairs atherosclerosis regression in mice.
Gaudreault, Nathalie; Kumar, Nikit; Olivas, Victor R; Eberlé, Delphine; Stephens, Kyle; Raffai, Robert L
2013-12-01
Diabetic patients are known to be more susceptible to atherosclerosis and its associated cardiovascular complications. However, the effects of hyperglycemia on atherosclerosis regression remain unclear. We hypothesized that hyperglycemia impairs atherosclerosis regression by modulating the biological function of lesional macrophages. HypoE (Apoe(h/h)Mx1-Cre) mice express low levels of apolipoprotein E (apoE) and develop atherosclerosis when fed a high-fat diet. Atherosclerosis regression occurs in these mice upon plasma lipid lowering induced by a change in diet and the restoration of apoE expression. We examined the morphological characteristics of regressed lesions and assessed the biological function of lesional macrophages isolated with laser-capture microdissection in euglycemic and hyperglycemic HypoE mice. Hyperglycemia induced by streptozotocin treatment impaired lesion size reduction (36% versus 14%) and lipid loss (38% versus 26%) after the reversal of hyperlipidemia. However, decreases in lesional macrophage content and remodeling in both groups of mice were similar. Gene expression analysis revealed that hyperglycemia impaired cholesterol transport by modulating ATP-binding cassette A1, ATP-binding cassette G1, scavenger receptor class B family member (CD36), scavenger receptor class B1, and wound healing pathways in lesional macrophages during atherosclerosis regression. Hyperglycemia impairs both reduction in size and loss of lipids from atherosclerotic lesions upon plasma lipid lowering without significantly affecting the remodeling of the vascular wall.
Search Moves Made by Novice End Users.
Wildemuth, Barbara M.; And Others
1992-01-01
Describes a study at the University of North Carolina at Chapel Hill that analyzed the transaction logs of medical students' searches of a factual database to determine the overall frequency of search moves, the interaction between the problem statement and students' search strategies, the search moves selected, and the tactics used by students.…
Fixing the Shadows While Moving the Gnomon
Gangui, Alejandro
2015-01-01
It is a common practice to fix a vertical gnomon and study the moving shadow cast by it. This shows our local solar time and gives us a hint regarding the season in which we perform the observation. The moving shadow can also tell us our latitude with high precision. In this paper we propose to exchange the roles and while keeping the shadows…
Moving Crystal Slow-Neutron Wavelength Analyser
Buras, B.; Kjems, Jørgen
1973-01-01
Experimental proof that a moving single crystal can serve as a slow-neutron wavelength analyser of special features is presented. When the crystal moves with a velocity h/(2 md) (h-Planck constant, m-neutron mass, d-interplanar spacing) perpendicular to the diffracting plane and the analysed...
Moving Crystal Slow-Neutron Wavelength Analyser
Buras, B.; Kjems, Jørgen
1973-01-01
Experimental proof that a moving single crystal can serve as a slow-neutron wavelength analyser of special features is presented. When the crystal moves with a velocity h/(2 md) (h-Planck constant, m-neutron mass, d-interplanar spacing) perpendicular to the diffracting plane and the analysed...
Online Risk Prediction for Indoor Moving Objects
Ahmed, Tanvir; Pedersen, Torben Bach; Calders, Toon
2016-01-01
Technologies such as RFID and Bluetooth have received considerable attention for tracking indoor moving objects. In a time-critical indoor tracking scenario such as airport baggage handling, a bag has to move through a sequence of locations until it is loaded into the aircraft. Inefficiency...
Testing linearity against nonlinear moving average models
de Gooijer, J.G.; Brännäs, K.; Teräsvirta, T.
1998-01-01
Lagrange multiplier (LM) test statistics are derived for testing a linear moving average model against an additive smooth transition moving average model. The latter model is introduced in the paper. The small sample performance of the proposed tests are evaluated in a Monte Carlo study and compared
Improvisation: Another Way to Move and Dance
Morgan, Rachel
2004-01-01
Using improvisation in movement and dance classes is an ideal way to help students relate to how their bodies move. Students can learn confidence from the way they move by experimenting with unconventional and different methods. Improvisation, as such, is responding spontaneously to stimuli (music) in order to create a composition that allows for…
Moving core beam energy absorber and converter
Degtiarenko, Pavel V.
2012-12-18
A method and apparatus for the prevention of overheating of laser or particle beam impact zones through the use of a moving-in-the-coolant-flow arrangement for the energy absorbing core of the device. Moving of the core spreads the energy deposition in it in 1, 2, or 3 dimensions, thus increasing the effective cooling area of the device.
Moving from production to services
Carassus, Jean; Andersson, Niclas; Kaklauskas, Arturas
2006-01-01
The construction industry is no longer focused on providing a single product - i.e. a building or a physical infrastructure, but a variety of services and improvement to the human environment. Major trends such as Performance-based Building as well as Sustainable Built Environment are calling...... of the industry. This new approach provides an excellent starting point for developing new performance indicators that will take into account the changing nature of the industry, for an integrative perspective providing a basis for strategic management, for studying sustainable development in construction...... and for understanding innovation processes and changes. A comprehensive perspective of the industry performance is crucial for policy initiatives as well as for strategic analysis of firms....
CERN. Geneva
2012-01-01
A Large Ion Collider Experiment (ALICE) is the heavy-ion detector designed to study the physics of strongly interacting matter and the quark-gluon plasma at the CERN Large Hadron Collider (LHC). Since its successful start-up in 2010, the LHC has been performing outstandingly, providing to the experiments long periods of stable collisions and an integrated luminosity that greatly exceeds the planned targets. To fully explore these privileged conditions, we aim at maximizing the experiment's data taking productivity during stable collisions. We present in this paper the evolution of the online systems in order to spot reasons of inefficiency and address new requirements. This paper describes the features added to the ALICE Electronic Logbook (eLogbook) to allow the Run Coordination team to identify, prioritize, fix and follow causes of inefficiency in the experiment. Thorough monitoring of the data taking efficiency provides reports for the collaboration to portray its evolution and evaluate the measures (fix...
CART III: improved camouflage assessment using moving target indication
Müller, Thomas; Honke, Thomas; Müller, Markus
2009-05-01
In order to facilitate systematic, computer aided improvements of camouflage and concealment assessment methods, the software system CART (Camouflage Assessment in Real-Time) was built up for the camouflage assessment of objects in image sequences (see contributions to SPIE 2007 and SPIE 2008 [1], [2]). It works with visual-optical, infrared and SAR image sequences. The system comprises a semi-automatic annotation functionality for marking target objects (ground truth generation) including a propagation of those markings over the image sequence for static as well as moving scene objects, where the recording camera may be static or moving. The marked image regions are evaluated by applying user-defined feature extractors, which can easily be defined and integrated into the system via a generic software interface. This article presents further systematic enhancements made in the recent year and addresses particularly the task of the detection of moving vehicles by latest image exploitation methods for objective camouflage assessment in these cases. As a main topic, the loop was closed between the two natural opposites of reconnaissance and camouflage, which was realized by incorporating ATD (Automatic Target Detection) algorithms into the computer aided camouflage assessment. Since object (and sensor) movement is an important feature for many applications, different image-based MTI (Moving Target Indication) algorithms were included in the CART system, which rely on changes in the image plane from an image to the successive one (after camera movements are automatically compensated). Additionally, the MTI outputs over time are combined in a certain way which we call "snail track" algorithm. The results show that their output provides a valuable measurement for the conspicuity of moving objects and therefore is an ideal component in the camouflage assessment. It is shown that image-based MTI improvements lead to improvements in the camouflage assessment process.
Competing Risks Quantile Regression at Work
Dlugosz, Stephan; Lo, Simon M. S.; Wilke, Ralf
2017-01-01
Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We us...... into the distribution of transitions out of maternity leave. It is found that cumulative incidences implied by the quantile regression model differ from those implied by a proportional hazards model. To foster the use of the model, we make an R-package (cmprskQR) available....... large-scale maternity duration data with multiple competing risks derived from German linked social security records to analyse how public policies are related to the length of economic inactivity of young mothers after giving birth. Our results show that the model delivers detailed insights...
SMOOTH TRANSITION LOGISTIC REGRESSION MODEL TREE
RODRIGO PINTO MOREIRA
2008-01-01
Este trabalho tem como objetivo principal adaptar o modelo STR-Tree, o qual é a combinação de um modelo Smooth Transition Regression com Classification and Regression Tree (CART), a fim de utilizá-lo em Classificação. Para isto algumas alterações foram realizadas em sua forma estrutural e na estimação. Devido ao fato de estarmos fazendo classificação de variáveis dependentes binárias, se faz necessária a utilização das técnicas empregadas em Regressão Logística, dessa forma a estimação dos pa...
Unsupervised K-Nearest Neighbor Regression
Kramer, Oliver
2011-01-01
In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or for face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regression framework for learning of low-dimensional manifolds. Similar to related approaches that are mostly based on kernel methods, unsupervised K-nearest neighbor (UKNN) regression optimizes latent variables w.r.t. the data space reconstruction error employing the K-nearest neighbor heuristic. The problem of optimizing latent neighborhoods is difficult to solve, but the UKNN formulation allows an efficient strategy of iteratively embedding latent points to fixed neighborhood topologies. The approaches will be tested experimentally.
LINEAR REGRESSION WITH R AND HADOOP
Bogdan OANCEA
2015-07-01
Full Text Available In this paper we present a way to solve the linear regression model with R and Hadoop using the Rhadoop library. We show how the linear regression model can be solved even for very large models that require special technologies. For storing the data we used Hadoop and for computation we used R. The interface between R and Hadoop is the open source library RHadoop. We present the main features of the Hadoop and R software systems and the way of interconnecting them. We then show how the least squares solution for the linear regression problem could be expressed in terms of map-reduce programming paradigm and how could be implemented using the Rhadoop library.