Future atmospheric CO2 leads to delayed autumnal senescence

Science.gov (United States)

Gail Taylor; Matthew J. Tallis; Christian P. Giardina; Kevin E. Percy; Franco Miglietta; Pooja S. Gupta; Beniamin Gioli; Carlo Calfapietra; Birgit Gielen; Mark E. Kubiske; Giuseppe E. Scarascia-Mugnozza; Katre Kets; Stephen P. Long; David F. Karnosky

2008-01-01

Growing seasons are getting longer, a phenomenon partially explained by increasing global temperatures. Recent reports suggest that a strong correlation exists between warming and advances in spring phenology but that a weaker correlation is evident between warming and autumnal events implying that other factors may be influencing the timing of autumnal phenology....

RSM Outlook Autumn 2005 : Branding

NARCIS (Netherlands)

G. Kemp (Gail); R. Morris (Rebecca)

2005-01-01

markdownabstract#### Contents The inaugural issue of RSM Outlook from autumn 2005 includes the opening of the new T-building, and how RSM celebrated its 35th birthday with a wine-tasting session. There are also articles on Professor Cees van Riel and reputation management, the re-branding of the

Novel complex therapy of autumnal allergic blepharoconjuctivitis

Directory of Open Access Journals (Sweden)

S. V. Yanchenko

2014-10-01

Full Text Available Aim. To assess the effectivity of autumnal allergic blepharoconjuctivitis complex therapy.Methods. 25 autumnal allergic blepharoconjuctivitis patients (50 eyes were examined before and after complex treatment that included olopatadine hydrochloride 1 mg / ml (instillations 2 times a day, cetirizine 10 mg (1 tablet a day, and steroid drug (insufflations 2 times a day. Dry eye patients additionally received hyaluronic acid 1 mg / ml (instillations 2 times a day. 10 controls (20 eyes were prescribed only the above-mentioned treatment. In 15 study group patients (30 eyes, Blepharogel 1 was applied on lid margins. Routine eye examination, clinical symptom assessment, Schirmer’s and Norn’s tests, xerosis meter and lissamine green staining evaluation, and anterior segment photography with computed morphometry were performed.Results. Compositae allergy was diagnosed in all patients. Dry eye due to tear film instability, lipid deficiency, and mucin deficiency and epitheliopathy were diagnosed in 55 %, 35.5 % and 28.3 %, respectively. In study group, the treatment significantly and rapidly reduced patient-reported symptoms and blepharoconjunctivitis signs as well as significantly improved tear stability, lipid deficiency, mucin deficiency, and epitheliopathy as compared with controls.Conclusion. Blepharogel 1 as a component of complex therapy increases the efficacy of autumnal allergic blepharoconjuctivitis treatment.

Novel complex therapy of autumnal allergic blepharoconjuctivitis

Directory of Open Access Journals (Sweden)

S. V. Yanchenko

2014-01-01

Full Text Available Aim. To assess the effectivity of autumnal allergic blepharoconjuctivitis complex therapy.Methods. 25 autumnal allergic blepharoconjuctivitis patients (50 eyes were examined before and after complex treatment that included olopatadine hydrochloride 1 mg / ml (instillations 2 times a day, cetirizine 10 mg (1 tablet a day, and steroid drug (insufflations 2 times a day. Dry eye patients additionally received hyaluronic acid 1 mg / ml (instillations 2 times a day. 10 controls (20 eyes were prescribed only the above-mentioned treatment. In 15 study group patients (30 eyes, Blepharogel 1 was applied on lid margins. Routine eye examination, clinical symptom assessment, Schirmer’s and Norn’s tests, xerosis meter and lissamine green staining evaluation, and anterior segment photography with computed morphometry were performed.Results. Compositae allergy was diagnosed in all patients. Dry eye due to tear film instability, lipid deficiency, and mucin deficiency and epitheliopathy were diagnosed in 55 %, 35.5 % and 28.3 %, respectively. In study group, the treatment significantly and rapidly reduced patient-reported symptoms and blepharoconjunctivitis signs as well as significantly improved tear stability, lipid deficiency, mucin deficiency, and epitheliopathy as compared with controls.Conclusion. Blepharogel 1 as a component of complex therapy increases the efficacy of autumnal allergic blepharoconjuctivitis treatment.

Experimental temperature manipulations alter songbird autumnal nocturnal migratory restlessness

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Berchtold Adrienne

2017-02-01

Full Text Available Migrating birds may respond to a variety of environmental cues in order to time migration. During the migration season nocturnally migrating songbirds may migrate or stop-over at their current location, and when migrating they may vary the rate or distance of migration on any given night. It has long been known that a variety of weather-related factors including wind speed and direction, and temperature, are correlated with migration in free-living birds, however these variables are often correlated with each other. In this study we experimentally manipulated temperature to determine if it would directly modulate nocturnal migratory restlessness in songbirds. We experimentally manipulated temperature between 4, 14, and 24°C and monitored nocturnal migratory restlessness during autumn in white-throated sparrows (Zonotrichia albicollis. White-throated sparrows are relatively shortdistance migrants with a prolonged autumnal migration, and we thus predicted they might be sensitive to weatherrelated cues when deciding whether to migrate or stopover. At warm temperatures (24°C none of the birds exhibited migratory restlessness. The probability of exhibiting migratory restlessness, and the intensity of this restlessness (number of infra-red beam breaks increased at cooler (14°C, 4°C temperatures. These data support the hypothesis that one of the many factors that birds use when making behavioural decisions during migration is temperature, and that birds can respond to temperature directly independently of other weather-related cues.

Timing and duration of autumn leaf development in Sweden, a 4-year citizen science study

Science.gov (United States)

Bolmgren, Kjell; Langvall, Ola

2017-04-01

Phenology monitoring has traditionally focused on the start of phenological phases and the start of the growing season, especially when it comes to species-specific observations on the ground. The patterns of and the mechanisms behind the end of particular phases and the growing season itself are less studied and poorly understood. With a changing climate, the need to understand and predict effects on the length as well as on the end of phenological phases increase in importance, e.g. in relation to estimations of carbon budgets and validation of remote sensing data. Furthermore, different species may be affected in different ways by changing conditions. In this 4-year-study, tens of thousands of pupils in ages from 6 to 19 years old were involved in observing autumn leaf development of common deciduous tree species. Their observations were made near schools all over Sweden (55-68°N). Observations were made weekly between late August and early November and followed an image-based observation protocol, classifying autumn leaf development into five levels, from a summer-green (level 0) to a 100% autumn-colored (level 4) canopy. As expected, there was a general (negative) correlation between latitude and the start of leaf senescence (level 2; 1/3 autumn-colored canopy), but the correlation differed largely among years and between species. There was a week correlation between latitude and duration of the leaf senescence period, defined as the period between 1/3 (level 2) and 100% (level 4) of autumn-colored canopy. A delayed onset of the leaf senescence affected the duration of the leaf senescence period more strongly; One (1) day later start was correlated with a 5-day shorter period. Different species had different length of their senescence period, with oak (mainly Quercus robur) and birches (Betula pendula and B. pubescence) having on average a 50% longer period than trembling aspen (Populus tremula) and Norway maple (Acer platanoides).

A robust empirical seasonal prediction of winter NAO and surface climate.

Science.gov (United States)

Wang, L; Ting, M; Kushner, P J

2017-03-21

A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how accurately models can predict the NAO. Most dynamical models, however, have limited skill in seasonal forecasts of the winter NAO. A new empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill than current dynamical models. The empirical model provides robust and skilful prediction of the December-January-February (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions of sea-ice concentration, stratospheric circulation, and sea-surface temperature. The predictability is, for the most part, derived from the relatively long persistence of sea ice in the autumn. The lower stratospheric circulation and sea-surface temperature appear to play more indirect roles through a series of feedbacks among systems driving NAO evolution. This MLR model also provides skilful seasonal outlooks of winter surface temperature and precipitation over many regions of Eurasia and eastern North America.

Outlier Loci Detect Intraspecific Biodiversity amongst Spring and Autumn Spawning Herring across Local Scales.

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Dorte Bekkevold

Full Text Available Herring, Clupea harengus, is one of the ecologically and commercially most important species in European northern seas, where two distinct ecotypes have been described based on spawning time; spring and autumn. To date, it is unknown if these spring and autumn spawning herring constitute genetically distinct units. We assessed levels of genetic divergence between spring and autumn spawning herring in the Baltic Sea using two types of DNA markers, microsatellites and Single Nucleotide Polymorphisms, and compared the results with data for autumn spawning North Sea herring. Temporally replicated analyses reveal clear genetic differences between ecotypes and hence support reproductive isolation. Loci showing non-neutral behaviour, so-called outlier loci, show convergence between autumn spawning herring from demographically disjoint populations, potentially reflecting selective processes associated with autumn spawning ecotypes. The abundance and exploitation of the two ecotypes have varied strongly over space and time in the Baltic Sea, where autumn spawners have faced strong depression for decades. The results therefore have practical implications by highlighting the need for specific management of these co-occurring ecotypes to meet requirements for sustainable exploitation and ensure optimal livelihood for coastal communities.

Effect of "owners" selection strategies on autumn weight in reindeer (Rangifer t. tarandus calves

Directory of Open Access Journals (Sweden)

Robert B. Weladji

2002-03-01

Full Text Available Many northern indigenous peoples, including the Sami are dependent on reindeer herding for their livelihood. In view of the socio-cultural and economical importance of reindeer herding, emphasis should be put on appropriate herd structure and selection strategies that maximise marketable products, such as meat (the primary marketable product nowadays. Empirical observations reveal that within a herd, some owners seem to have better productivity in term of carcass autumn weight of calves, than others. We hypothesized that there may be an "owner" effect in reindeer herding, i.e. some owners may be applying particular selection strategies that might be beneficial. We investigated this in three reindeer grazing districts in South Norway, using mixed linear models. We found that autumn carcass weight of calves varied significantly with year and "owner" within herd in all three districts. Consistently some particular owners within a herd had higher average autumn carcass weight of their calves than others. We attributed this difference to "individual selection strategies", meaning that some owners may follow more accurately the sex, age and weight-based recommended strategy and in addition, they may make superior choices when selecting animals for slaughtering. We conclude that individual owners have the capability, through appropriate selection decisions to improve the average annual autumn weight of their reindeer calves. This might be an important aspect of "Traditional Ecological Knowledge", in addition to the recommended modern sex, age and weight-based selection criteria.

Modular programming for tuberculosis control, the "AuTuMN" platform.

Science.gov (United States)

Trauer, James McCracken; Ragonnet, Romain; Doan, Tan Nhut; McBryde, Emma Sue

2017-08-07

Tuberculosis (TB) is now the world's leading infectious killer and major programmatic advances will be needed if we are to meet the ambitious new End TB Targets. Although mathematical models are powerful tools for TB control, such models must be flexible enough to capture the complexity and heterogeneity of the global TB epidemic. This includes simulating a disease that affects age groups and other risk groups differently, has varying levels of infectiousness depending upon the organ involved and varying outcomes from treatment depending on the drug resistance pattern of the infecting strain. We adopted sound basic principles of software engineering to develop a modular software platform for simulation of TB control interventions ("AuTuMN"). These included object-oriented programming, logical linkage between modules and consistency of code syntax and variable naming. The underlying transmission dynamic model incorporates optional stratification by age, risk group, strain and organ involvement, while our approach to simulating time-variant programmatic parameters better captures the historical progression of the epidemic. An economic model is overlaid upon this epidemiological model which facilitates comparison between new and existing technologies. A "Model runner" module allows for predictions of future disease burden trajectories under alternative scenario situations, as well as uncertainty, automatic calibration, cost-effectiveness and optimisation. The model has now been used to guide TB control strategies across a range of settings and countries, with our modular approach enabling repeated application of the tool without the need for extensive modification for each application. The modular construction of the platform minimises errors, enhances readability and collaboration between multiple programmers and enables rapid adaptation to answer questions in a broad range of contexts without the need for extensive re-programming. Such features are particularly

Sensitivity of the sea ice concentration over the Kara-Barents Sea in autumn to the winter temperature variability over East Asia

Science.gov (United States)

Cho, K. H.; Chang, E. C.

2017-12-01

In this study, we performed sensitivity experiments by utilizing the Global/Regional Integrated Model system with different conditions of the sea ice concentration over the Kara-Barents (KB) Sea in autumn, which can affect winter temperature variability over East Asia. Prescribed sea ice conditions are 1) climatological autumn sea ice concentration obtained from 1982 to 2016, 2) reduced autumn sea ice concentration by 50% of the climatology, and 3) increased autumn sea ice concentration by 50% of climatology. Differently prescribed sea ice concentration changes surface albedo, which affects surface heat fluxes and near-surface air temperature. The reduced (increased) sea ice concentration over the KB sea increases (decreases) near-surface air temperature that leads the lower (higher) sea level pressure in autumn. These patterns are maintained from autumn to winter season. Furthermore, it is shown that the different sea ice concentration over the KB sea has remote effects on the sea level pressure patterns over the East Asian region. The lower (higher) sea level pressure over the KB sea by the locally decreased (increased) ice concentration is related to the higher (lower) pressure pattern over the Siberian region, which induces strengthened (weakened) cold advection over the East Asian region. From these sensitivity experiments it is clarified that the decreased (increased) sea ice concentration over the KB sea in autumn can lead the colder (warmer) surface air temperature over East Asia in winter.

Conductive and Mixed Hearing Losses: A Comparison between Summer and Autumn.

Science.gov (United States)

Nickbakht, Mansoureh; Borzoo, Samira

2014-04-01

Conductive hearing loss is common among children and adults. This study aims at comparing the results of conductive hearing loss in summer and autumn. Puretone audiometry and tympanometry tests were done for all patients who referred to the Iranian-based audiology center of Imam Khomeini Hospital in Ahvaz. Data on the patients with conductive or mixed hearing loss were analyzed. The impacts of season, age, and etiology of the disease were analyzed on the patients who visited the audiology clinic due to the conductive hearing loss in summer and autumn. One hundred and fifty nine patients in summer and 123 patients in autumn had conductive or mixed hearing loss. Their age ranged from four to 82 years, with the average age of 35. The percentage of the patients, with acute otitis media and chronic otitis media (COM), who visited this clinic, was significantly higher than those with middle ear problems. COM and mastoid surgeries rate was higher in summer than autumn among adults. There is no relationship between season and middle ear diseases between children and juveniles, but COM and mastoid problems are more common in summer among adults visiting this clinic. Most of the patients had mild conductive hearing loss and bilateral middle ear impairments.

Decadal oscillation of autumn precipitation in Central Vietnam modulated by the East Pacific–North Pacific (EP–NP) teleconnection

International Nuclear Information System (INIS)

Li, R; Wang, S-Y; Gillies, R R; Cho, C; Buckley, B M; Truong, L H

2015-01-01

Autumn precipitation over Central Vietnam is associated with an increase in the occurrence of tropical cyclones that lead to frequent flooding and pose a significant threat to lives and property. The present analyses reveal a pronounced decadal oscillation of autumn precipitation in Central Vietnam within the 8–11 year frequency band that is modulated by the East Pacific–North Pacific (EP–NP) teleconnection. The negative phase of the EP–NP pattern is associated with a positive sea surface temperature (SST) anomaly in the South China Sea (SCS) that induces low-level convergence, enhances convection, and increases precipitation over Central Vietnam and adjacent islands including Hainan (China) and the Philippines. This circulation feature around the SCS is embedded in a large-scale circulation associated with SST anomalies across the Pacific Ocean—i.e., cooling in the Eastern and Central tropical Pacific sandwiched by warming in the North and South Pacific as well as the Western Pacific Ocean. The positive phase of the EP–NP features opposite SST and circulation anomalies, with the result being reduced rainfall in Central Vietnam. This out-of-phase relationship and shared decadal spectral coherence between the EP–NP index and autumn precipitation in Central Vietnam might be useful for future climate predictions and flood management. (letter)

Long-distance autumn migration across the Sahara by painted lady butterflies: exploiting resource pulses in the tropical savannah.

Science.gov (United States)

Stefanescu, Constantí; Soto, David X; Talavera, Gerard; Vila, Roger; Hobson, Keith A

2016-10-01

The painted lady, Vanessa cardui, is a migratory butterfly that performs an annual multi-generational migration between Europe and North Africa. Its seasonal appearance south of the Sahara in autumn is well known and has led to the suggestion that it results from extremely long migratory flights by European butterflies to seasonally exploit the Sahel and the tropical savannah. However, this possibility has remained unproven. Here, we analyse the isotopic composition of butterflies from seven European and seven African countries to provide new support for this hypothesis. Each individual was assigned a geographical natal origin, based on its wing stable hydrogen isotope (δ 2 H w ) value and a predicted δ 2 H w basemap for Europe and northern Africa. Natal assignments of autumn migrants collected south of the Sahara confirmed long-distance movements (of 4000 km or more) starting in Europe. Samples from Maghreb revealed a mixed origin of migrants, with most individuals with a European origin, but others having originated in the Sahel. Therefore, autumn movements are not only directed to northwestern Africa, but also include southward and northward flights across the Sahara. Through this remarkable behaviour, the productive but highly seasonal region south of the Sahara is incorporated into the migratory circuit of V. cardui. © 2016 The Author(s).

Vitamin D status and its determinants during autumn in children at northern latitudes

DEFF Research Database (Denmark)

Petersen, Rikke Agnete; Damsgaard, Camilla T.; Dalskov, Stine-Mathilde

2015-01-01

Sufficient summer/autumn vitamin D status appears important to mitigate winter nadirs at northern latitudes. We conducted a cross-sectional study to evaluate autumn vitamin D status and its determinants in 782 Danish 8-11-year-old children (55°N) using baseline data from the Optimal well-being...

Habitat use of migratory bats killed during autumn at wind turbines.

Science.gov (United States)

Voigt, Christian C; Lindecke, Oliver; Schönborn, Sophia; Kramer-Schadt, Stephanie; Lehmann, David

2016-04-01

The killing of large numbers of migratory bats at wind turbines is a pressing conservation problem. Even though avoidance and mitigation measures could benefit from a better knowledge of the species' migratory habits, we lack basic information about what habitats and corridors bats use during migration. We studied the isotopic niche dimensions of three bat species that are frequently killed at wind turbines in Germany: non-migratory Pipistrellus pipistrellus, mid-distance migratory Nyctalus noctula, and long- distance migratory Pipistrellus nathusii. We measured stable carbon and nitrogen isotope ratios (δ¹³C, δ¹⁵N) in five tissues that differed in isotopic retention time (fur, wing membrane tissue, muscle, liver, blood) to shed light on the species-specific habitat use during the autumn migration period using standard ellipse areas (SEAc). Further, we used stable isotope ratios of non-exchangeable hydrogen (δ²H(K)) in fur keratin to assess the breeding origin of bats. We inferred from isotopic composition (δ¹³C, δ¹⁵N) of fur keratin that isotopic niche dimensions of P. nathusii was distinct from that of N. noctula and P. pipistrellus, probably because P. nathusii was using more aquatic habitats than the other two species. Isoscape origin models supported that traveled distances before dying at wind turbines was largest for P. nathusii, intermediate for N. noctula, and shortest for P. pipistrellus. Isotopic niche dimensions calculated for each sample type separately reflected the species' migratory behavior. Pipistrellus pipistrellus and N. noctula showed similar isotopic niche breadth across all tissue types, whereas SEAc values of P. nathusii increased in tissues with slow turnaround time. Isotopic data suggested that P. nathusii consistently used aquatic habitats throughout the autumn period, whereas N. noctula showed a stronger association with terrestrial habitats during autumn compared to the pre-migration period.

The autumn when 27 gas power plants disappeared

International Nuclear Information System (INIS)

Braaten, Jan

2003-01-01

In the autumn of 2002 the influx of water to the hydropower plants in the Nordic countries was only half the normal value. The probability of such an extreme failure is less than 0.5 per cent. Prices rose during the autumn, which led to import to the hydropower system and start-up of stand by production capacity and consumption cut. The market and trade essentially worked well and the high prices were the prime mover. Hydropower is an unstable resource and one therefore needs trade possibilities and flexibility in consumption and production to handle variation of the influx. Also, perhaps, one should stimulate the reservoir owners to retain some more water in the reservoirs. A weak energy balance increases the risk of dry year crises. An active grip on this is necessary to reduce vulnerability in the future. While there was plenty of water in the first part of 2002, the influx to the power plants failed fatally during autumn; from week number 31 to the rest of the year only the equivalent of 35 TWh, which is half that of a normal year. This is a historic all-time low. Early next year the influx of water was less than normal. The energy shortage due to hydropower failure during the last 20 weeks of 2002 was greater than it would have been if instead all the Swedish nuclear power had failed. If an energy failure this size should have been covered by means of gas power, 24 gas power works of 400 MW each would be required. At the same time, the import from the Continent was restricted by problems with two cables corresponding to a loss of tree more gas power plants. Thus, the power crisis of the autumn of 2002 corresponds to 27 gas power plants. Gas power plants is a controversial issue in the energy policy debate in Norway, but there are plans for a 400 MW plant. This is food for thought for all those politicians who are worried about the possible environmental impact of gas power plants.

Radical budget and ozone chemistry during autumn in the atmosphere of an urban site in central China

Science.gov (United States)

Lu, Xingcheng; Chen, Nan; Wang, Yuhang; Cao, Wenxiang; Zhu, Bo; Yao, Teng; Fung, Jimmy C. H.; Lau, Alexis K. H.

2017-03-01

The ROx (=OH + HO2 + RO2) budget and O3 production at an urban site in central China (Wuhan) during autumn were simulated and analyzed for the first time using a UW Chemical Model 0-D box model constrained by in situ observational data. The daytime average OH, HO2, and RO2 concentrations were 2.2 × 106, 1.0 × 108, and 5.2 × 107 molecules cm-3, respectively. The average daytime O3 production rate was 8.8 ppbv h-1, and alkenes were the most important VOC species for O3 formation (contributing 45%) at this site. Our sensitivity test indicated that the atmospheric environment in Wuhan during autumn belongs to the VOC-limited regime. The daily average HONO concentration at this site during the study period reached 1.1 ppbv and played an important role in the oxidative capacity of the atmosphere. Without the source of excess HONO, the average daytime OH, HO2, RO2, and O3 production rates decreased by 36%, 26%, 27%, and 31% respectively. A correlation between the HONO to NO2 heterogeneous conversion efficiency and PM2.5 × SWR was found at this site; based on this relationship, if the PM2.5 concentration met the World Health Organization air quality standard (25 µg m-3), the O3 production rate in this city would decrease by 19% during late autumn. The burning of agricultural biomass severely affected the air quality in Wuhan during summer and autumn. Agricultural burning was found to account for 18% of the O3 formation during the study period. Our results suggest that VOC control and a ban on agricultural biomass burning should be considered as high-priority measures for improving the air quality in this region.

Drought-induced photosynthetic inhibition and autumn recovery in two Mediterranean oak species (Quercus ilex and Quercus suber).

Science.gov (United States)

Vaz, M; Pereira, J S; Gazarini, L C; David, T S; David, J S; Rodrigues, A; Maroco, J; Chaves, M M

2010-08-01

Responses of leaf water relations and photosynthesis to summer drought and autumn rewetting were studied in two evergreen Mediterranean oak species, Quercus ilex spp. rotundifolia and Quercus suber. The predawn leaf water potential (Ψ(lPD)), stomatal conductance (gs) and photosynthetic rate (A) at ambient conditions were measured seasonally over a 3-year period. We also measured the photosynthetic response to light and to intercellular CO₂ (A/PPFD and A/C(i) response curves) under water stress (summer) and after recovery due to autumn rainfall. Photosynthetic parameters, Vc(max), J(max) and triose phosphate utilization (TPU) rate, were estimated using the Farquhar model. RuBisCo activity, leaf chlorophyll, leaf nitrogen concentration and leaf carbohydrate concentration were also measured. All measurements were performed in the spring leaves of the current year. In both species, the predawn leaf water potential, stomatal conductance and photosynthetic rate peaked in spring, progressively declined throughout the summer and recovered upon autumn rainfall. During the drought period, Q. ilex maintained a higher predawn leaf water potential and stomatal conductance than Q. suber. During this period, we found that photosynthesis was not only limited by stomatal closure, but was also downregulated as a consequence of a decrease in the maximum carboxylation rate (Vc(max)) and the light-saturated rate of photosynthetic electron transport (J(max)) in both species. The Vc(max) and J(max) increased after the first autumnal rains and this increase was related to RuBisCo activity, leaf nitrogen concentration and chlorophyll concentration. In addition, an increase in the TPU rate and in soluble leaf sugar concentration was observed in this period. The results obtained indicate a high resilience of the photosynthetic apparatus to summer drought as well as good recovery in the following autumn rains of these evergreen oak species.

Uptake of rare earth elements by dryopteris erythrosora (autumn fern)

International Nuclear Information System (INIS)

Ozaki, Takuo; Enomoto, Shuichi

2001-01-01

Mechanisms of uptake of rare earth elements (REEs) were investigated, particularly those by REE accumulator species (autumn fern). Rare earth elements are practically insoluble under natural conditions, suggesting some unknown mechanisms in REE accumulator species. In the present investigation, two notable phenomena were observed. (1) Concerning the ionic-radius dependence of REE uptake by leaves, nonaccumulator species showed an extremely high uptake for Y compared with the adjacent-ionic-radius REEs in the multitracer, while accumulator species showed no anomaly. (2) REE uptake by autumn fern was influenced by the addition of chelating chemical reagents in the uptake solution, while no effect was observed for nonaccumulator species. (author)

Accidental poisoning with autumn crocus.

Science.gov (United States)

Gabrscek, Lucija; Lesnicar, Gorazd; Krivec, Bojan; Voga, Gorazd; Sibanc, Branko; Blatnik, Janja; Jagodic, Boris

2004-01-01

We describe a case of a 43-yr-old female with severe multiorgan injury after accidental poisoning with Colchicum autumnale, which was mistaken for wild garlic (Allium ursinum). Both plants grow on damp meadows and can be confused in the spring when both plants have leaves but no blossoms. The autumn crocus contains colchicine, which inhibits cellular division. Treatment consisted of supportive care, antibiotic therapy, and granulocyte-directed growth factor. The patient was discharged from the hospital after three weeks. Three years after recovery from the acute poisoning, the patient continued to complain of muscle weakness and intermittent episodes of hair loss.

Fluorescence measurements show stronger cold inhibition of photosynthetic light reactions in Scots pine compared to Norway spruce as well as during spring compared to autumn.

Science.gov (United States)

Linkosalo, Tapio; Heikkinen, Juha; Pulkkinen, Pertti; Mäkipää, Raisa

2014-01-01

We studied the photosynthetic activity of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst) in relation to air temperature changes from March 2013 to February 2014. We measured the chlorophyll fluorescence of approximately 50 trees of each species growing in southern Finland. Fluorescence was measured 1-3 times per week. We began by measuring shoots present in late winter (i.e., March 2013) before including new shoots once they started to elongate in spring. By July, when the spring shoots had achieved similar fluorescence levels to the older ones, we proceeded to measure the new shoots only. We analyzed the data by fitting a sigmoidal model containing four parameters to link sliding averages of temperature and fluorescence. A parameter defining the temperature range over which predicted fluorescence increased most rapidly was the most informative with in describing temperature dependence of fluorescence. The model generated similar fluorescence patterns for both species, but differences were observed for critical temperature and needle age. Down regulation of the light reaction was stronger in spring than in autumn. Pine showed more conservative control of the photosynthetic light reactions, which were activated later in spring and more readily attenuated in autumn. Under the assumption of a close correlation of fluorescence and photosynthesis, spruce should therefore benefit more than pine from the increased photosynthetic potential during warmer springs, but be more likely to suffer frost damage with a sudden cooling following a warm period. The winter of 2013-2014 was unusually mild and similar to future conditions predicted by global climate models. During the mild winter, the activity of photosynthetic light reactions of both conifers, especially spruce, remained high. Because light levels during winter are too low for photosynthesis, this activity may translate to a net carbon loss due to respiration.

Fluorescence measurements show stronger cold inhibition of photosynthetic light reactions in Scots pine compared to Norway spruce as well as during spring compared to autumn

Directory of Open Access Journals (Sweden)

Tapio eLinkosalo

2014-06-01

Full Text Available We studied the photosynthetic activity of Scots pine (Pinus sylvestris L. and Norway spruce (Picea abies [L.] Karst in relation to air temperature changes from March 2013 to February 2014. We measured the chlorophyll fluorescence of approximately 50 trees of each species growing in southern Finland. Fluorescence was measured 13 times per week. We began by measuring shoots present in late winter (i.e., March 2013 before including new shoots once they started to elongate in spring. By July, when the spring shoots had achieved similar fluorescence levels to the older ones, we proceeded to measure the new shoots only.We analysed the data by fitting a sigmoidal model containing four parameters to link sliding averages of temperature and fluorescence. A parameter defining the temperature range over which predicted fluorescence increased most rapidly was the most informative with in describing temperature dependence of fluorescence.The model generated similar fluorescence patterns for both species, but differences were observed for critical temperature and needle age. Down regulation of the light reaction was stronger in spring than in autumn. Pine showed more conservative control of the photosynthetic light reactions, which were activated later in spring and more readily attenuated in autumn. Under the assumption of a close correlation of fluorescence and photosynthesis, spruce should therefore benefit more than pine from the increased photosynthetic potential during warmer springs, but be more likely to suffer frost damage with a sudden cooling following a warm period. The winter of 20132014 was unusually mild and similar to future conditions predicted by global warming models. During the mild winter, the activity of photosynthetic light reactions of both conifers, especially spruce, remained high. Because light levels during winter are too low for photosynthesis, this activity may translate to a net carbon loss due to respiration.

Warming delays autumn declines in photosynthetic capacity in a boreal conifer, Norway spruce (Picea abies).

Science.gov (United States)

Stinziano, Joseph R; Hüner, Norman P A; Way, Danielle A

2015-12-01

Climate change, via warmer springs and autumns, may lengthen the carbon uptake period of boreal tree species, increasing the potential for carbon sequestration in boreal forests, which could help slow climate change. However, if other seasonal cues such as photoperiod dictate when photosynthetic capacity declines, warmer autumn temperatures may have little effect on when carbon uptake capacity decreases in these species. We investigated whether autumn warming would delay photosynthetic decline in Norway spruce (Picea abies (L.) H. Karst.) by growing seedlings under declining weekly photoperiods and weekly temperatures either at ambient temperature or a warming treatment 4 °C above ambient. Photosynthetic capacity was relatively constant in both treatments when weekly temperatures were >8 °C, but declined rapidly at lower temperatures, leading to a delay in the autumn decline in photosynthetic capacity in the warming treatment. The decline in photosynthetic capacity was not related to changes in leaf nitrogen or chlorophyll concentrations, but was correlated with a decrease in the apparent fraction of leaf nitrogen invested in Rubisco, implicating a shift in nitrogen allocation away from the Calvin cycle at low autumn growing temperatures. Our data suggest that as the climate warms, the period of net carbon uptake will be extended in the autumn for boreal forests dominated by Norway spruce, which could increase total carbon uptake in these forests. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model

Science.gov (United States)

Laiolo, P.; Gabellani, S.; Campo, L.; Silvestro, F.; Delogu, F.; Rudari, R.; Pulvirenti, L.; Boni, G.; Fascetti, F.; Pierdicca, N.; Crapolicchio, R.; Hasenauer, S.; Puca, S.

2016-06-01

The reliable estimation of hydrological variables in space and time is of fundamental importance in operational hydrology to improve the flood predictions and hydrological cycle description. Nowadays remotely sensed data can offer a chance to improve hydrological models especially in environments with scarce ground based data. The aim of this work is to update the state variables of a physically based, distributed and continuous hydrological model using four different satellite-derived data (three soil moisture products and a land surface temperature measurement) and one soil moisture analysis to evaluate, even with a non optimal technique, the impact on the hydrological cycle. The experiments were carried out for a small catchment, in the northern part of Italy, for the period July 2012-June 2013. The products were pre-processed according to their own characteristics and then they were assimilated into the model using a simple nudging technique. The benefits on the model predictions of discharge were tested against observations. The analysis showed a general improvement of the model discharge predictions, even with a simple assimilation technique, for all the assimilation experiments; the Nash-Sutcliffe model efficiency coefficient was increased from 0.6 (relative to the model without assimilation) to 0.7, moreover, errors on discharge were reduced up to the 10%. An added value to the model was found in the rainfall season (autumn): all the assimilation experiments reduced the errors up to the 20%. This demonstrated that discharge prediction of a distributed hydrological model, which works at fine scale resolution in a small basin, can be improved with the assimilation of coarse-scale satellite-derived data.

Formation Mechanisms of the Spring-Autumn Asymmetry of the Midlatitudinal NmF2 under Daytime Quiet Geomagnetic Conditions at Low Solar Activity

Science.gov (United States)

Pavlov, A. V.; Pavlova, N. M.

2018-05-01

Formation mechanism of the spring-autumn asymmetry of the F2-layer peak electron number density of the midlatitudinal ionosphere, NmF2, under daytime quiet geomagnetic conditions at low solar activity are studied. We used the ionospheric parameters measured by the ionosonde and incoherent scatter radar at Millstone Hill on March 3, 2007, March 29, 2007, September 12, 2007, and September 18, 1984. The altitudinal profiles of the electron density and temperature were calculated for the studied conditions using a one-dimensional, nonstationary, ionosphere-plasmasphere theoretical model for middle geomagnetic latitudes. The study has shown that there are two main factors contributing to the formation of the observed spring-autumn asymmetry of NmF2: first, the spring-autumn variations of the plasma drift along the geomagnetic field due to the corresponding variations in the components of the neutral wind velocity, and, second, the difference between the composition of the neutral atmosphere under the spring and autumn conditions at the same values of the universal time and the ionospheric F2-layer peak altitude. The seasonal variations of the rate of O+(4S) ion production, which are associated with chemical reactions with the participation of the electronically excited ions of atomic oxygen, does not significantly affect the studied NmF2 asymmetry. The difference in the degree of influence of O+(4S) ion reactions with vibrationally excited N2 and O2 on NmF2 under spring and autumn conditions does not significantly change the spring-autumn asymmetry of NmF2.

Changes in timing of autumn migration in North European songbird populations

DEFF Research Database (Denmark)

Tøttrup, Anders Peter; Thorup, Kasper; Rahbek, Carsten

2006-01-01

Although studies of changes in the timing of passerine spring migration are numerous, less is known about timing of their autumn departure. We present phenological data on 22 species based on mist-netted birds caught on the Baltic island of Christiansø during autumn migration between 1976 and 1997...... departure (-0.0426 days year-1, P = 0.40). Testing the 12 species for which the entire migration period was included (thus excluding many long-distance migrants), we found an overall earlier departure (-0.18 days year-1, P = 0.007). Short-distance migrants tended to show earlier departure, while long...

Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn

DEFF Research Database (Denmark)

Wu, Chaoyang; Chen, Xi Jing; Black, T. Andrew

2013-01-01

To investigate the importance of autumn phenology in controlling interannual variability of forest net ecosystem productivity (NEP) and to derive new phenological metrics to explain the interannual variability of NEP. North America and Europe. Flux data from nine deciduous broadleaf forests (DBF......, soil water content and precipitation, were also used to explain the phenological variations. We found that interannual variability of NEP can be largely explained by autumn phenology, i.e. the autumn lag. While variation in neither annual gross primary productivity (GPP) nor in annual ecosystem...

Mercury emission from a temperate lake during autumn turnover

International Nuclear Information System (INIS)

Wollenberg, Jennifer L.; Peters, Stephen C.

2009-01-01

Lakes in temperate regions stratify during summer and winter months, creating distinct layers of water differentiated by their physical and chemical characteristics. When lakes mix in autumn and spring, mercury cycling may be affected by the chemical changes that occur during mixing. Sampling was conducted in Lake Lacawac, Eastern Pennsylvania, USA, throughout the autumn of 2007 to characterize changes in emission of gaseous elemental mercury (Hg 0 ) from the lake surface and dissolved mercury profiles in the water column during mixing. Water chemistry and weather parameters were also measured, including dissolved organic carbon (DOC), iron, and solar radiation which have been shown to interact with mercury species. Results indicate that emission of Hg 0 from the lake to the atmosphere during turnover was controlled both by solar radiation and by surface water mercury concentration. As autumn turnover progressed through the months of October and November, higher mercury concentration water from the hypolimnion mixed with epilimnetic water, increasing mercury concentration in epilimnetic waters. Dissolved absorbance was significantly correlated with mercury concentrations and with iron, but DOC concentrations were essentially constant throughout the study period and did not exhibit a relationship with either dissolved mercury concentrations or emission rates. Positive correlations between dissolved mercury and iron and manganese also suggest a role for these elements in mercury transport within the lake, but iron and manganese did not demonstrate a relationship with emission rates. This research indicates that consideration of seasonal processes in lakes is important when evaluating mercury cycling in aquatic systems

Outlier loci detect intraspecific biodiversity amongst spring and autumn spawning herring across local scales

DEFF Research Database (Denmark)

Bekkevold, Dorte; Gross, Riho; Arula, Timo

2016-01-01

Herring, Clupea harengus, is one of the ecologically and commercially most important species in European northern seas, where two distinct ecotypes have been described based on spawning time; spring and autumn. To date, it is unknown if these spring and autumn spawning herring constitute genetica...... of these co-occurring ecotypes to meet requirements for sustainable exploitation and ensure optimal livelihood for coastal communities....

Report on the Autumn 2011 HEPiX Meeting

CERN Multimedia

CERN. Geneva; Lo Presti, Giuseppe; Martelli, Edoardo

2011-01-01

The Autumn 2011 meeting of HEPiX was held at TRIUMF, Vancouver, Canada on 24-28 October 2011. HEPiX is a global organization of service managers and support staff providing computing facilities for the High Energy and Nuclear Physics community. The speakers will present a summary of the subjects addressed during the meeting.

Insufficient autumn vitamin D intake and low vitamin D status in 7-year-old Icelandic children.

Science.gov (United States)

Bjarnadottir, Adda; Kristjansdottir, Asa Gudrun; Hrafnkelsson, Hannes; Johannsson, Erlingur; Magnusson, Kristjan Thor; Thorsdottir, Inga

2015-02-01

The aim was to investigate autumn vitamin D intake and status in 7-year-old Icelanders, fitting BMI and cardiorespiratory fitness as predictors. Three-day food records and fasting blood samples were collected evenly from September to November, and cardiorespiratory fitness was measured with an ergometer bike. Food and nutrient intakes were calculated, and serum 25-hydroxyvitamin D (s-25(OH)D) and serum parathyroid hormone were analysed. Suboptimal vitamin D status was defined s-25(OH)D <50 nmol/l, and deficient status as s-25(OH)D <25 nmol/l. School-based study in Reykjavik, Iceland in 2006. Of the 7-year-olds studied (n 265), 165 returned valid intake information (62 %), 158 gave blood samples (60 %) and 120 gave both (45 %). Recommended vitamin D intake (10 μg/d) was reached by 22·4 % of the children and 65·2 % had s-25(OH)D <50 nmol/l. Median s-25(OH)D was higher for children taking vitamin D supplements (49·2 nmol/l v. 43·2 nmol/l, respectively; P < 0·0 0 1). Median s-25(OH)D was lower in November (36·7 nmol/l) than in September (59·9 nmol/l; P < 0·001). The regression model showed that week of autumn accounted for 18·9 % of the variance in s-25(OH)D (P < 0·001), vitamin D intake 5·2 % (P < 0·004) and cardiorespiratory fitness 4·6 % (P < 0·005). A minority of children followed the vitamin D recommendations and 65 % had suboptimal vitamin D status during the autumn. Week of autumn was more strongly associated with vitamin D status than diet or cardiorespiratory fitness, which associated with vitamin D status to a similar extent. These results demonstrate the importance of sunlight exposure during summer to prevent suboptimal vitamin D status in young schoolchildren during autumn in northern countries. An increased effort is needed for enabling adherence to the vitamin D recommendations and increasing outdoor activities for sunlight exposure.

Deep Soil Conditions Make Mediterranean Cork Oak Stem Growth Vulnerable to Autumnal Rainfall Decline in Tunisia

Directory of Open Access Journals (Sweden)

Lobna Zribi

2016-10-01

Full Text Available Tree rings provide fruitful information on climate features driving annual forest growth through statistical correlations between annual tree growth and climate features. Indices built upon tree growth limitation by carbon sequestration (source hypothesis or drought-driven cambial phenology (sink hypothesis can be used to better identify underlying processes. We used both analytical frameworks on Quercus suber, a sparsely studied species due to tree ring methodological issues, and growing on a favorable sub-humid Mediterranean climate and deep soil conditions in Tunisia (North Africa. Statistical analysis revealed the major role of autumnal rainfall before the growing season on annual tree growth over the 1918–2008 time series. Using a water budget model, we were able to explain the critical role of the deep soil water refill during the wet season in affecting both the drought onset controlling growth phenology and the summer drought intensity affecting carbon assimilation. Analysis of recent climate changes in the region additionally illustrated an increase in temperatures enhancing the evaporative demand and advancing growth start, and a decline in rainfalls in autumn, two key variables driving stem growth. We concluded on the benefits of using process-based indices in dendrochronological analysis and identified the main vulnerability of this Mediterranean forest to autumnal rainfall decline, a peculiar aspect of climate change under summer-dry climates.

Autumn Weather and Winter Increase in Cerebrovascular Disease Mortality

LENUS (Irish Health Repository)

McDonagh, R

2016-11-01

Mortality from cerebrovascular disease increases in winter but the cause is unclear. Ireland’s oceanic climate means that it infrequently experiences extremes of weather. We examined how weather patterns relate to stroke mortality in Ireland. Seasonal data for Sunshine (% of average), Rainfall (% of average) and Temperature (degrees Celsius above average) were collected for autumn (September-November) and winter (December-February) using official Irish Meteorological Office data. National cerebrovascular mortality data was obtained from Quarterly Vital Statistics. Excess winter deaths were calculated by subtracting (nadir) 3rd quarter mortality data from subsequent 1st quarter data. Data for 12 years were analysed, 2002-2014. Mean winter mortality excess was 24.7%. Winter mortality correlated with temperature (r=.60, p=0.04). Rise in winter mortality correlated strongly with the weather in the preceding autumn (Rainfall: r=-0.19 p=0.53, Temperature: r=-0.60, p=0.03, Sunshine, r=0.58, p=0.04). Winter cerebrovascular disease mortality appears higher following cool, sunny autum

Autumn ichthyoplankton assemblage in the Yangtze Estuary shaped by environmental factors.

Science.gov (United States)

Zhang, Hui; Xian, Weiwei; Liu, Shude

2016-01-01

This study investigated the response of the ichthyoplankton community to environmental changes in the Yangtze Estuary using canonical correspondence analysis. Ichthyoplankton community and environmental data were recorded during the autumns of 1998, 2000, 2002, 2003, 2004, 2007 and 2009. Among the ichthyoplankton, the dominant larval and juvenile families were the Engraulidae, Gobiidae and Salangidae, and the most common eggs were from Trichiurus lepturus. The ichthyoplankton was identified via canonical correspondence analysis to three assemblages: an estuary assemblage dominated by Chaeturichthys stigmatias, a coastal assemblage dominated by Engraulis japonicus and Stolephorus commersonii, and an offshore assemblage dominated by Trichiurus lepturus. Regarding environmental factors in the Yangtze Estuary, suspended matter and surface seawater salinity were the main factors influencing the distributions of the different assemblages, while sediment from the Yangtze River during the rainy season and chlorophyll a were the principle drivers of the annual variances in the distribution of ichthyoplankton assemblages. Our aims in this study were to provide detailed characterizations of the ichthyoplankton assemblage in the autumns of seven years, examine the long-term dynamics of autumn ichthyoplankton assemblages, and evaluate the influence of environmental factors on the spatial distribution and inter-annual variations of ichthyoplankton assemblages associated with the Yangtze Estuary.

Autumn ichthyoplankton assemblage in the Yangtze Estuary shaped by environmental factors

Directory of Open Access Journals (Sweden)

Hui Zhang

2016-04-01

Full Text Available This study investigated the response of the ichthyoplankton community to environmental changes in the Yangtze Estuary using canonical correspondence analysis. Ichthyoplankton community and environmental data were recorded during the autumns of 1998, 2000, 2002, 2003, 2004, 2007 and 2009. Among the ichthyoplankton, the dominant larval and juvenile families were the Engraulidae, Gobiidae and Salangidae, and the most common eggs were from Trichiurus lepturus. The ichthyoplankton was identified via canonical correspondence analysis to three assemblages: an estuary assemblage dominated by Chaeturichthys stigmatias, a coastal assemblage dominated by Engraulis japonicus and Stolephorus commersonii, and an offshore assemblage dominated by Trichiurus lepturus. Regarding environmental factors in the Yangtze Estuary, suspended matter and surface seawater salinity were the main factors influencing the distributions of the different assemblages, while sediment from the Yangtze River during the rainy season and chlorophyll a were the principle drivers of the annual variances in the distribution of ichthyoplankton assemblages. Our aims in this study were to provide detailed characterizations of the ichthyoplankton assemblage in the autumns of seven years, examine the long-term dynamics of autumn ichthyoplankton assemblages, and evaluate the influence of environmental factors on the spatial distribution and inter-annual variations of ichthyoplankton assemblages associated with the Yangtze Estuary.

Early Autumn Senescence in Red Maple (Acer rubrum L.) Is Associated with High Leaf Anthocyanin Content.

Science.gov (United States)

Anderson, Rachel; Ryser, Peter

2015-08-05

Several theories exist about the role of anthocyanins in senescing leaves. To elucidate factors contributing to variation in autumn leaf anthocyanin contents among individual trees, we analysed anthocyanins and other leaf traits in 27 individuals of red maple (Acer rubrum L.) over two growing seasons in the context of timing of leaf senescence. Red maple usually turns bright red in the autumn, but there is considerable variation among the trees. Leaf autumn anthocyanin contents were consistent between the two years of investigation. Autumn anthocyanin content strongly correlated with degree of chlorophyll degradation mid to late September, early senescing leaves having the highest concentrations of anthocyanins. It also correlated positively with leaf summer chlorophyll content and dry matter content, and negatively with specific leaf area. Time of leaf senescence and anthocyanin contents correlated with soil pH and with canopy openness. We conclude that the importance of anthocyanins in protection of leaf processes during senescence depends on the time of senescence. Rather than prolonging the growing season by enabling a delayed senescence, autumn anthocyanins in red maple in Ontario are important when senescence happens early, possibly due to the higher irradiance and greater danger of oxidative damage early in the season.

Movement behavior preceding autumn mortality for white-tailed deer in central New York

Science.gov (United States)

Whitman, Brigham J.; Porter, W. F.; Dechen Quinn, Amy C.; Williams, David M.; Frair, Jacqueline L.; Underwood, Harold; Crawford, Joanne C.

2018-01-01

A common yet largely untested assumption in the theory of animal movements is that increased rates and a wider range of movements, such as occurs during breeding, make animals more vulnerable to mortality. We examined mortality among 34 white-tailed deer (Odocoileus virginianus) wearing GPS collars during the autumn breeding season of 2006 and 2007 in a heavily hunted, forest-agricultural landscape of central New York state. We evaluated whether individuals having higher rates of movement incurred higher rates of mortality and whether mortality risk was higher when deer were in less familiar areas. We used a Cox proportional hazards model to analyze how mortality risk changes with movement rates measured over 3 time periods: < 1 day, up to 2 weeks prior to death, and 3–4 weeks prior to death. Overall, deer increased their movement rates as autumn progressed, males more so than females. However, deer that died moved at a slower rate relative to surviving deer up to 2 weeks prior to death (ß = -2.22 ± 0.81; 95% confidence interval [CI] = -3.91 to -0.51) and a slower rate on their day of death compared to deer that survived (ß = -1.77 ± 0.73; 95% CI = -3.19 to -0.33). Site familiarity was not significantly related to mortality risk. Deer were equally likely to die within their 50% core use area as elsewhere within their autumn home range. We hypothesize that increased sociality associated with breeding may make animals more vulnerable to harvest mortality. Our findings contradict general assumptions about the influences of movement behavior on mortality risk, suggesting that patterns may be sensitive to the spatiotemporal context of the movement analysis.

VITAMINS’ DEFICIENCY IN CHILDREN DURING AUTUMN

Directory of Open Access Journals (Sweden)

O.A. Gromova

2009-01-01

Full Text Available Suboptimal provision with vitamins is widely spread among children. Its significance, to a considerable degree, is determined by season, and the most active forming of vitamins’ insufficiency occurs in the beginning of autumn. The necessity of correction of these states is assigned with the fact that the most of the vitamins are water-soluble, and organism can’t accumulate it. The administration of vitamin complexes allows achieving proper compensation of present vitamins’ insufficiency, and to prevent its development in future.Key words: children, vitamins, insufficiency, season.(Voprosy sovremennoi pediatrii — Current Pediatrics. 2009;8(5:111-114

Effect of growing degree days on autumn planted sunflower ...

African Journals Online (AJOL)

Sunflower (Helianthus annus L.) having high degree of adaptability under wide range of climatic conditions, allow the crop to be productive in broad range of environments. Field experiments in autumn were laid out at Pir Mehr Ali Shah, Arid Agriculture University Rawalpindi, Pakistan for two years (2007 and 2008), ...

Autumn predation of northern red oak seed crops

Science.gov (United States)

Kim C. Steiner

1995-01-01

Production and autumn predation of northern red oak acorns was measured over four years in five Pennsylvania stands dominated by this species. Mean annual production was 41,779/acre, of which an average of 7.9% was destroyed by insects or decay following insect attack, and an average of 38.6% was destroyed or removed by vertebrates. White-tailed deer appeared to be the...

Bats in Dutch offshore wind farms in autumn 2012

NARCIS (Netherlands)

Lagerveld, S.; Jonge Poerink, B.; Haselager, R.; Verdaat, J.P.

2014-01-01

In the autumn of 2012, we conducted a pilot study with ultrasonic recorders to assess the occurrence of bats over the North Sea. At Offshore Wind Farm Egmond aan Zee (OWEZ) a recorder was installed at the meteorological mast and at Princess Amalia Wind Farm (PAWP) a recorder was attached to the

Bootstrap prediction and Bayesian prediction under misspecified models

OpenAIRE

Fushiki, Tadayoshi

2005-01-01

We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

14C distribution and mobilization in young apple trees in autumn and spring

International Nuclear Information System (INIS)

Katzfuss, M.

1979-01-01

14 CO 2 was administered to young apple trees in autumn and the roots proved to be the most important storage organ for 14 C in this season. From autumn to spring the 14 C content of the roots, rootstocks, and the two-year-old shoots decreased strongly, while the respective level of the one-year-old shoots decreased only slightly. In spring the growing buds were the main consuming organs of 14 C-assimilates stored in the different organs of the tree at the end of the growing season

Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

OpenAIRE

David Ebert

2006-01-01

One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...

The effect of autumn ridging and inter-row subsoiling on potato tuber yield and quality on a sandy soil in Denmark

DEFF Research Database (Denmark)

Henriksen, Jens Christian Martin Bugge; Mølgaard, Jens Peter; Rasmussen, Jesper

2007-01-01

Autumn ridging is a modified version of the ridge tillage system. Instead of setting up ridges during the growing season, they are established in autumn and left for the winter. Previous studies have documented positive effects of autumn ridging on potato yield and we hypothesized that subsoiling...... could enhance these effects. To determine the effect of autumn ridging and inter-row subsoiling on potato yield and quality a field experiment was conducted on sandy soil from 2001 to 2003. Autumn ridging resulted in an average total and marketable tuber yield of 25.6 and 9.2 t ha1, which...... was not significantly different from the average total and marketable yield of 25.6 and 8.9 t ha1 with ploughing. However, autumn ridging significantly reduced the incidence of black scurf from 2.5% to 2.2%. Inter-row subsoiling in the growing season significantly increased marketable potato tuber yield from 8.4 to 9...

Conservation assessment for the autumn willow in the Black Hills National Forest, South Dakota and Wyoming

Science.gov (United States)

J. Hope Hornbeck; Carolyn Hull Sieg; Deanna J. Reyher

2003-01-01

Autumn willow, Salix serissima (Bailey) Fern., is an obligate wetland shrub that occurs in fens and bogs in the northeastern United States and eastern Canada. Disjunct populations of autumn willow occur in the Black Hills of South Dakota. Only two populations occur on Black Hills National Forest lands: a large population at McIntosh Fen and a small...

Joint Force Quarterly. Number 9, Autumn 1995

Science.gov (United States)

1995-11-01

Peters, Jr. Production Coordinator Calvin B. Kelley Senior Copy Editor Justin Burkhart Editorial Intern (Summer 1995) The Typography and Design ...J. Rokke ■ C O N T E N T S 2 JFQ / Autumn 1995 ■ O U T O F J O I N T 24 Jointness by Design , Not Accident by Michael C. Vitale ■ J F Q F O R U M...take power, or Iran and Iraq have a free hand, U.S. interests would suffer a serious setback. These factors have altered the region’s geo- graphic

Proceedings of the KNS 2016 Autumn Meeting

Energy Technology Data Exchange (ETDEWEB)

NONE

2016-10-15

This proceedings contains articles of 2016 autumn meeting of the Korean Nuclear Society. It was held on Oct. 26-28 in Kyungju, Korea. This proceedings is comprised of 11 sessions. The main subject titles of session are as follows: Reactor system technology, Reactor physics and computational science, Radioactive waste management, Nuclear fuel and materials, Thermal hydraulics and safety, Radiation utilization and protection, Quantum engineering and nuclear fusion, Nuclear power plant construction and operation technology, Nuclear policy, human resources and cooperation, Nuclear I and C, human factors and automatic remote systems, Competition Session. (Yi, J. H.)

Offshore Seabird Distributions during Summer and Autumn at West Greenland

DEFF Research Database (Denmark)

Boertmann, D.; Mosbech, A.

. During the autumn the numbers of seabirds increase as migrants from local and international populations of mainly thick-billed murre and little auk arrive to spend the winter in West Greenland waters. Huge concentrations of thick-billed murres, common eiders and king-eiders may occur then...

45th IGE (Institute of Gas Engineers) Autumn Meeting

Energy Technology Data Exchange (ETDEWEB)

Riley, T; De Winton, C

1980-01-01

Topics discussed at the 45th Institute of Gas Engineers Autumn Meeting (London, 1979) are outlined, including safety standards and recommendations for gas transmission and distribution systems, gas characteristics and utilization, heat transfer research, gas receiver stresses, the potential of hydrogen as an energy fuel, gas appliances and controls, pipe failure, refactories in gasifiers, synthetic natural gas, coal conversion techniques, and technological innovations.

Thermally driven interaction of the littoral and limnetic zones by autumnal cooling processes

Directory of Open Access Journals (Sweden)

Kolumban HUTTER

2005-02-01

Full Text Available In autumn, during the transition period, shores influence the interior dynamics of large temperate lakes by the formation of horizontal water-temperature gradients between the shallow and deep areas, whilst vertical temperature gradients are smoothed by convection due to surface cooling. A simple heat budget model, based on the heat balance of the water column without horizontal advection and turbulent mixing, allows deduction of the time-dependent difference between the mean temperature within the littoral area and the temperature in the upper mixed layer. The model corroborates that littoral areas cool faster than regions distant from shores, and provides a basis for an estimation of structure of flows from the beginning of cooling process till the formation of the thermal bar. It predicts the moment in the cooling process, when the corresponding density difference between the littoral and limnetic parts reaches a maximum. For a linear initial vertical temperature profile, the time-dependent "target depth" is explicitly calculated; this is the depth in the pelagic area with a temperature, characteristic of the littoral zone. This depth is estimated as 4/3 of the (concurrent thickness of the upper mixed layer. It is shown that, for a linear initial vertical temperature profile, the horizontal temperature profile between the shore and the lake has a self-similar behavior, and the temperature difference between the littoral waters and the upper mixed off-shore layer, divided by the depth of the upper mixed layer, is an invariant of the studied process. The results are in conformity with field data.

Differential timing and latitudinal variation in sex ratio of Aquatic Warblers during the autumn migration

Science.gov (United States)

Wojczulanis-Jakubas, Katarzyna; Chrostek, Małgorzata E.; Jiguet, Frédéric; Martínez, Carlos Zumalacárregui; Miguélez, David; Neto, Júlio M.

2017-12-01

Differential migration has been extensively reported in spring, but less so in autumn, particularly in relation to sex in monomorphic bird species. Here, we analysed the autumn passage of a monomorphic, globally threatened passerine, the Aquatic Warbler Acrocephalus paludicola throughout Western Europe, with regard to age and sex. We showed that, overall, adults migrated earlier than first-year birds, and males migrated earlier than females during the autumn migration. This may be caused by an overall social dominance of adults over immatures, and differentiated migration strategy of males and females. In addition, we found male-skewed sex proportions, with a tendency to an equalised ratio in more southern stopover sites. This may indicate a male bias in the global population or different migration strategies of the sexes. Differential migration may cause the age and sex classes to be exposed differently to various threats affecting demographic structure of the species.

Climatology of the autumn Red Sea trough

Science.gov (United States)

Awad, Adel M.; Mashat, Abdul-Wahab S.

2018-03-01

In this study, the Sudan low and the associated Red Sea trough (RST) are objectively identified using the mean sea level pressure (SLP) data from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis dataset covering the period 1955-2015. The Sudan low was detected in approximately 60.6% of the autumn periods, and approximately 83% of the detected low-pressure systems extended into RSTs, with most generated at night and during cold months. The distribution of the RSTs demonstrated that Sudan, South Sudan and Red Sea are the primary development areas of the RSTs, generating 97% of the RSTs in the study period. In addition, the outermost areas affected by RSTs, which include the southern, central and northern Red Sea areas, received approximately 91% of the RSTs originating from the primary generation areas. The synoptic features indicated that a Sudan low developed into an RST when the Sudan low deepened in the atmosphere, while the low pressures over the southern Arabian Peninsula are shallow and the anticyclonic systems are weakened over the northern Red Sea. Moreover, stabile areas over Africa and Arabian Peninsula form a high stability gradient around the Red Sea and the upper maximum winds weaken. The results of the case studies indicate that RSTs extend northward when the upper cyclonic and anticyclonic systems form a high geopotential gradient over Arabian Peninsula. Furthermore, the RST is oriented from the west to the east when the Azores high extends eastward and the Siberian high shrinks eastward or shifts northward.

[The short-term effects of particulate matter on lung function of college students in autumn and winter in Wuhan].

Science.gov (United States)

Li, Jiao-yuan; Ma, Lu; Liu, Li-zhi; Zhou, Jie; He, Ming-quan; Shima, Masayuki; Tamura, Kenji

2013-02-01

To evaluate the effects of indoor and outdoor PM2.5 (fine particulate matter, particulate matter with an aerodynamic diameter ≤ 2.5 µm) on lung function of college students in autumn and winter in Wuhan. In this panel study, 37 college students (excluded subject of respiratory disease and smoking history) aged 19 - 21 were investigated by cluster sampling in a university in Wuhan. The follow-up study lasted for 28 days in total, including two study periods, Oct. 29 to Nov. 11, 2009 (autumn) and Dec. 23, 2009 to Jan.5, 2010 (winter), the peak expiratory flow (PEF) of the college students were measured daily in the morning and evening in the university. PM10 and PM2.5 were monitored indoors and outdoors. The effects of PM on lung function of college students were analyzed by using generalized estimating equation (GEE). Average daily concentrations of indoor, outdoor PM2.5 in autumn were (91.3 ± 43.7) and (104.2 ± 49.4) µg/m(3) respectively, while in winter the concentrations of indoor and outdoor PM2.5 were (110.6 ± 42.3) and (143.5 ± 51.2) µg/m(3). The single pollutant model showed that in winter, the evening PEF decrement was significantly associated with increasing outdoor PM2.5. With an increase of 10 µg/m(3) outdoor PM2.5, the PEF measured in the evening decreased 1.27 L/min (95%CI: 0.02 - 2.52 L/min, respectively). Meanwhile, the results showed that 2-days lagged outdoor PM2.5 was also significantly associated with morning PEF. An increase of 10 µg/m(3) 2-days lagged outdoor PM2.5 caused the decrease of 1.82 L/min (95%CI: -3.53 - -0.11 L/min) of PEF measured in the morning. Controlling the influence of gaseous pollutants and building the two pollutants models, the results indicated that no significant changes of PEF of students being exposed to PM2.5 on same day (lag 0) were observed. However, under consideration of SO2 effect, significant association between an increase of 10 µg/m(3) 2-days lagged outdoor PM2.5 and changes of morning PEF (-1.81 L

A catchment-scale model to predict spatial and temporal burden of E. coli on pasture from grazing livestock.

Science.gov (United States)

Oliver, David M; Bartie, Phil J; Louise Heathwaite, A; Reaney, Sim M; Parnell, Jared A Q; Quilliam, Richard S

2018-03-01

Effective management of diffuse microbial water pollution from agriculture requires a fundamental understanding of how spatial patterns of microbial pollutants, e.g. E. coli, vary over time at the landscape scale. The aim of this study was to apply the Visualising Pathogen &Environmental Risk (ViPER) model, developed to predict E. coli burden on agricultural land, in a spatially distributed manner to two contrasting catchments in order to map and understand changes in E. coli burden contributed to land from grazing livestock. The model was applied to the River Ayr and Lunan Water catchments, with significant correlations observed between area of improved grassland and the maximum total E. coli per 1km 2 grid cell (Ayr: r=0.57; pE. coli burden between seasons in both catchments, with summer and autumn predicted to accrue higher E. coli contributions relative to spring and winter (PE. coli loading to land as driven by stocking density and livestock grazing regimes. Resulting risk maps therefore provide the underpinning evidence to inform spatially-targeted decision-making with respect to managing sources of E. coli in agricultural environments. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Proceedings of the Korean Society Nuclear Medicine Autumn Meeting 2002

International Nuclear Information System (INIS)

2002-01-01

This proceedings contains articles of 2002 autumn meeting of the Korean Society Nuclear Medicine. It was held on November 15-16, 2002 in Seoul, Korea. This proceedings is comprised of 5 sessions. The subject titles of session are as follows: Cancer, Physics of nuclear medicine, Neurology, Radiopharmacy and biology, General nuclear medicine. (Yi, J. H.)

Proceedings of the Korean Society Nuclear Medicine Autumn Meeting 1998

International Nuclear Information System (INIS)

1998-01-01

This proceedings contains articles of 1998 Autumn meeting of the Korean Society Nuclear Medicine. It was held on November 13-14, 1998 in Seoul, Korea. This proceedings is comprised of 5 sessions. The subject titles of session are as follows: general nuclear medicine, neurology, radiopharmacy and biology, nuclear cardiology, physics and instrumentation. (Yi, J. H.)

Proceedings of the Korean Society Nuclear Medicine Autumn Meeting 1997

International Nuclear Information System (INIS)

1997-01-01

This proceedings contains articles of 1997 autumn meeting of the Korean Society Nuclear Medicine. It was held on November 21, 1997 in Kwangju, Korea. This proceedings is comprised of 5 sessions. The subject titles of session are as follows: general nuclear medicine, neurology, radiopharmacy and biology, nuclear cardiology, physics and instrumentation. (Yi, J. H.)

Autumn Cooling of Western East Antarctica Linked to the Tropical Pacific

Science.gov (United States)

Clem, Kyle R.; Renwick, James A.; McGregor, James

2018-01-01

Over the past 60 years, the climate of East Antarctica cooled while portions of West Antarctica were among the most rapidly warming regions on the planet. The East Antarctic cooling is attributed to a positive trend in the Southern Annular Mode (SAM) and a strengthening of the westerlies, while West Antarctic warming is tied to zonally asymmetric circulation changes forced by the tropics. This study finds recent (post-1979) surface cooling of East Antarctica during austral autumn to also be tied to tropical forcing, namely, an increase in La Niña events. The recent increase in La Niña conditions forces a Rossby wave into the Southern Hemisphere that increases anticyclonic circulation over the South Atlantic. The South Atlantic anticyclone is associated with cold air advection, weakened northerlies, and increased sea ice concentrations across the western East Antarctic coast, which has increased the rate of cooling at Novolazarevskaya and Syowa stations after 1979. This enhanced cooling over western East Antarctica is tied more broadly to a zonally asymmetric temperature trend pattern across East Antarctica during autumn that is consistent with a tropically forced Rossby wave rather than a SAM pattern; the positive SAM pattern is associated with ubiquitous cooling across East Antarctica, which is not seen in temperature observations after 1979. We conclude that El Niño-Southern Oscillation-related circulation anomalies, particularly zonal asymmetries that locally enhance meridional wind, are an important component of East Antarctic climate variability during autumn, and future changes in tropical Pacific climate will likely have implications for East Antarctica.

International Atomic Energy Agency publications. New publications Autumn 2003

International Nuclear Information System (INIS)

2003-01-01

This Publications Catalogue lists all sales publications of the IAEA issued and forthcoming for the period Autumn 2003 - early 2004. Most Agency publications are issued in English, though some are also available in Arabic, Chinese, French, Russian or Spanish. This is indicated at the bottom of the book entry. A complete listing of all IAEA priced publications is available on the IAEA's web site: http://www.iaea.org/books

Optimum distribution between autumn-applied and spring-applied nitrogen in seed production of tall fescue (Festuca arundinacea Schreb.)

DEFF Research Database (Denmark)

Gislum, René; Deleuran, Lise Christina; Kristensen, Kristian

2012-01-01

The effect of different autumn and spring nitrogen (N) application rates on plant establishment, plant development, and seed yield were tested in a field experiment using tall fescue (Festuca arundinacea Schreb.). Results clearly showed that the optimum distribution of N between autumn and spring...... to achieve the highest seed yield and economical benefit was dependent on the choice of cover crops and location. The economically optimum N application rate was in the range from 44 to 73 kg ha−1 in autumn and 94 to 157 kg ha−1 in spring. The results are discussed in relation to Danish N regulations...... and plant establishment and development....

Proceedings of the Korean Society Nuclear Medicine Autumn Meeting 2001

International Nuclear Information System (INIS)

2001-01-01

This proceedings contains articles of 2001 autumn meeting of the Korean Society Nuclear Medicine. It was held on November 16-17, 2001 in Seoul, Korea. This proceedings is comprised of 6 sessions. The subject titles of session are as follows: Cancer, Physics of nuclear medicine, Neurology, Radiopharmacy and biology, Nuclear cardiology, General nuclear medicine. (Yi, J. H.)

THE CONTRIBUTION OF SPRINGTIME AND AUTUMN GLASS EELS (ANGUILLA ANGUILLA TO STOCK : RESULTS BASED ON OTOLITH MORPHOMETRY.

Directory of Open Access Journals (Sweden)

BRIAND C.

2003-07-01

Full Text Available A double mark, called a transition ring, or elver mark, was identifiable in light microscopy on otoliths of young yellow eels. In the Vilaine watershed, the radius of this mark decreased from 178 μm in yellow eels corresponding to glass eels arriving in autumn 1997 to 163 μm in yellow eels arriving in spring 1998. The mean transition ring radius of the freshwater eel population in the Vilaine river had an intermediate value between spring and autumn recruits. This implies that it consisted of a mixture of spring and autumn recruits. In the Vilaine estuary and the Frémur populations, the mean radius of the transition rings was close to the autumn one. The springtime recruits formed 68 % of freshwater and 15 % of estuarine population in the Vilaine for the 1998 cohort. This result was in sharp contrast with the available assessments of recruitment, which both in estuary and in the fluvial part of the watershed, were dominated by spring recruits. This contrast is possibly the consequence of density-dependent mortality, which would have been particularly important in springtime for glass eels whose migration was inhibited by the dam.

Tropical influence on Euro-Asian autumn rainfall variability

Energy Technology Data Exchange (ETDEWEB)

Mariotti, A. [University of Maryland, College Park, MD (United States); ENEA, Rome (Italy); Ballabrera-Poy, J. [University of Maryland, ESSIC, College Park, MD (United States); Zeng, N. [University of Maryland, ESSIC, College Park, MD (United States); University of Maryland, Department of Meteorology,, College Park, MD (United States)

2005-04-01

The connection between autumn rainfall variability in the Euro-Asian domain and tropical climate is documented using state-of-the-art global observational datasets and re-analyses. Results suggest a robust statistical relationship between the El Nino Southern Oscillation (ENSO) and autumn rainfall in parts of southwest Europe, northern Africa and southwest Asia. The correlation between area-mean anomalies over this region (P{sub ea}) and the NINO3.4 index is 0.68, stationary over the last 50 years. Global ENSO-like tropical climate anomalies are observed in conjunction with P{sub ea} anomalies confirming the relationship found with the NINO3.4 index. Overall, the connection with Indo-Pacific variability is stronger than that with the eastern Pacific.While rainfall anomalies in southwest Europe and southwest Asia appear to largely co-vary as one pattern under the influence of ENSO, our results suggest that different mechanisms may be contributing to the observed anomalies. In the North Atlantic/European region, it is speculated that while a PNA-like mode maybe the prevailing teleconnection mechanism for high P{sub ea}, for low P{sub ea} tropical Atlantic ENSO related SST anomalies may be playing a more relevant role forcing northeastward propagating Rossby waves. Over southwest Asia, a more direct connection to the Indo-Pacific region is suggested by the upper air anomaly observed over southern Asia, possibly the Rossby wave response to enhanced heating in the Indian Ocean. (orig.)

Decadal shifts in autumn migration timing by Pacific Arctic beluga whales are related to delayed annual sea ice formation.

Science.gov (United States)

Hauser, Donna D W; Laidre, Kristin L; Stafford, Kathleen M; Stern, Harry L; Suydam, Robert S; Richard, Pierre R

2017-06-01

Migrations are often influenced by seasonal environmental gradients that are increasingly being altered by climate change. The consequences of rapid changes in Arctic sea ice have the potential to affect migrations of a number of marine species whose timing is temporally matched to seasonal sea ice cover. This topic has not been investigated for Pacific Arctic beluga whales (Delphinapterus leucas) that follow matrilineally maintained autumn migrations in the waters around Alaska and Russia. For the sympatric Eastern Chukchi Sea ('Chukchi') and Eastern Beaufort Sea ('Beaufort') beluga populations, we examined changes in autumn migration timing as related to delayed regional sea ice freeze-up since the 1990s, using two independent data sources (satellite telemetry data and passive acoustics) for both populations. We compared dates of migration between 'early' (1993-2002) and 'late' (2004-2012) tagging periods. During the late tagging period, Chukchi belugas had significantly delayed migrations (by 2 to >4 weeks, depending on location) from the Beaufort and Chukchi seas. Spatial analyses also revealed that departure from Beaufort Sea foraging regions by Chukchi whales was postponed in the late period. Chukchi beluga autumn migration timing occurred significantly later as regional sea ice freeze-up timing became later in the Beaufort, Chukchi, and Bering seas. In contrast, Beaufort belugas did not shift migration timing between periods, nor was migration timing related to freeze-up timing, other than for southward migration at the Bering Strait. Passive acoustic data from 2008 to 2014 provided independent and supplementary support for delayed migration from the Beaufort Sea (4 day yr -1 ) by Chukchi belugas. Here, we report the first phenological study examining beluga whale migrations within the context of their rapidly transforming Pacific Arctic ecosystem, suggesting flexible responses that may enable their persistence yet also complicate predictions of how

Modelling bankruptcy prediction models in Slovak companies

Directory of Open Access Journals (Sweden)

Kovacova Maria

2017-01-01

Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

Modelling the current distribution and predicted spread of the flea species Ctenocephalides felis infesting outdoor dogs in Spain.

Science.gov (United States)

Gálvez, Rosa; Musella, Vicenzo; Descalzo, Miguel A; Montoya, Ana; Checa, Rocío; Marino, Valentina; Martín, Oihane; Cringoli, Giuseppe; Rinaldi, Laura; Miró, Guadalupe

2017-09-19

The cat flea, Ctenocephalides felis, is the most prevalent flea species detected on dogs and cats in Europe and other world regions. The status of flea infestation today is an evident public health concern because of their cosmopolitan distribution and the flea-borne diseases transmission. This study determines the spatial distribution of the cat flea C. felis infesting dogs in Spain. Using geospatial tools, models were constructed based on entomological data collected from dogs during the period 2013-2015. Bioclimatic zones, covering broad climate and vegetation ranges, were surveyed in relation to their size. The models builded were obtained by negative binomial regression of several environmental variables to show impacts on C. felis infestation prevalence: land cover, bioclimatic zone, mean summer and autumn temperature, mean summer rainfall, distance to urban settlement and normalized difference vegetation index. In the face of climate change, we also simulated the future distributions of C. felis for the global climate model (GCM) "GFDL-CM3" and for the representative concentration pathway RCP45, which predicts their spread in the country. Predictive models for current climate conditions indicated the widespread distribution of C. felis throughout Spain, mainly across the central northernmost zone of the mainland. Under predicted conditions of climate change, the risk of spread was slightly greater, especially in the north and central peninsula, than for the current situation. The data provided will be useful for local veterinarians to design effective strategies against flea infestation and the pathogens transmitted by these arthropods.

Predictive modeling of cholera using GRACE and TRMM satellite data

Science.gov (United States)

Jutla, A.; Akanda, A. S. S.; Colwell, R. R.

2015-12-01

Cholera outbreaks can be classified in three forms- epidemic (sudden or seasonal outbreaks), endemic (recurrence and persistence of the disease for several consecutive years) and mixed-mode endemic (combination of certain epidemic and endemic conditions) with significant spatial and temporal heterogeneity. Endemic cholera is related to floods and droughts in regions where water and sanitation infrastructure are inadequate or insufficient. With more than a decade of terrestrial water storage (TWS) data obtained from Gravity Recovery and Climate Experiment (GRACE), understanding dynamics of river discharge is now feasible. We explored lead-lag relationships between TWS in the Ganges-Brahmaputra-Meghna (GBM) basin and endemic cholera in Bangladesh. Since bimodal seasonal peaks in cholera in Bangladesh occur during the spring and autumn season, two separate models, between TWS and disease time series (2002 to 2010) were developed. TWS, hence water availability, showed an asymmetrical, strong association with spring (τ=-0.53; pcholera prevalence up to five to six months in advance. One unit (cm of water) decrease in water availability in the basin increased odds of above normal cholera by 24% [confidence interval (CI) 20-31%; pcholera in the autumn by 29% [CI:22-33%; pcholera is related with warm temperatures and heavy rainfall. Using TRMM data for several locations in Asia and Africa, probability of cholera increases 18% [CI:15-23%; p<0.05] after heavy precipitation resulted in a societal conditions where access to safe water and sanitation was disrupted. Results from mechanistic modeling framework using systems approach that include satellite based hydroclimatic information with tradition disease transmission models will also be presented.

Nitrogen reserves, spring regrowth and winter survival of field-grown alfalfa (Medicago sativa) defoliated in the autumn.

Science.gov (United States)

Dhont, Catherine; Castonguay, Yves; Nadeau, Paul; Bélanger, Gilles; Drapeau, Raynald; Laberge, Serge; Avice, Jean-Christophe; Chalifour, François-P

2006-01-01

The objective of the study was to characterize variations in proline, arginine, histidine, vegetative storage proteins, and cold-inducible gene expression in overwintering roots of field-grown alfalfa, in response to autumn defoliation, and in relation to spring regrowth and winter survival. Field trials, established in 1996 in eastern Canada, consisted of two alfalfa cultivars ('AC Caribou' and 'WL 225') defoliated in 1997 and 1998 either only twice during the summer or three times with the third defoliation taken 400, 500 or 600 growing degree days (basis 5 degrees C) after the second summer defoliation. The root accumulation of proline, arginine, histidine and soluble proteins of 32, 19 and 15 kDa, characterized as alfalfa vegetative storage proteins, was reduced the following spring by an early autumn defoliation at 400 or 500 growing degree days in both cultivars; the 600-growing-degree-days defoliation treatment had less or no effect. Transcript levels of the cold-inducible gene msaCIA, encoding a glycine-rich protein, were markedly reduced by autumn defoliation in 'WL 225', but remained unaffected in the more winter-hardy cultivar 'AC Caribou'. The expression of another cold-inducible gene, the dehydrin homologue msaCIG, was not consistently affected by autumn defoliation. Principal component analyses, including components of root organic reserves at the onset of winter, along with yield and plant density in the following spring, revealed that (a) amino acids and soluble proteins are positively related to the vigour of spring regrowth but poorly related to winter survival and (b) winter survival, as indicated by plant density in the spring, is associated with higher concentrations of cryoprotective sugars in alfalfa roots the previous autumn. An untimely autumn defoliation of alfalfa reduces root accumulation of specific N reserves such as proline, arginine, histidine and vegetative storage proteins that are positively related to the vigour of spring

Evaluation of land surface model representation of phenology: an analysis of model runs submitted to the NACP Interim Site Synthesis

Science.gov (United States)

Richardson, A. D.; Nacp Interim Site Synthesis Participants

2010-12-01

Phenology represents a critical intersection point between organisms and their growth environment. It is for this reason that phenology is a sensitive and robust integrator of the biological impacts of year-to-year climate variability and longer-term climate change on natural systems. However, it is perhaps equally important that phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating ecosystem processes, competitive interactions, and feedbacks to the climate system. Unfortunately, the phenological sub-models implemented in most state-of-the-art ecosystem models and land surface schemes are overly simplified. We quantified model errors in the representation of the seasonal cycles of leaf area index (LAI), gross ecosystem photosynthesis (GEP), and net ecosystem exchange of CO2. Our analysis was based on site-level model runs (14 different models) submitted to the North American Carbon Program (NACP) Interim Synthesis, and long-term measurements from 10 forested (5 evergreen conifer, 5 deciduous broadleaf) sites within the AmeriFlux and Fluxnet-Canada networks. Model predictions of the seasonality of LAI and GEP were unacceptable, particularly in spring, and especially for deciduous forests. This is despite an historical emphasis on deciduous forest phenology, and the perception that controls on spring phenology are better understood than autumn phenology. Errors of up to 25 days in predicting “spring onset” transition dates were common, and errors of up to 50 days were observed. For deciduous sites, virtually every model was biased towards spring onset being too early, and autumn senescence being too late. Thus, models predicted growing seasons that were far too long for deciduous forests. For most models, errors in the seasonal representation of deciduous forest LAI were highly correlated with errors in the seasonality of both GPP and NEE, indicating the importance of getting the underlying

High autumn temperature delays spring bud burst in boreal trees, counterbalancing the effect of climatic warming

Energy Technology Data Exchange (ETDEWEB)

Heide, O. M. [Agricultural Univesity of Norway, Department of Biology and Nature Conservation, As (Norway)

2003-09-01

The effect of temperature during short-day dormancy induction on the duration and stability of bud dormancy was examined in three boreal tree species (2 birches and 1 alder) grown in a controlled environment. The phenology of the latitudinal range of birch populations, and the relationship between spring bud burst and autumn and spring temperatures were also studied. Results showed that during short-day dormancy induction in the autumn high temperatures delayed bud burst in the following spring in both controlled and natural environments. It is suggested that this response to higher autumn temperatures may be a manifestation of a general synergism between high temperature and short-day photoperiodic processes, and may be an adaptive mechanism common to boreal trees. It is further conjectured that this mechanism may be important in counterbalancing the potentially adverse effects of higher winter temperatures on dormancy stability of boreal trees during climate warming. 23 refs., 2 tabs., 4 figs.

An Analysis on Langston Hughes’ Writing Technique——plotlessness in Early Autumn

Institute of Scientific and Technical Information of China (English)

娄佳丽

2014-01-01

Langston Hughes shows a different writing technique in his short story Early Autumn.He uses plotlessness, an irregular narrative technique, to reflect the character’s emotional undercurrents beneath the calm surface and also forms a sharp contrast with the character’s intense emotional change.

Sea surface salinity and temperature-based predictive modeling of southwestern US winter precipitation: improvements, errors, and potential mechanisms

Science.gov (United States)

Liu, T.; Schmitt, R. W.; Li, L.

2017-12-01

Using 69 years of historical data from 1948-2017, we developed a method to globally search for sea surface salinity (SSS) and temperature (SST) predictors of regional terrestrial precipitation. We then applied this method to build an autumn (SON) SSS and SST-based 3-month lead predictive model of winter (DJF) precipitation in southwestern United States. We also find that SSS-only models perform better than SST-only models. We previously used an arbitrary correlation coefficient (r) threshold, |r| > 0.25, to define SSS and SST predictor polygons for best subset regression of southwestern US winter precipitation; from preliminary sensitivity tests, we find that |r| > 0.18 yields the best models. The observed below-average precipitation (0.69 mm/day) in winter 2015-2016 falls within the 95% confidence interval of the prediction model. However, the model underestimates the anomalous high precipitation (1.78 mm/day) in winter 2016-2017 by more than three-fold. Moisture transport mainly attributed to "pineapple express" atmospheric rivers (ARs) in winter 2016-2017 suggests that the model falls short on a sub-seasonal scale, in which case storms from ARs contribute a significant portion of seasonal terrestrial precipitation. Further, we identify a potential mechanism for long-range SSS and precipitation teleconnections: standing Rossby waves. The heat applied to the atmosphere from anomalous tropical rainfall can generate standing Rossby waves that propagate to higher latitudes. SSS anomalies may be indicative of anomalous tropical rainfall, and by extension, standing Rossby waves that provide the long-range teleconnections.

Predictive modeling of complications.

Science.gov (United States)

Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

2016-09-01

Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

The Significance of Hunting : "The Bear" and "Delta Autumn"

OpenAIRE

海上, 順代; Nobuyo", "Unagami

2011-01-01

The aim of this paper is to discuss the significance of hunting in "The Bear" and "Delta Autumn", the fifth and sixth stories in William Faulkner‟s Go Down, Moses (1942). In this paper, I would like to show that hunting plays an important role in Faulkner's Southern society, referring to the studies of Maria Mies, a German sociologist. In her view, hunting is useful to a patriarchal society, which strictly distinguishes men from women. As a part of a social system, hunting succeeds in g...

Reception of the Warsaw Autumn Festival in Lithuania: Cultural Discourse and Political Context

Directory of Open Access Journals (Sweden)

Stanevičiūtė Rūta

2017-12-01

Full Text Available This article aims to offer a broader understanding of the Lithuanian reception of the Warsaw Autumn festival in relation to the modernisation of national music in Lithuania since the late 1950s – early 1960s. Based on a micro-historical and comparative approach to the network of individuals and events, it is intended to explore the shifts of reception through analysis of musical criticism, composers’ work and discourse, and artistic exchange between the Lithuanian and Polish new music scenes. The author discusses the cultural and political factors which affected the changing role of the Warsaw Autumn festival and its impact on the modernisation processes in Lithuanian music. In addition, the asymmetries of mutual understanding and interests between the Polish and Lithuanian music cultures have been highlighted both during the Cold War and the post-communist transformation periods.

Monitoring and forecasting local landslide hazard in the area of Longyearbyen, Svalbard - early progress and experiences from the Autumn 2016 events

Science.gov (United States)

Wang, Thea; Krøgli, Ingeborg; Boje, Søren; Colleuille, Hervé

2017-04-01

Since 2013 the Norwegian Water Resources and Energy Directorate (NVE) has operated a landslide early warning system (LEWS) for mainland Norway. The Svalbard islands, situated 800 km north of the Norwegian mainland, and 1200 km from the North Pole, are not part of the conventional early warning service. However, following the fatal snow avalanche event 19 Dec. 2015 in the settlement of Longyearbyen (78° north latitude), local authorities and the NVE have initiated monitoring of the hydro-meteorological conditions for the area of Longyearbyen, as an extraordinary precaution. Two operational forecasting teams from the NVE; the snow avalanche and the landslide hazard forecasters, perform hazard assessment related to snow avalanches, slush flows, debris flows, shallow slides and local flooding. This abstract will focus on recent experiences made by the landslide hazard team during the autumn 2016 landslide events, caused by a record setting wet and warm summer and autumn of 2016. The general concept of the Norwegian LEWS is based on frequency intervals of extreme hydro-meteorological conditions. This general concept has been transposed to the Longyearbyen area. Although the climate is considerably colder and drier than mainland Norway, experiences so far are positive and seem useful to the local authorities. Initially, the landslide hazard evaluation was intended to consider only slush flow hazard during the snow covered season. However, due to the extraordinary warm and wet summer and autumn 2016, the landslide hazard forecasters unexpectedly had to issue warnings for the local authorities due to increased risk of shallow landslides and debris flows. This was done in close cooperation with the Norwegian Meteorological Institute, who provided weather forecasts from the recently developed weather prediction model, AROME-Arctic. Two examples, from 14-15 Oct and 8-9 Nov 2016, will be given to demonstrate how the landslide hazard assessment for the Longyearbyen area is

Centre of the European gas market. The European Autumn Gas Conference

International Nuclear Information System (INIS)

Van Hasselt, F.; Van der Wal, W.; Ruinen, H.

1998-01-01

From the results of the 1997 European Autumn Gas Conference in Barcelona, Spain, it appears that the European gas industry is mainly focused on the liberalization of the European energy market. The main topic of the Conference was 'dealing with surplus'. A brief overview is given of the natural gas trade developments in the European countries. 1 ill., 1 tab. 2 ills

Candidate Prediction Models and Methods

DEFF Research Database (Denmark)

Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

2005-01-01

This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

Effect of spring versus autumn grass/clover silage and rapeseed supplementation on milk production, composition and quality in Jersey cows

DEFF Research Database (Denmark)

Larsen, Mette Krogh; Vogdanou, Stefania; Hellwing, Anne Louise Frydendahl

2016-01-01

of C16 : 0, riboflavin and α-tocopherol were decreased with autumn silage. The majority of C18 FAs in milk and α-tocopherol concentration increased with rapeseed whereas C11 : 0 to C16 : 0 FA were reduced. Autumn silage reduced biohydrogenation of C18 : 2n6, whereas rapeseed increased biohydrogenation...

The effect of autumn and spring planting time on seed yield and ...

African Journals Online (AJOL)

The objective of this study was to investigate the effects of autumn and spring plantings on seed yield and quality of chickpea genotypes. Fourteen chickpea genotypes were grown over the consecutive two growing seasons in northwest Turkey. The results showed that planting time had significant effects on the investigated ...

Feeding habits of garfish, Belone belone euxini Günther, 1866 in autumn and winter in Turkey’s south–east coast of the Black Sea

Directory of Open Access Journals (Sweden)

Kaya, Ş.

2017-02-01

Full Text Available We studied the stomach content of Belone belone in the south–east Black Sea during autumn and winter months in 2010–2011. The most frequent feeding items in the diet were insects in autumn and fish in winter. Other items in the diet were mollusks, crustaceans and isopods. Flying ants were mostly consumed by male garfish, particularly the smaller fish, in autumn.

Wind power prediction models

Science.gov (United States)

Levy, R.; Mcginness, H.

1976-01-01

Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

Inverse and Predictive Modeling

Energy Technology Data Exchange (ETDEWEB)

Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-09-27

The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

Archaeological predictive model set.

Science.gov (United States)

2015-03-01

This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

Potential Pasture Nitrogen Concentrations and Uptake from Autumn or Spring Applied Cow Urine and DCD under Field Conditions

Science.gov (United States)

Moir, Jim; Cameron, Keith; Di, Hong

2016-01-01

Nitrogen (N) cycling and losses in grazed grassland are strongly driven by urine N deposition by grazing ruminants. The objective of this study was to quantify pasture N concentrations, yield and N uptake following autumn and spring deposition of cow urine and the effects of fine particle suspension (FPS) dicyandiamide (DCD). A field plot study was conducted on the Lincoln University dairy farm, Canterbury, New Zealand from May 2003 to May 2005. FPS DCD was applied to grazed pasture plots at 10 kg·ha−1 in autumn and spring in addition to applied cow urine at a N loading rate of 1000 kg·N·ha−1, with non-urine control plots. Pasture N ranged between 1.9 and 4.8% with higher concentrations from urine. Results indicated that urine consistently increased N concentrations for around 220 days post deposition (mid December/early summer) at which point concentrations dropped to background levels. In urine patches, pasture yield and annual N uptake were dramatically increased on average by 51% for autumn and 28% for spring applied urine, in both years, when DCD was applied. This field experiment provides strong evidence that annual pasture N uptake is more strongly influenced by high urine N deposition than pasture N concentrations. FPS DCD has the potential to result in very high N uptake in urine patches, even when they are autumn deposited. PMID:27304974

Confidence scores for prediction models

DEFF Research Database (Denmark)

Gerds, Thomas Alexander; van de Wiel, MA

2011-01-01

In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

Fish larvae from the Canary region in autumn

Directory of Open Access Journals (Sweden)

J. M. Rodríguez

2000-03-01

Full Text Available In this paper, the taxonomic composition of the fish larvae community in the Canary region in autumn 1991 is presented. In total, 8699 larvae belonging to 58 fish families were studied. 176 taxonomic groups were identified, 149 at species level and the rest were identified at a higher level. The most numerous family and the one that presented the greatest number of species was Myctophidae. The most frequently caught species was Cyclothone braueri. The taxonomic composition (at family level of the fish larvae community, dominated by four mesopelagic families, was typical of oceanic regions of warm waters. The most remarkable feature of the fish larvae community was its high specific diversity.

Satellite tracking of two Montagu's Harriers (Circus pygargus) : Dual pathways during autumn migration

NARCIS (Netherlands)

Trierweiler, Christiane; Koks, Ben J.; Drent, Rudi H.; Exo, Klaus-Michael; Komdeur, Jan; Dijkstra, Cor; Bairlein, Franz

2007-01-01

Autumn migration routes of two Dutch female Montagu's Harriers (Circus pygargus) were documented for the first time using satellite telemetry. Both migrated to their African wintering area-one via the Straits of Gibraltar through the Mediterranean and the other via Italy/Tunisia. The rate of travel

Energy content of hybrid Rumex patienta L. x Rumex tianschanicus A. Los. (Rumex OK 2 samples from autumn months

Directory of Open Access Journals (Sweden)

Michal Rolinec

2018-03-01

Full Text Available Aim of this experiment was to determine the gross energy concentration of fresh, wilted and ensiled hybrid of Rumex patientia L. x Rumex tianschanicus A. Los. (Rumex OK 2. Samples were collected in autumn months of the year 2017. The plant of Rumex OK 2 consist during autumn months only from rosette of leaves. The height of leaves was in autumn months following, September 56.68±13.80 cm; October 59.29±11.93 cm and November 55.98±10.80 cm. Rumex OK 2 silage was made from wilted matter, with or without of addition of dried molasses. Gross energy was determined as the heat released after combustion of a sample (Leco AC 500 in MJ per kilogram of dry matter of the sample. By the autumn months the concentration of dry matter, as well as the concentration of gross energy increased, except Rumex OK 2 silage from November. The highest concentration of gross energy had wilted Rumex OK 2 from November (18.02 MJ.kg-1 of dry matter. There was no significant effect of addition of dried molasses to wilted Rumex OK 2 before ensiling on gross energy concentration in Rumex OK 2 silages (P>0.05. Gross energy concentration of all types of analysed samples had relative high value (16.98 to 18.02 MJ.kg-1 of dry matter. Fresh or ensiled Rumex OK 2 can be used as a part of feed ratio for ruminants or can be utilised in biogas station. However, due to the low content of dry mater in fresh or wilted material the production of silage can be in autumn months problematic.

Potential ocean–atmosphere preconditioning of late autumn Barents-Kara sea ice concentration anomaly

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Martin P. King

2016-02-01

Full Text Available Many recent studies have revealed the importance of the climatic state in November on the seasonal climate of the subsequent winter. In particular, it has been shown that interannual variability of sea ice concentration (SIC over the Barents-Kara (BK seas in November is linked to winter atmospheric circulation anomaly that projects on the North Atlantic Oscillation. Understanding the lead–lag processes involving the different components of the climate system from autumn to winter is therefore important. This note presents dynamical interpretation for the ice-ocean–atmosphere relationships that can affect the BK SIC anomaly in late autumn. It is found that cyclonic (anticyclonic wind anomaly over the Arctic in October, by Ekman drift, can be responsible for positive (negative SIC in the BK seas in November. The results also suggest that ocean heat transport via the Barents Sea Opening in September and October can contribute to BK SIC anomaly in November.

Chemical composition and sources of PM1 and PM2.5 in Beijing in autumn.

Science.gov (United States)

Zhang, Yanyun; Lang, Jianlei; Cheng, Shuiyuan; Li, Shengyue; Zhou, Ying; Chen, Dongsheng; Zhang, Hanyu; Wang, Haiyan

2018-02-20

Beijing, the capital of China, suffers from severe atmospheric aerosol pollution; nevertheless, a comprehensive study of the constituents and sources of PM 1 is still lacking, and the differences between PM 1 and PM 2.5 are still unclear. In this study, an intensive observation was conducted to reveal the pollution characteristics of PM 1 and PM 2.5 in Beijing in autumn. Positive matrix factorization (PMF), backward trajectories and a potential source contribution function (PSCF) model were used to identify the source categories and source areas of PM 1 and PM 2.5 . The results showed that the average concentrations of PM 1 and PM 2.5 reached 78.20μg/m 3 and 95.47μg/m 3 during the study period, respectively. PM 1 contributed greatly to PM 2.5 . The PM 1 /PM 2.5 value increased from 73.6% to 90.1% with PM 1 concentration growing from 150μg/m 3 . Higher secondary inorganic aerosol (SIA) proportions (31.3%-70.8%) were found in PM 1 . The higher fraction of SIA, OC, EC and typical elements in PM 1 illustrated that anthropogenic components accumulated more in smaller size particles. Three typical weather patterns causing the heavy pollution in autumn were found as follows: (1) Siberian high and uniform high pressure field, (2) cold front and low-voltage system, and (3) uniform low pressure field. A PMF analysis indicated that secondary aerosols and coal combustion, vehicle, industry, biomass burning, and dust were the important sources of PM, accounting for 53.8%, 8.0%, 13.0%, 13.2% and 12.0% of PM 1 , respectively, and for 47.5%, 9.9%, 12.4%, 8.4% and 21.8% of PM 2.5 , respectively. The HYSPLIT and chemical components analysis indicated the potential contribution from biomass burning and fertilization ammonia emissions to PM 1 in autumn. The source areas were similar for PM 1 and PM 1-2.5 under general polluted conditions, but during the heavily polluted periods, the source areas were distributed in farther regions from Beijing for PM 1 than for PM 1-2.5 . Copyright

European larch phenology in the Alps: can we grasp the role of ecological factors by combining field observations and inverse modelling?

Science.gov (United States)

Migliavacca, M.; Cremonese, E.; Colombo, R.; Busetto, L.; Galvagno, M.; Ganis, L.; Meroni, M.; Pari, E.; Rossini, M.; Siniscalco, C.; Morra di Cella, U.

2008-09-01

Vegetation phenology is strongly influenced by climatic factors. Climate changes may cause phenological variations, especially in the Alps which are considered to be extremely vulnerable to global warming. The main goal of our study is to analyze European larch ( Larix decidua Mill.) phenology in alpine environments and the role of the ecological factors involved, using an integrated approach based on accurate field observations and modelling techniques. We present 2 years of field-collected larch phenological data, obtained following a specifically designed observation protocol. We observed that both spring and autumn larch phenology is strongly influenced by altitude. We propose an approach for the optimization of a spring warming model (SW) and the growing season index model (GSI) consisting of a model inversion technique, based on simulated look-up tables (LUTs), that provides robust parameter estimates. The optimized models showed excellent agreement between modelled and observed data: the SW model predicts the beginning of the growing season (BGS) with a mean RMSE of 4 days, while GSI gives a prediction of the growing season length (LGS) with a RMSE of 5 days. Moreover, we showed that the original GSI parameters led to consistent errors, while the optimized ones significantly increased model accuracy. Finally, we used GSI to investigate interactions of ecological factors during springtime development and autumn senescence. We found that temperature is the most effective factor during spring recovery while photoperiod plays an important role during autumn senescence: photoperiod shows a contrasting effect with altitude decreasing its influence with increasing altitude.

Coupled hydrological and biogeochemical processes controlling variability of nitrogen species in streamflow during autumn in an upland forest

Science.gov (United States)

Sebestyen, Stephen D.; Shanley, James B.; Boyer, Elizabeth W.; Kendall, Carol; Doctor, Daniel H.

2014-01-01

Autumn is a season of dynamic change in forest streams of the northeastern United States due to effects of leaf fall on both hydrology and biogeochemistry. Few studies have explored how interactions of biogeochemical transformations, various nitrogen sources, and catchment flow paths affect stream nitrogen variation during autumn. To provide more information on this critical period, we studied (1) the timing, duration, and magnitude of changes to stream nitrate, dissolved organic nitrogen (DON), and ammonium concentrations; (2) changes in nitrate sources and cycling; and (3) source areas of the landscape that most influence stream nitrogen. We collected samples at higher temporal resolution for a longer duration than typical studies of stream nitrogen during autumn. This sampling scheme encompassed the patterns and extremes that occurred during base flow and stormflow events of autumn. Base flow nitrate concentrations decreased by an order of magnitude from 5.4 to 0.7 µmol L−1 during the week when most leaves fell from deciduous trees. Changes to rates of biogeochemical transformations during autumn base flow explained the low nitrate concentrations; in-stream transformations retained up to 72% of the nitrate that entered a stream reach. A decrease of in-stream nitrification coupled with heterotrophic nitrate cycling were primary factors in the seasonal nitrate decline. The period of low nitrate concentrations ended with a storm event in which stream nitrate concentrations increased by 25-fold. In the ensuing weeks, peak stormflow nitrate concentrations progressively decreased over closely spaced, yet similarly sized events. Most stormflow nitrate originated from nitrification in near-stream areas with occasional, large inputs of unprocessed atmospheric nitrate, which has rarely been reported for nonsnowmelt events. A maximum input of 33% unprocessed atmospheric nitrate to the stream occurred during one event. Large inputs of unprocessed atmospheric nitrate

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

Science.gov (United States)

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Autumn – a season for renewal

CERN Multimedia

2011-01-01

Autumn is not usually thought of as the season for renewal, but in the world of particle physics, this year is different. Around the world, many of CERN’s old friends and colleagues are moving on and making way for new faces.   In China, Chen Hesheng, who has been Director of the nation’s Institute for High Energy Physics since 1998, is taking up a new position, passing the baton to Wang Yifang. In Italy, Roberto Petronzio steps down after seven years at the helm of INFN, handing over the Presidency to Fernando Ferroni. In the UK, John Womersley has been appointed chief executive of the Science and Technology Facilities Council, taking over from Keith Mason. And in the USA, Jim Siegrist has been appointed to the leadership of the Department of Energy’s High Energy Physics Office following Denis Kovar’s retirement. Earlier in the year, Victor Matveev was elected to be the next Director of the Joint Institute of Nuclear Research in Dubna, Russia, followin...

Multi-model analysis in hydrological prediction

Science.gov (United States)

Lanthier, M.; Arsenault, R.; Brissette, F.

2017-12-01

Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been

Conductive and Mixed Hearing Losses: A Comparison between Summer and Autumn

OpenAIRE

Nickbakht, Mansoureh; Borzoo, Samira

2014-01-01

Background and Objectives Conductive hearing loss is common among children and adults. This study aims at comparing the results of conductive hearing loss in summer and autumn. Subjects and Methods Puretone audiometry and tympanometry tests were done for all patients who referred to the Iranian-based audiology center of Imam Khomeini Hospital in Ahvaz. Data on the patients with conductive or mixed hearing loss were analyzed. The impacts of season, age, and etiology of the disease were analyze...

Three responses to small changes in stream temperature by autumn-emerging aquatic insects

Science.gov (United States)

Judith L. Li; Sherri L. Johnson; Janel Banks. Sobota

2011-01-01

In this experimental study, conducted in coastal Oregon USA, we examined how small increases in summer water temperatures affected aquatic insect growth and autumn emergence. We maintained naturally fluctuating temperatures from 2 nearby streams and a 3rd regime, naturally fluctuating temperatures warmed by 3-5°C, in flow-through troughs from mid...

Predictive Modeling in Race Walking

Directory of Open Access Journals (Sweden)

Krzysztof Wiktorowicz

2015-01-01

Full Text Available This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.

Adding propensity scores to pure prediction models fails to improve predictive performance

Directory of Open Access Journals (Sweden)

Amy S. Nowacki

2013-08-01

Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

Model-free and model-based reward prediction errors in EEG.

Science.gov (United States)

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Autumn frost hardiness in Norway spruce plus tree progeny and trees of the local and transferred provenances in central Sweden.

Science.gov (United States)

Hannerz, Mats; Westin, Johan

2005-09-01

Reforestation with provenances from locations remote from the planting site (transferred provenances) or the progeny of trees of local provenances selected for superior form and vigor (plus trees) offer alternative means to increase yield over that obtained by the use of seed from unselected trees of the local provenance. Under Swedish conditions, Norway spruce (Picea abies (L.) Karst.) of certain transferred provenances generally has an advantage in productivity relative to the local provenance comparable to that of progeny of plus trees. The aim of this study was to explore the extent to which productivity gains achieved by provenance transfer or the use of plus tree progeny are associated with reductions in autumn frost hardiness, relative to that of trees of the local provenance. In a field trial with 19-year-old trees in central Sweden, bud hardiness was tested on four occasions during the autumn of 2002. Trees of the local provenance were compared with trees of a south Swedish provenance originating 3 degrees of latitude to the south, a Belarusian provenance and the progeny of plus trees of local origin. The Belarusian provenance was the least hardy and the local provenance the most hardy, with plus tree progeny and the south Swedish provenance being intermediate in hardiness. Both the Belarusian provenance and the plus tree progeny were significantly taller than trees of the other populations. Within provenances, tree height was negatively correlated with autumn frost hardiness. Among the plus tree progeny, however, no such correlation between tree height and autumn frost hardiness was found. It is concluded that although the gain in productivity achieved by provenance transfer from Belarus was comparable to that achieved by using the progeny of plus trees of the local provenance, the use of trees of the Belarus provenance involved an increased risk of autumn frost damage because of later hardening.

Nonlinear chaotic model for predicting storm surges

Directory of Open Access Journals (Sweden)

M. Siek

2010-09-01

Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

Southward autumn migration of waterfowl facilitates cross-continental transmission of the highly pathogenic avian influenza H5N1 virus

Science.gov (United States)

Xu, Yanjie; Gong, Peng; Wielstra, Ben; Si, Yali

2016-08-01

The highly pathogenic avian influenza subtype H5N1 (HPAI H5N1) is a worldwide zoonotic infectious disease, threatening humans, poultry and wild birds. The role of wild birds in the spread of HPAI H5N1 has previously been investigated by comparing disease spread patterns with bird migration routes. However, the different roles that the southward autumn and northward spring migration might play in virus transmission have hardly been explored. Using direction analysis, we analyze HPAI H5N1 transmission directions and angular concentration of currently circulating viral clades, and compare these with waterfowl seasonal migration directions along major waterfowl flyways. Out of 22 HPAI H5N1 transmission directions, 18 had both a southward direction and a relatively high concentration. Differences between disease transmission and waterfowl migration directions were significantly smaller for autumn than for spring migration. The four northward transmission directions were found along Asian flyways, where the initial epicenter of the virus was located. We suggest waterfowl first picked up the virus from East Asia, then brought it to the north via spring migration, and then spread it to other parts of world mainly by autumn migration. We emphasize waterfowl autumn migration plays a relatively important role in HPAI H5N1 transmission compared to spring migration.

Extracting falsifiable predictions from sloppy models.

Science.gov (United States)

Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

2007-12-01

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

Science.gov (United States)

Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

2012-01-01

The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

Fuel load and flight ranges of blackcaps Sylvia atricapilla in northern Iberia during autumn and spring migrations

Directory of Open Access Journals (Sweden)

JUAN ARIZAGA, EMILIO BARBA

2009-12-01

Full Text Available Fuel accumulation, mainly as fatty acids, is one of the main characteristics of migratory birds. Studying to what extent each population or species manages fuel load and how it varies along routes of migration or between seasons (autumn and spring migrations is crucial to our understanding of bird migration strategies. Our aim here was to analyse whether migratory blackcaps Sylvia atricapilla passing through northern Iberia differ in their mean fuel loads, rate of fuel accumulation and 'potential' flight ranges between migration seasons. Blackcaps were mist netted for 4 h-periods beginning at dawn from 16 September to 15 November 2003–2005, and from 1 March to 30 April 2004–2006 in a European Atlantic hedgerow at Loza, northern Iberia. Both fuel load and fuel deposition rate (this latter assessed with difference in body mass of within-season recaptured individuals were higher in autumn than in spring. Possible hypotheses explaining these results could be seasonal-associated variations in food availability (likely lower during spring than during autumn, the fact that a fraction of the migrants captured in spring could breed close to the study area and different selective pressures for breeding and wintering [Current Zoology 55 (6: 401–410, 2009].

The Warsaw Autumn International Festival of Contemporary Music Transformations of Programming Policies

Directory of Open Access Journals (Sweden)

Gąsiorowska Małgorzata

2017-12-01

Full Text Available The present paper surveys the history of the Warsaw Autumn festival focusing on changes in the Festival programming. I discuss the circumstances of organising a cyclic contemporary music festival of international status in Poland. I point out the relations between programming policies and the current political situation, which in the early years of the Festival forced organisers to maintain balance between Western and Soviet music as well as the music from the so-called “people’s democracies” (i.e. the Soviet bloc. Initial strong emphasis on the presentation of 20th-century classics was gradually replaced by an attempt to reflect different tendencies and new phenomena, also those combining music with other arts. Despite changes and adjustments in the programming policy, the central aim of the Festival’s founders – that of presenting contemporary music in all its diversity, without overdue emphasis on any particular trend – has consistently been pursued. The idea of introducing leitmotifs, different for each Festival edition (such as: music involving human voice, mainly electronic, etc. – is not inconsistent with this general aim since the selected works represent different aesthetics, and the “main theme” is not the only topic of any given edition of the Warsaw Autumn.

EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

OpenAIRE

Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.

2014-01-01

The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain, which...

Spatial Economics Model Predicting Transport Volume

Directory of Open Access Journals (Sweden)

Lu Bo

2016-10-01

Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

Lessons from the autumn 2014 flash floods in the city of Nîmes and its neighborhood (France: behavior of several mitigation dams and hydrological analysis

Directory of Open Access Journals (Sweden)

Fouchier Catherine

2016-01-01

Full Text Available The Languedoc area, in Southern France, is prone to autumnal flash floods which are characteristic of the Mediterranean climate. To cope with this threat, the local authorities have chosen to build several dams on the main dangerous rivers of the area. We have focused on the flood mitigation facilities of two operators: the City of Nîmes and the Gardons Rivers Managing authority. After the catastrophic flash flood of October 1988, the city of Nîmes built flood mitigation dams on many of its high-risk streams. These flood barriers worked several times during the intense rainfalls of autumn 2014. The on-site conclusions drawn from these floods and the computation carried out with hydrological models confirmed how well the dams functioned. In 2010, the Gardons Rivers Managing authority built a flood mitigation dam on the Esquielle River to protect the village of Saint-Geniès-de-Malgoirès. The spillway of this dam worked for the first time in the autumn of 2014. We analyzed one of the major floods monitored on that occasion at its outlet. The goals of this study are: (i to evaluate dams efficiency and (ii to test, on a catchment which was not used for its calibration, the AIGA flash flood warning method, which was developed by IRSTEA.

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

Directory of Open Access Journals (Sweden)

Jing Lu

2014-11-01

Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

Incorporating uncertainty in predictive species distribution modelling.

Science.gov (United States)

Beale, Colin M; Lennon, Jack J

2012-01-19

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Predictive user modeling with actionable attributes

NARCIS (Netherlands)

Zliobaite, I.; Pechenizkiy, M.

2013-01-01

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target

Advanced autumn migration of sparrowhawk has increased the predation risk of long-distance migrants in Finland.

Directory of Open Access Journals (Sweden)

Aleksi Lehikoinen

Full Text Available Predation affects life history traits of nearly all organisms and the population consequences of predator avoidance are often larger than predation itself. Climate change has been shown to cause phenological changes. These changes are not necessarily similar between species and may cause mismatches between prey and predator. Eurasian sparrowhawk Accipiter nisus, the main predator of passerines, has advanced its autumn phenology by about ten days in 30 years due to climate change. However, we do not know if sparrowhawk migrate earlier in response to earlier migration by its prey or if earlier sparrowhawk migration results in changes to predation risk on its prey. By using the median departure date of 41 passerine species I was able to show that early migrating passerines tend to advance, and late migrating species delay their departure, but none of the species have advanced their departure times as much as the sparrowhawk. This has lead to a situation of increased predation risk on early migrating long-distance migrants (LDM and decreased the overlap of migration season with later departing short-distance migrants (SDM. Findings highlight the growing list of problems of declining LDM populations caused by climate change. On the other hand it seems that the autumn migration may become safer for SDM whose populations are growing. Results demonstrate that passerines show very conservative response in autumn phenology to climate change, and thus phenological mismatches caused by global warming are not necessarily increasing towards the higher trophic levels.

Assessment and prediction of road accident injuries trend using time-series models in Kurdistan.

Science.gov (United States)

Parvareh, Maryam; Karimi, Asrin; Rezaei, Satar; Woldemichael, Abraha; Nili, Sairan; Nouri, Bijan; Nasab, Nader Esmail

2018-01-01

Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants' accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0) 12 , and SARIMA (1, 1, 1) (0, 0, 1) 12 , respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the

MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

Science.gov (United States)

Son, S. W.; Lim, Y.; Kim, D.

2017-12-01

The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.

Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

Science.gov (United States)

Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

2017-12-01

Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

Evaluation of high-resolution forecasts with the non-hydrostaticnumerical weather prediction model Lokalmodell for urban air pollutionepisodes in Helsinki, Oslo and Valencia

Directory of Open Access Journals (Sweden)

B. Fay

2006-01-01

Full Text Available The operational numerical weather prediction model Lokalmodell LM with 7,km horizontal resolution was evaluated for forecasting meteorological conditions during observed urban air pollution episodes. The resolution was increased to experimental 2.8 km and 1.1 km resolution by one-way interactive nesting without introducing urbanisation of physiographic parameters or parameterisations. The episodes examined are two severe winter inversion-induced episodes in Helsinki in December 1995 and Oslo in January 2003, three suspended dust episodes in spring and autumn in Helsinki and Oslo, and a late-summer photochemical episode in the Valencia area. The evaluation was basically performed against observations and radiosoundings and focused on the LM skill at forecasting the key meteorological parameters characteristic for the specific episodes. These included temperature inversions, atmospheric stability and low wind speeds for the Scandinavian episodes and the development of mesoscale recirculations in the Valencia area. LM forecasts often improved due to higher model resolution especially in mountainous areas like Oslo and Valencia where features depending on topography like temperature, wind fields and mesoscale valley circulations were better described. At coastal stations especially in Helsinki, forecast gains were due to the improved physiographic parameters (land fraction, soil type, or roughness length. The Helsinki and Oslo winter inversions with extreme nocturnal inversion strengths of 18°C were not sufficiently predicted with all LM resolutions. In Helsinki, overprediction of surface temperatures and low-level wind speeds basically led to underpredicted inversion strength. In the Oslo episode, the situation was more complex involving erroneous temperature advection and mountain-induced effects for the higher resolutions. Possible explanations include the influence of the LM treatment of snow cover, sea ice and stability-dependence of transfer

Modeling, robust and distributed model predictive control for freeway networks

NARCIS (Netherlands)

Liu, S.

2016-01-01

In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

Staying Power of Churn Prediction Models

NARCIS (Netherlands)

Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

Validation of regression models for nitrate concentrations in the upper groundwater in sandy soils

International Nuclear Information System (INIS)

Sonneveld, M.P.W.; Brus, D.J.; Roelsma, J.

2010-01-01

For Dutch sandy regions, linear regression models have been developed that predict nitrate concentrations in the upper groundwater on the basis of residual nitrate contents in the soil in autumn. The objective of our study was to validate these regression models for one particular sandy region dominated by dairy farming. No data from this area were used for calibrating the regression models. The model was validated by additional probability sampling. This sample was used to estimate errors in 1) the predicted areal fractions where the EU standard of 50 mg l -1 is exceeded for farms with low N surpluses (ALT) and farms with higher N surpluses (REF); 2) predicted cumulative frequency distributions of nitrate concentration for both groups of farms. Both the errors in the predicted areal fractions as well as the errors in the predicted cumulative frequency distributions indicate that the regression models are invalid for the sandy soils of this study area. - This study indicates that linear regression models that predict nitrate concentrations in the upper groundwater using residual soil N contents should be applied with care.

The diet of the garden dormouse (Eliomys quercinus) in the Netherlands in summer and autumn

NARCIS (Netherlands)

Kuipers, L.; Scholten, J.; Thissen, J.B.M.; Bekkers, L.; Geertsma, M.; Pulles, C.A.T.; Siepel, H.; Turnhout, van L.J.E.A.

2012-01-01

The food of the last remaining population of garden dormouse (Eliomys quercinus) in the Netherlands is studied by means of analysing faecal samples, collected in the summer and autumn of the year 2010. In total 139 scat samples were collected from 51 different nest boxes. The samples were visually

Prediction Models for Dynamic Demand Response

Energy Technology Data Exchange (ETDEWEB)

Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D²R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D²R, which we address in this paper. Our first contribution is the formal definition of D²R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D²R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D²R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D²R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D²R. Also, prediction models require just few days’ worth of data indicating that small amounts of

Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

Science.gov (United States)

Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

2015-01-01

We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

GROWTH AND SURVIVAL OF PRAWN Macrobrachium tenellum IN EXPERIMENTAL CULTURES DURING SUMMER AND AUTUMN IN THE TROPICAL MEXICAN PACIFIC COAST.

Directory of Open Access Journals (Sweden)

Fernando Vega Villasante

2011-03-01

Full Text Available For aquaculture purposes, Macrobrachium tenellum is considered as a good candidate, is not aggressive nor presents cannibalism and can tolerate an ample interval of temperatures, salinities and oxygen concentrations. The present work evaluates the semi-intensive culture of M. tenellum under environmental conditions of summer and autumn with special attention to water temperature. The results of the experimental cultures in the tropical Mexican Pacific coast, suggest this species demonstrates better growth during the end of the spring, summer and the beginning of the autumn, time at which the average temperature of the water is near 30°C. The experimental cultures of end of autumn and beginnings of winter demonstrate minimum growth, with an average temperature of the culture water of 27°C.  Other parameters like pH, O2 concentration and turbidity in the culture water were similar in all the experimental cultures reason why temperature is suggested the factor was the determinant in the differences found in growth. Â

Measurements and Mesoscale Modeling of Autumnal Vertical Ozone Profiles in Southern Taiwan

Directory of Open Access Journals (Sweden)

Yen-Ping Peng

2008-01-01

Full Text Available Vertical measurements of ozone were made using a tethered balloon at the Linyuan site in Kaohsiung County, southern Taiwan. Ozone was monitored at altitudes of 0, 100, 300, 500, and 1000 m from November 23 to 25 in 2005. The potential temperature profiles revealed a stable atmosphere during the study period, largely because of the dominance of the high-pressure system and nocturnal radiation cooling close to the surface. The mixing height was low (50 - 300 m, particularly in the late night and early morning. The surface ozone concentrations that were predicted using TAPM (The Air Pollution Model were high (33.7 - 119 ppbv in the daytime (10:00 - 16:00 and were low (10 - 40 ppbv at other times; the predictions of which were consistent with the observations. The simulated surface ozone concentrations reveal that costal lands typically had higher ozone concentrations than those inland, because most industrial parks are located in or close to the boundaries of Kaohsiung City. Both measurements and simulations indicate that daytime ozone concentrations decreased quickly with increasing height at altitudes below 300 m; while nighttime ozone concentrations were lower at low altitudes (50 to 300 m than at higher altitudes, partly because of dry deposition and titration of surface ozone by the near-surface nitrogen oxides (NOx and partly because of the existence of the residual layer above the stable nocturnal boundary layer. The simulations show a good correlation between the maximum daytime surface ozone concentration and average nighttime ozone concentration above the nocturnal boundary layer.

Accuracy assessment of landslide prediction models

International Nuclear Information System (INIS)

Othman, A N; Mohd, W M N W; Noraini, S

2014-01-01

The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

Mental models accurately predict emotion transitions.

Science.gov (United States)

Thornton, Mark A; Tamir, Diana I

2017-06-06

Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

Mental models accurately predict emotion transitions

Science.gov (United States)

Thornton, Mark A.; Tamir, Diana I.

2017-01-01

Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

Poisson Mixture Regression Models for Heart Disease Prediction.

Science.gov (United States)

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

Science.gov (United States)

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Comparisons of Faulting-Based Pavement Performance Prediction Models

Directory of Open Access Journals (Sweden)

Weina Wang

2017-01-01

Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

Unreachable Setpoints in Model Predictive Control

DEFF Research Database (Denmark)

Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

2008-01-01

In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

Increased air temperature during simulated autumn conditions does not increase photosynthetic carbon gain but affects the dissipation of excess energy in seedlings of the evergreen conifer Jack pine.

Science.gov (United States)

Busch, Florian; Hüner, Norman P A; Ensminger, Ingo

2007-03-01

Temperature and daylength act as environmental signals that determine the length of the growing season in boreal evergreen conifers. Climate change might affect the seasonal development of these trees, as they will experience naturally decreasing daylength during autumn, while at the same time warmer air temperature will maintain photosynthesis and respiration. We characterized the down-regulation of photosynthetic gas exchange and the mechanisms involved in the dissipation of energy in Jack pine (Pinus banksiana) in controlled environments during a simulated summer-autumn transition under natural conditions and conditions with altered air temperature and photoperiod. Using a factorial design, we dissected the effects of daylength and temperature. Control plants were grown at either warm summer conditions with 16-h photoperiod and 22 degrees C or conditions representing a cool autumn with 8 h/7 degrees C. To assess the impact of photoperiod and temperature on photosynthesis and energy dissipation, plants were also grown under either cold summer (16-h photoperiod/7 degrees C) or warm autumn conditions (8-h photoperiod/22 degrees C). Photosynthetic gas exchange was affected by both daylength and temperature. Assimilation and respiration rates under warm autumn conditions were only about one-half of the summer values but were similar to values obtained for cold summer and natural autumn treatments. In contrast, photosynthetic efficiency was largely determined by temperature but not by daylength. Plants of different treatments followed different strategies for dissipating excess energy. Whereas in the warm summer treatment safe dissipation of excess energy was facilitated via zeaxanthin, in all other treatments dissipation of excess energy was facilitated predominantly via increased aggregation of the light-harvesting complex of photosystem II. These differences were accompanied by a lower deepoxidation state and larger amounts of beta-carotene in the warm autumn

Late flooding combined with warm autumn – potential possibility for prolongation of transmission of mosquito-borne diseases

Czech Academy of Sciences Publication Activity Database

Šebesta, O.; Gelbič, Ivan

2016-01-01

Roč. 71, č. 11 (2016), s. 1292-1297 ISSN 0006-3088 Institutional support: RVO:60077344 Keywords : Aedes vexans * Aedes sticticus * autumn floods Subject RIV: GJ - Animal Vermins ; Diseases, Veterinary Medicine Impact factor: 0.759, year: 2016

The Calabrian Apennines: Important Bird Area (IBA for the Autumn migration of Raptors

Directory of Open Access Journals (Sweden)

Michele Panuccio

2012-09-01

Full Text Available Observations on the autumn migration of raptors were carried out on the Calabrian Appennines in the area where the Italian peninsula is only 30 km wide. We used three different watch points at the same time between 24 August and 12 September 2005 and 2006. We observed 4,842 raptors in 2005 and 5,324 in 2006; most of these were Honey Buzzards, Black Kites, Marsh Harriers and Montagu’s Harriers.

MULTIORGAN INJURY AFTER ACCIDENTAL POISONING WITH AUTUMN CROCUS

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Gorazd Lešničar

2004-04-01

Full Text Available Background. A case of accidental poisoning with autumn crocus (Colchicum autumnale that was misinterpreted for wild garlic (Allium ursinum is presented. Both plants grow on damp meadows and can be easily wrongly identified especially before blooming period as they have similar, pointed leaves.Results. Considering anamnestic data, clinical picture and laboratory findings in 43-yr-old female, a poisoning with the colchicine plant alkaloid was suspected. Later, it was confirmed by toxicology analyses (chromatography and spectrometry of the collected serum and urine samples. Severe initial gastrointestinal disorders progressed into ileus, bone-marrow suppression and multi-organ failure.Conclusions. After the patient had received a symptomatic treatment with granulocyte-directed growth factor and a suitable antibiotic therapy for secondary infection, she recovered within three weeks from the onset of condition. The most persistent problem was alopecia. The disease did not entailed any permanent sequellae which was confirmed 3 years after the patient was considered cured.

Spatial and temporal variability of seawater pCO2 within the Canadian Arctic Archipelago and Baffin Bay during the summer and autumn 2011

Science.gov (United States)

Geilfus, N.-X.; Pind, M. L.; Else, B. G. T.; Galley, R. J.; Miller, L. A.; Thomas, H.; Gosselin, M.; Rysgaard, S.; Wang, F.; Papakyriakou, T. N.

2018-03-01

The partial pressure of CO2 in surface water (pCO2sw) measured within the Canadian Arctic Archipelago (CAA) and Baffin Bay was highly variable with values ranging from strongly undersaturated (118 μatm) to slightly supersaturated (419 μatm) with respect to the atmospheric levels ( 386 μatm) during summer and autumn 2011. During summer, melting sea ice contributed to cold and fresh surface water and enhanced the ice-edge bloom, resulting in strong pCO2sw undersaturation. Coronation Gulf was the only area with supersaturated pCO2sw, likely due to warm CO2-enriched freshwater input from the Coppermine River. During autumn, the entire CAA (including Coronation Gulf) was undersaturated, despite generally increasing pCO2sw. Coronation Gulf was the one place where pCO2sw decreased, likely due to seasonal reduction in discharge from the Coppermine River and the decreasing sea surface temperature. The seasonal summer-to-autumn increase in pCO2sw across the archipelago is attributed in part to the continuous uptake of atmospheric CO2 through both summer and autumn and to the seasonal deepening of the surface mixed layer, bringing CO2-rich waters to the surface. These observations demonstrate how freshwater from sea ice melt and rivers affect pCO2sw differently. The general pCO2sw undersaturation during summer-autumn 2011 throughout the CAA and Baffin Bay give an estimated net oceanic sink for atmospheric CO2 over the study period of 11.4 mmol CO2 m-2 d-1, assuming no sea-air CO2 flux exchange across the sea-ice covered areas.

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

Science.gov (United States)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

Risk terrain modeling predicts child maltreatment.

Science.gov (United States)

Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

2016-12-01

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Autumn CO2 chemistry in the Japan Sea and the impact of discharges from the Changjiang River

Science.gov (United States)

Kosugi, Naohiro; Sasano, Daisuke; Ishii, Masao; Enyo, Kazutaka; Saito, Shu

2016-08-01

We made comprehensive surface water CO2 chemistry observations in the Japan Sea during each autumn from 2010 to 2014. The partial pressure of CO2 (pCO2) in surface water, 312-329 μatm, was 10-30 μatm lower in the Japan Sea than in the same latitude range of the western North Pacific adjacent to Japan. According to the sensitivity analysis of pCO2, the lower pCO2 in the Japan Sea was primarily attributable to a large seasonal decrease of pCO2 associated with strong cooling in autumn, particularly in the northern Japan Sea. In contrast, the lower pCO2 in relatively warm, freshwater in the southern Japan Sea was attributable to not only the thermodynamic effect of the temperature changes but also high total alkalinity. This alkalinity had its origin in Changjiang River and was transported by Changjiang diluted water (CDW) which seasonally runs into the Japan Sea from the East China Sea. The input of total alkalinity through CDW also elevated the saturation state of calcium carbonate minerals and mitigated the effects of anthropogenic ocean acidification, at least during autumn. These biogeochemical impacts of CDW in the Japan Sea last until November, although the inflow from the East China Sea to the Japan Sea almost ceases by the end of September. The long duration of the high saturation state of calcium carbonate benefits calcareous marine organisms.

Case studies in archaeological predictive modelling

NARCIS (Netherlands)

Verhagen, Jacobus Wilhelmus Hermanus Philippus

2007-01-01

In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing

Fingerprint verification prediction model in hand dermatitis.

Science.gov (United States)

Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

2015-07-01

Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

Finding Furfural Hydrogenation Catalysts via Predictive Modelling.

Science.gov (United States)

Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

2010-09-10

We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (k(H):k(D)=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R(2)=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model's predictions, demonstrating the validity and value of predictive modelling in catalyst optimization.

Model Predictive Control for Smart Energy Systems

DEFF Research Database (Denmark)

Halvgaard, Rasmus

pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...

Evidence for an autumn downstream migration of Atlantic salmon Salmo salar (Linnaeus) and brown trout Salmo trutta (Linnaeus) parr to the Baltic Sea

Science.gov (United States)

Taal, Imre; Kesler, Martin; Saks, Lauri; Rohtla, Mehis; Verliin, Aare; Svirgsden, Roland; Jürgens, Kristiina; Vetemaa, Markus; Saat, Toomas

2014-06-01

In the eastern Baltic rivers, anadromous salmonid parr are known to smoltify and migrate to the sea from March until June, depending on latitude, climate and hydrological conditions. In this study, we present the first records of autumn descent of brown trout Salmo trutta and Atlantic salmon Salmo salar from the Baltic Sea Basin. Otolith microchemistry analyses revealed that these individuals hatched in freshwater and had migrated to the brackish water shortly prior to capture. The fish were collected in 2006, 2008, 2009 and 2013 from Eru Bay (surface salinity 4.5-6.5 ‰), Gulf of Finland. This relatively wide temporal range of observations indicates that the autumn descent of anadromous salmonids is not a random event. These results imply that autumn descent needs more consideration in the context of the effective stock management, assessment and restoration of Baltic salmonid populations and their habitats.

Prediction skill of rainstorm events over India in the TIGGE weather prediction models

Science.gov (United States)

Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

2017-12-01

Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

Clinical characteristics of the autumn-winter type scrub typhus cases in south of Shandong province, northern China

Directory of Open Access Journals (Sweden)

Min Jing-Si

2009-06-01

Full Text Available Abstract Background Before 1986, scrub typhus was only found endemic in southern China. Because human infections typically occur in the summer, it is called "summer type". During the autumn-winter period of 1986, a new type of scrub typhus was identified in Shandong and northern Jiangsu province of northern China. This newly recognized scrub typhus was subsequently reported in many areas of northern China and was then called "autumn-winter type". However, clinical characteristics of associated cases have not been reported. Methods From 1995 to 2006, all suspected scrub typhus cases in five township hospitals of Feixian county, Shandong province were enrolled. Indirect immunofluorescent assay (IFA was used as confirmatory serodiagnosis test. Polymerase chain reaction (PCR connected with restriction fragment length polymorphism (RFLP and sequence analyses were used for genotyping of O. tsutsugamushi DNAs. Clinical symptoms and demography of confirmed cases were analyzed. Results A total of 480 scrub typhus cases were confirmed. The cases occurred every year exclusively between September and December with a peak occurrence in October. The case numbers were relatively higher in 1995, 1996, 1997, and 2000 than in other years. 57.9% of cases were in the group aged 21–50. More cases occurred in male (56% than in female (44%. The predominant occupational group of the cases was farmers (85.0%. Farm work was reported the primary exposure to infection in 67.7% of cases. Fever, rash, and eschar were observed in 100.0%, 90.4%, and 88.5% of cases, respectively. Eschars formed frequently on or around umbilicus, abdomen areas, and front and back of waist (34.1% in both genders. Normal results were observed in 88.7% (WBC counts, 84.5% (PLT counts, and 89.7% (RBC counts of cases, respectively. Observations from the five hospitals were compared and no significant differences were found. Conclusion The autumn-winter type scrub typhus in northern China occurred

Predicting climate-induced range shifts: model differences and model reliability.

Science.gov (United States)

Joshua J. Lawler; Denis White; Ronald P. Neilson; Andrew R. Blaustein

2006-01-01

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for...

Predicting and Mapping Soil Carbon Using Visible Near Infrared Spectroscopy at Different Scales

DEFF Research Database (Denmark)

Deng, Fan

. The third objective was to test whether SOC calibration models built for different subdivisions of the Danish soil spectral library according to pedological or geological stratification would improve estimation of SOC content from Vis-NIR scans. The fourth objective was to explore the use of Vis...... in situ measurements for soil spectra may be obtained in spring and autumn, when soils are slightly drier than field capacity. We assumed that the prediction capabilities of the Danish soil spectra library could be improved by dividing it into rather homogeneous subpopulations and building separate...... in these soil cores, but did not improve the calibration of SOC. Interestingly, the prediction ability for SOC increased when the Danish spectral library was spiked with local samples from Vindum. This indicates that the full variation in Danish soils is not yet fully represented in the library. The 3...

Predictive Modeling of a Paradigm Mechanical Cooling Tower Model: II. Optimal Best-Estimate Results with Reduced Predicted Uncertainties

Directory of Open Access Journals (Sweden)

Ruixian Fang

2016-09-01

Full Text Available This work uses the adjoint sensitivity model of the counter-flow cooling tower derived in the accompanying PART I to obtain the expressions and relative numerical rankings of the sensitivities, to all model parameters, of the following model responses: (i outlet air temperature; (ii outlet water temperature; (iii outlet water mass flow rate; and (iv air outlet relative humidity. These sensitivities are subsequently used within the “predictive modeling for coupled multi-physics systems” (PM_CMPS methodology to obtain explicit formulas for the predicted optimal nominal values for the model responses and parameters, along with reduced predicted standard deviations for the predicted model parameters and responses. These explicit formulas embody the assimilation of experimental data and the “calibration” of the model’s parameters. The results presented in this work demonstrate that the PM_CMPS methodology reduces the predicted standard deviations to values that are smaller than either the computed or the experimentally measured ones, even for responses (e.g., the outlet water flow rate for which no measurements are available. These improvements stem from the global characteristics of the PM_CMPS methodology, which combines all of the available information simultaneously in phase-space, as opposed to combining it sequentially, as in current data assimilation procedures.

Model predictive control classical, robust and stochastic

CERN Document Server

Kouvaritakis, Basil

2016-01-01

For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

Deciduous forest responses to temperature, precipitation, and drought imply complex climate change impacts.

Science.gov (United States)

Xie, Yingying; Wang, Xiaojing; Silander, John A

2015-11-03

Changes in spring and autumn phenology of temperate plants in recent decades have become iconic bio-indicators of rapid climate change. These changes have substantial ecological and economic impacts. However, autumn phenology remains surprisingly little studied. Although the effects of unfavorable environmental conditions (e.g., frost, heat, wetness, and drought) on autumn phenology have been observed for over 60 y, how these factors interact to influence autumn phenological events remain poorly understood. Using remotely sensed phenology data from 2001 to 2012, this study identified and quantified significant effects of a suite of environmental factors on the timing of fall dormancy of deciduous forest communities in New England, United States. Cold, frost, and wet conditions, and high heat-stress tended to induce earlier dormancy of deciduous forests, whereas moderate heat- and drought-stress delayed dormancy. Deciduous forests in two eco-regions showed contrasting, nonlinear responses to variation in these explanatory factors. Based on future climate projection over two periods (2041-2050 and 2090-2099), later dormancy dates were predicted in northern areas. However, in coastal areas earlier dormancy dates were predicted. Our models suggest that besides warming in climate change, changes in frost and moisture conditions as well as extreme weather events (e.g., drought- and heat-stress, and flooding), should also be considered in future predictions of autumn phenology in temperate deciduous forests. This study improves our understanding of how multiple environmental variables interact to affect autumn phenology in temperate deciduous forest ecosystems, and points the way to building more mechanistic and predictive models.

Model predictive Controller for Mobile Robot

OpenAIRE

Alireza Rezaee

2017-01-01

This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...

Deep Predictive Models in Interactive Music

OpenAIRE

Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

2018-01-01

Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

Risk prediction model: Statistical and artificial neural network approach

Science.gov (United States)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

Evaluation of CASP8 model quality predictions

KAUST Repository

Cozzetto, Domenico

2009-01-01

The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

Predictive models of moth development

Science.gov (United States)

Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

NARCIS (Netherlands)

Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

2014-01-01

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for

Predicting water main failures using Bayesian model averaging and survival modelling approach

International Nuclear Information System (INIS)

Kabir, Golam; Tesfamariam, Solomon; Sadiq, Rehan

2015-01-01

To develop an effective preventive or proactive repair and replacement action plan, water utilities often rely on water main failure prediction models. However, in predicting the failure of water mains, uncertainty is inherent regardless of the quality and quantity of data used in the model. To improve the understanding of water main failure, a Bayesian framework is developed for predicting the failure of water mains considering uncertainties. In this study, Bayesian model averaging method (BMA) is presented to identify the influential pipe-dependent and time-dependent covariates considering model uncertainties whereas Bayesian Weibull Proportional Hazard Model (BWPHM) is applied to develop the survival curves and to predict the failure rates of water mains. To accredit the proposed framework, it is implemented to predict the failure of cast iron (CI) and ductile iron (DI) pipes of the water distribution network of the City of Calgary, Alberta, Canada. Results indicate that the predicted 95% uncertainty bounds of the proposed BWPHMs capture effectively the observed breaks for both CI and DI water mains. Moreover, the performance of the proposed BWPHMs are better compare to the Cox-Proportional Hazard Model (Cox-PHM) for considering Weibull distribution for the baseline hazard function and model uncertainties. - Highlights: • Prioritize rehabilitation and replacements (R/R) strategies of water mains. • Consider the uncertainties for the failure prediction. • Improve the prediction capability of the water mains failure models. • Identify the influential and appropriate covariates for different models. • Determine the effects of the covariates on failure

Increased Air Temperature during Simulated Autumn Conditions Does Not Increase Photosynthetic Carbon Gain But Affects the Dissipation of Excess Energy in Seedlings of the Evergreen Conifer Jack Pine1[OA

Science.gov (United States)

Busch, Florian; Hüner, Norman P.A.; Ensminger, Ingo

2007-01-01

Temperature and daylength act as environmental signals that determine the length of the growing season in boreal evergreen conifers. Climate change might affect the seasonal development of these trees, as they will experience naturally decreasing daylength during autumn, while at the same time warmer air temperature will maintain photosynthesis and respiration. We characterized the down-regulation of photosynthetic gas exchange and the mechanisms involved in the dissipation of energy in Jack pine (Pinus banksiana) in controlled environments during a simulated summer-autumn transition under natural conditions and conditions with altered air temperature and photoperiod. Using a factorial design, we dissected the effects of daylength and temperature. Control plants were grown at either warm summer conditions with 16-h photoperiod and 22°C or conditions representing a cool autumn with 8 h/7°C. To assess the impact of photoperiod and temperature on photosynthesis and energy dissipation, plants were also grown under either cold summer (16-h photoperiod/7°C) or warm autumn conditions (8-h photoperiod/22°C). Photosynthetic gas exchange was affected by both daylength and temperature. Assimilation and respiration rates under warm autumn conditions were only about one-half of the summer values but were similar to values obtained for cold summer and natural autumn treatments. In contrast, photosynthetic efficiency was largely determined by temperature but not by daylength. Plants of different treatments followed different strategies for dissipating excess energy. Whereas in the warm summer treatment safe dissipation of excess energy was facilitated via zeaxanthin, in all other treatments dissipation of excess energy was facilitated predominantly via increased aggregation of the light-harvesting complex of photosystem II. These differences were accompanied by a lower deepoxidation state and larger amounts of β-carotene in the warm autumn treatment as well as by changes in

A multimodel approach to interannual and seasonal prediction of Danube discharge anomalies

Science.gov (United States)

Rimbu, Norel; Ionita, Monica; Patrut, Simona; Dima, Mihai

2010-05-01

Interannual and seasonal predictability of Danube river discharge is investigated using three model types: 1) time series models 2) linear regression models of discharge with large-scale climate mode indices and 3) models based on stable teleconnections. All models are calibrated using discharge and climatic data for the period 1901-1977 and validated for the period 1978-2008 . Various time series models, like autoregressive (AR), moving average (MA), autoregressive and moving average (ARMA) or singular spectrum analysis and autoregressive moving average (SSA+ARMA) models have been calibrated and their skills evaluated. The best results were obtained using SSA+ARMA models. SSA+ARMA models proved to have the highest forecast skill also for other European rivers (Gamiz-Fortis et al. 2008). Multiple linear regression models using large-scale climatic mode indices as predictors have a higher forecast skill than the time series models. The best predictors for Danube discharge are the North Atlantic Oscillation (NAO) and the East Atlantic/Western Russia patterns during winter and spring. Other patterns, like Polar/Eurasian or Tropical Northern Hemisphere (TNH) are good predictors for summer and autumn discharge. Based on stable teleconnection approach (Ionita et al. 2008) we construct prediction models through a combination of sea surface temperature (SST), temperature (T) and precipitation (PP) from the regions where discharge and SST, T and PP variations are stable correlated. Forecast skills of these models are higher than forecast skills of the time series and multiple regression models. The models calibrated and validated in our study can be used for operational prediction of interannual and seasonal Danube discharge anomalies. References Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part I: intearannual predictability. J. Climate, 2484-2501, 2008. Gamiz-Fortis, S., D. Pozo

Testing the predictive power of nuclear mass models

International Nuclear Information System (INIS)

Mendoza-Temis, J.; Morales, I.; Barea, J.; Frank, A.; Hirsch, J.G.; Vieyra, J.C. Lopez; Van Isacker, P.; Velazquez, V.

2008-01-01

A number of tests are introduced which probe the ability of nuclear mass models to extrapolate. Three models are analyzed in detail: the liquid drop model, the liquid drop model plus empirical shell corrections and the Duflo-Zuker mass formula. If predicted nuclei are close to the fitted ones, average errors in predicted and fitted masses are similar. However, the challenge of predicting nuclear masses in a region stabilized by shell effects (e.g., the lead region) is far more difficult. The Duflo-Zuker mass formula emerges as a powerful predictive tool

Comparison of Prediction-Error-Modelling Criteria

DEFF Research Database (Denmark)

Jørgensen, John Bagterp; Jørgensen, Sten Bay

2007-01-01

Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

Foundation Settlement Prediction Based on a Novel NGM Model

Directory of Open Access Journals (Sweden)

Peng-Yu Chen

2014-01-01

Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

Electrostatic ion thrusters - towards predictive modeling

Energy Technology Data Exchange (ETDEWEB)

Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

2014-02-15

The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

Predictive validation of an influenza spread model.

Directory of Open Access Journals (Sweden)

Ayaz Hyder

Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

Predictive Validation of an Influenza Spread Model

Science.gov (United States)

Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

2013-01-01

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

DEFF Research Database (Denmark)

Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

Predictive Surface Complexation Modeling

Energy Technology Data Exchange (ETDEWEB)

Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

2016-11-29

Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO₂ and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

NOx PREDICTION FOR FBC BOILERS USING EMPIRICAL MODELS

Directory of Open Access Journals (Sweden)

Jiří Štefanica

2014-02-01

Full Text Available Reliable prediction of NOx emissions can provide useful information for boiler design and fuel selection. Recently used kinetic prediction models for FBC boilers are overly complex and require large computing capacity. Even so, there are many uncertainties in the case of FBC boilers. An empirical modeling approach for NOx prediction has been used exclusively for PCC boilers. No reference is available for modifying this method for FBC conditions. This paper presents possible advantages of empirical modeling based prediction of NOx emissions for FBC boilers, together with a discussion of its limitations. Empirical models are reviewed, and are applied to operation data from FBC boilers used for combusting Czech lignite coal or coal-biomass mixtures. Modifications to the model are proposed in accordance with theoretical knowledge and prediction accuracy.

Experimental warming delays autumn senescence in a boreal spruce bog: Initial results from the SPRUCE experiment

Science.gov (United States)

Richardson, Andrew; Furze, Morgan; Aubrecht, Donald; Milliman, Thomas; Nettles, Robert; Krassovski, Misha; Hanson, Paul

2016-04-01

Phenology is considered one of the most robust indicators of the biological impacts of global change. In temperate and boreal regions, long-term data show that rising temperatures are advancing spring onset (e.g. budburst and flowering) and delaying autumn senescence (e.g. leaf coloration and leaf fall) in a wide range of ecosystems. While warm and cold temperatures, day length and insolation, precipitation and water availability, and other factors, have all been shown to influence plant phenology, the future response of phenology to rising temperatures and elevated CO2 still remains highly uncertain because of the challenges associated with conducting realistic manipulative experiments to simulate future environmental conditions. At the SPRUCE (Spruce and Peatland Responses Under Climatic and Environmental Change) experiment in the north-central United States, experimental temperature (0 to +9° C above ambient) and CO2 (ambient and elevated) treatments are being applied to mature, and intact, Picea mariana-Sphagnum spp. bog communities in their native habitat through the use of ten large (approximately 12 m wide, 10 m high) open-topped enclosures. We are tracking vegetation green-up and senescence in these chambers, at both the individual and whole-community level, using repeat digital photography. Within each chamber, digital camera images are recorded every 30 minutes and uploaded to the PhenoCam (http://phenocam.sr.unh.edu) project web page, where they are displayed in near-real-time. Image processing is conducted nightly to extract quantitative measures of canopy color, which we characterize using Gcc, the green chromatic coordinate. Data from a camera mounted outside the chambers (since November 2014) indicate strong seasonal variation in Gcc for both evergreen shrubs and trees. Shrub Gcc rises steeply in May and June, and declines steeply in September and October. By comparison, tree Gcc rises gradually from March through June, and declines gradually from

Prediction of pipeline corrosion rate based on grey Markov models

International Nuclear Information System (INIS)

Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin

2009-01-01

Based on the model that combined by grey model and Markov model, the prediction of corrosion rate of nuclear power pipeline was studied. Works were done to improve the grey model, and the optimization unbiased grey model was obtained. This new model was used to predict the tendency of corrosion rate, and the Markov model was used to predict the residual errors. In order to improve the prediction precision, rolling operation method was used in these prediction processes. The results indicate that the improvement to the grey model is effective and the prediction precision of the new model combined by the optimization unbiased grey model and Markov model is better, and the use of rolling operation method may improve the prediction precision further. (authors)

Sweat loss prediction using a multi-model approach.

Science.gov (United States)

Xu, Xiaojiang; Santee, William R

2011-07-01

A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15-40°C, RH 25-75%), and exercise intensities (250-600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30-39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.

Spring and Autumn Phenological Variability across Environmental Gradients of Great Smoky Mountains National Park, USA

Directory of Open Access Journals (Sweden)

Steven P. Norman

2017-04-01

Full Text Available Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI to understand the causes of variations in spring and autumn timing from 2000 to 2015, for a landscape renowned for its biological diversity. By filtering for cover type, topography and disturbance history, we achieved an improved understanding of the effects of seasonal weather variation on land surface phenology (LSP. Elevational effects were greatest in spring and were more important than site moisture effects. The spring and autumn NDVI of deciduous forests were found to increase in response to antecedent warm temperatures, with evidence of possible cross-seasonal lag effects, including possible accelerated green-up after cold Januarys and early brown-down following warm springs. Areas that were disturbed by the hemlock woolly adelgid and a severe tornado showed a weaker sensitivity to cross-year temperature and precipitation variation, while low severity wildland fire had no discernable effect. Use of ancillary datasets to filter for disturbance and vegetation type improves our understanding of vegetation’s phenological responsiveness to climate dynamics across complex environmental gradients.

Aespoe HRL - Geoscientific evaluation 1997/5. Models based on site characterization 1986-1995

International Nuclear Information System (INIS)

Rhen, I.; Stanfors, R.; Wikberg, P.

1997-10-01

The pre-investigations for the Aespoe Hard Rock Laboratory were started in 1986 and involved extensive field measurements, aimed at characterizing the rock formations with regard to geology, geohydrology, hydrochemistry and rock mechanics. Predictions for the excavation phase were made prior to excavation of the laboratory which was started in the autumn of 1990. The predictions concern five key issues: lithology and geological structures, groundwater flow, hydrochemistry, transport of solutes and mechanical stability. During 1996 the results from the pre-investigations and the excavation of the Aespoe Hard Rock Laboratory were evaluated and were compiled in geological, mechanical stability, geohydrological, groundwater chemical and transport-of-solutes models. The model concepts and the models of 1996 are presented in this report. The model developments from the pre-investigation phase up to the models made 1996 are also presented briefly

Aespoe HRL - Geoscientific evaluation 1997/5. Models based on site characterization 1986-1995

Energy Technology Data Exchange (ETDEWEB)

Rhen, I. [ed.; Gustafsson, Gunnar [VBB Viak AB, Goeteborg (Sweden); Stanfors, R. [RS Consulting, Lund (Sweden); Wikberg, P. [Swedish Nuclear Fuel and Waste Management Co., Stockholm (Sweden)

1997-10-01

The pre-investigations for the Aespoe Hard Rock Laboratory were started in 1986 and involved extensive field measurements, aimed at characterizing the rock formations with regard to geology, geohydrology, hydrochemistry and rock mechanics. Predictions for the excavation phase were made prior to excavation of the laboratory which was started in the autumn of 1990. The predictions concern five key issues: lithology and geological structures, groundwater flow, hydrochemistry, transport of solutes and mechanical stability. During 1996 the results from the pre-investigations and the excavation of the Aespoe Hard Rock Laboratory were evaluated and were compiled in geological, mechanical stability, geohydrological, groundwater chemical and transport-of-solutes models. The model concepts and the models of 1996 are presented in this report. The model developments from the pre-investigation phase up to the models made 1996 are also presented briefly. 317 refs, figs, tabs.

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

Science.gov (United States)

Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

2010-01-01

Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388

Alcator C-Mod predictive modeling

International Nuclear Information System (INIS)

Pankin, Alexei; Bateman, Glenn; Kritz, Arnold; Greenwald, Martin; Snipes, Joseph; Fredian, Thomas

2001-01-01

Predictive simulations for the Alcator C-mod tokamak [I. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] are carried out using the BALDUR integrated modeling code [C. E. Singer et al., Comput. Phys. Commun. 49, 275 (1988)]. The results are obtained for temperature and density profiles using the Multi-Mode transport model [G. Bateman et al., Phys. Plasmas 5, 1793 (1998)] as well as the mixed-Bohm/gyro-Bohm transport model [M. Erba et al., Plasma Phys. Controlled Fusion 39, 261 (1997)]. The simulated discharges are characterized by very high plasma density in both low and high modes of confinement. The predicted profiles for each of the transport models match the experimental data about equally well in spite of the fact that the two models have different dimensionless scalings. Average relative rms deviations are less than 8% for the electron density profiles and 16% for the electron and ion temperature profiles

Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

Science.gov (United States)

Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

2015-01-01

Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.

Predictive Modelling of Heavy Metals in Urban Lakes

OpenAIRE

Lindström, Martin

2000-01-01

Heavy metals are well-known environmental pollutants. In this thesis predictive models for heavy metals in urban lakes are discussed and new models presented. The base of predictive modelling is empirical data from field investigations of many ecosystems covering a wide range of ecosystem characteristics. Predictive models focus on the variabilities among lakes and processes controlling the major metal fluxes. Sediment and water data for this study were collected from ten small lakes in the ...

Stage-specific predictive models for breast cancer survivability.

Science.gov (United States)

Kate, Rohit J; Nadig, Ramya

2017-01-01

Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Ecological weed management by cover cropping : effects on weed growth in autumn and weed establishment in spring

NARCIS (Netherlands)

Kruidhof, H.M.; Bastiaans, L.; Kropff, M.J.

2008-01-01

Cover crops grown in the period between two main crops have potential as an important component of a system-oriented ecological weed management strategy. In late summer and autumn, the cover crop can suppress growth and seed production of weeds, whereas the incorporation of cover crop residues in

Impact of modellers' decisions on hydrological a priori predictions

Science.gov (United States)

Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

2014-06-01

In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of

A multivariate model for predicting segmental body composition.

Science.gov (United States)

Tian, Simiao; Mioche, Laurence; Denis, Jean-Baptiste; Morio, Béatrice

2013-12-01

The aims of the present study were to propose a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured variables and to compare its predictive capacity with that of the available univariate models that predict body fat percentage (BF%). The dual-energy X-ray absorptiometry (DXA) dataset (52% men and 48% women) with White, Black and Hispanic ethnicities (1999-2004, National Health and Nutrition Examination Survey) was randomly divided into three sub-datasets: a training dataset (TRD), a test dataset (TED); a validation dataset (VAD), comprising 3835, 1917 and 1917 subjects. For each sex, several multivariate prediction models were fitted from the TRD using age, weight, height and possibly waist circumference. The most accurate model was selected from the TED and then applied to the VAD and a French DXA dataset (French DB) (526 men and 529 women) to assess the prediction accuracy in comparison with that of five published univariate models, for which adjusted formulas were re-estimated using the TRD. Waist circumference was found to improve the prediction accuracy, especially in men. For BF%, the standard error of prediction (SEP) values were 3.26 (3.75) % for men and 3.47 (3.95)% for women in the VAD (French DB), as good as those of the adjusted univariate models. Moreover, the SEP values for the prediction of body and appendicular lean masses ranged from 1.39 to 2.75 kg for both the sexes. The prediction accuracy was best for age < 65 years, BMI < 30 kg/m2 and the Hispanic ethnicity. The application of our multivariate model to large populations could be useful to address various public health issues.

Two stage neural network modelling for robust model predictive control.

Science.gov (United States)

Patan, Krzysztof

2018-01-01

The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Hybrid Corporate Performance Prediction Model Considering Technical Capability

Directory of Open Access Journals (Sweden)

Joonhyuck Lee

2016-07-01

Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.

Dynamic Simulation of Human Gait Model With Predictive Capability.

Science.gov (United States)

Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

2018-03-01

In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.

Massive Predictive Modeling using Oracle R Enterprise

CERN Multimedia

CERN. Geneva

2014-01-01

R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions.

Science.gov (United States)

Hauri, D D; Huss, A; Zimmermann, F; Kuehni, C E; Röösli, M

2013-10-01

Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A burnout prediction model based around char morphology

Energy Technology Data Exchange (ETDEWEB)

T. Wu; E. Lester; M. Cloke [University of Nottingham, Nottingham (United Kingdom). Nottingham Energy and Fuel Centre

2005-07-01

Poor burnout in a coal-fired power plant has marked penalties in the form of reduced energy efficiency and elevated waste material that can not be utilized. The prediction of coal combustion behaviour in a furnace is of great significance in providing valuable information not only for process optimization but also for coal buyers in the international market. Coal combustion models have been developed that can make predictions about burnout behaviour and burnout potential. Most of these kinetic models require standard parameters such as volatile content, particle size and assumed char porosity in order to make a burnout prediction. This paper presents a new model called the Char Burnout Model (ChB) that also uses detailed information about char morphology in its prediction. The model can use data input from one of two sources. Both sources are derived from image analysis techniques. The first from individual analysis and characterization of real char types using an automated program. The second from predicted char types based on data collected during the automated image analysis of coal particles. Modelling results were compared with a different carbon burnout kinetic model and burnout data from re-firing the chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen across several residence times. An improved agreement between ChB model and DTF experimental data proved that the inclusion of char morphology in combustion models can improve model predictions. 27 refs., 4 figs., 4 tabs.

Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

Science.gov (United States)

Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

2015-07-01

Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

Prediction of resource volumes at untested locations using simple local prediction models

Science.gov (United States)

Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

2006-01-01

This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses. ?? Springer Science+Business Media, LLC 2007.

Zooplankton biomass and production in the North Sea during the Autumn Circulation experiment, October 1987–March 1988

DEFF Research Database (Denmark)

Hay, S.J.; Kiørboe, Thomas; Matthews, A.

1991-01-01

Distribution and abundance of zooplankton in the North Sea during the Autumn Circulation Experiment (October 1987–March 1988) were examined. From shipboard egg production incubations and the distributions of eggs, nauplii and females, the productivity of various copepod species was described. Aga...

A burnout prediction model based around char morphology

Energy Technology Data Exchange (ETDEWEB)

Tao Wu; Edward Lester; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical, Environmental and Mining Engineering

2006-05-15

Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coal particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.

Comparative Study of Bancruptcy Prediction Models

Directory of Open Access Journals (Sweden)

Isye Arieshanti

2013-09-01

Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

A grey NGM(1,1, k) self-memory coupling prediction model for energy consumption prediction.

Science.gov (United States)

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

Risk predictive modelling for diabetes and cardiovascular disease.

Science.gov (United States)

Kengne, Andre Pascal; Masconi, Katya; Mbanya, Vivian Nchanchou; Lekoubou, Alain; Echouffo-Tcheugui, Justin Basile; Matsha, Tandi E

2014-02-01

Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.

Model-based uncertainty in species range prediction

DEFF Research Database (Denmark)

Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel

2006-01-01

Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...

In situ autumn ozone fumigation of mature Norway spruce - Effects on net photosynthesis

DEFF Research Database (Denmark)

Mikkelsen, Teis Nørgaard; Ro-Poulsen, H.

2002-01-01

concentration. The experiment was conducted during 70 days during the autumn. Our system could not detect any ozone effects on dark respiration, but eventually effects on dark respiration could be masked in signal noise. An inhibition of daily net photosynthesis in ozone treated shoots was apparent......, and it is was found that a mean increase in ozone concentration of 10 nl l(-1) reduced net photosynthesis with 7.4 %. This effect should be related to a pre-exposure during the season of AOT40 12.5 mul l(-1) h....

From Restrictions to Freedom The Perilous Path to the First Warsaw Autumn Festival

Directory of Open Access Journals (Sweden)

Bylander Cindy

2017-12-01

Full Text Available The genesis of the Warsaw Autumn Festival was fraught with both potential and real complications. Musical life in Poland at the end of the first postwar decade was in a state of flux, if not turmoil, as ideological disagreements and material complications contributed to an atmosphere of dismay and distrust among musicians and authorities. This paper provides insight into the context in which the Festival’s organizers were operating, particularly the shortcomings of musical life in mid-decade that threatened to derail the Festival before it even began.

Survival prediction model for postoperative hepatocellular carcinoma patients.

Science.gov (United States)

Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao

2017-09-01

This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.

Methodology for Designing Models Predicting Success of Infertility Treatment

OpenAIRE

Alireza Zarinara; Mohammad Mahdi Akhondi; Hojjat Zeraati; Koorsh Kamali; Kazem Mohammad

2016-01-01

Abstract Background: The prediction models for infertility treatment success have presented since 25 years ago. There are scientific principles for designing and applying the prediction models that is also used to predict the success rate of infertility treatment. The purpose of this study is to provide basic principles for designing the model to predic infertility treatment success. Materials and Methods: In this paper, the principles for developing predictive models are explained and...

Hidden Semi-Markov Models for Predictive Maintenance

Directory of Open Access Journals (Sweden)

Francesco Cartella

2015-01-01

Full Text Available Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs with (i no constraints on the state duration density function and (ii being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL of the machine is calculated.

Modeling and Control of CSTR using Model based Neural Network Predictive Control

OpenAIRE

Shrivastava, Piyush

2012-01-01

This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...

Consensus models to predict endocrine disruption for all ...

Science.gov (United States)

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

Energy based prediction models for building acoustics

DEFF Research Database (Denmark)

Brunskog, Jonas

2012-01-01

In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

Comparison of Simple Versus Performance-Based Fall Prediction Models

Directory of Open Access Journals (Sweden)

Shekhar K. Gadkaree BS

2015-05-01

Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

Preclinical models used for immunogenicity prediction of therapeutic proteins.

Science.gov (United States)

Brinks, Vera; Weinbuch, Daniel; Baker, Matthew; Dean, Yann; Stas, Philippe; Kostense, Stefan; Rup, Bonita; Jiskoot, Wim

2013-07-01

All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.

A Grey NGM(1,1, k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction

Science.gov (United States)

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span. PMID:25054174

Bayesian Predictive Models for Rayleigh Wind Speed

DEFF Research Database (Denmark)

Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

2017-01-01

predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior...... and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented....

Modeling and Prediction Using Stochastic Differential Equations

DEFF Research Database (Denmark)

Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

2016-01-01

Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...

First record of entomopathogenic fungi on autumn leaf Caterpillar (Doleschallia bisaltide)

Science.gov (United States)

Dayanti, A. K.; Sholahuddin; Yunus, A.; Subositi, D.

2018-03-01

Caricature plant is one of the medicinal plants in Indonesia to cure hemorrhoids, menstruation, and others. The cultivation constraints of caricature plant is autumn leaf caterpillars (Doleschallia bisaltide). Utilization of synthetic insecticides is not allowed to avoid bioaccumulation of chemical residues. Entomopathogenic fungi is an alternative way to control D. bisaltide. The objective of the research was to obtain isolates of entomopathogenic fungi of D. bisaltide. The research conducted by two steps, which were exsploration of infecfted D. bisaltide. The second step was identification of the fungi. Exploration results of 16 pupae of D. Bisaltide were infected by fungi. Identification done by classify the mcroscopic and microscopic fungi isolate characteristic. One from five fungal isolates were entomopathogenic fungi from Verticillium genera.

Prediction of hourly solar radiation with multi-model framework

International Nuclear Information System (INIS)

Wu, Ji; Chan, Chee Keong

2013-01-01

Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

Seasonal changes in contents of phenolic compounds and sugar in Rhus, Euonymus and Acer leaves with special reference to anthocyanin formation in autumn

International Nuclear Information System (INIS)

Ishikura, Nariyuki

1976-01-01

The seasonal changes in the contents of sugar and phenolic compounds in the leaves of Rhus, Euonymus and two Acer species were examined in order to obtain information on the metabolic process inducing the autumn reddening. The incorporation of radioactivity of glucose-(U-14C) into anthocyanin was also examined. As a result of a preliminary test, the compositions of sugar and phenolic compounds were not altered by the drying treatment, therefore dried material was used. Dried leaves (ca. 2 g) were subject to the extraction with 80% methanol (3 ml, 3 hr) under refluxing. The extraction was repeated twice. Combined extracts (ca. 90 ml) were concentrated to ca. 20 ml at 35 deg C under reduced pressure. The concentrate was repeatedly washed with n-hexane and evaporated to remove the n-hexane. The resulting solution was made up to 30 ml with water, and used for the quantitative analysis. The solutions were fractionated in order to estimate total phenol and flavanol contents. Sugars were extracted from dry leaves (ca. 2 g) by boiling 70% ethanol (20 ml) for 3 hours. D-glucose-(U- 14 C)(280 mCi/mM) and phenylalanine-(U- 14 C)(422 mCi/mM) were fed to the leaves. It was found that antho-cyanin (mainly cyanidin 3-monoglucoside) was produced in the autumnal leaves of all plants examined, and that the red pigment was steadily accumulated in their leaves during the autumn. The sugar accumulated in autumnal Rhus leaves may be rapidly consumed by the formation of phenolic compounds. (Iwakiri, K.)

Revised predictive equations for salt intrusion modelling in estuaries

NARCIS (Netherlands)

Gisen, J.I.A.; Savenije, H.H.G.; Nijzink, R.C.

2015-01-01

For one-dimensional salt intrusion models to be predictive, we need predictive equations to link model parameters to observable hydraulic and geometric variables. The one-dimensional model of Savenije (1993b) made use of predictive equations for the Van der Burgh coefficient $K$ and the dispersion

Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.

Science.gov (United States)

Yin, Wen-Jun; Yi, Yi-Hu; Guan, Xiao-Feng; Zhou, Ling-Yun; Wang, Jiang-Lin; Li, Dai-Yang; Zuo, Xiao-Cong

2017-02-03

Several models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level. The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Time dependent patient no-show predictive modelling development.

Science.gov (United States)

Huang, Yu-Li; Hanauer, David A

2016-05-09

Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

Model predictive control using fuzzy decision functions

NARCIS (Netherlands)

Kaymak, U.; Costa Sousa, da J.M.

2001-01-01

Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

DEFF Research Database (Denmark)

Scheike, Thomas H.; Zhang, Mei-Jie

2002-01-01

Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...

Hormonal treatment before and after artificial insemination differentially improves fertility in subpopulations of dairy cows during the summer and autumn.

Science.gov (United States)

Friedman, E; Voet, H; Reznikov, D; Wolfenson, D; Roth, Z

2014-12-01

Reduced conception rate (CR) during the hot summer and subsequent autumn is a well-documented phenomenon. Intensive use of cooling systems can improve summer and autumn reproductive performance, but is unable to increase CR to winter and spring levels. We examined whether combined hormonal treatments--to increase follicular turnover before artificial insemination (AI) and progesterone supplementation post-AI--might improve fertility of cooled cows during the summer and autumn. The experiment was conducted from July to November in 3 commercial herds in Israel and included 707 Holstein cows at 50 to 60 d in milk (DIM). Cows were hormonally treated to induce 2 consecutive 9-d cycles, with GnRH administration followed by PGF2α injection 7 d later, followed by an intravaginal insert containing progesterone on d 5 ± 1 post-AI for 14 d. Both untreated controls (n=376) and treated cows (n=331) were inseminated following estrus, and pregnancy was determined by palpation 42 to 50 d post-AI. First-AI CR data revealed a positive interaction between treatment and cows previously diagnosed with postpartum uterine disease [odds ratio (OR) 2.24]. Interaction between treatment and low body condition score tended to increase the probability of first-AI CR (OR 1.95) and increased pregnancy rate at 90 DIM (OR 2.50) and at 120 DIM (OR 1.77). Low milk production increased the probability of being detected in estrus at the end of synchronization within treated cows (OR 1.67), and interacted with treatment to increase probability of pregnancy at 90 DIM (OR 2.39) relative to control counterparts. It is suggested that when administered with efficient cooling, combined hormonal treatment in specific subgroups of cows, that is, those previously diagnosed with postpartum uterine disease or those with low body condition score or low milk yield might improve fertility during the summer and autumn. Integration of such an approach into reproductive management during the hot seasons might improve

Evaluating the Predictive Value of Growth Prediction Models

Science.gov (United States)

Murphy, Daniel L.; Gaertner, Matthew N.

2014-01-01

This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

Uncertainties in model-based outcome predictions for treatment planning

International Nuclear Information System (INIS)

Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

2001-01-01

Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

The presence of Chernobil contamination in food chain in autumn 1986. In Brezhice-Krshko region

International Nuclear Information System (INIS)

Miklavzhicj, U.; Korun, M.; Zhele, M.; Fedina, Sh.; Juzhnich, K.; Brajnik, D.

1987-01-01

The influence of Chernobyl contamination in food chain measured in autumn 1986 in Bezhice-Krshko region is discussed and compared with the depth distribution of contaminating radionuclei in uncultivated and cultivated soil. It is felt that foliage transfer of resuspended Cs-137, Cs-134 still depositing at average rate of 30 Bq.m-2/month at the end of the year, could retain some future importance. (author). 3 refs.; 2 figs.; 2 tabs

Prediction error, ketamine and psychosis: An updated model.

Science.gov (United States)

Corlett, Philip R; Honey, Garry D; Fletcher, Paul C

2016-11-01

In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.

Predictive Capability Maturity Model for computational modeling and simulation.

Energy Technology Data Exchange (ETDEWEB)

Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

2007-10-01

The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

Model complexity control for hydrologic prediction

NARCIS (Netherlands)

Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

2008-01-01

A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

Predictive Model of Systemic Toxicity (SOT)

Science.gov (United States)

In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

Using Pareto points for model identification in predictive toxicology

Science.gov (United States)

2013-01-01

Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction

Science.gov (United States)

Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro

Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.

Nitrate leaching from organic arable crop rotations is mostly determined by autumn field management

DEFF Research Database (Denmark)

Askegaard, M; Olesen, Jørgen E; Rasmussen, Ilse Ankjær

2011-01-01

Two main challenges facing organic arable farming are the supply of nitrogen (N) to the crop and the control of perennial weeds. Nitrate leaching from different organic arable crop rotations was investigated over three consecutive four-year crop rotations in a field experiment at three locations....../volunteers had on avg. 30 kg N ha−1, and the largest N leaching losses were found after stubble cultivation (avg. 55 kg N ha−1). The N leaching losses increased with increasing number of autumn soil cultivations...

Use of aquaculture ponds and other habitats by autumn migrating shorebirds along the lower Mississippi river.

Science.gov (United States)

Lehnen, Sarah E; Krementz, David G

2013-08-01

Populations of many shorebird species are declining; habitat loss and degradation are among the leading causes for these declines. Shorebirds use a variety of habitats along interior migratory routes including managed moist soil units, natural wetlands, sandbars, and agricultural lands such as harvested rice fields. Less well known is shorebird use of freshwater aquaculture facilities, such as commercial cat- and crayfish ponds. We compared shorebird habitat use at drained aquaculture ponds, moist soil units, agricultural areas, sandbars and other natural habitat, and a sewage treatment facility in the in the lower Mississippi River Alluvial Valley (LMAV) during autumn 2009. Six species: Least Sandpiper (Calidris minutilla), Killdeer (Charadrius vociferous), Semipalmated Sandpiper (Calidris pusilla), Pectoral Sandpiper (C. melanotos), Black-necked Stilt (Himantopus himantopus), and Lesser Yellowlegs (Tringa flavipes), accounted for 92 % of the 31,165 individuals observed. Sewage settling lagoons (83.4, 95 % confidence interval [CI] 25.3-141.5 birds/ha), drained aquaculture ponds (33.5, 95 % CI 22.4-44.6 birds/ha), and managed moist soil units on public lands (15.7, CI 11.2-20.3 birds/ha) had the highest estimated densities of shorebirds. The estimated 1,100 ha of drained aquaculture ponds available during autumn 2009 provided over half of the estimated requirement of 2,000 ha by the LMAV Joint Venture working group. However, because of the decline in the aquaculture industry, autumn shorebird habitats in the LMAV may be limited in the near future. Recognition of the current aquaculture habitat trends will be important to the future management activities of federal and state agencies. Should these aquaculture habitat trends continue, there may be a need for wildlife biologists to investigate other habitats that can be managed to offset the current and expected loss of aquaculture acreages. This study illustrates the potential for freshwater aquaculture to

Onset of autumn shapes the timing of birth in Pyrenean chamois more than onset of spring.

Science.gov (United States)

Kourkgy, Charlotte; Garel, Mathieu; Appolinaire, Joël; Loison, Anne; Toïgo, Carole

2016-03-01

In seasonal environments, birth dates are a central component for a species' life history, with potential long-term fitness consequences. Yet our understanding of selective pressures of environmental changes on birth dates is limited in wild mammals due to the difficulty of data collection. In a context of rapid climate change, the question of a possible mismatch between plant phenology and birth phenology also remains unanswered for most species. We assessed whether and how the timing of birth in a mountain mammal (isard, also named Pyrenean chamois, Rupicapra pyrenaica pyrenaica) tracked changes in plant growing season, accounting for maternal traits, individual heterogeneity and population density. We not only focused on spring conditions but also assessed to what extent onset of autumn can be a driver of phenological biological events and compared the magnitude of the response to the magnitude of the environmental changes. We relied on a 22-year study based on intensively monitored marked individuals of known age. Births were highly synchronized (80% of kids born within 25 days) and highly repeatable (84%; between-female variation of 9.6 days, within-female variation of 4.2 days). Individual phenotypic plasticity allows females to respond rapidly to interannual changes in plant phenology but did not prevent the existence of a mismatch: a 10-day advance in the autumn or spring plant phenology led to 3.9 and 1.3 days advance in birth dates, respectively. Our findings suggest that plant phenology may act as a cue to induce important stages of the reproductive cycle (e.g. conception and gestation length), subsequently affecting parturition dates, and stressed the importance of focusing on long-term changes during spring for which females may show much lower adaptive potential than during autumn. These results also question the extent to which individual plasticity along with high heterogeneity among individuals will allow species to cope with demographic

Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

Directory of Open Access Journals (Sweden)

Nataša Šarlija

2017-01-01

Full Text Available This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1 to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2 to discuss common pitfalls and methodological errors in developing a model, and 3 to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.

Model output statistics applied to wind power prediction

Energy Technology Data Exchange (ETDEWEB)

Joensen, A; Giebel, G; Landberg, L [Risoe National Lab., Roskilde (Denmark); Madsen, H; Nielsen, H A [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)

1999-03-01

Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.

Neuro-fuzzy modeling in bankruptcy prediction

Directory of Open Access Journals (Sweden)

Vlachos D.

2003-01-01

Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Directory of Open Access Journals (Sweden)

Aiqing Kang

2017-01-01

Full Text Available Hybrid Ensemble Empirical Mode Decomposition (EEMD and Least Square Support Vector Machine (LSSVM is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP, Auto-Regressive Integrated Moving Average (ARIMA, combination of Empirical Mode Decomposition (EMD with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

PREDICTED PERCENTAGE DISSATISFIED (PPD) MODEL ...

African Journals Online (AJOL)

HOD

their low power requirements, are relatively cheap and are environment friendly. ... PREDICTED PERCENTAGE DISSATISFIED MODEL EVALUATION OF EVAPORATIVE COOLING ... The performance of direct evaporative coolers is a.

Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

Science.gov (United States)

Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

2013-04-01

In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

Comparison of midlatitude ionospheric F region peak parameters and topside Ne profiles from IRI2012 model prediction with ground-based ionosonde and Alouette II observations

Science.gov (United States)

Gordiyenko, G. I.; Yakovets, A. F.

2017-07-01

The ionospheric F2 peak parameters recorded by a ground-based ionosonde at the midlatitude station Alma-Ata [43.25N, 76.92E] were compared with those obtained using the latest version of the IRI model (http://omniweb.gsfc.nasa.gov/vitmo/iri2012_vitmo.html). It was found that for the Alma-Ata (Kazakhstan) location, the IRI2012 model describes well the morphology of seasonal and diurnal variations of the ionospheric critical frequency (foF2) and peak density height (hmF2) monthly medians. The model errors in the median foF2 prediction (percentage deviations between the median foF2 values and their model predictions) were found to vary approximately in the range from about -20% to 34% and showed a stable overestimation in the median foF2 values for daytime in January and July and underestimation for day- and nighttime hours in the equinoctial months. The comparison between the ionosonde hmF2 and IRI results clearly showed that the IRI overestimates the nighttime hmF2 values for March and September months, and the difference is up to 30 km. The daytime Alma-Ata hmF2 data were found to be close to the IRI predictions (deviations are approximately ±10-15 km) in winter and equinoctial months, except in July when the observed hmF2 values were much more (from approximately 50-200 km). The comparison between the Alouette foF2 data and IRI predictions showed mixed results. In particular, the Alouette foF2 data showed a tendency to be overestimated for daytime in winter months similar to the ionosonde data; however, the overestimated foF2 values for nighttime in the autumn equinox were in disagreement with the ionosonde observations. There were large deviations between the observed hmF2 values and their model predictions. The largest deviations were found during winter and summer (up to -90 km). The comparison of the Alouette II electron density profiles with those predicted by the adapted IRI2012 model in the altitude range hmF2 of the satellite position showed a great

Modeling the prediction of business intelligence system effectiveness.

Science.gov (United States)

Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

2016-01-01

Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

[Application of ARIMA model on prediction of malaria incidence].

Science.gov (United States)

Jing, Xia; Hua-Xun, Zhang; Wen, Lin; Su-Jian, Pei; Ling-Cong, Sun; Xiao-Rong, Dong; Mu-Min, Cao; Dong-Ni, Wu; Shunxiang, Cai

2016-01-29

To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA). SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation. The model of ARIMA (1, 1, 1) (1, 1, 0) 12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable. The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.

PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

Directory of Open Access Journals (Sweden)

Raphael Silveira Amaro

2016-03-01

Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

Seasonal predictability of Kiremt rainfall in coupled general circulation models

Science.gov (United States)

Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen

2017-11-01

The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

Assessing predictability of a hydrological stochastic-dynamical system

Science.gov (United States)

Gelfan, Alexander

2014-05-01

to those of the corresponding series of the actual data measured at the station. Beginning from the initial conditions and being forced by Monte-Carlo generated synthetic meteorological series, the model simulated diverging trajectories of soil moisture characteristics (water content of soil column, moisture of different soil layers, etc.). Limit of predictability of the specific characteristic was determined through time of stabilization of variance of the characteristic between the trajectories, as they move away from the initial state. Numerical experiments were carried out with the stochastic-dynamical model to analyze sensitivity of the soil moisture predictability assessments to uncertainty in the initial conditions, to determine effects of the soil hydraulic properties and processes of soil freezing on the predictability. It was found, particularly, that soil water content predictability is sensitive to errors in the initial conditions and strongly depends on the hydraulic properties of soil under both unfrozen and frozen conditions. Even if the initial conditions are "well-established", the assessed predictability of water content of unfrozen soil does not exceed 30-40 days, while for frozen conditions it may be as long as 3-4 months. The latter creates opportunity for utilizing the autumn water content of soil as the predictor for spring snowmelt runoff in the region under consideration.

Prediction of lithium-ion battery capacity with metabolic grey model

International Nuclear Information System (INIS)

Chen, Lin; Lin, Weilong; Li, Junzi; Tian, Binbin; Pan, Haihong

2016-01-01

Given the popularity of Lithium-ion batteries in EVs (electric vehicles), predicting the capacity quickly and accurately throughout a battery's full life-time is still a challenging issue for ensuring the reliability of EVs. This paper proposes an approach in predicting the varied capacity with discharge cycles based on metabolic grey theory and consider issues from two perspectives: 1) three metabolic grey models will be presented, including MGM (metabolic grey model), MREGM (metabolic Residual-error grey model), and MMREGM (metabolic Markov-residual-error grey model); 2) the universality of these models will be explored under different conditions (such as various discharge rates and temperatures). Furthermore, the research findings in this paper demonstrate the excellent performance of the prediction depending on the three models; however, the precision of the MREGM model is inferior compared to the others. Therefore, we have obtained the conclusion in which the MGM model and the MMREGM model have excellent performances in predicting the capacity under a variety of load conditions, even using few data points for modeling. Also, the universality of the metabolic grey prediction theory is verified by predicting the capacity of batteries under different discharge rates and different temperatures. - Highlights: • The metabolic mechanism is introduced in a grey system for capacity prediction. • Three metabolic grey models are presented and studied. • The universality of these models under different conditions is assessed. • A few data points are required for predicting the capacity with these models.

Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model.

Science.gov (United States)

Suresh, Krithika; Taylor, Jeremy M G; Spratt, Daniel E; Daignault, Stephanie; Tsodikov, Alexander

2017-11-01

Dynamic prediction incorporates time-dependent marker information accrued during follow-up to improve personalized survival prediction probabilities. At any follow-up, or "landmark", time, the residual time distribution for an individual, conditional on their updated marker values, can be used to produce a dynamic prediction. To satisfy a consistency condition that links dynamic predictions at different time points, the residual time distribution must follow from a prediction function that models the joint distribution of the marker process and time to failure, such as a joint model. To circumvent the assumptions and computational burden associated with a joint model, approximate methods for dynamic prediction have been proposed. One such method is landmarking, which fits a Cox model at a sequence of landmark times, and thus is not a comprehensive probability model of the marker process and the event time. Considering an illness-death model, we derive the residual time distribution and demonstrate that the structure of the Cox model baseline hazard and covariate effects under the landmarking approach do not have simple form. We suggest some extensions of the landmark Cox model that should provide a better approximation. We compare the performance of the landmark models with joint models using simulation studies and cognitive aging data from the PAQUID study. We examine the predicted probabilities produced under both methods using data from a prostate cancer study, where metastatic clinical failure is a time-dependent covariate for predicting death following radiation therapy. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Grey NGM(1,1,k Self-Memory Coupling Prediction Model for Energy Consumption Prediction

Directory of Open Access Journals (Sweden)

Xiaojun Guo

2014-01-01

Full Text Available Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1,k self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1,k model. The traditional grey model’s weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1,k self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

Improving Predictive Modeling in Pediatric Drug Development: Pharmacokinetics, Pharmacodynamics, and Mechanistic Modeling

Energy Technology Data Exchange (ETDEWEB)

Slikker, William; Young, John F.; Corley, Rick A.; Dorman, David C.; Conolly, Rory B.; Knudsen, Thomas; Erstad, Brian L.; Luecke, Richard H.; Faustman, Elaine M.; Timchalk, Chuck; Mattison, Donald R.

2005-07-26

A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

Characterization, Long-Range Transport and Source Identification of Carbonaceous Aerosols during Spring and Autumn Periods at a High Mountain Site in South China

Directory of Open Access Journals (Sweden)

Hong-yan Jia

2016-09-01

Full Text Available PM10 (particulate matter samples were collected at Mount Lu, a high elevation mountain site in south China (August and September of 2011; and March, April and May of 2012. Eight carbonaceous fractions of particles were analyzed to characterize the possible carbonaceous emission sources. During the sampling events, daily average concentrations of PM10 at Mount Lu were 97.87 μg/m3 and 73.40 μg/m3 in spring and autumn, respectively. The observed mean organic carbon (OC and element carbon (EC concentrations during spring in PM10 were 10.58 μg/m3 and 2.58 μg/m3, respectively, and those in autumn were 6.89 μg/m3 and 2.40 μg/m3, respectively. Secondary organic carbon concentration was 4.77 μg/m3 and 2.93 μg/m3 on average, accounting for 28.0% and 31.0% of the total OC in spring and autumn, respectively. Relationships between carbonaceous species and results of principal component analysis showed that there were multiple sources contributing to the carbonaceous aerosols at the observation site. Through back trajectory analysis, it was found that air masses in autumn were mainly transported from the south of China, and these have the highest OC but lowest EC concentrations. Air masses in spring transported from northwest China bring 7.77 μg/m3 OC and 2.28 μg/m3 EC to the site, with lower levels coming from other sites. These air mass sources were featured by the effective carbon ratio (ECR.

Predicting Effects of Water Regime Changes on Waterbirds: Insights from Staging Swans.

Science.gov (United States)

Nolet, Bart A; Gyimesi, Abel; van Krimpen, Roderick R D; de Boer, Willem F; Stillman, Richard A

2016-01-01

Predicting the environmental impact of a proposed development is notoriously difficult, especially when future conditions fall outside the current range of conditions. Individual-based approaches have been developed and applied to predict the impact of environmental changes on wintering and staging coastal bird populations. How many birds make use of staging sites is mostly determined by food availability and accessibility, which in the case of many waterbirds in turn is affected by water level. Many water systems are regulated and water levels are maintained at target levels, set by management authorities. We used an individual-based modelling framework (MORPH) to analyse how different target water levels affect the number of migratory Bewick's swans Cygnus columbianus bewickii staging at a shallow freshwater lake (Lauwersmeer, the Netherlands) in autumn. As an emerging property of the model, we found strong non-linear responses of swan usage to changes in water level, with a sudden drop in peak numbers as well as bird-days with a 0.20 m rise above the current target water level. Such strong non-linear responses are probably common and should be taken into account in environmental impact assessments.

Predicting Effects of Water Regime Changes on Waterbirds: Insights from Staging Swans.

Directory of Open Access Journals (Sweden)

Bart A Nolet

Full Text Available Predicting the environmental impact of a proposed development is notoriously difficult, especially when future conditions fall outside the current range of conditions. Individual-based approaches have been developed and applied to predict the impact of environmental changes on wintering and staging coastal bird populations. How many birds make use of staging sites is mostly determined by food availability and accessibility, which in the case of many waterbirds in turn is affected by water level. Many water systems are regulated and water levels are maintained at target levels, set by management authorities. We used an individual-based modelling framework (MORPH to analyse how different target water levels affect the number of migratory Bewick's swans Cygnus columbianus bewickii staging at a shallow freshwater lake (Lauwersmeer, the Netherlands in autumn. As an emerging property of the model, we found strong non-linear responses of swan usage to changes in water level, with a sudden drop in peak numbers as well as bird-days with a 0.20 m rise above the current target water level. Such strong non-linear responses are probably common and should be taken into account in environmental impact assessments.

Prediction models : the right tool for the right problem

NARCIS (Netherlands)

Kappen, Teus H.; Peelen, Linda M.

2016-01-01

PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

Science.gov (United States)

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

Robust predictions of the interacting boson model

International Nuclear Information System (INIS)

Casten, R.F.; Koeln Univ.

1994-01-01

While most recognized for its symmetries and algebraic structure, the IBA model has other less-well-known but equally intrinsic properties which give unavoidable, parameter-free predictions. These predictions concern central aspects of low-energy nuclear collective structure. This paper outlines these ''robust'' predictions and compares them with the data

An approach to model validation and model-based prediction -- polyurethane foam case study.

Energy Technology Data Exchange (ETDEWEB)

Dowding, Kevin J.; Rutherford, Brian Milne

2003-07-01

Enhanced software methodology and improved computing hardware have advanced the state of simulation technology to a point where large physics-based codes can be a major contributor in many systems analyses. This shift toward the use of computational methods has brought with it new research challenges in a number of areas including characterization of uncertainty, model validation, and the analysis of computer output. It is these challenges that have motivated the work described in this report. Approaches to and methods for model validation and (model-based) prediction have been developed recently in the engineering, mathematics and statistical literatures. In this report we have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical

Predictive modeling of liquid-sodium thermal–hydraulics experiments and computations

International Nuclear Information System (INIS)

Arslan, Erkan; Cacuci, Dan G.

2014-01-01

Highlights: • We applied the predictive modeling method of Cacuci and Ionescu-Bujor (2010). • We assimilated data from sodium flow experiments. • We used computational fluid dynamics simulations of sodium experiments. • The predictive modeling method greatly reduced uncertainties in predicted results. - Abstract: This work applies the predictive modeling procedure formulated by Cacuci and Ionescu-Bujor (2010) to assimilate data from liquid-sodium thermal–hydraulics experiments in order to reduce systematically the uncertainties in the predictions of computational fluid dynamics (CFD) simulations. The predicted CFD-results for the best-estimate model parameters and results describing sodium-flow velocities and temperature distributions are shown to be significantly more precise than the original computations and experiments, in that the predicted uncertainties for the best-estimate results and model parameters are significantly smaller than both the originally computed and the experimental uncertainties

Interpreting Disruption Prediction Models to Improve Plasma Control

Science.gov (United States)

Parsons, Matthew

2017-10-01

In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.

Predictability of Wave Energy and Electricity Markets

DEFF Research Database (Denmark)

Chozas, Julia Fernandez

2012-01-01

The articlw addresses an important challenge ahead the integration of the electricity generated by wave energy conversion technologies into the electric grid. Particularly, it looks into the role of wave energy within the day-ahead electricity market. For that the predictability of the theoretical...... power outputs of three wave energy technologies in the Danish North Sea are examined. The simultaneous and co-located forecast and buoy-measured wave parameters at Hanstholm, Denmark, during a non-consecutive autumn and winter 3-month period form the basis of the investigation. The objective...

In silico modeling to predict drug-induced phospholipidosis

International Nuclear Information System (INIS)

Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G.; Sadrieh, Nakissa

2013-01-01

Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL

Return Predictability, Model Uncertainty, and Robust Investment

DEFF Research Database (Denmark)

Lukas, Manuel

Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

Effective modelling for predictive analytics in data science ...

African Journals Online (AJOL)

Effective modelling for predictive analytics in data science. ... the nearabsence of empirical or factual predictive analytics in the mainstream research going on ... Keywords: Predictive Analytics, Big Data, Business Intelligence, Project Planning.

Statistical and Machine Learning Models to Predict Programming Performance

OpenAIRE

Bergin, Susan

2006-01-01

This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

Severe Autumn storms in future Western Europe with a warmer Atlantic Ocean

Science.gov (United States)

Baatsen, Michiel; Haarsma, Reindert J.; Van Delden, Aarnout J.; de Vries, Hylke

2015-08-01

Simulations with a very high resolution (~25 km) global climate model indicate that more severe Autumn storms will impact Europe in a warmer future climate. The observed increase is mainly attributed to storms with a tropical origin, especially in the later part of the twentyfirst century. As their genesis region expands, tropical cyclones become more intense and their chances of reaching Europe increase. This paper investigates the properties and evolution of such storms and clarifies the future changes. The studied tropical cyclones feature a typical evolution of tropical development, extratropical transition and a re-intensification. A reduction of the transit area between regions of tropical and extratropical cyclogenesis increases the probability of re-intensification. Many of the modelled storms exhibit hybrid properties in a considerable part of their life cycle during which they exhibit the hazards of both tropical and extratropical systems. In addition to tropical cyclones, other systems such as cold core extratropical storms mainly originating over the Gulf Stream region also increasingly impact Western Europe. Despite their different history, all of the studied storms have one striking similarity: they form a warm seclusion. The structure, intensity and frequency of storms in the present climate are compared to observations using the MERRA and IBTrACS datasets. Damaging winds associated with the occurrence of a sting jet are observed in a large fraction of the cyclones during their final stage. Baroclinic instability is of great importance for the (re-)intensification of the storms. Furthermore, so-called atmospheric rivers providing tropical air prove to be vital for the intensification through diabatic heating and will increase considerably in strength in the future, as will the associated flooding risks.

Predicting Protein Secondary Structure with Markov Models

DEFF Research Database (Denmark)

Fischer, Paul; Larsen, Simon; Thomsen, Claus

2004-01-01

we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

An empirical evaluation of landscape energetic models: Mallard and American black duck space use during the non-breeding period

Science.gov (United States)

Beatty, William S.; Webb, Elisabeth B.; Kesler, Dylan C.; Naylor, Luke W.; Raedeke, Andrew H.; Humburg, Dale D.; Coluccy, John M.; Soulliere, G.

2015-01-01

Bird conservation Joint Ventures are collaborative partnerships between public agencies and private organizations that facilitate habitat management to support waterfowl and other bird populations. A subset of Joint Ventures has developed energetic carrying capacity models (ECCs) to translate regional waterfowl population goals into habitat objectives during the non-breeding period. Energetic carrying capacity models consider food biomass, metabolism, and available habitat to estimate waterfowl carrying capacity within an area. To evaluate Joint Venture ECCs in the context of waterfowl space use, we monitored 33 female mallards (Anas platyrhynchos) and 55 female American black ducks (A. rubripes) using global positioning system satellite telemetry in the central and eastern United States. To quantify space use, we measured first-passage time (FPT: time required for an individual to transit across a circle of a given radius) at biologically relevant spatial scales for mallards (3.46 km) and American black ducks (2.30 km) during the non-breeding period, which included autumn migration, winter, and spring migration. We developed a series of models to predict FPT using Joint Venture ECCs and compared them to a biological null model that quantified habitat composition and a statistical null model, which included intercept and random terms. Energetic carrying capacity models predicted mallard space use more efficiently during autumn and spring migrations, but the statistical null was the top model for winter. For American black ducks, ECCs did not improve predictions of space use; the biological null was top ranked for winter and the statistical null was top ranked for spring migration. Thus, ECCs provided limited insight into predicting waterfowl space use during the non-breeding season. Refined estimates of spatial and temporal variation in food abundance, habitat conditions, and anthropogenic disturbance will likely improve ECCs and benefit conservation planners

On the Predictiveness of Single-Field Inflationary Models

CERN Document Server

Burgess, C.P.; Trott, Michael

2014-01-01

We re-examine the predictiveness of single-field inflationary models and discuss how an unknown UV completion can complicate determining inflationary model parameters from observations, even from precision measurements. Besides the usual naturalness issues associated with having a shallow inflationary potential, we describe another issue for inflation, namely, unknown UV physics modifies the running of Standard Model (SM) parameters and thereby introduces uncertainty into the potential inflationary predictions. We illustrate this point using the minimal Higgs Inflationary scenario, which is arguably the most predictive single-field model on the market, because its predictions for $A_s$, $r$ and $n_s$ are made using only one new free parameter beyond those measured in particle physics experiments, and run up to the inflationary regime. We find that this issue can already have observable effects. At the same time, this UV-parameter dependence in the Renormalization Group allows Higgs Inflation to occur (in prin...

Predictive modeling of neuroanatomic structures for brain atrophy detection

Science.gov (United States)

Hu, Xintao; Guo, Lei; Nie, Jingxin; Li, Kaiming; Liu, Tianming

2010-03-01

In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

Science.gov (United States)

King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

2011-01-01

Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

Directory of Open Access Journals (Sweden)

Michael King

Full Text Available Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL for the development of hazardous drinking in safe drinkers.A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873. The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51. External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846 and Hedge's g of 0.68 (95% CI 0.57, 0.78.The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Influence of Wind Model Performance on Wave Forecasts of the Naval Oceanographic Office

Science.gov (United States)

Gay, P. S.; Edwards, K. L.

2017-12-01

Significant discrepancies between the Naval Oceanographic Office's significant wave height (SWH) predictions and observations have been noted in some model domains. The goal of this study is to evaluate these discrepancies and identify to what extent inaccuracies in the wind predictions may explain inaccuracies in SWH predictions. A one-year time series of data is evaluated at various locations in Southern California and eastern Florida. Correlations are generally quite good, ranging from 73% at Pendleton to 88% at both Santa Barbara, California, and Cape Canaveral, Florida. Correlations for month-long periods off Southern California drop off significantly in late spring through early autumn - less so off eastern Florida - likely due to weaker local wind seas and generally smaller SWH in addition to the influence of remotely-generated swell, which may not propagate accurately into and through the wave models. The results of this study suggest that it is likely that a change in meteorological and/or oceanographic conditions explains the change in model performance, partially as a result of a seasonal reduction in wind model performance in the summer months.

Assessing the simulation and prediction of rainfall associated with the MJO in the POAMA seasonal forecast system

Energy Technology Data Exchange (ETDEWEB)

Marshall, Andrew G. [Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Hobart, (Australia); Hudson, Debra; Wheeler, Matthew C.; Hendon, Harry H.; Alves, Oscar [Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne (Australia)

2011-12-15

We assess the ability of the Predictive Ocean Atmosphere Model for Australia (POAMA) to simulate and predict weekly rainfall associated with the MJO using a 27-year hindcast dataset. After an initial 2-week atmospheric adjustment, the POAMA model is shown to simulate well, both in pattern and in intensity, the weekly-mean rainfall variation associated with the evolution of the MJO over the tropical Indo-Pacific. The simulation is most realistic in December-February (austral summer) and least realistic in March-May (austral autumn). Regionally, the most problematic area is the Maritime Continent, which is a common problem area in other models. Coupled with our previous demonstration of the ability of POAMA to predict the evolution of the large-scale structure of the MJO for up to about 3 weeks, this ability to simulate the regional rainfall evolution associated with the MJO translates to enhanced predictability of rainfall regionally throughout much of the tropical Indo-Pacific when the MJO is present in the initial conditions during October-March. We also demonstrate enhanced prediction skill of rainfall at up to 3 weeks lead time over the north-east Pacific and north Atlantic, which are areas of pronounced teleconnections excited by the MJO-modulation of tropical Indo-Pacific rainfall. Failure to simulate and predict the modulation of rainfall in such places as the Maritime Continent and tropical Australia by the MJO indicates, however, there is still much room for improvement of the prediction of the MJO and its teleconnections. (orig.)

Tracking the Autumn Migration of the Bar-Headed Goose (Anser indicus with Satellite Telemetry and Relationship to Environmental Conditions

Directory of Open Access Journals (Sweden)

Yaonan Zhang

2011-01-01

Full Text Available The autumn migration routes of bar-headed geese captured before the 2008 breeding season at Qinghai Lake, China, were documented using satellite tracking data. To assess how the migration strategies of bar-headed geese are influenced by environmental conditions, the relationship between migratory routes, temperatures, and vegetation coverage at stopovers sites estimated with the Normalized Difference Vegetation Index (NDVI were analyzed. Our results showed that there were four typical migration routes in autumn with variation in timing among individuals in start and end times and in total migration and stopover duration. The observed variation may be related to habitat type and other environmental conditions along the routes. On average, these birds traveled about 1300 to 1500 km, refueled at three to six stopover sites and migrated for 73 to 83 days. The majority of the habitat types at stopover sites were lake, marsh, and shoal wetlands, with use of some mountainous regions, and farmland areas.

Prediction Model for Gastric Cancer Incidence in Korean Population.

Directory of Open Access Journals (Sweden)

Bang Wool Eom

Full Text Available Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.During a median of 11.4 years of follow-up, 19,465 (1.4% and 5,579 (0.7% newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women.In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

Predictive modeling of coupled multi-physics systems: I. Theory

International Nuclear Information System (INIS)

Cacuci, Dan Gabriel

2014-01-01

Highlights: • We developed “predictive modeling of coupled multi-physics systems (PMCMPS)”. • PMCMPS reduces predicted uncertainties in predicted model responses and parameters. • PMCMPS treats efficiently very large coupled systems. - Abstract: This work presents an innovative mathematical methodology for “predictive modeling of coupled multi-physics systems (PMCMPS).” This methodology takes into account fully the coupling terms between the systems but requires only the computational resources that would be needed to perform predictive modeling on each system separately. The PMCMPS methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution based on a priori known mean values and uncertainties characterizing the parameters and responses for both multi-physics models. This “maximum entropy”-approximate a priori distribution is combined, using Bayes’ theorem, with the “likelihood” provided by the multi-physics simulation models. Subsequently, the posterior distribution thus obtained is evaluated using the saddle-point method to obtain analytical expressions for the optimally predicted values for the multi-physics models parameters and responses along with corresponding reduced uncertainties. Noteworthy, the predictive modeling methodology for the coupled systems is constructed such that the systems can be considered sequentially rather than simultaneously, while preserving exactly the same results as if the systems were treated simultaneously. Consequently, very large coupled systems, which could perhaps exceed available computational resources if treated simultaneously, can be treated with the PMCMPS methodology presented in this work sequentially and without any loss of generality or information, requiring just the resources that would be needed if the systems were treated sequentially

Comparison of the models of financial distress prediction

Directory of Open Access Journals (Sweden)

Jiří Omelka

2013-01-01

Full Text Available Prediction of the financial distress is generally supposed as approximation if a business entity is closed on bankruptcy or at least on serious financial problems. Financial distress is defined as such a situation when a company is not able to satisfy its liabilities in any forms, or when its liabilities are higher than its assets. Classification of financial situation of business entities represents a multidisciplinary scientific issue that uses not only the economic theoretical bases but interacts to the statistical, respectively to econometric approaches as well.The first models of financial distress prediction have originated in the sixties of the 20th century. One of the most known is the Altman’s model followed by a range of others which are constructed on more or less conformable bases. In many existing models it is possible to find common elements which could be marked as elementary indicators of potential financial distress of a company. The objective of this article is, based on the comparison of existing models of prediction of financial distress, to define the set of basic indicators of company’s financial distress at conjoined identification of their critical aspects. The sample defined this way will be a background for future research focused on determination of one-dimensional model of financial distress prediction which would subsequently become a basis for construction of multi-dimensional prediction model.

A model for predicting lung cancer response to therapy

International Nuclear Information System (INIS)

Seibert, Rebecca M.; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.

2007-01-01

Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during

Tectonic predictions with mantle convection models

Science.gov (United States)

Coltice, Nicolas; Shephard, Grace E.

2018-04-01

Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

Iowa calibration of MEPDG performance prediction models.

Science.gov (United States)

2013-06-01

This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

A predictive model for dimensional errors in fused deposition modeling

DEFF Research Database (Denmark)

Stolfi, A.

2015-01-01

This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

Comparison of two ordinal prediction models

DEFF Research Database (Denmark)

Kattan, Michael W; Gerds, Thomas A

2015-01-01

system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

Modeling pitting growth data and predicting degradation trend

International Nuclear Information System (INIS)

Viglasky, T.; Awad, R.; Zeng, Z.; Riznic, J.

2007-01-01

A non-statistical modeling approach to predict material degradation is presented in this paper. In this approach, the original data series is processed using Accumulated Generating Operation (AGO). With the aid of the AGO which weakens the random fluctuation embedded in the data series, an approximately exponential curve is established. The generated data series described by the exponential curve is then modeled by a differential equation. The coefficients of the differential equation can be deduced by approximate difference formula based on least-squares algorithm. By solving the differential equation and processing an inverse AGO, a predictive model can be obtained. As this approach is not established on the basis of statistics, the prediction can be performed with a limited amount of data. Implementation of this approach is demonstrated by predicting the pitting growth rate in specimens and wear trend in steam generator tubes. The analysis results indicate that this approach provides a powerful tool with reasonable precision to predict material degradation. (author)

Risk Prediction Models for Incident Heart Failure: A Systematic Review of Methodology and Model Performance.

Science.gov (United States)

Sahle, Berhe W; Owen, Alice J; Chin, Ken Lee; Reid, Christopher M

2017-09-01

Numerous models predicting the risk of incident heart failure (HF) have been developed; however, evidence of their methodological rigor and reporting remains unclear. This study critically appraises the methods underpinning incident HF risk prediction models. EMBASE and PubMed were searched for articles published between 1990 and June 2016 that reported at least 1 multivariable model for prediction of HF. Model development information, including study design, variable coding, missing data, and predictor selection, was extracted. Nineteen studies reporting 40 risk prediction models were included. Existing models have acceptable discriminative ability (C-statistics > 0.70), although only 6 models were externally validated. Candidate variable selection was based on statistical significance from a univariate screening in 11 models, whereas it was unclear in 12 models. Continuous predictors were retained in 16 models, whereas it was unclear how continuous variables were handled in 16 models. Missing values were excluded in 19 of 23 models that reported missing data, and the number of events per variable was models. Only 2 models presented recommended regression equations. There was significant heterogeneity in discriminative ability of models with respect to age (P prediction models that had sufficient discriminative ability, although few are externally validated. Methods not recommended for the conduct and reporting of risk prediction modeling were frequently used, and resulting algorithms should be applied with caution. Copyright © 2017 Elsevier Inc. All rights reserved.

Mathematical model for dissolved oxygen prediction in Cirata ...

African Journals Online (AJOL)

This paper presents the implementation and performance of mathematical model to predict theconcentration of dissolved oxygen in Cirata Reservoir, West Java by using Artificial Neural Network (ANN). The simulation program was created using Visual Studio 2012 C# software with ANN model implemented in it. Prediction ...

Quantifying nitrogen flux after application of 15N-labelled pig slurry on triticale in the late autumn

International Nuclear Information System (INIS)

Morvan, T.; Leterme, P.; Mary, B.

1996-01-01

Predicting nitrate leaching after spreading slurry in autumn is difficult because plant uptake, mineralization, immobilization, volatilization and denitrification occur and modify the nitrate pool available for leaching. To estimate these fluxes, pig slurry was labelled with (15NH4)2SO4 and spread in December (110 kg NH4-N.ha-1) on triticale. Soil microbial immobilization, crop uptake and soil inorganic nitrogen were measured at seven dates between day 2 and 63 after application. NH4-N in the slurry follows three ways of transformation: volatilization (38 kg N.ha-1 in 16 days), immobilization (29 kg N.ha-1 day 30) and nitrification (42 kg N.ha-1). This last one was achieved 48 days after spreading, despite the cold mean temperatures measured during the experiment. Gross mineralization of soil and slurry organic nitrogen was large (35 kg N.ha-1 for the 0-10 cm soil layer). The real utilization coefficient of 15N-labelled N was low, smaller than 4% at day 63. The leaching of nitrate was small because there was no rainfall after day 48. Thus, from the balance of 15N-labelled N, it is suggested that 22 kg NO3 N.ha-1 has been lost by denitrification [fr

Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.

Science.gov (United States)

Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

2018-01-12

Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

AN EFFICIENT PATIENT INFLOW PREDICTION MODEL FOR HOSPITAL RESOURCE MANAGEMENT

Directory of Open Access Journals (Sweden)

Kottalanka Srikanth

2017-07-01

Full Text Available There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

Compensatory versus noncompensatory models for predicting consumer preferences

Directory of Open Access Journals (Sweden)

Anja Dieckmann

2009-04-01

Full Text Available Standard preference models in consumer research assume that people weigh and add all attributes of the available options to derive a decision, while there is growing evidence for the use of simplifying heuristics. Recently, a greedoid algorithm has been developed (Yee, Dahan, Hauser and Orlin, 2007; Kohli and Jedidi, 2007 to model lexicographic heuristics from preference data. We compare predictive accuracies of the greedoid approach and standard conjoint analysis in an online study with a rating and a ranking task. The lexicographic model derived from the greedoid algorithm was better at predicting ranking compared to rating data, but overall, it achieved lower predictive accuracy for hold-out data than the compensatory model estimated by conjoint analysis. However, a considerable minority of participants was better predicted by lexicographic strategies. We conclude that the new algorithm will not replace standard tools for analyzing preferences, but can boost the study of situational and individual differences in preferential choice processes.

Prediction models for successful external cephalic version: a systematic review.

Science.gov (United States)

Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M; Molkenboer, Jan F M; Van der Post, Joris A M; Mol, Ben W; Kok, Marjolein

2015-12-01

To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015. We extracted information on study design, sample size, model-building strategies and validation. We evaluated the phases of model development and summarized their performance in terms of discrimination, calibration and clinical usefulness. We collected different predictor variables together with their defined significance, in order to identify important predictor variables for successful ECV. We identified eight articles reporting on seven prediction models. All models were subjected to internal validation. Only one model was also validated in an external cohort. Two prediction models had a low overall risk of bias, of which only one showed promising predictive performance at internal validation. This model also completed the phase of external validation. For none of the models their impact on clinical practice was evaluated. The most important predictor variables for successful ECV described in the selected articles were parity, placental location, breech engagement and the fetal head being palpable. One model was assessed using discrimination and calibration using internal (AUC 0.71) and external validation (AUC 0.64), while two other models were assessed with discrimination and calibration, respectively. We found one prediction model for breech presentation that was validated in an external cohort and had acceptable predictive performance. This model should be used to council women considering ECV. Copyright © 2015. Published by Elsevier Ireland Ltd.

Predictive QSAR Models for the Toxicity of Disinfection Byproducts

Directory of Open Access Journals (Sweden)

Litang Qin

2017-10-01

Full Text Available Several hundred disinfection byproducts (DBPs in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2 > 0.7, explained variance in leave-one-out prediction (Q2LOO and in leave-many-out prediction (Q2LMO > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3 > 0.7, and concordance correlation coefficient (CCC > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

Predictive QSAR Models for the Toxicity of Disinfection Byproducts.

Science.gov (United States)

Qin, Litang; Zhang, Xin; Chen, Yuhan; Mo, Lingyun; Zeng, Honghu; Liang, Yanpeng

2017-10-09

Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure-activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH-, DNA+ and DNA-. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination ( R ²) > 0.7, explained variance in leave-one-out prediction ( Q ² LOO ) and in leave-many-out prediction ( Q ² LMO ) > 0.6, variance explained in external prediction ( Q ² F1 , Q ² F2 , and Q ² F3 ) > 0.7, and concordance correlation coefficient ( CCC ) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs.

Modelling the predictive performance of credit scoring

Directory of Open Access Journals (Sweden)

Shi-Wei Shen

2013-07-01

Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

Modelling the winter distribution of a rare and endangered migrant, the Aquatic Warbler Acrocephalus paludicola

DEFF Research Database (Denmark)

Walther, Bruno A; Schäffer, Norbert; van Niekerk, Adriaan

2007-01-01

. Such model predictions may be useful guidelines to focus further field research on the Aquatic Warbler. Given the excellent model predictions in this study, this novel technique may prove useful to model the distribution of other rare and endangered species, thus providing a means to guide future survey......The Aquatic Warbler Acrocephalus paludicola is one of the most threatened Western Palearctic passerine species, classified as globally Vulnerable. With its breeding grounds relatively secure, a clear need remains for the monitoring and protection of the migration and wintering grounds of this rare...... and endangered migrant. Recent research has shown that the Aquatic Warbler migrates through northwest Africa in autumn and spring. The wintering grounds are apparently limited to wetlands of sub-Saharan West Africa, with records from only about 20 localities in Mauritania, Mali, Senegal and Ghana. Given the lack...

A predictive pilot model for STOL aircraft landing

Science.gov (United States)

Kleinman, D. L.; Killingsworth, W. R.

1974-01-01

An optimal control approach has been used to model pilot performance during STOL flare and landing. The model is used to predict pilot landing performance for three STOL configurations, each having a different level of automatic control augmentation. Model predictions are compared with flight simulator data. It is concluded that the model can be effective design tool for studying analytically the effects of display modifications, different stability augmentation systems, and proposed changes in the landing area geometry.

PSO-MISMO modeling strategy for multistep-ahead time series prediction.

Science.gov (United States)

Bao, Yukun; Xiong, Tao; Hu, Zhongyi

2014-05-01

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

Comparison of pause predictions of two sequence-dependent transcription models

International Nuclear Information System (INIS)

Bai, Lu; Wang, Michelle D

2010-01-01

Two recent theoretical models, Bai et al (2004, 2007) and Tadigotla et al (2006), formulated thermodynamic explanations of sequence-dependent transcription pausing by RNA polymerase (RNAP). The two models differ in some basic assumptions and therefore make different yet overlapping predictions for pause locations, and different predictions on pause kinetics and mechanisms. Here we present a comprehensive comparison of the two models. We show that while they have comparable predictive power of pause locations at low NTP concentrations, the Bai et al model is more accurate than Tadigotla et al at higher NTP concentrations. The pausing kinetics predicted by Bai et al is also consistent with time-course transcription reactions, while Tadigotla et al is unsuited for this type of kinetic prediction. More importantly, the two models in general predict different pausing mechanisms even for the same pausing sites, and the Bai et al model provides an explanation more consistent with recent single molecule observations

Questioning the Faith - Models and Prediction in Stream Restoration (Invited)

Science.gov (United States)

Wilcock, P.

2013-12-01

River management and restoration demand prediction at and beyond our present ability. Management questions, framed appropriately, can motivate fundamental advances in science, although the connection between research and application is not always easy, useful, or robust. Why is that? This presentation considers the connection between models and management, a connection that requires critical and creative thought on both sides. Essential challenges for managers include clearly defining project objectives and accommodating uncertainty in any model prediction. Essential challenges for the research community include matching the appropriate model to project duration, space, funding, information, and social constraints and clearly presenting answers that are actually useful to managers. Better models do not lead to better management decisions or better designs if the predictions are not relevant to and accepted by managers. In fact, any prediction may be irrelevant if the need for prediction is not recognized. The predictive target must be developed in an active dialog between managers and modelers. This relationship, like any other, can take time to develop. For example, large segments of stream restoration practice have remained resistant to models and prediction because the foundational tenet - that channels built to a certain template will be able to transport the supplied sediment with the available flow - has no essential physical connection between cause and effect. Stream restoration practice can be steered in a predictive direction in which project objectives are defined as predictable attributes and testable hypotheses. If stream restoration design is defined in terms of the desired performance of the channel (static or dynamic, sediment surplus or deficit), then channel properties that provide these attributes can be predicted and a basis exists for testing approximations, models, and predictions.

Qualitative and quantitative guidelines for the comparison of environmental model predictions

International Nuclear Information System (INIS)

Scott, M.

1995-03-01

The question of how to assess or compare predictions from a number of models is one of concern in the validation of models, in understanding the effects of different models and model parameterizations on model output, and ultimately in assessing model reliability. Comparison of model predictions with observed data is the basic tool of model validation while comparison of predictions amongst different models provides one measure of model credibility. The guidance provided here is intended to provide qualitative and quantitative approaches (including graphical and statistical techniques) to such comparisons for use within the BIOMOVS II project. It is hoped that others may find it useful. It contains little technical information on the actual methods but several references are provided for the interested reader. The guidelines are illustrated on data from the VAMP CB scenario. Unfortunately, these data do not permit all of the possible approaches to be demonstrated since predicted uncertainties were not provided. The questions considered are concerned with a) intercomparison of model predictions and b) comparison of model predictions with the observed data. A series of examples illustrating some of the different types of data structure and some possible analyses have been constructed. A bibliography of references on model validation is provided. It is important to note that the results of the various techniques discussed here, whether qualitative or quantitative, should not be considered in isolation. Overall model performance must also include an evaluation of model structure and formulation, i.e. conceptual model uncertainties, and results for performance measures must be interpreted in this context. Consider a number of models which are used to provide predictions of a number of quantities at a number of time points. In the case of the VAMP CB scenario, the results include predictions of total deposition of Cs-137 and time dependent concentrations in various

Evaluation of wave runup predictions from numerical and parametric models

Science.gov (United States)

Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.

2014-01-01

Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

Modelling the effect of temperature on hatching and settlement patterns of meroplanktonic organisms: the case of octopus

Directory of Open Access Journals (Sweden)

Stelios Katsanevakis

2006-12-01

Full Text Available The duration of embryonic development and the planktonic stage of meroplanktonic species is highly temperature dependent and thus the seasonal temperature oscillations of temperate regions greatly affect the patterns of hatching and benthic settlement. Based on data from the literature on embryonic development and planktonic duration of Octopus vulgaris (common octopus in relation to temperature, and on observed temperature patterns, several models of hatching and settlement patterns were created. There was a good fit between observed settlement patterns and model predictions. Based on these models we concluded that in temperate regions: (1 when temperature is increasing (from early spring to mid summer the hatching and settlement periods tend to shorten, while when the temperature is decreasing (during autumn the hatching and settlement periods tend to lengthen; (2 hatching and settlement peaks are narrower and more intense than a spring spawning peak but wider and less intense than an autumn spawning peak; (3 at lower latitudes, hatching and settlement patterns tend to follow the spawning pattern more closely, (4 the periodic temperature pattern of temperate areas has the potential to cause a convergence of hatching during spring.

Predictive models for PEM-electrolyzer performance using adaptive neuro-fuzzy inference systems

Energy Technology Data Exchange (ETDEWEB)

Becker, Steffen [University of Tasmania, Hobart 7001, Tasmania (Australia); Karri, Vishy [Australian College of Kuwait (Kuwait)

2010-09-15

Predictive models were built using neural network based Adaptive Neuro-Fuzzy Inference Systems for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used on-line for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematical models are found to be reliable predictive tools with an excellent accuracy of {+-}3% compared with experimental values. The predictive nature of these models did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications. (author)

State-space prediction model for chaotic time series

Science.gov (United States)

Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

1998-08-01

A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

A new, accurate predictive model for incident hypertension.

Science.gov (United States)

Völzke, Henry; Fung, Glenn; Ittermann, Till; Yu, Shipeng; Baumeister, Sebastian E; Dörr, Marcus; Lieb, Wolfgang; Völker, Uwe; Linneberg, Allan; Jørgensen, Torben; Felix, Stephan B; Rettig, Rainer; Rao, Bharat; Kroemer, Heyo K

2013-11-01

Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures. The primary study population consisted of 1605 normotensive individuals aged 20-79 years with 5-year follow-up from the population-based study, that is the Study of Health in Pomerania (SHIP). The initial set was randomly split into a training and a testing set. We used a probabilistic graphical model applying a Bayesian network to create a predictive model for incident hypertension and compared the predictive performance with the established Framingham risk score for hypertension. Finally, the model was validated in 2887 participants from INTER99, a Danish community-based intervention study. In the training set of SHIP data, the Bayesian network used a small subset of relevant baseline features including age, mean arterial pressure, rs16998073, serum glucose and urinary albumin concentrations. Furthermore, we detected relevant interactions between age and serum glucose as well as between rs16998073 and urinary albumin concentrations [area under the receiver operating characteristic (AUC 0.76)]. The model was confirmed in the SHIP validation set (AUC 0.78) and externally replicated in INTER99 (AUC 0.77). Compared to the established Framingham risk score for hypertension, the predictive performance of the new model was similar in the SHIP validation set and moderately better in INTER99. Data mining procedures identified a predictive model for incident hypertension, which included innovative and easy-to-measure variables. The findings promise great applicability in screening settings and clinical practice.

Cure modeling in real-time prediction: How much does it help?

Science.gov (United States)

Ying, Gui-Shuang; Zhang, Qiang; Lan, Yu; Li, Yimei; Heitjan, Daniel F

2017-08-01

Various parametric and nonparametric modeling approaches exist for real-time prediction in time-to-event clinical trials. Recently, Chen (2016 BMC Biomedical Research Methodology 16) proposed a prediction method based on parametric cure-mixture modeling, intending to cover those situations where it appears that a non-negligible fraction of subjects is cured. In this article we apply a Weibull cure-mixture model to create predictions, demonstrating the approach in RTOG 0129, a randomized trial in head-and-neck cancer. We compare the ultimate realized data in RTOG 0129 to interim predictions from a Weibull cure-mixture model, a standard Weibull model without a cure component, and a nonparametric model based on the Bayesian bootstrap. The standard Weibull model predicted that events would occur earlier than the Weibull cure-mixture model, but the difference was unremarkable until late in the trial when evidence for a cure became clear. Nonparametric predictions often gave undefined predictions or infinite prediction intervals, particularly at early stages of the trial. Simulations suggest that cure modeling can yield better-calibrated prediction intervals when there is a cured component, or the appearance of a cured component, but at a substantial cost in the average width of the intervals. Copyright © 2017 Elsevier Inc. All rights reserved.

Evaluation of burst pressure prediction models for line pipes

Energy Technology Data Exchange (ETDEWEB)

Zhu, Xian-Kui, E-mail: zhux@battelle.org [Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43201 (United States); Leis, Brian N. [Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43201 (United States)

2012-01-15

Accurate prediction of burst pressure plays a central role in engineering design and integrity assessment of oil and gas pipelines. Theoretical and empirical solutions for such prediction are evaluated in this paper relative to a burst pressure database comprising more than 100 tests covering a variety of pipeline steel grades and pipe sizes. Solutions considered include three based on plasticity theory for the end-capped, thin-walled, defect-free line pipe subjected to internal pressure in terms of the Tresca, von Mises, and ZL (or Zhu-Leis) criteria, one based on a cylindrical instability stress (CIS) concept, and a large group of analytical and empirical models previously evaluated by Law and Bowie (International Journal of Pressure Vessels and Piping, 84, 2007: 487-492). It is found that these models can be categorized into either a Tresca-family or a von Mises-family of solutions, except for those due to Margetson and Zhu-Leis models. The viability of predictions is measured via statistical analyses in terms of a mean error and its standard deviation. Consistent with an independent parallel evaluation using another large database, the Zhu-Leis solution is found best for predicting burst pressure, including consideration of strain hardening effects, while the Tresca strength solutions including Barlow, Maximum shear stress, Turner, and the ASME boiler code provide reasonably good predictions for the class of line-pipe steels with intermediate strain hardening response. - Highlights: Black-Right-Pointing-Pointer This paper evaluates different burst pressure prediction models for line pipes. Black-Right-Pointing-Pointer The existing models are categorized into two major groups of Tresca and von Mises solutions. Black-Right-Pointing-Pointer Prediction quality of each model is assessed statistically using a large full-scale burst test database. Black-Right-Pointing-Pointer The Zhu-Leis solution is identified as the best predictive model.

Evaluation of burst pressure prediction models for line pipes

International Nuclear Information System (INIS)

Zhu, Xian-Kui; Leis, Brian N.

2012-01-01

Accurate prediction of burst pressure plays a central role in engineering design and integrity assessment of oil and gas pipelines. Theoretical and empirical solutions for such prediction are evaluated in this paper relative to a burst pressure database comprising more than 100 tests covering a variety of pipeline steel grades and pipe sizes. Solutions considered include three based on plasticity theory for the end-capped, thin-walled, defect-free line pipe subjected to internal pressure in terms of the Tresca, von Mises, and ZL (or Zhu-Leis) criteria, one based on a cylindrical instability stress (CIS) concept, and a large group of analytical and empirical models previously evaluated by Law and Bowie (International Journal of Pressure Vessels and Piping, 84, 2007: 487–492). It is found that these models can be categorized into either a Tresca-family or a von Mises-family of solutions, except for those due to Margetson and Zhu-Leis models. The viability of predictions is measured via statistical analyses in terms of a mean error and its standard deviation. Consistent with an independent parallel evaluation using another large database, the Zhu-Leis solution is found best for predicting burst pressure, including consideration of strain hardening effects, while the Tresca strength solutions including Barlow, Maximum shear stress, Turner, and the ASME boiler code provide reasonably good predictions for the class of line-pipe steels with intermediate strain hardening response. - Highlights: ► This paper evaluates different burst pressure prediction models for line pipes. ► The existing models are categorized into two major groups of Tresca and von Mises solutions. ► Prediction quality of each model is assessed statistically using a large full-scale burst test database. ► The Zhu-Leis solution is identified as the best predictive model.

Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.

Science.gov (United States)

Hassanzadeh, S; Hosseinibalam, F; Alizadeh, R

2009-08-01

This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.

Hidden Markov Model for quantitative prediction of snowfall

Indian Academy of Sciences (India)

A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

Predictive Models and Computational Embryology

Science.gov (United States)

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

AUTUMNAL FORAGE YIELD AND NUTRITIVE VALUE OF THE LEGUME ARACHIS RENDIMENTO E VALOR NUTRITIVO DA FORRAGEM OUTONAL DE AMENDOIM-FORRAGEIRO

Directory of Open Access Journals (Sweden)

Pedro Lima Monks

2007-09-01

Full Text Available

Dry matter yield and nutritive value of forage le-gume Arachis    pintoi (Krap. & Greg. cv. Alqueire-1 (BRA 037036, was evaluated under different cutting mana-gement regimes and levels of P and K fertilization, in a yellow-red argisoil, at CAP-UFPEL, Capão do Leão, RS, Brazil during Spring-Summer and Fall. Cutting regimes compared were: no cutting, one, two, three, four and five cuttings, at 5 cm above ground. Fertilization levels con-sisted in supplying zero, 50 and 100% of requirements for P and K recommended by Brazilian Soil Science Society, for warm season perennial forage legumes. Fertilization treatments were alocated to main plots and cutting regi-mes to subplots, in a complete splitplot randomized block design, with three replications. Data of the following va-riables were submitted to analysis of variance and polino-mial regression: dry matter yield and quality of autumnal cutting, dry matter accumulation rate of autumnal cutting and total dry matter yield. If the purpose is the utilization of the forage during Autumn, 70% of the recommended phosphorus and potassium fertilization is sufficient to ob-tain maximum forage yield. However, if the objective are cuttings during the growing season (Spring-Summer and also in Autumn, it is necessary 100% of the recommended fertilization. The increase in number of cuttings during Spring-Summer decreases in the same proportion the fo-rage yield in Autumn. Forage nutritive value in Autumn is better when greater number of cuttings are made during Spring-Summer. Spring deferments also result in higher autumnal forage quality.

KEY-WORDS: Cutting, fertilization, tropical forage.

Num Argissolo vermelho amarelo eutrófico típi-co, do Centro Agropecuário da Palma, da UFPEL, Capão do Leão, RS,  foram avaliados os efeitos de cortes esti-vais e da adubação fosfatada e potássica sobre o rendi-mento e valor nutritivo da matéria seca (MS outonal de amendoim-forrageiro (Arachis

Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. II. Predicting wind components

Energy Technology Data Exchange (ETDEWEB)

Kamp, Derek van der [University of Victoria, Pacific Climate Impacts Consortium, Victoria, BC (Canada); University of Victoria, School of Earth and Ocean Sciences, Victoria, BC (Canada); Curry, Charles L. [Environment Canada University of Victoria, Canadian Centre for Climate Modelling and Analysis, Victoria, BC (Canada); University of Victoria, School of Earth and Ocean Sciences, Victoria, BC (Canada); Monahan, Adam H. [University of Victoria, School of Earth and Ocean Sciences, Victoria, BC (Canada)

2012-04-15

A regression-based downscaling technique was applied to monthly mean surface wind observations from stations throughout western Canada as well as from buoys in the Northeast Pacific Ocean over the period 1979-2006. A predictor set was developed from principal component analysis of the three wind components at 500 hPa and mean sea-level pressure taken from the NCEP Reanalysis II. Building on the results of a companion paper, Curry et al. (Clim Dyn 2011), the downscaling was applied to both wind speed and wind components, in an effort to evaluate the utility of each type of predictand. Cross-validated prediction skill varied strongly with season, with autumn and summer displaying the highest and lowest skill, respectively. In most cases wind components were predicted with better skill than wind speeds. The predictive ability of wind components was found to be strongly related to their orientation. Wind components with the best predictions were often oriented along topographically significant features such as constricted valleys, mountain ranges or ocean channels. This influence of directionality on predictive ability is most prominent during autumn and winter at inland sites with complex topography. Stations in regions with relatively flat terrain (where topographic steering is minimal) exhibit inter-station consistencies including region-wide seasonal shifts in the direction of the best predicted wind component. The conclusion that wind components can be skillfully predicted only over a limited range of directions at most stations limits the scope of statistically downscaled wind speed predictions. It seems likely that such limitations apply to other regions of complex terrain as well. (orig.)

Predicting acid dew point with a semi-empirical model

International Nuclear Information System (INIS)

Xiang, Baixiang; Tang, Bin; Wu, Yuxin; Yang, Hairui; Zhang, Man; Lu, Junfu

2016-01-01

Highlights: • The previous semi-empirical models are systematically studied. • An improved thermodynamic correlation is derived. • A semi-empirical prediction model is proposed. • The proposed semi-empirical model is validated. - Abstract: Decreasing the temperature of exhaust flue gas in boilers is one of the most effective ways to further improve the thermal efficiency, electrostatic precipitator efficiency and to decrease the water consumption of desulfurization tower, while, when this temperature is below the acid dew point, the fouling and corrosion will occur on the heating surfaces in the second pass of boilers. So, the knowledge on accurately predicting the acid dew point is essential. By investigating the previous models on acid dew point prediction, an improved thermodynamic correlation formula between the acid dew point and its influencing factors is derived first. And then, a semi-empirical prediction model is proposed, which is validated with the data both in field test and experiment, and comparing with the previous models.

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

Science.gov (United States)

Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

2017-05-22

PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

Science.gov (United States)

Diallo, Ousmane H.

2012-01-01

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Comparison of Linear Prediction Models for Audio Signals

Directory of Open Access Journals (Sweden)

2009-03-01

Full Text Available While linear prediction (LP has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.

Auditing predictive models : a case study in crop growth

NARCIS (Netherlands)

Metselaar, K.

1999-01-01

Methods were developed to assess and quantify the predictive quality of simulation models, with the intent to contribute to evaluation of model studies by non-scientists. In a case study, two models of different complexity, LINTUL and SUCROS87, were used to predict yield of forage maize

Models for predicting compressive strength and water absorption of ...

African Journals Online (AJOL)

This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...

Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation

International Nuclear Information System (INIS)

Voyant, Cyril; Muselli, Marc; Paoli, Christophe; Nivet, Marie-Laure

2012-01-01

We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/ARMA is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed. -- Highlights: ► Time series forecasting with hybrid method based on the use of ALADIN numerical weather model, ANN and ARMA. ► Innovative pre-input layer selection method. ► Combination of optimized MLP and ARMA model obtained from a rule based on the analysis of hourly data series. ► Stationarity process (method and control) for the global radiation time series.

An intermittency model for predicting roughness induced transition

Science.gov (United States)

Ge, Xuan; Durbin, Paul

2014-11-01

An extended model for roughness-induced transition is proposed based on an intermittency transport equation for RANS modeling formulated in local variables. To predict roughness effects in the fully turbulent boundary layer, published boundary conditions for k and ω are used, which depend on the equivalent sand grain roughness height, and account for the effective displacement of wall distance origin. Similarly in our approach, wall distance in the transition model for smooth surfaces is modified by an effective origin, which depends on roughness. Flat plate test cases are computed to show that the proposed model is able to predict the transition onset in agreement with a data correlation of transition location versus roughness height, Reynolds number, and inlet turbulence intensity. Experimental data for a turbine cascade are compared with the predicted results to validate the applicability of the proposed model. Supported by NSF Award Number 1228195.

Prediction-error variance in Bayesian model updating: a comparative study

Science.gov (United States)

Asadollahi, Parisa; Li, Jian; Huang, Yong

2017-04-01

In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes' Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model

Modeling of Complex Life Cycle Prediction Based on Cell Division

Directory of Open Access Journals (Sweden)

Fucheng Zhang

2017-01-01

Full Text Available Effective fault diagnosis and reasonable life expectancy are of great significance and practical engineering value for the safety, reliability, and maintenance cost of equipment and working environment. At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. Most of them need to be based on a large amount of data to achieve the problem. For this issue, we propose learning from the mechanism of cell division in the organism. We have established a moderate complexity of life prediction model across studying the complex multifactor correlation life model. In this paper, we model the life prediction of cell division. Experiments show that our model can effectively simulate the state of cell division. Through the model of reference, we will use it for the equipment of the complex life prediction.

Prediction models and control algorithms for predictive applications of setback temperature in cooling systems

International Nuclear Information System (INIS)

Moon, Jin Woo; Yoon, Younju; Jeon, Young-Hoon; Kim, Sooyoung

2017-01-01

Highlights: • Initial ANN model was developed for predicting the time to the setback temperature. • Initial model was optimized for producing accurate output. • Optimized model proved its prediction accuracy. • ANN-based algorithms were developed and tested their performance. • ANN-based algorithms presented superior thermal comfort or energy efficiency. - Abstract: In this study, a temperature control algorithm was developed to apply a setback temperature predictively for the cooling system of a residential building during occupied periods by residents. An artificial neural network (ANN) model was developed to determine the required time for increasing the current indoor temperature to the setback temperature. This study involved three phases: development of the initial ANN-based prediction model, optimization and testing of the initial model, and development and testing of three control algorithms. The development and performance testing of the model and algorithm were conducted using TRNSYS and MATLAB. Through the development and optimization process, the final ANN model employed indoor temperature and the temperature difference between the current and target setback temperature as two input neurons. The optimal number of hidden layers, number of neurons, learning rate, and moment were determined to be 4, 9, 0.6, and 0.9, respectively. The tangent–sigmoid and pure-linear transfer function was used in the hidden and output neurons, respectively. The ANN model used 100 training data sets with sliding-window method for data management. Levenberg-Marquart training method was employed for model training. The optimized model had a prediction accuracy of 0.9097 root mean square errors when compared with the simulated results. Employing the ANN model, ANN-based algorithms maintained indoor temperatures better within target ranges. Compared to the conventional algorithm, the ANN-based algorithms reduced the duration of time, in which the indoor temperature

Error analysis in predictive modelling demonstrated on mould data.

Science.gov (United States)

Baranyi, József; Csernus, Olívia; Beczner, Judit

2014-01-17

The purpose of this paper was to develop a predictive model for the effect of temperature and water activity on the growth rate of Aspergillus niger and to determine the sources of the error when the model is used for prediction. Parallel mould growth curves, derived from the same spore batch, were generated and fitted to determine their growth rate. The variances of replicate ln(growth-rate) estimates were used to quantify the experimental variability, inherent to the method of determining the growth rate. The environmental variability was quantified by the variance of the respective means of replicates. The idea is analogous to the "within group" and "between groups" variability concepts of ANOVA procedures. A (secondary) model, with temperature and water activity as explanatory variables, was fitted to the natural logarithm of the growth rates determined by the primary model. The model error and the experimental and environmental errors were ranked according to their contribution to the total error of prediction. Our method can readily be applied to analysing the error structure of predictive models of bacterial growth models, too. © 2013.

Predicting Power Outages Using Multi-Model Ensemble Forecasts

Science.gov (United States)

Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.

2017-12-01

Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.

Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance.

Science.gov (United States)

Smith, Lauren N; Makam, Anil N; Darden, Douglas; Mayo, Helen; Das, Sandeep R; Halm, Ethan A; Nguyen, Oanh Kieu

2018-01-01

Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown. We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models. Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions

Selection of tree roosts by male Indiana bats during the autumn swarm in the Ozark Highlands, USA

Science.gov (United States)

Roger W. Perry; Stephen C. Brandebura; Thomas S. Risch

2016-01-01

We identified 162 roosts for 36 male Indiana bats (Myotis sodalis) across 3 study areas in the Ozarks of northern Arkansas, USA, during the autumn swarm (late Aug to late Oct, 2005 and 2006). Bats utilized 14 tree species; snags of shortleaf pine (Pinus echinata) were the most utilized (30% of roosts) and pines were selected over hardwoods. Diameter of trees and snags...

A new ensemble model for short term wind power prediction

DEFF Research Database (Denmark)

Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan

2012-01-01

As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re-search...... of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset...

A new, accurate predictive model for incident hypertension

DEFF Research Database (Denmark)

Völzke, Henry; Fung, Glenn; Ittermann, Till

2013-01-01

Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures.......Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures....

Domestic appliances energy optimization with model predictive control

International Nuclear Information System (INIS)

Rodrigues, E.M.G.; Godina, R.; Pouresmaeil, E.; Ferreira, J.R.; Catalão, J.P.S.

2017-01-01

Highlights: • An alternative power management control for home appliances that require thermal regulation is presented. • A Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat. • Problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. • A modulation scheme of a two-level Model Predictive Control signal as an interface block is presented. • The implementation costs in home appliances with thermal regulation requirements are reduced. - Abstract: A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new

A state-based probabilistic model for tumor respiratory motion prediction

International Nuclear Information System (INIS)

Kalet, Alan; Sandison, George; Schmitz, Ruth; Wu Huanmei

2010-01-01

This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more

SHMF: Interest Prediction Model with Social Hub Matrix Factorization

Directory of Open Access Journals (Sweden)

Chaoyuan Cui

2017-01-01

Full Text Available With the development of social networks, microblog has become the major social communication tool. There is a lot of valuable information such as personal preference, public opinion, and marketing in microblog. Consequently, research on user interest prediction in microblog has a positive practical significance. In fact, how to extract information associated with user interest orientation from the constantly updated blog posts is not so easy. Existing prediction approaches based on probabilistic factor analysis use blog posts published by user to predict user interest. However, these methods are not very effective for the users who post less but browse more. In this paper, we propose a new prediction model, which is called SHMF, using social hub matrix factorization. SHMF constructs the interest prediction model by combining the information of blogs posts published by both user and direct neighbors in user’s social hub. Our proposed model predicts user interest by integrating user’s historical behavior and temporal factor as well as user’s friendships, thus achieving accurate forecasts of user’s future interests. The experimental results on Sina Weibo show the efficiency and effectiveness of our proposed model.

Development of Interpretable Predictive Models for BPH and Prostate Cancer.

Science.gov (United States)

Bermejo, Pablo; Vivo, Alicia; Tárraga, Pedro J; Rodríguez-Montes, J A

2015-01-01

Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. Statistical dependence with PC and BPH was found for prostate volume (P-value BPH prediction. PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced.

The role of spring and autumn phenological switches on spatiotemporal variation in temperate and boreal forest C balance: A FLUXNET synthesis

Science.gov (United States)

Richardson, A. D.; Reichstein, M.; Piao, S.; Ciais, P.; Luyssaert, S.; Stockli, R.; Friedl, M.; Gobron, N.; Fluxnet Site Pis, 21

2009-04-01

In temperate and boreal ecosystems, phenological transitions (particularly the timing of spring onset and autumn senescence) are thought to represent a major control on spatial and temporal variation in forest carbon sequestration. To investigate these patterns, we analyzed 153 site-years of data from the FLUXNET ‘La Thuile' database. Eddy covariance measurements of surface-atmosphere exchanges of carbon and water from 21 research sites at latitudes from 36°N to 67°N were used in the synthesis. We defined a range of phenological indicators based on the first (spring) and last (autumn) dates of (1) C source/sink transitions (‘carbon uptake period'); (2) measurable photosynthetic uptake (‘physiologically active period'); (3) relative thresholds for latent heat (evapotranspiration) flux; (4) phenological thresholds derived from a range of remote sensing products (JRC fAPAR, MOD12Q2, and the PROGNOSTIC model with MODIS data assimilation); and (5) a climatological metric based on the date where soil temperature equals mean annual air temperature. We then tested whether site-level flux anomalies were significantly correlated with phenological anomalies across these metrics, and whether the slopes of these relationships (representing the sensitivity to phenological variation) differed between deciduous broadleaf (DBF) and evergreen needleleaf (ENF) forests. Within sites, interannual variation in most phenological metrics was about 5-10 d, compared to 10-30 d across sites. Both spatial and temporal phenological variation were consistently larger at ENF, compared to DBF, sites. Averaged across metrics, phenological variability was roughly comparable in spring and autumn, both across (17 d) and within (9 d) sites. However, patterns of interannual variation in fluxes were less well explained by the derived phenological metrics than were patterns of spatial variation in fluxes. Also, the observed pattern strongly depended on the metric used, with flux-derived metrics

Modeling a multivariable reactor and on-line model predictive control.

Science.gov (United States)

Yu, D W; Yu, D L

2005-10-01

A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.

Plant water potential improves prediction of empirical stomatal models.

Directory of Open Access Journals (Sweden)

William R L Anderegg

Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

eTOXlab, an open source modeling framework for implementing predictive models in production environments.

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Carrió, Pau; López, Oriol; Sanz, Ferran; Pastor, Manuel

2015-01-01

Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments. We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series. The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by e

Real estate value prediction using multivariate regression models

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Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

A COMPARISON BETWEEN THREE PREDICTIVE MODELS OF COMPUTATIONAL INTELLIGENCE

Directory of Open Access Journals (Sweden)

DUMITRU CIOBANU

2013-12-01

Full Text Available Time series prediction is an open problem and many researchers are trying to find new predictive methods and improvements for the existing ones. Lately methods based on neural networks are used extensively for time series prediction. Also, support vector machines have solved some of the problems faced by neural networks and they began to be widely used for time series prediction. The main drawback of those two methods is that they are global models and in the case of a chaotic time series it is unlikely to find such model. In this paper it is presented a comparison between three predictive from computational intelligence field one based on neural networks one based on support vector machine and another based on chaos theory. We show that the model based on chaos theory is an alternative to the other two methods.

Greenland coastal air temperatures linked to Baffin Bay and Greenland Sea ice conditions during autumn through regional blocking patterns

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Ballinger, Thomas J.; Hanna, Edward; Hall, Richard J.; Miller, Jeffrey; Ribergaard, Mads H.; Høyer, Jacob L.

2018-01-01

Variations in sea ice freeze onset and regional sea surface temperatures (SSTs) in Baffin Bay and Greenland Sea are linked to autumn surface air temperatures (SATs) around coastal Greenland through 500 hPa blocking patterns, 1979-2014. We find strong, statistically significant correlations between Baffin Bay freeze onset and SSTs and SATs across the western and southernmost coastal areas, while weaker and fewer significant correlations are found between eastern SATs, SSTs, and freeze periods observed in the neighboring Greenland Sea. Autumn Greenland Blocking Index values and the incidence of meridional circulation patterns have increased over the modern sea ice monitoring era. Increased anticyclonic blocking patterns promote poleward transport of warm air from lower latitudes and local warm air advection onshore from ocean-atmosphere sensible heat exchange through ice-free or thin ice-covered seas bordering the coastal stations. Temperature composites by years of extreme late freeze conditions, occurring since 2006 in Baffin Bay, reveal positive monthly SAT departures that often exceed 1 standard deviation from the 1981-2010 climate normal over coastal areas that exhibit a similar spatial pattern as the peak correlations.

New tips for structure prediction by comparative modeling

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Rayan, Anwar

2009-01-01

Comparative modelling is utilized to predict the 3-dimensional conformation of a given protein (target) based on its sequence alignment to experimentally determined protein structure (template). The use of such technique is already rewarding and increasingly widespread in biological research and drug development. The accuracy of the predictions as commonly accepted depends on the score of sequence identity of the target protein to the template. To assess the relationship between sequence identity and model quality, we carried out an analysis of a set of 4753 sequence and structure alignments. Throughout this research, the model accuracy was measured by root mean square deviations of Cα atoms of the target-template structures. Surprisingly, the results show that sequence identity of the target protein to the template is not a good descriptor to predict the accuracy of the 3-D structure model. However, in a large number of cases, comparative modelling with lower sequence identity of target to template proteins led to more accurate 3-D structure model. As a consequence of this study, we suggest new tips for improving the quality of omparative models, particularly for models whose target-template sequence identity is below 50%. PMID:19255646

Complex versus simple models: ion-channel cardiac toxicity prediction.

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Mistry, Hitesh B

2018-01-01

There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model B net was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the B net model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

Complex versus simple models: ion-channel cardiac toxicity prediction

Directory of Open Access Journals (Sweden)

Hitesh B. Mistry

2018-02-01

Full Text Available There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model Bnet was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the Bnet model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

Tuning SISO offset-free Model Predictive Control based on ARX models

DEFF Research Database (Denmark)

Huusom, Jakob Kjøbsted; Poulsen, Niels Kjølstad; Jørgensen, Sten Bay

2012-01-01

, the proposed controller is simple to tune as it has only one free tuning parameter. These two features are advantageous in predictive process control as they simplify industrial commissioning of MPC. Disturbance rejection and offset-free control is important in industrial process control. To achieve offset......In this paper, we present a tuning methodology for a simple offset-free SISO Model Predictive Controller (MPC) based on autoregressive models with exogenous inputs (ARX models). ARX models simplify system identification as they can be identified from data using convex optimization. Furthermore......-free control in face of unknown disturbances or model-plant mismatch, integrators must be introduced in either the estimator or the regulator. Traditionally, offset-free control is achieved using Brownian disturbance models in the estimator. In this paper we achieve offset-free control by extending the noise...

Copula based prediction models: an application to an aortic regurgitation study

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Shoukri Mohamed M

2007-06-01

Full Text Available Abstract Background: An important issue in prediction modeling of multivariate data is the measure of dependence structure. The use of Pearson's correlation as a dependence measure has several pitfalls and hence application of regression prediction models based on this correlation may not be an appropriate methodology. As an alternative, a copula based methodology for prediction modeling and an algorithm to simulate data are proposed. Methods: The method consists of introducing copulas as an alternative to the correlation coefficient commonly used as a measure of dependence. An algorithm based on the marginal distributions of random variables is applied to construct the Archimedean copulas. Monte Carlo simulations are carried out to replicate datasets, estimate prediction model parameters and validate them using Lin's concordance measure. Results: We have carried out a correlation-based regression analysis on data from 20 patients aged 17–82 years on pre-operative and post-operative ejection fractions after surgery and estimated the prediction model: Post-operative ejection fraction = - 0.0658 + 0.8403 (Pre-operative ejection fraction; p = 0.0008; 95% confidence interval of the slope coefficient (0.3998, 1.2808. From the exploratory data analysis, it is noted that both the pre-operative and post-operative ejection fractions measurements have slight departures from symmetry and are skewed to the left. It is also noted that the measurements tend to be widely spread and have shorter tails compared to normal distribution. Therefore predictions made from the correlation-based model corresponding to the pre-operative ejection fraction measurements in the lower range may not be accurate. Further it is found that the best approximated marginal distributions of pre-operative and post-operative ejection fractions (using q-q plots are gamma distributions. The copula based prediction model is estimated as: Post -operative ejection fraction = - 0.0933 + 0

Chemical structure-based predictive model for methanogenic anaerobic biodegradation potential.

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Meylan, William; Boethling, Robert; Aronson, Dallas; Howard, Philip; Tunkel, Jay

2007-09-01

Many screening-level models exist for predicting aerobic biodegradation potential from chemical structure, but anaerobic biodegradation generally has been ignored by modelers. We used a fragment contribution approach to develop a model for predicting biodegradation potential under methanogenic anaerobic conditions. The new model has 37 fragments (substructures) and classifies a substance as either fast or slow, relative to the potential to be biodegraded in the "serum bottle" anaerobic biodegradation screening test (Organization for Economic Cooperation and Development Guideline 311). The model correctly classified 90, 77, and 91% of the chemicals in the training set (n = 169) and two independent validation sets (n = 35 and 23), respectively. Accuracy of predictions of fast and slow degradation was equal for training-set chemicals, but fast-degradation predictions were less accurate than slow-degradation predictions for the validation sets. Analysis of the signs of the fragment coefficients for this and the other (aerobic) Biowin models suggests that in the context of simple group contribution models, the majority of positive and negative structural influences on ultimate degradation are the same for aerobic and methanogenic anaerobic biodegradation.

Phenological models to predict the main flowering phases of olive ( Olea europaea L.) along a latitudinal and longitudinal gradient across the Mediterranean region

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Aguilera, Fátima; Fornaciari, Marco; Ruiz-Valenzuela, Luis; Galán, Carmen; Msallem, Monji; Dhiab, Ali Ben; la Guardia, Consuelo Díaz-de; del Mar Trigo, María; Bonofiglio, Tommaso; Orlandi, Fabio

2015-05-01

The aim of the present study was to develop pheno-meteorological models to explain and forecast the main olive flowering phenological phases within the Mediterranean basin, across a latitudinal and longitudinal gradient that includes Tunisia, Spain, and Italy. To analyze the aerobiological sampling points, study periods from 13 years (1999-2011) to 19 years (1993-2011) were used. The forecasting models were constructed using partial least-squares regression, considering both the flowering start and full-flowering dates as dependent variables. The percentages of variance explained by the full-flowering models (mean 84 %) were greater than those explained by the flowering start models (mean 77 %). Moreover, given the time lag from the North African areas to the central Mediterranean areas in the main olive flowering dates, the regional full-flowering predictive models are proposed as the most useful to improve the knowledge of the influence of climate on the olive tree floral phenology. The meteorological parameters related to the previous autumn and both the winter and the spring seasons, and above all the temperatures, regulate the reproductive phenology of olive trees in the Mediterranean area. The mean anticipation of flowering start and full flowering for the future period from 2081 to 2100 was estimated at 10 and 12 days, respectively. One question can be raised: Will the olive trees located in the warmest areas be northward displaced or will they be able to adapt their physiology in response to the higher temperatures? The present study can be considered as an approach to design more detailed future bioclimate research.

Perspective on the northwestward shift of autumn tropical cyclogenesis locations over the western North Pacific from shifting ENSO

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Hu, Chundi; Zhang, Chengyang; Yang, Song; Chen, Dake; He, Shengping

2017-11-01

During the recent decades of satellite era, more tropical cyclogenesis locations (TCLs) were observed over the northwestern part of the western North Pacific (WNP), relative to the southeastern part, during the boreal autumn. This increase in TCLs over the northwestern WNP is largely attributed to the synergy of shifting El Niño-Southern Oscillation (ENSO) and the 1998 Pacific climate regime shift. Both central Pacific (CP) La Niña and CP El Niño have occurred more frequently since 1998, with only one eastern Pacific El Niño observed in autumn 2015. The change in the mean longitude of TCLs is closely linked to the ENSO diversity, whereas the change in the mean latitude is dominated by the warming of the WNP induced by an interdecadal tendency of CP La Niña-like events. The physical mechanisms responsible for this shifting ENSO-TCL linkage can be potentially explained by the tacit-and-mutual configurations between tropical upper-tropospheric trough and monsoon trough, on both interannual and interdecadal timescales, which is mainly due to the ENSO-related large-scale environment changes in ocean and atmosphere that modulate the WNP TCL.

Short-term wind power prediction based on LSSVM–GSA model

International Nuclear Information System (INIS)

Yuan, Xiaohui; Chen, Chen; Yuan, Yanbin; Huang, Yuehua; Tan, Qingxiong

2015-01-01

Highlights: • A hybrid model is developed for short-term wind power prediction. • The model is based on LSSVM and gravitational search algorithm. • Gravitational search algorithm is used to optimize parameters of LSSVM. • Effect of different kernel function of LSSVM on wind power prediction is discussed. • Comparative studies show that prediction accuracy of wind power is improved. - Abstract: Wind power forecasting can improve the economical and technical integration of wind energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to forecast wind power accurately. For the purpose of utilizing wind power to the utmost extent, it is very important to make an accurate prediction of the output power of a wind farm under the premise of guaranteeing the security and the stability of the operation of the power system. In this paper, a hybrid model (LSSVM–GSA) based on the least squares support vector machine (LSSVM) and gravitational search algorithm (GSA) is proposed to forecast the short-term wind power. As the kernel function and the related parameters of the LSSVM have a great influence on the performance of the prediction model, the paper establishes LSSVM model based on different kernel functions for short-term wind power prediction. And then an optimal kernel function is determined and the parameters of the LSSVM model are optimized by using GSA. Compared with the Back Propagation (BP) neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction. Therefore, the proposed LSSVM–GSA is a better model for short-term wind power prediction

Calibration of PMIS pavement performance prediction models.

Science.gov (United States)

2012-02-01

Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

Testing process predictions of models of risky choice: a quantitative model comparison approach

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Pachur, Thorsten; Hertwig, Ralph; Gigerenzer, Gerd; Brandstätter, Eduard

2013-01-01

This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies. PMID:24151472

Testing Process Predictions of Models of Risky Choice: A Quantitative Model Comparison Approach

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Thorsten ePachur

2013-09-01

Full Text Available This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or nonlinear functions thereof and the separate evaluation of risky options (expectation models. Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models. We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter, Gigerenzer, & Hertwig, 2006, and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up and direction of search (i.e., gamble-wise vs. reason-wise. In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly; acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988 called similarity. In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies.

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

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Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

2018-02-01

To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

Approximating prediction uncertainty for random forest regression models

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John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

2016-01-01

Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

Deep Flare Net (DeFN) Model for Solar Flare Prediction

Science.gov (United States)

Nishizuka, N.; Sugiura, K.; Kubo, Y.; Den, M.; Ishii, M.

2018-05-01

We developed a solar flare prediction model using a deep neural network (DNN) named Deep Flare Net (DeFN). This model can calculate the probability of flares occurring in the following 24 hr in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., ≥M class versus statistically predict flares, the DeFN model was trained to optimize the skill score, i.e., the true skill statistic (TSS). As a result, we succeeded in predicting flares with TSS = 0.80 for ≥M-class flares and TSS = 0.63 for ≥C-class flares. Note that in usual DNN models, the prediction process is a black box. However, in the DeFN model, the features are manually selected, and it is possible to analyze which features are effective for prediction after evaluation.

Hidden markov model for the prediction of transmembrane proteins using MATLAB.

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Chaturvedi, Navaneet; Shanker, Sudhanshu; Singh, Vinay Kumar; Sinha, Dhiraj; Pandey, Paras Nath

2011-01-01

Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.

Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

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Volker J. Schmid

2007-10-01

Full Text Available The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted.

Model predictive control of a wind turbine modelled in Simpack

International Nuclear Information System (INIS)

Jassmann, U; Matzke, D; Reiter, M; Abel, D; Berroth, J; Schelenz, R; Jacobs, G

2014-01-01

Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine

Model predictive control of a wind turbine modelled in Simpack

Science.gov (United States)

Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.

2014-06-01

Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

Science.gov (United States)

Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M.; Kinter, James L., III; Paolino, Daniel A.; Zhang, Qin; vandenDool, Huug; Saha, Suranjana; Mendez, Malaquias Pena; Becker, Emily;