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Spatiotemporal dynamics of Lutzomyia longipalpis and macro-habitat characterization using satellite images in a leishmaniasis-endemic city in Argentina

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MEDICAL AND VETERINARY ENTOMOLOGY 2019年第1期33卷 89-98页

作者： Berrozpe, P. E. Lamattina, D. Santini, M. S. Araujo, A. V. Torrusio, S. E. Salomon, O. D. Ministerio Salud Nac Inst Nacl Med Trop INMeT Puerto Iguazu Misiones Argentina Consejo Nacl Invest Cient & Tecn Minist Ciencia Tecnol & Innovac Buenos Aires DF Argentina Minist Salud Nac Adm Nacl Labs Ctr Nacl Diagnost & Invest Endemo Endemo Epidemia Buenos Aires DF Argentina Minist Salud Nac Inst Salud Buenos Aires DF Argentina Univ Nacl Nordeste Fac Ciencias Exactas Nat & Agrimensura Corrientes Capital Corrientes Argentina Minist Ciencia Tecnol & Innovac Prod Comis Nacl Actividades Espaciales CONAE Buenos Aires DF Argentina

The spatiotemporal population dynamics of Lutzomyia longipalpis (Lutz & Neiva, 1912) (Diptera: Psychodidae) were evaluated in a city in Argentina in which visceral leishmaniasis is endemic. Over 14 sampling sessions, 5244 specimens of five species of Phlebotominae (Diptera: Psychodidae) were captured, of which 2458 (46.87%) specimens were L. longipalpis. generalized linear models were constructed to evaluate the associations between L. longipalpis abundance and explanatory variables derived from satellite images. The spatial variable 'stratum' and the temporal variable 'season' were also included in the models. Three variables were found to have significant associations: the normalized difference vegetation index;land surface temperature, and low urban coverage. The last two of these were associated with L. longipalpis abundance only during summer and winter, respectively. This variation between seasons supports the development of models that include temporal variables because models of distributions of the abundance of a species may show different critical variables according to the climatic period of the year. Abundance decreased gradually towards the downtown area, which suggests that L. longipalpis responds to a meta-population structure, in which rural-periurban source populations that persist over time may colonize adjacent areas. This information allows for a spatiotemporal stratification of risk, which provides public health authorities with a valuable tool to help optimize prevention measures against visceral leishmaniasis.

关键词： Leishmania infantum Phlebotominae generalized linear models land surface temperature normalized difference vegetation index urban coverage vegetation coverage visceral leishmaniasis

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Biological water quality in tropical rivers during dry and rainy seasons: A model-based analysis

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ECOLOGICAL INDICATORS 2020年 108卷 105769-000页

作者： Jerves-Cobo, Ruben Forio, Marie Anne Eurie Lock, Koen Van Butsel, Jana Pauta, Guillermina Cisneros, Felipe Nopens, Ingmar Goethals, Peter L. M. Univ Ghent Dept Anim Sci & Aquat Ecol Lab Environm Toxicol & Aquat Ecol Coupure Links 653 B-9000 Ghent Belgium Univ Ghent Dept Data Anal & Math Modelling BIOMATH Coupure Links 653 B-9000 Ghent Belgium Univ Cuenca Programa Manejo Agua & Suelo PROMAS Av 12 Abril S-N & Agustin Cueva Cuenca 010103 Ecuador Univ Cuenca Fac Ingn Av 12 Abril S-N & Agustin Cueva Cuenca 010103 Ecuador

Recent studies on water quality in tropical rivers indicate substantial differences between seasons. However, investigations on the needs and added value of season-specific models are lacking. Thus, this paper aims to determine the accuracy and relevance of season-specific and season-overarching models to predict biological water quality. Additionally, we investigated the variation of prediction accuracy using sub-datasets from different parts of the Cuenca River basin. This study was accomplished in the rivers that pass through the urban and suburban areas of the city of Cuenca, which is located in the southern Andes of Ecuador. The Andean Biotic Index (ABI) was used as an indicator of biological water quality. Subsequently, models were developed to predict the ABI, with physicochemical and morphological variables as predictors, which were collected in 43 sites during both the dry and the rainy seasons. The predictions were obtained using three kinds of generalized linear models (GLMs): Gaussian, Gamma and Inverse Gaussian. The season-specific models were more accurate than the season-overarching models. Similarly, the predictions of the biological water quality in sites sampled in the urban area were more accurate than the forecasts performed in reference sites. The major variables predicting the ABI during the dry season were five-day biological oxygen demand (BOD 5 ), ammonium and orthophosphate, while dissolved oxygen (DO), oxygen saturation (OS), nitrate, total solids proved to be important during the rainy season. The results of this research emphasize the importance of developing season-specific models and the implementation of different key actions for river restoration during both the dry and rainy seasons. The accuracy and the replication of these models could be improved and checked with more data taken from new sampling events. The modelling approach developed in this study can be applied to similar basins in the tropics and reveals that environmental i

关键词： Cuenca River basin Ecological modelling generalized linear models Pollution Ecological assessment Biological water quality Andes

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THE USE OF 3-D HIGHWAY DIFFERENTIAL GEOMETRY IN CRASH PREDICTION MODELING

THE USE OF 3-D HIGHWAY DIFFERENTIAL GEOMETRY IN CRASH PREDIC...

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作者： Kiriakos Amiridis University of Kentucky

学位级别：博士

The objective of this research is to evaluate and introduce a new methodology regarding rural highway safety. Current practices rely on crash prediction models that utilize specific explanatory variables, whereas the depository of knowledge for past research is the Highway Safety Manual (HSM). Most of the prediction models in the HSM identify the effect of individual geometric elements on crash occurrence and consider their combination in a multiplicative manner, where each effect is multiplied with others to determine their combined influence. The concepts of 3-dimesnional (3-D) representation of the roadway surface have also been explored in the past aiming to model the highway structure and optimize the roadway alignment. The use of differential geometry on utilizing the 3-D roadway surface in order to understand how new metrics can be used to identify and express roadway geometric elements has been recently utilized and indicated that this may be a new approach in representing the combined effects of all geometry features into single variables. This research will further explore this potential and examine the possibility to utilize 3-D differential geometry in representing the roadway surface and utilize its associated metrics to consider the combined effect of roadway features on crashes. It is anticipated that a series of single metrics could be used that would combine horizontal and vertical alignment features and eventually predict roadway crashes in a more robust manner. It should be also noted that that the main purpose of this research is not to simply suggest predictive crash models, but to prove in a statistically concrete manner that 3-D metrics of differential geometry, e.g. Gaussian Curvature and Mean Curvature can assist in analyzing highway design and safety. Therefore, the value of this research is oriented towards the proof of concept of the link between 3-D geometry in highway design and safety. This thesis presents the steps and rationale of th

关键词： 3-D Highway Geometric Design Differential Geometry Highway Safety & Crash Prediction models Gaussian Curvature Mean Curvature generalized linear models

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MLlib*: Fast Training of GLMs using Spark MLlib 35

MLlib*: Fast Training of GLMs using Spark MLlib

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IEEE 35th International Conference on Data Engineering (ICDE)

作者： Zhang, Zhipeng Jiang, Jiawei Wu, Wentao Zhang, Ce Yu, Lele Cui, Bin Peking Univ Sch EECS Beijing Peoples R China Peking Univ Key Lab High Confidence Software Technol MOE Beijing Peoples R China Peking Univ Ctr Data Sci Beijing Peoples R China Natl Engn Lab Big Data Anal & Applicat Beijing Peoples R China Microsoft Res Redmond WA USA Swiss Fed Inst Technol Zurich Switzerland Tencent Inc Shenzhen Peoples R China

ISBN: (纸本)9781538674741

In Tencent Inc., more than 80% of the data are extracted and transformed using Spark. However, the commonly used machine learning systems are TensorFlow, XGBoost, and Angel, whereas Spark MLlib, an official Spark package for machine learning, is seldom used. One reason for this ignorance is that it is generally believed that Spark is slow when it comes to distributed machine learning. Users therefore have to undergo the painful procedure of moving data in and out of Spark. The question why Spark is slow, however, remains elusive. In this paper, we study the performance of MLlib with a focus on training generalized linear models using gradient descent. Based on a detailed examination, we identify two bottlenecks in MLlib, i.e., pattern of model update and pattern of communication. To address these two bottlenecks, we tweak the implementation of MLlib with two state-of-the-art and well-known techniques, model averaging and AllReduce. We show that, the new system that we call MLlib*, can significantly improve over MLlib and achieve similar or even better performance than other specialized distributed machine learning systems (such as Petuum and Angel), on both public and Tencent's workloads.

关键词： Distributed Machine Learning Spark generalized linear models Gradient Descent

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Effects of a new waste collection policy on the population of yellow-legged gulls, Larus michahellis, in the historic centre of Venice (Italy)

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EUROPEAN JOURNAL OF WILDLIFE RESEARCH 2020年第4期66卷 50-50页

作者： Coccon, Francesca Fano, Shira CORILA Consortium Coordinat Res Act Venice Lagoon Syst Venice Italy Univ Naples Federico II Dept Econ & Stat DISES Naples Italy

Urban sprawl represents one of the most critical threats to biodiversity leading to the decline of rare and specialist species and the proliferation of a few dominant generalist ones. Among the winners there is the yellow-legged gull, Larus michahellis, which has recently colonised towns, nesting on the roofs of buildings and feeding on urban waste by spreading the contents of garbage bags on the streets. This situation has been recently documented in the historic centre of Venice (Italy) where it led to negative environmental impact and serious consequences in terms of urban hygiene. To counter such problems, the public company responsible for managing the urban waste in the Venice municipality established a new waste collection policy in the city to prevent accumulation of rubbish in the streets and limit the amount of trophic resources available for the species. We performed a monitoring program of the urban population of yellow-legged gulls and used generalized linear models to investigate the effectiveness of the new waste collection system on reducing the amount of waste in the streets and its effect on the target species. Results revealed a statistically significant positive correlation between urban waste and gulls and a significant effect of the new policy in lowering these two variables. Open areas, strongly frequented, with food and beverage vendors and illegal dumps were also highlighted as the main contributors to the presence of yellow-legged gulls. Such information can be used by local authorities to plan specific interventions to reduce the attractiveness of the city to gulls.

关键词： Human-wildlife conflict Urban waste collection Monitoring generalized linear models Pest species management

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Modeling and Forecasting Indian Malaria Incidence Using generalized Time Series models

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Communications in Statistics Case Studies Data Analysis and Applications 2019年第2期5卷 111-120页

作者： Mukhopadhyay, S. Tiwari, R. Shetty, P. Gogtay, N.J. Thatte, U.M. Department of Mathematics IIT Bombay Mumbai India Seth G.S. Medical College and KEM Hospital Mumbai India

In this article, time series for generalized linear models (GLMs) is used to fit weekly malaria data collected from the largest tertiary care hospital in Mumbai, India. Since the data are positive counts of malaria, conditional distribution of the disease counts given the past outcomes and the covariates are assumed to be distributed as a Poisson or a negative binomial. The data analysed show a downward trend, with the presence of seasonality and overdispersion. Various time series models for GLMs are compared and the best model is selected for forecasting the malaria counts. Forecasting regions and model fitting statistics are reported. © 2019, © 2019 Taylor & Francis Group, LLC.

关键词： ARMA forecasting generalized linear models negative binomial overdispersion

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Estimation of Prediction Intervals in Neural Network-based Regression models 20

Estimation of Prediction Intervals in Neural Network-based R...

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20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Miok, Kristian West Univ Timisoara Comp Sci Dept Timisoara Romania

ISBN: (纸本)9781728106250

Currently there are various methods allowing the construction of predictive models based on data. Measuring prediction uncertainty plays an essential role in fields such as medicine, physics and biology where the information about prediction accuracy can be essential. In this context only a few approaches address the question of how much the predicted values can be trusted. Neural networks are popular models, but unlike the statistical models, they do not quantify the uncertainty involved in the prediction process. In this work we investigate several regression models with a focus on estimating prediction intervals that statistical and machine learning models can provide. The analysis is conducted for a case study aiming to predict the number of crayfish in Romanian rivers based on landscape and water quality information.

关键词： Prediction intervals estimation generalized linear models Neural networks Bayesian neural networks Monte Carlo Dropout model

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Discussion of 'The class of CUB models: statistical foundations, inferential issues and empirical evidence' by Domenico Piccolo and Rosaria Simone

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STATISTICAL METHODS AND APPLICATIONS 2019年第3期28卷 459-463页

作者： Grilli, Leonardo Rampichini, Carla Univ Florence Dept Stat Comp Sci Applicat G Parenti Florence Italy

In this note we briefly discuss the structure of CUB models and their interpretation. Furthermore, we elaborate some issues related to the comparison of CUB models with mainstream approaches, focusing on generalized linear models for univariate ordinal responses and classical latent variable models for multivariate ordinal responses.

关键词： Data generating process generalized linear models Latent variable models

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Method of improving the performance of public-private innovation networks by linking heterogeneous DBs: Prediction using ensemble and PPDM models

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2020年 161卷 120258-120258页

作者： Seung-Pyo, Jun Jae-Seong, Lee Juyeon, Lee Univ Sci & Technol UST Data Anal Platform Ctr Korea Inst Sci & Technol Informat & Sci & Technol 66 Hoegi Ro Seoul 130741 South Korea

Due to the complexity of the innovation process and intensifying competition, small and medium enterprises (SMEs) have increased their participation in public-private innovation networks (PPINs). This study used both inference and prediction models by linking two heterogeneous databases (DBs), consisting of the responses of 1,439 manufacturing SMEs to the Korean Innovation Survey and the financial information of approximately 119,890 companies. In the inference model, we analyzed the determinants that affect the business performance and R&D investment performance of SMEs in PPINs, using generalized linear models. The prediction model utilized a machine learning based ensemble model and the method of linking heterogeneous DBs based on privacy-preserving data mining (PPDM). The findings of this study indicate that while PPINs do not have a significant effect on business performance, they do have a positive correlation to R&D investment. This study also proposes two prediction models for forecasting increases in R&D investment by SMEs, which is considered to be an indicator of PPIN performance. These two models can be respectively used in cases where the features of the companies targeted for prediction can be known in advance and in cases where the features are unknown.

关键词： Public-private innovation network National innovation system Small and medium enterprises generalized linear models Machine learning ensemble model Privacy-preserving data mining

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On fitting generalized linear and non-linear models of mortality

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SCANDINAVIAN ACTUARIAL JOURNAL 2016年第4期000卷 356-383页

作者： Currie, Iain D. Heriot Watt Univ Dept Actuarial Math & Stat Edinburgh Midlothian Scotland Heriot Watt Univ Maxwell Inst Math Sci Edinburgh Midlothian Scotland

Many common models of mortality can be expressed compactly in the language of either generalized linear models or generalized non-linear models. The R language provides a description of these models which parallels the usual algebraic definitions but has the advantage of a transparent and flexible model specification. We compare eight model structures for mortality. For each structure, we consider (a) the Poisson models for the force of mortality with both log and logit link functions and (b) the binomial models for the rate of mortality with logit and complementary log-log link functions. Part of this work shows how to extend the usual smooth two-dimensional P-spline model for the force of mortality with Poisson error and log link to the other smooth two-dimensional P-spline models with Poisson and binomial errors defined in (a) and (b). Our comments are based on the results of fitting these models to data from six countries: Australia, France, Japan, Sweden, UK and USA. We also discuss the possibility of forecasting with these models;in particular, the introduction of cohort terms generally leads to an improvement in overall fit, but can also make forecasting with these models problematic.

关键词： constraints forecasting generalized linear models identifiability mortality R language

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