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"Even though there is a growing interest in predictive policing, to date there have been few, if any, formal evaluations of these programs. This report documents an assessment of a predictive policing effort in Shreveport, Louisiana, in 2012, which was conducted to evaluate the crime reduction effects of policing guided by statistical predictions. RAND researchers led multiple interviews and focus groups with the Shreveport Police Department throughout the course of the trial to document the implementation of the statistical predictive and prevention models. In addition to a basic assessment of the process, the report shows the crime impacts and costs directly attributable to the strategy. It is hoped that this will provide a fuller picture for police departments considering if and how a predictive policing strategy should be adopted. There was no statistically significant change in property crime in the experimental districts that applied the predictive models compared with the control districts; therefore, overall, the intervention was deemed to have no effect. There are both statistical and substantive possibilities to explain this null effect. In addition, it is likely that the predictive policing program did not cost any more than the status quo."--"Abstract" on web page.

Crime prevention --- Offenses against property --- Law enforcement --- Police administration --- Regression analysis --- Forecasting --- Social prediction --- Social Welfare & Social Work --- Social Sciences --- Criminology, Penology & Juvenile Delinquency --- Prevention --- Statistical methods --- Prediction, Social --- Social forecasting --- Sociological prediction --- Forecasts --- Futurology --- Prediction --- Analysis, Regression --- Linear regression --- Regression modeling --- Police --- Police management --- Enforcement of law --- Crimes against property --- Crime --- Prevention of crime --- Administration --- Management --- Government policy --- Sociology --- Social indicators --- Multivariate analysis --- Structural equation modeling --- Criminal justice, Administration of --- Public safety --- Policing

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This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables.

Biometry. --- Multivariate analysis. --- Regression analysis. --- Agriculture. --- Biostatistics. --- Multivariate Analysis. --- Linear Models and Regression. --- Farming --- Husbandry --- Industrial arts --- Life sciences --- Food supply --- Land use, Rural --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Statistical methods --- Generalized Linear Mixed Models --- non normal distribution --- GLM --- GLMM --- Model Inference --- non normal response

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Over the past few decades, global warming and climate change have impacted the hydrologic cycle. Many models have been developed to simulate hydrologic processes. Obtaining accurate climatic data on local/meso, and global scales is essential for the realistic simulation of hydrologic processes. However, the limited availability of climatic data often poses a challenge to hydrologic modeling efforts. Hydrologic science is currently undergoing a revolution in which the field is being transformed by the multitude of newly available data streams. Historically, hydrologic models that have been developed to answer basic questions about the rainfall–runoff relationship, surface water, and groundwater storage/fluxes, land–atmosphere interactions, have been optimized for previously data-limited conditions. With the advent of remote sensing technologies and increased computational resources, the environment for water cycle researchers has fundamentally changed to one where there is now a flood of spatially distributed and time-dependent data. The bias in the climatic data is propagated through models and can yield estimation errors. Therefore, the bias in climatic data should be removed before their use in hydrologic models. Climatic data have been a core component of the science of hydrology. Their intrinsic role in understanding and managing water resources and developing sound water policies dictates their vital importance. This book aims to present recent advances concerning climatic data and their applications in hydrologic models.

statistical weather generator --- stochastic process --- Diyala River basin --- Wilks’ technique --- hydrological models --- rainfall --- surface runoff --- linear regression models --- curve number --- SCS.CN model --- mulching --- wildfire --- prescribed fire --- n/a --- CHIRPS --- GPM-IMERG --- rainfall data scarcity --- agro-hydrology --- Rift Valley Lake Basin --- hydrological research basin --- precipitation --- temperature --- long-term trends --- climate change --- evapotranspiration --- groundwater recharge --- thresholds --- seasonality --- spatiotemporal variations --- regional-scale --- long-term --- HydroBudget model --- cold and humid climates --- Quebec (Canada) --- tank cascade system --- dry zone --- water governance --- flood control --- traditional knowledge --- community participation --- Sri Lanka --- Wilks' technique

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This reprint presents various aspects of the future grid, which is the next generation of the electrical grid and will enable the smart integration of conventional, renewable, and distributed power generation, energy storage, transmission and distribution, and demand management. Renewable energy is crucial in transitioning to a less carbon-intensive economy and a more sustainable energy system. The high penetration and uncertain power outputs of renewable energy pose great challenges to the stable operation of energy systems. The deployment of the smart grid is revolutionary, and also imperative around the world. It involves and deals with multidisciplinary fields such as energy sources, control systems, communications, computational generation, transmission, distribution, customer operations, markets, and service providers. Smart grids are emerging in both developed and developing countries, with the aim of achieving a reliable and secure electricity supply. Smart grids will eventually require standards, policy, and a regulatory framework for successful implementation. This reprint addresses the emerging and advanced green energy technologies for a sustainable and resilient future grid, and provides a platform to enhance interdisciplinary research and share the most recent ideas.

Technology: general issues --- History of engineering & technology --- islanded mode --- microgrid --- decentralized control --- robust tracking --- invariant set --- thermal energy storage --- parabolic dish --- latent heat --- phase change material --- heat transfer fluid --- bio-inspired algorithms --- wireless sensor network --- genetic algorithm --- particle swarm optimization --- advanced metering infrastructure --- blockchain --- Ethereum --- isolated DC–DC converter --- photovoltaics --- LLC resonant converter --- dual-bridge --- wide voltage range --- power optimizer --- coordinated control --- vehicle-to-grid --- primary frequency control --- secondary frequency control --- state of charge --- decentralized --- Simulink model --- dimensionality reduction --- simple linear regression --- multiple linear regression --- polynomial regression --- load forecasting --- VSC (voltage source converter) --- PLL (Phase-Locked Loop) --- weak grid --- small signal stability --- eigenvalues --- demand-side management --- low-power consumer electronic appliances --- low-voltage distribution system --- non-intrusive identification of appliance usage patterns --- power quality --- smart home --- true power factor --- total harmonic distortion --- renewable energy sources --- energy management system --- communication technologies --- microgrid standards --- third-order sliding mode control --- asynchronous generators --- variable speed dual-rotor wind turbine --- direct field-oriented control --- integral-proportional --- transformer --- internal fault currents --- magnetic inrush currents --- extended Kalman filter (EKF) algorithm --- harmonic estimation --- DC microgrid --- fault --- cluster --- DC/DC converter --- fault current limiter (FCL) --- multi-objective --- renewable energy --- profit-based scheduling --- Equilibrium Optimizer --- smart grid --- campus microgrid --- batteries --- prosumer market --- distributed generation --- renewable energy resources --- energy storage system --- distributed energy resources --- demand response --- load clustering techniques --- sizing methodologies --- digital signal processing --- green buildings --- spectral analysis --- spectral kurtosis --- life-cycle cost --- optimal scheduling --- reinforcement learning --- enabling technologies --- energy community --- smart meter --- nanogrid --- platform --- power cloud --- n/a --- isolated DC-DC converter

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This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society.

Research & information: general --- earthquake --- damaged groups of buildings --- classification --- remote sensing images --- Convolution Neural Network (CNN) --- block vector data --- shoreline change --- landsat --- planet scope --- coastline --- morphological changes --- building extraction --- improved anchor-free instance segmentation --- high-resolution remote sensing images --- deep learning --- land use/land cover (LULC) --- GF-6 WFV --- object-oriented --- change detection --- double constraints --- REE mines --- mining and restoration assessment indicators (MRAIs) --- damage --- time trajectory --- effectiveness of management --- aeolian process --- desertification --- multi-sensor fusion --- interferometric SAR --- time-series analysis --- mussel farming --- high-resolution image --- transitional water management --- environmental pollution --- open source software --- synthetic aperture radar (SAR) --- target --- sea surface --- multiple scattering --- geo-hazard mapping --- Gaofen-1 satellite --- land cover --- environmental factors --- susceptibility --- post-classification differencing --- generalized difference vegetation index (GDVI) --- multiple linear regression --- logistic regression --- n/a