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Background: Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps. Methods: This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021. Results: A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically. Conclusions: While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals. [ABSTRACT FROM AUTHOR]

The majority of statistical methods to share information in basket trials are based on a Bayesian hierarchical model with a common normal distribution for the logit-transformed response rates. The methods are of varying complexity, yet they all use this basic model. Generally, complexity is an obstacle for the application in clinical trials and that includes the use of the logit-transformation. The transformation complicates the model and impedes a direct interpretation of the hyperparameters. On the other hand, there exist basket trial designs which directly work on the probability scale of the response rate which facilitates the understanding of the model for many stakeholders. In order to reduce unnecessary complexity, we considered using a hierarchical beta-binomial model instead of the transformed models. This article investigates whether this approach is a practicable alternative to the commonly applied sharing tools based on a logit-transformation of the response rates. For this purpose, we performed a systematic comparison of the two models, starting with the distributional assumptions for the response rates, continuing with the Bayesian behavior together with binomial data in an independent setting and ended with a simulation study for the hierarchical model under various data and prior scenarios. All Bayesian comparisons require equal starting points, wherefore we propose a calibration procedure to choose similar priors for the models. The evaluation of the sharing property additionally required an evaluation measure for simulation results, which we derived in this work. The conclusion of the comparison is that the hierarchical beta-binomial model is a feasible alternative basic model to share information in basket trials. [ABSTRACT FROM AUTHOR]

Extreme wildfire events, such as the Pantanal 2020 megafires, are expected to become more common. Assessing the impacts of such extreme events on wildlife is imperative for conservation planning. Direct observation of carcasses can provide valuable information on how these impacts relate to spatial heterogeneity. Here, we use double‐observer carcass surveys to assess the numbers and spatial patterns of direct mortalities of medium‐ to large‐sized mammals resulting from the Pantanal 2020 megafires in a large reserve (1080 km2) in the northern Pantanal wetland. Accounting for imperfect detection, we model the spatial variation in mortality occurrence and abundance, testing the effects of habitat‐related variables and wildfire severity using multi‐species N‐mixture models. We found that 26 out of 27 species of medium and large‐sized mammals died from the fires with a mean estimate of around 49 thousand individuals. The most affected species included capuchin monkeys, agoutis, peccaries, tapirs, brocket deer, tamanduas, coatis and capybaras. Direct mortality of mammals by wildfires was affected by landscape factors related to species habitats, species traits (probably related to escape or refuge strategies), and the intensity of the wildfires. Mortalities presented a general positive relationship with non‐flooded forests (a fire‐sensitive habitat in Pantanal) and with the severity of wildfires. Artificial water bodies, a common landscape structure in Pantanal, had 7.5 times more deaths than other areas. Synthesis and applications: With the approach used, we were able to: (i) identify mortality hotspots associated with landscape features; (ii) simultaneously identify the most affected species and assess the average relationships for all the assemblage of medium‐ and large‐sized mammals. We discuss conservation and management actions in two contexts: species prioritization for rescuing and monitoring; and territory prioritization for fire prevention and fighting. [ABSTRACT FROM AUTHOR]

Risky‐prescribing is the excessive or inappropriate prescription of drugs that singly or in combination pose significant risks of adverse health outcomes. In the United States, prescribing of opioids and other "risky" drugs is a national public health concern. We use a novel data framework—a directed network connecting physicians who encounter the same patients in a sequence of visits—to investigate if risky‐prescribing diffuses across physicians through a process of peer‐influence. Using a shared‐patient network of 10 661 Ohio‐based physicians constructed from Medicare claims data over 2014‐2015, we extract information on the order in which patients encountered physicians to derive a directed patient‐sharing network. This enables the novel decomposition of peer‐effects of a medical practice such as risky‐prescribing into directional (outbound and inbound) and bidirectional (mutual) relationship components. Using this framework, we develop models of peer‐effects for contagion in risky‐prescribing behavior as well as spillover effects. The latter is measured in terms of adverse health events suspected to be related to risky‐prescribing in patients of peer‐physicians. Estimated peer‐effects were strongest when the patient‐sharing relationship was mutual as opposed to directional. Using simulations we confirmed that our modeling and estimation strategies allows simultaneous estimation of each type of peer‐effect (mutual and directional) with accuracy and precision. We also show that failing to account for these distinct mechanisms (a form of model mis‐specification) produces misleading results, demonstrating the importance of retaining directional information in the construction of physician shared‐patient networks. These findings suggest network‐based interventions for reducing risky‐prescribing. [ABSTRACT FROM AUTHOR]

The effect of severe drought in the summer 2018 on the plant community composition and overall diversity was investigated in a replicated long-term grassland experiment where nitrogen availability was manipulated. The possible plant community response to the drought and the possible role of selected plant traits were investigated using model-based ordination techniques, which were tailored to the sampled pin-point cover data. Contrary to our prior expectation, that the severe drought had lasting effects on the plant community, we could not detect any significant effects of the severe drought on the plant community composition or Hill–Shannon diversity the following year and 3 years later. Furthermore, we did not detect any significant mediating effect of nitrogen on the possible effects of severe drought. The possible reasons for the observed high resistance of the grassland community to the severe drought event are discussed. Lasting effects of drought on plant communities cannot be predicted from observations during or immediately after the drought. [ABSTRACT FROM AUTHOR]

Hierarchical classification is important in automation and robotics in terms of providing extra pieces of information about the classified object. In this paper, a hierarchical classification model using a triplet attention-embedded deep learning model is proposed for household items for robotic applications. Here deep learning models are applied to modified office-home image datasets of the household to give out two outputs from the model. The first prediction gives the name of the class, and the second output gives the hierarchical output indicating the location of the object identified. Triplet attention layers are added intermittently for improvement of the overall performance of the classification. The attention-based deep learning model gives 86% accuracy for the coarse class and 80% accuracy for the fine class at 25 epochs. The analysis is also done based on precision, recall, and F1-score. Precision values are 100% for the coarse class and 83.1% for the fine class. Recall is 82.5% for coarse and 77.7% for the fine class. F1-score is 83% for the coarse class and 78.9% for the fine class. This experiment lays a foundation for hierarchical image classification. The performance was compared with the hierarchical classification models that were previously applied for fashion datasets. [ABSTRACT FROM AUTHOR]

Abstract Background Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps. Methods This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021. Results A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically. Conclusions While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets. We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species. We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA. spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.

In recent decades, the analysis of different geographic scales for studying the spatial patterning of crime has profoundly deepened our theoretical grasp of crime dynamics. However, a similar investigation is lacking when it comes to the patterning of offender residences, despite there being clear theoretical and empirical reasons for doing so, among them, the close relationship between where offenders live and where their corresponding crimes are committed. This paper delves into the concentration and variance of offender residences across different levels of spatial aggregation. The data used contains the locations of residence for known offenders in Birmingham between the years 2006 and 2016. Resident locations are aggregated to Output Areas (OA), nested within Lower Super Output Areas (LSOA), further nested within Middle Super Output Areas (MSOA). Descriptive and model-based statistics are deployed to quantify concentration and variation at each spatial scale. Results suggest that most variance (~48%) in offender residence concentrations is attributable to the largest spatial scale (MSOA level). Output Areas capture approximately 38\% of the variance. Findings open up discussions on the role of urban development in determining the appropriateness of spatial scale.

Recently, the low-rank representation (LRR) model has been widely used in the field of remote sensing image denoising due to its excellent noise suppression capability. However, those low-rank-based methods always discard important edge details as residuals, leading to a common issue of blurred edges in denoised results. To address this problem, we take a new look at low-rank residuals and try to extract edge information from them. Therefore, a hierarchical denoising framework was combined with a low-rank model to extract edge information from low-rank residuals within the edge subspace. A prior knowledge matrix was designed to enable the model to learn necessary structural information rather than noise. Also, such traditional model-driven approaches require multiple iterations, and the solutions may be very complex and computationally intensive. To further enhance the noise suppression performance and computing efficiency, a hierarchical low-rank denoising model based on deep unrolling (HLR-DUR) was proposed, integrating deep neural networks into the hierarchical low-rank denoising framework to expand the information capture and representation capabilities of the proposed shallow model. Sufficient experiments on optical images, hyperspectral images (HSI), and synthetic aperture radar (SAR) images showed that HLR-DUR achieved state-of-the-art (SOTA) denoising results. [ABSTRACT FROM AUTHOR]

In many applications that involve the inference of an unknown smooth function, the inference of its derivatives is also important. To make joint inferences of the function and its derivatives, a class of Gaussian processes called pth order Integrated Wiener's Process (IWP), is considered. Methods for constructing a finite element (FEM) approximation of an IWP exist but only focus on the case p = 2 and do not allow appropriate inference for derivatives. In this article, we propose an alternative FEM approximation with overlapping splines (O-spline). The O-spline approximation applies for any order p ∈ Z + , and provides consistent and efficient inference for all derivatives up to order p – 1. It is shown both theoretically and empirically that the O-spline approximation converges to the IWP as the number of knots increases. We further provide a unified and interpretable way to define priors for the smoothing parameter based on the notion of predictive standard deviation, which is invariant to the order p and the knot placement. Finally, we demonstrate the practical use of the O-spline approximation through an analysis of COVID death rates where the inference of derivative has an important interpretation in terms of the course of the pandemic. for this article are available online. [ABSTRACT FROM AUTHOR]

Hierarchical models can express ecological dynamics using a combination of fixed and random effects, and measurement of their complexity (effective degrees of freedom, EDF) requires estimating how much random effects are shrunk toward a shared mean. Estimating EDF is helpful to (1) penalize complexity during model selection and (2) to improve understanding of model behavior. I applied the conditional Akaike Information Criterion (cAIC) to estimate EDF from the finite‐difference approximation to the gradient of model predictions with respect to each datum. I confirmed that this has similar behavior to widely used Bayesian criteria, and I illustrated ecological applications using three case studies. The first compared model parsimony with or without time‐varying parameters when predicting density‐dependent survival, where cAIC favors time‐varying demographic parameters more than conventional Akaike Information Criterion. The second estimates EDF in a phylogenetic structural equation model, and identifies a larger EDF when predicting longevity than mortality rates in fishes. The third compares EDF for a species distribution model fitted for 20 bird species and identifies those species requiring more model complexity. These highlight the ecological and statistical insight from comparing EDF among experimental units, models, and data partitions, using an approach that can be broadly adopted for nonlinear ecological models. [ABSTRACT FROM AUTHOR]

Social robots require the ability to understand human emotions and provide affective and behavioral responses during human-robot interactions. However, current social robots lack empathy capabilities. In this work, we propose a novel Multi-modal Hierarchical Empathetic (MHE) framework for generating empathetic responses for social robots. MHE is composed of a multi-modal fusion and emotion recognition module, an empathetic dialogue generation module, and an expression generation module. By fusing the sensor signals of different modalities, the robot can recognize human emotions and generate affective responses. Multiple experiments are conducted on a real robot, Pepper, to evaluate the proposed framework. The experiments are conducted to discriminate between MHE-generated text and human responses in complete ignorance, and most experimenters agree that MHE can effectively generate human-like and empathetic responses. To better evaluate the similarity between human-robot and human-human interactions, a period eye movement map (PEM) captured by an eye tracker is proposed. The experimental results demonstrate the improvement in the MHE in human-robot interactions by comparing different PEMs. [ABSTRACT FROM AUTHOR]

Copyright of Comunicaciones en Estadística is the property of Universidad Santo Tomas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multimodal and asymmetric circular data manifest in diverse disciplines, underscoring the significance of fitting suitable distributions for the analysis of such data. This study undertakes a comprehensive comparative assessment, encompassing diverse extensions of the von Mises distribution and the associated statistical methodologies, spanning from Richard von Mises’ seminal work in 1918 to contemporary applications, with a particular focus on the field of wind energy. The primary objective is to discern the strengths and limitations inherent in each method. To illustrate the practical implications, three authentic datasets and a simulation study are incorporated to showcase the performance of the proposed models. Furthermore, this paper provides an exhaustive list of references related to von Mises distribution models. [ABSTRACT FROM AUTHOR]

Spatial similarity provides an important basis for geographic information processing and is widely applied in multi-source data fusion and update, data retrieval and query, and cartographic generalization. To address the shape description and similarity measurement of polygon entities, this study presents a new hierarchical shape description approach and examines its application in similarity measurement of polygon entities. Using the rotation and segmentation methods, we first constructed a hierarchical shape description model for target polygon entities, followed by measurement of global and hierarchical shape description of polygon entities, respectively, using the farthest-point-distance and geometric feature description methods. Finally, we constructed a comprehensive similarity measurement model through a weighted integration of position, size, direction, and shape. The hierarchical shape description approach proposed in this paper can be applied to the shape similarity measurement of polygon elements, similarity measurement after spatial object simplification, and multi-scale polygon entity matching. The experimental results showed that the hierarchical shape description approach and similarity measurement model are able to effectively measure spatial similarity between different polygon entities, and have obtained good application results. [ABSTRACT FROM AUTHOR]

In this article, we describe the package twostep, a bundle of programs to perform analyses of hierarchical data applying the two-step approach. We consider a two-level data setup in which "microlevel" units are nested within "macrolevel" units. One-step models (which can be fit using, for example, mixed) are the most common approach to modeling two-level data. The two-step approach is an alternative in which parameters associated with microlevel and macrolevel predictors are estimated separately for each level. It can be used as an alternative to one-step models if the estimand is a cross-level interaction. We also show how the two-step approach usefully complements one-step approaches by providing exploratory data analysis, descriptive graphs, and regression diagnostics. [ABSTRACT FROM AUTHOR]

Wildfire is an increasingly common disturbance in forested landscapes that can drastically alter local habitats. Under current climate change predictions, wildfires are likely to become more frequent and severe. In regions and ecosystems that have historically infrequent fire return intervals, there is little known about how organisms will respond to the more severe and frequent wildfires predicted under climate change. In the southern Appalachian Mountains, USA, fire has been suppressed and severe burns are historically uncommon. This region boasts immense biodiversity and is considered a biological hot spot for diversity in the salamander family Plethodontidae. These species rely upon cool, moist microclimates that may be impacted more by severe fire than by low-intensity wild or prescribed fire. In 2016, the Chimney Tops Two wildfire burned >6000 ha of Great Smoky Mountains National Park, USA, and left a mosaic of burn severity across the landscape. This presented an opportunity to examine how five plethodontid salamander species respond to and recover from a range of burn severity. Even though the landscape had been recovering for 5 yr at the time of study, populations of Plethodon jordani, Plethodon glutinosus spp., Desmognathus wrighti, Desmognathus imitator, and Eurycea wilderae within the burn boundary had lower abundance than those in unburned habitat. In addition, there was a trend of even lower abundances in more severely burned habitat. Evidence of recovery, as indicated by a relationship between population abundance and distance from the burn boundary, was only present for D. imitator. Finally, body size distributions were different between burned and unburned sites for three of the five species and individuals were larger, on average, in burned sites. This work provides insights into how terrestrial salamander populations may respond to the more severe and frequent wildfires predicted under climate change for the southern Appalachian Mountains region. [ABSTRACT FROM AUTHOR]

Copyright of Avian Conservation & Ecology is the property of Resilience Alliance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets.We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species.We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA.spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities. [ABSTRACT FROM AUTHOR]

This study explores the development of Chinese feminist media by presenting a case study of a leading feminist media account, Way to Non-Violence (WTNV) (Jieshu baoli zhi lu). Utilizing Shoemaker and Reese's Hierarchical Model, this study analyzes the factors shaping and constraining WTNV's development, and investigates the strategies WTNV deploys to meet its objectives amidst survival challenges. We carried out in-depth interviews with WTNV's staff members and contributors, observed their online meetings, and conducted thematic analyses of 1,331 WTNV's posts. We identify the influencing factors at five levels, specifically organizational level (WTNV), routines level (WTNV's daily operational practices), individual level (the staff and contributors), social system level (censorship and content regulation, misogyny patriarchy culture and anti-feminism), and extra-media level (economic pressures). We find that caught between ambition and reality, WTNV maintains a moderate and inclusive feminist stance, deploys survival tactics, keeps a low profile, and continues to advocate feminist ideas. This study offers feminist media scholars and practitioners insight into how political and cultural factors, organizational attributes and routines, and individual agency collectively shape feminist media in the Chinese context. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The hospitality industry has been highlighted as an important economic sector that is vulnerable to climate change. However, this industry is also considered to have a substantial contribution to the degradation of the environment by consuming excessive quantities of energy, producing approximately 8% of worldwide greenhouse gas emissions. Previous studies have proposed the circular economy as an optimal concept to address this issue by eliminating waste and maximizing resource utilization. In light of that, many hotels are striving to shift the traditional linear economy into a circular economy by adopting green marketing into their business operations. However, they still face many obstacles in making an effective transition to a circular‐style economy due to the lack of an appropriate approach. To fill this gap, this study aims to develop a framework to give insights on which aspects can foster the circular economy transition process through green marketing implementation. Therefore, this study proposes a hybrid method that integrates an exploratory factor analysis with a fuzzy synthetic method and a decision‐making trial and evaluation laboratory to structure the hierarchical model and provide guidance to decision‐makers for future improvements under resource constraints. The results reveal that advantage acquisition and behavior transition are the most influential aspects in accelerating the transition to the circular economy in the hospitality industry. The crucial theoretical and managerial implications are also discussed. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a modelling framework which can detect the simultaneous presence of two different types of spatial process. The first is the variation from a global mean resulting from a geographical unit's 'vertical' position within a nested hierarchical structure such as the county and region where it is situated. The second is the variation at the smaller scale of individual units due to the 'horizontal' influence of nearby locations. The former is captured using a multi-level modelling structure while the latter is accounted for by an autoregressive component at the lowest level of the hierarchy. Such a model not only estimates spatially-varying parameters according to geographical scale, but also the relative contribution of each process to the overall spatial variation. As a demonstration, the study considers the association of a selection of socio-economic attributes with voting behaviour in the 2019 UK general election. It finds evidence of the presence of both types of spatial effects, and describes how they suggest different associations between census profile and voting behaviour in different parts of England and Wales. [ABSTRACT FROM AUTHOR]

Migratory fish species have been particularly impacted by changes to the hydrologic and climatic cues to which migration and spawning behaviours have been adapted across generations. While conservation and recovery programs increasingly implement flow management actions to promote successful migration and spawning, uncertainty regarding how spawning migration phenology responds to changing environmental conditions can limit the ability to effectively target such recovery actions. Here, we use a Bayesian hierarchical modelling framework to analyse spawning migration phenology of individually tagged June suckers (Chasmistes liorus) – a federally threatened, long‐lived, iteroparous, adfluvial species endemic to Utah Lake (Utah, USA) and its tributaries. We then examine how annual hydrologic and thermal conditions relate to different components of annual migration phenology, including peak migration date, in‐stream residence time, and among‐individual variation in migration timing. Peak migration date occurred earlier in years with warmer spring air temperatures (a proxy for water temperatures), though this effect interacted with peak runoff timing. Both residence time and among‐individual variation in migration timing were greater in years with larger spring discharge and later peak flows. Residence time was also longer in warmer years. These results highlight how natural and anthropogenic changes to river flow and thermal regimes are likely to impact June sucker migration timing and duration, and our approach can be applied to other migratory species to identify the external drivers of the different components of migration phenology. [ABSTRACT FROM AUTHOR]

In engineering practice, many products exhibit multiple and dependent degrading performance characteristics (PCs). It is common to observe that these PCs' initial measurements are nonconstant and sometimes correlated with the subsequent degradation rate, which typically varies from one unit to another. To accommodate the unit-wise heterogeneity, PC-wise dependency, and "initiation-growth" correlation, this article proposes a broad class of multi-dimensional degradation models under a framework of hierarchical multivariate Wiener processes. These models incorporate dual multi-normally distributed random effects concerning the initial values and degradation rates. To infer model parameters, expectation-maximization (EM) algorithms and several tools for model validation and selection are developed. Various simulation studies are carried out to assess the performance of the inference method and to compare different models. Two case studies are conducted to demonstrate the applicability of the proposed methodology. The of this article contain derivations of EM estimators, additional numerical results, and R codes. [ABSTRACT FROM AUTHOR]

The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.

Abstract Purpose Trailing-edge populations at the low-latitude, receding edge of a shifting range face high extinction risk from climate change unless they are able to track optimal environmental conditions through dispersal. Methods We fit dispersal models to the locations of 3165 individually-marked black-throated blue warblers (Setophaga caerulescens) in the southern Appalachian Mountains in North Carolina, USA from 2002 to 2023. Black-throated blue warbler breeding abundance in this population has remained relatively stable at colder and wetter areas at higher elevations but has declined at warmer and drier areas at lower elevations. Results Median dispersal distance of young warblers was 917 m (range 23–3200 m), and dispersal tended to be directed away from warm and dry locations. In contrast, adults exhibited strong site fidelity between breeding seasons and rarely dispersed more than 100 m (range 10–1300 m). Consequently, adult dispersal kernels were much more compact and symmetric than natal dispersal kernels, suggesting adult dispersal is unlikely a driving force of declines in this population. Conclusion Our findings suggest that directional natal dispersal may mitigate fitness costs for trailing-edge populations by allowing individuals to track changing climate and avoid warming conditions at warm-edge range boundaries.

Obtaining precise ground-level fine particulate matter (PM2.5) information is significant for human health. Spatial PM2.5 maps can be obtained by remote sensing technology, but considerable uncertainty exists when suffering from high pollution with complicated aerosol types. To address this issue, we propose using a hierarchical machine learning model to retrieve high-accuracy and daily full-coverage PM2.5 concentrations from 2014 to 2023 in China. Our hierarchical model was validated by the sample-based 10 cross-validation . Results suggest that our model performs better in terms of RMSE of 12.12 µg/m3, MAE of 8.14 µg/m3 and R2 of 0.95 than traditional model with RMSE of 18.18 µg/m3, MAE of 12.21 µg/m3 and R2 of 0.89, showing 27.49–37.41% improvements for RMSE, 21.85–39.26% improvements for MAE and 8.31–15.39% improvements for R2 at three-folds samples. On longer time scales, our model also shows better results than previous studies. Additionally, for high-pollution provinces, our model can capture PM2.5 trends more preciously than the traditional model. Under severe haze, our hierarchical model can also rightly reflect PM2.5 changes. Overall, due to the hierarchical strategy, our ML-based model can obtain daily full-coverage PM2.5 maps in China with high accuracy and can be applied for follow-up studies.

Purpose: Trailing-edge populations at the low-latitude, receding edge of a shifting range face high extinction risk from climate change unless they are able to track optimal environmental conditions through dispersal. Methods: We fit dispersal models to the locations of 3165 individually-marked black-throated blue warblers (Setophaga caerulescens) in the southern Appalachian Mountains in North Carolina, USA from 2002 to 2023. Black-throated blue warbler breeding abundance in this population has remained relatively stable at colder and wetter areas at higher elevations but has declined at warmer and drier areas at lower elevations. Results: Median dispersal distance of young warblers was 917 m (range 23–3200 m), and dispersal tended to be directed away from warm and dry locations. In contrast, adults exhibited strong site fidelity between breeding seasons and rarely dispersed more than 100 m (range 10–1300 m). Consequently, adult dispersal kernels were much more compact and symmetric than natal dispersal kernels, suggesting adult dispersal is unlikely a driving force of declines in this population. Conclusion: Our findings suggest that directional natal dispersal may mitigate fitness costs for trailing-edge populations by allowing individuals to track changing climate and avoid warming conditions at warm-edge range boundaries. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the seroprevalence against COVID-19 by country and derive the seroprevalence over the world. To estimate seroprevalence among adults, we use serological surveys (also called the serosurveys) conducted within each country. When the serosurveys are incorporated to estimate world seroprevalence, there are two issues. First, there are countries in which a serological survey has not been conducted. Second, the sample collection dates differ from country to country. We attempt to tackle these problems using the vaccination data, confirmed cases data, and national statistics. We construct Bayesian models to estimate the numbers of people who have antibodies produced by infection or vaccination separately. For the number of people with antibodies due to infection, we develop a hierarchical model for combining the information included in both confirmed cases data and national statistics. At the same time, we propose regression models to estimate missing values in the vaccination data. As of 31st of July 2021, using the proposed methods, we obtain the $ 95\% $ 95% credible interval of the world seroprevalence as $ {[35.5\%,56.8\%]} $ [35.5%,56.8%]. [ABSTRACT FROM AUTHOR]

In Bayesian random‐effects meta‐analysis, the use of weakly informative prior distributions is of particular benefit in cases where only a few studies are included, a situation often encountered in health technology assessment (HTA). Suggestions for empirical prior distributions are available in the literature but it is unknown whether these are adequate in the context of HTA. Therefore, a database of all relevant meta‐analyses conducted by the Institute for Quality and Efficiency in Health Care (IQWiG, Germany) was constructed to derive empirical prior distributions for the heterogeneity parameter suitable for HTA. Previously, an extension to the normal‐normal hierarchical model had been suggested for this purpose. For different effect measures, this extended model was applied on the database to conservatively derive a prior distribution for the heterogeneity parameter. Comparison of a Bayesian approach using the derived priors with IQWiG's current standard approach for evidence synthesis shows favorable properties. Therefore, these prior distributions are recommended for future meta‐analyses in HTA settings and could be embedded into the IQWiG evidence synthesis approach in the case of very few studies. [ABSTRACT FROM AUTHOR]

Grass carp (Ctenopharyngodon idella), an invasive cyprinid within the Laurentian Great Lakes, is naturally reproducing in several Lake Erie tributaries, which has raised concerns of the species' spread throughout Lake Erie and the other Great Lakes. Knowledge of the recent invasion extent outside of the western basin of Lake Erie, particularly in eastern tributaries and nearshore waters, is limited. Understanding the invasion extent would improve the efficacy of ongoing coordinated multi-agency control efforts. Molecular tools, such as environmental DNA (eDNA), have shown promise for early detection of aquatic invasive species. In this study, water samples (N = 476) were collected for grass carp eDNA monthly between May and November in 2018 and 2019, at three sites in the Michigan waters of Lake Erie and the Detroit River. We fit Bayesian multi-scale occupancy models to determine differences in eDNA capture and detection probability among grass carp qPCR assays, sampling sites, and across time. To determine whether grass carp were physically present, and to validate eDNA samples, we quantified recent grass carp presence in sampled areas using an existing acoustic telemetry and field sampling framework. Our results indicate that grass carp eDNA capture probability differed among sites, but there was no difference among months. Positive grass carp eDNA detections were observed across multiple months at each site, with 69% of sitespecific sampling events testing positive for grass carp eDNA on at least one assay and replicate. The majority (65%) of weeks where positive eDNA sampling detections occurred also concurrently had one or more grass carp detected via acoustic telemetry 1-6 days prior. Our results highlight the potential utility of using eDNA to monitor the invasion extent of grass carp within the nearshore waters of Lake Erie. However, further evaluation of the factors that influence grass carp eDNA characteristics among sites within Lake Erie are needed to determine its efficacy for surveillance protocols by natural resource management agencies. [ABSTRACT FROM AUTHOR]

Management of solid waste from rural hospitals is amongst problems affecting Zimbabwe due to diseases, population, and hospital increase. Solid waste from rural hospitals is receiving little attention translating to environmental health problems. Therefore, 101 secondary sources were used to write a paper aiming to proffer a hierarchical model to achieve sustainable solid waste management at rural hospitals. Rural hospitals' solid waste encompasses electronic waste, sharps, pharmaceutical, pathological, radioactive, chemical, infectious, and general waste. General solid waste from rural hospitals is between 77.35 and 79% whilst hazardous waste is between 21 and 22.65%. Solid waste increase add burden to nearly incapacitated rural hospitals. Rural hospital solid waste management processes include storage, transportation, treatment methods like autoclaving and chlorination, waste reduction alternatives, and disposal. Disposal strategies involve open pits, open burning, dumping, and incineration. Rural hospital solid waste management is guided by legislation, policies, guidelines, and conventions. Effectiveness of legal framework is limited by economic and socio-political problems. Rural hospital solid waste management remain inappropriate causing environmental health risks. Developed hierarchical model can narrow the route to attain sustainable management of rural hospitals' solid waste. Proposed hierarchical model consists of five-layered strategies and acted as a guide for identifying and ranking approaches to manage rural hospitals' solid waste. Additionally, Zimbabwean government, Environmental Management Agency and Ministry of Health is recommended to collaborate to provide sufficient resources to rural hospitals whilst enforcing legal framework. Integration of all hierarchical model's elements is essential whereas all-stakeholder involvement and solid waste minimisation approaches are significant at rural hospitals. [ABSTRACT FROM AUTHOR]

Background: Minimal clinically important change (MCIC) represents the minimum patient-perceived improvement in an outcome after treatment, in an individual or within a group over time. This study aimed to determine MCIC of knee flexion in people with knee OA after non-surgical interventions using a meta-analytical approach. Methods: Four databases (MEDLINE, Cochrane, Web of Science and CINAHL) were searched for studies of randomised clinical trials of non-surgical interventions with intervention duration of ≤ 3 months that reported change in (Δ) (mean change between baseline and immediately after the intervention) knee flexion with Δ pain or Δ function measured using tools that have established MCIC values. The risk of bias in the included studies was assessed using version 2 of the Cochrane risk-of-bias tool for randomised trials (RoB 2). Bayesian meta-analytic models were used to determine relationships between Δ flexion with Δ pain and Δ function after non-surgical interventions and MCIC of knee flexion. Results: Seventy-two studies (k = 72, n = 5174) were eligible. Meta-analyses included 140 intervention arms (k = 61, n = 4516) that reported Δ flexion with Δ pain using the visual analog scale (pain-VAS) and Δ function using the Western Ontario and McMaster Universities Osteoarthritis Index function subscale (function-WOMAC). Linear relationships between Δ pain at rest-VAS (0–100 mm) with Δ flexion were − 0.29 (− 0.44; − 0.15) (β: posterior median (CrI: credible interval)). Relationships between Δ pain during activity VAS and Δ flexion were − 0.29 (− 0.41, − 0.18), and Δ pain-general VAS and Δ flexion were − 0.33 (− 0.42, − 0.23). The relationship between Δ function-WOMAC (out of 100) and Δ flexion was − 0.15 (− 0.25, − 0.07). Increased Δ flexion was associated with decreased Δ pain-VAS and increased Δ function-WOMAC. The point estimates for MCIC of knee flexion ranged from 3.8 to 6.4°. Conclusions: The estimated knee flexion MCIC values from this study are the first to be reported using a novel meta-analytical method. The novel meta-analytical method may be useful to estimate MCIC for other measures where anchor questions are problematic. Systematic review registration: PROSPERO CRD42022323927. [ABSTRACT FROM AUTHOR]

In this article, estimates of reliability performance characteristics are discussed when life test is conducted upon various test facilities under block progressive type-II censoring. When the differences in different test facilities are taken into account and lifetime of products follows a distribution with Kumaraswamy hazard function, different estimates are obtained from classical and hierarchical Bayesian frameworks, respectively. The existence and uniqueness of maximum likelihood estimators of model parameters are established, and approximate confidence intervals are obtained as well based on asymptotic theory and delta method. Moreover, associated Bayes estimators are also evaluated using a hybrid Metropolis–Hasting sampling in hierarchical framework. Finally, performances of the proposed approaches are compared via extensive simulation study, and two real data examples are also presented for application viewpoint. [ABSTRACT FROM AUTHOR]

Despite its importance in terms of survey participation, the literature is sparse on how face-to-face interviewers differentially affect specific groups of sample units. This paper demonstrates how an alternative parametrization of the random components in multilevel models, so-called separate coding, delivers valuable insights into differential interviewer effects for specific groups of sample members. In the example of a face-to-face recruitment interview for a probability-based online panel, we detect small interviewer effects regarding survey participation for non-Internet households, whereas we find sizable interviewer effects for Internet households. We derive practical guidance for survey practitioners to address differential interviewer effects based on the proposed variance decomposition. [ABSTRACT FROM AUTHOR]

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Pharmaceutical industries in most developing countries with limited resources are expected to encounter several barriers while incorporating Industry 4.0 to transform into Pharma 4.0. With limited resources, a developing country must prioritize the barriers consider their impacts, and make a resource utilization plan accordingly. In this study, We employed a hierarchical multiple criteria decision analysis (MCDM) technique to identify potential barriers to Pharma 4.0 in developing countries and examine their effects to generate a prioritization inventory. Firstly, we extracted the likely barriers using a systematic literature study and used an expert opinion-based Delphi Method to choose the most pertinent barriers. Subsequently, we analyzed the correlation and influence of the selected barriers on each other by formulating a hierarchical multi-criteria model integrating Interpretive Structural Modelling (ISM) and the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). As an outcome, we found three distinct categories of the selected 12 barriers: Prominent (4 of 12), Influencing (5 of 12), and Resulting (3 of 12). The results of this study are intended to assist the government in developing a solid adoption strategy for Pharma 4.0 and supply chain strategists in ensuring optimum resource utilization by resolving the examined barriers during the deployment of Pharma 4.0. The study is the first of its kind to discover barriers to Pharma 4.0 adoption and create hierarchical correlations within the context of the pharmaceutical sector from the perspective of a developing country.

Although significant progress in the task of producing fine-grained captions for portrait images has been made by the current models for generating detailed descriptions in captions, they still face challenges in attention allocation and in capturing the detailed characteristics of the subjects. This results in a difficulty to accurately generate refined captions for character images. In response to this issue, a model named Attention-guided Hierarchical Parsing (AHP) is innovatively proposed by us. This model leverages the exceptional segmentation performance of the Segment Anything Model (SAM) to guide the model to prioritize key information in character images, maintaining focus on the subject even in complex scenes. Additionally, the model utilizes a multi-level image feature encoding-decoding framework, significantly enhancing its capacity to capture intricate image details through a thorough analysis of multi-scale features within images. Extensive experimental results demonstrate the superior performance of the proposed model in generating fine-grained, high-quality captions, significantly improving the quality of image caption generation and introducing new perspectives and methods to the field of fine-grained image caption generation.

Abstract Background Automated feature selection methods such as the Least Absolute Shrinkage and Selection Operator (LASSO) have recently gained importance in the prediction of quality-related outcomes as well as the risk-adjustment of quality indicators in healthcare. The methods that have been used so far, however, do not account for the fact that patient data are typically nested within hospitals. Methods Therefore, we aimed to demonstrate how to account for the multilevel structure of hospital data with LASSO and compare the results of this procedure with a LASSO variant that ignores the multilevel structure of the data. We used three different data sets (from acute myocardial infarcation, COPD, and stroke patients) with two dependent variables (one numeric and one binary), on which different LASSO variants with and without consideration of the nested data structure were applied. Using a 20-fold sub-sampling procedure, we tested the predictive performance of the different LASSO variants and examined differences in variable importance. Results For the metric dependent variable Duration Stay, we found that inserting hospitals led to better predictions, whereas for the binary variable Mortality, all methods performed equally well. However, in some instances, the variable importances differed greatly between the methods. Conclusion We showed that it is possible to take the multilevel structure of data into account in automated predictor selection and that this leads, at least partly, to better predictive performance. From the perspective of variable importance, including the multilevel structure is crucial to select predictors in an unbiased way under consideration of the structural differences between hospitals.

Simple Summary: This paper provides a review of statistical methods for tumor-agnostic clinical trials. In particular, the review focuses on basket trials and provides methodological insights into various Bayesian approaches. The key concept of borrowing information through Bayesian hierarchical models is emphasized, and some novel trial designs are introduced. The review is expected to provide oncology and biostatistics researchers with more exposure to powerful Bayesian methods for the design and analysis of tumor-agnostic clinical trials. Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings. [ABSTRACT FROM AUTHOR]

As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge computing resources are exploited to process more of the data locally. This regime of analytics is coined as Federated Data Analytics (FDA). Despite the recent success stories of FDA, most literature focuses exclusively on deep neural networks. In this work, we take a step back to develop an FDA treatment for one of the most fundamental statistical models: linear regression. Our treatment is built upon hierarchical modeling that allows borrowing strength across multiple groups. To this end, we propose two federated hierarchical model structures that provide a shared representation across devices to facilitate information sharing. Notably, our proposed frameworks are capable of providing uncertainty quantification, variable selection, hypothesis testing, and fast adaptation to new unseen data. We validate our methods on a range of real-life applications, including condition monitoring for aircraft engines. The results show that our FDA treatment for linear models can serve as a competing benchmark model for the future development of federated algorithms. [ABSTRACT FROM AUTHOR]

Background. The conceptualization of negative symptoms (NS) in schizophrenia is still controversial. Recent confirmatory factor-analytic studies suggested that the bi-dimensional model (motivational deficit [MAP] and expressive deficit [EXP]) may not capture the complexity of NS structure, which could be better defined by a five-factor (five NS domains) or a hierarchical model (five NS domains as first-order factors, and MAP and EXP, as second-order factors). A validation of these models is needed to define the structure of NS. To evaluate the validity and temporal stability of the five-factor or the hierarchical structure of the brief negative symptom scale (BNSS) in individuals with schizophrenia (SCZ), exploring associations between these models with cognition, social cognition, functional capacity, and functioning at baseline and at 4 years follow-up. Methods. Clinical variables were assessed using state-of-the-art tools in 612 SCZ at two-time points. The validity of the five-factor and the hierarchical models was analyzed through structural equation models. Results. The two models had both a good fit and showed a similar pattern of associations with external validators at the two-time points, with minor variations. The five-factor solution had a slightly better fit. The associations with external validators favored the five-factor structure. Conclusions. Our findings suggest that both five-factor and hierarchical models provide a valid conceptualization of NS in relation to external variables and that five-factor solution provides the best balance between parsimony and granularity to summarize the BNSS structure. This finding has important implications for the study of pathophysiological mechanisms and the development of new treatments. [ABSTRACT FROM AUTHOR]

With the vast development in the cloud computing environment, many cloud users intend to outsource the data to a remote location and share the data with multiple users. The hierarchical model is an extensively used data organization process. It is highly complex to guarantee integrity, privacy, and confidentiality of the data and the structure of the model when the sensitive data is held in this manner. This research attempts to give a solution to hierarchically ensure the security and privacy of the information while data sharing occurs. A constructive hierarchical data sharing (CHDS) method is proposed to adopt symmetric encryption over the rooted hierarchical graph structure. The hierarchical graph model deals with incoming data features to establish the model's privacy and authenticity. Based on this model, the proposed CHDS is known to be transparent, secure, and confident in the public environment. Here, performance metrics like computational complexity, key generation, prediction accuracy, and execution time are evaluated. The outcomes provide the security of the multi-party environment without forfeiting sensible resources when the hierarchical model grows to a more significant number of siblings, edges, and vertices. The proposed CHDS gives a better trade-off when compared with various existing approaches. [ABSTRACT FROM AUTHOR]

Floods cause hundreds of fatalities and billions of dollars of economic loss each year in the United States. To mitigate these damages, accurate flood prediction is needed for issuing early warnings to the public. This situation is exacerbated in larger model domains flood prediction, particularly in ungauged basins. To improve flood prediction for both gauged and ungauged basins, we propose a spatio‐temporal hierarchical model (STHM) using above‐normal flow estimation with a 10‐day window of modeled National Water Model (NWM) streamflow and a variety of catchment characteristics as input. The STHM is calibrated (1993–2008) and validated (2009–2018) in controlled, natural, and coastal basins over three broad groups, and shows significant improvement for the first two basin types. A seasonal analysis shows the most influential predictors beyond NWM streamflow reanalysis are the previous 3‐day average streamflow and the aridity index for controlled and natural basins, respectively. To evaluate the STHM in improving above‐normal streamflow in ungauged basins, 20‐fold cross‐validation is performed by leaving 5% of sites. Results show that the STHM increases predictive skill in over 50% of sites' by 0.1 Nash‐Sutcliffe efficiency (NSE) and improves over 65% of sites' streamflow prediction to an NSE > 0.67, which demonstrates that the STHM is one of the first of its kind and could be employed for flood prediction in both gauged and ungauged basins. Key Points: We present a space‐time hierarchical modeling framework to predict daily above‐normal flow across CONUSThe model uses National Water Model prediction and hydroclimatic data to show major improvements in various basin types across CONUSThe method is shown to have good skill in predicting in ungauged basins providing enhanced above‐normal flow predictions for the county [ABSTRACT FROM AUTHOR]

In recent decades, the analysis of different geographic scales for studying the spatial patterning of crime has profoundly deepened our theoretical grasp of crime dynamics. However, a similar investigation is lacking when it comes to the patterning of offender residences, despite there being clear theoretical and empirical reasons for doing so, among them, the close relationship between where offenders live and where their corresponding crimes are committed. This paper delves into the concentration and variance of offender residences across different levels of spatial aggregation. The data used contains the locations of residence for known offenders in Birmingham between the years 2006 and 2016. Resident locations are aggregated to Output Areas (OA), nested within Lower Super Output Areas (LSOA), further nested within Middle Super Output Areas (MSOA). Descriptive and model-based statistics are deployed to quantify concentration and variation at each spatial scale. Results suggest that most variance (48%) in offender residence concentrations is attributable to the largest spatial scale (MSOA level). Output Areas capture approximately 38% of the variance. Findings open up discussions on the role of urban development in determining the appropriateness of spatial scale. [ABSTRACT FROM AUTHOR]