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It is difficult to predict exactly how events like extreme weather, pandemics, conflict, and politics will disrupt global food systems and cause people to go hungry. The destabilizing impacts of [...]

Marine spatial planning and conservation initiatives benefit from an understanding of species distributions across larger geographic areas than are often sampled by any one survey. Here, we test whether the integration of disparate survey data can improve habitat predictions across a region not well sampled by a single survey using Dungeness crab (Metacarcinus magister) from British Columbia as a case study. We assemble data from dive, trawl, and baited-trap surveys to generate six candidate generalized linear mixed-effect models with spatial random fields. To compare single-survey and integrated models, we evaluate predictive performance with spatially buffered leave-one-out cross-validation and independently with two novel approaches using fisheries catch data. We find improved predictive performance and reduced uncertainty when integrating data from surveys that suffer from small sample size, low detectability, or limited spatial coverage. We demonstrate the importance of robust model evaluation when integrating data and predicting to unsampled locations. In addition, we highlight the need for careful consideration of sampling biases and model assumptions when integrating data to reduce the risk of prediction errors. Key words: species distribution modelling, data integration, Gaussian random fields, Dungeness crab, 1. Introduction Species distribution models (SDMs), which relate species observations to environmental covariates, can be used to generate predictions in unsampled areas to inform a variety of management and conservation [...]

Rheumatic Heart Disease (RHD) involves damage of heart valves following acute rheumatic fever (ARF), itself an autoimmune reaction to untreated/recurrent Strep A throat and/or skin infections often resulting in premature [...]

Estimating abundance of migrating fishes is challenging. While sonars can be deployed continuously, improper assumptions about unidirectional migration and complete spatial coverage can lead to inaccurate estimates. To address these challenges, we present a framework for combining fixed-location count data from a dual-frequency identification sonar (DIDSON) with movement data from acoustic telemetry to estimate spawning run abundance of lake sturgeon (Acipenser fulvescens). Acoustic telemetry data were used to estimate the probability of observing a lake sturgeon on the DIDSON and to determine the probability that a lake sturgeon passing the DIDSON site had passed the site previously during the season. Combining probabilities with DIDSON counts, using a Bayesian integrated model, we estimated the following abundances: 99 (42-215 credible interval, CI) in 2017, 131 (82-248 CI) in 2018, and 92 (47-184 CI) in 2019. Adding movement data generated better inferences on count data by incorporating fish behavior (e.g., multiple migrations in a single season) and its uncertainty into abundance estimates. This framework can be applied to count and movement data to estimate abundance of spawning runs of other migratory fishes in riverine systems. L'estimation de l'abondance des poissons migrateurs n'est pas chose facile. Si des sonars peuvent etre deployes en continu, des hypotheses inadaptees sur le caractere unidirectionnel des migrations et l'exhaustivite de la couverture spatiale peuvent se traduire par des estimations inexactes. Pour surmonter ces difficultes, nous presentons un cadre permettant de combiner des donnees de denombrement dans des lieux precis obtenues par sonar d'identification a deux frequences (DIDSON) a des donnees de deplacement obtenues par telemetrie acoustique afin d'estimer l'abondance de migrations de frai d'esturgeons jaunes (Acipenser fulvescens). Les donnees de telemetrie acoustique sont utilisees pour estimer la probabilite d'observer un esturgeon jaune avec le DIDSON et pour determiner la probabilite qu'un esturgeon jaune qui passe devant un DIDSON soit deja passe par le meme site au cours de la saison. En combinant ces probabilites avec des denombrements par DIDSON dans un modele integre bayesien, nous estimons les abondances suivantes : 99 (intervalle de credibilite, IC : 42-215) en 2017, 131 (IC : 82-248) en 2018 et 92 (IC : 47-184) en 2019. L'inclusion de donnees de deplacement produit de meilleures inferences tirees des donnees de denombrement en incorporant le comportement des poissons (p. ex., plus d'une migration au cours d'une saison) et son incertitude aux estimations d'abondance. Ce cadre peut etre applique a des donnees de denombrement et de deplacement pour estimer l'abondance de migrations de frai d'autres poissons migrateurs dans des systemes fluviaux. [Traduit par la Redaction], Introduction Spawning migrations in rivers are a key part of the life history of diadromous as well some freshwater species. These migrations present an ideal time to monitor population trends [...]

: Objective: In this paper, we describe the design and baseline data of a study aimed at improving injury surveillance data quality of hospitals contributing to the Victorian Emergency Minimum Dataset (VEMD). Methods: The sequential study phases include a baseline analysis of data quality, direct engagement and communication with each of the emergency department (ED) hospital sites, collection of survey and interview data and ongoing monitoring. Results: In 2019/20, there were 371,683 injury‐related ED presentations recorded in the VEMD. Percentage unspecified, the indicator of (poor) data quality, was lowest for ‘body region’ (2.7%) and ‘injury type’ (7.4%), and highest for ‘activity when injured’ (29.4%). In the latter, contributing hospitals ranged from 3.0–99.9% unspecified. The ‘description of event’ variable had a mean word count of 10; 16/38 hospitals had a narrative word count of Conclusions: Baseline hospital injury surveillance data vary vastly in data quality, leaving much room for improvement and justifying intervention as described. Implications for public health: Hospital engagement and feedback described in this study is expected to have a marked effect on data quality from 2021 onwards. This will ensure that Victorian injury surveillance data can fulfil their purpose to accurately inform injury prevention policy and practice., Worldwide, injuries are a major cause of morbidity and mortality. In 2017/18 in Australia, there were more than half a million (532,562) injury‐related hospital admissions and 13,028 injury‐related deaths.[sup.1] Victoria [...]

Forest resource assessments based on multi-source and multi-temporal data have become more common. Therefore, enhancing the prediction capabilities of forestry dynamics by efficiently pooling and analyzing time-series and spatial sequential data is now more pivotal. Bayesian filtering and smoothing provide a well-defined formalism for the fusion or assimilation of various data. We ascertained how often the generic, standardized Bayesian framework is used in the scientific literature and whether such an approach is beneficial for forestry applications. A review of the literature showed that the use of Bayesian methods appears to be less common in forestry than in other disciplines, particularly remote sensing. Specifically, time-series analyses were found to favor ad hoc methods. Our review did not reveal strong numeric evidence for better performance by the various Bayesian approaches, but this result may be partly due to the challenge in comparing a variety of methods for different prediction tasks. We identified methodological challenges related to assimilating predictions of forest development; in particular, combining modelled growth with disturbances due to both forest operations and natural phenomena. Nevertheless, the Bayesian frameworks provide possibilities to efficiently combine and update prior and posterior predictive distributions and derive related uncertainty measures that appear under-utilized in forestry. Key words: forest inventory, hierarchical Bayes model, Kalman filter, Markov chain Monte Carlo (MCMC), credible interval. L'evaluation des ressources forestieres fondee sur des donnees de nombreuses sources et multitemporelles est devenue plus frequente. Par consequent, l'amelioration des capacites de previsions de la dynamique forestiere au moyen du regroupement et de l'analyse efficaces des donnees sequentielles spatiales et de series chronologiques est maintenant plus cruciale. Le filtrage et le nivellement bayesiens fournissent un formalisme bien defini pour la fusion ou l'assimilation des diverses donnees. Nous avons determine la frequence a laquelle le cadre bayesien generique et normalise est utilise dans la litterature scientifique et nous avons evalue si une telle approche est avantageuse pour les applications forestieres. Une revue de la litterature a montre que l'utilisation des methodes bayesiennes semble etre moins frequente en foresterie que dans d'autres disciplines, particulierement la teledetection. On a constate plus particulierement que les analyses des series chronologiques favorisaient les methodes ad hoc. Notre revue n'a pas revele de preuves numeriques importantes pour une meilleure performance par diverses approches bayesiennes, mais ce resultat peut etre du en partie a la difficulte de comparer un eventail de methodes pour differentes taches de previsions. Nous avons identifie des defis methodologiques relies a l'assimilation des previsions du developpement forestier; plus particulierement, en combinant la croissance modelisee aux perturbations dues aux operations forestieres et aux phenomenes naturels. Neanmoins, les cadres bayesiens offrent des possibilites de combiner efficacement et de faire une mise a jour avant et apres les distributions predictives et de calculer les mesures connexes de l'incertitude qui semblent sous-utilisees en foresterie. [Traduit par la Redaction] Mots-cles : inventaire forestier, modele hierarchique bayesien, filtre de Kalman, Monte Carlo par chaine de Markov (MCCM), intervalle de credibilite., 1. Introduction 1.1. Motivation and objectives Systematic forest inventories have been carried out for over a century (Kangas et al. 2018a) and have utilized remote sensing and other digital map [...]

Annual average daily traffic (AADT) is a fundamental input for numerous civil engineering applications, yet generating reliable estimates of AADT at a network-wide level poses challenges. This article explores the potential use of vehicle probe data to enhance conventional traffic monitoring practice for generating network-wide estimates of AADT by exploring relationships between site-specific traffic volume data and vehicle probe data collected in Manitoba, Canada. The analysis revealed that mean travel speed cannot be used to predict traffic volumes on Manitoba highways, since the mean travel speed did not deviate from the free-flow speed regardless of the volume measured. The quantity of probe data observations showed moderate correlation with traffic volume at some sites (R-squared up to 0.65), but these correlations were stronger (R-squared up to 0.9) when considering trucks only. These findings suggest that probe data could be used to estimate truck volumes at certain locations. Key words: probe data, traffic monitoring program, truck traffic, annual average daily traffic (AADT). Le trafic moyen journalier annuel (TMJA) est un intrant fondamental pour de nombreuses applications de genie civil, mais la production d'estimations fiables du TMJA a l'echelle du reseau pose des defis. Cet article explore l'utilisation potentielle des donnees de sondes de vehicules pour ameliorer la pratique de surveillance de la circulation conventionnelle afin de generer des estimations du TMJA a l'echelle du reseau en explorant les relations entre les donnees de volume de circulation propres au site et les donnees de sondes de vehicules recueillies au Manitoba, au Canada. L'analyse a revele que la vitesse de deplacement moyenne ne peut etre utilisee pour predire les volumes de circulation sur les routes du Manitoba, puisque la vitesse de deplacement moyenne ne s'ecarte pas de la vitesse de circulation libre, peu importe le volume mesure. La quantite d'observations de donnees de sondes a montre une correlation moderee avec le volume de circulation a certains sites (R au carre jusqu'a 0,65), mais ces correlations etaient plus fortes (R au carre jusqu'a 0,9) si l'on considere uniquement les camions. Ces constatations suggerent que des donnees de sondes pourraient etre utilisees pour estimer les volumes de camions a certains endroits. [Traduit par la Redaction] Mots-cles : donnees de sondes, programme de surveillance de la circulation, circulation de camions, trafic moyen journalier annuel (TMJA)., Introduction Traffic monitoring programs provide input data for highway and infrastructure design applications by measuring and estimating the volume, speed, and classification of vehicles using roadways. Annual average daily traffic [...]

In North America, impassable, man-made barriers block access to salmonid spawning habitat and require costly restoration efforts in the remaining habitats. Evaluating restored spawning habitat quality requires information on salmon water velocity and depth preferences, which may vary in relation to other variables (e.g., water temperature). We demonstrate a generalizable, low-cost method to gather and analyze these data by combining aerial redd surveys of winter-run Chinook salmon (Oncorhynchus tshawytscha), 2D hydraulic modeling, and generalized linear models to calculate spawning resource selection functions (RSFs). Our method permits the examination of interactions between environmental variables and habitat selection, which are frequently treated as independent. Our methods resulted in an RSF that shows interactions between both velocity and depth preference with changing temperature. Preferred depth increased and preferred velocity decreased with increasing temperature. Spawning RSFs for environmental variables may change as other environmental conditions (i.e., water temperature) change; thus, it is important to account for potential interactions when using or producing RSFs. En Amerique du Nord, des barrieres infranchissables construites par les humains bloquent l'acces a des habitats de frai des salmonides et necessitent des efforts de restauration couteux dans les habitats restants. Pour evaluer la qualite des habitats de frai restaures, de l'information sur la vitesse dans l'eau et les profondeurs de predilection des saumons, qui peuvent varier en fonction d'autres variables (p. ex. la temperature de l'eau), est necessaire. Nous faisons la demonstration d'une methode peu couteuse et generalisable pour obtenir et analyser ces donnees, qui combine des releves aeriens de nids de frai de saumons chinooks (Oncorhynchus tshawytscha) de la migration hivernale, la modelisation hydraulique en 2D et des modeles lineaires generalises pour calculer des fonctions de selection des ressources (FSR) de frai. Notre methode permet d'examiner les effets d'interactions entre differentes variables environnementales, souvent traitees comme etant independantes, sur la selection d'habitats. Elle produit une FSR qui montre les interactions entre la vitesse et la profondeur de predilection en fonction de la temperature. La profondeur de predilection augmente et la vitesse de predilection diminue quand la temperature augmente. Puisque les FSR de frai pour des variables environnementales peuvent changer en fonction des variations d'autres conditions ambiantes (c.-a-d. la temperature de l'eau), les interactions potentielles doivent etre prises en consideration lors de l'utilisation ou de la production de FSR. [Traduit par la Redaction], 1. Introduction Decisions species make about habitat selection are crucial for their fitness, and understanding these decisions is critical for management. One of the most important, and often final, decisions [...]

Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed-effects (PPM), generalized linear-mixed (GLM), and k nearest- neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95-1.47 and 0.96-1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%. Key words: generalized linear models, linear mixed-effects models, most similar neighbor approach, number of stems, systematic sampling. Les distributions de diametre a hauteur de poitrine (DHP) offrent des informations precieuses pour les decisions de gestion forestiere operationnelles et strategiques. Nous avons predit les distributions de DHP a l'aide de l'inventaire forestier national norvegien et de donnees de balayage laser aeroporte et nous avons compare les performances predictives des modeles lineaires a effets mixtes (LEM), lineaires generalises mixtes (LGM) et des k plus proches voisins (PPV). Bien que le modele LGM ait entraine des erreurs de prediction plus petites que le modele LEM, les deux ont ete clairement surpasses par l'approche PPV. Nous avons donc etudie la capacite du modele PPV a ameliorer la precision des estimations de la frequence des tiges par classes de DHP, dans la zone d'etude de 8,7 Mha, a l'aide d'un estimateur assiste par modele (AM) adapte a un echantillonnage systematique. Les estimations AM ont produit des efficacites superieures ou a peu pres egales a celle d'estimations directes utilisant uniquement des donnees de terrain. Les efficacites relatives (ER) associees aux estimations AM variaient entre 0,95-1,47 et 0,96-1,67 pour des largeurs de classe de DHP de respectivement 2 et 6 cm, lorsque les especes d'arbres dominantes etaient presumees connues. L'utilisation d'une carte de prediction des especes d'arbres, au lieu des informations observees, a reduit les ER jusqu'a 10 %. [Traduit par la Redaction] Mots-cles: modeles lineaires generalises, modeles lineaires a effets mixtes, approche des plus proches voisins, nombre de tiges, echantillonnage systematique., 1. Introduction Forest inventories provide essential information for the sustainable management of forest resources at different spatial levels. A key forest attribute is diameter at breast height (DBH), which is [...]

A variation of Shannon's relative entropy statistic is presented as a measure of configurational entropy for variables known to exhibit spatial autocorrelation using census tract datafor the city of Birmingham, Alabama. Standardized and non-standardized configurational entropy indices (CEIs) are introduced to measure the amount of spatial order in a geographic distribution. As the degree of spatial autocorrelation increases and the amount of entropy or uncertainty decreases, the CEIs produce values that diverge from Shannon's statistic, which tends to overstate the degree of disorder or uncertainty in the presence of spatial autocorrelation. The CEIs incorporate a spatial covariance approach to estimating spatial order based on connectivity and the differencing of values for adjacent areal units. While Shannon's entropy statistic is insensitive to spatial arrangement, the CEIs provide a scale-targeted quantification of the amount of inherent spatial order in a distribution as defined by the connective structure of the areal units and the degree to which a variable is spatially autocorrelated. The amount of spatial order, as manifested within an autocorrelated pattern at a given geographic scale and for a given connectivity structure, is directly proportional to the difference between Shannon's measure and the CEIs. KEYWORDS: Configurational entropy, Shannon's entropy, Connectivity, Spatial autocorrelation, Highlights * A useful alternative to Shannon's relative entropy index in form and function * Incorporates connectivity considerations in the estimation of entropy and spatial order * Useful descriptive statistic [...]

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics--however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis. This Perspective discusses how high-energy-density physics could tap the potential of AI-inspired algorithms for extracting relevant information and how data-driven automatic control routines may be used for optimizing high-repetition-rate experiments., Author(s): Peter W. Hatfield [sup.1] , Jim A. Gaffney [sup.2] , Gemma J. Anderson [sup.2] , Suzanne Ali [sup.2] , Luca Antonelli [sup.3] , Suzan Basegmez du Pree [sup.4] , [...]

Author(s): Giorgio Quer [sup.1] , Jennifer M. Radin [sup.1] , Matteo Gadaleta [sup.1] , Katie Baca-Motes [sup.1] , Lauren Ariniello [sup.1] , Edward Ramos [sup.1] [sup.2] , Vik Kheterpal [sup.2] [...], Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73-0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better (P < 0.01) than a model.sup.1 that considers symptoms alone (AUC = 0.71; IQR: 0.63-0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay. A smartphone app that combines smartwatch and activity tracker data together with self-reported symptoms allows continuous monitoring of SARS-CoV-2 infection.

Purpose and Need for Simulation in Child Welfare Training Simulated training environments are used in professional training to enhance learning complex skills prior to on-the-job performance in numerous fields other [...]

This article describes the results of an extensive review of reference transactions from multiple service points at the University of Utah's J. Willard Marriott Library. The review enabled us to better understand the types of questions asked at our service points and resulted in a new set of codes for categorizing reference transactions that focus on recording the kinds of expertise needed to answer each question. We describe the differences between our model and other scales for collecting reference questions. Our method for reviewing reference transactions and developing new codes may be useful to other libraries interested in updating how they collect reference statistics., In this paper we develop a strategy to evaluate public service points in an academic library based on the expertise sought by library patrons. (1) Although there is already an [...]

Nonprofit organizations use direct-mail marketing to cultivate one-time donors and convert them into recurring contributors. Cultivated donors generate much more revenue than new donors, but also lapse with time, making it important to steadily draw in new cultivations. The direct-mail budget is limited, but betterdesigned mailings can improve success rates without increasing costs. We propose an empirical model to analyze the effectiveness of several design approaches used in practice, based on a massive data set covering 8.6 million direct-mail communications with donors to the American Red Cross during 2009-2011. We find evidence that mailed appeals are more effective when they emphasize disaster preparedness and training efforts over post-disaster cleanup. Including small cards that affirm donors' identity as Red Cross supporters is an effective strategy, whereas including gift items such as address labels is not. Finally, very recent acquisitions are more likely to respond to appeals that ask them to contribute an amount similar to their most recent donation, but this approach has an adverse effect on donors with a longer history. We show via simulation that a simple design strategy based on these insights has potential to improve success rates from 5.4% to 8.1%. Keywords: business analytics; nonprofit operations; donor cultivation; charitable donations History: Received January 24, 2013; accepted December 18, 2014, by Serguei Netessine, operations management. Published online in Articles in Advance July 15, 2015., 1. Introduction When a major disaster strikes (e.g., Hurricane Katrina or the Haiti earthquake), nonprofit organizations experience sharp spikes in one-time donations. These donations are coordinated for immediate disaster relief, [...]

This investigation extends previous research on cross-functional integration. Building on earlier theoretical development, a graduated sequence of modes of integration is tested in relation to interunit task interdependence in a [...]