Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005–2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005–2012, and identified poverty status, relative humidity, and an interactive factor, ‘diurnal temperature range x mixed forest area’ as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases.

The need for increased performance of mobile device directly conflicts with the desire for longer battery life. Offloading computation to multiple devices is an effective method to reduce energy consumption and enhance performance for mobile applications. Android provides mechanisms for creating mobile applications but lacks a native scheduling system for determining where code should be executed. This paper presents Jade, a system that adds sophisticated energy-aware computation offloading capabilities to Android apps. Jade monitors device and application status and automatically decides where code should be executed. Jade dynamically adjusts offloading strategy by adapting to workload variation, communication costs, and energy status in a distributed network of Android and non-Android devices. Jade minimizes the burden on developers to build applications with computation offloading ability by providing easy-to-use Jade API. Evaluation shows that Jade can effectively reduce up to 39% of average power consumption for mobile application while improving application performance.

Colossus is designed to meet a growing need for survival analysis software capable of analyzing tens of millions of rows of radiation epidemiological data. Colossus is an R package devised to offer scalable survival analysis for the Million Person Study. The total and relative rate equations available in Colossus are outlined in this article, which are used in conjunction with Cox proportional hazards, Poisson, and Fine-Grey regression models. Following a comparison with existing software, validation with epidemiological cohort data is described. Exposure data and specific causes of death among workers at Los Alamos National Laboratory and U.S. nuclear power plants were analyzed by Colossus and 32-bit Epicure and compared with published results. Colossus results agreed with the results of existing software and previous publications.

As the volume of collected data continues to increase in the environmental sciences, so too does the need for effective means for accessing those data. We have developed an Open Modeling Interface (OpenMI) data component that retrieves input data for model components from environmental information systems and delivers output data to those systems. The adoption of standards for both model component input–output interfaces and web services make it possible for the component to be reconfigured for use with different linked models and various online systems. The data component employs three techniques tailored to the unique design of the OpenMI that enable efficient operation: caching, prefetching, and buffering, making it capable of scaling to large numbers of simultaneous simulations executing on a computational grid. We present the design of the component, an evaluation of its performance, and a case study demonstrating how it can be incorporated into modeling studies

Accurate identification of animal behaviors is fundamental to behavioral neuroscience, yet manual annotation remains a major bottleneck due to its subjectivity, limited scalability, and high labor cost. This work presents a unified deep learning and heuristic-based framework for automated behavior classification in the Novel Object Recognition Test (NORT), a widely used paradigm for assessing memory and cognitive function in rodents. We fine-tuned a YOLOv11 Pose model to detect nose and tail-base keypoints specific to Long-Evans rats, and used these keypoints to derive spatial heuristics for identifying NORT behaviors. In parallel, we trained YOLOv11 classification models to predict Standing, Object Interaction, and Other behaviors from individual frames, using datasets containing either 2-object or 5-object videos, as well as a combined dataset containing both 2-object and 5-object videos. Our model trained on the combined dataset, enhanced with heuristic-based post-processing to identify the specific objects that the rat is interacting with, achieved the best overall accuracy and generalization across arenas, outperforming heuristic-only and subset-specific 2-object or 5-object models. Detailed confusion matrices and error analyses reveal that most errors occur near behavioral transitions, reflecting the inherent ambiguity of manual labels at frame-level. The proposed framework enables scalable, reproducible, and objective annotation of NORT videos and provides a foundation for future extensions toward temporally-aware behavioral analysis. All the code and model weights, together with sample NORT videos, will be made publicly available to support future rodent behavior recognition research.