This paper introduces an approach to select the bandwidth or smoothing parameter in multiresolution (MR) density estimation and nonparametric density estimation. It is based on the evolution of the second, third and fourth central moments and the shape of the estimated densities for different bandwidths and resolution levels. The proposed method has been applied to density estimation by means of multiresolution densities as well as kernel density estimation (MRDE and KDE respectively). The results of the simulations and the empirical application demonstrate that the level of resolution resulting from the moments method performs better with multimodal densities than the Bayesian Information Criterion (BIC) for multiresolution densities estimation and the plug-in for kernel densities estimation.

To explore the spatiotemporal evolution characteristics of heat vulnerability in the Pearl River Delta urban agglomeration during heatwave disasters, this research employs the Entropy Weight Method (EWM) to calculate the heat vulnerability assessment results for nine cities in the region spanning from 2001 to 2022. Through the application of kernel density estimation, Moran\'s I, and the Geographically and Temporally Weighted Regression (GTWR) model, which is proven to be superior to traditional model such as OLS, this study analyzes the dynamic distribution patterns of heat vulnerability in the study area and dissect the trends of influencing factors. The results reveal that from 2001 to 2022, the overall heat vulnerability index in the study area demonstrates a fluctuating downward trend. Key contributors to heat vulnerability include high-frequency and long-duration heatwaves, population sensitivity, and changes in residents\' consumption levels. Throughout this period of development, the disparity in heat vulnerability among cities has gradually widened, indicating an overall pattern of uneven development in the region. Future attention should be focused on formulating heat adaptation strategies in areas with high vulnerability to enhance the overall sustainability of the study area.

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions. Subsequently, the cumulative sum (CUSUM) and the mahalanobis distance (MD) are used to reconstruct the hybrid statistic with spatial and temporal sequences. It helps to enhance the correlation between high-frequency temporal dynamics and space and improves fault detection precision. Moreover, the kernel density estimation (KDE) method is used to estimate the upper threshold of the hybrid statistic so as to optimize the fault detection process. Finally, simulations are conducted by applying the WT-based optimal STHM in the early fault detection of the Tennessee Eastman (TE) process, with the aim of proving that the fault detection method proposed has a high fault detection rate (FDR) and a low false alarm rate (FAR), and it can improve both production safety and product quality.

Interest in tracking and monitoring animals in livestock farming using wearable sensors has been steadily increasing. The use of these devices is particularly crucial in extensive livestock systems where direct interaction between animals and farmers is infrequent, necessitating strenuous efforts in long-distance herd monitoring. Internet of Things (IoT) technologies offer a promising solution to address the challenges posed by vast distances, enabling real-time and remote animal monitoring. In this study, an experimental trial was conducted using a custom-designed device, located in a Polyvinyl Chloride (PVC) case, specifically tailored to fit onto a collar. This case incorporates an integrated SigFox communication system, i.e., a Low Power Global Positioning System (LP-GPS) omnidirectional system, and a power supply. The trial took place in two grazing areas located in different territorial zones, designated as Case Study I and II. A LP-GPS collar was provided for each selected animal, and the data were recorded at 20-min intervals for Case Study I and 10-min intervals for Case Study II. The acquired data were then imported and analysed using Geographical Information Systems (GIS) software. Information was collected through a purpose-built web application (AppWeb). The objective was to analyze those territorial areas mostly occupied by animals within the two considered grazing areas by developing a GIS-based methodology. Specifically, customized algorithms such as Heatmap and Kernel Density Estimation (KDE) plugins were employed to conduct spatial analyses. The maps obtained through Heatmap plugin, showed the temporal-spatial distribution of animals within their grazing areas. Additionally, the KDE tool was used to classify preferred territorial areas, generating tailored charts for each animal in the sample. The individual Core Areas, determined through KDE evaluation for each animal, were overlaid to provide a comprehensive analysis of the monitored animals.The results achieved applying the GIS-based methodology facilitated the identification of animal positions and could be adopted to provide insights into feeding behavior and soil erosion, thereby aiding in the prevention of environmental issues.

Reinforcement learning algorithms are increasingly utilized across diverse domains within power systems. One notable challenge in training and deploying these algorithms is the acquisition of large, realistic datasets. It is imperative that these algorithms are trained on extensive, realistic datasets over numerous iterations to ensure optimal performance in real-world scenarios. In pursuit of this goal, we curated a comprehensive dataset capturing electric vehicle (EV) charging details over a span of 29,600 days within a designated parking facility. This dataset encompasses necessary information such as connection times, charging durations, and energy consumption of individual EVs. The methodology involved employing conditional tabular generative adversarial networks (CTGAN) to craft a pool of synthetic dataset from a smaller initial dataset collected from an EV charging facility located on the Caltech campus. Subsequently, multiple post-processing techniques were implemented to extract data from this pool, ensuring compliance with the charging station\'s capacity constraint while maintaining a realistic daily EV demand profile derived from historical data. Using kernel density estimation (KDE), the distributional characteristics of the historical data, especially concerning the timing of EV connections, were faithfully replicated. The developed dataset is specifically useful in training offline reinforcement learning algorithms.

BACKGROUND: Motor vehicle collisions are a leading source of mortality and injury on urban highways. From a temporal perspective, the determination of a road segment as being collision-prone over time can fluctuate dramatically, making it difficult for transportation agencies to propose traffic interventions. However, there has been limited research to identify and characterize collision-prone road segments with varying collision density patterns over time.
METHODS: This study proposes an identification and characterization framework that profiles collision-prone roads with various collision density variations. We first employ the spatio-temporal network kernel density estimation (STNKDE) method and time-series clustering to identify road segments with different collision density patterns. Next, we characterize collision-prone road segments based on spatio-temporal information, consequences, vehicle types, and contributing factors to collisions. The proposed method is applied to two-year motor vehicle collision records for New York City.
RESULTS: Seven clusters of road segments with different collision density patterns were identified. Road segments frequently determined as collision-prone were primarily found in Lower Manhattan and the center of the Bronx borough. Furthermore, collisions near road segments that exhibit greater collision densities over time result in more fatalities and injuries, many of which are caused by both human and vehicle factors.
CONCLUSIONS: Collision-prone road segments with various collision density patterns over time have distinct differences in the spatio-temporal domain and the collisions that occur on them.
CONCLUSIONS: The proposed method can help policymakers understand how collision-prone road segments change over time, and can serve as a reference for more targeted traffic treatment.

The inequality in CO2 emissions from agricultural energy consumption is a major challenge for coordinating low-carbon agricultural development across regions in China. However, the evolutionary characteristics and driving factors of inequality in China\'s agricultural energy-related CO2 emissions are poorly understood. In response, the Kaya-Theil model was adopted to examine the three potential factors influencing CO2 emission inequality in China\'s agricultural energy consumption. The results revealed that, from 1997 to 2021, agricultural energy-related CO2 emissions per capita showed a significant upward trend, with prominent polarization and right-tailing phenomena. Overall, the inequality was on a downward trend, with the Theil index falling from 0.4109 in 1997 to 0.1957 in 2021. Meanwhile, the decomposition of the national inequality revealed that the within-group inequality declined from 0.3991 to 0.1634, which was greater than between-group inequality, based on zoning the 28 provinces into three grain production functional areas. As for the three kaya factors, the energy intensity contributed the most to the overall inequality, followed by the agricultural economic development and CO2 emission intensity. Based on these results, this study provided some potential strategies to reduce agricultural-related CO2 emissions.

Home range and home range overlap can be used to describe use of space and movement of wildlife. During the last years, advancements in technology have greatly improved our understanding of animal movement, especially among large herbivores. Wild ungulate abundance and distribution have increased in temperate areas. Moreover, their diseases-including sarcoptic mange in the Iberian Ibex (Capra pyrenaica)-have become a cause of concern for livestock, public health, and wildlife conservation. In this study, we first reviewed existing literature on the home range of species in the genus Capra. We then analyzed data from 52 GPS-GSM-collared Iberian ibexes, of which 33 were healthy and 19 were affected by sarcoptic mange from 3 different populations in the southeastern Iberian Peninsula to analyze: (1) differences in size and characteristics of home ranges obtained by the 3 most commonly used methodologies-minimum convex polygon, kernel density estimation, and Brownian bridges movement models (BBMMs); and (2) the impact of endemic sarcoptic mange on Iberian Ibex home range. The literature review revealed that available information on spatial behavior of Capra spp. was based only on 3 species, including the Iberian Ibex, estimated through a diversity of methods which made it difficult to compare results. We found positive correlations among the different home range estimation methods in the Iberian Ibex, with BBMMs proving to be the most accurate. This study is the first to use BBMMs for estimating home range in this species, and it revealed a marked seasonal behavior in spatial use, although sarcoptic mange smoothed such seasonal pattern. The seasonal overlaps obtained suggest that core areas of the Iberian Ibex change within wider home range areas, which are ecological parameters relevant to identifying key areas for species management and conservation.

The aging intensity (AI), defined as the ratio of the instantaneous hazard rate and a baseline hazard rate, is a useful tool for the describing reliability properties of a random variable corresponding to a lifetime. In this work, the concept of AI is introduced in step-stress accelerated life testing (SSALT) experiments, providing new insights to the model and enabling the further clarification of the differences between the two commonly employed cumulative exposure (CE) and tampered failure rate (TFR) models. New AI-based estimators for the parameters of a SSALT model are proposed and compared to the MLEs in terms of examples and a simulation study.

Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more accessible, alternative approaches such as binned frequencies using histograms and k-nearest neighbors (k-NN) have been proposed. However, a systematic comparison of the applicability of these methods has been lacking. We wish to fill this gap by comparing kernel-density-based estimation (KDE) with these two alternatives in carefully designed synthetic test cases. Specifically, we wish to estimate the information-theoretic quantities: entropy, Kullback-Leibler divergence, and mutual information, from sample data. As a reference, the results are compared to closed-form solutions or numerical integrals. We generate samples from distributions of various shapes in dimensions ranging from one to ten. We evaluate the estimators\' performance as a function of sample size, distribution characteristics, and chosen hyperparameters. We further compare the required computation time and specific implementation challenges. Notably, k-NN estimation tends to outperform other methods, considering algorithmic implementation, computational efficiency, and estimation accuracy, especially with sufficient data. This study provides valuable insights into the strengths and limitations of the different estimation methods for information-theoretic quantities. It also highlights the significance of considering the characteristics of the data, as well as the targeted information-theoretic quantity when selecting an appropriate estimation technique. These findings will assist scientists and practitioners in choosing the most suitable method, considering their specific application and available data. We have collected the compared estimation methods in a ready-to-use open-source Python 3 toolbox and, thereby, hope to promote the use of information-theoretic quantities by researchers and practitioners to evaluate the information in data and models in various disciplines.