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Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power 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.)

Evaluating and addressing the risks posed by runoff and electricity prices in hydropower plants involved in medium-to long-term scheduling is a pressing issue. To address this， a scheduling model is proposed that aims to maximize expected net revenue while minimizing medium- to long-term operational risks. First， using the conditional value at risk （CVaR） theory， marginal distribution functions are utilized to characterize the risks of runoff uncertainty and electricity price volatility （EPV）， enabling accurate risk assessment for market-operated hydropower plants. Second， the least-squares cross validation （LSCV） is applied to determine the optimal bandwidth parameter in kernel density estimation （KDE）， ensuring a good fit for discrete runoff and electricity price data. Next， a Copula-Monte Carlo simulation method is used to model the joint risks of runoff and electricity prices， with Latin hypercube sampling （LHS） employed to enhance computational precision. Finally， case simulation and analysis are conducted to validate the effectiveness of the proposed model.

Background: Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling. Aim: Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones. Methods: On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach's training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal–Wallis or Kolmogorov–Smirnov tests for training impulse and KDE scores, respectively. Results: Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both P <.001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones. Conclusion: 2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations. [ABSTRACT FROM AUTHOR]

Background: The crested ibis, a species that relies on wetland ecosystems for survival, was once found throughout East Asia but has declined to near extinction in Korea, Russia, and Japan, except China. Artificial propagation of seven individuals found in Yangxian, Shaanxi Province, China has resulted in a stable population. Furthermore, South Korea and Japan are working on restoring populations through donations from China. Artificial propagation began in 2008, and in 2019, 40 individuals born between 2014 and 2018 were released into the natural habitat for the first time. We conducted this study to analyze the habitat environment, home range, and habitat usage patterns of a 2016-born male and a 2017-born female who attempted to reproduce naturally for the first time. Results: After forming a breeding pair on April 3, 2020, the pair made two breeding attempts, built a nest in Pinus densiflora, and succeeded in hatching the chicks, but failed to raise them. The home range analysis showed that the area was 1.777–2.425 km² for MCP 100%, and 0.347–2.085 km² for 95% KDE. Meanwhile, the core habitat ranged from 0.007 to 0.296 km² (KDE 50%), indicating differences depending on the time of year and the individual being studied. Breeding pairs were estimated to spend over 50% of their recorded occurrences within 50 m during nesting for incubation, resting, and other activities. They mainly used in paddy fields, but from April to June, when onions and garlic were growing, they searched for food in fields, cemeteries, reservoirs, and other areas. Conclusion: Breeding pairs have increasingly become more active near the nest, and Changnyeong-gun, where they were released, has large agricultural land suitable for crested ibis habitat. However, there is a problem that during the breeding season from April to June, most paddy fields are maintained as garlic and onion fields, which are then converted back for rice cultivation from May to June through double-cropping. Accordingly, for stable laying and rearing, it is necessary to contemplate how to maintain rice paddies, which serve as feeding grounds in the core habitats. [ABSTRACT FROM AUTHOR]

The devastating effects of rain-induced attenuation on communication links operating above 10 GHz during rainy events can significantly degrade signal quality, leading to interruptions in service and reduced data throughput. Understanding the spatial and seasonal distribution of rain heights is crucial for predicting these attenuation effects and for network performance optimization. This study utilized ten years of atmospheric temperature and geopotential height data at seven pressure levels (1000, 850, 700, 500, 300, 200, and 100 hPa) obtained from the Copernicus Climate Data Store (CDS) to deduce rain heights across nine stations in South Africa. The kernel density estimation (KDE) method was applied to estimate the temporal variation of rain height. A comparison of the measured and estimated rain heights shows a correlation coefficient of 0.997 with a maximum percentage difference of 5.3%. The results show that rain height ranges from a minimum of 3.5 km during winter in Cape Town to a maximum of about 5.27 km during the summer in Polokwane. The spatial variation shows a location-dependent seasonal trend, with peak rain heights prevailing at the low-latitude stations. The seasonal variability indicates that higher rain heights dominate in the regions (Polokwane, Pretoria, Nelspruit, Mahikeng) where there is frequent occurrence of rainfall during the winter season and vice versa. Contour maps of rain heights over the four seasons (autumn, spring, winter, and summer) were also developed for South Africa. The estimated seasonal rain heights show that rain-induced attenuations were grossly underestimated by the International Telecommunication Union (ITU) recommended rain heights at most of the stations during autumn, spring, and summer but fairly overestimated during winter. Durban had a peak attenuation of 15.9 dB during the summer, while Upington recorded the smallest attenuation of about 7.7 dB during winter at a 0.01% time exceedance. Future system planning and adjustments of existing infrastructure in the study stations could be improved by integrating these localized, seasonal radio propagation data in link budget design. [ABSTRACT FROM AUTHOR]

Climate change and human activities combine to alter river hydrology, thereby threatening the health of river ecosystems. Quantifying the impacts of climate change and human activities on ecological flow assurance is essential for water resource management and river ecological protection. However, fewer studies quantify the impacts of climate change and human activities on ecological flow assurance based on a complete set of frameworks. The present study introduces an integrated assessment framework designed to quantify the impacts of climate change and human activities on ecological flow security. The framework includes the following steps: (1) natural river runoff reconstruction utilizing a semi‐distributed hydrological model (SWAT), (2) calculation of the most suitable ecological stream flow of the watershed ecosystem by using the non‐parametric kernel density estimation method, (3) calculation of the safety and security levels under minimum ecological flow and appropriate ecological flow conditions in the watershed and (4) quantification of the influences of climate change and human activities on the security of ecological flow in the watershed through the application of a quantitative attribution method. The impact of climate change and human activities on the ecological flow assurance level was analysed using three hydrological stations in Xiangtan, Hengyang and Laobutou, which are the main tributaries of the Xiangjiang River Basin, as a case study. The findings indicated a substantial decrease in ecological flow assurance levels across the basin during the period of human impact (1991–2019). The quantitative assessment results suggest that human activities predominantly drive the degradation of ecological flow assurance throughout the period of human impact, accounting for 57.05% of the total impact. Extensive gradient reservoir scheduling and anthropogenic water withdrawals were the main factors contributing to the degradation of ecological flow assurance in the study basin. The methodology and findings presented in this study offer insights into the evolutionary characteristics and driving forces behind ecological flow security in a dynamic environment. Furthermore, they establish a scientific foundation for local water resource management and river ecosystem protection. [ABSTRACT FROM AUTHOR]

This paper focuses on the monitoring and prediction of the energy consumption of IT equipment to make informed decisions in terms of energy efficiency. The challenge with current monitoring systems lies in their specialization, scalability and integration complexities. To overcome these challenges, we propose a monitoring and prediction system for energy consumption of IT equipment. The proposed solution combines an adaptable, cost-effiective and energy-Efficient embedded device with open source software and a service-oriented architecture (SOA), which offers flexibility and integration capabilities, facilitating the easy inclusion of several workstation working from different environments. Several traditional Linear Regression (LR) models were evaluated prediction models using a temporal window of hour taking into account several features. As a result of the LR models evaluation, it is established that the Bayesian Ridge model was the best model since it presented the lowest error and the highest coefficient of determination. Finally, two approaches were evaluated to predict energy consumption: a Kernel Density Estimation (KDE)-based mechanism proposed to generate predictor variables in order to predict future energy using the best LR model, and a KDE-based energy model. Numerical results show that the energy prediction using KDE for the energy measurements provides lower time response than the LR based mechanism for the available dataset. [ABSTRACT FROM AUTHOR]

The global climate models (GCMs) are sophisticated tools for determining how the climate system will respond. However, the output of GCMs has a coarse resolution, which is unsuitable for basin-level modelling. Global climate models need to be downscaled at a local/basin scale to determine the impacts of climate change on hydrological responses. The present study attempted to evaluate how effectively various large-scale predictors could reproduce local-scale rain in 35 different locations in India using artificial neural networks (ANN), change-factors (CF), K-nearest neighbour (KNN), and multiple linear regression (MLR). The selection of predictors is made based on the correlation value. As potential predictors, air temperature, geo-potential height, wind velocity component, and relative humidity at specific mean sea-level pressure are selected. The comparison of four different downscaling methods concerning the reproduction of various statistics such as mean, standard deviation at chosen locations, quantile–quantile plots, cumulative distribution function, and kernel density estimation of the PDFs of daily rainfall for selected stations is examined. The CF approach outperforms the other methods at almost all sites (R2 = 0.92–0.99, RMSE = 1.37–28.88 mm, and NSE = –16.55–0.99). This also closely resembles the probability distribution pattern of IMD data. [ABSTRACT FROM AUTHOR]

Abstract Accurate wind power prediction can effectively alleviate the pressure of the power system peak frequency regulation, and is more conducive to the economic dispatch of the power system. To enhance wind power forecasting accuracy, a hybrid approach for wind power interval prediction is proposes in this study. Firstly, an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is applied to decompose the initial wind power sequence into multiple modes, and Variational Mode Decomposition is used to further decompose the high-frequency non-stationary components. Next, Fuzzy Entropy (FE) is utilized to assess the complexity of the post-decomposed Intrinsic Mode Functions (IMFs), and different forecasting methods are employed accordingly, the point predictions were obtained by linearly summing the component predictions.Additionally, an improved sparrow search algorithm (ISSA) is used to seek the optimal hyperparameters of the prediction algorithm. Finally, the prediction intervals are constructed using the point prediction results based on kernel density estimation (KDE). The root mean square errors (RMSE) of deterministic predictions are 2.8458 MW and 1.8605 MW, with uncertainty coverage rates of 95.83% and 97.92% at a 95% confidence level.

Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic accidents across the four major Californian cities—Los Angeles, Sacramento, San Diego, and San Jose—over five years. It achieves this through an integration of Geographic Information System (GIS) functionalities (space–time cube analysis) with non-parametric statistical and spatial techniques (DBSCAN, KDE, and the Getis-Ord Gi* method). Our findings from the temporal analysis showed that the most accidents occurred in Los Angeles over five years, while San Diego and San Jose had the least occurrences. The severity maps showed that the majority of accidents in all cities were level 2. Moreover, spatio-temporal dynamics, captured via the space–time cube analysis, visualized significant accident hotspot locations. The clustering of accidents using DBSCAN verified the temporal and hotspot analysis results by showing areas with high accident rates and different clustering patterns. Additionally, integrating KDE with the population density and the Getis-Ord Gi* method explained the relationship between high-density regions and accident occurrences. The utilization of GIS-based analytical techniques in this study shows the complex interplay between accident occurrences, severity, and demographic factors. The insight gained from this study can be further used to implement effective data-driven road safety strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: The expansion of road networks poses a significant threat to wildlife, particularly reptiles and amphibians, within protected areas (PAs). To address this concern, we examined road mortality patterns among herpetofauna in a Greek-protected wetland over 12 years (2008–2019), utilizing a combination of statistical modeling and spatial analysis. We aimed to identify the most vulnerable species, seasonal variations, and ecological determinants of roadkill patterns. Across 14 documented species, 340 roadkill incidents were recorded, with snakes comprising over 60% of encounters. Both environmental and road-related factors significantly influenced roadkill risk. Spatial analysis techniques pinpointed critical hotspots, particularly in the southeastern region of the study area. These findings highlight the need for targeted mitigation strategies to protect herpetofauna within this PA. Understanding the specific factors influencing roadkill patterns is crucial for implementing effective conservation measures and safeguarding these vulnerable species. The pervasive expansion of human-engineered infrastructure, particularly roads, has fundamentally reshaped landscapes, profoundly affecting wildlife interactions. Wildlife-vehicle collisions, a common consequence of this intricate interplay, frequently result in fatalities, extending their detrimental impact within Protected Areas (PAs). Among the faunal groups most susceptible to road mortality, reptiles and amphibians stand at the forefront, highlighting the urgent need for global comprehensive mitigation strategies. In Greece, where road infrastructure expansion has encroached upon a significant portion of the nation's PAs, the plight of these road-vulnerable species demands immediate attention. To address this critical issue, we present a multifaceted and holistic approach to investigating and assessing the complex phenomenon of herpetofauna road mortality within the unique ecological context of the Lake Karla plain, a rehabilitated wetland complex within a PA. To unravel the intricacies of herpetofauna road mortality in the Lake Karla plain, we conducted a comprehensive 12-year investigation from 2008 to 2019. Employing a combination of statistical modeling and spatial analysis techniques, we aimed to identify the species most susceptible to these encounters, their temporal and seasonal variations, and the ecological determinants of their roadkill patterns. We documented a total of 340 roadkill incidents involving 14 herpetofauna species in the Lake Karla's plain, with reptiles, particularly snakes, being more susceptible, accounting for over 60% of roadkill occurrences. Moreover, we found that environmental and road-related factors play a crucial role in influencing roadkill incidents, while spatial analysis techniques, including Kernel Density Estimation, the Getis-Ord Gi*, and the Kernel Density Estimation plus methods revealed critical areas, particularly in the south-eastern region of Lake Karla's plain, offering guidance for targeted interventions to address both individual and collective risks associated with roadkill incidents. [ABSTRACT FROM AUTHOR]

The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of "middle-high and low-outside." (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level. [ABSTRACT FROM AUTHOR]

The paper "Late Tripolye Culture Settlement Spatial Patterning: Case study from the Gordineşti II-Stînca goală site, Northern Moldova" deals with the archaeological excavations at the Gordineşti II-Stînca goală site in Northern Moldova. The authors present the results of investigations into the structural patterning of residential structures, ground-penetrating radar surveys, and desk-based kernel density estimation (KDE) analyses in order to gain a better understanding of the spatial organization of the settlement. The results indicate the high archaeological potential of the site, with likely multiple systematically and functionally arranged residential structures. The paper emphasizes the importance of studying the spatial organization of settlements for understanding prehistoric societies. [Extracted from the article]

In order to improve the reliability of power supply, the sophisticated design of the structure of electric cable network has become an important issue for modern urban distribution networks. In this paper, an electric cable path planning model based on heuristic optimization algorithm considering mixed-land scenario is proposed. Firstly, based on different land samples, the kernel density estimation (KDE) and the analytic hierarchy process (AHP) are used to estimate the construction cost of each unit grid, in order to construct the objective function of comprehensive investment for electric cable loop network. Then, the ant colony optimization (ACO) was improved in pheromone concentration, factor increment and search direction to accelerate the solving speed, and the cable path planning result with minimum construction cost is obtained. Finally, the feeder’s tie line of the cable loop network is planned by the genetic algorithm (GA) to achieve the minimum operating cost. In the case analysis, compared with the traditional method, not only the subjective factors in the process of investment estimation can be avoided, but also the speed of model solving and the quality of the optimal solution are improved. [ABSTRACT FROM AUTHOR]

Understanding the risks associated with the likelihood of extreme events and their respective consequences for the stability of hydraulic infrastructures is essential for flood forecasting and engineering design purposes. Accordingly, a hydrological methodology for providing reliable estimates of extreme discharge flows approaching hydraulic infrastructures was developed. It is composed of a preliminary assessment of missing data, quality and reliability for statistically assessing the frequency of flood flows, allied to parametric and non-parametric methods. Model and parameter uncertainties are accounted for by the introduced and proposed modified model averaging (modified MM) approach in the extreme hydrological event's prediction. An assessment of the parametric methods accuracy was performed by using the non-parametric Kernel Density Estimate (KDE) as a benchmark model. For demonstration and validity purposes, this methodology was applied to estimate the design floods approaching the case study 'new Hintze Ribeiro bridge', located in the Douro river, one of the three main rivers in Portugal, and having one of Europe's largest river flood flows. Given the obtained results, the modified MM is considered a better estimation method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology 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 current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.

[Display omitted] • Extraction TimeGAN is for data enhancement of aero engine gas path fault data. • Extraction TimeGAN can generate gas path combination fault data. • Extraction TimeGAN can simplify the fault diagnosis model. Aero-engine is the core power component of aircraft, and the accurate processing of engine monitoring data is the key to ensure its reliable operation and prevent faults. In the use and maintenance of aero-engines, the multi-combination fault data acquisition of gas path system is expensive and unavailable, thus intelligent multi-combination fault diagnosis under small samples scenarios poses a significant challenge. Aero-engine gas path combination fault data exhibit strong coupling, making it difficult for data augmentation methods to produce high-quality data. To solve the above problem, a novel aero-engine gas path combination fault data augmentation method based on Extraction TimeGAN is proposed. First, original data of multi-combination faults were obtained from Gasturb. To increase the complexity of generating data, Gaussian white noise of Signal-to-Noise Ratio (SNR) 20 was injected into the original data. Then, new samples with similar characteristics were generated from the original data according to different fault categories by TimeGAN, and the original data and generated data were both mapped to the latent space by Principal Component Analysis (PCA). Finally, the optimized extraction mechanism of generated data was established, and the high-density original data was identified and divided in the latent space through Kernel Density Estimation (KDE), the high-density regional hypersphere was constructed by Support Vector Data Description (SVDD), and the generated data located inside the hypersphere is extracted as the optimized generated data. In the multi-combination fault diagnosis experiment of aero-engine gas path, Area Under the Curve (AUC) of Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) reaches 99.74%, 99.82% and 99.85%. Under high noise conditions, the AUC of Extraction TimeGAN only decreases 0.94%. Furthermore, Under the condition of small sample, Extraction TimeGAN maintains the AUC of over 99%. [ABSTRACT FROM AUTHOR]

Abstract Monochrome drawn beads were widely circulated in South and Southeast Asia as early as the second century BC. This article aims to identify the glass beads unearthed from different sites in China and discuss their possible sources. Twenty-seven mineral soda alumina (m-Na-Al) glass and eighty-seven potash glass beads unearthed in different provinces in China were analysed by Laser Ablation Inductively Coupled Plasma Mass Spectrometry. The trace element analysis through Multivariate Kernel Density Estimation and Maximum Mean Discrepancy indicates the silica sources of m-Na-Al glass and most of the potash glass unearthed from Guangxi are identical. They were presumably produced somewhere in northeastern India or Southeast Asia and exported through the Maritime Silk Road. The silica sources of m-Na-Al glass in Henan and the rest of the potash glasses are geologically close. They were likely produced in southern India or Sri Lanka and exported through the North and Southwest Silk Roads. Future research on isotopic analysis will reveal more information about primary/secondary glass production in China, South and Southeast Asia.

The cutting force spectrum of the CNC lathe is the basic data for the reliability design, reliability test, and reliability evaluation of the CNC lathe and its components. Due to the complex and changeable turning conditions and different cutting processes, the cutting load presents multi-peak characteristics. At the same time, grouping the counted load cycles when parameter modeling will produce certain errors. As a result, the parameter distribution model cannot meet the modeling requirements. Thus, a compilation method based on kernel density estimation (KDE) of goodness-smoothness comprehensive evaluation (G-SCE) is proposed. The KDE is used to establish the dynamic cutting force distribution of the CNC lathe in which grouping the counted load cycles is not needed. For the bandwidth-determining methods, the rule of thumb method (ROT) and the least-squares cross-validation method (LCV) do not take into account the influence of different bandwidths on the goodness estimation and the smoothness of the estimated curve, and the G-CSE for KDE is proposed to determining the optimal bandwidth. It includes the estimation accuracy test method based on multiple goodness-of-fit tests and the smoothness test method based on the envelope curve, and the entropy method is used to comprehensively weights the estimated goodness index and the smoothness index to determine the optimal bandwidth. The results of the case analysis indicate that the method proposed can solve the problem of too large estimation error of parameter distribution for multimodal distribution. At the same time, it can better comprehensively evaluate the KDE under different bandwidths. In short, a new method of optimal bandwidth selection is proposed in the original method. [ABSTRACT FROM AUTHOR]

The great disparities in resource availability, power, wealth, social institution and opportunities have led to rise in crime incidents especially in urban agglomerations. Therefore, addressing violence and mapping of crime hotspots is important in order to build a socially sustainable community. Current work aims to apply Space–Time Permutation Model (STPM) inbuilt in SatScan (an open source statistical tool), for crime hotspot detection in Pune city, Maharashtra, India, and compare the result with two GIS-based statistical methods namely, Kernel Density Estimation (KDE), and Getis-Ord Gi*, being utilized extensively for crime hotspot detection and mapping. The datasets of four different crime namely robbery, molestation, rape and dacoity for a period from 2012 to 2015 were used. Twenty six significant crime clusters (p < 0.1, log likelihood ratio, LLR > 1.3) obtained through SatScan show similar pattern, as obtained from KDE (92% matching) and Getis-Ord Gi* (92% matching). A total of 11 clusters were obtained as most significant clusters (with p < 0.000, LLR > 2.72, predictive accuracy index, PAI > 57.11). Locations situated in the central part of the city such as Swargate, Bibwewadi, Deccan, Bund Garden, and Farashkhana show relatively more crime clusters possibly due to inter-state road connectivity, more liquor shops, famous tourist places, large no. of auto rickshaws and tongawals, and a variety of immigrants in the search of livelihood. [ABSTRACT FROM AUTHOR]

Migratory species interact with different ecosystems in different regions during migration, making them more environmentally sensitive and therefore more vulnerable to extinction. Long migration routes and limited conservation resources desire clear identification of conservation priorities to improve the allocation efficiency of conservation resources. Clarifying the spatio-temporal heterogeneity of the utilization intensity during migration is an effective way to guide the conservation areas and priority. 12 Oriental White Storks (Ciconia boyciana), listed as an “endangered” species by the IUCN, were equipped with satellite-tracking loggers to record their hourly location throughout the year. Then, combined with remote sensing and dynamic Brownian Bridge Movement Model (dBBMM), characteristics and differences between spring and autumn migration were identified and compared. Our findings revealed that: (1) the Bohai Rim has always been the core stopover area for the Storks’ spring and autumn migration, but the utilization intensity has spatial differences; (2) differences in habitat selection resulted in differences in the Storks’ spatial distribution, thus affecting the efficiency of existing conservation systems; (3) the shift of habitat from natural wetlands to artificial surfaces calls for the development of eco-friendly land use mode; (4) the development of satellite tracking, remote sensing, and advanced data analysis methods have greatly facilitated movement ecology, even though they are still under development.

Remotely sensed morphological traits have been used to assess functional diversity of forests. This approach is potentially spatial-scale-independent. Lidar data collected from the ground or by drone at a high point density provide an opportunity to consider multiple ecologically meaningful traits at fine-scale ecological units such as individual trees. However, high-spatial-resolution and multi-trait datasets used to calculate functional diversity can produce large volumes of data that can be computationally resource demanding. Functional diversity can be derived through a trait probability density (TPD) approach. Computing TPD in a high-dimensional trait space is computationally intensive. Reductions of the number of dimensions through trait selection and principal component analysis (PCA) may reduce the computational load. Trait selection can facilitate identification of ecologically meaningful traits and reduce inter-trait correlation. This study investigates whether kernel density estimator (KDE) or one-class support vector machine (SVM) may be computationally more efficient in calculating TPD. Four traits were selected for input into the TPD: canopy height, effective number of layers, plant to ground ratio, and box dimensions. When simulating a high-dimensional trait space, we found that TPD derived from KDE was more efficient than using SVM when the number of input traits was high. For five or more traits, applying dimension reduction techniques (e.g., PCA) are recommended. Furthermore, the kernel size for TPD needs to be appropriate for the ecological target unit and should be appropriate for the number of traits. The kernel size determines the required number of data points within the trait space. Therefore, 3–5 traits require a kernel size of at least 7 × 7 pixels. This study contributes to improving the quality of TPD calculations based on traits derived from remote sensing data. We provide a set of recommendations based on our findings. This has the potential to improve reliability in identifying biodiversity hotspots. [ABSTRACT FROM AUTHOR]

Geochronological databases are powerful tools for characterizing the crustal evolution and the age spectra of a region and allow comparison with other areas at a regional scale. In this contribution, we present the Colombian Geochronological Database (CGD), which contains a curated compilation of ca. 67,406 individual published U-Th-Pb, K-Ar, Ar-Ar, Rb-Sr, Sm-Nd, Lu-Hf, Fission-track, U-Th-He, and Re-Os mineral and whole rock ages that are reported in the published literature. Each date includes geographic coordinates, geological setting, petrologic and chemical information extracted from the respective publications. The structure of the database provides a powerful interface for constructing queries and allows searching and extracting information on geographic domains, provinces, stratigraphic units, isotopic systems, date interpretations, references, etc. This information establishes a framework for regional and global geological interpretations with geochronological, stratigraphic, structural and palaeogeographic implications. With the present effort we present to the geoscience community a clear insight, from a regional perspective, to the geology and tectonics of Colombia since Precambrian times. The comparison of all (detrital and magmatic) single zircon U-Pb dates from the Colombian (Gondwana sourced) geochronological database with the Global and North American (Laurentia sourced) databases provides a temporal constraint on the evolution of the South American continent. U-Pb zircon ages in Colombia define 13 peak clusters centred at 1767, 1530, 1325, 1178, 1007, 605,540, 468, 271, 237, 182, 76 and 10 Ma but of those, only few have a good correlation in all three databases: 1007 (Grenvillian/Orinoquian/Putumayo Orogeny), 605 (Braziliano/Pan-African Orogeny), 468 (Famatinian/Taconic Orogeny), and 182 Ma (Break-up of Pangea) zircon peaks. This correlation suggests that some tectonic events in Colombia are global and might represent crustal production and preservation while the other peaks might just represent local arc magmatic events. [ABSTRACT FROM AUTHOR]

Numerous facets of scientific research implicitly or explicitly call for the estimation of probability densities. Histograms and kernel density estimates (KDEs) are two commonly used techniques for estimating such information, with the KDE generally providing a higher fidelity representation of the probability density function (PDF). Both methods require specification of either a bin width or a kernel bandwidth. While techniques exist for choosing the kernel bandwidth optimally and objectively, they are computationally intensive, since they require repeated calculation of the KDE. A solution for objectively and optimally choosing both the kernel shape and width has recently been developed by Bernacchia and Pigolotti (2011). While this solution theoretically applies to multidimensional KDEs, it has not been clear how to practically do so. A method for practically extending the Bernacchia-Pigolotti KDE to multidimensions is introduced. This multidimensional extension is combined with a recently-developed computational improvement to their method that makes it computationally efficient: a 2D KDE on 105 samples only takes 1 s on a modern workstation. This fast and objective KDE method, called the fastKDE method, retains the excellent statistical convergence properties that have been demonstrated for univariate samples. The fastKDE method exhibits statistical accuracy that is comparable to state-of-the-science KDE methods publicly available in R, and it produces kernel density estimates several orders of magnitude faster. The fastKDE method does an excellent job of encoding covariance information for bivariate samples. This property allows for direct calculation of conditional PDFs with fastKDE. It is demonstrated how this capability might be leveraged for detecting non-trivial relationships between quantities in physical systems, such as transitional behavior.

Numerous facets of scientific research implicitly or explicitly call for the estimation of probability densities. Histograms and kernel density estimates (KDEs) are two commonly used techniques for estimating such information, with the KDE generally providing a higher fidelity representation of the probability density function (PDF). Both methods require specification of either a bin width or a kernel bandwidth. While techniques exist for choosing the kernel bandwidth optimally and objectively, they are computationally intensive, since they require repeated calculation of the KDE. A solution for objectively and optimally choosing both the kernel shape and width has recently been developed by Bernacchia and Pigolotti (2011). While this solution theoretically applies to multidimensional KDEs, it has not been clear how to practically do so. A method for practically extending the Bernacchia-Pigolotti KDE to multidimensions is introduced. This multidimensional extension is combined with a recently-developed computational improvement to their method that makes it computationally efficient: a 2D KDE on 105 samples only takes 1 s on a modern workstation. This fast and objective KDE method, called the fastKDE method, retains the excellent statistical convergence properties that have been demonstrated for univariate samples. The fastKDE method exhibits statistical accuracy that is comparable to state-of-the-science KDE methods publicly available in R, and it produces kernel density estimates several orders of magnitude faster. The fastKDE method does an excellent job of encoding covariance information for bivariate samples. This property allows for direct calculation of conditional PDFs with fastKDE. It is demonstrated how this capability might be leveraged for detecting non-trivial relationships between quantities in physical systems, such as transitional behavior.

Within a supply chain organisation, where millions of messages are processed, reliability and performance of message throughput are important. Problems can occur with the ingestion of messages; if they arrive more quickly than they can be processed, they can cause queue congestion. This paper models data interchange (EDI) messages. We sought to understand how best DevOps should model these messages for performance testing and how best to apply smart EDI content awareness that enhance the realms of Ambient Intelligence (Aml) with a Business-to business (B2B) supply chain organisation. We considered key performance indicators (KPI) for over- or under-utilisation of these queueing systems. We modelled message service and inter-arrival times, partitioned data along various axes to facilitate statistical modelling and used continuous parametric and non-parametric techniques. Our results include the best fit for parametric and non-parametric techniques. We noted that a one-size-fits-all model is inappropriate for this heavy-tailed enterprise dataset. Our results showed that parametric distribution models were suitable for modelling the distribution's tail, whilst non-parametric kernel density estimation models were better suited for modelling the head of a distribution. Depending on how we partitioned our data along the axes, our data suffer from quantisation noise. [ABSTRACT FROM AUTHOR]

Uncertainty of getting admission into universities / institutions is one of the global challenges in academic environment. The students are having good marks with high credential but not sure about getting their admission into universities / institutions. In this research study the researcher built a predictive model using Naïve Bayes Classifiers --machine learning algorithm to extract and analyze hidden pattern in students' academic records and their credentials. The main objective of this research study is to reduce uncertainty for getting admission into universities / institutions on the basis of their previous credentials and some other essentials parameters. This research study presents a joint venture of Naïve Bayes Classification and Kernel Density Estimations (KDE) approach to predict student's admission into universities or any higher institutions. The predictive model is built on training dataset of students' examination score such as GPA, GRE, RANK and some other essential features that offered the admission with a predictor accuracy rate of 72% and has been experimentally verified. To improve the quality of accuracy of predictive model the researcher used the Shapiro-Walk Normality Test and Gaussian distribution on large datasets. The predictive model helps in reducing the admission uncertainty and enhances the universities decision making capabilities. The significance of this research study is to reduce human intervention for making decisions with respect to students' admission into universities or any higher academic institutions, and it demonstrates that many universities and higher-level institutions could use this predictive model to improve their admission process without human intervention. [ABSTRACT FROM AUTHOR]

Smart cities are attracting much interest in terms of future development. As new technologies come on stream, ordinary towns are reshaping themselves as smart cities, where technology is used to improve connections between all elements of the town. The technology can be embedded everywhere and can harvest data for dedicated smart city applications. Smart cities will have a huge number of different devices running these applications. There will be a substantial amount of data associated with these devices. In the interlinked smart city environment, many different messages could be shared between them. Such devices will be associated with many security risks and privacy issues, as many of the shared statistics could also hold personal data. A substantial review of research has been recently undertaken to ensure that data will be safe in the smart city environment. This review has included all the latest research in the area and is intended to ensure that all the data required to run green smart cities and the devices required for them will remain secure and confidential. [ABSTRACT FROM AUTHOR]

The spatial relationship between transport networks and retail store locations is an important topic in studies related to commercial activities. Much effort has been made to study physical street networks, but they are seldom empirically discussed with considerations of transport flow networks from a temporal perspective. By using Beijing's bus and subway smart card data (SCD) and point of interest (POI) data, this study examined the location patterns of various retail stores and their daily dynamic relationships with three weighted centrality indices in the networks of public transport flows: degree, betweenness, and closeness. The results indicate that most types of retail stores are highly correlated with weighted centrality indices. For the network constructed by total public transport flows in the week, supermarkets, convenience stores, electronics stores, and specialty stores had the highest weighted degree value. By contrast, building material stores and shopping malls had the weighted closeness and weighted betweenness values, respectively. From a temporal perspective, most retail types' largest correlations on weekdays occurred during the after-work period of 19:00 to 21:00. On weekends, shopping malls and electronics stores changed their favorite periods to the daytime, while specialty stores favored the daytime on both weekdays and weekends. In general, the higher store type level of the shopping malls correlates more to weighted closeness or betweenness, and the lower-level store type of convenience stores correlates more to weighted degree. This study provides a temporal analysis that surpasses previous studies on street centrality and can help with urban commercial planning. [ABSTRACT FROM AUTHOR]

Fire Service is the fundamental civic service to protect citizens from irrecoverable, heavy losses of lives and property. Hotspot analysis of structure fires is essential to estimate people and property at risk. Hotspot analysis for the peak period of last decade, using a GIS-based spatial analyst and statistical techniques through the Kernel Density Estimation (KDE) and Getis-Ord Gi* with Inverse Distance Weighted (IDW) interpolation is performed, revealing fire risk zones at the city ward micro level. Using remote sensing, outputs of hotspot analysis are integrated with the built environment of Land Use Land Cover (LULC) to quantify the accurate built-up areas and population density of identified fire risk zones. KDE delineates 34 wards as hotspots, while Getis-Ord Gi* delineates 17 wards within the KDE hotspot, the central core areas having the highest built-up and population density. A temporal analysis reveals the maximum fires on Thursday during the hot afternoon hours from 12 noon to 5 p.m. The study outputs help decision makers for effective fire prevention and protection by deploying immediate resource allocations and proactive planning reassuring sustainable urban development. Furthermore, updating the requirement of the National Disaster Management Authority (NDMA) to build urban resilient infrastructure in accord with the Smart City Mission. [ABSTRACT FROM AUTHOR]

Green parks in urban areas are believed to enhance the well-being of residents. The importance of green spaces to support health and fitness in urban areas has recently regained interest. Reports released in 2010-2016 by the World Health Organization (WHO) on urban planning, environment, and health stated that green spaces can have a positive impact on physical activity, social and mental well-being, enhance air quality and decrease noise exposure. We analyzed the number of check-ins in various parks of Shanghai by utilizing geotagged social media network check-in data. This article presents a descriptive study using social media data by obtaining the three-year comparison of spatial and temporal patterns of park visits to raise public awareness that green parks provide a healthy environment that can be beneficial for the well-being of urban citizens. We investigated the visitor spatiotemporal behavior in more than 115 green parks in 10 districts of Shanghai with approximately 250,000 check-ins. We examined 3 years of geotagged data and our main findings are: (i) the spatial and temporal variations of users in urban green parks (ii) the gender differences in space and time with relation to urban green parks. The main objective of this article is to present evident data for policymakers on the advantages of providing green spaces access to urban citizens and to facilitate cities with systematic approaches to provide green space access to improve the health of urban citizens.

Random variables and their distributions are a central part in many areas of statistical methods. The Distributions.jl package provides Julia users and developers tools for working with probability distributions, leveraging Julia features for their intuitive and flexible manipulation, while remaining highly efficient through zero-cost abstractions.

Urban green spaces are really vital for the well-being of human in urban areas. In urban planning for green space site selection, the study of the bond among the usage of green spaces and their categories that really influence their use can provide useful references. A spatial and temporal research on the allocation of visitors in 157 green areas was carried out in Shanghai to know which green spaces are denser or crowdsourced by utilizing social media big data. We evaluated the association with statistical testing and Kernel Density Estimation among the spatial pattern of the visitor spread in urban green areas. We used check-in data from social media to test this study comparing the number of humans who visit various green parks. We have classified green areas into various categories and our main findings are focused on their characteristics: (1) famous category of green parks according to visitors' preferences, (2) Differences in the number of visitors by daytime, and (3) crowdsourced area based upon number of check-ins. The main aim of this article is to remind policy makers of the value of providing local people access to green areas and to empower cities with a framework for contacting green parks with the purpose of increasing the comfort of urban people with the architecture of smart city. [ABSTRACT FROM AUTHOR]

The objectives of this study are to present a methodology for identifying high-crash density zones using geographical information systems and apply a kernel density estimation (KDE) for determining most hazardous road segments. Moreover, we connect weather information with the crash analysis to demonstrate the interactions between adverse weather conditions and the surface pavement condition (friction and texture) and apply their relationship to the crash frequencies. The motivation of this study was to identify mainly local regions where most crashes occur and to conduct interventions there, thus improving the safety of the studied segment of the highway. To develop this methodology and conduct the analysis, data of crashes of 7 years (2009-2015) occurring on a heavy-duty Brazilian highway were used. The findings demonstrate that when data of total crashes (e.g., rear-end collision, side-impact collision, sideswipe collision, head on collision, and rollover) are compared with those of skidding crashes, which are generally caused by wet pavements, the critical sections are essentially the same. This indicates that the wet pavement is a significant factor influencing crash occurrences. Therefore, solutions for asphaltic resurfacing that increase friction and aims to improve drainage have the potential to decrease the number of crashes. This study presents a spatial perspective to research on delineating hazardous road segments and the complex issue of how they can be measured. [ABSTRACT FROM AUTHOR]

A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose. The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the techniques are also highlighted with a conclusion as to the most accurate one for vehicle detection. [ABSTRACT FROM AUTHOR]

Granular computing (GrC) is an essential tool to solve human real problem since the information granules is close to human perception schemes. In GrC, both classification accuracy and interpretability play significant roles. Fuzzy rule (FR) based classification systems are effective methods solving this problem. However, the accuracy of FR may be decreased when solving some complex application. In this paper, a novel model called FR–KDE integrating the FR and kernel density estimation (KDE) in the framework of Dempster–Shafer evidence theory is proposed to deal with the classification problem. By fusing the result of FR and KDE via the Dempster's combination rule, it can reduce the uncertainty of FR and obtain better accuracy. To illustrate the effect of the FR–KDE approach, it is applied to the medical data classification problem. Experimentally, the results demonstrate that the FR–KDE method is effective in handling biomedical data classification problems. [ABSTRACT FROM AUTHOR]

Within a supply chain organisation, where millions of messages are processed, reliability and performance of message throughput are important. Problems can occur with the ingestion of messages; if they arrive more quickly than they can be processed, they can cause queue congestion. This paper models data interchange (EDI) messages. We sought to understand how best DevOps should model these messages for performance testing and how best to apply smart EDI content awareness that enhance the realms of Ambient Intelligence (Aml) with a Business-to business (B2B) supply chain organisation. We considered key performance indicators (KPI) for over- or under-utilisation of these queueing systems. We modelled message service and inter-arrival times, partitioned data along various axes to facilitate statistical modelling and used continuous parametric and non-parametric techniques. Our results include the best fit for parametric and non-parametric techniques. We noted that a one-size-fits-all model is inappropriate for this heavy-tailed enterprise dataset. Our results showed that parametric distribution models were suitable for modelling the distribution’s tail, whilst non-parametric kernel density estimation models were better suited for modelling the head of a distribution. Depending on how we partitioned our data along the axes, our data suffer from quantisation noise.

Forest fires have been showing an increasing trend in the past few years. Hundreds of hectares of forests are damaged every year. Thus, it is crucial to identify and implement appropriate forest fire management measures. GIS hotspot analysis has become an advantageous technique for the analysis of spatial clustering of forest fires. However, inadequate research has been undertaken on the validation of forest fire hotspot analysis in GIS. The objective of this paper is to validate forest fire hotspots identified by two statistical-based and one non-statistical-based GIS hotspot analysis methods, namely Getis-Ord Gi*, Anselin Local Moran's I and Kernel Density Estimation (KDE), in a study area. The three hotspot analyses were validated by evaluating the spatial interference between the identified forest fire hotspots by each method and existing forest fire contributory factors. The study found that KDE resulted in better spatial matching of forest fire hotspots and forest fire contributory factors, compared to Getis-Ord Gi*. However, Anselin Local Moran's I did not identify any statistically significant forest fire hotspots in the study area. [ABSTRACT FROM AUTHOR]