Identification Of Hotspots Research Articles

Improved thermal network model for hot spot location in traction transformer windings

Obtaining accurate location information of the hot spot in advance is a prerequisite for locating the hot spot. To tackle this problem, we propose an improved thermal network model, which provides a quantitative method for placing fiber optic sensors at hot-spot locations. The initial step is to construct an enhanced thermal network model that takes into account the thermal resistance of the boundary layer using heat transfer theory. Then, we compare this model with Computational Fluid Dynamics (CFD) in example calculations to validate its effectiveness and precision. The results show that our proposed model can accurately and rapidly calculate temperature distribution within winding areas, enabling the identification of hot spots. This information is reliable for installing monitoring equipment to measure transformer temperatures.

Thorough Assessment of Machine Learning Techniques for Predicting Protein-Nucleic Acid Binding Hot Spots

Background: Proteins and nucleic acids are vital biomolecules that contribute significantly to biological life. The precise and efficient identification of hot spots at protein-nucleic acid interfaces is crucial for guiding drug development, advancing protein engineering, and exploring the underlying molecular recognition mechanisms. As experimental methods like alanine scanning mutagenesis prove to be time-consuming and expensive, a growing number of machine learning techniques are being employed to predict hot spots. However, the existing approach is distinguished by a lack of uniform standards, a scarcity of data, and a wide range of attributes. Currently, there is no comprehensive overview or evaluation of this field. As a result, providing a full overview and review is extremely helpful. Methods: In this study, we present an overview of cutting-edge machine learning approaches utilized for hot spot prediction in protein-nucleic acid complexes. Additionally, we outline the feature categories currently in use, derived from relevant biological data sources, and assess conventional feature selection methods based on 600 extracted features. Simultaneously, we create two new benchmark datasets, PDHS87 and PRHS48, and develop distinct binary classification models based on these datasets to evaluate the advantages and disadvantages of various machine-learning techniques. Results: Prediction of protein-nucleic acid interaction hotspots is a challenging task. The study demonstrates that structural neighborhood features play a crucial role in identifying hot spots. The prediction performance can be improved by choosing effective feature selection methods and machine learning methods. Among the existing prediction methods, XGBPRH has the best performance. Conclusion: It is crucial to continue studying hot spot theories, discover new and effective features, add accurate experimental data, and utilize DNA/RNA information. Semi-supervised learning, transfer learning, and ensemble learning can optimize predictive ability. Combining computational docking with machine learning methods can potentially further improve predictive performance.

Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data

Rural tourism serves as a crucial means for fostering rural economic prosperity and inheriting rural culture. The assessment of the quality of rural tourism development and the identification of disparities in rural tourism development among regions have become focal points in current research. This paper utilizes tourism big data to establish a system for evaluating rural tourism popularity and proposes a method for identifying rural tourism hotspots. The study explores the spatiotemporal evolution characteristics and formation mechanisms of the cold and hot patterns of rural tourism in China during two periods (pre-pandemic and post-pandemic) and on two spatial scales (provincial and municipal levels). The research findings indicate that (1) the annual variation in rural tourism popularity exhibits a fluctuating upward trend, with significant seasonal variations on a monthly basis. (2) The spatial pattern of rural tourism popularity changes with the scale effect. At the provincial level, hotspot areas form an east–west dual-core pattern, while at the municipal level, hotspot areas demonstrate an evolution from a three-core to a four-core pattern. In the post-pandemic era, rural tourism popularity in the northwest and southwest regions is experiencing a counter-trend growth. (3) At different spatiotemporal scales, influencing factors and their impact intensities vary. At the provincial level, road density and reception capacity consistently play dominant roles, and per capita disposable income significantly influences early-stage popularity enhancement. At the municipal level, resident population and tourism resources influence are the dominant factors, and the influence of air quality and regional media attention gradually strengthens. This article provides a new perspective on quantitative research in rural tourism, offering significant guidance for the rational allocation of resources in rural tourism, regional tourism collaboration, and the sustainable development of rural tourism in the post-pandemic era.

AirSUM - Structure from Motion supported stock unearthing Method: Erosion modeling in Viennese vineyards

Soil mobilization is particularly high in viticulture. In Austria and especially in Vienna, soil protection measures are frequently implemented without the vintners knowing how high their erosion rates are and where in particular they can introduce additional measures. New, highly accurate and cost-efficient methods for erosion estimation are expected to improve this situation.A relatively fast and low-cost option is the stock unearthing method (SUM), which provides a rough estimation of erosion based on biomarkers, but neglects the inter-row area. The improved stock unearthing method (ISUM) approach uses additional measurement points in this area and therefore delivers more accurate erosion volumes. Additionally, the use of Structure from Motion (SfM) DEMs provides respectable results on small plots in vineyards. This study combines and evaluates the unmanned aerial vehicle (UAV) SfM approach with the SUM. This results in a millimeter scale resolution and can be applied to larger study areas. The combination of SUM and SfM forms the new airSUM approach that provides a more accurate representation of the recent relief, rendering interpolation techniques unnecessary, reproducing visible runoff patterns, and enhancing erosion volume estimations. The use of airSUM enabled the detection of 32.7 ± 17.5 m3 (avg. of ∼ 84.1 ± 45 t ha−1 yr−1) soil erosion on an area of 700 m2 in a period of 8 years. Erosion hotspots could be modeled mainly in the wheel tracks with depths of up to 20.5 cm parallel to the slope and correspond excellently with field observations. This is partly due to the compaction of the surface, but mainly due to the preferential runoff and erosion. The identification of the erosion hot spots, runoff breaches and consequent runoff concentration allows the precise allocation of mitigation funds to reduce overland flow and erosion.

Linking the future likelihood of large fires to occur on mountain slopes with fuel connectivity and topography

AbstractIn the long run, ongoing climate change is expected to alter fuel production as well as the frequency and severity of fire weather, which may result in an unprecedented frequency of extreme fire events. In this paper we propose a simplified and spatially explicit method to assess the probability of experiencing large fires, based on topography (slope length) as well as extent and aggregation of the forested area (fuel connectivity). We considered 21 homogeneous pyroregions covering entire Switzerland as a study case and computed the length of the upslope paths within the forested areas, simulating ignition points on a systematic 100 × 100 m square grid. We then compared the obtained path lengths for each pyroregion with selected historical large forest fire statistics (e.g., mean area of the largest 5% of fires, maximum burnt area per fire) collected over the course of the last 30 years. This resulted in rather high R2 values, ranging from 0.558 to 0.651. The proposed approach was shown to allow for an easy identification and geo-localization of potential hotspots in terms of the likelihood for large fires to occur in mountainous regions, which is a prerequisite for a targeted planning of fire management measures aimed at preventing large fires and related post-fire gravitative natural hazards.

A district-level geospatial analysis of anaemia prevalence among rural men in India, 2019-21

BackgroundDespite its considerable impact on health and productivity, anemia among men has received limited attention. In a country as diverse as India, characterized by extensive geographic variations, there is a pressing need to investigate the nuanced spatial patterns of anemia prevalence among men. The identification of specific hotspots holds critical implications for policymaking, especially in rural areas, where a substantial portion of India’s population resides.MethodsThe study conducted an analysis on a sample of 61,481 rural men from 707 districts of India, utilizing data from the National Family Health Survey-5 (2019-21). Various analytical techniques, including Moran’s I, univariate LISA (Local Indicators of Spatial Association), bivariate LISA, and spatial regression models such as SLM (Spatial Lag Model), and SEM (Spatial Error Model) were employed to examine the geographic patterns and spatial correlates of anaemia prevalence in the study population.ResultsIn rural India, three out of every ten men were found to be anemic. The univariate Moran’s I value for anaemia was 0.66, indicating a substantial degree of spatial autocorrelation in anaemia prevalence across the districts in India. Cluster and outlier analysis identified five prominent ‘hotspots’ of anaemia prevalence across 97 districts, primarily concentrated in the eastern region (encompassing West Bengal, Jharkhand, and Odisha), the Dandakaranya region, the Madhya Pradesh-Maharashtra border, lower Assam, and select districts in Jammu and Kashmir. The results of SLM revealed significant positive association between anaemia prevalence at the district-level and several key factors including a higher proportion of Scheduled Tribes, men in the 49–54 years age group, men with limited or no formal education, individuals of the Muslim faith, economically disadvantaged men, and those who reported alcohol consumption.ConclusionsSubstantial spatial heterogeneity in anaemia prevalence among men in rural India suggests the need for region-specific targeted interventions to reduce the burden of anaemia among men in rural India and enhance the overall health of this population.

Risk from future climate change to Pakistan's protected area network: A composite analysis for hotspot identification

As climate change becomes a primary driver of global ecosystem deterioration and biodiversity loss, protected areas (PAs) are posed to play a crucial conservation role. At a global scale, 17 % of land is currently covered by PAs; a figure expected to reach 30 % by 2030 under the UN post-2020 global biodiversity framework. However, focusing only on the percent coverage of PAs without assessing their efficacy may not accomplish the intended conservation goals. Here, we present the first assessment of the risk from climate change to existing PAs and non-protected lands across Pakistan by combining data on the local exposure and vulnerability of 409 species of birds, mammals, reptiles and amphibians to multidimensional changes in climate by mid (2040–2060) and late (2061–2080) century under two climate emission scenarios (RCP4.5 and RCP8.5). We find that between 7 % (2050 RCP4.5) and 19 % (2080 RCP8.5) of the current network of PAs, mostly located in the eastern and southeastern parts of the country, are projected to be under future extreme risk (i.e., highly exposed areas containing highly vulnerable communities). Importantly, hotspots of risk within these PAs are projected to significantly expand over time and with increasing severity of the scenario. In contrast, PAs in the northern part of the country are projected to remain under moderate to low risk. Results are subject to variability across the country reflecting interesting differences in climate change exposure and species vulnerability between protected and non-protected lands. Importantly, significantly lower level of risks from future climate change are projected for PAs than non-protected lands across emission scenarios and periods suggesting potential candidate areas for the future expansion of the country's PA network. Our analysis provides novel insights that can help inform conservation decisions and management at a time when the country is investing in ambitious efforts to expand its network of protected areas.

Temporally dynamic environmental impact assessment of a building stock: Coupling MFA and LCA

In this study, MFA and LCA are coupled in a prospective analysis of the environmental impact of a building stock at a Danish university campus. The existing buildings are mapped and future growth is identified to create a dynamic LCA inventory. The prospective model is applied in a case study to determine the accumulated environmental impacts associated with the growth of the campus building stock from 2023 to 2050.The findings indicate that the national decarbonization of electricity and heat supply from 2023 to 2035 will deliver notable impact reductions, however the reduction in the overall impact of the building stock by 2050 will be counteracted by growth in new buildings and potential renovation activities. If decarbonization continues post-2035, impact will decrease for particularly global warming potential. The results allow identification of environmental impact hotspots, both spatially and temporally. This supports the development of mitigation strategies to reduce environmental impacts.

A district-level emission inventory of anthropogenic PM2.5 from the primary sources over the Indian Indo Gangetic Plain: Identification of the emission hotspots

A district-wise emission inventory was made for the states and union territories (UTs) of the Indian Indo-Gangetic Plain for the base year of 2018 to estimate the emissions of PM2.5 from various sectors. In addition to conventional sectors, emissions from road dust, fossil-fuelled irrigation pumps, and construction dust were also taken into account. Total primary anthropogenic PM2.5 emission was estimated to be 3157.3 Gg (or kilo-tones) for the year 2018 of which 32 % originated from the industrial sector, 27 % from domestic fuel consumption, 23 % from open burning, 14 % from road dust, 2 % from vehicular and 2 % from various unorganized sectors. The highest emissions were observed during the premonsoon (1013 Gg/year) followed by postmonsoon (802Gg/year), winter (788 Gg/year), and lowest during the monsoon (554Gg/year). Among the states and UTs, Uttar Pradesh contributes the most in total emissions (39 %), followed by Punjab (19 %), Bihar (17 %), West Bengal (13 %), Haryana (11 %), Delhi (0.9 %) and Chandigarh (0.1 %). Emission for per capita and for billion-rupee of state gross domestic product (GDP) were the highest for Punjab and Haryana. Results have identified the districts of Punjab (Firozpur, Ludhiana, Jalandhar), scattered pockets of Uttar Pradesh (Sonbhadra, Agra, Varanasi, Kanpur, Lucknow, Prayagraj) and lower Gangetic delta (Gaya, Muzaffarpur, Burdwan, both 24-parganas and Murshidabad) as potent hotspots of cumulative PM2.5 emissions. On the other hand, the districts of Punjab (Faridkot, Mansa, Muktsar, Fatehgarh) were found to be the hotspots for per capita emissions. High emissions were observed from the domestic sector, brick kilns, and micro and small-scale industries, and regulating norms should be more stringent for these sectors. Such a study will be a value add for the policymakers and health experts to assess emission hot spots, pollution simulation, and associated mortality analysis of the region.

Data-driven decarbonization framework with machine learning

Abstract Eight major supply chains contribute to more than 50% of the global greenhouse gas emissions (GHG). These supply chains range from raw materials to end-product manufacturing. Hence, it is critical to accurately estimate the carbon footprint of these supply chains, identify GHG hotspots, explain the factors that create the hotspots, and carry out what-if analysis to reduce the carbon footprint of supply chains. Towards this, we propose an enterprise decarbonization accelerator framework with a modular structure that automates carbon footprint estimation, identification of hotspots, explainability, and what-if analysis to recommend measures to reduce the carbon footprint of supply chains. To illustrate the working of the framework, we apply it to the cradle-to-gate extent of the palm oil supply chain of a leading palm oil producer. The framework identified that the farming stage is the hotspot in the considered supply chain. As the next level of analysis, the framework identified the hotspots in the farming stage and provided explainability on factors that created hotspots. We discuss the what-if scenarios and the recommendations generated by the framework to reduce the carbon footprint of the hotspots and the resulting impact on palm oil tree yield.

Spatial Analysis to Determine Black Spot Area in Kulon Progo Regency, Yogyakarta, Indonesia

Nowadays the number of traffic accidents increase due to the surging volume of road traffic. The efforts to reduce number of accidents is important, especially to identify the location and causes the accidents. Therefore, this study aims to identify black spot areas, which are areas with a high concentration of traffic accidents, in Kulon Progo Regency in Yogyakarta, Indonesia. The method used is spatial analysis by combining analysis of traffic accident data and geographic information system (GIS) data using ArcGIS software. The study focused on provincial road consist of primary collector 2 and 3, as well as national road segments. The analysis of accident data allowed the identification of hotspots where accidents occur most frequently. Hotspot analysis methods such as Local Statistic Analysis and Kernel Density Estimation were used to assess the spatial patterns and clustering of accidents. The study found that the black spot area in Kulon Progo Regency was mainly concentrated on the National Road Segment, with fewer occurrences on the Provincial Road with function of Primary Collector 2 and minimal presence on the Primary Collector 3 Road Segment. This result can be used to develop strategies to mitigate accidents and improve road safety in the area.

Spatial-temporal analysis of hepatitis E in Hainan Province, China (2013-2022): insights from four major hospitals.

Exploring the Incidence, Epidemic Trends, and Spatial Distribution Characteristics of Sporadic Hepatitis E in Hainan Province from 2013 to 2022 through four major tertiary hospitals in the Province. We collected data on confirmed cases of hepatitis E in Hainan residents admitted to the four major tertiary hospitals in Haikou City from January 2013 to December 2022. We used SPSS software to analyze the correlation between incidence rate and economy, population density and geographical location, and origin software to draw a scatter chart and SAS 9.4 software to conduct a descriptive analysis of the time trend. The distribution was analyzed using ArcMap 10.8 software (spatial autocorrelation analysis, hotspot identification, concentration, and dispersion trend analysis). SAS software was used to build an autoregressive integrated moving average model (ARIMA) to predict the monthly number of cases in 2023 and 2024. From 2013 to 2022, 1,922 patients with sporadic hepatitis E were treated in the four hospitals of Hainan Province. The highest proportion of patients (n = 555, 28.88%) were aged 50-59 years. The annual incidence of hepatitis E increased from 2013 to 2019, with a slight decrease in 2020 and 2021 and an increase in 2022. The highest number of cases was reported in Haikou, followed by Dongfang and Danzhou. We found that there was a correlation between the economy, population density, latitude, and the number of cases, with the correlation coefficient |r| value fluctuating between 0.403 and 0.421, indicating a linear correlation. At the same time, a scatter plot shows the correlation between population density and incidence from 2013 to 2022, with r2 values fluctuating between 0.5405 and 0.7116, indicating a linear correlation. Global Moran's I, calculated through spatial autocorrelation analysis, showed that each year from 2013 to 2022 all had a Moran's I value >0, indicating positive spatial autocorrelation (p < 0.01). Local Moran's I analysis revealed that from 2013 to 2022, local hotspots were mainly concentrated in the northern part of Hainan Province, with Haikou, Wenchang, Ding'an, and Chengmai being frequent hotspot regions, whereas Baoting, Qiongzhong, and Ledong were frequent cold-spot regions. Concentration and dispersion analysis indicated a clear directional pattern in the average density distribution, moving from northeast to southwest. Time-series forecast modeling showed that the forecast number of newly reported cases per month remained relatively stable in 2023 and 2024, fluctuating between 17 and 19. The overall incidence of hepatitis E in Hainan Province remains relatively stable. The incidence of hepatitis E in Hainan Province increased from 2013 to 2019, with a higher clustering of cases in the northeast region and a gradual spread toward the southwest over time. The ARIMA model predicted a relatively stable number of new cases each month in 2023 and 2024.

Tying up loose ends together: Cetaceans, maritime traffic and spatial management tools in the Strait of Gibraltar

Abstract The transboundary area of the Strait of Gibraltar is home to seven protected cetacean species that are threatened by high intensity of maritime traffic. More comprehensive knowledge of cetaceans and maritime traffic is required, together with analyses of legislations, strategies and policies. This study quantitatively investigates cetacean distribution and maritime traffic intensity and, for the species of community interest bottlenose dolphin, habitat suitability. Results are qualitatively discussed considering the overlap of cetacean hot spots with different maritime activities and the consistency of spatial conservation management measures in force. The Fixed Line Transect Mediterranean Monitoring Network protocols were followed for 59 visual surveys using ferries as observation platforms for monitoring cetaceans and maritime traffic. Surveys were carried out along the transects between Algeciras and Ceuta and between Algeciras and Tanger‐Med, in 2018 and 2019. 264 cetacean sightings, including seven different species and four near‐miss collision events (involving pilot, sperm and fin whales), were reported. Monitoring cetaceans from ferries in the Strait provided insights into cetacean distribution and maritime traffic, enabling the identification of cetacean hot spots, suitable habitats and maritime traffic high‐risk zones. A transboundary management effort is required, together with an adaptive approach for protecting highly mobile species such as cetaceans. Proposals include a long‐term cetacean monitoring program carried out by dedicated observers on board ferries as a cost‐effective methodology and mandatory training for crew members, to increase cetacean knowledge and reduce collision risk. The designation of an international temporal or, in some zones, permanent speed reduction area (i.e., Cetacean Critical Navigation Zone, with a maximum speed of 13 knots) and of a micro‐sanctuary with a seasonal no‐take zone in the Bay between Algeciras and Gibraltar, together with international surveillance, are recommended measures for the enhancement of conservation efforts in the Strait.

Identification of novel genomic hotspots and tumor-relevant genes via comprehensive analysis of HPV integration in Chinese patients of cervical cancer.

Cervical cancer accounts for 10-15% of cancer-related mortality among women globally. Infection with high-risk human papillomavirus (HPV) types constitutes a significant etiological factor in the development of cervical carcinoma. The integration of HPV DNA into the host genome is considered a pivotal event in cervical carcinogenesis. Nevertheless, the precise mechanisms underlying HPV integration and its role in promoting cancer progression remain inadequately understood. Therefore, this study aims to identify potential common denominators at HPV DNA integration sites and to analyze the adjacent cellular sequences. We conducted whole-genome sequencing on 13 primary cervical cancer samples, employing the chromosomal coordinates of 537 breakpoints to assess the statistical overrepresentation of integration sites in relation to various chromatin features. Our analysis, which encompassed all chromosomes, identified several integration hotspots within the human genome, notably at 14q32.2, 10p15, and 2q37. Additionally, our findings indicated a preferential integration of HPV DNA into intragenic and gene-dense regions of human chromosomes. A substantial number of host cellular genes impacted by the integration sites were associated with cancer, including IKZF2, IL26, AHRR, and PDCD6. Furthermore, the cellular genes targeted by integration were enriched in tumor-related terms and pathways, as demonstrated by gene ontology and KEGG analysis. In conclusion, these findings enhance our understanding of HPV integration sites and provide deeper insights into the molecular mechanisms underlying the pathogenesis of cervical carcinoma.

Prevalence of Extended-Spectrum Beta-Lactamase (ESBL)-Producing Escherichia coli in Humans, Food, and Environment in Kathmandu, Nepal: Findings From ESBL E. coli Tricycle Project.

The need to address antimicrobial resistance (AMR) through a One Health (OH) approach is now well recognized. There is, however, limited guidance on how AMR surveillance should be implemented across sectors to generate meaningful AMR and AMU data for decision-making. Using a sympatric approach to cross-sector sample collection, Nepal adopted the WHO extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli (E. coli) Tricycle Project as a step toward OH surveillance for assessing the prevalence of ESBL-producing E. coli across human, veterinary, and environment sectors. This involved a three-stage approach: identification of human hotspots (Stage 1) and sample collection sites for poultry (Stage 2) and wastewater (Stage 3). A total of 53 blood cultures from patients with bloodstream infections (BSIs), 100 stool samples from healthy pregnant women, 220 poultry ceca from slaughterhouses and live markets, and 48 wastewater samples were processed for bacterial culture and analyzed for the presence of ESBL-producing E. coli. The prevalence of ESBL-producing E. coli among isolated E. coli was the highest in wastewater samples (91%) followed by human BSIs (49%), poultry (38.6%), and fecal carriage isolates from healthy pregnant females (15%). A statistically significant association was seen in the prevalence of multidrug resistance among ESBL producers (52%) and nonproducers (26%). ESBL-producing E. coli was detected in all wastewater samples tested except for the upstream river. The findings of the study showed a high prevalence of ESBL-producing E. coli in samples from all three sectors and provided baseline data based upon which strategies for the safe disposal of communal and hospital waste, integrated AMR surveillance, and control strategies could be planned and implemented.

Global trends in the application of fluorescence imaging in pancreatic diseases: a bibliometric and knowledge graph analysis.

In recent years, with the continuous development of fluorescence imaging technology, research on its application in pancreatic diseases has surged. This area is currently of high research interest and holds the potential to become a non-invasive and effective tool in the diagnosis and treatment of pancreatic diseases. The objective of this study is to explore the hotspots and trends in the field of fluorescence imaging technology applications in pancreatic diseases from 2003 to 2023 through bibliometric and visual analysis. This study utilized the Web of Science (core collection) to identify publications related to the application of fluorescence imaging technology in pancreatic diseases from 2003 to 2023. Tools such as CiteSpace (V 6.2.R6), VOSviewer (v1.6.20), and R Studio (Bibliometrix: R-tool version 4.1.4) were employed to analyze various dimensions including publication count, countries, institutions, journals, authors, co-cited references, keywords, burst words, and references. A comprehensive analysis was conducted on 913 papers published from January 1, 2003, to December 1, 2023, on the application of fluorescence imaging technology in pancreatic diseases. The number of publications in this field has rapidly increased, with the United States being the central hub. The University of California, San Diego emerged as the most active institution. "Biomaterials" was identified as the most influential journal. Authors with the most publications and the highest average citations per article are Hoffman, Robert M. and Luiken, George A., respectively. Keywords such as pancreatic cancer, cancer, expression, indocyanine green, and nanoparticles received widespread attention, with indocyanine green and nanoparticles being current active research hotspots in the field. This study is the first bibliometric analysis in the field of fluorescence imaging technology applications in pancreatic diseases. Our data will facilitate a better understanding of the developmental trends, identification of research hotspots, and direction in this field. The findings provide practical information for other scholars to grasp key directions and cutting-edge insights.

Inherently spatial: data and analytical insights for the identification of forest socio-ecological hotspots

We draw insights regarding intricacies with spatially explicit data and analyses when studying the vulnerability of forest socio-ecological systems to disruptive abiotic and biotic factors. Common issues associated with data include location precision, spatial delimitation, methodological comparability, and measurement consistency. Spatial data analyses are challenged by issues of interpolation and extrapolation, inferences using data at different spatial scales, and assessment of disruption impacts at detectable spatial scales. The inextricable empirical nature of spatial data and analyses requires carefully conducting and disclosing the sensitivity of findings, and including robustness tests to openly inform decision-makers on issues of uncertainty associated with possible interventions. These considerations might be central to identifying forest socio-ecological hotspots as forest-dominated geographic areas encompassing social and ecological systems vulnerable to disruptions caused by abiotic and biotic factors, but where risks to human wellbeing may be considerably reduced through adaptive interventions.

Cholera detection system using CNN machine learning algorithm

A comprehensive cholera detection system leveraging cutting-edge technologies such as neural networks, machine learning, chatbots, live maps, and real-time statistical graphs is proposed. The system integrates a user-friendly chatbot interface to interact with individuals, prompting them to input relevant health information and symptoms. Behind the scenes, neural networks and machine learning algorithms analyze the data to detect potential cholera cases, offering users instant insights into their health status. The system incorporates live maps to track reported cases geographically, enabling a swift response from health authorities. Moreover, real-time statistical graphs provide dynamic visualizations of cholera trends, aiding in the identification of potential outbreak hotspots. By amalgamating these technologies, the cholera detection system not only facilitates early diagnosis and intervention but also enhances public health monitoring and management, contributing to the overall control and prevention of cholera outbreaks.

A Hot Spot Identification Approach for Photovoltaic Module Based on Enhanced U-Net With Squeeze-and-Excitation and VGG19

A Hot Spot Identification Approach for Photovoltaic Module Based on Enhanced U-Net With Squeeze-and-Excitation and VGG19

Enhancing Photovoltaic Reliability: A Global and Local Feature Selection Approach with Improved Harris Hawks Optimization for Efficient Hotspot Detection Using Infrared Imaging

The photovoltaic (PV) systems’ inherent ability to transform solar light directly into electrical energy has contributed to their increasing popularity. However, malfunctions can reduce system dependability. Therefore, rapid hotspot identification is critical for efficient, dependable, and risk‐free PV operation. This work presents a method for determining the most optimal hybrid features using the infrared (IR) images of PV panels for hotspot and fault detection. The information at the global (texture, HoG, and color histograms) and local (local binary pattern, SURF, and KAZE) levels were extracted from the IR images of PV panels using a uniform window size of 8 × 8. A binary improved Harris hawks optimization (b‐IHHO) optimal feature selection strategy was used to get the optimal feature subset for model training using PV IR images. The IR images of PV were acquired to test the presented framework. The findings suggested that the proposed framework can classify the IR images of solar panels with an accuracy of 98.41% with lesser feature vector size into three classes (normal, hotspot, and defective). Furthermore, the findings were also compared with the latest literature. The presented technique plays a vital role in carbon‐free cities and is simple to adopt for PV system inspection.