Hotspot Analysis Research Articles

Spectro-environmental factors integrated ensemble learning for urban river network water quality remote sensing

Remote sensing water quality monitoring technology can effectively supplement the shortcomings of traditional water quality monitoring methods in spatiotemporal dynamic monitoring capabilities. At present, although the spectral feature-based remote sensing water quality inversion models have achieved many successes, there could still be a problem of insufficient generalization ability in monitoring the water quality of complex river networks in large cities. In this paper, we propose a spectro-environmental factors integrated ensemble learning model for urban river network water quality inversion. We analyzed the correlation between water quality parameters, spectral reflectance, and environmental factors based on an in-situ dataset collected in the northern part of Shanghai. Using the Hot Spot Analysis (Getis-Ord Gi*), we found that river network water quality parameters have different patterns in different urban functional zones. Furthermore, daily average temperature, total rainfall within the seven days, and several band combinations were also selected as the environmental and spectral features using factor analysis and Pearson correlation coefficient analysis. After the feature analysis, the spectro-environmental factors integrated ensemble learning model was trained. Compared with the spectral-based machine learning inversion models, the coefficients of determination R2 increased by about 0.50. Our model was also tested in three different test areas within and outside the in-situ sampling areas in Shanghai based on low-altitude multispectral remote sensing images. The R2 results for total phosphorus (TP), ammonia nitrogen (NH3-N), and chemical oxygen demand (COD) within the in-situ sampling areas were 0.52, 0.58, and 0.56 respectively. The mean absolute percentage error (MAPE) results were 53.36%, 63.95%, and 22.46% respectively. After adding the area outside the in-situ sampling areas, the R2 results for TP, NH3-N, and COD were 0.47, 0.47, and 0.53. The MAPE were 49.38%, 74.46%, and 20.49%. Our research provided a new remote sensing water quality inversion method to be utilized in complex urban river networks which exhibited solid accuracy and generalization ability.

Research Hotspots and Evolution Trends of Green Logistics in China

In this study, the CiteSpace literature metrology analysis tool was employed to undertake a multi-dimensional metrological analysis of the research hotspots and development trends in the domestic green logistics research domain within China National Knowledge Network from 2014 to 2024. Through the visual analysis of relevant literature, the core research themes, major research institutions, and research frontiers in the field were identified. The results indicate that the research on green logistics in China is ascending. The collaboration among authors and research institutions from different regions is inadequate, and the cooperation between research institutions requires further enhancement. Domestic green logistics research centers on logistics parks, green supply chain management, carbon emissions, and environmental protection policies. Additionally, this study also deliberates on the development direction of green logistics research in the future and puts forward research topics that demand further attention, in order to provide references for relevant researchers.

First national mapping of cadmium in cacao beans in Colombia

Cacao crops in Colombia play a fundamental role in its economy. Cacao bean exports have a rising tendency due to increased area and production. However, cadmium (Cd) in cacao beans has affected market in Colombia. The aim of this study is to assess Cd distribution hotspots for cacao beans, Cd in beans and soil relationships, geological ages and soil chemical parameters, overall Cd dynamics, soil-to-plant translocation, and EU regulation impacts on Cd presence in Colombian cacao production. A hot and cold spot analysis combined with a cluster and outlier analysis was used to build the geographic Cd distribution in cacao beans in Colombia. Cadmium in the subsurface and the relationship with Cd in the soil surface of cacao farms were analysed using the geoaccumulation index (Igeo). The bioconcentration factor (BCF) and soil chemical features in cacao beans were quantified. Positive correlations were observed within all the studied variables except for Mn and Fe. The highest correlation was observed within Ca, pH, and Cd in soil. The Valanginian-Albian and Albian-Maastrichtian geological ages are directly related to Cd in beans, Zn, P, soil Cd, and Fe. Regions with the highest cacao productivity and export registered an average Cd concentration in beans of 2.39_ ± _2.82 mg kg−1. Two municipalities in Santander were ‘partially contaminated’ according to Igeo. The BCF was not directly proportional to soil and bean Cd level and did not fully coincide with hot spots. This evidence reinforces the idea that even areas with apparently low Cd levels may register a latent problem associated with genetic/ontogenetic, climatic, or management factors. This first distribution map of Cd in cacao beans constitutes a tool to implement actions to minimize risks and face challenges posed by the imposed regulations that may limit raw cacao bean exports in Colombia.

Trends and research foci in immunoregulatory mechanisms of allergic rhinitis: a bibliometric analysis (2014-2024).

This study aims to provide a comprehensive bibliometric analysis of research trends, hotspots, and future directions in the immunoregulatory mechanisms of allergic rhinitis (AR) from 2014 to 2024. Data were sourced from the Web of Science Core Collection (WoSCC), covering articles and reviews published between April 1, 2014, and March 31, 2024. The search terms included "Allergic Rhinitis," "AR," and related terms along with specific keywords related to immune cells and inflammatory mediators. Bibliometric tools such as CiteSpace, VOSviewer, and SCImago Graphica were used to analyze institutional cooperation networks, keyword co-occurrence, citation bursts, and research topic evolution. Microsoft Excel 2019 was employed to display annual publication trends. A total of 2200 papers met the inclusion and exclusion criteria. The number of publications showed an upward trend over the past decade, with a significant peak in 2021. China (583 papers) and the United States (454 papers) were the major contributing countries. Imperial College London emerged as the leading institution. Key research frontiers identified include the roles of NF kappa B and air pollution in AR. Keyword burst analysis revealed emerging topics such as respiratory allergy and personalized treatment strategies. Notable limitations include the exclusive use of the WoSCC database and the restriction to English-language publications. The field of immunoregulatory mechanisms in allergic rhinitis has seen significant growth, with China and the United States leading the research. Future research should focus on developing personalized treatment plans and understanding the comprehensive impact of environmental factors. Continued interdisciplinary collaboration and international cooperation will be essential for advancing therapeutic strategies in AR.

Assessing the impact of sediment characteristics on vegetation recovery in debris flow fans: A case study of the Ohya Region, Japan

What factors influence natural vegetation recovery in debris flow-prone areas, and how do sediment dynamics play a role? This study investigates these questions in the Ohya debris flow fan, Japan, utilizing various analytical techniques. Through the application of Support Vector Machine (SVM) classification, grain size analysis, accuracy assessment, debris flow analysis, and hotspot analysis, this study assess the distribution of sediments, vegetation classes, and the extent of debris flow events. The findings shed light on the dynamics of vegetation recovery and its relationship with sediment dynamic. The SVM classification outcomes reveal distinct trends in sediment and vegetation distribution along the flow path, particularly noting a significant decrease in vegetation cover from upstream to downstream sections. By employing SVM classification, this study successfully identified 3,282,910 sediment particles and determined their average, minimum, maximum, and standard deviation grain sizes as 7.3 cm, 0.27 cm, 415 cm, and 9.18, respectively. Accuracy assessments of image classification and grain size measurements demonstrate high levels of accuracy, with an overall classification accuracy of 98.82 % and a kappa coefficient of 0.977. Validation of grain size measurements reveals a strong correlation (R2 = 0.997 and y = 1.005× + 0.0661) between field-observed sediment sizes and sizes derived from classified images. Debris flow analysis reveals that the total area affected by debris flow in 2022 was 36,232.7 square meters, decreasing to 14,213.9 square meters in 2023. Hotspot analysis identifies regions of both high and low sediment size concentrations, providing valuable insights into sediment distribution patterns. Examining the natural recovery of vegetation, present study identifies vegetation spots across different study sections. Results show that 55 % of naturally recovering vegetation areas are located in regions unaffected by debris flow events between 2022 and 2023. Among the study sections, the area affected by debris flow in 2023 exhibits the lowest density of vegetation spots. Overall, this study highlights the new generation vegetation recovery and its association with sediment dynamic in the Ohya debris flow fan. The findings contribute valuable insights for understanding natural recovery processes in highly dynamic sedimentation areas, informing the development of effective strategies for ecosystem restoration and management in debris flow-prone regions.

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in glaucoma from 2013 to 2022.

To conduct a bibliometric analysis of research on artificial intelligence (AI) in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions for future studies. Relevant articles on the application of AI in the field of glaucoma from the Web of Science Core Collection were retrieved, covering the period from January 1, 2013, to December 31, 2022. In order to assess the contributions and co-occurrence relationships among different countries/regions, institutions, authors, and journals, CiteSpace and VOSviewer software were employed and the research hotspots and future trends within the field were identified. A total of 750 English articles published between 2013 and 2022 were collected, and the number of publications exhibited an overall increasing trend. The majority of the articles were from China, followed by the United States and India. National University of Singapore, Chinese Academy of Sciences, and Sun Yat-sen University made significant contributions to the published works. Weinreb RN and Fu HZ ranked first among authors and cited authors. American Journal of Ophthalmology is the most impactful academic journal in the field of AI application in glaucoma. The disciplinary scope of this field includes ophthalmology, computer science, mathematics, molecular biology, genetics, and other related disciplines. The clustering and identification of keyword nodes in the co-occurrence network reveal the evolving landscape of AI application in the field of glaucoma. Initially, the hot topics in this field were primarily "segmentation", "classification" and "diagnosis". However, in recent years, the focus has shifted to "deep learning", "convolutional neural network" and "artificial intelligence". With the rapid development of AI technology, scholars have shown increasing interest in its application in the field of glaucoma. Moreover, the application of AI in assisting treatment and predicting prognosis in glaucoma may become a future research hotspot. However, the reliability and interpretability of AI data remain pressing issues that require resolution.

Analyzing the Fertility Rate Trends in Kuwait with GIS Between 2014 and 2024

A highly evident trend globally has emerged since the middle of last century, namely the decline in fertility rates. Whilst the underlying reasons are complicated and multifaceted, it is sufficient to say that there is no easy solution or permanent reversal plan that works. This study investigates the fertility rate in the state of Kuwait and uses GIS tools, such as Hotspot analysis to compare fertility rates between 2014 and 2024 along with their spatial distribution. The aim is to evaluate a government attempt to slow down or stop the decline by offering significant financial support to families with children. Furthermore, the method attempts to identify differences in fertility behavior between ethnic groups in Kuwait (Hadar and Badu) by spatially comparing the fertility rates in suburbs dominated by each group. The output results suggest that the government policies have only been partially successful in stopping the fertility rates; the fertility rates decreased, although only slightly, on average between 2014 and 2024. Additionally, it was clear that the Badu group of Kuwaitis, residing in the new peripheral regions, form significantly larger families than Hadar who live close to the older central suburbs. Combined, these findings suggest that further modifications to the policy umbrella for fertility are necessary that should match the motivations and address the concerns of each ethnic group and district in a more tailored way, rather than simply offering a lump sum to every family.

A Python toolkit for integrating geographic information system into regulatory dispersion models for refined pollution modeling

AERMOD is designated as U.S. Environmental Protection Agency (EPA)'s preferred air dispersion model for refined transportation project hot-spot analyses beginning in 2020. One of the key challenges in its modeling process is spatially encoding roadway geometry, especially when simulating highways with complex geometric designs. This research proposed an open-source Python package, GTA, which enables conversion of publicly available roadway Geographic Information System (GIS) layers into defined sources, and source-based emission rates from MOtor Vehicle Emissions Simulator (MOVES) output for AERMOD modeling. The research selected a suburban area in Atlanta, and conducted a comprehensive analysis in terms of annual PM2.5 concentration results and the speed of preparing AERMOD input files for highway network modeling both manually and using software developed based on the proposed methodology. The results prove that the proposed methodology significantly expedites the AERMOD input preparation process, and facilitates convenient testing of multiple modeling configurations for multi-scenario or sensitivity analysis.

Unveiling urban air quality dynamics during COVID-19: a Sentinel-5P TROPOMI hotspot analysis

In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.

Exploring Cesarean Section Delivery Patterns in South India: A Bayesian Multilevel and Geospatial analysis of Population-Based Cross-Sectional Data

BackgroundThis paper focuses on the period from 2019 to 2021 and investigates the factors associated with the high prevalence of C-section deliveries in South India. We also examine the nuanced patterns, socio-demographic associations, and spatial dynamics underlying C-section choices in this region. A cross-sectional study was conducted using large nationally representative survey data.MethodsNational Family Health Survey data (NFHS) from 2019 to 2021 have been used for the analysis. Bayesian Multilevel and Geospatial Analysis have been used as statistical methods.ResultsOur analysis reveals significant regional disparities in C-section utilization, indicating potential gaps in healthcare access and socio-economic influences. Maternal age at childbirth, educational attainment, healthcare facility type size of child at birth and ever pregnancy termination are identified as key determinants of method of C-section decisions. Wealth index and urban residence also play pivotal roles, reflecting financial considerations and access to healthcare resources. Bayesian multilevel analysis highlights the need for tailored interventions that consider individual household, primary sampling unit (PSU) and district-level factors. Additionally, spatial analysis identifies regions with varying C-section rates, allowing policymakers to develop targeted strategies to optimize maternal and neonatal health outcomes and address healthcare disparities. Spatial autocorrelation and hotspot analysis further elucidate localized influences and clustering patterns.ConclusionIn conclusion, this research underscores the complexity of C-section choices and calls for evidence-based policies and interventions that promote equitable access to quality maternal care in South India. Stakeholders must recognize the multifaceted nature of healthcare decisions and work collaboratively to ensure more balanced and effective healthcare practices in the region.

Spatial distribution and associated factors of cesarean section in Ethiopia using mini EDHS 2019 data: a community based cross-sectional study

Maternal health is a major public health tricky globally. Cesarean section delivery reduces morbidity and mortality when certain complications occur throughout pregnancy and labor. Cesarean section subjected to the availability and use of essential obstetric services in regional factors in Ethiopia. There was a scarcity of studies that assess the spatial distribution and associated factors of cesarean section. Consequently, this study aimed to assess the spatial variation of cesarean section and associated factors using mini EDHS 2019 national representative data. A community based cross-sectional study was conducted in Ethiopia from March to June 2019. A two-stage stratified sampling design was used to select participants. A Global Moran’s I and Getis-Ord Gi* statistic hotspot analysis was used to assess the spatial distribution. Kuldorff’s SaTScan was employed to determine the purely statistically significant spatial clusters. A multilevel binary logistic regression model fitted to identify factors. A total of 5753 mothers were included. More than one-fourth of mothers delivered through cesarean section at private health institutions and 54.74% were not educated. The proportion of cesarean section clustered geographically in Ethiopia and hotspot areas were observed in Addis Ababa, Oromia, Tigray, Derie Dewa, Amhara, and SNNR regions. Mothers’ age (AOR = 1.07, 95% CI 1.02–1.12), mother’s had secondary education (AOR = 2.113, 95% CI 1.414, 3.157), mother’s higher education (2.646, 95% CI 1.724, 4.063), Muslim religion followers (AOR = 0.632, 95% CI 0.469, 0.852), poorer (AOR = 1.719, 95% CI 1.057, 2.795), middle wealth index (AOR = 1.769, 95% CI 1.073, 2.918), richer (AOR = 2.041, 95% CI 1.246, 3.344), richest (AOR = 3.510, 95% CI 2.197, 5.607), parity (AOR = 0.825, 95% CI 0.739, 0.921), and multiple pregnancies (AOR = 4.032, 95% CI 2.418, 6.723) were significant factors. Therefore, geographically targeted interventions are essential to reduce maternal and infant mortality with WHO recommendations for those Muslim, poorest and not educated mothers.

Spatial-temporal evolution patterns of influenza incidence in Xinjiang Prefecture from 2014 to 2023 based on GIS

Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p < 0.01). Global spatial autocorrelation analysis indicated significant positive spatial autocorrelation in influenza incidence rates from 2016 and from 2018 to 2023 (Moran’s I > 0, P < 0.05). Local spatial autocorrelation analysis further revealed clustering patterns in different regions, with high-high clustering and low-high clustering predominating in northern Xinjiang, and low-low clustering predominating in southern Xinjiang. Hotspot analysis indicated a progressive rise in the number of influenza incidence hotspots, primarily concentrated in northern Xinjiang, particularly in Urumqi, Ili Kazakh Autonomous Prefecture, and Hotan Prefecture. Standard deviation ellipse analysis and the trajectory of influenza incidence gravity center migration showed that the transmission range of influenza in Xinjiang has been expanding, with the epidemic center gradually moving northward. The spatiotemporal heterogeneity of influenza incidence in Xinjiang highlights the need for differentiated and precise influenza prevention and control strategies in different regions to address the changing trends in influenza prevalence.

Analysis of Research Hot Spots in Chinese and International English Ecohydrological Literature

ABSTRACTTo enhance comprehension of current research progress in the field of ecohydrology and to provide valuable references for future related research, policy formulation and international collaboration, this article relies on ecohydrology‐related literature and citation records in four databases: China Knowledge Network (CNKI), Wanfang Database, Web of Science (WOS) and Elsevier, up until the end of 2022. We use publication and citation trends analysis along with keyword hotspot analysis using VOS viewer to investigate publication and citation trends in Chinese and English literature. The study systematically analyse the process of keyword changes and research hotspots in Chinese and international English literature, providing a detailed explanation of the differences in research hotspots and the reasons for their formation. The research indicates that since the inception of the term ‘ecohydrology’, relevant literature has displayed sluggish growth alongside rapid development. Additionally, alterations in primary keywords have differed between Chinese and English literature over time. However, ‘groundwater’ has consistently retained a significant amount of attention within Chinese literature. Groundwater has been extensively studied in Chinese literature, with a particular focus on eco‐hydrological processes, their effects, modelling and techniques. Similarly, English language literature is abundant with keywords denoting pivotal concepts like the environment, hydrology, fauna and rivers. These areas have been the focal points of research for international scholars. Regional characteristics, ecological conditions, economic development level and scientific research priorities, among other factors, are all significant contributors to the development of the field of ecohydrology. Differences in research hotspots can also be attributed to geographical characteristics, ecological environment conditions and scientific research priorities. Over the past decade, there has been a gradual convergence in research between Chinese and English literature. The current hotspots of research in ecohydrology include basic research, theoretical applications and adaptive wide‐area mechanistic research. In the future, the discipline of ecohydrology is likely to face numerous challenges and opportunities related to climate change, resource management, and societal needs. Interdisciplinary collaboration, technological innovation and global cooperation will continue to drive breakthroughs in ecohydrology research.

Analysis of EGFR binding hotspots for design of new EGFR inhibitory biologics.

The epidermal growth factor (EGF) receptor (EGFR) is activated by the binding of one of seven EGF-like ligands to its ectodomain. Ligand binding results in EGFR dimerization and stabilization of the active receptor conformation subsequently leading to activation of downstream signaling. Aberrant activation of EGFR contributes to cancer progression through EGFR overexpression/amplification, modulation of its positive and negative regulators, and/or activating mutations within EGFR. EGFR targeted therapeutic antibodies prevent dimerization and interaction with endogenous ligands by binding the ectodomain of EGFR. However, these antibodies have had limited success in the clinic, partially due to EGFR ectodomain resistance mutations, and are only applicable to a subset of patients with EGFR-driven cancers. These limitations suggest that alternative EGFR targeted biologics need to be explored for EGFR-driven cancer therapy. To this end, we analyze the EGFR interfaces of known inhibitory biologics with determined structures in the context of endogenous ligands, using the Rosetta macromolecular modeling software to highlight the most important interactions on a per-residue basis. We use this analysis to identify the structural determinants of EGFR targeted biologics. We suggest that commonly observed binding motifs serve as the basis for rational design of new EGFR targeted biologics, such as peptides, antibodies, and nanobodies.

How sustainability can get a competitive advantage: State of the art for stationary battery storage systems

Stationary battery storage systems are becoming a critical energy infrastructure around the world. Therefore, responsible handling of battery materials is a fundamental precondition to avoid future social, environmental, and political conflicts. Global battery regulations support sustainable batteries to drive new business models on reuse, remanufacturing and recycling. With strict environmental market entry barriers, the EU will set minimum sustainability standards with the new EU-Battery Directive. The US Inflation Reduction Act provides financial incentives for a scale-up of the domestic battery industry. A hotspot analysis for the residential storage system VARTA.wall shows that a combination of reuse and recycling strategies can reduce the climate change impact by up to 45% and mineral resource use by up to 50% compared to initial battery designs. However, specific sustainability criteria and manufacturer-independent standards need to be set up by politics and industry organizations to bring the necessary technical and logistic infrastructure to the market. The challenge is to set up sustainability criteria strict enough to ensure responsible material handling but still allow cost-effective, practical solutions as well as affordable battery standards. Therefore, our analysis shows the limits of current and the need for future regulations to shift market incentives to sustainable batteries and their infrastructure.

Identifying Conservation Priority Areas of Hydrological Ecosystem Service Using Hot and Cold Spot Analysis at Watershed Scale

Identifying Conservation Priority Areas of Hydrological Ecosystem Service Using Hot and Cold Spot Analysis at Watershed Scale

Spatial disparities in cause-specific mortality in Ukraine: A district-level analysis, 2006–19

Turbulent socio-economic development, recent political challenges, and remarkable regional diversity with deep historical roots make Ukraine an important case study for understanding mortality trends in Eastern Europe. In this paper, we provide the first comprehensive, spatially detailed analysis of cause-specific mortality trends and patterns in Ukraine, focusing on the period 2006–19. We rely on official mortality data and use various demographic and spatial analysis techniques. Our results suggest a notable attenuation of the long-standing West–East and West–South–East mortality gradients. Cardiovascular mortality at older ages largely explains the gap between the vanguard (lowest mortality) and laggard (highest mortality) areas, especially for females and in the most recent period. By contrast, the impact of mortality from external causes has greatly diminished over time. Hotspot analyses reveal strong and persistent clustering of mortality from suicide, HIV, and lung cancer. Further research should focus on in-depth assessment of the mechanisms causing the observed patterns.

Life cycle assessment of decommissioned silicon photovoltaic module recycling using different technological configurations in China

The intricate encapsulation structure and material composition of photovoltaic modules necessitate full materials recycling involving multiple stages and different technological configurations, thereby increasing environmental burden of recycling process. Consequently, environmental impact assessments are imperative. However, previous studies primarily focused on a single technology or compared different technologies within a specific recycling stage, overlooking various technological configurations and thus engendering incomprehensive assessment. Hence, we employ a comparative life-cycle assessment to evaluate the environmental performance of six recycling alternatives with different technological configurations for silicon photovoltaic waste in China, which encompasses five recycling stages and glass/silicon remanufacturing processes. Results shows thermal delamination reduces the normalized environmental impact by 8.73% and 4.62% compared with mechanical and chemical delamination, respectively; electrolysis for metals extraction carries 35.72%–36.35% higher environmental benefits than precipitation. Additionally, introducing silicon/glass remanufacturing provides an additional 6.27%–11.55% environmental benefits. Therefore, integrating disassembly, thermal delamination, leaching & etching, electrolysis, and remanufacturing exhibits the best environmental performance, with −4796 kg CO2-eq/tonne carbon emission and −46400 MJ/tonne energy demand. Environmental hotspots analysis identifies key contributors to environmental impact and benefits. Further sensitivity analysis highlights the importance of enhancing silver and copper recovery efficiency. Finally, targeted strategies are proposed for green recycling routes of photovoltaic waste.

Unveiling spatio-temporal mysteries: A quest to decode India's Dengue and Malaria trend (2003-2022)

Dengue and malaria are two mosquito-borne diseases that are dangerous globally, especially in tropical and subtropical regions. In India, these two diseases pose severe health issues as they account for 74.37 % of the total vector-borne disease burden in the country. The present study examined the spatio-temporal patterns of prevalence of dengue and malaria across all states in India. Data related to epidemiological statistics were obtained from the Central Bureau of Health Intelligence (CBHI) and the National Vector Borne Disease Control Program (NVBDCP) for 2003–2017 and 2018–2022, respectively. In this study, we have utilized the Mann-Kendall test, Modified Mann-Kendall test, Sens's slope, Innovative trend analysis, and Percent Bias for trend analysis. Furthermore, a hotspot analysis was conducted to compare and examine the evolving patterns of these diseases over space and time. The Mann-Kendall test showed a significant increase in dengue cases throughout India, with Sen's slope showing the fastest growth in Punjab. West Bengal exhibited the most significant ITA slope increase. The PBIAS slope showed a gradual rise from the southern to the northern and north-eastern states. Mann-Kendall results indicated a statistically significant decline in malaria cases, dropping mostly in Odisha, followed by the northern, southern, and north-eastern states. Only Mizoram displayed an insignificant upward trend in malaria cases. Hotspot analysis revealed that dengue fever hotspots expanded in India's central, western, and northern regions, affecting 66.72 % of the country, whereas significant coldspots remain unchanged. Malaria hotspots covered 47.46 % of north-eastern, eastern coastal, and northern areas, while coldspots almost remained unchanged. This study provides valuable insights for health authorities to prioritize and identify the regions that need immediate intervention regarding these two mosquito-borne diseases.

Spatial Aspects of Acute Respiratory Disease Syndrome: An Application of Scan Statistics Using SaTScan in Identification and Analysis of Hotspot in India

Spatial Aspects of Acute Respiratory Disease Syndrome: An Application of Scan Statistics Using SaTScan in Identification and Analysis of Hotspot in India