Search Results (1,151)

Extreme heat events are associated with increased morbidity and mortality, disproportionately affecting vulnerable populations. However, evidence from disadvantaged urban settings in Africa remains scarce. This systematic review aimed to synthesize existing research on the health impacts of extreme heat in Africa’s disadvantaged urban communities. A systematic literature search was conducted in accordance with PRISMA guidelines across eleven databases (PubMed, Cochrane, VHL, Google Scholar, ScienceDirect, Research4Life, DOAJ, Emerald, Taylor & Francis, Annual Reviews, and IRIS), carried out over two search periods (December 2023–March 2024 and December 2024–February 2025). Eligible studies were published between 2010 and 2024 in English, French, or Portuguese, and examined direct or indirect heat-health effects in disadvantaged urban settings. Screening of 96,608 titles and 59 abstracts yielded 21 eligible studies. Geographically, the evidence was concentrated in South Africa (n = 7), Kenya (n = 6), and Tanzania (n = 4), with single studies from Cameroon, Egypt, Nigeria, and Ghana. Heat-related morbidity was examined in 17 studies and mortality in four. Commonly reported conditions included dehydration, heat exhaustion, skin rashes, heatstroke, and cardiovascular and respiratory diseases. Extreme heat was further associated with mortality from non-communicable diseases, pneumonia, and acute respiratory infections. Children, older adults, and the most economically deprived communities exhibited heightened vulnerability, driven by adverse socioeconomic and environmental conditions. The available evidence confirms substantial heat-health impacts on disadvantaged urban populations across Africa, while revealing a notable research gap in French- and Portuguese-language literature. These findings underscore the urgent need for expanded, geographically and linguistically diverse research to inform climate adaptation strategies and health protection policies. Full article

Accurate and up-to-date knowledge of land use and land cover represents one of the central challenges in spatial planning and landscape sciences. In this context, the present work introduces GANCIU (Geospatial Analysis with Neural Classification and Image Understanding), an original hybrid pipeline for the automatic extraction of man-made infrastructure from high-resolution satellite imagery. The primary methodological contribution lies in the sequential integration of four technologically heterogeneous components: a per-pixel Random Forest classifier, a guided image modulation step, edge detection via the Mumford–Shah variational functional solved through the Ambrosio–Tortorelli approximation, and final object delineation via the Segment Anything Model (SAM). Each component does not operate independently but conditions and informs the next: The RF probability map guides the modulation, which in turn directs the sensitivity of the variational step exclusively towards regions of interest; the AT edges provide spatial prompts to SAM, for which its masks are finally filtered by the RF probability in an adaptive manner through a Gaussian Mixture Model. This progressive conditioning scheme constitutes the architectural core of GANCIU and distinguishes it from approaches that combine classification and segmentation in parallel or in purely sequential fashion with each stage conditioning the next but without any reverse correction between them. The Random Forest classifier was trained on 44 manually annotated scenes, geographically disjoint from the twelve independent scenes used for quantitative validation. This validation, based on an instance matching protocol (precision, recall, F1 score, and IoU), confirms the contribution of the full pipeline over a Random-Forest-only baseline: Pooled false positives fall by close to two orders of magnitude (from 8320 to 209), while true positives rise nearly twentyfold (from 5 to 95), with a mean IoU of 0.742 ± 0.060 on correctly matched objects. Notably, the entire pipeline—including SAM-based segmentation—runs end-to-end on a modest, GPU-free consumer laptop (four logical CPU cores, under 16 GB RAM), demonstrating that competitive infrastructure-extraction performance does not require specialised computing hardware. Full article

This paper discusses the dynamic mechanism of stability and the possibility for breakthrough of the Hu Line based on the relationship between population, socioeconomic development and the environment. Against the background of sustainable development and climate change, we used historical census data from China and comparative analysis of three versions (IIASA, NUIST and Tsinghua) of the Shared Socioeconomic Pathways (SSPs) to analyze multidimensional demographic characteristics such as population size, age and education on both sides of the Hu Line (China’s population boundary). The study found that SSP scenarios and regional differences in the natural growth rate (defined by fertility and mortality) and urbanization rate jointly influenced the changes in the proportion on both sides of the Hu Line. In addition to the breakthrough of the population proportion on the west of the Hu Line, population quality deserves greater attention. The provinces on the east and west sides of the Hu Line can adopt different social and economic development paths to achieve sustained development of the population, economy, society, resources and environment. On the premise of sustained social and economic development, the total population will not rise or fall significantly, keeping the trend of aging under control, and enhancing the educational level. The central government should increase investment in basic education in the provinces west of the Hu Line and create more employment opportunities through the Belt and Road Initiative and the new urbanization strategy. In areas with abundant water resources on the west of the Hu Line, urbanization efforts should be rationally planned and advanced to gradually establish clusters of talent hubs. This will facilitate the effective conversion of human resources by retaining and attracting higher-education professionals to pursue employment and entrepreneurship opportunities. Full article

Ecological niche modeling (ENM) is widely used to predict species distributions and support biodiversity conservation under environmental change, yet its application to Amazonian wetland plants has not been systematically synthesized. We conducted a systematic review following the PRISMA 2020 guidelines to evaluate methodological approaches, environmental predictors, model performance, and research gaps. Literature searches were performed in Web of Science, Scopus, and Consensus (as a complementary AI-assisted academic search platform), yielding 1789 records, of which 48 met the eligibility criteria. Correlative models predominated, with MaxEnt, Random Forest, and ensemble frameworks being the most frequently applied algorithms. Across studies, integrating hydrological and edaphic predictors consistently improved model performance and ecological realism compared with climate-only approaches. Future climate projections indicated greater vulnerability for habitat-specialist species, whereas western Amazonia and the Andean foothills were repeatedly identified as potential climatic refugia. Major limitations included geographically biased occurrence records, limited high-resolution environmental datasets, and the underrepresentation of several Amazonian wetland ecosystems. Overall, the evidence indicates that reliable ENMs for Amazonian wetlands require integrating climatic, hydrological, and edaphic drivers rather than relying solely on macroclimate. Future research should prioritize geographically representative sampling, improved environmental datasets, transparent workflows, and process-informed modeling to strengthen ecological forecasting and conservation planning under climate and land-use change. Full article

This paper systematically reviews the emerging field of smart urban agriculture, defined as the integration of information and communication technologies (ICTs) into food production practices within and around built-up urban areas. Although urban agriculture and digital agriculture have each generated substantial scholarship, their intersection remains conceptually fragmented and empirically uneven. Following the PRISMA 2020 guidelines, this study reviews 143 English-language articles indexed in Web of Science and Scopus between 2016 and 2026, combining bibliometric mapping with structured thematic coding. The analysis shows that smart urban agriculture has expanded rapidly since 2021, but remains geographically concentrated and disciplinarily dispersed. Current research is organized mainly around IoT and sensor networks, machine learning, soilless cultivation, vertical farming, plant factories, and controlled-environment agriculture. ICT applications are most mature in enclosed, data-rich, and technically controllable systems, where they support monitoring, prediction, automation, and resource optimization. By contrast, community-based, open-space, and governance-oriented forms of urban agriculture remain underexplored. By systematically linking ICT families with different urban agriculture production settings, this review clarifies the field’s emerging knowledge structure and demonstrates that technological development remains uneven across agricultural forms and socio-institutional contexts. Full article

Background/Objectives: Burnout among healthcare professionals has emerged as a major occupational and organizational challenge with important implications for workforce sustainability, patient safety, and healthcare system performance. Although the scientific literature on burnout has expanded substantially, an integrated understanding of its global development, research structure, and scientific impact remains limited. This study aimed to examine the evolution, thematic development, and citation dynamics of burnout research among healthcare professionals through an integrated bibliometric approach combining science mapping, multilevel citation modelling, and critical literature synthesis. Methods: A bibliometric analysis was conducted using publications indexed in the Web of Science Core Collection between 1987 and 2024. Science mapping techniques were applied using Bibliometrix and VOSviewer to evaluate publication trends, collaboration networks, thematic development, and citation patterns. In addition, a multilevel negative binomial regression model was used to identify publication characteristics associated with citation impact. Results: A total of 1232 publications were included in the analysis. Scientific production increased markedly after 2020, temporally coinciding with the COVID-19 pandemic and growing scientific interest in healthcare workforce wellbeing. Research output was concentrated in high-income countries, particularly the United States, China, the United Kingdom, Canada, and Australia. Physicians and nurses dominated the literature, while non-clinical healthcare workers remained underrepresented. The thematic analysis indicated an increasing emphasis on organizational and system-level determinants of burnout alongside the continued presence of individual-centered perspectives. The multilevel negative binomial model identified longer title length and a greater number of author-provided keywords as being associated with lower expected citation counts, whereas a greater number of Keywords Plus terms was associated with higher expected citation counts. Conclusions: Burnout research has evolved into a rapidly expanding and increasingly interdisciplinary field. However, important gaps persist regarding geographic representation, workforce diversity, methodological standardization, and organizational intervention research. Future studies should move beyond descriptive approaches and further evaluate organizational interventions and workforce-related strategies to strengthen the evidence base that may inform healthcare policy and organizational practice. Full article

Background: Pericoronary adipose tissue (PCAT) radiomics on coronary computed tomography angiography (CCTA) may capture inflammatory and structural information beyond conventional attenuation measures. We assessed whether PCAT radiomics predicts future coronary events or quantifies plaque progression. Methods: PubMed/MEDLINE, Embase, and Web of Science were searched from inception to 29 August 2025. Eligible studies included adults undergoing CCTA with quantitative PCAT, or anatomically equivalent coronary perivascular adipose tissue (PVAT), radiomics, and longitudinal clinical or serial-CCTA outcomes. Findings were synthesised narratively because no common estimand supported meta-analysis. Methodological quality was assessed using QUADAS-2 and the Radiomics Quality Score (RQS). Results: Eleven full-text studies, representing at least 6307 participants were included; one study also analysed 127 plaques in 97 patients, and one reported a separate 202-person model-development sample whose overlap with its 1575-person cohort evaluation was not quantified. Eight studies evaluated coronary clinical events, and three evaluated plaque progression. Seven clinical-event studies reported better discrimination for radiomics-based or integrated models than at least one conventional comparator, although calibration and clinical-utility reporting were inconsistent. Two studies included clearly independent external validation. QUADAS-2 identified high patient-selection risk in 4/11 studies; RQSs ranged from 10/36 to 15/36 (mean: 12.5/36). Conclusions: PCAT radiomics shows preliminary prognostic potential, but confidence in the evidence is low because of retrospective designs, heterogeneous outcomes and workflows, restricted geographic sampling, and limited independent external validation. Current evidence is insufficient for routine clinical decision-making. Prospective multicentre validation with standardised acquisition, analysis, reporting, and clinically consistent endpoints is required. Full article

Air travel is widely regarded as a safe mode of transport, yet the aviation journey remains a significant source of psychological and physiological stress for many passengers. Existing research has largely focused on fear of flying during the flight itself, with limited synthesis of stress experienced across the entire passenger journey. This integrative review aimed to synthesize evidence on stressors affecting commercial airline passengers during the pre-flight, in-flight, and post-flight stages of travel. A comprehensive search of Google Scholar, Scopus, Web of Science, and PubMed identified peer-reviewed studies published between 2013 and 2025. Following screening and quality appraisal using an adaptation of Critical Appraisal Skills Program (CASP) principles, 46 studies met the inclusion criteria. The review identified 12 major categories of passenger stressors across the aviation journey. Pre-flight stress was primarily associated with procedural, socio-psychological, and financial factors, while in-flight stress centered on perceived loss of control, physical cabin constraints, environmental conditions, and disruptive passenger behavior. Post-flight stress was linked to physical fatigue, communication gaps, baggage-related challenges, immigration procedures, and ground transport difficulties. The review also revealed a substantial geographical imbalance, with most evidence originating from high-income countries. By providing a comprehensive temporal synthesis of passenger stressors, this review offers an evidence-based framework to inform passenger wellbeing initiatives, aviation policy, and future cross-cultural research. Full article

Background/Objectives: Authentic assessment—the systematic observation of naturally occurring social–emotional behavior by familiar caregivers—is increasingly mandated by international early childhood frameworks, yet the field lacks a comprehensive tool inventory, conceptual clarity on “authenticity,” and synthesized evidence on contextual coverage. This scoping review maps authentic social–emotional development (SED) assessment tools for children aged 0–8, examines how authenticity is conceptualized, and identifies gaps in cultural adaptation, disability inclusion, and age-range coverage. Methods: Following the Arksey and O’Malley framework enhanced by Levac et al. and JBI methodology, six databases (PsycINFO, ERIC, Education Source, MEDLINE, Scopus, Web of Science) were searched for English-language peer-reviewed publications (2006–2026), supplemented by hand-searching and forward citation tracking. Inclusion criteria targeted children aged 0–8 assessed via authentic modalities (naturalistic observation, play-based assessment, portfolio, performance-based tasks, curriculum-embedded measurement) in any global setting. Data charting used an integrated sociocultural and interpretive assessment framework. Reporting followed PRISMA-ScR. Results: Thirty-three studies yielded 43 instruments, 29 meeting authentic assessment criteria. Naturalistic observation predominated (23 studies; 69.7%), followed by play-based assessment (10; 30.3%); portfolio and curriculum-embedded approaches were under-represented. Most studies originated from the United States (57.6%) and targeted preschoolers (3–5 years; 75.8%), with thin coverage of infants/toddlers and primary-grade children. “Authenticity” was operationalized across ecological, participatory, and cultural dimensions rarely integrated theoretically. Conclusions: This review provides the first systematic landscape map of authentic SED assessment for children aged 0–8, confirming feasibility and diversity while revealing gaps in age coverage, geographic reach, and cultural adaptation. The integrated framework offers a potentially transferable analytic model, and findings can inform equitable, contextually responsive assessment practice and policy. Full article

Background/Objectives: Biallelic mutations of NLRP7, a maternal-effect gene encoding a key component of the subcortical maternal complex, are the major monogenic cause of recurrent hydatidiform mole (RHM) and confer a substantial lifetime risk of gestational trophoblastic neoplasia (GTN), including choriocarcinoma. Although oocyte donation is currently the standard reproductive option, anecdotal live births from autologous oocytes in carriers of specific missense variants raise mechanistic and clinical questions that have not been integrated. We aimed to synthesize the current understanding of NLRP7 biology, oncogenic mechanisms in GTN, and genotype-stratified reproductive outcomes, and to propose a genetic permissiveness framework that links cancer surveillance with reproductive decision-making. We also place this monogenic disease in an epidemiological context, given the marked geographical variation in molar pregnancy incidence. Methods: PubMed, Embase and Web of Science were searched from January 2006 to December 2025 using terms related to NLRP7, hydatidiform mole, GTN, choriocarcinoma, maternal-effect genes, genomic imprinting, and assisted reproduction. Original studies, case series, expert consensus statements, and reviews were included. Results: NLRP7 loss disrupts subcortical maternal complex assembly, maternal-DMR methylation, trophoblast differentiation, and HLA-C-dependent immune privilege; in choriocarcinoma, NLRP7 is conversely reported to be up-regulated, where it has been implicated in tumor proliferation and immune evasion through inflammasome-independent pathways. Genetic permissiveness for autologous-oocyte pregnancy is mutation-type dependent: complete loss-of-function variants confer near-zero permissiveness, whereas missense variants with retained partial function may permit rare normal pregnancies. Conclusions: A genetic-permissiveness framework integrating variant-level functional data, GTN risk, and reproductive priorities can support shared decision-making in this rare but high-stakes clinical scenario, and it identifies clear directions for future translational research. We emphasize, however, that this framework has not been prospectively validated and should be regarded as hypothesis-generating rather than as a clinically established decision-making model. Recognizing the biallelic NLRP7 mutation as a heritable cancer-predisposition state further supports genotype-informed, risk-stratified oncological surveillance. Full article

Precision agriculture (PA) has emerged as a transformative approach for improving agricultural productivity, resource-use efficiency, and environmental sustainability through the integration of digital technologies, including Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and autonomous systems. Although previous reviews have primarily emphasized technological innovation, adoption trends, or environmental outcomes, they have provided limited synthesis of the economic mechanisms linking technology adoption, resource allocation, production efficiency, investment performance, and long-term sustainability. A structured narrative–systematic review was conducted using peer-reviewed research retrieved from Scopus, Web of Science, and Google Scholar, covering studies published between 2004 and 2026. An integrated analytical framework combining technology adoption theory, resource economics, and production-efficiency models was employed to explain how digital technologies generate economic value while identifying methodological limitations, geographical bias, unresolved research questions, and future research priorities. The review demonstrates that GPS-guided machinery, variable-rate technologies, smart irrigation systems, AI-driven decision-support tools, and integrated digital platforms improve water- and nutrient-use efficiency, labor productivity, production efficiency, and farm profitability. However, economic performance remains highly context-dependent, varying according to farm size, crop type, climatic conditions, institutional support, digital infrastructure, resource scarcity, and policy environments. Methodological inconsistencies in return on investment (ROI), net present value (NPV), lifecycle costing, ecosystem-service valuation, and environmental externality assessment reduce comparability among studies and complicate evidence-based policymaking. The review further identifies a pronounced geographical concentration of evidence in North America, Europe, and Australia, with comparatively limited understanding of PA economics in China, India, Brazil, Sub-Saharan Africa, and Southeast Asia. Persistent challenges include high capital costs, unequal access among smallholder farmers, data governance concerns, interoperability limitations, uncertainty in long-term investment performance, and limited integration of agricultural insurance, climate-risk management, and digital finance. By integrating economic theory, methodological comparison, geographical analysis, sustainability valuation, and policy perspectives within a unified conceptual framework, this review highlights the need for standardized economic evaluation methodologies, broader geographical representation, and interdisciplinary research to support evidence-based policy and the sustainable digital transformation of global agriculture. Full article

Energy is fundamental to economic development and human well-being, yet its consumption patterns remain highly complex and influenced by a wide range of factors. This systematic literature review aims to comprehensively examine the determinants of energy consumption in the residential, commercial, and industrial sectors to promote sustainable use and support efforts to alleviate energy poverty. A structured search was conducted across three major databases: PubMed, Scopus, and Web of Science, for peer-reviewed articles published in English between January 2014 and July 2025. Seventy-three eligible studies were identified. The results reveal a significant research imbalance: 72 eligible studies were identified over the 11-year period, of which approximately 77% focused on the residential sector, while the commercial and industrial sectors, despite accounting for more than half of the global energy use and CO₂ emissions, remain underexplored. Moreover, existing studies are largely concentrated in developed and major emerging economies such as the United States (U.S.) and China, with minimal attention given to the Global South, where energy poverty is most severe. This review highlights the critical need for more inclusive and sectorally balanced research to uncover inefficiencies, inform effective policy interventions, and guide equitable energy transitions. Understanding the full spectrum of factors influencing energy use across all sectors and geographic contexts is essential to achieving global energy sustainability and addressing disparities in energy access. Full article

The compelling visualization of Digital Elevation Models (DEMs) constitutes a vital intersection between Geographic Information Science (GIS) and the digital humanities. Nevertheless, traditional Generative Adversarial Networks (GANs) frequently demonstrate a “geography-blind” characteristic, resulting in structural “topographic drift” by dissociating geomorphic complexity from cartographic constraints. To overcome this limitation, we propose Geo-InkGAN, a geo-heuristic framework that integrates geographic principles with generative processes to achieve high-fidelity ink-wash style synthesis. A key component of our approach is an adaptive optimization strategy grounded in the Slope Standard Deviation (SSD). By establishing a quantitative relationship between geomorphological entropy and the cycle-consistency loss weight (${\lambda }_{cyc}$), we effectively address the Pareto trade-off between geomorphic accuracy and esthetic representation. Our results indicate that alluvial plains benefit from low-intensity constraints to facilitate fluid ink diffusion, whereas rugged terrains require high-intensity constraints to maintain the integrity of the topological framework. Additionally, the HCEG-SE mechanism (Hillshade-Contour Edge-Guided Stroke Enhancement) narrows the semantic divide between terrain skeletons and artistic textures by combining multi-directional non-photorealistic rendering with precise edge extraction techniques. Evaluated across five geomorphologically diverse regions—from karst towers to loess plateaus—Geo-InkGAN demonstrably surpasses existing benchmarks in Geomorphological Structure Correlation (GSC). This geomorphology-aware approach advances the scientific rigor of AI-driven cartography and offers a refined methodology for the cultural representation of digital twin landscapes. Full article

This study analyses the evolution of the financial literature on climate risk, examining the integration of artificial intelligence techniques into its measurement and management. To this end, a bibliometric approach is employed based on 221 articles indexed in the Web of Science Core Collection, using the Bibliometrix package. Moving beyond existing descriptive bibliometric reviews on ESG and green finance, the novelty of this paper lies in its analytical focus on how financial science operationalises quantitative AI mechanisms to price and integrate climate transition risk into asset and portfolio valuation. The structural analysis reveals that natural language processing (NLP) and digital transformation acting as driving motor themes, suggesting that the reviewed literature associates AI innovation policies with the mitigation of corporate greenwashing and enhance information transparency. Furthermore, while machine learning algorithms establish the cross-cutting predictive foundation for risk assessment, empirical evidence unveils a critical academic shift of traditional ‘financial performance’ towards a declining quadrant, indicating that empirical studies frequently find that that multi-phase investments in risk technologies do not yield immediate financial returns. Finally, the study maps a persistent geographical gap where emerging markets lack the data infrastructure of advanced economies, alongside isolated high-dimensional causal econometric niches like double machine learning. This analytical mapping provides key implications for global risk management and future quantitative research avenues. Full article

Deep eutectic solvents (DESs) have undergone a remarkable transformation over the past two decades, evolving from a laboratory curiosity into one of the most actively investigated solvent platforms in green chemistry. Yet, despite this rapid expansion, and although the field is well served by numerous topical reviews, it still lacks a corpus-wide, cross-disciplinary synthesis capable of guiding strategic research priorities, identifying critical knowledge gaps, and informing policy and industrial investment decisions. The present work addresses this need through a thorough analysis of global DES research from 2003 to 2025, based on a deduplicated corpus of 17,757 publications retrieved from the Web of Science Core Collection and Scopus following PRISMA-adapted screening guidelines. The analysis maps temporal publication dynamics, geographic and institutional contributions, thematic evolution, journal landscape, component usage patterns, international collaboration networks, market projections, and alignment with the United Nations Sustainable Development Goals. The results document an exponential growth trajectory—from a single publication in 2004 to 3954 in 2025 (CAGR > 30%)—and reveal a clear thematic transition from early electrochemistry-dominated research toward extraction, pharmaceutical, and environmental applications, with machine-learning-assisted design and hydrophobic DES formulations emerging as the most dynamic current frontiers. China leads global output with 6819 publications (38.4%), while the United States and Malaysia achieve the highest citation-per-publication ratios among the leading nations (≈46.7 and ≈38.9, respectively, versus ≈27.6 for China), and Spain pairs a comparatively modest output with a high h-index, indicating that impact is large relative to volume. Type III DESs and NADESs collectively account for approximately 69% of the literature, with choline chloride present in 72% of reported formulations. The global DES market, valued at approximately USD 166 million in 2024, is projected to reach USD 370 million by 2030. Despite this progress, critical translational barriers persist: fewer than 0.3% of publications include techno-economic or life cycle assessment analysis, standardized characterization protocols remain absent, and toxicological datasets are systematically incomplete. This panoramic analysis is intended to serve as an evidence-based reference for researchers prioritizing future directions, for funding agencies assessing the maturity and needs of the field, and for industrial stakeholders evaluating the readiness of DES technologies for scale-up. Full article