Objective MPs are ubiquitous terrestrial ecosystem pollutants, posing risks to soil health and biota, and eventually food security. Though microbial and insect-mediated degradation of MPs has been explored, the roles of terrestrial gastropods, especially snails, remain underexplored. Methods This review synthesizes the available evidence on the presence of MPs in soils and critically assesses how land snails interact with and transform plastic particles. Literature published within the period 2000–2025 was analyzed to identify mechanisms of ingestion, fragmentation, and partial chemical alteration of plastics by snails. Results The results showed that radular abrasion and digestive processes can cause mechanical fragmentation and partial oxidation of polystyrene, as well as other polymers, in snails. This could increase the bioavailability of microplastics in soil microbial communities. Differences among species in feeding behavior, physiological responses, and gut microbiota composition affect the potential for degradation. Conclusion The review pinpoints the major gaps in knowledge, which relate to the scarce enzymatic evidence and the rarity of long-term or field-based studies. Integrated research is required to establish the ecological relevance and biotechnological potential of gastropods for mitigating soil microplastic pollution. Graphical Abstract

Aquatic ecosystems worldwide are increasingly degraded by eutrophication, habitat loss, hydrological alterations, invasive species, and climate change. At

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To address the problems of lagging remote monitoring, complex fault mechanisms, and inefficient maintenance decisions for key equipment in hydropower stations, this paper proposes a remote diagnosis and intelligent maintenance method based on edge-cloud collaboration and digital twin-driven approaches. A layered architecture encompassing equipment, perception, transmission, analysis, and service layers is constructed. A physical-geometric-behavior coupled digital twin model is established, and PLC-SCADA control systems, multi-source monitoring data, fault diagnosis models, and remaining life prediction methods are integrated to achieve a closed loop of equipment status perception, anomaly identification, degradation assessment, and maintenance optimization. Experimental verification is conducted on a dual-unit Pelton hydropower system. Results show that the proposed method exhibits higher fault diagnosis accuracy, better life prediction performance, and superior maintenance economy under complex operating conditions. Unlike existing digital twin-based monitoring systems, the proposed framework integrates edge–cloud collaboration, real-time physical–virtual synchronization, fault diagnosis, RUL prediction, and maintenance optimization into a unified workflow for critical hydropower equipment.

Flood is one of the most common hazards in Nepal that have a severe socio-economic effect especially in Himalayan basins that are dominated by monsoons. In this study, we constructed the rainfall thresholds of flood early warning in the Babai River Basin (BRB) by using a calibrated and validated Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) rainfall-runoff model. The model showed good performance, with the calibration values of NSE = 0.84, R² = 0.87, and PBIAS = − 5.6%, and validation values of NSE = 0.78, R² = 0.81, and PBIAS = − 7.9%. The historical flood occurrences and rain data (1990–2018) were examined at Chepang (mid-basin) and Bhada Bridge (basin outlet). The findings indicate that cumulative rainfall and peak discharge have a significant positive association, and amplified downstream runoff is by virtue of integrated basin runoff. The rainfall thresholds were calculated to obtain bankfull, warning, and danger levels of the river for 1-day, 3-day, and 5-day accumulations. Chepang had 105–110 mm of bankfull, 135–140 mm of warning, and 200–205 mm of danger levels, and the Bhada Bridge needed higher thresholds of 140–145 mm, 185–190 mm, and 240–245 mm for bankfull, warning, and danger, respectively. These thresholds provide a practical advice on anticipatory flood management in addition to current stage-based monitoring. Although the downstream discharge information is not well known and there may be some land-use and climate shifts, the study provides a feasible model of improving early warning of floods in the Himalayan basins. Communities at floods risk are ensured safer and more resilient with real time monitoring combined with risk-based planning.

Large Language Models (LLMs) often generate fluent answers that appear confident even when they contain factual errors. This creates a reliability problem because users may trust incorrect answers when the model provides no meaningful signal of uncertainty. This paper proposes CLAIM-CAL, a claim-level self-verification framework for calibrated LLM reliability. Instead of assigning confidence to a complete answer directly, CLAIM-CAL decomposes an answer into atomic factual claims, verifies each claim using multiple verification probes, converts claim-level verdicts into a risk score, and then applies post-hoc calibration to obtain a more reliable confidence estimate. We evaluate CLAIM-CAL on the TruthfulQA generation benchmark using a 200-example experimental run with 60 examples for calibration and 140 examples for held-out testing. The proposed method is compared against direct answering, verbal confidence, self-consistency, and simple self-verification. In the held-out comparison, raw CLAIM-CAL achieved the highest observed accuracy among the evaluated methods but remained overconfident. Isotonic calibration reduced Expected Calibration Error (ECE), a bin-weighted gap between predicted confidence and empirical accuracy, from 0.212 for raw CLAIM-CAL to 0.038 [95% CI: 0.015, 0.106] after calibration, while maintaining accuracy at 0.757. To address calibration as a possible confound, we additionally applied isotonic calibration to the variable-confidence baselines on the same 60-example calibration split. In this fair calibrated comparison, CLAIM-CAL + Calibration achieved the strongest point estimate for 10-bin equal-width ECE (0.038) and Brier Score (0.146), although paired bootstrap confidence intervals show that ECE differences versus the closest calibrated baselines should be interpreted cautiously. The calibrated method also achieved selective accuracy of 0.824 at a 0.7 confidence threshold with 0.893 coverage. A second-pass judge prompt-robustness check on 50 sampled evaluations achieved 96.0% agreement and Cohen's kappa of 0.896. These findings suggest that claim-level verification produces a useful reliability signal, and that calibration is necessary to convert that signal into trustworthy confidence estimates for selective answering.

Following Bangladesh’s deadliest dengue outbreak, we examined how existing surveillance systems capture dengue burden and where gaps remain. This post shares the story behind our narrative review and why integrated surveillance matters.

When we think of traditional Chinese Shanshui (mountain-water) paintings, we imagine serene, timeless scenes of misty mountains and flowing rivers—a harmony between nature and artistic expression that has been refined over centuries. But what if these tranquil landscapes could come to life?

Cetaceans are some of the most charismatic marine species receiving regular widespread public attention. They display highly complex behaviour including post-mortem attentive behaviour sometimes referred to as grieving behaviour. Post-mortem attentive behaviour in cetaceans has been documented predominantly among odontocetes and remains largely absent from the literature on baleen whales, limiting current understanding of the cognitive and emotional dimensions of death-related responses in mysticete cetaceans. Here, we describe a case of prolonged post-mortem behaviour by a female humpback whale and her escort toward a deceased calf. The adult female remained in close proximity to the calf, engaging in repeated interactions and maintaining attendance over several hours and possibly days. Unlike reports in odontocetes, the female did not attempt to lift the calf to the surface. The observed behaviours were consistent with epimeletic and post-mortem attentive responses documented in socially complex mammals and indicating a strong maternal attachment to the calf. This case study highlights the need for continued systematic documentation of rare neonatal mortality events to better understand the cognitive, emotional, and evolutionary significance of post-mortem behaviour in large whales. Understanding how nonhuman animals respond to death provides insight into their emotional lives, social bonds, and cognitive capacities.

Purpose To review the current advances, persistent challenges, and future prospects of methanol fuel cell technologies, thereby supporting the transition toward low-carbon energy systems. Design/methodology This is a comprehensive review that analyzes recent global progress across key aspects of methanol fuel cell technology. The focus areas include catalyst development, membrane innovation, performance optimization, and system integration. Findings Recent advances in electrocatalysts, nanostructured membranes, and hybrid system designs have substantially enhanced the efficiency, durability, and operational flexibility of methanol fuel cells. This progress has enabled their expansion in transportation, portable electronics, stationary power, and industrial energy generation. Originality/value This review offers a holistic overview of the current state-of-the-art and identifies critical limiting factors, such as methanol crossover, limited long-term durability, and high production costs, that continue to constrain commercialization. It emphasizes the need for coordinated, multidisciplinary efforts in materials science and system engineering, alongside favorable policies, to accelerate adoption and position methanol fuel cells as an integral component of the future renewable-energy landscape.

The accurate estimation of reference evapotranspiration (ETo) on seasonal and annual scale is vital for determination of water requirement of crops and impact of climate change on irrigated agriculture. This study investigates the influence of climatic variables (temperature, relative-humidity, solar-radiation, and wind speed) on ETo and comparison of the performance evaluation of temperature and radiation-based models over Cross River basin on seasonal and annual basis. Estimation of ETo was done using temperature-based; Schendel, Samani, Trajkovic, Droogers & Allen-2, Dorji, Hadria, Hargreaves-samani, Blaney-Morin-Nigeria, and radiation-based models; Jensen-Haise, Stephens and Stewart, Oudin, Abtew, Irmak-1, Copais, Tabari & Talaee-4, and Hargreaves under humid-tropic condition, with Penman–Monteith (PM-ETo) as a reference. Remotely-sensed meteorological variables from 1987 to 2017 were sourced from the Climatic Research Unit (CRU) database, over 22 stations. These variables were used for estimating ETo. Models were evaluated with coefficient of determination, a root-mean-square-error, Willmott’s index of agreement, Percentage-Bias and Nash–Sutcliffe Efficiency. Sensitivity analysis (± 5%) revealed that PM-ETo was most sensitive to solar-radiation and temperature, compared to relative-humidity and wind speed. Blaney-Morin-Nigeria demonstrated seasonal estimation bias. Further analysis revealed that radiation-based models out-performed the temperature-based models across all categories. Blaney-Morin-Nigeria and Copais recorded the best performance in dry season, while Hargreaves–Samani and Abtew recorded the best performance in rainy season. Abtew and Hargreaves–Samani recorded the best performance on annual basis. An increasing trend (slope = 0.0029 mm/day) of PM-ETo further suggests global warming scenario. This demonstrates the ability of radiation-driven models for crop water requirement estimation in data-scarce regions.

Abstract Alzheimer's disease (AD) is a common form of dementia characterized by memory loss, cognitive and linguistic abilities declining and self-care capabilities diminishment. With the aging population globally, AD poses a significant threat to public health. Current treatments for AD aim to alleviate symptoms and slow down disease progression, but due to the limited understanding of underlying disease mechanisms, AD is still impossible to be cured yet. In recent years, there has been an exponential growth in exosome-related research because of their excellent biocompatibility ability, loading capacity and cellular internalization, making exosome to be one of the hotspots and a promising strategy in AD therapy research. This comprehensive review systematically explores the potential roles of various exosome-based nanotherapeutic strategy in AD treatment, with a particular focus on their specific biological mechanisms of action. Firstly, we elaborated on the pathological mechanisms of AD formation as well as the mechanisms related to the formation, secretion and function of exosome. Additionally, we highlighted the research progress in the development of exosome-based nanotherapeutic strategies for AD treatment and their corresponding biological mechanisms. Furthermore, we delved into the challenges and opportunities these strategies facing in clinical application. Looking forward to future research directions and trends, our review aims to provide a more comprehensive understanding and guidance with the application of exosome in AD treatment. Exosome-based nanotherapeutic strategies, as a new therapeutic approach, have opened up new possibilities for the treatment of AD and brought new light to patients. Graphical abstract Schematic diagram of exosome-based nanotherapy strategies for the treatment of AD. It can be roughly classified as: exosomes-based methods treating AD and bioengineered exosomes for the treatment of AD.

Clean energy transitions are not just about technology. They are also about people, equity, and justice. Women play a pivotal role in advancing sustainable energy solutions, yet sociocultural, financial, and institutional barriers continue to limit their participation in decision-making and access to clean energy. This research combines BERTopic modeling, SDG mapping, and case study analysis to bridge quantitative insights with real-world narratives, offering a comprehensive examination of the gender‒energy nexus. Grounded in energy justice, gender empowerment, and SDG frameworks, the study applies Kabeer’s and Friedmann’s empowerment models to link agency, resources, and achievements with distributional, procedural, and recognitional justice in energy transitions. The study covered 616 publications identified through an extensive Scopus database search, spanning literature from 2015—coinciding with the adoption of SDGs—to 2024, specifically mapped to SDG 5 (gender) and SDG 7 (energy). Addressing the main energy justice dimensions and relevant SDGs, the findings of this systematic review reveal that clean energy adoption reduces unpaid domestic work (SDG 5.4), enhances women’s leadership (SDG 5.5), and strengthens economic opportunities (SDG 7.1, SDG 7.2) but remains constrained by gendered power dynamics, technology adaptation barriers, and financial accessibility issues. The study highlights how women’s participation in energy transitions leads to greater community engagement, increased household energy efficiency, and a shift toward sustainable energy behaviors. However, moderating factors of gender empowerment interventions show that intrahousehold bargaining, a lack of financial incentives, and limited representation in governance structures continue to restrict equitable energy access. Additionally, the findings emphasize that policies designed without a gender lens risk reinforcing existing inequalities rather than alleviating them. By embedding SDG goals in the analysis, this study ensures alignment with global sustainability goals and reinforces the urgency of justice-oriented energy policies. Advocating for inclusive, community-driven approaches, this research underscores the need for intersectional frameworks that integrate energy justice and gender empowerment, ensuring that energy transitions are not only technologically sound but also socially equitable and accessible to all.

This study presents a comprehensive analytical evaluation of the axial compression behavior of fiber-reinforced polymer–reinforced concrete (FRP-RC) columns. The investigation focuses on the combined effects of transverse confinement, FRP material type, column geometry, and concrete compressive strength, while explicitly considering both short and slender column configurations to evaluate the influence of geometric slenderness on structural response. The compiled experimental database covers concrete strengths ranging from approximately 10 to 90 MPa and includes columns reinforced with glass fiber–reinforced polymer (GFRP) and carbon fiber–reinforced polymer (CFRP) longitudinal bars and transverse reinforcement. The database analysis indicates that reducing spiral pitch from relatively wide spacing (100–120 mm) to dense configurations (35–40 mm) leads to a significant increase in normalized axial strength (approximately 50–100%) and enhances post-peak stability. Increasing concrete compressive strength from 30 to 50 MPa is associated with an increase in peak load of approximately 20–40%, accompanied by a reduction in ductility of about 25–35%. Columns reinforced with CFRP generally exhibit higher axial capacity than their GFRP counterparts, with observed strength gains in the range of 15% to 50%, primarily due to the higher stiffness and confinement efficiency of CFRP. Geometric effects are also pronounced. Circular columns tend to provide 10–25% higher normalized capacity compared to square sections. In addition, increasing column slenderness (higher L/D ratio) is associated with reductions in axial strength of approximately 20–30%, reflecting the influence of stability and second-order effects. A nonlinear finite element model was employed to support and extend the experimental trends observed in the database analysis. The numerical simulations captured consistent behavioral patterns, confirming the sensitivity of peak load and post-peak response to confinement intensity, concrete strength, FRP stiffness, and slenderness ratio. Comparisons with common design provisions indicate generally good agreement between predicted and experimental strengths, with experimental-to-predicted ratios typically ranging from 0.95 to 1.10, although both conservative and unconservative predictions are observed, particularly for lightly confined or slender columns. The combined experimental synthesis and numerical validation provide a unified understanding of the governing mechanisms controlling the axial behavior of FRP-RC columns and offer a strengthened basis for future refinement of design recommendations.

Objective This study systematically reviews the integration of artificial intelligence (AI) with investment funds in the asset management industry and emphasizes its transformative impact. Aiming to bridge knowledge gaps, this study explores AI's position within the industry, analyzes its variety, and assesses the transformational implications of existing practices. Methodology Adhering to systematic review methodology, a comprehensive search was conducted across the Web of Science and Scopus databases, identifying 27 high-quality studies published from 2020 to 2024. The study was then analyzed thematically. Findings The first theme of the review classifies AI applications into front-end and back-end roles, illustrating the transition from traditional processes. On the front-end, AI assists simple activities by analyzing an investor's profile to determine a suitable fund, similar to a human financial consultant. The back-end sees AI performing autonomous trading and managing pooled fund investments, resembling a human fund manager. As a secondary theme, this review analyse AI deployment include using robo-advisors and chatbots for front-end tasks, screening analysis, predictive analytics, automated algorithmic trading, and automated trading technical analysis for back-end tasks. This study also includes a deductive discussion on the implications and transofrmation of AI deployment. Contribution/implications This review serves as an analytical framework to highlight AI in investment funds in asset management and serves as a springboard for future research and industrial applications.

This study addresses the challenge of achieving sustainable marketing performance in fragile ecotourism destinations, where marketing activities must balance competitiveness with environmental preservation. A hybrid Smart Digital Marketing (SDM) framework that integrates Digital Six Sigma Marketing (DSSM) with fuzzy logic-based decision systems is developed and empirically evaluated to support adaptive decision-making under ecological constraints. Using an explanatory sequential mixed-methods design, the study combines survey data (N = 250), stakeholder interviews (N = 18), environmental observation scorecards, and computational modeling. The framework operationalizes three ecological and behavioral variables—environmental fragility, visitor load, and tourist satisfaction—as inputs to a Mamdani-type fuzzy inference system with 27 if–then rules, producing adaptive marketing intensity decisions aligned with carrying capacity. The pre–post analysis showed statistically significant observed improvements associated with framework implementation: campaign response time was reduced by 35.7%, process variance decreased by 39.4%, the process capability index increased from 0.94 to 1.42, and competitive positioning improved by 24.3%. The fuzzy inference system demonstrated adaptive modulation of marketing intensity from 0.80 under stable conditions to 0.19 under high ecological risk. The study contributes a practical, data-driven decision-support framework that enables organizations to align marketing strategies with ecological thresholds, extending Smart Digital Marketing as a dynamic capability within sustainability-sensitive tourism contexts. The framework provides a potentially transferable model for translating qualitative and ambiguous variables into structured, adaptive marketing decisions under environmental uncertainty.

Artificial intelligence (AI) tools such as chatbots have become an inseparable part of education with the advancements in large language models. As more studies have been conducted, researchers and practitioners may have challenges in finding the gaps in the literature and/or deciding on which tools are beneficial or not for instruction. These challenges can be addressed with bibliometric reviews that can provide a comprehensive overview of the current trends in AI use in ELT. This study addresses that need with a novel methodology by examining the ERIC and Scopus databases. Using computational data analysis tools and natural language processing, we examined 856 journal articles and provided several trends, such as keywords, the most cited papers, subject matrices, and geolocations of the articles, as well as several implications for practitioners. A key result is that the research on the use of AI tools is increasing substantially, with a clear focus on teaching methods and writing instruction. Our study offers valuable information for the researchers and practitioners who seek techniques to integrate AI tools into language instruction.

Preparing and organising a relief effort using useful social media content in a crisis situation is crucial. Twitter, Facebook, LinkedIn, and many more social media platform produces immense data. Practical implementation for necessary automatic sorting and extracting specific text by classifying tweets into useful information, which helps humanitarian needs. Demanding less computational resources to concentrate on text models to scale the work from the CrisisMMD dataset. Machine Learning (ML) classifier test for converting text by using Term Frequency Inverse Document Frequency (TF-IDF), Word2Vec, and Bag of Words (BoW). Performance measure is used to evaluate accuracy, macro-averaged precision, recall, F1-score, and ROC-AUC, and tested for statistical significance and ROC curves analysis, with standard metrics on the classifiers Logistic Regression (LR), Support Vector Machines (SVM), Multinomial Naïve Bayes (MNB), Decision Trees (DT), Random Forests (RF), XGBoost, and a combined ensemble model. Better results were obtained with the Word2Vec model than with TF-IDF, and BoW consistently delivers effective word-count-based methods, indicating simpler methods. Logistic Regression (LR) and SVMs achieve consistent and reliable results in linear models when used in an ensemble approach with measured criteria yielding the most well-rounded performance. To identify humanitarian needs and class distribution, and to correct the models to strengthen especially critical disaster response. The research focuses on demonstrating traditional ML techniques, paired with reliable test filtering and the right extraction features. Disaster-related test filtering to present real-time monitoring supports humanitarian decision-making.

To address the problems of lagging remote monitoring, complex fault mechanisms, and inefficient maintenance decisions for key equipment in hydropower stations, this paper proposes a remote diagnosis and intelligent maintenance method based on edge-cloud collaboration and digital twin-driven approaches. A layered architecture encompassing equipment, perception, transmission, analysis, and service layers is constructed. A physical-geometric-behavior coupled digital twin model is established, and PLC-SCADA control systems, multi-source monitoring data, fault diagnosis models, and remaining life prediction methods are integrated to achieve a closed loop of equipment status perception, anomaly identification, degradation assessment, and maintenance optimization. Experimental verification is conducted on a dual-unit Pelton hydropower system. Results show that the proposed method exhibits higher fault diagnosis accuracy, better life prediction performance, and superior maintenance economy under complex operating conditions. Unlike existing digital twin-based monitoring systems, the proposed framework integrates edge–cloud collaboration, real-time physical–virtual synchronization, fault diagnosis, RUL prediction, and maintenance optimization into a unified workflow for critical hydropower equipment.

Seaweed farming is increasingly promoted as a nature-based solution for marine carbon dioxide removal (mCDR), offering the dual promise of climate mitigation and ecosystem enhancement. However, here we highlight a fundamental paradox: while macroalgae cultivation can significantly boost carbon sequestration and support biodiversity, it also introduces site-specific ecological risks—most notably eutrophication, hypoxia, and acidification—particularly in semi-enclosed coastal systems with limited water exchange. We synthesize current understanding of both the positive and negative impacts of large-scale macroalgae farming, examining pathways of carbon uptake, storage, and export alongside biogeochemical and food web disruptions. Critically, we identify the overlooked roles of hydrodynamic conditions and benthic-pelagic coupling in mediating ecological outcomes. To ensure that macroalgae aquaculture contributes effectively to climate goals while safeguarding coastal ecosystem resilience, we call for the development of a targeted and comprehensive evaluation framework capable of accurately assessing its impacts on adjacent waters. Such a framework should incorporate site-specific water-exchange characteristics and biogeochemical vulnerability, thereby enabling more informed and adaptive management strategies—including hydrodynamically guided site zoning—to support sustainable, long-term ecosystem benefits.

In the Global South, a disproportionate number of young married women who experience unwanted and mistimed pregnancies undergo unsafe abortion practices, leading to a heightened burden of maternal and infant deaths in developing countries. The present qualitative study attempts to examine the practice of induced abortion at the village as well as assess the socio-structural barriers leading to poor sexual and reproductive health in rural Uttar Pradesh, India. The study underlines the lived experience and process of decision-making related to induced abortion, and the intertwined family, social, and healthcare-associated challenges faced by young married women. Evidence from the study suggests that the inability to use safe contraception methods, unsupportive behaviour of the spouse towards childcare, financial hardship, lack of accessibility and affordability of contraception methods, lesser decision-making power, and social norms and customs appeared to pose critical barriers to safe abortion practices among young married women. Findings from the study highlight an abysmal dearth of access to safe, affordable, and quality abortion care services in the study area. This calls for policymakers to increase investments in high-quality, comprehensive sexual and reproductive health services to ensure safe pregnancy and child health experiences in the Indian context.

The accurate estimation of reference evapotranspiration (ETo) on seasonal and annual scale is vital for determination of water requirement of crops and impact of climate change on irrigated agriculture. This study investigates the influence of climatic variables (temperature, relative-humidity, solar-radiation, and wind speed) on ETo and comparison of the performance evaluation of temperature and radiation-based models over Cross River basin on seasonal and annual basis. Estimation of ETo was done using temperature-based; Schendel, Samani, Trajkovic, Droogers & Allen-2, Dorji, Hadria, Hargreaves-samani, Blaney-Morin-Nigeria, and radiation-based models; Jensen-Haise, Stephens and Stewart, Oudin, Abtew, Irmak-1, Copais, Tabari & Talaee-4, and Hargreaves under humid-tropic condition, with Penman–Monteith (PM-ETo) as a reference. Remotely-sensed meteorological variables from 1987 to 2017 were sourced from the Climatic Research Unit (CRU) database, over 22 stations. These variables were used for estimating ETo. Models were evaluated with coefficient of determination, a root-mean-square-error, Willmott’s index of agreement, Percentage-Bias and Nash–Sutcliffe Efficiency. Sensitivity analysis (± 5%) revealed that PM-ETo was most sensitive to solar-radiation and temperature, compared to relative-humidity and wind speed. Blaney-Morin-Nigeria demonstrated seasonal estimation bias. Further analysis revealed that radiation-based models out-performed the temperature-based models across all categories. Blaney-Morin-Nigeria and Copais recorded the best performance in dry season, while Hargreaves–Samani and Abtew recorded the best performance in rainy season. Abtew and Hargreaves–Samani recorded the best performance on annual basis. An increasing trend (slope = 0.0029 mm/day) of PM-ETo further suggests global warming scenario. This demonstrates the ability of radiation-driven models for crop water requirement estimation in data-scarce regions.

Introduction The preservation of tangible cultural heritage artifacts, particularly those created through intangible cultural heritage (ICH) practices such as traditional craftsmanship, is essential for sustainable urban development. Such artifacts embody the skills and knowledge transmitted through ICH. Digital twin technology offers a systematic approach to document and preserve these artifacts digitally. However, obtaining complete 3D models through close-range photogrammetry remains challenging due to occlusion, surface properties, and sensor limitations, resulting in incomplete point cloud data. Methods This study proposes a generative adversarial network with self-attention mechanism to complete missing 3D point cloud data of cultural heritage artifacts. The network employs a multi-layer perceptron for global feature extraction, self-attention modules for local detail capture, and a feature pyramid decoder for hierarchical point cloud generation. Results Quantitative evaluation on the ShapeNet dataset demonstrates that the proposed method achieves average errors of 5.541 (P→GT) and 4.183 (T→P) for complete point cloud completion, outperforming FinerPCN, PF-Net, and PFG-Net. For missing point cloud regions, the method achieves errors of 24.303 (P→GT) and 20.008 (GT→P). Discussion The proposed framework integrates digital twin concepts with deep learning-based 3D reconstruction to enhance the digital preservation of cultural heritage artifacts. By generating more complete and accurate point clouds, the method enables higher-quality 3D models suitable for virtual exhibition, documentation, and cultural transmission. A limitation is that validation on diverse heritage artifact geometries beyond ShapeNet has not yet been conducted.

Health professional mobility has become a defining feature of 21st-century health systems, highlighting deep structural inequalities within global health labour markets. International movement of health workers can unfairly disadvantage source countries by losing publicly funded training investments, although it offers benefits at both individual and system levels. This narrative review compiles evidence on current patterns, drivers, and impacts of health worker migration, and explores the emerging idea of proportionate co-investment as a framework for fairer global health workforce management. Literature was gathered through database searches of PubMed, Scopus, Google Scholar, and major organisational and government websites. Evidence was presented following SANRA guidelines. The review emphasises dominant South–North migration flows. Widening wage gaps, poor working conditions, and power imbalances allow high-income countries to benefit from “implicit subsidies” in health workforce development. For source countries, this results in service gaps, poor distribution, higher health system costs, and slower progress toward universal health coverage. Current ethical recruitment codes and bilateral agreements are mostly voluntary, fragmented, and inadequate to fix labour-market imbalances. Proportionate co-investment redefines health worker migration as a shared global duty. Destination countries should systematically support health workforce education, retention, and working conditions in source countries. This review advocates a proactive framework grounded in proportionality, predictability, system alignment, and shared governance, drawing on emerging models such as skills partnerships and destination-country financing. Embedding proportionate co-investment could shift global policy from managing health worker losses to sharing responsibilities, promoting ethical mobility, and strengthening health systems worldwide.

The rapid expansion of research on green-synthesized nanoparticles (GSNPs), particularly those produced through biological processes, has generated a highly fragmented literature that obscures structural trends and translational gaps. This study conducts a comprehensive bibliometric analysis of publications indexed in Scopus between 2020 and 2025 to map the intellectual landscape of GSNPs and identify priority directions for future research. The findings demonstrate that silver nanoparticles (Ag NPs) dominate the field, driven by their established antibacterial effectiveness and broad biomedical and environmental utility. At the same time, plant extracts remain the primary biosynthetic resources, while alternative biological sources, including bacteria and fungi, are still inadequately investigated. The analysis also highlights the limited use of high-resolution characterization tools, including field emission scanning electron microscopy (FESEM), atomic force microscopy (AFM), and X-ray photoelectron spectroscopy (XPS), compared to the prevalent conventional techniques, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), revealing a persistent characterization gap at the nano–bio interface. The geographic analysis highlights Asia, especially India and China, as leading contributors, supported by dense international collaboration networks. Conceptually, the study clarifies green synthesis within the 12 principles of green chemistry, integrating biological, physical, and chemical methods and proposing a taxonomy that resolves the frequent conflation of green synthesis with biosynthesis alone. Finally, by linking bibliometric patterns to toxicity and regulatory keywords, the analysis exposes key barriers to commercialization. It outlines research strategies to advance GSNPs toward scalable and regulation-ready nanotechnology platforms. Graphical abstract

Clean energy transitions are not just about technology. They are also about people, equity, and justice. Women play a pivotal role in advancing sustainable energy solutions, yet sociocultural, financial, and institutional barriers continue to limit their participation in decision-making and access to clean energy. This research combines BERTopic modeling, SDG mapping, and case study analysis to bridge quantitative insights with real-world narratives, offering a comprehensive examination of the gender‒energy nexus. Grounded in energy justice, gender empowerment, and SDG frameworks, the study applies Kabeer’s and Friedmann’s empowerment models to link agency, resources, and achievements with distributional, procedural, and recognitional justice in energy transitions. The study covered 616 publications identified through an extensive Scopus database search, spanning literature from 2015—coinciding with the adoption of SDGs—to 2024, specifically mapped to SDG 5 (gender) and SDG 7 (energy). Addressing the main energy justice dimensions and relevant SDGs, the findings of this systematic review reveal that clean energy adoption reduces unpaid domestic work (SDG 5.4), enhances women’s leadership (SDG 5.5), and strengthens economic opportunities (SDG 7.1, SDG 7.2) but remains constrained by gendered power dynamics, technology adaptation barriers, and financial accessibility issues. The study highlights how women’s participation in energy transitions leads to greater community engagement, increased household energy efficiency, and a shift toward sustainable energy behaviors. However, moderating factors of gender empowerment interventions show that intrahousehold bargaining, a lack of financial incentives, and limited representation in governance structures continue to restrict equitable energy access. Additionally, the findings emphasize that policies designed without a gender lens risk reinforcing existing inequalities rather than alleviating them. By embedding SDG goals in the analysis, this study ensures alignment with global sustainability goals and reinforces the urgency of justice-oriented energy policies. Advocating for inclusive, community-driven approaches, this research underscores the need for intersectional frameworks that integrate energy justice and gender empowerment, ensuring that energy transitions are not only technologically sound but also socially equitable and accessible to all.

Aquatic ecosystems worldwide are increasingly degraded by eutrophication, habitat loss, hydrological alterations, invasive species, and climate change. At

This study focuses on the impact of treated greywater (TG) on the yield, growth and soil properties of irrigated pepper in Akure, Nigeria. Raw greywater was acquired from the Federal University of Technology, Akure (FUTA) hall of residence. Fresh water (FW) was obtained from the FUTA water reservoir. FW and TG were used for irrigation in a Randomized Complete Block Design (RCBD) with three treatments and three repetitions in years 2016, 2017, and 2018 respectively. Soil samples were collected to determine the soil physico-chemical properties including Soil pH, macronutrients and fertility status. The growth parameters of pepper considered are fruit's number, fruit’s width and number of leaves. Results of the soil analysis revealed improvement in the fertility status of the soil with respect to macronutrients after harvest of hot pepper. TG resulted in remarkable increase in pepper yield from 0.19 kg/m2 to 0.405 kg/m2, 0.208 kg/m2 to 0.423 kg/m2, and 0.175 kg/m2 to 0.353 kg/m2 in 2016, 2017, and 2018 respectively. At 5% probability value, TG significantly improved the growth and yield of pepper. The findings of this study strongly emphasize that effective wastewater management will enhance irrigated agriculture and FW conservation.

Many energy production and exploration facilities in the petroleum industry are susceptible to the microbial attachment and biofilm formation. Microorganis

Publishing is about trust as much as visibility. In this video, authors share their experiences of publishing with Discover journals, highlighting the import...

It started with the palpable heat of Taipei's summers. This personal experience fueled our research journey—developing a novel deep learning model, decoding decades of satellite data, and ultimately discovering how the synergy of green and blue spaces holds the key to cooler urban futures.

This research has a story, like all research. And it is, above all, a human story, like all of them.

Breast cancer has the highest incidence among cancers in women, with estrogens playing a pivotal role in the estrogen receptor-positive (ER+) subtype. Loca