An NLP-enhanced large-scale review of AI trends and research gaps in English language education - Discover Computing

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.

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A feature-optimized machine learning system for real-time crisis tweet identification - Discover Computing

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.

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Digital twin-driven edge–cloud collaborative remote fault diagnosis and intelligent predictive maintenance for critical hydropower equipment - Discover Artificial Intelligence

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.

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Reassessing the climate mitigation benefits and environmental risks of coastal seaweed farming - Discover Oceans

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.

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Socio-structural barriers to safe abortion and reproductive health among women in rural Uttar Pradesh, India - Discover Public Health

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.

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Evaluation of temperature and radiation based reference evapotranspiration methods over Cross River basin Nigeria - Discover Environment

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.

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Enhancing preservation of tangible cultural heritage artifacts through digital twin technology and deep learning-based 3D point cloud completion - Discover Computing

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.

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The global health implications of proportionate co-investment in health workforce migration - Discover Health Systems

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.

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Current advancements and future research directions in the green synthesis and applications of nanoparticles - Discover Nano

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

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Energy justice and gender: bridging equity, access, and policy for sustainable development - Discover Sustainability

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.

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Macrophytes-based solutions as tools to halt the collapse of freshwater biodiversity, functions and benefits - Discover Ecology

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

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Impacts of treated greywater on soil properties, growth and yield of irrigated pepper (Capsicum annum) in Akure Nigeria - Discover Environment

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.

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Advances in understanding microbial corrosion of oil pipelines - Discover Bacteria

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

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Rigorous peer review & research integrity | Discover Journals

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

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The Blue-Green Synergy: Discovering a Powerful Partnership for Cooler Cities

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.

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The "Stela Effect" of bright and dark narcissism on educational innovation in higher education: exploratory psychometric validation

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

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Beneficial effects of an indole derivative melatonin analogue in estrogen receptor positive breast cancer - Discover Endocrinology and Metabolism

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

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AI driven urban heat equity atlas integrating machine learning for climate risk assessment - Discover Environment

Urban heat island (UHI) exposure is unevenly distributed and often co-locates with social vulnerability, motivating tools that integrate physical heat burden with environmental justice considerations. We developed an Intelligent Urban Heat and Equity Atlas for 55,871 U.S. urban census tracts, integrating remotely sensed heat indicators, urban form, meteorology, and tract-level sociodemographic vulnerability measures. Machine-learning models were trained to predict tract-level annual daytime UHI intensity (UHI_annual_day) using spatially grouped cross-validation, and outputs were summarized into two GIS-ready equity products: (i) the Vulnerability-Adjusted Urban Heat Index (VA-UHI), a normalized composite prioritization index combining heat exposure and vulnerability, and (ii) a complementary Heat Exposure Equity Index (HEEI) for equity-focused comparison across vulnerability strata. Across the national tract sample, higher VA-UHI values systematically coincide with higher vulnerability, indicating that the most heat-burdened neighborhoods frequently overlap with areas facing structural socioeconomic disadvantage. Category-based comparisons further show that “Extreme” VA-UHI tracts exhibit higher vulnerability index scores than “Low” VA-UHI tracts (e.g., 0.412 vs. 0.242; + 0.170 index units), reflecting differences in normalized index magnitude rather than calibrated increases in health risk. These atlas outputs support interpretable, tract-scale prioritization for urban heat mitigation and equity-informed planning.

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Rainfall thresholds for flood early warning in the Babai River Basin, Nepal using rainfall-runoff modelling - Discover Geoscience

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.

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Nanoparticles that target nonamyloid and nontau pathways in Alzheimer’s disease - Discover Nano

Alzheimer’s disease (AD) is a complex neurodegenerative disorder whose progression involves multiple pathways beyond the canonical amyloid and tau cascades. Neuroinflammation, mitochondrial dysfunction, and lysosomal impairment represent key nonamyloid and nontau pathways; preclinical evidence suggests that targeting these pathways may aid in the development of more effective treatments, although clinical validation remains pending. Owing to their ability to cross the blood‒brain barrier and their potential for precise targeting, nanostructured materials represent promising tools for modulating these pathways in preclinical models. Lipid, chitosan, and gold nanoparticles, when employed as carriers of anti-inflammatory and antioxidant compounds such as curcumin and resveratrol, have been shown in animal studies to reduce neuroinflammation and improve mitochondrial function. NPs functionalized with ligands such as triphenylphosphonium specifically target mitochondria, reducing oxidative stress and increasing ATP production by increasing drug bioavailability. Polymeric and carbon nanostructures improve lysosomal function and restore cellular homeostasis. These technologies slow disease progression by reducing neuroinflammation, improving mitochondrial dynamics, and enhancing autophagy processes. This article provides a strictly narrative review of recent advances in the use of nanostructured materials for targeting nonamyloid and nontau pathways in AD and to examine the therapeutic potential of this technology in the development of effective strategies to combat this disease.

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