Search Results (26,767)

High-temperature (HT) stress during flowering causes male sterility and yield loss in soybean. MYB transcription factors are key regulators under abiotic stress, yet their function and mechanism in regulating male fertility under HT stress in soybean are not fully understood. In this study, a MYB transcription factor GmMYB21a in soybean was identified. GmMYB21a was induced by HT stress in soybean restorer line and was specifically expressed in pollen. Through overexpression and knockout experiments, we demonstrated that GmMYB21a positively regulated pollen viability and germination under HT stress. Overexpression of GmMYB21a significantly enhanced these traits in restorer line, whereas knockout plants exhibited the opposite effect. Transcriptome sequencing revealed that GmMYB21a overexpression upregulated numerous stress-responsive genes, particularly those involved in flavonoid biosynthesis and sugar metabolism. In addition, molecular experiments confirmed that GmMYB21a bound to the promoter of flavonoid synthesis gene GmCHI2-A and promoted its expression. In summary, our research indicated GmMYB21a enhanced the HT-tolerance of male fertility in soybean restorer line through reactive oxygen species scavenging and flavonoid synthesis. This study aims to elucidate the thermotolerance mechanism in soybean male fertility and identify genetic resources for breeding HT-tolerant restorer lines. Full article

The increasing global demand for sustainable water purification technologies has accelerated research on catalytic degradation and advanced oxidation processes for the removal of refractory pollutants. This study provides a comprehensive bibliometric analysis of global research trends in catalytic water and wastewater treatment from 2010 to 2025, combining quantitative mapping with a qualitative synthesis of emerging technological directions. Bibliographic data were retrieved from the Scopus database and screened using the PRISMA framework, followed by analysis using VOSviewer (v1.6.20) and OriginPro (version 2023, OriginLab Corporation, Northampton, MA, USA) to examine publication growth, citation patterns, international collaboration networks, and thematic evolution. A total of 1550 publications, including 1265 research articles and 285 review papers, were analyzed. The results show a significant increase in research output after 2015, reflecting growing global attention to water sustainability and environmental remediation. China, the United States, and India were identified as the leading contributors, with strong international collaboration networks. Keyword co-occurrence analysis revealed three dominant research themes: photocatalytic degradation and semiconductor engineering, Fenton and Fenton-like advanced oxidation processes, and emerging hybrid catalytic systems involving carbon-based materials and metal–organic frameworks. The analysis also indicates a recent shift toward multifunctional hybrid catalysts designed to improve efficiency, stability, and performance in complex wastewater systems. These findings highlight key scientific developments and suggest future research priorities, including green catalyst synthesis, reactor and process scale-up, AI-assisted catalyst design, and life-cycle sustainability assessment to support the transition from laboratory research to practical water treatment applications. Full article

Lung squamous cell carcinoma (LUSC) tends to arise in the setting of interstitial or emphysematous lung diseases, including idiopathic pulmonary fibrosis (IPF), pulmonary emphysema (PE), and smoking-related interstitial fibrosis (SRIF), where field cancerization may extend. DNA methylation of promoter regions of p16, CDH13, and RASSF1A and p16 exon 2 was assessed by methylation-specific PCR. Tumor, adjacent (<3 cm), and distant (≥3 cm) lung tissues were obtained from 25 patients with LUSC (IPF, n = 7; PE, n = 8; SRIF, n = 10). p16 exon 2 methylation was significantly higher in tumors than in non-tumorous tissues in PE and SRIF cases. In contrast, IPF cases showed p16 exon 2 hypermethylation also in distant tissues. Across tumor samples, p16 promoter hypermethylation was frequently observed in stage II or higher. p16 expression in tumors was generally reduced in IPF and PE cases, compared with SRIF cases. No consistent methylation or expression patterns were observed for CDH13 or RASSF1A. p16-associated molecular alterations exhibited disease- and stage-related differences, suggesting heterogeneity in LUSC carcinogenesis. These findings indicate a broader epigenetic field effect, as reflected by p16 exon 2, in IPF-associated LUSC and suggest that complex, elusive mechanisms underlying p16 aberrations may contribute to this phenomenon. Full article

Objective: The objective of this study was to map and critically synthesize empirical evidence on ML/AI applications across surface mining unit operations, and to characterize models, validation practices, and evidence gaps. Eligibility criteria: Our eligibility criteria comprised peer-reviewed studies (2020–2025) applying ML/AI to surface mining activities, training/validating models on empirical datasets, and reporting quantitative performance metrics. Information sources: Scopus, ScienceDirect, Dimensions, and Web of Science were our information sources, last searched December 2025 and supplemented by website and citation snowballing. Risk of bias: Risk of bias was assessed using an adapted domain-based approach based on PROBAST, used to interpret findings without excluding studies. Synthesis method: Our research employed a narrative synthesis (no meta-analysis due to heterogeneity in datasets, algorithms, contexts, and metrics), grouped by application domain. Results: From 5317 records, 57 studies were included, concentrated in blasting (43), followed by load and haul (6), post-dismantling management (4), extraction (2), and overall exploitation (2). Studies predominantly reported statistical metrics (e.g., R², RMSE, and MAE), with limited operational performance indicators; validation was frequently site-specific. Dataset sizes were not reported consistently across studies. Limitations: This study’s limitations were database coverage, restricted timeframe, and incomplete reporting (e.g., software/tooling). Conclusions: ML/AI shows strong potential, especially in blasting, but scalable deployment is constrained by site specificity, inconsistent reporting, and heterogeneous validation; standardized reporting and operational indicators are priorities. Registration: The systematic review protocol was registered in OSF with DOI 10.17605/OSF.IO/5UMKB. Funding: EU Erasmus+ STRIM project (1010832727). Full article

Speaking proficiency remains one of the most challenging skills for learners of English as a Foreign Language (EFL), particularly in contexts where sustained spoken interaction is limited. This systematic review synthesises 36 empirical studies (2015–2025) identified through a PRISMA-guided Scopus search to examine how artificial intelligence (AI)-mediated instruction supports EFL speaking development. The included studies were analysed according to AI modality, pedagogical integration, instructional input characteristics, and linguistic and affective outcomes. Findings indicate that AI tools—such as chatbots, automatic speech recognition systems, and large language models—consistently support affective outcomes, including reduced speaking anxiety and increased willingness to communicate. Improvements in fluency, pronunciation, and accuracy were frequently reported, particularly when AI tools were embedded within task-based and pedagogically structured instructional designs. However, evidence for sustained development of higher-order communicative competence was more variable. The review proposes a mediated input framework conceptualising AI as a design-sensitive instructional resource rather than an autonomous teaching agent. Full article

The rapid deployment of Large Language Models (LLMs) in multilingual, production-scale systems has made inference-time energy consumption a critical yet systematically under-evaluated dimension of model quality. While accuracy-centric benchmarks dominate current evaluation practice, they fail to capture the energy cost of reasoning, particularly across languages and task complexities where consumption profiles diverge substantially. In this work, we present a comprehensive energy–performance evaluation of five instruction-tuned LLMs, spanning Transformer, Grouped-Query Attention, and State Space Model architectures, across thirteen typologically diverse languages and multiple task difficulty levels under controlled GPU-level energy measurement on NVIDIA H200 hardware. Our analysis encompasses 65 model–language configurations totaling over 5100 individual inference runs, supported by rigorous non-parametric statistical testing (Friedman tests, pairwise Wilcoxon signed-rank with Holm correction, and paired Cohen’s d effect sizes). We report four principal findings. First, energy consumption varies up to threefold across models under identical workloads (${\chi }^2=49.42$, $p=4.78\times {10}^{-10}$, Friedman test), stratifying into three distinct energy regimes driven by architecture and generation dynamics rather than parameter count. Second, energy expenditure and reasoning performance are only weakly coupled, as confirmed by Spearman rank correlation analysis ($r_s=0.109$, $p=0.386$). Third, task category and difficulty level introduce substantial and model-dependent variation in both energy demand and performance, with cross-lingual performance variance amplifying at higher difficulty levels. Fourth, language choice acts as a measurable deployment parameter as follows: Romance languages on average achieve lower energy consumption than English across multiple models, while model efficiency rankings shift across languages, yielding language-dependent Pareto-optimal frontiers. We formalize these trade-offs through multi-objective Pareto analysis and introduce a composite AI Energy Score metric that captures reasoning quality per unit of energy. Of the 65 evaluated configurations, only four are Pareto-optimal, three Mistral-7B configurations at the low-energy extreme and one Phi-4-mini-instruct configuration at the high-performance end, while three of the five models are entirely dominated across all language configurations. These findings provide actionable guidelines for energy-aware model selection in multilingual deployments and support the integration of AI Energy Scores as a standard complementary criterion in LLM evaluation frameworks. Full article

Artificial intelligence (AI) is increasingly used in healthcare to support clinical decision-making through clinical decision support systems (CDSS). Human-in-the-loop (HITL) approaches introduce clinician oversight to improve model interpretability, reliability, and adaptability, while explainable AI (XAI) helps clinicians understand model behaviour. This review categorises HITL AI approaches in healthcare into pre-deployment and post-deployment stages and provides a dedicated review focusing specifically on post-deployment HITL systems. It also introduces the concept of closed-loop AI, where real-time expert feedback can refine AI outputs without requiring model retraining. A systematic review following PRISMA guidelines was conducted using the Scopus and PubMed databases for studies published between 2020 and July 2025. From 3466 identified records, 3012 remained after duplicate removal. After title and abstract screening, 1630 articles were assessed through full-text review, and 15 studies met the predefined inclusion criteria related to HITL, post-deployment adaptation, and interactive XAI in healthcare. The selected studies indicate growing interest in post-deployment HITL systems that allow clinicians to refine AI outputs, provide real-time feedback, and support adaptive CDSS. These findings highlight a shift toward human-centred, closed-loop AI frameworks that integrate expert feedback into deployed systems to improve transparency, trust, and responsiveness in clinical decision-making. Full article

Generative AI is reshaping university teaching and creating tension around authority, evidence, and accountability when decisions are made using algorithms. From a student perspective, this study constructed a typology of distributed teacher–AI agency (TAI) and examined the discursive mechanisms that produce the illusion of teacher autonomy. A non-experimental, cross-sectional, explanatory study was conducted: a lexicometric analysis of the ALCESTE (IRAMUTEQ) questionnaire, using open-ended responses from 3120 students (Mexico, n = 2051; Ecuador, n = 1069), segmented into 1077 units, and analyzed using positioning theory. Co-agency was operationalized using Teacher Agency (A), Delegation to AI (D), Governance (G: disclosure, criteria, verification), and the Illusion Index (II = A/(D + G + 1)). Three configurations emerged: Immediate Customizer (28.8%) with very high A and minimal D/G (II = 25.4); Technological Literacy Facilitator (27.3%) with visible delegation and safeguards (II ≈ 2.0); and Operational Optimizer (43.9%) oriented toward accelerating tasks with moderate governance (II ≈ 2.7). The illusion was associated with the agentive erasure of AI and a rhetoric of immediacy/efficiency that replaced verifiable criteria. These findings transform the student voice into a criteria-based diagnostic tool for strengthening traceability, minimal verification, and responsible orchestration of AI in higher education. Full article

Based on data from 208 students involved in entrepreneurship studies at Tecnológico de Monterrey, Mexico, this paper examines whether prior experience with AI, expectations, gender, and age reinforce future AI use. To achieve this objective, we applied binary logistic regression with random oversampling to balance the dataset. We complemented it with additional model performance metrics, including the confusion matrix, sensitivity, specificity, and area under the ROC curve. The results show that prior experience with AI, age-related technology use, and positive expectations regarding AI are associated with a higher likelihood of reinforcing future AI use. In terms of gender, the results indicate a gender gap favoring women, who are more likely to use AI when they perceive greater utility and confidence, as well as a stronger desire to succeed. Full article

The rapid expansion of mobile healthcare (mHealth) applications has transformed health-related e-commerce, creating new challenges for understanding and responding to user needs. This study proposes a data-driven framework to systematically identify and prioritize unmet user requirements from negative reviews of Chinese mHealth applications. Using a dataset of 31,124 user reviews collected between 2019 and 2025, the framework integrates sentiment analysis, topic modeling, and machine learning regression to uncover six key areas of user concern and examine their temporal evolution. Among several predictive models linking user concerns to app ratings, the k-nearest neighbors (KNN) model demonstrated superior performance. Subsequent SHAP-based interpretability analysis reveals that account authentication, system accessibility, and application stability have the most significant impact on user ratings, highlighting the critical roles of trust and technical reliability in health e-commerce. This research not only provides actionable insights for platform governance but also contributes a generalizable methodology for leveraging user-generated content to inform evidence-based management and policy decisions in mobile digital services. Full article

To better understand the complexity of biological systems, research has shifted from a reductionist to a holistic approach, expanding the focus from single genes to a genome-scale view of gene activity and regulation. This is known as transcriptomics, a continuously growing field generating gene expression signatures from different technologies. A comparable paradigm shift has occurred in computational systems biology with the implementation of Artificial Intelligence (AI) learning models for gene expression analysis and integration. These models enable transcriptome-based profiling to address challenges of data heterogeneity, integration, and updating, assisting human intelligence and enhancing their ability to retrieve, analyze, integrate, and generate data recursively, thanks to their intrinsic predictive, inferential, reinforcement, and generative capabilities. Additionally, while scientists worldwide are still learning how to leverage AI methods that can maintain the human-in-the-loop, a new fundamental change is emerging: agentic AI, which can autonomously act and employ other AI methods to pursue its objectives. As a futuristic perspective, the proposed data analysis pipeline imagines agentic AI systems allowing the automated retrieval and pre-processing of heterogeneous transcriptomics data, analysis and integration with other omics datasets, performed with an incremental updating and recurrent analysis (IURA) model that could allow the detection of guideline updates (e.g., disease reclassification) and the generation of new hypotheses, such as candidate biomarkers or transcriptome–phenotype correlations. Since personalized medicine could derive profound benefits from its use, this scenario also raises important considerations regarding the advantages and concerns associated with the use of scientific AI agents in research and clinical practice. Full article

Biomass combustion, as a key technology for achieving a low-carbon transformation of the energy system, faces multiple challenges in its efficient and clean utilization, including the high heterogeneity of fuels, the complex multi-scale coupling of the combustion process, and the attainment of ultra-low emissions. Traditional research methods have significant disconnections between microscopic mechanism understanding, macroscopic performance prediction of reactors, and end-of-pipe pollution control, which restricts the improvement of system performance. This review presents recent advances in advanced numerical simulation, pollutant control strategies, and bioenergy with carbon capture and storage (BECCS) pathways targeting ultra-low emissions in biomass combustion. This work synthesizes progress across three interconnected domains. First, methodologies are examined for integrating detailed chemical kinetics, particle-scale models, and reactor-scale simulations to develop high-fidelity predictive tools. Second, low-nitrogen combustion and synergistic pollutant control strategies for primary furnace types (e.g., grate, fluidized bed) are evaluated, alongside process optimization from fuel pretreatment to flue gas purification. Third, the potential for integrated design of biomass energy systems with carbon capture is assessed, emphasizing that system efficiency hinges on holistic “fuel-combustion-capture” chain optimization rather than isolated unit improvements. Future research directions are highlighted, including the development of physics-informed AI modeling paradigms, deeper co-design of multiple processes, and the establishment of robust life-cycle assessment frameworks. This review aims to provide a structured reference to inform both fundamental research and the practical development of next-generation clean biomass combustion technologies. Full article

The integration of Geographic Information Systems (GISs) with hydrologic science has evolved over seven decades from manual catchment delineation and output visualization to AI-native spatial water intelligence, reshaping how the water cycle is observed, modeled, and managed. This review explores that evolution, from the progressively tightening coupling between GIS software and hydrologic models to an AI-assisted future in which the line between these two fields blurs and eventually dissolves completely. The evolution of GISs in hydrology is traced through four eras, stratified as: (1) the formalization of governing equations and digital terrain representations (1950–1985); (2) the initial GIS–model coupling era and the rise in watershed simulation (1985–2000); (3) open source and the start of the open data deluge (2000–2015); and (4) machine learning and cloud-native computing (2015–present). A four-level vision for the role of artificial intelligence in the next generation of spatial hydrology is then articulated, from AI-assisted GIS operation to spatially aware AI water intelligence that reasons directly over geospatial data without requiring a traditional GIS or simulation software as an intermediary. Broader limitations and challenges are also discussed. Full article

Artificial Intelligence (AI) is increasingly embedded in China’s environmental supervision, reshaping how environmental risks are detected and regulated. Existing research mainly focuses on technical performance or isolated policy initiatives, while paying limited attention to the institutional sustainability of regulatory systems integrating AI, understood as the capacity to operate consistently, transparently, and accountably over time. This article examines how AI becomes institutionally embedded in environmental supervision. It focuses on three dimensions: the formation of regulatory evidence, the allocation of responsibility, and the exercise of administrative capacity. Drawing on qualitative analysis of legal and policy documents issued between 2017 and 2025 and two case studies (AI-enabled air-quality governance in Beijing and AI-enabled water-quality monitoring in the Yangtze River Basin), the study shows that AI deployment generates recurring governance tensions, including opacity in algorithmic evidence formation, fragmented accountability chains, and uneven administrative capacity. The article argues that sustainable AI-enabled supervision depends less on technological intensification than on institutional and governance conditions ensuring transparency, reviewability, and responsibility in routine regulatory practice, thereby contributing to debates on algorithmic regulation and providing policy-relevant insights for maintaining sustainable environmental governance in rapidly digitalizing regulatory contexts. Full article