Search Results (8,311)

Artificial intelligence technology holds significant importance for building intelligent question-answering systems in the field of coal mine safety and enhancing safety management levels. Currently, there is a lack of specialized large language models and high-quality question-answering datasets in this field. To address this, this study proposes a two-stage fine-tuning method based on Low-Rank Adaptation (LoRA) and Group Sequence Policy Optimization (GSPO) for training a question-answering model tailored to the coal mine safety domain. The research begins by constructing a dedicated question-answering dataset based on domain-specific regulatory documents. Subsequently, using Qwen2.5-7B Instruct as the base model, the study fine-tunes the model through supervised learning with LoRA technology, followed by further optimization of the model’s performance using the GSPO reinforcement learning algorithm. Experiments show that the model trained with this method exhibits significant improvements in coal mine safety-related tasks, achieving superior results on multiple automated evaluation metrics compared to contrast models of similar scale. This study validates the effectiveness of the two-stage fine-tuning method in adapting large language models (LLMs) to specific domains, providing a new technical approach for the intelligentization of coal mine safety. It should be noted that due to the lack of external data, this study relies on a self-constructed dataset and has not yet undergone external independent validation, which constitutes the main limitation of the current work. Full article

Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability. However, harnessing their full potential and lifetime requires intelligent operational strategies that balance technological performance, economic viability, and environmental sustainability. This systematic review examines how artificial intelligence (AI)-based intelligent optimization enhances GS-BESS performance, focusing on its techno-economic, environmental impacts, and policy and regulatory implications. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we review the evolution of GS-BESS, analyze its advancements, and assess state-of-the-art applications and emerging AI techniques for GS-BESS optimization. AI techniques, including machine learning (ML), predictive modeling, optimization algorithms, deep learning (DL), and reinforcement learning (RL), are examined for their ability to improve operational efficiency and control precision in GS-BESSs. Furthermore, the review discusses the benefits of advanced dispatch strategies, including economic efficiency, emissions reduction, and improved grid resilience. Despite significant progress, challenges persist in data availability, model generalization, high computational requirements, scalability, and regulatory gaps. We conclude by identifying emerging opportunities to guide the next generation of intelligent energy storage systems. This work serves as a foundational resource for researchers, engineers, and policymakers seeking to advance the deployment of AI-enhanced GS-BESS for sustainable, resilient power systems. By analyzing the latest developments in AI applications and BESS technologies, this review provides a comprehensive perspective on their synergistic potential to drive sustainability, cost-effectiveness, and energy systems reliability. Full article

Microplastics (MPs) and Antifouling Paint Particles (APPs) are pervasive anthropogenic pollutants that threaten global ecosystems, with distinct yet overlapping environmental behaviors and toxic impacts. MPs disperse widely in aquatic systems via runoff and wastewater; their toxicity stems from physical, chemical, and synergistic effects. APPs are concentrated in coastal zones, estuaries, and shipyard areas, and are acutely toxic due to their high metal and biocide content. This study systematically characterized the composition, concentration, and size distribution of common MPs and APPs in ship-hull derusting wastewater produced by ultra-high-pressure water jetting, using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) coupled with particle size analysis. The wastewater exhibited a total suspended solids (TSS) concentration of 20.04 g·L⁻¹, within which six types of MPs were identified at 3.29 mg·L⁻¹ in total and APPs were quantified at 330.25 mg·L⁻¹, representing 1.65% of TSS. The residual fraction primarily consisted of algae, biological debris, and inorganic particles. Particle size distribution ranged from 3.55 to 111.47 μm, with a median size (D50) of 31 μm, while APPs were mainly 5–100 μm, with 81.4% < 50 μm. Extrapolation to the annual treated ship-hull surface area in 2024 indicated the generation of ~57,440 m³ wastewater containing ~0.2 tons of MPs and ~19 tons of APPs. These findings highlight the magnitude of pollutant release from ship maintenance activities and underscore the urgent need for targeted treatment technologies and regulatory policies to mitigate microplastic pollution in marine environments. Full article

Increasing global water scarcity has intensified the adoption of water reuse as a sustainable strategy, particularly in regions affected by drought and pressure on natural resources. This paper presents a systematic review of the application of Life Cycle Assessment (LCA) in water reuse projects, focusing on research trends, methodological approaches, and opportunities for improvement. A systematic search was conducted in Web of Science, ScienceDirect, and Google Scholar for studies published from 2020 onwards using combinations of the keywords “life cycle assessment”, “LCA”, “water reuse”, “water recycling”, and “wastewater recycling”. Twelve studies were selected from 57 records identified, based on predefined eligibility criteria requiring quantitative LCA of water reuse systems. The results reveal a predominance of European research, reflecting regulatory advances and strong academic engagement in this field. The most frequently assessed impact categories were global warming, eutrophication, human toxicity and ecotoxicity, highlighting the environmental relevance of reuse systems. Energy consumption and water transport were identified as critical hotspots, especially in scenarios involving long distances and fossil-based energy sources. Nevertheless, most studies demonstrate that water reuse is environmentally viable, particularly when renewable energy and optimized logistics are applied. The review also emphasizes the need to better integrate economic and social dimensions and to adapt LCA methodologies to local conditions. Overall, the findings confirm LCA as a robust decision-support tool for sustainable planning and management of water reuse systems. Full article

Artificial intelligence (AI) is now embedded across many aspects of healthcare, yet most implementations remain fragmented, task-specific, and layered onto legacy workflows. This paper does not review AI applications in healthcare per se; instead, it examines what an AI-first healthcare system would look like, one in which AI functions as a foundational organizing principle of care delivery rather than an adjunct technology. We synthesize evidence across ambulatory, inpatient, diagnostic, post-acute, and population health settings to assess where AI capabilities are sufficiently mature to support system-level integration and where critical gaps remain. Across domains, the literature demonstrates strong performance for narrowly defined tasks such as imaging interpretation, documentation support, predictive surveillance, and remote monitoring. However, evidence for longitudinal orchestration, cross-setting integration, and sustained impact on outcomes, costs, and equity remains limited. Key barriers include data fragmentation, workflow misalignment, algorithmic bias, insufficient governance, and lack of prospective, multi-site evaluations. We argue that advancing toward AI-first healthcare requires shifting evaluation from accuracy-centric metrics to system-level outcomes, emphasizing human-enabled AI, interoperability, continuous learning, and equity-aware design. Using hypertension management and patient journey exemplars, we illustrate how AI-first systems can enable proactive risk stratification, coordinated intervention, and continuous support across the care continuum. We further outline architectural and governance requirements, including cloud-enabled infrastructure, interoperability, operational machine learning practices, and accountability frameworks—necessary to operationalize AI-first care safely and at scale, subject to prospective validation, regulatory oversight, and post-deployment surveillance. This review contributes a system-level framework for understanding AI-first healthcare, identifies priority research and implementation gaps, and offers practical considerations for clinicians, health systems, researchers, and policymakers. By reframing AI as infrastructure rather than isolated tools, the AI-first approach provides a pathway toward more proactive, coordinated, and equitable healthcare delivery while preserving the central role of human judgment and trust. Full article

The maritime sector is undergoing a profound transformation driven by disruptive technologies and global decarbonization objectives, placing new demands on Maritime Education and Training (MET) systems. Equipping maritime professionals with competencies for low-carbon shipping is now as critical as technological advancement itself. This study examines how disruptive technologies can be effectively integrated into MET frameworks to support environmental sustainability, using Panama as a representative case study of a major flag and maritime service state. A mixed-methods approach was adopted, combining a structured literature review, expert surveys, and a multi-criteria decision-making analysis based on the Analytic Hierarchy Process (AHP). The findings reveal a significant misalignment between existing MET curricula and the competencies required for decarbonized maritime operations. Key gaps include limited training in alternative fuels, emissions measurement and reporting, energy-efficient technologies, digital analytics, and regulatory compliance. Stakeholders also reported fragmented training provision, uneven access to emerging technologies, and weak coordination between academia, industry, and regulators, particularly in developing contexts. The results highlight the urgent need for curriculum reform and stronger cross-sector collaboration to align MET with evolving technological and regulatory demands. The study provides an applied, evidence-based framework for MET reform, with insights transferable to other systems facing similar decarbonization challenges. Full article

Atherosclerosis remains a leading cause of cardiovascular mortality despite advances in pharmacological and interventional therapies. Current treatment approaches face limitations including systemic side effects, inadequate local drug delivery, and restenosis following vascular interventions. Gel-based technologies offer unique advantages through tunable mechanical properties, controlled degradation kinetics, high drug-loading capacity, and potential for stimuli-responsive therapeutic release. This review examines gel platforms across multiple scales and applications in atherosclerosis research and intervention. First, gel-based in vitro models are discussed. These include hydrogel matrices simulating plaque microenvironments, three-dimensional cellular culture platforms, and microfluidic organ-on-chip devices. These devices incorporate physiological flow to investigate disease mechanisms under controlled conditions. Second, therapeutic strategies are addressed through macroscopic gels for localized treatment. These encompass natural polymer-based, synthetic polymer-based, and composite formulations. Applications include stent coatings, adventitial injections, and catheter-delivered depots. Natural polymers often possess intrinsic biological activities including anti-inflammatory and immunomodulatory properties that may contribute to therapeutic effects. Third, nano- and microgels for systemic delivery are examined. These include polymer-based nanogels with stimuli-responsive drug release responding to oxidative stress, pH changes, and enzymatic activity characteristic of atherosclerotic lesions. Inorganic–organic composite nanogels incorporating paramagnetic contrast agents enable theranostic applications by combining therapy with imaging-guided treatment monitoring. Current challenges include manufacturing consistency, mechanical stability under physiological flow, long-term safety assessment, and regulatory pathway definition. Future opportunities are discussed in multi-functional integration, artificial intelligence-guided design, personalized formulations, and biomimetic approaches. Gel technologies demonstrate substantial potential to advance atherosclerosis management through improved spatial and temporal control over therapeutic interventions. Full article

Regulatory T cells (Tregs), particularly their phenotypically distinct subpopulations, are critical for the establishment of maternal immune tolerance during embryo implantation. Despite advances in assisted reproductive technologies, implantation failure remains a frequent and often unexplained clinical challenge. Variations in Treg frequency and phenotype have been proposed to influence implantation success, particularly under differing hormonal conditions. This study aimed to investigate peripheral blood Treg levels and their subpopulations on the day of blastocyst transfer in both stimulated in vitro fertilization (IVF/ICSI) cycles involving controlled ovarian hyperstimulation (COH) and true natural cycles with frozen embryo transfer (FET), and to examine their associations with systemic hormone levels and anti-Müllerian hormone (AMH). A prospective observational study was conducted including women undergoing IVF/ICSI with fresh embryo transfer (ET) and women undergoing natural cycle FET. Peripheral blood samples were collected on the day of ET and analyzed using 13-colour flow cytometry, enabling detailed subdivision of Tregs into multiple subpopulations based on the expression of differentiation and chemokine markers, including CXCR5. In addition, because common $\gamma$-chain cytokines may influence pregnancy success by modulating the balance between suppressive Treg and non-Treg subsets, intracellular STAT5 signaling was assessed using phospho-specific flow cytometry. Serum estradiol, progesterone, FSH, LH, and AMH levels were measured in parallel. Significant differences were observed in Treg subpopulation distributions between women who conceived and those who did not. Higher frequencies of naïve CXCR5⁻ Tregs were associated with clinical pregnancy, independent of age, and correlated with serum progesterone levels. Moreover, both naïve Treg frequency and enhanced IL-7-dependent STAT5 signaling in naïve Tregs from women undergoing COH were associated with AMH levels, suggesting a link between ovarian reserve and Treg homeostasis mediated by signal transducer and activator of transcription 5 (STAT5) signaling. In conclusion, Treg subpopulations, particularly CXCR5⁻ naïve Tregs, appear to play a central role in implantation success following ET. Their distribution differs between stimulated and natural cycles and is influenced by systemic progesterone levels and STAT5 signaling. These findings suggest that peripheral Treg profiling may represent a potential biomarker of implantation competence and could inform personalized approaches in assisted reproduction. Full article

Yeast biosensors represent a promising biotechnological innovation for ensuring the safety and quality of fermented beverages such as beer, wine, and kombucha. These biosensors employ genetically engineered yeast strains to detect specific contaminants, spoilage organisms, or hazardous compounds during fermentation or the final product. By integrating synthetic biology tools, researchers have developed yeast strains that can sense and respond to the presence of heavy metals (e.g., lead or arsenic), mycotoxins, ethanol levels, or unwanted microbial metabolites. When a target compound is detected, the biosensor yeast activates a reporter system, such as fluorescence, color change, or electrical signal, providing a rapid, visible, and cost-effective means of monitoring safety parameters. These biosensors offer several advantages: they can operate in real time, are relatively low-cost compared to conventional chemical analysis methods, and can be integrated directly into the fermentation system. Furthermore, as Saccharomyces cerevisiae is generally recognized as safe (GRAS), its use as a sensing platform aligns well with existing practices in beverage production. Yeast biosensors are being investigated for the early detection of contamination by spoilage microbes, such as Brettanomyces and lactic acid bacteria. These contaminants can alter the flavor profile and shorten the product’s shelf life. By providing timely feedback, these biosensor systems allow producers to intervene early, thereby reducing waste and enhancing consumer safety. In this work, we review the development and application of yeast-based biosensors as potential safeguards in fermented beverage production, with the overarching goal of contributing to the manufacture of safer and higher-quality products. Nevertheless, despite their substantial conceptual promise and encouraging experimental results, yeast biosensors remain confined mainly to laboratory-scale studies. A clear gap persists between their demonstrated potential and widespread industrial implementation, underscoring the need for further research focused on robustness, scalability, and regulatory integration. Full article

Early life is a critical window for immune system development, during which the gut microbiome shapes innate immunity, antigen presentation, and adaptive immune maturation. Disruptions in microbial colonization—driven by factors such as cesarean delivery, antibiotic exposure, and formula feeding—deplete beneficial early-life taxa (e.g., Bifidobacterium, Bacteroides, and Enterococcus) and impair key microbial functions, including short-chain fatty acid (SCFA) production by these keystone species, alongside regulatory T cell induction. These dysbiosis patterns are associated with an increased risk of pediatric autoimmune diseases, notably type 1 diabetes, inflammatory bowel disease, celiac disease, and juvenile idiopathic arthritis. This review synthesizes current evidence on how the early-life microbiota influences immune maturation, with potential effects on the development of autoimmune diseases later in life. We specifically focus on human observational and intervention studies, where treatments with probiotics, synbiotics, vaginal microbial transfer, or maternal fecal microbiota transplantations have been shown to partially restore a disrupted microbiome. While restoration of the gut microbiome composition and function is the main reported outcome of these studies, to date, no reports have disclosed direct prevention of autoimmune disease development by targeting the early-life gut microbiome. In this regard, a better understanding of the early-life microbiome–immune axis is essential for developing targeted preventive strategies. Future research must prioritize longitudinal evaluation of autoimmune outcomes after microbiome modulation to reduce the burden of chronic immune-mediated diseases. Full article

Optimizing immunosuppressant dosing presents significant challenges in kidney transplantation due to narrow therapeutic ranges and considerable inter-patient pharmacokinetic differences. Emerging strategies for precision dosing, encompassing Bayesian population pharmacokinetic models, pharmacogenomic integration, and artificial intelligence algorithms, aim to enhance drug monitoring by moving beyond traditional trough-based approaches. This review critically assesses available evidence for predictive dosing models targeting immunosuppressants, including calcineurin inhibitors, antimetabolites, and mTOR inhibitors in kidney transplant patients. Available observational and simulation studies demonstrate substantial methodological diversity, with Bayesian PopPK-guided strategies showing 15–35% better target exposure achievement compared to trough-based monitoring. The absence of pooled estimates precludes a precise summary effect size, and evidence from randomized controlled trials remains limited. Machine learning models, particularly for tacrolimus, frequently reduced prediction error relative to traditional regression approaches, but substantial heterogeneity in study design, outcome definitions, and external validation limits quantitative synthesis. Hybrid Bayesian–AI frameworks and explainable AI tools show conceptual promise but are largely supported by proof-of-concept studies rather than reproducible clinical implementations. Overall, Bayesian pharmacokinetic modelling represents the most mature and clinically interpretable approach for precision dosing in transplantation, whereas AI-driven and hybrid systems remain investigational. Key gaps include the need for standardized reporting, rigorous risk-of-bias assessment, prospective validation, and clearer regulatory and implementation pathways to support safe and equitable clinical adoption. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

The delisting of Binance USD (BUSD) constitutes a major regulatory intervention in the stablecoin market and provides a unique opportunity to examine how targeted regulation affects liquidity allocation, market concentration, and short-run systemic risk in crypto-asset markets. Using daily data for 2023 and a linear and nonlinear Local Projections event-study framework, this paper analyzes the dynamic market responses to the BUSD delisting across major stablecoins and cryptocurrencies. The results show that liquidity displaced from BUSD is reallocated primarily toward USDT and USDC, leading to a measurable increase in stablecoin market concentration, while decentralized and algorithmic stablecoins absorb only a limited share of the shock. At the same time, Bitcoin and Ethereum experience temporary liquidity contractions followed by a relatively rapid recovery, suggesting conditional resilience of core crypto-assets. Overall, the findings document how a regulatory-induced exit of a major stablecoin reshapes short-run market dynamics and concentration patterns, highlighting potential trade-offs between regulatory enforcement and market structure. The paper contributes to the literature by providing the first empirical analysis of the BUSD delisting and by illustrating the usefulness of Local Projections for studying regulatory shocks in cryptocurrency markets. Full article

Plant roots form highly specialized apoplastic barriers that regulate the exchange of water, ions, and solutes between the soil and vascular tissues, thereby protecting plant survival under environmental stress. Among these barriers, the endodermis and exodermis play essential roles, enhanced by suberin lamellae and lignin-rich Casparian strips (CS). Recent advances have shown that these barriers are not static structures but are dynamic systems, rapidly adapting in response to drought, salinity and nutrient limitation. The R2R3-MYB transcription factor (TF) family is essential to this adaptive plasticity. These TFs serve as key regulators of hormonal and developmental signals to regulate suberin and lignin biosynthesis. Studies across different species demonstrate both conserved regulatory structure and species-specific adaptations in barrier formation. Suberization provides a hydrophobic structure that limits water loss and ion toxicity, while lignification supports structural resilience and pathogen defense, with the two pathways exhibiting adaptive and interactive regulation. However, significant knowledge gaps remain regarding MYB regulation under combined abiotic stresses, its precise cell-type-specific activity, and the associated ecological and physiological trade-offs. This review summarizes the central role of root barrier dynamics in plant adaptation, demonstrating how MYB TFs regulate suberin and lignin deposition to enhance crop resilience to environmental stresses. Full article

Promoting the value realization of ecological products is a central issue in practicing the concept that “lucid waters and lush mountains are invaluable assets.” This is particularly urgent for alpine regions, which are vital ecological security barriers but face stringent developmental constraints. This study takes Golog Tibetan Autonomous Prefecture in Qinghai Province as a case study. It establishes a Gross Ecosystem Product (GEP) accounting framework tailored to the characteristics of alpine ecosystems and conducts continuous empirical accounting for the period 2020–2023. The findings reveal that: (i) The total GEP of Golog is immense (reaching 655.586 billion yuan in 2023) but exhibits significant dynamic non-stationarity driven by climatic fluctuations, with a coefficient of variation as high as 11.48%. (ii) The value structure of the GEP is highly unbalanced, with regulatory services contributing over 97.6%. Water conservation and biodiversity protection are the two pillars, highlighting its role as a supplier of public ecological products and the predicament of market failure. (iii) The spatial distribution of GEP is highly heterogeneous. Maduo County, comprising 34% of the prefecture’s land area, contributes 48% of its total GEP, with its value per unit area being 1.68 times that of Gande County, revealing the spatial agglomeration of key ecosystem services. To address the dynamic, structural, and spatial constraints identified by these quantitative features, this paper proposes synergistic realization pathways centered on “monetizing regulatory services,” “precision policy regulation,” and “capacity and institution building”. The aim is to overcome the systemic bottlenecks—“difficulties in measurement, trading, coarse compensation, and weak incentives”—in alpine ecological functional zones. This provides a systematic theoretical and practical solution for fostering a virtuous cycle between ecological conservation and regional sustainable development. Full article