Search Results (9,060)

The growing worldwide prevalence of Diabetes Mellitus highlights the urgent need for effective early detection methods to enable prompt intervention. This study develops a machine learning-based decision-support prototype for predicting diabetes risk using health metrics from the DiaHealth dataset, a recently published Bangladeshi open-source dataset for Type 2 diabetes prediction. Five supervised learning algorithms were evaluated: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF). Models were assessed across three stages: before feature scaling, after standardisation, and following hyperparameter optimisation via GridSearchCV, using accuracy, precision, recall, and F1-score as evaluation metrics. LR and SVM showed marked improvements after standardisation, consistent with their sensitivity to feature magnitude, whilst tree-based approaches such as DT and RF remained largely unchanged. KNN displayed minimal sensitivity to scaling, which is discussed in relation to the feature distributions of the dataset. Following hyperparameter tuning, RF achieved the highest accuracy of 95%, outperforming all other models. RF predictions were interpreted using Local Interpretable Model-agnostic Explanations (LIME) to promote transparency in model decision-making. The best-performing model was subsequently deployed as an interactive web-based prototype application using Streamlit, providing real-time prediction outputs. These findings demonstrate how preprocessing choices and hyperparameter tuning can differentially affect algorithm performance and illustrate the potential of combining explainable AI with practical deployment for diabetes risk assessment in a research context. Full article

Selenium (Se) is a beneficial element involved in plant growth, metabolism, stress adaptation, and crop quality improvement, but its effects are strongly influenced by chemical form, application dose, plant species, growth stage, and environmental conditions. To integrate mechanistic understanding with global research trends, this study combines a concise mini-review with a bibliometric analysis of Se research in plants from 2000 to 2025. The mini-review summarizes Se speciation and bioavailability in the soil–plant–microbe system, root uptake and long-distance transport, metabolic assimilation and detoxification, physiological regulation, stress tolerance, biofortification, and nano-Se applications. Bibliographic data were retrieved from the Web of Science Core Collection and analyzed using CiteSpace, VOSviewer, and Scimago Graphica. A total of 3451 valid publications were identified, showing a sustained increase in annual output, especially after 2018. The field has expanded from early studies on Se speciation, uptake, assimilation, and antioxidant responses toward broader themes involving crop biofortification, molecular regulation, stress physiology, foliar application, nano-Se applications, green synthesis, and phytoremediation. Overall, plant Se research has evolved into an interdisciplinary field linking mechanistic studies with safe agricultural application. Future work should emphasize standardized experimental frameworks, causal mechanism validation, precise biofortification, field-based evaluation, and safety assessment of emerging Se-based technologies. Full article

Oscillating water column (OWC) wave energy converters equipped with dielectric elastomer generators (DEGs) represent a promising technology for harnessing ocean wave energy. This study emphasises the critical role of functional specifications in guiding the development of these devices from initial concept to full-scale deployment. A comprehensive analysis of key design parameters that influence the performance and efficiency of flexible OWCs with DEG-based power take-off systems is presented. This investigation focuses on the effects of draft, membrane diameter, deformation characteristics, number of layers, and membrane thickness on power output. Utilising a combination of analytical tools, including Wave Venture software, MATLAB, and Abaqus, detailed simulations and analyses are conducted to optimise these parameters. Our results demonstrate that increasing the DEG diameter significantly enhances power output, with diameters between 5 and 12 m showing optimal efficiency. A critical strain threshold of approximately 32% is identified, beyond which power output efficiency diminishes. Furthermore, the study reveals that multi-layer DEG configurations can substantially increase energy production, with thinner membranes generally yielding higher outputs. These findings provide valuable insights for developing functional specifications that balance performance, manufacturability, and long-term reliability in marine environments. This research advances OWC technology by offering a parameter-screening framework to guide device design towards optimised configurations and to accelerate the path to commercial viability in the wave energy sector. Full article

Large language models (LLMs) are increasingly adopted in life-science research for scientific writing, coding, literature synthesis, workflow troubleshooting, and preliminary data interpretation. In microbial genomics and bioinformatics, their appeal is clear because researchers routinely integrate genome annotations, antimicrobial resistance profiles, virulence determinants, taxonomic assignments, microbiome outputs, workflow scripts, and primary literature. Yet this domain also highlights major risks, including hallucinated biological claims, inaccurate citations, irreproducible code, unsupported genotype-to-phenotype inference, and inappropriate clinical or public health framing. This narrative review examines responsible LLM use in microbial genomics as a representative life-science setting where interpretation depends on database provenance, validated workflows, expert assessment, and reproducible evidence chains. It considers applications in genome annotation, antimicrobial resistance interpretation, virulence analysis, microbiome and metagenomics workflows, coding support, and scientific writing. The review further presents MicrobeGuardGPT as a conceptual reliability framework for assessing LLM-assisted microbial genomics outputs before scientific, clinical, or public health use. By connecting task domains, evidence verification, expert validation, and reliability classification, the framework supports risk-aware LLM integration in bioinformatics. Responsible implementation will require domain-specific benchmarks, curated database linkage, transparent reporting, reproducible workflows, human oversight, and governance standards tailored to biological interpretation across research, diagnostic, surveillance, outbreak-response, educational, and translational contexts. Full article

Background: Research on the acute effects of β-alanine supplementation has primarily focused on performance outcomes, with limited attention to the underlying physiological mechanisms. This study aimed to investigate the acute effects of two β-alanine doses on performance, mechanical output, and acid–base balance during a 10 min cycling time trial (10’-TT), and to explore the relationship between buffering-related variables and performance. Methods: Eighty-five recreational cyclists performed a 10’-TT under indoor conditions before (control) and following the acute ingestion of β-alanine (moderate-dose β-alanine 10 g—BAM; high-dose β-alanine 20 g—BAH) or placebo (PLA), with each condition tested on separate days. Data were analyzed using two-way repeated-measures ANOVA and correlation analyses. Results: No significant differences were observed in performance variables (distance, speed, cadence, or heart rate; p ≥ 0.751). However, total external mechanical work (kJ) was significantly reduced following acute supplementation (p = 0.028). Notably, the BAM condition reduced the mechanical cost of exercise without impairing performance, and this effect was moderately associated with changes in bicarbonate levels. Conclusions: Acute β-alanine supplementation did not improve performance outcomes but may alter buffering-related physiological responses associated with reduced mechanical work during high-intensity cycling exercise. These findings highlight the relevance of buffering-related mechanisms, particularly bicarbonate dynamics, in modulating the mechanical cost (work performed relative to performance achieved) of high-intensity exercise. Full article

Methicillin-resistant Staphylococcus aureus (MRSA) remains a significant multidrug-resistant pathogen, frequently associated with persistent infections and biofilm formation, underscoring the urgent need for alternative antimicrobial strategies. Bioactive compounds derived from fungi have attracted considerable attention due to their structural diversity and demonstrated antibacterial activity against MRSA. This study employed a scientometric approach to assess global research trends, thematic evolution, and collaborative networks concerning fungi-derived anti-MRSA compounds. Bibliographic data were collected from the Scopus database, and a total of 1666 English-language articles and reviews published up to 2025 were analyzed using Bibliometrix/Biblioshiny and VOSviewer. The findings indicate a marked increase in research output after 2010, reflecting heightened scientific interest in fungal natural products for MRSA management. China and the United States emerged as leading contributors in terms of publication volume and international collaboration. Thematic analysis revealed a shift from broad antimicrobial screening to more specialized investigations, including antibiofilm activity, secondary metabolites, endophytic fungi, molecular docking, and antimicrobial resistance. Nonetheless, several challenges persist, such as insufficient mechanistic validation, limited toxicity and pharmacokinetic assessments, and a lack of clinically relevant in vivo studies. Overall, the field is increasingly multidisciplinary, integrating microbiology, natural product chemistry, and computational methodologies to advance the discovery of anti-MRSA agents. Full article

Heart failure (HF) continues to be a major global health burden, with persistent morbidity and mortality despite guideline-directed and device-based therapies. Evidence suggests the gut–heart axis is a critical and underrecognized contributor to HF progression. Alterations in cardiac output and systemic venous congestion in HF lead to intestinal hypoperfusion, mucosal edema, and loss of barrier integrity, increasing intestinal permeability, gut dysbiosis, and translocation of microbial products. This systemic translocation is associated with chronic low-grade inflammation that activates innate immune pathways that correlate with endothelial dysfunction, oxidative stress, fibroblast activation, and adverse cardiac remodeling. Gut-derived metabolites derived by microbial metabolism modulate cardiovascular health by altering the metabolic profiles. Dysbiosis results in loss of protective short-chain fatty acid (SCFA)-producing bacteria and enriches pro-inflammatory taxa such as trimethylamine N-oxide (TMAO)-producing bacteria. Elevated TMAO is associated with increased mortality and hospitalization in HF, whereas SCFAs enhance barrier integrity and immune tolerance. Secondary bile acids and uremic toxins such as indoxyl sulfate and p-cresyl sulfate further link dysbiosis to fibrosis and vascular stiffness. Circulating markers such as TMAO, lipopolysaccharide-binding protein (LBP), and soluble CD14 carry prognostic value beyond traditional cardiac biomarkers. This review highlights current experimental, translational, and clinical evidence describing gut dysbiosis and its molecular links to HF progression. Targeting the gut–heart axis represents a novel therapeutic approach in HF. Dietary modulation, probiotics/prebiotics, fecal microbiota transplantation, and inhibitors of microbial metabolic pathways show promise. Future research should emphasize microbiota-based interventions in HF management. Full article

Energy storage systems (ESSs) installed alongside traditional photovoltaic (PV) power plants are primarily used to track planned output, which often results in low utilization rates and extended payback periods. Moreover, existing research inadequately addresses actual grid frequency fluctuation characteristics and lacks multi-timescale optimization frameworks. To address these issues, this paper proposes a day-ahead and intraday multi-market coordinated rolling optimization strategy that integrates energy market trading with Automatic Generation Control (AGC) frequency regulation services through a zone-based frequency regulation control strategy. The strategy first defines distinct regulation zones based on regional control deviations, enabling a dynamic power allocation approach for the energy storage system. Recognizing that conventional constant power control can lead to battery overcharging, over-discharging, and reduced cycle life, the strategy introduces state of charge (SOC)-based variable power charging and discharging constraint coefficients. These constraints ensure the battery operates safely within its optimal range. Furthermore, an electrochemical energy storage life decay model is developed to quantify battery degradation. To accommodate the uncertainty in PV output, Latin hypercube sampling is employed. A day-ahead dispatch model is established to maximize the system’s total daily operating revenue, and rolling optimization is applied during the intraday phase to correct deviations from the day-ahead forecast. Finally, simulation studies using actual data from a PV power plant demonstrate that the proposed strategy achieves a total daily revenue of 107,477 ¥, representing a 24.6% improvement over energy market-only participation; battery aging costs are reduced by 11.1% compared to the scenario without zone-based frequency regulation control. Results indicate that the proposed strategy effectively balances battery life degradation against market revenue, significantly improving the overall operational efficiency and economic viability of PV-storage hybrid systems. Full article

This study investigates the impact of internal muffler geometry on flow-related dissipation characteristics potentially relevant to acoustic behavior using steady-state Computational Fluid Dynamics (CFD) simulations. Four variants were analyzed: an empty tube, considered to be a baseline model, a three-chamber baffle system, a single spiral channel, and a complex multi-channel insert manufacturable only via advanced additive technologies. Simulations were conducted in SimScale using a compressible flow model with the k-ω SST turbulence formulation. Key outputs included static pressure distribution and turbulent kinetic energy (TKE), both of which were evaluated as qualitative surrogate indicators associated with flow-induced energy dissipation phenomena. The results indicate that geometries incorporating spiral features modify flow redistribution patterns, pressure gradients and localized turbulence intensity, suggesting potential applicability for future acoustic optimization studies. The study highlights how additive manufacturing enables the integration of geometrically complex internal structures otherwise unattainable through conventional methods. By comparing pressure drop and TKE patterns with internal design features, the research offers a preliminary CFD-based framework for geometry screening and conceptual evaluation of muffler insert designs for automotive exhaust systems. This approach provides computational support for rapid comparative assessment prior to experimental validation and detailed acoustic analysis. Full article

Driven by advances in multifunctional materials design, silver halides—both natural (AgCl, AgBr, AgI, and mixed phases such as embolite) and synthetic—have emerged as versatile functional materials characterized by tunable crystallography, phase stability, and compositional variability. This study investigates global research trends, interdisciplinary development, and emerging application areas of silver halides through a bibliometric analysis of 23,841 publications indexed in the Web of Science (1975–2025). CDPI, TELM, VOSviewer, and Excel were employed to evaluate publication growth, disciplinary integration, and thematic evolution. Research output increased markedly after 2005, reaching approximately 700–1000 publications annually during 2020–2025. China (18.3%) and the United States (17.5%) were the leading contributors, while the Chinese Academy of Sciences, Russian Academy of Sciences, and CNRS showed the highest scientific impact. Materials Science Multidisciplinary (CDPI = 0.72), Chemistry Multidisciplinary (0.70), and Physical Chemistry (0.67) exhibited the strongest interdisciplinary integration, whereas Nanoscience and Nanotechnology demonstrated the fastest growth. Keyword co-occurrence analysis identified six major research domains focused on functional materials engineering, including environmental remediation, catalysis, crystal growth, antibacterial materials, interfacial processes, and electroanalytical systems. Recent studies increasingly emphasize structure–property relationships and synthetic control of crystal size, morphology, and surface characteristics to enhance performance in photocatalysis, sensing, antimicrobial coatings, and advanced optical applications. Overall, the results highlight the growing importance of silver halides as strategic functional materials and provide a quantitative framework for future research and technological development. A limitation of this study is its exclusive reliance on the Web of Science database, which may underrepresent relevant publications indexed elsewhere. Full article

With the rapid expansion of artificial intelligence infrastructure and cloud computing services, data centres are evolving from rigid electricity loads into flexible resources capable of contributing to renewable energy integration, grid regulation and cross-regional computing power allocation. Addressing the shortcomings in existing research regarding the differences between various types of computing tasks, the mechanisms of green migration under network constraints, and the characterisation of curtailment risks for renewable energy, this paper proposes a risk-aware collaborative optimisation and scheduling method for a power–computing–network–storage coupled system across remote data centres. Firstly, a hierarchical model of multi-type computing tasks is constructed, classifying data centre loads into fixed real-time tasks, online inference tasks, long-duration AI training tasks, and opportunistic elastic tasks, to characterise the differences between these tasks in terms of latency, time-shift, migration, and completion volume constraints. Secondly, a graph-attention-inspired green affinity prior is proposed, mapping grid topological distance, renewable energy availability, data centre PUE, and energy storage regulation capacity into interpretable migration signals, thereby guiding flexible computing power to migrate towards nodes with abundant green electricity and favourable grid support conditions. Subsequently, we introduce the CVaR metric to quantify the tail risk of renewable energy curtailment, establishing a multi-scenario stochastic linear optimisation model that incorporates DC power flow, unit output, renewable energy utilisation, campus energy storage, task SLAs, and cross-node migration constraints. A 24 h simulation based on the IEEE 10-machine, 39-node system demonstrates that the proposed method can reduce the expected curtailment volume from 176.939 MWh to 0 MWh, lower the CVaR curtailment risk from 694.085 MWh to 0 MWh, and increase the proportion of green computing power by 9.283 percentage points compared to the fixed-load baseline, whilst improving the five-tier collaborative score by 4.885 points. Full article

In the current context, where sustainability has become a global imperative, emerging markets have increasingly incorporated green finance as a strategic pillar to foster long-term growth and stability. This study examines the evolution, trends, and key challenges of sustainable investment in emerging economies, with a particular focus on the integration of environmental, social, and governance (ESG) criteria. A systematic literature review was conducted using Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, based on a sample of 399 articles published over the past decade. The findings reveal a significant expansion in academic output on ESG investments in emerging markets, with an average annual growth rate of 14.06% and an international co-authorship rate of 37.34%. China, the United Kingdom, South Africa, and the United States emerge as leading contributors, particularly since 2020. However, critical gaps persist, including inconsistencies in ESG ratings and the limited adaptation of ESG frameworks to local socioeconomic and institutional conditions. Future research should focus on strengthening public policy frameworks, designing effective fiscal incentives, assessing the distributive implications of green finance, and leveraging technologies such as fintech, blockchain, and artificial intelligence to enhance ESG rating consistency, transparency, risk measurement, and the overall efficiency of sustainable investments. Full article

This bibliometric study maps olive (Olea europaea L.) yield prediction research as a coherent scientific domain for the first time. A Scopus query (27 February 2026) yielded 84 peer-reviewed articles (2002–2025), from which co-authorship network analysis, Bradford’s and Lotka’s Laws, Latent Dirichlet Allocation topic modelling (LDA), and OLS regression on citation counts were applied. Publication output increased nearly fourfold across three periods: 1.7 articles yr⁻¹ (2002–2014), 4.4 yr⁻¹ (2015–2019), and 6.7 yr⁻¹ (2020–2025). The 84 articles involve 382 authors, 61 journals, and 1551 citations (H-index = 22). Network analysis reveals a concentrated Spanish–Italian co-authorship axis. OLS regression (adj. R² = 0.267) identifies article age and abstract length as the only significant citation predictors, consistent with cumulative exposure time and study scope as structural drivers. Term-frequency screening against 18 a priori concepts finds that transfer learning, federated learning, hyperspectral imaging, digital twins, and SHAP-based explainability are absent or marginal. The field is producing more papers than ever on a narrowing methodological base geographically concentrated in the Mediterranean basin. Priority gaps—explainable AI, multi-region datasets, sensor-fusion pipelines, and federated data infrastructure—align directly with European Farm to Fork and Horizon Europe objectives. Full article

The convergence of artificial intelligence (AI), explainable AI (XAI), and neuroscience is fostering new opportunities for understanding both machine and biological intelligence through interpretable and human-centered learning paradigms. In this Perspective, we introduce XAI2Brain as a conceptual framework for brain–AI alignment, positioning mechanistic interpretability as an intermediate layer connecting neural network representations, human understanding, and neuroscience-inspired AI design. Rather than viewing XAI solely as a post hoc transparency tool, we emphasize its emerging role in enabling mechanistic analysis of internal model representations, concept-level reasoning, and interactive human–AI alignment. We define XAI2Brain as a multi-level conceptual framework rather than a deployable system, explicitly aimed at structuring brain–AI alignment across representation-level, mechanism-level, and interaction-level perspectives. We survey the evolution of XAI methodologies—from feature attribution and concept-based explanations to mechanistic and human-centric interpretability approaches—and discuss how these methods may support bidirectional knowledge transfer between AI systems and cognitive neuroscience. Importantly, we adopt a cautious stance on brain–AI analogy, explicitly recognizing that artificial neural representations are not equivalent to biological neural representations, and instead focusing on functional and informational correspondences rather than structural equivalence. Unlike conventional human-in-the-loop or reinforcement learning from human feedback paradigms that primarily optimize behavioral outputs, XAI2Brain focuses on cognitively interpretable and mechanistically grounded alignment between AI systems and human reasoning processes. This alignment promotes interactive human-in-the-loop intelligence, empowering humans to comprehend, guide, and refine AI systems, while enabling AI systems to better interpret human instructions, intentions, and contextual reasoning. We further discuss the challenges of scaling explainability to large generative and multimodal models, including issues of interpretability robustness, cognitive compatibility, evaluation, and ethical accountability. We also highlight key limitations of current mechanistic interpretability methods, including explanation instability, representation superposition, and lack of causal guarantees, underscoring that these challenges remain open research problems. Rather than proposing a complete artificial brain architecture, this Perspective outlines a research roadmap toward more interpretable, adaptive, and neuroscience-inspired AI systems capable of supporting future brain–AI integration and collaborative intelligence. We additionally clarify that this work follows a narrative perspective review methodology with structured thematic synthesis of the literature. By framing explainability as a bridge between mechanistic AI understanding, cognitive science, and human-centered interaction, XAI2Brain highlights the importance of interpretable alignment for the next generation of brain-inspired AI systems. Full article

Walnut multiplication technology for obtaining high-quality planting material consists of grafting, followed by forcing bionts in protected spaces under controlled microclimatic conditions, and completed by acclimatization under field conditions. The present research substantiates the hypothesis that the use of protected spaces (polyethylene tunnels) enables rigorous control of limiting factors. The main objectives of the paper are the comparative evaluation of two grafting methods (chip and patch budding) on the grafting success of eight native genotypes (‘Anica’, ‘Grădinar’, ‘Miroslava’, and ‘Velnița’, ‘Bălțăți’, ‘Belcești’, ‘Săbăuani’, and ‘Șorogari’) grafted on ‘Bălțați’ local biotype, determining the optimal moment of grafting by identifying the time window (April vs. August) that maximizes the success rate of the grafting association. The study, carried out from 2022 to 2024, evaluated the performance of chip and patch budding executed under high-tunnel conditions, quantifying the scion/rootstock growth, callus formation, and anatomical symbiont similarity through cross-sectional microscopy and image analysis software to measure vessel number, density, and diameter; the results are presented as the mean values of three annual repetitions across the experimental period. Preliminary results indicate a superior efficiency of the chip budding method, with a 51.3% success rate compared to 32.9% for the patch budding method. Another objective of the study was the ranking of the experimental variants. Thus, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision analysis method, ranked the experimental variants and identified the chip budding performed in April (variant a1/b2) as the optimal solution across all analyzed physiological and morphological parameters. These findings are highly significant for the nursery sector, as they demonstrate that transitioning from unpredictable field conditions to controlled high-tunnel conditions stabilizes production outcomes. By establishing a clear methodological hierarchy and a precise chronological window, this study provides actionable guidelines to standardize walnut multiplication, mitigate seasonal climate risks, and substantially increase the output of high-quality certified planting material. Full article