Search Results (1,121)

The Asian swamp eel (Monopterus albus), a commercially important freshwater aquaculture species in China and Southeast Asia, has attracted growing scientific interest owing to its natural protogynous hermaphroditism and substantial economic significance. Recent advances in genomics technologies have provided new insights into genetic improvement, disease resistance, and growth optimization in this species. However, a cohesive, up-to-date synthesis of the current genomic research landscape remains lacking. This review systematically summarizes progress in Asian swamp eel genomics: (i) reference genome development; (ii) population-level genetic diversity analysis; (iii) genome annotation and functional gene prediction; and (iv) applications of genomics in selective breeding, disease resistance enhancement, and growth performance optimization. We further evaluate emerging tools and platforms, highlight key technical constraints, and address ethical considerations and regulatory gaps in genome-informed aquaculture practices. Finally, we propose priority research avenues to strengthen the scientific foundation for resilient and sustainable swamp eel aquaculture. Full article

This review synthesizes findings from more than 100 journal articles, reports, and design standards on the design, simulation, and testing of steel beam-to-column connections, with emphasis on semi-rigid bolted extended end-plate (EEP) joints. The core objective of this study is to highlight the critical importance of accurately capturing this semi-rigid behavior, given the significant implications of improper modeling for the global response, safety, and design reliability of steel frames. While connections are often idealized as fully rigid or pinned, EEP connections typically exhibit a semi-rigid response governed by nonlinear moment–rotation (M–θ) behavior. The reviewed literature is organized around: (i) mechanical response and key failure mechanisms (end-plate yielding, bolt fracture, and prying action); (ii) analytical and numerical prediction methods, including component-based models and finite-element approaches capable of representing contact, bolt pretension, and cyclic degradation; and (iii) system-level implications for steel frames. Approaches used in major standards (AISC and Eurocode 3) for classifying connection stiffness and strength are compared, and experimental programs are summarized to identify the dominant parameters controlling resistance, ductility, and failure mode. Translating these component-level findings to the structural-system level, the review highlights how appropriately detailed semi-rigid EEP connections can enable moment redistribution, reduce member demands, and support stable inelastic deformation under seismic actions. Key research gaps include three-dimensional and multiaxial loading, impact and other high-rate actions, and the performance of alternative materials such as stainless steel. Full article

Climate change and soil degradation pose a challenge to grape quality, motivating the development of integrated monitoring approaches combining soil analysis with remote sensing techniques. However, harmonized information addressing this multidisciplinary challenge remains scarce. Therefore, this systematic review synthesizes the scientific literature published since 2020 with the aim of (i) identifying key soil properties and techniques applied, (ii) evaluating remote sensing approaches and their integration with soil data, and (iii) highlighting knowledge gaps and challenges for sustainable precision viticulture. A search in Scopus yielded 197 full-text articles classified into three thematic groups and analyzed using a standardized extraction protocol. Our synthesis reveals that pH, electrical conductivity, soil organic matter, and cation exchange capacity are the most consistently reported physicochemical parameters across the reviewed studies, while next-generation sequencing and multi-omics approaches are increasingly adopted in microbiological research to characterize rhizosphere communities and their links to terroir expression. In remote sensing, multispectral UAV platforms and satellite missions (Sentinel-2, Landsat) combined with vegetation indices, principally NDVI, dominate the toolset for monitoring vine vigor and water status. Nevertheless, genuine integration of remote-sensing outputs with root-zone soil measurements remains uncommon, with most studies treating both data streams independently. The principal knowledge gaps identified concern the absence of standardized sustainability assessment frameworks, limited cross-terroir transferability of predictive models, and insufficient long-term multi-site datasets to underpin climate change adaptation in vineyard management. Full article

Background: The click-evoked Auditory Brainstem Response (ABR) is the gold standard electrophysiological tool for assessing auditory pathway integrity in infants and young children. As normative data are inherently equipment-specific, the absence of pediatric reference values for the NeuroAudio system (Neurosoft, Ivanovo, Russia) represents a significant gap in clinical practice, given that existing normative datasets for this system are restricted to adult populations. Objective: To establish normative data for click ABR recorded with the NeuroAudio system in children under three years of age, stratified by age group according to auditory maturation patterns. Methods: A prospective, cross-sectional study was conducted at the Electrophysiology Laboratory of the Department of Speech Therapy, Paulista School of Medicine, Federal University of São Paulo (UNIFESP/EPM), under the approval of the Research Ethics Committee (protocol 7.939.564). A total of 203 children (121 males, 82 females; age range: 2 weeks to 36 months) with confirmed normal peripheral auditory function were included. Click stimuli (0.1 ms, rarefaction polarity) were delivered monaurally via ER-3A insert earphones at 80 dB nHL and a repetition rate of 19.3/s. Two average runs of 2000 artifact-free sweeps were recorded per ear. Absolute latencies of waves I, III, and V, interpeak intervals I–III, III–V, and I–V, and amplitudes of waves I and V were analyzed. Results: Statistical modeling supported the consolidation of 12 initial age bins into three clinically and statistically validated categories: 0–3, 4–12, and 13–36 months. Wave I latency remained stable across age groups, whereas waves III and V and all interpeak intervals showed progressive shortening with increasing age. Wave V amplitude increased progressively with age, while wave I amplitude remained unchanged. Females presented shorter latencies than males for waves III and V and for all interpeak intervals. The right ear exhibited a shorter III–V interpeak interval than the left ear, with a significant ear × age interaction indicating that this asymmetry is modulated during early maturation. Age, sex, and ear-stratified normative values (two SD and three SD reference limits) are reported. Conclusion: This study provides the first pediatric normative dataset for click-evoked ABR acquired with the NeuroAudio system in children under three years of age. The proposed three age stratifications, together with sex- and ear-specific reference values for the III–V interpeak interval, offer a clinically actionable framework for the accurate interpretation of pediatric ABR recordings and for the early identification of auditory pathway abnormalities. Full article

The fifth annual pan-tumour Early Age Onset Cancer (EAOC) Symposium, held in November 2025 and organized by the Colorectal Cancer Resource & Action Network (CCRAN), convened clinicians, researchers, policymakers, patients, and caregivers to address the rising incidence of cancers in individuals under 50 years. In addition to discussions around diagnostic and therapeutic advances for patients with late-stage disease, content centered on addressing critical gaps along the EAOC care continuum, including (i) diagnostic delays related to limited awareness and suboptimal primary care pathways, (ii) screening eligibility criteria for colorectal cancer (CRC) that no longer reflect current disease epidemiology, and (iii) insufficient age-appropriate infrastructure to meet the EAOC population’s unique unmet needs with respect to psychosocial support, fertility counseling, financial navigation, and survivorship planning. The symposium generated consensus recommendations such as the embedding of EAOC education into medical training curricula to increase the index of suspicion of EAOC in primary care, lowering the CRC screening age to 45 years to match this population’s rising disease incidence, and expanding multidisciplinary adolescent and young adult (AYA) and EAOC programs—including through the use of virtual models—to ensure that patients receive coordinated, comprehensive, equitable and age-appropriate care across the country. Full article

The genera Bombax and Pseudobombax (Malvaceae) are widely used in traditional medicine. This narrative review provides a hierarchical appraisal of their phytochemical and pharmacological profiles based on 35 studies, identifying 22 biological activities. A pronounced taxonomic bias was observed, with research heavily concentrated on Bombax ceiba, while other species, particularly within Pseudobombax, remain poorly explored. Hierarchical analysis indicates that Bombax reaches Level I evidence in metabolic and organ-protective activities, whereas Pseudobombax is largely limited to preliminary Levels II–III. Although antioxidant activity is the most frequently reported effect, it is predominantly supported by in vitro assays with limited physiological relevance. A morphological bias was also evident, with studies prioritizing stem bark and leaves over seeds and roots. Overall, the evidence reveals a significant translational gap, marked by the scarcity of pharmacokinetic data and mechanistic studies. Future research should prioritize standardized, mechanism-driven approaches and expand taxonomic coverage to advance the therapeutic potential of these genera. Full article

Morocco has developed one of the most comprehensive sustainability governance architectures among middle-income emerging economies, yet the relationship between its formal regulatory ambition and on-the-ground implementation effectiveness remains poorly understood. This narrative literature review provides an integrated, critically analytical account of Morocco’s sustainability governance system, organised around three interlocking dimensions: (i) a progressively strengthened legislative corpus anchored by the 2011 Constitution and Framework Law 99-12; (ii) a portfolio of national sustainability strategies aligning domestic policy with Paris Agreement commitments, Nationally Determined Contributions (NDCs), and the United Nations Sustainable Development Goals (SDGs); and (iii) corporate sustainability practices driven by regulatory obligations, international supply chain pressures, and ESG disclosure norms. Drawing on 124 sources, comprising 62 peer-reviewed articles, 38 legislative texts, and 24 institutional reports, and applying institutional isomorphism theory as an integrating analytical lens, the review advances three theoretical propositions concerning the conditions under which formal governance architectures translate into effective sustainability outcomes. It further proposes a validated conceptual framework and develops a comparative positioning of Morocco against peer economies (Tunisia, Egypt, South Africa, and Turkey). Critical implementation gaps are identified in enforcement capacity, SME integration, sustainability data infrastructure, and green finance, contributing a balanced and evidence-grounded assessment of Morocco’s sustainability transition. These findings offer actionable insights for policymakers, regulators, and business leaders operating in the Moroccan and broader African context. Full article

Photovoltaic (PV) power forecasting plays a central role in power system operation, electricity markets, and the integration of high shares of renewable energy. Over the past decade, forecasting approaches have evolved from classical statistical time-series models to advanced machine learning and deep learning architectures. This review analyses 119 studies published between 2016 and 2026, providing a structured assessment of PV forecasting methodologies, including model types, data requirements, validation strategies, and performance evaluation practices. Beyond summarizing existing approaches, the paper identifies three major methodological gaps in the literature: (i) fragmentation of evaluation metrics, which limits cross-study comparability; (ii) insufficient reporting of data preprocessing procedures and temporal leakage prevention; and (iii) limited integration of forecasting accuracy with economic and operational performance metrics. A systematic comparison of representative studies is conducted to highlight dominant modelling trends and persistent limitations. Beyond a descriptive summary, this review highlights significant limitations in methodological reporting across the 119 studies analysed, particularly regarding temporal leakage prevention in Deep Learning-based forecasting. To address these issues, we introduce a reproducibility checklist and propose a strategic roadmap aimed at strengthening the link between statistical accuracy (e.g., RMSE/MAE) and operational relevance in electricity markets. Full article

Modern logistics and manufacturing environments simultaneously demand mobility platforms that are compact enough to navigate narrow aisles and powerful enough to transport oversized or heavy components. We previously developed a compact Steering–Drive–Suspension (SDS) module that integrates steering, in-wheel drive, and suspension within a single wheel envelope, achieving $\pm {90}^{\circ }$ wide-angle steering with a single actuator. The present paper extends that hardware-centric work by treating the two-wheel (2WD) configuration assembled from two SDS modules as the unit module of the platform, building a four-wheel (4WD) operation by coupling two such 2WD units, and developing a unified balance and impedance-based control scheme. We derive a cart–pole inverted-pendulum model for the 2WD configuration and a planar 2-DOF bicycle model for the coupled and cooperative configurations, with full controllability proof and quantitative LQR robustness margins. Three Python 3.12 based scenarios validate the framework: (i) a 2WD inverted-pendulum tracking task, (ii) a forward and lateral relocation maneuver compared across SDS Crab, Ackermann, and four-wheel-steering modes, and (iii) cooperative transport of a $100\mathrm{kg}$ steel plate by two impedance-coupled 2WD units. Across all scenarios the proposed controllers achieve sub-centimetre tracking gap, pitch deviation within $\pm 2^{\circ }$, and well-damped cooperative behavior without payload sloshing. The results substantiate the central design claim that the SDS module’s compactness enables a single hardware platform to act simultaneously as an autonomous small-payload mover, a building block of a 4WD platform, and a cooperative agent for oversized loads. Full article

Rural youth often face barriers to accessing mental health services, including workforce shortages, limited resources, and persistent stigma. Schools are well-positioned to address these gaps through comprehensive school mental health systems (CSMHSs) embedded within multi-tiered systems of support (MTSSs). This study evaluated the implementation and effectiveness of a CSMHS in a small Midwestern rural school district over seven years. A longitudinal case study design was used to describe implementation across seven years. Universal mental health screening data were analyzed to determine the proportion of students receiving tiered supports over time. Implementation fidelity was assessed annually using the School Health Assessment and Performance Evaluation (SHAPE) system. Across seven years, more than 80% of students consistently demonstrated mental wellness within Tier I supports, with Tier II and Tier III needs aligned with expected MTSS distributions. SHAPE data indicated steady implementation improvement, particularly in universal screening, teaming, and tiered support. Ongoing challenges included monitoring Tier II intervention fidelity and demonstrating system-level impact. Findings suggest that CSMHSs can be effectively implemented and sustained in rural school settings when aligned with existing MTSS frameworks, supported by strong partnerships, and adapted to local contexts. This study provides evidence supporting the feasibility of rural CSMHS implementation and offers implications for practice and sustainability. Full article

This study examines how the Internet of Things (IoT) acts as a key enabler of sustainability in industrial production systems within the Industry 4.0 paradigm, addressing the fragmented understanding of the mechanisms linking digital technologies to environmental, operational, and emerging human-centric outcomes. A systematic literature review was conducted following PRISMA 2020 guidelines using the Web of Science Core Collection. After applying explicit inclusion and exclusion criteria, 69 peer-reviewed studies published between 2016 and 2026 were analyzed through qualitative thematic synthesis and comparative analysis. The findings reveal that IoT functions as a foundational digital infrastructure enabling real-time monitoring, operational transparency, and data-driven decision-making in production environments. Four dominant application domains are identified: (i) energy and resource efficiency, (ii) production monitoring and control, (iii) predictive maintenance and asset management, and (iv) emerging human-centric production systems aligned with Industry 5.0. While IoT consistently improves operational reliability and resource efficiency, its contribution to the social dimension of sustainability remains comparatively underdeveloped. This study advances the existing literature by providing a mechanism-oriented synthesis that explains how IoT-enabled infrastructures generate sustainability outcomes across production systems. Furthermore, it establishes a conceptual bridge between Industry 4.0 digitalization and the transition toward human-centric and resilient manufacturing models associated with Industry 5.0. From a practical perspective, the results highlight that IoT adoption contributes to reducing energy consumption, optimizing resource utilization, and enhancing operational performance, while also supporting safer and more adaptive working environments. However, challenges related to data integration, workforce adaptation, and digital capability gaps persist, underscoring the need for inclusive and strategically aligned digital transformation processes. Full article

Introduction: Species Distribution Models (SDMs) are widely used to infer environmental drivers of freshwater fish distributions and to project biodiversity responses to climate and land-use change. However, freshwater ecosystems present specific conceptual and methodological challenges, including dendritic network structure, strong spatial autocorrelation, dispersal constraints, and scale mismatches between biological processes and environmental predictors that remain insufficiently addressed. At the same time, emerging data sources such as environmental DNA (eDNA) and high-resolution remote sensing offer new opportunities to improve data coverage and ecological realism in SDMs. Methodology: Focusing on Iberian systems as illustrative case studies, here, we synthesize the following recent advances and challenges in SDM applications to freshwater fishes: (i) the implications of using presence–absence versus abundance data; (ii) the integration of hydrological and connectivity metrics as predictors; (iii) approaches to explicitly account for spatial structure and biotic interactions; and (iv) the contribution of novel datasets, including eDNA and remote sensing. Furthermore, we examine the performance and transferability of correlative models under analogue and non-analogue climate conditions. Results: Our synthesis highlights the importance of incorporating network topology, seasonality, dispersal constraints, and novel data sources to improve ecological realism and predictive performance. The integration of emerging biodiversity and environmental data can substantially reduce data gaps and improve model calibration and validation, particularly in poorly sampled systems. Nonetheless, model transferability remains a challenge, particularly for endemic and range-restricted species. Advancing freshwater SDMs through the integration of hydrologically explicit frameworks and novel data sources will strengthen their capacity to support evidence-based management of freshwater fish assemblages facing accelerating environmental changes. Full article

Industrial control systems (ICS), which manage critical infrastructure such as power grids and water treatment, are increasingly exposed to cyber threats and operational faults as their connectivity to external networks grows. AI-based anomaly detection has emerged as a key defense, yet three limitations restrict its practical deployment: (i) detected anomalies are treated uniformly without distinguishing between transient faults and intentional attacks, hindering tailored incident response; (ii) the trade-off between detection accuracy and the false-positive rate burdens experts with extensive manual triage and delays prompt action; and (iii) prevailing feature-attribution Explainable AI (XAI) techniques such as SHAP and LIME produce fragmented sensor-level explanations and fail to capture correlations among sensors in time-series data, undermining trust in model decisions. To address these gaps, this paper proposes a graph-based deep learning framework that (a) defines anomaly types in terms of the anomalous-sensor ratio measured before and after smoothing—which operationalizes the correlation-maintenance principle that faults keep coupled sensors jointly anomalous while attacks isolate them—enabling explicit separation of faults, attacks, false positives, and false negatives; (b) identifies ambiguous decisions near the detection threshold as candidate false alarms via dynamic threshold smoothing; and (c) provides correlation-aware graph visualizations for intuitive interpretation. Experiments on the Secure Water Treatment (SWaT) dataset center on this post-detection layer: built on a standard graph-based detector (F1-score 0.787 at Top-K = 10) that serves only as the substrate, the categorization separates faults from attacks, and the subsequent ambiguity analysis identifies false negatives with 83% precision and false positives with 73% precision. By separating attacks from faults and surfacing high-likelihood false alarms together with intuitive sensor-correlation explanations, the proposed approach reduces analyst workload and supports more reliable, prioritized incident response in ICS environments. Full article

Introduction: Despite more than two decades of implementation, the European Water Framework Directive (WFD) still faces major challenges in achieving good ecological status across European water bodies. Key limitations persist in connectivity restoration, transboundary harmonization, monitoring network design, and biological assessment of complex systems such as large rivers, reducing the Directive’s capacity to provide consistent ecological diagnoses and support effective river basin management. Objective: This work had four objectives: (I) incorporate ecological status into connectivity assessments; (II) evaluate harmonization in Iberian transboundary basins; (III) optimize the national fish monitoring network through co-creation; (IV) develop a fish-based multimetric index for Portuguese large rivers. Methodology: The work combined four approaches: (1) graph-based connectivity analysis integrating the probability of achieving good ecological status to evaluate functional connectivity across European river networks; (2) cross-border comparison of ecological classifications between Portugal and Spain in shared Iberian basins; (3) optimization of the Portuguese fish monitoring network through a co-creation approach involving the national authority; (4) development of a fish-based multimetric index designed for Portuguese large rivers. Results: Integrating ecological status into connectivity analyses reduced estimated connectivity and highlighted the combined effects of fragmentation and degradation. Cross-border comparisons showed that formal harmonization does not ensure consistent ecological classification. The optimized monitoring networks improved ecological representativeness without increasing sampling effort, while co-creation ensured operational feasibility. The new fish index for large rivers captures spatial variation in ecological quality and responds to pressure gradients, addressing a recognized methodological gap. Conclusions: Improving WFD implementation requires progress across multiple complementary components rather than isolated advances. More effective river management depends on integrating ecological processes, comparable assessment outputs, representative monitoring networks, and system-specific tools. These approaches provide transferable pathways for strengthening freshwater assessment and supporting more coherent river restoration and management across Europe. Full article

The evaluation of biomass boilers using partial approaches limits system understanding, because energy, exergy, dynamic, and emergy analyses describe complementary, but not equivalent, dimensions of thermo-industrial performance. In response to this gap, an integrated methodological framework is proposed to analyze two representative steam generator technologies in the sugar industry, fueled with ternary mixtures of sugarcane bagasse, Agricultural Crop Residues (ACR), and Dichrostachys cinerea, with the aim of identifying robust operating windows from a simultaneously thermal, exergetic, transient, and sustainability perspective. The methodology combines: (i) a direct and indirect steady-state model to quantify thermal losses and efficiency; (ii) an exergy model to assess conversion quality; (iii) a two-node coupled transient dynamic model capable of representing the differentiated response of the combustion zone and the water/steam system to moisture perturbations; and (iv) an emergy model to estimate the overall sustainability of the process. The results show that the effective moisture content of the mixture is the dominant control variable, since it determines the lower heating value on a wet basis, the specific fuel consumption, the main thermal loss, and the dynamic stability of the system. In the transient domain, a +5% step perturbation in moisture generates drops of 11.14–12.20 °C and 17.76–19.39 °C in furnace temperature for G1 and G2, respectively, while the steam response is damped to 1.03–1.14 °C and 2.39–2.65 °C. Likewise, moisture explains the magnitude of the response with coefficients of determination above 0.99, and the sensitivity analysis identifies the controller time constant, the thermal mass of the water/steam system, and the emissivity as the most influential parameters. Overall, the proposed framework makes it possible to go beyond isolated efficiency assessment and move toward a holistic characterization of biomass boiler performance under technically plausible ternary mixtures. Although the proposed methodological framework is transferable to other biomass combustion contexts, the numerical results—including optimal compositional zones, emergy indicators, and dynamic sensitivity coefficients—are specific to the Cuban sugar industry conditions, adopted transformities, and the biomass types evaluated herein. Full article