Search Results (13,423)

Despite being frequently proposed as a low-carbon solution for wastewater treatment and solar fuel production, the feasibility of photocatalytic processes in large-scale deployments remains unclear. This review evaluates the scalability of photocatalytic technologies by synthesizing a decade (2015–2025) of techno-economic analysis (TEA) and life-cycle assessment (LCA) studies. Using a systematic search and programmatic screening, 77 assessment-focused publications were identified from an initial corpus of 854 studies. Across applications, TEA and LCA consistently highlight two dominant barriers to scale-up: high electricity demand in UV-driven systems and significant cradle-to-gate impacts associated with catalyst synthesis, particularly for nanostructured materials. When solar irradiation replaces artificial light, environmental and economic hotspots shift from energy use to material production, catalyst durability, and reuse assumptions. Wide variability in reported costs and impacts reflects heterogeneous methodologies, limited pilot-scale data, and a lack of standardized reporting. Overall, assessment-based evidence indicates that photocatalysis is not yet ready for widespread industrial deployment as a large industrial process. However, continuous advances in solar-driven reactor design, low-impact and circular catalyst synthesis, hybrid process integration, and harmonized TEA/LCA frameworks could substantially improve its prospects for scalable, climate-positive implementation, especially in the context of emerging green energy alternatives. Full article

Construction progress monitoring remains predominantly manual, labor-intensive, and reliant on subjective human interpretation. Human dependence often leads to redundant or unreliable information, resulting in scheduling delays and increased costs. Advances in drones, point cloud generation, and multisensor data acquisition have expanded access to high-resolution as-built data. However, transforming data into reliable automated indicators of progress poses a challenge. A limitation is the lack of robust material-level segmentation, particularly for structural materials such as concrete and steel. Concrete and steel are crucial for verifying progress, ensuring quality, and facilitating construction management. Most studies in point cloud segmentation focus on object- or scene-level classification and primarily use geometric features, which limit their ability to distinguish materials with similar geometries but differing physical properties. A consolidated and systematic understanding of the performance of multispectral and multimodal segmentation methods for material-specific classification in construction environments remains unavailable. The systematic review addresses the existing gap by synthesizing and analyzing literature published from 2020 to 2025. The review focuses on segmentation methodologies, multispectral and multimodal data sources, performance metrics, dataset limitations, and documented challenges. Additionally, the review identifies research directions to facilitate automated progress monitoring of construction and to enhance digital twin frameworks. The review indicates strong quantitative performance, with multispectral and multimodal segmentation approaches achieving accuracies of 93–97% when integrating spectral information into point cloud or image-based pipelines. Large-scale environments benefit from combined LiDAR and high-resolution imagery approaches, which achieve classification quality metrics of 85–90%, thereby demonstrating robustness under complex acquisition conditions. Automated inspection workflows reduce inspection time from 24 h to less than 2 h and yield cost reductions of more than 50% compared to conventional methods. Additionally, deep-learning-based defect detection achieves inference times of 5–6 s per structural element, with reported accuracies of around 97%. The findings confirm productivity gains for construction monitoring. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) often coexists with obstructive sleep apnea (OSA) due to overlapping metabolic risk factors. Whether continuous positive airway pressure (CPAP) influences hepatic outcomes in MASLD remains uncertain. This systematic review, using updated criteria for MASLD, evaluated the effects of OSA treatment on liver and metabolic outcomes. Methods: PubMed, Web of Science, and CINAHL were searched for randomized controlled trials (RCTs) and observational studies in adults with MASLD and OSA treated with CPAP, lifestyle interventions, pharmacotherapy, or surgery. Outcomes included liver stiffness, fat content, enzymes, fibrosis scores, HbA1c, lipids, and anthropometrics. Risk of bias was assessed with RoB 2 (RCTs) and ROBINS-I (non-randomized studies) and certainty of evidence with GRADE. Results: Eight studies (three RCTs, five observational; n = 1006; 73.5% male) met criteria. Studies evaluated CPAP for from 4 weeks to 3 years, with adherence ≥ 4 h/night in most. CPAP produced modest, inconsistent reductions in alanine aminotransferase and aspartate aminotransferase, small improvements in HbA1c and triglycerides, and minimal changes in liver stiffness, steatosis, weight, or anthropometrics. No RCT demonstrated significant improvement in fibrosis or steatosis. Risk of bias was low in one RCT, “some concerns” in two, and moderate in observational studies; one study had serious confounding risk. Conclusions: CPAP may modestly improve liver enzymes and select metabolic parameters in MASLD with OSA, but evidence for salutary effects on steatosis, fibrosis, and body composition is limited. Level of evidence was low due to methodological limitations, heterogeneity, and imprecision. High-quality, longitudinal trials are needed. Full article

This paper presents a novel, accessible pipeline for the prediction and prevention of motion-related joint injuries in multiple individuals. Current methodologies for biomechanical analysis often rely on complex, restrictive setups such as multi-camera systems, wearable sensors, or markers, limiting their applicability in everyday environments. To overcome these limitations, we propose a comprehensive solution that utilizes only single-camera 2D images. Our pipeline comprises four distinct stages: (1) extraction of 2D human pose keypoints for multiple persons using a pretrained Human Pose Estimation model; (2) a novel ensemble learning model for person-view classification—distinguishing between front, back, and side perspectives—which is critical for accurate subsequent analysis; (3) a view-specific module that calculates body-segment angles, robustly handling movement pairs (e.g., flexion–extension) and mirrored joints; and (4) a pose assessment module that evaluates calculated angles against established biomechanical Range of Motion (ROM) standards to detect potentially injurious movements. Evaluated on a custom dataset of high-risk poses and diverse images, the end-to-end pipeline demonstrated an 87% success rate in identifying dangerous postures. The view classification stage, a key contribution of this work, achieved a 90% overall accuracy. The system delivers individualized, joint-specific feedback, offering a scalable and deployable solution for enhancing human health and safety in various settings, from home environments to workplaces, without the need for specialized equipment. Full article

Drug-resistant epilepsy (DRE) remains a major therapeutic challenge, as a considerable proportion of epilepsy patients fail to achieve seizure control with conventional anti-seizure medications or surgical therapy. Closed-loop systems have emerged as a promising alternative, offering patient-specific, on-demand neuromodulation. Despite notable advances in the academic domain, clinical translation has stagnated, and surgical resection remains the intervention with the highest probability of achieving seizure freedom. In this review, we delineate the principal limitations currently constraining progress in epilepsy neuromodulation and conceptualise these systems as instantiations of the read-write architecture characteristic of brain–computer interfaces. The read component entails the continuous acquisition and analysis of neurophysiological signals to predict or detect imminent seizures. In contrast, the write component involves the delivery of targeted interventions to disrupt epileptiform dynamics and prevent clinical seizure manifestation. We outline the closed-loop processing pipeline, survey the current state of the art, and discuss key methodological and translational challenges, particularly in algorithm validation and long-term reliability. Finally, we address patients’ and caregivers’ perspectives on the acceptance and practical integration of such technologies. This work synthesises current advances in the field and delineates the path toward fully autonomous clinically effective closed-loop neuromodulation as a viable treatment paradigm for DRE, aiming to improve patients’ quality of life. Full article

Ultrasound pretreatment offers a promising approach for improving spice drying efficiency while preserving bioactive compounds. This study explores the optimization of ultrasound pretreatment parameters for black pepper processing, using response surface methodology (RSM) to maximize piperine retention, drying efficiency, and moisture reduction. Compared to traditional one-factor-at-a-time (OFAT) screening, RSM identifies a multi-objective optimal balance that achieves superior results for all three responses. Our optimized conditions (35 kHz, 40 min, 50 °C, 80 W/cm³) achieved 18.64 mg/g DW piperine, a drying time of 444.51 min, and a 9.6% moisture content, demonstrating significant improvements in both bioactive preservation and energy efficiency compared with conventional methods. Compared to control samples requiring 600.69 ± 12.5 min drying time, optimal conditions reduced drying time by 26% to 444.51 min (a net process time reduction of 19%, including a 40 min pretreatment) while achieving the target moisture content (9.6%) and limiting piperine loss to approximately 6% in dried samples. Dual-method validation using UV spectrophotometry and HPLC confirmed model predictions with relative errors below 1%, establishing a consistent UV:HPLC ratio (1:2.12). Multi-analytical characterization revealed that ultrasound-induced cavitation selectively disrupted cellular structures, enhancing mass transfer without significant degradation of piperine’s functional groups. Scanning electron microscopy showed increased porosity and microfractures, while FTIR confirmed preservation of key chemical bonds with minor spectral shifts. The process achieved a five-fold improvement in product consistency (reducing the standard deviation from 0.68 to 0.12 mg/g) compared to conventional drying. These findings demonstrate that optimized ultrasound pretreatment provides a reproducible, scalable, and energy-efficient method for spice processing, supporting industrial adoption where consistent quality and bioactive stability are critical. Full article

Wireless Power Transfer (WPT) technologies are rapidly maturing, offering alternatives to traditional wired connections in applications ranging from consumer electronics to industrial automation. This review provides a technical analysis of WPT methodologies published between 2010 and 2025, explicitly distinguishing between non-radiative near-field techniques (specifically Inductive Power Transfer [IPT] and Capacitive Power Transfer [CPT]) and radiative far-field systems (Microwave Power Transfer [MPT] and Laser Power Transfer [LPT]). Unlike previous reviews that categorize primarily by coupling mechanism, this paper proposes a novel multi-parametric classification framework incorporating efficiency, alignment sensitivity, and emerging operational paradigms such as AI-optimized tuning and acoustic transfer. The analysis evaluates the engineering trade-offs between short-range, high-efficiency inductive systems and long-range, lower-efficiency radiative links. Furthermore, the paper identifies critical technical barriers to commercialization, specifically focusing on electromagnetic compatibility (EMC), biological safety (SAR) limits, and end-to-end system efficiency. Finally, the review extends beyond the physics to provide a rigorous economic analysis of the Total Cost of Ownership (TCO) for electric vehicle infrastructure and industrial IoT, highlighting the strategic viability of WPT in future smart grids. Full article

Background/Objectives: Incarcerated people experience high rates of trauma, psychological distress, and social marginalization. Yoga has been introduced in prisons as a trauma-sensitive mind–body practice, yet its rehabilitative contribution remains uncertain. This systematic review aimed to synthesize evidence on the feasibility and effectiveness of yoga interventions delivered in correctional settings. Methods: Following PRISMA guidelines and a preregistered PROSPERO protocol, we searched PubMed, PsycINFO, Cochrane CENTRAL, and Scopus for peer-reviewed publications from May 2012 to November 2025. Eligible studies involved structured yoga interventions for incarcerated populations and reported psychological, behavioral, or institutional outcomes. Two reviewers independently performed screening, data extraction, and quality appraisal using the Mixed-Methods Appraisal Tool (MMAT). Results: Ten studies reported in twelve publications and involving 1815 incarcerated individuals met the inclusion criteria. Interventions included Hatha-based protocols, Krimyoga, trauma-informed approaches, and multicomponent programs. Across randomized, quasi-experimental, and pre–post designs, yoga was feasible and acceptable. Reported benefits included reduced psychological distress, negative affect, anger, and trauma-related symptoms, as well as improved mood, self-regulation, and mindfulness. Evidence specific to women and girls was limited, but the available trauma-informed and gender-responsive studies suggested potential reductions in post-traumatic stress, depression, and anxiety, alongside increases in self-compassion. One large quasi-experimental cohort found lower reincarceration rates among yoga participants, although institutional outcomes were otherwise limited. Evidence was constrained by small samples, heterogeneous intervention formats, short follow-up, and variable outcome measures. Conclusions: Yoga appears to be a promising adjunct to rehabilitation in correctional settings. However, methodological limitations prevent firm conclusions. Larger, well-controlled studies with standardized outcomes and longer follow-up are needed to clarify effectiveness and support integration into correctional health and rehabilitation policy. Full article

Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder influenced not only by genetics, diet, and lifestyle but also by the gut microbiota. Dysbiosis (imbalances in microbial composition) can disrupt gut barrier integrity, alter microbial metabolites, and trigger low-grade inflammation, contributing to insulin resistance and β-cell dysfunction. Nutritional interventions, such as probiotics, prebiotics, synbiotics, postbiotics, and bioactive food components, represent potential therapeutic approaches for ameliorating gut eubiosis and advancing glycemic regulation. This narrative review incorporates evidence from selected studies identified by searches in PubMed, Scopus, and Google Scholar for studies published up to November 2025. The methodology included a structured literature search of in vitro, animal, and human studies, with a focus on intervention trials and mechanistic research. There are many positive signals from randomized controlled trials (RCTs), but heterogeneity and short follow-up limit definitive recommendations. Evidence from clinical and experimental studies indicates a beneficial effect on fasting glucose, hemoglobin A1c, and inflammatory markers, though heterogeneity of the individual and the variability in study designs limit generalization. There is insufficient evidence to recommend microbiota modulation as standard therapy in any disease. Key knowledge gaps include standardized interventions, stratified analyses by medication use (e.g., metformin), clinically meaningful endpoints, and long-term safety data. This review summarizes current knowledge on gut microbiota-driven mechanisms in T2DM and evaluates emerging microbiota-targeted therapies as adjunctive strategies for metabolic improvement. Full article

Strategic reverse logistics management is a key driver of sustainability in supply chains, where challenges in recyclable waste must be aligned with transportation systems to achieve optimal outcomes. A systematic review using the PRISMA methodology was conducted in December 2024 by searching Scopus, Google Scholar, and Thai Journals Online to examine the global research landscape and the strategic approaches applied in reverse logistics for recyclable waste and transportation. Analysis of 32 publications shows a steady rise in research, with most studies in Asia and dispersed across multiple journals, reflecting the field’s multidisciplinary nature. Four strategic approaches were identified. Model-driven approaches demonstrate strong capability through mathematical, computational, conceptual, and hybrid models, achieving reductions of 44% in climate impacts and 34% in costs. Technology-driven approaches contribute innovations to enhance battery transport safety. Exploratory approaches reveal contextual policy gaps and financial limitations. Hybrid approaches can improve efficiency and reduce CO₂ emissions. The future development of hybrid approaches still offers substantial room for broader application and deeper integration. This review supports the development of more effective systems, policies, and future research. Full article

Trametes polyzona is a white-rot basidiomycete with increasing relevance in environmental biotechnology due to its ligninolytic enzymes, biodegradation capacity, and versatile metabolic responses to diverse substrates. To provide an integrated and updated understanding of its biotechnological potential, we conducted a systematic review following PRISMA guidelines. A total of 46 studies published between 1991 and 2024 were analyzed, covering enzymatic production profiles, degradation of xenobiotics, extraction of bioactive metabolites, and experimental conditions influencing performance. Across the literature, T. polyzona consistently exhibits high ligninolytic activity, including laccase specific activities reported up to 1637 U/mg, together with efficient transformation of dyes, pesticides, and phenolic pollutants, and promising antioxidant and antimicrobial properties. However, substantial methodological heterogeneity was identified, particularly in strain characterization, fermentation parameters, and analytical approaches used to quantify enzymatic and biodegradation outcomes. These inconsistencies limit cross-study comparability and hinder process standardization. This review integrates current evidence; highlights critical gaps, such as limited ecotoxicological assessment of degradation products and scarce multi-omics characterization; and identifies key opportunities for process optimization in submerged/solid-state fermentation, bioreactor scaling, and the valorization of fungal metabolites. Overall, T. polyzona remains an underutilized resource with distinct advantages for applied mycology, environmental remediation, and industrial biotechnology. Full article

The rapid growth of online chess has intensified the challenge of distinguishing engine-assisted from authentic human play, exposing the limitations of existing approaches that rely solely on deterministic evaluation metrics. This study introduces a proof-of-concept hybrid framework for discriminating between engine-like and human-like chess play patterns, integrating Stockfish’s deterministic evaluations with stylometric behavioral features derived from the Maia engine. Key metrics include Centipawn Loss (CPL), Mismatch Move Match Probability (MMMP), and a novel Curvature-Based Stability (ΔS) indicator. These features were incorporated into a convolutional neural network (CNN) classifier and evaluated on a controlled benchmark dataset of 1000 games, where ‘suspicious’ gameplay was algorithmically generated to simulate engine-optimal patterns, while ‘clean’ play was modeled using Maia’s human-like predictions. Results demonstrate the framework’s ability to discriminate between these behavioral archetypes, with the hybrid model achieving a macro F1-score of 0.93, significantly outperforming the Stockfish-only baseline (F1 = 0.87), as validated by McNemar’s test (p = 0.0153). Feature ablation confirmed that Maia-derived features reduced false negatives and improved recall, while ΔS enhanced robustness. This work establishes a methodological foundation for behavioral pattern discrimination in chess, demonstrating the value of combining deterministic and human-centric modeling. Beyond chess, the approach offers a template for behavioral anomaly analysis in cybersecurity, education, and other decision-based domains, with real-world validation on adjudicated misconduct cases identified as the essential next step. Full article

Fixed-bed adsorption is widely employed in the scaling-up of liquid-phase adsorption processes because it offers significant operational advantages over batch systems. However, conventional approaches for scaling up adsorption columns are subject to important limitations. In this regard, the Axial Dispersion Model (ADM) coupled with the Pore Volume and Surface Diffusion Model (PVSDM) provides a framework capable of overcoming these constraints. In this study, ADM–PVSDM was applied predictively using equilibrium relationships and diffusion coefficients obtained from batch experiments. Model validity was assessed against nine experimental breakthrough curves, yielding an average deviation of 7.6% in breakthrough time. Furthermore, the model successfully predicted system behavior across a feed-flux range of 18–174 g h⁻¹m⁻². The integration of ADM–PVSDM was supported by the characterization of the Mass Transfer Zone (MTZ), which underpins the scaling approach proposed herein. The predicted breakthrough curves were also used to estimate MTZ length and velocity, which ranged from 0.97 to 8.7 cm and from 0.56 × 10⁻³ to 20 × 10⁻³ cm min⁻¹, respectively, with mean percentage deviations of 6.4% and 7.3%. These predictions enabled the development of a methodology which is capable of scaling adsorption columns over a wide operational range while requiring substantially fewer experiments compared to conventional scaling methods. Finally, it was demonstrated that commonly used empirical models, such as the Bohart–Adams model, failed to predict breakthrough curves with sufficient accuracy, thus rendering them unsuitable for developing this scaling methodology. Full article

Hydrofoil vessels have recently re-gained interest, presenting a more efficient and comfortable alternative to regular vessels. However, research in hydrofoils is limited by the high cost and complexity of developing a complete vessel to use for experimental testing. This paper presents a novel low-cost, small autonomous hydrofoil that can be used to research different hydrofoil hardware and configurations, control routines for autonomous balancing while foiling, and autonomous driving algorithms for hydrofoil crafts. A modular design with low-cost, off-the-shelf electronics is proposed and developed. Three independent PID control loops were implemented and validated, enabling the boat to remain stable in a foilborne state. An onboard GPS was used to implement autonomous driving, allowing the boat to navigate between GPS waypoints in a river. Experimental testing of the vessel indicated suitability as a low-cost, easy-to-modify, and easy-to-use hydrofoil test bed. Future research should focus on aspects of the mechanical design, investigating new control methodologies to improve performance, and investigating the efficiency gains and feasibility of performing long-range autonomous missions. Full article

The Guizhou Plateau, with the most extensive and representative karst landforms worldwide, is characterized by severe soil erosion and a highly fragile ecological environment. However, large-scale assessments of soil conservation services in this region remain limited. A key challenge lies in identifying appropriate datasets and methodologies for regional-scale soil conservation service evaluations, particularly under conditions of data scarcity or limited data accuracy. In this study, Unmanned Aerial Vehicle imagery, runoff plot observations, ground survey data, and multi-source satellite remote sensing data were integrated to refine LS and C in the Revised Universal Soil Loss Equation (RUSLE), thereby establishing a parameterized and localized soil erosion model. This improvement provided a methodological foundation for soil conservation service research in the region. Subsequently, the spatiotemporal variations in soil conservation services in the Guizhou Plateau over the past two decades were assessed. Furthermore, the relationships between soil conservation services and other key ecosystem services, including water conservation and carbon sequestration, were quantitatively examined, and the driving factors were analyzed. Soil conservation on the Guizhou Plateau exhibited an improving trend from 2000 to 2020. In karst areas, the relationship between soil conservation and water conservation was primarily influenced by temperature, altitude, and vegetation coverage, whereas in non-karst areas, it was regulated by rainfall and slope. Ecological restoration projects have enhanced the synergy between soil conservation and carbon sequestration by promoting vegetation cover. These findings could contribute to the next stage of ecological engineering initiatives and ecological policy implementation in Guizhou. Full article