Search Results (125,583)

Paralympic athletes are challenged by unique systemic strain due to impairment-related physiological and psychological stressors. This study aims to synthesize the current evidence regarding post-exercise recovery modalities in Paralympic athletes, providing an overview of their physiological considerations and practical applications. A narrative review was conducted across PubMed/MEDLINE, Scopus, and Web of Science (inception to December 2025). Inclusion criteria prioritized original research on competitive para-athletes evaluated through physiological or performance-based markers. Evidence identifies four critical domains: (1) Thermoregulation: In spinal cord injury (SCI), upper-body cooling is significantly more effective than lower-body strategies for core temperature reduction; objective monitoring of playing time is essential, as subjective perception is unreliable. (2) Systemic recovery: Sleep quality is compromised by secondary complications (e.g., nocturia and spasticity), and heart rate variability (HRV) serves as a sensitive autonomic marker to validate readiness. (3) Neuromuscular restoration: The early-phase rate of force development (RFD ≤ 50 ms) is more sensitive than the peak strength for detecting neural fatigue, particularly in SCI. (4) Contextual modulators: Infrastructure accessibility and psychological resilience are primary determinants of intervention efficacy. Effective recovery in para-sports requires a shift toward “active-assisted” impairment-specific interventions. Future research must validate specialized monitoring tools and longitudinal impacts on long-term health. Full article

The objective of this study was to evaluate the impact of seasonal changes in the chemical and structural composition of giant miscanthus (Miscanthus × giganteus) biomass on the performance, kinetics, and efficiency of anaerobic digestion (AD), as well as on the overall energy and techno-economic balance of the conversion chain. The AD performance was assessed using batch biochemical methane potential (BMP) assays conducted for eight harvest dates (June–January). Comprehensive characterization included fundamental physicochemical properties of the biomass, lignocellulosic fraction composition, AD kinetics, and methane production yield. A statistically significant (p < 0.05) increase in structural fiber fractions was observed with advancing plant maturity, accompanied by a progressive decline in specific methane yield from 281 ± 32 mL CH₄/g VS in June to 170 ± 11–172 ± 13 mL CH₄/g VS in winter harvests. Despite a relatively stable theoretical biochemical methane potential (TBMP) ranging from 425 to 443 mL CH₄/g VS, the conversion efficiency (BMP/TBMP) decreased from approximately 66% to below 40%, indicating increasing structural and kinetic limitations to substrate biodegradability. Kinetic parameters deteriorated systematically in late harvests, as reflected by a reduction in the first-order rate constant k_CH₄ from 0.115 to approximately 0.072 1/d and an extension of the lag phase λ from 2.19 to over 4 days. Regression analysis revealed strong negative correlations between lignocellulosic complex content and both BMP and k_CH₄, whereas the C/N ratio exhibited a positive association with process performance under the experimental conditions applied. The highest methane production per hectare (3904 ± 720 m³CH₄/ha) and the most favorable economic outcome (1979 ± 465 EUR/ha) were achieved for the September harvest. The results demonstrate that harvest timing constitutes a critical optimization parameter in lignocellulosic biogas systems, governing not only methane yield and process kinetics but also the overall energy output and economic viability of the bioenergy production chain. Full article

Despite the widespread adoption of BIM, information exchange across disciplines remains hindered by heterogeneous structures at the tabular data level, particularly when integrating data across multiple discipline-specific models. Manual mapping, rigid templates, or one-off programming scripts are labor-intensive and difficult to scale, limiting automated querying, cross-model aggregation, and schedule-level analytics. This study proposes a lightweight, workflow-driven approach for semantic normalization and cross-model integration of BIM schedule data, with optional script-supported workflow configuration used only to assist the configuration of deterministic, rule-guided mapping logic, rather than serving as a core analytical method. By introducing a customizable subcategory layer, the workflow enables fine-grained semantic alignment and efficient normalization across diverse schedule datasets, implemented through lightweight Python scripting and rule-guided semantic matching used solely as a supporting mechanism for deterministic field mapping. Using structural, architectural, and HVAC models, we demonstrate a stepwise process including data cleaning, hierarchical classification, consistency checking, batch analytics, and automated computation of cross-model metrics such as opening-to-wall ratios. Sample-based validation confirms the workflow’s reliability, achieving semantic mapping agreement rates above 95% and reducing manual processing time by more than 85%. The workflow is readily extensible to other disciplines and modeling conventions, supporting high-throughput data integration for tasks such as design coordination, semantic alignment, RFI reduction, accelerated design reviews, and data-driven decision making. Overall, rather than introducing a new algorithm, the contribution of this work lies in formalizing a reusable, schedule-level workflow abstraction that enables consistent semantic alignment and automated cross-model aggregation without relying on rigid ontologies or training-intensive learning-based models. Any optional tooling used during workflow configuration is auxiliary and does not constitute a standalone learning-based method requiring model training or performance benchmarking. This provides a reusable methodological foundation for scalable, schedule-level BIM data integration and cross-model analytics. Full article

Background: The pain experienced by people with fibromyalgia (FM) is thought to be the result of altered nociceptive processing, impaired descending inhibition and reduced tolerance to physical load. However, the relationship between the amount of exercise and pain reduction remains unclear. Methods: This study synthesized randomized controlled trials of exercise interventions for FM to quantify the combined analgesic effects of different types of exercise. A secondary aim was to standardize exposure using metabolic equivalent of task (MET)-based metrics and examine the association between cumulative intervention dose (MET·h) and analgesic response (Hedges’ g) across intervention arms. Following the PRISMA guidelines, a search was conducted in PubMed for randomized controlled trials published up to 31 December 2025. After screening and a full-text assessment, 15 trials were included. The protocols were converted into MET-defined intensity and weekly MET·min exposure, and the cumulative dose was calculated as the total MET·h accrued over the intervention period. Random-effects models were used to estimate the pooled effects within modality subgroups. Results: Across modalities, exercise was associated with reductions in pain, with effects typically falling within the small-to-moderate range. Larger improvements were observed in structured or supervised programs. The dose-response scatter plot showed wide variability across the dose range, with overlapping confidence intervals. An exploratory fourth-degree polynomial fit explained limited variance (R² = 0.1615) and did not indicate a monotonic dose-response pattern. This suggests that cumulative workload alone is a weak proxy for therapeutic response. Conclusions: Based on these findings, a pain-responsive algorithm combining weekly Visual Analogue Scale (VAS), ΔVAS and Talk Test thresholds was implemented as a preliminary online calculator to support the prescription of exercise tailored to symptoms. Full article

Persistent endodontic infections are frequently associated with Enterococcus faecalis, a microorganism capable of penetrating dentinal tubules and surviving conventional disinfection procedures. This in vitro study evaluated the antimicrobial activity of Chihuahuan propolis against E. faecalis using planktonic and intratubular infection models. Propolis extract was tested at concentrations of 15, 35, and 70 mg/mL and compared with triple antibiotic paste (TAP) as a clinically relevant intracanal medicament. Antimicrobial efficacy was assessed by disk diffusion, minimum inhibitory concentration (MIC), colony-forming unit (CFU) reduction in infected dentinal tubules, and scanning electron microscopy (SEM). Chihuahuan propolis exhibited concentration-dependent antimicrobial activity, with a MIC of 17.5 mg/mL. In the intratubular model, propolis at 70 mg/mL achieved a CFU reduction comparable to TAP after seven days of application. SEM analysis confirmed a marked reduction of bacterial colonization within dentinal tubules. Within the limitations of this in vitro, monoespecies model, Chihuahuan propolis demonstrated antimicrobial efficacy against E. faecalis comparable to TAP, supporting its further investigation as a potential non-antibiotic intracanal medicament. Full article

Activated sludge treatment is plagued by high secondary pollution risks, and ship antifouling paint particles (APPs) as hazardous heavy metal-rich solid wastes generated from hull derusting wastewater, pose severe environmental threats and intractable disposal dilemmas. This study developed a novel pilot-scale activated sludge reduction process coupling APPs-derived carbon-based catalysts with ultrasound-Fe²⁺/peroxydisulfate (PDS) advanced oxidation. Columnar catalysts were fabricated via direct carbonization-molding using waste APPs from an 82,000 deadweight bulk carrier were used as the sole raw material to prepare columnar catalysts via direct carbonization-molding; single-factor and orthogonal experiments optimized process parameters, Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS) and X-ray Photoelectron Spectroscopy (XPS) characterized catalyst and sludge properties, free radical quenching experiments elucidated reaction mechanisms and a 90-day continuous pilot run assessed catalytic stability. The process achieved a 43.5% sludge removal rate under optimal conditions, accompanied by 100% toluene and 92.3% phenolic compound degradation, as well as efficient total phosphorus (TP) and total nitrogen (TN) removal. Mechanistic studies via characterization and quenching experiments confirmed the catalyst enhanced PDS activation through free/non-free radical synergy and accelerated Fe²⁺/Fe³⁺ redox cycling. A 90-day continuous pilot operation demonstrated excellent long-term catalytic stability, with sludge removal rate remaining above 38%. This “waste treating waste” technology realizes high-value APPs resource utilization, provides a low-carbon sludge disposal pathway, and offers a scalable solution for collaborative pollution control in the wastewater treatment and shipping industries. Full article

Imine reductases (IREDs) have emerged as valuable biocatalysts for the asymmetric synthesis of chiral amines, key intermediates in numerous active pharmaceutical ingredients. Their ability to operate under mild reaction conditions with high chemo- and stereoselectivity provides an attractive alternative to conventional metal-catalyzed or chemical reduction processes. However, the broader industrial application of wild-type IREDs is often constrained by their limited substrate scope and moderate catalytic efficiency. Recent advances in biocatalysis have demonstrated that engineered IREDs can catalyze the reduction of a wide range of natural and non-natural imines, significantly expanding their applicability in pharmaceutical and fine chemical synthesis. In parallel, enzyme immobilization strategies have proven highly effective for improving operational stability, facilitating enzyme reuse, and enabling continuous flow biocatalytic processes. Efficient cofactor regeneration systems have further enhanced the practical implementation of IRED-based transformations. Advances in protein engineering, including structure-guided design, semi-rational mutagenesis, and directed evolution, have generated enzyme variants with improved catalytic activity, stereoselectivity, and substrate tolerance. The integration of high-throughput screening technologies and machine-learning-assisted enzyme design has further accelerated the discovery and optimization of efficient IRED biocatalysts. This review summarizes recent progress in the protein engineering and immobilization of IREDs and discusses future perspectives for their industrial application. Full article

The electrodeposition of rare earth metal alloys has attracted considerable interest, not only due to the challenges associated with the reduction in metal ions, but also because of their unique material properties and promising technological applications. This review presents a comprehensive analysis of the state-of-the-art in the electrochemical deposition of these alloys, focusing on various electrolytic systems, including aqueous solutions, organic molecular solvents, ionic liquids, and deep eutectic solvents. Despite inherent problematic factors such as low reduction potentials, competing hydrogen evolution reactions, and difficulties in controlling metal formation, recent advancements have enabled improved control over film formation, typically through the induced codeposition of lanthanides with iron-group metals. The influence of key factors, such as electrolyte composition and current/potential modes, on alloy codeposition, elemental and phase composition, structure, and deposition efficiency is discussed. The magnetic properties, electrocatalytic behavior, and corrosion resistance of the deposited films are also shown, highlighting their relevance for high-performance applications. Full article

To address the challenges of small targets, severe background clutter, and high deployment cost in UAV-based power-line defect detection, this paper proposes a lightweight defect detection model based on an improved YOLOv8n. In the downsampling stage, we design an improved lightweight adaptive downsampling module (ADownPro) to replace part of conventional convolutions, which uses a dual-branch parallel structure for stronger feature interaction and depthwise separable convolutions (DSConv) for complexity reduction. In the feature extraction stage, an integration of cross-stage partial connections and partial convolution (CSPPC) is proposed to replace the C2F module for efficient multi-scale feature fusion. In the detection head, mixed local channel attention (MLCA), which combines channel-spatial information and local–global contextual features, is introduced to strengthen defect-focused representations under complex backgrounds. For the loss function, a scale-annealed mixed-quality EIoU loss (SAMQ-EIoU) is proposed by combining iso-center scale transformation, scale factor annealing and focal-style quality reweighting to improve localization accuracy at high IoU thresholds. Experiments on a constructed dataset covering six typical defect categories show that the improved YOLOv8n achieves 91.4% mAP@0.50 and 64.5% mAP@0.50:0.95, with only 1.59 M parameters and 4.9 GFLOPs. Compared with mainstream detectors, the proposed model achieves a better balance between detection accuracy and lightweight design. In particular, compared with the recently proposed YOLOv8n-DSN and IDD-YOLO, it improves mAP@0.50 by 0.6% and 0.8%, and mAP@0.50:0.95 by 1.2% and 4.8%, respectively, while further reducing the parameter count by 1.00 M and 1.26 M, and the FLOPs by 1.7 G and 0.2 G. Moreover, the cross-dataset evaluation on the public UPID and SFID datasets further demonstrate the robustness and generalization ability of the proposed method. Full article

This study presents a comprehensive comparative analysis of the performance, combustion, and emission characteristics of a single-cylinder, four-stroke spark-ignition engine fueled by commercial gasoline and raw biogas enhanced with cerium oxide (CeO₂) nanoparticles. Raw biogas containing 58% methane was tested without carbon dioxide removal to reflect practical rural applications, while CeO₂ nanoparticles were ultrasonically dispersed in the fuel to promote homogeneous suspension and catalytic activity. Experiments were conducted under wide-open and part-throttle conditions across a range of engine speeds, with simultaneous measurement of brake thermal efficiency, brake-specific fuel consumption, volumetric efficiency, in-cylinder pressure, heat release rate, combustion phasing, and regulated emissions. The results showed that while gasoline consistently outperformed biogas in torque and power due to its higher heating value and flame speed, the addition of CeO₂ significantly reduced the performance gap. For the biogas mode, CeO₂ addition increased brake thermal efficiency by up to 5%, lowered brake-specific fuel consumption by up to 8%, and shifted the start of main combustion to earlier crank angles, indicating faster and more complete combustion, particularly at high loads where higher temperatures activate CeO₂’s catalytic behavior. Emission analysis revealed that CeO₂-blended biogas reduced carbon monoxide emissions by approximately 25% and unburned hydrocarbons by up to 55% compared with gasoline, while nitrogen oxide emissions were consistently 15–22% lower. These reductions were observed across both wide-open and part-throttle conditions, confirming improved combustion completeness and lower peak flame temperatures. These improvements are attributed to CeO₂’s oxygen-storage capability, catalytic oxidation activity, and enhanced thermal conductivity, which collectively strengthen combustion completeness and cyclic stability. The findings demonstrate that nanoparticle-enhanced biogas can substantially improve the environmental and operational viability of spark-ignition engines, offering a practical pathway for integrating renewable gaseous fuels into existing transportation systems. Full article

Background/Objectives: Retinoblastoma represents the most common intraocular malignancy in childhood; however, the clinical applicability of mitomycin C (MMC) is restricted by dose-dependent ocular toxicity. Consequently, the development of pharmacological strategies that sensitize tumor cells to MMC while allowing dose reduction remains an unmet therapeutic objective. In this context, quercetin, a bioactive flavonoid with pleiotropic anticancer properties, has emerged as a potential chemosensitizing agent. Methods: Human retinoblastoma cell lines Y79 and WERI-Rb1 were exposed to MMC and quercetin, administered either individually or in fixed-ratio combinations. Cytotoxic responses were quantified through dose–response modeling and IC₅₀ determination following 24 and 48 h of treatment. Drug–drug interactions were quantitatively characterized using the Chou–Talalay combination index (CI) approach and isobologram analysis. Cell cycle distribution was assessed by propidium iodide (PI)-based flow cytometric analysis to evaluate treatment-associated alterations in cell cycle progression. Apoptotic cell death was assessed by Annexin V-FITC/PI flow cytometry, while transcriptional modulation of genes associated with apoptosis, cell cycle regulation, and oxidative stress (BAX, BCL-2, TP53, CASP3, CDKN1A, and HMOX1) was evaluated by qRT-PCR. Modulation of tumor-supportive signaling was examined by measuring VEGF and IL-6 secretion. Translational relevance was further investigated using a three-dimensional (3D) tumor spheroid model, and the functional contribution of reactive oxygen species (ROS) was interrogated through N-acetyl-L-cysteine (NAC) rescue experiments. Results: Quercetin significantly enhanced the cytotoxic activity of MMC in both retinoblastoma cell lines, with CI values below 1 across IC₅₀–IC₉₀ effect levels, indicating a synergistic pharmacological interaction. PI–FACS analysis revealed that combined MMC and quercetin treatment induced a pronounced accumulation of cells in the G2/M phase, consistent with cell cycle arrest, with a more marked effect observed in Y79 cells compared with WERI-Rb1 cells. Combination treatment resulted in a pronounced increase in apoptotic cell populations compared with single-agent exposure and triggered a coordinated pro-apoptotic transcriptional response, characterized by increased expression of BAX, TP53, CASP3, CDKN1A, and HMOX1, alongside suppression of BCL-2 and a marked shift in the BAX/BCL-2 ratio. Concurrently, VEGF and IL-6 secretion were significantly reduced, reflecting attenuation of pro-angiogenic and pro-inflammatory signaling. Notably, synergistic cytotoxicity was maintained in 3D tumor spheroids, where combined treatment induced spheroid shrinkage, architectural disruption, and reduced viability. NAC pretreatment diminished ROS accumulation and partially restored cell viability, indicating that oxidative stress contributes to, but does not solely account for, the observed synergistic cytotoxic effect. Conclusions: Collectively, these findings indicate that quercetin appears to function as an effective chemosensitizing adjuvant to MMC in retinoblastoma models, through transcriptional changes consistent with p53-associated apoptotic signaling at the transcriptional level, G2/M cell cycle arrest, and partial involvement of ROS-related cellular stress responses, along with suppression of tumor-supportive signaling pathways. The preservation of synergistic activity in 3D tumor spheroids supports the potential preclinical relevance of this combination. However, these findings are based on transcriptional and phenotypic analyses and should be interpreted as hypothesis-generating, requiring further validation through protein-level and in vivo studies before translational application. Full article

Co-pyrolysis of sludge and plastics has gradually emerged as a crucial technical approach for waste reduction and resource recovery. This study develops high-precision, interpretable prediction models and quantifies the contributions of core risk factors to environmental risks. Based on the experimental datasets from 2015 to 2025, which include operational parameters and eight potential toxic elements (PTEs) with four chemical speciation fractions: acid-soluble/exchangeable (F1), reducible (F2), oxidizable (F3), and residual (F4), we constructed six machine learning models. Based on the experimental datasets from 2015 to 2025, which include operational parameters and eight potential toxic elements (PTEs) chemical speciation (F1–F4), we constructed six machine learning models. Feature importance analysis and Shapley Additive Explanation (SHAP) analysis were employed to identify core risk factors and interpret the model’s decision logic. Results indicate that XGBoost, Random Forest and CatBoost outperform other models, achieving test accuracies of 0.94, 0.92, and 0.90, with weighted F1-Scores of 0.94, 0.92, and 0.90, respectively. Feature importance highlights the most important features for the six different models, with Cd-F4, As-F1, and Cu-F4 contributing most significantly to the model predictions. SHAP analysis quantified the contributions of each feature to the model predictions, verified Cd-F4 as the primary risk discriminant, and further revealed that F1 and F4 of PTEs are key factors in distinguishing risk levels. This study proposes an interpretable machine learning framework, providing a theoretical basis for the optimization of the sludge and plastic co-pyrolysis process and the assessment of potential risks. Full article

The continuous demand for advanced composite materials with superior mechanical and electrical properties has driven the exploration of copper matrix composites for high-performance applications. The Cu–2Zr–0.6B (wt.%) powder mixtures were mechanically alloyed (MA) using two different ball-to-powder weight ratios (BPR: 10:1 and 15:1) to investigate the influence of milling conditions on the final composite material’s properties. MA powders milled with BPR 15:1 exhibited the highest values of dislocation densities, which induce higher hardness of Cu–ZrB₂ bulk materials. The MA powders were consolidated using three different methods: conventional cold pressing followed by sintering (CPS), hot pressing (HP), and spark plasma sintering (SPS). The in situ forming of ZrB₂ (3.5 vol.%) reinforcements during consolidation processes in Cu matrix proved to have a major impact on enhancing the hardness and structural stability, while the use of SPS and HP offered superior control over grain growth and porosity reduction compared to CPS. Main findings related to electrical and mechanical properties showed similar values for SPS (~38% IACS, ~173 HV1) and HP compacts (~39% IACS, ~155 HV1) but proved to be much higher compared to values of CPS compacts (~21% IACS, ~80 HV1). Full article

Stainless steel (SS) remains widely used in orthopedic implants but is susceptible to corrosion and implant-associated infections in physiological environments. This study aimed to develop a multifunctional multilayer coating combining corrosion resistance, bioactivity, and antimicrobial performance. A ZnO base layer was deposited on 316L SS via pulsed laser deposition, followed by matrix-assisted pulsed laser evaporation of a lovastatin-functionalized bioactive glass (BG57 + LOV) top layer. Two LOV concentrations were initially evaluated, and BG57+0.1LOV was selected based on structural homogeneity, cytocompatibility, and antimicrobial balance. Physicochemical characterization confirmed preservation of chemical integrity and formation of continuous, moderately rough coatings. Electrochemical impedance spectroscopy in simulated body fluid demonstrated progressive improvement in corrosion resistance from bare SS to ZnO-coated and finally to the BG57+0.1LOV/ZnO multilayer, which exhibited the most electropositive corrosion potential and effective suppression of charge-transfer reactions. Biological assays revealed high viability of osteoblasts, fibroblasts, keratinocytes, and macrophages without significant oxidative or nitrosative stress. Antimicrobial testing showed strain-dependent activity, with enhanced efficacy against MRSA and significant reduction in P. aeruginosa, associated with increased ROS/RNS generation. Overall, the BG57+0.1LOV/ZnO system represents a promising multifunctional coating strategy for corrosion-resistant and infection-resistant SS implants. Full article

Microstructural and morphological effects of cobalt (Co), nickel (Ni), and cerium (Ce) microalloying on the SAC305 lead-free solder alloy were investigated, with emphasis on the solidification behavior under slow cooling conditions. Although the individual effects of these elements have been previously reported, their combined influence remains scarcely addressed. Thermal behavior, elemental composition, and surface integrity of the solder joints were analyzed. The addition of Co, Ni, and Ce resulted in a significant shift of the onset temperature during cooling, indicating reduced undercooling. Microalloying led to a transformation of the intermetallic layer (IML) morphology from scalloped to planar, and a 60% reduction in the number of shrinkage voids. The average β-Sn grain size decreased by 37.5%, while the eutectic area increased from 32% to 38%. The substitution of Cu atoms by Co and Ni within the Cu₆Sn₅ lattice formed thermodynamically stable (Cu,Co,Ni)₆Sn₅ phases. These findings demonstrate that the synergistic effect of Co, Ni, and Ce microadditives effectively refines the microstructure, suppresses undercooling, and enhances the overall reliability of SAC305 solder joints. Full article