Search Results (8,382)

The optimization of warm-up strategies is essential to maximize athletic performance, especially in resistance training. While prolonged static stretching is associated with reduced power output, post-activation potentiation is known to increase it. However, the effects of short-term static stretching and its interaction with post-activation potentiation remain unclear. This study examined the acute effects of short-term static stretching alone and static stretching combined with post-activation potentiation on bench press power in trained individuals. The experimental method was used in this study. Twenty-four resistance-trained males successfully completed four sessions. The findings showed that static stretching alone significantly increased both peak and mean power compared to baseline. Combining static stretching with post-activation potentiation further increased the peak and mean power compared to baseline, but not by significantly more than static stretching alone. As a main conclusion, short-term static stretching can notably increase upper body strength, and although the addition of post-activation potentiation provides a numerical benefit, it does not outperform static stretching alone in this regard. Both static stretching and static stretching + post-activation potentiation may be viable warm-up strategies to improve acute bench press performance in trained individuals. Full article

High-intensity/sprint interval training (HIIT/SIT) improves aerobic and anaerobic performance, but it is unknown if HIIT/SIT increases strength, muscle mass/size, and muscle endurance (ME). We aimed to determine if HIIT/SIT increases strength, muscle mass/size, and ME. Databases (Ovid Medline, Sport Discus, EMBASE, and CINAHL) and the gray literature (Google Scholar) were searched for original research articles investigating the impact of HIIT/SIT on strength, muscle mass/size, and ME (23 March 2025). The risk of bias (ROB) was assessed via the Cochrane ROB 2 Tool. Meta-analyses were performed when three or more randomized controlled trials compared HIIT/SIT to a common comparator. Fifty-four studies were included (N = 1136). Twenty-five studies had a high ROB, while twenty-nine had some concerns. Standardized mean differences (SMD) (95% CI) of 0.16; (−0.09, 0.40), 0.33; (−0.21, 0.87) were observed for meta-analyses comparing the effect of HIIT/SIT to moderate intensity continuous training (MICT) and non-exercise controls (CON) on FFM, respectively. A meta-analysis comparing the effect of HIIT/SIT to resistance training (RT) on leg press strength yielded a SMD of −0.82; 95% CI: (−1.97, 0.33). HIIT/SIT may induce slightly greater gains than MICT and CON for FFM, while RT is likely superior to HIIT/SIT for improving leg press strength. However, the certainty of evidence is low, and 95% CIs intersect zero for all analyses. Full article

Digital pollution, encompassing energy consumption, e-waste, and the environmental impact of digital technologies, poses a significant and increasingly pressing environmental challenge that has received insufficient research attention. This study explores public perceptions, attitudes, and behaviors related to digital pollution, focusing on both individuals’ willingness to pay for environmentally friendly digital solutions and their actions to reduce digital environmental impact. Through a comprehensive survey of 300 UK participants, we examined the associations between demographic factors, knowledge levels, perceptions, and environmental actions. While traditional demographic factors such as age and income showed no significant correlation with willingness to pay, we found strong positive correlations with the frequency of environmental consideration (r = 0.47), willingness to act (r = 0.42), and perceived importance of digital pollution (r = 0.40). Notably, knowledge of digital pollution was not correlated with willingness to pay, while self-assessed tech-savviness and environmental knowledge had positive correlations with both willingness to pay and actions taken. Based on a robust cluster analysis, we identified four distinct participant groups: ’Engaged Eco-Tech Enthusiasts’ (youngest, most tech-savvy, and with the highest willingness to act), ‘Knowledgeable Traditionalists’ (oldest, highest knowledge scores, and moderate action), ‘Unengaged Pragmatists’ (lowest engagement), and ‘Affluent Moderates’ (wealthiest and with moderate engagement). These findings provide valuable insights for developing targeted interventions and communication strategies to address this emerging environmental challenge. Full article

Advancing urban scholarship and addressing pressing challenges such as gentrification, housing affordability, and urban sprawl require robust predictive models. In urban sciences, the performance of these models depends heavily on hyperparameter tuning, yet systematic evaluations of tuning approaches remain limited. This study compares two traditional hyperparameter tuning methods, Random Search and Grid Search, with Optuna, a more recent and advanced optimization framework, using housing transaction data as an illustrative case. Our findings show that Optuna substantially outperforms the other methods, running 6.77 to 108.92 times faster while consistently achieving lower error values across multiple evaluation metrics. By demonstrating both efficiency and accuracy gains, this research underscores the potential of advanced tuning strategies to accelerate urban analytics and provide more reliable evidence for policy-making. Full article

Background/Objectives: Resistance of pathogenic microorganisms to antibiotics poses a serious threat to public health and often leads to devastating consequences. In this context, one of the pressing challenges in pharmacochemistry is the search for new, effective antibiotics to combat severe human diseases. Cyclic lipopeptides have emerged as some of the most promising candidates and have been widely studied. These compounds are a class of microbial secondary metabolites produced by various microorganisms, and they possess significant medical and biotechnological importance. The defining structural feature of these compounds is the presence of both a hydrophobic fragment, primarily a hydrocarbon tail of varying length, and a hydrophilic cyclic peptide moiety. This hydrocarbon tail confers amphiphilic properties to the lipopeptides, which are essential for their broad spectrum of biological activities. Their mechanism of action involves disruption of the cell membrane, and in many cases, the formation of ion-permeable defects has also been shown. Results: This review summarizes the data on cyclic lipopeptides produced by Pseudomonas spp., Streptomyces spp., and Bacillus spp. that modify membrane permeability through the formation of ion channels. The main emphasis is on understanding how the structure of the CLP can be related to the probability and mode of pore formation. Conclusions: The findings can contribute to expanding the arsenal of effective antimicrobial agents with a mechanism of action that reduces the risk of developing resistance. Full article

A good way to produce large-sized wood products from small-diameter logs is by using laminated boards. The lamina undergoes an impregnation pretreatment to improve its quality before being formed into laminated boards (LBs). This research was performed to analyze the effects of an impregnation treatment on Jabon lamina with citric acid, boric acid, and polystyrene solutions on the physical and mechanical properties of Jabon LB. The Jabon lamina was first pretreated with citric acid, boric acid, and polystyrene by vacuuming for 30 min and pressing for 30 min at a pressure of 6.6 bar. The laminas were glued using isocyanate adhesive with a spreading rate of 280 g/m², consisting of three layers, which were cold pressed for 24 h. LB’s physical and mechanical properties were affected by the nature of the impregnating agent. Impregnating the lamina with citric acid and boric acid increased the density and moisture content of the laminated board, decreasing its mechanical properties. On the contrary, polystyrene-impregnated LB improved. After soaking in hot water, no LB displayed delamination, indicating high bonding performance. The best impregnating agent for lamina pretreatment was polystyrene, followed by boric acid and citric acid. The chemical compound, functional group, and degree of crystallinity of treated Jabon LB all changed due to the impregnation process. Full article

The study undertook a comprehensive review and comparative analysis of natural language processing techniques for news article classification, with a particular focus on Java language libraries. The dataset comprised an excess of 200,000 items of news metadata sourced from The Huffington Post. The traditional algorithms based on mathematical statistics and deep machine learning were evaluated. The libraries chosen for tests were Apache OpenNLP, Stanford CoreNLP, Waikato Weka, and the Huggingface ecosystem with the Pytorch backend. The efficacy of the trained models in forecasting specific topics was evaluated, and diverse methodologies for the feature extraction and analysis of word-vector representations were explored. The study considered aspects such as hardware resource management, implementation simplicity, learning time, and the quality of the resulting model in terms of detection, and it examined a range of techniques for attribute selection, feature filtering, vector representation, and the handling of imbalanced datasets. Advanced techniques for word selection and named entity recognition were employed. The study compared different models and configurations in terms of their performance and the resources they consumed. Furthermore, it addressed the difficulties encountered when processing lengthy texts with transformer neural networks, and it presented potential solutions such as sequence truncation and segment analysis. The elevated computational cost inherent to Java-based languages may present challenges in machine learning tasks. OpenNLP model achieved 84% accuracy, Weka and CoreNLP attained 86% and 88%, respectively, and DistilBERT emerged as the top performer, with an accuracy rate of 92%. Deep learning models demonstrated superior performance, training time, and ease of implementation compared to conventional statistical algorithms. Full article

Rural depopulation and the abandonment of historic settlements are pressing challenges for contemporary spatial planning, particularly in regions with a rich architectural and cultural heritage. This article examines the ruins of Moya, in Cuenca (Spain), as a case study to develop an integrated rural revitalisation strategy. The research combines historical building analysis, assessment of structural deterioration, and planning for economic reactivation to create a comprehensive framework for transforming abandoned sites into a viable cultural resource. The proposed model favours temporary and flexible occupation over permanent repopulation, promoting forms of use that respect and preserve the site’s historical identity. The approach builds on principles of activating monumental heritage, integrating the vernacular fabric, and organising the site into distinct functional areas. Moya is thus presented not only as an example of abandonment, but also as a replicable prototype for intervention in other declining rural environments, where heritage can serve as a strategic resource for sustainable development and territorial cohesion. The main objectives are to develop and test an integrated strategy for rural regeneration that goes beyond physical restoration, combining adaptive reuse, heritage conservation, and educational and cultural programmes to stimulate the local economy and reinforce territorial identity. The approach is evaluated through the case of Moya and contrasted with other national and international experiences to assess its replicability. Full article

Background/Objectives: There is growing interest in non-diet approaches to support health, well-being, and performance in different populations. The aim of this study was to evaluate the effects of a 12-week intuitive eating (IE) intervention on participants’ body composition, body image, eating behaviors, and athletic performance. Methods: The study included both an intervention group and a control group. It was conducted between September and December 2021. Participants were recruited from a sports center in Istanbul, where they had applied for nutrition and exercise counseling. Inclusion criteria included being 18–65 years old, not having engaged in regular physical activity in the past month, having no chronic disease, and not using any regular medications. Participants were not randomly assigned to groups; allocation was based on availability and willingness to attend the intervention sessions. The study involved 57 participants who were healthy adults between 18 and 65 years old and followed a structured exercise program. At the beginning of the study, a demographic questionnaire was administered. The anthropometric measurements were taken at the beginning and at the end of the intervention. In addition, validated performance and psychometric assessment tools were used, including the Cooper test for cardiovascular endurance, the Davies test for upper-body agility, and the 1-RM bench press for muscular strength, alongside standardized self-report questionnaires for eating attitudes (EAT-26), IE (IES-2), and body image (BCS). Results: The intervention group did not show any statistically significant changes in body composition (p > 0.05). The post-intervention means of the intervention and control groups were not statistically different (p > 0.05). The intervention group showed significant improvements in cardiovascular endurance, agility, and strength performance scores compared to the control group after the intervention (p < 0.05). The intervention group showed significant improvements in body image scores (p < 0.05) and eating attitude scores (p < 0.05). The post-intervention eating attitude and body image scores of the intervention group were significantly different from those of the control group (p < 0.05). The results of the correlation analysis showed a significant positive correlation between intuitive eating and body image (r = 0.455; p < 0.05) and a significant negative correlation between IE and disordered eating attitudes (r = −0.449; p < 0.05). Conclusions: These findings suggest that longer-term interventions may be beneficial and warrant further investigation. IE may serve as a promising strategy to enhance psychological well-being and performance outcomes without focusing on weight control. Full article

This study proposes a novel non-destructive approach to assessing and predicting the quality of stored date fruits using a composite quality index (Qi) modeled via visible–near-infrared (VIS–NIR) spectroscopy and artificial neural networks (ANNs). Two leading cultivars, Sukkary and Khlass, were stored for 12 months using three temperature regimes (25 °C, 5 °C, and −18 °C) and five types of packaging. The samples were grouped into six moisture content categories (4.36–36.70% d.b.), and key physicochemical traits, namely moisture, pH, hardness, total soluble solids (TSSs), density, color, and microbial load, were used to construct a normalized, dimensionless Qi. Spectral data (410–990 nm) were preprocessed using second-derivative transformation and modeled using partial least squares regression (PLSR) and the ANNs. The ANNs outperformed PLSR, achieving the correlation coefficient (R²) values of up to 0.944 (Sukkary) and 0.927 (Khlass), with corresponding root mean square error of prediction (RMSEP) values of 0.042 and 0.049, and the relative error of prediction (REP < 5%). The best quality retention was observed in the dates stored at −18 °C in pressed semi-rigid plastic containers (PSSPCs), with minimal microbial growth and superior sensory scores. The second-order Qi model showed a significantly better fit (p < 0.05, AIC-reduced) over that of linear alternatives, capturing the nonlinear degradation patterns during storage. The proposed system enables real-time, non-invasive quality monitoring and could support automated decision-making in postharvest management, packaging selection, and shelf-life prediction. Full article

Fu tea is a unique microbially fermented tea from China, and has two types. The primary distinction between these two types—Fu brick tea (FBT) and Fu loose tea (FLT)—is that FBT is compressed into bricks, whereas FLT is maintained as loose leaves. To investigate the differences in the chemical composition and sensory characteristics between the two types of Fu tea, this study utilized samples produced from the same batch of raw dark tea material to ensure comparability. Multiple analytical approaches were applied, including main active component analysis, sensory flavor evaluation, metabolomics, and differential characteristic component analysis. These methods were employed to comprehensively compare and characterize the two tea types. The results showed that compared to FBT, FLT exhibited a longer-lasting fungal flower aroma and a mellower taste. Furthermore, the quantity of Eurotium cristatum in FLT was 56.7% higher than that in FBT, indicating a significant difference. Untargeted ultrahigh-performance liquid chromatography–mass spectrometry was used to screen metabolites in Fu tea samples, and after multivariate statistical analysis, 12 differential metabolites were finally identified, including phenolic acids and their derivatives, coumarins and their derivatives, phenylpropanoids, and flavonoids and their glycosides. Subsequently, a targeted UHPLC-QQQ-MS/MS-based method was established and validated for the main differential metabolites, phenolic acids and catechins. The results indicated that gallic acid, catechin, and epicatechin can serve as characteristic markers for distinguishing between FBT and FLT. Notably, the content of gallic acid in FLT was 168.6% higher than that in FBT. These findings elucidate the impact of the pressing process on Fu tea, provide guidance for discriminating between FBT and FLT, and are significant for quality control in the industrial production of Fu tea. Full article

Background: Obesity, characterized by an abnormal and excessive accumulation of fat, significantly affects health by increasing the probability of chronic diseases and has become a pressing global health issue. Among natural compounds with therapeutic potential, rutin exhibits diverse biological effects, such as antioxidant, anti-inflammatory, and hypolipidemic properties. Objective: The purpose of this work is to evaluate the preventive effects of rutin loaded on chitosan nanoparticles on metabolic and oxidative alterations in male albino rats fed a high-fat diet (HFD). Method: The rats were allocated to four distinct groups: control, HFD, HFD treated with 50 mg/kg rutin, and HFD treated with 50 mg/kg nano-rutin, respectively, for six weeks. Results: Molecular docking analysis revealed that rutin exhibits an inhibitory interaction with PPAR-γ, suggesting its potential role in suppressing adipogenesis and contributing to its preventive effect against obesity. Nano-rutin markedly improved glycemic control, reducing fasting glucose from 161.75 ± 8.37 mg/dL in the HFD group to 133.50 ± 3.55 mg/dL, compared to 92.17 ± 3.53 mg/dL in controls. Serum leptin levels decreased from 28.95 ± 1.06 ng/mL in the HFD group to 15.58 ± 0.65 ng/mL with nano-rutin, approaching the control value of 10.43 ± 0.80 ng/mL. Oxidative stress was also significantly alleviated, as shown by a reduction in malondialdehyde (MDA) from 8.43 ± 0.20 U/µL in HFD rats to 6.57 ± 0.08 U/µL with nano-rutin, versus 1.29 ± 0.13 U/µL in controls. Conclusions: Rutin loaded on chitosan nanoparticles demonstrated protective effects against high-fat diet-induced obesity, mainly through modulation of leptin signaling and oxidative stress pathways. These findings highlight the promise of nano-rutin as a natural agent for preventing metabolic disorders related to obesity. Full article

Background/Objective: Insomnia is a clinically important symptom in Long COVID; however, few studies have addressed the presentation and course of insomnia symptoms in patients with Long COVID. Methods: The Insomnia Severity Index (ISI) was administered as part of a comprehensive baseline neuropsychological evaluation (Time 1) for patients with Long COVID at an Academic Medical Center (AMC). Data were gathered on 172 consecutively referred patients between the dates of November 2020 and May 2022. The mean age of patients at Time 1 was 49 years (range: 18 to 78), with a mean of 15 years of education. Patients were 70% female and 30% male and identified as White/Caucasian (78%), Black/African American (21%), or American Indian (1%). Patients’ severity of COVID-19 infection and self-reported emotional, somatic, cognitive, and fatigue symptoms were also gathered to identify concomitant risk factors for insomnia in Long COVID. Patients were then followed to observe the natural trajectory of insomnia complaints in Long COVID, with the Time 2 evaluation a mean of 9 months after the Time 1 evaluation. Results: Seventy-eight percent of Long COVID patients reported insomnia symptoms at Time 1, with 30% reporting Subthreshold Insomnia symptoms (ISI Score = 8–14), 30% reporting Moderate Insomnia symptoms (ISI Score = 15–21), and 18% reporting Severe Clinical Insomnia (ISI Score = 22–28). Severity of acute COVID-19 infection was not correlated with severity of insomnia in Long COVID; however, being non-white (r = 0.24, n = 172, p < 0.01) and having higher self-reported levels of anxiety (r = 0.41, n = 172, p < 0.01), depression (r = 0.52, n = 172, p < 0.01), perceived stress (r = 0.38, n = 172, p < 0.01), somatic symptoms (r = 0.51, n = 172, p < 0.01), cognitive failures, and fatigue were significantly correlated with insomnia symptoms. Insomnia was also significantly correlated with lower global cognitive function (r = 0.51, n = 172, p < 0.01) and lower cognitive flexibility (r = −0.17, n = 172, p < 0.05). There was a statistically significant decrease in reported ISI scores from Time 1 to Time 2 (t = −3.04; p = 0.003); however, ISI mean scores at both Time 1 (ISI Score = 14) and Time 2 (ISI Score = 12) remained in the Subthreshold Insomnia range (ISI score 8–14). Conclusions: Findings suggest that a large majority of Long COVID patients experience insomnia symptoms. Additionally, insomnia symptoms did not dissipate over time in a clinically meaningful way and were highly correlated with reduced global cognitive function, reduced cognitive flexibility, and higher levels of reported mood symptoms, fatigue, somatic symptoms, and experience of cognitive failures. Thus, there is a pressing need for intervention strategies to treat insomnia in Long COVID patients. Full article

Marsh wetland degradation and shrub expansion, driven by human activities and climate change, can impact carbon, nitrogen, and sulfur cycles by soil microorganisms. There is a paucity of systematic and in-depth research on the impact of shrub expansion in temperate wetlands on soil element cycles, which is a pressing scientific issue that demands resolution. This study used metagenomic sequencing and soil analysis methods to investigate the impact of shrub expansion in the Sanjiang Plain wetlands on carbon, nitrogen, and sulfur cycles in temperate wetland soils, as well as on functional microbial communities. Shrub expansion significantly altered soil carbon, nitrogen, and sulfur cycle processes and the composition (β diversity) of associated functional microbial communities, despite minimal changes in overall α diversity. Significant shifts occurred in the abundance of cycle pathways and related functional genes. Ammonia nitrogen, moisture, and total phosphorus were identified as the primary factors influencing these cycles and the functional microbial communities. Changes in the abundance of specific cycling pathways following shrub expansion are key drivers of functional community structure transformation. These changes may significantly reduce the long-term carbon sequestration potential of wetlands and affect regional climate feedback by altering greenhouse gas fluxes. The findings provide a theoretical basis for managing shrub expansion and assessing wetland function. Full article

Wood-derived ceramics represent a novel class of bio-based composite materials that integrate the hierarchical porous architecture of natural wood with high-performance ceramic phases such as silicon carbide (SiC). This review systematically summarizes recent advances in the fabrication of SiC woodceramics via two predominant sintering routes—reactive infiltration sintering and hot-press sintering—and elucidates their effects on the resulting microstructure and mechanical properties. This review leverages the intrinsic anisotropic vascular network and multiscale porosity and mechanical strength, achieving ultralightweight yet mechanically robust ceramics with tunable anisotropy and dynamic energy dissipation capabilities. Critical process–structure–property relationships are highlighted, including the role of ceramic reinforcement phases, interfacial engineering, and multiscale toughening mechanisms. The review further explores emerging applications spanning extreme protection (e.g., ballistic armor and aerospace thermal shields), multifunctional devices (such as electromagnetic shielding and tribological components), and architectural innovations including seismic-resistant composites and energy-efficient building materials. Finally, key challenges such as sintering-induced deformation, interfacial bonding limitations, and scalability are discussed alongside future prospects involving low-temperature sintering, nanoscale interface reinforcement, and additive manufacturing. This mini overview provides essential insights into the design and optimization of wood-derived ceramics, advancing their transition from sustainable biomimetic materials to next-generation high-performance structural components. This review synthesizes data from over 50 recent studies (2011–2025) indexed in Scopus and Web of Science, highlighting three key advancements: (1) bio-templated anisotropy breaking the porosity–strength trade-off, (2) reactive vs. hot-press sintering mechanisms, and (3) multifunctional applications in extreme environments. Full article