Search Results (382)

Background/Objectives: In soccer, scanning before receiving the ball helps players better perceive and interpret their surroundings, enabling faster and more effective passes. Despite its importance, no standardized tests currently incorporate scanning actions into assessments of passing abilities. In this study, we test the reliability and validity of a battery of passing tests that assess a player’s ability to control and pass the ball while also scanning for the appropriate target. Methods: We designed three passing tests that reflect different scanning demands that are routinely placed upon players during matches. Using players from the first and reserve teams of two professional clubs in Ghana (Club A, first-team n = 11, reserve-team n = 10; Club B, first-team n = 16, reserve-team n = 17), we: (i) tested the repeatability of each passing test (intraclass correlations), (ii) assessed whether the tests could distinguish between first and reserve team players (linear mixed-effects model), and (iii) examined whether players who were better in the passing tests had higher performances in 3v1 Rondo possession games (linear models). Results: All passing tests were significantly repeatable (ICCs = 0.77–0.85). Performance was highest in the 120-degree test (30.11 ± 7.22 passes/min), where scanning was not required, and was lowest in the 360-degree test (25.55 ± 5.94 passes/min), where players needed to constantly scan behind them. When players were scanning through an arc of 180 degrees, their average performance was 27.41 ± 6.14 passes/min. Overall passing performance significantly distinguished first from reserve team players (β = −1.47, t (51) = −4.32, p < 0.001)) and was positively associated with 3v1 Rondo possession performance (R² = 0.51, p < 0.001). Conclusions: Our results show that these passing tests are reliable, distinguish players across competitive levels, and correlate with performance in possession games. These tests offer a simple, ecologically valid way to assess scanning and passing abilities for elite players. Full article

The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aims to provide a comprehensive analysis of machine learning (ML) models and statistical methods applied in forecasting and classification of water quality. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy and robustness. This study also reviews water quality standards and highlights the environmental context that necessitates advanced predictive tools. Statistical techniques such as residual analysis, principal component analysis (PCA), and feature importance assessment are also explored to enhance model interpretability and reliability. Comparative tables of model performance, strengths, and limitations are presented alongside real-world applications. Despite recent advancements, challenges remain in data quality, model interpretability, and integration of spatio-temporal and fuzzy logic techniques. This review identifies key research gaps and proposes future directions for developing transparent, adaptive, and accurate models. The findings can also guide researchers and policymakers towards the development of smart water quality management systems that enhance decision-making and ecological sustainability. Full article

Beach Sand Mycobiome is currently among the most important health challenges for viticulture in the world. Remarkably, the study of fungal communities in coastal beach sand and recreational waters remains underexplored despite their potential implications for human health. This research aimed to assess the prevalence of fungal species and the antifungal susceptibility profiles of fungi recovered from the beaches of the Persian Gulf and the Sea of Oman. Sand and seawater samples from 39 stations distributed within 13 beaches along the coastline were collected between May and July 2023. The grown isolates were identified at the species level based on morphological characteristics and DNA sequencing. Antifungal susceptibility testing was performed according to the Clinical Laboratory Standards Institute guidelines. Of 222 recovered isolates, 206 (92.8%) filamentous fungi and 16 (7.2%) yeast strains were identified. Sand-recovered fungi comprised 82.9%, while water-originated fungi accounted for 17.1%. The DNA sequencing technique categorized 191 isolates into 13 genera and 26 species. The most recovered genus was Aspergillus (68.9%), and Aspergillus terreus sensu stricto was the commonly identified species (26.14%). Voriconazole was the most effective antifungal drug against Aspergillus species. Research on fungal contamination levels at these locations could provide a foundation for establishing regulatory frameworks to diminish fungal risks, thereby enhancing public health protection. The ecological significance of fungal communities in sandy beaches to human infections remains to be explored, and earlier reports in the literature may motivate researchers to focus on detecting this mycobiome in natural environments where further investigation is warranted. Ultimately, our discovery serves as a reminder that much remains to be learned about pathogenic fungi and underscores the need for vigilance in areas where emerging pathogens have not yet been identified. Full article

The rising demand for sustainable energy storage has fueled the development of green batteries as alternatives to conventional systems. However, a major research gap lies in the unified integration of environmentally friendly materials and processes across all battery components—electrodes, electrolytes, and separators—without compromising performance or scalability. This review addresses this gap by highlighting recent advances in eco-conscious battery technologies, focusing on green electrode fabrication using water-based methods, electrophoretic deposition, solvent-free dry-press coating, 3D printing, and biomass-derived materials. It also examines the shift toward safer electrolytes, including ionic liquids, deep eutectic solvents, water-based systems, and solid biopolymer matrices, which improve both environmental compatibility and safety. Additionally, biodegradable separators made from natural polymers such as cellulose and chitosan offer enhanced thermal stability and ecological benefits. The review emphasizes the importance of lifecycle considerations like recyclability and biodegradability, aligning battery design with circular economy principles. While significant progress has been made, challenges such as standardization, long-term stability, and industrial scalability remain. By identifying key strategies and future directions, this article contributes to the foundation for next-generation green batteries, promoting their adoption in environmentally sensitive applications ranging from wearable electronics to grid storage. Full article

Green roofs are increasingly being adopted as sustainable, nature-based solutions for managing urban stormwater, mitigating the urban heat island effect, and saving energy in buildings. However, the long-term performance of their individual components—particularly filter geotextiles—remains understudied, despite their critical role in maintaining system functionality. The filter layer, responsible for preventing clogging of the drainage layer with fine substrate particles, directly affects the hydrological performance and service life of green roofs. While most existing studies focus on the initial material properties, there is a clear gap in understanding how geotextile filters behave after prolonged exposure to real-world environmental conditions. This study addresses this gap by assessing the mechanical and structural integrity of geotextile filters after five years of use in both extensive and intensive green roof systems. By analyzing changes in surface morphology, microstructure, and porosity through tensile strength tests, digital imaging, and scanning electron microscopy, this research offers new insights into the long-term performance of geotextiles. Results showed significant retention of tensile strength, particularly in the machine direction (MD), and a 56% reduction in porosity, which may affect filtration efficiency. Although material degradation occurs, some geotextiles retain their structural integrity over time, highlighting their potential for long-term use in green infrastructure applications. This research emphasizes the importance of material selection, long-term monitoring, and standardized evaluation techniques to ensure the ecological and functional resilience of green roofs. Furthermore, the findings contribute to advancing knowledge on the durability and life-cycle performance of filter materials, promoting sustainability and longevity in urban green infrastructure. Full article

Industrial wastewater management remains a critical barrier to achieving Sustainable Development Goal 6 (SDG 6) in many developing countries, where regulatory frameworks exist but affordable and scalable treatment solutions are lacking. In Pakistan, the textile sector is a leading polluter, with untreated effluents routinely discharged into rivers and agricultural lands despite stringent National Environmental Quality Standards (NEQS). This study presents a pilot-scale case from Faisalabad’s Khurrianwala industrial zone, where a decentralized, nature-based bioreactor was piloted to bridge the gap between policy and practice. The system integrates four treatment stages—anaerobic digestion (AD), floating treatment wetland (FTW), constructed wetland (CW), and sand filtration (SF)—and was further intensified via nutrient amendment, aeration, and bioaugmentation with three locally isolated bacterial strains (Acinetobacter junii NT-15, Pseudomonas indoloxydans NT-38, and Rhodococcus sp. NT-39). The fully intensified configuration achieved substantial reductions in total dissolved solids (TDS) (46%), total suspended solids (TSS) (51%), chemical oxygen demand (COD) (91%), biochemical oxygen demand (BOD) (94%), nutrients, nitrogen (N), and phosphorus (P) (86%), sulfate (26%), and chloride (41%). It also removed 95% iron (Fe), 87% cadmium (Cd), 57% lead (Pb), and 50% copper (Cu) from the effluent. The bacterial inoculants persist in the system and colonize the plant roots, contributing to stable bioremediation. The treated effluent met the national environmental quality standards (NEQS) discharge limits, confirming the system’s regulatory and ecological viability. This case study demonstrates how nature-based systems, when scientifically intensified, can deliver high-performance wastewater treatment in industrial zones with limited infrastructure—offering a replicable model for sustainable, SDG-aligned pollution control in the Global South. Full article

Unmanned Aerial Vehicles (UAVs) have become powerful tools for high-resolution, quantitative remote sensing in ecological and environmental studies. In this study, we present a novel approach to accurately mapping and estimating the biomass of Suaeda salsa using UAV-based visible-light imagery combined with hue angle inversion modeling. By integrating diffuse reflectance standard plates into the flight protocol, we converted RGB pixel values into reflectance and derived hue angle metrics with enhanced radiometric accuracy. A hue angle cutoff threshold of 249.01° was identified as the optimal cutoff to distinguish Suaeda salsa from the surrounding land cover types with high confidence. To estimate biomass, we developed an exponential inversion model based on hue angle data calibrated through extensive field measurements. The resulting model—Biomass = 3.57639 × 10⁻¹⁵ × e^0.12201×α—achieved exceptional performance (R² = 0.99696; MAPE = 3.616%; RMSE = 0.02183 kg/m²), indicating strong predictive accuracy and robustness. This study highlights a cost-effective, non-destructive, and scalable method for the real-time monitoring of coastal vegetation, offering a significant advancement in remote sensing applications for wetland ecosystem management. Full article

Background: Amphetamine-type stimulants (ATS) in water pose significant public health and ecological risks, necessitating reliable and efficient detection methods. Current approaches often involve time-consuming pH adjustments and post-processing steps, limiting their practicality for high-throughput analysis. This study aimed to develop a streamlined method integrating pH regulation and adsorption into a single material to simplify sample preparation and enhance analytical efficiency. Methods: A novel Fe₃O₄/MWCNTs-OH/CaO composite adsorbent was synthesized via a one-pot grinding method, embedding pH adjustment and adsorption functionalities within a single material. This innovation enabled magnetic solid-phase extraction (MSPE) without pre-adjusting sample pH or post-desorption steps. The method was coupled with liquid chromatography–tandem mass spectrometry (LC-MS/MS) for ATS detection. Optimization included evaluating adsorption/desorption conditions and validating performance in real water matrices. Results: The method demonstrated exceptional linearity (R² > 0.98), low detection limits (0.020–0.060 ng/mL), and high accuracy with relative recoveries of 92.8–104.8%. Precision was robust, with intra-/inter-day relative standard deviations (RSDs) below 11.6%. Single-blind experiments confirmed practical applicability, yielding consistent recoveries (relative errors: 1–8%) for ATS-spiked samples at 0.8 and 8 ng/mL. Compared to existing techniques, the approach reduced processing time to ~5 min by eliminating external pH adjustments and post-concentration steps. Conclusions: This work presents a rapid, reliable, and user-friendly method for ATS detection in complex environmental matrices. The integration of pH regulation and adsorption into a single adsorbent significantly simplifies workflows while maintaining high sensitivity and precision. The technique holds promise for large-scale environmental monitoring and forensic toxicology, offering a practical solution for high-throughput analysis of emerging contaminants. Full article

Against the background of the global concern for environmental protection and the prevalence of the green building concept, the requirements for building design are increasing, as are the technological content and functional requirements. Meanwhile, the urgency to address challenges related to the ecological environment and performance requirements has become increasingly pronounced. Taking a dormitory building in China as an example. Autodesk Revit 2018 software is employed in this study to establish a building information modeling (BIM). Green building software (GBSWARE) simulates and analyzes outdoor wind environment, indoor thermal comfort, calculates building energy conservation, does daylighting analysis, and calculates building daylighting. Although the building’s energy-saving design aligns with the requirements, the lighting and indoor thermal comfort of the rooms do not meet the standards. Additionally, the outdoor wind environment has problems with the wind zone and a wind speed amplification coefficient that surpasses the limit. The thermal environment within the residential building fails to satisfy the requirements. This study leverages a BIM-based model for multifaceted applications, integrating tailored retrofit strategies that align with the building’s inherent characteristics and detailed analyses of its components. By harnessing the building’s energy-saving potential, it enhances energy use efficiency, offering a valuable reference for the conceptual design of green buildings and energy-efficient retrofits. Full article

Electrolytic manganese residue (EMR), an acidic by-product from manganese production, presents dual challenges of environmental pollution and resource waste. This study developed a silicon–manganese organic compound fertilizer (SMOCF) via the aerobic fermentation of EMR supplemented with bagasse, molasses, and activated sludge. The physicochemical analysis revealed that the EMR’s composition was dominated by silicon (7.1% active Si), calcium, sulfur, and trace elements. Critical parameters during composting—including water-soluble Mn (1.48%), organic matter (8.05%), pH (7.4), moisture (20.28%), and germination index (GI = 87.78%)—met organic fertilizer standards, with the GI exceeding the phytotoxicity threshold (80%). The final SMOCF exhibited favorable agronomic properties: neutral pH, earthy texture, and essential macronutrients (1.36% K, 1.11% N, 0.48% P). Heavy metals (As, Cd, Cr, Pb) in the SMOCF predominantly existed in stable residual forms, with total concentrations complying with China’s organic fertilizer regulations (GB/T 32951-2016). The ecological risk assessment confirmed a minimal mobilization potential (risk assessment code < 5%), ensuring environmental safety. This work demonstrates a circular economy strategy to repurpose hazardous EMRs into agriculturally viable fertilizers, achieving simultaneous pollution mitigation and resource recovery. The optimized SMOCF meets quality benchmarks for organic fertilizers while addressing heavy metal concerns, providing a scalable solution for industrial EMR valorization. Further studies should validate the field performance and long-term ecological impacts to facilitate practical implementation. Full article

Sustainable architectural heritage conservation focuses on preserving historical buildings while promoting environmental sustainability. It involves using eco-friendly materials and methods to ensure that the cultural value of these structures is maintained while minimizing their ecological impact. In this paper, the use of the hydroxyapatite (HAp) in various combinations on masonry samples is presented, with the aim of identifying the ideal solution to be applied to an entire historical building in Banloc monument. The new solution has various advantages: compatibility with historical lime mortars (chemical and physical), increased durability under aggressive environmental conditions, non-invasive and reversible, aligning with conservation ethics, bioinspired material that avoids harmful synthetic additives, preservation of esthetics—minimal visual change to treated surfaces, and nanostructural (determined via SEM and AFM) reinforcement to improve cohesion without altering the porosity. An innovative approach involving hydroxiapatite addition to commercial mortars is developed and presented within this paper. Physico-chemical, mechanical studies, and architectural and economic trends will be addressed in this paper. Some specific tests (reduced water absorption, increased adhesion, high mechanical strength, unchanged chromatic aspect, high contact angle, not dangerous freeze–thaw test, reduced carbonation test), will be presented to evidence the capability of hydroxyapatite to be incorporated into green renovation efforts, strengthen the consolidation layer, and focus on its potential uses as an eco-material in building construction and renovation. The methodology employed in evaluating the comparative performance of hydroxyapatite (HAp)-modified mortar versus standard Baumit MPI25 mortar includes a standard error (SE) analysis computed column-wise across performance indicators. To further substantiate the claim of “optimal performance” at 20% HAp addition, independent samples t-tests were performed. The results of the independent samples t-tests were applied to three performance and cost indicators: Application Cost, Annualized Cost, and Efficiency-Cost-Performance (ECP) Index. This validates the claim that HAp-modified mortar offers superior overall performance when considering efficiency, cost, and durability combined. Full article

Background: Falls are common among individuals with multiple sclerosis (MS), yet standard clinical mobility assessments—such as the Timed Up and Go (TUG)—may not fully capture the complexities of real-world ambulation, leading to suboptimal fall identification. There is a critical need to evaluate the ecological validity of these assessments and identify alternative tests that better reflect real-world mobility and more accurately detect falls. This study examined the ecological validity of the TUG and novel dual-task clinical assessments by comparing laboratory-based gait metrics to community ambulation in individuals with MS and evaluated their ability to identify fallers. Methods: Twenty-seven individuals with MS (age 59.11 ± 10.57) completed the TUG test and three novel dual-task mobility assessments (TUG-extended, 25-foot walk and turn, and Figure 8 walk), each performed concurrently with a phonemic verbal fluency task. After lab assessments, the participants wore accelerometers for three consecutive days. Gait speed and stride regularity data was collected during both the in-lab clinical assessments and identified walking bouts in the community. The participants were stratified as fallers or non-fallers based on self-reported fall history over the previous six months. Findings: Significant differences were observed between the TUG and real-world ambulation for both gait speed (p < 0.01) and stride regularity (p = 0.04). No significant differences were found in gait metrics between real-world ambulation and both the 25-foot walk and turn and TUG-extended. Intraclass correlation coefficient analysis demonstrated good agreement between the 25-foot walk and turn and real-world ambulation for both gait speed (ICC = 0.75) and stride regularity (ICC = 0.81). When comparing the TUG to real-world ambulation, moderate agreement was observed for gait speed (ICC = 0.56) and poor agreement for stride regularity (ICC = 0.41). The 25-foot walk and turn exhibited superior predictive ability of fall status (AUC = 0.76) compared to the TUG (AUC = 0.67). Conclusions: The 25-foot walk and turn demonstrated strong ecological validity. It also exhibited superior predictive ability of fall status compared to the TUG. These findings support the 25-foot walk and turn as a promising tool for assessing mobility and fall risk in MS, warranting further study. Full article

In this study, we analysed cocoa (a dried and fully fermented seed of Theobroma cacao L.) from two Amazonian cultivars and a commercial sample of the Amazonian variety EETP801, grown under sustainable organic conditions, in comparison to CCN51 cocoa grown on a neighbouring commercial farm using standard practises and a European commercial cacao powdered beverage. The overall metabolite profile of the 70% aq acetone sample cocoa extracts was analysed using high-performance TLC analyses (HPTLC), and the xanthine alkaloids were analysed using quantitative liquid chromatography–UV photodiode array (HPLC-DAD) analyses. The volatile fraction in the headspace of the freshly ground cocoa was subjected to solid phase micro-extraction and analysed by gas chromatography–mass spectrometry (HS-SPME/GC-MS). Total polyphenol content was determined by the Folin–Ciocalteu method. Despite the reduced production of cocoa by the EETP801 cultivar in comparison with the CCN51 cultivar, the obtained produce is significantly richer in theobromine (130 mg vs. 170 mg per g of cacao), with CCN51 having a double concentration of theophylline (12.6 vs. 6.5 mg per g of cacao). Qualitatively, the two Amazonian cocoa samples had a similar polyphenolic composition (per the HPTLC fingerprint). HS-SPME/GC-MS analyses revealed that all the samples show a spontaneous emission profile mainly rich in non-terpene derivatives, of which hydrocarbons and pyrazines are the most abundant groups. The most represented volatile organic compound is n-tridecane for both EETP801 and CCN51. The variability in the artisan fermentation and roasting processes influenced certain aspects of the volatile composition as reflected by the trimethyl pyrazine/tetramethyl pyrazine ratio, which was zero in EETP-801 and lower than 1 in CCN51. Acetic acid was absent in CCN51 but significant (c.a. 5.5.%) in EETP801 and the commercial samples. The cultivar EETP801 is a viable option for a more ecologically conscious sector of the cocoa beverages consumer group. Full article

Polycyclic aromatic hydrocarbons (PAHs) have attracted significant attention due to their severe threats to both ecological systems and human health. In this paper, a high-performance surface-enhanced Raman spectroscopy (SERS) substrate based on NCF/GO/Au@Ag nanocomposites was developed, which enabled sensitive and stable detection of PAHs. The NCF/GO/Au@Ag substrate synergistic utilizes the localized surface plasmon resonance (LSPR) effect of Au@Ag core–shell nanorods and the additional interfacial charge transfer provided by graphene oxide (GO) to exhibit extremely high sensitivity. And the three-dimensional fibrous network of nanocellulose (NCF) improved nanoparticle dispersion uniformity. Combined finite element simulations and experimental studies verified that the dual plasmonic resonances (512 nm and 772 nm) of Au@Ag nanorods optimally match 785 nm excitation, yielding an enhancement factor of 5.21 × 10⁵. GO integration enhanced Raman signals by 1.68-fold through interfacial charge transfer, while the introduction of NCF reduced the signal relative standard deviation (RSD) from 36.88% to 4.29%. The NCF/GO/Au@Ag substrate achieved a detection limit of 10 μg/L for PAHs, demonstrating exceptional sensitivity and reproducibility. Full article

This study presents a novel approach for classifying Discomycetes species using deep learning and explainable artificial intelligence (XAI) techniques. The EfficientNet-B0 model achieved the highest performance, reaching 97% accuracy, a 97% F1-score, and a 99% AUC, making it the most effective model. MobileNetV3-L followed closely, with 96% accuracy, a 96% F1-score, and a 99% AUC, while ShuffleNet also showed strong results, reaching 95% accuracy and a 95% F1-score. In contrast, the EfficientNet-B4 model exhibited lower performance, achieving 89% accuracy, an 89% F1-score, and a 93% AUC. These results highlight the superior feature extraction and classification capabilities of EfficientNet-B0 and MobileNetV3-L for biological data. Explainable AI (XAI) techniques, including Grad-CAM and Score-CAM, enhanced the interpretability and transparency of model decisions. These methods offered insights into the internal decision-making processes of deep learning models, ensuring reliable classification results. This approach improves traditional taxonomy by advancing data processing and supporting accurate species differentiation. In the future, using larger datasets and more advanced AI models is recommended for biodiversity monitoring, ecosystem modeling, medical imaging, and bioinformatics. Beyond high classification performance, this study offers an ecologically meaningful approach by supporting biodiversity conservation and the accurate identification of fungal species. These findings contribute to developing more precise and reliable biological classification systems, setting new standards for AI-driven research in biological sciences. Full article