Search Results (118,136)

Fungi contribute to ecosystem function through nutrient cycling and decomposition but may be affected by major disturbances such as fire. Some ecosystems are fire-adapted, such as prairies which require cyclical burning to mitigate woody plant encroachment and reduce litter. While fire suppresses fire-sensitive fungi, pyrophilous fungi may continue providing ecosystem functions. Using litter bags, we measured the litter decomposition at three prairies with unburned and burned sections, and we used Illumina sequencing to examine litter communities. We hypothesized that (H1) decomposition would be higher at unburned sites than burned, (H2) increased decomposition at unburned sites would be correlated with higher overall saprotroph diversity, with a lower diversity in autoclaved samples, and (H3) pyrophilous fungal diversity would be higher at burned sites and overall higher in autoclaved samples. H1 was not supported; decomposition was unaffected by burn treatments. H2 and H3 were somewhat supported; saprotroph diversity was lowest in autoclaved litter at burned sites, but pyrophilous fungal diversity was the highest. Pyrophilous fungal diversity significantly contributed to litter decomposition rates, while saprotroph diversity did not. Our findings indicate that fire-adapted prairies host a suite of pyrophilous saprotrophic fungi, and that these fungi play a primary role in litter decomposition post-fire when other fire-sensitive fungal saprotrophs are less abundant. Full article

In the summer of 2022, central and eastern China experienced prolonged extreme high temperatures and severe drought, leading to significant economic losses. To gain a more profound understanding of this drought event and furnish a reference for forecasting similar events in the future, this study examines the circulation anomalies associated with the drought. Employing a diagnostic method focused on temperature and moisture anomalies, this study introduces a novel approach to quantify and compare the relative significance of moisture transport and warm air dynamics in contributing to the drought. This study examines the atmospheric circulation anomalies linked to the drought event and compares the relative contributions of water vapor transport and warm air activity in causing the drought, using two parameters defined in the paper. The results show the following: (1) The West Pacific Subtropical High (WPSH) was more intense than usual and extended westward, consistently controlling the Yangtze River Basin. Simultaneously, the polar vortex area was smaller and weaker, the South Asian High area was larger and stronger, and it shifted eastward. These factors collectively led to weakened water vapor transport conditions and prevailing subsiding air motions in the Yangtze River Basin, causing frequent high temperatures. (2) By defining I_q and I_t to represent the contributions of moisture and temperature to precipitation, we found that the drought event in the Yangtze River Basin was driven by both reduced moisture supplies in the lower troposphere and higher-than-normal temperatures, with temperature playing a dominant role. Full article

Wildfire regimes are closely linked to changes in landscape structure, yet the influence of accelerated land use transitions on fire activity remains poorly understood, particularly in rapidly transforming regions like central Chile. Although land use change has been extensively documented in the country, the specific role of the speed, extent, and spatial configuration of these transitions in shaping fire dynamics requires further investigation. To address this gap, we examined how landscape transitions influence fire frequency in central Chile, a region experiencing rapid land use change and heightened fire activity. Using multi-temporal remote sensing data, we quantified land use transitions, calculated landscape metrics to describe their spatial characteristics, and applied intensity analysis to assess their relationship with fire frequency changes. Our results show that accelerated landscape transitions significantly increased fire frequency, particularly in areas affected by forest plantation rotations, new forest establishment, and urban expansion, with changes exceeding uniform intensity expectations. Regional variations were evident: In the more densely populated northern areas, increased fire frequency was primarily linked to urban development and deforestation, while in the more rural southern regions, forest plantation cycles played a dominant role. Areas with a high number of large forest patches were especially prone to fire frequency increases. These findings demonstrate that both the speed and spatial configuration of landscape transitions are critical drivers of wildfire activity. By identifying the specific land use changes and landscape characteristics that amplify fire risks, this study provides valuable knowledge to inform fire risk reduction, landscape management, and urban planning in Chile and other fire-prone regions undergoing rapid transformation. Full article

Background/Objectives: Health education plays a vital role in training health professionals and caregivers, supporting both prevention and the promotion of self-care. In this context, technology serves as a valuable ally by enabling continuous and flexible learning. Among the various domains of health education, nutrition stands out as a key element in the management of Amyotrophic Lateral Sclerosis (ALS), helping to prevent malnutrition and enhance patient well-being. Accordingly, this study aimed to evaluate the effectiveness of the teaching and learning processes within a learning pathway focused on food and nutrition in the context of ALS. Methods: This study adopted a longitudinal, quantitative design. The learning pathway, titled “Food and Nutrition in ALS,” consisted of four self-paced and self-instructional Massive Open Online Courses (MOOCs), offered through the Virtual Learning Environment of the Brazilian Health System (AVASUS). Participants included health professionals, caregivers, and patients from all five regions of Brazil. Participants had the autonomy to complete the courses in any order, with no prerequisites for enrollment. Results: Out of 14,263 participants enrolled nationwide, 182 were included in this study after signing the Informed Consent Form. Of these, 142 (78%) completed at least one course and participated in the educational intervention. A significant increase in knowledge was observed, with mean pre-test scores rising from 7.3 (SD = 1.8) to 9.6 (SD = 0.9) on the post-test across all courses (p < 0.001). Conclusions: The self-instructional, technology-mediated continuing education model proved effective in improving participants’ knowledge about nutrition in ALS. Future studies should explore knowledge retention, behavior change, and the impact of such interventions on clinical outcomes, especially in multidisciplinary care settings. Full article

In this study, we applied a new technique for vegetatively transplanting kelp Eisenia bicyclis to restore macroalgal habitats. We aimed to assess the restoration success of the E. bicyclis bed by comparing the carbon and nitrogen stable isotope ratios of macrobenthic consumers and their isotopic niches in artificial and control (barren ground) habitats. Except for the deposit feeding group, no significant differences were observed in isotopic values of the other feeding groups (suspension feeders, herbivores, omnivores, and carnivores) between the two sites. In contrast, our results showed wider isotopic niche indices for all feeding groups at the transplantation site compared to those at the control site, suggesting increased trophic diversity in the artificial habitat. Overall, these results indicate that the macroalgal bed created using the new method can play an ecological role in restoring functional properties of food web structures via trophic support of degraded coastal ecosystems. Full article

The aim was to investigate the genetic effects of a SNP located in the precursor region of gga-miR-3528. (1) Single-nucleotide polymorphisms within precursor regions of microRNAs play crucial biological roles. (2) Utilizing a Gushi–Anka F₂ resource population (n = 860), we screened and validated miRNA SNPs. A SNP mutation in the miR-3528 precursor region was identified. Specific primers were designed to amplify the polymorphic fragment. Genotyping was performed for this individual SNP across the population, using the MassArray system. Association analyses were conducted between this SNP and chicken growth and body measurement traits, carcass traits, meat quality traits, and serum enzyme activities. (3) The rs14098602 (+12 bp A > G) was identified within the precursor region of gga-miR-3528. Significant associations (p < 0.05) were observed between this SNP and chicken growth traits (body weight at the age of 0 day, body weight at the age of 2 weeks, and body weight at the age of 4 weeks), carcass traits (evisceration weight), meat quality traits (subcutaneous fat rate and pectoral muscle density), and serum enzyme activities (total protein, albumin, globulin, cholinesterase, and lactate dehydrogenase). (4) These findings suggest that the polymorphism at rs14098602 may influence chicken growth, meat quality, and serum biochemical indices, through specific mechanisms. The gga-miR-3528 gene likely plays an important role in chicken development. Therefore, this SNP can serve as a molecular marker for genetic breeding and auxiliary selection of growth-related traits, facilitating the rapid establishment of elite chicken populations with superior genetic resources. Full article

In the context of the GCC, the adoption of integrated reporting (IR) remains limited, due in part to weak regulatory enforcement, a lack of awareness of the strategic benefits of IR, and a strong focus on short-term financial results. This limited reporting context presents a significant challenge for firms to credibly demonstrate their value to the market and attract potential investors, thus communicating long-term value. Given these limitations, this study considers how IR contributes to firm value, but also examines the mediating role that firm image (FI) plays in this relationship as a reputational construct representing stakeholder perspectives of a firm’s transparency and accountability. The research employs a quantitative methodology, analysing secondary data from corporate governance and integrated reports spanning 2017–2018 to 2022–2023. Findings indicate a positive and robust relationship between integrated reporting and the firm’s value, which was assessed using Tobin’s Q. The findings highlight the significant mediating role of firm image, illustrating how IR practices, via increased transparency, accountability, and sustainability, enhance firm value. This study provides significant insights for researchers, policymakers, and corporate managers, highlighting the strategic relevance of IR in the GCC region. The findings demonstrate that integrated reporting improves transparency, accountability, and sustainability, thereby assisting corporate managers in utilising IR to enhance firm image and facilitate value creation. Policymakers can utilise these insights to develop regulatory frameworks that promote integrated reporting practices, thereby enhancing transparency and sustainable growth within the corporate sector. Full article

In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO₂). This has been a recurring concern associated with agricultural intensification for food production. This study aimed to evaluate the optimization of tractor gears and engine speed during crop operations to minimize CO₂ emissions and promote sustainability. The experiment was conducted using a strip plot design with subdivided sections and six replications, following a double factorial structure. The first factor evaluated was the type of agricultural implement (disc harrow, subsoiler, or sprayer), while the second factor was the engine speed setting (nominal or reduced). Operational and energy performance metrics were analyzed, including fuel consumption and CO₂ emissions, travel speed, effective working time, wheel slippage, and working depth. Optimized gear selection and engine speeds resulted in a 20 to 40% reduction in fuel consumption and CO₂ emissions. However, other evaluated parameters remain unaffected by the reduced engine speed, regardless of the implement used, ensuring the operation’s quality. Thus, optimizing operator training or configuring machines allows for environmental impact reduction, making agricultural practices more sustainable. Full article

Building change detection and building damage assessment are two essential tasks in post-disaster analysis. Building change detection focuses on identifying changed building areas between bi-temporal images, while building damage assessment involves segmenting all buildings and classifying their damage severity. These tasks play a critical role in disaster response and urban development monitoring. Although supervised learning has significantly advanced building change detection and damage assessment, its reliance on large labeled datasets remains a major limitation. In contrast, self-supervised learning enables the extraction of meaningful data representations without explicit training labels. To address this challenge, we propose a self-supervised learning approach that unifies denoising autoencoders and contrastive learning, enabling effective data representation for building change detection and damage assessment. The proposed architecture integrates a dual denoising autoencoder with a Vision Transformer backbone and contrastive learning strategy, complemented by a Feature Pyramid Network-ResNet dual decoder and an Edge Guidance Module. This design enhances multi-scale feature extraction and enables edge-aware segmentation for accurate predictions. Extensive experiments were conducted on five public datasets, including xBD, LEVIR, LEVIR+, SYSU, and WHU, to evaluate the performance and generalization capabilities of the model. The results demonstrate that the proposed Denoising AutoEncoder-enhanced Dual-Fusion Network (DAEDFN) approach achieves competitive performance compared with fully supervised methods. On the xBD dataset, the largest dataset for building damage assessment, our proposed method achieves an F1 score of 0.892 for building segmentation, outperforming state-of-the-art methods. For building damage severity classification, the model achieves an F1 score of 0.632. On the building change detection datasets, the proposed method achieves F1 scores of 0.837 (LEVIR), 0.817 (LEVIR+), 0.768 (SYSU), and 0.876 (WHU), demonstrating model generalization across diverse scenarios. Despite these promising results, challenges remain in complex urban environments, small-scale changes, and fine-grained boundary detection. These findings highlight the potential of self-supervised learning in building change detection and damage assessment tasks. Full article

Accurately identifying driver fatigue in complex driving environments plays a crucial role in road traffic safety. To address the challenge of reduced fatigue detection accuracy in complex cabin environments caused by lighting variations, we propose YOLO-FDCL, a novel algorithm specifically designed for driver fatigue detection under complex lighting conditions. This algorithm introduces MobileNetV4 into the backbone network to enhance the model’s ability to extract fatigue-related features in complex driving environments while reducing the model’s parameter size. Additionally, by incorporating the concept of structural re-parameterization, RepFPN is introduced into the neck section of the algorithm to strengthen the network’s multi-scale feature fusion capabilities, further improving the model’s detection performance. Experimental results show that on the YAWDD dataset, compared to the baseline YOLOv8-S, precision increased from 97.4% to 98.8%, recall improved from 96.3% to 97.5%, mAP@0.5 increased from 98.0% to 98.8%, and mAP@0.5:0.95 increased from 92.4% to 94.2%. This algorithm has made significant progress in the task of fatigue detection under complex lighting conditions. At the same time, this model shows outstanding performance on our self-developed Complex Lighting Driving Fatigue Dataset (CLDFD), with precision and recall improving by 2.8% and 2.2%, respectively, and improvements of 3.1% and 3.6% in mAP@0.5 and mAP@0.5:0.95 compared to the baseline model, respectively. Full article

Background/Objective: This study explores a novel approach for diagnosing common middle ear pathologies using Pressure-Less Acoustic Immittance (PLAI™), a non-invasive alternative to conventional tympanometry. Methods: A total of 516 ear measurements were collected and stratified into three age groups: 0–3, 3–12, and 12+ years, reflecting key developmental stages. PLAI™-derived acoustic parameters, including resonant frequency, peak admittance, canal volume, and resonance peak frequency boundaries, were analyzed using Random Forest classifiers, with SMOTE addressing class imbalance and SHAP values assessing feature importance. Results: Age-specific models demonstrated superior diagnostic accuracy compared to non-stratified approaches, with macro F1-scores of 0.79, 0.84, and 0.78, respectively. Resonant frequency, ear canal volume, and peak admittance consistently emerged as the most informative features. Notably, age-based stratification significantly reduced false negative rates for conditions such as Otitis Media with Effusion and tympanic membrane retractions, enhancing clinical reliability. These results underscore the relevance of age-aware modeling in pediatric audiology and validate PLAI™ as a promising tool for early, pressure-free middle ear diagnostics. Conclusions: While further validation on larger, balanced cohorts is recommended, this study supports the integration of machine learning and acoustic immittance into more accurate, developmentally informed screening frameworks. Full article

Thin-film detection technology plays a significant role in particle physics, X-ray imaging and radiation monitoring. In this paper, the detection capability of a Cs₃Cu₂I₅ thin-film scintillator toward α particles is investigated. The flexible thin-film scintillator is fabricated by a facile and cost-effective in situ strategy, exhibiting excellent scintillation properties. Upon α-particle excitation, the light yield of the Cs₃Cu₂I₅ thin-film is 2400 photons/MeV, which greatly benefits its application for single-particle events detection. Moreover, it shows linear energy response within the range of 4.7–5.5 MeV and moderate decay time of 667 ns. We further explored the cryogenic scintillation performance of Cs₃Cu₂I₅@PMMA film. As the temperature decreases from 300 K to 50 K, its light yield gradually increases to 1.3 fold of its original value, while its decay time remains almost unchanged. This scintillator film also shows excellent low-temperature stability and flexible operational stability. This work demonstrates the great potential of the Cs₃Cu₂I₅@PMMA film for the practical utilization in α-particle detection application. Full article

Globally, endometriosis affects almost 10% of reproductive-aged women, leading to chronic pain and discomfort. Endocrine-disrupting compounds (EDCs) seem to play a pivotal role as a causal factor. The current manuscript aims to explain potential molecular pathways, synthesize current evidence regarding EDCs as causative agents of endometriosis, and highlight implications in the general population and clinical work. A thorough review of experimental, epidemiologic, and mechanistic research studies was conducted to explain the association between EDCs and endometriosis. Among the primary EDCs under investigation are polychlorinated biphenyls, dioxins, phthalates, and bisphenol A (BPA). Despite methodological heterogeneity and some discrepancies, epidemiologic evidence supports a positive association between some increased levels of BPA, phthalates, and dioxins in urine or in blood, and endometriosis. Experiments support some effect of EDCs on endometrial cells and causing endometriosis. EDCs function as xenoestrogens, alter immune function, induce oxidative stress, and disrupt progesterone signaling. Epigenetic reprogramming may play a role in mediating EDC-induced endometriosis. Endocrine, immunological, and epigenetic pathways link EDCs and endometriosis. Prevention techniques require deeper comprehension of those factors. Causal linkages and possible treatment targets should be based on longitudinal studies and multi-omics techniques. Restriction of EDCs could be beneficial for endometriosis prevalence limitation. Full article

Groundwater plays a crucial role in sustaining agriculture and livelihoods in the arid Middle Draa Valley (MDV) of southeastern Morocco. However, increasing groundwater extraction, declining rainfall, and the absence of effective floodwater harvesting systems have led to severe aquifer depletion. This study applies and compares six machine learning (ML) algorithms—decision trees (CART), ensemble methods (random forest, LightGBM, XGBoost), distance-based learning (k-nearest neighbors), and support vector machines—integrating GIS, satellite data, and field observations to delineate zones suitable for groundwater recharge. The results indicate that ensemble tree-based methods yielded the highest predictive accuracy, with LightGBM outperforming the others by achieving an overall accuracy of 0.90. Random forest and XGBoost also demonstrated strong performance, effectively identifying priority areas for artificial recharge, particularly near ephemeral streams. A feature importance analysis revealed that soil permeability, elevation, and stream proximity were the most influential variables in recharge zone delineation. The generated maps provide valuable support for irrigation planning, aquifer conservation, and floodwater management. Overall, the proposed machine learning–geospatial framework offers a robust and transferable approach for mapping groundwater recharge zones (GWRZ) in arid and semi-arid regions, contributing to the achievement of Sustainable Development Goals (SDGs))—notably SDG 6 (Clean Water and Sanitation), by enhancing water-use efficiency and groundwater recharge (Target 6.4), and SDG 13 (Climate Action), by supporting climate-resilient aquifer management. Full article

Precise recognition of individual ovine specimens plays a pivotal role in implementing smart agricultural platforms and optimizing herd management systems. With the development of deep learning technology, sheep face recognition provides an efficient and contactless solution for individual sheep identification. However, with the growth of sheep, their facial features keep changing, which poses challenges for existing sheep face recognition models to maintain accuracy across the dynamic changes in facial features over time, making it difficult to meet practical needs. To address this limitation, we propose the lifelong biometric learning of the sheep face network (LBL-SheepNet), a feature decoupling network designed for continuous adaptation to ovine facial changes, and constructed a dataset of 31,200 images from 55 sheep tracked monthly from 1 to 12 months of age. The LBL-SheepNet model addresses dynamic variations in facial features during sheep growth through a multi-module architectural framework. Firstly, a Squeeze-and-Excitation (SE) module enhances discriminative feature representation through adaptive channel-wise recalibration. Then, a nonlinear feature decoupling module employs a hybrid channel-batch attention mechanism to separate age-related features from identity-specific characteristics. Finally, a correlation analysis module utilizes adversarial learning to suppress age-biased feature interference, ensuring focus on age-invariant identifiers. Experimental results demonstrate that LBL-SheepNet achieves 95.5% identification accuracy and 95.3% average precision on the sheep face dataset. This study introduces a lifelong biometric learning (LBL) mechanism to mitigate recognition accuracy degradation caused by dynamic facial feature variations in growing sheep. By designing a feature decoupling network integrated with adversarial age-invariant learning, the proposed method addresses the performance limitations of existing models in long-term individual identification. Full article