Search Results (8,351)

Micro(nano)plastics are important emerging contaminants and a current research hotspot in the environmental field. Micro(nano)plastics widely exist in various organic wastes such as waste sludge, food waste (FW) and livestock manure and often enter into digesters along with anaerobic digestion (AD) treatment of these wastes, thereby exerting extensive and profound influences on anaerobic process performance. This study reviews sources of micro(nano)plastics and their pathways entering the anaerobic system and summarizes the quantities, sizes, shapes and micromorphology of various micro(nano)plastics in waste sludge, FW, livestock manure, yard waste and municipal solid waste. The current advances on the effects of multiple micro(nano)plastics mainly polyvinyl chloride (PVC), polystyrene (PS) and polyethylene (PE) with different sizes and quantities (or concentrations) on AD of organic wastes in terms of methane production, organic acid degradation and process stability are comprehensively overviewed and mechanisms of micro(nano)plastics affecting AD involved in microbial cells, key enzymes, microbial communities and antibiotic resistance genes are analyzed. Meanwhile, coupling effects of micro(nano)plastics with some typical pollutants such as antibiotics and heavy metals on AD are also reviewed. Due to the extreme complexity of the anaerobic system, current research still lacks full understanding concerning composite influences of different types, sizes and concentrations of micro(nano)plastics on AD under various operating modes. Future research should focus on elucidating mechanisms of micro(nano)plastics affecting organic metabolic pathways and the expression of specific functional genes of microorganisms, exploring the fate and transformation of micro(nano)plastics along waste streams including but not limited to AD, investigating the interaction between micro(nano)plastics and other emerging contaminants (such as perfluorooctanoic acid and perfluorooctane sulphonate) and their coupling effects on anaerobic systems, and developing accurate detection and quantification methods for micro(nano)plastics and technologies for eliminating the negative impacts of micro(nano)plastics on AD. Full article

Accurate extraction of building features from remotely sensed data is essential for supporting research and applications in urban planning, land management, transportation infrastructure development, and disaster monitoring. Despite the prominence of deep learning as the state-of-the-art (SOTA) methodology for building extraction, substantial challenges remain, largely stemming from the diversity of building structures and the complexity of background features. To mitigate these issues, this study introduces DeepSwinLite, a lightweight architecture based on the Swin Transformer, designed to extract building footprints from very high-resolution (VHR) imagery. The model integrates a novel local–global attention module to enhance the interpretation of objects across varying spatial resolutions and facilitate effective information exchange between different feature abstraction levels. It comprises three modules: multi-scale feature aggregation (MSFA), improving recognition across varying object sizes; multi-level feature pyramid (MLFP), fusing detailed and semantic features; and AuxHead, providing auxiliary supervision to stabilize and enhance learning. Experimental evaluations on the Massachusetts and WHU Building Datasets reveal the superior performance of DeepSwinLite architecture when compared to existing SOTA models. On the Massachusetts dataset, the model attained an OA of 92.54% and an IoU of 77.94%, while on the WHU dataset, it achieved an OA of 98.32% and an IoU of 92.02%. Following the correction of errors identified in the Massachusetts ground truth and iterative enhancement, the model’s performance further improved, reaching 94.63% OA and 79.86% IoU. A key advantage of the DeepSwinLite model is its computational efficiency, requiring fewer floating-point operations (FLOPs) and parameters compared to other SOTA models. This efficiency makes the model particularly suitable for deployment in mobile and resource-constrained systems. Full article

Silver thin films are widely utilized in plasmonic, electronic, and catalytic devices due to their excellent conductivity, optical properties, and surface activity. However, the nanostructure and performance of Ag films are highly dependent on deposition parameters, particularly during radio-frequency magnetron sputtering (RF-MS). In this study, we systematically investigate the effects of RF power, sputtering time, and substrate type on the growth behavior, crystallinity, and electrical conductivity of Ag films. Optical emission spectroscopy (OES) and Langmuir probe diagnostics were employed to analyze the plasma environment, revealing the evolution of electron temperature and plasma density with varying RF powers. Structural characterizations using XRD, SEM, and AFM demonstrate that higher RF power results in reduced grain size, increased film density, and improved crystallinity, while deposition time influences film thickness and grain coalescence. Substrate material also plays a key role, with Cu substrates promoting better crystallinity due to improved lattice matching. Electrical measurements show that denser films with larger grains exhibit lower sheet resistance. These findings provide a comprehensive understanding of the plasma–film interplay and offer strategic insights for optimizing silver nanofilms in high-performance optoelectronic and catalytic systems. Full article

Background: Venous blood collection in pediatric patients is a critical procedure for diagnostic and monitoring purposes, yet it remains considerably more challenging than in adults. Factors such as small vein size, limited cooperation, and heightened sensitivity to pain contribute to technical difficulties and increased error rates. Objectives: This mini-review aims to provide a concise synthesis of current clinical practices and emerging technologies that support safer, more efficient venipuncture in children. Results: Key findings include the anatomical and procedural considerations relevant to pediatric venipuncture, age-specific recommendations for technique and positioning, as well as evidence-based strategies to reduce pain and anxiety. Common preanalytical errors, particularly hemolysis and insufficient sample volumes, are also addressed, along with their implications for clinical outcomes. Recent advances in medical digitalization, including the use of venous ultrasound, near-infrared projection, and transillumination, offer valuable support in overcoming procedural challenges. These technologies are not meant to replace human expertise but to complement it, improving vein visualization and increasing first-attempt success rates when integrated into a child-centered approach. Conclusions: Venous blood collection in pediatric patients requires a delicate balance between technical proficiency and human-centered care. Emphasis is placed on the importance of a child-centered approach, combining technical skill with empathy and clear communication. Enhancing the quality and safety of venous sampling in children requires not only training and standardization, but also a deeper understanding of the psychological dimensions involved in pediatric care. Full article

The Mediterranean soft-shell turtle (Trionyx triunguis) is classified as critically endangered by the IUCN. Effective conservation requires a clear understanding of its reproductive strategies and population structure. By combining mitochondrial DNA tandem repeat-region profiling with genome-wide SNP data obtained through 3RADseq, we gained high-resolution insights into the genetic composition and breeding behavior of Mediterranean populations. Our results revealed complex reproductive dynamics, including multiple paternity, sperm storage, and repeated nesting within a single season—strategies that enhance genetic diversity in small, fragmented populations. Using SNP-based kinship inference, we estimated the number of breeding females and identified full and half-sibling groups, offering a robust genomic framework for assessing population size and structure. Genetic similarity patterns highlighted moderate differentiation among Israeli river populations, suggesting some connectivity, while samples from Türkiye were clearly distinct, reflecting long-term geographic and genetic separation. This integrative approach provides a scalable, repeatable tool for long-term monitoring. The combined use of maternal and biparental markers enables detailed tracking of genetic diversity, breeding contributions, and demographic trends—key elements for designing informed, adaptive conservation strategies. Full article

Conversion of burned forestlands into rice croplands is often practised to increase food production. However, this practice can lead to a severe decline in soil quality and functioning. Unfortunately, no research has previously evaluated how and to what extent physico-chemical properties and overall quality of forest soils change when converted to rice paddy fields. This study has evaluated the changes in key soil properties and Soil Quality Index (SQI) when burned forests are converted to rice croplands in Guilan Province (Northern Iran). This conversion results in noticeable worsening of soil structure (shown by the decreases in size and stability of macro-aggregates, ~50%) and reductions in organic matter (−30%) and nutrient contents (−43% of TN and −49% of P) of soil in rice paddy fields in comparison to burned forest soils. In contrast, soil salinity increased by 180% and potassium by 12%, while pH remained unchanged between forestland and rice fields. The calculation of the SQI showed that the overall quality of the soil was severely affected by this change. The main message of this study is that replacement of forest ecosystems with rice croplands should be carefully controlled, in order to avoid noticeable impacts on soil properties and theiroverall quality. In sites where this conversion has occurred, sustainable land management practices, such as moderate supply of organic amendments and fertilisers, should be implemented to mitigate soil degradation. Full article

With the advancement of China’s “dual-carbon” strategy, optimizing the thermal performance of small-to-medium-sized sports buildings—key contributors to urban energy consumption and carbon emissions—has become a critical area of green building research. This study conducts a systematic literature review following the PRISMA framework, analyzing 96 high-relevance articles sourced from Web of Science, ScienceDirect, and CNKI. The review focuses on four key dimensions: building morphology, envelope thermal performance, eco-friendly material application, and thermal comfort strategies. Findings indicate that building geometry significantly influences natural ventilation and solar gain; optimizing the envelope system can enhance energy efficiency by 12–18%; and incorporating sustainable materials contributes to lifecycle carbon reduction. Furthermore, effective thermal comfort regulation requires the integration of climate-responsive strategies with intelligent control systems. The growing use of AI-assisted technologies—such as fuzzy logic, reinforcement learning, and real-time environmental feedback—is facilitating a shift from single-dimensional energy-saving approaches to multidimensional coupled optimization. This review establishes a comprehensive theoretical and practical framework for low-carbon design in small-to-medium sports buildings and highlights the urgent need for empirical validation and integrated design approaches across diverse climate zones. Full article

A barrier pillar between the surface and underground mining sections provides a critical buffer zone in the transition from the boxcut highwall to underground sections by isolating stress fields from underground sections and preventing them from affecting the boxcut highwall slope. In this study, an empirical scaled span method and Rocscience RS2 software were used to conduct parametric studies on key parameters for designing barrier pillars and analyzing the room and pillar design for a planned underground mine on the Great Dyke, Zimbabwe. The approach included analyzing the effect of barrier pillar width, assuming a 10° dipping angle of the orebody, with room and pillar dimensions of 7 m and 6 m, respectively. The impact on boxcut slope stability and the roof of the first stope was monitored. The stability of the barrier pillar was analyzed for varying widths (6 m, 10 m, 20 m, 30 m, and 40 m) and orebody dipping angles (0°, 10°, 20°, 30°, and 40°). The effect of deteriorated rock mass conditions, represented by Geological Strength Index (GSI) values from 30 to 50, was assessed. The optimum room and pillar design was evaluated against the planned 6 m pillar sizes. This comprehensive study aims to support the integrity and longevity of the critical structures of the mining operation. Full article

Our previous studies identified the Gcp/TsaD protein as essential for Staphylococcus aureus survival and implicated it in tRNA modification. Here, we demonstrate its broader role in bacterial physiology. Through a morphological analysis, RNA sequencing, network-based bioinformatics, and antibiotic susceptibility testing, we show that Gcp/TsaD influences cell morphology, cell wall integrity, transcriptional regulation, virulence, and antibiotic response. Gcp/TsaD depletion caused reduced cell size and increased cell wall thickness, suggesting its roles in cell division and peptidoglycan biosynthesis. The kinetic transcriptomic analysis revealed widespread changes in gene expression, particularly in the translation and amino acid biosynthesis pathways, supporting its function in maintaining translational fidelity via tRNA modification. Its depletion also upregulated the genes involved in cell envelope biosynthesis, including capsule formation, enhancing resistance to antimicrobial peptides, while downregulating the key virulence genes, indicating a role in pathogenicity. Functionally, the Gcp/TsaD-deficient cells were more susceptible to fosfomycin, reinforcing its importance in cell wall integrity. Together, these findings highlight the multifaceted contribution of Gcp/TsaD to S. aureus physiology and underscore its potential as a therapeutic target, particularly against antibiotic-resistant strains. Full article

Streptococcus pharyngitis (strep throat) poses a significant health challenge in rural and remote Indigenous communities in Australia, where access to medical resources is limited. Delays in diagnosis and treatment increase the risk of serious complications, including acute rheumatic fever and rheumatic heart disease. This paper presents a proof-of-concept AI-based diagnostic model designed to support clinicians in underserved communities. The model combines a lightweight Swin Transformer–based image classifier with a BLIP-2-based explainable image annotation system. The classifier predicts strep throat from throat images, while the explainable model enhances transparency by identifying key clinical features such as tonsillar swelling, erythema, and exudate, with synthetic labels generated using GPT-4o-mini. The classifier achieves 97.1% accuracy and an ROC-AUC of 0.993, with an inference time of 13.8 ms and a model size of 28 million parameters; these results demonstrate suitability for deployment in resource-constrained settings. As a proof-of-concept, this work illustrates the potential of AI-assisted diagnostics to improve healthcare access and could benefit similar research efforts that support clinical decision-making in remote and underserved regions. Full article

The fw2.2 (fruit weight 2.2) gene negatively regulates cell division and significantly influences fruit size, but its regulatory mechanisms in pears remain unclear. Here, we investigated how pear FWL (fw2.2-like) genes control cell division using Duli pear, Korla fragrant pear, and Yali pear. During the cell division phase, fluorescence in situ hybridization (FISH) revealed stronger expression of FWL1 and FWL5 in smaller fruits compared to larger ones, with both genes localized in the core and flesh tissues. Gene silencing experiments demonstrated that silencing of FWL5 leads to a significant increase in the number of cells, with a concomitant enlargement of the fruit. Yeast two-hybrid screening identified 147 proteins interacting with FWL5, showing substantial overlap with FWL1 interactors. Key candidates included metallothionein-like protein (MT) and Ole-e-1, with the latter displaying a positive correlation with fruit size during cell division. Bimolecular fluorescence complementation (BiFC) confirmed direct interactions between Ole-e-1 and both FWL1/FWL5. Functional analysis indicated the Ole-e-1 gene family has diverse roles in pear development. We propose that Ole-e-1 interacts with FWL genes to modulate cell division, thereby determining final fruit size. This study uncovers a novel regulatory axis linking cell cycle control and fruit size in pears. Full article

In Ecuador’s financial system, private banks and savings and credit cooperatives coexist, both playing a key role in financial intermediation and the economic inclusion of traditionally underserved sectors. During the COVID-19 pandemic, these institutions faced unprecedented challenges that tested their adaptability and operational efficiency. In this context, the present study evaluates the technical efficiency of banks and cooperatives in Ecuador over the 2015–2023 period, using a combined approach involving Data Envelopment Analysis (DEA) and mixed linear models (MLMs). A longitudinal and comparative methodology is adopted, allowing for the analysis of efficiency trends over time and the identification of their main structural determinants. The results show that cooperatives exhibit a higher average technical efficiency than banks, as well as greater resilience during the health crisis. The analysis reveals that operating expenses negatively impact efficiency, while equity and social capital show no significant effects. By combining DEA and MLMs, the study offers a more comprehensive and nuanced understanding of the factors influencing efficiency, underscoring the importance of tailored policies and institutional strategies focused on resource optimization and continuous improvement. The study concludes that efficiency does not rely solely on size or asset volume, but rather on managerial capacity and organizational adaptability in complex and changing environments. Full article

Scientific creativity is a crucial indicator of adolescents’ potential in science and technology, and its automated evaluation plays a vital role in the early identification of innovative talent. To address challenges such as limited sample sizes, high annotation costs, and modality heterogeneity, this study proposes a multimodal assessment method that integrates data augmentation and pseudo-labeling techniques. For the first time, a joint enhancement approach is introduced that combines textual and visual data with a pseudo-labeling strategy to accommodate the characteristics of text–image integration in elementary students’ cognitive expressions. Specifically, SMOTE is employed to expand questionnaire data, EDA is used to enhance hand-drawn text–image data, and text–image semantic alignment is applied to improve sample quality. Additionally, a confidence-driven pseudo-labeling mechanism is incorporated to optimize the use of unlabeled data. Finally, multiple machine learning models are integrated to predict scientific creativity. The results demonstrate the following: 1. Data augmentation significantly increases sample diversity, and the highest accuracy of information alignment was achieved when text and images were matched. 2. The combination of data augmentation and pseudo-labeling mechanisms improves model robustness and generalization. 3. Family environment, parental education, and curiosity are key factors influencing scientific creativity. This study offers a cost-effective and efficient approach for assessing scientific creativity in elementary students and provides practical guidance for fostering their innovative potential. Full article

Liming is an essential practice for neutralizing soil acidity, influenced by factors like lime particle size and application rate, addressing challenges from climate change, acid rain, nitrate leaching, and mineral oxidation. This study evaluated the efficiency of fine (0.1 mm) and coarse lime (1–2 mm) applied at rates of 3 t/ha and 6 t/ha in mitigating soil acidity, with a particular focus on their impact on subsoil characteristics. Over two years, key soil parameters were monitored, including pH, cation exchange capacity (CEC), and exchangeable base cations (Ca²⁺, Mg²⁺, K⁺), along with exchangeable aluminum (Al³⁺). Fine lime particles demonstrated superior effectiveness compared to coarser ones, leading to faster and more uniform pH increases due to their greater surface area and higher solubility. Lime application significantly improved CEC by reducing exchangeable aluminum and increasing calcium availability, particularly in the topsoil. While these effects were most pronounced in surface layers, aluminum toxicity remained a concern in deeper soil levels. Strong positive correlations were observed between lime application and soil parameters such as pH, CEC, and exchangeable cations, while aluminum showed a negative correlation. Principal component analysis confirmed the benefits of higher lime doses, with fine lime producing rapid improvements and coarse lime offering a slower but sustained effect on soil health. Full article

Accurate malware family classification from dynamic sandbox reports continues to be a fundamental cybersecurity challenge. Most prior works depend on random splits that tend to overestimate accuracy, whereas deployment requires robustness under temporal drift as well as changing behaviors. We present a leakage-aware pipeline that transforms CAPEv2 sandbox JSON reports into structured visual heatmaps and evaluate models under stratified and chronological splits. The pipeline rigorously flattens behavioral keys, builds normalized representations, and benchmarks Random Forest, MLP, CNN64, HybridNet, and a modern ResNeXt-50 backbone. On the Avast–CTU CAPEv2 dataset containing ten malware families, Random Forest achieves nearly state-of-the-art accuracy (97.2% accuracy, 0.993 AUC) with high efficiency on CPUs, making it attractive for triage. ResNeXt-50 achieves the best overall performance (98.4% accuracy, 0.998 AUC) and provides visual interpretability via Grad-CAM, enabling analysts to verify predictions. We further quantify efficiency trade-offs (inference throughput and GPU memory) and report ablation studies on vocabulary size and keyset choices. These results affirm that though ensemble methods are still robust, heatmap-based CNNs provide better accuracy, interpretability, and robustness against drift. Full article