Search Results (15,475)

Motor vehicle exhaust in urban tunnels can cause nitric oxide (NO) to accumulate, severely degrading air quality both inside the tunnel and in the surrounding environment. Photocatalytic technology is an efficient, secondary-pollution-free approach with clear potential for treating tunnel exhaust; however, parametric analyses for practical tunnel engineering applications remain limited. Using computational fluid dynamics (CFD), this study developed a numerical model to simulate photocatalytic NO degradation in a congested tunnel and examined how the surface reaction rate, coating extent, and longitudinal coated section affect NO reduction performance. The results show that NO reduction efficiency increased with the surface reaction rate; however, once the surface reaction rate constant exceeded 2.11 × 10⁻⁴ m/s, further gains diminished and the efficiency approached a plateau due to mass-transfer limitations. With respect to the coating extent, full four-wall coating (sidewalls, ceiling, and road surface) provided the best performance, followed by three-wall coating (excluding the ceiling). Moreover, because the road surface lies in a region of high pollutant concentration and low air velocity, coating on the road surface achieved a markedly stronger reduction effect than coating on the sidewalls or the ceiling. In the simulated 500 m tunnel, the downstream coated section achieved a markedly higher NO reduction efficiency in the ambient environment outside the tunnel (5.9%) than the upstream coated section (1.0%), approaching that of the full-length (500 m) coated section (6.6%). Therefore, in practical engineering applications, priority should be given to coating strategies targeting the downstream section and the road surface in order to balance NO reduction performance and economic cost. Such a strategy is beneficial not only for improving tunnel air quality, but also for promoting sustainable pavement and tunnel-surface engineering by reducing unnecessary coating area and enabling a more resource-efficient and cost-effective use of photocatalytic materials. These findings provide theoretical and methodological support for the sustainable design and application of photocatalytic coating systems in urban tunnels. Full article

Bacterial stem and root rot (BSRR), caused by Dickeya dadantii, poses a severe threat to global sweetpotato production, yet the genetic architecture underlying resistance remains elusive. To dissect these mechanisms, we conducted a high-resolution genome-wide association study (GWAS) on 135 diverse accessions, integrating two-year field phenotyping with best linear unbiased prediction (BLUP) and 6.8 million single-nucleotide polymorphism (SNP) markers. This approach mapped nine quantitative trait loci (QTLs) exhibiting significant allelic dosage-dependent effects, with the major locus, qBSRR.6.1 was the primary discriminator between resistant and susceptible genotypes. Crucially, transcriptomic profiling within these loci revealed distinct expression patterns: IbTCP5 and IbERF003 (located in qBSRR.5.1 and qBSRR.6.2) were highly expressed in the susceptible cultivar ‘Xinxiang’ but suppressed in the resistant ‘Guangshu87’. Furthermore, BSRR challenge identified IbPUB4, IbKCS5, and IbLig1 as priority candidate genes involved in defense, with expression patterns suggesting roles in ubiquitin-mediated protein turnover, cuticular wax biosynthesis, and DNA repair, respectively. In stark contrast, IbPUB25 was constitutively upregulated in ‘Xinxiang’, potentially acting as a negative regulator of immunity via degradation of target proteins. These findings elucidate the polygenic, dosage-sensitive nature of BSRR resistance and prioritize specific targets for future functional characterization. Pyramiding favorable alleles of positive candidates while silencing potential negative regulators like IbPUB25 offers a promising avenue for developing durable, high-resistance sweetpotato varieties. Full article

Background/Objectives: Motor competence is a key indicator of children’s developmental readiness and an important component of health and well-being education. It is conceptualized as a latent construct shaped by both individual and contextual factors. The objective of this study was to examine the influence of sex, age and class context on motor competence, with particular emphasis on skill-specific and contextual variability. Methods: Motor competence was assessed in 312 Greek primary school children aged 6–12 years (156 girls) using the Movement Assessment Battery for Children–Second Edition. Standard scores for manual dexterity, aiming–catching, and balance were analyzed using a multilevel modeling approach. Results: Balance showed the highest standard scores, while manual dexterity was the lowest-performing domain. Boys outperformed girls in aiming–catching, with a modest effect. Age effects were domain-specific, with relative age within the classroom negatively associated with manual dexterity but not with other domains. Class-level factors explained substantial variance, indicating heterogeneity across classes. Conclusions: Motor competence in primary school children is strongly domain-specific and meaningfully associated with classroom context. Manual dexterity emerges as a potential priority for curriculum development, and age-related effects appear to operate selectively across domains. Full article

Vulnerabilities in conventional agri-food supply chains (CAFSCs) necessitate a shift toward resilient, localized production models. Within the Agri-Food 4.0 landscape, urban Controlled-Environment Hydroponic Agriculture (CEHA) systems address these challenges by shortening supply chains and mitigating climate-induced breakdowns. However, structurally aligning Triple Bottom Line (TBL)-oriented stakeholder needs with complex technical specifications remains a critical challenge in sustainable CEHA system design. To address this challenge, the present study proposes a novel framework integrating the Full Consistency Method (FUCOM) and Quality Function Deployment (QFD) within a Finite Interval Type-2 (FIT2) Gaussian fuzzy environment. This approach systematically translates TBL-oriented priorities into precise engineering specifications, mapping 17 stakeholder needs (SNs) to 30 technical design requirements (TDRs) while capturing linguistic uncertainty and hesitation. The findings reveal a clear strategic focus on environmental and social sustainability. Specifically, high product quality, food safety and traceability, consumer acceptance, and minimization of environmental impacts emerge as the primary drivers of CEHA adoption. The QFD translation identifies scalable IoT infrastructure, sensor maintenance and calibration, and AI-enabled decision support as the most critical TDRs. The framework’s reliability and structural robustness were rigorously validated through comprehensive analyses, including Kendall’s W test to confirm expert consensus, alongside a Leave-One-Out (LOO) approach, weight perturbations, and a structural evaluation of TDR intercorrelations. These findings provide a scientifically grounded roadmap for designing sustainable, intelligent urban agricultural systems. Ultimately, this framework offers actionable managerial implications for agribusiness stakeholders to bridge strategic TBL-oriented goals with practical engineering, significantly enhancing Agri-Food 4.0 supply chain resilience. Full article

The occurrence of Escherichia coli resistant to high-priority critically important antimicrobials (HPCIA) in the food chain is a growing concern for food safety and public health. This study aimed to evaluate whether HPCIA-resistant E. coli isolated from pork and chicken meat at retail markets in La Plata, Buenos Aires, Argentina, exhibit source-associated genomic differentiation through whole-genome sequencing. The isolates displayed a polyclonal population structure, encompassing multiple phylogenetic groups and sequence types. Virulence gene profiles were highly diverse, with chicken-derived isolates harbouring a substantially higher number of virulence genes than pork isolates. Notably, one pork isolate carried a complete set of virulence genes characteristic of diarrheagenic E. coli. Single Nucleotide Polymorphism-based phylogenetic analysis revealed several closely related subclusters, including strains recovered from both pork and chicken meat from the same retail markets, suggesting recent clonal sharing or cross-contamination at the point of sale. These findings highlight the circulation of genetically diverse HPCIA-resistant E. coli in retail meat, underscoring the potential public health risk and the importance of monitoring resistance and virulence determinants throughout the food production chain. Full article

Timely classification of cardiovascular diseases is crucial to improve medical outcomes. Emerging remote patient monitoring systems help achieve this by enabling continuous monitoring of electrocardiogram signals in home environments. However, these systems struggle with unique challenges like missing genuine medical emergencies, rising energy demands, scalability challenges, handling vast medical databases, data processing delays, and safeguarding patient records. To overcome these challenges, we propose a single framework with three main phases: (a) an embedded hardware-driven K-Nearest Neighbor (KNN)-assisted real-time ECG monitoring and classification method; (b) a differentiated communication strategy (DCS) formed with a priority-based ECG data packaging framework and multi-layered security protocols; and (c) a multi-level blockchain network (MLBN) architecture armed with adaptive security mechanisms and real-time cross-chain medical data communication bridges. Simulations are conducted using the ECG signals (1000 fragments) dataset and the Ganache Ethereum development framework. The classification accuracies obtained for patient urgent categories U1 to U5 are 91.43%, 95.71%, 94.23%, 90.00%, and 91.43%, respectively. The performance evaluation results of the KNN-guided classification method, along with DCS and MLBN simulation results obtained from average gas consumption analysis, confirms reliability and viability of our framework, while also revolutionizing remote patient monitoring technology and addressing critical challenges in existing systems. Full article

Vegetation carbon stock is a key component of the terrestrial carbon cycle and supports climate-change mitigation and carbon-neutrality strategies. While field inventories provide accurate references, they are constrained by cost and limited scalability, motivating the rapid adoption of remote sensing for large-scale spatial estimation and mapping. However, the literature lacks a consolidated bibliometric and critical synthesis focused on above-ground vegetation carbon stock estimation. Therefore, this review aims to provide a quantitative overview of publication trends, synthesise methodological developments, and identify key research gaps in remote-sensing-based above-ground vegetation carbon stock estimation. A total of 1825 Web of Science records (2015–2024) were retrieved, of which 763 were included for bibliometric mapping using VOSviewer version 1.6.20 and CiteSpace version 6.3.R2, complemented by a critical review of 32 high-quality studies. Results indicate a shift from passive optical and single-index approaches toward active sensing and multi-sensor, multi-platform integration, alongside broad uptake of machine learning and an emerging dominance of deep learning for nonlinear modelling and feature learning. Research attention is expanding beyond forests to non-forest ecosystems, yet challenges persist in spatial resolution, validation data availability, and cross-biome generalizability. This review summarizes methodological trajectories and identifies priorities for robust, transferable above-ground carbon estimation. Full article

Aspergillus fumigatus is a leading cause of invasive fungal disease in humans and is classified as a critical priority threat by the World Health Organization. Triazole antifungals remain the cornerstone of therapy, yet their effectiveness is steadily being eroded by the continuous rise in drug resistance. Most resistance mechanisms trace back to mutations in Cyp51A, spawning well-defined genotypes such as TR₃₄/L98H and TR₄₆/Y121F/T289A. However, the Cyp51A genotype–phenotype landscape in A. fumigatus is far from straightforward. Isolates that share an identical TR genotype can display strikingly divergent susceptibility profiles, and mutational hotspots in Cyp51A, such as G54, M220 and G448, are linked to varying resistances, challenging assumptions about predictable resistance behavior. Complicating matters further, an expanding array of resistance mechanisms, independent of Cyp51A, is now being uncovered. This review summarizes the current state of knowledge on azole resistance in A. fumigatus, dissecting the intricate genotype–phenotype relationships, spotlighting emerging non-Cyp51A pathways and outlining future strategies to enhance the detection and clinical management of antifungal resistance. Full article

Despite recent advances in polycyclic aromatic hydrocarbon (PAH) research on the Tibetan Plateau (TP), studies investigating the transport potential and accumulation dynamics of these contaminants in the Hengduan Mountains, especially in forest soils which are important sinks for atmospheric PAHs, remain scarce. In the present study, soil and lichen samples (partially located under the forest canopy) were concurrently collected from 62 sampling sites across the Hengduan Mountains to characterize the occurrence, spatial distribution patterns, and underlying controlling factors of PAHs. The total concentrations of the 16 US EPA priority PAHs (∑₁₆PAHs) in soils and lichens ranged from 59.8 to 1163 ng/g and 174 to 3362 ng/g, respectively—values consistently higher than those reported in corresponding matrices from the northern and northwestern TP. Further, concentrations of PAHs in both soil and lichen under the forest canopy are significantly higher than those on the leeward slope without forest. Compositional fractionation of PAHs along the longitudinal and latitudinal gradients of sampling locations indicates significant modulation of PAH distribution by both the Indian monsoon and East Asian monsoon, a pattern further corroborated by air mass backward trajectory analysis. Our results confirm that PAHs can be transported to the southeastern TP slope via long-range atmospheric transport (LRAT). Notably, the combined effects of mountain cold-trapping and forest filtering jointly govern the deposition and spatial distribution of PAHs in this region. Full article

This study proposes a scalable, AI-driven, and Geographic Information System (GIS)-integrated framework for intelligent route-level classification in large-scale airport transportation networks to support airport operations, logistics planning, and network-level decision-making. The framework addresses the need for practical artificial intelligence applications that combine spatial network analysis with supervised machine learning to improve route assessment and resource allocation in complex air transport systems. A structured dataset was developed using operational and traffic-related attributes, including route distance, aircraft capacity, weekly frequency, annual passenger volume, demand variability, and route performance indicators, with additional normalized features to improve data representation. A Gradient Boosting ensemble classifier was trained to categorize routes into high-, medium-, and low-priority classes. The model achieved strong predictive performance, with a testing area under the ROC curve of 0.961, accuracy of 0.922, F1-score of 0.915, precision of 0.918, and a recall of 0.922. Feature importance analysis identified demand variability and route-density indicators as the main drivers of classification, enhancing interpretability and practical trust. The proposed framework demonstrates the real-world potential of AI for scalable, explainable, and efficient decision support in airport logistics and transportation network management. Full article

Over the past years, burn care has evolved from a discipline focused on survival to one centered on restoring long-term health, function, and quality of life. Significant advances in critical care, early excision and grafting, infection control, and metabolic support have transformed survival outcomes for even the most severe injuries. As a result, the field now faces a new frontier: understanding and managing the long-term physical, psychological, and systemic sequelae of survival. This review traces the evolution of burn care over the last century and outlines the challenges and priorities for the next 25 years. The first era of progress, defined by innovations in resuscitation, surgery, and critical care, has given rise to a growing cohort of long-term survivors. Research over the past decade has revealed that major burns induce chronic multisystem alterations, including metabolic, cardiovascular, neurocognitive, and immunological dysfunctions. Emerging concepts such as burn-associated heart failure exemplify this shift from acute to chronic disease understanding. Looking ahead, the future of burn medicine lies in personalized and lifelong care, supported by translational research, digital health, regenerative therapies, and interdisciplinary collaboration. Overall, burn care stands at a pivotal crossroads. By integrating precision medicine, rehabilitation science, and psychosocial care, we aim to move the field from survival toward sustained, holistic recovery over the next 25 years. Full article

Indonesia has identified clean hydrogen as one of the strategic initiatives for its energy transition, recognizing its potential as an energy carrier that can support the achievement of net zero emissions. To deepen the understanding of this emerging technology, this study assesses the readiness of green hydrogen development in Indonesia through a multi-stakeholder perspective combined with a technology readiness evaluation and insights from global developments. Based on stakeholder interviews across government, industry, academia, and energy institutions, this analysis identifies key enabling conditions and barriers for hydrogen deployment in the Indonesian context. This analysis indicates that the readiness level of green hydrogen technology in Indonesia has reached approximately technology readiness level (TRL) 5–TRL 6, suggesting that most initiatives remain at the pilot and demonstration stages. In addition, seven key factors influencing green hydrogen adoption were identified: infrastructure and technology, policy and regulation, finance, application sectors, public acceptance, standardization, and private sector participation. These results provide policy-relevant insights for accelerating hydrogen development and highlight priority areas for advancing Indonesia’s transition toward a low-carbon energy system. Full article

Soil erosion assessment in Southeast Europe’s mountainous regions often lacks systematic field validation, limiting confidence in model-based predictions. This study integrates the Erosion Potential Model (EPM) with remote sensing and field verification across 26,570 km² in the Federation of Bosnia and Herzegovina (FBiH) and Brčko District (BD). We developed a two-stage framework: initial GIS-based assessment using digital elevation models, soil maps, climate data, CORINE Land Cover, and Landsat imagery, followed by field calibration at 190 representative sites. Spectral indices (NDVI, BSI) provided dynamic corrections for vegetation cover and visible erosion features. Field validation significantly improved model performance; the erosion coefficient increased from Z = 0.21 to Z = 0.24, while discriminatory power improved AUC from 0.82 to 0.85, with corresponding gains in overall accuracy from 0.78 to 0.84 and F1-score from 0.78 to 0.85. The field-validated model estimated mean annual sediment production of 546.60 m³·km⁻²·year⁻¹, with total erosion material production of 14,074,940.2 m³·year⁻¹. Field calibration revealed substantial spatial redistribution, with medium-to-excessive erosion categories expanding by 30.37%, affecting 1319.12 km² requiring priority intervention. The Kappa coefficient (0.81) confirms high classification reliability. This field-validated framework enables evidence-based identification of degradation hotspots and provides actionable guidance for soil conservation planning in geomorphologically heterogeneous, data-limited regions. Full article

Background/Objectives: Developing novel strategies to combat respiratory infections caused by multidrug-resistant “priority pathogens” like the ESKAPEE Pseudomonas aeruginosa and Staphylococcus aureus is an urgent priority. Methods: We investigated two shortened variants of the proline-rich antimicrobial peptide (PrAMP) B7-005, B7-006 (15-mer) and B7-007 (13-mer). Evaluation included MIC assays against laboratory and clinical multidrug-resistant isolates, mechanistic studies of membrane permeabilization, cytotoxicity testing on BEAS-2B bronchial epithelial cells, and proteolytic stability assays in human elastase and sputum. Results: Despite their reduced size, lower positive charge, and decreased proline content, both variants retained full antimicrobial activity against clinical pathogens with consistent MIC values ≤ 25 µM. These variants exhibit membrane permeabilization in P. aeruginosa but may also relay on a hybrid mode of action involving also intracellular targets. Notably, B7-006 and B7-007 displayed low cytotoxicity compared to the lytic peptide BMAP-18. While B7-007 showed greater susceptibility to proteolytic degradation than its parent B7-005, it preserved partial integrity during the initial hours of exposure. Conclusions: Overall, these findings demonstrate that the B7 scaffold tolerates substantial truncation while preserving potency and selectivity, identifying a minimal 13-amino-acid active core. This work provides critical insights into structure–activity relationships and supports the development of compact, mechanistically versatile antimicrobial peptides to address the growing threat of multidrug-resistant respiratory pathogens. Full article

Smart feedlots are increasingly adopting Precision Livestock Farming technologies to enable continuous, individual-animal monitoring and more proactive management in intensive beef production systems. This narrative review synthesises evidence from approximately 350 academic publications, of which 117 are formally cited, complemented by industry deployments and the authors’ experience in smart feedlot system development. We cover enabling digital infrastructure (power, sensing networks, wireless connectivity, and gateways), animal identification and sensing (RFID, automated weighing, wearables, and pen-side sensors), machine vision (RGB, thermal, and multispectral imaging from fixed and mobile platforms), and AI-based analytics and decision support for health, welfare, performance, and environmental management. Across the literature, key components have progressed beyond proof-of-concept toward operation under commercial constraints. Reported outcomes include reduced reliance on routine pen-rider observation and yard handling, earlier triage of emerging morbidity risk and behavioural change, and more standardised welfare auditing. Vision-based methods are repeatedly validated against trained human scorers in both on-farm and abattoir contexts, while automated weighing and image-based liveweight estimation support higher-frequency growth monitoring with low single-digit percentage error in representative studies. Precision feeding and targeted supplementation are associated with improved feed utilisation and reduced resource wastage, although effectiveness and adoption vary across animal classes and production stages. We identify priorities for robust, scalable deployment: resilient communications in harsh environments, appropriate edge–cloud partitioning under intermittent connectivity, and interoperable multi-sensor data fusion to deliver trustworthy alerts and actionable insights. Persistent barriers remain cost, durability, maintenance burden, integration and interoperability, data governance, and workforce capability. Full article