Search Results (29,488)

Pinyon–juniper (PJ) woodlands, one of the most extensive mature and old-growth woodland types in the Western United States, provide critical ecological, cultural, and economic benefits but face increasing threats from climate change, altered disturbance regimes, invasive species, and pests. We developed the PJ Woodland Climate Adaptation Management Menu, a decision support tool designed to guide adaptive, climate-informed management of PJ ecosystems, particularly within the Colorado Plateau ecoregion. The menu was created through an iterative, collaborative process involving literature review, integration of strategies from existing adaptation frameworks, and extensive input from scientists, land managers, and community partners during workshops and focus groups. The menu links specific, evidence-based approaches to each of six broad strategies, including soliciting community input, mitigating disturbance, enhancing and maintaining biodiversity, conserving ecotones, timing actions for optimal outcomes, and accepting climate-driven changes when appropriate. It is intended for use with the Adaptation Workbook to help managers connect local goals and climate vulnerabilities to tailored management tactics. Hypothetical scenarios demonstrate the menu’s application to contrasting PJ woodland conditions, from die-off events to old-growth maintenance. Lessons learned during development underscore the value of early stakeholder engagement, cross-sector collaboration, and balancing diverse ecological objectives. This menu offers a flexible, transferable framework to strengthen climate resilience in PJ woodlands and serves as a model that could improve adaptation planning in other dryland forest ecosystems. Full article

Extreme meteorological phenomena—characterized by gale-force winds, torrential rainfall, and ice-snow accumulation—pose significant threats to the operational safety of high-speed railways, with wind-induced hazards being especially critical. Such events can trigger catastrophic incidents, including train derailments and service disruptions, as evidenced by numerous documented cases worldwide. To bolster the wind resilience of high-speed railway systems, high-precision wind speed prediction has become a cornerstone for ensuring operational safety. This research presents a systematic review of international advancements in railway wind early warning systems, critically evaluating the technical attributes and performance constraints of four primary paradigms: physical numerical models, statistical methods, machine learning algorithms, and hybrid frameworks. Moving beyond a simple taxonomy, this paper delineates the strengths, limitations, and domain-specific applicability of each approach within the high-speed railways context. Furthermore, it assesses the transformative potential of emerging large-scale Artificial Intelligence (AI) meteorological models for wind speed forecasting. A quantitative comparison is provided to facilitate rigorous methodological assessment. The findings reveal four critical technical bottlenecks: (1) low computational efficiency of numerical models; (2) insufficient spatiotemporal resolution of monitoring data; (3) poor generalization of predictive models; and (4) the “black-box” nature and weak interpretability of AI models. To address these, this paper posits that future research should prioritize key technologies including multi-source heterogeneous data fusion, algorithmic optimization, design of intelligent algorithms, probabilistic risk forecasting, and the synergistic integration of AI with numerical weather prediction (NWP). Such advancements will catalyze the development of more robust HSR wind warning systems, ensuring sustained safety and operational efficiency under volatile meteorological conditions. Full article

Background: The coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, remains a significant threat to global health. This continued threat is due to the emergence of new variants, the immune system’s limited ability to respond, and the limited effectiveness of available treatments for all individuals. Therefore, leveraging drug repurposing, a fast and inexpensive way to find other drugs that have already been shown to be safe and efficacious, becomes useful. This study leverages ADMET profiling, network pharmacology, and molecular docking to evaluate the repurposing of Product Nkabinde for COVID-19 treatment. Methods: ADMET analysis involving the bioactive phytochemicals of PN was evaluated for pharmacokinetic appropriateness and drug-likeness. Using topological analysis, a network of protein–protein interactions was built to identify hub genes, and predicted compound targets were intersected with COVID-19-associated genes to find shared targets. Their biological importance was characterized using functional enrichment analysis. The binding affinities of PN phytochemicals against hub proteins and SARS-CoV-2 viral proteases (Mpro and PLpro) were assessed by molecular docking using AutoDock Vina. To confirm docking accuracy, co-crystallized ligands were redocked using Schrodinger 2022-1. The multi-target therapeutic potential of PN in COVID-19 was assessed using this integrative network pharmacology and molecular docking technique. Results: Molecular docking demonstrated that PN phytochemicals displayed robust and persistent binding affinities for both viral and host targets. Oleanolic acid showed the best affinity toward Mpro (−12.9 kcal/mol vs. −8.3 kcal/mol), while quercetin-3-O-β-D-(6′-galloyl)-glucopyranoside showed better binding to PLpro (−8.4 kcal/mol vs. −6.4 kcal/mol). Procyanidin B2 toward HCK (−10.5 vs. −7.9 kcal/mol), diosgenin toward EGFR (−9.4 vs. −8.4 kcal/mol), rutin toward SRC (−10.5 vs. −7.8 kcal/mol), and pimelea factor P2 toward PIK3R1 (−11.0 vs. −8.2 kcal/mol) all showed significantly higher affinities than their corresponding co-crystallized ligands. Furthermore, procyanidin B2 demonstrated consistent binding to STAT1 and STAT3, confirming its role in modulating immune signals. Most of the PN phytochemicals show advantageous pharmacokinetic properties, including elevated anticipated gastrointestinal absorption and adherence to Lipinski’s rule of five, signifying favorable oral bioavailability and drug-like properties. Moreover, PN exhibits a remarkable multi-target binding capacity against both SARS-CoV-2 proteases and key host signaling proteins involved in immune regulation and inflammatory responses, as determined by this integrative network pharmacology and molecular docking investigation. Conclusions: PN’s prospects as a host-directed, antiviral treatment for COVID-19 are demonstrated by its coordinated modulation of the PI3K/AKT, JAK–STAT, SRC-family kinase, EGFR, and SYK pathways. These results necessitate further experimental and clinical validation, providing a solid computational basis for repurposing PN in the treatment of COVID-19. Full article

Background/Objectives: Reducing inappropriate use of antimicrobial agents in healthcare settings is a critical strategy to mitigate the growing threat of antimicrobial resistance. Globally, the highest consumption of antimicrobials in human healthcare originates from antibiotic prescriptions made in General Practice settings. Small group learning has long held a key role in General Practice education, characterized by active participation, common learning goals, and opportunities for reflection. This mode of delivery has been explored as a potential approach to increase appropriate antibiotic prescribing, supported by research indicating that more didactic educational interventions are unlikely to effectively improve physician prescribing behaviours. This systematic review specifically sought to synthesise the evidence on the effectiveness of small group-based, interventions in improving appropriate antibiotic prescribing behaviours in general practice. Methods: A mixed methods systematic review was employed. Studies were eligible if they reported on in-person, small group-based educational interventions to improve antibiotic prescribing among GPs. Full-text screening resulted in 19 eligible studies. Key characteristics, such as study design, intervention content, and outcomes, were extracted. Results: The 19 included studies used single and multi-modal interventions, with 68% focusing on respiratory tract infections. Common topics were patient communication (n = 11) and adherence to prescribing guidelines (n = 8). Most (n = 11) reported positive outcomes like reduced prescribing and were acceptable to GPs. Conclusions: These types of interventions can be effective in increasing the appropriate use of antibiotics in General Practice and are well received by GP participants. However, further research is required on the optimal content delivered in interventions and their associated long-term impact. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a global health threat, with raw milk serving as a potential reservoir for antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs). This study characterized the resistome and mobilome of raw milk from cows (Bos taurus) and water buffalo (Bubalus bubalis) in the Brazilian Amazon, a region where unpasteurized dairy consumption is culturally ingrained. Methods: Using shotgun metagenomic sequencing, we analyzed 32 pooled milk samples from extensive and semi-intensive farms in the Manaus Metropolitan Region. Results: Sequencing yielded over 3.1 million contigs. While cow milk showed a higher prevalence of positive samples (80%), buffalo milk exhibited a significantly higher abundance and diversity of ARG-associated contigs (301 contigs vs. 85 in cows). Clinically relevant genes were identified, including AbaQ, ArnT, and KpnF, alongside complex multi-AMR cassettes co-occurring with plasmids and widespread viral sequences (dominated by Caudoviricetes). Integrons were ubiquitous in cattle and highly prevalent in buffalo samples. Conclusions: These findings indicate that raw milk in the Amazon harbors a rich reservoir of resistance determinants and MGEs, likely driven by farm-level antibiotic usage. This underscores a critical food safety risk and highlights the need for One Health-based surveillance in the region. Full article

The overuse of antibiotics has led to their widespread environmental residues, posing a significant threat to the ecological environment. In this study, a flower-like spherical CoFe-layered double hydroxide (CoFe-LDH) catalyst was prepared using a hydrothermal method. The degradation performance of the CoFe-LDH/peroxymonosulfate (PMS) system was systematically investigated using tetracycline (TC) as a model pollutant. The CoFe-LDH exhibited a three-dimensional nanoflower-like spherical structure formed by interlaced nanosheets, featuring smooth surfaces and well-defined edges. This hierarchical porous structure facilitates the exposure of active sites. The CoFe-LDH/PMS system demonstrated remarkable degradation efficiency, achieving over 90.17% TC removal within 10 min. As the dosage of CoFe-LDH and PMS increases, the degradation rate of TC improves significantly, but the marginal improvement effect decreases. TC degradation efficiency increased with pH up to an optimum at pH 5.0, beyond which it declined. The anions—Cl⁻, $\mathrm{N}{\mathrm{O}}_3^{-}$, and $\mathrm{S}{\mathrm{O}}_4^{2-}$—all exhibited inhibitory effects on TC degradation; the TC removal rates decreased to 77.88%, 80.58%, and 82.78%, respectively. The removal experiments of different organic pollutants, such as oxytetracycline (88.91%), methylene blue (98.36%), and ciprofloxacin (84.52%), as well as actual water experiments, such as lake water (92.48%) and tap water (80.86%), have demonstrated the good universality of the CoFe-LDH/PMS system. Radical quenching experiments confirmed that ^•OH and $\mathrm{S}{\mathrm{O}}_4^{\text{•}-}$ were the dominant reactive species. Full article

Climate change poses a significant threat to water resources and agricultural sustainability, particularly in semi-arid and socio-economically vulnerable regions such as South Africa. This review synthesizes empirical, modelling, and policy-based evidence on the impacts of climate change on water availability, crop production, and adaptation strategies in the country, drawing on approximately 162 peer-reviewed studies and institutional reports published between 2010 and 2025. The findings indicate that rising temperatures, shifting rainfall patterns, and an increasing frequency of extreme events, such as droughts and floods, are intensifying water stress and disrupting agricultural systems. Hydrological models consistently project declines in runoff, soil moisture, and streamflow, while crop simulation models predict reductions in the yields of major staple crops, including maize, wheat, and sorghum, particularly under high-emission scenarios. Although localized improvements in water availability and crop productivity may occur, these tend to be limited and highly context-specific. In response, South Africa has implemented a range of adaptation strategies, including climate-smart agriculture, water-efficient irrigation, ecosystem-based approaches, and policy-driven interventions. However, their effectiveness remains constrained by institutional fragmentation, limited financial capacity, and persistent socio-economic inequalities, particularly among smallholder farmers. The review underscores the need for integrated, inclusive, and context-specific adaptation strategies that strengthen governance, enhance the science–policy interface, and improve access to climate finance. The insights provided offer valuable guidance for advancing climate resilience in South Africa and other vulnerable regions across the Global South. Full article

Soil contamination by complex pesticide mixtures poses a systemic threat to ecosystem health, yet the mechanisms of microbial community assembly under the coexistence of legacy and modern pollutants remain insufficiently understood. This study evaluated the influence of legacy organochlorine pesticides (OCPs) versus current-use agrochemicals on the structure and inferred functional potential of soil bacterial communities in the Almaty Region, Kazakhstan, using high-throughput 16S rRNA gene sequencing and GC–MS/MS analysis of 217 compounds. Results revealed a clear contrast between contamination regimes: modern organophosphate insecticides and herbicides, such as simazine (up to 32.3 mg kg⁻¹ at the Amangeldy site), were associated with lower alpha diversity (Shannon ≈ 3.03) and enrichment of copiotrophic taxa such as Pseudomonas and Sphingobium. In contrast, persistent OCP residues, such as p,p′-DDE (up to 1.43 mg kg⁻¹ at the Kyzylkairat site), were associated with higher diversity (Shannon ≈ 5.46) and enrichment of more stress-tolerant oligotrophic lineages, including Acidobacteria and Vicinamibacteraceae. Procrustes analysis supported significant concordance between pesticide profiles and taxonomic structure (M² = 0.286, p < 0.001), indicating that pesticide residue composition was strongly associated with bacterial community structure across the studied soils. The observed shift in community balance, particularly the relative increase in Pseudomonas versus Acidobacteria, is proposed as a candidate compositional indicator of ecosystem instability in semi-arid agricultural soils and may inform future remediation-oriented studies. Full article

The accumulation of microplastics in aquatic ecosystems poses a significant threat to fish physiology and welfare. This study investigated the impact of exposure to virgin polystyrene microplastics (15 µm) on energy balance and welfare in goldfish (Carassius auratus). Fish were exposed for 14 days, and the effects were assessed through an integrated analysis of behavioral, metabolic, neuroendocrine, and physiological parameters. Microplastic exposure significantly reduces feed intake and feed anticipatory activity, indicating a potent anorexigenic effect. This effect was driven by neuroendocrine disruption, characterized by the downregulation of orexigenic neuropeptides (npy, agrp, hcrt) and the upregulation of anorexigenic signaling (pomca, cartpt, lepa). Simultaneously, exposed fish exhibited increased oxygen consumption, suggesting elevated metabolic demands. These factors converged to impaired growth and reduced hepatosomatic index, suggesting altered energy allocation. Furthermore, microplastic exposure induced anxiety-like responses and increased plasma cortisol levels, confirming the activation of the physiological stress response. Overall, these findings demonstrate that microplastics disrupt energy homeostasis and trigger behavioral shifts that ultimately compromise fish welfare and the biological resilience of aquatic species. Full article

The H5N1 subtype of highly pathogenic avian influenza (HPAI) poses a major zoonotic threat due to its high fatality rate and capacity for cross species transmission. In early 2025, Argentina detected a novel triple reassortant A(H5N1) virus in Chaco Province, combining Eurasian, North American, and South American lineage segments. Genomic analyses of subsequent outbreaks in Buenos Aires and Entre Ríos confirmed persistence of this reassortant and additional HA substitutions (T204K, P251S) potentially linked to increased mammalian receptor affinity. Although PB2 sequences lacked canonical mammalian-adaptive markers (E627K, Q591K, D701N), all contained I292M, a mutation associated with human adaptation. Phylogenetic analyses revealed distinct genotypes and increasing divergence. These findings indicate ongoing viral evolution and adaptation within Argentina, emphasizing the urgent need for sustained genomic surveillance, timely data sharing, and integrated One Health strategies to mitigate zoonotic and socioeconomic risks associated with H5N1 spread in South America. Full article

Modern user authentication systems increasingly need user and device-behavior-aware adaptive mechanisms to detect evolving threats beyond the traditional authentication framework of static credential verification. This paper proposes a hybrid multi-model framework for personalized user-level anomaly detection using a data-driven Hybrid Anomaly Score (HAS). The primary contribution lies in deriving the HAS using the joint integration of three adaptive attributes: dynamically computed per-user deviation thresholds conditioned on individual behavioral history, profile-age-aware baseline weights reflecting user cohort maturity, and criticality-scaled aggregation with the security impact of each detection methodology. The framework is evaluated on a large-scale real-world dataset and demonstrates strong detection performance, while achieving low inference latency suitable for real-time enterprise deployment. The ablation analysis of the framework confirms that dynamic weighting and personalized threshold substantially improve detection stability and convergence with an effective and deployable solution for large-scale authentication environments. Full article

Weed infestation poses a significant threat to bok choy (Brassica rapa subsp. chinensis) cultivation, reducing crop yield and quality through resource competition and pest facilitation. Traditional weed detection methods face two major bottlenecks: one is data annotation, arising from the need for extensive, species-diverse datasets, and the other is visual discrimination, due to the high morphological similarity between crops and weeds at certain growth stages. To address these challenges, this study proposed an indirect weed detection framework that combines an optimized You Only Look Once version 10 (YOLOv10) model for crop detection with Excess Green ExG-based segmentation of residual vegetation. The model incorporates RFD and C2f-WDBB modules to improve feature preservation and multi-scale fusion. Compared with baseline YOLOv10, the final proposed RCW-YOLOv10 reduced the number of parameters by 1.04 million and improved detection performance, achieving increases of 3.5%, 1.5%, and 1.1% percentage points in Precision, Recall, and mAP50, respectively, under field conditions. The system initially detected bok choy plants, subsequently localizing weeds by masking crop regions and thresholding residual ExG signals in the uncovered areas. The detected weed coordinates were used to construct a distribution map that may support targeted control in precision agriculture. This approach simplifies weed identification under the tested bok choy field conditions and may be adaptable to other crops after further validation. Full article

Landslides are widespread geohazards in mountainous regions and pose serious threats to human safety, infrastructure, and ecosystems. Accurate detection from high-resolution optical remote sensing imagery remains challenging because landslide targets often exhibit irregular morphology, large scale variation, weak boundaries, and strong background interference. To address these issues, this study proposes L-SAINet, a shape-adaptive and inner-scale interaction network for landslide detection in complex remote sensing scenarios. Built on a lightweight one-stage detection framework, the proposed method introduces an L-SAI module that integrates adaptive deformable convolution, channel–spatial attention, and inner-scale feature interaction. The shape-adaptive branch improves geometric alignment for irregular and elongated landslide bodies, while the attention branch enhances semantic discrimination under heterogeneous background conditions. The two branches are further fused at the same feature scale to construct a more unified landslide representation. Experiments on the Bijie Landslide Remote Sensing Dataset show that L-SAINet consistently outperforms the baseline detector and single-branch variants in Precision, Recall, mAP@0.5, and mAP@0.5:0.95. Additional analyses based on precision–recall curves, confusion matrices, convergence behavior, model complexity, and representative complex-scene examples further confirm its effectiveness and robustness. The results demonstrate that jointly modeling geometric adaptability and semantic refinement is an effective strategy for landslide detection in complex mountain environments. Full article

The global navigation satellite system (GNSS), the main time synchronization method for phasor measurement units (PMUs) in smart grids, is highly vulnerable to time synchronization attacks (TSAs). This affects the timing of results and poses a serious threat to the safe and stable operation of power systems. To quickly detect TSAs and minimize the impact of time errors on PMU sensor networks, a TSA detection method based on carrier Doppler Pearson correlation coefficient estimation is proposed. This method can be directly implemented on existing commercial receivers without modifications. The method leverages the fact that carrier Doppler shifts in each satellite channel exhibit consistent changes when subjected to a TSA; therefore, if there is a correlation between channels, a consistent change in carrier Doppler shift caused by the TSA can be quickly detected through Pearson correlation coefficient estimation. In the TSA detection experiment, the proposed method was compared against four existing TSA detection methods on a self-developed experimental platform. The experimental results show that compared with the other four methods, the proposed method responds 4–22 s faster and has better detection speed, with more significant changes in the detection statistics. Notably, these advantages become more pronounced as the spoofing speed decreases and the spoofing stealthiness increases, indicating that this method has robust detection capability against sophisticated attacks. Meanwhile, it offers a lightweight computational overhead suitable for embedded PMU implementations, enhancing sensor-layer security in critical infrastructure. This work provides reliable synchronized measurements for power system monitoring and control over a wide area. Full article

semantic communication (SemCom) has emerged as a task-oriented communication paradigm that prioritizes meaning delivery over exact bit recovery. The integration of generative artificial intelligence (GenAI) into SemCom further enables knowledge-guided inference, multimodal reconstruction, and semantic compression through architectures such as large language models, variational autoencoders, generative adversarial networks, and diffusion models. At the same time, this integration introduces new security and privacy risks, including semantic eavesdropping, model inversion, semantic jamming, covert backdoors, prompt manipulation, and knowledge-base leakage, which are not adequately captured by conventional communication security models. In this survey, we provide a security-centric review of GenAI-assisted semantic communication systems by organizing the literature according to threat models, attack surfaces, defence strategies, and semantic modalities across text, image, and multimodal settings. The survey was conducted using IEEE Xplore, ACM Digital Library, SpringerLink, arXiv, and Google Scholar. Approximately 180 papers were initially screened, and 53 representative studies published between 2021 and 2026 were selected for detailed review. Based on this analysis, we classify the major threats into adversarial perturbation, jamming, poisoning and backdoor attacks, privacy leakage and semantic eavesdropping, and generative-model-specific vulnerabilities involving diffusion, large language models, and multimodal foundation models. We further map the corresponding defences, including adversarial training, model ensembling, semantic-aware encryption, diffusion-guided denoising, privacy-preserving representation learning, and secure resource allocation. The survey also identifies persistent open challenges, including the lack of standardized semantic security metrics, unified benchmarks, cross-layer evaluation frameworks, and robust defences for GenAI-native and multimodal semantic communication systems. Overall, this work provides a structured reference for the design of secure, trustworthy, and attack-resilient generative semantic communication systems for future intelligent networks. Full article