Search Results (1,880)

Background/Objectives: Fibromyalgia (FM) is a nociplastic pain syndrome characterized by chronic widespread pain, fatigue, sleep disturbance, cognitive dysfunction, and heightened sensitivity to somatic and environmental stimuli. Depressive symptoms and major depressive disorder (MDD) frequently co-occur with FM and are associated with greater disability, pain catastrophizing, sleep disruption, and poorer treatment response. Although pharmacological treatments can reduce symptoms in some patients, many individuals experience persistent functional and affective burden, highlighting the need for mechanism-based adjunctive strategies. Mindfulness-based interventions (MBIs), particularly mindfulness-based stress reduction (MBSR), have shown promise for improving functional impact, pain catastrophizing, perceived stress, depressive symptoms, sleep-related burden, and quality of life in FM. However, the effects of MBIs on pain intensity and biomarkers are less consistent. Methods: This narrative mechanistic review develops a hypothesis-generating multisystem neurobehavioral framework to explain how MBIs may influence pain–mood coupling in FM with depressive symptom burden. We define pain–mood recalibration as a reduction in the extent to which pain intensity, bodily vigilance, and interoceptive threat appraisal automatically drive depressive symptoms, rumination, avoidance, and functional disengagement. Results: Fibromyalgia-specific evidence most strongly supports cognitive–emotional mechanisms, including reduced catastrophizing, psychological inflexibility, rumination, perceived stress, and avoidance. Interoceptive, neural-network, autonomic, immune-inflammatory, and sleep-fatigue-behavioral pathways remain exploratory and require prospective mediation and moderation testing. Conclusions: Future trials should prioritize active controls, ecological momentary assessment, longitudinal biomarkers, neuroimaging, interoceptive tasks, and formal mediation/moderation models to determine which patients benefit, through which mechanisms, and under what biological and psychological conditions. Full article

Tomato (Solanum lycopersicum L.) is one of the most important solanaceous crops due to its economic and nutritional relevance. It hosts a wide range of plant pathogens, including a recombinant strain of potato virus Y (PVY^C-to), which is considered one of the major threats in tomato cultivation. PVY^C-to, a single-stranded RNA plant virus (family Potyviridae), mainly causes symptoms of mosaic, chlorosis, and vein necrosis in the leaves of horticultural crops, resulting in significant production losses. The ongoing evolution of viral strains, combined with the absence of natural resistance genes in commercial germplasm, requires the exploration of alternative management strategies, such as grafting, and the identification of resistant genetic resources within local biodiversity. This study aims to evaluate the response of five local tomato ecotypes (LTEs) from the Campania region—Corbarino, Giallo determinato da serbo, Pizzutello, San Marzano, and Vesuviano—to PVY^C-to infection. The goal is to identify a naturally tolerant ecotype for use as a rootstock for commercial tomato varieties and expand the pool of resilient genetic resources against viral diseases. Visual symptom evaluation at 14 and 28 days post-inoculation (dpi), quantification of virus accumulation by qDot-blot, and thermal imaging analysis showed that all tested tomato ecotypes were susceptible to PVY^C-to infection, although at different levels among the tested ecotypes. More specifically, Corbarino showed no significant changes in canopy temperature values, fewer symptoms, and reduced virus accumulation upon PVY^C-to infection, compared to the other LTEs, and was therefore selected as a rootstock in subsequent experiments. The reduction in both symptom severity and viral RNA accumulation observed in grafted plants, combined with the mitigation of thermal stress in grafted scions, confirmed the efficacy of grafting as a viable management strategy and eco-friendly cornerstone for safeguarding regional agro-biodiversity against escalating viral pressures. Full article

Industrial water treatment plants are increasingly dependent on cyber–physical systems (CPS) and automated control processes for their operational safety and efficiency. However, the embedding of digital control networks exposes these critical infrastructures to sophisticated cyber–physical attacks, including malicious tampering with chemical dosing units and physical actuators. This paper proposes a robust, AI-enhanced anomaly detection framework designed to identify multi-stage malicious activities in water treatment systems using real-world industrial datasets. The proposed system is developed and validated on the Secure Water Treatment (SWaT) dataset, which contains multivariate sensor and actuator time-series data collected from a fully operational physical testbed under both normal operations and targeted cyber–physical attacks. First, high-frequency sensor noise is filtered, and cross-channel measurement reliability is maximized using a Kalman filter-based sensor fusion module. Subsequently, the fused-state vector is analyzed using an unsupervised Isolation Forest algorithm optimized for high-dimensional boundary isolation. To eliminate false negatives caused by stealthy, low-amplitude data injections that bypass purely statistical models, a deterministic, rule-based verification layer derived from physical process control logic is integrated. By integrating a discrete linear Kalman filter with an unsupervised Isolation Forest and deterministic physical rules, the framework effectively suppresses high-frequency sensor noise, achieving a 67.8% reduction in root mean square error (RMSE), while maintaining high detection accuracy across complex industrial attack scenarios. Experimental results demonstrate that the proposed hybrid framework yields superior detection capability, achieving a Precision of ≈95%, a Recall of ≈93%, a scenario-level F1-score of 94.1 % (alongside a sample-level F1-score of 21.5 %) and an edge inference latency of 0.6 ms, effectively demonstrating its suitability for deployment within simulated real-time industrial edge computing environments. The findings further confirm that combining statistical machine learning, state-space sensor fusion, and invariant physical process logic provides a resilient defense paradigm for securing critical industrial infrastructure against modern cyber–physical threats. Full article

When a water treatment plant, power grid, or pipeline is compromised, the consequences extend beyond data loss: a manipulated sensor reading can trigger physical damage, and a disabled safety interlock can endanger lives. Cyber–physical systems (CPSs) sit at this intersection of digital control and physical process, yet existing security reviews treat threats and defences in separate silos, leaving practitioners without a clear picture of which defences fail against which attacks, and why. This paper fills that gap with a structured narrative review of 82 sources (70 from the primary window January 2020 to April 2026, plus 12 foundational pre-2020 works), organised through the CPS Defence-Gap Taxonomy (CPS-DGT)—a framework that classifies 14 attack mechanisms by architectural layer and physical impact, evaluates six defensive technology categories against documented failure modes, and maps five governance dimensions to the institutional conditions required for deployment. Across five intrusion detection system (IDS) studies that differ in dataset, attack selection, training regime and evaluation scope, reported F1 scores lie between 0.796 and 0.969 under each study’s own standard conditions; these values are not a controlled comparison and are reported descriptively. For the one architecture evaluated under adversarial evasion, F1 falls by 37.4 percentage points in absolute terms, a relative reduction of 38.6%. The defence-gap matrix identifies seven entries with insufficient coverage. Five of the 14 attack mechanisms are uncovered: A03, A09, A10, A11 and A14. Two further mechanisms, A01 and A12, have only partial defences. Adversarial evasion of learned detectors is reported separately as a transversal failure mode of one defensive category rather than as an attack mechanism. The uncovered mechanisms cluster at the cyber–physical boundary and in supply-chain channels. We conclude with five concrete research challenges, each with a direct path from the identified gap to a tractable research agenda. Full article

Wind erosion poses a serious threat to land stability and agricultural sustainability across global arid and semi-arid zones. This study was conducted in Tuoksun County, China’s sole county situated below sea level; its unique low-elevation landform generates distinctive wind–sand movement processes, forming a representative research platform for elucidating shelterbelt functional mechanisms under extreme arid environments. In this work, we systematically monitored wind field characteristics, shelterbelt windbreak performance, and soil wind erodibility and quantitatively analyzed the regulatory effects of different shelterbelt configurations on sand-fixing efficiency and soil anti-wind erosion capacity. The results revealed an obvious decoupling between wind velocity and wind direction frequency within the study area: the maximum wind speed (≈5.5 m s⁻¹) occurred in the NNW direction, whereas the W and WSW directions exhibited the highest wind occurrence frequency. Shelterbelt height showed an extremely significant positive correlation with wind speed reduction efficiency (r = 0.817 ***), while ambient wind speed was significantly negatively correlated with windproof benefit (r = −0.690 ***). Among forest types, F2 and PF achieved significantly higher efficiency and lower wind speeds than F1. Intact shelterbelts reached >85% efficiency, while degraded belts fell below 40%. Soil texture acted as the dominant factor controlling soil erodibility: clay, fine sand, and very fine sand increased soil wind erodibility, while coarse sand and gravel suppressed erodibility. Hierarchical clustering analysis of vertical wind speed profiles confirmed that shelterbelts can substantially reduce near-ground wind velocity; compared with shelterbelt types, spatial position (windward side, leeward side, and central zone) exerted a stronger influence on wind field regulation. This study elucidates the internal correlations among shelterbelt spatial configuration, sand-fixing efficiency, and soil wind erodibility, providing scientific support for the optimization of shelterbelt layout in low-elevation arid ecological regions. Full article

Pseudomonas aeruginosa (PA) is a formidable environmental pathogen. It causes severe and refractory bovine mastitis. The escalating threat of antimicrobial resistance requires new non-antibiotic therapies. These alternative therapies should focus on targeting host-directed responses. Sodium butyrate (SB) is a prominent short-chain fatty acid. It possesses potent immunomodulatory properties. However, its protective mechanisms against PA-induced mammary injury remain elusive. This study investigated the efficacy and underlying molecular mechanisms of SB. bMECs were pretreated with 0.5 mmol/L SB for 18 h prior to challenge with P. aeruginosa (1 × 10⁷ CFU/mL, 6 h). We evaluated its ability to alleviate PA-induced cytotoxicity in bovine mammary epithelial cells (bMECs). Flow cytometry and ELISA demonstrated the strong protective effects of SB. SB pretreatment significantly reduced PA-induced cellular apoptosis. It also suppressed the hypersecretion of pro-inflammatory cytokines, including IL-6 and TNF-α. Next, transcriptomic sequencing (RNA-seq) was performed. We identified 589 differentially expressed genes (DEGs) between the PA-challenged and SB-treated groups. These DEGs were significantly enriched in the Toll-like receptor (Tlr), Mapk, and autophagy signaling pathways. We subsequently conducted molecular validations via RT-qPCR, Western blotting, and immunofluorescence. The results revealed that SB significantly suppressed the overactivation of the TLR4/MAPK cascade. Specifically, SB significantly downregulated the expression of TLR4. It also decreased the downstream phosphorylation levels of p38, ERK, and JNK. Furthermore, PA infection induced a severe blockade of autophagic flux. This dysfunction was evidenced by the concurrent cellular accumulation of LC3-II and the autophagic substrate p62. Remarkably, SB intervention was associated with the reduction in autophagic marker accumulation, evidenced by facilitated lysosomal clearance of p62. Collectively, sodium butyrate protects bMECs against PA-induced inflammation and apoptosis. These protective effects are closely associated with the suppression of the TLR4/MAPK signaling cascade and the alleviation of autophagic marker accumulation. This highlights the potential of SB as a promising preventive strategy for the clinical management of bovine mastitis. Full article

Task offloading in vehicular edge networks must satisfy strict latency limits. At the same time, it must resist security threats such as replay attacks. Existing offloading models treat cryptographic settings as fixed, separate from the scheduling decision. This is a gap, and it exists in part because jointly choosing the best node and the best cryptographic curve for each task is computationally hard. We prove that this joint problem reduces to a generalized assignment problem, a well-known NP-hard problem, once curve-selection variables are fixed. Because of this hardness, we design a polynomial-time greedy heuristic as a practical approximation. Our model treats Elliptic Curve Cryptography (ECC) curve selection as an adaptive decision, and jointly optimizes it together with latency, CPU load, and bandwidth, using one unified scoring function. Results: Task success rate (TSR) with dynamic ECC is within 0.3 percentage points of latency-only scheduling (85.26% vs. 85.58%), while replay success falls from 100% to 8.93%, more than a 10-fold reduction. A single fixed curve that never refreshes (static ECC) performs far worse on both counts (40.29% TSR, 84.54% replay success), since it cannot serve tasks needing stronger security and accumulates staleness without bound. Measured cryptographic overhead is 0.35–1.54 ms per task depending on curve, and the heuristic runs within roughly 2.34% of an exact ILP bound at full scale. These results, from an openly available Python (version 3.9 or later) simulation, confirm real-time feasibility for IoT-enabled vehicular deployments. Full article

Páramo ecosystems are among the most biodiverse and hydrologically critical landscapes on Earth, yet their long-term sustainability is increasingly threatened by climate-driven wildfires. Vegetation Cover Fires (VCFs) in these high-altitude environments degrade carbon stocks, disrupt freshwater regulation, and undermine biodiversity conservation goals central to the UN Sustainable Development Goals (SDGs 13, 15, and 6). Between 2001 and 2023, 128 fire events consumed approximately 815 ha in the Sumapaz locality (the world’s largest continuous páramo), representing 64.9% of all fires recorded across Bogotá’s 20 localities. Despite this disproportionate ecological and social impact, no spatially explicit, operational risk management framework has been available for the region, representing a critical sustainability governance gap. This study addresses that gap by proposing an integrated climate-adaptive risk assessment and management strategy based on (i) the Canadian Forest Fire Danger Rating System Fire Weather Index (FWI), derived from ERA5 reanalysis climate data; (ii) spatial and temporal hotspot analysis of MODIS FIRMS active fire detections; and (iii) IDEAM’s multi-component vulnerability and threat scoring protocol. Spatial data were processed using ArcGIS, and FWI sub-indices were computed for each month of the 2001–2023 period. The FWI averaged 0.78 (low danger) across the study period yet peaked at 13.7 in February 2010 (moderate-to-high danger), consistent with the year of highest recorded fire activity (19 events). High- and very high-risk areas (3.70% combined) coincide with slopes >25%, the presence of the invasive and pyrogenic Ulex europaeus, and proximity to populated and agricultural lands. This study concludes with a three-pillar risk management framework—risk knowledge, risk reduction, and disaster management—providing spatially targeted, operationally viable strategies for local and institutional actors that directly support the sustainable conservation of páramo ecosystem services (water supply, carbon sequestration, biodiversity). The framework is designed to be updatable on a monthly basis using freely available ERA5 data, enabling continuous adaptive governance of wildfire risk as a contribution to long-term territorial sustainability. Limitations regarding MODIS detection uncertainty, ERA5 spatial resolution in complex terrain, and the need for probabilistic modeling are explicitly acknowledged. Full article

Background/Objectives: Epidemic infectious diseases remain a persistent global threat, but whether declining outbreak frequency translates into reduced mortality is unclear. This gap is critical for emergency care systems, where timely intervention determines survival. This study aims to characterize global epidemic trends and to assess whether reductions in outbreak frequency have been accompanied by reductions in epidemic mortality. Methods: We performed a retrospective global analysis of epidemic infectious disease events recorded in the Emergency Events Database (EM-DAT) from 2000 to 2025, following the STROBE reporting guideline. Data on disease type, geographic distribution, deaths, and affected populations were extracted. Completeness of mortality and affected-population reporting was quantified by region and disease type. Crude mortality ratios (CMRs) were compared across regions and pathogens using Kruskal–Wallis tests, and temporal trends were assessed with Mann–Kendall analysis and Sen’s slope estimation. Results: We identified 893 epidemic events across 131 countries, resulting in 125,262 deaths and over 12.7 million affected individuals. Mortality data were available for 773 events (86.6%), with completeness ranging from 92.0% in Africa to 23.5% in Europe. Burden was overwhelmingly concentrated in sub-Saharan Africa, which accounted for 80.2% of deaths. Cholera was the most frequent pathogen, while Ebola showed extreme lethality (CMR 42.71%). Despite a significant decline in annual epidemic frequency (τ = −0.668, p < 0.001), the greater part of which preceded the COVID-19 pandemic, neither annual mortality (τ = −0.237, p = 0.094) nor the annual mortality ratio (τ = −0.114, p = 0.427) showed a commensurate decline. Marked heterogeneity across regions and diseases underscores the dominant role of healthcare system capacity. Conclusions: Global progress in reducing epidemic frequency has not been matched by gains in survival. The persistence of high mortality in regions with constrained emergency care capacity highlights a critical failure in translating outbreak control into lives saved. Strengthening emergency care systems, improving access to timely treatment, and addressing structural inequities are essential. Fewer epidemics are not enough; fewer deaths when they occur is what is needed. Full article

Yield losses due to water-deficit (WD) conditions, especially during the reproductive stages of plant development, pose a significant threat to global canola (Brassica napus L.) production. Therefore, it is critical to investigate traits contributing to improved productivity under increased WD conditions. Here we present phenotypic, physiological and transcriptomic changes in response to WD across contrasting canola accessions exhibiting variation in drought resistance-related traits. WD significantly reduced shoot biomass, plant height, harvest index, leaf water content, photosynthetic CO₂ assimilation rate, intrinsic water-use efficiency and carbon isotope discrimination. WD caused 49 to 100% of the seed yield reduction: the minimum seed yield reduction (49.66%) was observed in a doubled-haploid (DH) line, 06-5101.137, while the maximum yield reduction (94.1 to 100%) occurred in the late-flowering DH lines (06.5101.088 and 06-5101.306). Seed yield showed a positive correlation (r = 0.29 to 0.95) with shoot biomass and harvest index, leaf water content, photosynthetic CO₂ assimilation rate, intrinsic water use efficiency and carbon isotope discrimination. However, it showed negative correlations with days to flower, leaf specific weight, root length, root biomass (r = −0.04 to −0.79) across water treatments. The specific leaf transcriptome analysis of the two parental lines of DH population that exhibit variation for effective water use under well-watered and water-deficient conditions revealed different categories of differentially expressed genes (DEGs): WD-responsive DEGs in BC1329 parental line (1116) and BC9102 (1205) with 754 and 853 DEGs unique to BC1329 and BC9102, respectively, WD-responsive DEGs (906), genotype-dependent DEGs (8465) and genotype × treatment interaction DEGs (353). DEG annotations revealed that the WD-treatment-affected genes were involved in stress responses and growth and development. We further located 235 DEGs within the QTL regions underlying agronomic and physiological performance. Our study provides a conceptual framework for the morphological, physiological and molecular determinants involved in water-use efficiency. Seedlings’ traits with high heritability values, such as shoot biomass, leaf weight, leaf water content and Δ¹³C, serve as proxies for trait-based selection for improved seed yield under both water-limited and non-water-limited conditions. Full article

The Australian construction industry generated approximately 29.2 million metric tonnes (Mt) of construction and demolition waste (C&DW) in 2022–2023, of which about 23.7 Mt (81.2%) was recovered, primarily through recycling (80.6%). Recovery concentrated on concrete, bricks, and rubble from large constructions, while C&DW from smaller projects was often directed to landfills. Most recovered materials are downcycled, have low market demand and contribute to unstable markets and continued reliance on virgin materials. To investigate these gaps, nine semi-structured expert interviews, two case studies, and a SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis were conducted. The findings were organised into four clusters: (1) regulatory and governance, (2) operational and technological, (3) procurement, cost and market, and (4) stakeholder management barriers significantly impact C&D waste management practices. The study recommends four drivers and six enablers to enhance circular economy 3R (reduce, reuse, recycle) practices, including standardisation and harmonisation of reused and recycled products’ quality and cost, mandating circular procurement, increasing incentives and penalties, on-site sorting, awareness and capacity building, and digital technology application for data management. This integrated approach can guide policymakers and practitioners in strengthening C&DW reduction practices and developing an economically viable market for reused and recycled products. Full article

The increasing sophistication of cyber threats and severe class imbalance in network traffic continue to challenge traditional intrusion detection systems. This study proposes a hybrid framework that integrates SMOTE and CTGAN for minority-class augmentation, a Bidirectional Long Short-Term Memory (Bi-LSTM) network for supervised traffic classification, and a benign-trained Variational Autoencoder (VAE) for validating low-confidence predictions. The framework was evaluated on the CSE-CIC-IDS2018 dataset. On a final holdout test partition of 2,759,227 network flows, it achieved a binary accuracy of 98.51%, an F1-score of 92.59%, and a ROC-AUC of 0.9935. In the 15-class evaluation, it achieved 98.50% accuracy and a weighted F1-score of 98.42%, while stratified 10-fold cross-validation yielded a mean accuracy of 98.79%. The VAE was activated for 519,389 low-confidence flows, representing 18.82% of the complete holdout test partition, and primarily reduced false-positive predictions, although this improvement was accompanied by a measurable reduction in attack recall. SHAP analysis was applied to the supervised Bi-LSTM component to provide feature-level interpretability. The framework also achieved a mean inference latency of 0.5195 ms per network flow. These findings demonstrate strong aggregate detection performance, stable generalisation, effective false-alarm reduction, and low inference latency, while highlighting continuing challenges in rare-class and open-set detection. Full article

Exposure to nitrogen oxide (NOx) in underground mining workings poses a significant threat to the health and safety of workers, while NOx emissions also represent an important environmental challenge associated with the operation of diesel-powered mining equipment. This article provides an overview of primary and secondary methods for limiting emissions and reducing exposure to NOx under underground mining conditions. The primary methods include intensified ventilation, extended ventilation time after blasting, the use of low-emission fuels, and modifications to combustion engines, including exhaust gas recirculation (EGR). The secondary methods include the use of exhaust gas purification technologies such as photocatalysis, oxidation catalysts (DOCs), selective catalytic reduction (SCR), and nitrogen oxide traps (LNTs). The presented solutions were compared in terms of NOx reduction efficiency, implementation costs, technical requirements, and practical applicability in underground workings. The most effective strategies in the short and long term were identified, taking into account the growing role of machine park electrification as a potentially sustainable solution to reducing emissions at their sources. Additionally, an SWOT analysis of NOx emission reduction methods designed for deep underground ore mines was conducted, enabling assessment of their strengths and weaknesses as well as opportunities and threats related to their implementation. The results can support the selection of NOx emission reduction methods that take into account environmental protection, worker safety, technical possibilities, and implementation costs and thus contribute to more sustainable underground mining. Full article

Agricultural drought is an important issue for food security, water availability and rural livelihoods, particularly in the semi-arid regions of the Indian state of Tamil Nadu, especially in Sivagangai District. This study combines multi-temporal remote sensing data and GIS techniques to evaluate the agricultural drought vulnerability during a 30-year time frame (1994–2024). Landsat satellite imagery and climate data were used to derive some key bio-physical indicators such as NDVI, NDWI, VCI, SMI, LST, LULC and SPI. An AHP was used to assign weights to each parameter and was created. The spatiotemporal analysis shows that there is a substantial reduction in vegetated and moist areas as well as a high growth of high and very high temperature areas and built-up land. The percentage of areas classified as ‘Very High’ drought vulnerability has increased significantly from 1.08% (1994) to 40.04% (2024), highlighting a growing threat from drought conditions. NDVI and NDWI were chosen as the most significant indices that affect drought. The findings reflect the deteriorating environmental condition and stress the need for specific mitigation measures, including afforestation, sustainable land-use planning and management of water resources. This study is both spatially explicit and scalable; it provides key information to local planners, policymakers and stakeholders to support agricultural resilience in drought-prone regions. Full article

Hydraulic short-circuit (HSC) operation is an important approach to enhancing the operational flexibility of pumped-storage power plants (PSPPs). However, under this new operating mode, the flow characteristics in the bifurcated pipe deteriorate significantly, posing a threat to the efficiency of the piping system and potentially affecting the inflow conditions for the turbine. In this study, six improved bifurcated pipe models were designed, and their internal flows under pumping, generating, and HSC modes were numerically simulated. Entropy production theory and vortex identification method were employed for flow field analysis. The results show that local modifications confined to the bifurcation are insufficient to simultaneously improve energy characteristics across different modes. In contrast, the bypass pipe enables early flow diversion, weakening the original high-dissipation regions while introducing controllable additional losses. M6 achieves an average energy loss reduction of 47.85% in the mid-to-high flow split ratio range (FSR > 0.3). A strong correlation is observed between vortex suppression and energy loss reduction: the bypass pipe substantially shortens the main vortex length at the inlet section of the generating branch, while simultaneously inducing new shear vortices at the junction; adjustment of its installation position is expected to further shorten their extension, thereby ensuring the normal operation of the turbine. This study provides a new technical pathway for extending the operating range of HSC operation and contributes to enhancing the grid-regulation capability of PSPPs. Full article