Search Results (5,816)

Distributed acoustic sensing (DAS) systems have been widely employed in oil and gas resource exploration, pipeline monitoring, traffic and transportation, structural health monitoring, hydrophone usage, and perimeter security due to their ability to perform large-scale distributed acoustic measurements. Conventional DAS relies on Rayleigh backscattering (RBS) from standard single-mode fibers (SMFs), which inherently limits the signal-to-noise ratio (SNR) and sensing robustness. Ultra-weak fiber Bragg grating (UWFBG) arrays can significantly enhance backscattering intensity and thereby improve DAS performance. This review provides a comprehensive overview of recent advances in UWFBG arrays for high-performance DAS. We introduce major inscription techniques for UWFBG arrays, including the drawing tower grating method, ultraviolet (UV) exposure through UV-transparent coating fiber technologies, and femtosecond laser direct writing methods. Furthermore, we summarize the applications of UWFBG arrays in DAS systems for the enhancement of RBS intensity, suppression of fading, improvement of frequency response, and phase noise compensation. Finally, the prospects of UWFBG-enhanced DAS technologies are discussed. Full article

In regions with limited meteorological monitoring systems, spatial drought modeling is of importance for efficient water resource management. This study recommends an alternative drought modeling strategy for Standardized Precipitation Evapotranspiration Index (SPEI) prediction at multiple target stations using data from neighboring stations. The Multi-Output Random Forest (MORF) model is implemented in this study to consider the spatial correlations among stations for the simultaneous prediction of SPEI for multiple stations instead of training independent models for each station. The efficiency of MORF is further compared to Multi-Output Support Vector Regression (MOSVR) and three baselines; a single-output RF, a monthly climatology model, and a persistence model. In addition to statistical performance criteria, drought characteristics are evaluated using intensity–duration–frequency analysis for three temporal scales (SPEI-3, SPEI-6, and SPEI-12). Results demonstrate that MORF outperformed MOSVR and RF in approximating observed drought intensity, duration, and frequency under moderate, severe, and extreme drought scenarios. Furthermore, spatial analysis reveals that MORF accurately captured the seasonal evolution of drought conditions including onset and recovery phases. The remarkable success of MORF in contrast to MOSVR and three traditional baselines can be explained by its ability to detect nonlinear and complex interactions of drought condition among various neighboring stations. This study emphasizes the promise of multi-output machine learning algorithms for drought monitoring in water resource management and climate adaptation planning in data-scarce regions. Full article

Background/Objectives: Swimming requires precise motor control, sustained attention, and optimal cognitive–motor integration, making it an ideal model for investigating neural efficiency—the phenomenon whereby expert performers achieve optimal outcomes with reduced neural resource expenditure, operationalized as lower activation, sparser connectivity, and enhanced functional integration. This systematic review examined cognitive performance and neural adaptations in swimming athletes, investigating neuroimaging and behavioral outcomes distinguishing swimmers from non-athletes across performance levels. Methods: Following PRISMA 2020 guidelines, seven databases were searched (1999–2024) for studies examining cognitive/neural outcomes in swimmers using neuroimaging or validated assessments. A total of 24 studies (neuroimaging: n = 9; behavioral: n = 15) met the inclusion criteria. Risk of bias assessment used adapted Cochrane RoB2 and Newcastle–Ottawa Scale criteria. Results: Neuroimaging modalities included EEG (n = 4), fMRI (n = 2), TMS (n = 1), and ERP (n = 2). Key associations identified included the following: (1) Neural Efficiency: elite swimmers showed sparser upper beta connectivity (35% fewer connections, d = 0.76, p = 0.040) and enhanced alpha rhythm intensity (p ≤ 0.01); (2) Cognitive Performance: superior attention, working memory, and executive control correlated with expertise (d = 0.69–1.31), with thalamo-sensorimotor functional connectivity explaining 41% of world ranking variance (r² = 0.41, p < 0.001); (3) Attention: external focus strategies improved performance in intermediate swimmers but showed inconsistent effects in experts; (4) Mental Fatigue: impaired performance in young adult swimmers (1.2% decrement, d = 0.13) but not master swimmers (p = 0.49); (5) Genetics: COMT Val158Met polymorphism associated with performance differences (p = 0.026). Effect sizes ranged from small to large, with Cohen’s d = 0.13–1.31. Conclusions: Swimming expertise is associated with specific neural and cognitive characteristics, including efficient brain connectivity and enhanced cognitive control. However, cross-sectional designs (88% of studies) and small samples (median n = 36; all studies underpowered) preclude causal inference. The lack of spatially quantitative synthesis and visualization of neuroimaging findings represents a methodological limitation of this review and the field. The findings suggest potential applications for talent identification, training optimization, and mental health promotion through swimming but require longitudinal validation and development of standardized swimmer brain atlases before definitive recommendations. Full article

Growing concerns over greenhouse gas (GHG) emissions have positioned hydrogen fuel cell buses (HFCBs) as a promising alternative for sustainable urban mobility. By eliminating tailpipe emissions and enabling significant reductions in well-to-wheel GHG intensities when hydrogen is sourced from renewables, HFCBs can contribute to improved urban air quality, energy diversification, and alignment with climate goals. Despite these benefits, large-scale adoption faces challenges related to production costs, hydrogen infrastructure, and efficiency improvements across the supply chain. Life cycle assessment (LCA) provides a valuable framework to assess these trade-offs holistically, capturing environmental, economic, and social dimensions of HFCB deployment. However, inconsistencies in system boundaries, functional units, and impact categories highlight the need for more standardized and comprehensive methodologies. This paper examines the potential of hydrogen buses by synthesizing evidence from peer-reviewed studies and identifying opportunities for integration into urban fleets. Findings suggest that when combined with robust LCA approaches, hydrogen buses offer a pathway toward decarbonized, cleaner, and more resilient public transport systems. Strategic adoption could not only enhance environmental performance but also foster innovation, infrastructure development, and long-term economic viability, positioning HFCBs as a cornerstone of sustainable urban transportation transitions. Full article

The end-to-end efficiency of radio-frequency (RF)-powered wireless communication networks (WPCNs) in post-disaster underground mine environments can be enhanced through adaptive beamforming. The primary challenges in such scenarios include (i) identifying the most energy-constrained nodes, i.e., nodes with the lowest residual energy to prevent the loss of tracking and localization functionality; (ii) avoiding reliance on the computationally intensive channel state information (CSI) acquisition process; and (iii) ensuring long-range RF wireless power transfer (LoRa-RFWPT). To address these issues, this paper introduces an adaptive and safety-aware deep reinforcement learning (DRL) framework for energy beamforming in LoRa-enabled underground disaster networks. Specifically, we develop a Safe Adaptive Deep Q-Network (SADQN) that incorporates residual energy awareness to enhance energy harvesting under mobility, while also formulating a SADQN approach with dual-variable updates to mitigate constraint violations associated with fairness, minimum energy thresholds, duty cycle, and uplink utilization. A mathematical model is proposed to capture the dynamics of post-disaster underground mine environments, and the problem is formulated as a constrained Markov decision process (CMDP). To address the inherent NP hardness of this constrained reinforcement learning (CRL) formulation, we employ a Lagrangian relaxation technique to reduce complexity and derive near-optimal solutions. Comprehensive simulation results demonstrate that SADQN significantly outperforms all baseline algorithms: increasing cumulative harvested energy by approximately 11% versus DQN, 15% versus Safe-DQN, and 40% versus PSO, and achieving substantial gains over random beamforming and non-beamforming approaches. The proposed SADQN framework maintains fairness indices above 0.90, converges 27% faster than Safe-DQN and 43% faster than standard DQN in terms of episodes, and demonstrates superior stability, with 33% lower performance variance than Safe-DQN and 66% lower than DQN after convergence, making it particularly suitable for safety-critical underground mining disaster scenarios where reliable energy delivery and operational stability are paramount. Full article

Given global efforts to promote sustainable energy transitions, this study investigates how the deployment of renewable energy technologies (RETs) relates to multidimensional societal welfare and provides empirical evidence on these linkages in Lithuania. The purpose of the study is to provide an integrated, Lithuania-specific assessment of how economic, social, and environmental determinants associated with RET deployment are related to multiple dimensions of societal welfare. Drawing on scientific literature, an integrated indicator framework is developed that links the economic, social, and environmental determinants of renewable energy technology (RET) deployment to six societal welfare dimensions, as defined by the Lithuanian Quality of Life Index. Using official Lithuanian statistics for 2020–2024, a standardized panel dataset is constructed and Pearson correlation analysis and multiple linear regression are applied using aggregated determinant categories, with model assumptions verified using the Breusch–Pagan and Durbin–Watson tests. Correlation results show very strong positive links between RET intensity indicators and key economic welfare measures (for example, wages, GDP per capita, foreign direct investment, disposable income), with absolute correlation coefficients typically between 0.90 and 0.99 (p < 0.05), and strong negative correlations between air-pollution indicators and GDP, income, FDI, and education (correlation coefficients between −0.96 and −0.90; p < 0.05). The results indicate that RET-related economic determinants have a statistically significant positive effect on the societal welfare dimensions of material living conditions; entrepreneurship/business competitiveness; and public infrastructure, living-environment quality/safety. Social factors also significantly support the societal welfare dimensions of entrepreneurship/business competitiveness and public infrastructure, living-environment quality/safety. In the retained regression models, explanatory power is very high (R² between 0.91 and 0.999), with positive and statistically significant coefficients for the economic determinant (regression coefficients between 0.43 and 0.96; p < 0.05) and negative, statistically significant coefficients for the environmental determinant in the entrepreneurship and public-infrastructure dimensions (regression coefficients between −1.13 and −1.51; p < 0.05). Environmental determinants are associated with lower air pollution but show negative effects on the societal welfare dimensions of entrepreneurship/business competitiveness and public infrastructure, living-environment quality/safety. Overall, the findings suggest that RET deployment is an important correlate of the economic aspects of societal welfare, while environmental and social dimensions display more complex, domain-specific impacts. Full article

Exploring the spatiotemporal evolution of building stock in African countries is of great significance for understanding the urbanization process, regional development disparities, and sustainable development pathways in the Global South. Integrating long-term (1975–2020), 100 m resolution building stock data for Tanzania with multi-source environmental and socioeconomic datasets, this study employed GIS spatial analysis techniques—including optimized hotspot analysis, standard deviational ellipse, and geographical detector—to investigate the spatiotemporal evolution characteristics and influencing factors of building differentiation. The results indicate that over the 45-year period, Tanzania’s building stock underwent rapid expansion, with a 3.83-fold increase in volume and a 4.93-fold increase in area, while the average height decreased continuously by 1.04 m. This growth was predominantly driven by the expansion of residential buildings. The spatial distribution of buildings exhibited a “north-dense, south-sparse” pattern with agglomeration along traffic axes. During 1975–1990, building growth hotspots were concentrated in western and southern regions, shifting to areas surrounding Lake Victoria and central administrative centers during 2005–2020. In contrast, coldspots expanded progressively from northern, northeastern regions and Zanzibar Island to parts of the southern and eastern coasts. The building distribution consistently maintained a northwest–southeast spatial orientation, with increasingly prominent directional characteristics; the centroid of building distribution moved more than 90 km northwestward, and the agglomeration intensity continued to increase. Socioeconomic factors—including population density, road network density, and GDP density—have a significantly stronger influence on building distribution than natural factors. Among natural factors, only river network density exhibits a significant effect, while constraints such as slope and terrain relief are relatively insignificant. Full article

Background and Objectives: Degenerative lumbar spinal stenosis (DLSS) frequently manifests as lower leg radiating pain (LLRP), requiring selective nerve root block (SNRB). Comprehensive and Integrative Medical Services (CIMS)—a multimodal program consisting of acupuncture, cupping, and manual therapy—have been increasingly incorporated into clinical practice in Korea. However, randomized evidence remains limited. This study evaluated the efficacy and safety of adjunctive CIMS in patients with DLSS presenting neuropathic LLRP requiring SNRB. Materials and Methods: In a single-center, parallel-group, assessor-blinded randomized controlled trial (CRIS KCT0006036), adults with DLSS (LANSS > 7; VAS > 5) were randomized 1:1 to experimental or control groups (n = 77; experimental 38, control 39). All participants received SNRB plus pharmacotherapy (limaprost, pregabalin). The experimental group additionally received CIMS, delivered eight times over 4 weeks. The primary outcome was pain intensity (VAS) at baseline and weeks 4, 8, and 12. Secondary outcomes included SF-36, ODI, and RMDQ at baseline and weeks 4, 8, and 12. Repeated-measures two-factor ANOVA assessed the main effects and time × group interaction. Results: Mean VAS (experimental vs. control) was 4.73 ± 1.67 vs. 4.70 ± 1.95 at baseline; 3.74 ± 1.68 vs. 4.66 ± 1.60 at week 4; 3.93 ± 2.03 vs. 4.79 ± 1.55 at week 8; and 3.98 ± 1.98 vs. 4.98 ± 1.68 at week 12. The significant time × group interaction was identified (p = 0.040), indicating a greater pain reduction with CIMS. No significant time × group interactions were observed across SF-36 domains. Adherence to CIMS modalities was high, and no unexpected adverse events occurred. Conclusions: In DLSS patients receiving SNRB and pharmacotherapy, adjunctive CIMS resulted in greater pain reduction over 12 weeks compared with standard care alone, without introducing new safety concerns. These findings support the clinical utility of CIMS as an effective adjunctive treatment option for DLSS. Full article

Compound dry–hot events increasingly threaten ecosystem productivity under global warming. Using ERA5-Land and MODIS NPP (2002–2024) for the Yangtze River Basin, we built climate indices and developed a Copula-based standardized compound dry–hot index (SCDHI) to detect events and examine spatiotemporal patterns. Trend and correlation analyses quantified NPP sensitivity and lag, and an NPP–SCDHI coupling framework assessed resistance and resilience across major vegetation types. Basin-wide monthly NPP increased slightly, while SCDHI decreased, indicating a warmer and drier tendency. Under dry–hot conditions, NPP was mainly negatively related to event intensity in the upper basin but positively related across much of the middle–lower plains. The mean NPP response time was approximately 2 months, with forests and croplands typically lagging 2–3 months. Under extreme stress, forests showed high resistance but limited recovery, whereas shrublands showed moderate resistance and low resilience. Cultivated vegetation exhibited the lowest resistance and weak resilience, grasslands had low resistance but relatively rapid recovery, and alpine vegetation showed moderate resistance and the highest resilience. Cultivated vegetation and grasslands may therefore represent high-risk types for ecological management. Full article

High-resolution climate projections are essential for regional risk assessment; however, Earth System Models (ESMs) operate at scales far too coarse for local impacts. This review examines how machine learning (ML) downscaling can bridge this divide and addresses a key knowledge gap: how to achieve reliable, physically consistent downscaling under future climate change. This article synthesizes ML downscaling developments from 2010 to 2025, spanning early statistical methods to modern deep learning (e.g., convolutional neural networks (CNNs), generative adversarial networks (GANs), diffusion models, and transformers). The analysis introduces a new taxonomy of model families and frames the discussion around the “performance paradox”—the tendency for models with excellent historical skill to falter under non-stationary climate shifts. Our analysis finds that convolutional approaches efficiently capture spatial structure but tend to smooth out extremes, whereas generative models better reproduce high-intensity events at the cost of greater complexity. The study also highlights emerging solutions like physics-informed models and improved uncertainty quantification to tackle persistent issues of physical consistency and trust. Finally, the synthesis outlines a practical roadmap for operational ML downscaling, emphasizing standardized evaluation, out-of-distribution stress tests, and hybrid physics–ML approaches to bolster confidence in future projections. Full article

This pilot study examined the feasibility, usability, and physiological effects of a high-intensity exercise program delivered through immersive virtual reality (IVR) in adults with Down syndrome (DS). Twenty participants (mean age: 29.85 ± 9.37 years) completed a 12-week intervention using the FitXR exergame on Meta Quest 3, with two sessions per week. Usability, safety, and personal experiences were assessed via the System Usability Scale (SUS), Simulator Sickness Questionnaire (SSQ), and Game Experience Questionnaire (GEQ), while body composition and strength were measured using bioelectrical impedance analysis and standardized tests (handgrip dynamometry, Five Sit-to-Stand Test). Results indicated excellent usability (SUS: 92.88–95.03/100), minimal cybersickness (SSQ: 2.12 → 1.98/48), and high adherence (90%). Positive experiences increased significantly, with no negative experiences reported. Lower-limb strength has been considered as a primary outcome, which has shown to improve significantly (p = 0.018; Cohen’s d = 0.89), whereas upper-limb strength and body composition changes were minimal. These findings suggest that IVR-based exercise is a safe, engaging, and feasible strategy for promoting physical activity and enhancing functional strength in adults with DS. Further controlled trials with longer duration and nutritional strategies are warranted to optimize body composition outcomes. Full article

Background: Food allergy is a leading cause of severe allergic reactions in children and often results in restrictive elimination diets. The oral food challenge (OFC) remains the diagnostic gold standard but is resource-intensive and carries a risk of adverse reactions. This study aimed to identify epidemiological, clinical, and laboratory predictors of OFC outcomes and reaction severity in children with suspected immediate-type food allergies. Methods: We conducted a retrospective review of 148 children who underwent hospital-based, open OFCs due to suspected immediate-type food reactions. Data on demographics, comorbidities, characteristics of the initial reaction, sensitisation profiles (specific IgE [sIgE], skin prick test [SPT]), and OFC outcomes were analysed. Reactions were graded using the Ring and Messmer scale. Results: OFC was positive in 44 of 148 children (29.7%). However, no clinical or laboratory parameters—including prior reaction severity and the magnitude of allergy test results—were associated with the severity of reactions during OFC. Comorbidities—specifically asthma, atopic dermatitis, and allergic rhinitis—were significantly associated with a positive OFC (p < 0.01), as were elevated sIgE levels and larger SPT wheal diameters (p < 0.01 for both). The optimal thresholds for predicting a positive OFC were 0.73 IU/mL for sIgE and 3.5 mm for SPT. Conclusions: Oral food challenge (OFC) remains essential for confirming food allergies in children. Given that the severity of reactions during OFCs cannot be reliably predicted and that low cut-off values of allergy tests were identified for predicting a positive OFC outcome, OFCs should be performed in a controlled and fully equipped medical setting, particularly in children with atopic comorbidities. Full article

Mesenchymal stromal cells (MSCs) are intensively investigated in oncology owing to their intrinsic tumor-homing ability and capacity to deliver therapeutic agents directly into the tumor microenvironment (TME). Recent advances in genetic engineering have enabled precise modification of MSCs, allowing controlled expression of therapeutic genes and other cargo delivery, thus improving targeting efficiency. As cellular carriers, MSCs have been engineered to transport oncolytic viruses, suicide genes in gene-directed enzyme prodrug therapy (GDEPT), multifunctional nanoparticles, and therapeutic factors such as IFN-β or TRAIL, while engineered MSC-derived extracellular vesicles (MSC-EVs) offer a promising cell-free alternative. These strategies increase intratumoral drug concentration, amplify bystander effects, and synergize with standard therapies while reducing systemic toxicity. Conversely, accumulating evidence highlights the tumor-promoting properties of MSCs: once recruited by inflammatory and hypoxic cues, they remodel the tumor microenvironment by stimulating angiogenesis, suppressing immune responses, differentiating into cancer-associated fibroblasts, and promoting epithelial-to-mesenchymal transition (EMT), ultimately enhancing invasion, metastasis, and therapy resistance. This duality has sparked both enthusiasm and concern in the oncology field. The present review outlines the paradoxical role of MSCs in oncology—ranging from their potential to promote tumor growth to their emerging utility as vehicles for targeted drug delivery. By highlighting both therapeutic opportunities and biological risks, we aim to provide a balanced perspective on how MSC-based strategies might be refined, optimized, and safely integrated into future cancer therapies. Full article

Melatonin is recognised as a multifunctional regulatory molecule that enhances plant tolerance to abiotic stresses, but its effectiveness is often strongly genotype-dependent. This study aimed to elucidate how exogenous melatonin (200 µM) modulates the physiological and biochemical responses of wheat during drought and subsequent recovery in two genotypes with contrasting grain pigmentation: the standard cv. Bohemia (red grain) and an experimental purple-pericarp (PP) line. Plants were exposed to drought at the early vegetative stage (BBCH 15), and gas exchange, leaf water potential, and biochemical markers (proline, malondialdehyde, phenolics, and flavonoids) were assessed during drought and after rehydration. In cv. Bohemia, water deficit led to a pronounced decrease in CO₂ assimilation, stomatal conductance, and leaf water potential, accompanied by strong increases in proline (Pro) and malondialdehyde (MDA). Melatonin application in this genotype markedly reduced the accumulation of Pro and MDA and accelerated the recovery of gas exchange, indicating a significant protective effect. The lower Pro levels in melatonin-treated Bohemia plants suggest that melatonin mitigated the perceived stress intensity, thereby reducing the physiological demand for osmotic adjustment. In contrast, the PP line exhibited higher inherent stability of the photosynthetic apparatus and more moderate biochemical shifts; its recovery was almost complete and independent of melatonin. Overall, these results indicate that the functional benefit of exogenous melatonin is greater in genotypes with a lower intrinsic stress-buffering capacity. This study highlights the importance of considering constitutive genotype traits and the recovery phase when using physiological regulators to improve wheat drought resilience. Full article

Coastal zones, as critical ocean–land–atmosphere ecotones, face significant ecological threats from persistent organic pollutants like organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs). However, there are still obvious deficiencies in the understanding of the pollution characteristics and ecological risks of OCPs and PCBs in the coastal environment of South China, especially in western Guangdong. Due to the absence of prior research on these pollutants in the Maoming area, we measured the grain sizes from 157 sediment samples and the concentrations of PCBs and OCPs from 11 key locations to assess their environmental occurrence and risks. As analyzed by the GC-MS system, OCP levels range from 0.39 to 50.20 ng/g (mean 10.25 ng/g), while PCB concentrations range from 1.6 to 92.59 ng/g. Through the analysis of pollutant data and analysis of similar areas, we found that OCPs and PCBs in the Maoming coastal zone primarily originate from fishing port operations, ship antifouling paints, and historical legacy pollutants. In addition, the distribution of pollution is significantly controlled by hydrodynamic conditions and the semi-enclosed geomorphological characteristics of the bay. As grain size increases, the correlation with pollutant concentrations shifts from positive to negative. This trend reveals that finer-grained sediments in low-energy environments accumulate significantly higher levels of pollution compared to their coarser counterparts in more dynamic settings. Compared to other coastal regions globally, the study area demonstrates relatively lower pollution intensity. Dual assessments using Sediment Quality Guidelines (SQGs) and Sediment Quality Standards (SQSs) indicate a generally low probability of adverse biological effects, with elevated risk localized to sites near port activities. This study provides a scientific basis for the prevention and control of OCP and PCB pollution in the Maoming coastal zone and also provides a reference for pollution assessment in similar areas. Full article