Search Results (13,145)

The Hubble tension, a persistent discrepancy between early and late Universe measurements of $H_0$, poses a significant challenge to the standard cosmological model. In this work, we present a new Bayesian hierarchical framework designed to meticulously decompose this observed tension into its constituent parts: standard measurement errors, information loss arising from parameter-space projection, and genuine physical tension. Our approach, employing Fisher matrix analysis with MCMC-estimated loss coefficients and explicitly modeling information loss via variance inflation factors ($\lambda$), is particularly important in high-precision analysis where even seemingly small information losses can impact conclusions. We find that the real tension component ($T_{real}$) has a mean value of 5.94 km/s/Mpc (95% CI: [3.32, 8.64] km/s/Mpc). Quantitatively, approximately 78% of the observed tension variance is attributed to real tension, 13% to measurement error, and 9% to information loss. Despite this, our decomposition indicates that the observed ∼$6.39\sigma$ discrepancy is predominantly a real physical phenomenon, with real tension contributing ∼$5.64\sigma$. Our findings strongly suggest that the Hubble tension is robust and probably points toward new physics beyond the $\Lambda$CDM model. Full article

To investigate the effects of seasonal grazing on ground-dwelling insect communities in desert steppe, this study conducted a controlled experiment in the desert steppe of Yanchi County, Ningxia, during 2022–2023. Five grazing regimes were established: spring-summer grazing (Sp+Su), spring-autumn grazing (Sp+Au), summer-autumn grazing (Su+Au), year-round continuous grazing (Annual), and no grazing (Control, CK). Insects were collected using pitfall traps and categorized into herbivorous and predatory functional groups. Combined with monitoring of vegetation community structure, we analyzed the regulatory mechanisms of grazing on insect diversity. The results showed that different grazing regimes had significantly divergent effects on herbivorous and predatory insects. Herbivorous insect diversity was significantly highest under the Annual grazing regime, while Sp+Au grazing effectively controlled herbivorous insect occurrence, resulting in the lowest abundance. Predatory insects exhibited the highest abundance but the lowest diversity under Su+Au grazing, whereas the CK regime increased their species richness. Beta diversity analysis indicated that total replacement diversity (Repl) was dominant, suggesting that grazing primarily influenced community structure by altering species composition rather than changing species number. Non-metric multidimensional scaling (NMDS) results revealed clustering characteristics of insect community structures under different grazing regimes. Redundancy analysis (RDA) and generalized additive models (GAMs) identified vegetation height and predatory insect abundance as key factors driving changes in herbivorous insects. Vegetation density and biomass exhibited nonlinear regulatory effects on herbivorous insects. Based on these findings, we recommend adopting either a hybrid strategy of “year-round continuous grazing combined with seasonal rest” or specifically the “spring + autumn” (Sp+Au) grazing regime. These approaches aim to synergistically achieve the goals of pest control and biodiversity conservation in desert steppe ecosystems. Full article

This study investigates the effect of seasonal conductor sag on electromagnetic field (EMF) exposure to near 400 kV double-bundle overhead transmission lines. The conductor sag study resulted in clearance values of 28.0 m for winter (−10 °C, sag ≈ 7.0 m) and 23.4 m for summer (+35 °C, sag ≈ 11.65 m). For both seasonal examples, the electric field strength and magnetic flux density were calculated at a pedestrian height of 1.5 m, and the image approach to account for ground effects. The winter setup resulted in maximum values of 1.35 kV/m (E) and 27.2 µT (B), while the summer configuration produced higher values of 1.96 kV/m and 38.5 µT, respectively. Autumn field measurements, representing intermediate seasonal circumstances, produced average values of 1.294 kV/m and 1.399 µT, with peaks of 8.39 kV/m and 6.85 µT for electric field and magnetic flux density, respectively. The electric field projections were nearly identical to measurements; however, the magnetic field predictions were significantly higher, most likely due to the model’s assumptions of balanced currents and ideal geometry. These findings suggest that seasonal conductor sag variation is a real and substantial factor in assessing EMF exposure, with the electric field being particularly sensitive to clearance changes. The findings emphasize the need to incorporate a large analysis into EMF compliance assessments, especially in cases where terrain relief between towers may further diminish clearance in mid-span regions. Full article

Multilevel active bridge converters are potential candidates for many modern high-power DC applications due to their ability to integrate multiple sources while minimizing weight and volume. Therefore, this paper deals with an analytical, simulation-based, and experimentally verified investigation of their circulating current behavior, power factor performance, and power loss characteristics. A high-frequency link analysis framework is developed to characterize voltage, current, and power transfer waveforms, providing insight into reactive power generation and its impact on overall efficiency. By introducing a modulation-based control approach, the proposed converters significantly reduce circulating currents and enhance the power factor, particularly under varying phase-shift conditions. Compared to quadruple active bridge topologies, the discussed multilevel architectures offer reduced transformer complexity and improved power quality, making them suitable for demanding applications such as electric vehicles and aerospace systems. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

Objective: Stroke is one of the leading causes of death and disability worldwide. Compared to developed countries, the prognosis of stroke is less favourable in developing countries. The objective of this study is to identify inter-ethnic variation in risk profiles and stroke outcomes amongst Bangladeshi, Indian, Nepalese, Pakistani, and Sri Lankan expatriates living in Qatar. Methods: Data from the Qatar Stroke Registry were retrospectively analyzed from April 2014 to June 2025. A total of 8825 patients were included. The chi-square test was used to analyze sociodemographic variables, while the Kruskal–Wallis test was used to analyze continuous variables. Post hoc analysis was performed. Multivariate logistic regression and multivariate multiple regression were used to identify the predictors associated with poor clinical outcomes and mortality at 90 days. Results: Ischemic stroke was the predominant stroke type in all groups, with Nepalese patients presenting with stroke at a younger age, whilst Pakistanis tended to be older (p < 0.001). In terms of stroke outcomes, Nepalese patients had the highest proportion of a poor functional outcome at 90 days as well as NIHSS at discharge (p < 0.05). However, Bangladeshis had the highest proportion of mortality at 90 days compared to the other cohorts. Multivariable logistic regression revealed that undiagnosed dyslipidemia, Nepalese ethnicity, and moderate and severe NIHSS admission scores were independent predictors of a poor functional outcome at 90 days, whilst male sex and prior antidiabetic therapy were protective factors (p < 0.001). In terms of mortality at 90 days, only a severe NIHSS admission score (>10) was a significant predictor (p < 0.001). A severe NIHSS admission score was also the only predictive factor of mortality and poor functional outcome at 90 days (p < 0.05). Conclusions: There was a significant variation in stroke presentation and outcomes among South Asian subpopulations in Qatar, suggesting the importance of tailored public health strategies as a uniform approach to stroke care is insufficient for this diverse population. Full article

Seed storage proteins (SSPs) play a pivotal role in determining the development, quality, and nutritional value of rice seeds. In this study, we conducted a transcriptome-based correlation analysis to identify novel transcription factors (TFs) potentially involved in the biosynthesis and accumulation of SSPs. Our analysis revealed nine TFs—OsGATA8, OsMIF1, OsMIF2, OsGZF1, OsbZIP58, OsS1Fa1, OsS1Fa2, OsICE2, and OsMYB24—that exhibit strong co-expression with key SSP genes, including those encoding glutelin and prolamin. Gene expression profiling using quantitative RT-PCR and GUS reporter assays revealed that these TFs are predominantly expressed during seed development, with peak expression observed at 10 days after flowering (DAF). Promoter analysis further demonstrated an enrichment of seed-specific and hormone-responsive cis-regulatory elements, reinforcing the seed-preferential expression patterns of these TFs. Collectively, our findings identify a set of candidate TFs likely involved in SSP regulation and seed maturation, providing a foundation for the genetic enhancement of rice seed quality and nutritional content through targeted breeding and biotechnological approaches. Full article

Shared cargo bike services hold significant potential for promoting sustainable urban mobility, yet their adoption remains limited—especially for private, everyday use. This study investigates how such systems can be more effectively integrated into urban transport by identifying key enablers and operationalizing them through a GIS-based multi-criteria analysis (MCA). Using a mixed-methods approach, expert interviews were conducted to explore success factors and barriers. Results highlight the dual function of shared cargo bikes: enabling occasional use while increasing long-term uptake by fostering trial and visibility. The study identifies both spatial and non-spatial enablers. Key spatial factors include high visibility, pedestrian flows, access to public transport and cycling networks, and placement in mixed-use areas. Non-spatial enablers include technical reliability, ease of use, strong visual identity, subsidies, and trial opportunities. The spatial enablers were operationalized into seven criteria in the MCA. Based on qualitative expert interviews and thematic analysis, the highest weights were assigned to visibility and pedestrian flows, followed by proximity to public transport and local centers, while lower weights were given to proximity to residences, population density, and access to cycle paths. The results offer guidance for station placement and demonstrate the role of shared cargo bikes in sustainable urban transport. Full article

Agricultural cooperatives are essential in mitigating climate change and food insecurity through the promotion of sustainable agricultural practices and the conservation of biodiversity. However, weaknesses in governance, economic restrictions, market pressures, and regulatory obstacles frequently hinder their efficacy. This study investigates the main factors leading to cooperative failures through qualitative analysis of twenty-three (23) expert interviews. Research demonstrates that strong governance, efficient communication, financial stability, and supportive policies are crucial for the viability of cooperatives. Leadership issues, bureaucratic inefficiencies, and market competition were seen as significant roadblocks. It is essential to tackle these difficulties via governance adjustments, economic resilience approaches, and policy advocacy to strengthen the role of cooperatives in climate change mitigation and food security. Full article

Coalbed methane (CBM) resources hold broad development prospects in China, with deep CBM reservoirs increasingly becoming a focal point for exploration. However, compared to shallow CBM, the factors influencing the productivity of deep CBM are more complex and less studied. This study integrates statistical methods—grey correlation analysis and principal component analysis—with the machine learning approach of random forests, and further employs a fuzzy mathematics-based comprehensive evaluation method to propose a systematic evaluation framework for identifying key controlling factors of productivity. Using field data from the No. 8+9 coal seam in the eastern margin of the Ordos Basin, the results indicate that the primary geological factors affecting cumulative gas production are gas content and coal seam thickness, while the key engineering factors are proppant intensity and proppant volume. These findings align with practical field experience and provide a rational basis for the design of fracturing strategies in deep CBM reservoirs. Full article

Background/Objectives: Students’ psychological health problems have been widely studied for a long time. However, with the COVID-19 pandemic and due to the additional challenges related to the need for individual and contextual adjustment, a more comprehensive approach to psychological health and well-being is needed. The main goal of the present study was to identify the individual and contextual factors that could discriminate middle school and high school students based on well-being and psychological symptoms. Methods: In this study, carried out within the scope of the second wave of the study “Psychological Health and Wellbeing | School Observatory”, promoted by the Ministry of Education, 3037 students from different regions and levels of public education in Portugal, 49.5% female, aged between 9 and 18 years, participated by completing a research protocol after informed consent was given. Results: Cluster analysis allowed the identification of four groups based on the scores of well-being and psychological symptoms: complete psychological health, incomplete psychological distress, incomplete psychological health and complete psychological distress. The analysis of discriminant variables additionally showed relevant differences between the two extreme groups: complete psychological health students reported higher socio-emotional skills, whereas complete psychological distress students reported higher stress and anxiety scores and low life satisfaction. Conclusions: The obtained results highlight the need for early identification of psychological distress using effective measures to prevent psychological symptoms and to promote socio-emotional skills in the school context. Full article

Alkaline stress, driven by high pH and carbonate accumulation, results in severe physiological damage in plants. While the molecular mechanisms underlying alkaline tolerance have been partially elucidated in many crops, they remain largely unexplored in wheat. We hypothesize that alkaline stress tolerance in wheat is genotype-dependent. This study employed an integrated multi-omics approach to assess alkaline stress responses, combining genome-wide association study (GWAS) and RNA-seq analyses. Systematic phenotyping revealed severe alkaline stress-induced root architecture remodeling—with 57% and 73% length reductions after 1- and 3-day treatments, respectively—across 258 accessions. Analysis of the GWAS results identified nine significant alkaline tolerance QTLs on chromosomes 1A, 3B, 3D, 4A, and 5B, along with 285 associated candidate genes. Using contrasting genotypes—Dingxi 38 (tolerant) and TDP.D-27 (sensitive)—as experimental materials, physiological analyses demonstrated that root elongation was less inhibited in Dingxi 38 under alkaline stress compared to TDP.D-27, with superior root integrity observed in the tolerant genotype. Concurrently, Dingxi 38 exhibited enhanced reactive oxygen species (ROS) scavenging capacity. Subsequent RNA-seq analysis identified differentially expressed genes (DEGs) involved in ion homeostasis, oxidative defense, and cell wall remodeling. Integrated GWAS and RNA-seq analyses allowed for the identification of seven high-confidence candidate genes, including transcription factors (MYB38, bHLH148), metabolic regulators (ATP-PFK3), and transporters (OCT7), elucidating a mechanistic basis for adaptation to alkaline conditions. These findings advance our understanding of alkaline tolerance in wheat and provide candidate targets for molecular breeding of saline- and alkaline-tolerant crops. Full article

As Shared Autonomous Vehicles (SAVs) emerge as a transformative force in urban mobility, integrating them into dense, historic urban environments presents distinct spatial and planning challenges—such as narrow street patterns, irregular road networks, and the need to protect cultural heritage. This study investigates the spatial adaptability of SAVs in Suzhou old town, a representative example of East Asian heritage cities. To assess spatial readiness, a hybrid weighting approach combining the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM) is used to evaluate 22 spatial indicators across livability, mobility, and spatial quality. These weighted indicators are mapped using a spatial density analysis based on Point of Interest (POI) data, revealing urban service distribution patterns and spatial mismatches. Results show that “Accessibility to Transportation Hubs” receives the highest composite weight, emphasizing the priority of linking SAVs with existing subway and bus networks. Environmental comfort factors—such as air quality, noise reduction, and access to green and recreational spaces—also rank highly, reflecting a growing emphasis on urban livability. Drawing on these findings, this study proposes four strategic directions for SAV integration that focus on network flexibility, public service redistribution, ecological enhancement, and cultural preservation. The proposed framework provides a transferable planning reference for historic urban areas transitioning toward intelligent, human-centered mobility systems. Full article

Evaluation of socio-ecological vulnerability is crucial for sustainable management in mining cities. This study selected Xintai City, China, as a case and constructed a comprehensive vulnerability assessment framework based on 2010–2020 multi-source data. By integrating the Geodetector, spatial autocorrelation analysis, and ordered weighted averaging (OWA), we systematically explored the spatio-temporal patterns and driving mechanisms of socio-ecological vulnerability. The Theil index at the village level revealed finer spatial heterogeneity than large-scale analyses. The results show the following: (1) Socio-ecological vulnerability in Xintai City is generally moderate, with high-vulnerability areas concentrated in the urban center and former coal mining zones. Over the past decade, high—vulnerability levels in these areas have improved, whereas the urban-rural fringe has experienced a significant increase in vulnerability, primarily driven by industrial transfer and uneven resource allocation. (2) Geodetector analysis indicated a shift in dominant drivers from natural to socio-economic factors, with population density and construction land proportion surpassing natural conditions such as average annual rainfall by 2020. Additionally, mining land proportion, land use change, and the spatial distribution of social services played key roles in shaping vulnerability patterns, while ecological and public service factors showed weaker explanatory power. (3) Scenario simulation based on OWA demonstrated that an economic-priority pathway leads to the outward expansion of vulnerable areas into mountainous regions, while an ecological-priority approach promotes spatial contraction and optimization of vulnerability zones. These findings provide scientific guidance for identifying key vulnerable areas and formulating differentiated management strategies, offering reference value for the sustainable development of resource-exhausted mining cities. Full article

This study examines patterns of artificial intelligence (AI) adoption in Ecuadorian micro and small enterprises (MSEs), with an emphasis on functional diversity across value chain activities. Based on a cross-sectional dataset of 781 enterprises and an entropy-based model, it assesses internal variability in AI use and explores its relationship with strategic perception and dynamic capabilities. The findings reveal predominant partial adoption, alongside high functional entropy in sectors such as mining and services, suggesting an ongoing phase of technological experimentation. However, a significant gap emerges between perceived strategic use and actual functional configurations—especially among microenterprises—indicating a misalignment between intent and organizational capacity. Barriers to adoption include limited technical skills, high costs, infrastructure constraints, and cultural resistance, yet over 70% of non-adopters express future adoption intentions. Regional analysis identifies both the Andean Highlands and Coastal regions as “innovative,” although with distinct profiles of digital maturity. While microenterprises focus on accessible tools (e.g., chatbots), small enterprises engage in data analytics and automation. Correlation analyses reveal no significant relationship between functional diversity and strategic value or capability development, underscoring the importance of qualitative organizational factors. While primarily descriptive, the entropy-based approach provides a robust diagnostic baseline that can be complemented by multivariate or qualitative methods to uncover causal mechanisms and strengthen policy implications. The proposed framework offers a replicable and adaptable tool for characterizing AI integration and informing differentiated support policies, with relevance for Ecuador and other emerging economies facing fragmented digital transformation. Full article