Search Results (1,625)

Return-type cable suspension bridges transfer the main-cable force to the anchorage predominantly in the horizontal direction, which may induce coupled sliding–overturning instability of the anchorage–foundation system. This study examines the stability of return-type cable gravity anchorage using the composite anchorage of the Jixin Expressway Yellow River Three Gorges Bridge as the prototype. A 1:100 laboratory specimen was designed based on similarity theory and tested under incremental loading until failure. Four configurations were considered by combining two embedment ratios (1/4 and 1/2) with two base types (flat-base and shear-keyed). Horizontal displacement, overturning angle, interface contact stress, and foundation strain were monitored throughout loading. Because the return-type cable transmits a predominantly horizontal force, the anchorage–foundation contact stress exhibits pronounced asymmetry between the toe and heel regions, and this stress asymmetry governs the coupled sliding–overturning instability mode. The shallow flat-base case exhibited a distinct displacement and contact stress jump at high load levels, followed by rapid rotation, indicating slip–tilt coupled instability. Increasing embedment improved confinement and delayed the onset of nonlinear deformation, but the flat-base configuration still showed pronounced toe stress concentration. By contrast, the shear-keyed base mobilized cooperative bearing of the surrounding foundation, producing smoother stress–strain evolution and higher ultimate capacity. Moreover, the shear-keyed base mitigates the stress asymmetry at the anchorage–foundation interface, leading to a more symmetric distribution of contact pressure and improved overall stability. Three-dimensional finite-element simulations reproduced the measured trends in displacement, stress concentration near the toe, and strain development, providing independent verification. The results clarify the dominant instability mechanism of return-type cable gravity anchors and offer design implications for embedment depth and shear-keyed base detailing. Full article

Anthropogenic polymers have become an increasingly important class of emerging contaminants in terrestrial ecosystems. While extensive research has focused on microplastics in aquatic environments, their interactions with soil systems and particularly with soil organic matter (SOM) remain insufficiently understood. Soil represents a major environmental sink for polymer residues originating from agricultural practices, urban activities, and atmospheric deposition. Accordingly, associations between polymers and SOM, including humic substances, may significantly influence the retention, mobility, and transformation of carbon in soil systems. This review synthesizes current knowledge on the influence of synthetic polymers on soil organic matter dynamics. A bibliometric and qualitative literature analysis based on publications indexed in Web of Science and Scopus from 1979 to 2025 was conducted to identify major research trends and knowledge gaps. The results indicate that polymer particles can alter soil structure, microbial activity, and sorption processes, thereby affecting the stability and cycling of soil organic carbon. Interactions between polymer surfaces and humic substances may modify aggregation processes and influence the persistence and mobility of both polymers and organic carbon compounds. Despite the rapid growth of research on microplastics, studies addressing polymer–SOM interactions remain limited and methodologically heterogeneous. Greater integration between polymer research, soil science, and land use studies is necessary to better understand the implications of polymer contamination for soil quality and carbon cycling. The findings highlight the need for standardized analytical approaches and interdisciplinary research frameworks to assess the long-term effects of polymers in soil ecosystems. Full article

With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article

Obesity represents a major public health crisis in Mexico, affecting over 70% of adults, and although strength training is effective for improving body composition, direct maximal strength testing (1RM) poses risks in this population. Mobile applications have emerged as popular tools for exercise prescription, yet their effectiveness compared to supervised, scientifically-based protocols remains unknown. To compare the effects of a supervised strength training program based on indirect 1RM estimation (S1RM group) versus a mobile application-generated program (App group) on body composition, anthropometric measures, and strength gains in male adults with obesity. Twenty male participants (BMI ≥ 30 kg/m²) were randomly assigned to either the S1RM group or the App group. Both groups trained three times per week for 12 weeks. Body composition (bioelectrical impedance), anthropometric measures (waist and hip circumference), and estimated 1RM were assessed pre- and post-intervention. A mixed repeated-measures ANOVA (Group × Time) was conducted, with effect sizes (${\eta }^2$p) and 95% confidence intervals calculated. Both groups showed significant improvements in most outcomes (p < 0.05). However, significant group × time interactions favored the S1RM group for waist circumference (F(1,18) = 14.50, p = 0.001, ${\eta }^2$p = 0.45) and hip circumference (F(1,18) = 217.90, p < 0.001, ${\eta }^2$p = 0.92). A significant between-group difference was also observed for visceral fat (F(1,18) = 4.91, p = 0.040, ${\eta }^2$p = 0.21). For muscle and fat mass, interactions showed large effect sizes (${\eta }^2$p = 0.18–0.19) with trends toward significance (p = 0.057–0.096). Strength increased significantly in all exercises for the S1RM group (14.9–22.0%, p < 0.01). These findings support the implementation of indirect 1RM estimation methods in obesity populations and highlight the added value of professional supervision in strength training programs. Full article

Antimicrobial resistance (AMR) is an ever-growing threat to global healthcare. It is largely driven by delayed or inadequate pathogen identification and antimicrobial susceptibility testing in routine clinical workflows. By the time the clinician receives results to guide treatment from traditional culture-based diagnostics, several days may have elapsed, leading to the use and potential over-prescription of broad-spectrum antibiotics and the development of resistant pathogens. A rapid and clinically actionable diagnostic approach at the clinical point of care (POC) may help address this gap. This review examines current and emerging POC diagnostic technologies for AMR and outlines the fundamental principles and mechanistic classifications of POC diagnostic technologies. These include phenotypic, genotypic, immunological, and biosensor-based approaches. A critical overview of key technological platforms, including rapid phenotypic antimicrobial susceptibility testing (AST), microfluidics and isothermal nucleic acid amplification (e.g., LAMP and RPA), CRISPR-based diagnostics, nanomaterial-enhanced biosensors, and mobile-integrated systems is provided. The impact of POC diagnostics on antimicrobial stewardship, time to appropriate therapy, and patient outcomes in primary care settings, hospitals, intensive care units, and resource-limited settings is presented and discussed. In addition to clinical implementation challenges, this review considers the issues of analytical performance, workflow, regulatory pathways, cost, and implementation readiness. In addition, it outlines key trends regarding digital integration, surveillance, workforce training, and policy frameworks. Overall, the review outlines the role of POC diagnostics in enhancing antimicrobial response surveillance and the global fight against AMR. Among emerging platforms, rapid phenotypic AST, microfluidic and isothermal-based assays, CRISPR-based diagnostics, and integrated biosensor systems show the greatest potential for near-term clinical impact; however, widespread implementation remains constrained by challenges related to clinical validation, cost, workflow integration, and alignment with antimicrobial stewardship frameworks. Full article

Abnormal gait associated with neuromuscular and musculoskeletal disorders represents a growing clinical burden, particularly in aging populations. This study presents a modular, low-cost Smart Rehabilitation Walker (SRW) that integrates multimodal sensing and real-time haptic feedback to enable simultaneous gait monitoring and corrective intervention in both clinical and home environments. The system combines force-sensing resistors for bilateral load symmetry assessment, inertial measurement units for fall detection, and surface electromyography (sEMG) for neuromuscular activity monitoring within a closed-loop assistive feedback architecture. A 15-day pilot study involving ten individuals with rheumatoid arthritis and clinically observed neurological gait abnormalities demonstrated measurable improvements in gait biomechanics. The Force Symmetry Index (FSI), calculated using the Robinson symmetry metric, decreased from an average of 0.9691 to 0.2019, corresponding to a 79.26% average reduction in inter-limb load asymmetry. Concurrently, sEMG measurements showed a substantial increase in neuromuscular activation (ΔEMG = 4.28), with statistical analysis confirming a significant improvement across participants (paired t-test: t(9) = 13.58, p < 0.001). To model rehabilitation trajectories, a nonlinear predictive framework based on Gaussian Process Regression achieved high predictive accuracy (R² ≈ 0.9, with a mean RMSE of 0.0385), while providing uncertainty-aware trend estimation. Validation using an independent amyotrophic lateral sclerosis gait dataset further demonstrated the transferability of the analytical pipeline. These results highlight the potential of sensor-enabled assistive walkers as scalable platforms for quantitative gait rehabilitation, adaptive feedback, and long-term mobility monitoring. Full article

The sixth-generation (6G) mobile communication systems are anticipated to be operational by 2030, prompting extensive research efforts by governments and private entities. Designed to meet societal, economic, and technological demands unaddressed by fifth-generation (5G) networks, 6G integrates scalability, security, and reliability with ubiquity and resource-intensive artificial intelligence. Envisaged as multi-band, decentralized, autonomous, flexible, and user-centric, 6G networks incorporate innovative technologies, including cell-free (CF), three-dimensional heterogeneous networks (3D HetNet), reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), as well as artificial intelligence/machine learning (ML). In 6G 3D HetNets, the densification of access points (APs) continues, accommodating increased connections and traffic volumes, alongside the use of higher frequency bands. Although 6G networks are not fully standardized, they target demanding Quality of Service (QoS) standards, such as a peak data rate of 1.0 Tbps and latency of 0.1 ms. This paper conducts a comprehensive literature review on radio resource management (RRM) in 6G cell-free and 3D HetNet systems, emphasizing challenges such as interference mitigation. It presents a taxonomy of RRM approaches, systematically studying, categorizing, and qualitatively analyzing recent techniques, outlining the current state, and indicating future trends, technologies, and challenges shaping 6G systems. Full article

Background: Unhoused individuals face disproportionately high rates of preventable chronic disease due to fragmented access to care and prolonged exposure to environmental stressors. Street medicine programs offer a mobile, low-barrier model to assess and address these unmet needs. Despite well-documented disparities, no publications in the current literature provide numerically specific screening recommendation guidelines tailored to unhoused populations. This study fills that gap using clinical data from Street Medicine Phoenix (SMP), a mobile healthcare initiative serving urban Arizona. Methods: We retrospectively reviewed 1322 clinical encounters recorded by SMP between August 2023 and October 2024. Diagnoses and treatments were manually categorized. Blood pressure (BP) and glucose values were analyzed using descriptive statistics and compared against national norms (CDC 50th percentile and ADA guidelines). Kruskal–Wallis and Dunn’s tests assessed age-based differences, while chi-square and Mann–Whitney U tests examined glucose patterns. Results: The mean patient age was 51.4 years; 34.5% identified as female. Cardiovascular issues (39.4%) and routine screenings (39.6%) were most frequently documented. Systolic and diastolic BP values were significantly elevated across all age groups except those 60+, with even the 18–39 group showing median systolic BP above CDC norms (124.0 mmHg). Among 60 patients with fasting glucose data, 41.4% met ADA criteria for diabetes, and 10.7% of those without a known diagnosis had diabetic-range values. Conclusions: Our findings suggest that cardiometabolic disease may emerge earlier and more aggressively among unhoused individuals than in the general U.S. population, reflecting patterns of accelerated biological aging. The elevation of cohort-based BP percentiles suggests that current national benchmarks may underrepresent clinical risk in this group. We propose initiating blood pressure screening at age 18 and fasting glucose screening by age 35 in unhoused individuals—adaptations of existing USPSTF recommendations based on cohort-specific trends. These screening thresholds can be feasibly implemented in street medicine settings to promote earlier detection and improve long-term health outcomes. Full article

This paper examines how emerging economies, with a focus on India, transition from being primarily recipients of capital to becoming outward investors. It investigates whether domestic institutional governance, rather than rapid liberalization or extensive investment treaty networks, accounts for the sustained growth of both inward FDI and outward ODI. The study combines a detailed timeline of institutional developments with structural break tests, vector autoregression (VAR), and dynamic panel GMM analysis. This approach tracks the timing, spread, and longevity of reforms like the shift from FERA to FEMA and the digitalization of administration, examining their effect on capital flow patterns. Results show that major turning points in India’s FDI and ODI movements correspond with key governance reforms, such as replacing the Foreign Exchange Regulation Act with the Foreign Exchange Management Act, unifying investment policies, digitizing administration, and renegotiating treaties post-2016. Improvements in governance have a more significant and enduring impact on FDI than macroeconomic factors, while clearer regulation and stronger institutions are vital for boosting ODI. Once domestic institutional capacity is taken into account, the number of investment treaties does not significantly influence capital movements. The paper introduces a “transferability matrix” that highlights effective, low-cost reforms, such as civil penalty systems and digital governance, which other emerging economies can implement. It stresses that integrating into global capital markets depends more on developing solid domestic regulations than on rapid deregulation. The study also advances previous research by (1) combining FDI and ODI within a single institutional framework explaining both flows; (2) moving beyond static, perception-based measures to develop a comprehensive timeline showing how regulatory credibility is built over three decades; and (3) providing empirical proof that credible domestic institutions can replace large treaty networks in ensuring capital mobility. Full article

Chronological age incompletely captures neurodegenerative burden and functional vulnerability in multiple sclerosis (MS). Brain-predicted age difference (Brain-PAD; predicted minus chronological age) provides an MRI-derived index of accelerated brain aging, but links to mobility and real-world behavior remain unclear. Forty-three adults with MS completed structural MRI, mobility testing, and six months of free-living physical activity monitoring. Brain age was estimated using PyBrainAge applied to FreeSurfer-derived cortical thickness and subcortical volumes. Hierarchical regressions tested whether Brain-PAD explained additional variance in mobility (Mini-BESTest total and subscores; forward/backward walking velocity) and moderate-to-vigorous physical activity (MVPA) beyond age and disability (PDDS). Predicted brain age exceeded chronological age (Brain-PAD = 8.4 ± 11.1 years; p < 0.001). After accounting for age and PDDS, Brain-PAD explained additional variance in Mini-BESTest total (ΔR² = 0.05, p = 0.042) and anticipatory control (ΔR² = 0.08, p = 0.034), with a trend for sensory orientation. Brain-PAD was not associated with walking velocity beyond PDDS. Higher Brain-PAD was associated with lower MVPA (β = −0.91, p = 0.005) and explained additional variance (ΔR² = 0.19). Brain-PAD is elevated in MS and relates to balance control and real-world physical activity beyond age and disability, highlighting its potential to identify functional vulnerability. Full article

This trend is inevitable when aluminum electrolyte raw materials containing a higher content of LiF and KF are being widely used. Though these substances are often used as regulators in aluminum electrolysis due to their high corrosivity, measuring the physical parameters of the electrolyte through ordinary measurement methods is difficult. In this paper, electrolytes with different Li and K contents at different temperatures were synthesized as simulation samples, and viscosity and surface tension were measured by a new method. The results showed that in the aluminum electrolyte with a cryolite ratio of three, KF and LiF had similar impacts on viscosity when the mechanisms were completely different. As the content increased, KF reduced the viscosity of the material through the ion lattice spacing effect, and the high mobility of LiF contributed greatly to the viscosity reduction. However, the viscosity reduced significantly only when there was a higher KF concentration. The effects of the two regulators on the surface tension were different. The rising LiF content presented a greater effect on the surface tension, and the temperature was also a main factor affecting the surface tension of the electrolyte. When the two were added together in the electrolyte, a high content of KF helped reduce the surface tension. The research in this paper plays a role in promoting the exploration of phase parameters of aluminum electrolytes with high Li and K content. Full article

Recent geopolitical events have led to an increased research focus on the experiences of female refugees. As careers play a crucial role in socio-economic integration, this study aims to examine the scope and characteristics of research findings on the careers of refugee women in host countries. Following the general research questions for bibliometric analysis, the major trends and intellectual structures of the research field of women refugees’ careers were identified. Four hundred and fifty-three articles selected from the Web of Science database (search by title, abstract, and keywords) for the period 2000–2023 were analyzed using VOSviewer (1.6.20). The results show that key challenges faced by forcibly displaced women include mental health disorders, language barriers, discrimination, downward career mobility, and pressure of traditional gender roles. The research reveals that critical enablers for female refugees’ workforce participation and economic independence are language training, culturally sensitive healthcare, and access to childcare. Simultaneously, empowerment strategies, including entrepreneurship and participation in professional networks, are proved to foster resilience and create pathways for successful career steps. Full article

Climate change is reshaping hydrologic regimes in snow-dominated watersheds, with important implications for mine rock drainage quantity and contaminant mobilization. This study quantifies potential long-term changes in drainage quantity by coupling a previously published physics-informed machine learning model with a Monte Carlo framework driven by downscaled monthly climate projections from ClimateNA. The proposed methodology was applied to three drainage monitoring stations at a mine site in Western Canada to assess projected drainage responses over the 2011–2100 period. An ensemble of daily weather sequences was generated by sampling historical within-month variability and scaling the resulting series to match projected monthly climate statistics, which were then used as inputs for the drainage model. Trends were assessed using the Mann–Kendall test modified for serial correlation, and their magnitudes were summarized using the Theil–Sen slopes. The trend analysis results indicate scenario-dependent changes in annual drainage across stations, alongside consistent seasonal shifts toward higher spring (April–May) and lower early-summer (June–July) drainage. These patterns are consistent with earlier snowmelt and earlier snowpack depletion. Corresponding shifts in intra-annual flow timing suggest that a larger fraction of annual drainage occurs earlier in the year. Overall, these findings provide a physics-informed basis for changes in drainage quantity and for guiding monitoring, design, and mitigation strategies under a warming climate. Full article

Casablanca, Morocco’s most populous and economically dynamic metropolis, is undergoing rapid and unregulated expansion, leading to accelerated agricultural land artificialization, landscape fragmentation, and growing socio-environmental vulnerability in peri-urban territories. This study investigates the spatio-temporal dynamics of urban expansion within a 40 km buffer around the city, using multi-temporal Landsat imagery (2015–2025), a GIS-based framework, and supervised classification. Four land-cover classes were extracted (urban, vegetation, forest and water) enabling a diachronic comparison of land transformation processes. Two spatial indicators were mobilized to quantify urban dynamics: the Average Urban Expansion Rate (AUER) and the Urban Expansion Intensity Index (UEII). Results reveal that urban areas expanded by up to 387.9% in some communes, with 15 exceeding an AUER of 25% and 17 falling within the “very high development” category based on UEII thresholds. Land artificialization was most intense along southern and southeastern peripheries, notably Deroua, Tit Mellil, Had Soualem, and Sidi Moussa Ben Ali, resulting in severe fragmentation of agricultural land. The classification of communes into four profiles (fast, slow, consolidated, and stable) highlights varying degrees of territorial vulnerability. By integrating demographic trends (2014–2024), the study exposes mismatches between population growth and land consumption, underscoring the urgent need for integrated spatial diagnostics and governance reforms toward sustainable peri-urban land management. Full article

Thermoplastic polyurethane properties are governed by the interplay between soft-segment mobility, hard-segment interactions, and segmented morphology, yet the extent to which atomistic predictions of their thermal and mechanical behavior depend on force-field choice remains insufficiently benchmarked. Here, we combine FTIR, DSC, TGA, and tensile testing with all-atom molecular dynamics simulations to investigate HDI–PEG polyurethane systems across a controlled soft-segment series. Experimentally, films with PEG molecular weights of 400, 1000, and 1500 g/mol were characterized, while simulations were extended to 400–2000 g/mol to compare two complementary force-field frameworks under a consistent protocol: OPLS-AA, a conventional atom-type-based force field, and OpenFF/Sage, a direct-chemical-perception framework augmented here with bespoke torsional refinements. Both force fields reproduce the composition-driven decrease in Tg and density with increasing PEG length, but differ systematically in absolute values, with OPLS-AA predicting higher densities and Tg values than OpenFF. Tensile experiments show the highest elastic modulus for PEG400, a marked decrease at PEG1000, and a partial recovery at PEG1500. Although nanosecond-scale deformation simulations overestimate absolute moduli because they probe high-rate elastic response, they recover composition-dependent stiffness differences, with OpenFF yielding a more pronounced non-monotonic trend than OPLS-AA. Overall, this work provides an experimentally anchored benchmark for assessing which composition-driven trends in HDI–PEG polyurethanes are robust across force-field families, and which observables remain sensitive to model assumptions and simulation scale. Full article