Search Results (34,323)

The rapid expansion of artificial intelligence and cloud computing is straining terrestrial data center infrastructure, motivating exploration of space-based data centers (SBDCs) as a scalable and energy-efficient alternative. While orbital platforms offer unique advantages, including continuous solar energy, radiative cooling, and global coverage, their practical deployment is constrained by unresolved reliability challenges across the mission lifecycle. This study presents a lifecycle-oriented reliability and risk assessment for SBDCs spanning launch, orbital operation, maintenance, and end-of-life phases, using a structured systems-level analysis of failure modes and operational dependencies. This paper focuses on compute-centric SBDC architectures, treating storage solely as a supporting resource. We identify and classify space-environment-specific risks, including launch-induced mechanical stress, radiation-driven degradation, thermal extremes, and single points of failure in power and communication subsystems. By integrating engineering constraints with economic considerations, we develop a unified risk-chain framework that shows how reliability limitations propagate from component design to system cost and operational viability. The analysis reveals a critical trade-off: achieving terrestrial-grade reliability in orbit requires substantial redundancy and radiation hardening, increasing mass and cost and reducing economic feasibility, whereas lower-reliability designs introduce operational and financial risks that challenge sustainability. These findings establish reliability as the central determinant of SBDC viability, providing an applied foundation for fault-tolerant, modular, and lifecycle-aware design strategies essential for transitioning orbital cloud infrastructure from concept to scalable reality. Full article

Background: Mobile health has been increasingly integrated into tuberculosis care to support patient education, communication, and treatment engagement. However, evidence remains limited regarding whether positive engagement with mHealth is associated with knowledge, attitudes, and practices among patients with multidrug-resistant tuberculosis. This study aimed to examine the association between positive mHealth engagement and knowledge, attitude, practice, and total KAP among patients with multidrug-resistant tuberculosis, and to evaluate the psychometric properties of the engagement score used as the primary exposure variable. Methods: A cross-sectional study was conducted among patients with multidrug-resistant tuberculosis. A positive mHealth engagement score was constructed from 12 mHealth-related items after harmonizing item directionality so that higher scores indicated more favorable engagement. The 12 items reflected five behavioural domains: intensity of use, ease and acceptability of use, functional engagement (communication with providers, access to health information, and perceived benefit for disease self-management), continuity of use, and barriers to sustained engagement. The composite score was computed as the mean of the 12 standardised items, with higher values indicating more positive engagement. Internal consistency was assessed using Cronbach’s alpha and corrected item–total correlations, and structural validity was explored using principal component analysis. Adjusted linear regression models were used to examine associations between the engagement score and Knowledge, Attitude, Practice, and total KAP scores, controlling for age, sex, and occupation. Sensitivity analyses were performed after excluding a poorly performing item, and tertile analyses were used to assess dose–response patterns. Results: The positive mHealth engagement score showed good internal consistency, with a Cronbach’s alpha of 0.852. One item demonstrated poor psychometric performance, and Cronbach’s alpha increased to 0.864 after its exclusion. The data were suitable for dimensionality assessment, with a Kaiser–Meyer–Olkin value of 0.870 and a significant Bartlett’s test. Principal component analysis identified a dominant first component explaining 43.29% of the total variance. Using the refined score, higher positive mHealth engagement was significantly associated with higher Knowledge scores (β = 2.06; 95% CI: 1.28–2.85; p < 0.001), higher Attitude scores (β = 4.68; 95% CI: 3.30–6.06; p < 0.001), and higher total KAP scores (β = 6.68; 95% CI: 4.62–8.74; p < 0.001), whereas no significant association was observed for the Practice score (β = −0.07; 95% CI: −0.63 to 0.49; p = 0.804). In tertile analyses, Knowledge, Attitude, and total KAP scores increased significantly across engagement levels, while Practice scores did not. Conclusions: Positive mHealth engagement was associated with better knowledge, attitudes, and overall KAP among patients with multidrug-resistant tuberculosis, but not with practice. These findings are associative; the cross-sectional design does not permit causal conclusions. The engagement score demonstrated good reliability and acceptable structural validity and may be a useful summary measure for evaluating patient interaction with mHealth interventions in tuberculosis care. Integrated strategies combining mHealth with clinical follow-up, adherence counseling, and structural support may be needed to translate informational and attitudinal gains into practice change. Full article

Graphene derivatives and nano-silicon dioxide (nano-SiO₂) have been widely studied as functional nanofillers for architectural coatings. They have the potential to improve mechanical performance, barrier properties, durability, and versatility. However, despite encouraging results in laboratory settings, their use in commercial coating formulations is still limited. This is mainly due to challenges with dispersing nanoparticles, ensuring compatibility with polymer binders, maintaining long-term durability, and achieving formulation stability. In this work, we conducted a thorough review and meta-analysis of 20 peer-reviewed studies to evaluate the performance and limitations of graphene-based materials and nano-SiO₂ in architectural and protective coatings. Our literature search followed PRISMA guidelines and included studies that provided quantitative data on dispersion methods, surface functionalization strategies, nanofiller loading levels, and coating performance metrics. This review highlights key relationships between structure, properties, and processing. It points out ongoing challenges that prevent practical use and suggests future research directions to enhance formulation design, improve dispersion stability, and extend the long-term performance of graphene- and nano-SiO₂-modified architectural and protective coatings. Full article

Concrete wall cavities are common hidden defects in construction engineering that seriously reduce structural safety, durability, and construction quality, especially in old buildings and projects without complete design documents. Traditional detection methods have obvious limitations: the manual tapping method relies heavily on subjective experience and lacks quantitative standards, while advanced non-destructive testing methods such as ultrasonic testing and infrared thermography are expensive, complex to operate, and difficult to apply on a large scale. At present, the quantitative correlation between acoustic signal characteristics and cavity defects has not been fully studied. To address these problems, this study combines literature analysis, controlled experiments, and acoustic signal processing to propose a quantitative identification method for concrete wall cavities based on autocorrelation analysis of sound signals. Tapping signals from normal and cavity walls are collected and processed using band-pass filtering and amplitude normalization. The autocorrelation function (ACF) is then used to extract characteristic parameters. The results show that the proposed method exhibits significantly improved accuracy and efficiency compared with traditional manual detection. Obvious differences in autocorrelation characteristics can be observed between normal and cavity walls. The method realizes the transformation from subjective auditory judgment to objective quantitative identification, with low cost, strong anti-interference ability, and high sensitivity to small defects. It provides a reliable technical tool for the rapid and quantitative non-destructive testing of concrete wall cavities in engineering practice. Full article

The rapid expansion of artificial intelligence has intensified the demand for hardware systems capable of emulating brain-like information processing with high accuracy, energy efficiency, and reliability. Neuromorphic computing based on memristive artificial synapses has emerged as a promising approach to overcome the limitations of conventional von Neumann architectures. Although inorganic and oxide-based synaptic memristors have been widely explored for neuromorphic systems, they often suffer from poor linearity, asymmetric potentiation/depression behavior, limited conductance states, and device variability, which restrict learning accuracy and scalability. In contrast, polymer-based memristors have gained significant attention owing to their intrinsic advantages, including mechanical flexibility, molecular tunability, controllable electronic/ionic transport, low-temperature processability, and compatibility with large-area fabrication. This review critically examines recent advances in polymer—based memristive materials and devices for achieving linear and symmetric artificial synaptic behavior. Polymer synapses are classified into pure polymer, polymer composite, and polymer-hybrid systems through a mechanism to function framework. Rather than providing a general compilation of organic memristor studies, this review analyzes how polymer chemistry, ion-migration control, trap state distribution, redox activity, electrode selection, active layer thickness, and interface engineering govern conductance update linearity, symmetry, and uniformity. Fundamental switching mechanisms, material classifications, device architectures, key synaptic characteristics, and system-level neuromorphic performance, including pattern-recognition applications, are critically discussed. By explicitly linking material and device design to conductance update fidelity, learning accuracy, training convergence, and pattern-recognition reliability, this review provides practical design guidelines and future perspectives for next-generation polymer-based neuromorphic hardware with improved linearity, symmetry, reliability, and scalability. Full article

The abdominal pressure–volume (P–V) relationship during laparoscopic insufflation is curvilinear and subject to substantial inter-individual variability, yet clinical practice relies on universal pressure targets derived from population-level guidelines. The Smart Inline Compliance Module (SICM) is a novel inline retrofit device that acquires intra-abdominal pressure and insufflation gas flow through physically separated sensing circuits, reconstructs insufflated volume by numerical integration of the flow signal, and derives the abdominal P–V curve and its biomechanical parameters in real time. This study reports the first two-arm pilot technical evaluation of the SICM system. Arm A comprised an exploratory biomechanical phantom with three defined stiffness levels (Soft, Medium, Rigid) tested under Continuous and Stepwise insufflation protocols (30 curves). Arm B comprised three female feline cadavers assessed under the same dual-protocol design (18 curves). This study should be interpreted as an early-stage technical evaluation rather than as a definitive validation benchmark. Signal quality was consistently high across both arms (Curve Quality Index: 1.0000 in the phantom arm; 0.9974 ± 0.0009 in the cadaveric arm). Volume integration accuracy was confirmed against an independent offline reference (mean absolute percentage difference: 0.07%). The system extracted reproducible biomechanical parameters under the Continuous protocol: in the cadaveric arm, maximum compliance (Cmax) ranged from 116.8 to 191.4 mL/mmHg across subjects, with intra-session coefficients of variation below 16%; Knee Pressure (Pknee), defined as a working operational index of the compliance transition, was 3.33–4.17 mmHg with CV below 8%. The Rigid phantom and cadaveric datasets showed partial numerical overlap in selected shape-derived parameters, which was interpreted only as an internal consistency check and not as evidence of biomechanical equivalence. The Stepwise protocol exposed the current methodological limits of the parameter-extraction workflow and identified specific targets for the next development iteration. These results are interpreted exclusively within the scope of technical feasibility and preliminary biomechanical characterisation; clinical applicability and optimal pressure guidance require adequately powered in vivo studies. Full article

Background/Objectives: The practices that parents use to feed their infants have important implications for life course health and well-being. However, little is known about the infant feeding experiences and decisions of parents with developmental disabilities. This study used a mixed methods design to gain an in-depth understanding of the infant feeding experiences and decisions of parents with developmental disabilities in the United States. Methods: Between July 2024 and June 2025, 18 parents with developmental disabilities completed a one-time quantitative survey, seven of whom also completed three individual qualitative interviews. Analytical procedures included descriptive statistics of quantitative survey data and thematic analysis of qualitative interviews, followed by integration of the two forms of data. All interview participants completed member checking of preliminary themes. Results: Parents with developmental disabilities described varied experiences with breastfeeding, formula feeding, and introducing solid foods to their infants at around six months. Four disability-related factors influenced parents’ decisions across different infant feeding practices: (1) sensitivity to sensory stimuli; (2) demands on executive function; (3) “rigid thinking” about breastfeeding; and (4) medication use. Conclusions: Findings suggest parents with developmental disabilities may benefit from direct and customized support with infant feeding. Changes to improve access to disability-affirming care are warranted. Full article

This study evaluates the structural performance of sliding-type evacuation ladders under realistic fire evacuation loading conditions using parametric numerical analysis. A series of finite element models was developed based on the original ladder design, and key parameters—including member thickness (1–4 mm), overlap length between modular units (40–70 mm), loading configurations, and boundary conditions at the ladder base—were systematically varied. A total of 288 numerical cases were analyzed to investigate their influence on global displacement behavior. The results indicate that a minimum member thickness of 2 mm is required to satisfy displacement-based serviceability criteria; however, this threshold may be insufficient when connection flexibility is considered. The overlap length has a more pronounced effect on structural performance for thinner members, while the loading height has a significant effect on the displacement response. In addition, the boundary condition at the ladder base plays a critical role, with vertical support conditions substantially reducing overall displacement. These findings highlight the importance of system-level structural evaluation beyond component-based testing. They also provide practical insights for improving the design criteria and installation conditions of evacuation ladders in high-rise residential buildings during fire emergencies. Full article

The expansion of accessible, fine-grained city data has significantly increased opportunities for evidence-based and informed policy-making. Despite this evolution, extracting actionable insights from heterogeneous data sources and effectively communicating findings remain persistent challenges. Most existing visualisation approaches and research prioritise technical implementation by focusing on how to visualise, often neglecting the importance of policy-driven visualisation questions and data contexts. This led to flawed analyses, particularly in complex domains such as smart cities and urban policy-making using digital twins. This article presents a novel, practical, step-by-step policy visualisation methodology grounded in empirical smart city research, shifting the emphasis toward policy-element-based questions informed by data-informed evidence. The methodology was successfully applied, tested, and adapted, resulting in an implementable, structured, and integrative approach that aligns with policymakers’ established policy design, implementation, and evaluation cycles. Through this approach, 20 user-driven smart city policy visualisations were operationalised and implemented in strategic policy decision-making contexts across smart city domains, including mobility, spatial planning, and environment. The results demonstrate how dashboards, algorithmic simulations, and digital twins visualisations can be systematically deployed to support evidence-informed decision-making. Full article

This study examines how peer perceptions and school power dynamics shape the identity development of learners with disabilities in South African full-service schools. Guided by Michel Foucault’s lens, particularly ‘care of the self,’ the research explores pathways from vulnerability to empowerment. Using a qualitative, exploratory design, data were generated through semi-structured interviews with learners (n = 20; ages 12–18) and non-participant classroom observations across four full-service schools, followed by thematic analysis with double-coding to enhance reliability. Findings show that negative peer perceptions and routine categorisation practices intensify isolation, self-doubt, and internalised stigma; yet many learners actively navigate identity threats via self-advocacy, supportive relationships, and self-care practices (e.g., mindfulness, journaling, goal setting). These practices are associated with greater self-awareness, confidence, and agency, particularly where school climates promote inclusion, positive peer interaction, and arts/sport participation. The analysis highlights a persistent tension between biopower in schooling (assessment, surveillance, normalisation) and students’ self-care efforts; however, supportive environments mitigate this tension and enable identity affirmation. The study concludes that embedding structured self-care opportunities, strengthening positive peer cultures, and integrating disability perspectives across the curriculum can convert harmful perceptions into opportunities for resilient identity formation and learner empowerment. Full article

Transition metal sulfides are promising anode materials for lithium-ion batteries, but their practical application is limited by severe volume variation and sluggish reaction kinetics during cycling. Inspired by the natural mineral assemblage of seafloor massive sulfides (SMS), FeS₂/CuFeS₂ composite anodes were prepared by a mechanochemical ball-milling method with mass ratios of 9:1 and 7:3 to reflect the major compositional characteristics of SMS. Among them, the 9:1 composite (F9C1) exhibited the best overall electrochemical performance, delivering a reversible capacity of 763.4 mAh g⁻¹ after 300 cycles at 1 C and retaining 46% of its baseline capacity at 5 C. Structural and electrochemical analyses suggested that the introduction of a small amount of CuFeS₂ likely promoted interfacial interactions between FeS₂ and CuFeS₂ phases, reduced charge-transfer resistance, and enhanced pseudocapacitive contribution, while preserving the capacity advantage of the FeS₂ host phase. These results demonstrate that mineral-inspired compositional design is an effective strategy for improving the lithium-storage performance of sulfide anodes and provides a feasible route for developing electrode materials inspired by naturally coexisting sulfide minerals. Full article

The horizontal bearing mechanism of pile–soil composite foundations adjacent to retaining walls is significantly affected by asymmetric lateral constraints caused by retaining wall movement, a scenario that remains inadequately explored in conventional design. This study employs a validated three-dimensional finite element model to investigate the response of such foundations to rotational displacement of a nearby wall. A comprehensive parametric analysis quantifies the influence of pile configuration, cushion properties, soil modulus, and loading conditions. The results demonstrate that rotational displacement (RB mode) induces a highly non-uniform load distribution within the pile group. The middle-front row piles emerge as critical load-bearing components, experiencing significant load amplification (load-transfer coefficients η_p up to 2.3). Key parameters, including pile length and cushion stiffness, selectively regulate system stiffness or optimize load sharing. Increasing the pile–wall distance is identified as an effective measure to reduce load concentration on front-row piles. The findings provide quantitative insights and practical guidance for the performance-based design of composite foundations under asymmetric constraints. Full article

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly impact a company’s economic performance. This paper proposes a hybrid decision-support framework that integrates the Analytic Hierarchy Process (AHP) with a normalized scoring model. In this approach, classical AHP pairwise comparisons are used to derive consistent criteria weights, while project alternatives are evaluated on a 1–10 normalized scale to ensure the model remains scalable and practical for an industrial setting. The framework was empirically validated through a case study in an automotive company evaluating twelve DFSS project concepts. The results reveal that experts prioritize Product Quality (33%) and Cost/Functionality (33%) above all other factors, with these two criteria accounting for 66% of the total decision weight. Furthermore, the study established classification rules where projects scoring above 7.2 showed high implementation potential, while those below 5.2 were frequently discontinued. This structured approach enables a transparent and justifiable prioritization process that supports economic and operational sustainability by significantly reducing wasted engineering hours and prototype costs. Full article

The reinforcement and retrofit of damaged steel buildings has emerged as a primary focus in civil engineering. It should be noted that completing the reasonable strengthening design for avoiding the sudden collapse of a structure in extreme engineering conditions was an urgent task, while the existing method required a long time which significantly influenced the reinforcing practice. In the present study, an improved explainable machine learning (ML) framework was developed for the rapid assessment of the bending property of repaired laminated channel beams. Firstly, a comprehensive database of 192 samples combining experimental and finite element data was established. The Mahalanobis distance analysis and Pearson correlation analysis were sequentially performed to evaluate the singularity of the samples and the dependencies between the variables. Secondly, the adversarial tests were conducted on the randomly selected 10 pairs of training and testing sets to determine the database with the best distribution consistency. Then, three machine-learning models of artificial neural networks (ANN), random forest (RF), and extreme gradient boosting tree (XGBoost) were respectively trained and validated. Finally, the explainability analysis of the XGBoost model was carried out in the global and local perspectives based on the SHAP method. The prediction accuracy (R²) of all ML models exceeded 90%, demonstrating good accuracy and providing a useful reference within the current database for the reinforcement design of damaged steel beams in emergency situations. In addition, the XGBoost model achieved superior prediction accuracy (R² = 97.98%) and stability (CoV = 0.82%) compared to ANN and RF. The explainability analysis revealed that boundary conditions and load type had the most significant influence on bending capacity. The proposed ML approach enabled efficient and reliable bending capacity estimation, supporting rapid decision-making in emergency reinforcement scenarios for damaged steel structures. Full article

Off-road semantic segmentation is challenging due to irregular terrain, vegetation clutter, class-level similarity, and ambiguous boundary annotations. Existing decoder designs often rely on compact bottlenecks that oversmooth fine structures or repeated multi-scale fusion that can amplify annotation noise and increase computational cost. To address these limitations, we propose a Cross-Scale Decoder for robust off-road semantic segmentation. The proposed decoder first stabilizes semantic representations through Global–Local Token Refinement (GLTR) on a compact bottleneck lattice. It then selectively incorporates fine-scale structural cues using Boundary-Guided Correction (BGC) and Gated Cross-Scale Interaction (GCS), avoiding dense and repeated feature fusion. In addition, uncertainty-guided class-aware point refinement focuses computation on ambiguous and low-confidence regions. Experiments on standard off-road benchmarks demonstrate that the proposed method improves segmentation accuracy and boundary consistency over existing approaches while maintaining practical inference efficiency. Full article