Search Results (315)

Gait Anomaly Detection is crucial for early diagnosis of neurodegenerative conditions and for monitoring rehabilitation progress. Surface electromyography provides a direct view into muscle synergy and motor intention, but the field suffers from a chronic scarcity of labeled pathological data. Traditional supervised methods, although accurate, rely on large, balanced datasets and often fail to generalize to unseen anomalies, limiting applicability in real-world clinical scenarios. An unsupervised framework based on a deep Autoencoder architecture is proposed. Instead of classifying anomalies, the model learns a robust representation of normal gait patterns through One-Class Classification, in a design that is inherently personalizable to each subject. Reconstruction errors are subsequently analyzed to identify deviations from normality, providing a sensitive measure of abnormal muscle coordination and motor patterns. The framework is validated on two distinct datasets: a controlled dataset of simulated anomalies, such as toe-walking, and a clinical dataset of real pathological gait patterns. Results demonstrate high detection accuracy: about 88% on real clinical knee pathologies and about 99% on the controlled setting of simulated toe-walking anomalies, the latter representing an easier, fully controlled scenario. These findings indicate that the framework effectively captures the muscle-activation synergies of normal gait and can detect deviations without requiring labeled pathological data. The unsupervised, reconstruction-based approach provides a practical, label-free solution for gait monitoring, with a normality model that can be adapted to the individual at deployment. Full article

The deep synergy between artificial intelligence and human capital facilitates enterprise green innovation transformation. Adopting the TOE framework, this paper constructs theoretical hypotheses and uses the panel data of Chinese listed firms to explore how human–artificial intelligence collaboration affects green innovation quantity and quality, along with its mediating paths and boundary conditions. Empirical results show that: (1) Human–artificial intelligence collaboration significantly promotes green innovation quantity and quality with quantile heterogeneity. High-innovation enterprises show stronger gains in green innovation quality, while cross-quantile differences are limited for green innovation quantity. (2) Organizational and employee digital capabilities act as mediators. However, the organizational digital capabilities path is not statistically significant for green innovation quantity, whereas other mediating paths are supported. (3) The promotional effect is shaped by digital infrastructure thresholds. Enterprise digital infrastructure produces a double-threshold increasing effect, and regional digital infrastructure creates a single threshold. Past this regional threshold, the effect on green innovation quantity turns from negative to positive. The regional threshold for green innovation quality is marginally significant, with its positive effect only present under low regional digital infrastructure and disappearing once the threshold is crossed. This paper identifies the digital constraints of human–artificial intelligence collaboration for green innovation and provides practical implications for enterprise digital and green development. Full article

Sustainable construction adoption in developing economies is often discussed in terms of financial, regulatory, and institutional barriers, while the organizational capabilities required for implementation have received less attention. This study examines the associations between key organizational and external factors and the adoption of sustainable construction practices in Jordan using partial least squares structural equation modeling (PLS-SEM) and the Technology–Organization–Environment (TOE) framework. Data were collected through a cross-sectional questionnaire completed by 192 contractors, consultants, developers, and other construction professionals. Financial Barriers, Economic Support, Regulatory Support, Human Capacity, and Technological Readiness were modeled as antecedents of adoption, while Perceived Sustainability Value (PSV) was modeled as the outcome. Human Capacity (β = 0.273, p = 0.001) and Technological Readiness (β = 0.300, p = 0.002) were positively associated with adoption, whereas Financial Barriers, Economic Support, and Regulatory Support were not statistically significant in the full model. Adoption was positively associated with PSV (β = 0.563, p < 0.001). The model accounted for 44.5% and 31.7% of the variance in adoption and PSV, respectively. The findings distinguish between barriers identified by practitioners as important and organizational capabilities most strongly associated with reported implementation. Strengthening training, technical expertise, equipment, and access to appropriate technologies may support sustainable construction adoption in Jordan. Because the data are cross-sectional and self-reported, the results are interpreted as associations rather than causal effects. Full article

Background/Objectives: Computer-assisted posturography is utilized to quantify human postural control, yet existing dedicated systems frequently present physical constraints, such as limited sensing surfaces and rigid hardware barriers. In this study, a methodological framework is presented and a structural arrangement (OpenBalance) is designed to implement balance assessment accommodating a wider anthropometric range through zone-based foot placement using a decentralized load cell array, with preliminary implementation details provided in a repository. Methods: The configuration comprises an array of 16 discrete uniaxial vertical-force load cells embedded within four mechanically decoupled sub-platforms to minimize mechanical cross-talk. Biomechanical moment equations were implemented in a custom Python pipeline to compute a localized Center of Pressure (COP) for each sub-platform independently. A vector-based data fusion algorithm maps these local coordinates into a unified global coordinate system. The system evaluation incorporated baseline signal-to-noise ratio (SNR) analysis, mechanical crosstalk testing, and digital low-pass filtering. Results: A proof-of-concept evaluation using empirical data confirmed that the cascading coordinate model produces a continuous global COP trajectory and quadrant-specific load distributions. The platform dimensions (355 × 460 mm) and sensor topography geometrically accommodate natural external foot rotation (incorporating a 10° toe-out angle projection) and foot lengths corresponding to EU shoe sizes up to 55. Mechanical crosstalk between adjacent sub-platforms remained minimal (<1% of applied load). Conclusions: The OpenBalance framework confirms the technical feasibility of deriving a continuous global COP from a decentralized array of distributed load cells. While baseline component specifications and static verifications are established, comprehensive dynamic cross-validation against reference standards remains a necessary next step for the future development of open hardware that can be produced using 3D printing. Full article

Artificial intelligence (AI) provides significant benefits for hydroponic systems by enabling precise nutrient management, efficient water recycling, and predictive decision support. However, adoption of AI in Omani hydroponic operations remains inconsistent, and the mechanisms linking contextual factors to farm-level performance are not well understood. This study investigates how technological, organizational, and environmental conditions relate to AI adoption, the association between adoption and perceived performance, and the mediating role of adoption in this relationship. An exploratory sequential mixed-methods approach, based on the Technology–Organization–Environment (TOE) framework, was employed. Data from nineteen interviews produced 563 coded references, and that material then shaped the item wording of a structured survey we administered to seventy-nine respondents. Reliability analysis using Cronbach’s alpha demonstrated acceptable to excellent internal consistency (α = 0.71–0.92). All three TOE dimensions were significant predictors of AI adoption (β = 0.29–0.33; R² = 0.58). AI adoption emerged as the sole significant direct predictor of perceived performance (β = 0.70; R² = 0.67), with none of the TOE dimensions retaining a significant direct association. Bootstrap analysis with five thousand resamples confirmed significant indirect effects for the Technological and Environmental dimensions, while the indirect effect for the Organizational dimension was inconclusive: its confidence interval crossing zero—a pattern we read as jointly reflecting heterogeneous organizational capacity across farms and the limited statistical power of a study this size for smaller effects. This study advances the TOE framework within Gulf-region hydroponic agriculture, identifies AI adoption as a key mediating mechanism, and provides evidence-based recommendations for policymakers, agricultural institutions, and technology providers. Since we define performance here in terms of water recycling, resource use and yield stability, the observed associations speak directly to sustainable production in arid settings. Full article

Smart farming (SF) integrates digital technologies, automation, and data analytics to enhance agricultural decision-making and sustainability. However, adoption in emerging economies remains limited due to structural constraints. This study examines the determinants of SF adoption intention in Honduras using an extended Technology–Organization–Environment (TOE) framework that incorporates technological attributes, farm manager characteristics, organizational factors, and business environmental conditions. A cross-sectional survey of 313 agricultural workers, mainly young and female, was analyzed using partial least squares structural equation modeling (PLS-SEM). The model explained 80.8% of the variance in adoption intention. Among the eleven hypothesized predictors, only government support and changes in the digital environment showed significant positive effects. Technological factors, managerial capabilities, organizational conditions, and competitive pressure were not significant. These findings indicate that, in structurally constrained agricultural systems, external enabling conditions—particularly institutional support and digital infrastructure—play a decisive role in shaping adoption intention. The results highlight the need for policies that strengthen rural connectivity, technical training, financing mechanisms, and institutional assistance to facilitate effective implementation of smart farming technologies. The study validates the TOE framework in an emerging economy and underscores the contextual variability of its explanatory dimensions. Full article

Artificial intelligence (AI) has increasingly been recognized as an important driver of green innovation, yet how cities leverage AI capabilities to achieve green innovation outcomes under different configurations of conditions remains insufficiently understood. This study develops a configurational framework based on the technology–organization–environment (TOE) perspective and applies dynamic qualitative comparative analysis (QCA) to panel data from 35 Chinese cities, comprising municipalities directly under the central government, provincial capitals, and sub-provincial cities, during 2019–2023. The results identify three configurations associated with high urban green innovation. AI Technology Development and AI Institutional Support consistently emerge as core conditions across all configurations, while AI Organizational Scale is identified as a recurring peripheral condition. The findings further reveal causal asymmetry between high and non-high levels of urban green innovation, showing that individual AI-related conditions alone are insufficient to achieve high green innovation outcomes without complementary conditions. Dynamic analysis indicates that the overall configurational structures remain relatively stable over time, although the consistency of individual configurations varies as AI applications and policy environments evolve. This study advances research on AI and green innovation by demonstrating that AI-driven urban green innovation depends on the interplay among multiple conditions and involves both stable foundational factors and evolving complementary conditions. Full article

Against the background of coordinated digital and green transformation, this study uses data on Shanghai- and Shenzhen-listed A-share firms from 2010 to 2024. The first year in which both policies take effect in a firm’s city is taken as the coordinated policy shock year. Using a staggered difference-in-differences (DID) model and double machine learning (DML), this study evaluates how public-data openness and green data center policies affect corporate sustainability performance. The results indicate that entering dual-policy status significantly improves corporate sustainability performance. Further restricted-sample analysis documents a positive incremental dual-policy effect beyond a single-policy implementation, which provides suggestive evidence for theoretical policy complementarity. Mechanism tests reveal that dual-policy status is associated with enhanced credit availability, greater ambidextrous green innovation, reduced agency costs, and optimized human-capital allocation. These patterns provide suggestive evidence consistent with the four theoretically proposed transmission channels, though formal causal mediation is not established in the current empirical setup. Analysis based on the technology–organization–environment (TOE) framework shows that technological foundations, organizational capabilities, and the external green institutional environment all strengthen the policy synergy effect. Further analysis has found that each policy has a positive effect when implemented separately; compared with a single-pilot status, the dual-pilot status produces a significant positive net effect. Both implementation sequences generate positive synergy effects; based on the point estimates, the synergy effect is larger when green data centers are built before public-data openness is promoted. From a policy-combination perspective, this study reveals the complementary mechanism between data element supply and green computing capacity and provides firm-level evidence for the coordinated advancement of digitalization and greening. Full article

Anastomotic angle and flow allocation change together in end-to-side junctions, complicating interpretation of angle-dependent wall shear. We used MITOS Flow Lab, a two-dimensional D2Q9 two-relaxation-time lattice-Boltzmann environment, to examine this coupling in steady, rigid-walled, Newtonian models at Re ≈ 91. Angles of 30–120° were compared under equal outlet pressures and at approximately matched branch-flow fractions of 0.25 and 0.21, achieved with angle-specific static outlet-pressure offsets. Under equal outlet pressures, the branch-flow fraction decreased from 0.356 to 0.151 across this angle range, while the minimum normalized signed recipient-floor shear increased from 0.140 to 0.450. Matching flow allocation substantially reduced angle-associated variation in this endpoint and in the sub-toe response, whereas sub-heel and sub-ostial responses remained angle dependent under the adjusted boundary conditions. This qualitative contrast persisted when the lumen resolution was increased from 32 to 64 nodes for the 0.25 target, although minimum-shear attenuation changed from approximately 83% to 74%. The 0.21 target was examined only on the production grid. These experiments demonstrate that the interpretation of angle-associated shear depends on the flow-allocation condition used for comparison. They do not identify a boundary-independent geometric effect or a causal mediation fraction. Absolute values and attenuation magnitudes remain sensitive to discretization and have not been independently validated. These controlled comparisons provide a framework for interpreting angle-associated shear together with achieved flow allocation and the specified outlet conditions. Full article

Accurate detection of early minor faults in electric vehicle traction batteries is important for preventing thermal runaway under complex operating conditions. Aging-related capacity degradation and measurement noise can mask the weak voltage distortions caused by early faults, leading to false alarms in data-driven diagnostic models. To improve robustness to these disturbances, this paper develops an early multi-fault decoupling and diagnosis framework based on PatchTSSA, a lightweight Transformer architecture that adapts time-series patching and Token Statistics Self-Attention (TSSA) to battery diagnostic sequences. The framework combines static–dynamic feature fusion with time-series patching to capture both global voltage drift and local morphological gradients. Within this adapted framework, TSSA replaces quadratic dot-product attention with second-order moment pooling, giving linear complexity with respect to the number of tokens and supporting future investigation of embedded Battery Management System (BMS) implementation. A physics-informed fault-injection strategy is used to construct a five-class dataset comprising the healthy state (E00), minor internal short circuit (E01), severe internal short circuit (E02), penetration fault (E03), and sensor drift (E04) from public Center for Advanced Life Cycle Engineering (CALCE) and National Aeronautics and Space Administration (NASA) battery-aging data. Across five raw-cycle-grouped splits and training seeds under 5 mV Gaussian white noise, the complete PatchTSSA configuration achieves $91.5\pm 1.9\%$ overall accuracy, $94.1\pm 1.2\%$ macro recall, and $85.4\pm 5.3\%$ E01 recall for the simulated fault patterns. The direct E01–E04 confusion rate is $0.09\pm 0.20\%$, whereas the E00-to-E01 false-alarm rate is $15.1\pm 6.9\%$. CALCE–NASA protocol differences are used only to describe cross-dataset domain shift; no transfer-performance claim is made without a matched capacity-free evaluation. The results indicate the potential of the framework for online fault-pattern discrimination, while validation using real fault data and embedded hardware remains necessary. Full article

Existing studies have extensively examined the factors influencing community resilience; however, insufficient attention has been paid to the synergistic effects of multiple conditions and differentiated configurational pathways under resource constraints. Drawing on the Technology–Organization–Environment (TOE) framework, this study examines 30 communities in small and medium-sized cities in Southwest China and integrates Necessary Condition Analysis (NCA) with fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate the multiple formation mechanisms and configurational pathways of community resilience. The results show that (1) resource constraints do not constitute a deterministic barrier to high community resilience but instead serve as an important contextual condition shaping pathway selection; communities facing high resource constraints can still achieve high resilience through capability reconstruction; (2) no single condition is necessary for high community resilience, while seven configurational pathways can lead to this outcome, with an overall solution consistency of 0.9749 and an overall solution coverage of 0.7601; these pathways can be grouped into three mechanisms—technology-oriented strengthening, organization-based compensation, and multi-dimensional collaboration; (3) infrastructure and response capacity provides stable foundational support across multiple high-resilience pathways, while the conditions exhibit patterns of potential functional substitution and configurational complementarity; and (4) low resilience is not simply a consequence of resource scarcity but primarily reflects the failure of key conditions to form effective configurations. By moving beyond a simple linear association between resource stocks and resilience levels, this study reveals a resilience formation logic of “resource allocation–capability reconstruction–configurational fit” and provides theoretical and practical implications for differentiated resilience governance in resource-constrained communities. Full article

Artificial intelligence (AI) is increasingly transforming recruitment and selection. This conceptual study aims to develop an integrative socio-technical framework explaining HR professionals’ behavioral intention to adopt AI-based recruitment systems. Using a theory-driven conceptual review, the study integrates the Technology Acceptance Model (TAM), selected technological and organizational dimensions of the Technology–Organization–Environment (TOE) framework, and a socio-technical systems perspective. The framework combines perceived usefulness and relative advantage; trust, transparency, data privacy concerns, job replacement anxiety, and resistance to change; and HR readiness and top management support. Trust, transparency, perceived usefulness, and HR readiness are theorized to support behavioral intention; relative advantage is proposed to strengthen perceived usefulness; data privacy concerns are proposed to weaken trust; and job replacement anxiety and resistance to change are theorized to negatively influence behavioral intention. Top management support is conceptualized as moderating the resistance–intention relationship. The framework extends additive adoption models by emphasizing reciprocal socio-technical adaptation among technological characteristics, human interpretations, organizational practices, and governance conditions. As a conceptual model, it requires empirical validation and provides a basis for future research and responsible AI implementation in recruitment. Full article

Although 5D Building Information Modeling (5D BIM) offers potential for cost control, its implementation remains constrained by multiple barriers. This study examines the relative perceived importance of six factors associated with 5D BIM implementation and their direct associations with project cost performance among higher-tier general contractors in Guangdong Province, China. Survey data from 234 contractor organizations, each represented by a knowledgeable key informant, were analyzed using SPSS, PLS-SEM, and IPMA to rank the factors, estimate their direct associations, and identify managerial priorities. Environmental factors received the highest perceived-importance ranking. The structural model shows significant positive direct associations between project cost performance and four factors: technological, organizational, environmental, and government policy; government policy has the largest standardized path coefficient among the six predictors tested. Operational and project-related factors show no statistically significant direct associations. IPMA places organizational and environmental factors in the high-importance, high-performance area, whereas government policy has high importance but below-average performance. This first empirical test of the previously developed extended TOE framework distinguishes perceived implementation importance from direct cost–performance relevance and clarifies its theoretical boundary. These context-specific findings inform contractor management and policy implementation and support future validation across regions, contractor types, and stakeholder groups. Full article

Qatar offers small and medium-sized enterprises (SMEs) an unusually uniform environment for digitalization, with advanced infrastructure, generous public programs, and strong market pressure, yet digital adoption varies widely between firms. This study asks whether that environment still explains the variation. Using the technology-organization-environment (TOE) framework, we surveyed owners and managers of service-sector SMEs through 165 invitations in April 2026 and analyzed 158 screened responses with partial least squares structural equation modeling (PLS-SEM). The model tested the effects of technological readiness, organizational readiness, environmental support, competitive pressure, and customer expectations on digital adoption, each measured on five-point scales. Organizational readiness was the strongest predictor ($\beta$ = 0.671), technological readiness had a smaller significant effect ($\beta$ = 0.297), and the model explained 74.5% of the variance. No environmental construct was significant, although four of the five environmental conditions were rated near the top of the scale. We interpret this as environmental saturation, a reading the diagnostics favor over a measurement ceiling: conditions shared by nearly all firms no longer distinguish adopters, leaving a divide in internal capability. Policymakers should fund diagnosed capability gaps, training, and implementation advice rather than further general provision, and managers should prioritize leadership commitment and staff skills. Full article

Agricultural carbon reduction (ACR) is important for the green transformation of agriculture and ecological protection in the Yellow River Basin. However, ACR is not driven by a single factor. How different conditions combine and change over time remains unclear. Based on the technology–organization–environment (TOE) framework, this study examines 77 cities in the Yellow River Basin from 2017 to 2022. Six antecedent conditions are considered: agricultural technological innovation (ATI), agricultural mechanization (AM), government environmental attention (GEA), government fiscal intervention (GFI), agricultural industrial structure (AIS), and urbanization (URB). Multi-period fuzzy-set qualitative comparative analysis (fsQCA) is used to identify the pathways to high ACR, their temporal evolution, and regional differences. The results show that (1) no single antecedent condition is necessary for high ACR in either period; (2) four high-ACR pathways are identified in the baseline period (2017–2019), comprising three types: agricultural structure–urbanization synergy, agricultural technological innovation-led, and technology–equipment–urbanization synergy; (3) four high-ACR pathways are also identified in the transition period (2020–2022). AIS is present in all four pathways, compared with only two pathways in the baseline period, and combines with AM or URB in different configurations; and (4) regional comparisons reveal alternative configurations of production and governance conditions. Upstream pathways include technology–equipment–government combinations, midstream pathways retain both technological and structural alternatives, and downstream pathways show a more pervasive role for AIS in the later period. These findings suggest that effective ACR policies should match local production and governance conditions rather than uniformly increase individual policy inputs. Full article