Search Results (3,102)

The Wolf Motor Function Test (WMFT) is widely used to evaluate upper limb motor performance after stroke. However, conventional approaches may overlook domain-specific neuromuscular and kinematic differences during task execution. This study classified WMFT tasks into three functional domains: proximal reaching and transport (PRT), fine motor manipulation (FMM), and gross motor functional control (GMFC). Interlimb differences in muscle activation, joint mobility, and movement amplitude were examined using sensor-based measurements. Twelve individuals with chronic stroke performed 16 WMFT tasks. Surface electromyography (EMG) and inertial measurement units (IMUs) recorded upper limb muscle activity, joint angles, and segmental displacement. Wilcoxon signed-rank tests and Spearman correlations were conducted for each functional domain. Significant asymmetries in EMG, range of motion (ROM), and root mean square (RMS) acceleration were found in PRT and FMM tasks. These results reflect increased proximal muscle activation and reduced distal engagement on the paretic side. GMFC tasks elicited more symmetrical patterns but still showed subtle deficits in distal control. Correlation analyses demonstrated strong interdependencies among neuromuscular and kinematic measures. This finding underscores the integrated nature of compensatory strategies. Categorizing WMFT tasks by functional domain and integrating multimodal sensor analysis revealed nuanced impairment patterns. These patterns were not detectable by conventional observational scoring. These findings support the use of sensor-based, domain-specific assessment to guide individualized rehabilitation strategies. Such approaches may ultimately enhance long-term functional recovery in stroke survivors. Full article

This empirical investigation explores the complex interdependencies between the concept of the Smart University Campus and the broader ecosystem of Smart City policies, with a particular focus on the New European Bauhaus initiative as a catalyst for educational transformation. The study examines how university campuses can evolve into paradigmatic models of innovation, sustainability, and inclusion through the strategic integration of emerging technologies, circular bioeconomy principles, and holistic ecological strategies. A comprehensive case study, grounded in rigorous quantitative analysis, including Principal Component Analysis (PCA), Importance-Performance Analysis (IPA), and Cluster Analysis (CA), based on questionnaires administered to a sample of 245 high school and university students—primarily from the academic community of the “King Mihai I” University of Life Sciences in Timișoara (USVT)—provides empirical insights into perceptions and expectations regarding the Smart Campus ecosystem and its core components: Smart Learning, Smart Living, Smart Safety and Security, Smart Socialization and Smart Health. The distinctive contribution of this research lies in its empirical demonstration that the strategic alignment between university campuses and Smart City initiatives, guided by the principles of the New European Bauhaus, can enhance educational efficiency by creating integrated learning ecosystems that simultaneously address academic needs, sustainability imperatives, and goals of sustainable urban development. Full article

Dynamic evaluation of airport terminal capacity is critical for efficient operations, yet it remains challenging due to the complex interplay of spatial and temporal factors. Existing approaches often handle spatial connectivity and temporal fluctuations separately, limiting their predictive power under rapidly changing conditions. Here the ST-GTNet (Spatio-Temporal Graph Transformer Network) is presented, a novel deep learning model that integrates Graph Convolutional Networks (GCNs) with a Transformer architecture to simultaneously capture spatial interdependencies among airport gates and temporal patterns in operational data. To ensure interpretability and efficiency, a feature selection mechanism guided by XGBoost and SHAP (Shapley Additive Explanations) is incorporated to identify the most influential features. This unified spatio-temporal framework overcomes the limitations of conventional methods by learning spatial and temporal dynamics jointly, thereby enhancing the accuracy of dynamic capacity predictions. In a case study at a large international airport with a U-shaped corridor terminal, the ST-GTNet delivered robust and reliable capacity forecasts, validating its effectiveness in a complex real-world scenario. These findings highlight the potential of the ST-GTNet as a powerful tool for dynamic airport capacity evaluation and management. Full article

Societal challenges are inherently complex and multi-tiered, arising from the interplay of diverse stakeholders with a spectrum of purposes and different perceptions and expectations, interdependent systems, and dynamic contextual factors that transcend single domains or disciplines. This paper presents a novel approach to developing a Reference Model of Governance tailored to a specific complex, multi-organisational enterprise facing socially complex challenges. Drawing on Angyal’s systems framework, the model introduces a three-dimensional structure with vertical, progression, and transverse dimensions, integrated within a holistic contextual whole. By mapping selected systems methodologies, including Soft Systems Methodology (SSM), Viable System Model (VSM), System Dynamics (SD), and dependency modelling, to these dimensions, the model offers a pragmatic, structured way to explore and regulate complex organisational behaviour. It enables collaborative inquiry, supports adaptive governance, and enhances the enterprise’s ability to address dynamic societal problems such as health, education, and public service delivery. The result is a governance reference model that captures both the operational and contextual realities of the enterprise, providing actionable insight for strategic design or diagnostic intervention. The novel approach is grounded in systemic and critical systems thinking and emphasises the use of methods for understanding to develop a common and shared understanding of the enterprise context and to surface multiple stakeholder perspectives. Full article

In urban underground construction, Earth Pressure Balance (EPB) tunneling faces complex geological uncertainties and dynamic operational risks. Traditional safety management approaches often struggle under such conditions. This paper proposes an integrated safety resilience assessment framework for EPB tunneling that combines an entropy-weighted TOPSIS method, the Analytic Network Process (ANP), and an extension cloud model to capture interdependencies and uncertainties. A hierarchical indicator system with four primary dimensions (stability, redundancy, efficiency, and fitness) is constructed. The entropy-TOPSIS algorithm provides objective initial weights and scenario ranking, while ANP models the feedback relationships among criteria. The extension cloud model quantifies fuzziness in expert judgments and converts qualitative assessments into probabilistic resilience ratings. The methodology is applied to a case study of the EPB shield tunnel section of Jinan Metro Line 6 (China). The section’s resilience is classified as a medium level, which agrees with expert evaluation. The results demonstrate that the proposed approach yields accurate and robust safety resilience evaluations, supporting data-driven decision-making. This framework offers a quantitative tool for resilience-based safety management of shield tunneling projects, providing guidance for shifting from traditional risk control toward a resilience-enhancement strategy. Full article

This essay proposes a transdisciplinary framework that positions cooperation as a foundational principle for ecosystem stewardship in the Anthropocene. Drawing from microbial ecology, evolutionary theory, and sustainability science, we argue that cooperation, rather than competition, is a robust and scalable strategy for resilience across biological and institutional systems. In particular, microbial behaviors such as biofilm formation, quorum sensing, and horizontal gene transfer are especially pronounced in extreme environments, where cooperation becomes essential for survival. These strategies serve as functional analogues that illuminate the structural logics of resilience: interdependence, redundancy, distributed coordination, and adaptation. As the Anthropocene progresses toward increasingly extreme conditions, including potential “Hothouse Earth” scenarios driven by climate disruption, such ecological heuristics offer concrete insights into how human institutions can adapt to stress and uncertainty. Rather than reiterating familiar calls for hybrid governance, we use microbial cooperation as a heuristic to reveal the functional architecture already present in many resilient governance practices. These microbial strategies emerging from life in extreme environments demonstrate how interdependence, redundancy, and distributed coordination can create system resilience and sustainability in the long run. By translating microbial survival strategies into institutional design principles, this framework reframes ecosystem stewardship not as a normative ideal, but as an ecological imperative grounded in the evolutionary logic of cooperation. Full article

This article explores an integrative framework for medical rehabilitation that combines regenerative medicine, systemic homeostasis, and microbiome modulation to optimize recovery and long-term health. Moving beyond conventional rehabilitation approaches focused on symptomatic recovery, this multidimensional paradigm emphasizes cellular repair, physiological balance, and microbial health as interdependent pillars of effective recovery. The framework leverages advancements in stem cell therapy, immune system modulation, and microbiota-targeted interventions to address both immediate functional restoration and long-term systemic resilience. By highlighting the synergistic interplay between these components, this article provides actionable insights into transforming medical rehabilitation into a proactive and holistic endeavor, paving the way for enhanced therapeutic outcomes and sustained patient well-being. Full article

Urban tree diversity plays a crucial role in enhancing the resilience of cities by contributing to ecosystem services such as mitigating the effects of land degradation, combating urban heat islands, improving air quality, and fostering biodiversity habitats. A diverse tree population enhances resilience to vulnerabilities related to climatic stress, disease, and habitat loss by promoting stability, adaptability, and efficiency within the ecosystem. Little is known about urban tree diversity in Serbia; therefore, this study examines the diversity of tree species in the City of Niš, Serbia, to assess its implications for urban resilience and biodiversity preservation in the context of land-use change. Using the Shannon Diversity Index, we quantify species richness and evenness across both central and suburban zones of the city. The results are benchmarked against similar indices in five other European cities to assess how patterns of urban tree distribution vary under different urbanisation pressures. The study reveals that tree diversity is markedly lower in the city centre than in peripheral areas, highlighting spatial inequalities in green infrastructure that may accelerate biodiversity loss due to compact urban development. These findings demonstrate how urban expansion and infrastructure density contribute to ecological fragmentation, potentially leading to long-term effects on ecosystem services. This study emphasises the strategic importance of integrating greenery diversity into urban and landscape planning, particularly in rapidly growing urban centres in Southeastern Europe. This research contributes to the existing body of literature, providing a deeper understanding of the interdependencies between urban tree diversity, land degradation, and biodiversity loss, offering data-driven insights. This enables urban planners, landscape architects, and policy advisors to make informed decisions about street tree diversity and green city infrastructure, contributing to the development of sustainable cities. Full article

Economic challenges place lower-income, economically marginalized families at heightened risk for experiencing financial stress, which is associated with a host of adverse family outcomes. Among lower-income families raising young children, existing economic challenges are often exacerbated by the added needs of children, including child-specific expenses (e.g., childcare) and decreased parental earning capacity. In these families, financial stress may strain the coparenting alliance; however, scant research has explored the association, particularly in families with young infants. Informed by family systems theory and the family stress model, the present study utilized an actor–partner interdependence model to explore the relationship between financial stress and the quality of the coparenting alliance within a sample of 214 lower-income opposite-sex couples with or expecting a new baby. This study further examined potential differences between (a) mothers and fathers, (b) cohabiting and married parents, and (c) those in different parenting contexts (i.e., new vs. established parents, recent vs. anticipated births). Results indicated that mothers’ and fathers’ perceptions of financial stress were negatively associated with their own report of coparenting alliance but not their partner’s coparenting alliance. This association was consistent across couple relationship structures and parenting contexts. Implications for policy and practices are provided. Full article

Local variations in mechanical properties are commonly observed in engineering structures, driven by complex manufacturing histories and harsh service environments. The evaluation of mechanical properties accurately constitutes a fundamental requirement for structural integrity assessment. The Instrumented Indentation Technique (IIT) can play a critical role in the in-site testing of local properties. However, it could be often a challenge to correlate indentation characteristics with uniaxial stress–strain relationships. In this study, we investigated quantitatively the connection between the indentation responses of commonly used metals and their plastic properties using the finite element inversion method. Materials typically exhibit plastic deformation mechanisms characterized by either linear or power-law hardening behaviors. Consequently, conventional prediction methods based on a single constitutive model may no longer be universally applicable. Hence, this study developed methods for acquiring mechanical properties suitable for either the power-law and linear hardening model, or combined, respectively, based on analyses of microstructures of materials exhibiting different hardening behaviors. We proposed a novel integrated IIT incorporating microstructures and material-specific constitutive models. Moreover, the inter-dependency between microstructural evolution and hardening behaviors was systematically investigated. The proposed method was validated on representative engineering steels, including austenitic stainless steel, structural steel, and low-alloy steel. The predicted deviations in yield strength and strain hardening exponent are broadly within 10%, with the maximum error at 12%. This study is expected to provide a fundamental framework for the advancement of IIT and structural integrity assessment. Full article

Hyperspectral band selection utilizes a crucial band subset to represent original data. In hyperspectral anomaly detection tailored for specific tasks, detection performance can be enhanced by pre-selecting a subset of bands that are more representative. However, existing methods remain constrained in modeling spatial–spectral dependencies and simultaneously extracting distinct bands’ contribution from the established model, thus struggling to balance effectiveness and stability. To address these issues, we propose a reliable band selection method for anomaly detection. Concretely, we conduct a convolution–transformer hybrid autoencoder architecture to fully exploit the local and global spatial–spectral interdependencies. Next, we design an anomaly–background separability constraint to seamlessly integrate the task priors of anomaly detection into network optimization. Furthermore, we design a spectral attention module to quantify the contribution of different bands during network optimization. Simultaneously, an adaptive band allocation method is designed to optimize the internal structure of the selected band subset. Extensive experiments demonstrate that the proposed method achieves more robust band selection results compared to existing related methods. Full article

Micro-expressions, characterized by brief and subtle facial muscle movements, are essential for conveying nuanced emotions in digital humans, yet existing rendering techniques often produce rigid or emotionally monotonous animations due to the inadequate modeling of temporal dynamics and action unit interdependencies. This paper proposes a graph-driven framework for micro-expression rendering that generates emotionally diverse and lifelike expressions. We employ a 3D-ResNet-18 backbone network to perform joint spatio-temporal feature extraction from facial video sequences, enhancing sensitivity to transient motion cues. Action units (AUs) are modeled as nodes in a symmetric graph, with edge weights derived from empirical co-occurrence probabilities and processed via a graph convolutional network to capture structural dependencies and symmetric interactions. This symmetry is justified by the inherent bilateral nature of human facial anatomy, where AU relationships are based on co-occurrence and facial anatomy analysis (as per the FACS), which are typically undirected and symmetric. Human faces are symmetric, and such relationships align with the design of classic spectral GCNs for undirected graphs, assuming that adjacency matrices are symmetric to model non-directional co-occurrences effectively. Predicted AU activations and timestamps are interpolated into continuous motion curves using B-spline functions and mapped to skeletal controls within a real-time animation pipeline (Unreal Engine). Experiments on the CASME II dataset demonstrate superior performance, achieving an F1-score of 77.93% and an accuracy of 84.80% (k-fold cross-validation, k = 5), outperforming baselines in temporal segmentation. Subjective evaluations confirm that the rendered digital human exhibits improvements in perceptual clarity, naturalness, and realism. This approach bridges micro-expression recognition and high-fidelity facial animation, enabling more expressive virtual interactions through curve extraction from AU values and timestamps. Full article

Our goal was to identify and understand perspectives of different stakeholders in the field of climate policy and test a process of co-creative policy development to support the implementation of climate protection measures. As the severity of climate change grows globally, perceptions of climate science and climate-based policy have become increasingly polarized. The one-solution consensus or compromise that has encapsulated environmental policymaking has proven insufficient or unable to address accurately or efficiently the climate issue. Because climate change is often described as a wicked problem (multiple causes, widespread impacts, uncertain outcomes, and an array of potential solutions), a clumsy solution that incorporates ideas and actions representative of varied and divergent worldviews is best suited to address it. This study used the Theory of Plural Rationality, which uses a two-dimensional spectrum to identify four interdependent worldviews as well as a fifth autonomous perspective to define the differing perspectives in the field of climate policy in Austria. Stakeholder inputs regarding general worldviews, climate change, and climate policy were evaluated to identify agreeable actions representative of the multiple perspectives. Thus, we developed and tested a co-creative process for developing clumsy solutions. This study concludes that while an ideological consensus is unlikely, agreement is more likely to occur on the practical level of concrete actions (albeit perhaps for different reasons). Findings suggested that creating an ecological tax reform was an acceptable policy action to diverse stakeholders. Furthermore, the study illuminated that the government is perceived to have the most potential influence on climate protection policy and acts as a key “broker”, or linkage, between other approaches that are perceived to be more actualized but less impactful. Full article

To address the challenges of complex harmonic characteristics, multi-source coupling, and strong time variability in aggregated loads downstream of high-voltage substations, this paper proposes an Adaptive Multi-Task Gaussian Process Regression (AMT-GPR) method for harmonic modeling. First, field measurements from the medium-voltage side of a 500 kV substation are denoised and analyzed using Fourier transform to reveal the dynamic patterns and interdependencies of harmonic current magnitudes. Then, a multi-task GPR framework is constructed, incorporating task correlation modeling and adaptive kernel functions to capture inter-task coupling and differences in feature scales. Finally, a probabilistic harmonic model is developed based on multiple sets of measured data, and the modeling performance of AMT-GPR is compared with single-task GPR, conventional MT-GPR, and mainstream machine learning approaches including RBF, LS-SVM, and LSTM. Simulation results demonstrate that traditional harmonic modeling methods are insufficient to capture the dynamic behavior and uncertainty of aggregated loads and AMT-GPR maintains strong robustness under small-sample conditions, significantly reduces prediction errors, and yields narrower uncertainty intervals, outperforming the baseline models. These findings validate the effectiveness of the proposed method in modeling harmonics of aggregated loads in high-voltage substations and provide theoretical support for subsequent harmonic assessment and mitigation strategies. Full article

The growing adoption of electric buses in public transport has intensified the need for efficient scheduling algorithms. In the context of tactical planning, public transport operators must address two interdependent scheduling problems: the Single Depot Vehicle Scheduling Problem for Electric Buses (EB-SD-VSP) and the Crew Scheduling Problem for Electric Buses (EB-CSP). This study introduces a sequential approach, solving EB-SD-VSP via a Mixed-Integer Quadratic Programming (MIQP) model, and then using its solution to generate service blocks for the EB-CSP, which is then solved as a Mixed-Integer Linear Programming (MILP) model. The proposed sequential optimization approach ultimately solves the combined problem of Vehicle and Crew Scheduling for a fleet of Electric Buses (EB-SD-VCSP). Experiments on real-world bus line data from Athens, Greece demonstrate practical applicability of the approach. When compared to a baseline scenario where the services are executed with conventional buses, the proposed method can calculate efficient vehicle timetables and crew schedules for operations with electric buses. The results highlight the benefit of decomposing joint electric bus and crew planning into tractable subproblems while preserving solution quality. These findings offer a scalable tactical-level planning tool for transit agencies transitioning to electric fleets and suggest promising directions for future extensions to multi-depot and real-time scenarios. Full article