Search Results (28,454)

Background/Objectives: The modern prosthetic foot market is characterized by a pronounced polarization between affordable but low-function devices and high-performance yet costly composite prostheses. The aim of this study was to develop and comprehensively evaluate cost-effective, functional prosthetic feet manufactured by fused deposition modeling (FDM). Methods: An iterative design methodology was employed, combining finite element analysis to optimize the biomechanical response of the device, the incorporation of user-specific requirements and experimental validation. Two TPU 95A-based 3D-printed prosthetic foot designs were designed and developed, and their strength and functional characteristics were assessed numerically under the ISO 22675:2024 normative loading cycle. Bench-top mechanical tests were conducted on the fabricated prototypes. Functional performance was evaluated by a transtibial amputee using an inertial motion capture system to analyze gait kinematics. Results: The results demonstrated that both designs operate predominantly within the elastic range with an adequate safety margin. The pilot feasibility gait assessment indicated feasibility and plausibility within the tested protocol and participant for both prototypes. Conclusions: The developed TPU 95A-based FDM prosthetic feet demonstrated promising structural integrity and functional feasibility, supporting the potential of low-cost additive manufacturing as a viable approach for producing affordable prosthetic feet. Further studies with larger participant cohorts and extended testing are needed to confirm clinical applicability and long-term performance. Full article

As wind farms in North America near the end of their design life, different end-of-life options need to be considered. Common options include decommissioning, lifetime extension, and repowering. In this research, a methodology to support early-stage repowering decisions is presented. Performance decline and repowering forecasts are obtained by combining analysis of past performance data and preliminary site plans for new turbines with turbine performance models from windPRO software. Financial metrics are computed using a simple techno-economic model with parameters informed by historical financial records. Repowering decisions are often sensitive to assumptions on key parameters, such as capital cost of repowering, which are poorly defined at the beginning of the process and subject to change quickly. This makes it difficult to provide guidance that will remain relevant as more information is obtained during future project planning stages. In this work, constrained optimization methods are used to identify sets of the key inputs that lie on the break-even point at which repowering is more profitable than continuing operation. Using this approach, which is novel in this context, the client gains an intuition for the ‘envelope’ within which the recommended guidance still holds. This decision-making process is applied to a case study using performance data and cost ranges from a real, anonymous wind farm. Full article

Inferring demographic history from whole-genome data is a central objective in evolutionary and conservation genomics. However, the Pairwise Sequentially Markovian Coalescent (PSMC) framework, one of the most widely used demographic inference methods for whole-genome sequence data, is highly sensitive to sequencing coverage, with low coverage producing systematic underestimation of heterozygosity, which biases effective population size trajectories. Here, we present PSMC-FAC, an automated method designed to optimize false-negative rate correction in low-coverage genomes by minimizing geometric distances between FNR-corrected low-coverage trajectories and their corresponding high-coverage references. Whole-genome datasets from humans, gray wolves, and cattle were downsampled across multiple coverage levels and processed through standard demographic inference pipelines. Corrected trajectories, projected onto a common temporal grid, were compared using Hausdorff and discrete Fréchet distance metrics and optimal correction factors were modeled as a function of sequencing depth using second-degree polynomial regression. Across species and demographic contexts, PSMC-FAC substantially improved concordance between low- and high-coverage trajectories and revealed highly predictable coverage-dependent correction patterns. Overall, PSMC-FAC provides a reproducible and mathematically grounded alternative to subjective correction approaches, enabling reliable demographic inference from moderate-coverage genomes and facilitating broader population-scale genomic analyses. Full article

Online asynchronous learning offers considerable flexibility but frequently faces challenges in sustaining engagement, interactivity, and inclusivity across diverse learner populations. This study introduces the OPTIMAL framework—an Online, Pedagogy- and Technology-Integrated, Microcurricula Approach for interactive and inclusive Learning—synthesising universal design for learning, active learning, and constructive alignment with technology integration frameworks (TPACK and PICRAT), operationalised through a microcurricula-as-a-service architecture. A three-year longitudinal case study (2022/23 to 2024/25) examined the application of the framework to a data literacy and analytics module serving over 5000 students across more than 15 programs and five faculties at Dublin City University. The module design constructively aligned learning outcomes, content, and technology at three levels to support multiple learning pathways, formative assessment, and transdisciplinary engagement, deliberately fostering transformative uses of technology in a fully asynchronous environment. Mixed-methods evaluation—combining learning analytics, surveys (n = 1743), and qualitative feedback—demonstrated sustained positive outcomes across all three years, including 95–99% completion rates, consistently high satisfaction, and longitudinal gains in engagement and pass rates. These findings demonstrate how the deliberate integration of pedagogical theory, technological frameworks, and modular curriculum architecture can deliver scalable, inclusive, and high-engagement online education, offering both a transferable, evidence-based model for educators and curriculum designers and longitudinal empirical validation for researchers. Full article

Mixed-signal simulation is indispensable for verifying modern integrated circuits that tightly couple analog and digital subsystems, yet the field lacks a unified framework for systematically comparing its diverse methodologies. This paper addresses that gap by proposing a novel three-axis taxonomy that classifies simulation methods along abstraction level, solver methodology, and analysis type, together with a comparative evaluation framework based on five quantitative metrics: accuracy, throughput, capacity, convergence reliability, and scalability. Applying this framework, we systematically compare thirteen classical method categories—spanning SPICE, FastSPICE, RF/periodic steady-state, behavioral modeling, co-simulation, and model order reduction—and eight AI/ML approaches including Gaussian process surrogates, graph neural networks, physics-informed neural networks, Bayesian optimization, and reinforcement learning. Our analysis reveals a clear maturity stratification: classical methods remain the only signoff-accurate approaches, Bayesian optimization represents the most industrially validated AI contribution with integration across all three major EDA platforms, while Neural ODE solvers and LLM-based design tools remain at the research stage. We identify a persistent academic-to-industry gap driven by foundry model complexity, limited benchmark diversity, and topology-specific overfitting. The proposed taxonomy and comparative framework provide practitioners with structured guidance for simulation method selection and highlight specific research directions needed to bridge the gap between AI promise and industrial deployment. Full article

The synergistic control of multiple pollutants is critically challenged by complex nonlinear interactions, strong spatiotemporal heterogeneity, and the difficulty of tracing causal drivers. Deep learning offers high predictive power but suffers from the “black-box” problem, limiting its acceptance in environmental decision-making. Explainable Deep Learning (XDL) integrates physical mechanisms with interpretable algorithms, achieving both prediction accuracy and explanatory transparency. This review systematically evaluates the effectiveness and limitations of XDL in analyzing multi-pollutant interactions, with a comparative focus on atmospheric and aquatic environments. Key techniques, including SHAP, attention mechanisms, and physics-informed neural networks, are examined for their roles in synergistic monitoring, source apportionment, and regulatory optimization. The main findings reveal that: (1) XDL, particularly the “tree model + SHAP” paradigm, has become a dominant tool for quantifying driving factors, yet most attributions remain correlational rather than causal; (2) physics-informed fusion (soft vs. hard constraints) improves physical consistency but faces unresolved conflicts between data and physical laws, with current models lacking a conflict detection mechanism; (3) cross-media comparison shows a unified technical logic of “physical mechanism guidance + post hoc feature attribution”, but atmospheric applications lead in embedding advection–diffusion constraints, while aquatic research excels in spatial topology modeling via graph neural networks; (4) critical bottlenecks include the lack of causal inference, uncertainty-unaware interpretations, and data scarcity. Future directions demand a shift from correlation-only to causal-aware attribution, from blind fusion to conflict-detecting systems, and from no evaluation standards to domain-specific validation benchmarks. XDL is poised to transform multi-pollutant governance from experience-driven to intelligence-driven approaches, provided that verifiable interpretability and physical consistency become core design principles. Full article

In the context of the aesthetic economy and the rapid development of digital intelligence, product design is increasingly required to address not only functional performance but also users’ emotional needs. However, due to the ambiguity and subjectivity of perceptual requirements, it remains difficult to accurately translate user emotions into specific design solutions. To address this challenge, this study proposes an integrated Kansei Engineering–machine learning framework for optimizing product design. First, user perceptual data are collected through questionnaires and interviews, and key perceptual imagery words are extracted using the Latent Dirichlet Allocation (LDA) model and factor analysis. Then, product design elements are systematically decomposed, and their relative importance is determined using the fuzzy analytic hierarchy process (FAHP). Based on this, a mapping relationship between perceptual imagery and design elements is established. Subsequently, the XGBoost model is employed to predict and optimize design element combinations. The optimized design schemes are further generated using AIGC technology and validated through eye-tracking experiments and subjective evaluations.The results show that the proposed method achieves high predictive accuracy (R² = 0.87) and significantly improves the emotional expression of product design. This study contributes to the integration of Kansei Engineering and machine learning by providing a data-driven approach for emotional design optimization, offering theoretical, practical, and strategic guidance for intelligent product design in industrial contexts. Full article

The study presents an engineering solution for maintaining traffic flow during road and utility operations, such as trench excavation. The analysis of existing organizational and technical approaches, along with global experience in temporary bridge use, showed that most foreign analogs were developed for military purposes and are not fully suitable for urban conditions in Kazakhstan and CIS countries. As an alternative solution, the development of a mobile overpass adapted for operation in dense urban environments is proposed. The present study continues earlier research focused on optimizing the placement of mobile overpass supports while accounting for the nonlinear deformation behavior of the soil foundation. At the previous stage, a rational distance between the supports and the trench edge was substantiated, and horizontal soil deformations were reduced. In the current study, the primary focus is on the design of the undercarriage, which determines the mobility, stability, and operational feasibility of the structure. A morphological analysis and synthesis method is applied to select a rational configuration of the undercarriage. A 3D model and a 1:4 scale test bench were developed, followed by load tests of 50–200 kg. The maximum deflection of −1.19 mm at 200 kg demonstrated an almost linear deformation pattern. The constructed regression model ($R^2=0.97$) confirmed the accuracy and reliability of the design. The developed mobile overpass is versatile, cost-effective, and practical, improving the resilience of urban transport infrastructure, reducing traffic congestion during roadworks, and creating a foundation for serial production in Kazakhstan and CIS countries. Full article

Personalized federated learning (PFL) faces significant challenges in resource-constrained edge environments, where strict communication budgets and severe system heterogeneity must be jointly addressed. Although one-shot federated learning reduces communication overhead, existing methods typically impose unified model architectures or rely on coarse manual selection strategies, limiting their adaptability to highly heterogeneous data distributions and restricting personalized representation capability. To overcome these limitations, we propose Personalized Federated Zero-shot Knowledge Distillation (pFedZKD), a data-free one-shot federated learning framework designed for structurally heterogeneous scenarios. The framework follows a decouple-and-reconstruct collaborative paradigm. On the client side (decoupling stage), we introduce Particle Swarm Optimization-based Federated Neural Architecture Search (PSO-FedNAS), a gradient-free neural architecture search method that enables each client to autonomously discover a customized convolutional architecture aligned with its local data distribution, eliminating the need for architectural consistency across clients. On the server side (reconstruction stage), to address parameter-space incompatibility caused by structural heterogeneity, we develop an architecture-agnostic multi-teacher zero-shot knowledge distillation mechanism (Multi-ZSKD). This method synthesizes pseudo-samples in latent space to extract semantic consensus from heterogeneous client models and transfers the aggregated knowledge to a unified global student model without accessing real data. The entire collaborative process is completed within a single communication round, substantially reducing communication cost while enhancing privacy preservation. Extensive experiments on MNIST, FashionMNIST, SVHN, and CIFAR-10 under heterogeneous data settings demonstrate that pFedZKD consistently achieves superior personalization accuracy, global generalization performance, and communication efficiency compared with state-of-the-art PFL methods. Full article

Against the backdrop of low-carbon transportation and urban logistics transformation, metro-based freight is regarded as an important pathway for emission reduction. This paper constructs a tripartite evolutionary game model involving the government, logistics enterprises, and metro operators, and analyzes multi-agent strategy evolution and the influence of key parameters using replicator dynamics equations and numerical simulation. The results show that well-designed subsidies and penalties can effectively promote a stable state characterized by “active government intervention, active response from logistics enterprises, and low-carbon integrated passenger and freight transportation by metro operators”. Reducing the cost of transformation can improve evolutionary efficiency, while excessively high subsidies may weaken the government’s willingness to intervene. This study provides insights for optimizing low-carbon transportation policies and supporting the development of metro-based freight systems. Full article

Desertsand concrete (DSC) is a sustainable alternative to natural river sand; however, its application in cold regions is restricted by inadequate crack resistance and freeze–thaw durability. This study investigates the freeze–thaw performance of steel–polypropylene hybrid fiber-reinforced desert sand concrete (SPHF-DSC), with emphasis on durability enhancement and service life prediction. A three-factor, three-level orthogonal experimental design was employed to evaluate the effects of desert sand replacement ratio (DSR), steel fiber (SF) content, and polypropylene fiber (PPF) content on mass loss, relative dynamic elastic modulus, and compressive strength under 25–100 freeze–thaw cycles. The results demonstrate that hybrid fiber reinforcement significantly improves freeze–thaw resistance due to the synergistic interaction between SF and PPF. After 100 cycles, the mass loss of all specimens remained within a narrow range of 0.65% to 0.73%, and the relative dynamic elastic modulus retention stayed above 90%. The optimal mixture (DSR = 30%, SF = 2%, PPF = 0.05%) exhibited superior frost resistance with the lowest deterioration indices among all groups. A freeze–thaw damage model based on damage mechanics was established and validated ($R^2$ > 0.96), enabling prediction of a service life exceeding 38 years under typical cold-region climatic conditions. These findings provide a durability-oriented design reference for the engineering application of DSC in cold-region infrastructure. Furthermore, the utilization of local desert sand reduces transportation energy consumption and promotes the sustainable development of energy infrastructure. Full article

In layered systems with porous substrates and a dense solid surface, stiffness and strength are inherently coupled through porosity-dependent relations, influencing their load-bearing behaviour. This work presents a systematic methodology for the assessment and design of such layer-substrate systems based on a criterion of balanced material utilization. A dimensionless parameter is defined to characterize the stress state in both components relative to their admissible limits, from which the optimal layer thickness is determined at equal stress levels in both constituents. Stress distributions are calculated using a numerical half-space model for layered contacts and evaluated through material-dependent equivalent stress criteria. The relationship between material utilization and load-carrying capacity is reduced to a scaling factor that combines the influence of porosity-dependent parameters. The approach establishes a direct link between the governing material parameters and structural design variables. Across the investigated parameter range, the utilization rate scales linearly with optimal layer thickness, whereas the load-carrying capacity follows a cubic relation. For a representative Ashby strength scaling coefficient of 𝐶𝜎 = 0.3, for example, a substrate porosity of 90% leads to a scaling factor of 1.6, corresponding to a possible load amplification of 60% relative to the homogeneous reference. Full article

As urbanization transitions from incremental expansion to the optimized utilization of existing construction land, the precise identification of land-use status and changes has become a core requirement for enhancing refined land resource management. However, in urban built environments characterized by dense object distributions and complex geometric contours, existing change detection methods often struggle to capture subtle boundaries, leading to edge blurring and loss of detail. To address these challenges, this study proposes an Edge-aware Change Detection Network for urban construction land change identification. The model features a shared Siamese encoding network based on MiT-B1, leveraging its hierarchical multi-scale attention mechanism to balance local detail extraction with long-range semantic dependency capture, thereby overcoming the limitations of monolithic feature extraction. Furthermore, a multi-level feature concatenation and fusion strategy is designed to align and interact with bi-temporal features along the channel dimension, significantly enhancing the saliency and discriminative representation of change areas. Experimental results on the Yongzhou building change detection dataset demonstrate that the proposed model outperforms state-of-the-art methods in both visual recognition and quantitative metrics. It effectively resolves the difficulty of boundary definition in complex urban scenarios, providing localized high-precision technical support for the assessment and dynamic monitoring of construction land within the study area. Full article

Global aging is accelerating, with the proportion of the population aged 60 and above projected to reach 22% by 2050. In cold-region communities, the visual landscape environment is closely associated with the health of older adults, particularly showing associations with blood pressure (BP) and emotion states. However, associations between these factors across different landscape spaces and potential gender differences remain underexplored. This study utilized eye-tracking experiments to collect visual attention data from older adults in three types of cold-region community spaces: inter-building spaces, walkways and squares. The ground, buildings, trees, lawn, and the sky were identified as the primary Areas of Interest (AOIs). The Profile of Mood States (POMS) scale was used to assess emotion during walking experiments, revealing suggestive gender–environment interaction characteristics. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) were measured, and a Mann–Whitney U test indicated that DBP in community squares exhibited significant environmental dependency (U = 114.5, p = 0.004, r = 0.44). Hierarchical Linear Models (HLMs) revealed that, after controlling for individual differences, the number of fixation points on ground was independently associated (i.e., independent of measured individual characteristics) with elevated SBP ($\gamma =0.31, p=0.011$), while fixation on trees was associated with reduced SBP ($\gamma =-0.24, p=0.018$). Furthermore, gender moderation effects were observed: the association between ground fixation and SBP was stronger in females ($\gamma =0.18, p=0.022$), whereas the association between sports facilities and DBP was stronger in males ($\gamma =0.29, p=0.009$). Based on these findings, evidence-based design strategies are proposed, including the optimization of ground safety, gender-differentiated planting configurations, and targeted layouts for sports facilities. These results provide empirical support for age-friendly community design in cold regions. Full article

Efficient design of desalination and brine management systems, which are central to a water circular economy, requires accurate thermodynamic data such as the osmotic coefficient. This property is key to understanding salt behavior in aqueous solutions, directly impacting the energy efficiency and sustainability of treatment processes. This study presents a predictive framework that combines machine learning with the Gazelle Optimization Algorithm (GOA) to accurately estimate osmotic coefficients for various inorganic salt solutions. The GOA was employed to automatically tune the hyperparameters of two models: Decision Tree (DT) and Gradient Boosting Machine (GBM). Using a comprehensive dataset of 893 samples with 27 salt-specific parameters, the GOA-GBM hybrid model delivered the highest predictive accuracy, achieving an R² of 0.9734 on test data. The GOA-DT model also performed robustly (R² = 0.9260), providing a more interpretable alternative. By creating a reliable tool for predicting osmotic coefficients, this methodology enables more precise process simulation and optimization. This directly supports the development of energy-efficient desalination technologies and informed decision-making for water reuse and resource recovery. The integration of advanced digital tools like GOA with machine learning offers a powerful approach to enhancing process efficiency and environmental safety, contributing directly to the design of sustainable, circular economy-based water treatment solutions for industrial and municipal applications. Full article