Search Results (351)

With the continuous expansion of urban metro systems, balancing passenger experience and operational efficiency has become a central concern in contemporary public transportation design. However, most existing metro service studies continue to focus on perceptual comfort or isolated usability tasks and lack an integrated, behavior-centered perspective that accounts for the full travel chain and diverse user groups. This study develops the Bi-directional Service Behavioral Experience Model (BSBEM), which systematically integrates one-way navigation behaviors and interactive operational behaviors within a unified dual-path framework to identify behavioral patterns and experiential disparities across user groups. Based on the People–Touchpoints–Environments–Messages–Services–Time–Emotion (POEMSTI) behavioral observation framework, this study employs a mixed-method approach combining video-based behavioral coding, usability testing, and subjective evaluation. An empirical study conducted at Beidajie Station on Xi’an Metro Line 2 involved three representative passenger groups: high-frequency commuters, urban leisure travelers, and special-care passengers. Multi-source data were collected to capture temporal, spatial, and interactional dynamics throughout the travel process. Results show that high-frequency commuters demonstrate the highest operational fluency, urban leisure travelers exhibit greater visual dependency and exploratory pauses, and special-care passengers are most affected by accessibility and feedback latency. Further analysis reveals a positive correlation between route complexity and interaction delay, highlighting discontinuous information feedback as a key experiential bottleneck. By jointly modeling one-way and interactive behaviors and linking group-specific patterns to concrete metro touchpoints, this research extends behavioral evaluation in metro systems and offers a novel behavior-based perspective along with empirical evidence for inclusive, adaptive, and human-centered service design. Full article

Background: This scoping review systematically maps and synthesises contemporary literature on the biomechanics of active below-knee prosthetic devices, focusing on gait kinematics, kinetics, energy expenditure, and muscle activation. It further evaluates design advancements, including powered ankle–foot prostheses and variable impedance systems, that seek to emulate physiological ankle function and enhance mobility outcomes for transtibial amputees. Methods: This review followed the PRISMA-ScR guidelines. A comprehensive literature search was conducted on ScienceDirect, PubMed and IEEE Xplore for studies published between 2013 and 2023. Search terms were structured according to the Population, Intervention, Comparator, and Outcome (PICO) framework. From 971 identified articles, 27 peer-reviewed studies were found to meet the inclusion criteria between January 2013 and December 2023. Data were extracted on biomechanical parameters, prosthetic design characteristics, and participant demographics to identify prevailing trends and research gaps. This scoping review was registered with Research Registry under the following registration number: reviewregistry 2055. Results: The reviewed studies demonstrate that active below-knee prosthetic systems substantially improve gait symmetry and ankle joint range of motion compared with passive devices. However, compensatory trunk and pelvic movements persist, indicating that full restoration of natural gait mechanics remains incomplete. Metabolic efficiency varied considerably across studies, influenced by device design, control strategies, and user adaptation. Notably, the literature exhibits a pronounced gender imbalance, with only 10.7% female participants, and a reliance on controlled laboratory conditions, limiting ecological validity. Conclusions: Active prosthetic technologies represent a significant advancement in lower-limb rehabilitation. Nevertheless, complete biomechanical normalisation has yet to be achieved. Future research should focus on long-term, real-world evaluations using larger, more diverse cohorts and adaptive technologies such as variable impedance actuators and multi-level control systems to reduce asymmetrical loading and optimise gait efficiency. Full article

Informal learning spaces (ILSs) have received widespread attention owing to their diversity, flexibility, and richness. Many university libraries are undergoing renovation. After partial renovation, the ILS of the library often appears in a ‘group embedded’ organisational model. This study used a study cluster of a university library as an example to research the quality of the internal spatial environment and its influencing factors in the study cluster. In terms of research methods, this study adopted a combination of high-precision positioning, questionnaires, and environmental data measurement. The questionnaires integrated the opinions of both users and designers. Drawing on the literature, this study surveyed multiple university libraries, summarised the spatial quality and influencing factors of ‘group embedded’ libraries, and compared them with the ILS of other two organisational models. There is currently no targeted framework for the design of ILSs, and no scholars have discussed the specifics of their organisational models. This study established a multi-factor analysis model for ‘group embedded’ ILSs. Finally, this study found four key determinants and their weights; they were physical environment (30.65%), environmental atmosphere (26.76%), spatial ontology (25.03%), and spatial facilities (17.56%). Among the 20 key factors, the first three factors and their weights are privacy (10.34%), illumination (9.20%), and noise (8.62%). Unlike the other two spatial organisation models, users of clustered embedded libraries paid more attention to space privacy. This paper proposed six major improvement measures to address privacy, illumination, noise, temperature, air quality, and nature friendly design. Full article

Public spaces in peri-urban towns are becoming key focal points of urban regeneration in China due to their geographic advantages, resource endowments, and diverse populations. Substantial investments have been made to improve residents’ living environments and well-being. As over-commercialized urban centers increasingly face congestion and homogenization, the distinctive landscapes and authentic everyday life of peri-urban towns are attracting growing attention from tourists. Understanding both residents’ and visitors’ perceptions of these public spaces is therefore essential for successful regeneration. This study examines Nanquan Street, which lies ina peri-urban heritage town in Wuxi, Jiangnan region, China. Drawing on user-generated content from major Chinese social media platforms (Xiaohongshu and Dianping) and field observations guided by the AEIOU framework, a three-stage grounded theory approach was employed to identify the key factors influencing user satisfaction. The analysis identified twelve sub-dimensions grouped into three overarching categories: foundational preconditions, social developmental factors, and spatial-operational factors, which collectively shape sustained satisfaction in Peri-urban heritage towns. By translating the satisfaction model into sustainable design strategies, this study proposes a set of renewal pathways applicable not only to Nanquan Street but also to similar peri-urban towns facing comparable challenges. Emphasizing multi-user experience, low-intervention strategies, and contextual adaptability, this research contributes to theoretical understandings of sustainable renewal in peri-urban towns. It provides actionable guidance for balancing everyday life, cultural heritage, and sustainable tourism development. Full article

The development of Natural Language Processing applications tailored for diverse Arabic-speaking users requires specialized Arabic corpora, which are currently lacking in existing Arabic linguistic resources. Therefore, in this study, a multidialectal parallel Arabic corpus is built, focusing on the travel and tourism domain. By leveraging the text generation and dialectal transformation capabilities of Large Language Models, an initial set of approximately 100,000 parallel sentences was generated. Following a rigorous multi-stage deduplication process, 50,010 unique parallel sentences were obtained from Modern Standard Arabic (MSA) and five major Arabic dialects—Saudi, Egyptian, Iraqi, Levantine, and Moroccan. This study presents the detailed methodology of corpus generation and refinement, describes the characteristics of the generated corpus, and provides a comprehensive statistical analysis highlighting the corpus size, lexical diversity, and linguistic overlap between MSA and the five dialects. This corpus represents a valuable resource for researchers and developers in Arabic dialect processing and AI applications that require nuanced contextual understanding. Full article

In recent years, the frequent emergence of Android malware has posed a significant threat to user security. The redundancy of features in malicious software samples and the instability of individual model performance have also introduced numerous challenges to malware detection. To address these issues, this paper proposes a malware detection framework named Mass-Droid, based on Multi-feature and Multi-layer Screening for adaptive Stacking integration. First, three types of features are extracted from APK files: permission features, API call features, and opcode sequences. Then, a three-layer feature screening mechanism is designed to effectively eliminate feature redundancy, improve detection accuracy, and reduce the computational complexity of the model. To tackle the problem of high performance fluctuations and limited generalization ability in single models, this paper proposes an adaptive Stacking integration method (Adaptive-Stacking). By dynamically adjusting the weights of base classifiers, this method significantly enhances the stability and generalization performance of the ensemble model when dealing with complex and diverse malware samples. The experimental results demonstrate that the MaSS-Droid framework can effectively mitigate overfitting, improve the model’s generalization capability, reduce feature redundancy, and significantly enhance the overall stability and accuracy of malware detection. Full article

Increasing spectral bandwidth in 5G networks improves capacity but cannot fully address the heterogeneous and rapidly growing traffic demands. Heterogeneous ultra-dense networks (HUDNs) play a key role in offloading traffic across multi-tier deployments; however, their diverse base-station characteristics and diverse quality-of-service (QoS) requirements make resource allocation highly challenging. Traditional static resource-allocation approaches lack flexibility and often lead to inefficient spectrum utilization in such complex environments. This study aims to develop a joint user association–resource allocation (UA–RA) framework for 5G HUDNs that dynamically adapts to real-time network conditions to improve spectral efficiency and service ratio under high traffic loads. A software-defined networking controller centrally manages the UA–RA process by coordinating inter-cell resource redistribution through the lending of underutilized resource blocks between macro and small cells, mitigating repeated congestion. To further enhance adaptability, a neural network–adaptive resource allocation (NN–ARA) model is trained on UA–RA-driven simulation data to approximate efficient allocation decisions with low computational cost. A real-world evaluation is conducted using the downtown Los Angeles deployment. For performance validation, the proposed NN–ARA approach is compared with two representative baselines from the literature (Bouras et al. and Al-Ali et al.). Results show that NN–ARA achieves up to 20.8% and 11% higher downlink data rates in the macro and small tiers, respectively, and improves spectral efficiency by approximately 20.7% and 11.1%. It additionally reduces the average blocking ratio by up to 55%. These findings demonstrate that NN–ARA provides an adaptive, scalable, and SDN-coordinated solution for efficient spectrum utilization and service continuity in 5G and future 6G HUDNs. Full article

A methodology of statistical processing has been developed and practical implementations of the application of vibration signals in the analysis of the evolution of damage accumulation in ship bearings have been performed. It has been shown that vibration signals are multicomponent, representing a finite additive set of multi-scale components localized in time and frequency domains of different vibration types. A conceptual model and algorithm for searching for optimal information indicators in technical condition monitoring systems, based on reducing the dimensionality of input information using the principal component method, have been developed. In this paper, the principal component method is approximated by an n-dimensional observation region in an n-dimensional ellipsoid, the semiaxes of which will be the main components. In this case, the input data matrix is transformed into a matrix of normalized centered values, and the set of points is represented by their distances to straight lines and planes. The tangent to the exponential trend of change in the corresponding component in the pre-destruction area has been chosen as the criteria for assessing the approach to the state of degradation and failure. It is shown that among the studied components, the first and third components are the most informative. The specification of the main components reflects the linear diversity of statistical features of vibration signals and can be an indicator of the state of the object under study. Thanks to vibration analysis, the user receives information about the technical condition and approach to degradation of the material. Full article

Next Point-of-Interest (POI) recommendation is a crucial task in personalized location-based services, aiming to predict the next POI that a user might visit based on their historical trajectories. Although sequence models and Graph Neural Networks (GNNs) have achieved significant success, they often overlook the diversity and dynamics of user preferences. To address these issues, researchers have begun to employ Hypergraph Convolutional Networks (HGCNs) for disentangled representation learning. However, two critical problems have received less attention: (1) the limited expressive capacity of conventional hypergraph convolution layers, which restricts the modeling of complex nonlinear user–POI preference interactions and consequently weakens generalization performance, and (2) the inadequate utilization of contrastive learning mechanisms, which prevents fully capturing cross-view collaborative signals and limits the exploitation of complementary multi-view information. To tackle these challenges, we propose a Nonlinear Enhanced Disentangled Contrastive Hypergraph Learning (NE-DCHL) for next POI recommendation. The proposed model enhances nonlinear modeling capability and generalization by integrating ReLU activation, residual connections, and dropout regularization within the hypergraph convolution layer. A K-Nearest Neighbor (KNN)-based weighted adjacency matrix is employed to construct the geographical-view hypergraph, reducing computational complexity while maintaining essential spatial correlations. Moreover, a mini-batch InfoNCE loss and the GRACE (deep GRAph Contrastive rEpresentation learning) framework are utilized to improve efficiency and cross-view collaboration. Extensive experiments on two real-world datasets demonstrate that NE-DCHL consistently outperforms the original DCHL and other state-of-the-art approaches. Full article

Solid-State Drives (SSDs) have become the dominant storage medium in performance-sensitive systems due to their high throughput, reliability, and energy efficiency. However, inherent constraints in NAND flash memory—such as out-of-place writes, block-level erase operations, and data fragmentation—necessitate frequent garbage collection (GC), which can significantly degrade user I/O performance when not properly managed. This paper presents a channel-aware GC control mechanism for multi-channel SSD architectures that limits GC concurrency based on real-time storage utilization. Unlike conventional controllers that allow GC to proceed simultaneously across all channels—often leading to complete I/O stalls—our approach adaptively throttles the number of GC-active channels to preserve user responsiveness. The control logic uses a dynamic thresholding function that increases GC aggressiveness only as the SSD approaches full capacity, allowing the system to balance space reclamation with quality-of-service guarantees. We implement the proposed mechanism in an SSD simulator and evaluate its performance under a range of real-world workloads. Experimental results show that the proposed adaptive GC control significantly improves SSD responsiveness across various workloads. Across all workloads, the proposed adaptive GC control achieved an average latency improvement factor of 4.86×, demonstrating its effectiveness in mitigating GC-induced interference. Even when excluding extreme outlier cases, the method maintained an average improvement of 1.55×, with a standard deviation of 1.17, confirming its consistency and robustness across diverse workload patterns. Full article

With respect to the current campus energy systems, the research on energy storage deployment has focused mostly on single users or a single metric, making it difficult to accommodate diverse multiuser needs while efficiently utilizing the available resources. This results in narrow evaluation dimensions and underutilized storage assets. To address this issue, an integrated method for multiuser energy storage, optimal sizing and leasing is proposed in this paper; the method is aimed at improving the economics and utilization of storage. First, we construct a campus energy system architecture that includes an energy storage service provider and develop a storage sizing model that minimizes the average daily total cost, yielding the optimal power ratings and capacities for different users. Second, we construct a comprehensive evaluation framework from both economic and technical perspectives and apply quantitative methods to select the best configuration scheme. On this basis, we propose a multicriteria optimization-based storage leasing mechanism that enables resource sharing among users and maximizes the revenue received by the service provider. Simulation results reveal that across five typical user scenarios, the proposed method outperforms the traditional single-configuration models: the overall storage utilization rate increases by 3.84%, the cost-reduction rates for some users exceed 16%, and the investment payback period decreases by approximately one year. Compared with configuration-only approaches, the proposed integrated configuration–leasing framework simultaneously enhances user-side economics and the profitability of the service provider. The integrated sizing and leasing method not only demonstrates solid economic and technical feasibility but is also applicable to multiuser campuses, shared storage cases, and cloud storage scenarios, providing a reference path for future multidimensional value extraction processes and commercial operations. Full article

The development of effective and user-friendly brain–computer interface (BCI) systems is essential for enhancing mobility and autonomy among individuals with physical disabilities. Recent studies have demonstrated significant advances in BCI technologies, particularly in the areas of motor imagery (MI), blink detection, and attention-level analysis. However, existing systems often face limitations, such as low classification accuracy, high latency, and poor robustness in dynamic, real-world environments. Furthermore, most traditional BCIs rely on single-modality approaches, which restrict their adaptability and real-time performance. This paper aims to address these challenges by presenting a multi-modal Electroencephalography (EEG)–fusion neurointerface wheelchair system integrating MI, intentional blink detection, and attention-level analysis. The proposed system improves on previous methods by employing a novel eight-channel needle-shaped dry electrode EEG headset, which significantly enhances signal quality through better electrode–skin contact without the need for conductive gels. Additionally, the system processes EEG signals in real-time using a Jetson Nano platform, incorporating a dual-threshold blink detection algorithm for emergency stops, an optimized random forest classifier for decoding directional MI, and a support vector machine (SVM) for attention-level assessment. Experimental evaluations involving classification accuracy, response latency, and trajectory-following precision confirmed robust system performance. MI classification accuracy averaged around 80%, with optimized attention-level analysis reaching up to 94.1%. Trajectory control tests demonstrated minimal deviation from predefined paths (typically less than 0.25 m). These results highlight the system’s advancements over existing single-modality BCIs, showcasing its potential to significantly improve the quality of life for mobility-impaired users. Future studies should focus on enhancing lateral MI detection accuracy, expanding datasets, and validating system robustness across diverse real-world scenarios. Full article

Accurate generation–load forecasting is essential for the stability and efficiency of modern power systems. However, large-scale renewable integration and diverse user demand introduce strong nonlinearity and uncertainty, making probabilistic forecasting challenging. To address this, we propose a Temporal Convolutional Network with Adaptive Diffusion for generation–load probabilistic forecasting, called TCN-AD. TCN-AD employs a temporal convolutional encoder to capture long-term dependencies and local variations. In addition, an adaptive diffusion mechanism dynamically adjusts noise intensity to model time-varying uncertainty. Notably, a multi-scale fusion module and periodic attention mechanism further enhance the perception of multi-scale and cyclical patterns. Finally, a TCN-based denoising decoder refines the reverse diffusion process to reconstruct temporal dependencies effectively. Experiments on real-world load, solar, and wind datasets show that TCN-AD consistently outperforms baselines in both deterministic and probabilistic forecasting. Full article

Urban green spaces, as vital land use components, play a crucial role in promoting public mental health and well-being. This study investigates the differential restorative benefits and stress relief pathways in urban green spaces for populations with varying stress thresholds. This study employed a controlled experiment (pre-test–free activity–post-test) with 120 park users, integrating subjective scales (DASS-21, SRRS, etc.). We innovatively stratified participants by stress threshold to analyze recovery mechanisms. Key findings reveal: (1) Park visits were associated with significant restorative benefits across all stress groups (p < 0.05), yet the recovery patterns and potential pathways appear to be stress-threshold-dependent. (2) Our findings suggest distinct patterns: low-stress individuals benefit via cognitive-behavioral routes (environmental awareness, dynamic activities), while medium-high stress groups rely more on physiological regulation (environmental enclosure, static relaxation). (3) Crucially, these mechanisms suggest stratified landscape design strategies: multi-sensory interactive spaces for low-stress, static rest areas for medium-stress, and low-interference, high-enclosure meditative environments for high-stress individuals. However, given the single-group pre-post design, observed benefits should be interpreted as associations and plausible pathways rather than definitive causal effects. By introducing stress threshold stratification into restorative landscape research, this study provides actionable, evidence-based guidelines for optimizing urban green space planning and design. It offers a crucial scientific foundation for creating healthier, more inclusive, and sustainable urban environments that effectively address diverse mental health needs and contribute to public health promotion through sustainable land use practices. Full article

Emergency evacuation planning in high-rise buildings presents complex optimization challenges critical to achieving sustainable and inclusive urban development. Traditional evacuation models inadequately address vulnerable groups’ needs—particularly persons with disabilities—while neglecting fire spread dynamics, congestion effects, and real-time risk assessment. This neglect undermines both human safety and social equity—core dimensions of sustainable communities. Sustainable cities must integrate inclusive design and emergency preparedness into high-rise development. This paper develops a comprehensive mathematical optimization framework for disability-inclusive emergency evacuation that integrates dynamic fire spread modeling, congestion-aware routing mechanisms, and explicit accessibility constraints within a unified formulation. The proposed approach balances evacuation efficiency, safety, and fairness across diverse population groups through a multi-objective optimization model that incorporates time-varying risk assessments, elevator priority systems for wheelchair users, and group-specific mobility coefficients. To address the computational scalability challenges inherent in large-scale mixed-integer nonlinear programming problems, we introduce an innovative solution methodology that combines Graph Neural Networks (GNN) with Proximal Policy Optimization (PPO) algorithms. The graph neural network component captures spatial-temporal feature representations of building geometry, occupant distributions, and hazard dynamics, while the reinforcement learning algorithm develops adaptive routing policies that respond to evolving emergency conditions. Experimental results on a representative high-rise building scenario demonstrate that the proposed GNN-PPO method achieves substantial improvements in safety, efficiency, and equity. The dynamic policy successfully prioritizes vulnerable populations, utilizes elevator systems effectively for persons with disabilities, and adapts to real-time emergency conditions, providing a robust framework for inclusive emergency evacuation planning in complex building environments. This work demonstrates how advanced computational methods can advance sustainability objectives by ensuring equitable safety outcomes across diverse populations—a prerequisite for truly sustainable cities. Full article