Search Results (1,264)

Land transition in China has led to the emergence of highly heterogeneous neighborhoods. This process challenges the social sustainability of public green spaces. This research investigates the driving mechanisms of place attachment within green space across diverse community typologies in Beijing. This study constructed a structural equation model (SEM) based on 626 valid questionnaires, using the Stimulus–Organism–Response (S-O-R) framework. The overall SEM results indicate that place identity significantly contributes to civic behavior (β = 0.439, p < 0.001). However, a persistent ‘value-action’ gap remains, with 65.81% of residents demonstrating high identity yet low participation. Furthermore, the multi-group analysis (MGA) reveals that place attachment logic diverges significantly across groups. Regarding user identity, public events promote visitors’ place identity, but this effect remains insignificant among residents (β = −0.064, p > 0.05). Regarding generational differences, the macro-spatial environment is significantly associated with place dependence for young people (β = 0.330, p < 0.001) but is insignificant for the elderly. Community heterogeneity reveals distinct failure modes. In commodity housing communities, a disconnect exists where daily usage fails to foster dependence (β = 0.026, p > 0.05). Conversely, urban–rural resettlement communities display an identity deficit where public events fail to translate into place identity (β = 0.131, p > 0.05). The study proposes differentiated renewal pathways tailored to three community types. For commercial housing communities, it advocates precise interventions that prioritize social engagement. Meanwhile, for urban–rural resettlement communities, the focus shifts to accessibility and culturally rooted activities to help reconnect displaced populations. Full article

The rapid digitization of cultural heritage materials has led to the substantial growth of digital library collections, particularly large and heterogeneous archives of periodicals. This expansion has intensified challenges related to content discovery, accessibility, and user engagement. Users increasingly struggle to navigate large periodical collections and identify relevant materials. In this context, intelligent interaction with cultural content has become an essential aspect of effectively accessing and utilizing resources in modern digital libraries, highlighting the need for adaptive and user-oriented mechanisms that support navigation and discovery. Artificial intelligence-driven personalization offers promising solutions. However, digital library environments often contain sparse interaction data, evolving user interests, and continuously growing collections. These characteristics limit the effectiveness of standalone content-based or collaborative approaches. This work proposes an integrated personalization approach that combines behavioral interaction data with semantic relationships between documents to support adaptive content delivery in digital libraries. The approach facilitates the discovery of both established and newly digitized or rarely accessed materials, supporting more effective access, exploration, and reuse of large and diverse digital library collections. Full article

The rapid digital transformation of industrial systems in the 21st century has led to an exponential growth in data generated by manufacturing processes and end-user products, particularly in the automotive sector. While this big data creates new opportunities for monitoring and diagnostics, it also introduces significant challenges related to system complexity, scalability, and nonlinearity, as well as the increasing shortage of experienced domain experts. These challenges motivate the adoption of intelligent, automated fault and anomaly detection techniques capable of operating reliably under real-world conditions. The primary objective of this paper is to provide a comprehensive and structured review of the anomaly detection methodologies for automotive applications, with particular emphasis on intelligent fault diagnosis, tolerance, and monitoring architectures. To this end, the paper systematically categorizes existing approaches, including model-based, data-driven, and hybrid techniques, and analyzes their underlying principles, data requirements, computational complexity, and applicability to safety-critical systems. Based on this analysis, the paper highlights current limitations, open research challenges, and emerging trends, including the integration of machine learning and artificial intelligence with domain knowledge and control-oriented frameworks. The main contribution of this work is a unified perspective that supports researchers and practitioners in selecting, designing, and deploying effective anomaly detection solutions for next-generation automotive and cyber-physical systems. Full article

The demand for concrete has led to increased use of raw materials and significant waste generation. Recycled aggregate concrete (RAC) offers a viable approach to sustainable concrete; however, the use of weakly bonded mortar on aggregate leads to low strength and crack formation. Fiber reinforcement, specifically hybrid fiber reinforcement combining steel, glass, basalt, and polypropylene fibers, can increase the tensile and flexural properties of RAC. This study developed machine learning models to enable the prediction of hybrid fiber-reinforced RAC’s compressive, splitting tensile, and flexural strength performance; these new models overcome the limitations of previous research, which relied on only one fiber type and regular methods of optimization. Two models (a deep neural network (DNN) and an XGBoost model) were trained and optimized using bald eagle search (BES), particle swarm optimization (PSO), and the Bayesian optimization (BO) algorithm to improve performance. Among the three optimization analyses, PSO-XGBoost achieved the highest accuracy for compressive strength and splitting tensile strength, while BES-XGBoost achieved the highest accuracy for flexural strength. The most significant influences on the compressive strength were curing age and silica fume, while the main drivers of splitting tensile strength and flexural strength were fiber volume and fiber characteristics. The use of SHAP-based methodology with a user-friendly interface further improved the design of RAC mixtures, reducing waste from raw materials, enhancing the structural performance of RAC, and enabling data-driven decision-making in the manufacturing of eco-friendly concrete products. Full article

The rapid integration of artificial intelligence (AI) into higher education is reshaping how learners develop academic, linguistic, and research competencies. This mixed-methods study examines how second-year EFL computer science students employ prompt engineering techniques across four task domains—research summarization, academic video note-taking, style transformation, and concept mapping—within a smart learning environment. Sixty-nine students completed a structured survey requiring AI-assisted draft generation followed by student-led revision. Quantitative analyses included descriptive statistics, chi-square tests, Cramer’s V, t-tests, ANOVA, Kruskal–Wallis tests, and three text-similarity measures (cosine, Jaccard, and Levenshtein). Qualitative evidence was drawn from students’ revised outputs and reflective responses. Results indicate that students consistently preserved semantic meaning while significantly rephrasing AI-generated text, demonstrating moderate conceptual alignment but substantial lexical and structural transformation. Frequent AI users said they were better at searching and revising, but the type of prompt didn’t have much of an effect on how deep the revision was or how well they learned. Iterative prompting and revision emerged as central drivers of metacognitive growth, academic language development, and sustainable learning behaviors. Across tasks, students viewed AI prompts as effective scaffolds for organizing information and synthesizing multimodal input, though reliance varied by learner. The findings underscore that sustainable AI use in EFL technical education depends not on AI output alone, but on structured prompting, iterative human revision, and critical engagement—practices that cultivate autonomy, digital literacy, and long-term academic resilience. Full article

Overlapping illumination in multi-LED visible light communication (VLC) networks introduces cross-LED coupling that reshapes the received-signal composition and may trigger error propagation in successive interference cancellation (SIC) for layered ADO-OFDM NOMA. This work employs an overlap factor $\beta \in [0,1]$ to quantify the severity of overlap-induced cross-LED coupling and develops a $\beta$-aware resource-allocation framework for a dual-LED indoor downlink. The proposed design integrates channel-aware MCGAD user grouping with three-level coefficient adaptation, including the inter-LED power split $\eta$, the inter-layer ACO/DCO split $\rho$, and the intra-layer two-user NOMA coefficients $\alpha$. Monte Carlo evaluations over $\beta \in \{0,0.2,0.5\}$ show that stronger coupling drives the system into an interference-limited regime with a pronounced high-SNR BER floor for strong users after SIC; the proposed $\beta$-aware design consistently reduces this floor relative to a $\beta$-blind fixed-coefficient baseline. Meanwhile, the spectral-efficiency curves remain close to the baseline, with only a minor gap at moderate-to-high SNR, and the Shannon-rate energy-efficiency trends remain comparable across coupling scenarios. The grouping-and-allocation procedure is dominated by sorting and deterministic pairing, exhibiting $\mathcal{O}(UlogU)$ complexity and avoiding the combinatorial growth of exhaustive grouping. Full article

Background: Blockchain technology has emerged as a transformative communication solution for securing distributed systems. However, several vulnerabilities exist during transactions, including latency and network congestion issues during mempool processing, topology weaknesses, cross-chain bridge exploits, and cryptographic weaknesses. These vulnerabilities have led to attacks that have threatened system integrity, including Block Extractable Value (BEV) attacks, Maximal Extractable Value (MEV) attacks, sandwich attacks, liquidation, and Decentralized Finance (DeFi) reordering attacks, among others. Thus, implementing a robust security framework based on the Confidentiality, Integrity, and Availability (CIA) triad remains critical for addressing modern blockchain technology threats. Objective: This paper examines blockchain technology, its various vulnerabilities, and attacks to determine how criminals exploit the system during transactions. Further, it evaluates its impact on users. Then, implement a blockchain attack in a “MasterChain” virtual environment to demonstrate how vulnerable spots can be practically exploited and discuss the application of the CIA security triad through modern cryptographic primitives. Methods: The approach considers Hevner’s design science framework, which emphasizes creating innovative artifacts that address identified problems while contributing to the knowledge base through rigorous evaluation. Furthermore, we developed a MasterChain tool using Python with Flask for distributed node communication, utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) with the Standards for Efficient Cryptography Prime 256-bit Koblitz curve 1 (secp256k1) for digital signatures and Secure Hash Algorithm 3 (SHA-3) (Keccak-256) hashing for block integrity. Results: show how the CIA has been implemented to provide secure communication through ECDSA-based transactions, SHA-3 chain integrity verification, and a multi-node distributed architecture, respectively. The performance analysis shows that ECDSA provides 256-bit security with 64-byte signatures compared to 2048-bit Rivest–Shamir–Adleman (RSA)’s 256-byte signatures, achieving a 75% reduction in bandwidth overhead. SHA-3 provides immunity to length extension attacks while maintaining equivalent collision resistance to SHA-256. Conclusions: The MasterChain framework provides a practical foundation for implementing blockchain security that addresses both classical and emerging vulnerabilities. The adoption of ECDSA and SHA-3 (Keccak-256) positions the system favourably for modern blockchain applications, while providing insights into the cryptographic trade-offs between performance, security, and compatibility. Full article

The rapid growth of vehicle ownership has led to increasingly congested road networks, which significantly reduces the energy efficiency of electric vehicles (EVs) and intensifies user range anxiety. To address these challenges, a dynamic EV route planning process is proposed by integrating traffic flow (TF) prediction, charging service modelling, and time-varying path optimization within a unified framework. First, future TF is predicted using a data-driven forecasting module based on the iTransformer model, which captures multivariate temporal dependencies across road links and provides accurate inputs for downstream decision-making. Based on the predicted traffic states, a time-dependent queuing process is formulated to estimate charging station waiting times by modelling the dynamic interaction between vehicle arrivals and service capacity. These components are then embedded into a time-varying shortest path optimization process that explicitly considers mid-journey charging constraints, with the objective of minimizing total travel time and economic cost. The proposed framework establishes a closed-loop decision-making process that couples traffic evolution, charging service dynamics, and routing behaviour. Extensive comparative experiments against classical Time-Dependent Shortest Path (TDSP) methods under different network scales, together with a real-world case study, demonstrate that the proposed approach achieves higher computational efficiency and improved routing performance under dynamic conditions. The results indicate that the proposed process-oriented method provides an effective and practical solution for EV routing in intelligent transportation systems characterized by time-varying traffic and service processes. Full article

This study examines the impact of lifestyle migration on housing development in the coastal regions of Northern Cyprus through a comparative analysis of Girne (Kyrenia) and Iskele (Trikomo). A mixed-methods approach was employed, combining a literature review with semi-structured interviews with real estate and construction stakeholders and structured surveys to analyse housing production patterns, user preferences, and spatial outcomes. The findings indicate that although both regions have experienced rapid housing growth driven by lifestyle-oriented demand, their development trajectories differ markedly. In Girne, housing production has evolved gradually, resulting in a fragmented and heterogeneous settlement structure shaped by mountainous topography and incremental planning practices. In contrast, Iskele has undergone rapid and large-scale development characterized by high-rise, high-density, and more homogeneous residential projects that are facilitated by flat terrain and investment-led growth. The results demonstrate that coastal housing transformation cannot be explained by lifestyle migration alone but emerges from the interaction between migration demand, planning regimes, and market dynamics. By providing a comparative and spatially grounded analysis within an island context characterized by limited planning control, this study offers empirical insights that contribute to debates on residential tourism, second homes, and sustainable coastal planning in Mediterranean regions. Full article

Background/Objectives: We aim to codesign and test a nurse-led, large language model-empowered agent to increase hepatitis B screening and vaccination for inclusion health populations. Methods: This study employs a double diamond model-guided codesign methodology. It includes four phases: (i) Discover: To identify intervention targets, a systematic review was undertaken that synthesized 51 factors influencing hepatitis B screening and vaccination among inclusion health populations. A qualitative study will later be conducted to further elucidate specific cultural barriers in the Chinese context. (ii) Define: To delineate effective intervention designs, two systematic reviews were performed, informing the integration of nurse-led intervention components (e.g., counseling, case management, and care coordination) and adaptation of a large language model to address identified intervention targets. (iii) Develop: To codesign an agent, hepatitis B prevention datasets will be constructed with subsequent model adaptations through fine-tuning and retrieval-augmented generation, as well as collaborations among diverse stakeholders. It will facilitate human–agent interactive consultation, intelligent case management, and care coordination, as well as collaborate with a nurse-led multidisciplinary team to manage hepatitis B screening, vaccination, and care linkage. (iv) Deliver: To evaluate and refine the agent, a mixed-methodology will be adopted, encompassing quantitative evaluation of model response, as well as qualitative evaluation of user experience, technical barriers, and potential benefits. Discussion: This intervention is expected to improve hepatitis B screening and vaccination rates among inclusion health populations, thereby enhancing diagnosis, immunity, and care linkage. It will establish a codesign framework for nursing-specific large language models, broadening the impact of nurses on preventive health equity. Full article

The rapid expansion of the Internet of Things (IoT) has led to billions of interconnected devices generating and exchanging sensitive data across diverse domains, which introduces challenges in identity management (IdM) regarding privacy, scalability, and verifiability. While blockchain technology provides decentralization and tamper resistance, its transparency and increasing on-chain storage demands make it unsuitable for large-scale IoT identity ecosystems. To overcome these challenges, IoT-SBIdM is proposed as a lightweight, privacy-preserving, and stateless blockchain-based identity management framework designed for IoT environments. This framework incorporates Elliptic Curve Cryptography (ECC)-based accumulators and Zero-Knowledge Proofs (ZKPs) to facilitate selective disclosure, enabling entities to prove credential authenticity without exposing sensitive identity information. Furthermore, the framework adopts W3C-compliant Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to promote interoperability and user-controlled identity ownership. The experimental results indicate that IoT-SBIdM achieves efficient smart contract execution by reducing gas costs through optimized registry logic. Moreover, the system maintains a compact block size of only 45 MB at higher block heights, outperforming comparable schemes in storage efficiency by achieving a 55% reduction relative to recent models and an approximate 94% reduction relative to older systems, thereby demonstrating superior scalability and storage efficiency, making it suitable for identity management solutions for IoT environments. Full article

Indigenous Australians experience a disproportionate burden of type 2 diabetes mellitus and cardiovascular disease. While clinician-led, community-based exercise programs are effective in general populations, limited peer-reviewed evidence is available describing culturally adapted exercise interventions with Indigenous Australians that transparently reports governance, cultural adaptation, and theoretical design. This paper reports the co-design and development of tools for the Preventing Indigenous Cardiovascular Disease and Diabetes through Exercise (PrIDE) study, an adaptation of the Beat It program that incorporates wearable technology. Using the Co-design Health Research and Innovation Model, four tools were developed with Indigenous governance through a Consumer Advisory Group and a project-specific Consumer User Panel. Three tools were culturally adapted—the PrIDE Exercise Program, the Strong Spirit Strong Self self-efficacy assessment, and Keep Your Heart Strong educational materials—and a newly developed tool, the Success Plan. Cultural adaptations were prospectively documented using the Model for Adaptation Design and Impact, and all tools were assessed using the Aboriginal and Torres Strait Islander Quality Appraisal Tool. Behavior change mechanisms were mapped using the COM-B model. This paper provides transparent documentation of culturally adapted theory-informed tool development to support reproducibility and knowledge translation. The evaluation of effectiveness, acceptability, and psychometric properties will be reported following PrIDE implementation. Full article

Background: Falls are a leading cause of morbidity and mortality among older adults, creating a need for technologies that can automatically detect falls and summon timely assistance. The rapid evolution of sensor technologies and artificial intelligence has led to a proliferation of fall detection systems (FDS). This systematic review synthesizes the recent literature to provide a comprehensive overview of the current technological landscape. Objective: The objective of this review is to systematically analyze and synthesize the evidence from the academic literature on fall detection technologies. The review focuses on three primary areas: the sensor modalities used for data acquisition, the computational models employed for fall classification, and the emerging trend of shifting from reactive detection to proactive fall risk prediction. Methods: A systematic search of electronic databases was conducted for studies published between 2008 and 2025. Following the PRISMA guidelines, 130 studies met the inclusion criteria and were selected for analysis. Information regarding sensor technology, algorithm type, validation methods, and key performance outcomes was extracted and thematically synthesized. Results: The analysis identified three dominant categories of sensor technologies: wearable systems (primarily Inertial Measurement Units), ambient systems (including vision-based, radar, WiFi, and LiDAR), and hybrid systems that fuse multiple data sources. Computationally, the field has shown a progression from threshold-based algorithms to classical machine learning and is now dominated by deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Many studies report high performance, with accuracy, sensitivity, and specificity often exceeding 95%. An important trend is the expansion of research from post-fall detection to proactive fall risk assessment and pre-impact fall prediction, which aim to prevent falls before they cause injury. Conclusions: The technological capabilities for fall detection are well-developed, with deep learning models and a variety of sensor modalities demonstrating high accuracy in controlled settings. However, a critical gap remains; our analysis reveals that 98.5% of studies rely on simulated falls, with only two studies validating against real-world, unanticipated falls in the target demographic. Future research should prioritize real-world validation, address practical implementation challenges such as energy efficiency and user acceptance, and advance the development of integrated, multi-modal systems for effective fall risk management. Full article

Despite skepticism and distrust in artificial intelligence (AI), it is increasingly integrated into daily life, with its potential benefits drawing interest. Yet little is known about the attitudinal and psychological effects of human–AI interactions, and whether consistent interactions with AI chatbots can change users’ attitudes and perceptions. Our within-subjects experiment (N = 52) investigated how five days of socially oriented, friendlike interactions with an AI chatbot, versus a journaling control, influenced changes in attitudes and perceptions of AI. Participants’ attitudes towards AI, trust, perceived empathy, anthropomorphism, animacy, likeability, perceived intelligence and safety, dependency, and exploratory well-being indicators were recorded. Results indicated that consistent friendlike interaction with AI chatbots led to significant increases in perceived empathy and animacy of technology, but no changes in global attitudes and perceptions of anthropomorphism. Participants also reported higher self-esteem levels after journaling, compared to after AI interaction. This suggests that although friendly engagement with AI chatbots may lead to perceptions of empathy and lifelikeness, where users interpret it to be genuinely understanding and supportive, this comes with trade-offs for self-esteem. Concurrently, empathy and perceived lifelikeness increased without corresponding increases in anthropomorphism, indicating that users may regard AI chatbots as separate living entities rather than having human-like qualities. Full article

The automation of public services represents a key area of development at the national level, with the main goal of facilitating citizens’ access to comprehensive, integrated and high-quality services in the shortest possible time. National strategies emphasize the need to integrate open geospatial data and artificial intelligence into information, transparency and decision-making processes. The evolution of artificial intelligence, particularly large language models (LLMs), has led to the development of virtual assistants capable of understanding user requirements and providing answers in natural, easy-to-understand language. This paper presents directions for the development and use of large-language-model-based virtual assistants, focusing on their ability to understand and interact with the geospatial domain through an LLM API. Geospatial modeling contributes significantly to the automation of public services, but access to this technology is often limited by technical expertise or dedicated software programs. The development of AI-based virtual assistants removes these barriers, facilitating access, reducing time and ensuring transparency and accuracy of information. The proposed approach is implemented using a commercial large language model API, integrated with domain-specific geospatial functions and authoritative spatial databases. This study highlights practical examples of virtual assistants capable of understanding the geospatial field and contributing to the optimization and automation of public services in the country. In addition, the paper presents comparative analyses, challenges encountered and potential directions for future research. Full article