Search Results (895)

As the core infrastructure for providing all-weather, full-coverage, high-speed, and diversified information services, spatial information networks (SINs) possess significant social, economic, and military value. However, due to the inherent characteristics of their network architecture, SINs are susceptible to core service paralysis and functional failure under large-scale targeted attacks or random disturbances, posing a critical bottleneck that constrains their stable operation. Current research on SIN vulnerability is predominantly confined to a single network topology perspective, lacking an integrated consideration of the task execution perspective. Consequently, it fails to accommodate the dual requirements of “network topology stability” and “task execution effectiveness”. To address the aforementioned research needs and challenges, this study adopts a “topology-task” dual-perspective fusion approach and proposes a vulnerability analysis framework for SINs that integrates multi-layer networks and hypernetworks. First, a two-layer SIN topology model encompassing the user layer and the satellite layer is constructed. Leveraging hypernetwork theory, information tasks involving multiple network entities are formally defined, and an integrated multi-layer hypernetwork model is established. Second, based on distinct task types, three categories of task efficiency evaluation metrics are defined, and corresponding quantitative methods for calculating SIN vulnerability are derived. Third, during the vulnerability analysis phase, a novel strategy for identifying and removing overlapping nodes in hypernetworks is introduced to enable precise localization of critical nodes within the network. Concurrently, a pre-attack node hardening strategy is designed to minimize the impact of attacks on network performance. Finally, through systematic analysis of vulnerability performance and critical node characteristics under different node removal strategies, the results demonstrate enhanced network performance. The effectiveness of the proposed method is validated by comparing the defense performance of the hardening strategy across various attack scenarios. To verify the feasibility and superiority of the proposed method, this study designs 5 × 5 groups of simulation experiments with varying network parameters. The results indicate that, compared with traditional methods, the proposed strategy can more accurately identify core nodes affecting the stable operation of SINs, significantly reducing network vulnerability and improving network survivability. In addition, a comprehensive sensitivity analysis of SIN vulnerability is conducted from three key influencing dimensions—mission scale, satellite count, and constellation configuration—clarifying the impact of each dimension on network invulnerability. Thus, this paper provides a reliable theoretical foundation and technical support for the planning, design, optimal deployment, and operation and maintenance management of SINs. Full article

Most existing studies on the cost competitiveness of self-consumption PV systems fail to jointly consider key technical, economic, and user-specific factors—such as the share of PV electricity self-consumed, energy exported or imported from the grid, and time-of-use electricity pricing—all of which significantly influence investment viability. To address these gaps, this study introduces a novel method based on a new model to calculate the unit cost of electricity consumption from the user’s perspective (C_EC, in €·kWh⁻¹). The array DC power rating is then optimally sized—assuming ideal orientation and tilt—to minimize C_EC. A self-consumption PV system is considered cost-competitive when the annualized minimized C_EC is lower than the applicable regulated electricity tariff. Colombia is selected as a case study to demonstrate the novel method due to the limited deployment and analysis of self-consumption PV systems in the country. The method is applied across residential, commercial, and industrial sectors in various locations. The resulting annualized minimized C_EC values (0.35–8.85 c€/kWh) are consistently below the corresponding regulated tariffs, demonstrating the economic viability of properly sized PV systems. The method’s adaptability to international tariff frameworks makes it a valuable tool for global application and a useful resource for policymakers and stakeholders. Full article

This study explores how digital media ecosystems shape collective visions of the future under conditions of rapid technological innovation and the growing influence of artificial intelligence (AI). Drawing on a large corpus of social media content comprising 50,036,592 tokens, the research examines institutional narratives and user-generated responses through a hybrid methodological framework. This framework combines information-wave detection, network analysis, semantic and associative modeling (TextAnalyst 2.32), and interpretation supported by a large language model (GPT-5). The methodological contribution of the study lies in the integration of network-based and semantic algorithms with AI-driven analytical tools for the examination of large-scale textual data. The findings indicate that media discourses about the future operate as key mechanisms through which societies interpret the environmental, social, and economic consequences of technological change. Institutional actors promote multiple future-oriented models that often conflict with one another at both discursive and practical levels. In contrast, user-generated content reflects widespread fear, skepticism, and distrust. Prominent themes include nostalgia for the past, anxiety about socio-economic and environmental consequences, and concerns related to expanding forms of digital control. The analysis also reveals divergent perspectives on urban development. Positive narratives emphasize ecological balance, a comfortable urban environment, thoughtfully designed mixed-use development, and solutions to transportation challenges. Negative narratives, by contrast, focus on over-densification, environmental degradation, and the erosion of privacy in technologically saturated urban spaces. Full article

Urban public spaces are an essential component of everyday life in urban communities, and micro public spaces have become an important vehicle for improving living environment quality and enhancing ecological benefits in community renewal within built-up areas. From an economic perspective, this study focuses on the quantification of carbon value generated through the renewal of micro public spaces. Four micro public spaces of comparable size but differing locational and functional characteristics, completed in Wuhan in 2023, were selected as case studies. Plant carbon sequestration was estimated using the biomass expansion factor method combined with field-based forestry surveys, while indirect emission reduction associated with residents’ outdoor activities was assessed through spatiotemporal observations of user behavior. These results were further translated into carbon value based on China’s carbon trading standards to support comparative analysis and design-oriented recommendations. The results indicate that direct sequestration significantly outweighs indirect reduction: the carbon storage density of trees ranges from 0.40 to 1.97 kg/m², with the total storage of HRP reaching 11,587.36 kg; in contrast, annual indirect reduction from resident activities is only 1.34–141.19 kg. Carbon sequestration performance is strongly influenced by the presence of large trees, while micro public spaces located in newly developed and commercial areas exhibit substantially lower emission reduction efficiency than those in older and densely populated residential neighborhoods. In addition, the functional attributes of micro public spaces shape age-specific use patterns, thereby significantly affecting emission reduction outcomes. Based on these findings, targeted optimization strategies for micro public space renewal are proposed to support people-oriented, sustainable, and systematic low-carbon urban regeneration. Full article

Background: The delivery of nursing care based on available evidence and centered on the individuals who require it and has a positive impact on the quality of professional interventions, leading to health benefits for the population across all core domains of nursing practice, as well as contributing to the advancement of the profession. Objective: To analyze nurses’ perspectives on the effects of their participation in evidence implementation programs in clinical nursing. Methods: In the Qualitative Descriptive Study, the study participants were nurses who took part in the “Safe Transition” project, a collaborative initiative involving three institutions: a nursing school, a hospital, and a network of primary healthcare institutions. The data were collected through semi-structured interviews that were analyzed by two researchers using content analysis techniques, with the support of the qualitative data analysis software. Results: From the content analysis of semi-structured interviews conducted with 17 nurses involved in knowledge implementation projects in clinical practice, the following categories emerged: Cultivating a spirit of inquiry and an evidence-based practice culture; Critically appraising established practices and evidence-based recommendations; and Integrating evidence into clinical expertise to drive change, improve outcomes, and enhance the quality of care. Conclusions: Effective communication and structured opportunities for knowledge sharing emerged as central to the critical examination of clinical practice and to the development of professionals’ competencies in evidence use. Evidence implementation was further motivated by professionals’ recognition that it can generate tangible benefits for healthcare service users. Collectively, these findings inform recommendations for clinical practice, nursing education, and future nursing research. Full article

This study provides a comprehensive empirical examination of the factors influencing the adoption of smart technologies in the Serbian hotel industry by integrating structural equation modeling (SEM), mediation and multigroup analyses, and machine-learning-based robustness testing. Grounded in the UTAUT framework, the research investigates how perceptual, organizational, and social determinants shape employees’ Behavioural Intention (BI) and actual Use Behaviour (USE). A key theoretical contribution is the introduction of the construct Perceived Spatial Impact of Technology (PST), which captures employees’ perceptions of how smart technologies transform the architectural concept, spatial organization, aesthetics, and functional logic of hotels. Although UTAUT traditionally focuses on users, neither prior studies nor the present one examine these dynamics from the perspective of architects or designers who create hotel spaces. Thus, the findings serve as an initial step from the user viewpoint, while future research should incorporate expert architectural reasoning to better understand how spatial knowledge and design logic intersect with user perceptions. All core UTAUT constructs significantly predict BI and USE, with Performance Expectancy and BI emerging as the strongest predictors across SEM and Random Forest models. PST exerts a fully mediated effect on USE through BI, and multigroup analysis reveals notable differences across job roles, hotel categories, and age groups. Overall, the results highlight that digital transformation in hospitality is not only technological and organizational, but also fundamentally architectural. Full article

This integrative literature review examines how virtual reality (VR) design can transform environmental understanding by changing users from passive observers to active participants in ecological systems. We aimed to analyze the interaction strategies through which VR enables environmental awareness and to identify the most effective approaches for fostering ecological connection. Through systematic analysis of studies published between 2015 and 2025, we found that effective VR implementations share three core design mechanisms: progressive engagement that builds connection over time, a careful balance between interaction and reflection, and multisensory integration that creates believable immersive experiences. These design mechanisms, in turn, build ecological connection through three fundamental pillars: perspective-taking that generates empathy, the creation of authentic sensory experiences, and the development of network thinking to understand complex interconnections. This review contributes to the field by mapping the development of environmental VR applications, identifying successful implementation strategies, and highlighting research gaps. Our analysis provides a comprehensive interaction framework for designing more effective environmental experiences and advancing this emerging field when innovative approaches are most needed. Full article

In the field of cultural architecture design, the deep impact mechanisms of wooden material design perception on users’ psychological experiences have not yet been fully elucidated. The interior environmental design of concert halls, as venues for immersive artistic experiences, especially the use of natural materials such as wood, is considered a key factor shaping audience perception and experience. However, existing research has largely focused on the acoustic performance of or visual preferences for wooden materials, while there remains a lack of mechanistic explanations for how wooden design perception systematically enhances users’ overall satisfaction through a series of internal psychological processes. Based on the “stimulus–organism–response” theoretical framework, this study proposes a chain mediation model aimed at exploring how perception of wooden design in concert halls enhances user satisfaction by promoting users’ flow experience and subsequently strengthening their place attachment. Through a cross-sectional survey of 1017 audiences with actual experience in wooden concert halls and analysis of the data using covariance-based structural equation modeling, the findings reveal that: (1) perception of wooden design has a significant direct positive effect on user satisfaction; (2) both flow experience and place attachment independently mediate the influence of wooden design perception on user satisfaction; (3) there exists a significant chain mediation path: “perception of wooden design → flow experience → place attachment → user satisfaction”. This study validates, from an architectural psychology perspective, the role of flow and place attachment as consecutive psychological mechanisms. The research provides empirical evidence for architects to use wood as a psychological intervention tool in cultural spaces, transforming material selection from an aesthetic consideration into a systematic design strategy with measurable psychological outcomes. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

This study examines sector-specific patterns of remarketing implementation in manufacturing and service industries and evaluates their effectiveness during digital transformation from a sustainability perspective. Using a mixed-method approach, the research combines descriptive analysis of enterprises’ digital maturity with Monte Carlo simulation modeling of remarketing campaign performance based on key parameters such as budget allocation, conversion efficiency, customer lifetime value, personalization intensity, and investment in AI-driven analytics. The results demonstrate that remarketing enhances traffic, user engagement, and return on investment; however, its sustainability depends on sectoral characteristics and behavioral responsiveness. AI-powered personalization is identified as a critical factor in reducing ad fatigue and improving conversion stability. While manufacturing firms tend to achieve higher but more volatile returns, service-sector companies demonstrate more stable outcomes due to greater digital adaptability and more intensive use of dynamic advertising tools. The findings highlight that sustainable remarketing strategies require sector-specific adaptation to balance economic efficiency, technological investment, and long-term consumer engagement, thereby supporting resilient and sustainable business development in the digital economy. Full article

Wireless sensor networks ($WSNs$) are extensively used in $IoT$ applications. Secure access control and data protection are essential. Nonetheless, the wireless environment has an open nature. The limited resources of sensor devices render $WSNs$ susceptible to a variety of security attacks, causing significant difficulties in the design phase of efficient authentication and key agreement ($AKA$) protocols. This study proposes a physically unclonable function ($PUF$)-based lightweight and secure $AKA$ protocol for $WSNs$ based on elliptic curve cryptography ($ECC$). A secure password update scheme is offered, which would allow legitimate users to reset forgotten passwords without re-registration. According to formal security analysis using $BAN$ logic and $ProVerif$, the proposed protocol is secure against common attacks. Moreover, from an entropy perspective, the use of dynamic pseudonyms and fresh session randomness increase an adversary’s uncertainty about user identities, thereby limiting identity-related information leakage. Performance evaluation shows that the proposed protocol achieves lower computational and communication overhead than the existing ones, making it suitable for $WSNs$ with resource constraints. Full article

Attribute-based encryption (ABE) is an advanced public key encryption mechanism that enables the precise control of access to encrypted data based on attributes assigned to users and data. Attribute-based access control (ABAC), which is built on ABE, is crucial in providing dynamic, fine-grained, and context-aware security management in modern Internet of Things (IoT) applications. ABAC controls access based on attributes associated with users, devices, resources, and environmental conditions rather than fixed roles, making it highly adaptable to the complex and heterogeneous nature of IoT ecosystems. ABE can significantly improve the security and manageability of modern military IoT systems. Nevertheless, its practical implementation requires obtaining a range of performance data and assessing the additional overhead, particularly regarding data transmission efficiency. This paper provides a comparative analysis of the performance of two cryptographic schemes for attribute-based encryption in the context of special Internet of Things (IoT) applications. This applies to special environments, both military and civilian, where infrastructure is unreliable and dynamic and decisions must be made locally and in near-real time. From a security perspective, there is a need for strong authentication, precise access control, and a zero-trust approach at the network edge as well. The CIRCL scheme, based on traditional pairing-based ABE (CP-ABE), is compared with the newer Covercrypt scheme, a hybrid key encapsulation mechanism with access control (KEMAC) that provides quantum resistance. The main goal is to determine which scheme scales better and meets the performance requirements for two different scenarios: large corporate networks (where scalability is key) and tactical edge networks (where minimal bandwidth and post-quantum security are paramount). The benchmark results are used to compare the operating costs in detail, such as the key generation time, message encryption and decryption times, public key size, and cipher overhead, showing that Covercrypt provides a reduction in ciphertext overhead in tactical scenarios, while CIRCL offers faster decryption throughput in large-scale enterprise environments. It is concluded that the optimal choice depends on the specific constraints of the operating environment. Full article

Against the backdrop of global climate change, the rising frequency and intensity of extreme weather events pose severe challenges to urban transport and commercial systems. As a core capacity for managing uncertainty and risk, urban resilience requires infrastructure to resist shocks, recover rapidly, and adaptively evolve. From a resilience perspective, this study develops a comprehensive evaluation system for spatial accessibility between subway stations and commercial complexes, operationalized by 21 indicators across five dimensions: Connectivity, Redundancy, Robustness, Dynamic adaptability, and Comfort. Spatial accessibility is simulated and measured using sDNA spatial network analysis, while an in-depth questionnaire survey collects, feeds back, and validates users’ subjective perceptions. By constructing a dual evaluation model that integrates spatial configuration and behavioral psychology, we find that the integrated development of subway stations and commercial complexes can maintain stable functional performance and sustained vitality under complex urban conditions by optimizing connectivity, enhancing redundancy, and improving adaptability. This is manifested in the expansion of residents’ pedestrian networks and the spillover of social service functions. In parallel, underground spaces can be transformed into resilient infrastructure to enhance civil air defense performance and provide diversified evacuation routes. The findings offer theoretical support and practical guidance for the construction of resilient cities. Full article

Although research on hotel service robots has been continuously increasing, the existing literature still lacks a systematic exploration of the multiple concurrent mechanisms involved in the formation of user experience. Based on the theory of value co-creation, this study first extracted the key service robot attributes that affect user experience by analyzing 3200 online user notes from the Chinese platform Xiaohongshu. Then, using 433 valid questionnaires, it employed SEM and fsQCA to examine the influence mechanism of service robot comprehensively attributes on user experience. The results of the SEM study showed that the attributes of perceived usefulness, perceived ease of use, anthropomorphism, and service remediation ability of service robots positively influenced customers’ willingness to co-create value, thereby further enhancing the user experience. The perceived privacy risk attribute did not significantly affect customers’ willingness to co-create value. The fsQCA analysis further identified multiple effective configurations, including antecedent configurations with high customer willingness to co-create value, high usage intention, high satisfaction, and high forgiveness intention as outcome variables. This study, through a combination of methods, revealed the complex experiences users encounter during interactions with service robots and regarded customers’ willingness to participate in value creation as a front-end psychological mechanism, providing a new theoretical perspective on the value co-creation process in human–machine collaboration. At the same time, this study, from the user perspective, provided strategies for optimizing user experience and service deployment for Chinese hotel managers. Full article

This study presents Care-MOVE, a smartphone-based application designed for continuous, passive, and unobtrusive monitoring of mobility, environmental exposure, and cognitive status in older adults within a telemedicine framework. The system integrates movement-related data collected through smartphone sensors (GPS, activity recognition, and caloric expenditure estimation) with contextual air quality information and standardized neuropsychological assessments, resulting in a comprehensive multimodal dataset (Care-MOVE Dataset). An exploratory proof-of-concept study was conducted on a subsample of 53 participants aged over 65, each monitored continuously for five days, contributing on average more than 30,000 longitudinal records. To investigate whether daily motor behavior can serve as a digital biomarker of cognitive functioning, several Machine Learning and Deep Learning models were evaluated using a Leave-One-User-Out (LOUO) cross-validation strategy. The comparative analysis included traditional classifiers (Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, and Support Vector Machines) as well as temporal deep learning architectures (1D CNN, LSTM, GRU, and Transformer). Among all of the evaluated approaches, the Support Vector Machine with RBF kernel achieved the best performance, reaching an accuracy of 98.1%, a balanced accuracy of 0.988, and an F1-score of 0.981, demonstrating robust generalization across unseen subjects. For this reason, the study was designed and presented as an exploratory proof-of-concept rather than a definitive clinical validation. This integrated approach not only enables the collection of detailed and contextualized data but also opens new perspectives for proactive digital healthcare, focused on risk prevention, improving quality of life, and promoting autonomy in elderly patients. Full article