Search Results (317)

Urban regeneration is critical for addressing contemporary urban challenges, yet its complexity arises from the dynamic interactions among different participants’ preference and strategic behavior factors, making it a multi-agent system driven by strategic behaviors. This study, based on a Chinese urban regeneration case, develops a dynamic evolutionary game model for industrial park renewal to explore the strategic interactions among three key stakeholders: government, social capital, and property owners. The findings reveal three insights: Firstly, the probabilities of social capital participation and property owner cooperation exhibit opposing trends, highlighting conflicting incentives. Secondly, social capital participation follows an inverted U-shaped trajectory with investment ratios, reflecting a strategic trade-off between risk and control; further robustness checks incorporating time delays and phased investments confirm that the curvature of this trajectory is highly sensitive to the project’s development cycle. Thirdly, lower land repayment costs, higher rental income, greater project returns, and a higher profit-sharing ratio promote cooperative strategies among property owners, though this effect remains marginal. The study further demonstrates that non-cooperative behavior among property owners results in a single evolutionary stable strategy (1, 1, 0) where the government repurchases land property rights, and social capital acquires these rights for redevelopment. The findings suggest that this conclusion applies specifically to industrial park renewal in urban centers held by property owners in cities, where it is government-led facilitation, with property owners exiting and social capital entering simultaneously, thereby ensuring alignment of multi-agent strategic behavior in China. Full article

Under the paradigm of the Internet of Things (IoT), the processing of large-scale data not only imposes higher demands on data-sharing efficiency but also increases the risk of user privacy leakage. To address these challenges, this paper proposes a blockchain-assisted traceable and revocable broadcast encryption scheme for preventing malicious encryptors (BATR). To resist trapdoor attacks by malicious encryptors, the scheme utilizes the uniform distribution property of hash function outputs to generate the random numbers required for the encryption algorithm. To block malicious users from leaking private keys, which attackers could exploit to construct piracy decoders with decryption capabilities, the scheme enhances the traditional broadcast encryption system by incorporating public tracing and revocation mechanisms. The scheme employs personalized transmission technology, allowing data owners to share public data with a set of authorized users while also sharing personalized data with specific authorized users. Additionally, users communicate using pseudonyms to ensure that their real identities are not accessible to third parties, thereby meeting privacy protection requirements. With the assistance of blockchain, trusted authorities and users can invoke smart contract interfaces to trigger blockchain peer nodes to execute smart contracts, thereby acquiring or updating identity authentication information stored on the blockchain to achieve secure authentication. This paper provides an analysis of the correctness and security of BATR, demonstrating that BATR satisfies chosen-ciphertext security under the Random Oracle Model. We also present performance evaluations and describe the experimental setup used to obtain operation-time baselines. Finally, this paper conducts a performance analysis of the BATR scheme, which exhibits high computational efficiency and compact communication bandwidth, resulting in significant performance improvements. Full article

Current seismic design practices primarily emphasize the structural safety of buildings, while research on the safety of non-structural components in interior design remains relatively insufficient. In this study, from the perspective of interior design, we explored the performance of non-structural components during earthquakes and how design strategies can reduce damage and enhance the adaptability of interior spaces, to establish a design framework that integrates safety considerations with behavioral guidance. We conducted a literature review, questionnaire survey, and comprehensive analysis in this study. The questionnaire was structured to investigate (1) the development of interior finishing and seismic design, (2) the seismic performance of non-structural components, and (3) the application trends of seismic disaster prevention and evacuation strategies. The respondents included property owners, design and construction professionals, government agencies, and academic experts. Their responses were analyzed for the differences in perception and needs regarding safety and spatial adaptability among different stakeholder groups. Through analysis, influencing factors were identified, and an integrated design framework of non-structural components—spatial planning and behavioral guidance—was established for the development of an interior design strategy toward earthquake disaster prevention. Among the three dimensions, application trends of seismic disaster prevention and evacuation strategies received the highest evaluation score, with an average score of 4.7 (a standard deviation of 0.4) and a reliability coefficient of α = 0.93. 90% of respondents supported the integration of virtual reality, building information modeling, and simulation-based training to improve evacuation efficiency, demonstrating the high feasibility and promotion potential of disaster-prevention technologies. Full article

Dynamic response measurements support bridge monitoring and structural assessment because they are obtainable under operational loading and are sensitive to changes in stiffness, boundary conditions, and mass distribution. This article presents a structured scoping review of dynamic-response-based bridge monitoring and assessment. It covers damage-sensitive indicators, stiffness/capacity proxy inference, interpretation under operational and extreme loading, sensing with acquisition (contact, and indirect/drive-by), and data processing, machine learning and digital-twin integration for decision support. Evidence was identified through targeted searches in Scopus and The Lens with duplicate resolution in Zotero. The cited studies are compiled into a traceable evidence inventory linked to method families and decision objectives. The synthesis shows that global modal properties enable change screening but are highly confounded by environmental/operational variability. Localization and state characterization typically require denser or higher-fidelity sensing and signal conditioning. Finally, capacity-related inference using calibrated conversion models or machine learning (ML) surrogates remains context-bounded and validation-dependent. This review provides an end-to-end pipeline, evidence-maturity rubric, and conservative failure-mode checks with escalation logic that tie SHM outputs to inspection and analysis rather than direct condition declarations for bridge owners. This review is intentionally scoped and does not claim PRISMA-style comprehensiveness. Full article

This study examines the relationship between house prices and the factors driving their growth during the transition from a long-standing currency board regime to Eurozone membership. The main objective is to identify and quantify the key factors explaining the variation in house price growth in Bulgaria under conditions of prolonged currency convergence. The study applies a set of econometric techniques, including stationarity tests (ADF and KPSS), diagnostic checks for normality, serial correlation and heteroscedasticity, and robustness checks. The study is based on 40 quarterly observations covering the period 2015Q4–2025Q3 and 48 selected predictors of the General house price index. The final ARIMAX(0,2,1) model is estimated using second-differenced data. The model includes a first-order moving average component and three exogenous regressors: the owner-occupiers’ housing expenditures, the actual rentals for housing in Bulgaria and the homeowners’ utility expenses. The model explains 87% of the variation in house price acceleration, with a comparatively low mean squared error. The diagnostic analysis confirms model adequacy. The three exogenous regressors are statistically significant at the 1% level with strong and stable effects on house price dynamics. No statistically significant relationship is found for the set of traditional macroeconomic, demographic, financial, and sectoral factors. The results show that during Bulgaria’s transition from a currency board to the Eurozone, the sustained house price growth was driven by country-specific factors. The three statistically significant determinants of the house price acceleration in Bulgaria reflect, respectively, the active investment behaviour of homeowners in improving existing properties, the rational assessment by housing market participants of the balance between mortgage and rental payments, and the burden of utility and maintenance costs borne by owners and tenants, depending on property size and energy efficiency. The first factor is most influential for homeowners, the second for tenants, and the third has a similarly significant impact on both groups. Full article

Qualification is the evidence-based process through which confidence is established that a component will perform its intended function, in its intended environment, for its intended lifetime, with the required reliability. It is an owner-led activity that defines the type, quantity and quality of data required for codification and for the industrial deployment of components and their structural materials. This paper presents a structured qualification framework and applies it to a fusion machine breeder blanket structure as a representative component. It demonstrates that qualification, rather than material properties alone, dictates the use of fusion structural materials and the deployment of such materials under ASME BPV and AFCEN RCC codes. Current limitations in addressing irradiation synergy, liquid metal corrosion, and joint integrity expose gaps that these codes cannot yet prescribe. Two contrasting structural blanket material case studies: metallic-based ferritic-martensitic steel Eurofer97 and non-metallic-based silicon carbide fibre-reinforced composites (SiC_f/SiC) are used to illustrate the differing evidence requirements for each system type. Industrial scale-up considerations, including alloy specifications, manufacturing readiness, inspection reliability, and supply-chain maturity, are evaluated alongside the need for internationally harmonised datasets and design methodologies. Fusion programmes can use a phased qualification strategy in which early, time-limited operation under controlled conditions builds the evidence needed for codification and scale-up, with the required pre-operation qualification level depending on risk, component criticality and failure consequences, and with the pace of qualification ultimately setting how quickly industry can supply components for commercial fusion. Codification remains essential for commercial deployment because construction codes express codified material behaviour through allowable stresses and permitted fabrication routes, enabling designers to use advanced materials without disclosing proprietary data. In jurisdictions where ASME BPV compliance is mandatory, codification determines whether a material may enter pressure boundary service and must therefore form part of the fusion machine owner’s long-term strategy for deployment. Full article

Accurate remaining useful life (RUL) prediction is essential for the safe and cost-effective operation of safety-critical systems such as electronic components and engines. While data-driven machine learning approaches have demonstrated strong performance for RUL estimation, their effectiveness is limited by the lack of full run-to-failure data and by strict privacy and intellectual property constraints in industrial settings. Federated learning (FL) enables collaborative model training across multiple data owners without direct data sharing, but it does not, by itself, provide formal privacy guarantees and remains vulnerable to information leakage. This paper presents a privacy-preserving DP-enhanced FL setup for RUL prediction that combines federated learning with differential privacy (DP). We describe an end-to-end implementation based on the Opacus DP library, highlight practical challenges arising from the integration of DP into recurrent neural network architectures, and propose solutions to address them. Using two representative RUL datasets (CMAPSS and SiC MOSFET), we analyze the effect of DP noise on prediction performance and on the functional dependence between the predicted RUL and the already lived life feature. The results demonstrate that differential privacy can be integrated into federated RUL prediction with limited degradation in predictive performance, providing practical insights for deploying privacy-aware collaborative models in industrial environments. Full article

Background: Rare cancers combine low incidence with high biological heterogeneity and multi-institutional care trajectories. These features make single-center learning structurally incomplete and render pathway fragmentation a dominant driver of preventable harm, variability, and waste. In this context, care quality is best understood as a property of pathway integrity across routing, diagnostics (imaging/biopsy planning), multidisciplinary intent-setting, definitive treatment, and surveillance—rather than as a department-level attribute. Objective: To define a pragmatic, transferable operating blueprint for a rare-cancer Learning Health System (LHS) that turns routine care into continuous, auditable learning under explicit governance, while maintaining claims discipline and protecting measurement validity. Approach: We synthesize an implementation-oriented operating model using the Swiss Sarcoma Network (SSN) as an exemplar. The blueprint couples clinical governance (Integrated Practice Unit logic, hub-and-spoke routing, auditable multidisciplinary team decision systems) with an interoperable real-world-time data backbone designed for benchmarking, pathway mapping, and feedback. The operating logic is expressed as a closed-loop control cycle: capture → harmonize → benchmark → learn → implement → re-measure, with explicit owners, minimum requirements, and failure modes. Results/Blueprint: (i) The model specifies a minimal set of data primitives—time-stamped and traceable decision points covering baseline and tumor characteristics, pathway timing, treatment exposure, outcomes and complications, and feasible longitudinal PROMs and PREMs; (ii) a VBHC-ready, multi-domain measurement backbone spanning outcomes, harms, timeliness, function, process fidelity, and resource stewardship; and (iii) two non-negotiable validity guardrails: explicit applicability (“N/A”) rules and mandatory case-mix/complexity stratification. Implementation is treated as a governed step with defined workflow levers, fidelity criteria, balancing measures, and escalation thresholds to prevent “dashboard medicine” and surrogate-driven optimization. Conclusions: This perspective contributes an operating model—not a platform or single intervention—that enables credible improvement science and establishes prerequisites for downstream causal learning and minimum viable digital twins. By distinguishing enabling infrastructure from the governed clinical system as the primary intervention, the blueprint supports scalable, learnable excellence in rare-cancer care while protecting against gaming, inequity, and inference drift. Distinct from generic LHS or VBHC frameworks, this blueprint specifies validity gates required for rare-cancer benchmarking—explicit applicability (“N/A”) rules, denominator integrity/capture completeness disclosure, anti-gaming safeguards, and escalation governance. These elements are critical in rare cancers because small denominators, high heterogeneity, and multi-institutional pathways otherwise make benchmarking prone to artifacts and unsafe inferences. Full article

Despite the global increase in cat ownership, some cats exhibit owner-directed aggression, resulting in caregiver injury, infection, and anxiety. Severe cases are commonly treated with selective serotonin reuptake inhibitors such as fluoxetine; however, adverse effects, particularly transient anorexia, often discourage treatment initiation. Cannabidiol (CBD), a natural compound with reported anxiolytic properties and minimal anorexic effects, may represent an alternative therapy. This study aimed to characterise owner-directed feline aggression in Thailand, identify associated factors, and compare the efficacy of CBD with fluoxetine. Most caregivers were females aged 20–40 years, and most cats were neutered mixed-breeds aged 1–6 years living indoors in multi-human and multi-cat households. For demographic variables, only human–cat interactions (e.g., petting) were significantly associated with aggression. Handling-induced aggression was universal, with grooming as the most common trigger (56%). In a single-blind, 4–8-week trial, 100 cats were randomly assigned to control, CBD 1 mg/kg/day, CBD 2 mg/kg/day, fluoxetine 0.5–1 mg/kg/day, or combined CBD and fluoxetine. Aggression scores decreased significantly in all treatment groups compared with control (p < 0.05), with no differences among active treatments. CBD at 1 mg/kg/day showed efficacy comparable to fluoxetine without anorexic effects. Full article

Deep neural networks (DNNs) represent critical intellectual property that model owners urgently need to protect. With the increasing value of models, malicious attackers increasingly attempt to extract model functionality through techniques such as fine-tuning, distillation, and pruning. Model fingerprinting has emerged as a mainstream protection strategy. However, existing fingerprinting methods either exhibit vulnerability to model modifications due to reliance on decision boundary features or require prohibitively large query budgets for accurate verification. This paper proposes ProbeSpec, which captures model fingerprints through dynamic behavioral analysis rather than static output matching. We discover that a model’s response patterns under multi-level perturbations form a unique “behavioral spectrum”, originating from implicit decision mechanisms learned during training and preserved even after various attacks. ProbeSpec employs three complementary probe types to elicit this characteristic and leverages DCT frequency-domain transformation for efficient fingerprint extraction. Extensive experiments show that ProbeSpec achieves 100% detection rate in the majority of attack scenarios, with an overall accuracy exceeding 95% across all tested architectures. Meanwhile, it effectively distinguishes independently trained models and requires only 80 probe samples for fingerprint extraction. Full article

Historic buildings are crucial for urban cultural continuity and sustainable development, but their protection and adaptive reuse are often constrained by institutional, financial, and managerial challenges. This study, using five historic buildings in Xuzhou as case studies, analyzes the key mechanisms influencing adaptive reuse, focusing on the impact of property rights structures, governance systems, and operational models on protection and reuse outcomes. Through semi-structured interviews with government officials, property owners, and the public, combined with on-site surveys and historical data, the study identifies fragmented property rights, limited funding, and homogeneous reuse models as the main barriers. It further highlights that clear property responsibility, a coordinated institutional framework, and diversified operational strategies are linked to successful adaptive reuse. The paper proposes a comprehensive framework covering policy regulation, financial investment, cultural activation, and restoration techniques. Five strategic recommendations are made: policy optimization, diversified funding, strengthened awareness, operational model upgrades, and multi-dimensional revitalization strategies. This research offers an empirical framework for the adaptive reuse of historic buildings, providing insights applicable to similar institutional and developmental contexts. Full article

This study aims to explore the relationship between the digital financial literacy of homestay business owners and economic sustainability. A conceptual model is developed by integrating three primary constructs—performance expectancy, effort expectancy, and facilitating conditions—from the unified theory of acceptance and use of technology (UTAUT) with digital financial literacy and FinTech use by homestay business owners. Further, the effect of FinTech use on economic sustainability is examined through the interaction between facilitating conditions and financial inclusion. Data were collected from Southern India, and hypothesized relationships were tested after checking the measurement properties of the survey instrument. The findings indicate that (i) the digital financial literacy of homestay business owners is a precursor to FinTech use, which, in turn, is positively associated with economic sustainability; (ii) digital financial literacy interacting with performance expectancy (first moderator) and effort expectancy (second moderator) significantly influenced FinTech use; and (iii) FinTech use interacting with facilitating conditions (first moderator) and financial inclusion (second moderator) increased economic sustainability. The three-way interactions in this study provide insights into the boundary conditions that increase FinTech use and economic sustainability, particularly in the context of homestay businesses. The proposed digital financial literacy and FinTech adoption model contributes to the information technology adoption research by extending the UTAUT, in which performance expectancy and effort expectancy play a vital role in FinTech adoption by homestay business owners. The three-way model developed and tested, to the best of our knowledge, is the first of its kind in the Indian context and hence makes a pivotal contribution to the advancement of the UTAUT model through its application to homestay business owners. The implications for theory and practice are discussed. Full article

Blockchain is widely promoted as a tool for enhancing transparency, trust, and sustainability in business, yet little is known about how creative micro, small, and medium enterprises (MSMEs) in emerging economies can meaningfully adopt it for finance and accounting purposes in times of global uncertainty. This study explores how blockchain can be harnessed for transparent and sustainable accounting in Indonesian creative MSMEs amid rapid digital disruption. Using an exploratory qualitative design, we conducted semi-structured, in-depth interviews with 18 owners and key decision-makers across diverse creative subsectors and analysed the data thematically through an integrated Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) lens. The findings show that participants recognise blockchain’s potential benefits for transaction transparency, verifiable records, intellectual property protection, and secure payments, but adoption is constrained by technical complexity, financial constraints, limited digital and accounting capabilities, and perceived regulatory and reputational risks. Government initiatives are seen as important for legitimacy yet insufficient without concrete guidance, capacity-building, and financial support. The study extends TAM–DOI applications to blockchain-enabled accounting in creative MSMEs and highlights the need for sequenced, ecosystem-based interventions to translate blockchain’s technical promise into accessible, ESG- and SDG-oriented accounting solutions in the creative economy. Full article

Amidst Portugal’s ongoing housing crisis, particularly pronounced in the Lisbon Metropolitan Area, thousands of residential units remain vacant. This article investigates why property owners often refrain from placing these homes on the rental market, despite high demand and rising prices. Drawing on empirical data from successive editions of the ALP (Lisbon Landlords Association) Barometer and framed by the literature on housing financialization, institutional trust, and patrimonial ownership cultures, the study shows that vacancy is not merely a result of speculation or neglect. Rather, it emerges as a rational response to a complex interplay of regulatory instability, legal mistrust, and deeply rooted socio-cultural norms. Landlords act not only as economic agents but also as custodians of family heritage, navigating uncertainty in a legal and symbolic environment increasingly perceived as hostile. The article argues that mobilizing Lisbon’s empty housing stock requires more than tax incentives or coercive measures. It demands rebuilding trust, ensuring legal predictability, and acknowledging the cultural meanings that shape property decisions. Policy recommendations include stabilizing rental legislation and designing culturally sensitive engagement strategies for small landlords. Full article

Adaptive reuse of heritage buildings is a vital strategy for balancing cultural preservation with modern functionality needs. This study provides a post-adaptive reuse evaluation of Dar Al Saraya in Madaba, Jordan, a significant Ottoman-era landmark, to examine how adaptive reuse strategies influence interior environments and heritage value. The analysis employs Zhang and Zhang’s evaluation framework focusing on existing fabric, special character, and policy and value, operationalized through 15 factors. A qualitative methodology was adopted, integrating site observations, photographic documentation, and semi-structured interviews with heritage experts, municipal representatives, residents, visitors, and site staff. Fieldwork was conducted in two phases (November 2024 and October 2025) to capture evolving conditions and perceptions. Findings indicate that challenges in spatial reconstruction were few and well addressed, but gaps in adaptation and reuse function strategies created significant issues. These included a lack of coordinated policies and the failure of municipal authorities and property owners to sustain the building’s reuse and involve the local community in reuse decisions. Despite various initiatives, from a museum, hotel, cultural center and gallery to its recent adaptation into a café, these efforts lacked sustainability and inclusive strategic planning. Consequently, the café has faced difficulties since opening, leaving its future uncertain. These findings highlight the importance of post-adaptive reuse evaluation and of integrating policy, planning, and community participation into adaptive reuse strategies to promote sustainable, community-centred conservation. Full article