Search Results (797)

The real estate industry, known for its complexity and exposure to systemic and idiosyncratic risks, requires increasingly sophisticated investment risk assessment tools. In this study, we present the Real Estate Construction Investment Risk (RECIR) model, a machine learning-based framework designed to quantify and manage multi-dimensional investment risks in construction projects. The model integrates diverse data sources, including macroeconomic indicators, property characteristics, market dynamics, and regulatory variables, to generate a composite risk metric called the total risk score. Unlike previous artificial intelligence (AI)-based approaches that primarily focus on forecasting prices, we incorporate regulatory compliance, forensic risk assessment, and explainable AI to provide a transparent and accountable decision support system. We train and validate the RECIR model using structured datasets such as the American Housing Survey and World Development Indicators, along with survey data from domain experts. The empirical results show the relatively high predictive accuracy of the RECIR model, particularly in highly volatile environments. Location score, legal context, and economic indicators are the dominant contributors to investment risk, which affirms the interpretability and strategic relevance of the model. By integrating AI with ethical oversight, we provide a scalable, governance-aware methodology for analyzing risks in the real estate sector. Full article

Accurate heavy-duty vehicle load estimation is crucial for transportation and environmental regulation, yet current methods lack precision in data accuracy and practicality for field implementation. We propose a Self-Supervised Reconstruction Heterogeneous Graph Convolutional Network (SSR-HGCN) for load estimation using On-Board Diagnostics (OBD) data. The method integrates physics-constrained heterogeneous graph construction based on vehicle speed, acceleration, and engine parameters, leveraging graph neural networks’ information propagation mechanisms and self-supervised learning’s adaptability to low-quality data. The method comprises three modules: (1) a physics-constrained heterogeneous graph structure that, guided by the symmetry (invariance) of physical laws, introduces a structural asymmetry by treating kinematic and dynamic features as distinct node types to enhance model interpretability; (2) a self-supervised reconstruction module that learns robust representations from noisy OBD streams without extensive labeling, improving adaptability to data quality variations; and (3) a multi-layer feature extraction architecture combining graph convolutional networks (GCNs) and graph attention networks (GATs) for hierarchical feature aggregation. On a test set of 800 heavy-duty vehicle trips, SSR-HGCN demonstrated superior performance over key baseline models. Compared with the classical time-series model LSTM, it achieved average improvements of 20.76% in RMSE and 41.23% in MAPE. It also outperformed the standard graph model GraphSAGE, reducing RMSE by 21.98% and MAPE by 7.15%, ultimately achieving < 15% error for over 90% of test samples. This method provides an effective technical solution for heavy-duty vehicle load monitoring, with immediate applications in fleet supervision, overloading detection, and regulatory enforcement for environmental compliance. Full article

African swine fever (ASF) has reshaped wild boar (Sus scrofa) populations and management across Europe since its reintroduction in 2007. ASF reached Slovakia in August 2019, when wild boar population size and harvest were at six-decade maximums. We analyzed data from six years (2019–2024) of national surveillance and control to quantify spatio-temporal ASF patterns in free-ranging wild boar. Using monthly virological (PCR) and serological (antibody) data from active (hunted) and passive (found dead) surveillance, we (1) estimated temporal variation in the effective reproduction number (R_t); (2) modeled spatio-temporal prevalence in Slovakia and its eastern, central, and western regions; (3) linked these dynamics to management indicators such as wild boar density, harvest, and mortality; and (4) proposed measures to increase surveillance and control effectiveness. Passive surveillance showed greater diagnostic sensitivity than active surveillance for case detection (PCR: 46.5% vs. 0.48%; antibodies: 7.62% vs. 0.75%). R_t peaked at 3.83 in March 2021, then declined but periodically exceeded 1.0 through late 2024. Virological prevalence showed strong late-winter/early-spring seasonality and a persistent east-to-west gradient: peaks occurred first in the east (March 2021, March 2023), with the center surpassing the east in October 2023 and a subsequent rise in the west. Seroprevalence lagged and shifted westward later, peaking in March 2023 and increasing in western Slovakia from mid-2024. Wild boar density decreased by 36.3% from 2019 to 2024 and harvest-based density by 42.8%, returning to post-classical swine fever levels (2009–2013). We recommend prioritizing targeted carcass searches and rapid removal, maintaining low wild boar densities through sustained harvest of adult females, modernizing population monitoring methods, enhancing hunters’ compliance, and strengthening cross-border coordination to improve surveillance and control, thereby slowing ASF spread across Europe. Full article

Occupational health and safety (OHS) is essential for protecting the life, health, and physical integrity of workers. In a complex and dynamic professional context, the prevention of occupational risks has become a priority for employers and decision-makers, going beyond legal compliance to create a safe and efficient work environment. This article explores the history and the main theoretical aspects of OHS and explores the implementation of the ISO 45001 standard and introduces managing workplace health and safety (WHS) risks based on the 5M Method and a weighted composite algorithm for OHS risk assessment integrating factors such as severity, probability, frequency of exposure, number of exposed employees, organizational response capacity, and incident history. Applied in a mixed industrial case study, this approach demonstrated superior risk prioritization compared to the classic severity–probability model. The findings have practical applications: organizations can use the Weighted Composite Score to prioritize interventions, allocate resources efficiently, and prevent high-risk incidents. The approach is adaptable across industries, supporting data-driven safety decisions. The integration of this method supports ISO 45001’s principles of a systematic, proactive, and continuous improvement approach to OHS management. Full article

With the growing application of soft robot technology in complex, dynamic environments, the limitations of traditional rigid robots have become increasingly prominent, urgently demanding novel soft actuation technologies. Dielectric elastomer actuators (DEAs) have gradually emerged as a research focus in soft robotics due to their high energy density, rapid response, low noise, and excellent compliance. This paper systematically reviews the research progress of DEA-based soft robots over the past decade. Using classification and comparative analysis, DEAs are categorized into four basic types according to their initial shape—planar, saddle-shaped, cylindrical, and conical—with detailed elaboration on their working principles, structural features, and typical applications. Furthermore, from two major application scenarios (underwater and terrestrial), this paper analyzes the adaptability of various DEAs in robot design and corresponding optimization strategies and summarizes their performance and research challenges in bionic propulsion, multi-modal motion, and environmental adaptability. Finally, it provides the prospective future research directions of DEAs in material development, structural design, intelligent control, and system integration, providing theoretical support and technical references for their wide application in fields such as medical treatment, detection, and human–robot interaction. Full article

The digital transformation of Tibetan cultural tourism is hindered by high manual costs, weak semantic adaptability, and cultural security risks. To address these, this paper proposes RLT2C, a “Rule+LLM-Verify” approach to automated and culturally secure KG construction. It employs a lightweight-large model collaboration mechanism, where a fine-tuned lightweight model generates initial Cypher statements, rigorously verified by LLMs for local semantic accuracy and cultural compliance. This two-stage process, combined with a dynamic-static cultural constraint system, ensures high efficiency and preserves cultural integrity, supporting knowledge-driven naked-eye 3D immersive experiences. Experimental results on 1200 Tibetan tourism-related texts show that RLT2C outperforms baselines in construction efficiency (14.5 triples/100 words), relationship accuracy (91.5%), local semantic adaptability (87.9%), and graph redundancy rate (5.4%). RLT2C exhibits strong practicality and scalability. The constructed KG serves not only as an information repository but also as a foundational engine for immersive visualization. By acting as a “central index” for 3D assets and a “safety gatekeeper” for content generation, it enables the dynamic and secure rendering of culturally authentic naked-eye 3D experiences from natural language queries. Full article

Monitoring process stability and tool condition is essential for ensuring machining quality and efficiency. This study investigates the influence of sensorized tool holders on dynamic properties and machining results. Three clamping conditions, one conventional and two different sensor-integrated tool holders (equipped with strain gauges or piezoelectric force sensors), are compared. Experimental modal analyses are carried out to determine the frequency-dependent dynamic compliance of the systems. Machining tests using a developed reference workpiece enable the investigation of process forces, wear development, and the surface quality achieved under real conditions. The results show that the dynamic behavior of the tools varies significantly depending on the respective excitation frequency, whereby the different structural properties of the tool holders have a clearly measurable influence on their dynamic properties, particularly near process-relevant excitation frequencies. However, no clear deterioration in terms of process stability or machining performance can be determined. In some cases, the sensorized tool holders can contribute to reduced tool wear and improved process stability. These findings emphasize that sensorized tool holders do not necessarily worsen the machining results and can be applied without negative effects when aligned with the system’s modal characteristics. Full article

This paper addresses the critical multiple-uncertainty challenge in laser link acquisition for space gravitational wave detection missions—a key bottleneck where spacecraft attitude alignment for laser link establishment is perturbed by inherent random disturbances in such missions, while also needing to balance ultra-high attitude precision, fuel efficiency, and compliance with engineering constraints. To tackle this, a convex optimization-based attitude control strategy integrating covariance control and free terminal time optimization is proposed. Specifically, a stochastic attitude dynamics model is first established to explicitly incorporate the aforementioned random disturbances. Subsequently, an objective function is designed to simultaneously minimize terminal state error and fuel consumption, with three key constraints (covariance constraints, pointing constraints, and torque saturation constraints) integrated into the convex optimization framework. Furthermore, to resolve non-convex terms in chance constraints, this study employs a hierarchical convexification method that combines Schur’s complementary theorem, second-order cone relaxation, and Taylor expansion techniques. This approach ensures lossless relaxation, renders the optimization problem computationally tractable without sacrificing solution accuracy, and overcomes the shortcomings of traditional convexification methods in handling chance constraints. Finally, numerical simulations demonstrate that the proposed method adheres to engineering constraints while maintaining spacecraft attitude errors below 1 μrad under environmental uncertainties. This study provides a convex optimization solution for laser link acquisition in space gravitational wave detection missions considering uncertainty conditions, and its framework can be extended to the optimal design of other stochastically uncertain systems. Full article

Background/Objectives: Ocular drug delivery faces significant challenges due to anatomical and physiological barriers that limit drug bioavailability, particularly with conventional eye drops. Levofloxacin (LEVO), a broad-spectrum antibiotic, is widely used in the treatment of bacterial conjunctivitis, but its therapeutic efficacy is hindered by rapid precorneal clearance and short residence time. Methods: This study introduces a biorelevant 2 mL dissolution model to simulate ocular conditions better and evaluate the release kinetics of LEVO-loaded nanofibrous ophthalmic inserts. Compared to the conventional 40 mL setup, the 2 mL system demonstrated a slower and more sustained drug release profile, with kinetic modeling confirming a more controlled release behavior. Difference and similarity factor analysis further validated the distinct release profiles, highlighting the impact of dissolution volume on release dynamics. Results: Preliminary pharmacokinetic modeling suggested that the nanofiber inserts, particularly when applied twice daily, maintained levofloxacin concentrations above minimum inhibitory and bactericidal levels for extended durations across three bacterial strains (Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus), potentially outperforming traditional eye drops. Conclusions: These findings suggest that small-volume dissolution testing may provide a more realistic method for evaluating ophthalmic insert formulations, though in vivo validation is needed. Moreover, the nanofibrous inserts show potential as a sustained-release alternative that warrants further investigation to improve patient compliance and therapeutic outcomes in ocular disease management. Full article

Driven by the rapid evolution of AI technology, compatible management mechanisms have become a systematic project involving the participation of multiple stakeholders. However, constrained by the rigidity and lag of traditional laws, the “one-size-fits-all” regulatory model will exacerbate the vulnerability of the complex system of AI governance, hinder the sustainable evolution of the AI ecosystem that relies on the dynamic balance between innovation and responsibility, and ultimately fall into the dilemma of “chaos when laissez-faire, stagnation when over-regulated”. To address this challenge, this study takes the multi-stakeholder collaborative mechanism co-established by governments, enterprises, and third-party technical audit institutions as its research object and centers on the issue of “strategic fluctuations” caused by key factor disturbances. From the perspective of the full life cycle of technological development, the study integrates the historical compliance performance of stakeholders and develops a nonlinear dynamic reward and punishment mechanism based on Credit Entropy. Through evolutionary game simulation, it further examines this mechanism as a realization path to promote the transformation from passive campaign-style AI supervision to agile governance of AI, which is characterized by rapid response and minimal intervention, thereby laying a foundation for the sustainable development of AI technology that aligns with long-term social well-being, resource efficiency, and inclusive growth. Finally, the study puts forward specific governance suggestions, such as setting access thresholds for third-party institutions and strengthening their independence and professionalism, to ensure that the iterative development of AI makes positive contributions to the sustainability of socio-technical systems. Full article

The concept of synergistic water resources, water environment, water ecology remediation, and restoration (3WRR) is essential for addressing the interlinked challenges of water scarcity, pollution, and ecological degradation. An intelligent platform of remediation and restoration project optimization was developed, integrating multi-source data fusion, a coupled air–land–water model, and dynamic decision optimization to support 3WRR in river basins. Applied to the Jinshan Lake Basin (JLB) in China’s Greater Bay Area, the platform assessed 894 scenarios encompassing diverse remediation and restoration plans, including point/non-point source reduction, sediment dredging, recycled water reuse, ecological water replenishment, and sluice gate control, accounting for inter-annual meteorological variability. The results reveal that source control alone (95% reduction in point and non-point loads) leads to limited improvement, achieving less than 2% compliance with Class IV water quality standards in tributaries. Integrated engineering–ecological interventions, combining sediment dredging with high-flow replenishment from the Xizhijiang River (26.1 m³/s), increases compliance days of Class IV water quality standards by 10–51 days. Concerning the lake plans, including sluice regulation and large-volume water exchange, the lake area met the Class IV standard for COD, NH₃-N, and TP by over 90%. The platform’s multi-objective optimization framework highlights that coordinated, multi-scale interventions substantially outperform isolated strategies in both effectiveness and sustainability. These findings provide a replicable and data-driven paradigm for 3WRR implementation in complex river–lake systems. The platform’s application and promotion in other watersheds worldwide will serve to enable the low-cost and high-efficiency management of watershed water environments. Full article

This study analyzes the relational architecture of Brazilian traceable beef exports using a tripartite network model that connects certified meatpacking plants, AgriTrace sustainability protocols, and importing countries. By leveraging export authorization data from the Brazilian Ministry of Agriculture, it is shown that certification protocols function not merely as compliance tools but as relational governance infrastructures, mediating legitimacy, market access, and coordination within global value chains. Bipartite projections allowed the deriving and analyzing of two secondary networks: one mapping connections between meatpacking plants that share certifications, and the other linking consumer nations through common supply channels. The meatpacking plant network displays high modularity, featuring two dominant clusters alongside several smaller, regionally coherent clusters. This structure reflects diverse governance capabilities and strategic certification adoptions. Conversely, the consumer nation network shows lower modularity but identifies central hubs that organize international demand and signal regulatory alignment. These patterns reveal underlying dynamics of coopetition, where actors collaborate through shared standards yet compete through innovation. By integrating the Inner Development Goals (IDG) framework, it is revealed internal capacities, such as trust, complexity awareness, and shared purpose, underpinning the efficacy of traceability systems as ethical and adaptive infrastructures. This values-based lens provides a novel perspective on how technical systems can foster resilient, inclusive, and sustainable trade, thereby contributing to planetary health and human-centered development in global livestock governance. Full article

The significance of human capital (HC) has been gaining attention worldwide. However, practices of human resource management (HRM) vary across countries. In Japan, these HRM practices have been shaped by top-down initiatives such as the Charter of Work–Life Balance, the Work Style (WS) Reform, and the mandatory disclosure of HC for publicly listed companies. This study examines the evolution of HRM perceptions in Japanese companies from 2008 to 2024, as well as the quantitative trends and semantic shifts in the mentions of a series of institutional reforms. This study performed text analysis on 51,666 narrative disclosure documents from 3970 listed Japanese companies. Results show that mandatory HC disclosure marked a semantic turning point, shifting the corporate focus from short-term productivity to long-term sustainability. Moreover, the sequential introduction of new concepts has fostered coexistence and semantic reconstruction, rather than competition or exclusion between HC and WS. This study empirically demonstrates that top-down institutional reforms can reshape corporate perceptions beyond mere compliance. It clarifies the dynamics of conceptual coexistence and semantic evolution when related ideas are introduced sequentially. In addition, the findings highlight the value of large-scale narrative disclosure documents in analyzing semantic change. Full article

Following the stablecoin legislation (GENIUS Act) enacted under the second Trump administration in 2025, blockchain has become core digital economy infrastructure. However, privacy risks from decentralization and transparency constrain adoption in regulated industries, requiring solutions that harmonize blockchain architecture with regulatory compliance. Existing research relies on reactive auditing or post-execution rule checking, which wastes computational resources or provides only basic encryption or access controls without comprehensive privacy compliance. The proposed Artificial Intelligence-enhanced Regulatory Proof-of-Compliance (AIRPoC) framework addresses this gap through a two-phase consensus mechanism that integrates AI legal agents with semantic web technologies for autonomous regulatory compliance enforcement. Unlike existing research, AIRPoC implements a dual-layer architecture where AI-powered regulatory validation precedes consensus execution, ensuring that only compliant transactions proceed to blockchain finalization. The system employs AI legal agents that automatically construct and update regulatory databases via multi-oracle networks, using SPARQL-based inference engines for real-time General Data Protection Regulation (GDPR) compliance validation. A simulation-based experimental evaluation conducted across 24 tests with 116,200 transactions in a controlled environment demonstrates 88.9% compliance accuracy, with 9502 transactions per second (TPS) versus 11,192 TPS for basic Proof-of-Stake (PoS) (4.5% overhead). This research represents a paradigm shift to dynamic, transaction-based regulatory models that preserve blockchain efficiency. Full article

This study systematically investigates the synergistic modification effects of two high-modulus additives on SBS-modified asphalt through microstructural characterization and performance evaluation. Fluorescence microscopic analysis reveals that the additive particles undergo swelling over time and form an interconnected network structure via phase separation dynamics. Rheological tests demonstrate a significant enhancement in high-temperature performance: at the optimal dosage of 10 wt%, the complex modulus increases by approximately 215%, and the rutting factor improves by about 300% compared to the control group. The results from multiple stress creep recovery (MSCR) tests confirm the material’s superior elastic recovery capability and reduced non-recoverable creep compliance. However, the incorporation of the additives adversely affects low-temperature ductility. The penetration of (two distinct high-modulus agents, designated as HMA-A and HMA-B) HMA-B decreases by approximately 36.8% more than that of HMA-A, accompanied by significantly lower low-temperature toughness. A dosage of 10% is identified as the critical threshold, which maximizes rutting resistance while minimizing low-temperature performance degradation. Based on these findings, this paper proposes an integrated design paradigm of “microstructure–performance–dosage,” recommending HMA-B for high-stress pavement channels and HMA-A for regions with substantial temperature variations. Full article