Search Results (2,863)

AI-enabled cyber–physical systems (CPSs) are increasingly deployed in public governance contexts where they sense human populations, infer classifications or risks, and trigger interventions that can shape liberty, equality, and access to essential services. In these deployments, governance failures often arise not only from model error but from systems-level interactions across data generation, model updates, organizational practices, and downstream actuation. This paper introduces a Risk–Rights–Rules (3R) architecture that treats fundamental rights and legal rules as enforceable constraints on the sensing–inference–actuation loop, rather than as external ethical aspirations. Building on established risk-management baselines and safety engineering practice, we specify a testable assurance object, a structured 3R assurance case, that links rights claims to explicit assumptions, measurable evidence, and accountable control points across the lifecycle. The approach is designed to reduce “legitimacy drift” in stochastic decision pipelines by making uncertainty, demographic error, contestability, and procurement leverage auditable at the system level. The result is a governance blueprint for high-consequence public-sector AI deployments for governance failures, which is both technically robust and institutionally defensible. Full article

Advanced Driver Assistance Systems (ADAS) have significant potential to improve road safety. However, drivers’ perceptions and acceptance of these systems in Thailand have not been explored. This study investigated Thai drivers’ perceptions towards ADAS and examined factors associated with trust and intention to use. A cross-sectional survey was conducted with 849 licenced drivers. The questionnaire measured perceived usefulness, perceived ease of use, trust, barriers and concerns, expectations and preferences, and intention to use ADAS. Data were analyzed using Mann–Whitney U tests, Spearman’s rank correlations, and multiple linear regression. Results indicated that Thai drivers reported positive perceptions of ADAS regarding perceived usefulness, expectations, preferences, and intention to use. Trust was most strongly associated with constructs such as perceived usefulness, perceived ease of use, and intention to use. Multiple regression identified perceived usefulness, trust, and expectations and preferences as significant positive predictors of intention to use ADAS, whereas barriers and concerns were negatively associated with intention to use. Perceived ease of use was not a significant predictor. These findings highlight the importance of perceived usefulness, trust, and user expectations in shaping intention to use ADAS and support the need for new policies regarding driver education and awareness initiatives in Thailand. Full article

Biological thin-sheet systems, including leaves, insect wings, and flowering organs, achieve adaptive deformation through distributed compliance, segmentation, curvature, and controlled opening. Kirigami offers a bio-inspired route for translating such deformation logics into programmable thin-sheet surfaces; however, the geometric parameters that most strongly influence elastic displacement remain insufficiently quantified, especially across different loading regimes. This study investigates Bio-Inspired Regime-Dependent Parameter Selection in Parametric Kirigami through twenty-five laser-cut specimens spanning five boundary shapes and three thermoplastic substrates. Specimens were tested under two contrasting regimes: quasi-static tensile loading and gravity-drape loading. Elastic displacement was measured under eight-point boundary fixation and analyzed using regime-separated Pearson correlations, Bonferroni-corrected significance testing (α/18 = 0.0028), and shape-controlled partial correlations. Under tensile loading, the Number of Offsets (r = 0.807), Segments per Offset (r = −0.603), and outer-boundary void perimeter (r = 0.621) showed the strongest Bonferroni-robust associations with displacement. Under gravity-drape loading, effects were weaker and more curvature-sensitive, indicating that parameter relevance is not universal but regime-dependent. Within the tested parametric design space, the study provides an experimentally grounded basis for selecting Kirigami geometric parameters in thin-sheet structures whose adaptive deformation logic is analogous to compliant systems found in nature. Full article

Rapid and non-contact surface inspection is essential for quality control in modern production. Optical coherence tomography (OCT) can image a surface without contact, but turning those images into roughness parameters usually requires specialized processing software. This study examined whether a multimodal large language model (LLM) could estimate roughness parameters directly from OCT B-scans as a screening tool. The study was designed as a controlled macro-scale proof of concept using periodic, analytically defined phantoms rather than as validation on stochastic industrial micro-roughness. Five test surfaces with exactly known geometries were designed, 3D-printed, and scanned with a spectral-domain OCT system. For each surface, roughness values were computed from the theoretical shape, extracted from the OCT image using MATLAB, and also estimated by the LLM from the same image. The repeatability of the LLM was checked by running the same prompt ten times per surface. On a sawtooth profile, the LLM estimates varied by 3.8% for Ra, 4.2% for Rq, 3.5% for Rp, 2.8% for Rv, and 3.1% for Rt. Across all five surfaces, the variation in Ra and Rq was around 3–5%, and for Rt, it stayed below 5%. The results show that a generative AI approach can produce repeatable roughness estimates that are useful for comparative screening. This method offers a flexible option for surface comparison and AI-assisted quality control when calibrated measurements are not required. Full article

Rapid urbanization in tropical Asia has led to a critical loss of green cover, exacerbating urban environmental challenges. While green roofs offer a promising Nature-based solution, their implementation in Asian countries is hindered by the prevalence of sloped roofs and high structural conversion costs. This research addresses this gap by developing a novel, lightweight vegetative roof tile designed as a direct structural replacement for conventional roofing materials in Sri Lanka. Existing roofing systems were studied, followed by a laboriousness study to determine the optimum tile dimensions. To meet these requirements, a modular tile measuring 900 mm × 1200 mm with a wave-shaped corrugated profile (a 10 mm rise and a 200 mm pitch) was engineered using SolidWorks 2024 and ABAQUS 2024 to meet Eurocode standards. Field investigations into plant health helped to finalize the depth of the roof tile as 2.5 cm. Following root penetration testing, fiber-reinforced plastic was selected for the tile structure to ensure durability while maintaining a total saturated weight of 52.5 kg/m². Biological testing demonstrated robust greening performance, with Axonopus compressus and Zoysia matrella achieving 100% survival rates and over 80% canopy coverage. This design methodology can be adapted across tropical Asia, contributing significantly to regional green infrastructure development and sustainable building practices. Full article

Koji production is a critical process that determines the flavor and quality of the final soy sauce product. However, the complex mechanisms underlying microbial metabolism and the evolution of the physicochemical environment still require further analysis. This study focuses on three parallel koji rooms in an industrialized koji fermentation process. This work tracked the dynamics of physicochemical indices, volatile flavor compounds, and microbial communities over a full 40 h cycle. Data integration and correlation analysis elucidated the close linkage between the microbial community, the fermentation environment, and flavor formation. Koji moisture declined gradually, with faster losses at later fermentation stages. This physiological dehydration arose from microbial metabolic heat, forced aeration and structural loosening of koji, not simple physical evaporation. System pH displayed a typical U-shaped trend across fermentation. Values dropped early, most likely driven by accumulating organic acids, before rising from mid to late fermentation. This pH rebound was tentatively attributed to ammonia release from proteolytic breakdown, which may neutralize acidic compounds. These observations cast doubt on the conventional assumption that organic acid levels may be reliably estimated solely from pH measurements. Physicochemical analysis showed continuous accumulation of amino acid nitrogen (0.6–0.9 g/100 g) and total acidity throughout fermentation. By contrast, reducing sugar concentrations differed across individual koji rooms, presumably owing to divergent microbial adaptation in early fermentation. A total of 77 common compounds were identified, among which 13 key odor-active compounds with OAV ≥ 1, such as 4-vinylguaiacol and 3-methylbutyraldehyde, constitute the characteristic flavor profile of soy sauce starter culture. High-throughput sequencing uncovered a distinct ecological pattern: eukaryotic communities, dominated by Aspergillus oryzae, converged under controlled regulation. While prokaryotic communities differentiated dynamically, driven by spatial heterogeneity in the semi-open fermentation environment. Spearman correlation analysis further indicated potential functional partitioning: high-abundance taxa (e.g., Aspergillus oryzae, Weissella) were predominantly associated with macromolecular substrate degradation, whereas rare low-abundance taxa (e.g., Alternaria) displayed significant correlations with the biosynthesis of key characteristic flavor compounds. This study clarifies the synergistic regulatory mechanisms linking physicochemical conditions, microbial metabolism, and flavor precursor formation during industrial koji production. The findings establish a scientific foundation for optimizing process parameters and achieving standardized quality control in soy sauce manufacturing. Full article

This study presents numerical and experimental investigations of a voltage-controlled active preload adjustment system for an ultrasonic traveling wave piezoelectric motor intended for potential use in space-related systems. The proposed preload system consists of two ring-shaped piezoceramic elements driven by a DC voltage of up to 300 V_DC. The passive conical spring provides the nominal rotor preload, while the piezoelectric ring stack enables open-loop remote fine adjustment of the stator–rotor contact force by modifying the axial compression of the spring. Finite element simulations were performed over a temperature range from −25 °C to 55 °C to evaluate the electromechanical response and thermal sensitivity of the preload system. The numerical results indicated that the active preload system can generate a simulated preload force variation of approximately 0.47 N at 300 V_DC, corresponding to approximately 21.4% of the nominal initial preload force of 2.2 N. Experimental tests were conducted in a thermal vacuum chamber at a pressure of 5.6 × 10⁻⁶ mbar. The measured displacement of the piezoceramic preload stack ranged from 0.33 µm to 2.36 µm and showed good agreement with the numerical displacement results. Motor speed measurements demonstrated that increasing the preload-control voltage from 0 to 300 V_DC resulted in an average angular speed increase of approximately 17–20 RPM, depending on temperature. The results demonstrate that the proposed system can provide compact open-loop preload fine adjustment under thermal vacuum conditions, with preload force variation supported by FEM estimation and experimentally validated displacement response. Full article

This study critically investigates the scientific literature on the intersection of management accounting and intellectual capital using a bibliometric performance analysis and science-mapping approach. Drawing on a sample of 59 publications from the Scopus and Web of Science databases, the paper maps the intellectual structure, key contributors, and thematic evolution of the field. This study conceptualizes management accounting not merely as a neutral technical system but as a socio-political mechanism that shapes how intellectual capital is rendered visible, measurable, and governable within organizations. The findings identify five dominant research clusters (intellectual capital and corporate strategy, management accounting and performance, green intellectual capital, digitalization and value creation, and management control and intangibles), revealing how accounting practices actively participate in constructing organizational realities and legitimizing particular forms of value and knowledge. The analysis highlights that measurement and reporting practices privilege certain dimensions of intellectual capital while potentially obscuring others, raising critical questions about power, visibility, and accountability in knowledge-based economies. In particular, the growing emphasis on digitalization and sustainability reflects shifting governance regimes in which accounting systems extend their influence over organizational conduct and strategic decision-making. By integrating bibliometric techniques with a critical interpretive lens, this study contributes to the literature by reframing management accounting as a key site where knowledge, control, and organizational value are negotiated. It also identifies gaps for future research, particularly regarding the ethical and political implications of accounting for intangible resources in increasingly digital and transparency-driven environments. Full article

Connected vehicle platforms enable large-scale collection of vehicle dynamics data from production fleets, creating opportunities for passive roadway monitoring using onboard sensing systems. While existing vibration-based approaches primarily focus on pavement roughness estimation, the ability of fused onboard signals to capture defect-level characteristics remains insufficiently explored. This study investigates whether Road Surface Monitoring (RSM) signals, developed by Honda as an integrated OEM sensing approach, contain distinguishable patterns associated with specific road surface defects. A framework is developed to analyze, detect, and classify defect-related vibration signatures using these fused signals. The approach introduces the Defect Consistency Index (DCI), which measured a 29% average difference between pothole and patching signal signatures within the dataset. A threshold-based Defect Identification Algorithm (DIA) was then applied to detect defective segments, achieving 89% detection accuracy. A machine learning pipeline using shape-based features was subsequently used to classify potholes and patching, achieving up to 90% classification accuracy on the evaluated dataset. The framework was evaluated using real-world RSM data collected from a single instrumented vehicle within a limited geographic region. The results indicate that fused vibration signals contain recurring defect-related patterns that may support defect-level analysis using compact, non-visual measurements. These findings indicate the potential of connected vehicle vibration sensing for scalable roadway monitoring while highlighting the need for broader validation across vehicles, environments, and defect conditions. Full article

Arnold tongues are wedge-shaped regions in parameter space associated with mode locking and synchronization phenomena in nonlinear dynamical systems. The Caputo fractional standard map extends the classical standard map by incorporating long-memory effects through fractional derivatives and is known to generate Arnold tongue structures as the fractionality parameter approaches unity. In this paper, we investigate the fractional standard map applied to matrix-valued state variables, with particular emphasis on systems governed by high-dimensional nilpotent matrices. We show that the interplay between fractional memory and nilpotent algebra produces hierarchical families of Arnold tongues associated with divergent dynamics. This phenomenon is not observed in either the classical standard map or the non-fractional standard map of nilpotent matrices alone. For idempotent matrices, the fractional standard map retains the same level of dynamical complexity as its scalar counterpart. For nilpotent matrices, higher-order terms induce coupling between the map coefficients, giving rise to substantially richer dynamical behavior. This combination of fractional memory and nilpotent algebra provides a systematic framework for studying higher-dimensional nonlinear dynamics beyond the scalar setting. To support numerical investigations, an efficient computational scheme for the auxiliary parameters is derived and calibrated using the H-rank algorithm, which provides a concise measure of algebraic complexity in sequences generated by dynamical systems. Numerical simulations reveal hierarchical structures of Arnold tongues of divergence together with characteristic divergence rates of the auxiliary parameters. The hierarchical level of a given auxiliary parameter is identified as a key quantity determining the algebraic complexity of the transient dynamics, with potential implications for information encoding in applications exploiting transient dynamical processes. Full article

High-end vertical five-axis machining centers commonly adopt dual-motor direct-drive configurations for their cradle-type A-axis to improve dynamic performance; however, this approach introduces control challenges in balancing counteracting torque and synchronization accuracy due to high-rigidity coupling. To address this issue, this study presents a novel error compensation control strategy based on a synchronous state observer. First, a system dynamic model incorporating dual-axis coupling effects is developed to systematically investigate the coupling mechanism between synchronization error and counteracting torque. Based on this model, a synchronous state observer is designed, which achieves real-time reconstruction and feedforward compensation of synchronization disturbances induced by factors such as transmission parameter mismatches and inter-axis torque imbalance, thereby enabling coordinated control of high-precision position synchronization and torque balance. The effectiveness of the proposed method is verified through simulation and experiments conducted on a VMC630 vertical five-axis machining center. Results show that under various speed and acceleration conditions, the maximum position synchronization error remained below $6.3\mathrm{e}-4$${}^{\circ }$, with comparable convergence performance; the current deviation between the dual motors was constrained to within $\pm 0.25$$\mathrm{A}$, demonstrating effective mitigation of counteracting torque. In machining tests of S-shaped specimens, all measured contour deviations fell within the $\pm 0.060$$\mathrm{m}$$\mathrm{m}$ tolerance range, and the specimens exhibited excellent contour consistency and surface quality. These results validate the proposed strategy’s status as an engineering-viable solution for precision motion control in high-rigidity coupled dual-motor systems. Full article

Rockfall from slope unstable rock masses is a typical geological hazard induced by brittle failure, with abrupt occurrence, limited macroscopic deformation before failure, and a short warning lead time. Conventional static analysis methods are useful for design-stage stability checks, but they cannot continuously capture structural-plane damage or update the stability state in real time. Dynamic evaluation based on structural dynamics links measurable parameters such as natural frequency, damping ratio, mode shape, vibration trajectory, wave velocity, and energy dissipation to the degradation of structural planes. This review synthesizes the dynamic behavior mechanism, parameter system, theoretical models, sensing technologies, and engineering applications for slope unstable rock masses. Different from previous reviews that mainly summarize rockfall monitoring or conventional slope stability analysis, this paper organizes the literature by failure mode, monitoring scale, model assumptions, field validation, uncertainty sources, and engineering applicability. The single-degree-of-freedom models for sliding-, toppling-, and falling-type rock masses, multi-block chain-collapse models, and data-physics dual-driven surrogate models are compared critically. Contact monitoring based on MEMS sensors, non-contact LDV monitoring, acoustic emission, microseismic monitoring, coda wave interferometry, and cloud-edge early-warning architectures are further reviewed. Key challenges include field-scale validation under heterogeneous and anisotropic geological conditions, environmental compensation, robust threshold calibration, and probabilistic linkage between dynamic indicators and failure probability. The review provides guidance for selecting dynamic evaluation models, designing field monitoring systems, and developing full-life-cycle digital-twin platforms for rockfall risk mitigation. Full article

Ensuring geometric accuracy in prefabricated bridge decks is essential for successful onsite assembly and maintaining structural performance. Conventional dimensional and geometric inspections rely heavily on manual measurements, which are time-consuming, labor-intensive, and prone to human error. This study proposes a BIM-based Mixed-Reality (MR) application that enables rapid, intuitive, and accurate geometric inspection of prefabricated bridge decks. The system integrates design BIM models with the physical environment through marker-based registration on the Microsoft HoloLens 2 (HL2), allowing inspectors to visualize dimensional attributes and shapes, assess positional deviations, and verify tolerance compliance directly within the MR workspace. To evaluate system accuracy, drift and translational error experiments were conducted, demonstrating stable hologram performance and marker-detection accuracy. The proposed method was validated on both small-scale and full-scale prefabricated decks, showing reliable detection of dimensional deviations and shear-pocket misalignments. The results confirm that the BIM–MR approach significantly improves inspection efficiency, accuracy, and decision-making, offering a practical and effective alternative for fabrication quality control and preassembly analysis in prefabricated bridge construction. Full article

Robotic sorting-and-filling tasks in flexible manufacturing require robots to reproduce specified in-box arrangements while adapting to variations in container poses, object availability, sensing conditions, and external interventions. This paper proposes a box-centric one-shot teaching framework for robotic packing tasks with relative pose constraints. In the teaching stage, a human operator demonstrates the desired packing layout only once. The system uses reference-prompted SAM-based contour refinement to extract box and in-box object contours, object categories, quantities, and relative position and orientation constraints. These constraints are then converted from pixel-plane measurements into box-local pose constraints, forming a reusable box-centric packing template that preserves both translational and angular layout information. During execution, the recorded template is transferred to detected box instances with different global poses, and executable pick-and-place commands are generated through a task-level perception-to-command pipeline. A mechanism for continuous assignment and state updates is further introduced to maintain residual target slots, update object-to-slot allocation, and report missing or redundant objects across execution rounds. Single-box template transfer experiments achieved mean placement errors of 7.16 mm and 7.57 mm for two recorded templates, while representative post-execution images further showed that the relative object orientations were visually preserved with respect to the taught template footprints. Multi-box experiments demonstrated that unfinished residual slots could be preserved and completed after scene updates without re-teaching. Additional validation with different container types and object shapes showed the feasibility of extending the framework beyond cube-only cases. Ablation tests under nine exposure settings further showed that SAM refinement improved template-acquisition robustness compared with the previous recognition method. These results verify that the proposed framework enables one-shot template acquisition, box-centric layout transfer, relative pose preservation, and persistent task-level execution for constrained robotic packing tasks. Full article

The rapid adoption of artificial intelligence (AI) in healthcare is reshaping service delivery and enabling more personalized, data-driven care. However, cross-country differences in AI implementation and perceived strategic importance remain insufficiently understood. This study proposes a dual-dimensional framework to assess AI maturity across 50 countries in the WHO European Region, distinguishing between actual AI applications and perceived opportunities. Using data from the WHO 2024–2025 Artificial Intelligence for Health survey, the AI Applications Index (AIA) is constructed using an intuitionistic fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method that accounts for uncertainty in implementation. In parallel, the AI Opportunities Index (AIO) is developed using a belief-structure TOPSIS approach to capture perceptions of AI’s strategic relevance. To better understand underlying patterns, Multiple Correspondence Analysis and Ward hierarchical clustering are applied to identify latent structures, homogeneous groups, and transitional development pathways. An Index of Alignment (IA) is introduced to measure coherence between AI applications and perceived opportunity. Countries are grouped into four development trajectories based on the mean values of the AI Applications and AI Opportunities indexes: AI leaders, implementation-driven systems, opportunity-driven systems, and lagging systems. These results are further compared with Ward clustering, revealing hybrid and transitional profiles not fully captured by aggregate classifications. The findings indicate that AI maturity is shaped not only by implementation levels but also by the alignment between technological capacity and strategic perception. The results highlight the multi-speed and institutionally differentiated nature of AI transformation in European healthcare systems. Full article