Search Results (17,479)

Generative artificial intelligence (GenAI) tools such as ChatGPT have attracted growing attention in higher education, particularly in relation to how students perceive their usefulness, usability, and educational value. However, existing studies are often descriptive and rarely translate perception data into exploratory quantitative indicators that can support structured evaluation under uncertainty. To address this gap, this study develops an exploratory Monte Carlo simulation framework for quantifying perception-based student success in the context of GenAI use. The term Perception-Based Student Success Score is used here as an exploratory proxy indicator derived from students’ positive evaluations of usability, efficiency, learnability, and perceived integration; it does not represent direct academic achievement, grades, retention, or objectively measured learning outcomes. A PRISMA-informed structured literature search in Scopus identified nineteen empirical studies published between 2023 and 2025, of which six reported item-level means and standard deviations suitable for probabilistic modelling. One coherent 10-item, 5-point Likert-scale usability-oriented instrument was selected as a canonical proof-of-concept dataset and used to parameterise an inverse-variance-weighted Monte Carlo simulation generating 10,000 synthetic observations. The results show that the weighting structure substantially influences the simulated outcome. In particular, System Efficiency and Learning Burden received the largest inverse-variance weight and therefore had the strongest influence on the composite score. This dominance should be interpreted cautiously because low variance in Likert-scale data may reflect response homogeneity or ceiling effects rather than substantive importance alone. The study offers a transparent, reproducible, and privacy-preserving proof-of-concept framework linking structured literature search, item-level summary statistics, and probabilistic modelling. Full article

Vertical axis wind turbines (VAWTs) have regained attention for distributed, urban, and floating offshore applications, yet the literature remains fragmented across competing rotor concepts and modelling traditions. This review consolidates the principal archetypes—Savonius, H-Darrieus, troposkein Darrieus, helical Darrieus, and Savonius–Darrieus hybrids—through five governing parameters: drag-versus-lift-driven operating principle, tip speed ratio λ = ωR/V_∞ (0.6–1.2 for Savonius; 2.5–5.0 for Darrieus), solidity σ = Nc/R (0.1–0.4), chord-based Reynolds number R_{e_c} (10⁵ − 10⁶), and peak power coefficient C_{p_max} (0.15–0.25 for Savonius; 0.35–0.45 for optimized H-Darrieus). Off-design performance is dominated by unsteady mechanisms that quasi-steady streamtube models cannot resolve—leading edge vortex shedding, dynamic stall hysteresis, blade–wake interaction, and flow-curvature-induced virtual camber—each examined for its contribution to the instantaneous torque C_T(θ) and the cycle-averaged C_p. Turbulence closures are benchmarked against phase-locked PIV and torque measurements: k – ω SST URANS captures peak-region C_p to within ±5–10% but over-predicts torque below λ_opt; the γ – R_{e_θ} transition SST model reduces this error to ±3–5%; DES, DDES, and LES reach ±2 – 3% at one to two orders of magnitude higher cost. Best practice computational fluid dynamics (CFD) guidelines are consolidated: domain extents of 15 D upstream, 10 D downstream, and 20 D lateral; rotating sub-domain D_rot » 1.5 D; y⁺ ≤ 1; Δθ ≤ 0.1°; and 20–30 revolutions before sampling. Performance enhancement strategies (variable pitch, guide vanes, helical twist, and hybridization) are reviewed quantitatively, with reported C_p gains of 5–30%. Four research priorities are identified: (i) transition-sensitive turbulence closures validated below R_{e_c} = 5 × 10⁵; (ii) coupled aero-hydro-servo-elastic models for floating offshore VAWTs; (iii) machine-learning-augmented turbulence modelling—including physics-informed neural networks (PINNs) and neural-network-corrected RANS closures—to improve unsteady flow prediction at sub-LES cost; and (iv) integrated aeroacoustic–aeroelastic frameworks for urban and building-integrated deployment. Full article

Part surface topography is characterized by complex multi-scale and multi-feature coupling, and accurate topography modeling is essential for predicting assembly precision in high-performance mechanical systems. Gaussian Process Regression (GPR) offers a principled, probabilistic framework for surface modeling from sparse measurements, but its performance depends critically on kernel function selection. A fixed single kernel lacks the flexibility to represent surfaces that simultaneously exhibit smooth trends, periodic textures, and linear drift. To address this limitation, an adaptive composite kernel method is proposed. Initial GPR residuals are analyzed through statistical hypothesis tests and spectral decomposition to identify which geometric features are present; matching base kernels—Squared Exponential (SE), Periodic (PER), and Linear (LIN)—are then selected and combined additively or multiplicatively. Experiments on three representative synthetic surfaces show that the composite kernels reduce RMSE by up to 95.09% relative to the single SE kernel. Validation on a machined part confirms that the method successfully transfers to real measured data, achieving a 30.65% RMSE reduction and raising R² from 0.9536 to 0.9777. The results demonstrate that residual-analysis-driven kernel selection yields physically interpretable models with substantially improved reconstruction accuracy. Full article

Background/Objectives: While the health effects of economic fluctuations are well-documented, the role of policy-related uncertainty remains underexplored. The objective of this study is to examine the association between economic policy uncertainty (EPU) and mortality. Furthermore, we investigate whether changes in lifestyle behaviors are associated with EPU and may help shed light on the relationship between EPU and health outcomes. Methods: We utilize a mixed data sampling (MIDAS) framework to analyze US state-level data from 2009 to 2020. The model controls for unemployment, income, demographic characteristics, as well as state and year fixed effects. This approach enables the incorporation of high-frequency uncertainty measures to capture dynamic mortality responses. Results: The results indicate a statistically significant inverse association between EPU and total mortality. The association is negative across both genders, with a stronger effect observed among males. Across age cohorts, the retirement-age group exhibits the highest sensitivity. In terms of cause-specific mortality, EPU is positively associated with mortality from respiratory diseases and suicide, while it is negatively associated with mortality from homicide, accidents, and pneumonia and influenza. In addition, EPU is significantly associated with a lower prevalence of current drinking and smoking, a higher likelihood of being in a healthy weight range, improved self-reported health, and reduced time spent traveling. Conclusions: The findings suggest heterogeneous associations between EPU and mortality outcomes across demographic groups and causes of death, highlighting the complex and multifaceted nature of the relationship between policy-related uncertainty and population health rather than a uniform response across health outcomes. Full article

Background: Despite growing scholarly interest in Sustainable Entrepreneurship Orientation (SEO), the field lacks a theoretically grounded measurement approach, limiting the generalizability and comparability of SEO adaptation. Methods: This paper proposes an evidence-based formative measurement approach to assess the degree of SEO within an enterprise. A multidisciplinary literature review identified four SEO dimensions, namely Entrepreneurial, People, Environmental, and Communal Orientation (EPEC), and their observable firm behavior indicators. A Multiple Indicators and Multiple Causes (MIMIC) model framework is used to position SEO as a latent formative construct rated across defined maturity levels. A longitudinal single-case study of a Hungarian private medical clinic, conducted over four quarterly measurement cycles using onsite observations and semi-structured interviews, was used to demonstrate the feasibility of the instrument, its ability to rate SEO adaptation levels consistently across independent raters from different organizational roles, and its ability to generate meaningful, trackable SEO maturity data that evolves. Results: Fleiss’ Kappa values confirmed substantial inter-rater agreement across 21 raters, and progressive SEO maturity was observed across all four quarters. Conclusions: The paper offers a theoretically grounded, methodologically replicable measurement instrument with potential applications for researchers, practitioners, and policymakers, subject to further validation across diverse organizational and cultural contexts. Full article

This study presents an AI-assisted long-distance fiber Bragg grating (FBG)-based sensing approach for simultaneous temperature and vibration measurement using a single bare FBG sensor. To address the strong coupling between temperature- and vibration-induced effects in the wavelength time series, a signal processing framework based on adaptive variational mode decomposition (AVMD) is developed. With power-spectral-density-guided parameter selection, the mixed wavelength signal is separated into a low-frequency temperature-related component and a high-frequency vibration-related component, enabling stable temperature–vibration decoupling within a single-sensor architecture. Experiments conducted with a 10 km fiber link between the sensor and the interrogator demonstrate that the proposed method can stably track the dominant vibration frequency under various temperature and vibration conditions, while the reconstructed low-frequency component remains consistent with the thermal evolution trend even in the presence of vibration. Random vibration tests and low-frequency vibration resolution analysis further confirm the stability and practicality of the proposed approach under long-distance fiber transmission conditions. In addition, an AI-assisted condition-monitoring scheme is demonstrated using a one-dimensional convolutional autoencoder trained solely with normal wavelength time-series data. Rather than relying on raw reconstruction error alone, the diagnostic layer derives a latent transition score from encoder bottleneck features through temporal pooling, L2 normalization, cosine-distance evaluation, smoothing, and baseline removal. Deviations from steady operating conditions can thereby be preliminarily indicated, highlighting the potential for integrating physics-driven signal processing with data-driven artificial intelligence in long-distance fiber sensing systems. Full article

Accurate measurement of chewing events in natural eating conditions is important for unobtrusive monitoring of feeding behavior and masticatory function. Yet, existing methods often rely on contact sensors, dedicated wearables, or manual annotation. This work presents a non-contact, video-based framework for chewing-event detection using frontal facial video, normalized 3D facial landmark dynamics, and recurrent temporal modeling. To obtain physiologically grounded reference labels, synchronized bilateral anterior temporalis surface electromyography was acquired during real-meal sessions and used to derive chewing-event annotations during dataset construction, whereas inference relied exclusively on video. Facial motion was represented from frame-wise 3D landmarks and processed by recurrent neural networks, with model selection performed through Bayesian hyperparameter optimization. On an independent hold-out test set comprising five sessions and 18,836 frames, the proposed method detected 577 chewing events versus 589 ground truth events, corresponding to a mean absolute error of 4.4 chews/session and a mean absolute percentage error of 4.32%. A comparison with a related rule-based video method from the literature showed substantially larger counting errors (MAE = 39.4, MAPE = 30.39%), particularly in sessions that included concurrent activities such as speaking, suggesting that the proposed approach can reduce counting errors relative to the considered rule-based baseline under the specific meal conditions tested in this feasibility study. The effect of landmark-localization uncertainty on the predicted chewing probability was assessed through Monte Carlo propagation, showing limited impact for most prediction instants and greater sensitivity for intermediate probability values. Finally, the ONNX implementation achieved a mean latency of 8.96 ± 5.74 ms on CPU and 6.89 ± 3.58 ms with CUDA execution on the test workstation, supporting real-time applicability. To support practical deployment, the pipeline was also implemented as a native Kotlin Android application and tested on a commercial tablet, achieving real-time operation at 20 fps. Full article

In recent years, multi-criteria group decision-making (MCGDM) methods have attracted widespread attention in the academic community. However, most existing MCGDM approaches suffer from limitations in decision-makers’ expressive capacity and the loss of uncertain information. To address these issues, this study proposes a novel multi-criteria group decision-making (MCGDM) framework. First, we developed an evaluation information representation method called the hybrid probabilistic information set (HPIS), which allows DMs to fully express their opinions based on individual cognition using the most suitable form of representation. Second, the criteria importance through inter-criteria correlation (CRITIC) and the combined compromise solution (CoCoSo) methods are extended into the cloud model environment, ensuring that the rich uncertainty information is fully preserved and transmitted throughout the entire evaluation process. Finally, we apply the proposed MCGDM framework to a practical case study evaluating urban flood resilience within an urban agglomeration, to identify its vulnerable components. The results indicate that Baoding, Zhangjiakou, and Chengde are identified as the most vulnerable cities, necessitating immediate and targeted measures to bolster their flood defense capabilities. At the same time, decision-makers can select both qualitative and quantitative comments simultaneously and carry uncertainty information throughout the entire calculation process. Furthermore, the sensitivity and comparative analyses demonstrate the robustness and practical utility of the proposed method under the tested scenarios. Full article

At a time when China confronts the dual challenges of intensifying international competition and urgent industrial transformation, enhancing enterprises’ new quality productivity (NQP) has become a critical pathway to strengthening market competitiveness. This study constructs a comprehensive micro-level NQP index system for enterprises, encompassing three core dimensions: revolutionary breakthroughs in science and technology, deep transformation and upgrading of industrial systems, and innovative allocation of production factors. Using panel data from listed enterprises in China’s three major eastern urban agglomerations (Beijing–Tianjin–Hebei, Yangtze River Delta, and Guangdong–Hong Kong–Macao Greater Bay Area), we systematically examine the spatiotemporal evolution patterns and market expansion effects of enterprise NQP. The results reveal that while enterprises’ NQP has shown a generally upward trend, significant regional disparities and pronounced polarization persist across the three urban agglomerations. Development is notably path-dependent and spatially correlated, being easily influenced by neighboring cities. More importantly, empirical evidence from benchmark regression and spatial Durbin models indicates that enhancing NQP significantly boosts enterprises’ market potential, with substantial positive spatial spillover effects. This study contributes to the literature by developing a novel micro-level measurement framework for new quality productivity and providing robust evidence that NQP serves as a powerful driver for expanding market potential in an era of technological and industrial transformation. Full article

Geometric errors in CNC machine tools accumulate along the tool path and directly affect machining accuracy. Traditional serial-chain-based volumetric error models, such as those based on the homogeneous transformation matrix (HTM) or screw theory, often exhibit ambiguous geometric definitions, weak traceability to measurement points, and increased computational cost due to repeated coordinate transformations and inverse mappings, limiting their suitability for real-time control. To overcome these challenges, this study proposes a parallel-type unified error vector transfer (EVT) framework, based on the Abbe and Bryan principles. In this framework, axis error motions are directly expressed as vectors and transferred to the tool center point (TCP), where they are superimposed to obtain total error contributions. Building on this principle, a unified normal volumetric error model (NVEM) is formulated using survival and sign factors. The unified NVEM is applicable to various types of three-axis machining centers, including horizontal configurations. In other words, differences in coordinate system definitions can be reconciled through coordinate transformation, allowing the unified NVEM to be consistently applied. Furthermore, a real-time error compensation controller (RECC) is embedded into the CNC kernel to compute compensation values within each interpolation cycle, ensuring deterministic and low-latency operation without external computation. Experimental validations on an XYFZ-type vertical machining center demonstrate that the proposed framework improves positioning accuracy by more than 72% and machining accuracy by 60.4%. These results confirm the feasibility, efficiency, and universality of the parallel-type unified EVT framework for real-time volumetric error compensation. Here, ‘parallel-type’ denotes the parallel superposition of independent error vector contributions, rather than a parallel kinematic mechanism. Full article

Critical infrastructure restoration (CIR) is a disaster-management and sustainability challenge because prolonged disruption of energy, water, transport, communications, healthcare, and public-administration services can amplify social, economic, and environmental losses. This PRISMA 2020-reported systematic review synthesizes post-2016 scientific literature and official policy, legal, standards, and technical documents on CIR and AI decision support. The review identified 55 records, removed 1 duplicate, excluded 1 ineligible record, and retained 53 core sources for qualitative synthesis, including 31 scholarly publications and 22 official documents. Manual screening was used; no automated screening or AI-assisted exclusion tools were applied. The results are organized around four research questions covering regulatory frameworks, recovery practices, supporting systems, and AI model families. The synthesis shows that CIR is shaped by layered governance through NIS2, the CER Directive, the AI Act, and national measures; by operational recovery practices such as continuity planning, cyber crisis coordination, interdependency mapping, and model-supported restoration; by digital platforms including SCADA/ICS, IoT sensing, GIS/common operating pictures, decision-support systems, simulation environments, and digital twins; and by AI methods ranging from classical machine learning and computer vision to reinforcement learning and generative assistants. However, evidence maturity remains uneven, with many AI applications still simulation-based, sector-specific, or weakly validated in real restoration settings. The review contributes an integrated CIR-oriented framework showing that AI creates practical value when embedded in interoperable, human-supervised, regulation-aware, and empirically validated restoration architectures that support sustainable service continuity rather than isolated automation. Full article

In recent years, the integration of renewable energy sources and the widespread use of nonlinear loads have increased dynamic uncertainties in modern power systems, making real-time and synchronized monitoring essential. Accurate M-class synchrophasor estimation under these nonstationary and spectrally uncertain conditions remains a challenging problem due to dynamic variations, harmonics/interharmonics, out-of-band interference, and measurement noise. This study proposes a suitably constrained optimization-based framework for M-class synchrophasor estimation, in which a hybrid structure integrating an ESPRIT-based subspace method with the Adaptive Fitness Distance Balance Artificial Rabbit Optimization (ES-AFDB-ARO) algorithm is employed. In this framework, the optimization stage is guided by spectral information obtained via the subspace stage to narrow the search space and improve convergence stability. Performance is evaluated under IEEE C37.118 steady-state and dynamic conditions via Monte Carlo simulations, showing that total vector error, frequency error, and rate-of-change-of-frequency error values remain within standard limits. Comparative analyses at 60 dB and 40 dB SNR demonstrate that the ES-AFDB-ARO method exhibits improved and more stable performance than the widely used interpolated discrete Fourier transform, Taylor weighted least squares and Taylor–Kalman filter methods. The results show that the proposed framework offers a reliable solution for synchrophasor estimation under dynamic operating conditions. Full article

The quantum Zeno and anti-Zeno effects describe how frequent measurements can either suppress or accelerate quantum dynamics. While extensively studied in various platforms, their manifestation in dark-state dynamics remains largely unexplored. Here we investigate the stability of dark states in a cavity quantum electrodynamics (QED) system consisting of two atoms coupled to a single-mode cavity, subject to thermal dephasing that models continuous quantum non-demolition monitoring. Using the Tavis–Cummings model within a Lindblad master equation framework, we perform numerical simulations to investigate how measurement-induced dephasing affects dark-state retention and stabilization time. Through systematic numerical scans, we identify distinct parameter regimes corresponding to Zeno and anti-Zeno behavior: at low dephasing intensities, increasing the measurement strength accelerates the loss of dark-state coherence (anti-Zeno regime), while at higher intensities, it slows down the dynamics and partially recovers dark-state weight (Zeno regime). The transition between these regimes is controlled by the dephasing rates, the cavity photon exchange, and the asymmetry in atom–field couplings. We show that even under strong dephasing, a finite dark-state component persists, demonstrating remarkable robustness. Our results provide insights into the interplay between measurement back-action and decoherence in open quantum systems, with implications for quantum control and information storage. Full article

High-sensitivity detection of direct current (DC) partial discharge (PD) in HVDC gas-insulated equipment (GIE) remains challenging because conventional electrical measurements are susceptible to ambient interference and DC PD lacks a phase reference for phase-resolved analysis. Although photon counting techniques provide exceptional sensitivity and noise immunity, their diagnostic application has so far been confined to alternating current (AC) conditions. In this study, a photon-counting-based measurement platform was developed to investigate DC PD generated by three representative gas–solid insulation defects, namely conductor protrusion, surface-attached metal, and free metallic particle. Photon pulse sequences were acquired under both positive and negative voltage polarities. Successive inter-pulse time intervals were then mapped into two-dimensional kernel density estimation heatmaps to visualize defect-dependent temporal characteristics. A Random Forest classifier, integrated with SHapley Additive exPlanations (SHAP) for feature reduction, was employed for quantitative classification. The proposed method achieved classification accuracies of 97.50% and 99.17% for positive and negative polarities, respectively. Notably, the model adaptively prioritized angular-distribution features over radial-distribution features under space-charge-suppressed conditions. These results demonstrate the feasibility of photon-counting-based time-domain characterization and defect classification for DC PD, providing a quantitative, less experience-dependent framework for insulation defect identification in DC gas-insulated systems. Full article

The expansion of the transport sector in Thailand has resulted in a continuous increase in greenhouse gas emissions and air pollution. Therefore, promoting the adoption of commercial electric vehicles (EVs) has become an important approach to mitigating environmental impacts and enhancing sustainability. This study integrates the TAM, TPB, and 7Ps frameworks to examine factors influencing the intention to adopt EVs among freight transport operators in Thailand. A total of 876 freight operators were surveyed, and the data were analyzed using a random parameters probit model with heterogeneity in means. The results indicate that environmental motivation, perceived safety, ease of use, reductions in operational costs, social benefits, dealership credibility, and perceived quality-of-life improvement positively influence the intention to adopt EVs. In contrast, gaps between EV attitudes and purchasing readiness, along with over-reliance on promotional and online channels, negatively affect EV adoption intention. Furthermore, perceptions of price appropriateness show heterogeneous effects across respondents, reflecting hidden costs and operational uncertainties. Based on these findings, the study proposes an integrated set of policy measures to support a sustainable transition toward EV adoption in the freight transport sector. These results provide useful guidance for policymakers and freight transport operators in developing strategies and policies that encourage the long-term adoption of electric vehicles in freight transportation. Full article