Search Results (1,619)

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

Road traffic crashes remain a global public health burden and a persistent resource allocation problem that undermines progress toward the sustainable development of safe, equitable mobility systems. Saudi Arabia’s Vision 2030 targets fewer than 10 fatalities per 100,000 population, a goal aligned with United Nations Sustainable Development Goal 3.6 (halving road traffic deaths) and SDG 11.2 (safe and sustainable transport), yet a gap persists between crash prediction research and how agencies deploy enforcement resources. This paper builds a closed-loop predict–optimize–evaluate framework connecting Long Short-Term Memory (LSTM) neural networks to a goal-distance gap metric and constrained optimization, feeding forecast outputs directly into enforcement scheduling decisions. Using monthly casualty data from official Saudi sources covering the entire kingdom (all 13 administrative regions) from 2010 through 2024 (N = 42,856 fatal and serious injuries across 180 monthly observations), we validate LSTM forecasting against five benchmarks plus a GRU and a Transformer baseline, apply gap analysis as a standardized goal-distance metric, optimize enforcement allocation with triangulated elasticity estimates, and evaluate past policy reforms through multi-method counterfactual analysis. A headline finding is that roughly 28% of fatal and serious injuries cluster within only about 6% of weekly hours, creating an unusually concentrated target for enforcement reallocation. The LSTM achieves RMSE = 2.47 with MASE = 0.83, beating ARIMA by 35% while maintaining robustness during COVID disruptions (RMSE = 2.38 in the post-acute period 2022–2024 versus 2.61 in the acute period 2020–2021). Temporal analysis confirms 28% of fatalities (95% CI: 26.0–30.0%) cluster within 6% of weekly hours. Enforcement elasticity triangulated from three independent sources converges at α ≈ 0.31 (90% CI: 0.25–0.40). The optimization model allocates 56% of enforcement resources to Thursday–Friday midnight-to-4 AM windows, projecting a 17.1% casualty reduction (90% CI: 13.5–20.6% under Monte Carlo uncertainty in α). Monte Carlo sensitivity analysis with 10,000 iterations confirms a median benefit-cost ratio of 1.88 (90% CI: 1.18–2.97), with P (BCR > 1.0) = 98.9%, using locally calibrated VSL = SAR 4.2 million (equivalent to approximately USD 1.12 million at the SAMA-pegged rate of 3.75 SAR/USD, in constant 2024 prices). Counterfactual evaluation finds that the post-2018-reform period was associated with a 22.1% casualty reduction (95% CI: 16.4–27.8%), with magnitude robust across four methods (LSTM counterfactual, Bayesian Structural Time-Series, Synthetic Control, and an inverse-variance-weighted synthesis of the three); we stress, however, that attribution to the driving reform itself cannot be cleanly separated from concurrent Saher camera expansion, public awareness campaigns, and trauma-care improvements. By translating prediction into evidence-based, resource-efficient enforcement, the framework supports sustainable road safety policy in middle-income and rapidly motorizing settings. 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, 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 European Union aims to reduce its dependency on imported critical and strategic raw materials. Therefore, the EU’s Critical Raw Materials Act defines benchmarks for strategic raw materials on domestic mining, recycling, refining, and the diversification of import sources to be achieved by 2030. This study investigates the feasibility of the EU’s Critical Raw Materials Act mining benchmark for strategic rare earth elements, which aims for 10% of the EU’s annual demand to be met through domestic mining. We assess whether domestic rare earth element supply from mining within the EU can meet the projected future demand for 2030 and 2050. The study also examines the extent to which the total demand of rare earth elements for the EU could be met proportionally. An uncertainty estimation with Monte Carlo simulation with consideration of uniform and Gaussian distribution, based on individual project development stages, highlights that reaching the 10% benchmark for strategic rare earth elements is theoretically likely by 2030; however, with an incorporated nine-year lead time, meeting the 2030 benchmark is no longer feasible. Furthermore, obstacles such as social license to operate, mining permits and appeals in practice may additionally prolong procedures. The study concludes that in order to mine domestic rare earth elements and to reduce import dependency, the EU needs to invest in geological exploration and mining. Moreover, establishing a whole rare earth elements supply chain from mining to refining is highly complex and, as illustrated by the Japan–Australia partnership, which required 14 years without including the geological exploration phase. Full article

This paper develops an accountability-aware fractional control framework for embodied intelligent systems in shared human environments. The approach combines a Caputo fractional-order stabilizing law, an intent-evidence realization with softmax belief reconstruction, and a conditional proxemic safety layer. Sufficient conditions are established for local Mittag-Leffler stability of the augmented error dynamics and forward invariance of the safe set. Numerical results are presented as a theorem-validation benchmark. For the base case with $\alpha =0.9$, the augmented error norm decays from $1.2359$ to $9.90\times {10}^{-3}$ while the safety margin remains strictly positive, and the robustness condition is satisfied with a margin of $1.8641$. An $\alpha$-sweep and a step-size convergence study further show that the fractional order induces a systematic safety–performance trade-off and that the reported behaviors are numerically stable. Additional simulations with four intent classes, bounded observation noise, and Monte Carlo uncertainty stress tests are included to strengthen the numerical evidence beyond the two-intent theorem-validation case. The manuscript also clarifies the quantitative interpretation of the accountability index, the conditional nature of the safety theorem, and an implementable sampled safety-filter realization for concrete robotic platforms. The results support the proposed framework as a mathematically consistent tool for shaping the balance between regulation and proxemic safety. Full article

In modern reliability engineering, modeling bounded lifetime data under realistic experimental conditions is still challenging, especially when censoring schemes and unit removals are random. This study proposes a new and unified reliability framework by combining the flexible powering new power (PNP) distribution with a grouping-based progressive first-failure mechanism using a beta-binomial random design. The proposed approach explicitly accounts for the randomness in group removals, providing a more realistic description of practical life-testing experiments. Classical estimation is carried out using maximum likelihood methods with the Newton-Raphson algorithm, along with confidence intervals constructed under both standard and log-transformed parameterizations. To increase flexibility in inference, a Bayesian approach is developed based on a joint gamma and shifted log-normal prior, which respects parameter constraints and incorporates prior uncertainty. Since the posterior distributions cannot be obtained in closed form, a Metropolis-Hastings Markov chain Monte Carlo algorithm is used to generate reliable posterior estimates and credible intervals. Additionally, beyond sensitivity analysis, multiple prior robustness diagnostics are incorporated to ensure reliable hyperparameter calibration and to safeguard against prior misspecification. The performance of the proposed estimators is carefully examined through extensive Monte Carlo simulations under different censoring schemes and parameter settings. The simulation results indicate that the proposed Bayesian procedures often provide more stable estimation and shorter interval estimates with competitive coverage probabilities compared with the corresponding classical methods, particularly under moderate-to-heavy censoring settings. To demonstrate its practical usefulness, the proposed model is applied to two real datasets on tensile strength of carbon and polyester fibers, where it provides a good fit and useful insights into material reliability and failure behavior. In the same applications, the practical relevance and superior performance of the proposed distribution are demonstrated, where it outperforms existing bounded versions of several well-known models, including the gamma, Weibull, and Birnbaum-Saunders distributions. Overall, this work contributes to reliability analysis by offering a flexible and computationally efficient framework that accounts for both random censoring and complex lifetime patterns, with potential applications in engineering, materials science, and applied reliability studies. Full article

Surrogate models for nuclear reactor calculations typically provide point predictions without quantifying uncertainty, limiting their use in risk-informed applications. While several studies have applied machine learning to reactor physics, systematic evaluation of prediction interval calibration against Monte Carlo statistical uncertainty remains underexplored. This study develops a Gaussian Process regression (GPR) surrogate model that provides both accurate predictions and calibrated uncertainty estimates for the infinite multiplication factor ($k_{\infty }$) of a pressurized water reactor pin-cell. A dataset of 400 OpenMC Monte Carlo simulations was generated using Latin Hypercube Sampling across boron concentration (0–2000 ppm), fuel temperature (600–1200 K), and moderator temperature (500–600 K). The GPR model achieves $R^2=0.9971$ with prediction errors below the Monte Carlo statistical uncertainty ($\mathrm{MAE}/{\sigma }_{\mathrm{MC}}=0.75$), indicating that model accuracy is limited only by inherent training data noise. The key contribution is demonstrating that GPR prediction intervals are well-calibrated, achieving 92.5% coverage for 95% confidence bounds (bootstrap 95% CI: [87.5%, 97.5%], containing the nominal level; binomial test p = 0.297), with mean prediction uncertainty closely matching the Monte Carlo statistical uncertainty (${\sigma }_{\mathrm{GPR}}=0.00192$ vs. ${\sigma }_{\mathrm{MC}}=0.00200$). This near-perfect match suggests the surrogate has captured essentially all deterministic variation, with residual uncertainty attributable to Monte Carlo noise alone. Variance-based sensitivity analysis confirms boron concentration accounts for 99% of output variance. The surrogate preserves physically meaningful reactivity coefficients (Doppler: $-2.1$ pcm/K; boron worth: $-6.1$ pcm/ppm) while providing ${10}^5$-fold computational speedup. The framework is restricted to fresh fuel with fixed enrichment; extension to burnup-dependent scenarios is left for future work. Full article

To address the challenge of exorbitant computational costs in the reliability analysis of complex steel structures, which stems from the impact of multiple sources of uncertainty throughout their entire lifecycle, this paper presents a comparative evaluation of the explicit reconstruction of the Limit State Function (LSF) using SVM combined with Hyperparameter Optimization (HPO) for structural reliability analysis under constrained computational budgets. Although traditional Monte Carlo simulation (MCS) exhibits high accuracy, it requires a substantial number of finite element calculations, rendering it difficult to satisfy the efficiency requirements of engineering projects. Conversely, the first-order and second-order reliability methods (FORM/SORM) offer high computational efficiency but rely on explicit limit state functions, posing challenges for their direct application to complex structural systems. Thus, this study initially acquires response samples of the structure under various combinations of random variables through a limited number of finite element analyses (FEA). Subsequently, it employs an SVM to develop a highly accurate equivalent explicit limit state function, which serves as a substitute for the original implicit limit state function. Finally, it integrates Monte Carlo simulation to efficiently evaluate the structure’s failure probability and reliability index. Meanwhile, to tackle the problem of SVM model performance being highly susceptible to hyperparameters, this study presents a comparative analysis of four strategies: Bayesian Optimization (BO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Random Search (RS), aiming to identify the optimal parameter combination and improve the model’s generalization capability. Through verification with four progressive examples, including linear, nonlinear, truss, and multistory frame structures, the results demonstrate that the proposed method can accurately characterize the nonlinearity of structural responses. The obtained failure probabilities and reliability indices are in close agreement with those obtained from the direct Monte Carlo simulation (MCS) and existing research. Moreover, while maintaining computational accuracy, the method significantly reduces computational costs, thereby providing an efficient and practical solution for structural reliability analysis in engineering practice. Full article

Reliable solar energy harvesting demands advanced control strategies capable of maintaining thermal precision despite inherent environmental unpredictability. This research addresses the critical challenge of temperature regulation in the solar furnace system, which is hindered by severe non-linearities and stochastic environmental uncertainties. The study aims to transition Fractional-Order PID (FOPID) control from theoretical design to reliable industrial application by accounting for the Uncertain Design Vector (UDV) during the tuning phase. A Reliability-Based Design Optimization (RBDO) framework is proposed, utilizing a hybrid Multi-Objective Imperialist Competitive Algorithm (MOICA) integrated with Monte Carlo Analysis (MCAR). This approach simultaneously optimizes the Maximum Sensitivity (Ms), the integral of Time-weighted Absolute Error (ITAE) and their sensitivities, while ensuring physical realizability through the FOPID structure. Crucially, the simulation results demonstrate that the RBDO-tuned FOPID design achieves optimal performance levels comparable to deterministic methods while significantly reducing the overall system sensitivity by 35% to 55% compared to both deterministic and literature-based methods (GA-FOPID and PSO-FOPID). The study concludes that integrating probabilistic reliability into multi-objective metaheuristics provides a robust control strategy for high-temperature solar facilities, effectively mitigating the performance degradation caused by real-world parameter fluctuations and ensuring consistent operational stability. Full article

This study investigates the prediction of carbon emissions from fly ash and ground granulated blast furnace slag-based geopolymer concrete (GPC) using advanced ensemble machine learning (ML) techniques. Although ML has been extensively utilized to model GPC’s mechanical performance, its application in estimating environmental impacts, specifically carbon emissions, is limited. The research employs six ensemble ML models, such as random forest, gradient boosting, extreme gradient boosting (XGB), CatBoost, and light gradient boosting machine (LGBM), including versions optimized using ant colony optimization (ACO). Among them, the ACO-enhanced XGB model demonstrated the highest predictive accuracy with a coefficient of determination (R²) of 0.97, with low prediction errors (MAE = 3.92, RMSE = 6.17). However, cross-validation and uncertainty analyses indicate that the performance differences among top models are relatively small. Conversely, LGBM exhibited the least predictive reliability. Feature importance analysis revealed that curing parameters, specifically initial curing time, curing temperature, and the dosage of dry sodium hydroxide, had the most influence on carbon emissions. To evaluate model robustness and interpretability, Monte Carlo simulation and Gaussian white noise analyses were conducted. Results confirmed that CatBoost and ACO–gradient boosting (ACO-GB) demonstrated greater stability under varying and noisy conditions, whereas XGB-based models, although highly accurate, were comparatively more sensitive to input variability. Overall, the research establishes a data-driven, efficient framework for quantifying carbon emissions in GPC, highlighting the importance of evaluating both predictive accuracy and model robustness, advancing sustainable material design through intelligent modelling. Full article

Benthic AUVs (underwater quadruped robots) merge the cruising efficiency of submersibles with the bottom-crawling stability of legged robots for unstructured deep-sea exploration. However, the deliberate separation of the center of gravity and buoyancy—essential for static stability—generates a significant righting moment. When climbing steep slopes, this moment resists hull alignment. If the slope exceeds the robot’s maximum hydrostatic pitch limit, conventional inverse kinematics algorithms fail: the hind legs lose ground contact and propulsion is lost. To overcome this, this paper proposes a framework integrating optimal force distribution, adaptive trajectory probing, and admittance control. An analytical multi-point moment balance model derives the terrain-adaptive pitch boundaries. A Quadratic Program (QP) then distributes contact forces, tasking front legs with stabilizing the righting moment while hind legs provide thrust. During the swing phase, adaptive Bezier sequences prevent anterior slope collisions and ensure posterior ground contact. Furthermore, a Cartesian admittance controller provides active compliance to manage the nonlinear friction of dynamic waterproof seals. Validated via a high-fidelity physics-based simulation model calibrated against physical pool trials, the robot achieved robust traversal of $15°$ and $33°$ steep slopes. Statistical robustness is substantiated via a 30-trial Monte Carlo study, where postural stability remained remarkably consistent with a mean Pitch RMSE of $2.88°$ across a $\pm 10\%$ parameter uncertainty envelope. Compared to traditional baseline algorithms, the proposed method successfully suppressed torque chattering by $54.1\%$ in the high-frequency band (2–$50\mathrm{Hz}$) and improved energetic efficiency by up to $43\%$ on steep gradients. These findings offer a validated control architecture for heavy-duty deep-sea platforms navigating complex benthic topographies. Full article

To address the output volatility of distributed photovoltaics, the low utilization efficiency of energy storage resources, and the challenge of optimal revenue for PV-storage virtual power plants (VPPs) in multi-market environments, this paper proposes a three-stage stochastic optimal operation strategy for PV-storage VPPs under multi-market synergy and develops a benefit allocation model based on the Nash–Harsanyi bargaining game. A Monte Carlo simulation was adopted to capture the uncertainties of market electricity prices and PV power output, and the stochastic dual-dynamic-programming (SDDP) algorithm was employed to solve the three-stage optimization framework consisting of day-ahead bidding, real-time optimization, and real-time frequency regulation. Bargaining power was quantified from four dimensions—the marginal contribution rate, PV prediction accuracy, energy storage capacity, and utilization rate—to establish a fair and reasonable internal benefit allocation mechanism. Case studies verified that the proposed method improved the single-day market revenue by up to 20.79% compared with traditional operation modes, achieved a near-zero curtailment rate for distributed PV, and maintained frequency regulation performance scores above 0.4 at all times. The benefits of all investment entities in the alliance increased by 3.36–99.43%, significantly enhancing the multi-market profitability of PV-storage VPPs and the stability of alliance cooperation. Full article

Odor source localization in turbulent environments remains a major challenge for autonomous robots, as odor plumes are highly intermittent, spatially fragmented, and often lack stable concentration gradients. Here, we propose a bio-inspired navigation framework that translates key principles of bumblebee olfactory cognition into robotic decision-making. First, classical conditioning and olfactorily triggered spatial memory experiments demonstrated that bumblebees could form robust odor memories and that training frequency is positively correlated with both proboscis extension response retention and spatial directional preference. Based on these biological findings, a bio-inspired navigation framework, termed Bio-Nav, is constructed by integrating a Partially Observable Markov Decision Process, a Hidden Markov Model, short-term memory, long-term directional reference memory, fuzzy inference, and value iteration. High-fidelity two-dimensional turbulent simulations show that the proposed algorithm substantially outperforms moth-inspired search, Infotaxis, and standard POMDP-based navigation. In 100 Monte Carlo trials, Bio-Nav achieved a success rate of 96.0%, an average of 20.3 search steps, an average path length of 155.1 cm, and a path-to-straight-line distance ratio of 1.6. Even under strong turbulence, the success rate remained above 91%. These results indicate that memory–perception coupling, inspired by bumblebee navigation, provides an effective and robust strategy for odor source localization in complex turbulent environments, offering a generalizable principle for bio-inspired robotic search under uncertainty. Full article

Spaceborne detection of solar-induced chlorophyll fluorescence (SIF) requires extremely high radiometric accuracy, and the polarization characteristics of an ultra-wide swath spaceborne fluorescence ultraspectral camera directly affect the accuracy of SIF retrieval. This study takes an ultra-wide swath camera based on an off-axis three-mirror anastigmat telescope combined with a Littrow–Offner spectrometer as the research object. A full-field-of-view (FOV), full-spectral, pixel-by-pixel polarization testing system was established based on the Stokes–Muller formalism, achieving for the first time fine characterization and calibration of the pixel-level polarization properties of such a payload. The results show that: (1) polarization sensitivity (LPS) exhibits a strong linear positive correlation with wavelength (R² > 0.97), with good uniformity (fluctuation < 1%) across the full ±15° FOV; (2) the polarization sensitive axis (PSA) shows a symmetric distribution across the FOV and gradually approaches 90° as the wavelength increases, with a clear deviation in the short-wavelength bands and stabilization in the mid-to-long wavelength bands; (3) through multiple sets of cross-validation and Monte Carlo statistics, the calibration accuracy reaches 0.1%, and the system uncertainty is better than 0.05%. This study can provide data support and a reference basis for high-accuracy spaceborne SIF retrieval, payload polarization correction, and optical design optimization. Full article