Search Results (1,265)

Seismic reliability assessment of steel frame structures using nonlinear finite element analysis is often hindered by implicit limit state functions and high computational cost. To address these challenges, this study proposes a machine learning-based framework for global seismic reliability analysis. A nine-story steel frame model is established and validated through modal and pushover analysis. Global sensitivity analysis using the Sobol’ method is performed to identify key parameters governing the maximum inter-story drift ratio. Three machine learning models—PSO-SVR, PSO-XGBoost, and PSO-BPNN—are trained with the selected features and integrated into Monte Carlo simulation (MCS) for reliability calculation. The results show that the PSO-BPNN model achieves the highest accuracy with the maximum error of 1.0259% relative to direct MCS, outperforming the conventional MLE-based approach, which yields errors up to 11.9383% due to the non-standard distribution of the structural response. The impact of training sample size on model performance is also examined, with 1000 samples identified as a practical threshold for acceptable prediction accuracy. Existing code design methods require modifications based on the total probability approach for global reliability analysis. This study offers an efficient and precise methodology for seismic reliability design of steel frame structures, particularly when structural responses deviate from standard parametric distributions. Full article

The pharmacokinetics (PK) and pharmacokinetic–pharmacodynamic (PK/PD) relationships of cefquinome in small ruminants remain incompletely characterized, particularly for long-acting (LA) formulations. This study evaluated cefquinome disposition after intravenous (IV), subcutaneous (SC) and LA subcutaneous (SC-LA) administration in lactating sheep and goats using nonlinear mixed-effects models (NLMEs) and Monte Carlo (MC) simulations. Cefquinome exhibited low volumes of distribution (0.21–0.31 L/kg), with goats showing higher clearance and shorter terminal half-lives than sheep. The SC-LA formulation reduced the absorption rate constant and increased both the mean absorption time and terminal half-life by 4–6-fold, resulting in sustained systemic exposure over 48 h. PK/PD analysis showed higher PK/PD cut-off values for the LA formulation, with values of 0.125 μg/mL for the fT > MIC index and 0.25 μg/mL for the fAUC/MIC index, respectively, whereas IV and SC regimens achieved lower thresholds. MC simulations indicated that only the LA formulation achieved ≥ 90% probability of target attainment (PTA) values at MICs equivalent to tentative epidemiological cut-off values (TECOFF) for respiratory pathogens. Notably, fAUC/MIC provided a more informative descriptor of efficacy for the LA formulation. These findings highlight the advantage of LA formulations and demonstrate improved performance compared with conventional dosing regimens in sheep and goats. Full article

OnVeMCS 1.1 is a standalone software for probabilistic human health risk assessment of pollutants in soil, sediment, water, air, and food, enabling Monte Carlo simulation (MCS) of risks across multiple exposure pathways. The hazard index (HI) and cancer risk metrics (TCR/ILCR) for ingestion, inhalation, and dermal contact are quantified using the standard dose/concentration approach. Users can manually enter analyte concentrations with various probability distributions or import them from Excel templates, and select scenario-specific exposure factor sets for residents (children and adults), outdoor and indoor workers, and food consumers. The software supports both one-dimensional (1D) and two-dimensional Monte Carlo simulation (2D MCS) modes. The results are presented through a variety of plots, including histograms and cumulative distribution functions (CDFs), pathway/analyte contribution charts, sensitivity analysis plots, nested CDFs, and uncertainty ribbons. The software also allows the overlay of two or more outputs and the inclusion of regulatory thresholds (HI = 1; TCR/ILCR = 10⁻⁶–10⁻⁴). The results are exported to a multi-sheet Excel workbook containing raw arrays, summary tables, exceedance probabilities, and sensitivity data. OnVeMCS operates quickly, with even 2D MCSs being completed in several seconds. OnVeMCS is distributed as a single Windows installer file with data examples and is free for the academic community. Full article

Polycyclic aromatic hydrocarbons (PAHs) are major carcinogenic pollutants in urban air, and inhalation exposure poses health risks, particularly for primary school children aged 6–14 years in school environments. Traditional deterministic models for incremental lifetime cancer risk (ILCR) assessment often fail to adequately quantify variability and epistemic uncertainty in exposure parameters. This study develops a multi-layered probabilistic framework that progresses from deterministic calculations through one-dimensional Monte Carlo and sensitivity-guided two-dimensional Monte Carlo to a hierarchical (second-order) two-dimensional Monte Carlo simulation. The hierarchical approach samples hyper-parameters of the input distributions (means, standard deviations, and modes) in the outer loop, while exposure variables are sampled in the inner loop using Latin hypercube sampling. Applied to PAH and BaP_eq concentrations measured indoors and outdoors during heating and non-heating seasons, the framework yielded mean total ILCR values of 1.42 × 10⁻⁶ for children and 1.18 × 10⁻⁶ for adults. The hierarchical 2D MC produced 95% confidence intervals on the 95th percentiles of [9.17 × 10⁻⁷, 5.67 × 10⁻⁶] for children and [6.48 × 10⁻⁷, 5.57 × 10⁻⁶] for adults, with outdoor heating identified as the dominant exposure pathway. Although the air sampling campaign was conducted in 2011–2012, the data remain representative for evaluating seasonal and microenvironmental variability of PAHs in urban school settings in the region, as PAH levels are predominantly driven by persistent combustion sources. This framework provides more comprehensive uncertainty quantification for complex environmental exposure scenarios. Full article

The quantitative measurement of nuclear material hold-up in the process equipment is one of the technical challenges in nuclear material measurement for nuclear facilities. Its results are directly related to the optimization of radiation protection, the criticality safety control of nuclear materials, and the accurate accounting of nuclear material. As a key core equipment in the nuclear material reprocessing process, the precipitation reactor is restricted by the complex on-site environment, compact spatial layout, and continuous operation process, making it difficult for traditional measurement technologies to conduct accurate quantitative analysis of the internal hold-up. To address this issue, this paper proposes a method for measuring and analyzing the hold-up in the precipitation reactor based on the passive neutron counting method. A laboratory model of the precipitation reactor is constructed, and a multi-detector neutron measurement system is developed in this work. By combining Monte Carlo (MC) simulation with experimental calibration of standard point sources, a mathematical model suitable for hold-up measurement of the precipitation reactor is established. Meanwhile, uncertainty analysis of key data was carried out, and the accuracy of the model was verified by operational Pu samples of various masses, effectively reducing the measurement deviation caused by the uneven distribution of hold-up in the equipment and model assumptions. This research provides a more reliable technical reserve and reference paradigm for the measurement of nuclear material hold-up in nuclear facilities. Full article

The accelerating worldwide shift toward renewable energy sources (RES) has increased the demand for reliable, efficient, and financially sound wind farm implementation. With the ongoing expansion of wind power within contemporary energy systems, the design and analysis of wind farms, frequently referred to as Power Parks, have become progressively intricate. This study employs probabilistic analysis using Monte Carlo simulation (MCS) to analyze the network in terms of losses, energy output, and profit to ensure the network’s economic stability before it is incorporated into the grid. The results indicate that the system will maintain its reliability throughout the year, as demonstrated in the Simulation Results section where the network operates within permissible values. Furthermore, the system is deemed economically viable, as illustrated in the conclusion, which presents the profit and loss figures. This study is significant as it employs effective techniques necessary for analyzing the entire power park in terms of losses, stability, profitability, and energy output over a specified period, taking into account the variability and uncertainty of wind conditions. Full article

This paper addresses the stabilization problem of hybrid stochastic McKean–Vlasov differential equations via a discrete-time state observation feedback control strategy. Utilizing the coupling method and particle system approximation, Itô’s formula for Markovian switching stochastic McKean–Vlasov differential equations is established. Based on the derived formula, we construct two novel Lyapunov functionals that incorporate state processes, probability distributions, and Markovian switching signals. Using the proposed Lyapunov functionals, we further analyze three stability properties of the closed-loop system, including H_∞ stability, asymptotic stability, and mean-square exponential stability. Due to the time-varying characteristics of system distributions, numerical simulation lacks fixed reference benchmarks and faces considerable difficulties. To overcome this challenge, this paper introduces a particle system approximation scheme. We further prove the exponential stability equivalence between the controlled McKean–Vlasov system and its corresponding particle system. This equivalence relation provides an effective new approach for the stability analysis of such controlled hybrid stochastic systems. Finally, an illustrative example is given to verify our theory results. Full article

Background: Walking a dog on-leash is a common activity for a large portion of our society. Many dogs consistently pull on the leash, which transmits potentially dangerous forces to the human body. The purpose of this in silico study was to determine the effects of dog-leash tension and human posture on lumbar compression, and how comparable the effects of dog walking on lumbar compression are to lifting, an activity known to contribute to low back pain. Methods: Dog-leash simulations were performed with 50–300 N directed along the arm segment of a static three-dimensional musculoskeletal model across a range of trunk segment and shoulder joint angles. Lifting simulations were performed across a range of test postures with the model holding a 50–300 N weight close to the ground. Lumbar compression was computed for each simulation using McGill’s polynomial equation and compared with the 3400 N cutoff used to develop occupational safety guidelines. Results: Lumbar compression increased as trunk segment flexion increased for all simulation conditions. With 200 N of leash tension, lumbar compression exceeded 3400 N for all postures with 25° or more of trunk segment flexion. When lifting 150 N, lumbar compression exceeded 3400 N for all postures with shank segment angle of 80° or greater and knee flexion angle of 100° or less. Conclusions: Our in silico results suggest that dog owners should seek intervention if their dog routinely pulls on the leash with a force of 200 N or greater and should attempt to lean backward when resisting leash pulling to reduce lumbar compression and injury risk. Full article

Background/Objectives: Osteoporosis remains a clinically important metabolic bone disorder with limited bone-forming therapeutic options. SET domain bifurcated protein 1 (SETDB1) is involved in osteogenic epigenetic regulation, but small-molecule discovery guided by SETDB1-associated structural regions remains limited. This study aimed to identify a candidate compound with in silico relevance to a SETDB1-associated ligand-bound pocket and assess its association with early osteogenic readouts. Methods: A computational–experimental workflow was used, including hierarchical molecular docking, MM-GBSA rescoring, ADMET-based prioritization, redocking validation, molecular dynamics simulations, and preliminary in vitro evaluation in MC3T3-E1 cells. Compound 271 (C271) was selected based on structure-based screening results and predicted developability-related properties. Cytocompatibility, alkaline phosphatase (ALP) activity and staining, selected molecular markers, and SETDB1–H3 molecular dynamics behavior were evaluated. Results: Redocking reproduced the reference binding mode, and molecular dynamics simulations indicated that C271 maintained a relatively persistent conformation around the predicted SETDB1-associated pocket. Comparative SETDB1–H3 simulations showed altered H3 dynamics and SETDB1–H3 contact patterns in the C271-containing system. In cell-based assays, C271 showed no appreciable cytotoxicity within the tested concentration range and was associated with increased ALP activity and staining. C271 treatment was accompanied by higher global H3K9me3 and Runx2 levels, whereas SETDB1 protein abundance remained largely unchanged. Conclusions: C271 was identified as a computationally prioritized SETDB1-related candidate compound associated with early osteogenic-associated cellular responses. The evidence supports computational plausibility and cell-level association, but does not establish direct SETDB1 engagement, SETDB1 enzymatic modulation, SETDB1-dependent causality, or late-stage osteogenic maturation/mineralization. Given the single-compound evaluation, further target-engagement, enzymatic, and functional studies are needed. Full article

Robust 3D registration is a fundamental problem in computer vision and robotics, where the goal is to estimate the geometric transformation between two sets of measurements in the presence of noise and outlier contamination. Existing robust registration methods are mainly built on either maximum consensus (MC) estimators, which first identify inliers and then estimate the transformation, or M-estimators, which directly optimize a robust objective. However, MC-based methods typically ignore residual magnitudes during inlier selection, while many M-estimators do not explicitly couple inlier/outlier identification with model estimation. Thus, a unified and efficient framework that jointly performs inlier identification and accurate transformation estimation remains desirable for challenging 3D registration. In this work, we introduce a unified truncated-loss based formulation for simultaneous inlier identification and model estimation (SIME) and study it in the context of 3D registration. We show that, compared with MC-based robust fitting, SIME can achieve a lower fitting residual because it incorporates residual magnitudes into the inlier selection process. To solve the resulting nonconvex problem, we develop an alternating minimization (AM) algorithm, and further propose an AM method embedded with semidefinite relaxation (AM-R) to alleviate the difficulty caused by the binary inlier variables. We instantiate the proposed framework for 3D rotation search and rigid point-set registration using quaternion-based formulations. Experimental results on both simulated and real-world registration tasks demonstrate that the proposed methods compare favorably with strong baseline solvers, especially in high noise and extreme outliers. In the synthetic experiments, the proposed methods are evaluated under outlier ratios up to 95% and consistently achieve competitive or better accuracy, with clear advantages in high-noise cases. On 3DMatch, SIME (AM) achieves a mean registration success rate of 91.0%. These results show the potential of SIME for reliable 3D registration in practical robotics, computer vision, and geometric perception applications. Full article

This paper investigates the conflicting multiple constraints and safety challenges in humanoid robot teleoperation for nonprehensile transportation tasks. The robot’s complex workspace and high degrees of freedom frequently conflict with highly dynamic task requirements, imposing stringent demands on coordinated motion. To address these issues, this paper proposes a Multiple-Constraint Safety-Critical Control Framework (MC-SCCF) featuring a hierarchical three-layer architecture. The top layer guarantees intrinsic safety against workspace boundaries using a continuously differentiable reachability surrogate model and an improved control barrier function (CBF)-based safe velocity filter for smooth deceleration. The middle layer maps user commands into pose-coupled reference trajectories to ensure task-level object safety, satisfying strict non-slip and non-toppling constraints. The bottom layer utilizes a quadratic programming (QP)-based inverse kinematics solver to achieve self-collision avoidance, coordinated motion, and optimal configuration while strictly enforcing joint and manipulability limits. Simulations and hardware experiments demonstrate that the MC-SCCF achieves real-time, high-precision reachability evaluation and successfully coordinates task dynamics with physical constraints, enhancing operational safety and the human–robot interaction experience. Full article

Statistical Process Control (SPC) plays a crucial role in monitoring and improving manufacturing processes to ensure product quality. Control charts using exponentially weighted moving averages (EWMA) and their extensions, including Extended EWMA (EEWMA) and New Extended EWMA (NEEWMA), have been developed to increase the sensitivity for detecting small to medium process changes. This research proposes a method for calculating the Average Run Length (ARL) and Standard Deviation of Run Length (SDRL) of control charts under a symmetric distribution using the Markov Chain Approach (MCA). This method is based on the probability of state transitions between controlled and uncontrolled states. The MCA method is more efficient than the Monte Carlo Simulation Approach (MC) in terms of accuracy and significantly reduces processing time. This research also demonstrates the application of ARL and SDRL calculations using the MCA method in various studies. Firstly, the performance of control charts is compared using the Mean Percentage Error (MPE) and Mean Absolute Percentage Error (MAPE). Secondly, the impact of symmetrically distributed process parameters on the performance of control charts is examined. Thirdly, a practical application of the control charts is presented. This research applies the proposed method to detect changes in unemployment insurance claims (UI) using seasonally adjusted initial claims assessment (ICSA) and continuing claims assessment (CCSA) rates from 2021 to 2025. The results show that the MCA method is more efficient than the MC method in terms of accuracy and significantly reduces processing time. Full article

Background/Objectives: Breast cancer remains a leading cause of cancer-related mortality among women worldwide. This study presents a systematically justified multi-modal breast cancer classification pipeline that combines established, physically motivated preprocessing operations, modality-specific deep learning models, late-fusion inference, and a deployment-aware federated learning evaluation. Rather than introducing new image restoration or federated optimization algorithms, this work formalizes how standard preprocessing methods can be organized according to the dominant degradation characteristics of ultrasound, MRI, and mammography, and evaluates their contribution under centralized and simulated federated learning settings. Methods: Patient-wise stratified five-fold cross-validation was applied across ultrasound (BUSI, $n=780$), dynamic contrast-enhanced MRI (DUKE, $n=922$), and mammography (CBIS-DDSM, $n=400$). A five-algorithm federated learning comparison, including FedAvg, FedProx, SCAFFOLD, FedNova, and FP16-FedAvg, was conducted under IID and non-IID conditions using a Dirichlet distribution with $\alpha =0.5$. The evaluation reports diagnostic performance together with per-round training time, communication time, latency-related measurements, and cumulative bandwidth. Ablation experiments, McNemar’s test, Cohen’s h effect sizes, and confidence intervals were used to support the analysis. Results: Per-modality models achieved 92.50 ± 1.2%, 90.63 ± 1.5%, and 92.00 ± 1.3% accuracy for ultrasound, MRI, and mammography, respectively, with statistically significant improvements over the corresponding baselines according to McNemar’s test ($p<0.05$). Weighted late fusion achieved 93.10 ± 1.1% accuracy and improved performance compared with the best individual modality ($p=0.031$). FP16 transmission reduced cumulative bandwidth from 8.14 GB to 1.23 GB ($-84.9\%$) without a statistically significant performance difference compared with FP32 transmission ($p=0.74$), while SCAFFOLD achieved the highest non-IID accuracy (90.50%). Conclusions: The findings demonstrate internal technical validity and deployment-relevant trade-offs, but they should be interpreted cautiously because the federated evaluation is simulation-based, key-slice extraction may require annotation-assisted assumptions, and external multi-center validation remains necessary before clinical deployment. Reported improvements are statistically significant in several comparisons, but corresponding Cohen’s h effect sizes are small, and clinical meaningfulness requires independent validation rather than inference from p-values alone. Full article

Soil monitoring is fundamental for maintaining global soil health, ensuring food security, and achieving sustainable development. While satellite platforms provide invaluable tools for this purpose, the accuracy of soil monitoring heavily relies on the appropriate design of their remote sensing payload parameters. This study focuses on enhancing the accuracy of satellite-based global soil monitoring. Key physicochemical soil parameters—including total nitrogen (TN), soil organic matter (SOM), total salt content (TSS), moisture content (MC), and clay fraction (Clay)—were analyzed. A full-chain analytical validation model integrating “instrument–radiative transfer–soil parameter inversion” was developed. Using spectral measurements and soil sample analyses from the black soil region of Northeast China, the spectral response characteristics of core soil parameters were simulated and cross-validated under varying spectral resolutions and integration times. Results indicate that, under specific parameter configurations, the ‘Linshi-1’ satellite achieved robust TN inversion accuracy with R² > 0.65. SOM consistently exhibited good inversion performance, with RMSE ranging between 5.04 and 5.76 g/kg across various spectral treatments (all < 6 g/kg). TSS inversion demonstrated strong stability, maintaining an RMSE of approximately 0.43–0.44 g/kg at resampled spectral resolutions≥10 nm (corresponding to an SNR > 263). MC inversion accuracy was sensitive to both spectral resolution and regional variations, requiring a resampled resolution below 10 nm for consistently high accuracy. Clay inversion required the highest resolution, achieving an RMSE of less than 6 g/kg only at resampled resolutions of 1 nm or 2 nm (SNR approximately 150–210). These findings guided the design of the ‘Linshi-1’ black soil monitoring satellite system and its hyperspectral payload prototype. This effort establishes a solid theoretical and methodological foundation for future deployment, providing crucial space-based support for China’s black soil resource management and sustainable utilization. Full article

A hybrid code index modulation based on multi-carrier differential chaos shift keying (DCSK), referred to as HCIM MC-DCSK, is proposed in this paper. In the proposed system, multiple data-bearing information signals are transmitted simultaneously with one reference signal. The number of separate physical channels required for data transmission is M + 1, where M is the number of subcarriers. These data-bearing information signals are separated by different Walsh codes. The chaotic signal and its Hilbert transform are utilized to complete the hybrid index modulation. In addition, analytical bit-error-rate expressions are derived for the proposed HCIM MC-DCSK system operating over AWGN and multipath Rayleigh fading channels. The spectral efficiency and data rate of the proposed system are analyzed. The validity of the analytical results and the superiority of the proposed system are confirmed through relevant simulations. Full article