Search Results (5,489)

Spontaneous combustion in fire-affected coal seams can degrade coal quality, alter rock mechanical parameters and reduce mining profitability. Traditional pit limit optimization methods ignore coal fire-induced quality degradation, ignore the coupling effect of economic fluctuation and slope stability, and lack quantitative optimization for fire-affected open-pit mines. Here, we optimize loss-reducing mining boundaries for the southern fire-affected highwall in the first mining district of the Dananhu No. 2 Mine, Hami, Xinjiang. The aim is to move beyond binary decisions that either sterilize or fully extract fire-affected reserves. We instead integrate economic return, slope stability and coal-price uncertainty into a single boundary-optimization framework. First, we established a three-dimensional Cartesian coordinate system for the study area. We then modeled and fitted the coal-seam roof and floor using MATLAB-based multiple integration, reducing edge errors in solid surfaces. Laboratory analyses of borehole coal samples defined how calorific value varied with advance distance. These data were used to derive the coal-quality curve. Net mining profit was then formulated as the objective function, replacing the conventional stripping-ratio criterion. Profit was calculated across advance distances to identify the economically optimal boundary. Mechanical parameters of thermally altered rocks were obtained from laboratory deformation tests. Rhino and FLAC3D 6.0 were then used to evaluate three-dimensional slope stability at critical locations. Coal-price perturbation scenarios were finally introduced to test the sensitivity of net profit and optimal advance distance. Under the baseline coal price, the slope remained stable at an advance distance of 193 m. At this boundary, net profit reached a maximum of RMB 676.608 million. The southern surface boundary contracted by 47 m relative to the initial boundary, reducing unnecessary land disturbance. Sensitivity analysis showed that lower coal prices sharply reduced both the optimal advance distance and maximum net profit. When coal price decreased by 30%, the optimal advance distance contracted to approximately 116.9 m. Maximum net profit fell to approximately RMB 248.239 million. Higher coal prices expanded the optimal boundary outward. Once coal price reached approximately 128.7 yuan/t, or 18.5% above baseline, the optimum reached the upper constraint of 240 m. Net profit then increased substantially with further price growth. These results provide a quantitative basis for dynamic boundary optimization and disturbance-reducing extraction in fire-affected open-pit coal mines. Full article

Affected by coupled environmental factors including temperature and water activity, food storage is restricted by fungal contamination, quality degradation, and energy limits. Conventional microbial growth prediction models typically rely on given initial states and environmental parameters, making it difficult to account for the effects of prior-parameter errors and thereby limiting the accurate quantification of fungal growth risk and the real-time regulation of storage environments. This paper develops a fungal growth risk prediction and optimal regulation method for food storage based on the forward reachable set (FRS). The method combines a fungal growth kinetic model for Aspergillus flavus with FRS theory to calculate the reachable domains of colony radius and cell states within a finite time horizon, adopts a risk margin to describe the maximum colony expansion relative to deterministic growth trajectories, and constructs a multi-objective index covering energy cost, fungal growth risk, quality loss, and control switching cost to select the optimal environmental control scheme. Numerical simulation results show that the risk margin reflects the expansion of fungal growth risk caused by the propagation and accumulation over time of prior-parameter errors, while the selected regulation strategy exhibits stronger conservatism. Full article

Proximate analysis is essential for assessing the nutritional composition of legumes. Near-Infrared Spectroscopy (NIRS) is widely used as a rapid, non-destructive method. However, commercial NIRS calibration models are primarily developed using major commercial legumes and may not represent the spectral variability of underutilised legumes, limiting their transferability across species. This study aimed to evaluate the proximate composition of local market legumes and adjust existing NIR equations for predicting proximate constituents. Twenty-six dry seed legume samples, representing four genera and multiple species from northern Thailand and southern China, were analysed using standard wet-chemistry methods. Reference analyses showed that soybeans contained the highest protein and fat levels among the legumes studied. Partial Least Squares regression was used to adjust prediction models, with 5–7 newly acquired samples per model. Model performance was evaluated using leave-two-out cross-validation. The adjusted models yielded root mean square error values of 0.987, 0.586, 0.671, 0.363, and 0.976 for protein, fat, moisture, ash, and fibre, respectively. Spectral adjustment improved agreement between predicted and reference values, particularly for protein, fat, and ash. This study demonstrated the initial potential of adapting existing NIRS calibration models to diverse legumes. Future work must prioritise independent external validation with expanded sample cohorts to establish operational robustness. Full article

To develop an analysis-oriented stress–strain model for prestressed fiber-reinforced polymer (FRP)-confined circular concrete, the path-dependent responses of prestressed FRP-confined concrete and actively confined concrete were systematically investigated. Existing experimental data were used to examine the applicability of the stress-path independence and strain-path independence assumptions under different prestressing methods. The filament winding method generally satisfies the stress-path independence assumption, with relative errors in axial stress mostly within 10%, whereas direct application of the actively confined concrete model to expansive-concrete method specimens results in errors close to 20%. After accounting for the initial confinement effect, these errors are generally reduced to within 10%. Strain-path comparisons further show that the prestress-induced initial lateral strain should be considered; after removing this initial strain component, the lateral strain–axial strain relationship shows strong consistency with that of actively confined concrete. Accordingly, the peak stress, peak strain, and lateral strain–axial strain relationship were modified, and a strain-controlled incremental calculation procedure was established to generate the complete stress–strain response. Validation against compiled published experimental data yielded an R² of 0.931 and a MAPE of 8.88% for compressive-strength prediction, while the predicted axial stress–strain and lateral dilation responses also showed reasonable agreement with the experimental results. The proposed model can therefore support nonlinear analysis over the complete loading range and quantitative assessment of strength and deformation for different prestress levels and FRP confinement parameters within the validated range. Full article

Electric dynamometer test benches generate heterogeneous multi-channel logs whose structural completeness, physical consistency, and operating-condition support vary across files. We propose Coupling-Gated Data Quality Index (CG-DQI) sample weighting, a fold-local preparation protocol for 60 s next-window temperature-change prediction. Deployable weights use training-side structural and physical quality signals, while a target-magnitude coupling gate screens candidate association with the absolute target. The dataset contains 30,093 valid windows from 134 files and 30 date-defined campaigns. File-level leave-one-file-out validation reduced Ridge MAE from 0.7035 to 0.6932 °C and the mean MAE across three fixed Random Forest initializations from 0.4556 to 0.4489 °C per window. Strict nested leave-one-campaign-out analysis retained sparse CG-DQI in all 30 folds and yielded a campaign-level Ridge MAE difference of 0.0152 °C with a 95% bootstrap confidence interval (CI) of 0.0054–0.0309, while the 99th percentile absolute error (P99) increased by 0.0710 °C. Fixed-parameter XGBoost preserved a positive mean MAE direction, although its campaign interval crossed zero. CG-DQI, therefore, provides a traceable MAE-oriented preparation option within the present bench and campaign range; tail-sensitive and cross-device use requires an explicit constraint or recalibration. Full article

High-frequency induction hardening enhances the surface wear resistance and contact fatigue life of high-speed linear guides, but simultaneously produces an inhomogeneous, layered cross-sectional structure comprising a high-strength, low-ductility outer hardened layer and a low-strength, high-ductility inner core. This structural heterogeneity renders conventional straightening stroke prediction models—predicated on homogeneous material assumptions—fundamentally inadequate. Moreover, the iterative trial-bending operations ubiquitous in industrial practice progressively accumulate plastic strain, causing guide rails to exhibit erratic positive-to-negative deflection reversal during sequential straightening passes. To address these critical challenges, this study proposes a novel two-stage hybrid straightening strategy based on inverse finite element analysis (FEA) and closed-loop experimental feedback. An equivalent hardened layer depth (HD₀) is introduced as a parametric descriptor to construct a layered elastoplastic finite element model, and an inverse simulation strategy is developed to generate a comprehensive three-dimensional stroke–residual deflection prediction dataset encompassing both vertical and lateral straightening conditions across multiple support spans. Displacement-controlled three-point bending experiments validate the layered model and elucidate the mechanism by which cumulative plasticity progressively amplifies cross-sectional plastic sensitivity under repeated loading. Grounded in this physical insight, a hybrid straightening algorithm is formulated, combining dataset-driven initial stroke prediction for rapid large-deformation elimination with an upper-bound constraint and a measurement-feedback-driven sequential reduction compensation scheme for fine-tuning. Comparative experiments demonstrate that the proposed strategy effectively suppresses the oscillatory over-straightening characteristic of conventional empirical trial-and-error approaches, consistently reducing residual deflection below 0.05 mm within two to three loading cycles. This work bridges the gap between theoretical simulation and the complex physical state of actual machining, substantially improving both the efficiency and precision of straightening for guide rails with induction-hardened layers. Full article

Antenna selection is investigated as a means of reducing radio-frequency (RF) chain power in massive multiple-input multiple-output (MIMO) base stations under realistic channel state information (CSI) impairments. The study is motivated by the mismatch between conventional selection objectives and multi-cell operation with estimation error, pilot contamination, spatial correlation and inter-cell interference. APCS-Boost-R is introduced as the primary contribution. An interference-whitened D-optimal seed is combined with projected rank-one exchanges and a calibrated surrogate that incorporates a user-side interference-plus-noise report and a closed-form estimation-error correction. APCS-Boost-RG is retained as an optional graph neural network (GNN) refinement in which residual exchanges are ranked after the algebraic solution has been formed, while feasibility and non-degradation of the calibrated surrogate are verified deterministically. In a three-cell urban macro configuration derived from Third Generation Partnership Project (3GPP) TR 38.901 with 64 antennas, 16 active RF chains and eight users per cell, APCS-Boost-R achieves 19.364 bit/s/Hz over 200 paired realizations. Improvements of 2.58 percent over APCS-Boost, 6.76 percent over greedy search and 10.16 percent over a genetic algorithm are obtained. APCS-Boost-RG adds 0.019 bit/s/Hz but is treated as an optional refinement because it requires a second-stage neighborhood evaluation and offline model maintenance. In the archived common timing record, APCS-Boost-R requires 20.376 ms per three-cell realization, compared with 12.728 ms for APCS-Boost, 57.775 ms for norm-initialized greedy search and 41.302 ms for the genetic algorithm, while APCS-Boost-RG requires 24.0 ms versus 20.4 ms for APCS-Boost-R in the separate archived learned-stage record. Separate reconstructions on the documented reproducibility host require $55.3\pm 14.5$ ms for APCS-Boost-R and $592.2\pm 181.9$ ms for a complete APCS-Boost-RG rebuild. Additional paired examinations confirm robustness across stronger search budgets, report imperfections, regularized precoding, coordination, near-field sensitivity, hardware perturbations, and configurations ranging from 32 to 128 antennas and one to seven cells. Full article

Life skills are cognitive, emotional and social competencies that help adolescents manage everyday demands and participate in school and social contexts. This study aimed to translate, culturally adapt and examine the psychometric properties of the European Portuguese Life Skills Scale (LSS). A cross-sectional methodological study included a convenience sample of 1807 Portuguese adolescents aged 13–19 years from public secondary schools in Central Portugal. The sample was randomly split for exploratory factor analysis (EFA; n = 903) and confirmatory factor analysis (CFA; n = 904). EFA supported an interpretable eight-factor solution explaining 50.7% of the variance. CFA compared the original ten-factor model with the EFA-derived eight-factor model. The ten-factor model showed significant standardised loadings (λ = 0.444–0.849) and comparatively better fit, with acceptable root mean square error of approximation (RMSEA = 0.045) and standardised root mean square residual (SRMR = 0.062), although the comparative fit index (CFI = 0.855) and Tucker–Lewis index (TLI = 0.849) remained below preferred thresholds. Internal consistency was satisfactory to excellent (α = 0.782–0.963; ω = 0.783–0.964). Average variance extracted (AVE) and heterotrait–monotrait ratio (HTMT) analyses provided partial evidence of convergent validity and additional support for discriminant validity. Supplementary multi-group CFA provided preliminary support for measurement invariance across female and male adolescents. Findings provide initial support for the European Portuguese LSS as a multidimensional measure of adolescent life skills. Full article

In multivariate statistical analysis, accurate modeling of the covariance structure is critical for high-dimensional data analysis, variable selection, and regularization. In high-dimensional settings, strong inter-variable correlation and redundancy are key factors limiting the performance of classical sparsity-based methods. While LASSO and its variants provide effective tools for coefficient shrinkage and variable selection, they may select redundant variables and produce unnecessarily complex models in highly correlated settings. In this study, a Correlation-Sensitive Adaptive LASSO (CDA-LASSO) method is proposed to address these limitations. The proposed approach is based on a hybrid weighting mechanism that makes the penalty term sensitive not only to initial coefficient magnitudes but also to the correlation structure between variables. This structure incorporates correlation-based redundancy information and imposes stronger penalties on predictors with higher directed redundancy scores. Under fixed-dimensional regularity conditions, the bounded correlation multiplier is shown to preserve the selection consistency and oracle limiting distribution of Adaptive LASSO. The method was evaluated through 14 high-dimensional simulation scenarios covering different sample sizes, dimensionalities, sparsity levels, correlation strengths, support structures, and normal or heavy-tailed errors. The results indicate that the Max and kMean variants generally reduce the false discovery rate and model size relative to LASSO and Elastic Net while maintaining broadly comparable predictive performance. Numerical improvements over Adaptive LASSO were also observed in several scenarios, although these differences were not uniformly statistically significant. Under very high correlation, reductions in false discoveries were sometimes accompanied by modest decreases in the true positive rate. The real-world Riboflavin analysis further showed that the CDA-LASSO variants produced smaller models than LASSO and Elastic Net while retaining comparable prediction errors. Overall, CDA-LASSO directly incorporates the internal correlation structure of the data into the penalty weights without requiring a predefined graphical structure and provides a practical methodological extension for more controlled and parsimonious variable selection in high-dimensional correlated settings. Full article

Background: Commercial digital mental health platforms have expanded across the Gulf since 2020, yet cancellation in this delivery model remains understudied. We aimed to identify factors independently associated with session cancellation on a Saudi Arabic-language commercial platform and to determine whether they describe user or provider behaviour. Methods: We conducted a retrospective cohort analysis of 36,544 booked sessions from 21,904 unique users (October 2023–April 2026); the outcome was binary cancellation (cancelled vs. completed). Multivariable logistic regression with cluster-robust standard errors estimated adjusted odds ratios (aORs) for user, provider, and booking characteristics; secondary analyses decomposed cancellation by recorded initiator and modelled age, lead time, and calendar time flexibly. Reporting followed STROBE guidelines. Results: Crude cancellation was 25.6%. Cancellation was strongly patterned by visit type (follow-up aOR 0.26, 95% CI 0.21–0.32), lead time (≥8 days 2.10, 95% CI 1.90–2.33), session type (free 1.19, 95% CI 1.10–1.27), and provider specialty; age reduced odds modestly (aOR 0.90 per decade, p < 0.001) and gender was null (aOR 0.95, p = 0.119). Cause-specific models showed that the lead-time gradient reverses by initiator: at ≥8 days versus same-day, user-initiated cancellation was less likely (aOR 0.62, 95% CI 0.49–0.78), whereas provider-initiated cancellation was far more likely (aOR 4.75, 95% CI 4.23–5.35), and provider-specialty differences were confined almost entirely to provider-initiated cancellation. The very low adjusted odds for emergency sessions (aOR 0.17, 95% CI 0.13–0.23) reflect how emergency bookings are administratively coded on the platform rather than genuine behavioural non-adherence and are regarded as artefactual. Discrimination was moderate (apparent AUC 0.700, development sample). Conclusions: Cancellation reflected operational features of the booking, but its largest correlates—lead time, free-session status, and provider specialty—operate predominantly through provider-side scheduling rather than patient non-adherence. Full article

Cutterhead driving torque is the primary load indicator of earth-pressure-balance shield machines, yet its dependence on strongly coupled multi-source operating parameters limits the reliability of empirical formulations. This study proposes IALA-edRVFL-FIS-Reg, a fuzzy ensemble deep random vector functional link regression model optimized by an improved artificial lemming algorithm (IALA). The base learner maps continuous operating parameters into fuzzy-state features through a Gaussian-membership Sugeno inference layer, propagates the concatenated raw and fuzzified inputs through stacked randomized hidden layers with direct input links, and obtains layer-wise output weights by regularized closed-form least squares before ensembling, thereby combining fuzzy-state representation with deep random feature mapping without gradient back-propagation. Distinct from the standard ALA, IALA introduces three explicitly defined mechanisms: an error-feedback exploration–exploitation transition factor normalized by the initial-population loss, which replaces the fixed energy factor; an adaptive step size coupling sigmoid error-gating with cosine annealing to preserve jumping capability while refining local search; and a stagnation-counter-triggered directional-disturbance jump for escaping local optima. Using 48,646 valid tunnelling records from 301 rings of Beijing Metro Line 22 and 65 raw and mechanism-based engineered features, the model attains R² = 0.9555, RMSE = 382.52 kN·m, MAE = 302.95 kN·m and MAPE = 9.18%, outperforming eleven benchmarks on a ring-disjoint holdout, previously unseen rings of the same section, IALA yields an R² gain of 0.0104 over ALA. Full article

This paper proposes a method for reducing the initial azimuth estimation time of a north-finding system employing low-cost sensors and a compact 3-axis turntable. The system is capable of operating in non-horizontal environments, magnetically disturbed environments, and environments where Global Navigation Satellite System (GNSS) signals are unavailable. Detection of due north without prior azimuth information was evaluated through indoor experiments under the aforementioned conditions. During each rotation, the compact 3-axis turntable was kept horizontal and the acceleration and angular velocity were measured in 16 directions at 22.5$°$ intervals. By including the final position coinciding with the initial one, a total of 17 measurement points were obtained per lap. This process was repeated for 77 laps. For statistical evaluation, 5000 bootstrap replications were generated. Detection of due north was then performed using these datasets and the relationship between the number of laps and the estimation error was statistically analyzed. Consequently, it was confirmed that the root mean square (RMS) error becomes less than 1$°$ after ten laps, corresponding to a data acquisition time of approximately 1.3 h. Compared to our previous study, the required estimation time is reduced by approximately 2 h. Full article

Safe cooperative interception must coordinate arrival, terminal geometry, closing speed, and internal spacing at the same time. This study formulates a spatial cooperative guidance scheme that couples range synchronization, terminal relative-speed regulation, adaptive transverse convergence, and active separation among interceptors. Along the line of sight (LOS), finite-time agreement of range-related states is superposed with a shared closing-speed servo, thereby separating the difference dynamics inside the multi-vehicle system from the mean radial-speed mode. In the two angular channels, the constant reaching gain is replaced with a time-, range-, and error-dependent gain that moderates the initial command peak while preserving terminal LOS-angle convergence. Collision risk is handled in two phases. During the early trajectory-reshaping phase, a smoothed neighbor-repulsion command is projected onto the transverse plane so that the radial speed loop is not perturbed. After the LOS-angle errors enter the assigned band, separated terminal approach directions are used to avoid late trajectory aggregation. Numerical tests show that the scheme removes early overload saturation and increases the minimum spacing while keeping the terminal constraints. In three deterministic cases, the terminal angle errors are on the order of ${10}^{-6}$, the relative impact-speed errors are on the order of ${10}^{-3}$, and the miss distances are approximately $0.8\mathrm{m}$. Two 200-run Monte Carlo tests with launch-condition perturbations and measurement noise give terminal angle errors on the order of ${10}^{-3}$, relative impact-speed errors around $0.5\mathrm{m}/\mathrm{s}$, and miss distances concentrated near $0.5\mathrm{m}$. These results indicate that the proposed design improves command smoothness and spacing performance without sacrificing cooperative terminal accuracy under the tested engagement conditions. Full article

This paper evaluates the sensitivity and calibration of the third-generation shallow-water wave model SWAN for Typhoon Doksuri (No. 202305) along the Fujian coast. Sensitivity analyses were conducted for model initialization, wind forcing, and key physical parameters. The results show that a 1-day spin-up period is sufficient to largely reduce the initial error caused by a cold start. A locally refined unstructured triangular grid was adopted, and ERA5 reanalysis winds were blended with the Holland empirical typhoon wind field to better represent extreme winds near the typhoon core. Further tests indicate that the combination of the Janssen wind input scheme, cds1 = 3.5, LTA triad wave interaction scheme, JONSWAP bottom friction scheme, and a wave-breaking parameter of 0.73 can effectively reproduce the typhoon wave process along the Fujian coast. The optimized simulations agree well with buoy observations and provide a reference for typhoon wave forecasting and coastal disaster risk assessment. Full article

This paper studies how hidden, stochastic continuation of informed participation shapes price discovery in a two-period Kyle-type market. Hidden participation separates two analytical questions that coincide in the standard Gaussian-linear model. Conditional on a specified linear continuation order, we first derive the exact Bayesian posterior mean and variance of a mixture comprising an informed-trading regime and a noise-only regime; this is a conditional-inference result, not a full nonlinear equilibrium. We then derive an equilibrium under a constrained best-linear-pricing protocol in which market makers use the minimum-mean-square-error affine projection and the insider optimizes pointwise against linear prices. The exact Bayesian posterior responds nonlinearly because order flow reveals both residual value and the likelihood of informed participation, while moderate flows can preserve substantial regime uncertainty. In the projection benchmark, a lower continuation probability accelerates first-period information revelation, shifts insider rents toward the initial round, and creates opposing early- and late-learning effects. A dimensionless analysis characterizes how inference varies with continuation probability and the informed-to-noise variance ratio and establishes scale invariance for the benchmark’s normalized comparative statics. The paper thus isolates a participation margin in price discovery and states precisely which results concern exact inference and which concern a constrained equilibrium. Full article