Search Results (279)

Quantum key distribution (QKD) employs quantum states to generate shared cryptographic keys. An attacker interacting with the modeled non-orthogonal quantum signals can affect the monitored statistics, and hence they can be detected under the specified protocol assumptions, but this trait does not inherently authenticate the classical channel, and it does not prevent implementation side channels. In this work, we introduce ModPhase-8 (QUEST), a proposed QKD modulation and adaptive-receiver architecture evaluated through analytical modeling and simulation. Instead of using only a few quantum signal types, our system uses eight carefully designed signal variations created by adjusting the phase between two very short light pulses. The eight phase states are organized into four phase bases, each containing two antipodal states that encode one binary raw-key value. The enlarged signal set diversifies the physical representation of the key bit and changes the state-discrimination problem faced by an eavesdropper, but it does not increase the raw-key payload beyond one bit per successfully sifted signal. On the receiving side, the system adaptively switches between two measurement techniques based on the prevailing channel conditions. This adaptive detection mechanism enhances reliability and helps maintain low error rates even when the communication channel is affected by noise. We provide an analytical security assessment under the stated collective-attack, source, channel, receiver, and trusted-device assumptions, supplemented by attack-specific analyses of intercept–resend, beam-splitting, source-side multi-photon leakage, and selected implementation-related vulnerabilities. Simulation studies were conducted to examine the physical-layer and post-processing behavior of the proposed protocol under explicitly stated channel, receiver, detector, and finite-sample values. Under the adopted simulation model, ModPhase-8 maintains low error rates in the low- and moderate-noise operating regimes and exhibits favorable receiver-level robustness across the investigated channel conditions. The reported rate values are model-based performance estimates rather than rigorously certified secret-key lower bounds. In particular, Qiskit simulation does not establish a composable security proof or an optimal bound on Eve’s information for the exact eight-state time-bin ensemble. A protocol-specific numerical security analysis incorporating the homodyne–heterodyne measurement operators, post-selection, reconciliation efficiency, finite-size effects, and Eve’s Holevo information remains necessary before definitive rate comparisons can be made. ModPhase-8 should therefore be interpreted as a practically motivated receiver and modulation framework whose security-rate performance remains subject to further protocol-specific analysis. Full article

Utah Lake and Great Salt Lake share a watershed yet face distinct pressures: eutrophication, harmful algal blooms, hydrologic decline, and exposed-playa dust hazards, but their combined literature has never been systematically characterized. I analyzed 1383 peer-reviewed records using bibliographic coupling, Louvain community detection, latent Dirichlet allocation, and large language model-assisted annotation. The coupling network (1012 records, 7536 edges) split into 379 communities ($Q=0.541$), of which 342 were singletons and only 14 reached 10 or more papers, together holding 59% of coupled records. Of 10 main-text clusters, 6 are anchored in a lake and 4 in a topic rather than a system; just 1 is Utah Lake dominant. Great Salt Lake research spans broader disciplinary communities (brine-shrimp ecology, mercury cycling, dust and paleoclimate); Utah Lake research is more applied (native-species management, eutrophication, harmful algal blooms). Recent Utah Lake studies report no lake-wide chlorophyll-a trend across 1068 Landsat scenes (1984–2021) and dissolved phosphorus near ∼0.02–0.04 mg L⁻¹ over ∼50 years despite ∼300% population growth, implying internal sediment cycling dominates; recent Great Salt Lake work centers on hydrologic decline and playa dust hazards. The synthesis identifies five cross-cutting gaps, including dust-emission-to-exposure assessment, Utah Lake nutrient modeling, and predictive water management, and yields a reproducible, transferable framework. Full article

Gob-side entry retaining by roof cutting and pressure relief (CRRE) eliminates coal pillar waste and mitigates mining-induced stress concentration. Although widely applied in mines shallower than 1000 m, systematic research on asymmetric deformation mechanisms and matched control technologies under ultra-deep conditions (>1000 m, σH > 60 MPa) remains limited. This study investigates the 5307 working face of Anju Coal Mine (burial depth: 1127–1195 m) using theoretical analysis, FLAC3D numerical simulation, and 480 m of field monitoring. The stress evolution, deviatoric stress field response, and asymmetric deformation mechanisms of the surrounding rock under ultra-deep mining conditions are systematically analyzed, based on which a targeted collaborative control technology is proposed. The key findings indicate that (1) CRRE significantly attenuates advanced abutment pressure compared with conventional pillar retention, with an average stress reduction of 20.1 ± 1.2% (95% CI: 17.8–22.4%, p < 0.01). (2) During the advanced mining stage, the second invariant of deviatoric stress exhibits a saddle-shaped distribution with a pronounced concentration at the mid-rib, identifying this as the dominant zone for rib bulging failure. (3) In the post-mining entry-forming stage, the roof deviatoric stress field demonstrates marked asymmetric evolution, with the distortion energy on the solid-coal side substantially exceeding that on the gob side; moreover, the low-position roof strata exhibit high distortion and poor stability, rendering them prone to bending fractures. Grounded in these mechanisms, a full-cycle differentiated surrounding rock control technology is developed, integrating pre-mining directional roof pre-splitting, active tough support reinforcement, post-mining temporary roof control and pressure relief, and gangue retaining with rib collaborative protection. The key parameters include a roof cutting height of 7 m, a cutting angle of 15°, NPR constant-resistance anchor cables with W-steel belts, and temporary support extending 300 m behind the working face. Field monitoring reveals staged deformation evolution, with stabilization achieved 250 m behind the working face. Maximum roof subsidence, floor heave, and total roof-floor convergence were 180 mm, 329 mm, and 422 mm, respectively, below the 500 mm allowable threshold for ultra-deep retained entries. Full article

To overcome the limited dust control efficiency and backward dust diffusion tendency in fully mechanized excavation faces, this study pioneeringly proposes a wind-splitting dust control method based on a mechanical iris structure. This method actively splits and precisely allocates the airflow from the forced air duct outlet, achieving a synergistic effect between axial dust suppression and radial dust blocking. Through laboratory experiments and numerical simulations, the influences of the radial split ratio and the installation distance of the wind-splitting device on airflow and dust distribution were investigated. Results indicate that the proposed method significantly outperforms traditional ventilation. Optimal dust-control performance, considering both roadway-average concentration reduction and rear-area protection, was obtained at a radial split ratio of 0.6 and an installation distance of 24 m. Under these conditions, the axial-radial airflow and the exhaust negative pressure form a stable dust-blocking air curtain and a dust-controlling vortex in the front roadway, confining dust primarily within 6.4 m from the heading face. The average breathing zone dust concentration decreased to 22.62 mg/m³, and the roadway average dropped to 31.50 mg/m³, with a dust control efficiency of 77.56% and a significant reduction in rear dust concentrations. This offers effective ventilation optimization. Full article

The telomere is one of the biological hallmarks of skin aging. Sodium acetylated hyaluronate (SAH) is an acetylated derivative of sodium hyaluronate, produced by a grafting technology that confers higher resistance to hyaluronidase. This study aimed to explore whether SAH is associated with telomere-related endpoints and with clinical signs of skin aging. Two vitro studies were performed, assessing telomerase activity in human adult keratinocytes and telomere length in human normal fibroblasts by quantitative PCR (qPCR) assay kits, with or without SAH. A randomized, split-face, double-centered, double-blind, placebo-controlled clinical trial was then conducted on 44 Caucasian and Asian female volunteers, comparing a facial cream containing 0.1% SAH with a placebo cream over 28 days; crow’s feet wrinkle depth, area, and volume, under-eye roughness (Rz), elasticity (R2), and firmness (F4) were measured by PRIMOS 3D and Cutometer^® MPA 580 at baseline, day 7, and day 28. In vitro, only the highest SAH concentration (0.15%) was associated with a significant increase in telomerase activity and in telomere length versus control, whereas lower concentrations had no significant effect. Clinically, the SAH cream significantly reduced crow’s feet wrinkle volume and area, improved skin elasticity, and reduced under-eye roughness versus placebo, with non-significant trends for wrinkle depth and firmness. These exploratory findings make SAH an interesting active ingredient for anti-aging skincare cosmetics, combining a curative dimension (clinical improvement of skin-aging parameters) with a preventive dimension (telomere preservation observed in vitro for the first time), and warrant further mechanistic investigation. Full article

Hard roof strata in deep coal mines commonly cause rib failure and roof collapse, restricting extraction efficiency. This study employs theoretical analysis, numerical simulation, and field experiments to investigate the fractal evolution of damage in coal–rock mass under loading and blasting disturbances, with the aim of quantifying progressive failure and optimizing roof control. Key findings include: (1) The fractal dimension D of fragment size distribution increases monotonically with loading rate, with fine-particle proportion rising from 48.5% to 52.3%, indicating more thorough fragmentation at higher rates. (2) Load intensity, elastic modulus, and seam thickness govern coal bearing capacity and energy accumulation, with D serving as a quantitative damage indicator. (3) Pre-splitting blasting shifts the stress peak away from the working face, with shear fractures dominating the fracture network and tensile fractures playing a secondary role. (4) Field application at Zhangji Coal Mine (9 coal seam, 7–23.5 m sandstone roof) confirms the effectiveness of segmented fan-shaped borehole pre-splitting blasting in controlling roof behavior; fractal dimension derived from borehole images quantifies fracture propagation. Dynamic adjustment of blasting parameters based on geological core samples is recommended to enhance fracture network complexity and improve roof control efficiency under varying hard rock conditions. Full article

Retinol, a bioactive small molecule of the vitamin A family, contributes epidermal and dermal tissue repair through retinoic acid receptor γ (RARγ)/retinoid X receptor (RXR) receptor-mediated transcriptional regulation of keratinocyte differentiation, extracellular matrix (ECM) remodeling, and barrier restoration. However, the relationship between applied concentration, tissue-level regenerative outcomes, and tolerability remains incompletely characterized. This prospective, randomized, single-blind study compared biophysical effects and tolerance of two retinol concentrations in EU-compliant facial creams, high-performance liquid chromatography (HPLC)-verified as 0.104% and 0.247% (w/w). Thirty-eight women aged 40–61 years (Fitzpatrick phototypes II–III) participated across three independent sub-studies: 28 were randomized to either concentration for 12 weeks; 5 underwent split-face ultrasound imaging (0.247% versus retinol-free control) for 8 weeks; and 5 participated in a tape stripping sub-study quantifying stratum corneum interleukin-1 alpha (IL-1α) and interleukin-1 receptor antagonist (IL-1ra) by ELISA before and after 6 weeks of 0.247% retinol treatment. Main cohort assessments included transepidermal water loss (TEWL), hydration, melanin, erythema, pH, sebum, biomechanical parameters, and wrinkle grading. Both concentrations significantly improved barrier parameters—hydration, TEWL, brightness, pH, and sebum—with no inter-group differences. In the ultrasound sub-group, 0.247% retinol increased epidermal thickness (+12.4 μm), epidermal density (+3.84%), and dermal density (+1.81%) versus control, consistent with ECM reorganization and epidermal stratification. Biomechanical parameters showed no significant changes, consistent with retinol’s remodeling timeline. Consumer assessment indicated comparable efficacy; 0.247% demonstrated superior smoothing but higher erythema incidence. In the tape stripping sub-study, IL-1α decreased in all participants (median: 25.1 → 13.2 picograms per tape [pg/Tape]; 5/5 concordant), while IL-1ra increased in 4/5 participants (median: 352.8 → 1403.1 picograms per three sequential tapes [pg/3sT]), indicating a directionally consistent shift in the IL-1α/IL-1ra balance; these results are exploratory and require replication in larger cohorts. These findings collectively support clinically meaningful barrier restoration and structural remodeling within current EU safety limits (Commission Regulation EU 2024/996), with 0.104% offering a favorable efficacy-to-tolerability profile for initial therapy. Full article

Tool wear detection is essential for machining quality control and predictive maintenance, but conventional inspection is often manual, time-consuming, and operator-dependent. Existing learning-based visual methods still face challenges in jointly achieving reliable wear-type recognition, accurate wear-region localization, and quantitative wear-width measurement under shop-floor imaging conditions. To address these issues, this study proposes MaskLenNet, a query-based instance segmentation and length prediction network for solid carbide end-milling tool diagnosis. MaskLenNet combines a Swin Transformer backbone, query-based instance-mask prediction, wear-oriented attention, and a key-point head that directly estimates the maximum wear-land width (VB). Evaluation uses 234 images from 54 physical tools under a tool-disjoint split, so different rotations of one tool cannot occur in both training and evaluation sets. On the held-out test set, MaskLenNet achieves 96.52% matched-instance classification accuracy, 95.75% foreground instance mIoU, and a VB mean absolute error of 0.010214 mm. Relative to BEiT-Base, the gains are 3.04 and 3.60 percentage points in accuracy and mIoU, respectively. These results demonstrate promising performance within the evaluated acquisition system; they do not establish equivalence to microscopy or generalization to other machines, optics, workpiece materials, or sites. Full article

Effective fetal health prediction is crucial for early diagnosis of fetal distress and avoiding negative perinatal effects. Cardiotocography (CTG), which measures fetal heart rate and uterine contractions, is a popular method for prenatal examination; however, conventional interpretation is arbitrary and unreliable. Fetal health prediction continues to face challenges due to scarce and imbalanced CTG datasets and data leakage during model evaluation, which can lead to poor generalization and inaccurate performance estimates. Moreover, the absence of explainable AI reduces model clarity and restricts clinical utilization. This study offers a hybrid deep learning model that uses TabNet, Residual Multi-Layer Perceptron (Residual MLP), and Focal Loss to classify normal, suspect, and pathological fetal states. TabNet allows for attention feature learning from CTG data, Residual MLP increases predictive robustness, and Focal Loss aids minority abnormal case diagnosis. To achieve a reliable evaluation, data splitting is used to create a pipeline designed to eliminate conventional train–test data leakage by performing data partitioning before model training and evaluation. SHAP and LIME enhance interpretability by offering clear global and local explanations. The suggested model obtains 99.30% accuracy, 99.10% balanced accuracy, and 98.65% pathological recall on the augmented dataset. A comparative evaluation shows that fixing data leakage drops overinflated baseline accuracy from 96.80% to 93.14%, emphasizing the necessity of a robust experimental design. The results show that the proposed framework outperforms cutting-edge fetal health prediction approaches, offering a robust, understandable, and clinically reliable alternative for CTG-based fetal health evaluation. Full article

Synthetic data are increasingly proposed as a strategy for addressing data scarcity and representation imbalance in medical AI, particularly for paediatric populations and darker skin tones. However, visually plausible synthetic images may still contain clinically implausible features or fairness-relevant inconsistencies that are not adequately captured by automatic image-quality metrics. In this study, we present and empirically evaluate a clinician-guided framework for validating and selecting synthetic paediatric dermatology images. The framework combines a clinician-facing evaluation platform with structured assessments of visual realism, mask quality, diagnostic plausibility, confidence, and skin-tone relevance. Four clinicians with complementary expertise in paediatrics and dermatology completed 282 assessments of 93 real and synthetic images. Synthetic images were often rated as visually realistic but showed lower inter-rater agreement and weaker mask-quality assessments than real images. Clinician realism and confidence ratings were then used to divide 30 synthetic images into 18 approved and 12 non-approved images. To assess downstream utility, we compared a real-only ResNet50 classifier with classifiers augmented using all synthetic images, clinician-approved synthetic images, or non-approved synthetic images. Across three patient-level experimental splits, the clinician-approved condition achieved the strongest overall classification performance and the largest gains for the under-represented Dark-Skin subgroup. Because the Dark-Skin subgroup contained only seven patients and the synthetic subsets differed in size and disease composition, these fairness results should be interpreted as exploratory. The present study therefore provides evidence for clinician-guided validation and data curation rather than for a completed iterative generator-retraining process. Future work will evaluate whether clinician feedback can also support repeated generative-model refinement in larger, multi-centre datasets. Full article

The height of a water-conducting fractured zone (WCFZ) is directly related to the design of water-preserved coal mining and water-hazard risk assessment in ecologically fragile mining areas in western China. Existing empirical formulas have limited regional adaptability, and individual machine learning models may show insufficient stability under small-sample and nonlinear data conditions. To address this issue, a heterogeneous Stacking ensemble prediction framework was constructed based on measured data from the Yushen mining area. Mining thickness, working face length, mining method, burial depth, coal seam dip angle, and hard strata proportion coefficient were selected as input variables. The base layer consisted of support vector regression (SVR), classification and regression tree (CART), random forest (RF), extreme gradient boosting (XGBoost), and back-propagation neural network (BPNN), while Ridge regression was used as the meta-learner. Under the current data split, the test set R², RMSE, MAE, and MAPE of the Stacking model were 0.953, 10.99 m, 8.79 m, and 9.847%, respectively, indicating overall superiority over individual models and other ensemble configurations. The field validation results showed that the relative errors of the model for boreholes LD-1 and LD-2 in the fully mined area were 1.99% and 1.28%, respectively; however, an overestimation of 52.70% occurred for LD-3 in the coal-pillar-adjacent area. This indicates that the model is more suitable for the regional-scale screening of the maximum fractured-zone height and should not be directly used for fine-scale prediction in local boundary-affected zones. SHAP analysis showed that mining thickness, working face length, and hard strata proportion coefficient were the main influencing variables, and their response trends were generally consistent with key-strata control and the transition toward full-mining conditions. This study provides a reference for the rapid prediction of WCFZ height and preliminary evaluation of water-preserved coal mining in weakly cemented mining areas in western China. Full article

This study aimed to investigate the effects of a facial serum containing Tubtim Chumphae rice extract on skin hydration, elasticity, oiliness, pigmentation, and erythema in healthy adults aged 30–50 years. Thirty participants applied two facial serums daily to opposite sides of the face for 12 weeks: (1) a serum containing Tubtim Chumphae rice extract (treatment intervention) and (2) a standard serum formulation containing 2% α-arbutin (control intervention). Skin hydration, elasticity, oiliness, melanin, and erythema were assessed at baseline and after the intervention using non-invasive skin measurement techniques. At baseline, no significant differences were observed between facial sides for any outcome. After 12 weeks, the side treated with the treatment intervention demonstrated significant within-side improvements in skin hydration at the forehead, skin elasticity at the cheeks and chin, and reductions in melanin and erythema in selected facial regions (all p < 0.05). However, paired split-face comparisons revealed that the treatment intervention produced significantly greater skin hydration at the cheek (mean difference = 3.88 AU, 95% CI: 0.23–7.53 AU, p = 0.039) and significantly greater skin elasticity at the forehead (mean difference = 0.090, 95% CI: 0.011–0.169, p = 0.028) compared with the control intervention. In addition, skin oiliness at the cheek was significantly lower on the side treated with the treatment intervention (mean difference = −6.62 µg/cm², 95% CI: −12.62 to −0.62 µg/cm², p = 0.032). No significant between-intervention differences were observed for melanin or erythema at any facial region. These findings suggest that topical application of a facial serum containing Tubtim Chumphae rice extract may improve skin hydration and elasticity while reducing skin oiliness in healthy middle-aged adults. However, no significant advantages over a standard 2% α-arbutin serum were observed for skin pigmentation or erythema after 12 weeks of treatment. Clinical Trial Registration: ClinicalTrials.gov, NCT06475222. Full article

Variations in goaf temperature and CO concentration during low-temperature oxidation are jointly affected by residual-coal oxidation, air-leakage oxygen supply, gas generation, migration, dilution, and ventilation disturbance, resulting in nonlinear multi-source responses. This study investigated a joint prediction framework based on multi-source monitoring feature fusion and covariance-adaptive whale optimization algorithm (CA-WOA)-optimized models. Daily monitoring data from goaf pipes, the working face, the upper corner, and the return-air side were integrated. Pearson correlation analysis and random forest feature-importance ranking were used to construct top-$k$ feature subsets. CA-WOA incorporates rank-weighted elite-center reconstruction, covariance-adaptive direction learning, WOA random-search injection, and geometric step-size decay, and was used to optimize RF, XGBoost, LightGBM, CatBoost, LSSVM, and TABM under a unified dual-output fitness function. CA-WOA improved all six models, with a maximum mean $R^2$ increase of 0.076 and a maximum fitness reduction of 22.8%. CA-WOA–TABM achieved the best fivefold performance, with a mean $R^2$ of 0.924 and $F=0.273$, while its out-of-fold $R^2$ values for temperature and CO concentration were 0.928 and 0.931, respectively. On the additional 1202 working-face dataset, TABM achieved a later-period test $R_{\mathrm{C}\mathrm{O}}^2$ of 0.766 under chronological splitting, and CA-WOA–TABM obtained a fivefold mean $R_{\mathrm{C}\mathrm{O}}^2$ of 0.948. SHAP and PDP analyses identified GoafPipe_CH4, GoafPipe_CO2, GoafPipe_C2H6, and GoafPipe_O2 as the main predictive variables. Full article

Electric vehicle manufacturing firms face increasing supply chain vulnerability due to component shortages, technological interdependence, raw material volatility, and demand uncertainty. This study aims to examine whether digital transformation can be translated into a resilience-building capability and to identify the transmission channels. Using panel data from Chinese A-share listed electric vehicle manufacturing firms from 2014 to 2023, we employ a double machine learning framework to estimate the relationship between digital transformation and supply chain resilience while accounting for high-dimensional firm-level controls. The results show that digital transformation is positively associated with supply chain resilience. This finding remains robust across alternative sample-splitting ratios, different machine learning algorithms, alternative winsorization thresholds, and sample restrictions. It also holds after addressing potential endogeneity using instrumental variable estimation. Mechanism tests indicate that digital transformation contributes to resilience by promoting technological innovation and reducing managerial transaction costs. Heterogeneity analysis further shows that the effect is more pronounced among firms with stronger market positions, larger firms, and vehicle manufacturers. These findings suggest that digital transformation is not merely a tool for operational upgrading but also an important organizational capability for strengthening supply chain resilience in electric vehicle manufacturing. Full article

Cementitious sand and gravel (CSG) is commonly used in construction engineering; however, its mix proportion design is complex, and traditional physical experiments face limitations such as long cycles, high costs, and susceptibility to external factors when obtaining high-quality sample data. In this study, a foundational dataset was first acquired through physical experiments: 100 sets of CSG specimens with different mix proportions (cement content 40, 50, 60, 70 kg/m³; water-to-binder ratio 1.0, 1.2, 1.4; sand ratio 0.1, 0.2, 0.3, 0.4; fly ash content 20, 30, 40, 50 kg/m³) were prepared. After 28 days of standard curing, compressive strength and splitting tensile strength tests were conducted using a WAW-1000 electro-hydraulic servo universal testing machine, yielding 100 sets of real mechanical property data. The coefficients of variation for all test groups were below 10%, confirming the reliability and repeatability of the experimental data. On this basis, a data augmentation method based on Conditional Tabular Generative Adversarial Networks (CTGAN) is proposed. Through adversarial training between the generator and the discriminator, the model learns the multi-dimensional distribution characteristics of the original CSG data and generates 100 synthetic samples, which are then merged with the original data to expand the dataset to 200 samples. The quality of the synthetic data is evaluated using Wasserstein distance and correlation matrix heatmaps. Furthermore, a hybrid XGBoost–LSTM prediction model is proposed—XGBoost is used for feature construction to capture nonlinear interactions among mix proportion variables, and the constructed features are then fed into an LSTM network for sequential learning and regression prediction. The results show that the CTGAN-generated data are highly consistent with the original data in terms of kernel density distributions and variable correlations, with Wasserstein distance significantly superior to four comparative methods: Bootstrap, SMOTE, GaussianCopula, and TVAE. After augmentation, the XGBoost–LSTM model achieves a coefficient of determination (R²) of 0.9897 for compressive strength prediction (vs. 0.9793 before augmentation) and 0.9801 for splitting tensile strength (vs. 0.9882 before augmentation, a slight decrease). The mean absolute percentage errors (MAPE) are 4.49% and 4.11%, and the root mean square errors (RMSE) are 0.201 and 0.049, respectively; both error metrics are reduced compared with those before augmentation. Compared with baseline models including XGBoost, LSTM, Random Forest (RF), and Support Vector Regression (SVR), the XGBoost–LSTM model exhibits the best performance across all evaluation metrics, and Wilcoxon signed-rank tests confirm that the performance differences are statistically significant (p < 0.05). The proposed method of CTGAN-based data augmentation combined with the XGBoost-LSTM hybrid model provides an effective solution to the problem of insufficient CSG sample data and offers a reference for data enhancement and performance prediction of other small-sample materials. Full article