Search Results (6,280)

With the increasing embeddedness of AI robots and other intelligent technologies in organizational workplaces, leader AI-focused attention has emerged as an important reference point for employees as they use and adapt to AI-related technologies. Drawing on work-related rumination theory, this study develops and tests an integrated mediation model to examine how leader AI-focused attention is related to employee proactive behavior through two parallel pathways: problem-solving pondering and affective rumination. It further investigates the moderating role of AI job role clarity. Based on structural equation modeling of multi-wave survey data from 514 employees, the results show that leader AI-focused attention positively predicts employees’ problem-solving pondering and affective rumination. Problem-solving pondering is positively related to employee proactive behavior, whereas affective rumination is negatively related to employee proactive behavior. In addition, AI job role clarity positively moderates the relationship between leader AI-focused attention and problem-solving pondering; specifically, this positive relationship is stronger when employees report higher AI job role clarity. From the perspective of work-related rumination, this study extends the explanation of the psychological mechanisms linking leader AI-focused attention to employee proactive behavior. It also provides theoretical insights and practical implications for understanding the boundary condition of leaders’ attentional signals in AI-related work contexts and for supporting employee proactive behavior. Full article

This paper develops a unified asymptotic theory for conditional single-index U-statistics and the associated conditional U-processes in the setting of locally stationary functional time series subject to missing-at-random response mechanisms. The proposed framework addresses, within a single nonparametric inferential architecture, three major sources of complexity in modern functional data analysis: infinite-dimensional covariates, smoothly time-varying stochastic dynamics, and incomplete response observations. The methodology is based on a class of kernel-type estimators combining temporal localization, functional single-index smoothing, and inverse-propensity correction. Temporal localization captures the gradual evolution of the underlying regression structure, the single-index projection provides an effective dimension-reduction mechanism for functional covariates, and the propensity adjustment restores the target conditional functional under the MAR sampling scheme. The principal contribution of the paper is the establishment of weak convergence, in a suitable space of bounded functions, for the resulting propensity-adjusted conditional U-process indexed by a general class of measurable kernels. Under absolute regularity conditions, local stationarity assumptions, small-ball probability requirements, entropy restrictions of VC type, and uniform consistency of the propensity-score estimator, the normalized process is shown to converge weakly to a tight centered Gaussian process. The limiting covariance structure explicitly reflects the interaction between temporal smoothing, functional concentration, dependence, and the random loss of responses. In parallel, uniform convergence rates are derived for the associated conditional single-index U-statistic estimators, thereby quantifying the respective contributions of smoothing bias, stochastic fluctuation, local-stationarity approximation error, and missingness-induced variance inflation. A substantial part of the analysis is devoted to the technical difficulties created by the simultaneous presence of dependence, nonstationarity, functional covariates, and incomplete observations. The proofs combine Hoeffding-type decompositions adapted to weighted incomplete data, blocking and coupling arguments for absolutely regular triangular arrays, refined entropy bounds for kernel-indexed function classes, and small-ball probability techniques for functional covariates. The MAR mechanism is incorporated via inverse-propensity weighting, and its effects on the effective sample size, asymptotic variance, and bias structure are made explicit. The theory also provides a rigorous foundation for bandwidth selection through blocked, propensity-adjusted cross-validation and clarifies its relation to the corresponding oracle risk. The proposed framework encompasses a broad class of statistical learning and inference problems involving pairwise or higher-order functionals of functional time series. In particular, it applies to conditional Kendall-type functionals, discrimination problems, metric learning with incomplete labels, and conditional independence testing under local stationarity. A simulation study illustrates the finite-sample behavior of the proposed estimators and supports the theoretical findings across varying regimes of temporal nonstationarity, serial dependence, functional concentration, and response missingness. Overall, the results provide a mathematically rigorous and methodologically flexible foundation for inference from evolving functional data when dependence, infinite dimensionality, and incomplete observation are present simultaneously. Full article

Koji production is a critical process that determines the flavor and quality of the final soy sauce product. However, the complex mechanisms underlying microbial metabolism and the evolution of the physicochemical environment still require further analysis. This study focuses on three parallel koji rooms in an industrialized koji fermentation process. This work tracked the dynamics of physicochemical indices, volatile flavor compounds, and microbial communities over a full 40 h cycle. Data integration and correlation analysis elucidated the close linkage between the microbial community, the fermentation environment, and flavor formation. Koji moisture declined gradually, with faster losses at later fermentation stages. This physiological dehydration arose from microbial metabolic heat, forced aeration and structural loosening of koji, not simple physical evaporation. System pH displayed a typical U-shaped trend across fermentation. Values dropped early, most likely driven by accumulating organic acids, before rising from mid to late fermentation. This pH rebound was tentatively attributed to ammonia release from proteolytic breakdown, which may neutralize acidic compounds. These observations cast doubt on the conventional assumption that organic acid levels may be reliably estimated solely from pH measurements. Physicochemical analysis showed continuous accumulation of amino acid nitrogen (0.6–0.9 g/100 g) and total acidity throughout fermentation. By contrast, reducing sugar concentrations differed across individual koji rooms, presumably owing to divergent microbial adaptation in early fermentation. A total of 77 common compounds were identified, among which 13 key odor-active compounds with OAV ≥ 1, such as 4-vinylguaiacol and 3-methylbutyraldehyde, constitute the characteristic flavor profile of soy sauce starter culture. High-throughput sequencing uncovered a distinct ecological pattern: eukaryotic communities, dominated by Aspergillus oryzae, converged under controlled regulation. While prokaryotic communities differentiated dynamically, driven by spatial heterogeneity in the semi-open fermentation environment. Spearman correlation analysis further indicated potential functional partitioning: high-abundance taxa (e.g., Aspergillus oryzae, Weissella) were predominantly associated with macromolecular substrate degradation, whereas rare low-abundance taxa (e.g., Alternaria) displayed significant correlations with the biosynthesis of key characteristic flavor compounds. This study clarifies the synergistic regulatory mechanisms linking physicochemical conditions, microbial metabolism, and flavor precursor formation during industrial koji production. The findings establish a scientific foundation for optimizing process parameters and achieving standardized quality control in soy sauce manufacturing. Full article

Environmental supply-chain governance increasingly requires firms to trace climate accountability across buyer–supplier relationships. This study examines whether downstream customer climate pressure is associated with suppliers’ green supply-chain management and value-chain carbon accountability among Chinese listed firms. We construct an exposure-weighted customer pressure measure by combining disclosed top-customer relationships with customer climate-accountability signals, and we decompose this measure into disclosure-based and non-disclosure-based components so that symbolic and substantive accountability can be separated. We then link this measure to supplier green supply-chain indicators, value-chain carbon-disclosure components, Scope 3 disclosure, environmental investment, and reported environmental performance indicators, including air emissions, water pollutant discharge, resource consumption, and environmental tax. Using firm-year panel regressions with fixed effects, alternative pressure measures, selection corrections, and extended outcome tests, we find an association between customer climate pressure and supplier value-chain disclosure. The depth of the association is concentrated where customer carbon-disclosure visibility is observed and is not separately identified in the smaller climate-only subsample, while the value-chain interaction association is positive but imprecisely estimated there. The value-chain disclosure associations are robust to a year-stratified randomization-inference placebo test. We do not find evidence that customer pressure is associated with supplier emissions, resource use, environmental investment, or environmental tax in the available matched samples. The pattern is consistent with symbolic compliance in supply-chain carbon accountability: customer disclosure visibility maps into supplier disclosure visibility, while we do not observe parallel movement in substantive environmental outcomes. The central finding is therefore that downstream customer climate pressure is associated with what suppliers disclose rather than with what they emit, shaping supplier disclosure behavior rather than substantive emission reduction. The estimates apply to supplier-year observations with disclosed and mappable listed-customer links, which we treat as the scope condition of the study rather than as an incidental data limitation. Full article

Background/Objectives: Multi-strain Limosilactobacillus fermentum supplementation has been reported to promote weight loss outcomes in free-living conditions, but limited evidence exists on these probiotic strains added to an energy-restricted diet and walking program in overweight adults. Methods: In a double-blind, placebo-controlled, parallel-arm randomized trial, overweight adults (35.2 ± 13.2 years old, 167.6 ± 8.6 cm, 79.9 ± 11.8 kg, 28.4 ± 2.7 kg/m² body mass index, 36.1 ± 6.6% body fat) completed a 12-week weight loss program that included a 500 kcal/day energy deficit and walking 10 k steps/d. Participants ingested one daily capsule containing a three-strain probiotic blend (L. fermentum K7-Lb1, L. fermentum K8-Lb1, L. fermentum K11-Lb3; 6 billion CFU/day) (PRO) or maltodextrin placebo (PLA). Assessments were performed at baseline, week 6, and week 12 and included body composition, resting energy expenditure, substrate utilization, peak oxygen uptake, dietary intake, step counts, blood biomarkers, quality of life, and side effects. Data were analyzed using multivariate and univariate repeated-measures general linear models (GLM), with mean changes from baseline presented alongside 95% confidence intervals. Results: All participants significantly reduced body weight, fat mass, body fat percentage, and waist circumference. At 12 weeks, PRO reduced fat mass more than PL (−2680.7 ± 1276.7 g; p = 0.039). In PRO, android and gynoid fat percentage decreased at 6 weeks (p < 0.001; p = 0.008) and 12 weeks (p = 0.004; p < 0.001), respectively. Visceral adipose tissue mass, volume, and area were lower at 6 weeks and trended lower at 12 weeks. In PRO, bone mineral content and bone mineral area decreased at 12 weeks, while bone mineral density paradoxically increased (0.007 ± 0.003 g/cm²; p = 0.024). Conclusions: During a 12-week weight loss program, supplementation of a multi-strain L. fermentum probiotic significantly reduced body fat and central adiposity. Full article

The non-parallel least squares support matrix machine (NPLSSMM), extending the original model with a non-parallel one, provides an enhanced framework to utilize the structural information of matrix data. However, its least squares loss makes it sensitive to imperfectly labeled data. To address this limitation, this paper proposes a correntropy-based non-parallel least squares support matrix machine (C-NPLSSMM). By incorporating correntropy, a symmetric and bounded similarity measure, into the loss function design, C-NPLSSMM adaptively adjusts the classification model to match the underlying structure of matrix data while mitigating the impact of mislabeled samples. Theoretical analysis is conducted to reveal the intrinsic relationship between C-NPLSSMM and the original NPLSSMM. Specifically, when the kernel size of correntropy loss is large enough, C-NPLSSMM degenerates into NPLSSMM, ensuring consistency with the original formulation. Experimental results on publicly available image and electroencephalogram datasets demonstrate that C-NPLSSMM achieves higher classification accuracy than other competitive methods in most cases. Full article

To address the challenges of quantifying expert experience, handling missing data, and managing class imbalance in evaluating the autonomous controllability of power equipment, this paper proposes a quantitative evaluation method that integrates expert prior rules with machine learning. First, building upon a five-dimensional evaluation indicator system, expert decision logic—including dimension-average threshold judgments, multi-dimensional weakness-based cumulative downgrading mechanisms, and key sub-item interaction rules—is formalized into a 15-dimensional rule prior feature vector, which is concatenated with the original 21-dimensional raw indicators to construct a RAW + RULE augmented feature space. Second, a KNN algorithm is employed for missing value imputation, while cost-sensitive learning combined with the SMOTE is adopted in a dual-path parallel scheme to address class imbalance. Six machine learning models are evaluated and compared via 30 repeated stratified cross-validations on a real-world dataset of 97 high-voltage bushing suppliers. Experimental results show that, on complete datasets, the RAW + RULE configuration with the Random Forest model achieves a mean test accuracy of 0.936 and a Kappa of 0.938, substantially outperforming the pure raw-feature model (accuracy 0.769, Kappa 0.766). Under weighted random missingness ranging from 10% to 50%, the RAW + RULE configuration demonstrates superior robustness, with ensemble tree models maintaining mean accuracies of 0.614–0.636 even at a 50% missing rate. This study provides a practically deployable technical solution and methodological reference for the quantitative assessment of autonomous controllability levels and early security warning in the power equipment supply chain. Full article

Multispectral temperature measurement technology based on blackbody radiation theory has been widely applied in the field of non-contact temperature measurement. However, its applicability is limited by the single-point measurement mode. To address this limitation, this study developed a spectral fusion temperature measurement device and proposed a new method for reconstructing the two-dimensional temperature field of deflagration fireballs by fusing spectral and imaging data. The device adopts a CCD sensor and a fiber optic spectrometer placed in parallel with parallel optical axes. To ensure the accuracy of the CCD’s response characteristics at different distances, the photo-response non-uniformity (PRNU) calculation method was used for precision validation. In this study, spectral and imaging data of deflagration fireballs were obtained through experiments. Spectral data of consecutive frames at 189 ms, 192 ms, 195 ms, and 198 ms were extracted and analyzed, confirming that the temperature range at the four time points is 1050 K to 1800 K. The proposed method generates temperature elements with equal temperature intervals and their probabilities within the temperature range, and calculates the theoretical radiation spectrum of each element. Then, least squares optimization fitting is performed on the experimentally measured spectra to obtain the optimal probabilities of the temperature elements in the temperature field. By combining these optimal probabilities with CCD grayscale images, the 2D temperature distribution of the deflagration fireball was reconstructed. Results show that: the PRNU value of the device at a distance of 9 m is less than 2.2% through experimental verification; fused images of the temperature field spectra of four consecutive frames of the deflagration fireball were obtained using the proposed method. The average temperatures reconstructed by the proposed method at 189 ms, 192 ms, 195 ms, and 198 ms were 1382 K, 1373 K, 1366 K, and 1357 K, respectively, while the corresponding temperatures obtained by conventional spectral inversion were 1430 K, 1422 K, 1414 K, and 1406 K. The relative errors were 3.2%, 3.4%, 3.3%, and 3.4%, respectively, with an average relative error of approximately 3.3%. Full article

The existing arresting efficiency evaluation method overlooks corrosion defects in its formulation. If directly applied to evaluate and design arrestors for corroded sandwich pipes, it often leads to conservative evaluations of arresting efficiency and unreasonably designed arrestors. Based on this, this paper first verifies the reliability of numerical simulation results through physical experiments. On this basis, the influence of the structural parameters and material parameters of the arrestor on the arresting efficiency of the integral arrestor is analyzed. The results show that an increase in the length, thickness and material strength of the arrestor not only affects the arresting efficiency of the arrestor but also changes the arresting crossing mode, from parallel crossing to orthogonal crossing. A chart of arresting efficiency suitable for engineering design is proposed. Finally, a systematic comparison is conducted of different modeling methods. The results show that, considering both prediction accuracy and training efficiency, the Genetic Algorithm–Back Propagation (GA-BP) model significantly outperforms the empirical model, the Whale Optimization Algorithm–Back Propagation (WOA-BP) model, and the Particle Swarm Optimization–Back Propagation (PSO-BP) model. The average prediction error is only 6.56%, and 94.42% of the data error is less than 20%. The model provides a theoretical basis for the arrestor design and failure assessment of sandwich pipes with corrosion defects and has clear engineering guidance value. Full article

Constant current/voltage (CC/CV) output of wireless power transfer (WPT) systems deviates due to increased load resistance during charging and mutual inductance variations caused by misalignment. Dynamically regulating the DC input voltage can maintain a stable output at the preset value, and predicting the mutual inductance and load resistance can help monitor charging status. However, joint prediction of characteristics and regulation degree can be nonlinear and complicated. This work proposes a data-driven method for characteristic prediction and output optimization for WPT systems based on the current waveform from only the transmitter side. A Multi-Scale Parallel Convolutional (MSPC) neural network is applied to simultaneously predict the load resistance, mutual inductance, output deviation factor and regulation coefficient. By leveraging its multi-scale feature extraction capabilities, it can accurately estimate the aforementioned parameters based on only the AC current waveform at the transmitter side. To improve the model’s generalizability under practical conditions, transfer learning (TL) is utilized to minimize the discrepancy between simulated and physical data. Finally, a 140 W prototype of the series-series (SS)-compensated WPT system is built to validate the effectiveness of the proposed method. Full article

Natural disasters frequently cause significant building damage, necessitating timely and accurate damage assessment for effective rescue operations and post-disaster reconstruction. Traditional building damage assessment methods commonly rely on paired pre- and post-disaster remote sensing images, which often face practical challenges in data acquisition and image pairing during emergency situations. To overcome these limitations, a hybrid swin–mamba U-shaped network (UNet) is developed for building damage assessment using only post-disaster remote sensing imagery. The proposed framework employs a Swin Transformer as the encoder to extract multi-scale features and capture long-range contextual information, while a Parallelized Patch-Aware Attention (PPA) convolution module is introduced in the decoder to restore spatial details and improve feature reconstruction. In addition, a Visual State Space (VSS) module is incorporated in the bottleneck layer to effectively model both global contextual dependencies and local structural information, thereby improving the representation of building damage characteristics from single-temporal imagery. Experiments conducted on the xBD dataset show that the proposed method outperforms the Swin–Unet by 1.7% in overall F1-score, achieving an overall F1-score of 55.2%. In addition, qualitative visualization results suggest that the proposed method has favorable generalization capability across different disaster scenarios. These results highlight the practical potential of the proposed framework for rapid post-disaster building damage assessment, particularly in emergency response scenarios where only post-disaster imagery is available. Full article

Background: Hemoglobinopathies, including thalassemia and structural hemoglobin variants, are among the most prevalent inherited disorders worldwide and represent a major public health concern in southern China. Accurate characterization of both common and rare variants is essential for carrier screening, genetic counseling, and prevention. However, routine molecular screening is generally restricted to common pathogenic variants, potentially overlooking rare hemoglobinopathy subtypes. This study aimed to characterize the spectrum of hemoglobinopathies in a large hospital-based cohort of reproductive-age individuals from Guangdong, China, and to evaluate a genotype–phenotype discordance-guided secondary testing strategy. Methods: A retrospective hospital-based study was conducted in 71,676 reproductive-age individuals who underwent hemoglobinopathy screening at our hospital in Guangdong, China, between 2018 and 2024. Hematological and routine genetic analyses were performed in parallel. Cases exhibiting genotype–phenotype discordance were further investigated using tailored secondary molecular approaches selected according to specific hematological findings. Results: In this cohort, 10,412 hemoglobinopathies were identified. Thalassemia accounted for 10,217 cases (98.13%), including α-thalassemia (8026), β-thalassemia (2561), δβ-thalassemia (17), γ-thalassemia (10), and δ-thalassemia (4). Structural hemoglobin variants comprised 195 cases (1.91%). Among the 788 genotype–phenotype discordant cases, secondary analysis yielded a positive detection rate of 36.80% (290/788). Conclusions: This study provides large-scale hospital-based data on the distribution of hemoglobinopathies among reproductive-age individuals in Guangdong. Review of genotype–phenotype discordance improved the detection of rare variants beyond routine screening and may facilitate the development of tailored secondary testing strategies. Further studies are warranted to validate its clinical utility and applicability. Full article

Animal abuse, encompassing active cruelty and neglect, is an underreported animal welfare and public safety concern. In Hungary, the parallel administrative and criminal law definitions of animal cruelty create additional uncertainty for citizens, professionals, and authorities, particularly regarding which institution should receive and evaluate a report. Existing reporting pathways are unstructured, and rarely produce documentation that is directly usable in subsequent administrative or criminal proceedings. This study presents the concept design of a structured online citizen-reporting interface developed for the Hungarian regulatory context. The interface functions as a structured intake tool: it guides non-expert reporters through standardised, category-based data entry; supports the submission of contextual evidence, including photographs, videos and location data; and prepares structured case files for transmission to the competent authority. The concept was shaped by a preliminary stakeholder needs assessment, in which people knowledgeable in animal welfare issues and members of the general public participated. The system does not perform legal or veterinary welfare assessment; instead, it standardises the information available to the responsible administrative, investigative or expert veterinary actor. Anticipated benefits include improved completeness of initial reports, clearer routing between administrative and criminal pathways, support for reporting, and a documentation format compatible with downstream expert evaluation. Full article

To address the pronounced interlayer productivity disparity and uneven reserve utilization during the development of multilayer low-permeability porous carbonate gas reservoirs, the G gas field on the right bank of the Amu Darya River was selected as the study area. Core-parallel physical simulation experiments, orthogonal numerical simulations, and production logging test (PLT) data were integrated to investigate the mechanisms of interlayer interference and its key controlling factors under multilayer commingled production. The results show that interlayer interference is primarily controlled by the permeability contrast and production differential. With increasing permeability contrast, high-permeability layers contribute a larger proportion of total production, whereas the utilization of medium- and low-permeability layers declines, thereby intensifying interlayer interference. Under the same permeability configuration, the interference coefficient increases with increasing production differential. Moreover, compared with the two-layer commingled-production cases, the three-layer system showed a stronger response to pressure-differential variation. When the production differential increased from 1 MPa to 5 MPa, the interference coefficient in the three-layer system increased from 9.84% to 27.83%, indicating more pronounced productivity loss in the medium- and low-permeability layers. Orthogonal numerical simulation indicates that the sensitivity of the main controlling factors follows the order of production differential ≥ permeability ratio > thickness ratio > gas viscosity. PLT data further validate the reliability of the experimental and numerical simulation results. During the development of Well G-22, the XVac layer consistently dominated gas production, whereas the XVm and XVp layers acted as supplementary contributors, indicating a dynamic production pattern in which high-permeability layers are preferentially activated and medium- and low-permeability layers contribute progressively at later stages. These findings demonstrate that permeability heterogeneity is the fundamental cause of interlayer interference, while the production differential serves as an important amplifying factor. This study provides a theoretical basis for zonal production allocation, optimization of the production differential, and stable production management in multilayer low-permeability porous carbonate gas reservoirs. Full article

The greater cane rat algorithm (GCRA) draws inspiration from the seasonal behavioral patterns of the greater cane rats: extensive roaming during the non-breeding period for global exploration, and aggregative foraging during the reproductive period for local exploitation. The GCRA leverages independent movement and population aggregation to iteratively update positions in pursuit of the optimal solution, which exhibits inherent structural deficiencies: precipitous population diversity collapse, lethargic convergence dynamics, suboptimal computational precision, high susceptibility to local optima, and severe dimensional scalability. This paper proposes a modified complex-valued encoding GCRA (CGCRA) that exploits the mathematical structure of complex numbers to construct a two-dimensional search domain on the complex plane and facilitate collaborative optimization. The CGCRA maps the decision variables onto the complex domain, the real part executes the native foraging mechanism for local fine-grained exploitation, and the imaginary part exploits phase rotation to generate global exploratory perturbations. The CGCRA leverages a dual-encoding redundancy mechanism with inherent error tolerance to attenuate result volatility, augment information capacity and population heterogeneity, elevate search adaptability and disturbance rejection, accelerate parallel computation and exploration efficiency, and facilitate spatial transformation and multi-dimensional data manipulation. Twenty-three benchmark functions and twelve real-world engineering designs are employed to assess the CGCRA’s stability and practical feasibility rigorously. The CGCRA delivers comprehensive spatial mapping and adaptive coordination to facilitate population collaboration and bolster resilience, expedite exhaustive research, and advance optimization efficiency. The experimental results demonstrate that the CGCRA emphasizes instructive superiority and practical utility to regulate exploration and exploitation, reduce result dispersion, mitigate search stagnation, accelerate convergence efficiency, elevate solution precision, and fortify stability and robustness. Full article