Search Results (29,843)

The line contact behavior of multiscale meshing interfaces necessitates synergistic investigation spanning nano-to centimeter-scale ranges. When nominally smooth gear teeth surfaces come into contact, the mechanical–thermal coupling effect at the meshing interface actually occurs over a collection of microscale asperities (roughness peaks) exhibiting hierarchical distribution characteristics. The emergent deformation phenomena across multiple asperity scales govern the self-organized evolution of interface conformity, thereby regulating both the load transfer efficiency and thermal transport properties within the contact zone. The fractal nature of the roughness topography on actual meshing interfaces calls for the development of a cross-scale theoretical framework that integrates micro-texture optimization with multi-physics coupling contact behavior. Conventional roughness characterization methods based on statistical parameters suffer from inherent limitations: their parameter values are highly dependent on measurement scale, lacking uniqueness under varying sampling intervals and instrument resolutions, and failing to capture the scale-invariant nature of meshing interface topography. A scale-independent parameter system grounded in fractal geometry theory enables essential feature extraction and quantitative characterization of three-dimensional interface morphology. This study establishes a progressive deformation theory for gear line contact interfaces with fractal geometric characteristics, encompassing elastic, elastoplastic transition, and perfectly plastic stages. By systematically investigating the force–thermal coupling mechanisms in textured meshing interfaces under multiscale conditions, the research provides a theoretical foundation and numerical implementation pathways for high-precision multiscale thermo-mechanical analysis of meshing interfaces. Full article

The present study investigates the twofold effect of home–work spillover on life satisfaction through intrinsic work motivation and meaning derived from work, with family flexibility as a moderator. Based on Self-Determination Theory and the Work–Home Resources model, we test a moderated parallel mediation model whereby both positive and negative spillover from home affect life satisfaction through motivational and meaning pathways, depending on the level of family flexibility. 735 working adults completed validated measures of work-related flow, work meaning, home–work interaction (negative and positive), family flexibility, and life satisfaction. PROCESS macro (Model 59) via 5000 bootstrapped samples indicated that home negatively influencing work was associated with lower life satisfaction, mainly via reduced work meaning, particularly for individuals with low family flexibility. Conversely, positive work–home interaction was associated with higher work meaning and, indirectly, greater life satisfaction, with this effect being stronger when family flexibility was lower. Intrinsic motivation was associated with life satisfaction through mediation only when family flexibility was higher. These results indicate work meaning and family context compensatory and buffering effects on well-being. The research adds to integrative work–life interface models by delineating conditional psychological processes that enable employee flourishing. Full article

This paper examines how organizations and regions integrate digital transformation with environmental sustainability (“twin transition”). Based on 43 semi-structured expert interviews across 27 countries, we identify five empirically grounded insights. First, adoption is propelled by competitive pressure, external shocks, and rising regulatory and stakeholder demands. Second, success depends on internal capabilities—clear leadership vision and workforce skills—together with supportive regional innovation ecosystems. Third, deliberate technological synergies—especially digital twins for lifecycle optimization, Artificial Intelligence (AI)/analytics and Internet of Things (IoT) for monitoring, and blockchain for traceability—enable measurable gains in resource efficiency and environmental performance. Fourth, integration strengthens eco-innovation capacity, resilience to disruption, competitive positioning, and regional innovation dynamics. Fifth, persistent barriers remain; organizational silos, key performance indicators (KPIs) misalignment, rebound effects from digital infrastructures, and uneven regional capabilities. By linking enabling conditions, integration mechanisms, and barriers, the study advances theory and offers actionable guidance for managers and policymakers on realizing the twin transition, using descriptive counts to indicate salience within a purposive expert sample rather than to draw statistical inferences. Full article

Coordinated cyber attacks tamper with measurement data to disrupt the situational awareness of active distribution systems. Various sensors report measurements asynchronously at different rates, which introduces challenges during state estimation. In addition, this forces cyber intruders to exert greater effort to compromise multiple communication channels and launch coordinated attacks. Therefore, multi-channel and asynchronous measurements could be harnessed to develop more secure cyber defense strategies. In this paper, a prediction-correction-based multi-rate observer is designed to exploit the value of asynchronous measurements for the detection of coordinated false data injection (FDI) attacks. First, a time-function-dependent prediction-correction strategy is proposed to adjust the sampling interval for each sensor’s measurement. Then, an observer is designed based on the trade-off between estimation error and the optimal period of the most recent sampling instant, with the convergence of estimation error with the maximum permitted sampling interval. Moreover, the conditions for exponential stability are developed using the Lyapunov–Krasovskii functional technique. Next, a coordinated FDI attack detection strategy is developed based on the dual nonlinear minimization problem. The proposed attack detection and secure state estimation strategies are tested on the IEEE 13-node system. Simulation results show that these schemes are effective in enhancing attack detection based on asynchronous measurements or compromised data. Full article

Amorphous cobalt-phosphorous (CoP) coatings are a candidate to replace hard chromium and other traditional coatings. Here, electrodeposition of both amorphous and crystalline CoP coatings was performed at room temperature and in an air environment. The bath composition and deposition conditions were optimized to offer a low cost, low maintenance, and safe process. The effects of various deposition variables such as solution composition, pH, duration, and mixing parameters were studied, and the reproducibility of the process was demonstrated. Selected coatings were then thoroughly characterized by a variety of techniques. The best amorphous/nanocrystalline coating contained ca. 6.4 wt.% P after 1.2 h of deposition, and 7.2 wt.% P after 4 h of deposition. The best crystalline coating contained ca. 2.7 wt.% P after 1.2 h of deposition and between 2.3 and 5.5 wt.% P after 4 h of deposition. The amorphous coating had excellent mechanical properties: a high hardness (7.8 ± 0.7 GPa), high Young’s modulus (153 ± 9 GPa), and surprisingly low coefficient of dry friction (between 0.11 ± 0.02 and 0.17 ± 0.01). The coating could not be scraped from the substrate using a diamond scalpel blade. In a standard adhesion test, the sample failed neither cohesively within the coating nor adhesively between the coating and the substrate. In the as-deposited conditions, the structure was uniform, nanocrystalline, or had nanocrystals embedded in an amorphous matrix. The crystallization temperature of the amorphous alloy was 284 °C, and the phase transformation occurred only between 300 and 400 °C. The coatings developed and comprehensively characterized herein may be considered for aerospace, magnetic storage, fuel cells, water splitting, and other applications. Full article

Gene regulatory networks (GRN) govern cellular identity and function through precise control of gene transcription. Single-cell technologies have provided powerful means to dissect regulatory mechanisms within specific cellular states. However, existing computational approaches for modeling single-cell RNA sequencing (scRNA-seq) data often infer local regulatory interactions independently, which limits their ability to resolve regulatory mechanisms from a global perspective. Here, we propose a deep learning framework (Planet) based on diffusion models for constructing cell-specific GRN, thereby providing a systems-level view of how protein regulators orchestrate transcriptional programs. Planet jointly optimizes local network structures in conjunction with gene expression profiles, thereby enhancing the structural consistency of the resulting networks at the global level. Specifically, Planet decomposes GRN generation into a series of Markovian evolution steps and introduces a Triple Hybrid-Attention Transformer to capture long-range regulatory dependencies across diffusion time-steps. Benchmarks on multiple scRNA-seq datasets demonstrate that Planet achieves competitive performance against state-of-the-art methods and yields only a slight improvement over DigNet under comparable conditions. Compared with conventional diffusion models that rely on fixed sampling schedules, Planet employs a fast-sampling strategy that accelerates inference with only minimal accuracy trade-off. When applied to mouse-lung Cd8⁺Gzmk⁺ T cells, Planet successfully reconstructs a cell-type-specific GRN, recovers both established and previously uncharacterized regulators, and delineates the dynamic immunoregulatory changes that accompany ageing. Overall, Planet provides a practical framework for constructing cell-specific GRNs with improved global consistency, offering a complementary perspective to existing methods and new insights into regulatory dynamics in health and disease. Full article

Background: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental condition that currently relies on subjective clinical judgment for diagnosis, emphasizing the need for objective, clinically applicable tools. Methods: We applied machine learning techniques to parent reports, self-reports, and performance-based measures in a sample of 255 Taiwanese children and adolescents (108 ADHD and 147 controls; mean age = 11.85 years). Models were trained under a nested cross-validation framework to avoid performance overestimation. Results: Most models achieved high classification accuracy (AUCs ≈ 0.886–0.906), while convergent feature importance across models highlighted parent-rated social problems, executive dysfunction, and self-regulation traits as robust predictors. Additionally, ex-Gaussian parameters derived from reaction time distributions on the Continuous Performance Test (CPT) proved more informative than raw scores. Conclusions: These findings support the utility of integrating multi-informant ratings and task-based measures in interpretable ML models to enhance ADHD diagnosis in clinical practice. Full article

Background and Objectives: Infantile colic affects 15–40% of infants ≤ 5 months, burdening families and health systems. While the effects of intrapartum oxytocin on neonatal outcomes have been widely investigated, its potential link with infantile colic remains poorly understood. We evaluated whether synthetic oxytocin is associated with infantile colic during the first five months of life and explored neonatal head circumference, feeding type and epidural anesthesia as additional factors. Materials and Methods: Prospective 1:1 matched case–control study in three Spanish pediatric outpatient clinics. Parents of 76 term infants aged 0–5 months (38 cases, 38 controls) completed face-to-face structured interviews documenting synthetic oxytocin and epidural use, infant anthropometry and feeding pattern. Infantile colic was diagnosed by Rome IV criteria. Associations were estimated with conditional logistic regression, producing adjusted odds ratios and 95% confidence intervals. Results: Synthetic oxytocin was used in 57.9% of deliveries and epidural anesthesia in 81.6 %. Synthetic oxytocin showed no association with infantile colic (aOR 1.24; 95% CI 0.50–3.09). Epidural strongly predicted synthetic oxytocin exposure (aOR 4.55; 95% CI 1.28–16.20) but had no independent link to infantile colic. Infants with colic had a smaller mean head circumference at birth, although this difference did not remain significant after adjusting for gestational age, likely reflecting limited sample size. Synthetic oxytocin was not associated with breastfeeding status. Conclusions: In this cohort, intrapartum synthetic oxytocin was not related to infantile colic or to feeding difficulties. Smaller head circumference among colic cases may warrant further investigation as a potential risk marker. The high co-use of synthetic oxytocin and epidural underscores the need for larger longitudinal studies to clarify their peripartum–neonatal interactions. Full article

Amidst global climate warming and increasingly severe food security challenges, the agroforestry economy, a green ecological industry that balances ecological conservation and economic development, has attracted widespread attention. This study constructs a theoretical analytical framework based on the diamond model to systematically identify key factors influencing the development of the agroforestry economy. Using 56 practical cases from the agroforestry economy in China as samples, the study applies Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to further explore the multiple driving paths of agroforestry economic development and their supporting elements. The research findings show that (1) forest resources, technological innovation, market demand, enterprise forms, related industries, and government support do not constitute necessary conditions for the development of the agroforestry economy. The path to the development of the agroforestry economy exhibits complex and concurrent multi-faceted characteristics. (2) Technological innovation has always been at the core of all configurations, and strengthening technological innovation plays a universal role in enhancing the level of agroforestry economic development. The role of government support in the process of the development of the agroforestry economy is limited. (3) The system identified four driving paths, including the endogenous type, characterized by resource technology enterprises; the collaborative type, characterized by a resource technology market with light promotion by the government; the external expansion type, characterized by market technology enterprises; and the linkage type, characterized by market technology enterprises assisted by related industries. The consistency level of the overall solution reached 0.91, and the coverage was 0.54. It reveals the different driving mechanisms with different combinations of elements for the development of the agroforestry economy. Therefore, each region should strengthen scientific and technological research, innovation, and the transformation and application of research outcomes. It should promote the coordinated development of diverse factors, establish tailored regional development models, and explore suitable pathways for developing the agroforestry economy based on its unique resource endowments. Full article

This study presents a novel approach to intensifying the anaerobic digestion of sewage sludge through enzymatic pretreatment using hydrolytic enzymes—cellulase and lysozyme. It aims to determine how enzymatic activation affects the efficiency of methane fermentation, defined as the degree of organic matter decomposition and yield and composition of biogas. An experiment was carried out under mesophilic conditions over 20 days, analyzing the physicochemical properties of sludge, biogas production, methane content, and sanitary parameters. The addition of cellulase and lysozyme significantly enhanced process efficiency, increasing both the rate of organic matter degradation and biogas yield. The highest biogas production values (0.73 L·g⁻¹ d.m. for cellulase and 0.72 L·g⁻¹ d.m. for lysozyme) were obtained at a 4% (w/w) enzyme concentration, with a corresponding increase in the degree of organic matter decomposition to 78.7% and 80.0%, respectively. The produced biogas contained 58–61% methane, exceeding the values observed in the control sample, which indicates a positive effect of enzymatic activation on methane selectivity. Enhanced biogas production was attributed to improved hydrolysis of complex organic compounds, resulting in greater substrate bioavailability for methanogenic microorganisms. Moreover, methane fermentation led to the complete elimination of E. coli from all supernatants, confirming the hygienization potential of the process. The results of this study indicate that enzymatic pretreatment may serve as a viable strategy to improve both the energy efficiency and hygienic safety of anaerobic digestion processes, with relevance for future optimization and full-scale wastewater treatment applications. Full article

The architectural heritage of the 20th century has proved to be highly vulnerable to the test of time, with slight variations in different geographical contexts. The lack of value recognition, restrictions imposition, and resulting protection has led to the loss of memory of material and immaterial values. Restoring dignity has been the primary goal of those who have given substance and vitality to the theme of Modern Restoration, inheriting from the past the method that requires, in order to catalogue each work, the essential stages of knowledge and documentation, preliminary to conservation and enhancement. It is precisely in this scenario, after analysing the experiences of institutions, bodies and associations in the field of filing and cataloguing, that the needs brought about by the digital transition were taken on board; the aim is to define, within the PRIN 2022 DIMHENSION project, an innovative operative protocol that is economically, socially and technically sustainable, aimed at the computerised management of 20th century architectural heritage. The steps are the identification of the global description of the history of the building, translation of the entire body of data into information assets (H-BIR), and the possibility of consultation using parametric models (H-BIM). A Digital Register has therefore been designed, initially for an international sample of late 20th century façade systems, which goes well beyond their dynamic documentation, creating the conditions for a platform for consulting the complex of information, structured in an H-BIR archive interfaced with an H-BIM object library. Full article

Markov chain Monte Carlo (MCMC) methods are fundamental to Bayesian inference but are often computationally prohibitive for large datasets, as the full likelihood must be evaluated at each iteration. Subsampling-based approximate Metropolis–Hastings (MH) algorithms offer a popular alternative, trading a manageable bias for a significant reduction in per-iteration cost. While this bias–variance trade-off is empirically understood, a formal theoretical framework for its optimization has been lacking. Our work establishes such a framework by bounding the mean squared error (MSE) as a function of the subsample size (m), the data size (n), and the number of epochs (E). This analysis reveals two optimal asymptotic scaling laws: the optimal subsample size is $m^{★}=O\left(E^{1/2}\right)$, leading to a minimal MSE that scales as ${\mathrm{MSE}}^{★}=O\left(E^{-1/2}\right)$. Furthermore, leveraging the large-sample asymptotic properties of the posterior, we show that when augmented with a control variate, the approximate MH algorithm can be asymptotically more efficient than the standard MH method under ideal conditions. Experimentally, we first validate the two optimal asymptotic scaling laws. We then use Bayesian logistic regression and Softmax classification models to highlight a key difference in convergence behavior: the exact algorithm starts with a high MSE that gradually decreases as the number of epochs increases. In contrast, the approximate algorithm with a practical control variate maintains a consistently low MSE that is largely insensitive to the number of epochs. Full article

PostCOVID-19 is a condition affecting approximately 10% of individuals infected with SARS-CoV-2, presenting significant challenges in diagnosis and clinical management. Portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), offer real-time insights into cerebral hemodynamics and represent a promising tool for studying postCOVID-19 in naturalistic settings. This study investigates the integration of fNIRS with machine learning to identify neural correlates of postCOVID-19. A total of six machine learning classifiers—Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), XGBoost, Logistic Regression, and Multi-Layer Perceptron (MLP)—were evaluated using a stratified subject-aware cross-validation scheme on a dataset comprising 29,737 time-series samples from 37 participants (9 postCOVID-19, 28 controls). Four different feature representation strategies were compared: raw time-series, PCA-based dimensionality reduction, statistical feature extraction, and a hybrid approach that combines time-series and statistical descriptors. Among these, the hybrid representation demonstrated the highest discriminative performance. The SVM classifier trained on hybrid features achieved strong discrimination ($\mathit{ROC}\text{-}\mathit{AUC}$ = 0.909) under subject-aware CV5; at the default threshold, $\mathit{Sensitivity}$ was moderate and $\mathit{Specificity}$ was high, outperforming all other methods. In contrast, models trained on statistical features alone exhibited limited $\mathit{Sensitivity}$ despite high $\mathit{Specificity}$. These findings highlight the importance of temporal information in the fNIRS signal and support the potential of machine learning combined with portable neuroimaging for postCOVID-19 identification. This approach may contribute to the development of non-invasive diagnostic tools to support individualized treatment and longitudinal monitoring of patients with persistent neurological symptoms. Full article

The article presents the results of research on the effect of 160 MeV xenon ions irradiation on the mechanical and tribological properties of the Nimonic 90 superalloy. The alloy samples were irradiated with xenon ion fluences ranging from 1 × 10¹⁴ to 5 × 10¹⁴ Xe²⁴⁺/cm² at a temperature of 60 °C. The investigations revealed significant changes in the crystal structure of the material, including the formation of new phases and partial amorphisation of the surface layer, particularly pronounced at the highest irradiation fluence. Measurements of microhardness, coefficient of friction, and wear revealed a deterioration in the mechanical and tribological properties of the samples irradiated with fluences of 1.0 and 2.5 × 10¹⁴ Xe²⁴⁺ ions/cm², attributed to the formation of radiation-induced defects. Increased friction and wear were observed at depths greater than the predicted range of xenon ions, indicating the occurrence of a long-range effect. After irradiation with a 5.0 × 10¹⁴ Xe²⁴⁺ ions/cm² fluence, a radiation annealing effect was observed, leading to a partial reduction in the earlier damage and resulting in improved microhardness and reduced wear. To our knowledge, this is the first observation of a radiation annealing effect under these specific irradiation and test conditions. The findings suggest limitations in the application of the Nimonic 90 superalloy in environments exposed to intense ionizing radiation, such as nuclear reactors. Full article

This study addresses the challenge of accurate classification under missing data conditions by integrating multiple imputation strategies with discriminant analysis frameworks. The proposed approach evaluates six imputation methods (Mean, Regression, KNN, Random Forest, Bagged Trees, MissRanger) across several discriminant techniques. Simulation scenarios varied in sample size, predictor dimensionality, and correlation structure, while the real-world application employed the Cirrhosis Prediction Dataset. The results consistently demonstrate that ensemble-based imputations, particularly regression, KNN, and MissRanger, outperform simpler approaches by preserving multivariate structure, especially in high-dimensional and highly correlated settings. MissRanger yielded the highest classification accuracy across most discriminant analysis methods in both simulated and real data, with performance gains most pronounced when combined with flexible or regularized classifiers. Regression imputation showed notable improvements under low correlation, aligning with the theoretical benefits of shrinkage-based covariance estimation. Across all methods, larger sample sizes and high correlation enhanced classification accuracy by improving parameter stability and imputation precision. Full article