Search Results (7,963)

We study statistical dependence in the frequency domain using the integral probability metric (IPM) framework. We propose the uniform Fourier dependence measure (UFDM) defined as the uniform norm of the difference between the joint and product-marginal characteristic functions. We provide a theoretical analysis, highlighting key properties, such as invariances, monotonicity in linear dimension reduction, and a concentration bound. For the estimation of the UFDM, we propose a gradient-based algorithm with singular value decomposition (SVD) warm-up and show that this warm-up is essential for stable performance. The empirical estimator of UFDM is differentiable, and it can be integrated into modern machine learning pipelines. In experiments with synthetic and real-world data, we compare UFDM with distance correlation (DCOR), Hilbert–Schmidt independence criterion (HSIC), and matrix-based Rényi’s $\alpha$-entropy functional (MEF) in permutation-based statistical independence testing and supervised feature extraction. Independence test experiments showed the effectiveness of UFDM at detecting some sparse geometric dependencies in a diverse set of patterns that span different linear and nonlinear interactions, including copulas and geometric structures. In feature extraction experiments across 16 OpenML datasets, we conducted 160 pairwise comparisons: UFDM statistically significantly outperformed other baselines in 20 cases and was outperformed in 13. Full article

The article presents the results of theoretical and experimental research into the adhesive properties of flax fiber and their impact on the scientific development of composite material production. The research established that combining natural fibers with a polymer material or matrix increases the complexity of the composite forming process and causes problems in the physicochemical processes of matrix–filler interaction. This is explained by the low wettability of flax bast (13.0–14.5 g). It was found that the presence of cutins on oil flax fibers determines their high degree of hydrophobicity. To improve the adhesive properties of the bast, it was chemically treated to remove cellulose companions and cutins, high-molecular-weight compounds. The bast was chemically treated using the oxidative method. After chemical treatment, a fiber enriched with cellulose and freed from waxy substances was obtained. Thus, the cellulose content increased from 47.67–53.33% to 90.01–97.68%, and the waxy substances were almost completely removed. Their content in the bast was 18.13–18.57%, but after chemical treatment, it decreased to 0.01–0.04%. After chemical treatment, the wettability of the fiber increased to the required levels −104.94–122.78 g, indicating that the adhesive properties were significantly improved. The results of studies on physical and mechanical indicators demonstrate the high quality of the obtained composites. In terms of fluidity, all samples were superior to the control sample reinforced with cotton fiber. The theoretical and experimental research enabled the collection of experimental samples of composite materials. Full article

Tissue regeneration is a highly complex and dynamic process critically influenced by the immune microenvironment and its multifaceted interactions with fibroblasts. Traditionally regarded as structural cells responsible for extracellular matrix (ECM) production, fibroblasts have recently emerged as active regulators orchestrating immune responses and tissue repair. This review focuses on the reciprocal crosstalk between fibroblasts and key immune components, including macrophages, T cells, ECM, local pH, and signaling proteins. These interactions coordinate the initiation and resolution phases of inflammation, regulating fibroblast migration, proliferation, differentiation, and ECM deposition, which collectively determine the efficiency and quality of tissue repair. Special attention is given to the dynamic modulation of the immune microenvironment that governs fibroblast behavior during injury and regeneration. Finally, recent therapeutic strategies targeting this crosstalk—from molecular inhibitors to cell-based therapies—are discussed, highlighting emerging avenues for enhancing regenerative outcomes and mitigating fibrotic diseases. This integrated perspective positions fibroblast–immune interactions as a promising frontier in regenerative medicine, offering new opportunities for targeted tissue repair and control of chronic inflammation. Full article

Water scarcity is fostering an urgent need to drive research into novel and synergistic water treatment approaches, with advanced oxidation processes (AOPs) emerging as a superior option for treating various contaminants. The spread of vinyl chloride (VC) through groundwater sources raises concerns for potable water production due to its toxic and carcinogenic properties. This study integrates ozone-based degradation experiments with data-driven modelling approaches to statistically characterize and predict VC removal under different water-matrix conditions. Ozonation alone enables partial removal of VC from two contaminated groundwater samples, while integration of O₃/H₂O₂ treatment further enhances the degradation efficacy (70–97%). Decreasing VC concentration below the parametric value of 0.5 µg/L requires application of the peroxone process or photodegradation by O₃/H₂O₂/UV for groundwater with higher levels of interfering compounds. Advanced machine learning models and ensemble methods were also tested to enhance predictive accuracy for target molecule degradation, considering water characteristics and treatment parameters as input features. An ensemble of Random Forest and Neural Network predictions yielded the best performance (R² = 0.99; Mean Squared Error = 10.8), demonstrating the effectiveness of ensemble approaches for complex chemical prediction tasks and highlighting areas for further refinement to improve interpretability and predictive consistency of AOP treatment outcomes. This study not only aligns with the current momentum in AI-assisted AOP research but also advances it by delivering a generalizable, reproducible, and interpretable ensemble model trained on experimentally diverse datasets. Full article

Accurate real-time prediction of labor productivity is crucial for the successful management of construction projects. However, it remains a significant challenge due to the dynamic and uncertain nature of construction environments. Existing models, while valuable for planning and post-analysis, often rely on historical data and static assumptions, rendering them inadequate for providing actionable, real-time insights during construction. This study addresses this gap by suggesting a novel hybrid AI-stochastic framework that integrates a Long Short-Term Memory (LSTM) network with Markov Chain modeling for dynamic productivity forecasting in repetitive construction activities. The LSTM component captures complex, long-term temporal dependencies in productivity data, while the Markov Chain models probabilistic state transitions (Low, Medium, High productivity) to account for inherent volatility and uncertainty. A key innovation is the use of a Bayesian-adjusted Transition Probability Matrix (TPM) to mitigate the “cold start” problem in new projects with limited initial data. The framework was rigorously validated across four distinct case studies, demonstrating robust performance with Mean Absolute Percentage Error (MAPE) values predominantly in the “Good” range (10–20%) for both the training and test datasets. A comprehensive sensitivity analysis further revealed the model’s stability under data perturbations, though performance varied with project characteristics. By enabling more efficient resource utilization and reducing project delays, the proposed framework contributes directly to sustainable construction practices. The model’s ability to provide accurate real-time predictions helps minimize material waste, reduce unnecessary labor costs, optimize equipment usage, and decrease the overall environmental impact of construction projects. Full article

Let $G=(V,E)$ be a simple graph with the vertex set V and the edge set E$\left(\left|V\right|=n,\left|E\right|=m\right)$. An example of a vertex-decorated graph $DG$ is a vertex-quadrangulated graph $QG$. The vertex quadrangulation $QG$ of 4-regular graph G visually looks like a graph whose vertices are depicted as empty squares, and the connecting edges are attached to the corners of the squares. If we contract each quadrangle of $QG$ to a point that takes over the incidence of the four edges that were previously joined to this quadrangle, then we can again get the original graph G. Any connected graph H that provides (some of) its vertices for external connections can play the role of a decorating graph, and any graph G with vertices of valency no greater than the number of contact vertices in H can be decorated with it. Herein, we consider the case when G is a regular graph. Since the decoration also depends on the way the edges are attached to the decorating graph, we clearly stipulate it. We show that all similarly decorated regular graphs $DG$ that meet our conditions have at least $\left|V\right(H\left)\right|$ predicted common eigenvalues. A number of related results are proven. As possible applications of these results in chemistry, cases of simplified findings of eigenvalues of a molecular graph even in the absence of the usual symmetry of the molecule may be of interest. This, in particular, can somewhat expand the possibilities of applying the simple Hückel method for large molecules. Full article

Addressing the urgent challenges of plastic pollution and food waste, this study develops a high-performance, fully biodegradable bio-nanocomposite film from renewable agricultural resources through a data-driven optimization approach. The ternary system combines cassava starch (matrix), cellulose nanofibrils (CNFs for reinforcement), and nano-silica materials (SiO₂-NPs as barrier enhancer). Response Surface Methodology synergistically coupled with the Firefly Algorithm—a metaheuristic optimization technique—systematically determined the optimal formulation (1.99% w/v starch, 1.38% w/v CNF, 0.30% w/v SiO₂-NPs). The optimized film achieved exceptional performance: tensile strength of 5.813 MPa, elongation at break of 12.37%, and water vapor permeability of 5.395 × 10⁻⁶ g·cm⁻¹·s⁻¹·Pa⁻¹. Critically, the film demonstrated over 80% biodegradation within 60 days and superior UV-shielding capabilities (>90%), effectively extending food shelf-life while minimizing environmental impact. This work establishes a robust strategy for designing sustainable packaging materials through intelligent optimization, valorizing agricultural by-products, and contributing to circular economy principles and UN Sustainable Development Goals. The integration of renewable resources with metaheuristic algorithms represents a significant advancement toward sustainable food packaging solutions. Full article

Data, as a key production factor in modern logistics systems, plays a crucial role in enhancing industry efficiency and promoting supply chain coordination. To address challenges in data sharing among logistics enterprises—such as conflicts of interest, unequal risk allocation, and insufficient security governance—this study develops a tripartite evolutionary game model involving logistics enterprises, data partners, and supervisory institutions. The payoff matrix incorporates prospect theory to account for risk attitudes, loss–gain perceptions, and subjective judgments. Stable equilibrium points are derived using the Jacobian matrix, and numerical simulations examine strategic evolution under varying parameters. Results indicate that increased returns for data partners reduce their motivation to provide truthful data, while higher enterprise profits suppress logistics enterprises’ willingness to share. Compensation levels have limited impact, whereas excessively high supervision subsidies weaken participation and oversight across all parties. Stronger penalties and higher-level enforcement significantly promote compliance and positive system evolution. Enterprise investment positively correlates with data-sharing behavior, and risk preferences of all parties accelerate convergence to stable equilibria. Conversely, excessively low risk preference in supervisory institutions may lead to an unstable “sharing–false data–non-regulation” pattern. These findings provide theoretical support and policy guidance for designing a dynamic governance mechanism that balances incentives, constraints, and collaboration, thereby facilitating secure and effective logistics data sharing and informing the development of the data factor market. Full article

An epoxy resin can be crosslinked with an imidazole-based ionic liquid (IL), whose excess, provided its high melting temperature, can potentially form a dispersed phase to store thermal energy and produce a phase-change material (PCM). This work investigates the crosslinking of diglycidyl ether of bisphenol A (DGEBA) using 1-ethyl-3-methylimidazolium chloride ([EMIM]Cl) at its mass fractions of 5, 10, 20, 40, and 60%. The effect of [EMIM]Cl on the viscosity, curing rate, and curing degree was studied, and the thermophysical properties and morphology of the resulting crosslinked epoxy polymer were investigated. During the curing, [EMIM]Cl changes its role from a crosslinking agent (an initiator of homopolymerization) and a diluent of the epoxy resin to a plasticizer of the cured epoxy polymer and a dispersed phase-change agent. An increase in the [EMIM]Cl content accelerates the curing firstly because of the growth in the number of reaction centers, and then the curing slows down because of the action of the IL as a diluent, which reduces the concentration of reacting substances. In addition, a rise in the proportion of [EMIM]Cl led to the predominance of the initiation over the chain growth, causing the formation of short non-crosslinked molecules. The IL content of 5% allowed for curing the epoxy resin and elevating the stiffness of the crosslinked product by almost 7 times compared to tetraethylenetriamine as a usual aliphatic amine hardener (6.95 GPa versus 1.1 GPa). The [EMIM]Cl content of 20–40% resulted in a thermoplastic epoxy polymer capable of flowing and molding at elevated temperatures. The formation of IL emulsion in the epoxy matrix occurred at 60% [EMIM]Cl, but its hygroscopicity and absorption of water from surrounding air reduced the crystallinity of dispersed [EMIM]Cl, not allowing for an effective phase-change material to be obtained. Full article

Glycation-induced modifications of extracellular matrix (ECM) proteins, including collagen, are increasingly recognized as critical modulators of cellular behavior, particularly in pathophysiological contexts such as aging and diabetes. While their impact on general cell adhesion has been explored, the specific consequences for mesenchymal stem cell (MSC) mechanotransduction remain poorly defined. In this study, we investigated the temporal and mechanistic aspects of adhesion and mechanosensitive signaling in adipose-derived MSCs (ADMSCs) cultured on native versus glycated collagen substrates. Our findings identify two temporally distinct adhesion mechanisms: an initial pathway mediated by the receptor for advanced glycation end-products (RAGE), which is activated within the first 30 min following substrate engagement, and a later-stage adhesion process predominantly governed by integrins. Immunofluorescence analysis demonstrated maximal nuclear localization of YAP/TAZ transcriptional regulators during the initial adhesion phase, coinciding with RAGE engagement. This nuclear enrichment was progressively attenuated as integrin-mediated focal adhesions matured, suggesting a dynamic shift in receptor usage and mechanotransductive signaling. Interestingly, glycated collagen substrates accelerated early cell attachment but impaired focal adhesion maturation, suggesting a disruption in integrin engagement. Endogenous collagen synthesis was consistently detected at all examined time points (30 min, 2 h, and 5 h), suggesting a constitutive biosynthetic activity that remains sensitive to the glycation state of the substrate. Atomic force microscopy (AFM) demonstrated that glycation disrupts collagen fibrillogenesis: while native collagen forms a well-organized network of long, interconnected fibrils, GL-1 substrates (glycated for 1 day) displayed sparse and disordered fibrillary structures, whereas GL-5 substrates (5-day glycation) exhibited partial restoration of fibrillar organization. These matrix alterations were closely associated with changes in adhesion kinetics and mechanotransduction profiles. Taken together, our findings demonstrate that collagen glycation modulates both adhesion dynamics and mechanosensitive signaling of MSCs through a dual-receptor mechanism. These insights have significant implications for the design of regenerative therapies targeting aged or metabolically compromised tissues, where ECM glycation is prevalent. Full article

Additive manufacturing offers several advantages, such as rapid prototyping, cost-efficient production, and flexibility in constructing components. This work presents the design and fabrication of a strain sensor capable of generating electrical signals under applied mechanical loads, enabling potential failure prediction. The sensor was manufactured using the Fused Deposition Modeling 3D-printing process, combining acrylonitrile-styrene-acrylate as structural and protective layers with a conductive polylactic acid matrix containing carbon nanotubes as the sensing element. To assess its performance, the sensor was embedded within a Glass Fiber-Reinforced Polyester composite and subjected to bending tests. The results demonstrate a reliable sensing response, characterized by a measurable increase in electrical resistance under bending load. Moreover, the change in resistance increased with applied bending force, demonstrating the sensor’s feasibility for structural health monitoring applications. Full article

Infant formulas are specialized foods designed for babies and toddlers who cannot be exclusively breastfed. However, acrylamide (AA) may form during the thermal processing involved in their production. Although chromatographic techniques offer high sensitivity and detection capability for AA analysis, their application remains limited due to the complexity of diverse food matrices, high operating costs, time requirements, and environmental concerns. A new validated liquid chromatography–mass spectrometry (LC-MS) protocol for AA detection in infant formula was developed using sequential hydration, acetonitrile (ACN) precipitation, and dual-sorbent clean-up, which minimized matrix effects and ensured clarity and high reproducibility. The validated method demonstrated excellent linearity (R² = 0.9985, solvent-based; 0.9903, matrix-based), a pronounced matrix effect (−67%), satisfactory sensitivity (limit of detection, LOD: 10 µg/kg; limit of quantification, LOQ: 20 µg/kg), and consistent recovery (82–99%) with less than 15% variation. AA analysis was performed on 31 infant formula samples. The highest individual AA level (268.2 µg/kg) was detected in an amino acid-based formula intended for infants under one year of age while the highest mean concentration was found in cereal-based samples (188.1 ± 100.8 µg/kg), followed by goat’s milk-based (52.7 ± 25.67), plant-based (48.8 ± 31.68), and cow’s milk-based (27.5 ± 29.62) formulas (p < 0.001). The wide variability in AA concentrations among infant formulas can be attributed to differences in formulation, ingredient composition, manufacturing processes, and analytical methodologies. These findings highlight the need for continuous monitoring of AA levels in infant foods to ensure their safety. Full article

This article proposes a dynamic causal inference framework that integrates theoretical analysis, numerical simulation, and industrial data mining to address the root-cause tracing problem of time-delay effects in strip thickness and shape quality during hot rolling. First, we analyze the key process parameters, equipment states, and material characteristics influencing geometric quality and clarify their dynamic interaction mechanisms. Second, a delay-correlation matrix calculation method based on Dynamic Time Warping (DTW) and Mutual Information (MI) is developed to handle temporal misalignment in multi-source industrial signals and quantify the strength of delayed correlations. Furthermore, a transformer-based information gain approximation mechanism is designed to replace traditional explicit probability modeling and learn dynamic information-flow relationships among variables in a data-driven manner. Experimental verification on real production data demonstrates that the proposed framework can accurately identify time-delay causal pathways, providing an interpretable and engineering-feasible solution for quality control under complex operating conditions. Full article

Oxidative stress, UV exposure, inflammation, and extracellular matrix degradation collectively drive skin aging, underscoring the need for safe, multi-target therapeutic options. We developed and applied an integrated analytical pipeline combining UHPLC–MS/MS metabolomics, computational analyses (network pharmacology, molecular docking, and molecular dynamics simulation), and experimental bioassays to efficiently identify and characterize novel natural products with anti-aging potential. This workflow was applied to Melampyrum roseum Maxim., a previously unassessed hemiparasitic plant of the Orobanchaceae family, to elucidate its bioactive potential against skin aging. UHPLC–MS/MS profiling annotated 13 secondary metabolites, predominantly flavone aglycones, iridoid glycosides, and phenylpropanoid derivatives. Network pharmacology analysis linked these metabolites to 172 potential skin-aging-associated targets, mainly within inflammatory, ECM, and oxidative-stress pathways. Molecular docking and 100-ns molecular dynamics simulations confirmed stable ligand-target interactions with favorable binding energies, particularly with AKT1, EGFR, PTGS2 and XDH. Validating these predictions, the M. roseum extract demonstrated significant antioxidant activity and effectively suppressed key inflammatory mediators (IL-6, TNF-α, COX-2) and MMP-1 levels in UVB-exposed fibroblasts, notably without significant cytotoxicity. Collectively, these findings demonstrate that M. roseum harbors multifunctional metabolites that modulate key inflammatory and matrix-regulatory pathways, providing preliminary mechanistic evidence for its potential as a promising candidate for natural anti-aging applications. Full article