Search Results (154)

The effect of annealing on the microstructure and tensile properties of low-density polyethylene (LDPE) non-woven fabric produced by melt electrospinning was systematically investigated using DSC, SAXS, SEM, etc. The results showed that, above an annealing temperature of 80 $°\mathrm{C}$, both the main melting point and crystallinity of LDPE decreased compared to the original sample, as did the tensile strength of the non-woven fabric. Additionally, the lamellar distribution became broader at annealing temperatures above 80 $°\mathrm{C}$. The recrystallization mechanism of molten lamellae (disordered chains) in LDPE was elucidated by fitting the data using a Gaussian function. It was found that secondary crystallization, forming thicker lamellae, and spontaneous crystallization, forming thinner lamellae, occurred simultaneously at rates dependent on the annealing temperature. Secondary crystallization dominated at temperatures ≤80 $°\mathrm{C}$, whereas spontaneous crystallization prevailed at temperatures above 80 $°\mathrm{C}$. These findings explain the observed changes in the microstructure and tensile properties of the LDPE non-woven fabric. Furthermore, a physical model describing the microstructural evolution of the LDPE non-woven fabric during annealing was proposed based on the experimental evidence. Full article

In this paper, we calibrate the luminosity relation of gamma−ray bursts (GRBs) by employing artificial neural networks (ANNs) to analyze the Pantheon+ sample of type Ia supernovae (SNe Ia) in a manner independent of cosmological assumptions. The A219 GRB dataset is used to calibrate the Amati relation ($E_{\mathrm{p}}$-$E_{\mathrm{iso}}$) at low redshift with the ANN framework, facilitating the construction of the Hubble diagram at higher redshifts. Cosmological models are constrained with GRBs at high redshift and the latest observational Hubble data (OHD) via the Markov chain Monte Carlo numerical approach. For the Chevallier−Polarski−Linder (CPL) model within a flat universe, we obtain ${\Omega }_{\mathrm{m}}={0.321}_{-0.069}^{+0.078}$, $h={0.654}_{-0.071}^{+0.053}$, $w_0={-1.02}_{-0.50}^{+0.67}$, and $w_a={-0.98}_{-0.58}^{+0.58}$ at the 1 −$\sigma$ confidence level, which indicates a preference for dark energy with potential redshift evolution ($w_a\ne 0$). These findings using ANNs align closely with those derived from GRBs calibrated using Gaussian processes (GPs). Full article

Fluids that exhibit self-rewetting properties, such as aqueous long-chain alcohol solutions, display a unique quadratic relationship between surface tension and temperature and are marked by a positive gradient. This characteristic leads to distinctive patterns of thermocapillary convection and associated interfacial dynamics, setting self-rewetting fluids apart from normal fluids (NFs). The potential to improve heat transfer using self-rewetting fluids (SRFs) is garnering interest for use in various technologies, including low-gravity conditions and microfluidic systems. Our research aims to shed light on the contrasting behaviors of SRFs in comparison to NFs regarding interfacial transport phenomena. This study focuses on the thermocapillary convection in SRF layers with a deformable interface enclosed inside a closed container modeled as a square cavity, which is subject to nonuniform heating, represented using a Gaussian profile for the heat flux variation on one of its sides, in the absence of gravity. To achieve this, we have enhanced a central-moment-based lattice Boltzmann method (LBM) utilizing three distribution functions for tracking interfaces, computing two-fluid motions with temperature-dependent surface tension and energy transport, respectively. Through numerical simulations, the impacts of several characteristic parameters, including the viscosity and thermal conductivity ratios, as well as the surface tension–temperature sensitivity parameters, on the distribution and magnitude of the thermocapillary-driven motion are examined. In contrast to that in NFs, the counter-rotating pair of vortices generated in the SRF layers, due to the surface tension gradient at the interface, is found to be directed toward the SRF layers’ hotter zones. Significant interfacial deformations are observed, especially when there are contrasts in the viscosities of the SRF layers. The thermocapillary convection is found to be enhanced if the bottom SRF layer has a higher thermal conductivity or viscosity than that of the top layer or when distributed, rather than localized, heating is applied. Furthermore, the higher the magnitude of the effect of the dimensionless quadratic surface tension sensitivity coefficient on the temperature, or of the effect of the imposed heat flux, the greater the peak interfacial velocity current generated due to the Marangoni stresses. In addition, an examination of the Nusselt number profiles reveals significant redistribution of the heat transfer rates in the SRF layers due to concomitant nonlinear thermocapillary effects. Full article

Given finite-dimensional random vectors Y, X, and Z that form a Markov chain in that order ($Y\to X\to Z$), we derive the upper bounds on the excess minimum risk using generalized information divergence measures. Here, Y is a target vector to be estimated from an observed feature vector X or its stochastically degraded version Z. The excess minimum risk is defined as the difference between the minimum expected loss in estimating Y from X and from Z. We present a family of bounds that generalize a prior bound based on mutual information, using the Rényi and $\alpha$-Jensen–Shannon divergences, as well as Sibson’s mutual information. Our bounds are similar to recently developed bounds for the generalization error of learning algorithms. However, unlike these works, our bounds do not require the sub-Gaussian parameter to be constant, and therefore, apply to a broader class of joint distributions over Y, X, and Z. We also provide numerical examples under both constant and non-constant sub-Gaussianity assumptions, illustrating that our generalized divergence-based bounds can be tighter than the ones based on mutual information for certain regimes of the parameter $\alpha$. Full article

Millimeter-wave (mmWave) MIMO systems have emerged as a key enabling technology for next-generation wireless networks, addressing the growing demand for ultra-high data rates through the utilization of wide bandwidths and large-scale antenna configurations. Beyond communication capabilities, these systems offer inherent advantages for integrated sensing applications, particularly in scenarios requiring precise object detection and localization. The sparse mmWave channel in the beamspace domain allows fewer radio-frequency (RF) chains by selecting dominant beams, boosting both communication efficiency and sensing resolution. However, existing channel estimation methods, such as learned approximate message passing (LAMP) networks, rely on computationally intensive iterations. This becomes particularly problematic in large-scale system deployments, where estimation inaccuracies can severely degrade sensing performance. To address these limitations, we propose a low-complexity channel estimator using a non-iterative reconstruction network (NIRNet) with a learning-based selection matrix (LSM). NIRNet employs a convolutional layer for efficient, non-iterative beamspace channel reconstruction, significantly reducing computational overhead compared to LAMP-based methods, which is vital for real-time sensing. The LSM generates a signal-aware Gaussian measurement matrix, outperforming traditional Bernoulli matrices, while a denoising network enhances accuracy under low SNR conditions, improving sensing resolution. Simulations show the NIRNet-based algorithm achieves a superior normalized mean squared error (NMSE) and an achievable sum rate (ASR) with lower complexity and reduced training overhead. Full article

Gas sensors can provide early warning of fires by detecting pyrolysis gas components in the sheaths of high-voltage cables. However, air humidity significantly affects the thermal decomposition gas production characteristics of the outer sheath of high-voltage cables, which in turn affects the accuracy of this warning method. In this paper, the thermal decomposition and gas production characteristics of the polyethylene (PE) outer jacket of high-voltage cables under different air humidities (20–100%) are studied, and the corresponding density functional theory (DFT) simulation calculations are performed using Gaussian 09W software. The results show that with the increase in humidity, the thermal decomposition gas yield of the PE outer jacket of high-voltage cables exhibits a decreasing trend. Under high-humidity conditions (≥68.28%RH), the generation of certain thermal decomposition gases is significantly reduced or even ceases. Meanwhile, the influence of moisture on the thermal decomposition characteristics of PE was analyzed at the micro level through simulation, indicating that the H-free radicals generated by moisture promote the initial decomposition of PE, but the subsequent combination of hydroxyl groups with terminal chain C forms a relatively stable alkoxy structure, increasing the activation energy of the reaction (by up to 44.7 kJ/mol) and thus inhibiting the generation of small-molecule gases. An experimental foundation is laid for the final construction of a fire warning method for high-voltage cables based on the information of thermal decomposition gas of the outer sheath. Full article

This article discusses and compares approaches to the visualization of the danger zone formed as a result of spreading toxic substances during a fire at an industrial enterprise, to create predictive models and scenarios for evacuation and environmental protection measures. The purpose of this study is to analyze the features and conditions for the application of algorithms for predicting the spread of a danger zone, based on the Gauss equation and the probabilistic algorithm of a cellular automaton. The research is also aimed at the analysis of the consequences of a fire at an industrial enterprise, taking into account natural and climatic conditions, the development of the area, and the scale of the fire. The subject of this study is the development of software and algorithmic support for the visualization of the danger zone and analysis of the consequences of a fire, which can be confirmed by comparing a computational experiment and actual measurements of toxic substance concentrations. The main research methods include a Gaussian model and probabilistic, frontal, and empirical cellular automation. The results of the study represent the development of algorithms for a cellular automation model for the visual forecasting of a dangerous zone. They are characterized by taking into consideration the rules for filling the dispersion ellipse, as well as determining the effects of interaction with obstacles, which allows for a more accurate mathematical description of the spread of a cloud of toxic combustion products in densely built-up areas. Since the main problems of the cellular automation approach to modeling the dispersion of pollutants are the problems of speed and numerical diffusion, in this article the frontal cellular automation algorithm with a 16-point neighborhood pattern is used, which takes into account the features of the calculation scheme for finding the shortest path. Software and algorithmic support for an integrated system for the visualization and analysis of fire consequences at an industrial enterprise has been developed; the efficiency of the system has been confirmed by computational analysis and actual measurement. It has been shown that the future development of the visualization of dangerous zones during fires is associated with the integration of the Bayesian approach and stochastic forecasting algorithms based on Markov chains into the simulation model of a dangerous zone for the efficient assessment of uncertainties associated with complex atmospheric processes. Full article

Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score. Full article

The research on high-order transverse modes in lasers was largely abandoned a few years after the invention of the laser in 1960. The main reason for this was that high-order beams are more divergent and less bright than the Gaussian beam. In the present paper, we showed that the behaviour of $L{G}_{p0}$ beams faced to the optical Kerr effect (OKE) varies considerably depending on the mode order (p = 0 or $p\ge 1$). We focused our attention on the properties of $L{G}_{00}$ and $L{G}_{10}$ beams when subject to OKE, and we found that the $L{G}_{10}$ beam keeps its focusability much better than the $L{G}_{00}$ beam. This property has at least two applications concerning first the conception of high-intensity laser chains not based on a Gaussian beam but on an $L{G}_{10}$ beam and second, the use of an $L{G}_{10}$ beam instead of the usual Gaussian beam which can reduce drastically the protection of optical limiters based on OKE; this constitutes a counter-measure against such limiters. Full article

A Bayesian network offers powerful knowledge representations for independence, conditional independence and causal relationships among variables in a given domain. Despite its wide application, the detection limits of modern measurement technologies make the use of the Bayesian networks theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper, we introduce the censored Gaussian Bayesian network (GBN), an extension of GBNs designed to handle left- and right-censored data caused by instrumental detection limits. We further propose the censored Structural Expectation-Maximization (cSEM) algorithm, an iterative score-and-search framework that integrates Monte Carlo sampling in the E-step for efficient expectation computation and employs the iterative Markov chain Monte Carlo (MCMC) algorithm in the M-step to refine the network structure and parameters. This approach addresses the non-decomposability challenge of censored-data likelihoods. Through simulation studies, we illustrate the superior performance of the cSEM algorithm compared to the existing competitors in terms of network recovery when censored data exist. Finally, the proposed cSEM algorithm is applied to single-cell data with censoring to uncover the relationships among variables. The implementation of the cSEM algorithm is available on GitHub. Full article

Developing prefabricated buildings (PBs) and optimizing the construction supply chain represent effective strategies for reducing carbon emissions in the construction industry. Prefabricated building supply chain (PBSC) carbon reduction suffers from synergistic difficulties, limited rationality, and environmental complexity. Therefore, investigating carbon emission reduction in PBSC is essential. In this study, PBSC participants are divided into four categories according to the operation process. Gaussian white noise is introduced to simulate the random perturbation factors, and a four-way stochastic evolutionary game model is constructed and numerically simulated. The study found the following: Stochastic perturbation factors play a prominent role in the evolution speed of the agent; the emission reduction benefit and cost of the participant significantly affect the strategy selection; the operation status of the PBSC is the key to strategy selection, and it is important to pay attention to the synergy of the participants at the first and the last end of the PBSC; the influence of the external environment on strategies is mainly manifested in the loss caused and the assistance provided; and the information on emission reduction is an important factor influencing strategies. Finally, we provide suggestions for promoting carbon emission reduction by participants in the PBSC from the perspective of resisting stochastic perturbation, enhancing participants’ ability, and strengthening PBSC management; external punishment and establishing a cross-industry information sharing platform is more important than the reward. Full article

The essence of coal spontaneous combustion lies in the existence of a large number of chemically active functional groups in the coal molecule, such as aldehyde group (-CHO) and methoxy group (-OCH₃) in the side chain structure of coal molecule, which can be easily oxidized, thus triggering the spontaneous combustion process. Retardant is a more widely used technology to prevent the spontaneous combustion of coal, but the research on the microscopic level of the mechanism of coal spontaneous combustion retardation has been weak for many years, so deepening the exploration in this field is crucial for the optimization of the retardation strategy. The inhibition effect of Li⁺, Na⁺, and K⁺ inhibitors was investigated through the programmed warming experiments, and the results showed that the carbon monoxide production and oxygen consumption of coal samples inhibited by Li⁺, Na⁺, and K⁺ inhibitors were reduced to different degrees compared with that of the original coal, which proved that it had an inhibitory effect on the spontaneous combustion of coal. In order to deeply investigate the interaction between the molecular structure properties of coal and alkali metal ions, the complexes formed by three typical alkali metal ions-Li⁺, Na⁺, and K⁺-with specific reactive groups (-CHO and -OCH₃) in coal were investigated with the help of the quantum chemical calculation software Gaussian 16W, and the following conclusions were made after analyzing the complexes: on the one hand, the complexes formed by Li⁺, Na⁺, and K⁺ with the reactive groups in coal can occupy the sites where the reactive groups bind with oxygen, reduce the chance of coal oxygen contact and inhibit its oxidation process; on the other hand, the coordinating action of alkali metal ions increases the maximum energy barrier that needs to be overcome for the reaction of the originally active groups, resulting in the coal molecules in the process of oxidation reaction, increasing the difficulty of the reaction, thus effectively curbing the tendency of spontaneous combustion of coal. Full article

Parameter identification problems in partial differential equations (PDEs) consist in determining one or more functional coefficient in a PDE. In this article, the Bayesian nonparametric approach to such problems is considered. Focusing on the representative example of inferring the diffusivity function in an elliptic PDE from noisy observations of the PDE solution, the performance of Bayesian procedures based on Gaussian process priors is investigated. Building on recent developments in the literature, we derive novel asymptotic theoretical guarantees that establish posterior consistency and convergence rates for methodologically attractive Gaussian series priors based on the Dirichlet–Laplacian eigenbasis. An implementation of the associated posterior-based inference is provided and illustrated via a numerical simulation study, where excellent agreement with the theory is obtained. Full article

The degradation of polystyrene (PS) represents a significant challenge in plastic waste management due to its chemical stability and low biodegradability. In this study, the catalytic degradation mechanisms of PS were investigated by density functional theory (DFT)-based calculations using the hybrid functional B3LYP and the 6-311G++(d,p) basis in Gaussian 16. The influence of acidic (AlCl₃, Fe₂(SO₄)₃) and basic (CaO) catalysts was evaluated in terms of activation energy, reaction mechanisms, and degradation products. The results revealed that acid catalysts induce PS fragmentation through the formation of carbocationic intermediates, promoting the selective cleavage of C-C bonds in branched chains with bond dissociation energies (BDE) of 176.8 kJ/mol (C1-C7) and 175.2 kJ/mol (C3-C8). In contrast, basic catalysts favor β-scission by stabilizing carbanions, reducing the BDE to 151.6 kJ/mol (C2-C3) and 143.9 kJ/mol (C3-C4), which facilitates the formation of aromatic products such as styrene and benzene. Fe₂(SO₄)₃ was found to significantly decrease the activation barriers to 328.12 kJ/mol, while the basic catalysts reduce the energy barriers to 136.9 kJ/mol. Gibbs free energy (ΔG) calculations confirmed the most favorable routes, providing key information for the design of optimized catalysts in PS valorization. This study highlights the usefulness of computational modeling in the optimization of plastic recycling strategies, contributing to the development of more efficient and sustainable methods. Full article

This research explores the systemic nature of competitiveness within the cereal sector of the European Union (EU) and addresses the structural interdependencies among key competitiveness drivers through a network-based model. The goal of this research is to offer policy alignment solutions based on the empirical findings derived from a sparse Gaussian graphical model that was operationalized to identify conditional dependencies, synergies, and decouplings across five dimensions: factor endowments, self-sufficiency, trade strategy, resource productivity, and environmental impact. The results showed systemic vulnerabilities, including the decoupling of factor endowments from strategic trade specialization, a pronounced East–West productivity divide, and the asymmetry between the economic valorization of harvested land and its environmental impact, reflected in land management practices. Research findings underscore the need for synergy-driven strategies to coherently align agricultural competitiveness outcomes with the economic and structural potential of each EU country. A critical policy incongruency has been identified: the current prioritization of ecological performance under the Common Agricultural Policy overlooks essential agricultural infrastructural disparities, thereby perpetuating competitiveness asymmetries across the Union. In response, this study introduces a systemic amelioration framework designed to reconcile environmental priorities with agricultural infrastructure development, fostering cohesive and resilient competitiveness throughout the EU cereal sector. Full article