Search Results (2,358)

Smart cities are confronted with a variety of disaster threats. Among them, natural fires pose a serious threat to human lives, the environment, and asset security. In view of the fact that existing research mostly focuses on the analysis of accident precursors, this paper proposes a dynamic risk-modeling method based on Stochastic Petri Nets (SPN) and Bayesian theory to deeply explore the evolution mechanism of urban natural fires. The SPN model is constructed through natural language processing techniques, which discretize the accident evolution process. Then, the Bayesian theory is introduced to dynamically update the model parameters, enabling the accurate assessment of key event nodes. The research results show that this method can effectively identify high-risk nodes in the evolution of fires. Their dynamic probabilities increase significantly over time, and key transition nodes have a remarkable impact on the emergency response efficiency. This method can increase the fire prevention and control efficiency by approximately 30% and reduce potential losses by more than 20%. The dynamic update mechanism significantly improves the accuracy of risk prediction by integrating real-time observation data and provides quantitative support for emergency decision making. It is recommended that urban management departments focus on strengthening the maintenance of facilities in high-risk areas (such as fire alarm systems and emergency passages), optimize cross-departmental cooperation processes, and build an intelligent monitoring and early-warning system to shorten the emergency response time. This study provides a new theoretical tool for urban fire risk management. In the future, it can be extended to other types of disasters to enhance the universality of the model. Full article

The transport and fate of antibiotics are significantly influenced by co-existing colloidal and nanosized substances, such as clay particles. Montmorillonite, a common clay mineral with a thin nano-sheet-like structure, enhances antibiotic (e.g., ciprofloxacin) mobility due to its strong adsorption properties. Nevertheless, little is known about how ciprofloxacin systematically influences the charging and aggregation properties of montmorillonite. This study examines the effect of ciprofloxacin on the electrophoretic mobility and hydrodynamic diameter of Na-montmorillonite under varying pH levels and NaCl concentrations. Results show ciprofloxacin promotes aggregation and alters the surface net charge of Na-montmorillonite at acidic to neutral pH, where ciprofloxacin is positively charged. At higher pH levels, where ciprofloxacin is negatively charged, no significant effects are observed. The observed aggregation behaviors align with predictions based on the Derjaguin–Landau–Verwey–Overbeek (DLVO) theory. Specifically, the slow aggregation regime, the fast aggregation regime, and the critical coagulation concentration are identified. The relationship between critical coagulation ionic strength and electrokinetic surface charge density is well explained by the DLVO theory with the Debye–Hückel approximations. Additionally, non-DLVO interactions are inferred. At low NaCl and ciprofloxacin concentrations, minimal changes in aggregation and surface charge suggest dispersed montmorillonite may facilitate ciprofloxacin transport, raising environmental concerns. Full article

Modeling complex conditions involving extensive engineering structures with large numbers of beams and columns often requires a mixture of analytical modeling based on beam theory and numerical simulations involving finite element models composed of solid or shell elements. However, high levels of deformation in the planar configuration of cross-sections arising under extreme external loads, such as intensive earthquakes, explosions, and hurricanes, greatly complicates the task of fitting the numerical simulation results to the planar cross-sections required by beam theory. The present work addresses this issue by proposing a fitting method based on a least squares approximation method. The fitting problem is first transformed into a process of solving a cubic equation whose coefficients are integrals over the simulated cross-section. The solution of the cubic equation is defined using explicit formulae developed for calculating the integrals over the surfaces of single solid or shell elements lying within the cross-section by combining the shape functions and degree of freedom results of the elements. The proposed fitting method is then applied for analyzing the blast resistance of steel structures. The potential application of the proposed method is demonstrated by evaluating the rotations, shear deformations, and moment–curvature relationships of the fitted cross-sections. Full article

For the first time, we explored the optical, electronic, mechanical, and structural properties of the Ruddlesden–Popper phase family member X₂CaTa₂O₇ (X = Li, Na, K, and Rb) by using density functional theory (DFT) with the Perdew–Burke–Ernzerhof (PBE) function in the generalized gradient approximation (GGA) framework. These materials show promising potential for energy conversion applications. Detailed investigations into structural parameters, band gaps, total and partial densities of states, and optical and mechanical properties demonstrate their suitability for photovoltaic technologies. The calculated electronic band gap structures and density of states demonstrate that X₂CaTa₂O₇ (X = Li, Na, K, and Rb) are semiconductors in nature with band gaps ranging from 1.52 eV to 3.02 eV. Measurements demonstrate substantial contributions from O-2p⁴, Ca-4p⁴, and Ta-4f¹⁴ orbitals to the electronic structures of the compounds. Moreover, the optical characteristics, like the reflectivity, absorption coefficients (10⁵ cm⁻¹), dielectric functions (8.5), refractive index (2–3), and optical conductivity (4–6 fs⁻¹), highlight the abilities of these compounds for optoelectronic and photovoltaic devices. Additionally, the mechanical properties measurements of the compounds show that they are capable for flexible electronic applications as well. This manuscript provides good insights into the design and development of the compounds capable for next-generation photovoltaic devices. Full article

Due to their broad applicability, gauge theories (GTs) play a crucial role in various areas of physics, from high-energy physics to condensed matter. Their formulations on lattices, lattice gauge theories (LGTs), can be studied, among many other methods, with tools coming from statistical mechanics lattice models, such as mean field methods, which are often used to provide approximate results. Applying these methods to LGTs requires particular attention due to the intrinsic local nature of gauge symmetry, how it is reflected in the variables used to formulate the theory, and the breaking of gauge invariance when approximations are introduced. This issue has been addressed over the decades in the literature, yielding different conclusions depending on the formulation of the theory under consideration. In this article, we focus on the mean field theoretical approach to the analysis of GTs and LGTs, connecting both older and more recent results that, to the best of our knowledge, have not been compared in a pedagogical manner. After a brief overview of mean field theory in statistical mechanics and many-body systems, we examine its application to pure LGTs with a generic compact gauge group. Finally, we review the existing literature on the subject, discussing the results obtained so far and their dependence on the formulation of the theory. Full article

Hexagonal BCN (h-BCN), an isoelectronic counterpart to graphene, exhibits chirality and offers the distinct advantage of optical activity in the vacuum ultraviolet (VUV) region, characterized by significantly higher wavelengths compared to graphene nanoflakes. h-BCN possesses a wide bandgap and demonstrates desirable semiconducting properties. In this study, we employ Density Functional Theory (DFT) calculations to investigate the proximity effects of adsorbed h-BCN flakes on two-dimensional (2D) substrates. The chosen substrates encompass monolayers of 3D transition metals and WSe₂, as well as a bilayer consisting of WSe₂/Ni. Notably, the hydrogen-terminated h-BCN nanoflakes retain their planar configuration following adsorption. We observe a strong interaction between h-BCN and fcc-based monolayers such as Ni(111), resulting in the closure of the optical bandgap, while the adsorption energy on WSe₂ is significantly weaker, preserving an approximate 1.1 eV bandgap. Furthermore, we demonstrate the magnetism induced by the proximity of adsorbed chiral h-BCN molecules, and the chiral-induced spin selectivity within the proposed systems. Full article

This research is based on the frequency response of angle-ply laminated cylindrical shells under higher-order shear deformation theory. The higher-order shear deformation theory is used to model the displacement and rotational functions, which are approximated by cubic and quintic splines. The eigenvalue problem is obtained with the simply supported boundary condition. The frequency of cylindrical shells is analyzed by varying the circumferential node number, length, number of layers, and layer alignment. The competence of the formulation is verified by comparing it with the available results of higher-order zigzag theory. Full article

Since its discovery by scientists, high-order harmonic generation has emerged as a focal research topic in the field of strong-field physics. Following decades of advancement, significant progress has been achieved in both experimental and theoretical investigations of high-order harmonic generation. Among various theoretical approaches, including the time-dependent Schrödinger equation, strong-field approximation, and quantitative rescattering, etc., time-dependent density functional theory stands out for its high computational accuracy and reduced resource demands. Consequently, it plays a crucial role in research on both gaseous and solid-state high-order harmonic generation. Time-dependent density functional theory enables real-time and real-space simulation of high-order harmonic generation in intense laser fields, incorporating all nonperturbative many-body effects. It is extensively employed in research within the domain of strong-field physics. This paper primarily presents selected key findings from the application of time-dependent density functional theory in studying the generation, regulation, and application of gas high-order harmonic generation. Full article

Machine learning (ML) in queueing theory combines the predictive and optimization capabilities of ML with the analytical frameworks of queueing models to improve performance in systems such as telecommunications, manufacturing, and service industries. In this paper we give an overview of how ML is applied in queueing theory, highlighting its use cases, benefits, and challenges. We consider a classical $GI/G/K$-type queueing system, which is at the same time rather complex for obtaining analytical results, consisting of K homogeneous servers with an arbitrary distribution of time between incoming customers and equally distributed service times, also with an arbitrary distribution. Different simulation techniques are used to obtain the training and test samples needed to apply the supervised ML algorithms to problems of regression and classification, and some results of the approximation analysis of such a system will be needed to verify the results. ML algorithms are used also to solve both parametric and dynamic optimization problems. The latter is achieved by means of a reinforcement learning approach. It is shown that the application of ML in queueing theory is a promising technique to handle the complexity and stochastic nature of such systems. Full article

A general theory is presented to analyze the time-dependent, transient diffusiophoresis of a charged spherical colloidal particle in an uncharged gel medium containing a symmetrical electrolyte when an electrolyte concentration gradient is suddenly applied. We derive the inverse Laplace transform of an approximate expression for the relaxation function R(t), which describes the time-course of the ratio of the diffusiophoretic mobility of a weakly charged spherical colloidal particle, possessing a thin electrical double layer, to its steady-state diffusiophoretic mobility. The relaxation function depends on the mass density ratio of the particle to the electrolyte solution, the particle radius, the Brinkman screening length, and the kinematic viscosity. However, it does not depend on the type of electrolyte (e.g., KCl or NaCl), which affects only the steady-state gel diffusiophoretic mobility. It is also found that the expression for the relaxation function in transient gel diffusiophoresis of a weakly charged spherical colloidal particle with a thin electrical double layer takes the same form as that for its transient gel electrophoresis. Full article

Yttria-Stabilized Zirconia (YSZ) is an important material in thermal barrier coatings (TBCs), which are widely applied in aviation engines and ground gas turbines. Therefore, the quality inspection of the YSZ layer is of great significance for the safety of engines and gas turbines. In this work, the YSZ powder is mixed with Polytetrafluoroethylene (also known as teflon) in different mass ratios and pressed into tablets with different thicknesses. A terahertz time-domain spectroscopy system is used to obtain their time-domain spectra, and their frequency spectra are then obtained by fast Fourier transform. Based on theory formulas, we obtained the frequency-dependent curves of the absorption coefficient, refractive index, and absorbance of the YSZ tablets. The results show that the YSZ tablets have characteristic absorption peaks in the terahertz band, and these peaks are affected by the mass ratio of YSZ to teflon and the thickness of the tablets. Finally, we conducted a terahertz Raman spectroscopy test of the YSZ tablets for the first time. The results show that in the range from 0 to 1600 cm⁻¹, there are about ten strong Raman peaks. More importantly, these peaks are approximately independent of the mass ratio and the thickness of tablets. This study is of great significance for the nondestructive testing of TBC quality using terahertz spectroscopy technology. Full article

This study presents the fabrication and characterization of ZnO-MoS₂ heterostructure-based ultra-broadband photodetectors capable of operating across the ultraviolet (UV) to mid-infrared (MIR) spectral range (365 nm–10 μm). The p-n heterojunction was synthesized via RF magnetron sputtering and spin coating, followed by annealing. Structural and optical analyses confirmed their enhanced light absorption, efficient charge separation, and strong built-in electric field. The photodetectors exhibited light-controlled hysteresis in their I-V characteristics, attributed to charge trapping and interfacial effects, which could enable applications in optical memory and neuromorphic computing. The devices operated self-powered, with a peak responsivity at 940 nm, which increased significantly under an applied bias. The response and recovery times were measured at approximately 100 ms, demonstrating their fast operation. Density functional theory (DFT) simulations confirmed the type II band alignment, with a tunable bandgap that was reduced to 0.20 eV with Mo vacancies, extending the detection range. The ZnO-MoS₂ heterostructure’s broad spectral response, fast operation, and defect-engineered bandgap tunability highlight its potential for imaging, environmental monitoring, and IoT sensing. This work provides a cost-effective strategy for developing high-performance, ultra-broadband, flexible photodetectors, paving the way for advancements in optoelectronics and sensing technologies. Full article

The increasing complexity and frequency of malware attacks pose significant challenges to cybersecurity, as traditional methods struggle to keep pace with the evolving threat landscape. Current malware classification techniques often fail to account for the heterogeneity of malware data and models across different clients, limiting their effectiveness. In this chapter, we propose a distributed model enhancement-based malware classification method that leverages federated learning to address these limitations. Our approach employs generative adversarial networks to generate synthetic malware data, transforming non-independent datasets into approximately independent ones to mitigate data heterogeneity. Additionally, we utilize knowledge distillation to facilitate the transfer of knowledge between client-specific models and a global classification model, promoting effective collaboration among diverse systems. Inspired by active defense theory, our method identifies suboptimal models during training and replaces them on a central server, ensuring all clients operate with optimal classification capabilities. We conducted extensive experimentation on the Malimg dataset and the Microsoft Malware Classification Challenge (MMCC) dataset. In scenarios characterized by both model heterogeneity and data heterogeneity, our proposed method demonstrated its effectiveness by improving the global malware classification model’s accuracy to 96.80%. Overall, our research presents a robust framework for improving malware classification while maintaining data privacy across distributed environments, highlighting its potential to strengthen cybersecurity defenses against increasingly sophisticated malware threats. Full article

The rationale for age-structured population migration system models lies in the significant impact of age patterns on migration dynamics, as age-specific migration rates exhibit distinct regularities and are influenced by life course transitions, socio-economic conditions, and demographic structures. Based on artificial neural networks, this article proposes a class of population models with age structure described by partial differential equations to predict the future trends of regional population changes. The population migration rate, as a complex nonlinear feature, can be trained through artificial neural networks, providing a population approximation system. By employing semigroup theory, we establish the well-posedness of the proposed system. It is shown that the solution of the approximation system can converge to that of the original system in the sense of the $L^2$-norm. Finally, several simulation experiments are provided to verify the effectiveness of the population forecasting model. Full article

As shown by Dyson in his famous paper “Missed Opportunities”, it follows, even from purely mathematical considerations, that quantum Poincare symmetry is a special degenerate case of quantum de Sitter symmetries. Thus, the usual explanation of why, in particle physics, Poincare symmetry works with a very high accuracy is as follows. A theory in de Sitter space becomes a theory in Minkowski space when the radius of de Sitter space is very high. However, the answer to this question must be given only in terms of quantum concepts, while de Sitter and Minkowski spaces are purely classical concepts. Quantum Poincare symmetry is a good approximate symmetry if the eigenvalues of the representation operators $M_{4\mu }$ of the anti-de Sitter algebra are much greater than the eigenvalues of the operators $M_{\mu \nu }$ ($\mu ,\nu =0,1,2,3$). We explicitly show that this is the case in the Flato–Fronsdal approach, where elementary particles in standard theory are bound states of two Dirac singletons. Full article