Advances in Differential Equations and Control Processes

A data-driven approach to coal mine safety performance using swarm intelligence and ensemble learning

Fri, 05 Jun 2026 07:07:06 +0000

At present, there are some shortcomings in the dynamic adaptability and subjectivity of coal mine safety performance evaluation, and it is difficult to realize the short-term safety performance evaluation with full staff participation. In this study, based on the Analytic Network Process-Technique for Order Preference by Similarity to an Ideal Solution (ANP-TOPSIS), a coal mine safety performance evaluation index system was constructed, and the evaluation index was optimized by a particle swarm optimization algorithm to improve the accuracy of dynamic index weight allocation. Emotional processing analysis technology is introduced, and the survey evaluation form is designed to quantify the subjective emotional tendency. Statistical methods such as the intra-group correlation coefficient, consistency test and regression model are used to improve the reliability of expert scoring data and quantitatively analyze individual subjective differences. Using the random forest classification method, combined with the term frequency-inverse document frequency (TF-IDF) to vectorize the text data, a bottom-up dynamic evaluation method of employee safety performance based on machine learning is established. The random forest model achieved an average F1-score of 0.929, with all six safety dimensions scoring above 0.8. The example shows that the short-process long-period safety performance evaluation based on ANP-TOPSIS-PSO, and a random forest model can accurately describe the coal mine safety appearance and provide scientific decision support for improving the coal mine safety performance level.

Quantum purity exchange dynamics in a qubit–resonator system subject to squeezed-vacuum driving

Leila Abdelgader, Chafaa Hamrouni — Wed, 01 Apr 2026 06:27:25 +0000

This work presents a theoretical framework to study the non-Markovian dynamics of a two-level quantum emitter interacting with a broadband squeezed electromagnetic reservoir, and both one- and two-photon interaction processes are incorporated. Mathematical modeling uses a time-convolution less projection operator technique. This yields a time-local master equation. The coefficients of this equation are derived from integrals over the reservoir's squeezed correlation functions: and . The model is validated through rigorous numerical simulation of the resulting dynamical equations. Testing involves computing key physical observables: the transient emission spectrum and the field linear entropy . These predictions are systematically analyzed against variations in squeezing parameters , coupling strengths , and detector bandwidth . The results confirm that the model successfully captures phase-dependent decoherence, spectral modulation, and purity oscillations. Notably, two-photon processes suppress decoherence under strong squeezing. The consistency between analytical derivations and numerical outcomes validates the framework. It is established as a predictive tool for quantum optics in engineered nonclassical environments. This study directly connects engineered reservoir properties specifically its nonclassical photon statistics to observable, time-dependent quantum phenomena. The findings offer fundamental insights and a predictive tool for quantum control, sensing, and information processing in tailored electromagnetic environments.

Effect of heater location and wall waviness on buoyant convection in a porous wavy cavity using heat function approach

Huey Tyng Cheong, Sivasankaran Sivanandam — Thu, 30 Apr 2026 03:39:00 +0000

Natural convection and thermal transport in a porous square cavity with a wavy cold wall and a localized heat source on the left sidewall are numerically examined in this work. The cavity is filled with a fluid-saturated porous medium and is governed by the Darcy model under steady, laminar flow conditions with the Boussinesq approximation. A heater of fixed length is mounted on the left sidewall at three different points, namely the lower, center, and upper positions, while the right sidewall is maintained at a constant cold temperature and modeled with varying waviness in terms of amplitude and number of undulations. The remaining walls are considered adiabatic. The governing dimensionless equations for energy and stream functions are discretized using the finite difference technique and solved iteratively for various heater positions, right sidewall waviness, and Darcy–Rayleigh values after transforming the physical wavy domain into a rectangular computational domain. Results are presented in the form of Nusselt numbers, isotherms, streamlines, and heatlines. The findings indicate that the heater position has a significant influence on the convection flow, and heat transfer performance. The averaged heat transmission rate is improved by the right sidewall’s increased waviness. Among the heater placements, lower heating produces the highest averaged heat transfer for higher Darcy–Rayleigh numbers, whereas center heating is more effective under weak convection conditions. This study provides useful insight into the thermal design of porous systems involving non-uniform heating, such as solar air conditioning, ventilation, and heating systems.

Within-host dynamics of ZIKV-CHIKV co-infection: Stability analysis and effective therapeutic strategies

Ahmed Elaiw, Zainab Alkhudhari , Aatef Hobiny — Wed, 06 May 2026 00:00:00 +0000

In this paper, we develop a mathematical model that describes the within-host co-dynamics of two arboviruses, Zika virus (ZIKV) and Chikungunya virus (CHIKV). The model is also modified to investigate the impact of various treatment strategies. The model incorporates four cell types: uninfected target cells, latently infected cells, actively infected cells, and antibodies. The analysis establishes that all solutions remain nonnegative and bounded over time. It further reveals the presence of four distinct steady states: the disease-free steady state, the ZIKV-only steady state, the CHIKV-only steady state, and the coexistence steady state representing co-infection. The next-generation matrix technique was applied to determine the reproduction numbers for the ZIKV-only model, the CHIKV-only model, and the ZIKV-CHIKV co-infection model (denoted by R_Z^L, R_C^Land R₀^L= max{R_Z^L, R_C^L }, respectively) as well as the invasion reproduction numbers R_Z^L,^invand R_C^L,^invwhich determine whether a virus can successfully invade an existing infection state. We conducted a mathematical analysis to determine the existence of equilibrium points and to establish the criteria for their global stability. Global stability is verified through the application of suitably constructed Lyapunov functions. The effects of four therapeutic strategies are included: (i) antiviral therapy that prevents viral infection of target cells, (ii) antiviral therapy that suppresses viral production, (iii) immune-stimulating treatment, and (iv) therapy that increases the rate of antibody circulation. Simulations show antivirals outperform immune-boosting strategies in clearing co-infection, while combining both offers synergy by suppressing replication and enhancing host defenses. The proposed model, along with the theoretical analysis, is new and offers a useful framework for studying viral co-infections.

Bielecki–Hyers–Ulam stability of non-linear fractional Volterra Fredholm Hammerstein integro-delay dynamic systems with instantaneous impulses on time scale

Syed Omar Shah, Jun Zheng — Tue, 12 May 2026 00:00:00 +0000

In this paper, we study the existence and uniqueness of solutions, Bielecki–Hyers–Ulam stability, and Bielecki–Hyers–Ulam–Rassias stability for non-linear fractional Volterra Fredholm Hammerstein integro-delay dynamic systems with instantaneous impulses on time scale. Such systems provide a unified framework that encompasses both continuous and discrete models, making them highly appropriate for describing complex real-world phenomena involving memory effects, hereditary properties, and sudden perturbations. Sufficient conditions are established for the existence and uniqueness of solutions to the considered systems. In particular, the Picard operator and the Banach fixed point theorem are utilized to prove the existence and uniqueness of solutions. Moreover, we analyze the qualitative behavior of solutions by proving Bielecki–Hyers–Ulam stability and Bielecki–Hyers–Ulam–Rassias stability. To obtain these stability results, Grönwall’s inequality on time scales is used as the main analytical tool. For our results, some suitable assumptions are imposed along with appropriate Lipschitz conditions on the nonlinear terms. By constructing appropriate contractive mappings in a suitably defined Bielecki-type normed space, we develop a unified and systematic framework to handle the combined effects of integral operators, fractional dynamics, delay arguments, and impulsive perturbations. Finally, an illustrative example is provided to demonstrate the effectiveness and applicability of the theoretical findings.

A novel mean curvature-based model for positive image restoration and blur kernel estimation in blind image deblurring

Azhar Iqbal, Shahid Saleem, Shahbaz Ahmad, Adel M. Al-Mahdi, Faisal Fairag — Wed, 20 May 2026 08:25:09 +0000

The premise of blind image deblurring revolves around the restoration of a clear image from a blurred one without prior knowledge of the specific blur kernel employed. Within this realm, various image priors have been extensively investigated and applied to address this inherently challenging problem. Throughout the image deblurring process, ensuring the resulting image intensities remain strictly non-negative is often imperative. However, prevalent numerical methodologies utilized to solve this issue have shown instances where the outcomes are not consistently favorable, leading to undesirable negative intensities that contribute to significant areas of darkness in the restored images. This study introduces a novel model designed to tackle the blind image deblurring problem by leveraging mean curvature. The proposed model not only assures positive outcomes but also confines the upper limit of image intensity values, thereby maintaining them within a predefined range. Additionally, new numerical algorithms are introduced, which not only restore the image but also estimate the blur kernel. Comparative analyses between these proposed algorithms and existing numerical techniques have been conducted to showcase the effectiveness and feasibility of our suggested approach.

Mapping and superposition of multi-modal data flows for system fault evolution

Tiejun Cui, Chongxin Wang, Shasha LI — Mon, 25 May 2026 00:00:00 +0000

To study system fault evolution using multi-modal data, multi-modal data are associated with multiple factors, and a mapping and superposition method for multi-modal data flows and factors is established. Multi-modal data and their characteristics are discussed. The mapping between multi-modal data flows and factors and the superposition of mapping results are investigated. The robustness of superposition operators, dynamic system modeling of factor evolution, and denoising performance of the mapping-superposition strategy are theoretically analyzed. The function of mapping in system fault evolution is explained, and a case study is provided. Results show that disaster data exhibit multi-modal characteristics. A multi-modal data flow consists of multiple single-modal data flows, which can be further subdivided into multifactor value data flows. These establish a mapping relationship between factors and time calibration and form a factor mapping model for single-/multi-modal data flows. It is necessary to consider the superposition forms of multi-factor value data flows mapped to the same factor, including scalar, vector, and max-min superposition forms, with corresponding mathematical models and superposition processes provided. The framework is embedded into a continuous-time dynamic model based on differential equations, supporting state estimation and optimal control. The proposed method is applied to analyze the fault process of an unmanned monitoring aircraft. The case is modeled with linear differential equations to simulate factor state trajectories and fault events. The results can provide a multi-dimensional data interface for the study of system fault processes, facilitating the analysis, prediction, early warning, and intervention of system faults.

Bipolar fuzzy dominance rough WASPAS approach for AI-based radar evaluation

Muhammad Iftikhar, Ubaid Ur Rehman, Tahir Mahmood — Fri, 29 May 2026 08:27:33 +0000

The selection of an AI-based radar system to detect drones is a multi-criteria decision-making (MCDM) problem with many conflicting criteria. Exchanges between positive and negative aspects of each radar system in uncertain and incomplete information should be assessed by decision-makers. The traditional MCDM models are usually not effective in dealing with such complexities, especially when both positive and negative aspects are involved, and comparative reasoning is needed (dominance). In order to address these shortcomings, this article suggests an advanced model using the bipolar fuzzy dominance rough set (BFDRS) approach. The suggested method combines fuzzy logic to deal with uncertainty, dominance-based rough sets to model preferences, and bipolar fuzzy sets to manage dual-natured assessments. In order to operationalize the framework, we propose two new aggregation operators, namely bipolar fuzzy dominance rough dombi averaging (BFDRDA) and bipolar fuzzy dominance rough dombi geometric (BFDRDG), to combine expert opinions in the context of multiple criteria successfully. After that, we develop an MCDM methodology, which is the WASPAS method, within the framework of BFDRS, to prioritize AI radar alternatives in the presence of uncertainty. An extensive case study proves the relevance of the suggested model, and a comparative analysis with the currently existing ones proves its strength and higher decision-support abilities in complex and contradictory environments.

A numerical approach to magnetic field calculation in Hall thrusters considering nonlinear magnetic permeability with Aitken extrapolation for convergence acceleration

Jiasong Li, Yanbin Xi, Yue Liu — Wed, 03 Jun 2026 00:00:00 +0000

This paper investigates the numerical computation of magnetic fields in a Hall thruster, a process governed by a nonlinear elliptic boundary value problem. The model rigorously accounts for the influence of ferromagnetic materials, where the relative permeability is defined as a magnetic field-dependent function exceeding unity in core regions and set to unity elsewhere. Consequently, the model equations are inherently nonlinear, and their coefficients exhibit discontinuity across material interfaces. To solve this complex system, the finite difference method is applied on a uniform staggered mesh to derive a system of nonlinear difference equations with discontinuous coefficients. An iterative algorithm featuring a nested loop structure is presented to tackle this nonlinearity: an inner loop computes the magnetic field for a fixed relative permeability, while an outer loop updates the permeability distribution based on the current field solution. A critical challenge in such simulations is the convergence difficulty under high excitation due to strong nonlinearity and magnetic saturation. To address this, we propose a robust nested iterative algorithm enhanced with Aitken extrapolation. The method is validated through numerical simulations on a miniature Hall thruster model across three distinct coil ampere-turn configurations. Results highlight a critical distinction: while the standard fixed-point iteration performs adequately under low-to-moderate excitation, it fails to converge under the high-excitation condition. In contrast, the proposed Aitken-accelerated algorithm achieves stable convergence across all test cases, successfully resolving the convergence bottleneck in high-field scenarios. This advancement provides a robust framework for the magnetic circuit design of high-power Hall thrusters.

From linear w-γ correlation to redshift-dependent dynamics: A complete phenomenological framework and testing roadmap for dark energy

Tongfeng Zhao — Thu, 16 Apr 2026 07:57:01 +0000

Based on an adaptive universe model, this perspective article presents a phenomenological framework that correlates the dark energy equation of state w with the cosmic growth index γ via the linear relation w(a) = −1 + η(γ(a) − 0.55). Recognizing that the coupling between dark energy and structure formation may evolve with cosmic time, the framework is extended to a redshift-dependent formulation: w(z) = −1 + η(z)[γ(z) − 0.55] + Δw_bg(z), where η(z) captures the structure-dependent coupling and Δw_bg(z) accounts for intrinsic background evolution. Several physically motivated parameterizations of η(z) are proposed, including continuous forms (smooth transition and oscillatory) and a phenomenological piecewise model aligned with distinct phases of structure formation history. Built upon an interacting dark sector model that strictly conserves energy and momentum, the framework maintains the spacetime geometry of General Relativity while naturally addressing both the Hubble tension (via enhanced late-time expansion) and the S₈tension (via suppressed structure growth). A hierarchical Bayesian testing roadmap with Fisher forecasts demonstrates that upcoming surveys (DESI, Euclid, Roman) can decisively detect couplings of magnitude |η| ≳ 0.05 at high significance. The framework yields distinctive, testable predictions—including correlated w(z) and fσ₈(z) evolution, a gravitational slip parameter η_slip= 1 that distinguishes it from modified gravity theories, and scale-dependent signatures in the nonlinear regime—providing a comprehensive path to either validate or falsify the hypothesized dark energy–structure growth connection.