Mathematics

42 pages, 564 KB

Open AccessArticle

Black-Box Bug Amplification for Multithreaded Software

by Yeshayahu Weiss, Gal Amram, Achiya Elyasaf, Eitan Farchi, Oded Margalit and Gera Weiss

Mathematics 2025, 13(18), 2921; https://doi.org/10.3390/math13182921 - 9 Sep 2025

Bugs, especially those in concurrent systems, are often hard to reproduce because they manifest only under rare conditions. Testers frequently encounter failures that occur only under specific inputs, often at low probability. We propose an approach to systematically amplify the occurrence of such [...] Read more.

Bugs, especially those in concurrent systems, are often hard to reproduce because they manifest only under rare conditions. Testers frequently encounter failures that occur only under specific inputs, often at low probability. We propose an approach to systematically amplify the occurrence of such elusive bugs. We treat the system under test as a black-box system and use repeated trial executions to train a predictive model that estimates the probability of a given input configuration triggering a bug. We evaluate this approach on a dataset of 17 representative concurrency bugs spanning diverse categories. Several model-based search techniques are compared against a brute-force random sampling baseline. Our results show that an ensemble stacking classifier can significantly increase bug occurrence rates across nearly all scenarios, often achieving an order-of-magnitude improvement over random sampling. The contributions of this work include the following: (i) a novel formulation of bug amplification as a rare-event classification problem; (ii) an empirical evaluation of multiple techniques for amplifying bug occurrence, demonstrating the effectiveness of model-guided search; and (iii) a practical, non-invasive testing framework that helps practitioners to expose hidden concurrency faults without altering the internal system architecture. Full article

(This article belongs to the Special Issue Computational Approaches in Computer Science: Methods, Algorithms, and Applications)

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31 pages, 685 KB

Open AccessReview

A Review of Fractional Order Calculus Applications in Electric Vehicle Energy Storage and Management Systems

by Vicente Borja-Jaimes, Jorge Salvador Valdez-Martínez, Miguel Beltrán-Escobar, Alan Cruz-Rojas, Alfredo Gil-Velasco and Antonio Coronel-Escamilla

Mathematics 2025, 13(18), 2920; https://doi.org/10.3390/math13182920 - 9 Sep 2025

Abstract

Fractional-order calculus (FOC) has gained significant attention in electric vehicle (EV) energy storage and management systems, as it provides enhanced modeling and analysis capabilities compared to traditional integer-order approaches. This review presents a comprehensive survey of recent advancements in the application of FOC [...] Read more.

Fractional-order calculus (FOC) has gained significant attention in electric vehicle (EV) energy storage and management systems, as it provides enhanced modeling and analysis capabilities compared to traditional integer-order approaches. This review presents a comprehensive survey of recent advancements in the application of FOC to EV energy storage systems, including lithium-ion batteries (LIBs), supercapacitors (SCs), and fuel cells (FCs), as well as their integration within energy management systems (EMS). The review focuses on developments in electrochemical, equivalent circuit, and data-driven models formulated in the fractional-order domain, which improve the representation of nonlinear, memory-dependent, and multi-scale dynamics of energy storage devices. It also discusses the benefits and limitations of current FOC-based models, identifies open challenges such as computational feasibility and parameter identification, and outlines future research directions. Overall, the findings indicate that FOC offers a robust framework with significant potential to advance next-generation EV energy storage and management systems. Full article

(This article belongs to the Special Issue The Applications of Fractional Calculus in Control Engineering, Dynamical Systems and Signal Processing)

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23 pages, 1137 KB

Open AccessArticle

Adaptive Lavrentiev Regularization of Singular and Ill-Conditioned Discrete Boundary Value Problems in the Robust Multigrid Technique

by Sergey I. Martynenko and Aleksey Yu. Varaksin

Mathematics 2025, 13(18), 2919; https://doi.org/10.3390/math13182919 - 9 Sep 2025

Abstract

The paper presents a multigrid algorithm with the effective procedure for finding problem-dependent components of smoothers. The discrete Neumann-type boundary value problem is taken as a model problem. To overcome the difficulties caused by the singularity of the coefficient matrix of the resulting [...] Read more.

The paper presents a multigrid algorithm with the effective procedure for finding problem-dependent components of smoothers. The discrete Neumann-type boundary value problem is taken as a model problem. To overcome the difficulties caused by the singularity of the coefficient matrix of the resulting system of linear equations, the discrete Neumann-type boundary value problem is solved by direct Gauss elimination on the coarsest level. At finer grid levels, Lavrentiev (shift) regularization is used to construct the approximate solutions of singular or ill-conditioned problems. The regularization parameter for the unperturbed systems can be defined using the proximity of solutions obtained at the coarser grid levels. The paper presents the multigrid algorithm, an analysis of convergence and perturbation errors, a procedure for the definition of the starting guess for the Neumann boundary value problem satisfying the compatibility condition, and an extrapolation of solutions of regularized linear systems. This robust algorithm with the least number of problem-dependent components will be useful in solving the industrial problems. Full article

26 pages, 2173 KB

Open AccessArticle

RAMHA: A Hybrid Social Text-Based Transformer with Adapter for Mental Health Emotion Classification

by Mahander Kumar, Lal Khan and Ahyoung Choi

Mathematics 2025, 13(18), 2918; https://doi.org/10.3390/math13182918 - 9 Sep 2025

Abstract

Depression, stress, and anxiety are mental health disorders that are increasingly becoming a huge challenge in the digital age; at the same time, it is critical that they are detected early. Social media is a rich and complex source of emotional expressions that [...] Read more.

Depression, stress, and anxiety are mental health disorders that are increasingly becoming a huge challenge in the digital age; at the same time, it is critical that they are detected early. Social media is a rich and complex source of emotional expressions that requires intelligent systems that can decode subtle psychological states from natural language. This paper presents RAMHA (RoBERTa with Adapter-based Mental Health Analyzer), a hybrid deep learning model that combines RoBERTa, parameter-efficient adapter layers, BiLSTM, and attention mechanisms and is further optimized with focal loss to address the class imbalance problem. When tested on three filtered versions of the GoEmotions dataset, RAMHA shows outstanding results, with a maximum accuracy of 92% in binary classification and 88% in multiclass tasks. A large number of experiments are performed to compare RAMHA with eight standard baseline models, including SVM, LSTM, and BERT. In these experiments, RAMHA is able to consistently outperform the other models in terms of accuracy, precision, recall, and F1-score. Ablation studies further confirm the contributions of the individual components of the architecture, and comparative analysis demonstrates that RAMHA outperforms the best previously reported F1-scores by a substantial margin. The results of our study not only indicate the potential of the adapter-enhanced transformer in emotion-aware mental health screening but also establish a solid basis for its use in clinical and social settings. Full article

(This article belongs to the Section E1: Mathematics and Computer Science)

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31 pages, 2377 KB

Open AccessArticle

Adaptive Control Method for Initial Support Force of Self-Shifting Temporary Support Based on Pressure Feedback

by Rui Li, Dongjie Wang, Weixiong Zheng, Tong Li and Miao Wu

Mathematics 2025, 13(18), 2917; https://doi.org/10.3390/math13182917 - 9 Sep 2025

Abstract

To address the challenge of effective roof support in fully mechanized excavation roadways, this paper proposes an adaptive control method for the initial support force of self-shifting temporary supports based on pressure sensors. First, the mechanical characteristics of the roof in fully mechanized [...] Read more.

To address the challenge of effective roof support in fully mechanized excavation roadways, this paper proposes an adaptive control method for the initial support force of self-shifting temporary supports based on pressure sensors. First, the mechanical characteristics of the roof in fully mechanized excavation faces were analyzed, a static model of the roadway roof thin plate was established, the mechanical criteria for heading support were determined, and the reasonable calculation of the initial support force and working resistance for heading support was completed. Then, the pressure-control system of the hydraulic cylinder was modeled, achieving real-time online adjustment of PID control parameters based on fuzzy neural network control, and an adaptive control system for initial support force based on feedback from pressure sensors inside the hydraulic cylinder was constructed. Finally, comparative experiments of fuzzy neural network PID (FNN-PID) and fuzzy PID control were conducted in both the AMESim 2304 and Matlab/Simulink 2016 co-simulation environment and real physical scenarios. The effectiveness and advancement of the proposed control algorithm were verified. Full article

24 pages, 4156 KB

Open AccessFeature PaperArticle

Optimizing a Sustainable Inventory Model Under Limited Recovery Rates and Demand Sensitivity to Price, Carbon Emissions, and Stock Conditions

by Xi-Bin Lin, Jonas Chao-Pen Yu and Jen-Ming Chen

Mathematics 2025, 13(18), 2916; https://doi.org/10.3390/math13182916 - 9 Sep 2025

Abstract

The recovery, rework, or remanufacturing of returned products has received significant attention, leading to considerable advancements in green supply chain management. However, the impact of recovery mechanisms under demand sensitivity remains understudied. This study develops a sustainability model that incorporates limited recovery rates [...] Read more.

The recovery, rework, or remanufacturing of returned products has received significant attention, leading to considerable advancements in green supply chain management. However, the impact of recovery mechanisms under demand sensitivity remains understudied. This study develops a sustainability model that incorporates limited recovery rates and demand sensitivity to price, carbon emissions, and stock conditions. The analysis investigates the difference in profit when considering recovery and proposes a procedure for deriving optimal solutions using two key decision variables: unit sales price and cycle time, within a nonlinear profit model. The findings show that (i) the increase in total profit is significant and (ii) both sellers and consumers benefit from this mechanism. In addition, total profit is 15% higher, while the total cost is 22% lower than in the case without recovery. Consumers can purchase products at lower prices (−12%), and sellers can sell more products (+4%), thereby earning higher profit (+15%). Such a win–win policy aligns with environmental, social, and governance (ESG) regulations and supports a healthy, long-term supply chain relationship. Numerical examples and sensitivity analysis illustrate the characteristics of the proposed model. The results also provide managerial insights into enterprises’ limited recovery capacity. Full article

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15 pages, 385 KB

Open AccessArticle

Influence of Flexoelectric Coupling and Interfacial Imperfection on Shear Horizontal Wave Propagation in a Piezoflexoelectric Layer over an Elastic Substrate

by Ayman Alneamy, Kulandhaivel Hemalatha and Mohammed Tharwan

Mathematics 2025, 13(18), 2915; https://doi.org/10.3390/math13182915 - 9 Sep 2025

Abstract

This study analytically investigates shear horizontal (SH) wave propagation in a layered structure consisting of a piezoflexoelectric (PFE) layer bonded to an elastic substrate, considering an imperfect interface. A frequency equation is derived by applying appropriate boundary and interfacial conditions, capturing the effects [...] Read more.

This study analytically investigates shear horizontal (SH) wave propagation in a layered structure consisting of a piezoflexoelectric (PFE) layer bonded to an elastic substrate, considering an imperfect interface. A frequency equation is derived by applying appropriate boundary and interfacial conditions, capturing the effects of flexoelectric coupling, interface imperfections, the layer thickness, and the material properties. The resulting dispersion relation reveals that both interface imperfections and the flexoelectric strength significantly alter the phase velocity of SH waves. Numerical simulations show that increasing flexoelectric coefficients or interface imperfections lead to notable changes in dispersion behavior. Comparative analyses under electrically open (EO)- and electrically short (ES)-circuited boundary conditions demonstrate their impacts on wave propagation. These findings offer new insights into the design and analysis of piezoflexoelectric devices with realistic interface conditions. Full article

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22 pages, 1085 KB

Open AccessFeature PaperArticle

Kyber AHE: An Easy-to-Implement Additive Homomorphic Encryption Scheme Based on Kyber and Its Application in Biometric Template Protection

by Roberto Román, Rosario Arjona and Iluminada Baturone

Mathematics 2025, 13(18), 2914; https://doi.org/10.3390/math13182914 - 9 Sep 2025

Abstract

Homomorphic encryption solutions tend to be costly in terms of memory and computational resources, making them difficult to implement. In this paper, we present Kyber AHE, a lightweight additive homomorphic encryption scheme for computing the addition modulo 2 of two binary strings in [...] Read more.

Homomorphic encryption solutions tend to be costly in terms of memory and computational resources, making them difficult to implement. In this paper, we present Kyber AHE, a lightweight additive homomorphic encryption scheme for computing the addition modulo 2 of two binary strings in the encrypted domain. It is based on the CRYSTALS-Kyber public key encryption (PKE) scheme, which is the basis of the NIST module-lattice-based key-encapsulation mechanism standard. Apart from being quantum-safe, Kyber PKE has other interesting features such as the use of compressed ciphertexts, reduced sizes of keys, low execution times, and the ability to easily increase the security level. The operations performed in the encrypted domain by Kyber AHE are the decompression of ciphertexts, the component-wise modulo q addition of polynomials, and the compression of the results. A great advantage of Kyber AHE is that it can be easily implemented along with CRYSTALS-Kyber without the need for additional libraries. Among the applications of homomorphic encryption, biometric template protection schemes are a promising solution to provide data privacy by comparing biometric features in the encrypted domain. Therefore, we present the application of Kyber AHE for the protection of biometric templates. Experimental results have been obtained using Kyber AHE in an iris biometric template protection scheme with 256-byte features using Kyber512, Kyber768, and Kyber1024 instances. The sizes of the encrypted iris features are 6.0, 8.5, and 12.5 kB for NIST security levels I, III, and V, respectively. Using a commercial laptop, the encryption ranges from 0.755 to 1.73 ms, the evaluation from 0.096 to 0.161 ms, and the decryption from 0.259 to 0.415 ms, depending on the security level. Full article

(This article belongs to the Special Issue Advanced Research on Information System Security and Privacy, 2nd Edition)

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16 pages, 325 KB

Open AccessArticle

Fast Cluster Bootstrap Methods for Spatial Error Models

by Yu Zheng and Honggang Fan

Mathematics 2025, 13(18), 2913; https://doi.org/10.3390/math13182913 - 9 Sep 2025

Abstract

Typically, the traditional bootstrap methods for parameter inference of spatial error models suffer from high computational costs, so this study proposes fast cluster bootstrap methods for spatial error models to deal with the dilemma. The key idea is to calculate the sufficient statistics [...] Read more.

Typically, the traditional bootstrap methods for parameter inference of spatial error models suffer from high computational costs, so this study proposes fast cluster bootstrap methods for spatial error models to deal with the dilemma. The key idea is to calculate the sufficient statistics for each cluster before performing the bootstrap loop of the spatial error model, and based on these sufficient statistics, all quantities needed for bootstrap inference can be computed. Furthermore, this study performed Monte Carlo simulations, and the result reveals that compared with traditional bootstrap methods, our proposed methods can reduce the computational cost substantially and improve the reliability for obtaining the bootstrap test statistics and confidence intervals of the parameters for spatial error models. Full article

(This article belongs to the Section D: Statistics and Operational Research)

24 pages, 2295 KB

Open AccessArticle

A VMD-Based Four-Stage Hybrid Forecasting Model with Error Correction for Complex Coal Price Series

by Qing Qin and Lingxiao Li

Mathematics 2025, 13(18), 2912; https://doi.org/10.3390/math13182912 - 9 Sep 2025

Abstract

This study proposes a four-module “decomposition–forecasting–ensemble–correction” framework to improve the accuracy of complex coal price forecasts. The framework combines Variational Mode Decomposition (VMD), adaptive Autoregressive Integrated Moving Average (ARIMA) and Gated Recurrent Unit (GRU)-Attention forecasting models, a data-driven weighted ensemble strategy, and an [...] Read more.

This study proposes a four-module “decomposition–forecasting–ensemble–correction” framework to improve the accuracy of complex coal price forecasts. The framework combines Variational Mode Decomposition (VMD), adaptive Autoregressive Integrated Moving Average (ARIMA) and Gated Recurrent Unit (GRU)-Attention forecasting models, a data-driven weighted ensemble strategy, and an innovative error correction mechanism. Empirical analysis using the Bohai-Rim Steam–Coal Price Index (BSPI) shows that the framework significantly outperforms benchmark models, as validated by the Diebold–Mariano test. It reduces the Mean Absolute Percentage Error (MAPE) by 30.8% compared to a standalone GRU-Attention model, with the error correction module alone contributing a 25.1% MAPE reduction. This modular and transferable framework provides a promising approach for improving forecasting accuracy in complex and volatile economic time series. Full article

(This article belongs to the Special Issue Probability Statistics and Quantitative Finance)

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24 pages, 3425 KB

Open AccessArticle

A Dynamical Systems Model of Port–Industry–City Co-Evolution Under Data Constraints

by Huajiang Xu and Changxin Xu

Mathematics 2025, 13(18), 2911; https://doi.org/10.3390/math13182911 - 9 Sep 2025

Abstract

This study develops a dynamical systems framework for analyzing the co-evolution of port–industry–city (PIC) systems, with particular attention to the data-limited contexts often encountered in developing coastal regions. The model integrates time-delay differential equations and stochastic disturbances to capture nonlinear behaviors such as [...] Read more.

This study develops a dynamical systems framework for analyzing the co-evolution of port–industry–city (PIC) systems, with particular attention to the data-limited contexts often encountered in developing coastal regions. The model integrates time-delay differential equations and stochastic disturbances to capture nonlinear behaviors such as investment cycles, policy lags, and external shocks. By introducing dimensionless indicators and dynamic parameter adjustment, the framework reduces dependence on extensive datasets and enhances cross-regional applicability. The Kribi Deep Seaport in Cameroon serves as an illustrative case, demonstrating how the approach can reveal emergent trajectories under alternative development regimes. Simulation results identify three distinct pathways: capital-driven expansion with risks of premature overinvestment, industrial clustering modes requiring coordinated urban services, and policy-led strategies constrained by ecological thresholds and institutional inertia. Compared with conventional static or equilibrium-based models, this approach provides a mathematically rigorous tool for examining delay-driven, nonlinear interactions in complex socio-ecological systems. The framework highlights the value of dynamical systems analysis for scenario exploration, policy design, and sustainable governance in resource-constrained environments. Full article

(This article belongs to the Special Issue Dynamical Systems and Complex Systems)

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22 pages, 805 KB

Open AccessArticle

A Symmetric Quantum Perspective of Analytical Inequalities and Their Applications

by Muhammad Zakria Javed, Nimra Naeem, Muhammad Uzair Awan, Yuanheng Wang and Omar Mutab Alsalami

Mathematics 2025, 13(18), 2910; https://doi.org/10.3390/math13182910 - 9 Sep 2025

Abstract

This study explores some new symmetric quantum inequalities that are based on Breckner’s convexity. By using these concepts, we propose new versions of Hermite–Hadamard (H-H) and Fejer-type inequalities. Additionally, we establish a new integral identity which helped us to derive a set of [...] Read more.

This study explores some new symmetric quantum inequalities that are based on Breckner’s convexity. By using these concepts, we propose new versions of Hermite–Hadamard (H-H) and Fejer-type inequalities. Additionally, we establish a new integral identity which helped us to derive a set of new quantum inequalities. Using the symmetric quantum identity, Breckner’s convexity, and several other classical inequalities, we develop blended bounds for a general quadrature scheme. To ensure the significance of this study, a few captivating applications are discussed. Full article

(This article belongs to the Special Issue Mathematical Inequalities and Fractional Calculus)

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37 pages, 787 KB

Open AccessFeature PaperReview

Machine Learning for Enhancing Metaheuristics in Global Optimization: A Comprehensive Review

by Antonio Bolufé-Röhler and Dania Tamayo-Vera

Mathematics 2025, 13(18), 2909; https://doi.org/10.3390/math13182909 - 9 Sep 2025

Abstract

The integration of machine learning with metaheuristic optimization has emerged as one of the most promising frontiers in artificial intelligence and global search. Metaheuristics offer flexibility and effectiveness in solving complex optimization problems where gradients are unavailable or unreliable, but often struggle with [...] Read more.

The integration of machine learning with metaheuristic optimization has emerged as one of the most promising frontiers in artificial intelligence and global search. Metaheuristics offer flexibility and effectiveness in solving complex optimization problems where gradients are unavailable or unreliable, but often struggle with premature convergence, parameter sensitivity, and poor scalability. ML techniques, especially supervised, unsupervised, reinforcement, and meta-learning, provide powerful tools to address these limitations through adaptive, data-driven, and intelligent search strategies. This review presents a comprehensive survey of ML-enhanced metaheuristics for global optimization. We introduce a functional taxonomy that categorizes integration strategies based on their role in the optimization process, from operator control and surrogate modeling to landscape learning and learned optimizers. We critically analyze representative techniques, identify emerging trends, and highlight key challenges and future directions. The paper aims to serve as a structured and accessible resource for advancing the design of intelligent, learning-enabled optimization algorithms. Full article

(This article belongs to the Special Issue Heuristic Optimization and Machine Learning)

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17 pages, 1593 KB

Open AccessArticle

Forecasting Upper Bounds for Daily New COVID-19 Infections Using Tolerance Limits

by Hsiuying Wang

Mathematics 2025, 13(18), 2908; https://doi.org/10.3390/math13182908 - 9 Sep 2025

Abstract

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in Wuhan, China, in December 2019. Since then, it has evolved into a global pandemic. Forecasting the number of COVID-19 cases is a crucial task that can [...] Read more.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in Wuhan, China, in December 2019. Since then, it has evolved into a global pandemic. Forecasting the number of COVID-19 cases is a crucial task that can greatly aid management decisions. Numerous methods have been proposed in the literature to forecast COVID-19 case numbers; however, most do not yield highly accurate results. Rather than focusing solely on predicting exact case numbers, providing robust upper bounds may offer a more practical approach to support effective decision-making and resource preparedness. This study proposes the use of tolerance interval methods to construct upper bounds for daily new COVID-19 case numbers. The tolerance limits derived from the normal, Poisson, and negative binomial distributions are compared. These methods rely either on historical data alone or on a combination of historical data and auxiliary data from other regions. The results demonstrate that the proposed methods can generate informative upper bounds for COVID-19 case counts, offering a valuable alternative to traditional forecasting models that emphasize exact number estimation. This approach can improve pandemic preparedness through better equipment planning, resource allocation, and timely response strategies. Full article

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22 pages, 1286 KB

Open AccessArticle

Multiclass Classification of Sarcopenia Severity in Korean Adults Using Machine Learning and Model Fusion Approaches

by Arslon Ruziboev, Dilmurod Turimov, Jiyoun Kim and Wooseong Kim

Mathematics 2025, 13(18), 2907; https://doi.org/10.3390/math13182907 - 9 Sep 2025

Abstract

This study presents a unified machine learning strategy for identifying various degrees of sarcopenia severity in older adults. The approach combines three optimized algorithms (Random Forest, Gradient Boosting, and Multilayer Perceptron) into a stacked ensemble model, which is assessed with clinical data. A [...] Read more.

This study presents a unified machine learning strategy for identifying various degrees of sarcopenia severity in older adults. The approach combines three optimized algorithms (Random Forest, Gradient Boosting, and Multilayer Perceptron) into a stacked ensemble model, which is assessed with clinical data. A thorough data preparation process involved synthetic minority oversampling to ensure class balance and a dual approach to feature selection using Least Absolute Shrinkage and Selection Operator regression and Random Forest importance. The integrated model achieved remarkable performance with an accuracy of 96.99%, an F1 score of 0.9449, and a Cohen’s Kappa coefficient of 0.9738 while also demonstrating excellent calibration (Brier Score: 0.0125). Interpretability analysis through SHapley Additive exPlanations values identified appendicular skeletal muscle mass, body weight, and functional performance metrics as the most significant predictors, enhancing clinical relevance. The ensemble approach showed superior generalization across all sarcopenia classes compared to individual models. Although limited by dataset representativeness and the use of conventional multiclass classification techniques, the framework shows considerable promise for non-invasive sarcopenia risk assessments and exemplifies the value of interpretable artificial intelligence in geriatric healthcare. Full article

(This article belongs to the Special Issue Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications, 2nd Edition)

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19 pages, 1130 KB

Open AccessArticle

Optimizing Mine Ventilation Systems: An Advanced Mixed-Integer Linear Programming Model

by Deyun Zhong, Lixue Wen, Yulong Liu, Zhaohao Wu and Liguan Wang

Mathematics 2025, 13(18), 2906; https://doi.org/10.3390/math13182906 - 9 Sep 2025

Abstract

In the underground mine ventilation area, the absence of robust solutions for nonlinear programming models has impeded progress for decades. To overcome the enduring difficulty of solving nonlinear optimization models for mine ventilation optimization, a major technical bottleneck, we first develop an advanced [...] Read more.

In the underground mine ventilation area, the absence of robust solutions for nonlinear programming models has impeded progress for decades. To overcome the enduring difficulty of solving nonlinear optimization models for mine ventilation optimization, a major technical bottleneck, we first develop an advanced linear optimization technique. This method transforms the nonlinear ventilation optimization and regulation model into a linear control model, avoiding the limitation of difficulty in solving the nonlinear mathematical model. The linear strategy opens up a new solution idea for the nonlinear calculation of the mine ventilation optimization and regulation. Furthermore, this study introduces evaluation metrics for ventilation scheme quality, including minimal energy consumption, fewest adjustment points, and optimal placement of these points, enhancing flexibility in ventilation network optimization. By analyzing the ventilation model control objectives and constraints, we formulated a linear optimization model and developed a multi-objective mixed-integer programming model for ventilation network optimization. This paper constructs and verifies a calculation example model for mine ventilation optimization, assessing its reliability based on airflow distribution calculations. Full article

(This article belongs to the Special Issue Mathematical Modeling and Analysis in Mining Engineering)

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28 pages, 6268 KB

Open AccessArticle

Robustness Evaluation and Enhancement Strategy of Cloud Manufacturing Service System Based on Hybrid Modeling

by Xin Zheng, Beiyu Yi and Hui Min

Mathematics 2025, 13(18), 2905; https://doi.org/10.3390/math13182905 - 9 Sep 2025

Abstract

In dynamic and open cloud service processes, particularly in distributed networked manufacturing environments, the complex and volatile manufacturing landscape introduces numerous uncertainties and disturbances. This paper addresses the common issue of cloud resource connection interruptions by proposing a path substitution strategy based on [...] Read more.

In dynamic and open cloud service processes, particularly in distributed networked manufacturing environments, the complex and volatile manufacturing landscape introduces numerous uncertainties and disturbances. This paper addresses the common issue of cloud resource connection interruptions by proposing a path substitution strategy based on alternative service routes. By integrating agent-based simulation and complex network methodologies, a simulation model for evaluating the robustness of cloud manufacturing service systems is developed, enabling dynamic simulation and quantitative decision-making for the proposed robustness enhancement strategies. First, a hybrid modeling approach for cloud manufacturing service systems is proposed to meet the needs of robustness analysis. The specific construction of the hybrid simulation model is achieved using the AnyLogic 8.7.4 simulation software and Java-based secondary development techniques. Second, a complex network model focusing on cloud manufacturing resource entities is further constructed based on the simulation model. By combining the two models, two-dimensional robustness evaluation indicators—comprising performance robustness and structural robustness—are established. Then, four types of edge attack strategies are designed based on the initial topology and recomputed topology. To ensure system operability after edge failures, a path substitution strategy is proposed by introducing redundant routes. Finally, a case study of a cloud manufacturing project is conducted. The results show the following: (1) The proposed robustness evaluation model fully captures complex disturbance scenarios in cloud manufacturing, and the designed simulation experiments support the evaluation and comparative analysis of robustness improvement strategies from both performance and structural robustness dimensions. (2) The path substitution strategy significantly enhances the robustness of cloud manufacturing services, though its effects on performance and structural robustness vary across different disturbance scenarios. Full article

(This article belongs to the Special Issue Interdisciplinary Modeling and Analysis of Complex Systems)

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Mathematics, Volume 13, Issue 18 (September-2 2025) – 17 articles

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