Mathematics

18 pages, 7011 KiB

Open AccessArticle

A Cooperative Game Hybrid Optimization Algorithm Applied to UAV Inspection Path Planning in Urban Pipe Corridors

by Chuanyue Wang, Lei Zhang, Yifan Gao, Xiaoyuan Zheng and Qianling Wang

Mathematics 2023, 11(16), 3620; https://doi.org/10.3390/math11163620 - 21 Aug 2023

Cited by 1 | Viewed by 831

This paper proposes an improved algorithm applied to path planning for the inspection of unmanned aerial vehicles (UAVs) in urban pipe corridors, which introduces a collaborative game between spherical vector particle swarm optimization (SPSO) and differential evolution (DE) algorithms. Firstly, a high-precision 3D [...] Read more.

This paper proposes an improved algorithm applied to path planning for the inspection of unmanned aerial vehicles (UAVs) in urban pipe corridors, which introduces a collaborative game between spherical vector particle swarm optimization (SPSO) and differential evolution (DE) algorithms. Firstly, a high-precision 3D grid map model of urban pipe corridors is constructed based on the actual urban situation. Secondly, the cost function is formulated, and the constraints for ensuring the safe and smooth inspection of UAVs are proposed to transform path planning into an optimization problem. Finally, a hybrid algorithm of SPSO and DE algorithms based on the Nash bargaining theory is proposed by introducing a cooperative game model for optimizing the cost function to plan the optimal path of UAV inspection in complex urban pipe corridors. To evaluate the performance of the proposed algorithm (GSPSODE), the SPSO, DE, genetic algorithm (GA), and ant colony optimization (ACO) are compared with GSPSODE, and the results show that GSPSODE is superior to other methods in UAV inspection path planning. However, the selection of algorithm parameters, the difference in the experimental environment, and the randomness of experimental results may affect the accuracy of experimental results. In addition, a high-precision urban pipe corridors scenario is constructed based on the RflySim platform to dynamically simulate the optimal path planning of UAV inspection in real urban pipe corridors. Full article

(This article belongs to the Special Issue Artificial Intelligence and Optimization in Aircraft Design and Unmanned Aerial Vehicles)

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28 pages, 12632 KiB

Open AccessArticle

Novel Integer Shmaliy Transform and New Multiparametric Piecewise Linear Chaotic Map for Joint Lossless Compression and Encryption of Medical Images in IoMTs

by Achraf Daoui, Haokun Mao, Mohamed Yamni, Qiong Li, Osama Alfarraj and Ahmed A. Abd El-Latif

Mathematics 2023, 11(16), 3619; https://doi.org/10.3390/math11163619 - 21 Aug 2023

Cited by 2 | Viewed by 911

Abstract

The discrete Shmaliy moment transform (DST) is a type of discrete orthogonal moment transform that is widely used in signal and image processing. However, DST is not suitable for lossless image applications due to its non-integer reversible nature. To overcome this limitation, we [...] Read more.

The discrete Shmaliy moment transform (DST) is a type of discrete orthogonal moment transform that is widely used in signal and image processing. However, DST is not suitable for lossless image applications due to its non-integer reversible nature. To overcome this limitation, we introduce the integer discrete Shmaliy transform (IDST) that performs integer-to-integer encoding, leading to a perfect and unique reconstruction of the input image. Next, a new 1D chaotic system model, the 1D multiparametric piecewise linear chaotic map (M-PWLCM), is presented as an extension of the existing 1D PWLCM. The M-PWLCM includes eight control parameters defined over an unlimited interval. To demonstrate the relevance of IDST and M-PWLCM in reversible image processing applications, they are used in a new scheme for lossless compression and encryption of medical images in the internet of medical things (IoMTs). On the one hand, the simulation results show that our scheme offers a good compression ratio and a higher level of security to resist differential attacks, brute force attacks and statistical attacks. On the other hand, the comparative analysis carried out shows the overall superiority of our scheme over similar state-of-the-art ones, both in achieving a higher compression ratio and better security when communicating medical images over unsecured IoMTs. Full article

(This article belongs to the Special Issue Recent Advances in Security, Privacy, and Applied Cryptography, 2nd Edition)

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18 pages, 8825 KiB

Open AccessArticle

Numerical Investigation on Suction Flow Control Technology for a Blunt Trailing Edge Hydrofoil

by Peng Yang, Chiye Zhang, Hongyeyu Yan, Yifan Ren, Changliang Ye, Yaguang Heng and Yuan Zheng

Mathematics 2023, 11(16), 3618; https://doi.org/10.3390/math11163618 - 21 Aug 2023

Viewed by 747

Abstract

The generation of hydro-mechanical resonance is related to the transition of the boundary layer and the development of vortex shedding. The application effect of suction control in hydrodynamics is equally deserving of consideration as an active control technique in aerodynamics. This study examines [...] Read more.

The generation of hydro-mechanical resonance is related to the transition of the boundary layer and the development of vortex shedding. The application effect of suction control in hydrodynamics is equally deserving of consideration as an active control technique in aerodynamics. This study examines how suction control affects the flow field of the NACA0009 blunt trailing edge hydrofoil using the γ transition model. Firstly, the accuracy of the numerical method is checked by performing a three-dimensional hydrofoil numerical simulation. Based on this, three-dimensional hydrofoil suction control research is conducted. According to the results, the suction control increases the velocity gradient in the boundary layer and delays the position of transition. The frequency of vortex shedding in the wake region lowers, and the peak value of velocity fluctuation declines. The hydrofoil hydrodynamic performance may be successfully improved with a proper selection of the suction coefficient via research of the suction coefficient and suction position on the flow field around the hydrofoil. The lift/drag ratio goes up as the suction coefficient goes up. The boundary layer displacement thickness and momentum thickness are at their lowest points, and the velocity fluctuation amplitude in the wake region is at its lowest point as the suction coefficient C_μ = 0.003. When the suction slots are at the leading edge, the momentum loss in the boundary layer is minimal and the velocity fluctuation in the wake zone is negligible. Full article

(This article belongs to the Special Issue Computational Methods and Applications for Numerical Analysis, 2nd Edition)

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15 pages, 1456 KiB

Open AccessArticle

Enhanced Internet of Things Security Situation Assessment Model with Feature Optimization and Improved SSA-LightGBM

by Baoshan Xie, Fei Li, Hao Li, Liya Wang and Aimin Yang

Mathematics 2023, 11(16), 3617; https://doi.org/10.3390/math11163617 - 21 Aug 2023

Viewed by 895

Abstract

In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using [...] Read more.

In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using independent component analysis (ICA), and the weights of all features are calculated and fused using the maximum relevance minimum redundancy (mRMR) algorithm, Spearman’s rank correlation coefficient, and extreme gradient boosting (XGBoost) feature importance method to filter out the optimal subset of features. Piecewise chaotic mapping and firefly perturbation strategies are then used to optimize the sparrow search algorithm (SSA) to achieve fast convergence and prevent getting trapped in local optima, and then the optimized algorithm is used to improve the light gradient boosting machine (LightGBM) algorithm. Finally, the improved LightGBM method is used for training to calculate situation values based on a threat impact to assess the IoT network security situation. The research findings reveal that the model attained an evaluation accuracy of 99.34%, sustained a mean square error at the 0.00001 level, and reached its optimum convergence value by the 45th iteration with the fastest convergence speed. This enables the model to more effectively evaluate the IoT network security status. Full article

(This article belongs to the Special Issue Analytical Frameworks and Methods for Cybersecurity)

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28 pages, 999 KiB

Open AccessReview

A Survey on Fair Allocation of Chores

by Hao Guo, Weidong Li and Bin Deng

Mathematics 2023, 11(16), 3616; https://doi.org/10.3390/math11163616 - 21 Aug 2023

Viewed by 1281

Abstract

Wherever there is group life, there has been a social division of labor and resource allocation, since ancient times. Examples include ant colonies, bee colonies, and wolf colonies. Different roles are responsible for different tasks. The same is true of human beings. Human [...] Read more.

Wherever there is group life, there has been a social division of labor and resource allocation, since ancient times. Examples include ant colonies, bee colonies, and wolf colonies. Different roles are responsible for different tasks. The same is true of human beings. Human beings are the largest social group in nature, among whom there are intricate social networks and interest networks between individuals. In such a complex relationship, how do decision makers allocate resources or tasks to individuals in a fair way? This is a topic worthy of further study. In recent decades, fair allocation has been at the core of research in economics, mathematics and other fields. The fair allocation problem is to assign a set of items to a set of agents so that each agent’s allocation is as fair as possible to satisfy each agent. The fairness measurements followed in current research include envy-freeness, proportionality, equitability, maximin share fairness, competitive equilibrium, maximum Nash social diswelfare, and so on. In this paper, the main concern is the allocation of chores. We discuss this problem in two parts: divisible and indivisible. We comprehensively review the existing results, algorithms, and approximations that meet various fairness criteria in chronological order. The relevant results of achieving fairness and efficiency are also discussed. In addition, we propose some open questions and future research directions for this problem based on existing research. Full article

(This article belongs to the Special Issue Optimisation Algorithms and Their Applications)

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20 pages, 12456 KiB

Open AccessArticle

ATC-YOLOv5: Fruit Appearance Quality Classification Algorithm Based on the Improved YOLOv5 Model for Passion Fruits

by Changhong Liu, Weiren Lin, Yifeng Feng, Ziqing Guo and Zewen Xie

Mathematics 2023, 11(16), 3615; https://doi.org/10.3390/math11163615 - 21 Aug 2023

Cited by 1 | Viewed by 1785

Abstract

Passion fruit, renowned for its significant nutritional, medicinal, and economic value, is extensively cultivated in subtropical regions such as China, India, and Vietnam. In the production and processing industry, the quality grading of passion fruit plays a crucial role in the supply chain. [...] Read more.

Passion fruit, renowned for its significant nutritional, medicinal, and economic value, is extensively cultivated in subtropical regions such as China, India, and Vietnam. In the production and processing industry, the quality grading of passion fruit plays a crucial role in the supply chain. However, the current process relies heavily on manual labor, resulting in inefficiency and high costs, which reflects the importance of expanding the application of fruit appearance quality classification mechanisms based on computer vision. Moreover, the existing passion fruit detection algorithms mainly focus on real-time detection and overlook the quality-classification aspect. This paper proposes the ATC-YOLOv5 model based on deep learning for passion fruit detection and quality classification. First, an improved Asymptotic Feature Pyramid Network (APFN) is utilized as the feature-extraction network, which is the network modified in this study by adding weighted feature concat pathways. This optimization enhances the feature flow between different levels and nodes, allowing for the adaptive and asymptotic fusion of richer feature information related to passion fruit quality. Secondly, the Transformer Cross Stage Partial (TRCSP) layer is constructed based on the introduction of the Multi-Head Self-Attention (MHSA) layer in the Cross Stage Partial (CSP) layer, enabling the network to achieve a better performance in modeling long-range dependencies. In addition, the Coordinate Attention (CA) mechanism is introduced to enhance the network’s learning capacity for both local and non-local information, as well as the fine-grained features of passion fruit. Moreover, to validate the performance of the proposed model, a self-made passion fruit dataset is constructed to classify passion fruit into four quality grades. The original YOLOv5 serves as the baseline model. According to the experimental results, the mean average precision (mAP) of ATC-YOLOv5 reaches 95.36%, and the mean detection time (mDT) is 3.2 ms, which improves the mAP by 4.83% and the detection speed by 11.1%, and the number of parameters is reduced by 10.54% compared to the baseline, maintaining the lightweight characteristics while improving the accuracy. These experimental results validate the high detection efficiency of the proposed model for fruit quality classification, contributing to the realization of intelligent agriculture and fruit industries. Full article

(This article belongs to the Special Issue Computer Vision, Image Processing Technologies and Artificial Intelligence)

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22 pages, 673 KiB

Open AccessArticle

Innovating and Pricing Carbon-Offset Options of Asian Styles on the Basis of Jump Diffusions and Fractal Brownian Motions

by Yue Qi and Yue Wang

Mathematics 2023, 11(16), 3614; https://doi.org/10.3390/math11163614 - 21 Aug 2023

Viewed by 835

Abstract

Due to CO

_{2}

emissions, humans are encountering grave environmental crises (e.g., rising sea levels and the grim future of submerged cities). Governments have begun to offset emissions by constructing emission-trading schemes (carbon-offset markets). Investors naturally crave carbon-offset options to effectively control risk. [...] Read more.

Due to CO

_{2}

emissions, humans are encountering grave environmental crises (e.g., rising sea levels and the grim future of submerged cities). Governments have begun to offset emissions by constructing emission-trading schemes (carbon-offset markets). Investors naturally crave carbon-offset options to effectively control risk. However, the research and practice for these options are relatively limited. This paper contributes to the literature in this area. Specifically, according to carbon-emission allowances’ empirical distributions, we implement fractal Brownian motions and jump diffusions instead of traditional geometric Brownian motions. We contribute to extending the theoretical model based on carbon-offset option-pricing methods. We innovate the carbon-offset options of Asian styles. We authenticate the options’ stochastic differential equations and analytically price the options in the form of theorems. We verify the parameter sensitivity of pricing formulas by illustrations. We also elucidate the practical implications of an emission-trading scheme. Full article

(This article belongs to the Special Issue Mathematical Methods in Energy Economy)

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16 pages, 16540 KiB

Open AccessArticle

ADDA: An Adversarial Direction-Guided Decision-Based Attack via Multiple Surrogate Models

by Wanman Li and Xiaozhang Liu

Mathematics 2023, 11(16), 3613; https://doi.org/10.3390/math11163613 - 21 Aug 2023

Viewed by 775

Abstract

Over the past decade, Convolutional Neural Networks (CNNs) have been extensively deployed in security-critical areas; however, the security of CNN models is threatened by adversarial attacks. Decision-based adversarial attacks, wherein an attacker relies solely on the final output label of the target model [...] Read more.

Over the past decade, Convolutional Neural Networks (CNNs) have been extensively deployed in security-critical areas; however, the security of CNN models is threatened by adversarial attacks. Decision-based adversarial attacks, wherein an attacker relies solely on the final output label of the target model to craft adversarial examples, are the most challenging yet practical adversarial attacks. However, existing decision-based adversarial attacks generally suffer from poor query efficiency or low attack success rate, especially for targeted attacks. To address these issues, we propose a query-efficient Adversarial Direction-guided Decision-based Attack (ADDA), which exploits the advantages of transfer-based priors and the benefits of a single query. The transfer-based priors provided by the gradients of multiple different surrogate models can be utilized to suggest the most promising search directions for generating adversarial examples. The query consumption during the ADDA attack is mainly derived from a single query evaluation of the candidate adversarial samples, which significantly saves the number of queries. Experimental results on several ImageNet classifiers, including

l_{\infty}

and

l_{2}

threat models, demonstrate that our proposed approach overwhelmingly outperforms existing state-of-the-art decision-based attacks in terms of both query efficiency and attack success rate. We show case studies of ADDA against a real-world API in which it is successfully able to fool the Google Cloud Vision API after only a few queries. Full article

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

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14 pages, 1900 KiB

Open AccessArticle

Consumer Sentiment and Luxury Behavior in the United States before and after COVID-19: Time Trends and Persistence Analysis

by Berta Marcos Ceron and Manuel Monge

Mathematics 2023, 11(16), 3612; https://doi.org/10.3390/math11163612 - 21 Aug 2023

Cited by 2 | Viewed by 1443

Abstract

This paper analyzes the stochastic properties of consumer sentiment to understand how they affected the luxury sector in the United States before and after COVID-19. The results were derived using fractional integration methodologies and suggest that, before the pandemic episode, both variables were [...] Read more.

This paper analyzes the stochastic properties of consumer sentiment to understand how they affected the luxury sector in the United States before and after COVID-19. The results were derived using fractional integration methodologies and suggest that, before the pandemic episode, both variables were expected to be mean reverting and the shocks were transitory, having similar behavior. However, after the appearance of COVID-19, results suggest that consumer sentiment recovered before the luxury sector. Results from the use of cointegration methodologies show that the effects of COVID-19 disappeared in the short-run. Finally, the sentiment of consumers acts as a leading indicator of the behavior of the luxury sector according to wavelet analysis. Thus, an increase in consumer sentiment implies an increase of 3.6% in the luxury sector. Full article

(This article belongs to the Special Issue Applied Time Series and Artificial Intelligence in Economics and Finance)

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14 pages, 1247 KiB

Open AccessFeature PaperArticle

EvolveNet: Evolving Networks by Learning Scale of Depth and Width

by Athul Shibu and Dong-Gyu Lee

Mathematics 2023, 11(16), 3611; https://doi.org/10.3390/math11163611 - 21 Aug 2023

Cited by 1 | Viewed by 767

Abstract

Convolutional neural networks (CNNs) have shown decent performance in a variety of computer vision tasks. However, these network configurations are largely hand-crafted, which leads to inefficiency in the constructed network. Various other algorithms have been proposed to address this issue, but the inefficiencies [...] Read more.

Convolutional neural networks (CNNs) have shown decent performance in a variety of computer vision tasks. However, these network configurations are largely hand-crafted, which leads to inefficiency in the constructed network. Various other algorithms have been proposed to address this issue, but the inefficiencies resulting from human intervention have not been addressed. Our proposed EvolveNet algorithm is a task-agnostic evolutionary search algorithm that can find optimal depth and width scales automatically in an efficient way. The optimal configurations are not found using grid search, and are instead evolved from an existing network. This eliminates inefficiencies that emanate from hand-crafting, thus reducing the drop in accuracy. The proposed algorithm is a framework to search through a large search space of subnetworks until a suitable configuration is found. Extensive experiments on the ImageNet dataset demonstrate the superiority of the proposed method by outperforming the state-of-the-art methods. Full article

(This article belongs to the Special Issue Deep Learning Algorithms in Computational Intelligence: Advances and Applications)

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33 pages, 1652 KiB

Open AccessArticle

Platform Operations under Dual-Channel Catering Supply Chain

by Xin Li, Kenan Li and Yongjian Li

Mathematics 2023, 11(16), 3610; https://doi.org/10.3390/math11163610 - 21 Aug 2023

Viewed by 861

Abstract

In the modern catering business model, restaurants usually use established platforms to promote their food and use two channels to sell their food: online and offline sales. We construct demand functions for online and offline, considering promotion and substitution relationships by a revised [...] Read more.

In the modern catering business model, restaurants usually use established platforms to promote their food and use two channels to sell their food: online and offline sales. We construct demand functions for online and offline, considering promotion and substitution relationships by a revised Bertrand model. We first consider three classic models: the decentralized decision model, the equilibrium decision model, and the centralized decision model. In the decentralized decision model, the platform decides both the promotional effort and the online discount; in the equilibrium decision model, the platform decides the online discount, while the food service provider decides the promotional effort. In the centralized decision model, the takeaway platform and the food service provider have maximized the overall profit as the decisive goal. We find that the online discount decreases in price when the impact factor of the online promotion is high but increases in price when the impact factor of the online promotion is low. Then, we analyze and compare the results under three models. We find that when the substitution factor is low enough, or the impactor factor of online promotion is low enough, the global optimal platform discount is higher than the equilibrium platform discount and the decentralized online discount; otherwise, the results are the opposite. In addition, the global optimal promotional effort is always higher than the optimal promotional effort in the decentralized model. When the substitution factor is low enough, or the impactor factor of online promotion is low enough, the global optimal promotional effort is higher than the equilibrium optimal promotional effort; otherwise, the result is the opposite. Full article

(This article belongs to the Special Issue Modeling, Simulation and Optimization of Supply Chains)

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13 pages, 2311 KiB

Open AccessArticle

Birational Quadratic Planar Maps with Generalized Complex Rational Representations

by Xuhui Wang, Yuhao Han, Qian Ni, Rui Li and Ron Goldman

Mathematics 2023, 11(16), 3609; https://doi.org/10.3390/math11163609 - 21 Aug 2023

Viewed by 618

Abstract

Complex rational maps have been used to construct birational quadratic maps based on two special syzygies of degree one. Similar to complex rational curves, rational curves over generalized complex numbers have also been constructed by substituting the imaginary unit with a new independent [...] Read more.

Complex rational maps have been used to construct birational quadratic maps based on two special syzygies of degree one. Similar to complex rational curves, rational curves over generalized complex numbers have also been constructed by substituting the imaginary unit with a new independent quantity. We first establish the relationship between degree one, generalized, complex rational Bézier curves and quadratic rational Bézier curves. Then we provide conditions to determine when a quadratic rational planar map has a generalized complex rational representation. Thus, a rational quadratic planar map can be made birational by suitably choosing the middle Bézier control points and their corresponding weights. In contrast to the edges of complex rational maps of degree one, which are circular arcs, the edges of the planar maps can be generalized to hyperbolic and parabolic arcs by invoking the hyperbolic and parabolic numbers. Full article

(This article belongs to the Section Computational and Applied Mathematics)

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3 pages, 149 KiB

Open AccessEditorial

Preface to the Special Issue on “Mathematical Methods for Computer Science”

by Zhongyun Hua and Yushu Zhang

Mathematics 2023, 11(16), 3608; https://doi.org/10.3390/math11163608 - 21 Aug 2023

Viewed by 546

Abstract

In the last few decades, the relationship between mathematics and algorithms has become increasingly important and influential in computer science [...] Full article

(This article belongs to the Special Issue Mathematical Methods for Computer Science)

18 pages, 361 KiB

Open AccessFeature PaperArticle

Development of the Method of Averaging in Clifford Geometric Algebras

by Dmitry Shirokov

Mathematics 2023, 11(16), 3607; https://doi.org/10.3390/math11163607 - 21 Aug 2023

Viewed by 587

Abstract

We develop the method of averaging in Clifford (geometric) algebras suggested by the author in previous papers. We consider operators constructed using two different sets of anticommuting elements of real or complexified Clifford algebras. These operators generalize Reynolds operators from the representation theory [...] Read more.

We develop the method of averaging in Clifford (geometric) algebras suggested by the author in previous papers. We consider operators constructed using two different sets of anticommuting elements of real or complexified Clifford algebras. These operators generalize Reynolds operators from the representation theory of finite groups. We prove a number of new properties of these operators. Using the generalized Reynolds operators, we give a complete proof of the generalization of Pauli’s theorem to the case of Clifford algebras of arbitrary dimension. The results can be used in geometry, physics, engineering, computer science, and other applications. Full article

(This article belongs to the Special Issue Applications of Geometric Algebra)

17 pages, 4761 KiB

Open AccessArticle

Computational Evaluation of IABP, Impella 2.5, TandemHeart and Combined IABP and Impella 2.5 Support in Cardiogenic Shock

by Rahmi Alkan, Beatrice De Lazzari, Massimo Capoccia, Claudio De Lazzari and Selim Bozkurt

Mathematics 2023, 11(16), 3606; https://doi.org/10.3390/math11163606 - 21 Aug 2023

Cited by 1 | Viewed by 1038

Abstract

Cardiogenic shock is a life-threatening condition consisting of low cardiac output status leading to end-organ hypoperfusion following either acute left or right ventricular failure or decompensation of chronic heart failure. Partial or failed response to inotropic support in the acute phase may require [...] Read more.

Cardiogenic shock is a life-threatening condition consisting of low cardiac output status leading to end-organ hypoperfusion following either acute left or right ventricular failure or decompensation of chronic heart failure. Partial or failed response to inotropic support in the acute phase may require the use of mechanical circulatory support. Although patients supported with different devices such as an IABP, Impella 2.5, or TandemHeart experience stability in the short term, the haemodynamic benefits of each device remain unclear. The aim of this study is to present a direct comparison of an IABP, Impella 2.5, TandemHeart, and combined IABP and Impella 2.5 support in cardiogenic shock to evaluate haemodynamic variables and left ventricular unloading using cardiovascular system modelling and simulation in terms of cardiac function, systemic, pulmonary, cardiac, and cerebral circulations. The simulation results showed that the IABP had a relatively low effect on the haemodynamic variables. Although both Impella 2.5 and TandemHeart improved the total blood flow rates, as well as coronary and cerebral perfusion with the increasing pump operating speed, TandemHeart had a more profound effect on the haemodynamic variables. Combining the IABP and Impella 2.5 also improved the haemodynamics, although at the expense of reverse blood flow in the cerebral circulation. Simulation results showed that TandemHeart support might have a more beneficial effect on the haemodynamics and left ventricular energetics in comparison to the IABP and Impella 2.5. Nevertheless, the combined use of the IABP and Impella 2.5 for short-term support may be considered an appropriate alternative. Full article

(This article belongs to the Special Issue Trends and Prospects of Numerical Modelling in Bioengineering)

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14 pages, 6883 KiB

Open AccessArticle

Searching for Optimal Oversampling to Process Imbalanced Data: Generative Adversarial Networks and Synthetic Minority Over-Sampling Technique

by Gayeong Eom and Haewon Byeon

Mathematics 2023, 11(16), 3605; https://doi.org/10.3390/math11163605 - 21 Aug 2023

Viewed by 1253

Abstract

Classification problems due to data imbalance occur in many fields and have long been studied in the machine learning field. Many real-world datasets suffer from the issue of class imbalance, which occurs when the sizes of classes are not uniform; thus, data belonging [...] Read more.

Classification problems due to data imbalance occur in many fields and have long been studied in the machine learning field. Many real-world datasets suffer from the issue of class imbalance, which occurs when the sizes of classes are not uniform; thus, data belonging to the minority class are likely to be misclassified. It is particularly important to overcome this issue when dealing with medical data because class imbalance inevitably arises due to incidence rates within medical datasets. This study adjusted the imbalance ratio (IR) within the National Biobank of Korea dataset “Epidemiologic data of Parkinson’s disease dementia patients” to values of 6.8 (raw data), 9, and 19 and compared four traditional oversampling methods with techniques using the conditional generative adversarial network (CGAN) and conditional tabular generative adversarial network (CTGAN). The results showed that when the classes were balanced with CGAN and CTGAN, they showed a better classification performance than the more traditional oversampling techniques based on the AUC and F1-score. We were able to expand the application scope of GAN, widely used in unstructured data, to structured data. We also offer a better solution for the imbalanced data problem and suggest future research directions. Full article

(This article belongs to the Special Issue Class-Imbalance and Cost-Sensitive Learning)

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11 pages, 289 KiB

Open AccessArticle

Unveiling the Potential of Sheffer Polynomials: Exploring Approximation Features with Jakimovski–Leviatan Operators

by Mohra Zayed, Shahid Ahmad Wani and Mohammad Younus Bhat

Mathematics 2023, 11(16), 3604; https://doi.org/10.3390/math11163604 - 21 Aug 2023

Cited by 1 | Viewed by 553

Abstract

In this article, we explore the construction of Jakimovski–Leviatan operators for Durrmeyer-type approximation using Sheffer polynomials. Constructing positive linear operators for Sheffer polynomials enables us to analyze their approximation properties, including weighted approximations and convergence rates. The application of approximation theory has earned [...] Read more.

In this article, we explore the construction of Jakimovski–Leviatan operators for Durrmeyer-type approximation using Sheffer polynomials. Constructing positive linear operators for Sheffer polynomials enables us to analyze their approximation properties, including weighted approximations and convergence rates. The application of approximation theory has earned significant attention from scholars globally, particularly in the fields of engineering and mathematics. The investigation of these approximation properties and their characteristics holds considerable importance in these disciplines. Full article

18 pages, 3533 KiB

Open AccessArticle

Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction

by Yoonjae Noh, Jong-Min Kim, Soongoo Hong and Sangjin Kim

Mathematics 2023, 11(16), 3603; https://doi.org/10.3390/math11163603 - 20 Aug 2023

Viewed by 1938

Abstract

The stock index is actively used for the realization of profits using derivatives and via the hedging of assets; hence, the prediction of the index is important for market participants. As market uncertainty has increased during the COVID-19 pandemic and with the rapid [...] Read more.

The stock index is actively used for the realization of profits using derivatives and via the hedging of assets; hence, the prediction of the index is important for market participants. As market uncertainty has increased during the COVID-19 pandemic and with the rapid development of data engineering, a situation has arisen wherein extensive amounts of information must be processed at finer time intervals. Addressing the prevalent issues of difficulty in handling multivariate high-frequency time-series data owing to multicollinearity, resource problems in computing hardware, and the gradient vanishing problem due to the layer stacking in recurrent neural network (RNN) series, a novel algorithm is developed in this study. For financial market index prediction with these highly complex data, the algorithm combines ResNet and a variable-wise attention mechanism. To verify the superior performance of the proposed model, RNN, long short-term memory, and ResNet18 models were designed and compared with and without the attention mechanism. As per the results, the proposed model demonstrated a suitable synergistic effect with the time-series data and excellent classification performance, in addition to overcoming the data structure constraints that the other models exhibit. Having successfully presented multivariate high-frequency time-series data analysis, this study enables effective investment decision making based on the market signals. Full article

(This article belongs to the Special Issue Economic Model Analysis and Application)

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9 pages, 261 KiB

Open AccessFeature PaperArticle

Hard to Detect Factors of Univariate Integer Polynomials

by Alberto Dennunzio, Enrico Formenti and Luciano Margara

Mathematics 2023, 11(16), 3602; https://doi.org/10.3390/math11163602 - 20 Aug 2023

Viewed by 709

Abstract

We investigate the computational complexity of deciding whether a given univariate integer polynomial

p (x)

has a factor

q (x)

satisfying specific additional constraints. When the only constraint imposed on

q (x)

is to have a degree [...] Read more.

We investigate the computational complexity of deciding whether a given univariate integer polynomial

p (x)

has a factor

q (x)

satisfying specific additional constraints. When the only constraint imposed on

q (x)

is to have a degree smaller than the degree of

p (x)

and greater than zero, the problem is equivalent to testing the irreducibility of

p (x)

and then it is solvable in polynomial time. We prove that deciding whether a given monic univariate integer polynomial has factors satisfying additional properties is NP-complete in the strong sense. In particular, given any constant value

k \in Z

, we prove that it is NP-complete in the strong sense to detect the existence of a factor that returns a prescribed value when evaluated at

x = k

(Theorem 1) or to detect the existence of a pair of factors—whose product is equal to the original polynomial—that return the same value when evaluated at

x = k

(Theorem 2). The list of all the properties we have investigated in this paper is reported at the end of Section Introduction. Full article

26 pages, 2644 KiB

Open AccessArticle

Efficient Harris Hawk Optimization (HHO)-Based Framework for Accurate Skin Cancer Prediction

by Walaa N. Ismail and Hessah A. Alsalamah

Mathematics 2023, 11(16), 3601; https://doi.org/10.3390/math11163601 - 20 Aug 2023

Cited by 2 | Viewed by 1143

Abstract

The prediction of skin cancer poses a number of challenges due to the differences in visual characteristics between melanoma, basal cell carcinomas, and squamous cell carcinomas. These visual differences pose difficulties for models in discerning subtle features and patterns accurately. However, a remarkable [...] Read more.

The prediction of skin cancer poses a number of challenges due to the differences in visual characteristics between melanoma, basal cell carcinomas, and squamous cell carcinomas. These visual differences pose difficulties for models in discerning subtle features and patterns accurately. However, a remarkable breakthrough in image analysis using convolutional neural networks (CNNs) has emerged, specifically in the identification of skin cancer from images. Unfortunately, manually designing such neural architectures is prone to errors and consumes substantial time. It has become increasingly popular to design and fine-tune neural networks by using metaheuristic algorithms that are based on natural phenomena. A nature-inspired algorithm is a powerful alternative to traditional algorithms for solving problems, particularly in complex optimization tasks. One such algorithm, the Harris hawk optimization (HHO), has demonstrated promise in automatically identifying the most appropriate solution across a wide range of possibilities, making it suitable for solving complex optimization problems. The purpose of this study is to introduce a novel automated architecture called “HHOForSkin” that combines the power of convolutional neural networks with meta-heuristic optimization techniques. The HHOForSkin framework uses an innovative custom CNN architecture with 26 layers for the analysis of medical images. In addition, a Harris hawk optimization algorithm (HHO) is used to fine-tune the developed model for multiple skin cancer classification problems. The developed model achieves an average accuracy of 99.1% and 98.93% F1 score using a publicly available skin cancer dataset. These results position the developed optimization-based skin cancer detection strategy at the forefront, offering the highest accuracy for seven-class classification problems compared to related works. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Applications and Advances)

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19 pages, 9933 KiB

Open AccessArticle

Numerical Computing Research on Tunnel Structure Cracking Risk under the Influence of Multiple Factors in Urban Deep Aquifer Zones

by Minglei Ma, Wei Wang, Jianqiu Wu, Lei Han, Min Sun and Yonggang Zhang

Mathematics 2023, 11(16), 3600; https://doi.org/10.3390/math11163600 - 20 Aug 2023

Viewed by 716

Abstract

During the operation period of tunnels in urban deep aquifer zones, the geological environment around the tunnel is complex and the surrounding strata are rich in groundwater, which often poses a risk of structure cracking and groundwater leakage, seriously threatening the tunnel’s safety. [...] Read more.

During the operation period of tunnels in urban deep aquifer zones, the geological environment around the tunnel is complex and the surrounding strata are rich in groundwater, which often poses a risk of structure cracking and groundwater leakage, seriously threatening the tunnel’s safety. To reduce the risk of tunnel cracking, a theoretical calculation model and a three-dimensional concrete–soil interaction thermo-mechanical coupling numerical computing model was established to analyze the tunnel structure cracking risk under the influence of multiple factors in urban deep aquifer zones. The response mechanism of structural stress and deformation under the influence of the grade of rock and soil mass, overburden thickness, temperature difference, structure’s length–height ratio, structure’s thickness, and structure’s elastic modulus was investigated, and the stress and deformation response characteristics of the structure with deformation joints were explored. The results show that the maximum longitudinal tensile stress of the structure increases with the increase in the grade of rock and soil mass, overburden thickness, temperature difference, structure’s length–height ratio, and elastic modulus. The temperature difference has the most significant impact on the longitudinal tensile stress of the structure, with the maximum tensile stress of the structure increasing by 2.8 times. The tunnel deformation joints can effectively reduce the longitudinal tensile stress of the structure, and the reduction magnitude of the tensile stress is the largest at the deformation joints, which is 64.7%. Full article

(This article belongs to the Special Issue Resilient Infrastructure: Mathematical Modeling, Assessment and Smart Sensing)

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18 pages, 3192 KiB

Open AccessArticle

Risk Assessment of Mining Heritage Reuse in Public–Private-Partnership Mode Based on Improved Matter–Element Extension Model

by Shan Yang, Shengyuan Zhuo, Zitong Xu and Jianhong Chen

Mathematics 2023, 11(16), 3599; https://doi.org/10.3390/math11163599 - 20 Aug 2023

Viewed by 800

Abstract

With the development and utilization of resources, mineral-resource cities face the dilemma of resource depletion, the environmental restoration of mines, and industrial transformation. Reusing their mining heritage is a good way for these cities to change their mono-industrial structure and vigorously develop successor [...] Read more.

With the development and utilization of resources, mineral-resource cities face the dilemma of resource depletion, the environmental restoration of mines, and industrial transformation. Reusing their mining heritage is a good way for these cities to change their mono-industrial structure and vigorously develop successor industries. Due to the complexity of reusing mining heritage, introducing the “Public–Private-Partnership” (PPP) mode can be a good solution to the problems of the government’s mining heritage reuse, such as large capital investment and a long construction-cycle time. To accurately classify the risk of reuse of mining heritage in the PPP mode, 26 indicators are selected to construct the evaluation index system of mining heritage reuse in the PPP mode based on five aspects: social capital-side, contractor-side, government-side, civilian-side, and the natural environment. The path coefficients of the structural equation model are used to calculate the weights of the indicators. The improved matter–element extension model is constructed to evaluate the reuse of mining heritage in the PPP mode. The Jiaozuo-Centennial Mining Heritage Park project is the object of research for applying the model. The results show that the risk evaluation index system combines the risk factors from the stakeholders’ perspective. The risk-evaluation model of the mining heritage reuse PPP project is constructed based on the combination of the improved matter–element extension model, the calculation of the asymmetric closeness, and the structural equation modeling method, which solves the drawbacks of the traditional model, such as the difficulty of determining the weights of the indicators, the incomplete scope of the material element domains, and the poor calculation of the comprehensive correlation degree. The case analysis shows that the risk level of the Jiaozuo-Centennial Mining Heritage Park project is Level II. This aligns with the actual situation and verifies the feasibility of the risk-evaluation model applied to the actual project. The research in this paper fills the gap in the risk model of mining heritage reuse in the PPP mode, enriches the theoretical system of risk evaluation of mining heritage reuse projects, and provides reference significance for similar mining heritage development projects in the future. Full article

(This article belongs to the Special Issue Mathematical Theories and Models in Environmental Science)

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16 pages, 361 KiB

Open AccessArticle

Di-Forcing Polynomials for Cyclic Ladder Graphs CL_n

by Yantong Wang

Mathematics 2023, 11(16), 3598; https://doi.org/10.3390/math11163598 - 20 Aug 2023

Viewed by 718

Abstract

The cyclic ladder graph

C L_{n}

is the Cartesian product of cycles

C_{n}

and paths

P_{2}

, that is

C L_{n} = C_{n} \times P_{2}

,

(n \geq 3)

. The di-forcing polynomial of [...] Read more.

The cyclic ladder graph

C L_{n}

is the Cartesian product of cycles

C_{n}

and paths

P_{2}

, that is

C L_{n} = C_{n} \times P_{2}

,

(n \geq 3)

. The di-forcing polynomial of

C L_{n}

is a binary enumerative polynomial of all perfect matching forcing and anti-forcing numbers. In this paper, we derive recursive formulas for the di-forcing polynomial of cyclic ladder graph

C L_{n}

by classifying and counting the matching cases of the associated edges of a given vertex, from which we obtain the number of perfect matching, the forcing and anti-forcing polynomials, and the generating function and by computing some di-forcing polynomials of the lower order

C L_{n}

. Full article

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23 pages, 1799 KiB

Open AccessArticle

Stochastic Growth Models for the Spreading of Fake News

by Antonio Di Crescenzo, Paola Paraggio and Serena Spina

Mathematics 2023, 11(16), 3597; https://doi.org/10.3390/math11163597 - 19 Aug 2023

Viewed by 883

Abstract

The propagation of fake news in online social networks nowadays is becoming a critical issue. Consequently, many mathematical models have been proposed to mimic the related time evolution. In this work, we first consider a deterministic model that describes rumor propagation and can [...] Read more.

The propagation of fake news in online social networks nowadays is becoming a critical issue. Consequently, many mathematical models have been proposed to mimic the related time evolution. In this work, we first consider a deterministic model that describes rumor propagation and can be viewed as an extended logistic model. In particular, we analyze the main features of the growth curve, such as the limit behavior, the inflection point, and the threshold-crossing-time, through fixed boundaries. Then, in order to study the stochastic counterparts of the model, we consider two different stochastic processes: a time non-homogeneous linear pure birth process and a lognormal diffusion process. The conditions under which the means of the processes are identical to the deterministic curve are discussed. The first-passage-time problem is also investigated both for the birth process and the lognormal diffusion process. Finally, in order to study the variability of the stochastic processes introduced so far, we perform a comparison between their variances. Full article

(This article belongs to the Special Issue Stability Problems for Stochastic Models: Analytics, Asymptotics, Estimation and Applications)

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24 pages, 5019 KiB

Open AccessArticle

Sizing, Modeling, and Performance Comparison of Squirrel-Cage Induction and Wound-Field Flux Switching Motors

by Chiweta E. Abunike, Udochukwu B. Akuru, Ogbonnaya I. Okoro and Chukwuemeka C. Awah

Mathematics 2023, 11(16), 3596; https://doi.org/10.3390/math11163596 - 19 Aug 2023

Cited by 1 | Viewed by 1642

Abstract

In this study, the analytical design and electromagnetic performance comparison of a squirrel-cage induction motor (SCIM) and a wound-field flux switching motor (WFFSM) for high-speed brushless industrial motor drives is undertaken for the first time. The study uses analytical sizing techniques and finite [...] Read more.

In this study, the analytical design and electromagnetic performance comparison of a squirrel-cage induction motor (SCIM) and a wound-field flux switching motor (WFFSM) for high-speed brushless industrial motor drives is undertaken for the first time. The study uses analytical sizing techniques and finite element analysis (FEA) to model and predict the performance of both motors at a 7.5 kW output power. This study includes detailed equations and algorithms for sizing and modeling of both types of motors, as well as performance calculations that aid in motor selection, design optimization, and system integration. The main findings show that the SCIM has superior torque performance for starting and overload conditions, while the WFFSM offers advantages in power factor, efficiency over a wide operating range, and potential for higher peak power output. To this end, the WFFSM is capable of high-speed and high-efficiency operation while the SCIM is suitable for applications requiring variable speed operation. The validation study shows good agreement between analytical and FEA calculations for both motors. The results provide insights into the design and performance characteristics of both motors, enabling researchers to explore innovative approaches for improving their efficiency, reliability, and overall performance. Full article

(This article belongs to the Special Issue Mathematical Modelling and Numerical Analysis in Electrical Engineering)

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26 pages, 602 KiB

Open AccessFeature PaperArticle

Spectral Applications of Vertex-Clique Incidence Matrices Associated with a Graph

by Shaun Fallat and Seyed Ahmad Mojallal

Mathematics 2023, 11(16), 3595; https://doi.org/10.3390/math11163595 - 19 Aug 2023

Viewed by 733

Abstract

Using the notions of clique partitions and edge clique covers of graphs, we consider the corresponding incidence structures. This connection furnishes lower bounds on the negative eigenvalues and their multiplicities associated with the adjacency matrix, bounds on the incidence energy, and on the [...] Read more.

Using the notions of clique partitions and edge clique covers of graphs, we consider the corresponding incidence structures. This connection furnishes lower bounds on the negative eigenvalues and their multiplicities associated with the adjacency matrix, bounds on the incidence energy, and on the signless Laplacian energy for graphs. For the more general and well-studied set

S (G)

of all real symmetric matrices associated with a graph G, we apply an extended version of an incidence matrix tied to an edge clique cover to establish several classes of graphs that allow two distinct eigenvalues. Full article

(This article belongs to the Special Issue Spectral Graph Theory and the Inverse Eigenvalue Problem of a Graph)

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22 pages, 1778 KiB

Open AccessArticle

AI-Enabled Condition Monitoring Framework for Outdoor Mobile Robots Using 3D LiDAR Sensor

by Sathian Pookkuttath, Povendhan Arthanaripalayam Palanisamy and Mohan Rajesh Elara

Mathematics 2023, 11(16), 3594; https://doi.org/10.3390/math11163594 - 19 Aug 2023

Viewed by 844

Abstract

An automated condition monitoring (CM) framework is essential for outdoor mobile robots to trigger prompt maintenance and corrective actions based on the level of system deterioration and outdoor uneven terrain feature states. Vibration indicates system failures and terrain abnormalities in mobile robots; hence, [...] Read more.

An automated condition monitoring (CM) framework is essential for outdoor mobile robots to trigger prompt maintenance and corrective actions based on the level of system deterioration and outdoor uneven terrain feature states. Vibration indicates system failures and terrain abnormalities in mobile robots; hence, five vibration threshold classes for CM in outdoor mobile robots were identified, considering both vibration source system deterioration and uneven terrain. This study proposes a novel CM approach for outdoor mobile robots using a 3D LiDAR, employed here instead of its usual use as a navigation sensor, by developing an algorithm to extract the vibration-indicated data based on the point cloud, assuring low computational costs without losing vibration characteristics. The algorithm computes cuboids for two prominent clusters in every point cloud frame and sets motion points at the corners and centroid of the cuboid. The three-dimensional vector displacement of these points over consecutive point cloud frames, which corresponds to the vibration-affected clusters, are compiled as vibration indication data for each threshold class. A simply structured 1D Convolutional Neural Network (1D CNN)-based vibration threshold prediction model is proposed for fast, accurate, and real-time application. Finally, a threshold class mapping framework is developed which fuses the predicted threshold classes on the 3D occupancy map of the workspace, generating a 3D CbM map in real time, fostering a Condition-based Maintenance (CbM) strategy. The offline evaluation test results show an average accuracy of vibration threshold classes of 89.6% and consistent accuracy during real-time field case studies of 89%. The test outcomes validate that the proposed 3D-LiDAR-based CM framework is suitable for outdoor mobile robots, assuring the robot’s health and operational safety. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0)

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25 pages, 3075 KiB

Open AccessArticle

Robust Model Predictive Control for Two-DOF Flexible-Joint Manipulator System

by Rong Li, Hengli Wang, Gaowei Yan, Guoqiang Li and Long Jian

Mathematics 2023, 11(16), 3593; https://doi.org/10.3390/math11163593 - 19 Aug 2023

Cited by 1 | Viewed by 906

Abstract

This paper presents a practical study on how to improve the

ℋ_{\infty}

performance and meet the input–output constraints of the two-degrees-of-freedom (DOF) flexible-joint manipulator system (FJMS) with parameter uncertainties and external disturbances. For this reason, a robust constrained moving-horizon

ℋ_{\infty}

controller [...] Read more.

This paper presents a practical study on how to improve the

ℋ_{\infty}

performance and meet the input–output constraints of the two-degrees-of-freedom (DOF) flexible-joint manipulator system (FJMS) with parameter uncertainties and external disturbances. For this reason, a robust constrained moving-horizon

ℋ_{\infty}

controller is designed to improve the system

ℋ_{\infty}

performance while still satisfying the input–output constraints of the uncertain system. First, the uncertain controlled system model of the two-DOF FJMS is established via the Lagrange equation method, Spong’s assumption, and the linear fractional transformation (LFT) technique. Then, the control requirements and input–output constraints of the uncertain system are transformed into the linear matrix inequality (LMI) via the theory of

ℋ_{\infty}

control and the full-block multiplier technique. Next, the LMI optimization problem refreshed by the current state is addressed at each sample moment with the idea of the moving-horizon control of the model predictive control (MPC), and the calculated gain is implemented to the nonlinear closed-loop system under the state feedback structure. The validity and feasibility of the designed control scheme is finally verified via the results of simulation experiments. Full article

(This article belongs to the Special Issue Mathematical Methods Applied in Artificial Intelligence and Multi-Agent Systems)

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25 pages, 363 KiB

Open AccessFeature PaperArticle

Asymptotic Domain Decomposition Method for Approximation the Spectrum of the Diffusion Operator in a Domain Containing Thin Tubes

by Andrey Amosov, Delfina Gómez, Grigory Panasenko and Maria-Eugenia Pérez-Martinez

Mathematics 2023, 11(16), 3592; https://doi.org/10.3390/math11163592 - 19 Aug 2023

Viewed by 654

Abstract

The spectral problem for the diffusion operator is considered in a domain containing thin tubes. A new version of the method of partial asymptotic decomposition of the domain is introduced to reduce the dimension inside the tubes. It truncates the tubes at some [...] Read more.

The spectral problem for the diffusion operator is considered in a domain containing thin tubes. A new version of the method of partial asymptotic decomposition of the domain is introduced to reduce the dimension inside the tubes. It truncates the tubes at some small distance from the ends of the tubes and replaces the tubes with segments. At the interface of the three-dimensional and one-dimensional subdomains, special junction conditions are set: the pointwise continuity of the flux and the continuity of the average over a cross-section of the eigenfunctions. The existence of the discrete spectrum is proved for this partially reduced problem of the hybrid dimension. The conditions of the closeness of two spectra, i.e., of the diffusion operator in the full-dimensional domain and the partially reduced one, are obtained. Full article

(This article belongs to the Section Difference and Differential Equations)

30 pages, 11545 KiB

Open AccessArticle

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

by Vladimir Shepelev, Aleksandr Glushkov, Ivan Slobodin and Mohammed Balfaqih

Mathematics 2023, 11(16), 3591; https://doi.org/10.3390/math11163591 - 19 Aug 2023

Cited by 3 | Viewed by 1079

Abstract

This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban [...] Read more.

This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban areas. Traffic cameras are used to collect real-time vehicle traffic data. Our system provides valuable insight into the relationship between traffic flows, emissions, and intersection capacity. This study shows how changes in the traffic composition reduce the traffic capacity of intersections and increase emissions, especially those involving fine dust particles. Using the combination of fuzzy logic methods and Gaussian spline distribution functions, we demonstrate the variability of these relationships and highlight the need to further study compromises between mobility and air quality. Ultimately, our results offer promising opportunities for the development of intelligent traffic management systems aimed at balancing the demands of urban mobility while minimizing environmental impact. This study demonstrates the importance of taking into account the correlation between the change in the composition of traffic queues due to a random change in the traffic flow and its impact on emissions and the traffic capacity of intersections. This study found that the presence of various groups of vehicles and their position in the queue can reduce the traffic capacity by up to 70% and increase the growth of harmful emissions by 14 fold. Full article

(This article belongs to the Special Issue Modeling and Optimization in Urban Transport and Ecology)

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Mathematics, Volume 11, Issue 16 (August-2 2023) – 184 articles

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