Mathematics doi: 10.3390/math11122616

Authors: Fiske Schijlen Lichao Wu Luca Mariot

Side-channel analysis (SCA) is a class of attacks on the physical implementation of a cipher, which enables the extraction of confidential key information by exploiting unintended leaks generated by a device. In recent years, researchers have observed that neural networks (NNs) can be utilized to perform highly effective SCA profiling, even against countermeasure-hardened targets. This study investigates a new approach to designing NNs for SCA, called neuroevolution to attack side-channel traces yielding convolutional neural networks (NASCTY-CNNs). This method is based on a genetic algorithm (GA) that evolves the architectural hyperparameters to automatically create CNNs for side-channel analysis. The findings of this research demonstrate that we can achieve performance results comparable to state-of-the-art methods when dealing with desynchronized leakages protected by masking techniques. This indicates that employing similar neuroevolutionary techniques could serve as a promising avenue for further exploration. Moreover, the similarities observed among the constructed neural networks shed light on how NASCTY effectively constructs architectures and addresses the implemented countermeasures.

]]>Mathematics doi: 10.3390/math11122617

Authors: Soroosh Shalileh

Enhancing the effectiveness of clustering methods has always been of great interest. Therefore, inspired by the success story of the gradient descent approach in supervised learning in the current research, we proposed an effective clustering method using the gradient descent approach. As a supplementary device for further improvements, we implemented our proposed method using an automatic differentiation library to facilitate the users in applying any differentiable distance functions. We empirically validated and compared the performance of our proposed method with four popular and effective clustering methods from the literature on 11 real-world and 720 synthetic datasets. Our experiments proved that our proposed method is valid, and in the majority of the cases, it is more effective than the competitors.

]]>Mathematics doi: 10.3390/math11122615

Authors: Meirun Chen Cheng-Kuan Lin

Motivated by two typical ways to construct multiprocessor systems, matching composition networks and cycle composition networks, we generalize the definition of the Cartesian product of networks and consider the classical diagnosability of the generalized Cartesian product of networks (GCPNs). In this paper, we determine the accurate value of the classical diagnosability of the generalized Cartesian product of networks (GCPNs) under the PMC model and the MM* model.

]]>Mathematics doi: 10.3390/math11122614

Authors: Meirbek Moldabekov Anna Sukhenko Yerkin Orazaly Alisher Aden

This study aims to analyze the nonlinear dynamics of a satellite attitude control system equipped with reaction wheels and a PD controller. Based on the angular momentum conservation theorem for a closed mechanical system, the nonlinear equations of the attitude control system dynamics are presented as a linear system of differential equations with time-varying parameters. The asymptotic properties of the angular momentum of a mechanical system including a satellite and reaction wheels as rigid bodies are investigated. A relation has been established between the dynamic parameters of the attitude control system and the initial value of the angular momentum of the satellite. The issue of asymptotic stability for differential equations with time-varying parameters is simplified to the asymptotic stability problem for the ultimate homogeneous system of linear differential equations with constant elements. The dependencies of the dynamic parameters of the attitude control system on the constant parameters of this ultimate system of linear differential equations, as well as the initial values of the satellite&rsquo;s angular momentum, enable us to apply proven and effective engineering methods. These methods are used not only for analyzing the stability of the control system but also for synthesizing the parameters of the control law based on the quality requirements of transient processes such as the stability margin, responsiveness, oscillation, transient time, and overshoot. In this case, the calculation of the control law parameters will be grounded in exact equations, not on approximate equations of the control system dynamics obtained by linearization.

]]>Mathematics doi: 10.3390/math11122611

Authors: Anam Habib Zareen A. Khan Muhammad Riaz Dragan Marinkovic

The concept of linear Diophantine fuzzy set (LDFS) theory with its control parameters is a strong model for machine learning and data-driven multi-criteria decision making (MCDM). The sine-trigonometric function (STF) has two significant features, periodicity and symmetry about the origin that are very useful tools for information analysis. Keeping in view the characteristics of both STF and LDFS theory, this article introduces the sine-trigonometric operations for linear Diophantine fuzzy numbers (LDFNs). These operational laws lay a foundation for developing new linear Diophantine fuzzy sine-trigonometric aggregation operators (LDFSTAOs). The integration of Industry 4.0 technology into healthcare has the potential to revolutionize patient care. One of the most challenging tasks is the selection of efficient suppliers for the healthcare supply chain (HSC). The traditional suppliers are not efficient in accordance with Industry 4.0, with particular uncertainties. A new MCDM framework is presented based on LDFSTAOs to examine the HSC performance in industry 4.0. A credibility test, sensitivity analysis and comparative analysis are performed to express the novelty, reliability, and efficiency of the proposed methodology.

]]>Mathematics doi: 10.3390/math11122613

Authors: Noorsufia Abd Shukor Tahir Ahmad Mujahid Abdullahi Amidora Idris Siti Rahmah Awang

Fuzzy Topological Topographic Mapping (FTTM) is a mathematical model that consists of a set of homeomorphic topological spaces designed to solve the neuro magnetic inverse problem. A sequence of FTTM, denoted as FTTMn, is an extension of FTTM that is arranged in a symmetrical form. The special characteristic of FTTM, namely the homeomorphisms between its components, allows the generation of new FTTM. Later, the FTTMn can also be viewed as a graph. Previously, a group of researchers defined an assembly graph and utilized it to model a DNA recombination process. Some researchers then used this to introduce the concept of tangled cords for assembly graphs. In this paper, the tangled cord for FTTM4 is used to calculate the Eulerian paths. Furthermore, it is utilized to determine the least upper bound of the Hamiltonian paths of its assembly graph. Hence, this study verifies the conjecture made by Burns et al.

]]>Mathematics doi: 10.3390/math11122610

Authors: Gennadii Alekseev

The optimal control problems for stationary magnetohydrodynamic equations under the inhomogeneous mixed boundary conditions for a magnetic field and the Dirichlet condition for velocity are considered. The role of controls in the control problems under study is played by normal and tangential components of the magnetic field given on different parts of the boundary and by the exterior current density. Quadratic tracking-type functionals for velocity, magnetic field or pressure are taken as cost functionals. The global solvability of the control problems under consideration is proved, an optimality system is derived and, based on its analysis, a mathematical apparatus for studying the local uniqueness and stability of the optimal solutions is developed. On the basis of the developed apparatus, the local uniqueness of solutions of control problems for specific cost functionals is proved, and stability estimates of optimal solutions are established.

]]>Mathematics doi: 10.3390/math11122612

Authors: Jiwei Wan Huimin Zhao Rui Li Rongjun Chen Tuanjie Wei

As a biological feature with strong spatio-temporal correlation, the current difficulty of gait recognition lies in the interference of covariates (viewpoint, clothing, etc.) in feature extraction. In order to weaken the influence of extrinsic variable changes, we propose an interval frame sampling method to capture more information about joint dynamic changes, and an Omni-Domain Feature Extraction Network. The Omni-Domain Feature Extraction Network consists of three main modules: (1) Temporal-Sensitive Feature Extractor: injects key gait temporal information into shallow spatial features to improve spatio-temporal correlation. (2) Dynamic Motion Capture: extracts temporal features of different motion and assign weights adaptively. (3) Omni-Domain Feature Balance Module: balances fine-grained spatio-temporal features, highlight decisive spatio-temporal features. Extensive experiments were conducted on two commonly used public gait datasets, showing that our method has good performance and generalization ability. In CASIA-B, we achieved an average rank-1 accuracy of 94.2% under three walking conditions. In OU-MVLP, we achieved a rank-1 accuracy of 90.5%.

]]>Mathematics doi: 10.3390/math11122607

Authors: Dan Stefanoiu Janetta Culita

John von Neumann (JvN) was one of the greatest scientists and minds of the 20th century. His research encompassed a large variety of topics (especially from mathematics), and the results he obtained essentially contributed to the progress of science and technology. Within this article, one function that JvN defined long time ago, namely the cardinal sinus (sinc), was employed to define transforms to be applied on 1D signals, either in continuous or discrete time. The main characteristics of JvN Transforms (JvNTs) are founded on a theory described at length in the article. Two properties are of particular interest: orthogonality and invertibility. Both are important in the context of data compression. After building the theoretical foundation of JvNTs, the corresponding numerical algorithms were designed, implemented and tested on artificial and real signals. The last part of the article is devoted to simulations with such algorithms by using 1D signals. An extensive analysis on JvNTs effectiveness is performed as well, based on simulation results. In conclusion, JvNTs prove to be useful tools in signal processing.

]]>Mathematics doi: 10.3390/math11122608

Authors: Hongsheng Su Yifan Zhao Xueqian Wang

Preventive maintenance is widely used in wind turbine equipment to ensure their safe and reliable operation, and this mainly includes time-based maintenance (TBM) and condition-based maintenance (CBM). Most wind farms only use TBM as the main maintenance strategy in engineering practice. Although this can meet certain reliability requirements, it cannot fully utilize the characteristics of TBM and CBM. For this, a state model based on the stochastic differential equation (SDE) is established in this paper to describe the spatio-temporal evolution process of the degradation behavior of wind turbine generators, in which the components&rsquo; failure is represented by a proportional hazards model, the random fluctuation of the state is simulated by the Brownian motion, and the SDE model is solved by a function transformation method. Based on the model, the characteristics of TBM and CBM, and the asymptotic relationship between them, are discussed and analyzed, the necessity and feasibility of their combination are expounded, and a joint maintenance strategy is proposed and analyzed. The results show that the stochastic model can better reflect the real deterioration state of the generator. Moreover, TBM has a fixed maintenance interval, depending on global sample tracks and, only depending on the local sample track, CBM can follow the component state. Finally, the rationality and effectiveness of the proposed model and results are verified by a practical example.

]]>Mathematics doi: 10.3390/math11122609

Authors: Zadoki Tabo Chester Kalinda Lutz Breuer Christian Albrecht

One of the most deadly neglected tropical diseases known to man is schistosomiasis. Understanding how the disease spreads and evaluating the relevant control strategies are key steps in predicting its spread. We propose a mathematical model to evaluate the potential impact of four strategies: chemotherapy, awareness programs, the mechanical removal of snails and molluscicides, and the impact of a change in temperature on different molluscicide performances based on their half-lives and the length of time they persist in contact with target species. The results show that the recruitment rate of humans and the presence of cercaria and miracidia parasites are crucial factors in disease transmission. However, schistosomiasis can be entirely eradicated by combining all of the four strategies. In the face of climate change and molluscicide degradation, the results show that increasing the temperatures and the number of days a molluscicide persists in the environment before it completely degrades decreases the chemically induced mortality rate of snails while increasing the half-life of different molluscicides increases the death rate of snails. Therefore, eradicating schistosomiasis effectively necessitates a comprehensive integration of all preventative measures. Moreover, regions with different weather patterns and seasonal climates need strategies that have been adapted in terms of the appropriate molluscicide and time intervals for reapplication and effective schistosomiasis control.

]]>Mathematics doi: 10.3390/math11122606

Authors: Mohamed Abdel-Basset Reda Mohamed Ibrahim M. Hezam Ahmad M. Alshamrani Karam M. Sallam

Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new obstacle or hazard is recognized or if the target is moved during the mission. In brief, the planning of UAV routes might optimize a specific problem determined by the application, such as the moving target problem (MTP), flight time and threats, or multiobjective navigation. The complexity of MTP ranges from NP-hard to NEXP-complete because there are so many probabilistic variables involved. Therefore, it is hard to detect a high-quality solution for this problem using traditional techniques such as differential calculus. Therefore, this paper hybridizes differential evolution (DE) with two newly proposed updating schemes to present a new evolution-based technique named hybrid differential evolution (HDE) for accurately tackling the MTP in a reasonable amount of time. Using Bayesian theory, the MTP can be transformed into an optimization problem by employing the target detection probability as the fitness function. The proposed HDE encodes the search trajectory as a sequence of UAV motion pathways that evolve with increasing the current iteration for finding the near-optimal solution, which could maximize this fitness function. The HDE is extensively compared to the classical DE and several rival optimizers in terms of several performance metrics across four different scenarios with varying degrees of difficulty. This comparison demonstrates the proposal&rsquo;s superiority in terms of the majority of used performance metrics.

]]>Mathematics doi: 10.3390/math11122605

Authors: Asma Al-Jaser Belgees Qaraad Omar Bazighifan Loredana Florentina Iambor

In this paper, new criteria for a class oscillation of second-order delay differential equations with distributed deviating arguments were established. Our method mainly depends on making sharper estimates for the non-oscillatory solutions of the studied equation. By using the Ricati technique and comparison theorems that compare the studied equations with first-order delay differential equations, we obtained new and less restrictive conditions that ensure the oscillation of all solutions of the studied equation. Further, we give an illustrative example.

]]>Mathematics doi: 10.3390/math11122604

Authors: Zhizhuang Zhang Xiangyu Zhou Gang Li Shouguo Qian Qiang Niu

The hyperbolic problem has a unique entropy solution, which maintains the entropy inequality. As such, people hope that the numerical results should maintain the discrete entropy inequalities accordingly. In view of this, people tend to construct entropy stable (ES) schemes. However, traditional numerical schemes cannot directly maintain discrete entropy inequalities. To address this, we here construct an ES finite difference scheme for the nonlinear hyperbolic systems of conservation laws. The proposed scheme can not only maintain the discrete entropy inequality, but also enjoy high-order accuracy. Firstly, we construct the second-order accurate semi-discrete entropy conservative (EC) schemes and ensure that the schemes meet the entropy identity when an entropy pair is given. Then, the second-order EC schemes are used as a building block to achieve the high-order accurate semi-discrete EC schemes. Thirdly, we add a dissipation term to the above schemes to obtain the high-order ES schemes. The term is based on the Weighted Essentially Non-Oscillatory (WENO) reconstruction. Finally, we integrate the scheme using the third-order Runge&ndash;Kutta (RK) approach in time. In the end, plentiful one- and two-dimensional examples are implemented to validate the capability of the scheme. In summary, the current scheme has sharp discontinuity transitions and keeps the genuine high-order accuracy for smooth solutions. Compared to the standard WENO schemes, the current scheme can achieve higher resolution.

]]>Mathematics doi: 10.3390/math11122603

Authors: Huda J. Saeed Ali Hasan Ali Rayene Menzer Ana Danca Poțclean Himani Arora

This research aims to propose a new family of one-parameter multi-step iterative methods that combine the homotopy perturbation method with a quadrature formula for solving nonlinear equations. The proposed methods are based on a higher-order convergence scheme that allows for faster and more efficient convergence compared to existing methods. It aims also to demonstrate that the efficiency index of the proposed iterative methods can reach up to 43&asymp;1.587 and 84&asymp;1.681, respectively, indicating a high degree of accuracy and efficiency in solving nonlinear equations. To evaluate the effectiveness of the suggested methods, several numerical examples including their performance are provided and compared with existing methods.

]]>Mathematics doi: 10.3390/math11122602

Authors: Alex Doboli Daniel-Ioan Curiac

Understanding the process of reaching consensus or disagreement between the members of a team is critical in many situations. Consensus and disagreement can refer to various aspects, such as requirements that are collectively perceived to be important, shared goals, and solutions that are jointly considered to be realistic and effective. Getting insight on how the end result of the interaction process is influenced by parameters such as the similarity of the participants&rsquo; experience and behavior (e.g., their available concepts, the produced responses and their utility, the preferred response generation method, and so on) is important for optimizing team performance and for devising novel applications, i.e., systems for tutoring or self-improvement and smart human computer interfaces. However, understanding the process of reaching consensus or disagreement in teams raises a number of challenges as participants interact with each other through verbal communications that express new ideas created based on their experience, goals, and input from other participants. Social and emotional cues during interaction are important too. This paper presents a new model, called Learning and Response Generating Agents, for studying the interaction process during problem solving in small teams. As compared to similar work, the model, grounded in work in psychology and sociology, studies consensus and disagreement formation when agents interact with each other through symbolic, dynamically-produced responses with clauses of different types, ambiguity, multiple abstraction levels, and associated emotional intensity and utility.

]]>Mathematics doi: 10.3390/math11122601

Authors: Vassilios N. Laskos Thomas Kotsopoulos Dimitrios Karpouzos Vassilios P. Fragos

The incompressible laminar isothermal flow of a Newtonian fluid at steady state around a surface-mounted rib is studied in a three-dimensional (3D) numerical experiment. The dimensionless Navier&ndash;Stokes equations are solved numerically using the Galerkin finite element method for Reynolds numbers 1 to 800. The expansion ratio of the problem is 1:9.6, while the aspect ratio is 1:20. The transition from the steady to the unsteady state and the identification of the critical Reynolds number are investigated in this paper. Numerical results of the skin-friction lines at the bottom and streamlines throughout the computational field are presented. A comparison between the 2D and 3D flow is made to show the effect of the walls on the flow, which reaches the plane of symmetry and affects the flow there; hence, also affecting the stability of the flow. It is concluded that the flow is three-dimensional even for a Reynolds number equal to 10. The critical Reynolds number is 600, and the steady-state equations can be used for any calculations up to this value.

]]>Mathematics doi: 10.3390/math11122600

Authors: Siraj Uddin Bang-Yen Chen Rawan Bossly

Recently, we studied CR-slant warped products B1&times;fM&perp;, where B1=MT&times;M&theta; is the Riemannian product of holomorphic and proper slant submanifolds and M&perp; is a totally real submanifold in a nearly Kaehler manifold. In the continuation, in this paper, we study B2&times;fM&theta;, where B2=MT&times;M&perp; is a CR-product of a nearly Kaehler manifold and establish Chen&rsquo;s inequality for the squared norm of the second fundamental form. Some special cases of Chen&rsquo;s inequality are given.

]]>Mathematics doi: 10.3390/math11122599

Authors: Chaojie Wang Jie Chen Shuen Sun

In this paper, a new preconditioning method is proposed for the linear system arising from the elliptic optimal control problem. It is based on row permutations of the linear system and approximations of the corresponding Schur complement inspired by the matching strategy. The eigenvalue bounds of the preconditioned matrices are shown to be independent of mesh size and regularization parameter. Numerical results illustrate the efficiency of the proposed preconditioning methods.

]]>Mathematics doi: 10.3390/math11122598

Authors: Ze Shi Hongyi Li Di Zhao Chengwei Pan

Relation classification is a significant task within the field of natural language processing. Its objective is to extract and identify relations between two entities in a given text. Within the scope of this paper, we construct an artificial dataset (CS13K) for relation classification in the realm of cybersecurity and propose two models for processing such tasks. For any sentence containing two target entities, we first locate the entities and fine-tune the pre-trained BERT model. Next, we utilize graph attention networks to iteratively update word nodes and relation nodes. A new relation classification model is constructed by concatenating the updated vectors of word nodes and relation nodes. Our proposed model achieved exceptional performance on the SemEval-2010 task 8 dataset, surpassing previous approaches with a remarkable F1 value of 92.3%. Additionally, we propose the integration of a ranking-based voting mechanism into the existing model. Our best results are an F1 value of 92.5% on the SemEval-2010 task 8 dataset and a value 94.6% on the CS13K dataset. These findings highlight the effectiveness of our proposed models in tackling relation classification tasks.

]]>Mathematics doi: 10.3390/math11122597

Authors: Ioannis S. Triantafyllou

In the present work, we study the combined m-consecutive-k-out-of-n: F and kc-out-of-n: F reliability systems, which consist of independent and identically distributed components. Two different redundancy policies are considered, and their general frameworks are described and illustrated. The main objective of the paper refers to the investigation of the effect of adding cold standby redundancy to the system at the the system level and the component level. Exact formulae for determining the crucial characteristics of the enhanced structure, such as its survival function, the mean time to failure and the mean residual lifetime, are provided. All formulae proved in the present manuscript are explicit expressions which are based on the signature vector of the resulting reliability schemes. An extensive numerical investigation is carried out to shed light on the performance of the combined m-consecutive-k-out-of-n: F and consecutive kc-out-of-n: F reliability systems with cold standby redundancy. Some concluding remarks and comments are provided upon the determination of the optimal design parameters.

]]>Mathematics doi: 10.3390/math11122596

Authors: Chao Wang Yinfang Song Fengjiao Zhang Yuxiao Zhao

This paper investigates the exponential stability of a class of neutral inertial neural networks with multi-proportional delays and leakage delays. By utilizing the Lyapunov stability theory, the approach of parametric variation, and the differential inequality technique, some criteria are acquired that can guarantee that all solutions of the addressed system converge exponentially to the equilibrium point. In particular, the neutral term, multi-proportional delays, and leakage delays are incorporated simultaneously, resulting in a more general model, and the findings are novel and refine the previous works. Finally, one example is provided to indicate that the dynamic behavior is consistent with the theoretical analysis.

]]>Mathematics doi: 10.3390/math11122595

Authors: Wilson Rojas-Preciado Mauricio Rojas-Campuzano Purificación Galindo-Villardón Omar Ruiz-Barzola

The scientific literature is abundant regarding control charts in multivariate environments for numerical and mixed data; however, there are few publications for qualitative data. Qualitative variables provide valuable information on processes in various industrial, productive, technological, and health contexts. Social processes are no exception. There are multiple nominal and ordinal categorical variables used in economics, psychology, law, sociology, and education, whose analysis adds value to decision-making; therefore, their representation in control charts would be useful. When there are many variables, there is a risk of redundant or excessive information, so the application of multivariate methods for dimension reduction to retain a few latent variables, i.e., a recombination of the original and synthesizing of most of the information, is viable. In this context, the T2Qv control chart is presented as a multivariate statistical process control technique that performs an analysis of qualitative data through Multiple Correspondence Analysis (MCA), and the Hotelling T2 chart. The interpretation of out-of-control points is carried out by comparing MCA charts and analyzing the &chi;2 distance between the categories of the concatenated table and those that represent out-of-control points. Sensitivity analysis determined that the T2Qv control chart performs well when working with high dimensions. To test the methodology, an analysis was performed with simulated data and with a real case applied to the graduate follow-up process in the context of higher education. To facilitate the dissemination and application of the proposal, a reproducible computational package was developed in R, called T2Qv, and is available on the Comprehensive R Archive Network (CRAN).

]]>Mathematics doi: 10.3390/math11122593

Authors: Hongquan Wang Xinshan Zhu Chao Ren Lan Zhang Shugen Ma

The rapid development of digital image inpainting technology is causing serious hidden danger to the security of multimedia information. In this paper, a deep network called frequency attention-based dual-stream network (FADS-Net) is proposed for locating the inpainting region. FADS-Net is established by a dual-stream encoder and an attention-based blue-associative decoder. The dual-stream encoder includes two feature extraction streams, the raw input stream (RIS) and the frequency recalibration stream (FRS). RIS directly captures feature maps from the raw input, while FRS performs feature extraction after recalibrating the input via learning in the frequency domain. In addition, a module based on dense connection is designed to ensure efficient extraction and full fusion of dual-stream features. The attention-based associative decoder consists of a main decoder and two branch decoders. The main decoder performs up-sampling and fine-tuning of fused features by using attention mechanisms and skip connections, and ultimately generates the predicted mask for the inpainted image. Then, two branch decoders are utilized to further supervise the training of two feature streams, ensuring that they both work effectively. A joint loss function is designed to supervise the training of the entire network and two feature extraction streams for ensuring optimal forensic performance. Extensive experimental results demonstrate that the proposed FADS-Net achieves superior localization accuracy and robustness on multiple datasets compared to the state-of-the-art inpainting forensics methods.

]]>Mathematics doi: 10.3390/math11122594

Authors: Boya Zhou Xiujun Cheng

In this paper, a novel second-order method based on a change of variable and the symmetrical and repeated quadrature formula is presented for numerical solving second kind Volterra integral equations with non-smooth solutions. Applying the discrete Gr&ouml;nwall inequality with weak singularity, the convergence order&nbsp;O(N&minus;2)&nbsp;in&nbsp;L&infin;&nbsp;norm is proved, where N refers to the number of time steps. Numerical results are conducted to verify the efficiency and accuracy of the method.

]]>Mathematics doi: 10.3390/math11122592

Authors: Chengxin Gong Jinwen Ma

As a reasonable statistical learning model for curve clustering analysis, the two-layer mixtures of Gaussian process functional regressions (TMGPFR) model has been developed to fit the data of sample curves from a number of independent information sources or stochastic processes. Since the sample curves from a certain stochastic process naturally form a curve cluster, the model selection of TMGPFRs, i.e., the selection of the number of mixtures of Gaussian process functional regressions (MGPFRs) in the upper layer, corresponds to the discovery of the cluster number and structure of the curve data. In fact, this is rather challenging because the conventional model selection criteria, such as BIC and cross-validation, cannot lead to a stable result in practice even with a heavy burden of repetitive computation. In this paper, we improve the original TMGPFR model and propose a Bayesian Ying-Yang (BYY) annealing learning algorithm for the parameter learning of the improved model with automated model selection. The experimental results of both synthetic and realistic datasets demonstrate that our proposed algorithm can make correct model selection automatically during parameter learning of the model.

]]>Mathematics doi: 10.3390/math11112591

Authors: Sulaiman Z. Almutairi Emad A. Mohamed Fayez F. M. El-Sousy

The optimal control of reactive powers in electrical systems can improve a system&rsquo;s performance and security; this can be provided by the optimal reactive power dispatch (ORPD). Under the high penetration of renewable energy resources (RERs) such as wind turbines (WTs), the ORPD problem solution has become a challenging and complex task due to the fluctuations and uncertainties of generated power from WTs. In this regard, this paper solved the conventional ORPD and the stochastic ORPD (SORPD) at uncertainties of the generated power from WTs and the load demand. An Adaptive Manta-Ray Foraging Optimization (AMRFO) was presented based on three modifications, including the fitness distance balance selection (FDB), Quasi Oppositional based learning (QOBL), and an adaptive Levy Flight (ALF). The ORPD and SORPD were solved to reduce the power loss (PLoss) and the total expected PLoss (TEPL), the voltage deviations (VD) and the total expected VD (TEVD). The normal and Weibull probability density functions (PDFs), along with the scenario reduction method and the Monte Carlo simulation (MCS), were utilized for uncertainty representations. The performance and validity of the suggested AMRFO were compared to other optimizers, including SCSO, WOA, DO, AHA, and the conventional MRFO on the IEEE 30-bus system and standard benchmark functions. These simulation results confirm the supremacy of the suggested AMRFO for the ORPD and SORPD solution compared to the other reported techniques.

]]>Mathematics doi: 10.3390/math11112590

Authors: Raul Moragues Juan Aparicio Miriam Esteve

In this paper, we propose and compare new methodologies for ranking the importance of variables in productive processes via an adaptation of OneClass Support Vector Machines. In particular, we adapt two methodologies inspired by the machine learning literature: one involving the random shuffling of values of a variable and another one using the objective value of the dual formulation of the model. Additionally, we motivate the use of these type of algorithms in the production context and compare their performance via a computational experiment. We observe that the methodology based on shuffling the values of a variable outperforms the methodology based on the dual formulation. We observe that the shuffling-based methodology correctly ranks the variables in 94% of the scenarios with one relevant input and one irrelevant input. Moreover, it correctly ranks each variable in at least 65% of replications of a scenario with three relevant inputs and one irrelevant input.

]]>Mathematics doi: 10.3390/math11112587

Authors: Hatairat Yingtaweesittikul Sayan Panma Penying Rochanakul

Let G and H be graphs. A mapping f from the vertices of G to the vertices of H is known as a homomorphism from G to H if, for every pair of adjacent vertices x and y in G, the vertices f(x) and f(y) are adjacent in H. A rectangular grid graph is the Cartesian product of two path graphs. In this paper, we provide a formula to determine the number of homomorphisms from paths to rectangular grid graphs. This formula gives the solution to the problem concerning the number of walks in the rectangular grid graphs.

]]>Mathematics doi: 10.3390/math11112589

Authors: Vladislav N. Kovalnogov Ruslan V. Fedorov Tamara V. Karpukhina Theodore E. Simos Charalampos Tsitouras

Runge-Kutta (RK) pairs are amongst the most popular methods for numerically solving Initial Value Problems. While using an RK pair, we may experience rejection of some steps through the interval of integration. Traditionally, all of the evaluations are then dropped, and we proceed with a completely new round of computations. In this work, we propose avoiding this waste and continuing by reusing the rejected RK stages. We focus especially on an RK pair of orders six and five. After step rejection, we reuse all the previously evaluated stages and only add three new stages. We proceed by evaluating the output using a smaller step. By this technique, we manage to significantly reduce the cost in a set of problems that are known to pose difficulties in RK algorithms with changing step sizes.

]]>Mathematics doi: 10.3390/math11112588

Authors: Federico Bizzarri Chiara Mocenni Silvia Tiezzi

We propose a Markov Decision Process Model that blends ideas from Psychological research and Economics to study decision-making in individuals with self-control problems. We have borrowed a dual-process of decision-making with self-awareness from Psychological research, and we introduce present bias in inter-temporal preferences, a phenomenon widely explored in Economics. We allow for both an exogenous and endogenous, state-dependent, present bias in inter-temporal decision-making and explore, by means of numerical simulations, the consequences on well-being emerging from the solution of the model. We show that, over time, self-awareness may mitigate present bias and suboptimal choice behaviour.

]]>Mathematics doi: 10.3390/math11112586

Authors: Nikita V. Martyushev Boris V. Malozyomov Svetlana N. Sorokova Egor A. Efremenkov Mengxu Qi

Currently, the estimated range of an electric vehicle is a variable value. The assessment of this power reserve is possible by various methods, and the results of the assessment by these methods will be quite different. Thus, building a model based on these cycles is an extremely important task for manufacturers of electric vehicles. In this paper, a simulation model was developed to determine the range of an electric vehicle by cycles of movement. A mathematical model was created to study the power reserve of an electric vehicle, taking into account four driving cycles, in which the lengths of cycles and the forces acting on the electric vehicle are determined; the calculation of the forces of resistance to movement was carried out taking into account the efficiency of the electric motor; thus, the energy consumption of an electric vehicle is determined. The modeling of the study of motion cycles on the presented model was carried out. The mathematical evaluation of battery life was based on simulation results. Simulation modeling of an electric vehicle in the MATLAB Simulink software environment was performed. An assessment of the power reserve of the developed electric vehicle was completed. The power reserve was estimated using the four most common driving cycles&mdash;NEDC, WLTC, JC08, US06. Studies have shown that the highest speed of the presented US06 cycle provides the shortest range of an electric vehicle. The JC08 and NEDC cycles have similar developed speeds in urban conditions, while in NEDC there is a phase of out-of-town traffic; therefore, due to the higher speed, the electric vehicle covers a greater distance in equal time compared to JC08. At the same time, the NEDC cycle is the least dynamic and the acceleration values do not exceed 1 m/s2. Low dynamics allow for a longer range of an electric vehicle; however, the actual urban operation of an electric vehicle requires more dynamics. The cycles of movement presented in the article provide a sufficient variety and variability of the load of an electric vehicle and its battery over a wide range, which made it possible to conduct effective studies of the energy consumed, taking into account the recovery of electricity to the battery in a wide range of loads. It was determined that frequent braking, taking into account operation including in urban traffic, provides a significant return of electricity to the battery.

]]>Mathematics doi: 10.3390/math11112585

Authors: Bowen Zhang Lingfeng Liu

Chaos has been one of the most effective cryptographic sources since it was first used in image-encryption algorithms. This paper closely examines the development process of chaos-based image-encryption algorithms from various angles, including symmetric and asymmetric algorithms, block ciphers and stream ciphers, and integration with other technologies. The unique attributes of chaos, such as sensitivity to initial conditions, topological transitivity, and pseudo-randomness, are conducive to cross-referencing with other disciplines and improving image-encryption methods. Additionally, this paper covers practical application scenarios and current challenges of chaotic image encryption, thereby encouraging researchers to continue developing and complementing existing situations, and may also serve as a basis of future development prospects for chaos-based image encryption.

]]>Mathematics doi: 10.3390/math11112584

Authors: Ausaina Niyomdecha Patchanok Srisuradetchai

Numerous lifetime distributions have been developed to assist researchers in various fields. This paper proposes a new continuous three-parameter lifetime distribution called the complementary gamma zero-truncated Poisson distribution (CGZTP), which combines the distribution of the maximum of a series of independently identical gamma-distributed random variables with zero-truncated Poisson random variables. The proposed distribution&rsquo;s properties, including proofs of the probability density function, cumulative distribution function, survival function, hazard function, and moments, are discussed. The unknown parameters are estimated using the maximum likelihood method, whose asymptotic properties are examined. In addition, Wald confidence intervals are constructed for the CGZTP parameters. Simulation studies are conducted to evaluate the efficacy of parameter estimation, and three real-world data applications demonstrate that CGZTP can be an alternative distribution for fitting data.

]]>Mathematics doi: 10.3390/math11112581

Authors: Lantao You Jianfeng Jiang Yuejuan Han

A k*-container of a graph G is a set of k disjoint paths between any pair of nodes whose union covers all nodes of G. The spanning connectivity of G, &kappa;*(G), is the largest k, such that there exists a j*-container between any pair of nodes of G for all 1&le;j&le;k. If &kappa;*(G)=&kappa;(G), then G is super spanning connected. Spanning connectivity is an important property to measure the fault tolerance of an interconnection network. The divide-and-swap cube DSCn is a newly proposed hypercube variant, which reduces the network cost from O(n2) to O(nlog2n) compared with the hypercube and other hypercube variants. The folded divide-and-swap cube FDSCn is proposed based on DSCn to reduce the diameter of DSCn. Both DSCn and FDSCn possess many better properties than hypercubes. In this paper, we investigate the super spanning connectivity of FDSCn where n=2d and d&ge;1. We show that &kappa;*(FDSCn)=&kappa;(FDSCn)=d+2, which means there exists an m-DPC(node-disjoint path cover) between any pair of nodes in FDSCn for all 1&le;m&le;d+2.

]]>Mathematics doi: 10.3390/math11112583

Authors: Yong Tang

The work considers traveling wave optical solutions for the nonlinear generalized fractional KMN equation. This equation is considered for describing pulse propagation in optical fibers and communication systems using two quite similar approaches, based on the expansion of these solutions in the exponential or polynomial forms. Both approaches belong to the direct solving class of methods for PDEs and suppose the use of an auxiliary equation. The solutions acquired in this paper are obtained from first- and second-order differential equations that act as auxiliary equations. In addition, we generated 3D, contour, and 2D plots to illustrate the characteristics of the obtained soliton solutions. To create these plots, we carefully selected appropriate values for the relevant parameters.

]]>Mathematics doi: 10.3390/math11112582

Authors: Athena Papargiri George Fragoyiannis Vasileios S. Kalantonis

In the present paper, the forward problem of EEG and MEG is discussed, where the head is modeled by a spherical two-shell piecewise-homogeneous conductor with a neuronal current source positioned in the exterior shell area representing the brain tissue, while the interior shell portrays a cerebral edema. We consider constant conductivity, which assumes different values in each compartment, where the expansions of the electric potential and the magnetic field are represented via spherical harmonics. Furthermore, we demonstrate the reduction of our analytical results to the single-compartment model while it is shown that the magnetic field in the exterior of the conductor is a function only of the dipole moment and its position. Consequently, it does not depend on the inhomogeneity dictated by the interior shell, a fact that verifies the efficiency of the model.

]]>Mathematics doi: 10.3390/math11112580

Authors: N. Seshagiri Rao Zoran D. Mitrović Dania Santina Nabil Mlaiki

In this study, we have new fixed point results for weak contraction mappings in complete and partially ordered b-metric spaces. Our findings expand and generalize the results of Jachymski and Mituku et al and many more results in the literature as well. To illustrate our work, we present an application on the existence and uniqueness of a nonlinear quadratic integral problem solution. Moreover, an open problem is presented to enable the scope for future research in this area.

]]>Mathematics doi: 10.3390/math11112579

Authors: Shandong Mou Kexin Zhong Yamin Ma

Nowadays, with the rapid development of the platform economy, Big Data-based Discriminatory Pricing (BDDP) has become a common phenomenon in which big data and algorithms are applied to excessively seize consumer surplus and thus damage the rights and interests of consumers. This work aims to explore the equilibrium strategies of the consumers, the government, and the service platform and discuss factors affecting the BDDP practice of the service platforms. This study constructs a tripartite evolutionary game model among consumers, service platforms, and the government. Two evolutionary equilibrium strategies are derived and validated using simulation. Numerical experiments are conducted using MATLAB to reveal players&rsquo; evolutionary stability strategies under various settings. The study shows that (1) the strategies of the government and the platform always influence each other, (2) a reasonable adjustment of tax rate helps regulate the platform&rsquo;s behavior, and (3) the proportion of consumers who switch the platform after they realize themselves suffering BDDP is an important factor influencing platform&rsquo;s strategy. This study lastly summarizes the managerial insights for dealing with the platform&rsquo;s BDDP behavior and safeguarding consumers&rsquo; rights from the perspectives of macro-regulation and privacy data protection. The conclusions of this study can help promote the high-quality development of the platform economy.

]]>Mathematics doi: 10.3390/math11112578

Authors: Ekram E. Ali Hari M. Srivastava Abeer M. Albalahi

The potential for widespread applications of the geometric and mapping properties of functions of a complex variable has motivated this article. On the other hand, the basic or quantum (or q-) derivatives and the basic or quantum (or q-) integrals are extensively applied in many different areas of the mathematical, physical and engineering sciences. Here, in this article, we first apply the q-calculus in order to introduce the q-derivative operator S&eta;,p,qn,m. Secondly, by means of this q-derivative operator, we define an interesting subclass T&alefsym;&lambda;,pn,m(&eta;,&alpha;,&kappa;) of the class of normalized analytic and multivalent (or p-valent) functions in the open unit disk U. This p-valent analytic function class is associated with the class &kappa;-UCV of &kappa;-uniformly convex functions and the class &kappa;-UST of &kappa;-uniformly starlike functions in U. For functions belonging to the normalized analytic and multivalent (or p-valent) function class T&alefsym;&lambda;,pn,m(&eta;,&alpha;,&kappa;), we then investigate such properties as those involving (for example) the coefficient bounds, distortion results, convex linear combinations, and the radii of starlikeness, convexity and close-to-convexity. We also consider a number of corollaries and consequences of the main findings, which we derived herein.

]]>Mathematics doi: 10.3390/math11112577

Authors: Nianyi Wang Huiling Wang Shan Pei Boyu Zhang

In this study, we develop a data-driven heuristic method to solve the irregular flight recovery problem. Based on operational data from China South Airlines, Beijing, China, we evaluate the importance of a flight in the flight network and the influence of a delay on a flight and its subsequent flights. Then, we classify historical states into three scenarios according to their delay reasons and investigate the recovery patterns for each scenario. Inspired by the results of the data analysis, we develop a heuristic algorithm that imitates dispatcher actions. The algorithm is based on two basic operations: swapping the tail numbers of two flights and resetting their flight departure times. The algorithm can provide multiple recovery plans in real time for different scenarios, and we continue to refine and validate the algorithm for more robust and general solutions through a cost analysis. Finally, we test the efficiency and effectiveness of the recovery method based on the flight schedule, with real and simulated delays, and compare it with two other methods and the recovery actions of dispatchers.

]]>Mathematics doi: 10.3390/math11112576

Authors: Rafael Magdalena-Benedicto Sonia Pérez-Díaz Adrià Costa-Roig

Over the past few decades, the mathematical community has accumulated a significant amount of pure mathematical data, which has been analyzed through supervised, semi-supervised, and unsupervised machine learning techniques with remarkable results, e.g., artificial neural networks, support vector machines, and principal component analysis. Therefore, we consider as disruptive the use of machine learning algorithms to study mathematical structures, enabling the formulation of conjectures via numerical algorithms. In this paper, we review the latest applications of machine learning in the field of geometry. Artificial intelligence can help in mathematical problem solving, and we predict a blossoming of machine learning applications during the next years in the field of geometry. As a contribution, we propose a new method for extracting geometric information from the point cloud and reconstruct a 2D or a 3D model, based on the novel concept of generalized asymptotes.

]]>Mathematics doi: 10.3390/math11112575

Authors: Fátima Pilar Eliana Costa e Silva Ana Borges

This study investigates the scheduling of mechanical repairs performed at a Portuguese firm in the automobile sector. The aim is to reduce the amount of time that vehicles spend inactive between interventions by developing a mathematical model that takes into account the available resources and mechanics, the necessary interventions, and the time required for each repair. To accomplish this, a mixed-integer linear programming (MILP) model was employed, incorporating various variables to schedule interventions, allocate resources, and determine start times for each vehicle. The problem was formulated using the AMPL modeling language, and real-world instances of the problem, derived from data provided by the company, were solved using the Gurobi solver. Results show that the developed model significantly improves the scheduling of the vehicles&rsquo; repairs at the firm, leading to a reduction of 67% on average in the downtime of the vehicles and allowing an automatic correct schedule of the mechanical interventions. Moreover, the comparison of the scheduling obtained from the developed model and the firm&rsquo;s procedure shows that interventions on vehicles arriving at the repair shop are mostly repaired on the day of entry, allowing for quicker delivery to the customer.

]]>Mathematics doi: 10.3390/math11112574

Authors: Can-Zhong Yao Ze-Kun Zhang Yan-Li Li

This study focused on analyzing the complexities and risk spillovers that arise among financial institutions due to the development of financial markets. The research employed the conditional value at risk (CoVaR) methodology to quantify the extent of tail risk spillover and constructed a risk spillover network encompassing Chinese financial institutions. The study further investigated the characteristics, transmission paths, and dynamic evolution of this network under different risk conditions. The empirical findings of this research highlighted several important insights. First, financial institutions play distinct roles in the risk spillover process, with the securities and banking sectors as risk exporters and the insurance and diversified financial sectors as risk takers. The closest risk spillover relationships were observed between banking and insurance and between securities and diversified financial sectors. Second, in high-risk scenarios, there is significant intrasectoral risk transmission between banks and the diversified financial sector, as well as dual-sectoral risk contagion between banks and securities, with the most-common transmission occurring between diversified financial and securities sectors. Finally, the securities sector acts as the pivotal node for risk spillovers, being the main transmitter of intersectoral risks. The formation and evolution of risk spillover networks are influenced by endogenous mechanisms, in particular the convergence effect.

]]>Mathematics doi: 10.3390/math11112572

Authors: Pablo O. Juárez-Moreno Agustín Santiago-Moreno José M. Sautto-Vallejo Carlos N. Bouza-Herrera

Warner proposed a methodology called randomized response techniques, which, through the random scrambling of sensitive variables, allows the non-response rate to be reduced and the response bias to be diminished. In this document, we present a randomized response technique using simple random sampling. The scrambling of the sensitive variable is performed through the selection of a report Ri, i = 1,2,3. In order to evaluate the accuracy and efficiency of the proposed estimators, a simulation was carried out with two databases, where the sensitive variables are the destruction of poppy crops in Guerrero, Mexico, and the age at first sexual intercourse. The results show that more accurate estimates are obtained with the proposed model.

]]>Mathematics doi: 10.3390/math11112573

Authors: Yihan Wang Jinjie Zhu

The phase reduction approach has manifested its efficacy in investigating synchronization behaviors in limit-cycle oscillators. However, spatial distributions of the phase value on the limit cycle may lead to illusions of synchronizations for oscillators close to bifurcations. In this paper, we compared the phase sensitivity function in the spatial domain and time domain for oscillators close to saddle-node homoclinic (SNH) bifurcation, also known as saddle-node bifurcation on an invariant circle. It was found that the phase sensitivity function in the spatial domain can show the phase accumulation feature on the limit cycle, which can be ignored in the phase sensitivity function in the time domain. As an example, the synchronization distributions of uncoupled SNH oscillators driven by common and independent noises were investigated, where the space-dependent coupling function was considered on common noise. These results shed some light on the phase dynamics of oscillators close to bifurcations.

]]>Mathematics doi: 10.3390/math11112571

Authors: Tianxiang Ren Jinwen Wu

Percolation theory is a subject that has been flourishing in recent decades. Because of its simple expression and rich connotation, it is widely used in chemistry, ecology, physics, materials science, infectious diseases, and complex networks. Consider an infinite-rooted N-ary tree where each vertex is assigned an i.i.d. random variable. When the random variable follows a Bernoulli distribution, a path is called head run if all the random variables that are assigned on the path are 1. We obtain the weak law of large numbers for the length of the longest head run. In addition, when the random variable follows a continuous distribution, a path is called an increasing path if the sequence of random variables on the path is increasing. By Stein&rsquo;s method and other probabilistic methods, we prove that the length of the longest increasing path with a probability of one focuses on three points. We also consider limiting behaviours for the longest increasing path in a special tree.

]]>Mathematics doi: 10.3390/math11112569

Authors: Irina Volinsky Roman Shklyar

Stabilization by a parametric distributed control function plays a very important role in aeronautics, aerospace and physics. Choosing the right parameters is necessary for handling the distributed control. In the current paper, we introduce stabilization criteria for an n-order functional-differential equation with a parametric distributed control function in n-term integrals and 2n parameter sets. In our article, we use properties of unimodal and log-concave polynomials.

]]>Mathematics doi: 10.3390/math11112570

Authors: Martin Kochol

We introduce a unifying approach for invariants of finite matroids that count mappings to a finite set. The aim of this paper is to show that if the cardinalities of mappings with fixed values on a restricted set satisfy contraction&ndash;deletion rules, then there is a relation among them that can be expressed in terms of linear algebra. In this way, we study regular chain groups, nowhere-zero flows and tensions on graphs, and acyclic and totally cyclic orientations of oriented matroids and graphs.

]]>Mathematics doi: 10.3390/math11112568

Authors: Giro Candelario Alicia Cordero Juan R. Torregrosa María P. Vassileva

In recent years, some Newton-type schemes with noninteger derivatives have been proposed for solving nonlinear transcendental equations by using fractional derivatives (Caputo and Riemann&ndash;Liouville) and conformable derivatives. It has also been shown that the methods with conformable derivatives improve the performance of classical schemes. In this manuscript, we design point-to-point higher-order conformable Newton-type and multipoint procedures for solving nonlinear equations and propose a general technique to deduce the conformable version of any classical iterative method with integer derivatives. A convergence analysis is given and the expected orders of convergence are obtained. As far as we know, these are the first optimal conformable schemes, beyond the conformable Newton procedure, that have been developed. The numerical results support the theory and show that the new schemes improve the performance of the original methods in some aspects. Additionally, the dependence on initial guesses is analyzed, and these schemes show good stability properties.

]]>Mathematics doi: 10.3390/math11112567

Authors: Vyacheslav Trofimov Maria Loginova Vladimir Egorenkov Yongqiang Yang Zhongwei Yan

In this paper, we consider the 3D problem of laser-induced semiconductor plasma generation under the action of the optical pulse, which is governed by the set of coupled time-dependent non-linear PDEs involving the Poisson equation with Neumann boundary conditions. The main feature of this problem is the non-linear feedback between the Poisson equation with respect to induced electric field potential and the reaction-diffusion-convection-type equation with respect to free electron concentration and accounting for electron mobility (convection&rsquo;s term). Herein, we focus on the choice of the numerical method for the Poisson equation solution with inhomogeneous Neumann boundary conditions. Despite the ubiquitous application of such a direct method as the Fast Fourier Transform for solving an elliptic problem in simple spatial domains, we demonstrate that applying a direct method for solving the problem under consideration results in a solution distortion. The reason for the Neumann problem&rsquo;s solvability condition violation is the computational error&rsquo;s accumulation. In contrast, applying an iterative method allows us to provide finite-difference scheme conservativeness, asymptotic stability, and high computation accuracy. For the iteration technique, we apply both an implicit alternating direction method and a new three-stage iteration process. The presented computer simulation results confirm the advantages of using iterative methods.

]]>Mathematics doi: 10.3390/math11112566

Authors: Vassili N. Kolokoltsov

L&eacute;vy walks represent important modeling tools for a variety of real-life processes. Their natural scaling limits are known to be described by the so-called material fractional derivatives. So far, these scaling limits have been derived for spatially homogeneous walks, where Fourier and Laplace transforms represent natural tools of analysis. Here, we derive the corresponding limiting equations in the case of position-depending times and velocities of walks, where Fourier transforms cannot be effectively applied. In fact, we derive three different limits (specified by the way the process is stopped at an attempt to cross the boundary), leading to three different multi-dimensional versions of Caputo&ndash;Dzherbashian derivatives, which correspond to different boundary conditions for the generators of the related Feller semigroups and processes. Some other extensions and generalizations are analyzed.

]]>Mathematics doi: 10.3390/math11112565

Authors: Ángel López-Oriona José A. Vilar

The 21st century has witnessed a growing interest in the analysis of time series data. While most of the literature on the topic deals with real-valued time series, ordinal time series have typically received much less attention. However, the development of specific analytical tools for the latter objects has substantially increased in recent years. The R package otsfeatures attempts to provide a set of simple functions for analyzing ordinal time series. In particular, several commands allowing the extraction of well-known statistical features and the execution of inferential tasks are available for the user. The output of several functions can be employed to perform traditional machine learning tasks including clustering, classification, or outlier detection. otsfeatures also incorporates two datasets of financial time series which were used in the literature for clustering purposes, as well as three interesting synthetic databases. The main properties of the package are described and its use is illustrated through several examples. Researchers from a broad variety of disciplines could benefit from the powerful tools provided by otsfeatures.

]]>Mathematics doi: 10.3390/math11112564

Authors: Usman Ali Mawia Osman

Activation energy can be elaborated as the minimal energy required to start a certain chemical reaction. The concept of this energy was first presented by Arrhenius in the year 1889 and was later used in the oil reservoir industry, emulsion of water, geothermal as well as chemical engineering and food processing. This study relates to the impacts of mass transfer caused by temperature differences (Soret) and heat transport due to concentration gradient (Dufour) in a Carreau model with nanofluids (NFs), mixed convection and a magnetic field past a stretched sheet. Moreover, thermal radiation and activation energy with new mass flux constraints are presumed. All chemical science specifications of nanofluid are measured as constant. As a result of the motion of nanofluid particles, the fluid temperature and concentration are inspected, with some physical description. A system of coupled partial differential frameworks is used mathematically to formulate the physical model. A numerical scheme named the Runge&ndash;Kutta (R-K) approach along with the shooting technique are used to solve the obtained equations to a high degree of accuracy. The MATLAB R2022b software is used for the graphical presentation of the solution. The temperature of the nanofluid encompasses a quicker rate within the efficiency of a Dufour number. An intensifying thermal trend is observed for thermophoresis and the Brownian motion parameter. The Soret effect causes a decline in the fluid concentration, and the opposite trend is observed for rising activation energy. In addition, the local Nusselt number increases with the Prandtl number. Further, the comparative outcomes for drag force are established, with satisfying agreement with the existing literature. The results acquired here are anticipated to be applied to improving heat exchanger thermal efficiency to maintain thermal balancing control in compact heat density equipment and devices.

]]>Mathematics doi: 10.3390/math11112563

Authors: Ting Huang Jieping Gu Yuting Ouyang Wentao Huang

This paper focuses on investigating the bifurcation of limit cycles and centers within a specific class of three-dimensional cubic systems possessing Z3-equivariant symmetry. By calculating the singular point values of the systems, we obtain a necessary condition for a singular point to be a center. Subsequently, the Darboux integral method is employed to demonstrate that this condition is also sufficient. Additionally, we demonstrate that the system can bifurcate 15 small amplitude limit cycles with a distribution pattern of 5&minus;5&minus;5 originating from the singular points after proper perturbation. This finding represents a novel contribution to the understanding of the number of limit cycles present in three-dimensional cubic systems with Z3-equivariant symmetry.

]]>Mathematics doi: 10.3390/math11112562

Authors: Melike Kaplan Rubayyi T. Alqahtani

The key objective of the current manuscript was to investigate the exact solutions of the fractional perturbed Radhakrishnan&ndash;Kundu&ndash;Lakshmanan model. For this purpose, we applied two reliable and efficient approaches; specifically, the modified simple equation (MSE) and exponential rational function (ERF) techniques. The methods considered in this paper offer solutions for problems in nonlinear theory and mathematical physics practice. We also present solutions obtained graphically with the Maple package program.

]]>Mathematics doi: 10.3390/math11112561

Authors: Alberto Marroquín Gonzalo Garcia Ernesto Fabregas Ernesto Aranda-Escolástico Gonzalo Farias

The current computational advance allows the development of technological solutions using tools, such as mobile robots and programmable electronic systems. We present a design that integrates the Khepera IV mobile robot with an NVIDIA Jetson Xavier NX board. This system executes an algorithm for navigation control based on computer vision and the use of a model for object detection. Among the functionalities that this integration adds to the Khepera IV in generating guided driving are trajectory tracking for safe navigation and the detection of traffic signs for decision-making. We built a robotic platform to test the system in real time. We also compared it with a digital model of the Khepera IV in the CoppeliaSim simulator. The navigation control results show significant improvements over previous works. This is evident in both the maximum navigation speed and the hit rate of the traffic sign detection system. We also analyzed the navigation control, which achieved an average success rate of 93%. The architecture allows testing new control techniques or algorithms based on Python, facilitating future improvements.

]]>Mathematics doi: 10.3390/math11112560

Authors: Zheng Xu Song Yan Cong Wu Qing Duan Sixia Chen Yun Li

To study the relationship between genetic variants and phenotypes, association testing is adopted; however, most association studies are conducted by genotype-based testing. Testing methods based on next-generation sequencing (NGS) data without genotype calling demonstrate an advantage over testing methods based on genotypes in the scenarios when genotype estimation is not accurate. Our objective was to develop NGS data-based methods for association studies to fill the gap in the literature. Single-variant testing methods based on NGS data have been proposed, including our previously proposed single-variant NGS data-based testing method, i.e., UNC combo method. The NGS data-based group testing method has been proposed by us using a linear model framework which can handle continuous responses. In this paper, we extend our linear model-based framework to a generalized linear model-based framework so that the methods can handle other types of responses especially binary responses which is a common problem in association studies. To evaluate the performance of various estimators and compare them we performed simulation studies. We found that all methods have Type I errors controlled, and our NGS data-based methods have better performance than genotype-based methods for other types of responses, including binary responses (logistics regression) and count responses (Poisson regression), especially when sequencing depth is low. We have extended our previous linear model (LM) framework to a generalized linear model (GLM) framework and derived NGS data-based methods for a group of genetic variables. Compared with our previously proposed LM-based methods, the new GLM-based methods can handle more complex responses (for example, binary responses and count responses) in addition to continuous responses. Our methods have filled the literature gap and shown advantage over their corresponding genotype-based methods in the literature.

]]>Mathematics doi: 10.3390/math11112559

Authors: Vladimir Temlyakov

In this paper, a new criterion for the evaluation of the theoretical efficiency of a greedy algorithm is suggested. Using this criterion, we prove some results on the rate of convergence of greedy algorithms, which provide expansions. We consider both the case of Hilbert spaces and the more general case of Banach spaces. The new component of this paper is that we bound the error of approximation by the product of two norms&mdash;the norm of f and the A1-norm of f. Typically, only the A1-norm of f is used. In particular, we establish that some greedy algorithms (Pure Greedy Algorithm (PGA) and its modifications) are as good as the Orthogonal Greedy Algorithm (OGA) in this new sense of the rate of convergence, while it is known that the PGA is much worse than the OGA in the standard sense. Our new results provide better bounds for the accuracy than known results in the case of small &#8741;f&#8741;.

]]>Mathematics doi: 10.3390/math11112558

Authors: Yeling Yang Feng Yi Chuancheng Deng Guang Sun

The chi-squared automatic interaction detector (CHAID) algorithm is considered to be one of the most used supervised learning methods as it is adaptable to solving any kind of problem at hand. We are keenly aware of the non-linear relationships among CHAID maps, and they can empower predictive models with stability. However, we do not precisely know how high its accuracy. To determine the perfect scope the CHAID algorithm fits into, this paper presented an analysis of the accuracy of the CHAID algorithm. We introduced the causes, applicable conditions, and application scope of the CHAID algorithm, and then highlight the differences in the branching principles between the CHAID algorithm and several other common decision tree algorithms, which is the first step towards performing a basic analysis of CHAID algorithm. We next employed an actual branching case to help us better understand the CHAID algorithm. Specifically, we used vehicle customer satisfaction data to compare multiple decision tree algorithms and cited some factors that affect the accuracy and some corresponding countermeasures that are more conducive to obtaining accurate results. The results showed that CHAID can analyze the data very well and reliably detect significantly correlated factors. This paper presents the information required to understand the CHAID algorithm, thereby enabling better choices when the use of decision tree algorithms is warranted.

]]>Mathematics doi: 10.3390/math11112556

Authors: Hang Yi Wenjun Peng Xiuchun Xiao Shaojin Feng Hengde Zhu Yudong Zhang

The field of position tracking control and communication engineering has been increasingly interested in time-varying quadratic minimization (TVQM). While traditional zeroing neural network (ZNN) models have been effective in solving TVQM problems, they have limitations in adapting their convergence rate to the commonly used convex activation function. To address this issue, we propose an adaptive non-convex activation zeroing neural network (AZNNNA) model in this paper. Using the Lyapunov theory, we theoretically analyze the global convergence and noise-immune characteristics of the proposed AZNNNA model under both noise-free and noise-perturbed scenarios. We also provide computer simulations to illustrate the effectiveness and superiority of the proposed model. Compared to existing ZNN models, our proposed AZNNNA model outperforms them in terms of efficiency, accuracy, and robustness. This has been demonstrated in the simulation experiment of this article.

]]>Mathematics doi: 10.3390/math11112557

Authors: Luyu Wang Houbo Xiong Yunhui Shi Chuangxin Guo

A multi-stage robust real-time economic dispatch model (MRRTD) for power systems is proposed in this paper. The MRRTD takes the dynamic form of multi-stage robust optimization as the framework to naturally simulate the operation of equipment that is temporally coupled, e.g., utility-level energy storage systems. For normal systems, the MRRTD can work directly in short time slots with a rolling horizon. For large-scale systems, the MRRTD expands the time-slot scale and generates optimal dispatch policies. With this guidance, the real-time dispatch decision can be swiftly made thereafter. In addition, a dynamic uncertainty set based on deep learning is proposed, which can dynamically refine the covering ability for probable occurred wind power scenarios. To efficiently solve the MRRTD, a novel fast robust dual dynamic programming method is employed. The effectiveness of the proposed model and solution algorithm, especially the improved scalability compared to several other dynamic economic dispatch methods, are demonstrated by simulation results from six benchmark test cases ranging from a modified IEEE 6-bus system to a 6495-bus system.

]]>Mathematics doi: 10.3390/math11112555

Authors: América Berenice Morales-Díaz Josué Gómez-Casas Chidentree Treesatayapun Carlos Rodrigo Muñiz-Valdez Jesús Salvador Galindo-Valdés Jesús Fernando Martínez-Villafañe

Data technology advances have increased in recent years, especially for robotic systems, in order to apply data-driven modelling and control computations by only considering the input and output signals&rsquo; relationship. For a data-driven modelling and control approach, the system is considered unknown. Thus, the initialization values of the system play an important role to obtain a suitable estimation. This paper presents a methodology to initialize a data-driven model using the pseudo-Jacobian matrix algorithm to estimate the model of a mobile manipulator robot. Once the model is obtained, a control law is proposed for the robot end-effector position tasks. To this end, a novel neuro-fuzzy network is proposed as a control law, which only needs to update one parameter to minimize the control error and avoids the chattering phenomenon. In addition, a general stability analysis guarantees the convergence of the estimation and control errors and the tuning of the closed-loop control design parameters. The simulations results validate the performance of the data-driven model and control.

]]>Mathematics doi: 10.3390/math11112554

Authors: Abdulaziz Attaallah Khalil al-Sulbi Areej Alasiry Mehrez Marzougui Syed Anas Ansar Alka Agrawal Md Tarique Jamal Ansari Raees Ahmad Khan

Neoteric biomedical, technological, and normative shifts have prompted care firms to establish clinical governance as a contrivance to assure high-quality service in an exceedingly intricate milieu. Web security is an epochal concern in the healthcare sector, although it has garnered scant attention since the inception of web applications. The necessity to provide adequate security for healthcare web applications (HWAs) cannot be exaggerated, as umpteen health agencies are contingent on them to carry out their operations. Every healthcare organization renders a humongous volume of data available online to practitioners, pharmacies, and patients. Researchers are continually endeavoring to ameliorate techniques to increase the security and longevity of HWAs. In this context, experts examined certain imperative security risks in HWAs to quantitatively evaluate them in the design phase and covered numerous facets of HWAs, along with their security attributes and risk factors. The authors have proposed a combined approach of fuzzy-based symmetric techniques, i.e., AHP-TOPSIS (Analytic Hierarchy Process&ndash;Technique for Order of Preference by Similarity to Ideal Solution), for the assessment of alternative HWAs, leveraging the multi-criteria decision-making (MCDM) approach. Ten consecutive HWAs from local hospitals in Uttar Pradesh, India, have been taken to estimate the security risk, incorporating this methodology to evaluate the priority of weightage and the impact of security attributes. Henceforth, the findings and methodology employed in this study can assist security practitioners in identifying and prioritizing the most influential risk factors to secure HWAs and encourage them to develop revamped or novel methods.

]]>Mathematics doi: 10.3390/math11112553

Authors: Cristina Lopes Ana Maria Rodrigues Valeria Romanciuc José Soeiro Ferreira Elif Göksu Öztürk Cristina Oliveira

Sectorization is concerned with dividing a large territory into smaller areas, also known as sectors. This process usually simplifies a complex problem, leading to easier solution approaches to solving the resulting subproblems. Sectors are built with several criteria in mind, such as equilibrium, compactness, contiguity, and desirability, which vary with the applications. Sectorization appears in different contexts: sales territory design, political districting, healthcare logistics, and vehicle routing problems (agrifood distribution, winter road maintenance, parcel delivery). Environmental problems can also be tackled with a sectorization approach; for example, in municipal waste collection, water distribution networks, and even in finding more sustainable transportation routes. This work focuses on sectorization concerning the location of the area&rsquo;s centers and allocating basic units to each sector. Integer programming models address the location-allocation problems, and various formulations implementing different criteria are compared. Methods to deal with multiobjective optimization problems, such as the &#1013;-constraint, the lexicographic, and the weighted sum methods, are applied and compared. Computational results obtained for a set of benchmarking instances of sectorization problems are also presented.

]]>Mathematics doi: 10.3390/math11112552

Authors: Yuchao Li Mingsong Yang Qin Zhao Zongjian Li Zhaoxi Ma Yunhe Liu Xinhong Hei

Compliance checking is a very important step in engineering construction. With the development of information technology, automated compliance checking (ACC) has been paid more and more attention by researchers. One of the most important steps in automated compliance checking is the representation of the code information. However, the relationship constraint is often ignored in the code information and spatial geometric relationship is challenging to represent. The general code representation method does not have enough ability to identify the situation that does not meet the checking conditions because it is easy to cause semantic ambiguity in the checking results. This paper proposes a code representation method, and the building code information is represented in five parts. Relationships in the engineering domain and spatial relationships can be represented in constraint mode; different spatial relationship constraint-checking methods are also explicated. Constraint subject and constraint item can distinguish checking conditions and requirements, which supports semantic checking results. The mapping between the building information ontology and the code concepts is established, which can be used to automatically generate reasoning rules for compliance checking. Finally, the proposed method is verified by the representation of the China Metro Design Code and the application of the actual Metro model.

]]>Mathematics doi: 10.3390/math11112551

Authors: Elena Zaitseva Vitaly Levashenko Ravil Mukhamediev Nicolae Brinzei Andriy Kovalenko Adilkhan Symagulov

Drones, or UAVs, are developed very intensively. There are many effective applications of drones for problems of monitoring, searching, detection, communication, delivery, and transportation of cargo in various sectors of the economy. The reliability of drones in the resolution of these problems should play a principal role. Therefore, studies encompassing reliability analysis of drones and swarms (fleets) of drones are important. As shown in this paper, the analysis of drone reliability and its components is considered in studies often. Reliability analysis of drone swarms is investigated less often, despite the fact that many applications cannot be performed by a single drone and require the involvement of several drones. In this paper, a systematic review of the reliability analysis of drone swarms is proposed. Based on this review, a new method for the analysis and quantification of the topological aspects of drone swarms is considered. In particular, this method allows for the computing of swarm availability and importance measures. Importance measures in reliability analysis are used for system maintenance and to indicate the components (drones) whose fault has the most impact on the system failure. Structural and Birnbaum importance measures are introduced for drone swarms&rsquo; components. These indices are defined for the following topologies: a homogenous irredundant drone fleet, a homogenous hot stable redundant drone fleet, a heterogeneous irredundant drone fleet, and a heterogeneous hot stable redundant drone fleet.

]]>Mathematics doi: 10.3390/math11112550

Authors: Sandi Ljubic Alen Salkanovic

In the field of human&ndash;computer interaction (HCI), text entry methods can be evaluated through controlled user experiments or predictive modeling techniques. While the modeling approach requires a language model, the empirical approach necessitates representative text phrases for the experimental stimuli. In this context, finding a phrase set with the best language representativeness belongs to the class of optimization problems in which a solution is sought in a large search space. We propose a genetic algorithm (GA)-based method for extracting a target phrase set from the available text corpus, optimizing its language representativeness. Kullback&ndash;Leibler divergence is utilized to evaluate candidates, considering the digram probability distributions of both the source corpus and the target sample. The proposed method is highly customizable, outperforms typical random sampling, and exhibits language independence. The representative phrase sets generated by the proposed solution facilitate a more valid comparison of the results from different text entry studies. The open source implementation enables the easy customization of the GA-based sampling method, promotes its immediate utilization, and facilitates the reproducibility of this study. In addition, we provide heuristic guidelines for preparing the text entry experiments, which consider the experiment&rsquo;s intended design and the phrase set to be generated with the proposed solution.

]]>Mathematics doi: 10.3390/math11112549

Authors: Renata Tavanielli Márcio Laurini

This study examines the effectiveness of various specifications of the dynamic Nelson&ndash;Siegel term structure model in analyzing the term structure of Brazilian interbank deposits. A key contribution of our research is the incorporation of regime changes and other time-varying parameters in the model, both when relying solely on observed yields and when incorporating macroeconomic variables. By allowing parameters in the latent factors to adapt to changes in persistence patterns and the overall shape of the yield curve, these mechanisms enhance the model&rsquo;s flexibility. To evaluate the performance of the models, we conducted assessments based on their in-sample fit and out-of-sample forecast accuracy. Our estimation approach involved Bayesian procedures utilizing Markov Chain Monte Carlo techniques. The results highlight that models incorporating macro factors and greater flexibility demonstrated superior in-sample fit compared to other models. However, when it came to out-of-sample forecasts, the performance of the models was influenced by the forecast horizon and maturity. Models incorporating regime switching exhibited better performance overall. Notably, for long maturities with a one-month ahead forecast horizon, the model incorporating regime changes in both the latent and macro factors emerged as the top performer. On the other hand, for a twelve-month horizon, the model incorporating regime switching solely in the macro factors demonstrated superior performance across most maturities. These findings have significant implications for the development of trading and hedging strategies in interest rate derivative instruments, particularly in emerging markets that are more prone to regime changes and structural breaks.

]]>Mathematics doi: 10.3390/math11112548

Authors: Andrei-Marius Avram Verginica Barbu Mititelu Vasile Păiș Dumitru-Clementin Cercel Ștefan Trăușan-Matu

Correctly identifying multiword expressions (MWEs) is an important task for most natural language processing systems since their misidentification can result in ambiguity and misunderstanding of the underlying text. In this work, we evaluate the performance of the mBERT model for MWE identification in a multilingual context by training it on all 14 languages available in version 1.2 of the PARSEME corpus. We also incorporate lateral inhibition and language adversarial training into our methodology to create language-independent embeddings and improve its capabilities in identifying multiword expressions. The evaluation of our models shows that the approach employed in this work achieves better results compared to the best system of the PARSEME 1.2 competition, MTLB-STRUCT, on 11 out of 14 languages for global MWE identification and on 12 out of 14 languages for unseen MWE identification. Additionally, averaged across all languages, our best approach outperforms the MTLB-STRUCT system by 1.23% on global MWE identification and by 4.73% on unseen global MWE identification.

]]>Mathematics doi: 10.3390/math11112547

Authors: Jianing Cao Hua Chen

In this paper, a mathematical model based on the T-S fuzzy model is proposed to solve the fault estimation (FE) and fault-tolerant control (FTC) problem for singular nonlinear time-varying delay (TVD) systems with sensor fault. TVD is is extremely difficult to solve and the Laplace transform is devised to build an equal system free of TVD. Additionally, the sensor fault is changed to actuator fault by the developed coordinate transformation. A fuzzy learning fault estimator is first built to estimate the detailed sensor fault information. Then, a PI FTC scheme is suggested aiming at minimizing the damage caused by the fault. Simulation results from multiple faults reveal that the FE and FTC algorithms are able to estimate the fault and guarantee the system performance properly.

]]>Mathematics doi: 10.3390/math11112546

Authors: Nagireddy Venkata Rajasekhar Reddy Pydimarri Padmaja Miroslav Mahdal Selvaraj Seerangan Vrince Vimal Vamsidhar Talasila Lenka Cepova

The Internet of Things (IoT) is rapidly expanding and becoming an integral part of daily life, increasing the potential for security threats such as malware or cyberattacks. Many embedded systems (ESs), responsible for handling sensitive data or facilitating secure online activities, must adhere to stringent security standards. For instance, payment processors employ security-critical components as distinct chips, maintaining physical separation from other network components to prevent the leakage of sensitive information such as cryptographic keys. Establishing a trusted environment in IoT and ESs, where interactions are based on the trust model of communication nodes, is a viable approach to enhance security in IoT and ESs. Although trust management (TM) has been extensively studied in distributed networks, IoT, and ESs, significant challenges remain for real-world implementation. In response, we propose a hybrid fuzzy rule algorithm (FRA) and trust planning mechanism (TPM), denoted FRA + TPM, for effective trust management and to bolster IoT and ESs reliability. The proposed system was evaluated against several conventional methods, yielding promising results: trust prediction accuracy (99%), energy consumption (53%), malicious node detection (98%), computation time (61 s), latency (1.7 ms), and throughput (9 Mbps).

]]>Mathematics doi: 10.3390/math11112545

Authors: Andrey M. Bramm Stanislav A. Eroshenko Alexandra I. Khalyasmaa Pavel V. Matrenin

At the current stage of the integration of renewable energy sources into the power systems of many countries, requirements for compliance with established technical characteristics are being applied to power generation. One such requirement is the installed capacity utilization factor, which is extremely important for optimally placing power facilities based on renewable energy sources and for the successful development of renewable energy. Efficient placement maximizes the installed capacity utilization factor of a power facility, increasing energy efficiency and the payback period. The installed capacity utilization factor depends on the assumed meteorological factors relating to geographical location and the technical characteristics of power generation. However, the installed capacity utilization factor cannot be accurately predicted, since it is necessary to know the volume of electricity produced by the power facility. A novel approach to the optimization of placement of renewable energy source power plants and their capacity factor forecasting was proposed in this article. This approach combines a machine learning forecasting algorithm (random forest regressor) with a metaheuristic optimization algorithm (grey wolf optimizer). Although the proposed approach assumes the use of only open-source data, the simulations show better results than commonly used algorithms, such as random search, particle swarm optimizer, and firefly algorithm.

]]>Mathematics doi: 10.3390/math11112544

Authors: Fan Zhang Mingang Hua Mengyu Gao

In this paper, the design of a dynamic output feedback controller for a networked control system with dual-channel data packet loss and special discrete-time delay is studied, in which the data packet loss is described by the Markov process. In order to effectively alleviate the problem of network congestion, a quantizer was added to the sensor-to-controller channel. The transition probabilities of the Markov process are uncertain, but they exist in the convex sets of known convex polyhedron types. The mode-dependent Lyapunov function was constructed, and a sufficient condition was given to make the closed-loop system stochastically stable and satisfy the performance index. The parameters of the controller were solved by the linear matrix inequality method. Finally, an example of aircraft shows the validity of the proposed approach. A numerical example is compared with other literature, showing the superiority of the proposed approach.

]]>Mathematics doi: 10.3390/math11112543

Authors: Aravindh Dharmarajan Parivallal Arumugam Sakthivel Ramalingam Kavikumar Ramasamy

This work focuses on the design of a unified control law, which enhances the accuracy of both the disturbance estimation and stabilization of nonlinear T-S fuzzy semi-Markovian jump systems. In detail, a proportional-integral observer based equivalent-input-disturbance (PIO-EID) approach is considered to model and develop the controller. The PIO approach includes a variable for relaxation in the system design along with an additional term for integration to improve the flexibility of the design and endurance of the system. The proposed stability criteria are formulated in the form of matrix inequalities using Lyapunov theory and depend on the sojourn time for robust control design. Final analyses are performed using MATLAB software with simulations to endorse the theoretical findings of this paper.

]]>Mathematics doi: 10.3390/math11112542

Authors: Qiaoling Guo Tingting Shan Bingliang Shen Tao Yang

Let A/B be a right H-Galois extension over a semisimple Hopf algebra H. The purpose of this paper is to give the relationship of Gorenstein flat dimensions between the algebra A and its subalgebra B, and obtain that the global Gorenstein flat dimension and the finitistic Gorenstein flat dimension of A is no more than that of B. Then the problem of preserving property of Gorenstein flat precovers for the Hopf-Galois extension will be studied. Finally, more relations for the crossed products and smash products will be obtained as applications.

]]>Mathematics doi: 10.3390/math11112540

Authors: Matías Jaque-Zurita Jorge Hinojosa Ignacio Fuenzalida-Henríquez

Computational simulation is a highly reliable tool used to solve structural analysis problems. In recent times, several techniques have been developed in the field of computational mechanics in order to analyze non-linearities in less time, helping decision-making when structures suffer damage. The global&ndash;local analysis is a technique to increase the efficiency of computational simulations by using a global model to obtain boundary conditions in a coupling zone imposed on a local model. Coupling can be performed through the primal&ndash;dual method, which is used for crack propagation using 2D and 3D models with fine meshes, thus saving computational time. However, it has not been implemented at a commercial level to analyze large structures such as multi-story buildings with focused non-linearities. In this work, a global&ndash;local analysis with non-intrusive methodology and simplified models was implemented in a cracked framed structure, using a 1D (global) and 3D (local) coupling considering crack propagation with primal&ndash;dual interface conditions. Different lengths of the local model were analyzed, studying their influence on the convergence of the problem, and compared with a 3D monolithic model to check the reliability of the results. The results show that the proposed methodology solves the problem with an error less than 10%. Furthermore, it was determined that the dimensions of the local model affect the convergence of the problem. This work also provides an implementation of the method for large structures containing focused non-linearities and using commercial software, reducing computational time for the cracked structural analysis.

]]>Mathematics doi: 10.3390/math11112541

Authors: Katarína Laššová Lucia Rumanová

Math trail is a type of outdoor activity conducted in groups. Group members work together, communicate, and find common and best strategies for solving problems. Thus, it combines the use of mathematics in real life with a pleasant walk. Pupils have the opportunity to explore mathematics linked to real objects, solving standard and non-standard problems. Teachers, in turn, can use mathematics in this way as a tool to connect it to other STEM disciplines. This article first describes the results of research aimed at assessing spatial ability and mental rotation in students at technical vocational schools in Slovakia. A stereometry test was solved by 455 students. We then created a mathematical walk aimed at developing students&rsquo; spatial ability. The experiences presented in the article may be helpful as other mathematics&rsquo; popularization stimuli. We believe that when students are engaged in problem-based learning activities that are related to STEM disciplines, in particular, by connecting with the subjects in the problem context of the real world, STEM disciplines may become more important for students. Interdisciplinary STEM learning imitates authentic real-world problem-solving.

]]>Mathematics doi: 10.3390/math11112539

Authors: Adel Alahmadi Patrick Solé Ramy Taki Eldin

We recall a classic lower bound on the minimum Hamming distance of constacyclic codes over finite fields, analogous to the well-known BCH bound for cyclic codes. This BCH-like bound serves as a foundation for proposing some minimum-distance lower bounds for single-generator quasi-twisted (QT) codes. Associating each QT code with a constacyclic code over an extension field, we obtain the first bound. This is the QT analogue to a result in the literature for quasi-cyclic codes. We point out some weaknesses in this bound and propose a novel bound that takes into account the Chinese remainder theorem approach to QT codes as well as the BCH bound of constacyclic codes. This proposed bound, in contrast to previous bounds in the literature, does not presuppose a specific form of code generator and does not require calculations in any extension field. We illustrate that our bound meets the one in the literature when the code generator adheres to the specific form assumed in that study. Various numerical examples enable us to compare and discuss these bounds.

]]>Mathematics doi: 10.3390/math11112538

Authors: Dongying Wang Sumin Wang

For order-of-addition experiments, the response is affected by the addition order of the experimental materials. Consequently, the main interest focuses on creating a predictive model and an optimal design for optimizing the response. Van Nostrand proposed the pairwise-order (PWO) model for detecting PWO effects. Under the PWO model, the full PWO design is optimal under various criteria but is often unaffordable because of the large run size. In this paper, we consider the D-, A- and M.S.-optimal fractional PWO designs. We first present some results on information matrices. Then, a flexible and efficient algorithm is given for generating these optimal PWO designs. Numerical simulation shows that the generated design has an appealing efficiency in comparison with the full PWO design, though with only a small fraction of runs. Several comparisons with existing designs illustrate that the generated designs achieve better efficiencies, and the best PWO designs and some selected 100% efficient PWO designs generated by the new algorithm are reported.

]]>Mathematics doi: 10.3390/math11112534

Authors: Mei-Chuan Cheng Hui-Chiung Lo Chih-Te Yang

This paper aims to propose a comprehensive inventory model including pricing, pre-sale incentives, advance sales, trade credit, and carbon tax policies. The novelty of this study lies in its holistic approach to addressing these relevant and practical issues. The major purpose is to determine the optimal pricing, pre-order discount, and replenishment decisions to maximize the total profit under carbon tax policy. Through theoretical analysis, this study develops several theorems to demonstrate properties of optimal solutions and an easy-to-use algorithm to derive optimal solutions. Further, several numerical examples are provided to demonstrate the solution process for different scenarios and the effects of various parameters on optimal alternatives and solutions. This study provides companies management implications to address the challenges posed by the global movement to reduce carbon emissions while maintaining their profitability.

]]>Mathematics doi: 10.3390/math11112537

Authors: Fang Ren Ziyi Wu Yaqi Xue Yanli Hao

In this paper, we propose a reversible data hiding scheme in an encrypted image based on bit-plane redundancy of prediction error. The scheme greatly improves the embedding capacity while maintaining lossless image recovery and error-free secret data extraction. Firstly, the original image is preprocessed to obtain the prediction error image. After the error matrix is divided into blocks, the corresponding block type is obtained. Secondly, the predicted error image is encrypted with stream cipher and the encryption matrix blocks are scrambled to ensure the security of the scheme. Finally, after embedding the block type value into the encrypted image, the spare room corresponding to each block was obtained, which was used to embed the secret data. The scheme makes full use of the spatial correlation of the pixels in the block, so it improves the embedding rate. By selecting 100 images in each dataset of BOSSbase and BOWS-2, when the block size is 3&times;3, the average embedding rate of our scheme can reach 3.56 bpp and 3.81 bpp, respectively. The performance of the proposed method is better than the other schemes with similar properties.

]]>Mathematics doi: 10.3390/math11112536

Authors: Nikolai A. Magnitskii

In this work, an analytical and numerical analysis of the transition to chaos in five nonlinear systems of ordinary and partial differential equations, which are models of autocatalytic chemical processes and interacting populations, is carried out. It is shown analytically and numerically that in all considered systems of equations, further complication of the dynamics of solutions and the transition to chemical and biological turbulence is carried out in full accordance with the universal Feigenbaum-Sharkovsky-Magnitskii bifurcation theory through subharmonic and homoclinic cascades of bifurcations of stable limit cycles. In this case, irregular (chaotic) attractors in all cases are exclusively singular attractors in the sense of the FShM theory. The obtained results once again indicate the wide applicability of the universal bifurcation FShM theory for describing laminar&ndash;turbulent transitions to chaotic dynamics in complex nonlinear systems of differential equations and that chaos in the system can be confirmed only by detection of some main cycles or tori in accordance with the universal bifurcation diagram presented in the article.

]]>Mathematics doi: 10.3390/math11112535

Authors: Yan Yang Fang-Wen Ge Xiang Liu

For plate structures, their random parameters can be regarded as a two-dimensional random field in the plane. To solve the plate theory considering a two-dimensional random field, an efficient strategy for the stochastic finite element method was adopted. Firstly, the stochastic finite element method was used to establish the plate structural model, in which the random field characteristics of the parameter were considered, and the mathematical expression of its random field was obtained through the Karhunen&ndash;Lo&egrave;ve expansion; secondly, the point estimate method was applied to calculate the statistics of random structures. The computational efficiency can be significantly improved through the reference point selection strategy. The accuracy and efficiency of the calculation strategy were verified, and the influences of correlation length and coefficient of variation of the parameter on the random response of plate structures under different plate types (including Kirchhoff plate and Mindlin plate) and boundary conditions (including simply supported and clamped supported) were discussed. The proposed method can provide some help in solving static problems of plate structures.

]]>Mathematics doi: 10.3390/math11112532

Authors: Aušra Gadeikytė Aušra Abraitienė Rimantas Barauskas

In this study, computational models of heat and mass exchange through textile structures with additional ventilation at the micro- and macro-scale were investigated. The finite element analysis of advanced textile materials provides a better understanding of their heat and mass transfer properties, which influence thermal comfort. The developed computational models can predict air permeability (AP), thermal resistance (Rct), and heat transfer (h) coefficients at the micro-scale. Moreover, the mesh size was taken into consideration and validated with experimental data presented in the literature. In addition, computational models were extended to micro- and macro-scale forced ventilation models. Macro-scale finite element models require input parameters such as an effective heat transfer coefficient that are usually obtained experimentally. In this research, the heat transfer coefficients (hmicrolayer = 25.603 W/(K&middot;m2), htotal = 8.9646 W/(K&middot;m2)) were obtained numerically from the micro-scale model and were applied to a macro-scale model. The proposed methodology and developed models facilitate the determination of average temperature and temperature distributions through different through-thickness positions along the axis Oz. The simulations were carried out using Comsol Multiphysics and Matlab software.

]]>Mathematics doi: 10.3390/math11112533

Authors: Maksim A. Pakhomov Viktor I. Terekhov

The features of the local mean and fluctuational flow structure, carrier phase turbulence and the propagation of the dispersed phase in the bubbly and droplet-laden isothermal round polydispersed jets were numerically simulated. The dynamics of the polydispersed phase is predicted using the Eulerian&ndash;Eulerian two-fluid approach. Turbulence of the carrier phase is described using the second-moment closure while taking into account the presence of the dispersed phase. The numerical analysis was performed in a wide range of variation of dispersed phase diameter at the inlet and particle-to-fluid density ratio (from gas flow laden with water droplets to carrier fluid flow laden with gas bubbles). An increase in the concentration of air bubbles and their size leads to jet expansion (as compared to a single-phase jet up to 40%), which indicates an increase in the intensity of the process of turbulent mixing with the surrounding space. However, this makes the gas-droplet jet narrower (up to 15%) and with a longer range in comparison with a single-phase flow. The addition of finely dispersed liquid droplets to an air jet suppresses gas phase turbulence (up to 15%). In a bubbly jet, it is found that small bubbles (Stk &lt; 0.1) accumulate near the jet axis in the initial cross-sections, while concentration of the large ones (Stk &gt; 0.2) along the jet axis decreases rapidly. In the gas-droplet jet, the effect of dispersed phase accumulation is also observed in the initial cross-section, and then its concentration decreases gradually along the jet axis. For gas bubbles (Stk &lt; 0.1), small turbulence attenuation (up to 6%) is shown.

]]>Mathematics doi: 10.3390/math11112530

Authors: Lin Chen Ting Dong Jin Peng Dan Ralescu

In recent years, there have been frequent cases of impact on the stable development of supply chain economy caused by uncertain events such as COVID-19 and extreme weather events. The creation, management, and impact coping techniques of the supply chain economy now face wholly novel requirements as a result of the escalating level of global uncertainty. Although a significant literature applies uncertainty analysis and optimization modeling (UAO) to study supply chain management (SCM) under uncertainty, there is a lack of systematic literature review and research classification. Therefore, in this paper, 121 articles published in 44 international academic journals between 2015 and 2022 are extracted from the Web of Science database and reviewed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Bibliometric analysis and CiteSpace software are used to identify current developments in the field and to summarize research characteristics and hot topics. The selected published articles are classified and analyzed by author name, year of publication, application area, country, research purposes, modeling methods, research gaps and contributions, research results, and journals to comprehensively review and evaluate the SCM in the application of UAO. We find that UAO is widely used in SCM under uncertainty, especially in the field of decision-making, where it is common practice to abstractly model the decision problem to obtain scientific decision results. This study hopes to provide an important and valuable reference for future research on SCM under uncertainty. Future research could combine uncertainty theory with supply chain management segments (e.g., emergency management, resilience management, and security management), behavioral factors, big data technologies, artificial intelligence, etc.

]]>Mathematics doi: 10.3390/math11112531

Authors: Jin Zhong Lin Lin

A square matrix is said to have property n if there exists a positive integer w such that Aw is nonnegative. In this paper, we study the core-EP monotonicity for property-n matrices. Some necessary and sufficient conditions for a property-n matrix to be core-EP monotone are given. Moreover, a necessary and sufficient condition for a real square matrix to have a nonnegative core-EP inverse is also presented.

]]>Mathematics doi: 10.3390/math11112529

Authors: Meijun Zhou Gang Mei

In practical engineering applications, there is a high demand for inverting parameters for various materials, and obtaining monitoring data can be costly. Traditional inverse methods often involve tedious computational processes, require significant computational effort, and exhibit slow convergence speeds. The recently proposed Physics-Informed Neural Network (PINN) has shown great potential in solving inverse problems. Therefore, in this paper, we propose a transfer learning-based coupling of the Smoothed Finite Element Method (S-FEM) and PINN methods for the inversion of parameters in elastic-plasticity problems. The aim is to improve the accuracy and efficiency of parameter inversion for different elastic-plastic materials with limited data. High-quality small datasets were synthesized using S-FEM and subsequently combined with PINN for pre-training purposes. The parameters of the pre-trained model were saved and used as the initial state for the PINN model in the inversion of new material parameters. The inversion performance of the coupling of S-FEM and PINN is compared with the coupling of the conventional Finite Element Method (FEM) and PINN on a small data set. Additionally, we compared the efficiency and accuracy of both the transfer learning-based and non-transfer learning-based methods of the coupling of S-FEM and PINN in the inversion of different material parameters. The results show that: (1) our method performs well on small datasets, with an inversion error of essentially less than 2%; (2) our approach outperforms the coupling of conventional FEM and PINN in terms of both computational accuracy and computational efficiency; and (3) our approach is at least twice as efficient as the coupling of S-FEM and PINN without transfer learning, while still maintaining accuracy. Our method is well-suited for the inversion of different material parameters using only small datasets. The use of transfer learning greatly improves computational efficiency, making our method an efficient and accurate solution for reducing computational cost and complexity in practical engineering applications.

]]>Mathematics doi: 10.3390/math11112528

Authors: Ming Jiang Zhiwei Liu

More accurate traffic prediction can further improve the efficiency of intelligent transportation systems. However, the complex spatiotemporal correlation issues in transportation networks pose great challenges. In the past, people have carried out a great deal of research to solve this problem. Most studies are based on graph neural networks to model traffic graphs and attempt to use fixed graph structures to obtain relationships between nodes. However, due to the time-varying spatial correlation of the transportation network, there is no stable node relationship. To address the above issues, we propose a new traffic prediction framework called the Dynamic Graph Spatial-Temporal Neural Network (DGSTN). Unlike other models that use predefined graphs, this model represents stable node relationships and time-varying node relationships by constructing static topology maps and dynamic information maps during the training and testing stages, to capture hidden node relationships and time-varying spatial correlations. In terms of network architecture, we designed multi-scale causal convolution and adaptive spatial self-attention mechanisms to capture temporal and spatial features, respectively, and assisted learning through static and dynamic graphs. The proposed framework has been tested on two real-world traffic datasets and can achieve state-of-the-art performance.

]]>Mathematics doi: 10.3390/math11112527

Authors: Rolando Rubilar-Torrealba Karime Chahuán-Jiménez Hanns de la Fuente-Mella

The purpose of this study was to identify and measure the impact of the different effects of entropy states over the high-frequency trade of the cryptocurrency market, especially in Bitcoin, using and selecting optimal parameters of the Bayesian approach, specifically through approximate Bayesian computation (ABC). ABC corresponds to a class of computational methods rooted in Bayesian statistics that could be used to estimate the posterior distributions of model parameters. For this research, ABC was applied to estimate the daily prices of the Bitcoin cryptocurrency from May 2013 to December 2021. The findings suggest that the behaviour of the parameters for our tested trading algorithms, in which sudden jumps are observed, can be interpreted as changes in states of the generated time series. Additionally, it is possible to identify and model the effects of the COVID-19 pandemic on the series analysed in the research. Finally, the main contribution of this research is that we have characterised the relationship between entropy and the evolution of parameters defining the optimal selection of trading algorithms in the financial industry.

]]>Mathematics doi: 10.3390/math11112526

Authors: Yulei Huang Ziping Ma Huirong Li Jingyu Wang

Semi-supervised non-negative matrix factorization (NMF) has achieved successful results due to the significant ability of image recognition by a small quantity of labeled information. However, there still exist problems to be solved such as the interconnection information not being fully explored and the inevitable mixed noise in the data, which deteriorates the performance of these methods. To circumvent this problem, we propose a novel semi-supervised method named DLRGNMF. Firstly, dual latent space is characterized by the affinity matrix to explicitly reflect the interrelationship between data instances and feature variables, which can exploit the global interconnection information in dual space and reduce the adverse impacts caused by noise and redundant information. Secondly, we embed the manifold regularization mechanism in the dual graph to steadily retain the local manifold structure of dual space. Moreover, the sparsity and the biorthogonal condition are integrated to constrain matrix factorization, which can greatly improve the algorithm&rsquo;s accuracy and robustness. Lastly, an effective alternating iterative updating method is proposed, and the model is optimized. Empirical evaluation on nine benchmark datasets demonstrates that DLRGNMF is more effective than competitive methods.

]]>Mathematics doi: 10.3390/math11112525

Authors: Abigail María Elena Ramírez-Mendoza Wen Yu Xiaoou Li

This paper introduces a new spike activation function (SPKAF) or spike membership function for fuzzy adaptive neurons (FAN), developed for decoding spatiotemporal information with spikes, optimizing digital signal processing. A solution with the adaptive network-based fuzzy inference system (ANFIS) method is proposed and compared with that of the FAN-SPKAF model, obtaining very precise simulation results. Stability analysis of systems models is presented. An application to voice recognition using solfeggio syllables in Spanish is performed experimentally, comparing the methods of FAN-step activation function (STEPAF)-SPKAF, Augmented Spiking Neuron Model, and Augmented FAN-STEPAF-SPKAF, achieving very good results.

]]>Mathematics doi: 10.3390/math11112524

Authors: María José Hernández-Molinos Angel J. Sánchez-García Rocío Erandi Barrientos-Martínez Juan Carlos Pérez-Arriaga Jorge Octavio Ocharán-Hernández

Software defect prediction is an important area in software engineering because it helps developers identify and fix problems before they become costly and hard-to-fix bugs. Early detection of software defects helps save time and money in the software development process and ensures the quality of the final product. This research aims to evaluate three algorithms to build Bayesian Networks to classify whether a project is prone to defects. The choice is based on the fact that the most used approach in the literature is Naive Bayes, but no works use Bayesian Networks. Thus, K2, Hill Climbing, and TAN are used to construct Bayesian Networks. On the other hand, three public PROMISE data sets are used based on McCabe and Halstead complexity metrics. The results are compared with the most used approaches in the literature, such as Decision Tree and Random Forest. The results from different performance metrics applied to a cross-validation process show that the classification results are comparable to Decision Tree and Random Forest, with the advantage that Bayesian algorithms show less variability, which helps engineering software to have greater robustness in their predictions since the selection of training and test data do not give variable results, unlike Decision Tree and Random Forest.

]]>Mathematics doi: 10.3390/math11112523

Authors: Javier Rico-Azagra Montserrat Gil-Martínez

Whenever additional states of a plant can be measured, closing nested feedback loops can be exploited in a variety of ways. The goal here is to reduce the bandwidth of feedback controllers and thus reduce the amplification of sensor noise that can otherwise spoil the expected performance when the actuator saturates. This can be particularly relevant for demanding tracking specifications and large plant uncertainties. In this context, the current work proposes a novel model-matching control architecture with a feedforward controller and two feedback controllers, which is accompanied by a new robust design method in the frequency domain of Quantitative Feedback Theory (QFT). The use of a feedforward controller reduces the amount of feedback to the minimum necessary to constrain the spread of the tracking error responses as specified. Furthermore, this amount of feedback is quantitatively distributed along the frequency between the inner and outer loops to reduce the total sensor noise at the control input as much as possible. A theoretical example illustrates the method and highlights the advantages of the new architecture over two other previously feasible QFT solutions: one with double feedback and another with single feedback plus feedforward. The importance of choosing the correct switching frequency between loops is also demonstrated. Finally, the angle of rotation of a commercial servo motor is successfully controlled using the motor speed as an internal measure.

]]>Mathematics doi: 10.3390/math11112522

Authors: Nusrat Shaheen Ismail Shah Amani Almohaimeed Sajid Ali Hana N. Alqifari

Regression analysis is a statistical process that utilizes two or more predictor variables to predict a response variable. When the predictors included in the regression model are strongly correlated with each other, the problem of multicollinearity arises in the model. Due to this problem, the model variance increases significantly, leading to inconsistent ordinary least-squares estimators that may lead to invalid inferences. There are numerous existing strategies used to solve the multicollinearity issue, and one of the most used methods is ridge regression. The aim of this work is to develop novel estimators for the ridge parameter &ldquo;&gamma;&rdquo; and compare them with existing estimators via extensive Monte Carlo simulation and real data sets based on the mean squared error criterion. The study findings indicate that the proposed estimators outperform the existing estimators.

]]>Mathematics doi: 10.3390/math11112521

Authors: Hristo Kostadinov Nikolai Manev

Integer codes have been successfully applied to various areas of communication and computer technology. They demonstrate good performance in correcting specific kinds of errors. In many cases, the used integer codes are constructed by computer search. This paper presents an algebraic construction of integer codes over the ring of integers modulo A=2n+1 capable of correcting at least up to two bit errors in a single b-byte. Moreover, the codes can correct some configurations of three or more erroneous bits, but not all possible ones. The construction is based on the use of cyclotomic cosets of 2 modulo A.

]]>Mathematics doi: 10.3390/math11112520

Authors: Rui Gu Hailong Hou

There are six different classes of endomorphisms for a graph. The sets of these endomorphisms always form a chain under the inclusion of sets. In order to study these different endomorphisms more systematically, B&ouml;ttcher and Knauer proposed the concept of the endomorphism type of a graph in 1992. In this paper, we explore the six different classes of endomorphisms of graph P(3m+1,3). In particular, the endomorphism type of P(3m+1,3) is given.

]]>Mathematics doi: 10.3390/math11112519

Authors: Ayesha Kalhoro Asif Ali Wagan Abdullah Ayub Khan Jim-Min Lin Chin Soon Ku Lip Yee Por Jing Yang

Non-fungible tokens (NFTs) are individual tokens with valuable information stored inside them over blockchain technology. They can be purchased and sold like other physical and virtual art pieces because their worth is mostly determined by the market and demand. The unique data of NFTs render it simple to verify and authenticate their ownership and transfer of tokens between owners. However, in Pakistan, developers cannot acquire different licences to accomplish their projects not because they cannot afford it, but because they cannot invest in every piece of software to accomplish each new sensitive task. Rather, they can render the product platform independent. Considering this technology, this paper provides IT professionals with a new NFT approach and business policies that solely belong to the information technology domain. In addition, this paper also introduces how NFT tokens can hold software applications. Since we can store files, we can let NFTs also store complete applications to help developers in further utilising virtuality and having the metaverse at their fingertips. Whenever they succeed in a project, they never receive rewards, and their skills only pay the bills. In a nutshell, this paper presents a prototype of NFTs that would be further polished to save and utilise applications in a decentralised manner while rewarding the developers.

]]>Mathematics doi: 10.3390/math11112518

Authors: Yupeng Shi Dayong Ye

This paper revisits the problem of stability analyses for neural networks with time-varying delay. A composite-matrix-based integral inequality (CMBII) is presented, which takes the delay derivative into account. In this case, the coupling information can be fully captured in integral inequalities with the delay derivative. Based on a CMBII, a new stability criterion is derived for neural networks with time-varying delay. The effectiveness of this method is verified by a numerical example.

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