Mathematics doi: 10.3390/math10193491

Authors: Vuk Stojiljković Rajagopalan Ramaswamy Ola A. Ashour Abdelnaby Stojan Radenović

In this work, various fractional convex inequalities of the Hermite&ndash;Hadamard type in the interval analysis setting have been established, and new inequalities have been derived thereon. Recently defined p interval-valued convexity is utilized to obtain many new fractional Hermite&ndash;Hadamard type convex inequalities. The derived results have been supplemented with suitable numerical examples. Our results generalize some recently reported results in the literature.

]]>Mathematics doi: 10.3390/math10193490

Authors: Junbo Qiu Xin Yin Yucong Pan Xinyu Wang Min Zhang

Uniaxial compressive strength (UCS) is a critical parameter in the disaster prevention of engineering projects, requiring a large budget and a long time to estimate in different rocks or the early stage of a project. If predicted accurately, the UCS of rocks significantly affects geotechnical applications. This paper develops a dataset of 734 samples from previous studies on different countries&rsquo; magmatic, sedimentary, and metamorphic rocks. Within the study context, three main factors, point load index, P-wave velocity, and Schmidt hammer rebound number, are utilized to estimate UCS. Moreover, it applies extreme learning machines (ELM) to map the nonlinear relationship between the UCS and the influential factors. Five metaheuristic algorithms, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), butterfly optimization algorithm (BOA), and sparrow search algorithm (SSA), are used to optimize the bias and weight of ELM and thus enhance its predictability. Indeed, several performance parameters are utilized to verify the proposed models&rsquo; generalization capability and predictive performance. The minimum, maximum, and average relative errors of ELM achieved by the whale optimization algorithm (WOA-ELM) are smaller than the other models, with values of 0.22%, 72.05%, and 11.48%, respectively. In contrast, the minimum and mean residual error produced by WOA-ELM are less than the other models, with values of 0.02 and 2.64 MPa, respectively. The results show that the UCS values derived from WOA-ELM are superior to those from other models. The performance indices (coefficient of determination (R2): 0.861, mean squared error (MSE): 17.61, root mean squared error (RMSE): 4.20, and value account for (VAF): 91% obtained using the WOA-ELM model indicates high accuracy and reliability, which means that it has broad application potential for estimating UCS of different rocks.

]]>Mathematics doi: 10.3390/math10193489

Authors: Iqbal M. Batiha Shameseddin Alshorm Adel Ouannas Shaher Momani Osama Y. Ababneh Meaad Albdareen

In this paper, we introduce new three-point fractional formulas which represent three generalizations for the well-known classical three-point formulas; central, forward and backward formulas. This has enabled us to study the function&rsquo;s behavior according to different fractional-order values of &alpha; numerically. Accordingly, we then introduce a new methodology for Richardson extrapolation depending on the fractional central formula in order to obtain a high accuracy for the gained approximations. We compare the efficiency of the proposed methods by using tables and figures to show their reliability.

]]>Mathematics doi: 10.3390/math10193488

Authors: Lu Niu Xiangdong Deng

The temporal linear instability of a viscoelastic liquid sheet moving around an inviscid gas in a transverse electrical field is analyzed. The fluid is described by the leaky dielectric model, which is more complex than existing models and enables a characterization of the liquid electrical properties. In addition, the liquid is assumed to be viscoelastic, and the dimensionless dispersion relation of the sinuous and varicose modes between the wavenumber and the temporal growth rate can be derived as a 3 &times; 3 matrix. According to this relationship, the effects of the liquid properties on the sheet instability are determined. The results suggest that, as the electrical Euler number and the elasticity number increase and the time constant ratio decreases, the sheet becomes more unstable. Finally, an energy budget approach is adopted to investigate the instability mechanism for the sinuous mode.

]]>Mathematics doi: 10.3390/math10193487

Authors: Ebubekir Kaya

Artificial neural networks (ANNs), one of the most important artificial intelligence techniques, are used extensively in modeling many types of problems. A successful training process is required to create effective models with ANN. An effective training algorithm is essential for a successful training process. In this study, a new neural network training algorithm called the hybrid artificial bee colony algorithm based on effective scout bee stage (HABCES) was proposed. The HABCES algorithm includes four fundamental changes. Arithmetic crossover was used in the solution generation mechanisms of the employed bee and onlooker bee stages. The knowledge of the global best solution was utilized by arithmetic crossover. Again, this solution generation mechanism also has an adaptive step size. Limit is an important control parameter. In the standard ABC algorithm, it is constant throughout the optimization. In the HABCES algorithm, it was determined dynamically depending on the number of generations. Unlike the standard ABC algorithm, the HABCES algorithm used a solution generation mechanism based on the global best solution in the scout bee stage. Through these features, the HABCES algorithm has a strong local and global convergence ability. Firstly, the performance of the HABCES algorithm was analyzed on the solution of global optimization problems. Then, applications on the training of the ANN were carried out. ANN was trained using the HABCES algorithm for the identification of nonlinear static and dynamic systems. The performance of the HABCES algorithm was compared with the standard ABC, aABC and ABCES algorithms. The results showed that the performance of the HABCES algorithm was better in terms of solution quality and convergence speed. A performance increase of up to 69.57% was achieved by using the HABCES algorithm in the identification of static systems. This rate is 46.82% for the identification of dynamic systems.

]]>Mathematics doi: 10.3390/math10193486

Authors: Luyao Wang Libin Chen Zhiwei Yang Minghao Li Kewei Yang Mengjun Li

In the military field, decision making has become the core of the new operational concept, known as the &ldquo;kill web&rdquo;. Although the theory of kill web has been widely recognized by many countries, the decision-making methods for the kill web are still in the early stage. Therefore, there is a need for a new decision-making method for the kill web. Firstly, different from the traditional scheme decision, the kill web is a complex system. The method of complex network provides a new perspective on complex systems, so the kill web was modeled based on complex network. Secondly, the kill web relies on artificial intelligence to provide decision-makers with operation loop solutions, and then decision-makers rely on the experience to make a final decision. However, the current decision-making methods only consider one of the intelligent and human decision-making methods, while the kill web needs to consider both. Hence, we combined intelligent decision making with human decision making through multi-objective optimization and the prospect theory. Finally, we designed a nondominated sorting ant colony genetic algorithm-II (NSACGA-II) to solve large-scale problems, since the kill web is a large-scale system. In addition, an illustrative case was used to verify the feasibility and effectiveness of the proposed model. The results showed that, compared with other classical multi-objective optimization algorithms, the NSACGA-II is superior to other superior algorithms in terms of the hypervolume (HV) and spacing (SP), which verifies the effectiveness of the method and greatly improves the quality of commanders&rsquo; decision-making.

]]>Mathematics doi: 10.3390/math10193485

Authors: Carlos Ramirez-Carrasco Fernando Córdova-Lepe Nelson Velásquez

This research studies a metapopulation model where each patch is considered a form of fragmentation of the environment produced by the spatio-temporal variability of anthropogenic noise. A deterministic mathematical model is proposed that describes two processes of migration between patches. The first process consists of migration due to chronic critical noise produced by an anthropogenic and biological source (self-generated acoustic signals of higher intensity, due to the Lombard effect). The second process consists of migration due to a higher level of stain occupancy. A simple and classical analysis of the local stability of the model is performed. The results indicate that no subpopulation goes extinct; in fact, a necessary condition for long-term stabilization of the size of the subpopulations is that the noise attenuation rate is higher. Moreover, as long as the noise is of low intensity the differences in the carrying capacity of each patch do not produce substantial, long-term differences in the sizes of the subpopulations. However, as the noise intensity increases, the difference in carrying capacities produce noticeable, long-term differences between subpopulation sizes. Finally, the results are corroborated by numerical simulations.

]]>Mathematics doi: 10.3390/math10193484

Authors: Nguyen Choi Park

Computer-aided diagnosis/detection (CADx) systems have been used to help doctors in improving the quality of diagnosis and treatment processes in many serious diseases such as breast cancer, brain stroke, lung cancer, and bone fracture. However, the performance of such systems has not been completely accurate. The key factor in CADx systems is to localize positive disease lesions from the captured medical images. This step is important as it is used not only to localize lesions but also to reduce the effect of noise and normal regions on the overall CADx system. In this research, we proposed a method to enhance the segmentation performance of thyroid nodules in ultrasound images based on information fusion of suggestion and enhancement segmentation networks. Experimental results with two open databases of thyroid digital image databases and 3DThyroid databases showed that our method resulted in a higher performance compared to current up-to-date methods.

]]>Mathematics doi: 10.3390/math10193483

Authors: Ching-Lung Fan Yu-Jen Chung

Damage to the surface construction of reinforced concrete (RC) will impact the security of the facility&rsquo;s structure. Deep learning can effectively identify various types of damage, which is useful for taking protective measures to avoid further deterioration of the structure. Based on deep learning, the multi-convolutional neural network (MCNN) has the potential for identifying multiple RC damage images. The MCNN6 of this study was evaluated by indicators (accuracy, loss, and efficiency), and the optimized architecture was confirmed. The results show that the identification performance for &ldquo;crack and rebar exposure&rdquo; (Type B) by MCNN6 is the best, with an accuracy of 96.81% and a loss of 0.07. The accuracy of the other five types of damage combinations is also higher than 80.0%, and the loss is less than 0.44. Finally, the MCNN6 model can be used in the detection of various damage to achieve automated assessment for RC facility surface conditions.

]]>Mathematics doi: 10.3390/math10193482

Authors: Xin Cao Qin Luo Peng Wu

In recent years, with the rapid development of Internet services in all walks of life, a large number of malicious acts such as network attacks, data leakage, and information theft have become major challenges for network security. Due to the difficulty of malicious traffic collection and labeling, the distribution of various samples in the existing dataset is seriously imbalanced, resulting in low accuracy of malicious traffic classification based on machine learning and deep learning, and poor model generalization ability. In this paper, a feature image representation method and Adversarial Generative Network with Filter (Filter-GAN) are proposed to solve these problems. First, the feature image representation method divides the original session traffic into three parts. The Markov matrix is extracted from each part to form a three-channel feature image. This method can transform the original session traffic format into a uniform-length matrix and fully characterize the network traffic. Then, Filter-GAN uses the feature images to generate few attack samples. Compared with general methods, Filter-GAN can generate more efficient samples. Experiments were conducted on public datasets. The results show that the feature image representation method can effectively characterize the original session traffic. When the number of samples is sufficient, the classification accuracy can reach 99%. Compared with unbalanced datasets, Filter-GAN has significantly improved the recognition accuracy of small-sample datasets, with a maximum improvement of 6%.

]]>Mathematics doi: 10.3390/math10193468

Authors: Rubén Jesús Pérez-López María Mojarro-Magaña Jesús Everardo Olguín-Tiznado Claudia Camargo-Wilson Juan Andrés López-Barreras Julio Cesar Cano Gutiérrez Jorge Luis Garcia-Alcaraz

This paper reports a second order structural equation model (SEM) with four latent variables and six hypotheses to analyze the Planning, Execution, and Control of the information and communication technologies (ICT) implementation in supply chains (SC) and the operational Benefits obtained. The model is validated with information obtained from 80 responses to a questionnaire applied direct to manufacturing companies in Baja California state (Mexico), specifically in Ensenada, Mexicali, Tecate, and Tijuana municipalities. The variables are statistically validated using the Cronbach&rsquo;s alpha index for internal and R-squared for predictive validity. Partial least squares algorithms are used to validate the model&rsquo;s hypotheses in software WarpPLS version7.0 ScripWarp Systems, Laredo, TX, US. Findings indicate that the direct impact of Execution and Control is positive and therefore are the basis for successful integration of ICT and obtaining agility and flexibility benefits in the SC.

]]>Mathematics doi: 10.3390/math10193481

Authors: Carlos Caleiro Sérgio Marcelino

Transfer theorems for combined logics provide essential tools and insight for reasoning about complex logical systems. In this paper, we present the first sufficient criterion (contextual extensibility) for decidability to be preserved through combination of propositional logics, and we study the complexity upper bounds induced by the method. In order to assess the scope and usability of our criterion, we illustrate its use in re-obtaining two standard important (though partial) results of the area: the preservation of decidability for disjoint combinations of logics, and the preservation of decidability for fusions of modal logics. Due to the very abstract nature and generality of the idea underlying contextual extensibility, we further explore its applicability beyond propositional logics. Namely, we explore the particular case of 2-deductive systems, and as a byproduct, we obtain the preservation of decidability for disjoint combinations of equational logics and discuss the relationship of this result and of our criterion with several related results with meaningful applications in satisfiability modulo theories.

]]>Mathematics doi: 10.3390/math10193480

Authors: Ana Irina Nistor

In this paper we study the magnetic trajectories as the solutions of the Lorentz equation defined by the cross product corresponding to the 7&minus;dimensional Euclidean space. We find several examples of such trajectories and moreover, we strongly motivate our results making a comparison with the 3&minus;dimensional Euclidean case, ambient space which was among the first ones approached in the study of magnetic trajectories.

]]>Mathematics doi: 10.3390/math10193478

Authors: Shujun Guo Lujing Jing Zhaopeng Dai Yang Yu Zhiqing Dang Zhihang You Ang Su Hongwei Gao Jinqiu Guan Yujun Song

This paper studies the problem of target control and how a virtual ellipsoid can avoid the static obstacle. During the motion to the target set, the virtual ellipsoid can achieve a motion under collision avoidance by keeping the distance between the ellipsoid and obstacle. We present solutions to this problem in the class of closed-loop (feedback) controls based on Hamilton&ndash;Jacobi&ndash;Bellman (HJB) equation. Simulation results verify the validity and effectiveness of our algorithm.

]]>Mathematics doi: 10.3390/math10193479

Authors: Álvaro Serrano Holgado

Much is known about the adele ring of an algebraic number field from the perspective of harmonic analysis and class field theory. However, its ring-theoretical aspects are often ignored. Here, we present a description of the prime spectrum of this ring and study some of the algebraic and topological properties of these prime ideals. We also study how they behave under separable extensions of the base field and give an indication of how this study can be applied in adele rings not of number fields.

]]>Mathematics doi: 10.3390/math10193477

Authors: Arseny A. Sorokin Gerd Leuchs Joel F. Corney Nikolay A. Kalinin Elena A. Anashkina Alexey V. Andrianov

Squeezed light&mdash;nonclassical multiphoton states with fluctuations in one of the quadrature field components below the vacuum level&mdash;has found applications in quantum light spectroscopy, quantum telecommunications, quantum computing, precision quantum metrology, detecting gravitational waves, and biological measurements. At present, quantum noise squeezing with optical fiber systems operating in the range near 1.5 &mu;m has been mastered relatively well, but there are no fiber sources of nonclassical squeezed light beyond this range. Silica fibers are not suitable for strong noise suppression for 2 &micro;m continuous-wave (CW) light since their losses dramatically deteriorate the squeezed state of required lengths longer than 100 m. We propose the generation multiphoton states of 2-micron 10-W class CW light with squeezed quantum fluctuations stronger than &minus;15 dB in chalcogenide and tellurite soft glass fibers with large Kerr nonlinearities. Using a realistic theoretical model, we numerically study squeezing for 2-micron light in step-index soft glass fibers by taking into account Kerr nonlinearity, distributed losses, and inelastic light scattering processes. Quantum noise squeezing stronger than &minus;20 dB is numerically attained for a customized As2Se3 fibers with realistic parameters for the optimal fiber lengths shorter than 1 m. For commercial As2S3 and customized tellurite glass fibers, the expected squeezing in the &minus;20&ndash;&minus;15 dB range can be reached for fiber lengths of the order of 1 m.

]]>Mathematics doi: 10.3390/math10193476

Authors: Meghea

Mountain Pass Theorem (MPT) is an important result in variational methods with multiple applications in partial differential equations involved in mathematical physics. Starting from a variant of MPT, a new result concerning the existence of the solution for certain mathematical physics problems involving p-Laplacian and p-pseudo-Laplacian has been obtained. Based on the main theorem, the existence, possibly the uniqueness, and characterization of solutions for models such as nonlinear elastic membrane, glacier sliding, and pseudo torsion problem have been obtained. The novelty of the work consists of the formulation of the central result under weaker conditions requested by the chosen variant of MPT, the proof of this statement, and its application in solving above mentioned problems. While the expressions of such Dirichlet and/or von Neumann problems were already completed, this proposed solving method suggests some specific numerical methods to construct the appropriate solution. A general goal of this paper is the extension of the applicative pallet of this way to construct the solutions encountered in modeling real processes developed within new emerging technologies.

]]>Mathematics doi: 10.3390/math10193475

Authors: Hongfang Liu Jinxia Liang Kinkar Chandra Das

Extra edge connectivity and diagnosability have been employed to investigate the fault tolerance properties of network structures. The p-extra edge connectivity &lambda;p(&Gamma;) of a graph &Gamma; was introduced by F&agrave;brega and Fiol in 1996. In this paper, we find the exact values of p-extra edge connectivity of some special graphs. Moreover, we give some upper and lower bounds for &lambda;p(&Gamma;), and graphs with &lambda;p(&Gamma;)=1,2,n2n2&minus;1,n2n2 are characterized. Finally, we obtain the three extremal results for the p-extra edge connectivity.

]]>Mathematics doi: 10.3390/math10193473

Authors: Xiaoji Shang Zhizhen Zhang Weihao Yang J.G. Wang Cheng Zhai

Heat treatment on shale reservoirs can promote the development of secondary fractures in a matrix on the basis of hydraulic fracturing, forming multi-scale gas&ndash;water seepage channels and strengthening the gas desorption. Experimental evidence shows that heat treatment can enhance gas recovery in the same mining life. Heat treatment on a shale gas reservoir is a multi-physical and multi-phase coupling process. However, how the thermal stimulation interacts with nonlinear two-phase flow in heterogeneous shale volume fracturing has not been clear. In this paper, a fully coupled THGM model for heating-enhanced shale-gas recovery in heterogeneous shale reservoirs is proposed. First, the governing equations are formulated for the shale-reservoir deformation involving both gas adsorption and thermal expansion, the permeability evolution model for the cracking process of fractured shale, the gas&ndash;water two-phase continuity equation considering the effects of gas solubility and the heat transfer equation for heat conduction and convection. The interactions among stress, temperature and seepage in a heterogeneous shale reservoir were studied. Secondly, a test on shale permeability after 50 &deg;C temperature treatment was conducted. The evolution of temperature, capillary pressure, water and gas saturation and the permeability of shale during the heat treatment of the reservoir were numerically analyzed. Finally, the gas production from a shale gas reservoir was numerically simulated with this THGM model. The numerical results indicated that the thermal-induced fracturing, gas desorption and separation from water make predominant contributions to the evolution of permeability. The heat treatment can enhance cumulative gas production by 58.7% after 27.4 years of heat injection through promoting gas desorption and matrix diffusion.

]]>Mathematics doi: 10.3390/math10193474

Authors: Manuel D. Ortigueira

In this paper, the lognormal distribution is studied, and a new series representation is proposed. This series uses the powers of the bilinear function. From it, a simplified form is obtained and used to compute the Laplace transform of the distribution.

]]>Mathematics doi: 10.3390/math10193472

Authors: Lin Hu Lin-Fei Nie

Considering the influences of uncertain factors on the reproduction of virus in vivo, a stochastic HIV model with CTLs&rsquo; immune response and logistic growth was developed to research the dynamics of HIV, where uncertain factors are white noise and telegraph noise. which are described by Brownian motion and Markovian switching, respectively. We show, firstly, the existence of global positive solutions of this model. Further, by constructing suitable stochastic Lyapunov functions with regime switching, some sufficient conditions for the existence and uniqueness of the stationary distribution and the conditions for extinction are obtained. Finally, the main results are explained by some numerical examples. Theoretical analysis and numerical simulation show that low-intensity white noise can maintain the persistence of the virus, and high intensity white noise can make the virus extinct after a period of time with multi-states.

]]>Mathematics doi: 10.3390/math10193467

Authors: Roberto M. Fuentes Jonathan M. Palma Hildo Guillardi Júnior Márcio J. Lacerda Leonardo de P. Carvalho Alejandro J. Rojas Ricardo C. L. F. Oliveira

This paper investigates the problem of control design for dc&ndash;dc converters, where the solution is especially suitable to address variations in the input voltage, a frequent situation in photovoltaic systems, and the problem of constant power load, where a nonlinear load is connected to the output of the converter. The proposed approach models the converters in terms of Linear Parameter-Varying (LPV) models, which are used to compute gain-scheduled robust gains. The synthesis conditions provide stabilizing controllers with an attenuation level of disturbances in terms of the H&infin; norm. Moreover, the design conditions can also overcome pole locations to comply with physical application restrictions when ensuring transient performance. The validation of the controllers is made via simulation of the classical converters (buck, boost and buck-boost), showing that the proposed method is a viable and generalized control solution that works for all three converters, with guarantees of closed-loop stability and good performance.

]]>Mathematics doi: 10.3390/math10193471

Authors: Huiling Cai Qingcheng Lin Hanwei Liu Xuefeng Li Hui Xiao

Studies have shown that illuminance and correlated colour temperature (CCT) are strongly correlated with body responses such as circadian rhythm, alertness, and mood. It is worth noting that these responses show a complex and variable coupling, which needs to be solved using accurate mathematical models for the regulation of indoor light parameters. Therefore, in this study, by weighing the evaluations of visual comfort, alertness, valence, and arousal of mood, a multi-objective optimisation mathematical model was developed with constraints conducive to the healthy rhythm. The problem was solved with the multi-objective evolutionary algorithm based on the decomposition differential evolution (MOEA/D-DE) algorithm. Taking educational space as the analysis goal, a dual-parameter setting strategy for illuminance and CCT covering four modes was proposed: focused learning, comfortable learning, soothing learning, and resting state, which could provide a scientific basis for the regulation of the lighting control system. The alertness during class time reached 3.01 compared to 2.34 during break time, showing a good light facilitation effect. The proposed mathematical model and analysis method also have the potential for application in the lighting design and control in other spaces to meet the era of intelligent, highly flexible, and sustainable buildings.

]]>Mathematics doi: 10.3390/math10193470

Authors: Natalia Alekseeva Viktoriia Podryga Parvin Rahimly Richard Coffin Ingo Pecher

2D numerical modeling algorithms of multi-component, multi-phase filtration processes of mass transfer in frost-susceptible rocks using nonlinear partial differential equations are a valuable tool for problems of subsurface hydrodynamics considering the presence of free gas, free water, gas hydrates, ice formation and phase transitions. In this work, a previously developed one-dimensional numerical modeling approach is modified and 2D algorithms are formulated through means of the support-operators method (SOM) and presented for the entire area of the process extension. The SOM is used to generalize the method of finite difference for spatially irregular grids case. The approach is useful for objects where a lithological heterogeneity of rocks has a big influence on formation and accumulation of gas hydrates and therefore it allows to achieve a sufficiently good spatial approximation for numerical modeling of objects related to gas hydrates dissociation in porous media. The modeling approach presented here consistently applies the method of physical process splitting which allows to split the system into dissipative equation and hyperbolic unit. The governing variables were determined in flow areas of the hydrate equilibrium zone by applying the Gibbs phase rule. The problem of interaction of a vertical fault and horizontal formation containing gas hydrates was investigated and test calculations were done for understanding of influence of thermal effect of the fault on the formation fluid dynamic.

]]>Mathematics doi: 10.3390/math10193469

Authors: Muhammad Aamir Ali Fongchan Wannalookkhee Hüseyin Budak Sina Etemad Shahram Rezapour

In both pure and applied mathematics, convex functions are used in many different problems. They are crucial to investigate both linear and non-linear programming issues. Since a convex function is one whose epigraph is a convex set, the theory of convex functions falls under the umbrella of convexity. However, it is a significant theory that affects practically all areas of mathematics. In this paper, we introduce the notions of g,h-convexity or convexity with respect to a pair of functions on co-ordinates and discuss its fundamental properties. Moreover, we establish some novel Hermite&ndash;Hadamard- and Ostrowski-type inequalities for newly introduced co-ordinated convexity. Additionally, it is presented that the newly introduced notion of the convexity and given inequalities are generalizations of existing studies in the literature. Lastly, we look at various mathematical examples and graphs to confirm the validity of the newly found inequalities.

]]>Mathematics doi: 10.3390/math10193466

Authors: Mohamed Abdel-Basset Reda Mohamed Karam M. Sallam Ripon K. Chakrabortty

This paper introduces a novel physical-inspired metaheuristic algorithm called &ldquo;Light Spectrum Optimizer (LSO)&rdquo; for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge&ndash;Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm.

]]>Mathematics doi: 10.3390/math10193465

Authors: Ahlam Anabousy Michal Tabach

The research community still faces challenges with respect to understanding and promoting mathematics teachers&rsquo; knowledge related to integrating technology into their instruction. This study&rsquo;s goals are: (1) to examine the relations between the various components of pedagogical technology knowledge (PTK), (2) to examine whether teachers&rsquo; PTK differed significantly after their participation in a professional development (PD) program designed to enhance Community of Inquiry (CoI) practices, and (3) to examine the effect of teachers&rsquo; personal characteristics on PTK components and on their development. This study involved 42 middle school mathematics teachers. The data, collected using Thomas and Palmer&rsquo;s PTK questionnaire, underwent a statistical analysis. Through the quantitative analysis, scores for each PTK component were computed and appropriate statistical tests were run. The results indicate that, aside from knowledge of mathematical content, all the components of PTK and PTK itself demonstrate strong correlations. In addition, the results showed that teachers&rsquo; PTK components differed significantly after they participated in a CoI PD program, except for the knowledge of mathematical content component. The background variables had significant effects on some PTK components&rsquo; scores and on their development among the participants.

]]>Mathematics doi: 10.3390/math10193464

Authors: Larry Su Elmina Homapour Fabio Caraffini Francisco Chiclana

There is a large volume of literature in international business on multinationality. There is an equally large volume of literature in finance on stock price crash risk. However, very few studies have attempted to provide a link between these two research areas. Using an unbalanced panel data consisting of 473 multinational corporations (MNCs) publicly listed in the Chinese stock markets during 2004 to 2020, this paper is one of the first to empirically investigate whether and to what extent multinationality affects stock price crash risk. The paper finds strong evidence that multinational operation is negatively related to stock price crash risk. In addition, MNCs with better corporate governance quality experience larger decline in stock price crash risk when the degree of multinationality increases. Furthermore, MNCs with higher stock market liquidity experience lower crash risk. An important implication is that companies should strengthen their corporate governance and market liquidity while &ldquo;going global&rdquo;.

]]>Mathematics doi: 10.3390/math10193463

Authors: Mauricio A. Inostroza-Osses Oswaldo López-Santos Yeison A. Aldana-Rodríguez Manuel G. Forero

This paper introduces a method for the reconfiguration of a transformer-based cascaded multilevel inverter (CT-MLI), which allows the use of alternative switching patterns when individual inverter stages cease to operate as a result of a failure in the circuit switches. Different possibilities of reconfiguration of the inverter stages to provide the output voltage signal are analyzed and optimal switching patterns are determined. The results obtained from the simulation in the PSIM software are provided to validate the proposal.

]]>Mathematics doi: 10.3390/math10193462

Authors: Qing-Yuan Xu Wan-Ying He Chuang-Tao Zheng Peng Xu Yun-Shan Wei Kai Wan

An adaptive fuzzy iterative learning control (ILC) algorithm is designed for the iterative variable reference trajectory problem of nonlinear discrete-time systems with input saturations and unknown control directions. Firstly, an adaptive fuzzy iterative learning controller is constructed by combining with the fuzzy logic system (FLS), which can compensate the loss caused by input saturation. Then, the discrete Nussbaum gain technique is adopted along the iteration axis, which can be embedded to the learning control method to identify the control direction of the system. Finally, based on the nonincreasing Lyapunov-like function, it is proven that the adaptive iterative learning controller can converge asymptotically when the number of iterations tends to infinity, and the system signals always remain bounded in the learning process. A simulation example verifies the feasibility and effectiveness of the learning control method.

]]>Mathematics doi: 10.3390/math10193461

Authors: Mantas Lukauskas Vaida Pilinkienė Jurgita Bruneckienė Alina Stundžienė Andrius Grybauskas Tomas Ruzgas

The outbreak of war and the earlier and ongoing COVID-19 pandemic determined the need for real-time monitoring of economic activity. The economic activity of a country can be defined in different ways. Most often, the country&rsquo;s economic activity is characterized by various indicators such as the gross domestic product, the level of employment or unemployment of the population, the price level in the country, inflation, and other frequently used economic indicators. The most popular were the gross domestic product (GDP) and industrial production. However, such traditional tools have started to decline in modern times (as the timely knowledge of information becomes a critical factor in decision making in a rapidly changing environment) as they are published with significant delays. This work aims to use the information in the Lithuanian mass media and machine learning methods to assess whether these data can be used to assess economic activity. The aim of using these data is to determine the correlation between the usual indicators of economic activity assessment and media sentiments and to forecast traditional indicators. When evaluating consumer confidence, it is observed that the forecasting of this economic activity indicator is better based on the general index of negative sentiment (comparisons with univariate time series). In this case, the average absolute percentage error is 1.3% lower. However, if all sentiments are included in the forecasting instead of the best one, the forecasting is worse and in this case the MAPE is 5.9% higher. It is noticeable that forecasting the monthly and annual inflation rate is thus best when the overall negative sentiment is used. The MAPE of the monthly inflation rate is as much as8.5% lower, while the MAPE of the annual inflation rate is 1.5% lower.

]]>Mathematics doi: 10.3390/math10193460

Authors: Jānis Bajārs Juan F. R. Archilla

We propose two classes of symplecticity-preserving symmetric splitting methods for semi-classical Hamiltonian dynamics of charge transfer by intrinsic localized modes in nonlinear crystal lattice models. We consider, without loss of generality, one-dimensional crystal lattice models described by classical Hamiltonian dynamics, whereas the charge (electron or hole) is modeled as a quantum particle within the tight-binding approximation. Canonical Hamiltonian equations for coupled lattice-charge dynamics are derived, and a linear analysis of linearized equations with the derivation of the dispersion relations is performed. Structure-preserving splitting methods are constructed by splitting the total Hamiltonian into the sum of Hamiltonians, for which the individual dynamics can be solved exactly. Symmetric methods are obtained with the Strang splitting of exact, symplectic flow maps leading to explicit second-order numerical integrators. Splitting methods that are symplectic and conserve exactly the charge probability are also proposed. Conveniently, they require only one solution of a linear system of equations per time step. The developed methods are computationally efficient and preserve the structure; therefore, they provide new means for qualitative numerical analysis and long-time simulations for charge transfer by nonlinear lattice excitations. The properties of the developed methods are explored and demonstrated numerically considering charge transport by mobile discrete breathers in an example model previously proposed for a layered crystal.

]]>Mathematics doi: 10.3390/math10193459

Authors: Xiao-Ting He Bo Pang Jie-Chuan Ai Jun-Yi Sun

The biparametric perturbation method is applied to solve the improved F&ouml;ppl&ndash;von K&aacute;rm&aacute;n equation, in which the improvements of equations come from two different aspects: the first aspect concerns materials, and the other is from deformation. The material considered in this study has bimodular functionally graded properties in comparison with the traditional materials commonly used in classical F&ouml;ppl&ndash;von K&aacute;rm&aacute;n equations. At the same time, the consideration for deformation deals with not only the large deflection as indicated in classical F&ouml;ppl&ndash;von K&aacute;rm&aacute;n equations, but also the larger rotation angle, which is incorporated by adopting the precise curvature formulas but not the simple second-order derivative term of the deflection. To fully demonstrate the effectiveness of the biparametric perturbation method proposed, two sets of parameter combinations, one being a material parameter with central defection and the other being a material parameter with load, are used for the solution of the improved F&ouml;ppl&ndash;von K&aacute;rm&aacute;n equations. Results indicate that not only the two sets of solutions from different parameter combinations are consistent, but also they may be reduced to the single-parameter perturbation solution obtained in our previous study. The successful application of the biparametric perturbation method provides new ideas for solving similar nonlinear differential equations.

]]>Mathematics doi: 10.3390/math10193458

Authors: Chun-Chieh Tseng Kuo-Ching Chiou Kuen-Suan Chen

The measurement of the process capability is a key part of quantitative quality control, and process capability indices are statistical measures of the process capability. Six sigma level represents the maximum achievable process capability, and many enterprises have implemented Six Sigma improvement strategies. In recent years, many studies have investigated Six Sigma quality indices, including Qpk. However, Qpk contains two unknown parameters, namely &delta; and &gamma;, which are difficult to use in process control. Therefore, whether a process quality reaches the k sigma level must be statistically inferred. Moreover, the statistical method of sampling distribution is challenging for the upper confidence limits of Qpk. We address these two difficulties in the present study and propose a methodology to solve them. Boole&rsquo;s inequality, Demorgan&rsquo;s theorem, and linear programming were integrated to derive the confidence intervals of Qpk, and then the upper confidence limits were used to perform hypothesis testing. This study involved a case study of the semiconductor assembly process in order to verify the feasibility of the proposed method.

]]>Mathematics doi: 10.3390/math10193456

Authors: Ahmed H. Arnous Luminita Moraru

In this paper, the optical solitons for the complex Ginzburg&ndash;Landau equation with Kudryashov&rsquo;s law of refractive index are established. An improved modified extended tanh&ndash;function technique is used to extract numerous solutions. Bright and dark solitons, as well as singular soliton solutions, are achieved. In addition, as the modulus of ellipticity approaches unity or zero, solutions are formulated in terms of Jacobi&rsquo;s elliptic functions, which provide solitons and periodic wave solutions.

]]>Mathematics doi: 10.3390/math10193457

Authors: Shimai Su Anna Tur

A two-player differential game of pollution control with uncertain initial disturbance stock is considered. In pace with contemporary policy in the resource extraction industry, we initiate our research based on a resource extraction differential model with a rehabilitation process in which the firms are required to compensate the local to rehabilitate the polluted and dilapidated areas. Given the reality that the initial pollution stock plays a critical role in the production, and we cannot rigorously determine its actual value, a simulation of the estimation of the initial stock is alternatively investigated through the Pontryagin maximum principle (PMP). The later analytical results by normalized value of information (NVI) indicate the precious influence brought to the final payoff under various estimations of the initial stock both in the cooperative and non-cooperative cases. With such guidance, the player is capable of making a much more judicious decision when it comes to the determination of the initial stock. Furthermore, a numerical example is additionally presented for better comprehension.

]]>Mathematics doi: 10.3390/math10193455

Authors: Chunru Li Zujun Ma

In this work, we analyze a delayed rumor-propagation model. First, we analyze the existence and boundedness of the solution of the model. Then, we give the conditions for the existence of the rumor-endemic equilibrium. Regrading the delay as a bifurcating parameter, we explore the local asymptotic stability and Hopf bifurcation of the rumor-endemic equilibrium. By a Lyapunov functional technique, we examine the global asymptotically stability of the rumor-free and the rumor-endemic equilibria. We provide two control variables in the rumor-spreading model with time delay, and get the optimal solution via the optimal procedures. Finally, we present some numerical simulations to verify our theoretical predictions. They illustrate that the delay is a crucial issue for system, and it can lead to not just Hopf bifurcation but also chaos.

]]>Mathematics doi: 10.3390/math10193453

Authors: Carlos Iglesias Pastrana Francisco Javier Navas González Elena Ciani María Esperanza Camacho Vallejo Juan Vicente Delgado Bermejo

This study evaluates a method to accurately, repeatably, and reliably extract camel zoo-metric data (linear and tridimensional) from 2D digital images. Thirty zoometric measures, including linear and tridimensional (perimeters and girths) variables, were collected on-field with a non-elastic measuring tape. A scaled reference was used to extract measurement from images. For girths and perimeters, semimajor and semiminor axes were mathematically estimated with the function of the perimeter of an ellipse. On-field measurements&rsquo; direct translation was determined when Cronbach&rsquo;s alpha (C&alpha;) &gt; 0.600 was met (first round). If not, Bayesian regression corrections were applied using live body weight and the particular digital zoometric measurement as regressors (except for foot perimeter) (second round). Last, if a certain zoometric trait still did not meet such a criterion, its natural logarithm was added (third round). Acceptable method translation consistency was reached for all the measurements after three correction rounds (C&alpha; = 0.654 to 0.997, p &lt; 0.0001). Afterwards, Bayesian regression corrected equations were issued. This research helps to evaluate individual conformation in a reliable contactless manner through the extraction of linear and tridimensional measures from images in dromedary camels. This is the first study to develop and correct the routinely ignored evaluation of tridimensional zoometrics from digital images in animals.

]]>Mathematics doi: 10.3390/math10193451

Authors: Sanjar M. Abrarov Rehan Siddiqui Rajinder K. Jagpal Brendan M. Quine

In this work we develop a new algorithm for the efficient computation of the Voigt/complex error function. In particular, in this approach we propose a two-domain scheme where the number of the interpolation grid-points is dependent on the input parameter y. The error analysis we performed shows that the MATLAB implementation meets the requirements for radiative transfer applications involving the HITRAN molecular spectroscopic database. The run-time test shows that this MATLAB implementation provides rapid computation, especially at smaller ranges of the parameter x.

]]>Mathematics doi: 10.3390/math10193454

Authors: Yu Yan Yiming Wang Yiming Lei

Let (X,d) be a directionally (&gamma;,m)-limited space with every &gamma; &isin;(0,&infin;). In this setting, we aim to study an analogue of the classical theory of Ap(&mu;) weights. As an application, we establish some weighted estimates for the Hardy&ndash;Littlewood maximal operator. Then, we introduce the relationship between directionally (&gamma;,m)-limited spaceand geometric doubling. Finally, we obtain the weighted norm inequalities of the Calder&oacute;n&ndash;Zygmund operator and commutator in non-homogeneous space.

]]>Mathematics doi: 10.3390/math10193452

Authors: Alina Vladimirovna Petukhova Anna Vladimirovna Kovalenko Anna Vyacheslavovna Ovsyannikova

Managerial decision-making is a complex process that has several problems. The more heterogeneous the system, the more immeasurable, non-numerical information it contains. To understand the cognitive processes involved, it is important to describe in detail their components, define the dependencies between components, and apply relevant algorithms for scenario modelling. Fuzzy cognitive maps (FCMs) is the popular approach for modeling a system&rsquo;s behavior over time and defining its main properties. This work develops a new algorithm for scenario analysis in complex systems represented by FCMs to provide support for decision-making. The algorithm allows researchers to analyze system-development scenarios to obtain the required change to the system&rsquo;s components that leads to the target state. The problem of determining a system&rsquo;s initial state is most conspicuous when constructing a compound or unbalanced fuzzy maps. Currently, a brute force algorithm is used to calculate the steps needed to approach a target, but that takes exponential time. The paper describes a new algorithm to obtain the initial values of the controlled concepts in fuzzy cognitive maps using the theory of neutrosophic fuzzy equations. This approach reduces the time needed to find the optimal solution to a problem, and it allows inverse problems to be solved in the fuzzy cognitive maps as a part of the scenario-modeling framework.

]]>Mathematics doi: 10.3390/math10193450

Authors: Suresh Chavhan Subhi R. M. Zeebaree Ahmed Alkhayyat Sachin Kumar

With an ever-increasing number of electric vehicles (EVs) on the roads, there is a high demand for EV charging infrastructure. The present charging infrastructure in the market requires a lot of space and sometimes leads to traffic congestion, increasing the risk of accidents and obstruction of emergency vehicles. As the current infrastructure requires ample space, the cost of setting up this charging infrastructure becomes very high in metropolitan cities. In addition, there are a lot of adverse effects on the power grid due to the integration of EVs. This paper discusses a space-efficient charging infrastructure and multi-agent system-based power grid balance to overcome these issues. The proposed multi-level EV charging station can save a lot of space and reduce traffic congestion as more vehicles can be accommodated in the space. Depending on the size, capacity, and type of multi-level vehicle charging system, it can serve as a reliable charging solution at sites with medium and high daily footfall. We integrated the EV charging station with IEEE 33 bus test system and analyzed the grid and charging stations. The proposed scheme is exhaustively tested by simulation in a discrete-time event simulator in MATLAB and analyzed with varying EV arrival rates, time periods, etc.

]]>Mathematics doi: 10.3390/math10193449

Authors: Mircea Şuşcă Vlad Mihaly Dora Morar Petru Dobra

The current journal paper proposes an end-to-end analysis for the numerical implementation of a two-degrees-of-freedom (2DOF) control structure, starting from the sampling rate selection mechanism via a quasi-optimal manner, along with the estimation of the worst-case execution time (WCET) for the specified controller. For the sampling rate selection, the classical Shannon&ndash;Nyquist sampling theorem is replaced by an optimization problem that encompasses the trade-off between the fidelity of the controllers&rsquo; representation, along with the fidelity of the resulting closed-loop systems, and the implementation difficulty of the controllers. Additionally, the WCET analysis can be seen as a verification step before automatic code generation, a computational model being provided. The proposed computational model encompasses infinite-impulse response (IIR) and finite-impulse response (FIR) filter models for the controller implementation, along with additional relevant phenomena being discussed, such as saturation, signal scaling and anti-windup techniques. All proposed results will be illustrated on a DC motor benchmark control problem.

]]>Mathematics doi: 10.3390/math10193448

Authors: Alexey V. Yakovlev Vladimir V. Alekseev Maria V. Volchikhina Sergey V. Petrenko

A combinatorial model is proposed for determining the probability and information losses in an organizational and technical system (OTS) under destructive external influences. Mathematical expressions are obtained to determine the loss of information in the clusters of the control system. It is shown that the use of this model for a quantitative analysis of the probability of occurrence of events and information losses in the control system, under varying external influences on the dynamic OTS, makes it possible to carry out a quantitative analysis and synthesis of the structure of the control system that is resistant to destructive external influences. A decomposition of the probabilities of occurrence of events and the corresponding loss of information by the levels of the hierarchy of the analyzed air traffic control system is presented. The achieved result is due to the sensitivity of the model for determining information losses relative to changes in the structure of the system and destructive external influences, as well as the use of the mathematical apparatus in combinatorial analyses.

]]>Mathematics doi: 10.3390/math10193445

Authors: Mohan Chaudhry Jing Gai

Bulk-service queueing systems have been widely applied in many areas in real life. While single-server queueing systems work in some cases, multi-servers can efficiently handle most complex applications. Bulk-service, multi-server queueing systems (compared to well-developed single-server queueing systems) are more complex and harder to deal with, especially when the inter-arrival time distributions are arbitrary. This paper deals with analytic and computational analyses of queue-length distributions for a complex bulk-service, multi-server queueing system GI/Ma,b/c, wherein inter-arrival times follow an arbitrary distribution, a is the quorum, and b is the capacity of each server; service times follow exponential distributions. The introduction of quorum a further increases the complexity of the model. In view of this, a two-dimensional Markov chain has to be involved. Currently, it appears that this system has not been addressed so far. An elegant analytic closed-form solution and an efficient algorithm to obtain the queue-length distributions at three different epochs, i.e., pre-arrival epoch (p.a.e.), random epoch (r.e.), and post-departure epoch (p.d.e.) are presented, when the servers are in busy and idle states, respectively.

]]>Mathematics doi: 10.3390/math10193446

Authors: Victor Fernandez-Viagas Luis Sanchez-Mediano Alvaro Angulo-Cortes David Gomez-Medina Jose Manuel Molina-Pariente

In this paper, we address the permutation flow shop scheduling problem with sequence-dependent and non-anticipatory setup times. These setups are performed or supervised by multiple servers, which are renewable secondary resources (typically human resources). Despite the real applications of this kind of human supervision and the growing attention paid in the scheduling literature, we are not aware of any previous study on the problem under consideration. To cover this gap, we start theoretically addressing the problem by: proposing three mixed-integer linear programming models to find optimal solutions in the problem; and proposing different decoding procedures to code solutions in approximated procedures. After that, the best decoding procedure is used to propose a new mechanism that generates 896 different dispatching rules, combining different measures, indicators, and sorting criteria. All these dispatching rules are embedded in the traditional NEH algorithm. Finally, an iterated greedy algorithm is proposed to find near-optimal solutions. By doing so, we provide academics and practitioners with efficient methods that can be used to obtain exact solutions of the problem; applied to quickly schedule jobs and react under changes; used for initialisation or embedded in more advanced algorithms; and/or easily updated and implemented in real manufacturing scenarios.

]]>Mathematics doi: 10.3390/math10193447

Authors: Manlika Ratchagit Honglei Xu

This paper proposes a new linear combination model to predict the closing prices on multivariate financial data sets. The new approach integrates two delays of deep learning methods called the two-delay combination model. The forecasts are derived from three different deep learning models: the multilayer perceptron (MLP), the convolutional neural network (CNN) and the long short-term memory (LSTM) network. Moreover, the weight combination of our proposed model is estimated using the differential evolution (DE) algorithm. The proposed model is built and tested for three high-frequency stock data in financial markets&mdash;Microsoft Corporation (MSFT), Johnson &amp; Johnson (JNJ) and Pfizer Inc. (PFE). The individual and combination forecast methods are compared using the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The state-of-the-art combination models used in this paper are the equal weight (EW), the inverse of RMSE (INV-RMSE) and the variance-no-covariance (VAR-NO-CORR) methods. These comparisons demonstrate that our proposed approach using DE weight&rsquo;s optimization has significantly lower forecast errors than the individual model and the state-of-the-art weight combination procedures for all experiments. Consequently, combining two delay deep learning models using differential evolution weights can effectively improve the stock price prediction.

]]>Mathematics doi: 10.3390/math10193444

Authors: Muhammad Sadiq Carlos Alfaro Aragon Yacine Terriche Syed Wajahat Ali Chun-Lien Su Ľuboš Buzna Mahmoud Elsisi Chung-Hong Lee

Zero-emission transportation is currently a public priority, especially in big cities. For this reason, the use of electric vehicles (EVs) is receiving much attention. To facilitate the adoption of EVs, a proper charging infrastructure together with energy management is essential. This article proposes a design guideline for a direct current (DC) charging station with bipolar properties. A bipolar system can convert a two-wire system into three wires in a microgrid system with a neutral line. The configuration of the bipolar system supports different loads; therefore, the unbalanced operation is inherent to the system. The proposed bipolar DC charging station (CS) has a three-level balancing converter that reduces the step-down effort chargers. Moreover, this paper proposes the continuous-control-set model predictive control (CCS-MPC)-based balancing strategy that allows the handling of different output loads while keeping the neutral-line voltage efficiently regulated with improved dynamic performance compared to a traditional controller. Stability and parameter robustness analyses are also performed for the control parameter selection. To ensure the performance of the proposed method, both simulation and experimental results are presented and compared with those obtained from the traditional methods.

]]>Mathematics doi: 10.3390/math10193443

Authors: Joan C. Micó

This paper presents the population pyramid dynamics model (PPDM) to study the evolution of the population pyramid of a determined country or society, deducing as a crucial objective its exact analytical solution. The PPDM is a first-order linear partial differential equation whose unknown variable is the population density (population per age unit) depending on time and age, jointly an initial condition in the initial time and a boundary condition given by the births in the zero age. In addition, the dynamical patterns of the crude birth, death, immigration and emigration rates depending on time, jointly with the mathematical pattern of the initial population pyramid depending on ages, take part of the PPDM. These patterns can be obtained from the historical data. An application case of the PPDM analytical solution is presented: Spain, in the 2007&ndash;2021 period for its validation, and in the 2021&ndash;2026 period for its future forecasting. This application case also permits to obtain the forecasting limits of the PPDM by comparing the historical data with those provided by the PPDM. Other variables that can be obtained from the historical population pyramids data, such as the dependency ratio and the life expectancy at birth, are considered.

]]>Mathematics doi: 10.3390/math10193442

Authors: Chi Qin Lai Haidi Ibrahim Shahrel Azmin Suandi Mohd Zaid Abdullah

In line with current developments, biometrics is becoming an important technology that enables safer identification of individuals and more secure access to sensitive information and assets. Researchers have recently started exploring electroencephalography (EEG) as a biometric modality thanks to the uniqueness of EEG signals. A new architecture for a convolutional neural network (CNN) that uses EEG signals is suggested in this paper for biometric identification. A CNN does not need complex signal pre-processing, feature extraction, and feature selection stages. The EEG datasets utilized in this research are the resting state eyes open (REO) and the resting state eyes closed (REC) EEG. Extensive experiments were performed to design this deep CNN architecture. These experiments showed that a CNN architecture with eleven layers (eight convolutional layers, one average pooling layer, and two fully connected layers) with an Adam optimizer resulted in the highest accuracy. The CNN architecture proposed here was compared to existing models for biometrics using the same dataset. The results show that the proposed method outperforms the other task-free paradigm CNN biometric identification models, with an identification accuracy of 98.54%.

]]>Mathematics doi: 10.3390/math10193441

Authors: Evgeny Popov Anna Veretennikova Sergey Fedoreev

The relevance of this study stems from the fact that the development of a market for financial instruments can significantly expand lending opportunities for small- and medium-sized businesses. While research on the impact of tokenization on financial markets is extensive, literature provides virtually no description of mathematical models that can be used in the design and development of information systems issuing tokenized financial instruments. Thus, the study aims to develop mathematical models representing the transformation of the over-the-counter (OTC) securities market induced by the tokenization of underlying assets. The development of crowdlending platforms is gradually transforming the financial market landscape. The key change trends consist in transactional fragmentation both on the demand and supply sides. This paper proposes a mathematical model of internal transformation occurring in the OTC financial market, which describes the process of managing rights to underlying assets during their issuance and circulation. The model is built by analogy with the Harrison&ndash;Ruzzo&ndash;Ullman (HRU) model, applying the same principles to the relations of economic agents in exercising access rights to underlying assets as those that regulate access rights to files. The research novelty of the presented model consists in the formalization of financial market transformation occurring in the context of asset tokenization, which significantly expands the mathematical apparatus of digital financial transactions. This paper also proposes a mathematical model of competitive tokenization-induced transformation occurring in the OTC financial market, which describes transaction costs associated with attracting investment in the OTC financial market and the market for tokenized assets. In addition, the barriers of the OTC financial market and the stock market are described indicating the supply and demand trends in the context of transformation occurring in the OTC financial market under the influence of underlying asset tokenization. The novelty of this model lies in the mathematical formalization of the investment attraction process in the market for tokenized assets. The theoretical value of the developed models consists in the confirmation of significantly expanded supply capabilities of tokenized assets on the graph showing the dependence of asset returns on invested capital.

]]>Mathematics doi: 10.3390/math10193440

Authors: Cheng Wang Gong Cai Zhang

This paper mainly studies the longitudinal and lateral deck motion compensation technology. In order to ensure the safe landing of the carrier-based aircrafts on the flight decks of carriers during the landing process, it is necessary to introduce deck motion information into the guidance law information of the automatic landing guidance system when the aircraft is about to land so that the aircraft can track the deck motion. To compensate the influence of the height change in the ideal landing point on the landing process, the compensation effects of the deck motion compensators with different design parameters are verified by simulation. For further phase-lead compensation for the longitudinal automatic landing guidance system, a deck motion predictor is designed based on the particle filter optimal prediction theory and the AR model time series analysis method. Because the influence of up and down motions on the vertical motion of the ideal landing point is the largest, the compensation effects of the designed predictor and compensator are simulated and verified based on the up and down motion of the power spectrum. For the compensation for the lateral motion, a tracking strategy of the horizontal measurement axis of the inertial stability coordinate system to the horizontal axis of the hull coordinate system (center line of the deck) is proposed. The tracking effects of the horizontal measurement axis of the designed integral and inertial tracking strategies are simulated and compared. Secondly, the lateral deck motion compensation commands are designed, and the compensation effects of different forms of compensation commands are verified by simulations. Finally, the compensation effects for the lateral deck motion under integral and inertial tracking strategies are simulated and analyzed.

]]>Mathematics doi: 10.3390/math10193439

Authors: Chen Cheng Zian Wang Chengxi Zhang Yang Yang

In order to meet the constraints of velocity and acceleration of displacement and attitude motion of an unmanned helicopter during an automatic carrier landing mission, an asymmetric tracking differentiator, which could set the speed and acceleration limits in two directions of tracked signal motion respectively, was derived based on the tracking differentiator in the active disturbance rejection control method. Based on the proposed asymmetric tracking differentiator, a fal-extended state observer based on the fal function was added to construct the attitude and angular velocity controller which is programmable during the transition process of unmanned helicopters. The mathematical simulation and result analysis show that the newly proposed attitude estimation algorithm effectively compensates for the deficiencies of the existing methods, improves the anti-jamming capability and the accuracy of attitude estimation in the maneuvering process, achieving the expected design purposes.

]]>Mathematics doi: 10.3390/math10193438

Authors: Guolin Hu Jian Zhang Zhiguo Yan

This paper further develops a relaxed method to reduce conservatism in H&infin; feedback control for continuous-time T-S fuzzy systems via a generalized non-quadratic Lyapunov function. Different from the results of some exisiting works, the generalized H&infin; state feedback controller is designed. The relaxed stabilization conditions are obtained by applying Finsler&rsquo;s lemma with the homogenous polynomial multipliers, and the H&infin; performance is acquired by solving an optimization problem. In addition, the proposed method could be expanded to handle other control problems for fuzzy systems. Two examples are given to show the validity of the proposed results.

]]>Mathematics doi: 10.3390/math10193437

Authors: Hua Shi Lin Huang Ke Li Xiang-Hu Wang Hu-Chen Liu

In recent years, different types of emergency events have taken place frequently around the world. Emergencies need to be addressed in the shortest possible time since inappropriate or delayed decisions may result in severe secondary disasters and economic losses. To make emergency decisions effectively within a limited time, a new emergency decision-making model is proposed in this study based on double hierarchy hesitant linguistic term sets (DHHLTSs) and the multi-attributive border approximation area comparison (MABAC) method. First, the performance assessment information on emergency solutions provided by domain experts is represented by the DHHLTSs, which are very useful for managing complex linguistic expressions in a prominent manner. Then, we make an extension of the MABAC method to determine the priority of alternative solutions and find out the optimal one for an emergency event. Furthermore, the criteria weights for emergency decision making are determined objectively with a maximum comprehensive method. Finally, a practical public health example is provided and a comparative analysis is performed to illustrate the applicability and advantages of the proposed emergency decision-making model.

]]>Mathematics doi: 10.3390/math10193436

Authors: Jiaming Guo Xiaofeng Luo Juan Zhang Mingtao Li

Brucellosis a the serious infectious disease in Hinggan League. Research has demonstrated that a large amount of transportation is one of the main reasons for so many cases. However, the specific transmission mechanism of brucellosis is not clear. In this paper, we utilize a multi-patch model to study the effect of the transportation of sheep on the spread of brucellosis in Hinggan League. Theoretically, we prove the global stability of the disease-free equilibrium and the uniform persistence of the endemic equilibrium. In a practical application, we apply the model to investigate the spread of brucellosis in Ulanhot city and Jalaid Banner, which are geographically adjacent in Hinggan League. The strains carried by humans are B.melitensis bv.1 and B.melitensis bv.3. We use the two-patch model to fit reported brucellosis cases data of two places by Markov Chain Monte Carlo (MCMC) simulations. It is found that the global basic reproduction number R0 is larger than 1, but the isolated basic reproduction numbers in Ulanhot city and Jalaid Banner are both less than 1. This indicates that the prevalence of brucellosis may be caused by the transportation of sheep. Sensitivity analysis of parameters on R0 shows that it is the most effective means to control the transportation of sheep from Jalaid to Ulanhot on preventing brucellosis. Moreover, we also discover that improving vaccine efficiency is an effective method compared with strengthening the vaccination coverage rate and improving the detection rate of sheep with brucellosis. Our dynamic behavior analysis of the two-patch model can provide a reference for the dynamic behavior analysis of the n-patch model, and our results provide a guide for how to control brucellosis based on transportation.

]]>Mathematics doi: 10.3390/math10193435

Authors: Zoran Perić Danijela Aleksić Jelena Nikolić Stefan Tomić

With increased network downsizing and cost minimization in deployment of neural network (NN) models, the utilization of edge computing takes a significant place in modern artificial intelligence today. To bridge the memory constraints of less-capable edge systems, a plethora of quantizer models and quantization techniques are proposed for NN compression with the goal of enabling the fitting of the quantized NN (QNN) on the edge device and guaranteeing a high extent of accuracy preservation. NN compression by means of post-training quantization has attracted a lot of research attention, where the efficiency of uniform quantizers (UQs) has been promoted and heavily exploited. In this paper, we propose two novel non-uniform quantizers (NUQs) that prudently utilize one of the two properties of the simplest UQ. Although having the same quantization rule for specifying the support region, both NUQs have a different starting setting in terms of cell width, compared to a standard UQ. The first quantizer, named the simplest power-of-two quantizer (SPTQ), defines the width of cells that are multiplied by the power of two. As it is the case in the simplest UQ design, the representation levels of SPTQ are midpoints of the quantization cells. The second quantizer, named the modified SPTQ (MSPTQ), is a more competitive quantizer model, representing an enhanced version of SPTQ in which the quantizer decision thresholds are centered between the nearest representation levels, similar to the UQ design. These properties make the novel NUQs relatively simple. Unlike UQ, the quantization cells of MSPTQ are not of equal widths and the representation levels are not midpoints of the quantization cells. In this paper, we describe the design procedure of SPTQ and MSPTQ and we perform their optimization for the assumed Laplacian source. Afterwards, we perform post-training quantization by implementing SPTQ and MSPTQ, study the viability of QNN accuracy and show the implementation benefits over the case where UQ of an equal number of quantization cells is utilized in QNN for the same classification task. We believe that both NUQs are particularly substantial for memory-constrained environments, where simple and acceptably accurate solutions are of crucial importance.

]]>Mathematics doi: 10.3390/math10193430

Authors: Peijie Jiang Tommy Tanu Wijaya Mailizar Mailizar Zulfah Zulfah Astuti Astuti

This study aimed to examine the potential of micro-lectures as effective technology-based learning media in mathematics. It proposed a hypothesis that using micro-lectures affects learning satisfaction and achievement in mathematics. Data were collected using a questionnaire developed from the acceptance model theory (TAM) and the extended Expectation Confirmation Model (ECM). Respondents comprised 233 students from six classes that used micro-lectures to learn mathematics for one semester at a public junior high school. The data were analyzed quantitatively using structural equation modeling assisted by SMART PLS 3.0 software. The results showed that perceived usefulness was the most significant factor in the learning achievement. Student attitude towards micro-lectures was the strongest positive factor in learning satisfaction. Furthermore, the proposed model explained 76.9% and 77.3% of the factors related to learning and satisfaction in using micro-lectures, respectively. It implies that micro-lectures affect learning satisfaction and achievement in mathematics. These results indicate that using micro-lectures in mathematics lessons increases learning satisfaction and achievement. They could assist schools, teachers, and local education ministries in planning, evaluating, and implementing micro-lectures in teaching and learning activities to improve education quality.

]]>Mathematics doi: 10.3390/math10193434

Authors: Khalid Javed Lieven Vandevelde Frederik De Belie

Rectifiers are required by the devices connected to the distribution end of the electrical power networks for AC/DC conversion. The line current becomes non-sinusoidal when a capacitor with a significant value is used to mitigate the output voltage ripple. This type of converter emulates a non-resistive impedance to the grid, due to which a bend occurs in the shape of the line current, which results in high total harmonic distortion and a low power factor. For perceiving sinusoidal current, power factor correction techniques are required. A digital controller for parallel-connected buck-boost power factor correctors is presented in this article to maintain a constant output voltage and to deal with circulating currents amongst parallel-connected converters. The proposed digital supervisory controller also regulates the input and line currents to keep them sinusoidal according to the input supply voltage to maintain the high power factor of the system. In this paper, using the differential equations of a buck-boost converter, the duty cycle calculations are performed for both Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM), which are responsible for providing a unity power factor. A supervisory controller encompasses a feed-forward control algorithm for tuning model parameters for eliminating the harmonics from the line current. The proposed scheme helps calculate duty cycles which provides a unity power factor and minimizes the circulating currents. The proposed method was simulated in MATLAB/Simulink and their digital-hardware validation testing was also performed using C2000 MCU Launchpad.

]]>Mathematics doi: 10.3390/math10193433

Authors: Sultana DIDI Ahoud AL AL HARBY Salim BOUZEBDA

The nonparametric estimation of density and regression function based on functional stationary processes using wavelet bases for Hilbert spaces of functions is investigated in this paper. The mean integrated square error over adapted decomposition spaces is given. To obtain the asymptotic properties of wavelet density and regression estimators, the Martingale method is used. These results are obtained under some mild conditions on the model; aside from ergodicity, no other assumptions are imposed on the data. This paper extends the scope of some previous results for wavelet density and regression estimators by relaxing the independence or the mixing condition to the ergodicity. Potential applications include the conditional distribution, curve discrimination, and time series prediction from a continuous set of past values.

]]>Mathematics doi: 10.3390/math10193432

Authors: Mahmood Ahmad Badr T. Alsulami Ramez A. Al-Mansob Saerahany Legori Ibrahim Suraparb Keawsawasvong Ali Majdi Feezan Ahmad

Resistance value (R-value) is one of the basic subgrade stiffness characterizations that express a material&rsquo;s resistance to deformation. In this paper, artificial intelligence (AI)-based models&mdash;especially M5P, support vector machine (SVM), and Gaussian process regression (GPR) algorithms&mdash;are built for R-value evaluation that meets the high precision and rapidity requirements in highway engineering. The dataset of this study comprises seven parameters: hydrated lime-activated rice husk ash, liquid limit, plastic limit, plasticity index, optimum moisture content, and maximum dry density. The available data are divided into three parts: training set (70%), test set (15%), and validation set (15%). The output (i.e., R-value) of the developed models is evaluated using the performance measures coefficient of determination (R2), mean absolute error (MAE), relative squared error (RSE), root mean square error (RMSE), relative root mean square error (RRMSE), performance indicator (&rho;), and visual framework (Taylor diagram). GPR is concluded to be the best performing model (R2, MAE, RSE, RMSE, RRMSE, and &rho; equal to 0.9996, 0.0258, 0.0032, 0.0012, 0.0012, and 0.0006, respectively, in the validation phase), very closely followed by SVM, and M5P. The application used for the aforementioned approaches for predicting the R-value is also compared with the recently developed artificial neural network model in the literature. The analysis of performance measures for the R-value dataset demonstrates that all the AI-based models achieved comparatively better and reliable results and thus should be encouraged in further research. Sensitivity analysis suggests that all the input parameters have a significant influence on the output, with maximum dry density being the highest.

]]>Mathematics doi: 10.3390/math10193431

Authors: Yu Chen Nick Gibbons

Transitional flow has a significant impact on vehicles operating at supersonic and hypersonic speeds. An economic way to simulate this problem is to use computational fluid dynamics (CFD) codes. However, not all CFD codes can solve transitional flows. This paper examines the ability of the Spalart&ndash;Allmaras one-equation BCM (SA-BCM) transitional model to solve hypersonic transitional flow, implemented in the open-source CFD code Eilmer. Its performance is validated via existing wind tunnel data. Eight different hypersonic flow conditions are applied. A flat plate model is built for the numerical tests. The results indicate that the existing SA-BCM model is sensitive to the freestream turbulence intensity and the grid size. It is not accurate in all the test cases, though the transitional length can be matched by tuning the freestream intensity. This is likely due to the intermittency term of the SA-BCM model not being appropriately calibrated for high-velocity flow, though if the model can be recalibrated it may be able to solve the general high-velocity flows. Although the current SA-BCM model is only accurate under certain flow conditions after one calibration process, it remains attractive to CFD applications. As a one-equation model, the SA-BCM model runs much faster than multiple-equation flow models.

]]>Mathematics doi: 10.3390/math10193429

Authors: Lei Shi Hari M. Srivastava Ayesha Rafiq Muhammad Arif Muhammad Ihsan

In the present paper, we aimed to discuss certain coefficient-related problems for the inverse functions associated with a bounded turning functions class subordinated with the exponential function. We calculated the bounds of some initial coefficients, the Fekete&ndash;Szeg&ouml;-type inequality, and the estimation of Hankel determinants of second and third order. All of these bounds were proven to be sharp.

]]>Mathematics doi: 10.3390/math10193428

Authors: Răzvan Diaconescu

Institution theory represents the fully axiomatic approach to model theory in which all components of logical systems are treated fully abstractly by reliance on category theory. Here, we survey some developments over the last decade or so concerning the institution theoretic approach to non-classical aspects of model theory. Our focus will be on many-valued truth and on models with states, which are addressed by the two extensions of ordinary institution theory known as L-institutions and stratified institutions, respectively. The discussion will include relevant concepts, techniques, and results from these two areas.

]]>Mathematics doi: 10.3390/math10193427

Authors: Evgeniya Gospodinova Penio Lebamovski Galya Georgieva-Tsaneva Galina Bogdanova Diana Dimitrova

In the article, a comparative analysis is performed regarding the accuracy parameter in determining the degree of self-similarity of fractal processes between the following methods: Variance-Time plot, Rescaled Range (R/S), Wavelet-based, Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). To evaluate the methods, fractal processes based of Fractional Gaussian Noise were simulated and the dependence between the length of the simulated process and the degree of self-similarity was investigated by calculating the Hurst exponent (H &gt; 0.5). It was found that the Wavelet-based, DFA and MFDFA methods, with a process length greater than 214 points, have a relative error of the Hurst exponent is less than 1%. A methodology for the Wavelet-based method related to determining the size of the scale and the wavelet algorithm was proposed, and it was investigated in terms of the exact determination of the Hurst exponent of two algorithms: Haar and Daubechies with different number of coefficients and different values of the scale. Based on the analysis, it was determined that the Daubechies algorithm with 10 coefficients and scale (i = 2, j = 10) has a relative error of less than 0.5%. The three most accurate methods are applied to the study of real cardiac signals of two groups of people: healthy and unhealthy (arrhythmia) subjects. The results of the statistical analysis, using the t-test, show that the proposed methods can distinguish the two studied groups and can be used for diagnostic purposes.

]]>Mathematics doi: 10.3390/math10193426

Authors: Jiang Wang Yang Gu Kang Rong Quan Xu Xi Zhang

Recently, the application of memristors to improve chaos complexity in discrete chaotic systems has been paid more and more attention to. To enrich the application examples of discrete memristor-based chaotic systems, this article proposes a new three-dimensional (3-D) memristor-based Lozi map by introducing a discrete memristor into the original two-dimensional (2-D) Lozi map. The proposed map has no fixed points but can generate hidden hyperchaos, so it is a hidden hyperchaotic map. The dynamical effects of the discrete memristor on the memristor-based Lozi map and two types of coexisting hidden attractors boosted by the initial conditions are demonstrated using some numerical methods. The numerical results clearly show that the introduced discrete memristor allows the proposed map to have complicated hidden dynamics evolutions and also exhibit heterogeneous and homogeneous hidden multistability. Finally, a digital platform is used to realize the memristor-based Lozi map, and its experimental phase portraits are obtained to confirm the numerical ones.

]]>Mathematics doi: 10.3390/math10193425

Authors: Ming-Zhu Chen Ning Wang A-Ming Liu Aleksandr A. Makhnev

In this paper, we determine the maximum signless Laplacian spectral radius of all graphs which do not contain small books as a subgraph and characterize all extremal graphs. In addition, we give an upper bound of the signless Laplacian spectral radius of all graphs which do not contain intersecting quadrangles as a subgraph.

]]>Mathematics doi: 10.3390/math10193423

Authors: Wei Wang Yuwei Zhou Jianbin Liu Baofeng Sun

On-street cruising by drivers impedes the effectiveness of road traffic conditions and increases energy consumption and environmental impact. Existing models of on-street cruising for parking mainly embody those intrinsic on-street parking factors and disregard the extrinsic impacts from off-street parking gaming factors. This research focused on both the intrinsic and extrinsic elements, especially gaming factors, of off-street parking, i.e., the price of off-street parking, the waiting time of off-street parking, and the difference in walking time between their parking lots to their destinations. On-street cruising for a parking model is reconstructed in this paper in consideration with the equilibrium cruising time, i.e., the maximum tolerable cruise time after evaluating the cost of on-street and off-street parking. Correlation analysis showed that the off-street parking gaming factors were all positively related with the maximum tolerable cruise time. A simulation model was further presented for on-street cruising for the parking model by the cellular automata approach with real-world data. Simulation experiments demonstrated that the average speed of vehicles on the street increases by 9.858 km/h, the average delay decreases by 44.934 s, and the price of on-street parking increases by 4.5 CNY/h. The proposed on-street cruising for parking model proved effective by decreasing the maximum tolerable cruising time to bring significant improvements in average speed, average delay, and on-street cruising vehicles in road traffic flow.

]]>Mathematics doi: 10.3390/math10193424

Authors: Osman Kazancı Sarka Hoskova-Mayerova Bijan Davvaz

An multi-polar fuzzy set is a robust mathematical model to examine multipolar, multiattribute, and multi-index data. The multi-polar fuzzy sets was created as a useful mechanism to portray uncertainty in multiattribute decision making. In this article, we consider the theoretical applications of multi-polar fuzzy sets. We present the notion of multi-polar fuzzy sets in ordered semihypergroups and define multi-polar fuzzy hyperideals (bi-hyperideals, quasi hyperideals) in an ordered semihypergroup. Relations between multi-polar fuzzy hyperideals, multi-polar fuzzy bi-hyperideals and multi-polar fuzzy quasi hyperideals are discussed.

]]>Mathematics doi: 10.3390/math10193421

Authors: Koketso Ntshabele Bassey Isong Naison Gasela Adnan M. Abu-Mahfouz

Low-Power Wide-Area Network (LPWAN) is a wireless WAN technology that connects low-powered and low-bandwidth devices with low bit rates atop Long Ranges (LoRa). It is characterized by improved scalability, wide area coverage, and low power consumption, which are beneficial to resource-constrained devices on the Internet of Things (IoT) for effective communication and security. Security in Long-Range Wide-Area Networks (LoRaWAN) widely employs Advanced Encryption Standard (AES) 128-bit symmetric encryption as the accepted security standard for a key generation that secures communication and entities. However, designing an efficient key manifestation and management model is still a challenge as different designs are based on different research objectives. To date, there is no global and well-accepted LoRaWAN security model for all applications. Thus, there is a need to continually improve the LoRaWAN security model. This paper, therefore, performed an in-depth analysis of some existing LoRaWAN key security models to identify security challenges affecting these security models and assess the strengths and weaknesses of the proposed solutions. The goal is to improve some of the existing LoRaWAN security models by analysing and bringing together several challenges that affect them. Several relevant studies were collected and analysed; the analysis shows that though there are few research works in this area, several existing LoRaWAN security models are not immune to attacks. Symmetry encryption is found to be the most used approach to manage key security due to its less computational operations. Moreover, it is possible to improve existing key security models in LPWAN with consideration of the resource constrained. Again, trusted third parties for key management were also widely used to defend against possible attacks and minimize operational complexities. We, therefore, recommend the design of lightweight and less complex LPWAN security models to sustain the lifespan of LPWAN devices.

]]>Mathematics doi: 10.3390/math10193422

Authors: Yehuda Arav Eyal Fattal Ziv Klausner

Understanding the factors that increase the transmissibility of the recently emerging variants of SARS-CoV-2 can aid in mitigating the COVID-19 pandemic. Enhanced transmissibility could result from genetic variations that improve how the virus operates within the host or its environmental survival. Variants with enhanced within-host behavior are either more contagious (leading infected individuals to shed more virus copies) or more infective (requiring fewer virus copies to infect). Variants with improved outside-host processes exhibit higher stability on surfaces and in the air. While previous studies focus on a specific attribute, we investigated the contribution of both within-host and outside-host processes to the overall transmission between two individuals. We used a hybrid deterministic-continuous and stochastic-jump mathematical model. The model accounts for two distinct dynamic regimes: fast-discrete actions of the individuals and slow-continuous environmental virus degradation processes. This model produces a detailed description of the transmission mechanisms, in contrast to most-viral transmission models that deal with large populations and are thus compelled to provide an overly simplified description of person-to-person transmission. We based our analysis on the available data of the Alpha, Epsilon, Delta, and Omicron variants on the household secondary attack rate (hSAR). The increased hSAR associated with the recent SARS-CoV-2 variants can only be attributed to within-host processes. Specifically, the Delta variant is more contagious, while the Alpha, Epsilon, and Omicron variants are more infective. The model also predicts that genetic variations have a minimal effect on the serial interval distribution, the distribution of the period between the symptoms&rsquo; onset in an infector&ndash;infectee pair.

]]>Mathematics doi: 10.3390/math10193414

Authors: Laifa Tao Chao Wang Yuan Jia Ruzhi Zhou Tong Zhang Yiling Chen Chen Lu Mingliang Suo

Due to the increasing complexity of the entire satellite system and the deteriorating orbital environment, multiple independent single faults may occur simultaneously in the satellite power system. However, two stumbling blocks hinder the effective diagnosis of simultaneous-fault, namely, the difficulty of obtaining the simultaneous-fault data and the extremely complicated mapping of the simultaneous-fault modes to the sensor data. To tackle the challenges, a fault diagnosis strategy based on a novel rough set model is proposed. Specifically, a novel rough set model named FN&zeta;DTRS by introducing a concise loss function matrix and fuzzy neighborhood relationship is proposed to accurately mine and characterize the relationship between fault and data. Furthermore, an attribute rule-based fault matching strategy is designed without using simultaneous-fault data as training samples. The numerical experiments demonstrate the effectiveness of the FN&zeta;DTRS model, and the diagnosis experiments performed on a satellite power system illustrate the superiority of the proposed approach.

]]>Mathematics doi: 10.3390/math10193420

Authors: Nien-Che Yang Yong-Chang Zhang Eunike Widya Adinda

In this study, the capacity and location of battery energy storage systems (BESSs) in a distribution network were evaluated to increase the stability and reliability of power systems by applying the proposed transient stability indicators. The search capability of particle swarm optimization (PSO) combined with Pareto optimality in MATLAB R2019b was used to solve multi-objective problems in steady-state conditions, including active power loss, voltage deviation, and total operating cost. The BESS capacity and location were then derived using the Manhattan distance method. Subsequently, the BESS control model was constructed using the commercial software DIgSILENT PowerFactory 2021 to evaluate generator power smoothing and short-term voltage stability index during the transient response and determine the final capacity and location of BESS in distribution networks. Finally, the accuracy of the proposed method was verified by considering the daily load and solar power generation curves using the IEEE 33-bus radial power distribution network.

]]>Mathematics doi: 10.3390/math10193419

Authors: Sajid Hussain Muhammad Sajid Rashid Mahmood Ul Hassan Rashid Ahmed

Here, we propose a new generalized exponential extended exponentiated (NGE3) family of distributions. Some statistical properties of proposed family are gained. The most extreme probability method, maximum likelihood (ML), is utilized for parameter estimation. We explore an exceptional model called NGE3-Exponential (NGE3E). NGE3E is estimated with ML, and the performance of estimators is demonstrated by utilizing a simulation. Moreover, two applications are given to show the significance and adaptability of the proposed model in comparison to some generalized models (GMs).

]]>Mathematics doi: 10.3390/math10193418

Authors: Jin Zhang Wenjun Meng Yufeng Yin Zhengnan Li Lidong Ma Weiqiang Liang

This paper presents a control method for the problem of trajectory jitter and poor tracking performance of the end of a three-joint rigid manipulator. The control is based on a high-order particle swarm optimization algorithm with an improved sliding mode control neural network. Although the sliding mode variable structure control has a certain degree of robustness, because of its own switching characteristics, chattering can occur in the later stage of the trajectory tracking of the manipulator end. Hence, on the basis of the high-order sliding mode control, the homogeneous continuous control law and super-twisting adaptive algorithm were added to further improve the robustness of the system. The radial basis function neural network was used to compensate the errors in the modeling process, and an adaptive law was designed to update the weights of the middle layer of the neural network. Furthermore, an improved particle swarm optimization algorithm was established and applied to optimize the parameters of the neural network, which improved the trajectory tracking of the manipulator end. Finally, MATLAB simulation results indicated the validity and superiority of the proposed control method compared with other sliding mode control algorithms.

]]>Mathematics doi: 10.3390/math10193417

Authors: Jianxun Li Wenjie Cheng Kin Keung Lai Bhagwat Ram

Because of their flexibility, controllability and convenience, Automated Guided Vehicles (AGV) have gradually gained popularity in intelligent manufacturing because to their adaptability, controllability, and simplicity. We examine the relationship between AGV scheduling tasks, charging thresholds, and power consumption, in order to address the issue of how AGV charging affects the scheduling of flexible manufacturing units with multiple AGVs. Aiming to promote AGVs load balance and reduce AGV charging times while meeting customer demands, we establish a scheduling model with the objective of minimizing the maximum completion time based on process sequence limitations, processing time restrictions, and workpiece transportation constraints. In accordance with the model&rsquo;s characteristics, we code the machine, workpiece, and AGV independently, solve the model using a genetic algorithm, adjust the crossover mutation operator, and incorporate an elite retention strategy to the population initialization process to improve genetic diversity. Calculation examples are used to examine the marginal utility of the number of AGVs and electricity and validate the efficiency and viability of the scheduling model. The results show that the AVGs are effectively scheduled to complete transportation tasks and reduce the charging wait time. The multi-AGV flexible manufacturing cell scheduling can also help decision makers to seek AGVs load balance by simulation, reduce the charging times, and decrease the final completion time of manufacturing unit. In addition, AGV utilization can be maximized when the fleet size of AGV is 20%-40% of the number of workpieces.

]]>Mathematics doi: 10.3390/math10193416

Authors: Jing Tian Yizhi Chen Hui Xu

Let &Sigma;+ be the set of all finite words over a finite alphabet &Sigma;. A word u is called a strict prefix of a word v, if u is a prefix of v and there is no other way to show that u is a subword of v. A language L&sube;&Sigma;+ is said to be prefix-strict, if for any u,v&isin;L, u is a subword of v always implies that u is a strict prefix of v. Denote the class of all prefix-strict languages in &Sigma;+ by P(&Sigma;+). This paper characterizes P(&Sigma;+) as a universe of a model of the free object for the ai-semiring variety satisfying the additional identities x+yx&asymp;x and x+yxz&asymp;x. Furthermore, the analogous results for so-called suffix-strict languages and infix-strict languages are introduced.

]]>Mathematics doi: 10.3390/math10193415

Authors: Yuanfeng Ding Yan Huang Li Tang Xizhong Qin Zhenhong Jia

In this paper, we study the joint optimization problem of the spectrum and power allocation for multiple vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) users in cellular vehicle-to-everything (C-V2X) communication, aiming to maximize the sum rate of V2I links while satisfying the low latency requirements of V2V links. However, channel state information (CSI) is hard to obtain accurately due to the mobility of vehicles. In addition, the effective sensing of state information among vehicles becomes difficult in an environment with complex and diverse information, which is detrimental to vehicles collaborating for resource allocation. Thus, we propose a framework of multi-agent deep reinforcement learning based on attention mechanism (AMARL) to improve the V2X communication performance. Specifically, for vehicle mobility, we model the problem as a multi-agent reinforcement learning process, where each V2V link is regarded an agent and all agents jointly intercommunicate with the environment. Each agent allocates spectrum and power through its deep Q network (DQN). To enhance effective intercommunication and the sense of collaboration among vehicles, we introduce an attention mechanism to focus on more relevant information, which in turn reduces the signaling overhead and optimizes their communication performance more explicitly. Experimental results show that the proposed AMARL-based approach can satisfy the requirements of a high rate for V2I links and low latency for V2V links. It also has an excellent adaptability to environmental change.

]]>Mathematics doi: 10.3390/math10193413

Authors: Ochkov Vasileva Orlov Chudova Tikhonov

A method has been obtained for the use of visualization in computer mathematical packages, which is an effective means of overcoming difficult situations that arise for students when mastering such packages and solving computational problems. Depending on the complexity of the problem being solved, either the teacher or the students themselves can create special visual graphic (animation) objects. Such objects allow, initially without going into the intricacies of the functioning of the package and the mathematical apparatus used, to competently describe a complete picture of a difficult situation for students and indicate ways to resolve it. The method is considered through the example of the process of solving systems of equations using the mathematical package Mathcad and the WolframAlpha online resource. Graphical and animated objects are presented that clearly demonstrate the areas of the location of initial approximations, allowing you to numerically obtain all the real roots of systems of trigonometric and nonlinear equations. Similar objects are built to find the critical points of the Himmelblau&rsquo;s special test function. Visualization materials are confirmed by the presented computational calculations. The proposed method is implemented in the form of plans for lectures and practical classes on mathematical modeling using computer technologies. The method was tested with university students at the National Research University Moscow Power Engineering Institute.

]]>Mathematics doi: 10.3390/math10193412

Authors: Araceli Queiruga-Dios María Jesus Santos Sánchez Fatih Yilmaz Deolinda M. L. Dias Rasteiro Jesús Martín-Vaquero Víctor Gayoso Martínez

This book contains the successful submissions [...]

]]>Mathematics doi: 10.3390/math10193411

Authors: Xiaoyan Li Shenghua Xu Tao Jiang Yong Wang Yu Ma Yiming Liu

Point-of-interest (POI) recommendation is the prevalent personalized service in location-based social networks (LBSNs). A single use of matrix factorization (MF) or deep neural networks cannot effectively capture the complex structure of user&ndash;POI interactions. In addition, to alleviate the data-sparsity problem, current methods primarily introduce the auxiliary information of users and POIs. Auxiliary information is often judged to be equally valued, which will dissipate some of the valuable information. Hence, we propose a novel POI recommendation method fusing auxiliary attribute information based on the neural matrix factorization, integrating the convolutional neural network and attention mechanism (NueMF-CAA). First, the k-means and term frequency&ndash;inverse document frequency (TF-IDF) algorithms are used to mine the auxiliary attribute information of users and POIs from user check-in data to alleviate the data-sparsity problem. A convolutional neural network and an attention mechanism are applied to learn the expression of auxiliary attribute information and distinguish the importance of auxiliary attribute information, respectively. Then, the auxiliary attribute information feature vectors of users and POIs are concatenated with their respective latent feature vectors to obtain the complete latent feature vectors of users and POIs. Meanwhile, generalized matrix factorization (GMF) and multilayer perceptron (MLP) are used to learn the linear and nonlinear interactions between users and POIs, respectively, and the last hidden layer is connected to integrate the two parts to alleviate the implicit feedback problem and make the recommendation results more interpretable. Experiments on two real-world datasets, the Foursquare dataset and the Weibo dataset, demonstrate that the proposed method significantly improves the evaluation metrics&mdash;hit ratio (HR) and normalized discounted cumulative gain (NDCG).

]]>Mathematics doi: 10.3390/math10183410

Authors: Manuel I. Capel

Automatic Machine Learning (AML) methods are currently considered of great interest for use in the development of cyber-physical systems. However, in practice, they present serious application problems with respect to fitness computation, overfitting, lack of scalability, and the need for an enormous amount of time for the computation of neural network hyperparameters. In this work, we have experimentally investigated the impact of continuous updating and validation of the hyperparameters, on the performance of a cyber-physical model, with four estimators based on feedforward and narx ANNs, all with the gradient descent-based optimization technique. The main objective is to demonstrate that the optimized values of the hyperparameters can be validated by simulation with MATLAB/Simulink following a mixed approach based on interleaving the updates of their values with a classical training of the ANNs without affecting their efficiency and automaticity of the proposed method. For the two relevant variables of an Induction Motor (IM), two sets of estimators have been trained from the input current and voltage data. In contrast, the training data for the speed and output electromagnetic torque of the IM have been established with the help of a new Simulink model developed entirely. The results have demonstrated the effectiveness of ANN estimators obtained with the Deep Learning Toolbox (DLT) that we used to transform the trained ANNs into blocks that can be directly used in cyber-physical models designed with Simulink.

]]>Mathematics doi: 10.3390/math10183409

Authors: Nikolay A. Kudryashov

The family of the generalized Schr&ouml;dinger equations with Kerr nonlinearity of unrestricted order is considered. The solutions of equations are looked for using traveling wave reductions. The Painlev&eacute; test is applied for finding arbitrary constants in the expansion of the general solution into the Laurent series. It is shown that the equation does not pass the Painlev&eacute; test but has two arbitrary constants in local expansion. This fact allows us to look for solitary wave solutions for equations of unrestricted order. The main result of this paper is the theorem of existence of optical solitons for equations of unrestricted order that is proved by direct calculation. The optical solitons for partial differential equations of the twelfth order are given in detail.

]]>Mathematics doi: 10.3390/math10183408

Authors: Pablo Concha-Vega Eric Goles Pedro Montealegre Martín Ríos-Wilson

A majority automata is a two-state cellular automata, where each cell updates its state according to the most represented state in its neighborhood. A question that naturally arises in the study of these dynamical systems asks whether there exists an efficient algorithm that can be implemented in order to compute the state configuration reached by the system at a given time-step. This problem is called the prediction problem. In this work, we study the prediction problem for a more general setting in which the local functions can be different according to their behavior in tie cases. We define two types of local rules: the stable majority and biased majority. The first one remains invariant in tie cases, and the second one takes the value 1. We call this class the heterogeneous majority cellular automata (HMCA). For this latter class, we show that in one dimension, the prediction problem for HMCA is in NL as a consequence of the dynamics exhibiting a type of bounded change property, while in two or more dimensions, the problem is P-Complete as a consequence of the capability of the system of simulating Boolean circuits.

]]>Mathematics doi: 10.3390/math10183407

Authors: Haonan Peng Yuanyuan Li Wei Zhang

Single-cell RNA sequencing (scRNA-seq) technology has been a significant direction for single-cell research due to its high accuracy and specificity, as it enables unbiased high-throughput studies with minimal sample sizes. The continuous improvement of scRNA-seq technology has promoted parallel research on single-cell multi-omics. Instead of sequencing bulk cells, analyzing single cells inspires greater discovery power for detecting novel genes without prior knowledge of sequence information and with greater sensitivity when quantifying rare variants and transcripts. However, current analyses of scRNA-seq data are usually carried out with unsupervised methods, which cannot take advantage of the prior distribution and structural features of the data. To solve this problem, we propose the SCAFG (Classifying Single Cell Types Based on an Adaptive Threshold Fusion Graph Convolution Network), a semi-supervised single-cell classification model that adaptively fuses cell-to-cell correlation matrices under various thresholds according to the distribution of cells. We tested the performance of the SCAFG in identifying cell types on diverse real scRNA-seq data; then, we compared the SCAFG with other commonly used semi-supervised algorithms, and it was shown that the SCAFG can classify single-cell data with a higher accuracy.

]]>Mathematics doi: 10.3390/math10183406

Authors: Serhii Lupenko

This work is devoted to the procedure for constructing of a cyclically correlated random process of a continuous argument as a mathematical model of cyclic signals in dynamic systems, which makes it possible to consistently describe cyclic stochastic signals, both with regular and irregular rhythms, not separating them, but complementing them within the framework of a single integrated model. The class of cyclically correlated random processes includes the subclass of cyclostationary (periodically) correlated random processes, which enable the use of a set of powerful methods of analysis and the forecasting of cyclic signals with a stable rhythm. Mathematical structures that model the cyclic, phase and rhythmic structures of a cyclically correlated random process are presented. The sufficient and necessary conditions that the structural function and the rhythm function of the cyclically correlated random process must satisfy have been established. The advantages of the cyclically correlated random process in comparison with other mathematical models of cyclic signals with a variable rhythm are given. The obtained results contribute to the emergence of a more complete and rigorous theory of this class of random processes and increase the validity of the methods of their analysis and computer simulation.

]]>Mathematics doi: 10.3390/math10183405

Authors: Yi Cui Ronghua Shi Jian Dong

In this paper, we proposed a tunicate swarm algorithm based on Tent-Lévy flight (TLTSA) to avoid converging prematurely or failing to escape from a local optimal solution. First, we combined nine chaotic maps with the Lévy flight strategy to obtain nine different TSAs based on a Chaotic-Lévy flight strategy (CLTSA). Experimental results demonstrated that a TSA based on Tent-Lévy flight (TLTSA) performed the best among nine CLTSAs. Afterwards, the TLTSA was selected for comparative research with other well-known meta-heuristic algorithms. The 16 unimodal benchmark functions, 14 multimodal benchmark functions, 6 fixed-dimension functions, and 3 constrained practical problems in engineering were selected to verify the performance of TLTSA. The results of the test functions suggested that the TLTSA was better than the TSA and other algorithms in searching for global optimal solutions because of its excellent exploration and exploitation capabilities. Finally, the engineering experiments also demonstrated that a TLTSA solved constrained practical engineering problems more effectively.

]]>Mathematics doi: 10.3390/math10183402

Authors: Lingxiao Li Jinliang Zhang Mingliang Wang

In this paper, three forms of (2 + l)-dimensional Burgers&rsquo; equations are investigated. More general multiple solutions of these Burgers&rsquo; equations are obtained by dependent variable transformation derived using the simplified homogeneous balance method.

]]>Mathematics doi: 10.3390/math10183404

Authors: K. N. Sneha U. S. Mahabaleshwar Mohsen Sharifpur Mohammad Hossein Ahmadi Mohammed Al-Bahrani

The consequence of magnetohydrodynamics (MHD) flow on entropy generation analysis and thermal radiation for carbon nanotubes via a stretched surface through a magnetic field has been discovered. The governing partial differential equations are altered into ordinary differential equations with the aid of the similarity variable. Here, water is considered the base fluid with two types of carbon nanotubes, such as single-wall carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes (MWCNTs). This domain is used in the energy equation, and then it is solved analytically and transferred in terms of hypergeometric function. The existence and nonexistence of solutions for stretching are investigated. Some of the primary findings discussed in this article show that the presence of carbon nanotubes, magnetic field, and Eckert number develop heat transfer in nanofluids and heat sources and that Eckert number reduces entropy formation. Different regulating parameters, such as Casson fluid, mass transpiration, thermal radiation, solid volume fractions, magnetic constraint, and heat source/sink constraint, can be used to analyze the results of velocity and temperature profiles. The novelty of the current study on the influence of magnetic field entropy analysis on CNTs flow with radiation, is that elastic deformation is the subject of this research, and this has not previously been examined. Higher values of heat sources and thermal radiation enhance the heat transfer rate. The study reveals that thermal radiation, Casson fluid; mass transpiration, Darcy number, and Prandtl number increase, and that decrease in the buoyancy ratio, magnetic parameter, and volume fraction decrease the values of the buoyancy ratio, and also control the transfer of heat.

]]>Mathematics doi: 10.3390/math10183403

Authors: Shaofeng Wang Xin Cai Jian Zhou Zhengyang Song Xiaofeng Li

With the increasing requirements for energy, resources and space, numerous rock engineering projects (e [...]

]]>Mathematics doi: 10.3390/math10183401

Authors: Jia Song Yuxie Luo Mingfei Zhao Yunlong Hu Yanxue Zhang

Aiming at the problem of the terminal guidance phase of hypersonic vehicles (HSV) under fault condition, and considering the existence of various uncertain parameters and actuator faults in the control system, a fault-tolerant integrated guidance and control design of a hypersonic vehicle based on the proximal policy optimization algorithm (PPO) is proposed. First, in view of the problem that the separate guidance and control loop design cannot make full use of the coupling relationship between the two, the relationship between the guidance loop and the control loop is considered and an integrated guidance and control system of HSV is established. Then, the integrated guidance and control problem is converted into a reinforcement learning model, the action space, state observation space, and reward function of the PPO agent are designed, and the network is initialized and designed. Simulations verify the feasibility of the proposed PPO-based IGC system.

]]>Mathematics doi: 10.3390/math10183400

Authors: Muhammad Shakeel Attaullah Mohammed Kbiri Alaoui Ahmed M. Zidan Nehad Ali Shah Wajaree Weera

In this study, the dispersal caused by the transverse Poisson&rsquo;s effect in a magneto-electro-elastic (MEE) circular rod is taken into consideration using the nonlinear longitudinal wave equation (LWE), a mathematical physics problem. Using the generalized exp-function method, we investigate the families of solitary wave solutions of one-dimensional nonlinear LWE. Using the computer program Wolfram Mathematica 10, these new exact and solitary wave solutions of the LWE are derived as trigonometric function, periodic solitary wave, rational function, hyperbolic function, bright and dark solitons solutions, sinh, cosh, and sech2 function solutions of the LWE. These solutions represent the electrostatic potential and pressure for LWE as well as the graphical representation of electrostatic potential and pressure.

]]>Mathematics doi: 10.3390/math10183399

Authors: Gurami Tsitsiashvili

The interest in large or extreme outliers in arrays of empirical information is caused by the wishes of users (with whom the author worked): specialists in medical and zoo geography, mining, the application of meteorology in fishing tasks, etc. The following motives are important for these specialists: the substantial significance of large emissions, the fear of errors in the study of large emissions by standard and previously used methods, the speed of information processing and the ease of interpretation of the results obtained. To meet these requirements, interval pattern recognition algorithms and the accompanying auxiliary computational procedures have been developed. These algorithms were designed for specific samples provided by the users (short samples, the presence of rare events in them or difficulties in the construction of interpretation scenarios). They have the common property that the original optimization procedures are built for them or well-known optimization procedures are used. This paper presents a series of results on processing observations by allocating large outliers as in a time series in planar and spatial observations. The algorithms presented in this paper differ in speed and sufficient validity in terms of the specially selected indicators. The proposed algorithms were previously tested on specific measurements and were accompanied by meaningful interpretations. According to the author, this paper is more applied than theoretical. However, to work with the proposed material, it is required to use a more diverse mathematical tool kit than the one that is traditionally used in the listed applications.

]]>Mathematics doi: 10.3390/math10183398

Authors: Yu-Huei Cheng Cheng-Yen Tseng Duc-Man Nguyen Yu-Da Lin

In traditional agricultural quality control, agricultural products are screened manually and then packaged and transported. However, long-term fruit storage is challenging in tropical climates, especially in the case of cherry tomatoes. Cherry tomatoes that appear rotten must be immediately discarded while grading; otherwise, other neighboring cherry tomatoes could rot. An insufficient agricultural workforce is one of the reasons for an increasing number of rotten tomatoes. The development of smart-technology agriculture has become a primary trend. This study proposed a You Only Look Once version 4 (YOLOv4)-driven appearance grading filing mechanism to grade cherry tomatoes. Images of different cherry-tomato appearance grades and different light sources were used as training sets, and the cherry tomatoes were divided into four categories according to appearance (perfect (pedicled head), good (not pedicled head), defective, and discardable). The AI server ran the YOLOv4 deep-learning framework for deep image learning training. Each dataset group was calculated by considering 100 of the four categories as the difference, and the total numbers of images were 400, 800, 1200, 1600, and 2000. Each dataset group was split into an 80% training set, 10% verification set, and 10% test set to overcome the identification complexity of different appearances and light source intensities. The experimental results revealed that models using 400&ndash;2000 images were approximately 99.9% accurate. Thus, we propose a new mechanism for rapidly grading agricultural products.

]]>Mathematics doi: 10.3390/math10183397

Authors: Josiane Mothe

The author wishes to make the following corrections to this paper [1]:In Abstract, (1) &ldquo;It depicts how data analytics has been used in IR for a better understanding system effectiveness&rdquo; should be &ldquo;It depicts how data analytics has been used in IR to gain a better understanding of system effectiveness&rdquo;; (2) &ldquo;This review concludes lack of full understanding of system effectiveness according to the context although it has been possible to adapt the query processing to some contexts successfully&rdquo; should be changed to &ldquo;This review concludes that we lack a full understanding of system effectiveness related to the context which the system is in, though it has been possible to adapt the query processing to some contexts successfully&rdquo;; (3) &ldquo;This review also concludes that, even if it is possible to distinguish effective from non effective system on average on a query set&rdquo; should be changed to &ldquo;This review also concludes that, even if it is possible to distinguish effective from non-effective systems for a query set&rdquo; [...]

]]>Mathematics doi: 10.3390/math10183396

Authors: Xudong Fan Aiwen Wang Pengcheng Jiang Sijin Wu Ying Sun

The nonlinear bending of the sandwich plates with graphene nanoplatelets (GPLs) reinforced porous composite (GNRPC) core and two metal skins subjected to different boundary conditions and various loads, such as the concentrated load at the center, linear loads with different slopes passing through the center, linear eccentric loads, uniform loads, and trapezoidal loads, has been presented. The popular four-unknown refined theory accounting for the thickness stretching effects has been employed to model the mechanics of the sandwich plates. The governing equations have been derived from the nonlinear Von Karman strain&ndash;displacement relationship and principle of virtual work with subsequent solution by employing the classical finite element method in combination with the Newton downhill method. The convergence of the numerical results has been checked. The accuracy and efficiency of the theory have been confirmed by comparing the obtained results with those available in the literature. Furthermore, a parametric study has been carried out to analyze the effects of load type, boundary conditions, porosity coefficient, GPLs weight fraction, GPLs geometry, and concentrated load radius on the nonlinear central bending deflections of the sandwich plates. In addition, the numerical results reveal that the adopted higher order theory can significantly improve the simulation of the transverse deflection in the thickness direction.

]]>Mathematics doi: 10.3390/math10183392

Authors: Dušan P. Nikezić Uzahir R. Ramadani Dušan S. Radivojević Ivan M. Lazović Nikola S. Mirkov

Mathematical methods are the basis of most models that describe the natural phenomena around us. However, the well-known conventional mathematical models for atmospheric modeling have some limitations. Machine learning with Big Data is also based on mathematics but offers a new approach for modeling. There are two methodologies to develop deep learning models for spatio-temporal image prediction. On these bases, two models were built&mdash;ConvLSTM and CNN-LSTM&mdash;with two types of predictions, i.e., sequence-to-sequence and sequence-to-one, in order to forecast Aerosol Optical Thickness sequences. The input dataset for training was NASA satellite imagery MODAL2_E_AER_OD from Terra/MODIS satellites, which presents global Aerosol Optical Thickness with an 8 day temporal resolution from 2000 to the present. The obtained results show that the ConvLSTM sequence-to-one model had the lowest RMSE error and the highest Cosine Similarity value. The advantages of the developed DL models are that they can be executed in milliseconds on a PC, can be used for global-scale Earth observations, and can serve as tracers to study how the Earth&rsquo;s atmosphere moves. The developed models can be used as transfer learning for similar image time-series forecasting models.

]]>Mathematics doi: 10.3390/math10183394

Authors: Xiaohong Zhang Rong Liang Benjamín Bedregal

After the research on naBL-algebras gained by the non-associative t-norms and overlap functions, inflationary BL-algebras were also studied as a recent kind of non-associative generalization of BL-algebras, which can be obtained by general overlap functions. In this paper, we show that not every inflationary general overlap function can induce an inflationary BL-algebra by a counterexample and thus propose the new concept of weak inflationary BL-algebras. We prove that each inflationary general overlap function corresponds to a weak inflationary BL-algebra; therefore, two mistaken results in the previous paper are revised. In addition, some properties satisfied by weak inflationary BL-algebras are discussed, and the relationships among some non-classical logic algebras are analyzed. Finally, we establish the theory of filters and quotient algebras of inflationary general residuated lattice (IGRL) and inflationary pseudo-general residuated lattice (IPGRL), and characterize the properties of some kinds of IGRLs and IPGRLs by naBL-filters, (weak) inflationary BL-filters, and weak inflationary pseudo-BL-filters.

]]>Mathematics doi: 10.3390/math10183395

Authors: Eglė Butkevičiūtė Aleksėjus Michalkovič Liepa Bikulčienė

Mental fatigue is a major public health issue worldwide that is common among both healthy and sick people. In the literature, various modern technologies, together with artificial intelligence techniques, have been proposed. Most techniques consider complex biosignals, such as electroencephalogram, electro-oculogram or classification of basic heart rate variability parameters. Additionally, most studies focus on a particular area, such as driving, surgery, etc. In this paper, a novel approach is presented that combines electrocardiogram (ECG) signal feature extraction, principal component analysis (PCA), and classification using machine learning algorithms. With the aim of daily mental fatigue recognition, an experiment was designed wherein ECG signals were recorded twice a day: in the morning, i.e., a state without fatigue, and in the evening, i.e., a fatigued state. PCA analysis results show that ECG signal parameters, such as Q and R wave amplitude values, as well as QT and T intervals, presented with the largest differences between states compared to other ECG signal parameters. Furthermore, the random forest classifier achieved more than 94.5% accuracy. This work demonstrates the feasibility of ECG signal feature extraction for automatic mental fatigue detection.

]]>Mathematics doi: 10.3390/math10183391

Authors: Jianxu Zhu Dingxuan Zhao Shuang Liu Zilong Zhang Guangyu Liu Jinming Chang

Due to the lack of body stability of emergency rescue vehicles, their attitude stability is insufficient and they are unable to realize working while driving, resulting in low rescue efficiency. Aiming at the water tower fire truck, which is equipped with an active suspension system, the vehicle attitude stability is studied. First, combined with the active suspension system and spray system, a 13-DOF integrated dynamic model for the water tower fire truck is established. Using the model-assisted active disturbance rejection control method, the controllers are designed for the vertical displacement, pitch angle, and roll angle of the vehicle attitude. Then, the computer simulation is carried out to verify the effectiveness of this control method. Finally, the water spray obstacle crossing experiment is carried out with a JP32G water tower fire truck. The results show that when the vehicle runs over the triangular obstacle on one side and two sides in the integrated spray-active suspension mode, the peak&ndash;peak values of body pitch angle and roll angle are reduced by 10.9% and 23.2%, and 23.7% and 16.3%, respectively, compared with the passive hydro pneumatic suspension.

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