#### Symmetry

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EISSN : 2073-8994
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#### Latest articles in this journal

Published: 29 September 2022
by MDPI
Journal: Symmetry
Abstract:
In our present study, two subclasses of starlike functions which are symmetric about the origin are considered. These two classes are defined with the use of the sigmoid function and the trigonometric function, respectively. We estimate the first four initial logarithmic coefficients, the Zalcman functional, the Fekete–Szegö functional, and the bounds of second-order Hankel determinants with logarithmic coefficients for the first class ${\mathcal{S}}_{\mathrm{seg}}^{*}$ and improve the obtained estimate of the existing second-order Hankel determinant of logarithmic coefficients for the second class ${\mathcal{S}}_{sin}^{*}$. All the bounds that we obtain in this article are proven to be sharp.
Published: 29 September 2022
by MDPI
Journal: Symmetry
Abstract:
A therapy’s outcome is determined by a tumor’s response to treatment which, in turn, depends on multiple factors such as the severity of the disease and the strength of the patient’s immune response. Gold standard cancer therapies are in most cases fragile when sought to break the ties to either tumor kill ratio or patient toxicity. Lately, research has shown that cancer therapy can be at its most robust when handling adaptive drug resistance and immune escape patterns developed by evolving tumors. This is due to the stochastic and volatile nature of the interactions, at the tumor environment level, tissue vasculature, and immune landscape, induced by drugs. Herein, we explore the path toward antifragile therapy control, that generates treatment schemes that are not fragile but go beyond robustness. More precisely, we describe the first instantiation of a control-theoretic method to make therapy schemes cope with the systemic variability in the tumor-immune-drug interactions and gain more tumor kills with less patient toxicity. Considering the anti-symmetric interactions within a model of the tumor-immune-drug network, we introduce the antifragile control framework that demonstrates promising results in simulation. We evaluate our control strategy against state-of-the-art therapy schemes in various experiments and discuss the insights we gained on the potential that antifragile control could have in treatment design in clinical settings.
Published: 29 September 2022
by MDPI
Journal: Symmetry
Abstract:
This paper is concerned with the oscillation and asymptotic behavior of certain third-order nonlinear delay differential equations with distributed deviating arguments. By establishing sufficient conditions for the nonexistence of Kneser solutions and existing oscillation results for the studied equation, we obtain new criteria which ensure that every solution oscillates by using the theory of comparison with first-order delay equations and the technique of Riccati transformation. Some examples are presented to illustrate the importance of main results.
Published: 29 September 2022
by MDPI
Journal: Symmetry
Abstract:
In recent years, the use of unmanned aerial vehicles (UAVs) has increased significantly. Asymmetrical factors, or frictional studies on the disc brake of UAVs, are one of the safety considerations taken into consideration during the design process because UAVs and their components have been built with the best safety in mind. This study focuses on choosing the optimal material for a UAV’s disc brake by using transient structural and thermal models. In order to compare the asymmetry-based frictional force produced by the two ways; the processes used in the transient simulation are validated using pin-on-disc (POD) testing. The foundation for this validation investigation is a metal matrix composite made of an aluminum alloy, and the basis tool is an ASTM G99-based computational test specimen. Steel-EN24 and carbon ceramic matrix composites testing are expanded using the same POD tests. A range of 3 percent to 8 percent error rates is found. As a result, the calculation techniques are applied to the UAV’s disc brake after they have proven to be trustworthy. This fixed-wing UAV’s extensions have a 5 kg payload capacity. The weight, avionics components, tire dimensions, and disc brake dimensions of the other UAV design parts are calculated using analytical formulas. The final designs are made using CATIA as a result. The grid convergence experiment is organized using a traditional finite element analysis tool. Finally, at its maximum rotational speed, a UAV’s disc brake is put through asymmetrical friction testing based on structural and thermal consequences. The correct materials for critical applications, such as carbon fiber-woven-wet-based reinforced polymer and Kevlar unidirectional-49-based reinforced polymer composites for changing rotating speeds, have now been made possible by fixed-wing UAVs.
Published: 29 September 2022
by MDPI
Journal: Symmetry
Abstract:
The quality of operation of neural networks in solving application problems is determined by the success of the stage of their training. The task of learning neural networks is a complex optimization task. Traditional learning algorithms have a number of disadvantages, such as «sticking» in local minimums and a low convergence rate. Modern approaches are based on solving the problems of adjusting the weights of neural networks using metaheuristic algorithms. Therefore, the problem of selecting the optimal set of values of algorithm parameters is important for solving application problems with symmetry properties. This paper studies the application of a new metaheuristic optimization algorithm for weights adjustment—the algorithm of the spiders-cycle, developed by the authors of this article. The approbation of the proposed approach is carried out to adjust the weights of recurrent neural networks used to solve the time series forecasting problem on the example of three different datasets. The results are compared with the results of neural networks trained by the algorithm of the reverse propagation of the error, as well as three other metaheuristic algorithms: particle swarm optimization, bats, and differential evolution. As performance criteria for the comparison of algorithms of global optimization, in this work, descriptive statistics for metrics of the estimation of quality of predictive models, as well as the number of calculations of the target function, are used. The values of the MSE and MAE metrics on the studied datasets were obtained by adjusting the weights of the neural networks using the cycling spider algorithm at 1.32, 25.48, 8.34 and 0.38, 2.18, 1.36, respectively. Compared to the inverse error propagation algorithm, the cycling spider algorithm reduced the value of the error metrics. According to the results of the study, it is concluded that the developed algorithm showed high results and, in the assessment of performance, was not inferior to the existing algorithm.
Published: 29 September 2022
by MDPI
Journal: Symmetry
Abstract:
The optimization of collaborative service scheduling is the main bottleneck restricting the efficiency and cost of collaborative service execution. It is helpful to reduce the cost and improve the efficiency to deal with the scheduling problem correctly and effectively. The traditional genetic algorithm can solve the multi-objective problem more comprehensively than the optimization algorithm, such as stochastic greedy algorithm. But in the actual situation, the traditional algorithm is still one-sided. The intelligent genetic scheme (IGS) proposed in this paper enhances the expansibility and diversity of the algorithm on the basis of traditional genetic algorithm. In the process of initial population selection, the initial population generation strategy is changed, a part of the population is randomly generated and the selection process is iteratively optimized, which is a selection method based on population asymmetric exchange to realize selection. Mutation factors enhance the diversity of the population in the adaptive selection based on individual innate quality. The proposed IGS can not only maintain individual diversity, increase the probability of excellent individuals, accelerate the convergence rate, but also will not lead to the ultimate result of the local optimal solution. It has certain advantages in solving the optimization problem, and provides a new idea and method for solving the collaborative service optimization scheduling problem, which can save manpower and significantly reduce costs on the premise of ensuring the quality. The experimental results show that Intelligent Genetic algorithm (IGS) not only has better scalability and diversity, but also can increase the probability of excellent individuals and accelerate the convergence speed.
Published: 28 September 2022
by MDPI
Journal: Symmetry
Abstract:
With the advent of the big data era, it is vital to explore the information involved in this type of data. With the continuous development of higher education, the K-means clustering algorithm is widely used to analyze students’ academic data. However, a significant drawback of this method is that it is seriously affected by initial centroids of clustering and easily falls into local optima. Motivated by the fact that the chaos and swarm intelligence algorithm are frequently combined, we propose an approach for data clustering by Memristive Chaotic Sparrow Search Algorithm (MCSSA) in this paper. First, we introduce a memristive chaotic system, which has a property of conditional symmetry. We use the sequences generated by the memristive chaotic system to initialize the location of the sparrows. Then, MCSSA is applied before K-means for finding the optimal locations in the search space. Those locations are used as initial cluster centroids for the K-means algorithm to find final data clusters. Finally, the improved clustering algorithm is applied to the analysis of college students’ academic data, demonstrating the value and viability of the approach suggested in this paper. Through empirical research, it is also confirmed that this method can be promoted and applied.
Published: 28 September 2022
by MDPI
Journal: Symmetry
Abstract:
In this paper, the geodesic motion of neutral and test particles around the time conformal (TC) Dilaton black hole (BH) is investigated using the ${e}^{ϵg\left(t\right)}$ as the time conformal factor in which g(t) is an arbitrary function of time and $ϵ$ is a perturbation parameter. The function $g\left(t\right)$ leads to $\left(\frac{t}{a}\right)$ by utilizing the well-known approximate Noether symmetry (ANS). Furthermore, we discuss the effect of magnetic fields and find the location of stable and unstable orbits w. r. t time, graphically. After that, in the presence and absence of a magnetic field, we interrogate the crucial physical parameters such as effective potential $\left({U}_{eff}\right)$, effective force $\left({F}_{eff}\right)$ and escape velocity $\left({\nu }_{\perp }\right)$. We find the unstable and stable regions of particles for different values of angular momentum $\left({L}_{z}\right)$ and magnetic field $\left(B\right)$ near the TC Dilaton BH. Moreover, the effects of the Dilaton parameter ($\mu$) on neutral and charged particles are also discussed, which provide some new features. The important results in this study could estimate the powerful relativistic jets originating from the BH.
Published: 28 September 2022
by MDPI
Journal: Symmetry
Abstract:
The weighted exponential distribution is a promising skewed distribution in the life-testing experiment. The joint progressive type-II censoring (JPC) scheme is an effective approach to reducing costs. In this paper, we consider the estimates of parameters of the weighted exponential distribution with the JPC data. Two populations, whose scale parameters are the same but the shape parameters of which are different, are chosen to be studied. We first evaluate the parameters with the maximum likelihood method. Because the maximum likelihood estimates of parameters cannot be obtained in closed form, we apply the Newton–Raphson method in this part. Bayesian estimates and the corresponding credible intervals under the squared error loss function are computed by using the Markov Chain Monte Carlo method. After that, we use the bootstrap method to calculate the associated confidence intervals of the unknown parameters. A simulation has been performed to test the feasibility of the above methods and real data analysis is also provided for illustrative purposes.
Published: 28 September 2022
by MDPI
Journal: Symmetry
Abstract:
We establish an elaborate numerical model with which to investigate the deformation characteristics of segmental lining. The numerical model contains reinforcement and connecting bolts that previous numerical studies have generally neglected. We validated the model parameters using a full-scale model test result. Based on this numerical model, we studied the deformation characteristics of segmental lining. Convergence, joint deformation, bolt stress, and reinforcement stress were systematically analyzed under different loading conditions. Furthermore, we discuss the relationships between convergence and joint opening, bolt stress and joint opening. The deformation characteristics of segmental lining are revealed. When the lining is deformed by earth pressure, plastic hinges form at the joints. The segment rotates around the plastic hinge, which is the main reason for segmental lining deformation under earth pressure. Horizontal convergence is a single index to reflect the deformation of tunnel rings, representing the overall deformation of the ring to a certain extent but not the deformation characteristics of the joint. When the loading conditions differ, the relationship between joint opening and horizontal convergence is consistent for some joints and inconsistent for others.