Concept Of Symmetry Research Articles

Simulation Tool to Evaluate Fault Diagnosis Techniques for DC-DC Converters

This paper presents a tool that evaluates the applicability of a fault diagnostics technique (FDT) for Switch-Mode Non-Isolated DC-DC converters (SMNI DC-DC). The proposed tool simulates the operation of a Type II controller for a buck converter under different operating conditions and, simultaneously, implements the FDT. In this way, it is possible to evaluate the applicability of the FDT in real-time. The proposed tool is implemented in Python, and requires the partitioning of the code into different modules. The first module is responsible for modeling the different states of the converter. The second module contributes to the production of the PWM signal. The concepts of symmetry and asymmetry are implicit to the signal processing methods used in the design of the FDTs that compose the third module. Finally, all of the modules are properly included in the main function, which represents the fourth module. The validation of the proposed tool is carried out by comparing the results with the LTspice simulator and with an experimental prototype built for this purpose. Different operating conditions are considered. In addition to the proposed tool, an algorithm for the design of both the power section and the converter compensator is presented.

Introduction to the special issue on symmetry in the life sciences - symmetry and asymmetry across biological scales.

In this special issue of Emerging Topics in Life Sciences, we present a series of mini-reviews of some of the most exciting research involving the concept of symmetry. This research spans the biological sciences from proteins to ecosystems. The reviews examine protein and floral symmetry, primate brain and behavioral asymmetries, geometric morphometrics, and various fluctuating asymmetries.

Soft Sensing and Multi-objective Optimization of Pulp Washing Process Based on Data-Driven Technology

Due to the multi-variable, time-delay, and nonlinear characteristics of the pulp washing process, it is difficult to accurately measure and optimize the process. An asymmetrical nonlinear control system can be decomposed into a group of low-dimensional subsystems, which brings great convenience to the analysis and design of the system. In this paper, the concept of symmetry is used to simplify nonlinear optimal control problems, and data-driven theory is used to solve optimal policy problems. This paper proposes a data-driven operating model optimization method to model and optimizes the pulp washing process. The most important quality indicators of pulp washing performance are the alkali residue in washing pulp and the Baumé degree of the extracted black liquor. Considering the difficulty of modeling, online measurement of these indicators, two-step neural network, and multiple logistic regression were used to build a prediction model for soda water and Baumé degree. The mathematical model of the washing process can be identified, and the indicators meet the production requirements. With the goal of better product quality, low cost, and low energy consumption, based on the optimized operation mode database, the ant colony optimization (ACO) algorithm was used to solve the multi-objective problem. Theoretical analysis and practical application were carried out, and the optimal control system of the pulp washing process was designed. The actual results showed that the pulp output was increased by 20%, and the water consumption was reduced by nearly 30%. This method is effective during pulp washing.

Floral symmetry: the geometry of plant reproduction.

The flower is an astonishing innovation that arose during plant evolution allowing flowering plants — also known as angiosperms — to dominate life on earth in a relatively short period of geological time. Flowers are formed from secondary meristems by co-ordinated differentiation of flower organs, such as sepals, petals, stamens, and carpels. The position, number and morphology of these flower organs impose a geometrical pattern — or symmetry type — within the flower which is a trait tightly connected to successful reproduction.During evolution, flower symmetry switched from the ancestral poly-symmetric (radial symmetry) to the mono-symmetric (bilateral symmetry) type multiple times, including numerous reversals, with these events linked to co-evolution with pollinators and reproductive strategies.In this review, we introduce the diversity of flower symmetry, trace its evolution in angiosperms, and highlight the conserved genetic basis underpinning symmetry control in flowers. Finally, we discuss the importance of building upon the concept of flower symmetry by looking at the mechanisms orchestrating symmetry within individual flower organs and summarise the current scenario on symmetry patterning of the female reproductive organ, the gynoecium, the ultimate flower structure presiding over fertilisation and seed production.

Some Fuzzy Inequalities for Harmonically s-Convex Fuzzy Number Valued Functions in the Second Sense Integral

Many fields of mathematics rely on convexity and nonconvexity, especially when studying optimization issues, where it stands out for a variety of practical aspects. Owing to the behavior of its definition, the idea of convexity also contributes significantly to the discussion of inequalities. The concepts of symmetry and convexity are related and we can apply this because of the close link that has grown between the two in recent years. In this study, harmonic convexity, also known as harmonic s-convexity for fuzzy number valued functions (F-NV-Fs), is defined in a more thorough manner. In this paper, we extend harmonically convex F-NV-Fs and demonstrate Hermite–Hadamard (H.H) and Hermite–Hadamard Fejér (H.H. Fejér) inequalities. The findings presented here are summaries of a variety of previously published studies.

A Differential Operator Associated with q-Raina Function

The topics studied in the geometric function theory of one variable functions are connected with the concept of Symmetry because for some special cases the analytic functions map the open unit disk onto a symmetric domain. Thus, if all the coefficients of the Taylor expansion at the origin are real numbers, then the image of the open unit disk is a symmetric domain with respect to the real axis. In this paper, we formulate the q-differential operator associated with the q-Raina function using quantum calculus, that is the so-called Jacksons’ calculus. We establish a new subclass of analytic functions in the unit disk by using this newly developed operator. The theory of differential subordination inspired our approach; therefore, we geometrically explore the most popular properties of this new operator: subordination properties, coefficient bounds, and the Fekete-Szegő problem. As special cases, we highlight certain well-known corollaries of our primary findings.

Abstraction of Interpolative Reich-Rus-Ćirić-Type Contractions and Simplest Proof Technique

The concept of symmetry is a very vast topic that is involved in the studies of several phenomena. This concept enables us to discuss the phenomenon in some systematic pattern depending upon the type of phenomenon. Each phenomenon has its own type of symmetry. The phenomenon that is used in the discussion of this article is a symmetric distance-measuring function. This article presents the notions of abstract interpolative Reich-Rus-Ćirić-type contractions with a shrink map and examines the existence of ϕ-fixed points for such maps in complete metric space. These notions are defined through special types of simulation functions. The proof technique of the results presented in this article is easy to understand compared with the existing literature on interpolative Reich-Rus-Ćirić-type contractions.

Improve Stock Price Model-Based Stochastic Pantograph Differential Equation

Although the concept of symmetry is widely used in many fields, it is almost not discussed in finance. This concept appears to be relevant in relation, for example, to mathematical models that can predict stock prices to contribute to the decision-making process. This work considers the stock price of European options with a new class of the non-constant delay model. The stochastic pantograph differential equation (SPDE) with a variable delay is provided in order to overcome the weaknesses of using stochastic models with constant delay. The proposed model is constructed to improve the evaluation process and prediction accuracy for stock prices. The feasibility of the proposed model is introduced under relatively weak conditions imposed on its volatility function. Furthermore, the sensitivity of time lag is discussed. The robust stochastic theta Milstein (STM) method is combined with the Monte Carlo simulation to compute asset prices within the proposed model. In addition, we prove that the numerical solution can preserve the non-negativity of the solution of the model. Numerical experiments using real financial data indicate that there is an increasing possibility of prediction accuracy for the proposed model with a variable delay compared to non-linear models with constant delay and the classical Black and Scholes model.

Symmetry and financial markets

It is hard to overstate the importance that the concept of symmetry has had in every field of physics, a fact alluded to by the Nobel Prize winner P. W. Anderson, who once wrote that “physics is the study of symmetry”. Whereas the idea of symmetry is widely used in science in general, very few (if not almost no) applications have found its way into the field of finance. Still, the phenomenon appears relevant in terms of for example the symmetry of strategies that can happen in the decision making to buy or sell financial shares. Game theory is therefore one obvious avenue where to look for symmetry but, as will be argued, also technical analysis and long-term economic growth could be phenomena which show the hallmark of a symmetry.

Higher-dimensional charged black holes in f(R)-gravity's rainbow surrounded by cloud of strings: Exact solution, shadow, and effective potential barrier

We construct a new class of spherically symmetric black hole solutions in f(R)-gravity's rainbow1 framework which is surrounded by string cloud configuration. In the thermodynamic analysis, we show that the black hole's temperature grows up for such small values of b parameter and also big values of it, graphically. In addition to the mass and charge of the black holes, we show that the presence of the rainbow function gr(ε) and cloud's strength b can also affect the size of the shadow. For the black hole solution, it is shown that the shadow size increases with the parameter b, but decreases with the parameter gr(ε). In addition, we have shown that the energy emission rate decreases with increase in gr(ε) and decrease in b parameter. We have analyzed the concept of effective potential barrier by transforming the radial equation of motion into standard Schrodinger form. The most important result derived from this study is that the height of this potential increases with decrease in gr(ε) parameter and increases in the coupling constant λ and the b parameter. We also investigate the impact of these parameters on the other thermodynamical quantities like temperature and heat capacity. There is a minimum on that between for one special value of b. From the temperature diagram versus the b parameter (T−b) and analogy with T−r+ diagrams, we can say that the b parameter behaves as the event horizon radius r+. Regarding the cosmological constant as a thermodynamic pressure and its conjugate quantity as a thermodynamic volume, we investigate the critical behavior of our black hole solutions. In d=5, the P-V diagrams are more complex than that of the standard Van der Waals. These diagrams behave like the Born–Infeld-AdS P-V diagrams. When d=6, the P-V diagrams are also more complex than that of the standard Van der Waals and we decompose them into two parts. One part behaves like the standard Van der Waals system. The other part is similar to the Schwarzschild-AdS black hole P-V diagrams where the isotherms turn and enter a region with negative pressures. The null geodesic equations are computed in d=5 spacetime dimensions by using the concept of symmetries and Hamilton-Jacobi equation and Carter separable method. With the null geodesics in hand, we then evaluate the celestial coordinates (x, y) and the radius Rs of the black hole shadow and represent it graphically.

Numerical Evidence for a Haagerup Conformal Field Theory.

We numerically study an anyon chain based on the Haagerup fusion category and find evidence that it leads in the long-distance limit to a conformal field theory whose central charge is ∼2. Fusion categories generalize the concept of finite group symmetries to noninvertible symmetry operations, and the Haagerup fusion category is the simplest one which comes from neither finite groups nor affine Lie algebras. As such, ours is the first example of conformal field theories which have truly exotic generalized symmetries. Basically the same result was independently obtained in the preceding Letter [Phys. Rev. Lett. 128, 231602 (2022)PRLTAO0031-900710.1103/PhysRevLett.128.231602].

Early Detection of Abnormal Attacks in Software-Defined Networking Using Machine Learning Approaches

Recent developments have made software-defined networking (SDN) a popular technology for solving the inherent problems of conventional distributed networks. The key benefit of SDN is the decoupling between the control plane and the data plane, which makes the network more flexible and easier to manage. SDN is a new generation network architecture; however, its configuration settings are centralized, making it vulnerable to hackers. Our study investigated the feasibility of applying artificial intelligence technology to detect abnormal attacks in an SDN environment based on the current unit network architecture; therefore, the concept of symmetry includes the sustainability of SDN applications and robust performance of machine learning (ML) models in the case of various malicious attacks. In this study, we focus on the early detection of abnormal attacks in an SDN environment. On detection of malicious traffic in SDN topology, the AI module in the topology is applied to detect and act against the attack source through machine learning algorithms, making the network architecture more flexible. Under multiple abnormal attacks, we propose a hierarchical multi-class (HMC) architecture to effectively address the imbalanced dataset problem and improve the performance of minority classes. The experimental results show that the decision tree, random forest, bagging, AdaBoost, and deep learning models exhibit the best performance for distributed denial-of-service (DDoS) attacks. In addition, for the imbalanced dataset problem of multiclass classification, our proposed HMC architecture performs better than previous single classifiers. We also simulated the SDN topology and scenario verification. In summary, we concatenated the AI module to enhance the security and effectiveness of SDN networks in a practical manner.

Does Post-truth Expand or Restrict Political Choice? Politics, Planning, and Expertise in a Post-truth Environment

Abstract Steve Fuller has replied to my critique of his endorsement of a post-truth epistemology. I trace the divergence in our approach to social epistemology by examining our distinct responses to the principle of symmetry in the sociology of scientific knowledge. Fuller has extended the concept of symmetry and challenged the field to embrace a post-truth condition that flattens the difference between experts and the public. By contrast, I have criticized the concept of symmetry for policing the field to rule ideology critique out of court. I argue that a focus on post-truth populism obscures the role of counter-elites and ideologies that restrict political choice. A better way to promote democracy would be to support minority positions within science that promise to open up suppressed political possibilities and to seek the coordinated use of different disciplines to address significant public problems.

The Cooperative Control of Subgrade Stiffness on Symmetrical Bridge–Subgrade Transition Section

In the field of civil engineering and architecture, the concept of symmetry has been widely accepted. The bridge can be treated as a typical symmetrical structure of civil engineering buildings. Among them, the Subgrade can be identified as an important part to bear the vehicle loads. Severe pavement problems and bridge service capabilities will be caused by problems of the bridge–subgrade transition section. Therefore, setting the rigid–flexible transition is an important method to solve this problem. The bridge–subgrade transition section has been set at both ends of the bridge, which can be regarded as a typical symmetrical structure. Based on nonlinear finite element numerical simulation and synergistic theory, the cooperative control problems of the bridge–subgrade transition section were studied in this work. The change rule of the stiffness of the transition section was discussed and the influence of stiffness variation of the bridge–subgrade transition section on the stress state of the structure was also analyzed. Furthermore, the influence of subgrade stiffness change on the stress and strain field was analyzed. A permanent strain prediction model was established and stiffness or subsidence difference coordination control was also discussed.

Statistically invariant eddy viscosity models

We presently generalize existing two-equation Reynolds-averaged Navier–Stokes models by using recent advances in our understanding of the Lie symmetries of governing turbulence. The motivation for this and the necessary steps are laid out using the conventional terminology of turbulence modeling, without requiring deep knowledge about the mathematical concept of symmetries. For illustration purposes, these steps are applied to the standard k–ε model and the k–ω model. The so-modified k–ε model is applied to a wide range of canonical flows. For all of them, it is shown to match or even improve the performance of its classical counterpart and is, thus, shown to be more general than the original k–ε model.

Damage Effectiveness Calculation of Hitting Targets with Ammunition Based on Bayesian Multinomial Distribution

Owning to the fact that ammunition can cause varying degrees of damage to its target, this article presents a damage effectiveness calculation method of hitting targets with ammunition based on Bayesian multinomial distribution to solve the problems of complex processes, few trial times and difficult calculations of damage probability in target-hitting tests with high-tech ammunition, according to a calculation index of damage effectiveness about the occurrence probability of different damage. Based on the concept of symmetry, the idea of “divide damage level—determine distribution—integrate information—solve distribution” is adopted. Firstly, this paper describes the damage effectiveness test of ammunition attacking targets with multiple distributions; secondly, this paper integrates the damage effectiveness information of ammunition strike targets with Dempster–Shafer evidence theory (D–S evidence theory) and symmetry advantage; finally, this paper attempts to solve the symmetric posterior distribution of damage effectiveness parameters with Bayesian theory and the Markov chain Monte Carlo (MCMC) method. The result demonstrates that this method is very significant in improving the calculation accuracy of ammunition damage effectiveness, which could describe the damage effectiveness of ammunition in detail by integrating the prior information with multiple types of damage effectiveness.

Some New Quantum Hermite-Hadamard Type Inequalities for s-Convex Functions

In this investigation, we first establish new quantum Hermite–Hadamard type integral inequalities for s-convex functions by utilizing newly defined Tq-integrals. Then, by using obtained inequality, we establish a new Hermite–Hadamard inequality for coordinated s1,s2-convex functions. The results obtained in this paper provide significant extensions of other related results given in the literature. Finally, some examples are given to illustrate the result obtained in this paper. These types of analytical inequalities, as well as solutions, apply to different areas where the concept of symmetry is important.

Perceptual Hash of Neural Networks

In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network structure tailored for a specific task, such as the classic encoder-decoder structure. Massive DNN models are diverse in category, quantity and open source frameworks for implementation. Therefore, the retrieval of DNN models has become a problem worthy of attention. To this end, we propose a new idea of generating perceptual hashes of DNN models, named HNN-Net (Hash Neural Network), to index similar DNN models by similar hash codes. The proposed HNN-Net is based on neural graph networks consisting of two stages: the graph generator and the graph hashing. In the graph generator stage, the target DNN model is first converted and optimized into a graph. Then, it is assigned with additional information extracted from the execution of the original model. In the graph hashing stage, it learns to construct a compact binary hash code. The constructed hash function can well preserve the features of both the topology structure and the semantics information of a neural network model. Experimental results demonstrate that the proposed scheme is effective to represent a neural network with a short hash code, and it is generalizable and efficient on different models.

Design and Optimization of Combined Cooling, Heating, and Power Microgrid with Energy Storage Station Service

This study aims to symmetrically improve the economy and environmental protection of combined cooling, heating and power microgrid. Hence, the characteristics of configuration ways of energy storage devices in traditional combined cooling, heating and power systems are analyzed, and a scheme for the operator to establish an energy storage station is designed. An improved aquila optimizer for the optimal configuration of the system is proposed to symmetrically enhance the economic and environmental protection performance. The feasibility of the proposed scheme is verified through experiments in three different places. The results show that the economic cost and exhaust emission of the system with energy storage station are reduced to varying degrees compared with the system with energy storage equipment alone and the system without energy storage equipment based on symmetry concept. Especially in Place 1, the scheme with energy storage station in the system can reduce the electric energy purchased from power grid by 43.29% and 61.09%, respectively, compared with other schemes. This study is conducive to promoting the development of clean energy, alleviating the energy crisis, reducing the power supply pressure of power grid, and improving the profits of operators by symmetrically considering the economic and environmental performance of the system.

New Generalized Class of Convex Functions and Some Related Integral Inequalities

There is a strong correlation between convexity and symmetry concepts. In this study, we investigated the new generic class of functions called the (n,m)–generalized convex and studied its basic algebraic properties. The Hermite–Hadamard inequality for the (n,m)–generalized convex function, for the products of two functions and of this type, were proven. Moreover, this class of functions was applied to several known identities; midpoint-type inequalities of Ostrowski and Simpson were derived. Our results are extensions of many previous contributions related to integral inequalities via different convexities.