Journal Description
Symmetry
Symmetry
is an international, peer-reviewed, open access journal covering research on symmetry/asymmetry phenomena wherever they occur in all aspects of natural sciences. Symmetry is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Astrophysics Data System, and other databases.
- Journal Rank: JCR - Q2 (Multidisciplinary Sciences) / CiteScore - Q1 (General Mathematics); Q1 (Physics and Astronomy); Q1 (Computer Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.2 days after submission; acceptance to publication is undertaken in 3.5 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Symmetry.
Impact Factor:
2.7 (2022);
5-Year Impact Factor:
2.7 (2022)
Latest Articles
Characterizations of Minimal Dominating Sets in γ-Endowed and Symmetric γ-Endowed Graphs with Applications to Structure-Property Modeling
Symmetry 2024, 16(6), 663; https://doi.org/10.3390/sym16060663 (registering DOI) - 27 May 2024
Abstract
Claude Berge (1987) introduced the concept of k-extendable graphs, wherein any independent set of size k is inherently a constituent of a maximum independent set within a graph . Graphs possessing the property of being
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Claude Berge (1987) introduced the concept of k-extendable graphs, wherein any independent set of size k is inherently a constituent of a maximum independent set within a graph . Graphs possessing the property of being 1-extendable are termedas Berge graphs. This introduction gave rise to the notion of well-covered graphs and well-dominated graphs. A graph is categorized as well-covered if each of its maximal independent sets is, in fact, a maximum independent set. Similarly, a graph attains the classification of well-dominated if every minimal dominating set (DS) within it is a minimum dominating set. In alignment with the concept of k-extendable graphs, the framework of -endowed graphs and symmetric -endowed graphs are established. In these graphs, each DS of size k encompasses a minimum DS of the graph. In this article, a study of -endowed dominating sets is initiated. Various results providing a deep insight into -endowed dominating sets in graphs such as those characterizing the ones possessing a unique minimum DS are proven. We also introduce and study the symmetric -endowed graphs and minimality of dominating sets in them. In addition, we give a solution to an open problem in the literature. which seeks to find a domination-based parameter that has a correlation coefficient of with the total -electronic energy of lower benzenoid hydrocarbons. We show that the upper dominating number studied in this paper delivers a strong prediction potential.
Full article
(This article belongs to the Special Issue Symmetry and Graph Theory)
Open AccessArticle
Sufficient Conditions for Linear Operators Related to Confluent Hypergeometric Function and Generalized Bessel Function of the First Kind to Belong to a Certain Class of Analytic Functions
by
Saiful R. Mondal, Manas Kumar Giri and Raghavendar Kondooru
Symmetry 2024, 16(6), 662; https://doi.org/10.3390/sym16060662 (registering DOI) - 27 May 2024
Abstract
Geometric function theory has extensively explored the geometric characteristics of analytic functions within symmetric domains. This study analyzes the geometric properties of a specific class of analytic functions employing confluent hypergeometric functions and generalized Bessel functions of the first kind. Specific constraints are
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Geometric function theory has extensively explored the geometric characteristics of analytic functions within symmetric domains. This study analyzes the geometric properties of a specific class of analytic functions employing confluent hypergeometric functions and generalized Bessel functions of the first kind. Specific constraints are imposed on the parameters to ensure the inclusion of the confluent hypergeometric function within the analytic function class. The coefficient bound of the class is used to determine the inclusion properties of integral operators involving generalized Bessel functions of the first kind. Different results are observed for these operators, depending on the specific values of the parameters. The results presented here include some previously published findings as special cases.
Full article
(This article belongs to the Special Issue Symmetries of Difference Equations, Special Functions and Orthogonal Polynomials II)
Open AccessArticle
Particle Swarm Optimization Algorithm Using Velocity Pausing and Adaptive Strategy
by
Kezong Tang and Chengjian Meng
Symmetry 2024, 16(6), 661; https://doi.org/10.3390/sym16060661 (registering DOI) - 27 May 2024
Abstract
Particle swarm optimization (PSO) as a swarm intelligence-based optimization algorithm has been widely applied to solve various real-world optimization problems. However, traditional PSO algorithms encounter issues such as premature convergence and an imbalance between global exploration and local exploitation capabilities when dealing with
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Particle swarm optimization (PSO) as a swarm intelligence-based optimization algorithm has been widely applied to solve various real-world optimization problems. However, traditional PSO algorithms encounter issues such as premature convergence and an imbalance between global exploration and local exploitation capabilities when dealing with complex optimization tasks. To address these shortcomings, an enhanced PSO algorithm incorporating velocity pausing and adaptive strategies is proposed. By leveraging the search characteristics of velocity pausing and the terminal replacement mechanism, the problem of premature convergence inherent in standard PSO algorithms is mitigated. The algorithm further refines and controls the search space of the particle swarm through time-varying inertia coefficients, symmetric cooperative swarms concepts, and adaptive strategies, balancing global search and local exploitation. The performance of VASPSO was validated on 29 standard functions from Cec2017, comparing it against five PSO variants and seven swarm intelligence algorithms. Experimental results demonstrate that VASPSO exhibits considerable competitiveness when compared with 12 algorithms. The relevant code can be found on our project homepage.
Full article
(This article belongs to the Special Issue Symmetry in Computing Algorithms and Applications)
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Open AccessArticle
Enhancing Integer Time Series Model Estimations through Neural Network-Based Fuzzy Time Series Analysis
by
Mohammed H. El-Menshawy, Mohamed S. Eliwa, Laila A. Al-Essa, Mahmoud El-Morshedy and Rashad M. EL-Sagheer
Symmetry 2024, 16(6), 660; https://doi.org/10.3390/sym16060660 (registering DOI) - 27 May 2024
Abstract
This investigation explores the effects of applying fuzzy time series (FTSs) based on neural network models for estimating a variety of spectral functions in integer time series models. The focus is particularly on the skew integer autoregressive of order one (NSINAR(1)) model. To
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This investigation explores the effects of applying fuzzy time series (FTSs) based on neural network models for estimating a variety of spectral functions in integer time series models. The focus is particularly on the skew integer autoregressive of order one (NSINAR(1)) model. To support this estimation, a dataset consisting of NSINAR(1) realizations with a sample size of n = 1000 is created. These input values are then subjected to fuzzification via fuzzy logic. The prowess of artificial neural networks in pinpointing fuzzy relationships is harnessed to improve prediction accuracy by generating output values. The study meticulously analyzes the enhancement in smoothing of spectral function estimators for NSINAR(1) by utilizing both input and output values. The effectiveness of the output value estimates is evaluated by comparing them to input value estimates using a mean-squared error (MSE) analysis, which shows how much better the output value estimates perform.
Full article
(This article belongs to the Topic Fuzzy Number, Fuzzy Difference, Fuzzy Differential: Theory and Applications)
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Open AccessArticle
The Additive Xgamma-Burr XII Distribution: Properties, Estimation and Applications
by
Hebatalla H. Mohammad, Faten S. Alamri, Heba N. Salem and Abeer A. EL-Helbawy
Symmetry 2024, 16(6), 659; https://doi.org/10.3390/sym16060659 - 27 May 2024
Abstract
This paper introduces a new four-parameter additive model, named xgamma-Burr XII distribution, by considering two competing risks: the former has the xgamma distribution and the latter has the Burr XII distribution. A graphical description of the xgamma-Burr XII distribution is presented, including plots
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This paper introduces a new four-parameter additive model, named xgamma-Burr XII distribution, by considering two competing risks: the former has the xgamma distribution and the latter has the Burr XII distribution. A graphical description of the xgamma-Burr XII distribution is presented, including plots of the probability density function, hazard rate and reversed hazard rate functions. The xgamma-Burr XII density has different shapes such as decreasing, unimodal, approximately symmetric and decreasing-unimodal. The main statistical properties of the proposed model are studied. The unknown model parameters, reliability, hazard rate and reversed hazard rate functions are estimated via the maximum likelihood method. The asymptotic confidence intervals of the parameters, reliability function, hazard rate function and reversed hazard rate function are also obtained. A simulation study is carried out to evaluate the performance of the maximum likelihood estimates. In addition, three real data are applied to show the superiority of the xgamma-Burr XII distribution over some known distributions in real-life applications.
Full article
(This article belongs to the Section Mathematics)
Open AccessEditorial
The Nuclear Physics of Neutron Stars
by
Charalampos Moustakidis
Symmetry 2024, 16(6), 658; https://doi.org/10.3390/sym16060658 - 27 May 2024
Abstract
Neutron stars are considered extraordinary astronomical laboratories for the physics of nuclear matter as they have the most fascinating constitution of energy and matter in the Universe [...]
Full article
(This article belongs to the Special Issue The Nuclear Physics of Neutron Stars)
Open AccessReview
The Scale-Invariant Vacuum Paradigm: Main Results and Current Progress Review (Part II)
by
Vesselin G. Gueorguiev and Andre Maeder
Symmetry 2024, 16(6), 657; https://doi.org/10.3390/sym16060657 - 26 May 2024
Abstract
This is a summary of the main results within the Scale-Invariant Vacuum (SIV) paradigm based on Weyl integrable geometry. We also review the mathematical framework and utilize alternative derivations of the key equations based on the reparametrization invariance as well. The main results
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This is a summary of the main results within the Scale-Invariant Vacuum (SIV) paradigm based on Weyl integrable geometry. We also review the mathematical framework and utilize alternative derivations of the key equations based on the reparametrization invariance as well. The main results discussed are related to the early universe; that is, applications to inflation, Big Bang Nucleosynthesis, and the growth of the density fluctuations within the SIV. Some of the key SIV results for the early universe are a natural exit from inflation within the SIV in a later time with value related to the parameters of the inflationary potential along with the possibility for the density fluctuations to grow sufficiently fast within the SIV without the need for dark matter to seed the growth of structure in the universe. In the late-time universe, the applications of the SIV paradigm are related to scale-invariant dynamics of galaxies, MOND, dark matter, and dwarf spheroidals, where one can find MOND to be a peculiar case of the SIV theory. Finally, within the recent time epoch, we highlight that some of the change in the length-of-the-day (LOD), about 0.92 cm/yr, can be accounted for by SIV effects in the Earth–Moon system.
Full article
(This article belongs to the Special Issue Nature and Origin of Dark Matter and Dark Energy II)
Open AccessArticle
On the Reducibility of a Class Nonlinear Almost Periodic Hamiltonian Systems
by
Nina Xue and Yanmei Sun
Symmetry 2024, 16(6), 656; https://doi.org/10.3390/sym16060656 - 26 May 2024
Abstract
Inthis paper, we consider the reducibility of a class of nonlinear almost periodic Hamiltonian systems. Under suitable hypothesis of analyticity, non-resonant conditions and non-degeneracy conditions, by using KAM iteration, it is shown that the considered Hamiltonian system is reducible to an almost periodic
[...] Read more.
Inthis paper, we consider the reducibility of a class of nonlinear almost periodic Hamiltonian systems. Under suitable hypothesis of analyticity, non-resonant conditions and non-degeneracy conditions, by using KAM iteration, it is shown that the considered Hamiltonian system is reducible to an almost periodic Hamiltonian system with zero equilibrium points for most small enough parameters. As an example, we discuss the reducibility and stability of an almost periodic Hill’s equation.
Full article
(This article belongs to the Special Issue Symmetry in Integrable Systems and Soliton Theories)
Open AccessArticle
MultiFuzzTOPS: A Fuzzy Multi-Criteria Decision-Making Model Using Type-2 Soft Sets and TOPSIS
by
Shumaila Manzoor, Saima Mustafa, Kanza Gulzar, Asim Gulzar, Sadia Nishat Kazmi, Syed Muhammad Abrar Akber, Rasool Bukhsh, Sheraz Aslam and Syed Muhammad Mohsin
Symmetry 2024, 16(6), 655; https://doi.org/10.3390/sym16060655 - 25 May 2024
Abstract
Effective and optimal decision-making can enhance system performance, potentially leading to a positive reputation and financial gains. Multi-criteria decision-making (MCDM) is an important research topic widely applied to practical decision-making problems. Using the basic idea of symmetry to balance the arrangement where elements
[...] Read more.
Effective and optimal decision-making can enhance system performance, potentially leading to a positive reputation and financial gains. Multi-criteria decision-making (MCDM) is an important research topic widely applied to practical decision-making problems. Using the basic idea of symmetry to balance the arrangement where elements or features have an equality or similarity in distribution, MCDM provides robust decisions in such multi-dimensional complex issues. This study proposes MultiFuzzTOPS, a decision-making model to deal with complexity of multi-criteria decision-making. The proposed MultiFuzzTOPS leverages the fuzzy logic and soft sets such as type-2 soft sets (T2SS) and technique for order preference by similarity to ideal solution (TOPSIS) for decision-making. We validate the proposed model by implementing it to solve the pesticide selection problem in food science by considering various criteria for the selection of pesticides. Our proposed MultiFuzzTOPS recommends the best pesticide compared with its counterparts because it covers the maximum information for the selection of the best alternative. Results are ranked on the basis of the Hamming distance and similarity coefficient. We also validate the effectiveness by performing the sensitivity analysis, and the validation shows the reliability and effectiveness of our proposed model.
Full article
(This article belongs to the Section Computer)
Open AccessArticle
A Modified Spectral Conjugate Gradient Method for Absolute Value Equations Associated with Second-Order Cones
by
Leifu Gao, Zheng Liu, Jingfei Zou and Zengwei Wang
Symmetry 2024, 16(6), 654; https://doi.org/10.3390/sym16060654 - 25 May 2024
Abstract
In this paper, we propose a modified spectral conjugate gradient (MSCG) method for solving absolute value equations associated with second-order cones (SOCAVEs). Some properties of the SOCAVEs are analyzed, and the global convergence of the MSCG method is discussed in depth. Numerical experiments
[...] Read more.
In this paper, we propose a modified spectral conjugate gradient (MSCG) method for solving absolute value equations associated with second-order cones (SOCAVEs). Some properties of the SOCAVEs are analyzed, and the global convergence of the MSCG method is discussed in depth. Numerical experiments are given to illustrate the effectiveness and competitiveness of our algorithm.
Full article
(This article belongs to the Section Mathematics)
Open AccessArticle
A Symmetric Multiprocessor System-on-a-Chip-Based Solution for Real-Time Image Dehazing
by
Dat Ngo and Bongsoon Kang
Symmetry 2024, 16(6), 653; https://doi.org/10.3390/sym16060653 - 25 May 2024
Abstract
The acquisition of digital images is susceptible to haze, and images captured under such adverse conditions may impact high-level applications designed for clean input data. Image dehazing emerges as a practical solution to this problem, as it can be employed to pre-process images
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The acquisition of digital images is susceptible to haze, and images captured under such adverse conditions may impact high-level applications designed for clean input data. Image dehazing emerges as a practical solution to this problem, as it can be employed to pre-process images immediately after acquisition. This paper presents a concise review of impactful algorithms, including those based on deep learning models, to identify the existing gap in real-time processing capabilities. Subsequently, a real-time dehazing system on a multiprocessor system-on-a-chip (MPSoC) platform is introduced to bridge this gap. The proposed system balances the trade-off between dehazing performance and computational complexity; hence, the name “Symmetric” is coined. Additionally, the entire system is implemented in programmable logic and wrapped by an interface circuit supporting double-buffering, rendering it highly suitable for seamless integration into existing camera systems. Implementation results on a Zynq UltraScale+ MPSoC ZCU106 Evaluation Kit demonstrate a maximum operating frequency of 356.51 MHz, equivalent to a maximum processing speed of 40.27 frames per second for DCI 4K resolution.
Full article
(This article belongs to the Special Issue Symmetry in Process Optimization)
Open AccessArticle
Critical Information Mining Network: Identifying Crop Diseases in Noisy Environments
by
Yi Shao, Wenzhong Yang, Zhifeng Lu, Haokun Geng and Danny Chen
Symmetry 2024, 16(6), 652; https://doi.org/10.3390/sym16060652 - 24 May 2024
Abstract
When agricultural experts explore the use of artificial intelligence technology to identify and detect crop diseases, they mainly focus on the research of a stable environment, but ignore the problem of noise in the process of image acquisition in real situations. To solve
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When agricultural experts explore the use of artificial intelligence technology to identify and detect crop diseases, they mainly focus on the research of a stable environment, but ignore the problem of noise in the process of image acquisition in real situations. To solve this problem, we propose an innovative solution called the Critical Information Mining Network (CIMNet). Compared with traditional models, CIMNet has higher recognition accuracy and wider application scenarios. The network has a good effect on crop disease recognition under noisy environments, and can effectively deal with the interference of noise to the recognition effect in actual farmland scenes. Consider that the shape of the leaves can be symmetrical or asymmetrical.First, we introduce the Non-Local Attention Module (Non-Local), which uses a unique self-attention mechanism to fully capture the context information of the image. The module overcomes the limitation of traditional convolutional neural networks that only rely on local features and ignore global features. Global features are particularly important when the image is disturbed by noise. Non-Local improves a more comprehensive visual understanding of crop disease recognition. Secondly, we have innovatively designed a Multi-scale Critical Information Fusion Module (MSCM). The module uses the Key Information Extraction Module (KIB) to dig into the shallow key features in the network deeply. The shallow key features strengthen the feature perception of the model to the noise image through texture and contour information, and then the shallow key features and deep features are fused to enrich the original deep feature information of the network. Finally, we conducted experiments on two public datasets, and the results showed that the accuracy of our model in crop disease identification under a noisy environment was significantly improved. At the same time, our model also showed excellent performance under stable conditions. The results of this study provide favorable support for the improvement of crop production efficiency.
Full article
(This article belongs to the Special Issue Symmetry in Image Processing: Novel Topics and Advancements)
Open AccessArticle
Numerical Study on Aerodynamic Noise Reduction in Passenger Car with Fender Shape Optimization
by
Dongqi Jiao, Haichao Zhou, Tinghui Huang and Wei Zhang
Symmetry 2024, 16(6), 651; https://doi.org/10.3390/sym16060651 - 24 May 2024
Abstract
Despite the rapid development of vehicle intelligent technology, the aerodynamic noise problem of internal combustion engine vehicles and pure electric vehicles at high speed has always been a growing problem. In this study, the effects of the car body fender shape on the
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Despite the rapid development of vehicle intelligent technology, the aerodynamic noise problem of internal combustion engine vehicles and pure electric vehicles at high speed has always been a growing problem. In this study, the effects of the car body fender shape on the aerodynamic noises of the rearview mirror and wheel region were investigated, and a noise reduction method was also proposed by optimizing the fender shape. To realize the parametric modeling of the fender, five positional variables were selected to define the fender configuration; the free-form deformation (FFD) method was used to establish the response fender model according the DOE schemes, and computational fluid dynamics (CFD) simulations are used to obtain the noise results. Then, with the help of the radial basis function (RBF) model and the adaptive simulated annealing (ASA) algorithm, the aerodynamic shape of the fender was optimized to reduce aerodynamic noise. Comparative analysis was then employed to assess flow field characteristics of the optimized model against the original model and elucidate the fender configuration’s contribution to aerodynamic noise reduction and its realization mechanism.
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(This article belongs to the Section Engineering and Materials)
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Open AccessArticle
Numerical Simulation Study Considering Discontinuous Longitudinal Joints in Soft Soil under Symmetric Loading
by
Xianwei He, Xiangyang Xu and Hao Yang
Symmetry 2024, 16(6), 650; https://doi.org/10.3390/sym16060650 - 24 May 2024
Abstract
In shield tunneling, the joint is one of the most vulnerable parts of the segmental lining. Opening of the joint reduces the overall stiffness of the ring, leading to structural damage and issues such as water leakage. Currently, the Winkler method is commonly
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In shield tunneling, the joint is one of the most vulnerable parts of the segmental lining. Opening of the joint reduces the overall stiffness of the ring, leading to structural damage and issues such as water leakage. Currently, the Winkler method is commonly used to calculate structural deformation, simplifying the interaction between segments and soil as radial and tangential Winkler springs. However, when introducing connection springs or reduction factors to simulate the joint stiffness of segments, the challenge lies in determining the reduction coefficient and the stiffness of the springs. Currently, the hyperstatic reflection method cannot simulate the discontinuity effect at the connection of the tunnel segments, while the state space method overlooks the nonlinear interaction between the tunnel and the soil. Therefore, this paper proposes a numerical simulation method considering the interaction between the tunnel and the soil, which is subjected to compression rather than tension, and the discontinuity of the joints between the segments. The model structure and external load are symmetrical, resulting in symmetrical calculation results. This method is based on the soft soil layers and shield tunnel structures of the Shanghai Metro, and the applicability of the model is verified through deformation calculations using three-dimensional laser scanning point clouds of sections from the Shanghai Metro Line 5. When the subgrade reaction coefficient is 5000 , the model can effectively simulate the deformation of operational tunnels. By adjusting the bending stiffness of individual connection springs, we investigate the influence of bending stiffness reduction on the bending moment, radial displacement, and rotational displacement of the ring. The results indicate that a decrease in joint bending stiffness significantly affects the mechanical response of the ring, and the extent and degree of this influence are correlated with the joint position and the magnitude of joint bending stiffness.
Full article
(This article belongs to the Special Issue Symmetry, Finite Element Analysis, and Intelligent Sensing and Monitoring: Applications in Engineering)
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Open AccessArticle
Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images
by
Yasin Ozkan and Pakize Erdogmus
Symmetry 2024, 16(6), 649; https://doi.org/10.3390/sym16060649 - 23 May 2024
Abstract
While there are many verification studies on signature images using deep learning algorithms in the literature, there is a lack of studies on the classification of signature images. Signatures are used as a means of identification for banking, security controls, symmetry, certificates, and
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While there are many verification studies on signature images using deep learning algorithms in the literature, there is a lack of studies on the classification of signature images. Signatures are used as a means of identification for banking, security controls, symmetry, certificates, and contracts. In this study, the aim was to design network architectures that work very fast in areas that require only signature images. For this purpose, a new Si-CNN network architecture with existing layers was designed. Afterwards, a new loss function and layer (Si-CL), a novel architecture using Si-CL as classification layer in Si-CNN to increase the performance of this architecture, was designed. This architecture was called Si-CNN+NC (New Classification). Si-CNN and Si-CNN+NC were trained with two datasets. The first dataset which was used for training is the “C-Signatures” (Classification Signatures) dataset, which was created to test these networks. The second dataset is the “Cedar” dataset, which is a benchmark dataset. The number of classes and sample numbers in the two datasets are symmetrical with each other. To compare the performance of the trained networks, four of the most well-known pre-trained networks, GoogleNet, DenseNet201, Inceptionv3, and ResNet50, were also trained with the two datasets with transfer learning. The findings of the study showed that the proposed network models can learn features from two different handwritten signature images and achieve higher accuracy than other benchmark models. The test success of the trained networks showed that the Si-CNN+NC network outperforms the others, in terms of both accuracy and speed. Finally, Si-CNN and Si-CNN+NC networks were trained with the gold standard dataset MNIST and showed superior performance. Due to its superior performance, Si-CNN and Si-CNN+NC can be used by signature experts as an aid in a variety of applications, including criminal detection and forgery.
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(This article belongs to the Special Issue Symmetry/Asymmetry in Neural Networks)
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Open AccessArticle
The Schwarzian Approach in Sturm–Liouville Problems
by
Nektarios Vlahakis
Symmetry 2024, 16(6), 648; https://doi.org/10.3390/sym16060648 - 23 May 2024
Abstract
A novel method for finding the eigenvalues of a Sturm–Liouville problem is developed. Following the minimalist approach, the problem is transformed to a single first-order differential equation with appropriate boundary conditions. Although the resulting equation is nonlinear, its form allows us to find
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A novel method for finding the eigenvalues of a Sturm–Liouville problem is developed. Following the minimalist approach, the problem is transformed to a single first-order differential equation with appropriate boundary conditions. Although the resulting equation is nonlinear, its form allows us to find the general solution by adding a second part to a particular solution. This splitting of the general solution into two parts involves the Schwarzian derivative: hence, the name of the approach. The eigenvalues that correspond to acceptable solutions can be found by requiring the second part to correct the asymptotically diverging behavior of the particular solution. The method can be applied to many different areas of physics, such as the Schrödinger equation in quantum mechanics and stability problems in fluid dynamics. Examples are presented.
Full article
(This article belongs to the Special Issue Feature Papers in 'Physics' Section 2024)
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Open AccessArticle
Inequalities for the Euclidean Operator Radius of n-Tuple Operators and Operator Matrices in Hilbert C∗-Modules
by
Mohammad H. M. Rashid and Wael Mahmoud Mohammad Salameh
Symmetry 2024, 16(6), 647; https://doi.org/10.3390/sym16060647 - 23 May 2024
Abstract
This study takes a detailed look at various inequalities related to the Euclidean operator radius. It examines groups of n-tuple operators, studying how they add up and multiply together. It also uncovers a unique power inequality specific to the Euclidean operator radius.
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This study takes a detailed look at various inequalities related to the Euclidean operator radius. It examines groups of n-tuple operators, studying how they add up and multiply together. It also uncovers a unique power inequality specific to the Euclidean operator radius. The research broadens its scope to analyze how n-tuple operators, when used as parts of operator matrices, illustrate inequalities connected to the Euclidean operator radius. By using the Euclidean numerical radius and Euclidean operator norm for n-tuple operators, the study introduces a range of new inequalities. These inequalities not only set limits for the addition, multiplication, and Euclidean numerical radius of n-tuple operators but also help in establishing inequalities for the Euclidean operator radius. This process involves carefully examining the Euclidean numerical radius inequalities of operator matrices with n-tuple operators. Additionally, a new inequality is derived, focusing specifically on the Euclidean operator norm of operator matrices. Throughout, the research keeps circling back to the idea of finding and understanding symmetries in linear operators and matrices. The paper highlights the significance of symmetry in mathematics and its impact on various mathematical areas.
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(This article belongs to the Section Mathematics)
Open AccessArticle
A Novel Self-Adaptive Deformable Convolution-Based U-Net for Low-Light Image Denoising
by
Hua Wang, Jianzhong Cao, Huinan Guo and Cheng Li
Symmetry 2024, 16(6), 646; https://doi.org/10.3390/sym16060646 - 23 May 2024
Abstract
Capturing images under extremely low-light conditions usually suffers from various types of noise due to the limited photon and low signal-to-noise ratio (SNR), which makes low-light denoising a challenging task in the field of imaging technology. Nevertheless, existing methods primarily focus on investigating
[...] Read more.
Capturing images under extremely low-light conditions usually suffers from various types of noise due to the limited photon and low signal-to-noise ratio (SNR), which makes low-light denoising a challenging task in the field of imaging technology. Nevertheless, existing methods primarily focus on investigating the precise modeling of real noise distributions while neglecting improvements in the noise modeling capabilities of learning models. To address this situation, a novel self-adaptive deformable-convolution-based U-Net (SD-UNet) model is proposed in this paper. Firstly, deformable convolution is employed to tackle noise patterns with different geometries, thus extracting more reliable noise representations. After that, a self-adaptive learning block is proposed to enable the network to automatically select appropriate learning branches for noise with different scales. Finally, a novel structural loss function is leveraged to evaluate the difference between denoised and clean images. The experimental results on multiple public datasets validate the effectiveness of the proposed method.
Full article
(This article belongs to the Special Issue Advances in Image Processing with Symmetry/Asymmetry)
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Open AccessArticle
Improvement and Application of Hale’s Dynamic Time Warping Algorithm
by
Hairong Wang and Qiufang Zheng
Symmetry 2024, 16(6), 645; https://doi.org/10.3390/sym16060645 - 23 May 2024
Abstract
Due to the different generation and propagation mechanisms of P- and S-waves, there may be significant differences in the seismic data collected by the two, which poses a great obstacle to the time domain matching of P- and S-waves in multiwave exploration. Furthermore,
[...] Read more.
Due to the different generation and propagation mechanisms of P- and S-waves, there may be significant differences in the seismic data collected by the two, which poses a great obstacle to the time domain matching of P- and S-waves in multiwave exploration. Furthermore, the quality and accuracy of the matching effect will directly affect the subsequent multiwave joint inversion and interpretation effect. Therefore, the study of P and S-wave-matching methods plays a crucial role in seismic exploration. In 2013, Hale improved the classical Dynamic Time Warping (DTW) algorithm applied to solve the problem of speech recognition, and obtained the DTW algorithm suitable for solving the matching of P-waves and S-waves. The seismic wave-matching results generated by this algorithm are horizontal discontinuous (different trajectories) and need further processing. This study analyses the algorithm based on simulations of seismic waves using Ricker wavelets. In response to existing problems, this paper proposes strategies to improve the DTW algorithm. The algorithm in this study significantly improved the continuity of the registration results of the actual seismic wave data in the horizontal direction (different traces).
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(This article belongs to the Special Issue Advanced Symmetry Methods for Dynamics, Control, Optimization and Applications in 2023)
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Open AccessRetraction
RETRACTED: Lin et al. A Perception Study for Unit Charts in the Context of Large-Magnitude Data Representation. Symmetry 2023, 15, 219
by
Yun Lin, Yi Tang, Yanfei Zhu, Fangbin Song and Wenzhe Tang
Symmetry 2024, 16(6), 644; https://doi.org/10.3390/sym16060644 - 23 May 2024
Abstract
The Symmetry Editorial Office retracts the article titled “A Perception Study for Unit Charts in the Context of Large-Magnitude Data Representation” [...]
Full article
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Fractal and Design of Multipoint Iterative Methods for Nonlinear Problems
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Algorithms, Future Internet, Information, Mathematics, Symmetry
Research on Data Mining of Electronic Health Records Using Deep Learning Methods
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Algorithms, Computation, Mathematics, Molecules, Symmetry, Nanomaterials, Materials
Advances in Computational Materials Sciences
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The Nuclear Physics of Neutron Stars
Guest Editor: Charalampos MoustakidisDeadline: 31 May 2024
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Time Series Forecasting in Physical Geography
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Quantum Mechanics: Concepts, Symmetries, and Recent Developments
Guest Editor: Tuong Trong TruongDeadline: 30 June 2024
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Symmetry in Hamiltonian Dynamical Systems
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