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
The Schwarzschild–de Sitter Metric of Nonlocal dS Gravity
Symmetry 2024, 16(5), 544; https://doi.org/10.3390/sym16050544 - 01 May 2024
Abstract
It is already known that a simple nonlocal de Sitter gravity model, which we denote as gravity, contains an exact vacuum cosmological solution that mimics dark energy and dark matter and is in very good agreement with the standard model of
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It is already known that a simple nonlocal de Sitter gravity model, which we denote as gravity, contains an exact vacuum cosmological solution that mimics dark energy and dark matter and is in very good agreement with the standard model of cosmology. This success of gravity motivated us to investigate how it works at a lower-than-cosmic scale—galactic and the solar system. This paper contains our investigation of the corresponding Schwarzschild–de Sitter metric of the gravity model. To obtain an exact solution, it is necessary to solve the corresponding nonlinear differential equation, which is a very complicated and difficult problem. What we obtained is a solution to a linearized equation, which is related to space metrics far from the massive body, where the gravitational field is weak. The obtained approximate solution is of particular interest for examining the possible role of nonlocal de Sitter gravity in describing the effects in galactic dynamics that are usually attributed to dark matter. This solution was tested on the Milky Way and the spiral galaxy M33 and is in good agreement with observational measurements.
Full article
(This article belongs to the Special Issue Symmetry/Asymmetry and the Dark Universe)
Open AccessArticle
Research on Mathematical Modeling of Critical Impact Force and Rollover Velocity of Coach Tripped Rollover Based on Numerical Analysis Method
by
Xinye Wu, Zhiwei Wang and Shenghui Chen
Symmetry 2024, 16(5), 543; https://doi.org/10.3390/sym16050543 - 01 May 2024
Abstract
Although the probability of a rollover accident is lower than that of other forms of collision, rollover is a serious accident that can break the symmetry of the vehicle and cause serious loss of life and property. There are many factors affecting rollovers,
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Although the probability of a rollover accident is lower than that of other forms of collision, rollover is a serious accident that can break the symmetry of the vehicle and cause serious loss of life and property. There are many factors affecting rollovers, such as the environment, the vehicle, and the driving control. A coach comprises a complex dynamic system; as such, the accuracy and rationality of the used mathematical model are decisive in the study of coach rollover warning and control. By analogy with the modeling method of an automobile collision accident, the general process of a coach rollover accident is analyzed in this study in combination with the contact form and freedom of motion characteristic of the coach body and external environment. According to the principle of conservation of energy, the mathematical models of critical rollover impact force in a collision between vehicles and obstacles and in a collision between two vehicles are established, allowing for analysis of the relationships between the critical tripped rollover impact forces required for a 90° rollover and the continuous action time and collision point height. During the collision between the vehicle and the obstacle, the occurrence of a vehicle rollover is related not only to the impact force in the collision process but also to the collision duration time. Even if the impact force is relatively small, the collision lasts long enough that a second collision may occur until the vehicle rolls over. In the process of a two-vehicle collision, the critical rollover impact force is not only related to the vehicle mass but also to the vehicle wheelbase and the height of the collision point. Based on the law of conservation of momentum, the mathematic models of 90-degree rollover and 180-degree rollover are established, and the critical rollover velocities are calculated. The purpose of this study is to provide reference and guidance for the research methods of vehicle rollover stability and anti-rollover control in the intelligent vehicle era.
Full article
(This article belongs to the Special Issue Design Theory, Optimal Control and Intelligent Algorithms of Electric Vehicles and Intelligent Vehicles)
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Open AccessArticle
A Novel Radar Cross-Section Calculation Method Based on the Combination of the Spectral Element Method and the Integral Method
by
Hongyu Zhao, Jingying Chen, Mingwei Zhuang, Xiaofan Yang and Jianliang Zhuo
Symmetry 2024, 16(5), 542; https://doi.org/10.3390/sym16050542 - 01 May 2024
Abstract
This article proposes a novel method for calculating radar cross-sections (RCSs) that combines the spectral element method and the integral method, allowing for RCS calculations at any position in a free space or a half-space. This approach replaces the field source with an
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This article proposes a novel method for calculating radar cross-sections (RCSs) that combines the spectral element method and the integral method, allowing for RCS calculations at any position in a free space or a half-space. This approach replaces the field source with an incident field using the scattered field equation of the spectral element method, enabling the arbitrary placement of the field source without being limited by the computational domain. By applying the superposition theorem and the volume equivalence principle, the scattered field of the objects at any position is obtained through integral equations, eliminating limitations on the computation points imposed by the computational domain. Based on Green’s function’s important role throughout the calculation process and its symmetry properties, the RCS calculation of symmetric models will be more advantageous. Finally, several examples, including symmetry models, are provided to validate both the feasibility and accuracy of this proposed method.
Full article
(This article belongs to the Special Issue Application of Symmetry in Innovative Microwave/Millimeter-Wave/THz Antenna, Circuit and Radar System)
Open AccessArticle
Experimental Investigations on the Cavitation Bubble Dynamics near the Boundary of a Narrow Gap
by
Zhifeng Wang, Yihao Yang, Zitong Guo, Qingyi Hu, Xiaoyu Wang, Yuning Zhang, Jingtao Li and Yuning Zhang
Symmetry 2024, 16(5), 541; https://doi.org/10.3390/sym16050541 - 01 May 2024
Abstract
Cavitation bubbles near narrow gaps widely exist within microfluidic control devices. In the present paper, a laser-induced cavitation bubble is arranged in a narrow gap composed of two parallel plates. The inception position of the bubble is set to be at the same
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Cavitation bubbles near narrow gaps widely exist within microfluidic control devices. In the present paper, a laser-induced cavitation bubble is arranged in a narrow gap composed of two parallel plates. The inception position of the bubble is set to be at the same distance from the two plates so that the dynamic behaviors of the bubble are symmetrical. The collapse and rebound dynamics of the bubble near the boundary of a narrow gap are investigated through high-speed photography. The bubble behaviors (e.g., shape deformation, translational movement, and jet characteristics) are analyzed while considering the influence of the dimensionless distance between the bubble and the boundary and the dimensionless gap width. The principal findings include the following: (1) When the dimensionless distance is small, a violent jet towards the gap is generated during the bubble collapse stage, along with a weak counter-jet towards the boundary appearing during the rebound stage. (2) As the dimensionless distance increases, the translational distance of the bubble during the collapse stage initially decreases, then increases, and finally decreases to zero. (3) Within the parameter range considered in this paper, the dimensionless width mainly affects the expansion degree and movement direction of the bubble cloud during its rebound and subsequent stages. The above research findings can provide experimental support for bubble-driven flow control, pumping, and liquid mixing in microfluidic channels.
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(This article belongs to the Section Physics)
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Optimizing Variance Estimation in Stratified Random Sampling through a Log-Type Estimator for Finite Populations
by
Gullinkala Ramya Venkata Triveni, Faizan Danish and Olayan Albalawi
Symmetry 2024, 16(5), 540; https://doi.org/10.3390/sym16050540 - 01 May 2024
Abstract
In this research, a logarithmic-type estimator was formulated for estimating the finite population variance in stratified random sampling. By ensuring that the sampling process is symmetrically conducted across the population, biases can be minimized, and the sample is more likely to be representative
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In this research, a logarithmic-type estimator was formulated for estimating the finite population variance in stratified random sampling. By ensuring that the sampling process is symmetrically conducted across the population, biases can be minimized, and the sample is more likely to be representative of the population as a whole. We conducted a comprehensive numerical study and simulation study to evaluate the performance of the proposed estimator. The mean squared error values were computed for both our proposed estimator and several existing ones, including the standard unbiased variance estimator, difference-type estimator, and other considered estimators. The results of the numerical study and simulation study demonstrated that the proposed log-type estimator outperforms the other considered estimators in terms of MSE and percentage relative efficiency. Graphical representations of the results are also provided to illustrate the efficiency of the proposed estimator. Based on the findings of this study, we conclude that the proposed log-type estimator is a valuable addition to the existing literature on variance estimation in stratified random sampling. It provides a more efficient and accurate estimate of the population variance, which can be beneficial for various statistical applications.
Full article
(This article belongs to the Section Mathematics)
Open AccessEditorial
Special Issue: “Fluctuating Asymmetry as a Measure of Stress: Influence of Natural and Anthropogenic Factors”
by
Elena Shadrina and Cino Pertoldi
Symmetry 2024, 16(5), 539; https://doi.org/10.3390/sym16050539 - 01 May 2024
Abstract
The main cause of stress, according to Selye [...]
Full article
(This article belongs to the Special Issue Fluctuating Asymmetry as a Measure of Stress: Influence of Natural and Anthropogenic Factors)
Open AccessArticle
Applications of Symmetry-Enhanced Physics-Informed Neural Networks in High-Pressure Gas Flow Simulations in Pipelines
by
Sultan Alpar, Rinat Faizulin, Fatima Tokmukhamedova and Yevgeniya Daineko
Symmetry 2024, 16(5), 538; https://doi.org/10.3390/sym16050538 - 30 Apr 2024
Abstract
This article presents a detailed examination of the methodology and modeling tools utilized to analyze gas flows in pipelines, rooted in the fundamental principles of gas dynamics. The methodology integrates numerical simulations with modern neural network techniques, particularly focusing on the PINN utilizing
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This article presents a detailed examination of the methodology and modeling tools utilized to analyze gas flows in pipelines, rooted in the fundamental principles of gas dynamics. The methodology integrates numerical simulations with modern neural network techniques, particularly focusing on the PINN utilizing the continuous symmetry data inherent in PDEs, which is called the symmetry-enhanced Physics-Informed Neural Network. This innovative approach combines artificial neural networks (ANNs) integrating physical equations, which provide enhanced efficiency and accuracy when modeling various complex processes related to physics with a symmetric and asymmetric nature. The presented mathematical model, based on the system of Euler equations, has been carefully implemented using Python language. Verification with analytical solutions ensures the accuracy and reliability of the computations. In this research, a comparative and comprehensive analysis was carried out comparing the outcomes obtained using the symmetry-enhanced PINN method and those from conventional computational fluid dynamics (CFD) approaches. The analysis highlighted the advantages of the symmetry-enhanced PINN method, which produced smoother pressure and velocity fluctuation profiles while reducing the computation time, demonstrating its capacity as a revolutionary modeling tool. The estimated results derived from this study are of paramount importance for ensuring ongoing energy supply reliability and can also be used to create predictive models related to gas behavior in pipelines. The application of modeling techniques for gas flow simulations has the potential to improve the integrity of our energy infrastructure and utilization of gas resources, contributing to advancing our understanding of symmetry principles in nature. However, it is crucial to emphasize that the effectiveness of such models relies on continuous monitoring and frequent updates to ensure alignment with real-world conditions. This research not only contributes to a deeper understanding of compressible gas flows but also underscores the crucial role of advanced modeling methodologies in the sustainable management of gas resources for both current and future generations. The numerical data covered the physics of the process related to the modeling of high-pressure gas flows in pipelines with regard to density, velocity and pressure, where the PINN model was able to outperform the classical CFD method for velocity by 170% and for pressure by 360%, based on values.
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(This article belongs to the Topic Artificial Intelligence (AI) Applied in Civil Engineering, 2nd Volume)
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A Novel Spatiotemporal Periodic Polynomial Model for Predicting Road Traffic Speed
by
Shan Jiang, Yuming Feng, Xiaofeng Liao, Hongjuan Wu, Jinkui Liu and Babatunde Oluwaseun Onasanya
Symmetry 2024, 16(5), 537; https://doi.org/10.3390/sym16050537 - 30 Apr 2024
Abstract
Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic,
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Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic, which integrates the temporal, spatial, and periodic features of speed time series and can effectively handle the nonlinear mapping relationship from input to output. In terms of the model, we establish a road traffic speed prediction model based on polynomial regression. In terms of spatial feature extraction methods, we introduce a maximum mutual information coefficient spatial feature extraction method. In terms of periodic feature extraction methods, we introduce a periodic trend modeling method into the prediction of speed time series, and effective fusion is carried out. Four strategies are evaluated based on the Guangzhou road speed dataset: a univariate polynomial model, a spatiotemporal polynomial model, a periodic polynomial model, and a spatiotemporal periodic polynomial model. The test results show that the three methods proposed in this article can effectively improve prediction accuracy. Comparing the spatiotemporal periodic polynomial model with multiple machine learning models and deep learning models, the prediction accuracy is improved by 5.94% compared to the best feedforward neural network. The research in this article can effectively deal with the temporal, spatial, periodic, and nonlinear characteristics of speed prediction, and to a certain extent, improve the accuracy of speed prediction.
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(This article belongs to the Section Engineering and Materials)
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Open AccessEditorial
Review of Advanced Digital Technologies, Modeling and Control Applied in Various Processes
by
Ilia Beloglazov
Symmetry 2024, 16(5), 536; https://doi.org/10.3390/sym16050536 - 30 Apr 2024
Abstract
This special issue reviews advanced digital technologies in modeling and control of technological processes [...]
Full article
(This article belongs to the Special Issue Advanced Digital, Modeling and Control Applies into Various Processes)
Open AccessArticle
Numerical Investigations on the Jet Dynamics during Cavitation Bubble Collapsing between Dual Particles
by
Zhifeng Wang, Zhengyang Feng, Jinsen Hu, Yuning Zhang and Yuning Zhang
Symmetry 2024, 16(5), 535; https://doi.org/10.3390/sym16050535 - 29 Apr 2024
Abstract
The jet dynamics during cavitation bubble collapsing between unequal-sized dual particles are investigated utilizing a numerical model that combines the finite volume approach alongside the volume of fluid approach. The model incorporates the compressibility of the two-phase fluid and accounts for mass and
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The jet dynamics during cavitation bubble collapsing between unequal-sized dual particles are investigated utilizing a numerical model that combines the finite volume approach alongside the volume of fluid approach. The model incorporates the compressibility of the two-phase fluid and accounts for mass and heat transfer between two phases. The computational model utilizes an axisymmetric model, where the axis of symmetry is defined as the line that connects the centers of the particles and the bubble. A comprehensive analysis is presented on the influence of the particle radius and bubble–particle distance on the jet behavior. Furthermore, the variations of surface pressure on the particles induced by jet impingement are quantitatively analyzed. Four distinct jet behaviors are categorized, depending on the formation mechanism, as well as the number and the direction of the jets. For case 1, the bubble produces a single jet directed toward a small particle; for case 2, the bubble fragments produces double jets receding from each other; for case 3, the bubble produces double jets approaching each other; and for case 4, the bubble produces a single jet directed toward a large particle. The pressure perturbations induced by jet impingement upon the particles exceed those caused by shock wave impacts. The larger the bubble volume at the moment of jet formation, the longer the duration of the pressure variation caused by the jet impinging on the particles.
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(This article belongs to the Section Physics)
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Coplanar Waveguide (CPW) Loaded with Symmetric Circular and Polygonal Split-Ring Resonator (SRR) Shapes
by
Supakorn Harnsoongnoen, Saksun Srisai and Pongsathorn Kongkeaw
Symmetry 2024, 16(5), 534; https://doi.org/10.3390/sym16050534 - 29 Apr 2024
Abstract
This paper investigates the performance of coplanar waveguide (CPW) structures loaded with symmetric circular and polygonal split-ring resonators (SRRs) for microwave and RF applications, leveraging their unique electromagnetic properties. These properties make them suitable for metamaterials, sensors, filters, resonators, antennas, and communication systems.
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This paper investigates the performance of coplanar waveguide (CPW) structures loaded with symmetric circular and polygonal split-ring resonators (SRRs) for microwave and RF applications, leveraging their unique electromagnetic properties. These properties make them suitable for metamaterials, sensors, filters, resonators, antennas, and communication systems. The objectives of this study are to analyze the impact of different SRR shapes on the transmission characteristics of CPWs and to explore their potential for realizing compact and efficient microwave components. The CPW-SRR structures are fabricated on a dielectric substrate, and their transmission properties and spectrogram are experimentally characterized in the frequency range of 4 GHz to 10 GHz with the rotation angles of the SRR gap. The simulation results demonstrate that the resonant frequencies and magnitude of the transmission coefficient of the CPW-SRR structures are influenced by the geometry of the SRR shapes and the rotation angles of the SRR gap, with certain shapes exhibiting enhanced performance characteristics compared to others. Moreover, the symmetric circular and polygonal SRRs offer design flexibility and enable the realization of miniaturized microwave components with improved performance metrics. Overall, this study provides valuable insights into the design and optimization of CPW-based microwave circuits utilizing symmetric SRR shapes, paving the way for advancements in the miniaturization and integration of RF systems.
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(This article belongs to the Section Engineering and Materials)
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A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images
by
Yi Zhang, Xinyu Liu and Yang Lv
Symmetry 2024, 16(5), 533; https://doi.org/10.3390/sym16050533 - 29 Apr 2024
Abstract
This paper presents an improved swarming algorithm that enhances low-illumination images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO-BIGA) and incomplete beta (IHHO-NBeta) functions. This paper integrates the concept of symmetry into the improvement steps of the image adaptive
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This paper presents an improved swarming algorithm that enhances low-illumination images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO-BIGA) and incomplete beta (IHHO-NBeta) functions. This paper integrates the concept of symmetry into the improvement steps of the image adaptive enhancement algorithm. The enhanced algorithm integrates chaotic mapping for population initialization, a nonlinear formula for prey energy calculation, spiral motion from the black widow algorithm for global search enhancement, a nonlinear inertia weight factor inspired by particle swarm optimization, and a modified Levy flight strategy to prevent premature convergence to local optima. This paper compares the algorithm’s performance with other swarm intelligence algorithms using commonly used test functions. The algorithm’s performance is compared against several emerging swarm intelligence algorithms using commonly used test functions, with results demonstrating its superior performance. The improved Harris Eagle algorithm is then applied for image adaptive enhancement, and its effectiveness is evaluated on five low-illumination images from the LOL dataset. The proposed method is compared to three common image enhancement techniques and the IHHO-BIGA and IHHO-NBeta methods. The experimental results reveal that the proposed approach achieves optimal visual perception and enhanced image evaluation metrics, outperforming the existing techniques. Notably, the standard deviation data of the first image show that the IHHO-NBeta method enhances the image by 8.26%, 120.91%, 126.85%, and 164.02% compared with IHHO-BIGA, the single-scale Retinex enhancement method, the homomorphic filtering method, and the limited contrast adaptive histogram equalization method, respectively. The processing time of the improved method is also better than the previous heuristic algorithm.
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(This article belongs to the Special Issue Asymmetric and Symmetric Study on Image Processing and Statistical Data Analysis)
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Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP
by
Yong Liu, Zhuo Wang, Dong Zhang, Mingshun Yang, Xinqin Gao and Li Ba
Symmetry 2024, 16(5), 532; https://doi.org/10.3390/sym16050532 - 29 Apr 2024
Abstract
Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data
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Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data collection, data pre-processing, and model interpretation. In this context, a quality anomaly recognition and diagnosis model for the complex product manufacturing process is constructed based on a deep residual network, support vector machine (SVM), and Shapley additive explanation (SHAP). Given the numerous complex product quality characteristic indexes and unpredictable accidental factors in the production process, it is necessary to mine the deep relationship between quality characteristic data and quality state. This mining is achieved by utilizing the strong feature extraction ability of the deep residual shrinkage network (DRSN) through self-learning. The symmetry of the data within the model has also been taken into account to ensure a more balanced and comprehensive analysis. The excellent binary classification ability of the support vector machine is combined with the DRSN to identify the quality anomaly state. The SHAP interpretable model is employed to diagnose the quality anomaly problem of a single product and to identify and diagnose quality anomalies in the manufacturing process of complex products. The effectiveness of the model is validated through case analysis. The accuracy of the DRSN-SVM quality anomaly recognition model reaches 99%, as demonstrated by example analysis, and the model exhibits faster convergence and significantly higher accuracy compared with the naive Bayesian model classification and support vector machine classification models.
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(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)
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Attached Flows for Reaction–Diffusion Processes Described by a Generalized Dodd–Bullough–Mikhailov Equation
by
Carmen Ionescu and Iulian Petrisor
Symmetry 2024, 16(5), 531; https://doi.org/10.3390/sym16050531 - 28 Apr 2024
Abstract
This paper uses the attached flow method for solving nonlinear second-order differential equations of the reaction–diffusion type. The key steps of the method consist of the following: (i) reducing the differentiability order by defining the first derivative of the variable as a new
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This paper uses the attached flow method for solving nonlinear second-order differential equations of the reaction–diffusion type. The key steps of the method consist of the following: (i) reducing the differentiability order by defining the first derivative of the variable as a new variable called the flow and (ii) a forced decomposition of the derivative-free term so that the flow appears explicitly in it. The resulting reduced equation is solved using specific balancing rules. Only step (i) would lead to an Abel-type equation with complicated integral solutions. Completed with (ii) and with the graduation procedure, the attached flow method used in the paper, without requiring such a great effort, allows for the obtaining of accurate analytical solutions. The method is applied here to a subclass of reaction–diffusion equations, the generalized Dodd–Bulough–Mikhailov equation, which includes a translation of the variable and nonlinearities up to order five. The equation is solved for each order of nonlinearity, and the solutions are discussed following the values of the parameters involved in the equation.
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(This article belongs to the Topic Nonlinear Phenomena, Chaos, Control and Applications to Engineering and Science and Experimental Aspects of Complex Systems)
Open AccessArticle
Second Hankel Determinant and Fekete–Szegö Problem for a New Class of Bi-Univalent Functions Involving Euler Polynomials
by
Semh Kadhim Gebur and Waggas Galib Atshan
Symmetry 2024, 16(5), 530; https://doi.org/10.3390/sym16050530 - 28 Apr 2024
Abstract
Orthogonal polynomials have been widely employed by renowned authors within the context of geometric function theory. This study is driven by prior research and aims to address the —Fekete-Szegö problem. Additionally, we provide bound estimates for the coefficients and an upper bound estimate
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Orthogonal polynomials have been widely employed by renowned authors within the context of geometric function theory. This study is driven by prior research and aims to address the —Fekete-Szegö problem. Additionally, we provide bound estimates for the coefficients and an upper bound estimate for the second Hankel determinant for functions belonging to the category of analytical and bi-univalent functions. This investigation incorporates the utilization of Euler polynomials.
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(This article belongs to the Special Issue Geometric Function Theory and Special Functions II)
Open AccessArticle
Flexible Techniques to Detect Typical Hidden Errors in Large Longitudinal Datasets
by
Renato Bruni, Cinzia Daraio and Simone Di Leo
Symmetry 2024, 16(5), 529; https://doi.org/10.3390/sym16050529 - 28 Apr 2024
Abstract
The increasing availability of longitudinal data (repeated numerical observations of the same units at different times) requires the development of flexible techniques to automatically detect errors in such data. Besides standard types of errors, which can be treated with generic error correction techniques,
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The increasing availability of longitudinal data (repeated numerical observations of the same units at different times) requires the development of flexible techniques to automatically detect errors in such data. Besides standard types of errors, which can be treated with generic error correction techniques, large longitudinal datasets may present specific problems not easily traceable by the generic techniques. In particular, after applying those generic techniques, time series in the data may contain trends, natural fluctuations and possible surviving errors. To study the data evolution, one main issue is distinguishing those elusive errors from the rest, which should be kept as they are and not flattened or altered. This work responds to this need by identifying some types of elusive errors and by proposing a statistical-mathematical approach to capture their complexity that can be applied after the above generic techniques. The proposed approach is based on a system of indicators and works at the formal level by studying the differences between consecutive values of data series and the symmetries and asymmetries of these differences. It operates regardless of the specific meaning of the data and is thus applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing two key variables: “Total academic staff” and “Total number of enrolled students”, which are two of the most important variables, often used in empirical analysis as a proxy for size, and are considered by policymakers at the European level. The results are very promising.
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(This article belongs to the Topic Decision-Making and Data Mining for Sustainable Computing)
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In Pursuit of BRST Symmetry and Observables in 4D Topological Gauge-Affine Gravity
by
Oussama Abdelghafour Belarbi and Ahmed Meziane
Symmetry 2024, 16(5), 528; https://doi.org/10.3390/sym16050528 - 28 Apr 2024
Abstract
The realization of a BRST cohomology of the 4D topological gauge-affine gravity is established in terms of a superconnection formalism. The identification of fields in the quantized theory occurs directly as is usual in terms of superconnection and its supercurvature components with the
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The realization of a BRST cohomology of the 4D topological gauge-affine gravity is established in terms of a superconnection formalism. The identification of fields in the quantized theory occurs directly as is usual in terms of superconnection and its supercurvature components with the double covering of the general affine group . Then, by means of an appropriate decomposition of the metalinear double-covering group with respect to the general linear double-covering group , one can easily obtain the enlargements of the fields while remaining consistent with the BRST algebra. This leads to the descent equations, allowing us to build the observables of the theory by means of the BRST algebra constructed using a algebra-valued superconnection. In particular, we discuss the construction of topological invariants with torsion.
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(This article belongs to the Special Issue Symmetries in Gravity Research: Classical and Quantum)
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On the Unified Concept of Generalizations of Λ-Sets
by
Emilia Przemska
Symmetry 2024, 16(5), 527; https://doi.org/10.3390/sym16050527 - 27 Apr 2024
Abstract
In this paper, we propose a unified concept encompassing generalizations of two types of families defined based on Levine’s notions of generalized closed sets and Maki’s sets. The methods used in this investigation are described in my previous work, where a unified
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In this paper, we propose a unified concept encompassing generalizations of two types of families defined based on Levine’s notions of generalized closed sets and Maki’s sets. The methods used in this investigation are described in my previous work, where a unified concept of general closedness is presented. From a methodology point of view, the present concept is symmetric to the previous. In generalizing open subsets, one can use the two methods. According to the first one, the family of Levine’s generalization is used as some base to build the family of closed subsets of the new topology. In the second method, the family of open subsets is extended, in the same way, as the family of closed subsets in the classic Levine’s method. The results obtained in this general conception easily extend and imply well-known theorems of this area of investigation. In the literature on this issue, many versions of generalizations of -sets have been investigated. The tools used in this paper enabled us to prove that there exist at most 10 generalizations of these types, and we show the relationships between them in the graph. As a result, it turns out that some generalizations investigated in the literature are trivial.
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(This article belongs to the Section Mathematics)
Open AccessArticle
Analysis of the Surrounding Rock Full-Displacement Variation in Large-Span Mudstone Highway Tunnels
by
Dechao Chi, Yanbin Luo, Chengwei Chen, Shengqing Wang, Yunfei Wu and Yuhang Hu
Symmetry 2024, 16(5), 526; https://doi.org/10.3390/sym16050526 - 27 Apr 2024
Abstract
Due to the increasing development of highway reconstruction and expansion projects in China, many large-span highway tunnels are being constructed near existing highway tunnels. Tunneling underneath will inevitably cause variation in the surrounding rock displacement and may even lead to collapse. In this
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Due to the increasing development of highway reconstruction and expansion projects in China, many large-span highway tunnels are being constructed near existing highway tunnels. Tunneling underneath will inevitably cause variation in the surrounding rock displacement and may even lead to collapse. In this study, based on an analysis of extensive field monitoring data from the Gucheng tunnel, the variation law for the surrounding rock full-displacement and the influence of the tunnel-face spatial effect in a large-span mudstone tunnel are analyzed. The change in the full displacement experienced the following sequence: slow pre-displacement growth → rapid increase → slow increase → gradual stability. The displacement released by the excavation of the tunnel construction accounts for 40~60% of the total displacement, and the closer to the excavation contour, the more obvious the displacement release. The final convergence value of vertical displacement is obtained by hyperbolic function regression prediction analysis. Based on this value, Lee and Hoek equations are used for parameter analysis and field-data fitting. It is concluded that the larger the proportion of the early displacement of the surrounding rock before construction to the total displacement, the smaller the influence of the tunnel-face spatial effect on the surrounding rock. The numerical simulation results are compared with actual monitoring results, and good agreement is observed. The larger the burial depth of the tunnel, the smaller the influence range in the tunnel-face spatial effect, and the more concentrated the displacement release. The variation law and the influential range for the surrounding rock full-displacement described in this paper can provide a reference for predicting and controlling the deformation during the construction of future large-span mudstone tunnels.
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(This article belongs to the Section Engineering and Materials)
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Open AccessArticle
Symmetric U-Net Model Tuned by FOX Metaheuristic Algorithm for Global Prediction of High Aerosol Concentrations
by
Dušan P. Nikezić, Dušan S. Radivojević, Nikola S. Mirkov, Ivan M. Lazović and Tatjana A. Miljojčić
Symmetry 2024, 16(5), 525; https://doi.org/10.3390/sym16050525 - 26 Apr 2024
Abstract
In this study, the idea of using a fully symmetric U-Net deep learning model for forecasting a segmented image of high global aerosol concentrations is implemented. As the forecast relies on historical data, the model used a sequence of the last eight segmented
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In this study, the idea of using a fully symmetric U-Net deep learning model for forecasting a segmented image of high global aerosol concentrations is implemented. As the forecast relies on historical data, the model used a sequence of the last eight segmented images to make the prediction. For this, the classic U-Net model was modified to use ConvLSTM2D layers with MaxPooling3D and UpSampling3D layers. In order to achieve complete symmetry, the output data are given in the form of a series of eight segmented images shifted by one image in the time sequence so that the last image actually represents the forecast of the next image of high aerosol concentrations. The proposed model structure was tuned by the new FOX metaheuristic algorithm. Based on our analysis, we found that this algorithm is suitable for tuning deep learning models considering their stochastic nature. It was also found that this algorithm spends the most time in areas close to the optimal value where there is a weaker linear correlation with the required metric and vice versa. Taking into account the characteristics of the used database, we concluded that the model is capable of generating adequate data and finding patterns in the time domain based on the ddc and dtc criteria. By comparing the achieved results of this model using the AUC-PR metric with the previous results of the ResNet3D-101 model with transfer learning, we concluded that the proposed symmetric U-Net model generates data better and is more capable of finding patterns in the time domain.
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(This article belongs to the Special Issue Symmetry in Mathematical Models)
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