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
SwinDPSR: Dual-Path Face Super-Resolution Network Integrating Swin Transformer
Symmetry 2024, 16(5), 511; https://doi.org/10.3390/sym16050511 - 23 Apr 2024
Abstract
Whether to use face priors in the face super-resolution (FSR) methods is a symmetry problem.Various face priors are used to describe the overall and local face features, making the generation of super-resolution face images expensive and laborious. FSR methods that do not require
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Whether to use face priors in the face super-resolution (FSR) methods is a symmetry problem.Various face priors are used to describe the overall and local face features, making the generation of super-resolution face images expensive and laborious. FSR methods that do not require any prior information tend to focus too much on the local features of the face, ignoring the modeling of global information. To solve this problem, we propose a dual-path facial image super-resolution network (SwinDPSR) fused with Swin Transformer. The network does not require additional face priors, and it learns global face shape and local face components through two independent branches. In addition, the channel attention ECA module is used to aggregate the global and local face information in the above dual-path sub-networks, which can generate corresponding high-quality face images. The results of face super-resolution reconstruction experiments on public face datasets and a real-scene face dataset show that SwinDPSR is superior to previous advanced methods both in terms of visual effects and objective indicators. The reconstruction results are evaluated with four evaluation metrics: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), learned perceptual image patch similarity (LPIPS), and mean perceptual score (MPS).
Full article
(This article belongs to the Section Computer)
Open AccessArticle
On Maximum Guaranteed Payoff in a Fuzzy Matrix Decision-Making Problem with a Fuzzy Set of States
by
Svajone Bekesiene and Serhii Mashchenko
Symmetry 2024, 16(5), 510; https://doi.org/10.3390/sym16050510 - 23 Apr 2024
Abstract
The current study delves into a fuzzy matrix decision-making problem involving fuzzy sets of states. It establishes that a maximum guaranteed payoff constitutes a type-2 fuzzy set defined on the real line. Additionally, it provides the associated type-2 membership function. Moreover, the paper
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The current study delves into a fuzzy matrix decision-making problem involving fuzzy sets of states. It establishes that a maximum guaranteed payoff constitutes a type-2 fuzzy set defined on the real line. Additionally, it provides the associated type-2 membership function. Moreover, the paper illustrates that the maximum guaranteed payoff type-2 fuzzy set of the decision-making problem can be broken down, based on the secondary membership grades, into a finite collection of fuzzy numbers. Each of these fuzzy numbers represents the maximum guaranteed payoff of the corresponding decision-making problem with a crisp set of states. This set corresponds to a specific cut of the original fuzzy set of states. Some properties of the maximum guaranteed payoff type-2 fuzzy set are investigated, and illustrative examples are provided. Since the problem formulation is symmetrical with respect to alternatives and states of nature, the results obtained can be used in the case of a fuzzy set of alternatives.
Full article
(This article belongs to the Special Issue Symmetry in Process Optimization)
Open AccessArticle
Construction of Ruled Surfaces from the W-Curves and Their Characterizations in
by
Samah Gaber, Adel H. Sorour and A. A. Abdel-Salam
Symmetry 2024, 16(5), 509; https://doi.org/10.3390/sym16050509 - 23 Apr 2024
Abstract
Ruled surfaces are considered one of the significant aspects of differential geometry. These surfaces are formed by the motion of a straight line called a generator, and every curve that intersects all the generators is called a directrix. In the present research paper,
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Ruled surfaces are considered one of the significant aspects of differential geometry. These surfaces are formed by the motion of a straight line called a generator, and every curve that intersects all the generators is called a directrix. In the present research paper, we explore a family of ruled surfaces constructed from circular helices (W-curve) using the Frenet frame in the Euclidean space . We derive the explicit formulas for the second mean curvature and second Gaussian curvature. We present some ruled surfaces, and we describe their properties. In addition, we determine the sufficient conditions for these surfaces to be minimal, flat, II-minimal, and II-flat. Also, we obtain sufficient conditions for the base curve for these ruled surfaces to be a geodesic curve, an asymptotic line, and a principal line. Furthermore, we present an application for a ruled surface whose base curve is a circular helix, we compute some quantities for this surface such as the mean curvature and Gaussian curvatures and we plot the ruled surface with its base curve, and at symmetric points and along a symmetry axis.
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(This article belongs to the Section Mathematics)
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Open AccessArticle
Efficient Multistep Algorithms for First-Order IVPs with Oscillating Solutions: II Implicit and Predictor–Corrector Algorithms
by
Theodore E. Simos
Symmetry 2024, 16(5), 508; https://doi.org/10.3390/sym16050508 - 23 Apr 2024
Abstract
This research introduces a fresh methodology for creating efficient numerical algorithms to solve first-order Initial Value Problems (IVPs). The study delves into the theoretical foundations of these methods and demonstrates their application to the Adams–Moulton technique in a five-step process. We focus on
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This research introduces a fresh methodology for creating efficient numerical algorithms to solve first-order Initial Value Problems (IVPs). The study delves into the theoretical foundations of these methods and demonstrates their application to the Adams–Moulton technique in a five-step process. We focus on developing amplification-fitted algorithms with minimal phase-lagor phase-lag equal to zero (phase-fitted). The request of amplification-fitted (zero dissipation) is to ensure behavior like symmetric multistep methods (symmetric multistep methods are methods with zero dissipation). Additionally, the stability of the innovative algorithms is examined. Comparisons between our new algorithm and traditional methods reveal its superior performance. Numerical tests corroborate that our approach is considerably more effective than standard methods for solving IVPs, especially those with oscillatory solutions.
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(This article belongs to the Section Mathematics)
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Open AccessArticle
Extended Deep-Learning Network for Histopathological Image-Based Multiclass Breast Cancer Classification Using Residual Features
by
Hiren Mewada
Symmetry 2024, 16(5), 507; https://doi.org/10.3390/sym16050507 - 23 Apr 2024
Abstract
Autonomy of breast cancer classification is a challenging problem, and early diagnosis is highly important. Histopathology images provide microscopic-level details of tissue samples and play a crucial role in the accurate diagnosis and classification of breast cancer. Moreover, advancements in deep learning play
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Autonomy of breast cancer classification is a challenging problem, and early diagnosis is highly important. Histopathology images provide microscopic-level details of tissue samples and play a crucial role in the accurate diagnosis and classification of breast cancer. Moreover, advancements in deep learning play an essential role in early cancer diagnosis. However, existing techniques involve unique models for each classification based on the magnification factor and require training numerous models or using a hierarchical approach combining multiple models irrespective of the focus of the cell features. This may lead to lower performance for multiclass categorization. This paper adopts the DenseNet161 network by adding a learnable residual layer. The learnable residual layer enhances the features, providing low-level information. In addition, residual features are obtained from the convolution features of the preceding layer, which ensures that the future size is consistent with the number of channels in DenseNet’s layer. The concatenation of spatial features with residual features helps better learn texture classification without the need for an additional texture feature extraction module. The model was validated for both binary and multiclass categorization of malignant images. The proposed model’s classification accuracy ranges from 94.65% to 100% for binary and multiclass classification, and the error rate is 2.78%. Overall, the suggested model has the potential to improve the survival of breast cancer patients by allowing precise diagnosis and therapy.
Full article
(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications—Second Volume)
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Open AccessArticle
Fixed Point Dynamics in a New Type of Contraction in b-Metric Spaces
by
María A. Navascués and Ram N. Mohapatra
Symmetry 2024, 16(4), 506; https://doi.org/10.3390/sym16040506 - 22 Apr 2024
Abstract
Since the topological properties of a b-metric space (which generalizes the concept of a metric space) seem sometimes counterintuitive due to the fact that the “open” balls may not be open sets, we review some aspects of these spaces concerning compactness, metrizability, continuity
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Since the topological properties of a b-metric space (which generalizes the concept of a metric space) seem sometimes counterintuitive due to the fact that the “open” balls may not be open sets, we review some aspects of these spaces concerning compactness, metrizability, continuity and fixed points. After doing so, we introduce new types of contractivities that extend the concept of Banach contraction. We study some of their properties, giving sufficient conditions for the existence of fixed points and common fixed points. Afterwards, we consider some iterative schemes in quasi-normed spaces for the approximation of these critical points, analyzing their convergence and stability. We apply these concepts to the resolution of a model of integral equation of Urysohn type. In the last part of the paper, we refine some results about partial contractivities in the case where the underlying set is a strong b-metric space, and we establish some relations between mutual weak contractions and quasi-contractions and the new type of contractivity.
Full article
(This article belongs to the Special Issue Symmetry in Nonlinear Dynamics and Chaos II)
Open AccessArticle
An Integrated Framework for Dynamic Vehicle Routing Problems with Pick-up and Delivery Time Windows and Shared Fleet Capacity Planning
by
Eyüp Tolunay Küp, Salih Cebeci, Barış Bayram, Gözde Aydın, Burcin Bozkaya and Raha Akhavan-Tabatabaei
Symmetry 2024, 16(4), 505; https://doi.org/10.3390/sym16040505 - 22 Apr 2024
Abstract
This paper proposes a novel route optimization framework to solve the problem of instant pick-up and delivery for e-grocery orders. The proposed framework extends the traditional time-windowed package delivery problem. We demonstrate the effectiveness of our approach for this integrated problem using actual
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This paper proposes a novel route optimization framework to solve the problem of instant pick-up and delivery for e-grocery orders. The proposed framework extends the traditional time-windowed package delivery problem. We demonstrate the effectiveness of our approach for this integrated problem using actual delivery data from HepsiJet, a leading e-commerce logistics provider in Turkey. We first employ several machine learning algorithms and simulations to investigate the capacity of the courier. Subsequently, a dynamic route planning workflow is executed with a highly specialized and novel routing algorithm. Our proposed heuristic approach considers combined fleet operations for delivering regular packages originating from a central depot and dynamic e-grocery orders picked up at local supermarkets and delivered to the customers. The heuristic algorithm constitutes k-opt and node transfer operation variations customized for this integrated problem. We report the performance of our approach in problem instances from the literature and instances from HepsiJet’s daily operations, which we also publicly share as new route optimization problem instances. Our results suggest that, despite the more complex nature of the integrated problem, our proposed algorithm and solution framework produce more efficient and cost-effective solutions that offer additional business opportunities for companies such as HepsiJet. The computational analyses reveal that implementing our proposed approach yields significant efficiency gains and cost reductions for the company, with a distance reduction of over 30%, underscoring our approach’s effectiveness in achieving substantial cost savings and enhanced efficiency through integrating two distinct delivery operations.
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(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications—Second Volume)
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Open AccessArticle
The Three Faces of U(3)
by
John LaChapelle
Symmetry 2024, 16(4), 504; https://doi.org/10.3390/sym16040504 - 22 Apr 2024
Abstract
is a semi-direct product group characterized by nontrivial homomorphisms mapping into the automorphism group of . For , there are three nontrivial homomorphisms that induce three
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is a semi-direct product group characterized by nontrivial homomorphisms mapping into the automorphism group of . For , there are three nontrivial homomorphisms that induce three separate defining representations. In a toy model of Yang–Mills (endowed with a suitable inner product) coupled to massive fermions, this renders three distinct covariant derivatives acting on a single matter field. Employing a permutation induced by a large gauge transformation acting on the defining representation vector space, the three covariant derivatives and one matter field can alternatively be expressed as a single covariant derivative acting on three distinct species of matter fields possessing the same quantum numbers. One can interpret this as three species of matter fields in the defining representation.
Full article
(This article belongs to the Special Issue Symmetry beyond the Standard Models of Cosmology, High Energy Physics and Quantum Field Theory II)
Open AccessArticle
Multi-Dimensional Data Analysis Platform (MuDAP): A Cognitive Science Data Toolbox
by
Xinlin Li, Yiming Wang, Xiaoyu Bi, Yalu Xu, Haojiang Ying and Yiyang Chen
Symmetry 2024, 16(4), 503; https://doi.org/10.3390/sym16040503 - 22 Apr 2024
Abstract
Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to
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Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to truncate data dimensions when dealing with data with multiple dimensions. This is not necessarily because of its merit in terms of mathematical algorithm, but partly because it is easy to conduct with commonly accessible statistical software. On the other hand, dimension reduction might not be the best analysis when modeling data with no more than 20 dimensions. Using state-of-the-art techniques, researchers in various research disciplines (e.g., computer vision) classified data with more than hundreds of dimensions with neural networks and revealed the inner structure of the data. Therefore, it might be more sophisticated to process human perception data directly with neural networks. In this paper, we introduce the multi-dimensional data analysis platform (MuDAP), a powerful toolbox for data analysis in cognitive science. It utilizes artificial intelligence as well as network analysis, an analysis method that takes advantage of data symmetry. With the graphic user interface, a researcher, with or without previous experience, could analyze multiple dimensional data with great ease.
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(This article belongs to the Section Computer)
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Open AccessArticle
Best Proximity Point Results for Multi-Valued Mappings in Generalized Metric Structure
by
Asad Ullah Khan, Maria Samreen, Aftab Hussain and Hamed Al Sulami
Symmetry 2024, 16(4), 502; https://doi.org/10.3390/sym16040502 - 21 Apr 2024
Abstract
In this paper, we introduce the novel concept of generalized distance denoted as and call it an extended b-generalized pseudo-distance. With the help of this generalized distance, we define a generalized point to set distance
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In this paper, we introduce the novel concept of generalized distance denoted as and call it an extended b-generalized pseudo-distance. With the help of this generalized distance, we define a generalized point to set distance a generalized Hausdorff type distance and a -property of a pair of nonempty subsets of extended b-metric space Additionally, we establish several best proximity point theorems for multi-valued contraction mappings of Nadler type defined on b-metric spaces and extended b-metric spaces. Our findings generalize numerous existing results found in the literature. To substantiate the introduced notion and validate our main results, we provide some concrete examples.
Full article
(This article belongs to the Special Issue Fixed Point Theory and Its Applications Dedicated to the Memory of Professor William Arthur Kirk)
Open AccessArticle
Differential Subordination and Superordination Using an Integral Operator for Certain Subclasses of p-Valent Functions
by
Norah Saud Almutairi, Awatef Shahen and Hanan Darwish
Symmetry 2024, 16(4), 501; https://doi.org/10.3390/sym16040501 - 21 Apr 2024
Abstract
This work presents a novel investigation that utilizes the integral operator in the field of geometric function theory, with a specific focus on sandwich theorems. We obtained findings about the differential subordination and superordination of a novel formula
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This work presents a novel investigation that utilizes the integral operator in the field of geometric function theory, with a specific focus on sandwich theorems. We obtained findings about the differential subordination and superordination of a novel formula for a generalized integral operator. Additionally, certain sandwich theorems were discovered.
Full article
(This article belongs to the Special Issue Symmetry in Geometric Theory of Analytic Functions)
Open AccessArticle
Symmetrical and Asymmetrical Sampling Audit Evidence Using a Naive Bayes Classifier
by
Guang-Yih Sheu and Nai-Ru Liu
Symmetry 2024, 16(4), 500; https://doi.org/10.3390/sym16040500 - 20 Apr 2024
Abstract
Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify
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Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify data using a Naive Bayes classifier into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring its representativeness. The user-based approach samples data symmetrically around the median of a class as audit evidence. It may be equivalent to a combination of monetary and variable samplings. The item-based approach represents asymmetric sampling based on posterior probabilities for obtaining risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary samplings. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples; handling complex patterns, correlations, and unstructured data; and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.
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(This article belongs to the Special Issue Symmetry or Asymmetry in Machine Learning)
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Open AccessArticle
Coupled Fixed Point Theory in Subordinate Semimetric Spaces
by
Areej Alharbi, Maha Noorwali and Hamed H. Alsulami
Symmetry 2024, 16(4), 499; https://doi.org/10.3390/sym16040499 - 19 Apr 2024
Abstract
The aim of this paper is to study the coupled fixed point of a class of mixed monotone operators in the setting of a subordinate semimetric space. Using the symmetry between the subordinate semimetric space and a JS-space, we generalize the results of
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The aim of this paper is to study the coupled fixed point of a class of mixed monotone operators in the setting of a subordinate semimetric space. Using the symmetry between the subordinate semimetric space and a JS-space, we generalize the results of Senapati and Dey on JS-spaces. In this paper, we obtain some coupled fixed point results and support them with some examples.
Full article
(This article belongs to the Special Issue Nonlinear Analysis and Its Applications in Symmetry II)
Open AccessArticle
Osculating Type Ruled Surfaces with Type-2 Bishop Frame in E3
by
Özgür Boyacıoğlu Kalkan and Süleyman Şenyurt
Symmetry 2024, 16(4), 498; https://doi.org/10.3390/sym16040498 - 19 Apr 2024
Abstract
The aim of this work is to investigate osculating type ruled surfaces with a type 2-Bishop frame in . We accomplish this by employing the symmetry of osculating curves. We examine osculating type ruled surfaces by taking into account the curvatures
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The aim of this work is to investigate osculating type ruled surfaces with a type 2-Bishop frame in . We accomplish this by employing the symmetry of osculating curves. We examine osculating type ruled surfaces by taking into account the curvatures of the base curve. We investigate the geometric properties of these surfaces, focusing on their cylindrical and developable characteristics. Moreover, we calculate the Gaussian and mean curvatures and provide the requirements for the surface to be flat and minimal. We determine the requirements for the curves lying on this surface to be geodesic, asymptotic curves, or lines of curvature. Furthermore, relations between osculating type ruled surfaces with central tangent and central normal vectors are given. Finally, some examples of these surfaces are presented.
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(This article belongs to the Special Issue Contact Geometry: Reduction, Symmetries and Applications)
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Directed Path 3-Arc-Connectivity of Cartesian Product Digraphs
by
Xiaosha Wei
Symmetry 2024, 16(4), 497; https://doi.org/10.3390/sym16040497 - 19 Apr 2024
Abstract
Let be a digraph of order n and let with . A directed -Steiner path (or an -path for short) is a directed path P beginning at r such that . Arc-disjoint between two -paths is characterized by the absence of common arcs. Let be the maximum number of arc-disjoint -paths in D. The directed path k-arc-connectivity of D is defined as In this paper, we shall investigate the directed path 3-arc-connectivity of Cartesian product and prove that if G and H are two digraphs such that , , and , , then moreover, this bound is sharp. We also obtain exact values for for some digraph classes G and H, and most of these digraphs are symmetric.
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(This article belongs to the Special Issue Advances in Graph Theory)
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Open AccessArticle
The Helicity of Magnetic Fields Associated with Relativistic Electron Vortex Beams
by
Norah Alsaawi and Vasileios E. Lembessis
Symmetry 2024, 16(4), 496; https://doi.org/10.3390/sym16040496 - 19 Apr 2024
Abstract
For radially extended Bessel modes, the helicity density distributions of magnetic fields associated with relativistic electron vortex beams are investigated for first time in the literature. The form of the distribution is defined by the electron beam’s cylindrically symmetric density flux, which varies
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For radially extended Bessel modes, the helicity density distributions of magnetic fields associated with relativistic electron vortex beams are investigated for first time in the literature. The form of the distribution is defined by the electron beam’s cylindrically symmetric density flux, which varies with the winding number ℓ and the electron spin. Different helicity distributions are obtained for different signs of the winding number , confirming the chiral nature of the magnetic fields associated with the electron vortex beam. The total current helicity for the spin-down state is smaller than that of the spin-up state. The different fields and helicities associated with opposite winding numbers and/or spin values will play an important role in the investigation of the interaction of relativistic electron vortices with matter and especially chiral matter. A comparison of the calculated quantities with the corresponding ones in the case of non-relativistic spin-polarized electron beams is performed.
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(This article belongs to the Section Physics)
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The Fox Trapezoidal Conjecture for Alternating Knots
by
Nafaa Chbili
Symmetry 2024, 16(4), 495; https://doi.org/10.3390/sym16040495 - 19 Apr 2024
Abstract
A long-standing conjecture due to R. Fox states that the coefficients of the Alexander polynomial of an alternating knot exhibit a trapezoidal pattern. In other words, these coefficients increase, stabilize, then decrease in a symmetric way. A stronger version of this conjecture states
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A long-standing conjecture due to R. Fox states that the coefficients of the Alexander polynomial of an alternating knot exhibit a trapezoidal pattern. In other words, these coefficients increase, stabilize, then decrease in a symmetric way. A stronger version of this conjecture states that these coefficients form a log-concave sequence. This conjecture has been recently highlighted by J. Huh as one of the most interesting problems on log-concavity of sequences. In this expository paper, we shall review the various versions of the conjecture, highlight settled cases and outline some future directions.
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(This article belongs to the Section Mathematics)
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The Expansion Methods of Inception and Its Application
by
Cuiping Shi, Zhenquan Liu, Jiageng Qu and Yuxin Deng
Symmetry 2024, 16(4), 494; https://doi.org/10.3390/sym16040494 - 18 Apr 2024
Abstract
In recent years, with the rapid development of deep learning technology, a large number of excellent convolutional neural networks (CNNs) have been proposed, many of which are based on improvements to classical methods. Based on the Inception family of methods, depthwise separable convolution
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In recent years, with the rapid development of deep learning technology, a large number of excellent convolutional neural networks (CNNs) have been proposed, many of which are based on improvements to classical methods. Based on the Inception family of methods, depthwise separable convolution was applied to Xception to achieve lightweighting, and Inception-ResNet introduces residual connections to accelerate model convergence. However, existing improvements for the Inception module often neglect further enhancement of its receptive field, while increasing the receptive field of CNNs has been widely studied and proven to be effective in improving classification performance. Motivated by this fact, three effective expansion modules are proposed in this paper. The first expansion module, Inception expand (Inception-e) module, is proposed to improve the classification accuracy by concatenating more and deeper convolutional branches. To reduce the number of parameters for Inception e, this paper proposes a second expansion module—Equivalent Inception-e (Eception) module, which is equivalent to Inception-e in terms of feature extraction capability, but which suppresses the growth of the parameter quantity brought by the expansion by effectively reducing the redundant convolutional layers; on the basis of Eception, this paper proposes a third expansion module—Lightweight Eception (Lception) module, which crosses depthwise convolution with ordinary convolution to further effectively reduce the number of parameters. The three proposed modules have been validated on the Cifar10 dataset. The experimental results show that all these extensions are effective in improving the classification accuracy of the models, and the most significant effect is the Lception module, where Lception (rank = 4) on the Cifar10 dataset improves the accuracy by 1.5% compared to the baseline model (Inception module A) by using only 0.15 M more parameters.
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(This article belongs to the Section Computer)
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Accelerated Stability Testing in Food Supplements Underestimates Shelf Life Prediction of Resveratrol with Super-Arrhenius Behavior
by
Andrea Biagini, Nicola Refrigeri, Concetta Caglioti, Paola Sabbatini, Silvia Ticconi, Giada Ceccarelli, Rossana Giulietta Iannitti, Federico Palazzetti and Bernard Fioretti
Symmetry 2024, 16(4), 493; https://doi.org/10.3390/sym16040493 - 18 Apr 2024
Abstract
Thermo-oxidative stability testing plays a critical role in accurately predicting shelf life. These tests are performed in real time and under stress conditions, where degradation processes are accelerated by increasing storage conditions. In this study, high-performance liquid chromatography (HPLC) analyses were performed to
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Thermo-oxidative stability testing plays a critical role in accurately predicting shelf life. These tests are performed in real time and under stress conditions, where degradation processes are accelerated by increasing storage conditions. In this study, high-performance liquid chromatography (HPLC) analyses were performed to evaluate the degradation of resveratrol in nutraceutical tablets as a function of time under different storage conditions in terms of temperature and relative humidity (RH), namely 25 °C/60% RH, 30 °C/65% RH, and 40 °C/75% RH. The latter is an accelerated test and is used to estimate shelf life for long-term storage. Resveratrol is present in both pure form and as a solid dispersion on magnesium dihydroxide microparticles (Resv@MDH). Degradation kinetic constants were determined at 25 °C, 30 °C, and 40 °C, and the Arrhenius behavior of the kinetic constants as a function of temperature was verified. The main results of this work are as follows: (i) the stability of resveratrol in nutraceutical tablets is affected by temperature; (ii) the dependence of the kinetic constants on temperature does not follow the Arrhenius equation, determining an overestimation of the degradation rate at 25 °C; in this regard a modified version of the Arrhenius equation that takes into account the deviation from linearity has been used to estimate the dependence of the kinetic constant on the temperature. These results suggest that accelerated testing does not provide a general model for predicting the shelf life of foods and dietary supplements. The reason may be due to possible matrix effects that result in different degradation mechanisms depending on the temperature. In this regard, symmetry relationships in the kinetics of chemical reactions resulting from microscopic reversibility and their relationship to the deviation from the Arrhenius equation are discussed. However, further research is needed to characterize the degradation mechanisms at different temperatures. The results of these studies would allow accurate prediction of food degradation to improve food safety and risk management and reduce food waste. In addition, knowledge of stability processes is necessary to ensure the maintenance of physiological processes by dietary supplements.
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(This article belongs to the Special Issue From Nanoclusters to Nanoparticles: Symmetry, Theory, Experiments, and Applications)
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Generalized Neuromorphism and Artificial Intelligence: Dynamics in Memory Space
by
Said Mikki
Symmetry 2024, 16(4), 492; https://doi.org/10.3390/sym16040492 - 18 Apr 2024
Abstract
This paper introduces a multidisciplinary conceptual perspective encompassing artificial intelligence (AI), artificial general intelligence (AGI), and cybernetics, framed within what we call the formalism of generalized neuromorphism. Drawing from recent advancements in computing, such as neuromorphic computing and spiking neural networks, as well
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This paper introduces a multidisciplinary conceptual perspective encompassing artificial intelligence (AI), artificial general intelligence (AGI), and cybernetics, framed within what we call the formalism of generalized neuromorphism. Drawing from recent advancements in computing, such as neuromorphic computing and spiking neural networks, as well as principles from the theory of open dynamical systems and stochastic classical and quantum dynamics, this formalism is tailored to model generic networks comprising abstract processing events. A pivotal aspect of our approach is the incorporation of the memory space and the intrinsic non-Markovian nature of the abstract generalized neuromorphic system. We envision future computations taking place within an expanded space (memory space) and leveraging memory states. Positioned at a high abstract level, generalized neuromorphism facilitates multidisciplinary applications across various approaches within the AI community.
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(This article belongs to the Section Mathematics)
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12 April 2024
Meet Us at the 31st International Conference on Neutrino Physics and Astrophysics (Neutrino 2024), 16–22 June 2024, Milan, Italy
Meet Us at the 31st International Conference on Neutrino Physics and Astrophysics (Neutrino 2024), 16–22 June 2024, Milan, Italy
12 April 2024
Interview with Dr. Kerlos Atia Abdalmalak Dawoud—Winner of the Symmetry 2024 Travel Award
Interview with Dr. Kerlos Atia Abdalmalak Dawoud—Winner of the Symmetry 2024 Travel Award
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Mathematical Modeling
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Algorithms, Axioms, Fractal Fract, Mathematics, Symmetry
Fractal and Design of Multipoint Iterative Methods for Nonlinear Problems
Topic Editors: Xiaofeng Wang, Fazlollah SoleymaniDeadline: 30 June 2024
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Algorithms, Future Internet, Information, Mathematics, Symmetry
Research on Data Mining of Electronic Health Records Using Deep Learning Methods
Topic Editors: Dawei Yang, Yu Zhu, Hongyi XinDeadline: 31 August 2024
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Algorithms, Computation, Mathematics, Molecules, Symmetry, Nanomaterials, Materials
Advances in Computational Materials Sciences
Topic Editors: Cuiying Jian, Aleksander CzekanskiDeadline: 30 September 2024
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Symmetry
The Dark Universe: The Harbinger of a Major Discovery
Guest Editor: Konstantin ZioutasDeadline: 30 April 2024
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The Qualitative Theory of Functional Differential Equations and their Applications
Guest Editors: Osama Moaaz, Higinio RamosDeadline: 15 May 2024
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The Nuclear Physics of Neutron Stars
Guest Editor: Charalampos MoustakidisDeadline: 31 May 2024
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Interplay between NISQ Devices and Symmetry
Guest Editors: Thi Ha Kyaw, Guillermo RomeroDeadline: 17 June 2024