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Symmetry, Volume 12, Issue 6 (June 2020) – 188 articles

Cover Story (view full-size image): Symmetry (https://www.mdpi.com/journal/symmetry) is an international peer-reviewed open access journal in a variety of subjects rank from physics, chemistry, biology, mathematics, to computer science. Our aim is to encourage scientists to publish their theoretical and experimental results in as much detail as possible.
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37 pages, 532 KiB  
Article
A Robust q-Rung Orthopair Fuzzy Einstein Prioritized Aggregation Operators with Application towards MCGDM
by Muhammad Riaz, Hafiz Muhammad Athar Farid, Humaira Kalsoom, Dragan Pamučar and Yu-Ming Chu
Symmetry 2020, 12(6), 1058; https://doi.org/10.3390/sym12061058 - 26 Jun 2020
Cited by 50 | Viewed by 3951
Abstract
A q-rung orthopair fuzzy set (q-ROFS) provides a significant mechanism for managing symmetrical aspects in real life circumstances. The renowned distinguishing feature of q-ROFS is that the sum of the qth powers to each membership degree (MD) and non-membership degree (NMD) is less [...] Read more.
A q-rung orthopair fuzzy set (q-ROFS) provides a significant mechanism for managing symmetrical aspects in real life circumstances. The renowned distinguishing feature of q-ROFS is that the sum of the qth powers to each membership degree (MD) and non-membership degree (NMD) is less than or equal 1, and therefore the comprehensive uncertain space for q-ROF information is broader. Numerous researchers have suggested several aggregation operators based on q-ROFSs. In order to discuss prioritized relationship in the criterion and a smooth approximation of q-ROF information, we introduced q-rung orthopair fuzzy Einstein prioritized weighted averaging (q-ROFEPWA) operator and q-rung orthopair fuzzy Einstein prioritized weighted geometric (q-ROFEPWG) operator. Additionally, we presented a multi-criteria group decision making (MCGDM) technique based on q-rung orthopair fuzzy Einstein prioritized aggregation operators. These operators can evaluate the possible symmetric roles of the criterion that express the real phenomena of the problem. In order to investigate characteristic of suggested operators regarding the symmetry of attributes and their symmetrical roles under q-ROF information, we presented an application of Einstein prioritized aggregation operators. Finally, by comparing it with some other established representative MCGDM models, an illustrative example is provided to check the feasibility, efficiency and supremacy of the proposed technique. Full article
(This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problems)
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<p>Graphical comparison between IF-value, PF-value and q-ROF-value.</p>
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16 pages, 4614 KiB  
Article
Adaptive Sliding Mode Control Based on Disturbance Observer for Placement Pressure Control System
by Qi Hong, Yaoyao Shi and Zhen Chen
Symmetry 2020, 12(6), 1057; https://doi.org/10.3390/sym12061057 - 26 Jun 2020
Cited by 7 | Viewed by 2700
Abstract
In the process of composite placement, irregularity and asymmetry pressure fluctuation will affect the density and evenness of composite products, which lead to the inconsistency of interfacial strength and fiber volume fraction. The dynamic performance of placement pressure systems will be affected by [...] Read more.
In the process of composite placement, irregularity and asymmetry pressure fluctuation will affect the density and evenness of composite products, which lead to the inconsistency of interfacial strength and fiber volume fraction. The dynamic performance of placement pressure systems will be affected by external disturbance, mechanism friction and measurement noise. In this paper, an adaptive sliding mode control (ASMC) strategy based on disturbance observer (DOB) is proposed. The disturbance observer is introduced to estimate the equivalent disturbance torque, and the estimation error is compensated by the switching term of sliding mode control. The adaptive method is used to ensure that the switching gain is not overestimated, and then the Lyapunov function is used to verify the stability of the closed-loop control system. The experimental results and simulation analysis show that ASMC-DOB has high control accuracy and good robustness. At the same time, the designed algorithm can effectively reduce the void content of composite products. Full article
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<p>Placement flow chart.</p>
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<p>Schematic of composite laminate.</p>
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<p>Structure of tension actuator and heating unit.</p>
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<p>Cutting mechanism of composite tape.</p>
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<p>Placement pressure control process.</p>
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<p>Schematic of pressure control system actuator.</p>
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<p>Block diagram of adaptive sliding mode control strategy based on disturbance observer (ASMC-DOB).</p>
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<p>Step response of PID (simulation). (<b>a</b>) Control error and tracking response; (<b>b</b>) Control input.</p>
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<p>Step response of SMC (simulation). (<b>a</b>) Control error and tracking response; (<b>b</b>) Control input.</p>
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<p>Step response of ASMC (simulation). (<b>a</b>) Control error and tracking response (<b>b</b>) Control input.</p>
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<p>Step response of PID (experiment). (<b>a</b>) Control error and tracking response (<b>b</b>) Control input.</p>
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<p>Step response of SMC (experiment). (<b>a</b>) Control error and tracking response (<b>b</b>) Control input.</p>
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<p>Step response of ASMC (Experiment). (<b>a</b>) Control error and tracking response (<b>b</b>) Control input.</p>
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<p>Control error comparison of different control algorithms.</p>
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<p>Process for sampling and measuring void content of composite tape placement products.</p>
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15 pages, 2174 KiB  
Article
Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network
by Yanan Guo, Xiaoqun Cao, Bainian Liu and Mei Gao
Symmetry 2020, 12(6), 1056; https://doi.org/10.3390/sym12061056 - 25 Jun 2020
Cited by 69 | Viewed by 6701
Abstract
Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence [...] Read more.
Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research. Full article
(This article belongs to the Special Issue Symmetry in Artificial Visual Perception and Its Application)
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<p>U-Net architecture diagram modified from the original study [<a href="#B27-symmetry-12-01056" class="html-bibr">27</a>]. Green/yellow boxes indicate multi-channel feature maps; red arrows indicate 3 × 3 convolution for feature extraction; cyan arrows indicate skip-connection for feature fusion; downward orange arrows indicate max pooling for dimension reduction; upward orange arrows indicate up-sampling for dimension recovery.</p>
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<p>The structure of the Cloud-AttU model. All the orange/white boxes correspond to multi-channel feature maps. The Cloud-AttU is equipped with skip connections to adaptively rescale feature maps in the encoding path with weights learned from the correlation of feature maps in the decoding path.</p>
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<p>The diagram of attention gate in Cloud-AttU.</p>
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<p>Cloud detection results of different scenes over Landsat-Cloud dataset [<a href="#B48-symmetry-12-01056" class="html-bibr">48</a>]. The first row shows the RGB images (<b>top</b>), the second row shows the ground truths (<b>middle</b>) and the third row shows the predictions of Cloud-AttU model (<b>bottom</b>). The yellow in the figure indicates that cloud exists and the purple indicates that no cloud exists.</p>
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<p>Cloud detection results of different scenes over Landsat-Cloud dataset [<a href="#B48-symmetry-12-01056" class="html-bibr">48</a>]. The first column is the RGB image (<b>left</b>), the second column is the ground truth (<b>center left</b>), the third column is the predictions of Cloud-Net model (<b>center right</b>) and the fourth column is the predictions of Cloud-AttU model (<b>right</b>). The yellow in the figure indicates that cloud exists and the purple indicates that no cloud exists.</p>
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<p>Cloud detection results under the influence of snow and ice ground over Landsat-Cloud dataset [<a href="#B48-symmetry-12-01056" class="html-bibr">48</a>], the first column is RGB image (<b>left</b>), the second column is ground truth (<b>middle left</b>), the third column is the prediction of the Cloud-Net model (<b>center right</b>), and the fourth column is the prediction of the Cloud-AttU model (<b>right</b>). The yellow in the figure indicates the presence of clouds and the purple indicates the absence of clouds.</p>
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<p>Cloud detection results under the influence of other factors over Landsat-Cloud dataset [<a href="#B48-symmetry-12-01056" class="html-bibr">48</a>], the first column is RGB image (<b>left</b>), the second column is ground truth (<b>middle left</b>), the third column is the prediction of the Cloud-Net model (<b>center right</b>), and the fourth column is the prediction of the Cloud-AttU model (<b>right</b>). The yellow in the figure indicates the presence of clouds and the purple indicates the absence of clouds.</p>
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17 pages, 1113 KiB  
Article
Parking Site Selection for Light Rail Stations in Muaeng District, Khon Kaen, Thailand
by Narueset Prasertsri and Satith Sangpradid
Symmetry 2020, 12(6), 1055; https://doi.org/10.3390/sym12061055 - 25 Jun 2020
Cited by 11 | Viewed by 3135
Abstract
Khon Kaen District is in the central, north-east part of Thailand and is being developed to handle the country’s growth. Khon Kaen District is undertaking the project of building a light rail as a facility for the people. Consequently, one of the problems [...] Read more.
Khon Kaen District is in the central, north-east part of Thailand and is being developed to handle the country’s growth. Khon Kaen District is undertaking the project of building a light rail as a facility for the people. Consequently, one of the problems is ensuring adequate parking for people using the light rail service. In general, the symmetry concept naturally used in decision making to finding an optimal solution for decision and optimization problems. In this paper, multi-criteria decision analysis (MCDA) and multi-objective decision making (MODM) were used to solve the parking site selection problem, which made the decision easier. This paper proposed an analytic hierarchy process (AHP) technique, combined with the geographical information system (GIS), to evaluate the weight of the criteria used in the analysis and find potential parking solutions. Furthermore, this paper proposed the application of a linguistic technique with fuzzy TOPSIS methods to analyze the appropriateness of parking site selections from potential candidates to support use of the light rail. The results of the MCDA show that the most suitable parking lot location is along the light rail and closest to the business area. The results of the fuzzy TOPSIS method, both positive and negative ideal decisions, can help inform decision makers in selecting which candidate site is optimal for parking. Full article
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<p>Suitable parking map from multi-criteria decision analysis (MCDA).</p>
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<p>Parking site candidates map from fuzzy technique for order preference by similarity to ideal solution (TOPSIS).</p>
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15 pages, 3679 KiB  
Article
Predicting the Volume of Response to Tweets Posted by a Single Twitter Account
by Krzysztof Fiok, Waldemar Karwowski, Edgar Gutierrez and Tareq Ahram
Symmetry 2020, 12(6), 1054; https://doi.org/10.3390/sym12061054 - 25 Jun 2020
Cited by 15 | Viewed by 4121
Abstract
Social media users, including organizations, often struggle to acquire the maximum number of responses from other users, but predicting the responses that a post will receive before publication is highly desirable. Previous studies have analyzed why a given tweet may become more popular [...] Read more.
Social media users, including organizations, often struggle to acquire the maximum number of responses from other users, but predicting the responses that a post will receive before publication is highly desirable. Previous studies have analyzed why a given tweet may become more popular than others, and have used a variety of models trained to predict the response that a given tweet will receive. The present research addresses the prediction of response measures available on Twitter, including likes, replies and retweets. Data from a single publisher, the official US Navy Twitter account, were used to develop a feature-based model derived from structured tweet-related data. Most importantly, a deep learning feature extraction approach for analyzing unstructured tweet text was applied. A classification task with three classes, representing low, moderate and high responses to tweets, was defined and addressed using four machine learning classifiers. All proposed models were symmetrically trained in a fivefold cross-validation regime using various feature configurations, which allowed for the methodically sound comparison of prediction approaches. The best models achieved F1 scores of 0.655. Our study also used SHapley Additive exPlanations (SHAP) to demonstrate limitations in the research on explainable AI methods involving Deep Learning Language Modeling in NLP. We conclude that model performance can be significantly improved by leveraging additional information from the images and links included in tweets. Full article
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<p>Box plots of annual responses to tweets from the official @USNavy account. Outliers were not plotted, for clarity. From left to right: replies, likes, and retweets.</p>
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<p>The workflow in the proposed classification framework.</p>
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<p>Procedure for extracting features from unstructured text.</p>
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<p>Procedure for the pre-processing of tweets.</p>
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<p>Deep Learning Feature Extractor architecture and training.</p>
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<p>Pseudo code describing computation procedures adopted in our study.</p>
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<p>Partial results regarding the prediction of replies. Mean F1 scores are presented as a function of selected LMs, machine learning classifiers, and feature groups I–III.</p>
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<p>SHAP explanations for a GB model trained on the group I features. Feature abbreviations: ym—publication time of the tweet, counted in months after January 2017; has_url2—whether the tweet contains any links; has_hash—whether the tweet contains any hashtags; is_reply_to—whether the tweet is a reply to another tweet; has_retweet—whether the tweet contains any retweets. Classes indicate a level of response: 0—low; 1—moderate; 2—high.</p>
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<p>SHAP explanations for a GB model trained on group II features obtained by RoBERTa LM. Features are numbered by the DLFE, and the model architecture prevents them from being decoded into any human-understandable explanation. Classes indicate a level of response: 0—low; 1—moderate; 2—high.</p>
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<p>SHAP explanations for a GB model trained on group III features (obtained by RoBERTa LM and structured features). Feature names are the same as in <a href="#symmetry-12-01054-f008" class="html-fig">Figure 8</a> and <a href="#symmetry-12-01054-f009" class="html-fig">Figure 9</a>.</p>
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24 pages, 1179 KiB  
Article
Quantum Key Distillation Using Binary Frames
by Luis A. Lizama-Perez and J. Mauricio López
Symmetry 2020, 12(6), 1053; https://doi.org/10.3390/sym12061053 - 24 Jun 2020
Cited by 6 | Viewed by 3198
Abstract
We introduce a new integral method for Quantum Key Distribution to perform sifting, reconciliation and amplification processes to establish a cryptographic key through the use of binary matrices called frames which are capable to increase quadratically the secret key rate. Since the eavesdropper [...] Read more.
We introduce a new integral method for Quantum Key Distribution to perform sifting, reconciliation and amplification processes to establish a cryptographic key through the use of binary matrices called frames which are capable to increase quadratically the secret key rate. Since the eavesdropper has no control on Bob’s double matching detection events, our protocol is not vulnerable to the Intercept and Resend (IR) attack nor the Photon Number Splitting (PNS) attack. The method can be implemented with the usual optical Bennett–Brassard ( B B 84 ) equipment allowing strong pulses in the quantum regime. Full article
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<p>BB84 pulses represented in the Bloch sphere. The quantum states prepared by Alice (<b>left</b>) could be <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math> and the measurement bases Bob could apply are <span class="html-italic">X</span> and <span class="html-italic">Z</span> (<b>right</b>).</p>
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<p>We represent pairs of quantum states: (<b>a</b>) orthogonal pairs (<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>) and (<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>), (<b>b</b>) non-orthogonal pairs (<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>), (<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>), (<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>) and (<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>) and (<b>c</b>) parallel pairs (<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>), (<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>),(<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>) and (<math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>).</p>
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<p>Alice sends the non-orthogonal pair <math display="inline"><semantics> <mrow> <mo>(</mo> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> <mo>,</mo> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> <mo>)</mo> </mrow> </semantics></math> to Bob. After a double matching detection event is produced at Bob’s optical system it could register <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math> or <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>Quantum states inside a non-orthogonal pair are separated temporally to avoid losses due to consecutive detection events. The order between two non-orthogonal states is not relevant for the present discussion.</p>
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<p>We see (at left) the states prepared by Alice, two pairs of non-orthogonal states: <math display="inline"><semantics> <mrow> <mo>(</mo> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> <mo>,</mo> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>(</mo> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>1</mn> <mi>X</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> <mo>,</mo> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mn>0</mn> <mi>Z</mi> </msub> <mrow> <mo>〉</mo> </mrow> </mrow> <mo>)</mo> </mrow> </semantics></math>. After a double matching detection event is produced at Bob’s side (in this example two double detection events) the possible matching results are exhibited at the right.</p>
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<p>The exchange of messages assuming an error free protocol. <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>O</mi> </mrow> </semantics></math> represents the pairs of non-orthogonal qubits, the sub-indices <span class="html-italic">k</span> denote the double matching detection events at Bob’s station, <span class="html-italic">f</span> represents the the required information to construct the frames and <span class="html-italic">s</span> denotes the sifting bits computed by Bob.</p>
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<p>From the experimental simulation in <a href="#symmetry-12-01053-t014" class="html-table">Table 14</a> we show the frame contribution when QBER = 30%, 605,060 frames have been created and 237,762 correspond to auxiliary frames. For each frame <math display="inline"><semantics> <msub> <mi>f</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>f</mi> <mn>3</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>f</mi> <mn>4</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>f</mi> <mn>6</mn> </msub> </semantics></math> we show the number of frames created (left) and the frames after error correction (right).</p>
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<p>Alice sends a pair of non-orthogonal states to Bob who obtains a double matching detection at his optical detectors. Eve has a copy of such states, however she has a 0.5 chance to choose the correct measurement basis (<span class="html-italic">X</span> or <span class="html-italic">Z</span>). Furthermore, the probability to get a double matching detection event is 0.5. Therefore, Eve’s probability to get Bob’s result is just 0.25.</p>
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<p>Alice sends a pair of non-orthogonal states to Bob who obtains a double matching detection event at his optical detectors. Eve has a copy of such states, however he has a 0.5 chance to choose the optimal measurement basis, in this case the <math display="inline"><semantics> <mrow> <mi>X</mi> <mo>−</mo> <mi>Z</mi> </mrow> </semantics></math> basis. Despite Eve choose between bases <math display="inline"><semantics> <mrow> <mi>X</mi> <mo>+</mo> <mi>Z</mi> </mrow> </semantics></math> or <math display="inline"><semantics> <mrow> <mi>X</mi> <mo>−</mo> <mi>Z</mi> </mrow> </semantics></math>, the chance to guess Bob’s result is <math display="inline"><semantics> <mrow> <mfrac> <mn>9</mn> <mn>16</mn> </mfrac> <mo>=</mo> <mn>0.5625</mn> </mrow> </semantics></math> so she obtains an inconclusive result with <math display="inline"><semantics> <mrow> <mfrac> <mn>6</mn> <mn>16</mn> </mfrac> <mo>=</mo> <mn>0.375</mn> </mrow> </semantics></math>. From here, the probability for Eve to obtain Bob’s measurement result is 0.28125.</p>
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8 pages, 1411 KiB  
Article
Asymmetries in Football: The Pass—Goal Paradox
by Daniel R. Antequera, David Garrido, Ignacio Echegoyen, Roberto López del Campo, Ricardo Resta Serra and Javier M. Buldú
Symmetry 2020, 12(6), 1052; https://doi.org/10.3390/sym12061052 - 24 Jun 2020
Cited by 5 | Viewed by 3988
Abstract
We investigate the relation between the number of passes made by a football team and the number of goals. We analyze the 380 matches of a complete season of the Spanish national league “LaLiga" (2018/2019). We observe how the number of scored goals [...] Read more.
We investigate the relation between the number of passes made by a football team and the number of goals. We analyze the 380 matches of a complete season of the Spanish national league “LaLiga" (2018/2019). We observe how the number of scored goals is positively correlated with the number of passes made by a team. In this way, teams on the top (bottom) of the ranking at the end of the season make more (less) passes than the rest of the teams. However, we observe a strong asymmetry when the analysis is made depending on the part of the match. Interestingly, fewer passes are made in the second half of a match, while, at the same time, more goals are scored. This paradox appears in the majority of teams, and it is independent of the number of passes made. These results confirm that goals in the first half of matches are more “costly” in terms of passes than those scored in second halves. Full article
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<p>Correlation between the number of completed passes and scored goals. Each point corresponds to a team and the red solid line is the linear regression of the points, which has a slope <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>0.026</mn> </mrow> </semantics></math>, an intercept of <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.087</mn> </mrow> </semantics></math> and a correlation coefficient of <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>0.672</mn> </mrow> </semantics></math>.</p>
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<p>Number of passes per part of the match. For each team <span class="html-italic">i</span>, in blue, number of passes <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> completed during the first half of the match. In red, the number of passes <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> completed in the second half. Teams are ordered, from left to right, according to the ranking at the end of the season.</p>
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<p>Number of goals per part of the match. For each team <span class="html-italic">i</span>, in blue, the number of goals <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> scored during the first half of the match. In red, the number of goals <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> scored in the second half. Teams are ordered, from left to right, according to the ranking at the end of the season.</p>
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<p>The cost of a goal, in number of passes. For each team <span class="html-italic">i</span>, in blue, the number of passes per scored goal (<math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>1</mn> </msub> <mo>/</mo> <msub> <mi>m</mi> <mn>1</mn> </msub> </mrow> </semantics></math>) during the first half of the match. In red, the same ratio in the second half (<math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mn>2</mn> </msub> <mo>/</mo> <msub> <mi>m</mi> <mn>2</mn> </msub> </mrow> </semantics></math>). Teams are ordered, from left to right, according to the ranking at the end of the season.</p>
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15 pages, 2485 KiB  
Article
Monaural Singing Voice and Accompaniment Separation Based on Gated Nested U-Net Architecture
by Haibo Geng, Ying Hu and Hao Huang
Symmetry 2020, 12(6), 1051; https://doi.org/10.3390/sym12061051 - 24 Jun 2020
Cited by 5 | Viewed by 3155
Abstract
This paper proposes a separation model adopting gated nested U-Net (GNU-Net) architecture, which is essentially a deeply supervised symmetric encoder–decoder network that can generate full-resolution feature maps. Through a series of nested skip pathways, it can reduce the semantic gap between the feature [...] Read more.
This paper proposes a separation model adopting gated nested U-Net (GNU-Net) architecture, which is essentially a deeply supervised symmetric encoder–decoder network that can generate full-resolution feature maps. Through a series of nested skip pathways, it can reduce the semantic gap between the feature maps of encoder and decoder subnetworks. In the GNU-Net architecture, only the backbone not including nested part is applied with gated linear units (GLUs) instead of conventional convolutional networks. The outputs of GNU-Net are further fed into a time-frequency (T-F) mask layer to generate two masks of singing voice and accompaniment. Then, those two estimated masks along with the magnitude and phase spectra of mixture can be transformed into time-domain signals. We explored two types of T-F mask layer, discriminative training network and difference mask layer. The experiment results show the latter to be better. We evaluated our proposed model by comparing with three models, and also with ideal T-F masks. The results demonstrate that our proposed model outperforms compared models, and it’s performance comes near to ideal ratio mask (IRM). More importantly, our proposed model can output separated singing voice and accompaniment simultaneously, while the three compared models can only separate one source with trained model. Full article
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<p>Proposed Separation Model.</p>
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<p>Illustration of 6-level nested U-Net architecture with gated linear units (GLUs) applied only on backbone. Dashed line columns denote the backbone of gated nested U-Net (GNU-Net), and the light-pink triangle denotes the nested part. Cubes denote the output of each layer or concatenation operation, except for <math display="inline"><semantics> <msup> <mi mathvariant="bold">X</mi> <mrow> <mn>0</mn> <mo>,</mo> <mn>0</mn> </mrow> </msup> </semantics></math> which denotes the input.</p>
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<p>Diagrams of a convolutional GLU block and a deconvolutional GLU block, where <math display="inline"><semantics> <mi>σ</mi> </semantics></math> denotes a sigmoid function.</p>
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<p>Two kinds of mask layer.</p>
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<p>Three evaluation metrics of estimated singing voice and accompaniment by various network models.</p>
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<p>(<b>a</b>) The mixture magnitude spectrogram of a clip in iKala dataset; (<b>b</b>,<b>c</b>) the ground truth spectra of clean singing voice and pure accompaniment; (<b>d</b>–<b>f</b>) the magnitude spectra of estimated singing voice by U-Net model and our proposed two models; (<b>g</b>–<b>i</b>) The magnitude spectra of estimated accompaniment by U-Net model and our proposed two models (model1, NU-Net+DML; model2, GNU-Net+DML). Accom denotes accompaniment.</p>
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18 pages, 911 KiB  
Article
AACS: Attribute-Based Access Control Mechanism for Smart Locks
by Zhenghao Xin, Liang Liu and Gerhard Hancke
Symmetry 2020, 12(6), 1050; https://doi.org/10.3390/sym12061050 - 23 Jun 2020
Cited by 8 | Viewed by 5533
Abstract
This article researched the security and application of smart locks in Internet of Things environments in the domain of computer and engineer science and symmetry. Smart locks bring much convenience for users. However, most smart lock systems are cloud-based and it is problematic [...] Read more.
This article researched the security and application of smart locks in Internet of Things environments in the domain of computer and engineer science and symmetry. Smart locks bring much convenience for users. However, most smart lock systems are cloud-based and it is problematic managing and enforcing the permissions of an authorized device if the device is offline. Moreover, most smart lock systems lack fine-grained access control and cascading removal of permissions. In this paper, we leverage attribute-based access control mechanisms to manage the access of visitors with different identities. We use identity-based encryption to verify the identity of the visitor. In our proposed system, the administrator uses the policy set in the smart lock to implement access control on the device side, which reduces the dependence of access control on the server. We set attributes such as role, time, date, and location to have fine-grained control over access to different permissions and roles that might appear in the house. And the scheme provides the cascading delete function while providing the group access function. Our solution considers multiple roles in the home as well as hierarchical management issues, and improves the applicability of the smart lock system in complex residential and commercial situations. In the experimental section, we show that our system can be applied to premises with many different inhabitant identities. Full article
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<p>This is the Device-Gateway-Cloud (DGC) model, a common communication mode between intelligent locks and the cloud. The smart lock USES the user’s smartphone as a mobile gateway to communicate with the cloud and connect with the user’s phone via Bluetooth Low Power (BLE). Since most smart locks store data in the cloud rather than inside the lock, users access the locks using a digital key on their smartphone. As a result, malicious users can take their phones off-line to avoid having their digital keys withdrawn by administrators.</p>
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<p>Attribute-Based Access Control Mechanism for Smart Locks (AACS) communication mode. Based on the DGC model, AACS takes the lock as the center and stores information in the lock, while the administrator directly manages the policy set within the lock. After authentication, the user sends access requests containing various attributes for authentication within the smart lock. In this case, malicious users can no longer retain access by disconnecting.</p>
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<p>System structure of AACS. NAR: Natural access request, AA: Attribute authority, AAR: Attribute access request, PEP: Policy enforcement point, PDP: Policy decision point, PAP: Policy administer point.</p>
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<p>Permission allocation diagram. Alice is the owner of the house. P1, P2, and P3 are regular members of the family (wife, boyfriend and girlfriend, children, etc.), P4 and P5 represent users with rights granted by P2, and P6 and P7 represent users with rights granted by P3.</p>
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<p>The time it takes AACS to process access requests under 100–2000 user conditions.</p>
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7 pages, 300 KiB  
Article
Prediction of the Neutrino Mass Scale Using Coma Galaxy Cluster Data
by Peter D. Morley
Symmetry 2020, 12(6), 1049; https://doi.org/10.3390/sym12061049 - 23 Jun 2020
Viewed by 2378
Abstract
The near degeneracy of the neutrino masses—a mass symmetry—allows condensed neutrino objects that may be the Dark Matter everybody is looking for. If the KATRIN terrestrial experiment has a neutrino mass signal, it will contradict the analysis of the Planck Satellite Consortium reduction [...] Read more.
The near degeneracy of the neutrino masses—a mass symmetry—allows condensed neutrino objects that may be the Dark Matter everybody is looking for. If the KATRIN terrestrial experiment has a neutrino mass signal, it will contradict the analysis of the Planck Satellite Consortium reduction of their raw cosmological microwave data. Using Condensed Neutrino Objects as the Dark Matter along with Coma Galaxy Cluster data, we predict that KATRIN will indeed see a neutrino mass signal. If this physics drama unfolds, there will be profound implications for cosmology, which are discussed in this paper. Full article
(This article belongs to the Special Issue Nature and Origin of Dark Matter and Dark Energy)
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<p>CNO fit to weak lensing data, which gives the neutrino mass scale [<a href="#B5-symmetry-12-01049" class="html-bibr">5</a>,<a href="#B6-symmetry-12-01049" class="html-bibr">6</a>].</p>
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<p>Approximate location of the CNO center using fastest galaxy GMP = 3176, which has offsets R.A. +0.0579 arc-minutes and Dec −13.465 arc-minutes. The figure background is taken from reference [<a href="#B9-symmetry-12-01049" class="html-bibr">9</a>], which shows the actual Coma Galaxy Cluster. At a distance of 101.3 Mpc, the 2.191 Mpc CNO radius translates to 74.35 arc-minutes.</p>
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<p>Spatial variation of the reduced Fermi momentum <span class="html-italic">x</span> for the CNO having boundary value <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> <mn>0.010</mn> </mrow> </semantics></math>. The units of length are 46128.98 pc/<math display="inline"><semantics> <msubsup> <mi>m</mi> <mrow> <mi>ν</mi> </mrow> <mn>2</mn> </msubsup> </semantics></math> with <math display="inline"><semantics> <msub> <mi>m</mi> <mi>ν</mi> </msub> </semantics></math> in units of eV/c<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>.</p>
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<p>CNO mass density for <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> <mn>0.010</mn> </mrow> </semantics></math> CNO. The units of mass density are <math display="inline"><semantics> <mrow> <mn>1.76307</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>20</mn> </mrow> </msup> <msubsup> <mi>m</mi> <mrow> <mi>ν</mi> </mrow> <mn>4</mn> </msubsup> </mrow> </semantics></math> gm/cm<math display="inline"><semantics> <msup> <mrow/> <mn>3</mn> </msup> </semantics></math> with <math display="inline"><semantics> <msub> <mi>m</mi> <mi>ν</mi> </msub> </semantics></math> in units of eV/c<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>.</p>
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18 pages, 2443 KiB  
Article
Estimation of Natural Radionuclides’ Concentration of the Plutonic Rocks in the Sakarya Zone, Turkey Using Multivariate Statistical Methods
by Fusun Yalcin, Nurdane Ilbeyli, Mehmet Demirbilek, Mustafa Gurhan Yalcin, Alper Gunes, Abdullah Kaygusuz and Suleyman Fatih Ozmen
Symmetry 2020, 12(6), 1048; https://doi.org/10.3390/sym12061048 - 23 Jun 2020
Cited by 22 | Viewed by 3418
Abstract
The study aimed to determine the natural radioactivity levels of 226Ra, 232Th, and 40K by the Gamma-Ray spectrometry method, and radiological hazard parameters of the plutonic rocks in the Western and Central Sakarya Zone and to analyze the data using [...] Read more.
The study aimed to determine the natural radioactivity levels of 226Ra, 232Th, and 40K by the Gamma-Ray spectrometry method, and radiological hazard parameters of the plutonic rocks in the Western and Central Sakarya Zone and to analyze the data using multivariate statistical methods. The average radiological values of samples were determined as 40K (1295.3 Bq kg−1) > 232Th (132.1 Bq kg−1) > 226Ra (119.7 Bq kg−1). According to the skewness values of the distributions of the examined radionuclides, 226Ra (2.1) and 232Th (0.7) seemed to be positively right-skewed while 40K (−0.2) had a negatively right-skewed histogram. On the other hand, the following kurtosis values were calculated for the distributions: 226Ra (5.8 > 3), 232Th (−0.7), and 40K (−0.8). Kolmogorov–Smirnov and Shapiro–Wilk tests were applied to the data to test their normality. Therefore, Spearman’s correlation coefficient method was performed. The radionuclides of 226Ra and 232Th were found to have a positive correlation with radiological hazard parameters of the samples. 2 (two)-related factors identified, and the cumulative value was calculated to be 98.7% on the basis of the Scree Plot. According to the hierarchical cluster analysis, the samples that are grouped with those from Camlik region are prominent. The average radioactivity values of Camlik, Sogukpinar, Karacabey, and Sogut (except for 232Th) regions were detected to be higher than the world averages while the value of 40K was also found to be higher than the average values of various countries in the world. Full article
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<p>Site location map of the study area, sample locations, and the distribution of <sup>40</sup>K samples.</p>
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<p>Frequency distributions of <sup>226</sup>Ra, <sup>232</sup>Th, and <sup>40</sup>K.</p>
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<p>The relation between <sup>226</sup>Ra and <sup>232</sup>Th concentrations.</p>
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<p>Scree plot of the principal component analysis.</p>
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<p>Component plot in the varimax-rotated space, component 1 (95.1%) and component 2 (3.5%).</p>
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<p>The clustering of radioactive variables.</p>
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<p>The activity concentration of <sup>226</sup>Ra, <sup>232</sup>Th, and <sup>40</sup>K in the plutonic rock samples.</p>
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23 pages, 7042 KiB  
Article
Landslide Susceptibility Mapping Using the Slope Unit for Southeastern Helong City, Jilin Province, China: A Comparison of ANN and SVM
by Chenglong Yu and Jianping Chen
Symmetry 2020, 12(6), 1047; https://doi.org/10.3390/sym12061047 - 23 Jun 2020
Cited by 49 | Viewed by 4243
Abstract
The purpose of this study is to produce a landslide susceptibility map of Southeastern Helong City, Jilin Province, Northeastern China. According to the geological hazard survey (1:50,000) project of Helong city, a total of 83 landslides were mapped in the study area. The [...] Read more.
The purpose of this study is to produce a landslide susceptibility map of Southeastern Helong City, Jilin Province, Northeastern China. According to the geological hazard survey (1:50,000) project of Helong city, a total of 83 landslides were mapped in the study area. The slope unit, which is classified based on the curvature watershed method, is selected as the mapping unit. Based on field investigations and previous studies, three groups of influencing Factors—Lithological factors, topographic factors, and geological environment factors (including ten influencing factors)—are selected as the influencing factors. Artificial neural networks (ANN’s) and support vector machines (SVM’s) are introduced to build the landslide susceptibility model. Five-fold cross-validation, the receiver operating characteristic curve, and statistical parameters are used to optimize model. The results show that the SVM model is the optimal model. The landslide susceptibility maps produced using the SVM model are classified into five grades—very high, high, moderate, low, and very low—and the areas of the five grades were 127.43, 151.60, 198.77, 491.19, and 506.91 km2, respectively. The very high and high susceptibility areas included 79.52% of the total landslides, demonstrating that the landslide susceptibility map produced in this paper is reasonable. Consequently, this study can serve as a guide for landslide prevention and for future land planning in the southeast of Helong city. Full article
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<p>Geographical position and landslide inventory of the study area. (<b>a</b>,<b>b</b>): geographical position of the study area; (<b>c</b>) landslide inventory of the study area; (<b>d</b>) geological map of the study area; (<b>e</b>,<b>f</b>) typical landslides of the study area.</p>
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<p>Flowchart of this study.</p>
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<p>Construction process of the slope units based on the curvature watershed method.</p>
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<p>(<b>a</b>) Elevation map; (<b>b</b>) slope angle map.</p>
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<p>Influencing factor maps of the study area: (<b>a</b>) lithology (downloaded using 91 Weitu software), (<b>b</b>) elevation, (<b>c</b>) slope angle, and (<b>d</b>) slope aspect.</p>
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<p>Influencing factors maps of the study area: (<b>a</b>) topographic relief, (<b>b</b>) curvature, (<b>c</b>) land-use, and (<b>d</b>) rainfall.</p>
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<p>Influencing factors maps of the study area: (<b>a</b>) distance to river and (<b>b</b>) distance to fault.</p>
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<p>Schematic diagram of a simple artificial neural network (ANN) model.</p>
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<p>Division of slope units of the study area.</p>
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<p>Area under the curve (AUC) values of model evaluation parameters when the hidden neurons of ANN model varied.</p>
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<p>Landslide susceptibility map. (<b>a</b>) ANN model; and (<b>b</b>) support vector machine (SVM) model.</p>
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<p>Statistics of morphological characteristics of slope units. (<b>a</b>) Slope unit area distribution diagram; (<b>b</b>) slope unit shape index distribution diagram.</p>
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20 pages, 4629 KiB  
Article
A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms
by Omar Almomani
Symmetry 2020, 12(6), 1046; https://doi.org/10.3390/sym12061046 - 23 Jun 2020
Cited by 191 | Viewed by 11068
Abstract
The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. [...] Read more.
The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal. Full article
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<p>Classification of anomaly detection [<a href="#B4-symmetry-12-01046" class="html-bibr">4</a>].</p>
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<p>ML procedures [<a href="#B4-symmetry-12-01046" class="html-bibr">4</a>].</p>
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<p>The architecture of the proposed model.</p>
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<p>Wolves’ hierarchy [<a href="#B17-symmetry-12-01046" class="html-bibr">17</a>].</p>
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<p>UNSW-NB15 testbed [<a href="#B42-symmetry-12-01046" class="html-bibr">42</a>].</p>
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<p>True Positive Rate (TPR).</p>
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<p>False Negative Rate (FNR).</p>
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<p>False Positive Rate (FPR).</p>
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<p>True Negative Rate (TNR).</p>
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<p>Accuracy.</p>
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<p>The precision rate.</p>
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<p>The sensitivity rate.</p>
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<p>The F-measure rate.</p>
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18 pages, 1920 KiB  
Article
Persistence Analysis and Prediction of Low-Visibility Events at Valladolid Airport, Spain
by Sara Cornejo-Bueno, David Casillas-Pérez, Laura Cornejo-Bueno, Mihaela I. Chidean, Antonio J. Caamaño, Julia Sanz-Justo, Carlos Casanova-Mateo and Sancho Salcedo-Sanz
Symmetry 2020, 12(6), 1045; https://doi.org/10.3390/sym12061045 - 23 Jun 2020
Cited by 27 | Viewed by 3224
Abstract
This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case [...] Read more.
This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case of long-term analysis, a Detrended Fluctuation Analysis (DFA) approach is applied in order to estimate large-scale RVR time series similarities. The short-term persistence analysis of low-visibility events is evaluated by means of a Markov chain analysis of the binary time series associated with low-visibility events. We finally discuss an hourly short-term prediction of low-visibility events, using different approaches, some of them coming from the persistence analysis through Markov chain models, and others based on Machine Learning (ML) techniques. We show that a Mixture of Experts approach involving persistence-based methods and Machine Learning techniques provides the best results in this prediction problem. Full article
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<p>Location of Villanubla airport, Valladolid, Spain; (<b>a</b>) Location in the Iberian Peninsula; (<b>b</b>) Orography of Valladolid area.</p>
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<p>Diagrams representing a two-state (<b>a</b>) First-order; (<b>b</b>) <span class="html-italic">N</span>-order Markov process.</p>
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<p>Support Vector Machine (SVM) Illustration: Linear decision hyperplane in a non-linearly transformed feature space <math display="inline"><semantics> <mi mathvariant="script">H</mi> </semantics></math>. The <span class="html-italic">slack</span> variables <math display="inline"><semantics> <msub> <mi>ξ</mi> <mi>i</mi> </msub> </semantics></math> are included to define the <span class="html-italic">soft-margin</span>.</p>
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<p>Multi-layer perceptron structure considered in the Extreme Learning Machine (ELM) algorithm.</p>
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<p>Runway Visual Range (RVR) time series and Detrended Fluctuation Analysis (DFA) carried out; (<b>a</b>) RVR time series in winter at Villanubla airport; (<b>b</b>) DFA obtained from the analysis. The DFA exponent is <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>.</mo> <mn>37</mn> </mrow> </semantics></math> in times under 10 h, and <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>97</mn> </mrow> </semantics></math> over this characteristic time, which indicates the strong correlation of the RVR signal.</p>
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<p>ROC space and plots of the methods considered for different time windows in the input data (<math display="inline"><semantics> <mrow> <mi>t</mi> <mo>−</mo> <mn>1</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mi>t</mi> <mo>−</mo> <mn>4</mn> </mrow> </semantics></math>), and the Naïve persistence operator; (<b>a</b>) MCM; (<b>b</b>) ELM; (<b>c</b>) SVM; (<b>d</b>) Mixture of Experts (MOE). An enlarged view is represented in the insets. Note that the exact location of the points is given in the zoomed insets.</p>
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4 pages, 165 KiB  
Editorial
Symmetry-Adapted Machine Learning for Information Security
by Jong Hyuk Park
Symmetry 2020, 12(6), 1044; https://doi.org/10.3390/sym12061044 - 22 Jun 2020
Cited by 6 | Viewed by 2754
Abstract
Nowadays, data security is becoming an emerging and challenging issue due to the growth in web-connected devices and significant data generation from information and communication technology (ICT) platforms. Many existing types of research from industries and academic fields have presented their methodologies for [...] Read more.
Nowadays, data security is becoming an emerging and challenging issue due to the growth in web-connected devices and significant data generation from information and communication technology (ICT) platforms. Many existing types of research from industries and academic fields have presented their methodologies for supporting defense against security threats. However, these existing approaches have failed to deal with security challenges in next-generation ICT systems due to the changing behaviors of security threats and zero-day attacks, including advanced persistent threat (APT), ransomware, and supply chain attacks. The symmetry-adapted machine-learning approach can support an effective way to deal with the dynamic nature of security attacks by the extraction and analysis of data to identify hidden patterns of data. It offers the identification of unknown and new attack patterns by extracting hidden data patterns in next-generation ICT systems. Therefore, we accepted twelve articles for this Special Issue that explore the deployment of symmetry-adapted machine learning for information security in various application areas. These areas include malware classification, intrusion detection systems, image watermarking, color image watermarking, battlefield target aggregation behavior recognition models, Internet Protocol (IP) cameras, Internet of Things (IoT) security, service function chains, indoor positioning systems, and cryptoanalysis. Full article
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
17 pages, 327 KiB  
Article
An Upper Bound of the Third Hankel Determinant for a Subclass of q-Starlike Functions Associated with k-Fibonacci Numbers
by Muhammad Shafiq, Hari M. Srivastava, Nazar Khan, Qazi Zahoor Ahmad, Maslina Darus and Samiha Kiran
Symmetry 2020, 12(6), 1043; https://doi.org/10.3390/sym12061043 - 22 Jun 2020
Cited by 39 | Viewed by 2615
Abstract
In this paper, we use q-derivative operator to define a new class of q-starlike functions associated with k-Fibonacci numbers. This newly defined class is a subclass of class A of normalized analytic functions, where class A is invariant (or symmetric) [...] Read more.
In this paper, we use q-derivative operator to define a new class of q-starlike functions associated with k-Fibonacci numbers. This newly defined class is a subclass of class A of normalized analytic functions, where class A is invariant (or symmetric) under rotations. For this function class we obtain an upper bound of the third Hankel determinant. Full article
(This article belongs to the Special Issue Integral Transformation, Operational Calculus and Their Applications)
31 pages, 7466 KiB  
Article
Effect of Zinc Oxide Nano-Additives and Soybean Biodiesel at Varying Loads and Compression Ratios on VCR Diesel Engine Characteristics
by Rakhamaji S. Gavhane, Ajit M. Kate, Abhay Pawar, Mohammad Reza Safaei, Manzoore Elahi M. Soudagar, Muhammad Mujtaba Abbas, Hafiz Muhammad Ali, Nagaraj R Banapurmath, Marjan Goodarzi, Irfan Anjum Badruddin, Waqar Ahmed and Kiran Shahapurkar
Symmetry 2020, 12(6), 1042; https://doi.org/10.3390/sym12061042 - 22 Jun 2020
Cited by 88 | Viewed by 6853
Abstract
The present investigation is directed towards synthesis of zinc oxide (ZnO) nanoparticles and steady blending with soybean biodiesel (SBME25) to improve the fuel properties of SBME25 and enhance the overall characteristics of a variable compression ratio diesel engine. The soybean biodiesel (SBME) was [...] Read more.
The present investigation is directed towards synthesis of zinc oxide (ZnO) nanoparticles and steady blending with soybean biodiesel (SBME25) to improve the fuel properties of SBME25 and enhance the overall characteristics of a variable compression ratio diesel engine. The soybean biodiesel (SBME) was prepared using the transesterification reaction. Numerous characterization tests were carried out to ascertain the shape and size of zinc oxide nanoparticles. The synthesized asymmetric ZnO nanoparticles were dispersed in SBME25 at three dosage levels (25, 50, and 75 ppm) with sodium dodecyl benzene sulphonate (SDBS) surfactant using the ultrasonication process. The quantified physicochemical properties of all the fuels blends were in symmetry with the American society for testing and materials (ASTM) standards. Nanofuel blends demonstrated enhanced fuel properties compared with SBME25. The engine was operated at two different compression ratios (18.5 and 21.5) and a comparison was made, and best fuel blend and compression ratio (CR) were selected. Fuel blend SBME25ZnO50 and compression ratio (CR) of 21.5 illustrated an overall enhancement in engine characteristics. For SBME25ZnO50 and CR 21.5 fuel blend, brake thermal efficiency (BTE) increased by 23.2%, brake specific fuel consumption (BSFC) were reduced by 26.66%, and hydrocarbon (HC), CO, smoke, and CO2 emissions were reduced by 32.234%, 28.21% 22.55% and 21.66%, respectively; in addition, the heat release rate (HRR) and mean gas temperature (MGT) improved, and ignition delay (ID) was reduced. In contrast, the NOx emissions increased for all the nanofuel blends due to greater supply of oxygen and increase in the temperature of the combustion chamber. At a CR of 18.5, a similar trend was observed, while the values of engine characteristics were lower compared with CR of 21.5. The properties of nanofuel blend SBME25ZnO50 were in symmetry and comparable to the diesel fuel. Full article
(This article belongs to the Special Issue Nanofluids in Advanced Symmetric Systems)
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Figure 1

Figure 1
<p>A flow chart of the synthesis of zinc oxide nanoparticles through the aqueous precipitation method.</p>
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<p>FESEM at 3 μm at 25,000× magnification level.</p>
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<p>Characterization tests: (<b>a</b>) XRD analysis; (<b>b</b>) UV-Vis Absorbance; (<b>c</b>) TEM at 100 nm magnification.</p>
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<p>Characterization tests: (<b>a</b>) XRD analysis; (<b>b</b>) UV-Vis Absorbance; (<b>c</b>) TEM at 100 nm magnification.</p>
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<p>The comprehensive steps involved in the preparation of nanofuel.</p>
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<p>Variable compression ratio (VCR) test engine setup.</p>
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<p>The variation of BTE at compression ratios of (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>The variation of BTE at compression ratios of (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>The variation of BTE at compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>The variation of BTE at compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>The variation of carbon monoxide at compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>The variation of hydrocarbon emissions at compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>The variation of nitrogen oxide emissions at compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>The variation of carbon dioxide emissions at compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>The variation of smoke emissions at compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>Variation of ignition delay period with load at compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>Variation of ignition delay period with load at compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>The mean gas temperature of fuel blends at varying crank angles at maximum load and CR 21.5.</p>
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<p>The variation of heat release rate with °CA at maximum load and compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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<p>The variation of heat release rate with °CA at maximum load and compression ratios: (<b>a</b>) 18.5 and (<b>b</b>) 21.5.</p>
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13 pages, 244 KiB  
Article
Ordered Gyrovector Spaces
by Sejong Kim
Symmetry 2020, 12(6), 1041; https://doi.org/10.3390/sym12061041 - 22 Jun 2020
Cited by 9 | Viewed by 2348
Abstract
The well-known construction scheme to define a partial order on a vector space is to use a proper convex cone. Applying this idea to the gyrovector space we construct the partial order, called a gyro-order. We also give several inequalities of gyrolines and [...] Read more.
The well-known construction scheme to define a partial order on a vector space is to use a proper convex cone. Applying this idea to the gyrovector space we construct the partial order, called a gyro-order. We also give several inequalities of gyrolines and cogyrolines in terms of the gyro-order. Full article
(This article belongs to the Special Issue Symmetry and Geometry in Physics)
11 pages, 1963 KiB  
Article
Simple Solutions of Lattice Sums for Electric Fields Due to Infinitely Many Parallel Line Charges
by Erik Vigren and Andreas Dieckmann
Symmetry 2020, 12(6), 1040; https://doi.org/10.3390/sym12061040 - 21 Jun 2020
Cited by 1 | Viewed by 4930
Abstract
We present surprisingly simple closed-form solutions for electric fields and electric potentials at arbitrary position ( x ,   y ) within a plane crossed by infinitely long line charges at regularly repeating positions using angular or elliptic functions with complex arguments. The [...] Read more.
We present surprisingly simple closed-form solutions for electric fields and electric potentials at arbitrary position ( x ,   y ) within a plane crossed by infinitely long line charges at regularly repeating positions using angular or elliptic functions with complex arguments. The lattice sums for the electric-field components and the electric potentials could be exactly solved, and the duality symmetry of trigonometric and lemniscate functions occurred in some solutions. The results may have relevance in calculating field configurations with rectangular boundary conditions. Several series related to Gauss’s constant are presented, established either as corollary results or via parallel investigations conducted in the spirit of experimental mathematics. Full article
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Figure 1
<p>Sections of size <math display="inline"><semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics></math> of electric potentials corresponding to Cases (<b>A</b>) (upper left), (<b>B</b>) (upper right), (<b>C</b>) (lower left) and (<b>D</b>) (lower right). Reference points set such that <math display="inline"><semantics> <mrow> <mi>U</mi> <mrow> <mo>(</mo> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> <mo>,</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> <mo>)</mo> </mrow> <mo>=</mo> <mn>0</mn> <mo>,</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>,</mo> </mrow> </semantics></math> <math display="inline"><semantics> <mi>λ</mi> </semantics></math> were set to 1.</p>
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<p>Equipotential lines and electric-field vectors of potential section shown in <a href="#symmetry-12-01040-f001" class="html-fig">Figure 1</a> for Cases (<b>A</b>), (<b>B</b>), (<b>C</b>) and (<b>D</b>) as indicated. Blue/yellow areas are below/above the chosen reference point of each potential. Space between charges shown in ochre carried potential values near zero. All potentials, lines, and vectors drawn using closed expressions from Equations (8)–(10), setting <span class="html-italic">a</span> and <math display="inline"><semantics> <mi>λ</mi> </semantics></math> to 1. ContourPlots with overlayed VectorPlots are shown using automatic vector scaling.</p>
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<p>(<b>left</b>) Field strengths and (<b>right</b>) electric potentials of (blue line) square tube and (orange line) Geiger counter, shown decreasing from the wire edge to the grounded wall.</p>
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13 pages, 241 KiB  
Article
Analysis of Homotopy Decomposition Varieties in Quotient Topological Spaces
by Susmit Bagchi
Symmetry 2020, 12(6), 1039; https://doi.org/10.3390/sym12061039 - 21 Jun 2020
Cited by 2 | Viewed by 2089
Abstract
The fundamental groups and homotopy decompositions of algebraic topology have applications in systems involving symmetry breaking with topological excitations. The main aim of this paper is to analyze the properties of homotopy decompositions in quotient topological spaces depending on the connectedness of the [...] Read more.
The fundamental groups and homotopy decompositions of algebraic topology have applications in systems involving symmetry breaking with topological excitations. The main aim of this paper is to analyze the properties of homotopy decompositions in quotient topological spaces depending on the connectedness of the space and the fundamental groups. This paper presents constructions and analysis of two varieties of homotopy decompositions depending on the variations in topological connectedness of decomposed subspaces. The proposed homotopy decomposition considers connected fundamental groups, where the homotopy equivalences are relaxed and the homeomorphisms between the fundamental groups are maintained. It is considered that one fundamental group is strictly homotopy equivalent to a set of 1-spheres on a plane and as a result it is homotopy rigid. The other fundamental group is topologically homeomorphic to the first one within the connected space and it is not homotopy rigid. The homotopy decompositions are analyzed in quotient topological spaces, where the base space and the quotient space are separable topological spaces. In specific cases, the decomposed quotient space symmetrically extends Sierpinski space with respect to origin. The connectedness of fundamental groups in the topological space is maintained by open curve embeddings without enforcing the conditions of homotopy classes on it. The extended decomposed quotient topological space preserves the trivial group structure of Sierpinski space. Full article
14 pages, 846 KiB  
Article
An Optimal Fourth Order Derivative-Free Numerical Algorithm for Multiple Roots
by Sunil Kumar, Deepak Kumar, Janak Raj Sharma, Clemente Cesarano, Praveen Agarwal and Yu-Ming Chu
Symmetry 2020, 12(6), 1038; https://doi.org/10.3390/sym12061038 - 21 Jun 2020
Cited by 38 | Viewed by 3102
Abstract
A plethora of higher order iterative methods, involving derivatives in algorithms, are available in the literature for finding multiple roots. Contrary to this fact, the higher order methods without derivatives in the iteration are difficult to construct, and hence, such methods are almost [...] Read more.
A plethora of higher order iterative methods, involving derivatives in algorithms, are available in the literature for finding multiple roots. Contrary to this fact, the higher order methods without derivatives in the iteration are difficult to construct, and hence, such methods are almost non-existent. This motivated us to explore a derivative-free iterative scheme with optimal fourth order convergence. The applicability of the new scheme is shown by testing on different functions, which illustrates the excellent convergence. Moreover, the comparison of the performance shows that the new technique is a good competitor to existing optimal fourth order Newton-like techniques. Full article
(This article belongs to the Special Issue Ordinary and Partial Differential Equations: Theory and Applications)
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Figure 1
<p>Basins of the new method for <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>Basins of the new method for <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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17 pages, 4655 KiB  
Article
Concrete Based Jeffrey Nanofluid Containing Zinc Oxide Nanostructures: Application in Cement Industry
by Nadeem Ahmad Sheikh, Dennis Ling Chuan Ching, Ilyas Khan, Afnan Ahmad and Syed Ammad
Symmetry 2020, 12(6), 1037; https://doi.org/10.3390/sym12061037 - 20 Jun 2020
Cited by 13 | Viewed by 3332
Abstract
Concrete is a non-Newtonian fluid which is a counterexample of Jeffrey fluid. The flow of Jeffrey fluid is considered containing nanostructures of zinc oxide in this study. The flow of the nanofluid is modeled in terms of partial fractional differential equations via Atangana–Baleanu [...] Read more.
Concrete is a non-Newtonian fluid which is a counterexample of Jeffrey fluid. The flow of Jeffrey fluid is considered containing nanostructures of zinc oxide in this study. The flow of the nanofluid is modeled in terms of partial fractional differential equations via Atangana–Baleanu (AB) fractional derivative approach and then solved using the integral transformation. Specifically, the applications are discussed in the field of concrete and cement industry. The variations in heat transfer rate and skin friction have been observed for different values of volume fractions of nanoparticles. The results show that by adding 4% Z n O nanoparticles increase skin friction up to 15%, ultimately enhancing the adhesion capacity of concrete. Moreover, Z n O increase the density of concrete, minimizing the pores in the concrete and consequently increasing the strength of concrete. The solutions are simplified to the corresponding solutions of the integer ordered model of Jeffrey-nanofluid. Applications of this work can be found in construction engineering and management such as buildings, roads, tunnels, bridges, airports, railroads, dams, and utilities. Full article
(This article belongs to the Special Issue Application of Nanotechnology in Human Life)
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Figure 1
<p>Geometry of the flow.</p>
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<p>Variation in velocity profile for different values of <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>λ</mi> <mo>=</mo> <mn>1.5</mn> <mo>,</mo> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Variation in temperature profile for different values of <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>λ</mi> <mo>=</mo> <mn>1.5</mn> <mo>,</mo> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Variation in Nusselt number for different values of <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>λ</mi> <mo>=</mo> <mn>1.5</mn> <mo>,</mo> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Variation in skin friction for different values of <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>λ</mi> <mo>=</mo> <mn>1.5</mn> <mo>,</mo> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Variation in velocity profile for different values of <math display="inline"><semantics> <mi>α</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mi>ϕ</mi> <mo>=</mo> <mn>0.02</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>λ</mi> <mo>=</mo> <mn>1.5</mn> <mo>,</mo> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Variation in temperature profile for different values of <math display="inline"><semantics> <mi>α</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mi>ϕ</mi> <mo>=</mo> <mn>0.02</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>λ</mi> <mo>=</mo> <mn>1.5</mn> <mo>,</mo> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Variation in velocity profile for different values of <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>ϕ</mi> <mo>=</mo> <mn>0.02</mn> <mo>,</mo> <mi>λ</mi> <mo>=</mo> <mn>1.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Variation in velocity profile for different values of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>ϕ</mi> <mo>=</mo> <mn>0.02</mn> <mo>,</mo> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Variation in velocity profile for different values of <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>r</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>ϕ</mi> <mo>=</mo> <mn>0.02</mn> <mo>,</mo> <mi>λ</mi> <mo>=</mo> <mn>1.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Variation in velocity profile for different values of <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>m</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>0.3</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>ϕ</mi> <mo>=</mo> <mn>0.02</mn> <mo>,</mo> <mi>λ</mi> <mo>=</mo> <mn>1.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Variation in velocity profile for different values of <math display="inline"><semantics> <mi>M</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>,</mo> <mi>α</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="normal">P</mi> <mi>e</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi>G</mi> <mi>r</mi> <mo>=</mo> <mi>G</mi> <mi>m</mi> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mi>τ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>ϕ</mi> <mo>=</mo> <mn>0.02</mn> <mo>,</mo> <mi>λ</mi> <mo>=</mo> <mn>1.5</mn> <mo>.</mo> </mrow> </semantics></math></p>
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16 pages, 1417 KiB  
Article
An Improved MV Method for Stock Allocation Based on the State-Space Measure of Cognitive Bias with a Hybrid Algorithm
by Liwen Wang, Hecheng Wu, Gang Li and Weixue Lu
Symmetry 2020, 12(6), 1036; https://doi.org/10.3390/sym12061036 - 20 Jun 2020
Cited by 1 | Viewed by 2563
Abstract
In classical finance theory, cognitive bias does not play any role in predicting returns. With the development of the economy, the classical theory gradually finds it difficult to offset the irrational demand through arbitrage. Due to the rise of behavioral economics, how to [...] Read more.
In classical finance theory, cognitive bias does not play any role in predicting returns. With the development of the economy, the classical theory gradually finds it difficult to offset the irrational demand through arbitrage. Due to the rise of behavioral economics, how to allocate stock portfolios in the highly subjective environment is an unavoidable problem. Considering the decision heterogeneity between the rational market and the irrational one, the mean-variance (MV) method was improved in the construction of a market bias index for stock portfolio allocation, which we called EMACB (exponential moving average of cognitive bias)-variance method. Besides, due to the lack of related research, we introduced a measure of aggregate investor cognitive bias by adopting the state-space model. Finally, the proposed method was applied in an investment allocation example to prove its feasibility, and its advantages were emphasized by a comparison with another relevant approach. Full article
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<p>Some characteristics of cognitive biases.</p>
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<p>The allocation results in different numbers of stocks in a portfolio.</p>
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<p>The allocation results in different periods.</p>
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21 pages, 870 KiB  
Article
An Extended Analysis on Robust Dissipativity of Uncertain Stochastic Generalized Neural Networks with Markovian Jumping Parameters
by Usa Humphries, Grienggrai Rajchakit, Ramalingam Sriraman, Pramet Kaewmesri, Pharunyou Chanthorn, Chee Peng Lim and Rajendran Samidurai
Symmetry 2020, 12(6), 1035; https://doi.org/10.3390/sym12061035 - 20 Jun 2020
Cited by 21 | Viewed by 2568
Abstract
The main focus of this research is on a comprehensive analysis of robust dissipativity issues pertaining to a class of uncertain stochastic generalized neural network (USGNN) models in the presence of time-varying delays and Markovian jumping parameters (MJPs). In real-world environments, most practical [...] Read more.
The main focus of this research is on a comprehensive analysis of robust dissipativity issues pertaining to a class of uncertain stochastic generalized neural network (USGNN) models in the presence of time-varying delays and Markovian jumping parameters (MJPs). In real-world environments, most practical systems are subject to uncertainties. As a result, we take the norm-bounded parameter uncertainties, as well as stochastic disturbances into consideration in our study. To address the task, we formulate the appropriate Lyapunov–Krasovskii functional (LKF), and through the use of effective integral inequalities, simplified linear matrix inequality (LMI) based sufficient conditions are derived. We validate the feasible solutions through numerical examples using MATLAB software. The simulation results are analyzed and discussed, which positively indicate the feasibility and effectiveness of the obtained theoretical findings. Full article
(This article belongs to the Special Issue Symmetry in Nonlinear Studies)
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<p>Time responses of the state variables <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>p</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with respect to Model (<a href="#FD6-symmetry-12-01035" class="html-disp-formula">6</a>) in Example 1.</p>
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<p>Transient responses of the state variables <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>p</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with respect to Model (<a href="#FD6-symmetry-12-01035" class="html-disp-formula">6</a>) in Example 1.</p>
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<p>The Markovian switching signal <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> in Example 1.</p>
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<p>Time responses of the state variables <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>p</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>p</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>p</mi> <mn>4</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with respect to Model (<a href="#FD46-symmetry-12-01035" class="html-disp-formula">46</a>) in Example 2.</p>
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<p>Time responses of the state variables <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>p</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with respect to Model (<a href="#FD48-symmetry-12-01035" class="html-disp-formula">48</a>) in Example 3.</p>
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<p>Transient responses of the state variables <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>p</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with respect to Model (<a href="#FD48-symmetry-12-01035" class="html-disp-formula">48</a>) in Example 3.</p>
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<p>The Markovian switching signal <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> in Example 3.</p>
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17 pages, 309 KiB  
Article
Trapezium-Type Inequalities for Raina’s Fractional Integrals Operator Using Generalized Convex Functions
by Miguel Vivas-Cortez, Artion Kashuri and Jorge E. Hernández Hernández
Symmetry 2020, 12(6), 1034; https://doi.org/10.3390/sym12061034 - 20 Jun 2020
Cited by 20 | Viewed by 2902
Abstract
The authors have reviewed a wide production of scientific articles dealing with the evolution of the concept of convexity and its various applications, and based on this they have detected the relationship that can be established between trapezoidal inequalities, generalized convex functions, and [...] Read more.
The authors have reviewed a wide production of scientific articles dealing with the evolution of the concept of convexity and its various applications, and based on this they have detected the relationship that can be established between trapezoidal inequalities, generalized convex functions, and special functions, in particular with the so-called Raina function, which generalizes other better known ones such as the hypergeometric function and the Mittag–Leffler function. The authors approach this situation by studying the Hermite–Hadamard inequality, establishing a useful identity using Raina’s fractional integral operator in the setting of ϕ -convex functions, obtaining some integral inequalities connected with the right-hand side of Hermite–Hadamard-type inequalities for Raina’s fractional integrals. Various special cases have been identified. Full article
23 pages, 842 KiB  
Article
On the Absolute Stable Difference Scheme for Third Order Delay Partial Differential Equations
by Allaberen Ashyralyev, Evren Hınçal and Suleiman Ibrahim
Symmetry 2020, 12(6), 1033; https://doi.org/10.3390/sym12061033 - 19 Jun 2020
Cited by 4 | Viewed by 2666
Abstract
The initial value problem for the third order delay differential equation in a Hilbert space with an unbounded operator is investigated. The absolute stable three-step difference scheme of a first order of accuracy is constructed and analyzed. This difference scheme is built on [...] Read more.
The initial value problem for the third order delay differential equation in a Hilbert space with an unbounded operator is investigated. The absolute stable three-step difference scheme of a first order of accuracy is constructed and analyzed. This difference scheme is built on the Taylor’s decomposition method on three and two points. The theorem on the stability of the presented difference scheme is proven. In practice, stability estimates for the solutions of three-step difference schemes for different types of delay partial differential equations are obtained. Finally, in order to ensure the coincidence between experimental and theoretical results and to clarify how efficient the proposed scheme is, some numerical experiments are tested. Full article
(This article belongs to the Special Issue Symmetry in Modeling and Analysis of Dynamic Systems)
15 pages, 3581 KiB  
Article
The Status of Edge Strands in Ferredoxin-Like Fold
by Mateusz Banach, Piotr Fabian, Katarzyna Stapor, Leszek Konieczny, Magdalena Ptak-Kaczor and Irena Roterman
Symmetry 2020, 12(6), 1032; https://doi.org/10.3390/sym12061032 - 19 Jun 2020
Cited by 2 | Viewed by 2308
Abstract
There is an opinion in professional literature that edge-strands in β-sheet are critical to the processes of amyloid transformation. Propagation of fibrillar forms mainly takes place on the basis of β-sheet type interactions. In many proteins, the edge strands represent only a partially [...] Read more.
There is an opinion in professional literature that edge-strands in β-sheet are critical to the processes of amyloid transformation. Propagation of fibrillar forms mainly takes place on the basis of β-sheet type interactions. In many proteins, the edge strands represent only a partially matched form to the β-sheet. Therefore, the edge-strand takes slightly distorted forms. The assessment of the level of arrangement can be carried out based on studying the secondary structure as well as the structure of the hydrophobic core. For this purpose, a fuzzy oil drop model was used to determine the contribution of each fragment with a specific secondary structure to the construction of the system being the effect of a certain synergy, which results in the construction of a hydrophobic core. Studying the participation of β-sheets edge fragments in the hydrophobic core construction is the subject of the current analysis. Statuses of these edge fragments in β-sheets in ferredoxin-like folds are treated as factors that disturb the symmetry of the system. Full article
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<p>Hydrophobicity density distribution profiles for 1URR (<b>A</b>) and chain A of 2BJD (<b>B</b>): theoretical (T—blue line) and observed (O—red line). Markers at the top denote secondary structure fragments (red hexagons—helices, yellow squares—sheets). Fragments marked by red shade exhibit RD (relative distance) &gt; 0.5. Fragments marked by blue shade exhibit RD ≤ 0.5. Yellow region on <b>A</b>—RD close to 0.5. Green triangle markers (facing up) denote residues engaged in ligand binding. Purple triangles (facing down) denote protein-protein interaction. Catalytic residues are shown as brown rhombuses, while members of the hydrophobic core are marked by white circles.</p>
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<p>3D presentation of 1URR (<b>A</b>) and chain A of 2BJD (<b>B</b>). Fragments marked by red color exhibit RD &gt; 0.5. Fragments marked by blue color exhibit RD ≤ 0.5. Yellow fragment on A—RD close to 0.5. Catalytic residues are shown as spheres (darker color—23R in 1URR and 30R in 2BJD, lighter color—41N in 1URR and 48N in 2BJD). Member of the hydrophobic core are represented by semi-transparent surface inside each protein.</p>
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<p>Hydrophobicity density distribution profiles for monomers of 4OIX (<b>A</b>), 4OJ3 (<b>B</b>), 4OJG (<b>C</b>), 4OJH (<b>D</b>): theoretical (T—blue line) and observed (O—red line). Markers at the top denote secondary structure fragments (red hexagons—helices, yellow squares—sheets). Fragments marked by red shade exhibit RD &gt; 0.5. Fragments marked by blue shade exhibit RD ≤ 0.5. Green fragments—β-strand with mutations. Purple triangles (facing down) denote protein-protein interaction. Orange stars mark the mutation in given protein, while black stars mark the mutations in other proteins.</p>
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<p>3D presentation of monomers of 4OIX (<b>A</b>), 4OJ3 (<b>B</b>), 4OJG (<b>C</b>), 4OJH (<b>D</b>). Fragments marked by red color exhibit RD &gt; 0.5. Fragments marked by blue color exhibit RD ≤ 0.5. Green fragments—β-strand with mutations. Orange spheres mark the mutation in given protein, while black spheres mark the mutations in other proteins.</p>
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<p>Hydrophobicity density distribution profiles for dimers of 2BJD (<b>A</b>), 4OJ3 (<b>B</b>), 4OJG (<b>C</b>), 4OJH (<b>D</b>): theoretical (T—blue line) and observed (O—red line). Green fragments—β-strand with mutations. Purple triangles (facing down) denote protein-protein interaction. Orange stars mark the mutation in given protein, while black stars mark the mutations in other proteins. Dashed vertical line is the chain separator.</p>
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<p>3D presentation of dimers of 2BJD (<b>A</b>), 4OJ3 (<b>B</b>), 4OJG (<b>C</b>), 4OJH (<b>D</b>). Fragments marked by red color exhibit RD &gt; 0.5. Fragments marked by blue color exhibit RD ≤ 0.5. Green fragments—β-strand with mutations. Orange spheres mark the mutation in given protein, while black spheres mark the mutations in other proteins. Residues engaged in protein-protein interaction have their side chains displayed as sticks and are surrounded by a semi-transparent surface.</p>
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12 pages, 791 KiB  
Article
Existence Results of Mild Solutions for the Fractional Stochastic Evolution Equations of Sobolev Type
by He Yang
Symmetry 2020, 12(6), 1031; https://doi.org/10.3390/sym12061031 - 19 Jun 2020
Cited by 4 | Viewed by 2090
Abstract
In this paper, by utilizing the resolvent operator theory, the stochastic analysis method and Picard type iterative technique, we first investigate the existence as well as the uniqueness of mild solutions for a class of α ( 1 , 2 ) -order [...] Read more.
In this paper, by utilizing the resolvent operator theory, the stochastic analysis method and Picard type iterative technique, we first investigate the existence as well as the uniqueness of mild solutions for a class of α ( 1 , 2 ) -order Riemann–Liouville fractional stochastic evolution equations of Sobolev type in abstract spaces. Then the symmetrical technique is used to deal with the α ( 1 , 2 ) -order Caputo fractional stochastic evolution equations of Sobolev type in abstract spaces. Two examples are given as applications to the obtained results. Full article
(This article belongs to the Special Issue Advances in Stochastic Differential Equations)
15 pages, 4088 KiB  
Article
Kane’s Formalism Used to the Vibration Analysis of a Wind Water Pump
by Gabriel Leonard Mitu, Eliza Chircan, Maria Luminita Scutaru and Sorin Vlase
Symmetry 2020, 12(6), 1030; https://doi.org/10.3390/sym12061030 - 19 Jun 2020
Cited by 5 | Viewed by 2409
Abstract
The paper uses Kane’s formalism to study two degrees of freedom (DOF) mechanisms with elastic elements = employed in a wind water pump. This formalism represents an alternative, in our opinion, that is simpler and more economical to Lagrange’s equation, used mainly by [...] Read more.
The paper uses Kane’s formalism to study two degrees of freedom (DOF) mechanisms with elastic elements = employed in a wind water pump. This formalism represents an alternative, in our opinion, that is simpler and more economical to Lagrange’s equation, used mainly by researchers in this type of application. In the problems where the finite element method (FEM) is applied, Kane’s equations were not used at all. The automated computation causes it to be reconsidered in the case of mechanical systems with a high DOF. Analyzing the planar transmission mechanism, these equations were applied for the study of an elastic element. An analysis was then made of the results obtained for this type of water pump. The matrices coefficients of the obtained equations were symmetric or skew-symmetric. Full article
(This article belongs to the Special Issue Multibody Systems with Flexible Elements)
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<p>Plane finite element.</p>
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<p>Kinematic mechanism of the transmission of a wind water pump.</p>
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<p>Position of the markers.</p>
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<p>Test bench.</p>
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<p>The trajectories of the five points analyzed at an engine angular velocity of 140 rpm.</p>
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<p>The trajectories of the five points analyzed at an engine angular velocity of 140 rpm.</p>
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<p>Acceleration field of the five points analyzed at an engine angular velocity of 140 rpm. The value of the maximum acceleration was 14.33 m/s<sup>2</sup>.</p>
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<p>Elastic lever BC’ divided in finite elements.</p>
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<p>Four eigenfrequencies (eigenfrequencies 3, 4, 5, and 6) of the beam discretized in 10 finite elements for an angular velocity at 140 rpm.</p>
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<p>The section from the procedure of deriving the motion equations differences using Kane’s or Lagrange’s formalism. The use of Lagrange’s formalism.</p>
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<p>The section from the procedure of deriving the motion equations differences using Kane’s or Lagrange’s formalism. The use of Kane’s formalism.</p>
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22 pages, 2066 KiB  
Review
Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing
by Anabi Hilary Kelechi, Mohammed H. Alsharif, Okpe Jonah Bameyi, Paul Joan Ezra, Iorshase Kator Joseph, Aaron-Anthony Atayero, Zong Woo Geem and Junhee Hong
Symmetry 2020, 12(6), 1029; https://doi.org/10.3390/sym12061029 - 18 Jun 2020
Cited by 12 | Viewed by 6468
Abstract
Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to [...] Read more.
Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today’s high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI. Full article
(This article belongs to the Special Issue Information Technologies and Electronics)
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<p>Minimum supply voltage of microprocessors [<a href="#B17-symmetry-12-01029" class="html-bibr">17</a>].</p>
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<p>Power (Thermal Design Power, TDP) of microprocessors [<a href="#B17-symmetry-12-01029" class="html-bibr">17</a>].</p>
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<p>High performance computing (HPC) deployment scenario.</p>
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<p>(<b>a</b>). Regression task. (<b>b</b>). Classification task.</p>
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<p>Google’s historical power usage effectiveness (PUE) performance [<a href="#B91-symmetry-12-01029" class="html-bibr">91</a>].</p>
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<p>Results of testing [<a href="#B92-symmetry-12-01029" class="html-bibr">92</a>].</p>
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<p>Tree layered neural network [<a href="#B90-symmetry-12-01029" class="html-bibr">90</a>].</p>
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