Symmetry doi: 10.3390/sym9100198

Authors: Lambert Jorba Romà Adillon

We propose a generalization of trapezoidal fuzzy numbers based on modal interval theory, which we name “modal interval trapezoidal fuzzy numbers”. In this generalization, we accept that the alpha cuts associated with a trapezoidal fuzzy number can be modal intervals, also allowing that two interval modalities can be associated with a trapezoidal fuzzy number. In this context, it is difficult to maintain the traditional graphic representation of trapezoidal fuzzy numbers and we must use the interval plane in order to represent our modal interval trapezoidal fuzzy numbers graphically. Using this representation, we can correctly reflect the modality of the alpha cuts. We define some concepts from modal interval analysis and we study some of the related properties and structures, proving, among other things, that the inclusion relation provides a lattice structure on this set. We will also provide a semantic interpretation deduced from the modal interval extensions of real continuous functions and the semantic modal interval theorem. The application of modal intervals in the field of fuzzy numbers also provides a new perspective on and new applications of fuzzy numbers.

]]>Symmetry doi: 10.3390/sym9090197

Authors: Kamran Siddique Zahid Akhtar Haeng-gon Lee Woongsup Kim Yangwoo Kim

Anomaly detection systems, also known as intrusion detection systems (IDSs), continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i) performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii) employs logistic regression and extreme gradient boosting techniques for classification; (iii) introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv) uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system.

]]>Symmetry doi: 10.3390/sym9090195

Authors: Stevo Stević

The solvability of the following three-dimensional product-type system of difference equations x n + 1 = α y n a z n − 1 b , y n + 1 = β z n c x n − 1 d , z n + 1 = γ x n f y n − 1 g , n ∈ N 0 , where a , b , c , d , f , g ∈ Z , α , β , γ ∈ C \ { 0 } and x − i , y − i , z − i ∈ C \ { 0 } , i ∈ { 0 , 1 } , is shown. This is the first three-dimensional system of the type with multipliers for which formulas are presented for their solutions in closed form in all the cases.

]]>Symmetry doi: 10.3390/sym9090196

Authors: Alexandra S. Maia Ana R. Ribeiro Paula M. L. Castro Maria Elizabeth Tiritan

The importance of stereochemistry for medicinal chemistry and pharmacology is well recognized and the dissimilar behavior of enantiomers is fully documented. Regarding the environment, the significance is equivalent since enantiomers of chiral organic pollutants can also differ in biodegradation processes and fate, as well as in ecotoxicity. This review comprises designed biodegradation studies of several chiral drugs and pesticides followed by enantioselective analytical methodologies to accurately measure the enantiomeric fraction (EF). The enantioselective monitoring of microcosms and laboratory-scale experiments with different environmental matrices is herein reported. Thus, this review focuses on the importance of evaluating the EF variation during biodegradation studies of chiral pharmaceuticals, drugs of abuse, and agrochemicals and has implications for the understanding of the environmental fate of chiral pollutants.

]]>Symmetry doi: 10.3390/sym9090194

Authors: Yuji Yamakawa Akio Namiki Masatoshi Ishikawa Makoto Shimojo

This paper demonstrates the relationship between the production process of a knot and manipulation skills. First, we define the description (rope intersections, grasp type and fixation positions) of a knot. Second, we clarify the characteristics of the manipulation skills from the viewpoint of the knot description. Next, in order to obtain the production process of the knot, we propose an analysis method based on the structure of the knot and the characteristics of the manipulation skills. Using the proposed analysis method, we analyzed eight kinds of knots, formed with a single rope, two ropes or a single rope and an object. Finally, in order to validate the production process obtained by the proposed analysis method, we show experimental results of an overhand knot and a half hitch produced by using a robot hand system.

]]>Symmetry doi: 10.3390/sym9090193

Authors: Shiyong Yin Jinsong Bao Yiming Zhang Xiaodi Huang

As the core of intelligent manufacturing, cyber-physical systems (CPS) have serious security issues, especially for the communication security of their terminal machine-to-machine (M2M) communications. In this paper, blockchain technology is introduced to address such a security problem of communications between different types of machines in the CPS. According to the principles of blockchain technology, we designed a blockchain for secure M2M communications. As a communication system, M2M consists of public network areas, device areas, and private areas, and we designed a sophisticated blockchain structure between the public area and private area. For validating our design, we took cotton spinning production as a case study to demonstrate our solution to M2M communication problems under the CPS framework. We have demonstrated that the blockchain technology can effectively solve the safety of expansion of machines in the production process and the communication data between the machines cannot be tampered with.

]]>Symmetry doi: 10.3390/sym9090192

Authors: Jaehee Jung Jong Kim Young-Sik Jeong Gangman Yi

Big data research on genomic sequence analysis has accelerated considerably with the development of next-generation sequencing. Currently, research on genomic sequencing has been conducted using various methods, ranging from the assembly of reads consisting of fragments to the annotation of genetic information using a database that contains known genome information. According to the development, most tools to analyze the new organelles’ genetic information requires different input formats such as FASTA, GeneBank (GB) and tab separated files. The various data formats should be modified to satisfy the requirements of the gene annotation system after genome assembly. In addition, the currently available tools for the analysis of organelles are usually developed only for specific organisms, thus the need for gene prediction tools, which are useful for any organism, has been increased. The proposed method—termed the genome_search_plotter—is designed for the easy analysis of genome information from the related references without any file format modification. Anyone who is interested in intracellular organelles such as the nucleus, chloroplast, and mitochondria can analyze the genetic information using the assembled contig of an unknown genome and a reference model without any modification of the data from the assembled contig.

]]>Symmetry doi: 10.3390/sym9090189

Authors: Mingyu Kim Jiwon Lee Changyu Jeon Jinmo Kim

This research proposes a gaze pointer-based user interface to provide user-oriented interaction suitable for the virtual reality environment on mobile platforms. For this purpose, a mobile platform-based three-dimensional interactive content is produced to test whether the proposed gaze pointer-based interface increases user satisfaction through the interactions in a virtual reality environment based on mobile platforms. The gaze pointer-based interface—the most common input method for mobile virtual reality content—is designed by considering four types: the visual field range, the feedback system, multi-dimensional information transfer, and background colors. The performance of the proposed gaze pointer-based interface is analyzed by conducting experiments on whether or not it offers motives for user interest, effects of enhanced immersion, provision of new experience, and convenience in operating content. In addition, it is verified whether any negative psychological factors, such as VR sickness, fatigue, difficulty of control, and discomfort in using contents are caused. Finally, through the survey experiment, this study confirmed that it is possible to design different ideal gaze pointer-based interface in mobile VR environment according to presence and convenience.

]]>Symmetry doi: 10.3390/sym9090190

Authors: João Ribeiro Maria Tiritan Madalena Pinto Carla Fernandes

The development of chiral stationary phases (CSPs) for liquid chromatography (LC) revolutionized the enantioseparation and, nowadays, different types of CSPs are commercially available. Polysaccharide-based CSPs are one of the most versatile and widely used for both analytical and preparative applications and they are able to resolve several classes of racemates. Phenylcarbamates of amylose and cellulose derivatives are the most successful; however, polysaccharide-based CSPs comprising marine-derived polysaccharides are also described revealing high chiral recognition abilities and wider range of mobile phases. A literature survey covering the report on chitin and chitosan based CSPs is presented. The chemical structure of the chiral selectors, their development and applications in chiral LC are emphasized.

]]>Symmetry doi: 10.3390/sym9090188

Authors: Jean Taylor Erin Teich Pablo Damasceno Yoav Kallus Marjorie Senechal

Where are the atoms in complex crystals such as quasicrystals or periodic crystals with one hundred or more atoms per unit cell? How did they get there? The first of these questions has been gradually answered for many materials over the quarter-century since quasicrystals were discovered; in this paper we address the second. We briefly review a history of proposed models for describing atomic positions in crystal structures. We then present a revised description and possible growth model for one particular system of alloys, those containing Tsai-type clusters, that includes an important class of quasicrystals.

]]>Symmetry doi: 10.3390/sym9090191

Authors: Hongjun Guan Shuang Guan Aiwu Zhao

The daily fluctuation trends of a stock market are illustrated by three statuses: up, equal, and down. These can be represented by a neutrosophic set which consists of three functions—truth-membership, indeterminacy-membership, and falsity-membership. In this paper, we propose a novel forecasting model based on neutrosophic set theory and the fuzzy logical relationships between the status of historical and current values. Firstly, the original time series of the stock market is converted to a fluctuation time series by comparing each piece of data with that of the previous day. The fluctuation time series is then fuzzified into a fuzzy-fluctuation time series in terms of the pre-defined up, equal, and down intervals. Next, the fuzzy logical relationships can be expressed by two neutrosophic sets according to the probabilities of different statuses for each current value and a certain range of corresponding histories. Finally, based on the neutrosophic logical relationships and the status of history, a Jaccard similarity measure is employed to find the most proper logical rule to forecast its future. The authentic Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets are used as an example to illustrate the forecasting procedure and performance comparisons. The experimental results show that the proposed method can successfully forecast the stock market and other similar kinds of time series. We also apply the proposed method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) to verify its effectiveness and universality.

]]>Symmetry doi: 10.3390/sym9090187

Authors: Haricharan Padmanabhan Maggie Kingsland Jason Munro Daniel Litvin Venkatraman Gopalan

Spatial symmetries occur in combination with temporal symmetries in a wide range of physical systems in nature, including time-periodic quantum systems typically described by the Floquet formalism. In this context, groups formed by three-dimensional point group symmetry operations in combination with time translation operations are discussed in this work. The derivation of these ’spatio-temporal’ groups from conventional point groups and their irreducible representations is outlined, followed by a complete listing. The groups are presented in a template similar to space group operations, and are visualized using a modified version of conventional stereographic projections. Simple examples of physical processes that simultaneously exhibit symmetry in space and time are identified and used to illustrate the application of spatio-temporal groups.

]]>Symmetry doi: 10.3390/sym9090186

Authors: Wei He Zhixiong Li Reza Malekian Xinglong Liu Zhihe Duan

Automatic Identification System (AIS), as a major data source of navigational data, is widely used in the application of connected ships for the purpose of implementing maritime situation awareness and evaluating maritime transportation. Efficiently extracting featured data from AIS database is always a challenge and time-consuming work for maritime administrators and researchers. In this paper, a novel approach was proposed to extract massive featured data from the AIS database. An Evidential Reasoning rule based methodology was proposed to simulate the procedure of extracting routes from AIS database artificially. First, the frequency distributions of ship dynamic attributes, such as the mean and variance of Speed over Ground, Course over Ground, are obtained, respectively, according to the verified AIS data samples. Subsequently, the correlations between the attributes and belief degrees of the categories are established based on likelihood modeling. In this case, the attributes were characterized into several pieces of evidence, and the evidence can be combined with the Evidential Reasoning rule. In addition, the weight coefficients were trained in a nonlinear optimization model to extract the AIS data more accurately. A real life case study was conducted at an intersection waterway, Yangtze River, Wuhan, China. The results show that the proposed methodology is able to extract data very precisely.

]]>Symmetry doi: 10.3390/sym9090183

Authors: You-Hong Li Jian-Qiang Wang Xue-Jun Wang Yue-Long Zhao Xing-Hua Lu Da-Long Liu

Community detection (CD) has become an important research direction for data mining in complex networks. Evolutionary algorithm-based (EA-based) approaches, among many other existing community detection methods, are widely used. However, EA-based approaches are prone to population degradation and local convergence. Developing more efficient evolutionary algorithms thus becomes necessary. In 2013, Cuevas et al. proposed a new differential evolution (DE) hybrid meta-heuristic algorithm based on the simulated cooperative behavior of spiders, known as social spider optimization (SSO). On the basis of improving the SSO algorithm, this paper proposes a community detection algorithm based on differential evolution using social spider optimization (DESSO/CD). In this algorithm, the CD detection process is done by simulating the spider cooperative operators, marriage, and operator selection. The similarity of nodes is defined as local fitness function; the community quality increment is used as a screening criterion for evolutionary operators. Populations are sorted according to their contribution and diversity, making evolution even more different. In the entire process, a random cloud crossover model strategy is used to maintain population diversity. Each generation of the mating radius of the SSO algorithm will be adjusted appropriately according to the iterative times and fitness values. This strategy not only ensures the search space of operators, but also reduces the blindness of exploration. On the other hand, the multi-level, multi-granularity strategy of DESSO/CD can be used to further compensate for resolution limitations and extreme degradation defects based on modular optimization methods. The experimental results demonstrate that the DESSO/CD method could detect the community structure with higher partition accuracy and lower computational cost when compared with existing methods. Since the application of the SSO algorithm in CD research is just beginning, the study is competitive and promising.

]]>Symmetry doi: 10.3390/sym9090184

Authors: JongBeom Lim HeonChang Yu Joon-Min Gil

It is well known that cloud computing has many potential advantages over traditional distributed systems. Many enterprises can build their own private cloud with open source infrastructure as a service (IaaS) frameworks. Since enterprise applications and data are migrating to private cloud, the performance of cloud computing environments is of utmost importance for both cloud providers and users. To improve the performance, previous studies on cloud consolidation have been focused on live migration of virtual machines based on resource utilization. However, the approaches are not suitable for multimedia big data applications. In this paper, we reveal the performance bottleneck of multimedia big data applications in cloud computing environments and propose a cloud consolidation algorithm that considers application types. We show that our consolidation algorithm outperforms previous approaches.

]]>Symmetry doi: 10.3390/sym9090185

Authors: Yanhui Guo Yaman Akbulut Abdulkadir Şengür Rong Xia Florentin Smarandache

Segmentation is considered as an important step in image processing and computer vision applications, which divides an input image into various non-overlapping homogenous regions and helps to interpret the image more conveniently. This paper presents an efficient image segmentation algorithm using neutrosophic graph cut (NGC). An image is presented in neutrosophic set, and an indeterminacy filter is constructed using the indeterminacy value of the input image, which is defined by combining the spatial information and intensity information. The indeterminacy filter reduces the indeterminacy of the spatial and intensity information. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Numerous experiments have been taken to test its performance, and it is compared with a neutrosophic similarity clustering (NSC) segmentation algorithm and a graph-cut-based algorithm. The results indicate that the proposed NGC approach obtains better performances, both quantitatively and qualitatively.

]]>Symmetry doi: 10.3390/sym9090182

Authors: Natalie Uomini Rebecca Lawson

The evolutionary origins of the human bias for 85% right-handedness are obscure. The Apprenticeship Complexity Theory states that the increasing difficulty of acquiring stone tool-making and other manual skills in the Pleistocene favoured learners whose hand preference matched that of their teachers. Furthermore, learning from a viewing position opposite, rather than beside, the demonstrator might be harder because it requires more mental transformation. We varied handedness and viewpoint in a bimanual learning task. Thirty-two participants reproduced folding asymmetric origami figures as demonstrated by a videotaped teacher in four conditions (left-handed teacher opposite the learner, left-handed beside, right-handed opposite, or right-handed beside). Learning performance was measured by time to complete each figure, number of video pauses and rewinds, and similarity of copies to the target shape. There was no effect of handedness or viewpoint on imitation learning. However, participants preferred to produce figures with the same asymmetry as demonstrated, indicating they imitate the teacher's hand preference. We speculate that learning by imitation involves internalising motor representations and that, to facilitate learning by imitation, many motor actions can be flexibly executed using the demonstrated hand configuration. We conclude that matching hand preferences evolved due to socially learning moderately complex bimanual skills.

]]>Symmetry doi: 10.3390/sym9090181

Authors: Haiping Ren Guofu Wang Laijun Luo

Integral inequalities play critical roles in measure theory and probability theory. Given recent profound discoveries in the field of fuzzy set theory, fuzzy inequality has become a hot research topic in recent years. For classical Sandor type inequality, this paper intends to extend the Sugeno integral. Based on the (s,m)-convex function in the second sense, a new Sandor type inequality is proposed for the Sugeno integral. Examples are given to verify the conclusion of this paper.

]]>Symmetry doi: 10.3390/sym9090180

Authors: Yuxiang Li Yinliang Zhao Bin Liu

Speculative multithreading (SpMT) is a thread-level automatic parallelization technique that can accelerate sequential programs, especially for irregular applications that are hard to be parallelized by conventional approaches. Thread partition plays a critical role in SpMT. Conventional machine learning-based thread partition approaches applied machine learning to offline guide partition, but could not explicitly explore the law between partition and performance. In this paper, we build a parametric model (Qinling) with a multiple regression method to discover the inherent law between thread partition and performance. The paper firstly extracts unpredictable parameters that determine the performance of thread partition in SpMT; secondly, we build a parametric model Qinling with extracted parameters and speedups, and train Qinling offline, as well as apply it to predict the theoretical speedups of unseen applications. Finally, validation is done. Prophet, which consists of an automatic parallelization compiler and a multi-core simulator, is used to obtain real speedups of the input programs. Olden and SPEC2000 benchmarks are used to train and validate the parametric model. Experiments show that Qinling delivers a good performance to predict speedups of unseen programs, and provides feedback guidance for Prophet to obtain the optimal partition parameters.

]]>Symmetry doi: 10.3390/sym9090179

Authors: Yaman Akbulut Abdulkadir Sengur Yanhui Guo Florentin Smarandache

k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parametric supervised classifier. It aims to determine the class label of an unknown sample by its k-nearest neighbors that are stored in a training set. The k-nearest neighbors are determined based on some distance functions. Although k-NN produces successful results, there have been some extensions for improving its precision. The neutrosophic set (NS) defines three memberships namely T, I and F. T, I, and F shows the truth membership degree, the false membership degree, and the indeterminacy membership degree, respectively. In this paper, the NS memberships are adopted to improve the classification performance of the k-NN classifier. A new straightforward k-NN approach is proposed based on NS theory. It calculates the NS memberships based on a supervised neutrosophic c-means (NCM) algorithm. A final belonging membership U is calculated from the NS triples as U = T + I − F . A similar final voting scheme as given in fuzzy k-NN is considered for class label determination. Extensive experiments are conducted to evaluate the proposed method’s performance. To this end, several toy and real-world datasets are used. We further compare the proposed method with k-NN, fuzzy k-NN, and two weighted k-NN schemes. The results are encouraging and the improvement is obvious.

]]>Symmetry doi: 10.3390/sym9090178

Authors: Qiang Guo Guoqing Ruan Jian Wan

This paper proposes a novel method of sparse signal reconstruction, which combines the improved double chains quantum genetic algorithm (DCQGA) and the orthogonal matching pursuit algorithm (OMP). Firstly, aiming at the problems of the slow convergence speed and poor robustness of traditional DCQGA, we propose an improved double chains quantum genetic algorithm (IDCQGA). The main innovations contain three aspects: (1) a high density quantum encoding method is presented to reduce the searching space and increase the searching density of the algorithm; (2) the adaptive step size factor is introduced in the chromosome updating, which changes the step size with the gradient of the objective function at the search points; (3) the quantum π / 6 -gate is proposed in chromosome mutation to overcome the deficiency of the traditional NOT-gate mutation with poor performance to increase the diversity of the population. Secondly, for the problem of the OMP algorithm not being able to reconstruct precisely the effective sparse signal in noisy environments, a fidelity orthogonal matching pursuit (FOMP) algorithm is proposed. Finally, the IDCQGA-based OMP and FOMP algorithms are applied to the sparse signal decomposition, and the simulation results show that the proposed algorithms can improve the convergence speed and reconstruction precision compared with other methods in the experiments.

]]>Symmetry doi: 10.3390/sym9090176

Authors: Zbigniew Marszałek

Modern architectures make possible development in new algorithms for large data sets and distributed computing. The newly proposed versions can benefit both from faster computing on the multi core architectures, and intelligent programming techniques that use efficient procedures available in the latest programming studios. Frequently used algorithms to sort arrays of data in NoSQL databases is merge sort, where as NoSQL we understand any database without typical SQL programming interpreter. The author describes how to use the parallelization of the sorting processes for the modified method of sorting by merging for large data sets. The subject of this research is the claim that the parallelization of the sorting method is faster and beneficial for multi-core systems. Presented results show how the number of processors influences the sorting performance. The results are presented in theoretical assumptions and confirmed in practical benchmark tests. The method is compared to other sorting methods like quick sort, heap sort, and merge sort to show potential efficiency.

]]>Symmetry doi: 10.3390/sym9090177

Authors: Wei Zhang Yumei Xing Dong Qiu

In this paper, based on a partial order, we study the characterizations of directional derivatives and the subdifferential of fuzzy function. At the same time, we also discuss the relation between the directional derivative and the subdifferential.

]]>Symmetry doi: 10.3390/sym9090175

Authors: Bo Hu Lvqing Bi Songsong Dai

A complex fuzzy set is a set whose membership values are vectors in the unit circle in the complex plane. This paper establishes the orthogonality relation of complex fuzzy sets. Two complex fuzzy sets are said to be orthogonal if their membership vectors are perpendicular. We present the basic properties of orthogonality of complex fuzzy sets and various results on orthogonality with respect to complex fuzzy complement, complex fuzzy union, complex fuzzy intersection, and complex fuzzy inference methods. Finally, an example application of signal detection demonstrates the utility of the orthogonality of complex fuzzy sets.

]]>Symmetry doi: 10.3390/sym9090174

Authors: Kuppusamy Kanagaraj Kangjie Lin Wanhua Wu Guowei Gao Zhihui Zhong Dan Su Cheng Yang

Buckybowls are polynuclear aromatic hydrocarbons that have a curved aromatic surface and are considered fragments of buckminsterfullerenes. The curved aromatic surface led to the loss of planar symmetry of the normal aromatic plane and may cause unique inherent chirality, so-called bowl chirality, which it is possible to thermally racemize through a bowl-to-bowl inversion process. In this short review, we summarize the studies concerning the special field of bowl chirality, focusing on recent practical aspects of attaining diastereo/enantioenriched chiral buckybowls through asymmetric synthesis, chiral optical resolution, selective chiral metal complexation, and chiral assembly formation.

]]>Symmetry doi: 10.3390/sym9090173

Authors: Dae Yoon Dong-Soo Kim Young Kim Jae Lee

Our principal goal is to study the prescribed curvature problem in a manifold with density. In particular, we consider the Euclidean 3-space R 3 with a positive density function e ϕ , where ϕ = - x 2 - y 2 , ( x , y , z ) ∈ R 3 and construct all the helicoidal surfaces in the space by solving the second-order non-linear ordinary differential equation with the weighted Gaussian curvature and the mean curvature functions. As a result, we give a classification of weighted minimal helicoidal surfaces as well as examples of helicoidal surfaces with some weighted Gaussian curvature and mean curvature functions in the space.

]]>Symmetry doi: 10.3390/sym9090172

Authors: Igor Forain Robson de Oliveira Albuquerque Ana Sandoval Orozco Luis García Villalba Tai-Hoon Kim

Increasingly sophisticated antivirus (AV) software and the growing amount and complexity of malware demand more processing power from personal computers, specifically from the central processor unit (CPU). This paper conducted performance tests with Clam AntiVirus (ClamAV) and improved its performance through parallel processing on multiple cores using the Open Multi-Processing (OpenMP) library. All the tests used the same dataset constituted of 1.33 GB of data distributed among 2766 files of different sizes. The new parallel version of ClamAV implemented in our work achieved an execution time around 62% lower than the original software version, reaching a speedup of 2.6 times faster. The main contribution of this work is to propose and implement a new version of the ClamAV antivirus using parallel processing with OpenMP, easily portable to a variety of hardware platforms and operating systems.

]]>Symmetry doi: 10.3390/sym9090171

Authors: Mi Kim Nam Lee Jin Park

Internet of Things (IoT) platforms are the key for the development of scalable IoT applications and services that connect real and virtual worlds between objects, systems, and people. However, as the IoT platform market represents a truly new market segment that was almost non-existent a few years ago, the platforms are complex and changing quickly. These IoT platforms perform simple functions such as providing useful information, and others can provide services through collaborations with IoT devices. This situation needs a generic service interface, and results in a range of IoT architectures through not only the configuration setting of IoT devices and resources but also the varied environments of collaboration of each device. Due to these heterogeneities, it is quite challenging to develop applications working with diverse IoT services, and it is even more difficult to maintain such applications. Therefore, this paper presents a security generic service interface with the effective common characteristics of an IoT platform by defining a set of generic interfaces and adopting well-known design patterns. The generic interface solves the heterogeneity-driven problems and makes it possible to effectively adopt a platform-independent Generic Interface that could be operated in diverse IoT platforms.

]]>Symmetry doi: 10.3390/sym9090169

Authors: Jiansen Zhao Xiao-Yue You Hu-Chen Liu Song-Man Wu

Supplier selection is a complex multiple criteria decision-making (MCDM) problem, which considers a number of alternative suppliers as well as conflicting and noncommensurable criteria. Considering the fact that it is difficult to precisely determine criteria weights and the ratings of alternatives on each criterion in real-life situations, the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method has been modified for intuitionistic fuzzy data in this study for supplier evaluation and selection. Further, we take into account both subjective and objective weights of criteria in the decision-making process, as most supplier selection approaches consider only subjective criteria weights. Finally, two supplier selection examples are provided to illustrate the proposed intuitionistic fuzzy hybrid VIKOR (IFH-VIKOR) method, and its merits are highlighted by comparing it with other relevant approaches.

]]>Symmetry doi: 10.3390/sym9090170

Authors: Cátia Silva Cláudia Ribeiro Alexandra Maia Virgínia Gonçalves Maria Tiritan Carlos Afonso

The accurate assessment of racemic pharmaceuticals requires enantioselective analytical methods. This study presents the development and validation of an enantioselective liquid chromatography with a fluorescence detection method for the concomitant quantification of the enantiomers of tramadol and their metabolites, N-desmethyltramadol and O-desmethyltramadol, in wastewater samples. Optimized conditions were achieved using a Lux Cellulose-4 column 150 × 4.6 mm, 3 µm isocratic elution, and 0.1% diethylamine in hexane and ethanol (96:4, v/v) at 0.7 mL min−1. The samples were extracted using 150 mg Oasis® mixed-mode cation exchange (MCX) cartridges. The method was validated using a synthetic effluent of a laboratory-scale aerobic granular sludge sequencing batch reactor. The method demonstrated to be selective, accurate, and linear (r2 &gt; 0.99) over the range of 56 ng L−1 to 392 ng L−1. The detection and the quantification limits of each enantiomer were 8 ng L−1 and 28 ng L−1 for tramadol and N-desmethyltramadol, and 20 ng L−1 and 56 ng L−1 for O-desmethyltramadol. The feasibility of the method was demonstrated in a screening study in influent and effluent samples from a wastewater treatment plant. The results demonstrated the occurrence of tramadol enantiomers up to 325.1 ng L−1 and 357.9 ng L−1, in the effluent and influent samples, respectively. Both metabolites were detected in influents and effluents.

]]>Symmetry doi: 10.3390/sym9090168

Authors: Rafał Dreżewski Krzysztof Doroz

Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach—the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

]]>Symmetry doi: 10.3390/sym9080167

Authors: Ismael Amezcua Valdovinos Jesus Perez Diaz Luis Garcia Villalba Tai-hoon Kim

The Transmission Control Protocol (TCP) is the most used transport protocol to exchange reliable data between network devices. A considerable number of extensions have been implemented into TCP to achieve better performance. In this paper, we will present, describe, implement, and analyze a new protocol extension called Bandwidth-Aggregation TCP (BATCP), which enables the concurrent use of network interfaces, to improve network performance on multi-homed nodes. BATCP allows the use of multiple TCP connections to accept multiple IP addresses from a multi-homed node, scheduling segments among them based on a scheduling algorithm. Our results show that BATCP achieves full exploitation of each network interface, achieving up to 100 % network utilization using two ADSL connections in real-world scenarios. MultiPath TCP (MPTCP) is currently being standardized, and achieves up to 96 % of network utilization when in ideal conditions. BATCP and MPTCP are the only protocols tested on real-world scenarios. Related work such as the Proxy Inverse Multiplexer, called PRISM, and bandwidth aggregation with Stream Control Transmission Protocol (SCTP) achieve 80 % utilization or less with network simulators.

]]>Symmetry doi: 10.3390/sym9080166

Authors: María Carmen Carnero

Abstract: Until the last few decades, maintenance has not been considered of special importance by organisations. Thus, the number of studies that assess maintenance performance in a country is still very small, despite the relevance this area has to the level of national competitiveness. This article describes a multicriteria model integrating the fuzzy analytic hierarchy process (FAHP) with multi-attribute utility theory (MAUT) to assess the maintenance performance of large, medium and small enterprises in Spain, before and after the recession, as well as the asymmetries in the state of maintenance between different activity sectors. The weightings are converted to utility functions which allow the final utility of an alternative to be calculated via a Multi-Attribute Utility Function. From the Spanish maintenance data for different industrial sectors in 2005 and 2010, 2400 discrete probability distributions have been produced. Finally, a Monte Carlo simulation is applied for the estimation of the uncertainty. The results show that the economic crisis experienced by Spain since 2008 has negatively affected the level of maintenance applied, rather than it being considered an area that could deliver cost reductions and improvements in productivity and quality to organisations.

]]>Symmetry doi: 10.3390/sym9080163

Authors: Dong Wang Hualing Ren Fubo Shao

Various distributed optimization methods have been developed for consensus optimization problems in multi-agent networks. Most of these methods only use gradient or subgradient information of the objective functions, which suffer from slow convergence rate. Recently, a distributed Newton method whose appeal stems from the use of second-order information and its fast convergence rate has been devised for the network utility maximization (NUM) problem. This paper contributes to this method by adjusting it to a special kind of consensus optimization problem in two different multi-agent networks. For networks with Hamilton path, the distributed Newton method is modified by exploiting a novel matrix splitting techniques. For general connected multi-agent networks, the algorithm is trimmed by combining the matrix splitting technique and the spanning tree for this consensus optimization problems. The convergence analyses show that both modified distributed Newton methods enable the nodes across the network to achieve a global optimal solution in a distributed manner. Finally, the distributed Newton method is applied to solve a problem which is motivated by the Kuramoto model of coupled nonlinear oscillators and the numerical results illustrate the performance of the proposed algorithm.

]]>Symmetry doi: 10.3390/sym9080164

Authors: Jin Park Jong Park

Blockchain has drawn attention as the next-generation financial technology due to its security that suits the informatization era. In particular, it provides security through the authentication of peers that share virtual cash, encryption, and the generation of hash value. According to the global financial industry, the market for security-based blockchain technology is expected to grow to about USD 20 billion by 2020. In addition, blockchain can be applied beyond the Internet of Things (IoT) environment; its applications are expected to expand. Cloud computing has been dramatically adopted in all IT environments for its efficiency and availability. In this paper, we discuss the concept of blockchain technology and its hot research trends. In addition, we will study how to adapt blockchain security to cloud computing and its secure solutions in detail.

]]>Symmetry doi: 10.3390/sym9080160

Authors: Aifang Xie

In this work, by Zadeh’s extension principle, we extend representable uninorms and their fuzzy implications (coimplications) to type-2 fuzzy sets. Emphatically, we investigate in which algebras of fuzzy truth values the extended operations are type-2 uninorms and type-2 fuzzy implications (coimplications), respectively.

]]>Symmetry doi: 10.3390/sym9080165

Authors: Miloslav Znojil František Růžička Konstantin Zloshchastiev

Schrödinger equations with non-Hermitian, but PT -symmetric quantum potentials V ( x ) found, recently, a new field of applicability in classical optics. The potential acquired there a new physical role of an “anomalous” refraction index. This turned attention to the nonlinear Schrödinger equations in which the interaction term becomes state-dependent, V ( x ) → W ( ψ ( x ) , x ) . Here, the state-dependence in W ( ψ ( x ) , x ) is assumed logarithmic, and some of the necessary mathematical assumptions, as well as some of the potential phenomenological consequences of this choice are described. Firstly, an elementary single-channel version of the nonlinear logarithmic model is outlined in which the complex self-interaction W ( ψ ( x ) , x ) is regularized via a deformation of the real line of x into a self-consistently constructed complex contour C. The new role played by PT -symmetry is revealed. Secondly, the regularization is sought for a multiplet of equations, coupled via the same nonlinear self-interaction coupling of channels. The resulting mathematical structures are shown to extend the existing range of physics covered by the logarithmic Schrödinger equations.

]]>Symmetry doi: 10.3390/sym9080161

Authors: Shikai Guo Rong Chen Hui Li

Crowdsourcing is an appealing and economic solution to software application testing because of its ability to reach a large international audience. Meanwhile, crowdsourced testing could have brought a lot of bug reports. Thus, in crowdsourced software testing, the inspection of a large number of test reports is an enormous but essential software maintenance task. Therefore, automatic prediction of the severity of crowdsourced test reports is important because of their high numbers and large proportion of noise. Most existing approaches to this problem utilize supervised machine learning techniques, which often require users to manually label a large number of training data. However, Android test reports are not labeled with their severity level, and manual labeling is time-consuming and labor-intensive. To address the above problems, we propose a Knowledge Transfer Classification (KTC) approach based on text mining and machine learning methods to predict the severity of test reports. Our approach obtains training data from bug repositories and uses knowledge transfer to predict the severity of Android test reports. In addition, our approach uses an Importance Degree Reduction (IDR) strategy based on rough set to extract characteristic keywords to obtain more accurate reduction results. The results of several experiments indicate that our approach is beneficial for predicting the severity of android test reports.

]]>Symmetry doi: 10.3390/sym9080162

Authors: Haibin Liu Xinyang Deng Wen Jiang

Failure mode and effects analysis (FMEA) is a popular and useful approach applied to examine potential failures in different products, designs, processes, and services. As a vital index, the risk priority number (RPN) can determine the risk priorities of failure modes by some risk factors such as occurrence (O), severity (S), and detection (D). However, in FMEA, the traditional risk priority number approach has some shortcomings, especially in setting the weight of risk factors. This paper presents an improved risk priority number approach based on a fuzzy measure and fuzzy integral. A fuzzy measure is used to reflect the importance of the individual indicators and the indicator set and a fuzzy integral is a nonlinear function defined on the basis of fuzzy measure. The weights of risk factors given by domain experts are seen as fuzzy densities to generate a λ -fuzzy measure which can reflect the weights’ difference and relevance about risk factors. Then, the Choquet integral is used to fuse every value of risk factors about failure modes so as to obtain the comprehensive evaluation result. The result can reflect the comprehensive risk level, so it has a definite physical significance. Finally, an illustrative example and a comparison with another approach are given to show the effectiveness of the proposed approach in the paper.

]]>Symmetry doi: 10.3390/sym9080158

Authors: Zia Bashir Jarosław Wątróbski Tabasam Rashid Wojciech Sałabun Jawad Ali

The classical matrix theory is deficient to express the vagueness of the real life. The fuzzy set theory has been successfully applied to bridge this gap. Much work has already been done on a two-person zero sum matrix game with fuzzy goals. In continuation, this paper is dedicated to define and study a multi-criteria two-person zero sum game with intuitionistic fuzzy goals. It is shown that solving such games is equivalent to solving two crisp multi object linear programming problems. Our work generalizes the previous study on a multi-criteria game with fuzzy goals by adopting the approach of linear programming with intuitionistic fuzzy sets. Finally, an illustrative numerical example is provided to elaborate the proposed approach.

]]>Symmetry doi: 10.3390/sym9080157

Authors: Lili Guo Shanya Cong

Heterogeneous networks (HetNets) are regarded as a promising approach to handle the deluge of mobile data traffic. With the co-channel deployment of small cells, the coverage and capacity of the network will be improved. However, the conventional maximum-received-power (MRP) user association scheme and cross-tier interference issue significantly diminish the performance gain provided by small cells. In this paper, we propose a novel location-aware cross-tier cooperation (LA-CTC) scheme for jointly achieving load balancing and interference mitigation in two-tier HetNets. In detail, we define an inner region for each macro base station (MBS) where the femto base stations (FBSs) will be deactivated, and thereby the users within the inner region will only be served by the MBS. Subsequently, for the users located in the outer region, the proposed scheme only uses coordinated multipoint (CoMP) transmission by two tiers of BSs to eliminate the excessive cross-tier interference suffered by offloaded users, whereas users with good locations are served directly by either a MBS or a FBS. Using tools of stochastic geometry, we derived the analytical expressions for the coverage probability and average rate of a randomly chosen user. Meanwhile, the analytical results were validated through Monte Carlo simulations. The numerical results show that the proposed scheme can improve the performance of networks significantly. Moreover, we compare the performance of the proposed scheme with that of the conventional MRP scheme, the cell range expansion (CRE) scheme and the location-aware cross-tier CoMP transmission (LA-CTCT) scheme in the literature. Numerical comparisons revealed that the proposed LA-CTC scheme outperforms the other three schemes.

]]>Symmetry doi: 10.3390/sym9080156

Authors: Jianwei Gao Ru Yi

In this paper, we develop a new linguistic aggregation operator based on the cloud model for solving linguistic group decision-making problem. First, an improved generating cloud method is proposed so as to transform linguistic variables into clouds, which modifies the limitation of the classical generating cloud method. We then address some new cloud algorithms, such as cloud possibility degree and cloud support degree which can be respectively used to compare clouds and determine the weights. Combining the cloud support degree with power aggregation operator, we develop a new cloud aggregation operator dubbed the cloud generalized power ordered weighted average (CGPOWA) operator. We study the properties of the CGPOWA operator and investigate its family including a wide range of aggregation operators such as the CGPA operator, CPOWA operator, CPOWGA operator, CPWQA operator, CWAA and CWGA operator. Furthermore, a new approach for linguistic group decision-making is presented on the basis of the improved generating cloud method and CGPOWA operator. Finally, an illustrative example is provided to examine the effectiveness and validity of the proposed approach.

]]>Symmetry doi: 10.3390/sym9080159

Authors: Wei Zhang Yumei Xing Dong Qiu

A multi-objective bi-matrix game model based on fuzzy goals is established in this paper. It is shown that the equilibrium solution of such a game model problem can be translated into the optimal solution of a multi-objective, non-linear programming problem. Finally, the results of this paper are demonstrated through a numerical example.

]]>Symmetry doi: 10.3390/sym9080155

Authors: Vladimir D. Ivashchuk

The review is devoted to exact solutions with hidden symmetries arising in a multidimensional gravitational model containing scalar fields and antisymmetric forms. These solutions are defined on a manifold of the form M = M0 x M1 x . . . x Mn , where all Mi with i &gt;= 1 are fixed Einstein (e.g., Ricci-flat) spaces. We consider a warped product metric on M. Here, M0 is a base manifold, and all scale factors (of the warped product), scalar fields and potentials for monomial forms are functions on M0 . The monomial forms (of the electric or magnetic type) appear in the so-called composite brane ansatz for fields of forms. Under certain restrictions on branes, the sigma-model approach for the solutions to field equations was derived in earlier publications with V.N.Melnikov. The sigma model is defined on the manifold M0 of dimension d0 ≠ 2 . By using the sigma-model approach, several classes of exact solutions, e.g., solutions with harmonic functions, S-brane, black brane and fluxbrane solutions, are obtained. For d0 = 1 , the solutions are governed by moduli functions that obey Toda-like equations. For certain brane intersections related to Lie algebras of finite rank—non-singular Kac–Moody (KM) algebras—the moduli functions are governed by Toda equations corresponding to these algebras. For finite-dimensional semi-simple Lie algebras, the Toda equations are integrable, and for black brane and fluxbrane configurations, they give rise to polynomial moduli functions. Some examples of solutions, e.g., corresponding to finite dimensional semi-simple Lie algebras, hyperbolic KM algebras: H2(q, q) , AE3, HA(1)2, E10 and Lorentzian KM algebra P10 , are presented.

]]>Symmetry doi: 10.3390/sym9080153

Authors: Jiqian Chen Jun Ye Shigui Du

A refined single-valued/interval neutrosophic set is very suitable for the expression and application of decision-making problems with both attributes and sub-attributes since it is described by its refined truth, indeterminacy, and falsity degrees. However, existing refined single-valued/interval neutrosophic similarity measures and their decision-making methods are scarcely studied in existing literature and cannot deal with this decision-making problem with the weights of both attributes and sub-attributes in a refined interval and/or single-valued neutrosophic setting. To solve the issue, this paper firstly introduces a refined simplified neutrosophic set (RSNS), which contains the refined single-valued neutrosophic set (RSVNS) and refined interval neutrosophic set (RINS), and then proposes vector similarity measures of RSNSs based on the Jaccard, Dice, and cosine measures of simplified neutrosophic sets in vector space, and the weighted Jaccard, Dice, and cosine measures of RSNSs by considering weights of both basic elements and sub-elements in RSNS. Further, a decision-making method with the weights of both attributes and sub-attributes is developed based on the weighted Jaccard, Dice, and cosine measures of RSNSs under RSNS (RINS and/or RSVNS) environments. The ranking order of all the alternatives and the best one can be determined by one of weighted vector similarity measures between each alternative and the ideal solution (ideal alternative). Finally, an actual example on the selecting problem of construction projects illustrates the application and effectiveness of the proposed method.

]]>Symmetry doi: 10.3390/sym9080154

Authors: Jing Fu Jun Ye

When a physician carries out the clinical survey of a patient with benign prostatic hyperplasia (BPH) symptoms to reach the initial evaluation/diagnosis of BPH, the existing initial evaluation method of BPH based on the international prostate symptom score (I-PSS) usually uses the objective evaluation/diagnosis method with crisp values without considering fuzzy information. However, this common evaluation/diagnosis method may lead to the loss of a great deal of useful incomplete, uncertain, and inconsistent information in the clinical survey and initial evaluation process of the BPH symptoms for a patient, resulting in an unreasonable evaluation and diagnosis distortion of the BPH symptoms. To overcome this drawback, this paper aims to propose new exponential similarity measures (ESMs) between simplified neutrosophic sets (SNSs), including single-valued neutrosophic ESMs and interval neutrosophic ESMs, and their initial evaluation/diagnosis method of the BPH symptoms with simplified neutrosophic information. Finally, two evaluation/diagnosis examples of the BPH symptoms are provided to demonstrate the effectiveness and rationality of the proposed method.

]]>Symmetry doi: 10.3390/sym9080152

Authors: Donghwoon Kwon Suwoo Park Jeong-Tak Ryu

A smart connected car in conjunction with the Internet of Things (IoT) is an emerging topic. The fundamental concept of the smart connected car is connectivity, and such connectivity can be provided by three aspects, such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X). To meet the aspects of V2V and V2I connectivity, we developed modules in accordance with international standards with respect to On-Board Diagnostics II (OBDII) and 4G Long Term Evolution (4G-LTE) to obtain and transmit vehicle information. We also developed software to visually check information provided by our modules. Information related to a user’s driving, which is transmitted to a cloud-based Distributed File System (DFS), was then analyzed for the purpose of big data analysis to provide information on driving habits to users. Yet, since this work is an ongoing research project, we focus on proposing an idea of system architecture and design in terms of big data analysis. Therefore, our contributions through this work are as follows: (1) Develop modules based on Controller Area Network (CAN) bus, OBDII, and 4G-LTE; (2) Develop software to check vehicle information on a PC; (3) Implement a database related to vehicle diagnostic codes; (4) Propose system architecture and design for big data analysis.

]]>Symmetry doi: 10.3390/sym9080151

Authors: Sui-Zhi Luo Peng-Fei Cheng Jian-Qiang Wang Yuan-Ji Huang

Project delivery system selection is an essential part of project management. In the process of choosing appropriate transaction model, many factors should be under consideration, such as the capability and experience of proprietors, project implementation risk, and so on. How to make their comprehensive evaluations and select the optimal delivery system? This paper proposes a decision-making approach based on an extended linguistic preference structure: simplified neutrosophic linguistic preference relations (SNLPRs). The basic elements in SNLPRs are simplified neutrosophic linguistic numbers (SNLNs). First, several distance measures of SNLNs are introduced. A distance-based consistency index is provided to measure the consistency degree of a simplified neutrosophic linguistic preference relation (SNLPR). When the SNLPR is not acceptably consistent, a consistency-improving automatic iterative algorithm may be used. Afterwards, a decision-making method with SNLPRs is developed. The example of its application in project delivery systems’ selection is offered, and a comparison analysis is given in the end as well.

]]>Symmetry doi: 10.3390/sym9080149

Authors: Weizhang Liang Guoyan Zhao Hao Wu

The investment in and development of mineral resources play an important role in the national economy. A good mining project investment can improve economic efficiency and increase social wealth. Faced with the complexity and uncertainty of a mine’s circumstances, there is great significance in evaluating investment risk scientifically. In order to solve practical engineering problems, this paper presents an extended TOPSIS method combined with linguistic neutrosophic numbers (LNNs). Firstly, considering that there are several qualitative risk factors of mining investment projects, the paper describes evaluation information by means of LNNs. The advantage of LNNs is that major original information is reserved with linguistic truth, indeterminacy, and false membership degrees. After that, a number of distance measures are defined. Furthermore, a common status is that the decision makers can’t determine the importance degrees of every risk factor directly for a few reasons. With respect to this situation, the paper offers a weight model based on maximizing deviation to obtain the criteria weight vector objectively. Subsequently, a decision-making approach through improving classical TOPSIS with LNNs comes into being. Next, a case study of the proposed method applied in metallic mining projects investment is given. Some comparison analysis is also submitted. At last, the discussions and conclusions are finished.

]]>Symmetry doi: 10.3390/sym9080150

Authors: Yuval Ben-Abu

Potassium channels are integral membrane proteins that selectively transport K+ ions across cell membranes. They function through a pair of gates, which work in tandem to allow the passage of the ions through the channel pore in a coupled system, to which I refer to here as the “gate linker”. The functional mutation effects, as described in the literature, suggest that the gate linker functions analogously to a triad of coiled springs arranged in series. Accordingly, I constructed a physical model of harmonic oscillators and analyzed it mechanically and mathematically. The operation of this model indeed corresponds to the phenomena observed in the mutations study. The harmonic oscillator model shows that the strength of the gate linker is crucial for gate coupling and may account for the velocity, direction, and efficiency of ion transfer through the channel. Such a physical perspective of the gating process suggests new lines of investigation regarding the coupling mode of potassium channels and may help to explain the importance of the gate linker to channel function.

]]>Symmetry doi: 10.3390/sym9080148

Authors: Nazife Koca Mehmet Koca

Vertices and symmetries of regular and irregular chiral polyhedra are represented by quaternions with the use of Coxeter graphs. A new technique is introduced to construct the chiral Archimedean solids, the snub cube and snub dodecahedron together with their dual Catalan solids, pentagonal icositetrahedron and pentagonal hexecontahedron. Starting with the proper subgroups of the Coxeter groups W ( A 1 ⊕ A 1 ⊕ A 1 ) , W ( A 3 ) , W ( B 3 ) and W ( H 3 ) , we derive the orbits representing the respective solids, the regular and irregular forms of a tetrahedron, icosahedron, snub cube, and snub dodecahedron. Since the families of tetrahedra, icosahedra and their dual solids can be transformed to their mirror images by the proper rotational octahedral group, they are not considered as chiral solids. Regular structures are obtained from irregular solids depending on the choice of two parameters. We point out that the regular and irregular solids whose vertices are at the edge mid-points of the irregular icosahedron, irregular snub cube and irregular snub dodecahedron can be constructed.

]]>Symmetry doi: 10.3390/sym9080147

Authors: Qingsong Ai Yanan Zhang Weili Qi Quan Liu and Kun Chen

Since surface electromyograghic (sEMG) signals are non-invasive and capable of reflecting humans’ motion intention, they have been widely used for the motion recognition of upper limbs. However, limited research has been conducted for lower limbs, because the sEMGs of lower limbs are easily affected by body gravity and muscle jitter. In this paper, sEMG signals and accelerometer signals are acquired and fused to recognize the motion patterns of lower limbs. A curve fitting method based on median filtering is proposed to remove accelerometer noise. As for movement onset detection, an sEMG power spectral correlation coefficient method is used to detect the start and end points of active signals. Then, the time-domain features and wavelet coefficients of sEMG signals are extracted, and a dynamic time warping (DTW) distance is used for feature extraction of acceleration signals. At last, five lower limbs’ motions are classified and recognized by using Gaussian kernel-based linear discriminant analysis (LDA) and support vector machine (SVM) respectively. The results prove that the fused feature-based classification outperforms the classification with only sEMG signals or accelerometer signals, and the fused feature can achieve 95% or higher recognition accuracy, demonstrating the validity of the proposed method.

]]>Symmetry doi: 10.3390/sym9080146

Authors: Xiaowu Li Lin Wang Zhinan Wu Linke Hou Juan Liang Qiaoyang Li

To compute the minimum distance between a point and a parametric surface, three well-known first-order algorithms have been proposed by Hartmann (1999), Hoschek, et al. (1993) and Hu, et al. (2000) (hereafter, the First-Order method). In this paper, we prove the method’s first-order convergence and its independence of the initial value. We also give some numerical examples to illustrate its faster convergence than the existing methods. For some special cases where the First-Order method does not converge, we combine it with Newton’s second-order iterative method to present the hybrid second-order algorithm. Our method essentially exploits hybrid iteration, thus it performs very well with a second-order convergence, it is faster than the existing methods and it is independent of the initial value. Some numerical examples confirm our conclusion.

]]>Symmetry doi: 10.3390/sym9080145

Authors: Sebastian Ocklenburg Jutta Peterburs Janet Mertzen Judith Schmitz Onur Güntürkün Gina Grimshaw

Hemispheric asymmetries are a major organizational principle in human emotion processing, but their interaction with prefrontal control processes is not well understood. To this end, we determined whether hemispheric differences in response inhibition depend on the emotional valence of the stimulus being inhibited. Participants completed a lateralised Go/Nogo task, in which Nogo stimuli were neutral or emotional (either positive or negative) images, while Go stimuli were scrambled versions of the same pictures. We recorded the N2 and P3 event-related potential (ERP) components, two common electrophysiological measures of response inhibition processes. Behaviourally, participants were more accurate in withholding responses to emotional than to neutral stimuli. Electrophysiologically, Nogo-P3 responses were greater for emotional than for neutral stimuli, an effect driven primarily by an enhanced response to positive images. Hemispheric asymmetries were also observed, with greater Nogo-P3 following left versus right visual field stimuli. However, the visual field effect did not interact with emotion. We therefore find no evidence that emotion-related asymmetries affect response inhibition processes.

]]>Symmetry doi: 10.3390/sym9080144

Authors: Beatrice Bonati Caterina Quaresmini Gionata Stancher Valeria Sovrano

As recent studies have shown a left-eye preference during exploration in Podarcis muralis, which could be strictly related to its territoriality, we tested the same behaviour in a similar species, but one living in different habitats and showing a different ecology. In particular, we assessed the preferential turning direction in adults of a non-territorial lizard, Zootoca vivipara, during the exploration of an unknown maze. At the population level, no significant preference emerged, possibly for the lack of the territorial habit and the characteristics of the natural environment. Nevertheless, females turned to the left more frequently than males did. We hypothesize this as a motor bias, possibly due to a necessity for females to be coordinated and fast in moving in the environment, because of their viviparous condition and the resultant reduction of physical performance during pregnant periods, which are likely to increase vulnerability to predators.

]]>Symmetry doi: 10.3390/sym9080143

Authors: Mansik Kim Kyung-Soo Lim Jungsuk Song Moon-seog Jun

As the Internet of Things (IoT) has developed, the emerging sensor network (ESN) that integrates emerging technologies, such as autonomous driving, cyber-physical systems, mobile nodes, and existing sensor networks has been in the limelight. Smart homes have been researched and developed by various companies and organizations. Emerging sensor networks have some issues of providing secure service according to a new environment, such as a smart home, and the problems of low power and low-computing capacity for the sensor that previous sensor networks were equipped with. This study classifies various sensors used in smart homes into three classes and contains the hierarchical topology for efficient communication. In addition, a scheme for establishing secure communication among sensors based on physical unclonable functions (PUFs) that cannot be physically cloned is suggested in regard to the sensor’s low performance. In addition, we analyzed this scheme by conducting security and performance evaluations proving to constitute secure channels while consuming fewer resources. We believe that our scheme can provide secure communication by using fewer resources in a smart home environment in the future.

]]>Symmetry doi: 10.3390/sym9080142

Authors: Yaman Akbulut Abdulkadir Şengür Yanhui Guo Florentin Smarandache

Extreme learning machine (ELM) is known as a kind of single-hidden layer feedforward network (SLFN), and has obtained considerable attention within the machine learning community and achieved various real-world applications. It has advantages such as good generalization performance, fast learning speed, and low computational cost. However, the ELM might have problems in the classification of imbalanced data sets. In this paper, we present a novel weighted ELM scheme based on neutrosophic set theory, denoted as neutrosophic weighted extreme learning machine (NWELM), in which neutrosophic c-means (NCM) clustering algorithm is used for the approximation of the output weights of the ELM. We also investigate and compare NWELM with several weighted algorithms. The proposed method demonstrates advantages to compare with the previous studies on benchmarks.

]]>Symmetry doi: 10.3390/sym9080141

Authors: Ya-huei Wang Hung-Chang Liao

This study attempted to test whether the use of computer-assisted language learning (CALL) and innovative collaborative learning could be more effective than the use of traditional collaborative learning in improving students’ English proficiencies. A true experimental design was used in the study. Four randomly-assigned groups participated in the study: a traditional collaborative learning group (TCLG, 34 students), an innovative collaborative learning group (ICLG, 31 students), a CALL traditional collaborative learning group (CALLTCLG, 32 students), and a CALL innovative collaborative learning group (CALLICLG, 31 students). TOEIC (Test of English for International Communication) listening, reading, speaking, and writing pre-test and post-test assessments were given to all students at an interval of sixteen weeks. Multivariate analysis of covariance (MANCOVA), multivariate analysis of variance (MANOVA), and analysis of variance (ANOVA) were used to analyze the data. The results revealed that students who used CALL had significantly better learning performance than those who did not. Students in innovative collaborative learning had significantly better learning performances than those in traditional collaborative learning. Additionally, students using CALL innovative collaborative learning had better learning performances than those in CALL collaborative learning, those in innovative collaborative learning, and those in traditional collaborative learning.

]]>Symmetry doi: 10.3390/sym9080140

Authors: Francisco Lupiáñez

In an earlier paper, we proved that Smarandache’s definition of neutrosophic paraconsistent topology is neither a generalization of Çoker’s intuitionistic fuzzy topology nor a generalization of Smarandache’s neutrosophic topology. Recently, Salama and Alblowi proposed a new definition of neutrosophic topology, that generalizes Çoker’s intuitionistic fuzzy topology. Here, we study this new definition and its relation to Smarandache’s paraconsistent neutrosophic sets.

]]>Symmetry doi: 10.3390/sym9080139

Authors: Yao-Te Tsai Shu-Ching Wang Kuo-Qin Yan Chih-Ming Chang

In the complex environment of the IoT (Internet of Things), the amount of information available is enormous and the number of users also increases at a blistering pace. With a huge number of users, e-commerce marketing strategies in the IoT become extremely important and must be altered accordingly in response to changes in the environment and industry. Hence, the application of IoT technology to mobile commerce allows users to receive integrated information according to time, location, and context using location-based service, and provides them with a more effective shopping experience. The validation results show that external variables indirectly influence behavioral intention through perceived usefulness. The investigation of behavioral intention is used to understand users’ acceptance and using willingness of the store app, which can help narrow the gap between stores and consumers, and help improve operations.

]]>Symmetry doi: 10.3390/sym9080138

Authors: Kh Tohidul Islam Ram Gopal Raj Ghulam Mujtaba

The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the well-maintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.

]]>Symmetry doi: 10.3390/sym9080137

Authors: Di Wang Chunying Zhao Jun Kong

Over the past four decades, the prophecy from computer pundits and prognosticators pointed to the looming arrival of the paperless office era. However, forty years later, physical paper documents are still playing a significant role due to the ease of use, superior readability, and availability. The drawbacks of paper sheets are that they are hard to modify and retrieve, have limited space, and are environmentally unfriendly. Augmenting paper documents with digital information from mobile devices extends the two-dimensional space of physical paper documents. Various camera-based recognition and detection devices have been proposed to augment paper documents with digital information. However, there are still some limitations that exist in these systems. This paper presents a novel, low cost, spatially aware, mobile system called Ultrasonic PhoneLens. The Ultrasonic PhoneLens adopts two-dimensional dynamic image presentation and ultrasonic sound positioning techniques. It consists of two ultrasonic sound sensors, one Arduino mini-controller board, and one android mobile device. Based on the location of the mobile device over the physical paper, Ultrasonic PhoneLens can retrieve pre-saved digital information from a mobile database for the object (such as a text, a paragraph, or an image) in a paper document. An empirical study was conducted to evaluate the system performance. The results indicate that our system has a better performance in tasks such as browsing multivalent documents and sharing digital information than the Wiimote PhoneLens system.

]]>Symmetry doi: 10.3390/sym9080136

Authors: Shahzad Faizi Wojciech Sałabun Tabasam Rashid Jarosław Wątróbski Sohail Zafar

There are many real-life problems that, because of the need to involve a wide domain of knowledge, are beyond a single expert. This is especially true for complex problems. Therefore, it is usually necessary to allocate more than one expert to a decision process. In such situations, we can observe an increasing importance of uncertainty. In this paper, the Multi-Criteria Decision-Making (MCDM) method called the Characteristic Objects Method (COMET) is extended to solve problems for Multi-Criteria Group Decision-Making (MCGDM) in a hesitant fuzzy environment. It is a completely new idea for solving problems of group decision-making under uncertainty. In this approach, we use L-R-type Generalized Fuzzy Numbers (GFNs) to get the degree of hesitancy for an alternative under a certain criterion. Therefore, the classical COMET method was adapted to work with GFNs in group decision-making problems. The proposed extension is presented in detail, along with the necessary background information. Finally, an illustrative numerical example is provided to elaborate the proposed method with respect to the support of a decision process. The presented extension of the COMET method, as opposed to others’ group decision-making methods, is completely free of the rank reversal phenomenon, which is identified as one of the most important MCDM challenges.

]]>Symmetry doi: 10.3390/sym9080134

Authors: Ting-Yi Chang Min-Shiang Hwang Chou-Chen Yang

In this paper, we propose new password authenticated key exchange (PAKE) and protected password change (PPC) protocols without any symmetric or public-key cryptosystems. The security of the proposed protocols is based on the computational Diffie-Hellman assumption in the random oracle model. The proposed scheme can resist both forgery server and denial of service attacks.

]]>Symmetry doi: 10.3390/sym9080135

Authors: Elisa Frasnelli Giorgio Vallortigara

Several species of social bees exhibit population-level lateralization in learning odors and recalling olfactory memories. Honeybees Apis mellifera and Australian social stingless bees Trigona carbonaria and Austroplebeia australis are better able to recall short- and long-term memory through the right and left antenna respectively, whereas non-social mason bees Osmia rufa are not lateralized in this way. In honeybees, this asymmetry may be partially explained by a morphological asymmetry at the peripheral level—the right antenna has 5% more olfactory sensilla than the left antenna. Here we looked at the possible correlation between the number of the antennal sensilla and the behavioral asymmetry in the recall of olfactory memories in A. australis and O. rufa. We found no population-level asymmetry in the antennal sensilla distribution in either species examined. This suggests that the behavioral asymmetry present in the stingless bees A. australis may not depend on lateral differences in antennal receptor numbers.

]]>Symmetry doi: 10.3390/sym9080131

Authors: Ana Granados Domingo Pestana Ana Portilla José Rodríguez

Since the characterization of Gromov hyperbolic graphs seems a too ambitious task, there are many papers studying the hyperbolicity of several classes of graphs. In this paper, it is proven that every Mycielskian graph G M is hyperbolic and that δ ( G M ) is comparable to diam ( G M ) . Furthermore, we study the extremal problems of finding the smallest and largest hyperbolicity constants of such graphs; in fact, it is shown that 5 / 4 ≤ δ ( G M ) ≤ 5 / 2 . Graphs G whose Mycielskian have hyperbolicity constant 5 / 4 or 5 / 2 are characterized. The hyperbolicity constants of the Mycielskian of path, cycle, complete and complete bipartite graphs are calculated explicitly. Finally, information on δ ( G ) just in terms of δ ( G M ) is obtained.

]]>Symmetry doi: 10.3390/sym9080132

Authors: Gabriel Barragán-Ramírez Alejandro Estrada-Moreno Yunior Ramírez-Cruz Juan A. Rodríguez-Velázquez

In a graph G = ( V , E ) , a vertex v ∈ V is said to distinguish two vertices x and y if d G ( v , x ) ≠ d G ( v , y ) . A set S ⊆ V is said to be a local metric generator for G if any pair of adjacent vertices of G is distinguished by some element of S. A minimum local metric generator is called a local metric basis and its cardinality the local metric dimension of G. A set S ⊆ V is said to be a simultaneous local metric generator for a graph family G = { G 1 , G 2 , … , G k } , defined on a common vertex set, if it is a local metric generator for every graph of the family. A minimum simultaneous local metric generator is called a simultaneous local metric basis and its cardinality the simultaneous local metric dimension of G . We study the properties of simultaneous local metric generators and bases, obtain closed formulae or tight bounds for the simultaneous local metric dimension of several graph families and analyze the complexity of computing this parameter.

]]>Symmetry doi: 10.3390/sym9080133

Authors: Yanyan Wang Yingsong Li

A joint-optimization method is proposed for enhancing the behavior of the l 1 -norm- and sum-log norm-penalized NLMS algorithms to meet the requirements of sparse adaptive channel estimations. The improved channel estimation algorithms are realized by using a state stable model to implement a joint-optimization problem to give a proper trade-off between the convergence and the channel estimation behavior. The joint-optimization problem is to optimize the step size and regularization parameters for minimizing the estimation bias of the channel. Numerical results achieved from a broadband sparse channel estimation are given to indicate the good behavior of the developed joint-optimized NLMS algorithms by comparison with the previously proposed l 1 -norm- and sum-log norm-penalized NLMS and least mean square (LMS) algorithms.

]]>Symmetry doi: 10.3390/sym9080128

Authors: Ivana Bianchi Marco Bertamini Roberto Burro Ugo Savardi

Symmetry is a salient aspect of biological and man-made objects, and has a central role in perceptual organization. Two studies investigate the role of opposition and identicalness in shaping adults’ naïve idea of “symmetry”. In study 1, both verbal descriptions of symmetry (either provided by the participants or selected from among alternatives presented by the experimenter) and configurations drawn as exemplars of symmetry were studied. In study 2, a pair comparison task was used. Both studies focus on configurations formed by two symmetrical shapes (i.e., between-objects symmetry). Three main results emerged. The explicit description of symmetry provided by participants generally referred to features relating to the relationship perceived between the two shapes and not to geometrical point-by-point transformations. Despite the fact that people tended to avoid references to opposition in their verbal definition of symmetry in study 1, the drawings that they did to represent their prototypical idea of symmetry manifested opposition as a basic component. This latter result was confirmed when the participants were asked to select the definition (in study 1) or the configuration (in study 2) that best fitted with their idea of symmetry. In conclusion, identicalness is an important component in people’s naïve idea of symmetry, but it does not suffice: opposition complements it.

]]>Symmetry doi: 10.3390/sym9080130

Authors: Dong Qiu Yumei Xing Shuqiao Chen

In this article, we put forward the multi-objective matrix game model based on fuzzy payoffs. In order to solve the game model, we first discuss the relationship of two fuzzy numbers via the lower limit - 1 2 of the possibility degree. Then, utilizing this relationship, we conclude that the equilibrium solution of this game model and the optimal solution of multicriteria linear optimization problems are of equal value. Finally, to illustrate the effectiveness and correctness of the obtained model, an example is provided.

]]>Symmetry doi: 10.3390/sym9080129

Authors: Qianhui Dong Yibing Li Qian Sun Ya Zhang

As a typical navigation system, the strapdown inertial navigation system (SINS) is crucial for autonomous underwater vehicles (AUVs) since the SINS accuracy determines the performance of AUVs. Initial alignment is one of the key technologies in SINS, and initial alignment time and initial alignment accuracy affect the performance of SINS directly. As actual systems are nonlinear, the nonlinear filter is widely used to improve the accuracy of the initial alignment. Due to its higher precision and lower computational load, the cubature Kalman filter (CKF) has done well in state estimation. However, the noise characteristics need to be known exactly as prior knowledge, which is difficult or even impossible to achieve. Thus, the adaptive filter should be introduced in the initial alignment algorithm to suppress the uncertainty effect caused by the unknown system noise. Therefore, taking the nonlinearity and uncertainty into account, a novel initial alignment algorithm for AUVs is proposed in this manuscript, based on CKF and the adaptive variance components estimation (VCE) filter (VCKF). Additionally, the simulation and experiment results show that not only the accuracy, but also the convergence speed can be improved with this proposed method. The validity and superiority of this novel adaptive initial alignment algorithm based on VCKF are verified.

]]>Symmetry doi: 10.3390/sym9080127

Authors: Wen Jiang Yehang Shou

The single-valued neutrosophic set is a subclass of neutrosophic set, and has been proposed in recent years. An important application for single-valued neutrosophic sets is to solve multicriteria decision-making problems. The key to using neutrosophic sets in decision-making applications is to make a similarity measure between single-valued neutrosophic sets. In this paper, a new method to measure the similarity between single-valued neutrosophic sets using Dempster–Shafer evidence theory is proposed, and it is applied in multicriteria decision-making. Finally, some examples are given to show the reasonable and effective use of the proposed method.

]]>Symmetry doi: 10.3390/sym9070124

Authors: Jingyuan Jia Aiwu Zhao Shuang Guan

Most existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertain. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relationships of the training time series. Firstly, we compare each piece of data with the data of theprevious day in a historical training time series to generate a new fluctuation trend time series (FTTS). Then, we fuzzify the FTTS into a fuzzy-fluctuation time series (FFTS) according to the up, equal, or down range and orientation of the fluctuations. Since the relationship between historical FFTS and the fluctuation trend of the future is nonlinear, a particle swarm optimization (PSO) algorithm is employed to estimate the proportions for the lagged variables of the fuzzy AR (n) model. Finally, we use the acquired parameters to forecast future fluctuations. In order to compare the performance of the proposed model with that of the other models, we apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets. The experimental results and the comparison results show that the proposed method can be successfully applied in stock market forecasting or similarkinds of time series. We also apply the proposed method to forecast Shanghai Stock Exchange Composite Index (SHSECI) and DAX30 index to verify its effectiveness and universality.

]]>Symmetry doi: 10.3390/sym9070126

Authors: Chao Zhang Deyu Li Arun Sangaiah Said Broumi

As a significant business activity, merger and acquisition (M&amp;A) generally means transactions in which the ownership of companies, other business organizations or their operating units are transferred or combined. In a typical M&amp;A procedure, M&amp;A target selection is an important issue that tends to exert an increasingly significant impact on different business areas. Although some research works based on fuzzy methods have been explored on this issue, they can only deal with incomplete and uncertain information, but not inconsistent and indeterminate information that exists universally in the decision making process. Additionally, it is advantageous to solve M&amp;A problems under the group decision making context. In order to handle these difficulties in M&amp;A target selection background, we introduce a novel rough set model by combining interval neutrosophic sets (INSs) with multigranulation rough sets over two universes, called an interval neutrosophic (IN) multigranulation rough set over two universes. Then, we discuss the definition and some fundamental properties of the proposed model. Finally, we establish decision making rules and computing approaches for the proposed model in M&amp;A target selection background, and the effectiveness of the decision making approach is demonstrated by an illustrative case analysis.

]]>Symmetry doi: 10.3390/sym9070125

Authors: Hsiao-Ting Tseng Hsin-Ginn Hwang Wei-Yen Hsu Pei-Chin Chou I-Chiu Chang

Population ageing is an important global issue. The Taiwanese government has used various Internet of Things (IoT) applications in the “10-year long-term care program 2.0”. It is expected that the efficiency and effectiveness of long-term care services will be improved through IoT support. Home-delivered meal services for the elderly are important for home-based long-term care services. To ensure that the right meals are delivered to the right recipient at the right time, the runners need to take a picture of the meal recipient when the meal is delivered. This study uses the IoT-based image recognition system to design an integrated service to improve the management of image recognition. The core technology of this IoT-based image recognition system is statistical histogram-based k-means clustering for image segmentation. However, this method is time-consuming. Therefore, we proposed using the statistical histogram to obtain a probability density function of pixels of a figure and segmenting these with weighting for the same intensity. This aims to increase the computational performance and achieve the same results as k-means clustering. We combined histogram and k-means clustering in order to overcome the high computational cost for k-means clustering. The results indicate that the proposed method is significantly faster than k-means clustering by more than 10 times.

]]>Symmetry doi: 10.3390/sym9070123

Authors: Jiqian Chen Jun Ye Shigui Du Rui Yong

In nature, the mechanical properties of geological bodies are very complex, and their various mechanical parameters are vague, incomplete, imprecise, and indeterminate. However, we cannot express them by the crisp values in classical probability and statistics. In geotechnical engineering, we need to try our best to approximate exact values in indeterminate environments because determining the joint roughness coefficient (JRC) effectively is a key parameter in the shear strength between rock joint surfaces. In this original study, we first propose neutrosophic interval probability (NIP) and define the confidence degree based on the cosine measure between NIP and the ideal NIP. Then, we propose a new neutrosophic interval statistical number (NISN) by combining the neutrosophic number with the confidence degree to express indeterminate statistical information. Finally, we apply NISNs to express JRC under indeterminate (imprecise, incomplete, and uncertain, etc.) environments. By an actual case, the results demonstrate that NISNs are suitable and effective for JRC expressions and have the objective advantage.

]]>Symmetry doi: 10.3390/sym9070121

Authors: Zhikang Lu Jun Ye

The neutrosophic cubic set can contain much more information to express its interval neutrosophic numbers and single-valued neutrosophic numbers simultaneously in indeterminate environments. Hence, it is a usual tool for expressing much more information in complex decision-making problems. Unfortunately, there has been no research on similarity measures of neutrosophic cubic sets so far. Since the similarity measure is an important mathematical tool in decision-making problems, this paper proposes three cosine measures between neutrosophic cubic sets based on the included angle cosine of two vectors, distance, and cosine functions, and investigates their properties. Then, we develop a cosine measures-based multiple attribute decision-making method under a neutrosophic cubic environment in which, from the cosine measure between each alternative (each evaluated neutrosophic cubic set) and the ideal alternative (the ideal neutrosophic cubic set), the ranking order of alternatives and the best option can be obtained, corresponding to the cosine measure values in the decision-making process. Finally, an illustrative example about the selection problem of investment alternatives is provided to illustrate the application and feasibility of the developed decision-making method.

]]>Symmetry doi: 10.3390/sym9070120

Authors: Paolo Gregorio Sara Bonella Lamberto Rondoni

We derive the quantum analogues of some recently discovered symmetry relations for time correlation functions in systems subject to a constant magnetic field. The symmetry relations deal with the effect of time reversal and do not require—as in the formulations of Casimir and Kubo—that the magnetic field be reversed. It has been anticipated that the same symmetry relations hold for quantum systems. Thus, here we explicitly construct the required symmetry transformations, acting upon the relevant quantum operators, which conserve the Hamiltonian of a system of many interacting spinless particles, under time reversal. Differently from the classical case, parity transformations always reverse the sign of both the coordinates and of the momenta, while time reversal only of the latter. By implementing time reversal in conjunction with ad hoc “incomplete” parity transformations (i.e., transformations that act upon only some of the spatial directions), it is nevertheless possible to achieve the construction of the quantum analogues of the classical maps. The proof that the mentioned symmetry relations apply straightforwardly to quantal time correlation functions is outlined.

]]>Symmetry doi: 10.3390/sym9070122

Authors: Duc Manh Nguyen Sunghwan Kim

In this paper, new construction methods of entanglement-assisted quantum error correction code (EAQECC) from circulant matrices are proposed. We first construct the matrices from two vectors of constraint size, and determine the isotropic subgroup. Then, we also propose a method for calculation of the entanglement subgroup based on standard forms of binary matrices to satisfy the constraint conditions of EAQECC. With isotropic and entanglement subgroups, we determine all the parameters and the minimum distance of the EAQECC. The proposed EAQECC with small lengths are presented to explain the practicality of this construction of EAQECC. Comparison with some earlier constructions of EAQECC shows that the proposed EAQECC is better.

]]>Symmetry doi: 10.3390/sym9070119

Authors: Zhi-Lian Guo Yan-Ling Liu Hai-Long Yang

In this paper, we extend the rough set model on two different universes in intuitionistic fuzzy approximation spaces to a single-valued neutrosophic environment. Firstly, based on the ( α , β , γ ) -cut relation R ˜ { ( α , β , γ ) } , we propose a rough set model in generalized single-valued neutrosophic approximation spaces. Then, some properties of the new rough set model are discussed. Furthermore, we obtain two extended models of the new rough set model—the degree rough set model and the variable precision rough set model—and study some of their properties. Finally, we explore an example to illustrate the validity of the new rough set model.

]]>Symmetry doi: 10.3390/sym9070118

Authors: Boliang Lin Siqi Liu Ruixi Lin Jianping Wu Jiaxi Wang Chang Liu

China’s railway network is one of the largest railway networks in the world. By the end of 2016, the total length of railway in operation reached 124,000 km and the annual freight volume exceeded 3.3 billion tons. However, the structure of network does not completely match transportation demand, i.e., there still exist a few bottlenecks in the network, which forces some freight flows to travel along non-shortest paths. At present, due to the expansion of the high-speed railway network, more passengers will travel by electric multiple unit (EMU) trains running on the high-speed railway. Therefore, fewer passenger trains will move along the regular medium-speed lines, resulting in more spare capacity for freight trains. In this context, some shipments flowing on non-shortest paths can shift to shorter paths. And consequently, a combinatorial optimization problem concerning which origin-destination (O-D) pairs should be adjusted to their shortest paths will arise. To solve it, mathematical models are developed to adjust freight flows between their shortest paths and non-shortest paths based on the 0-1 knapsack problem. We also carry out computational experiments using the commercial software Gurobi and a greedy algorithm (GA), respectively. The results indicate that the proposed models are feasible and effective.

]]>Symmetry doi: 10.3390/sym9070117

Authors: Washington Encalada José Sequera

In recent years, we have seen a significant number of new technological ideas appearing in literature discussing the future of education. For example, E-learning, cloud computing, social networking, virtual laboratories, virtual realities, virtual worlds, massive open online courses (MOOCs), and bring your own device (BYOD) are all new concepts of immersive and global education that have emerged in educational literature. One of the greatest challenges presented to e-learning solutions is the reproduction of the benefits of an educational institution’s physical laboratory. For a university without a computing lab, to obtain hands-on IT training with software, operating systems, networks, servers, storage, and cloud computing similar to that which could be received on a university campus computing lab, it is necessary to use a combination of technological tools. Such teaching tools must promote the transmission of knowledge, encourage interaction and collaboration, and ensure students obtain valuable hands-on experience. That, in turn, allows the universities to focus more on teaching and research activities than on the implementation and configuration of complex physical systems. In this article, we present a model for implementing ecosystems which allow universities to teach practical Information Technology (IT) skills. The model utilizes what is called a “social cloud”, which utilizes all cloud computing services, such as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Additionally, it integrates the cloud learning aspects of a MOOC and several aspects of social networking and support. Social clouds have striking benefits such as centrality, ease of use, scalability, and ubiquity, providing a superior learning environment when compared to that of a simple physical lab. The proposed model allows students to foster all the educational pillars such as learning to know, learning to be, learning to live together, and, primarily, learning to do, through hands-on IT training from a MOOCs. An aspect of the model has been verified experimentally and statistically through a course of computer operating systems.

]]>Symmetry doi: 10.3390/sym9070116

Authors: Kim-Phung Truong Ming-Hung Shu Thanh-Lam Nguyen Bi-Min Hsu

The inevitability of measurement errors and/or humans of subjectivity in data collection processes make accumulated data imprecise, and are thus called fuzzy data. To adapt to this fuzzy domain in a manufacturing process, a traditional u control chart for monitoring the average number of nonconformities per unit is required to extend. In this paper, we first generalize the u chart, named fuzzy u-chart, whose control limits are built on the basis of resolution identity, which is a well-known fuzzy set theory. Then, an approach to fuzzy-logic reasoning, incorporating the decision-maker’s varying levels of optimism towards the online process, is proposed to categorize the manufacturing conditions. In addition, we further develop a condition-based classification mechanism, where the process conditions can be discriminated into intermittent states between in-control and out-of-control. As anomalous conditions are monitored to some extent, this condition-based classification mechanism can provide the critical information to deliberate the cost of process intervention with respect to the gain of quality improvement. Finally, the proposed fuzzy u-chart is implemented in the Vietnam textile dyeing industry to replace its conventional u-chart. The results demonstrate that the industry can effectively evade unnecessary adjustments to its current processes; thus, the industry can substantially reduce its operational cost and potential loss.

]]>Symmetry doi: 10.3390/sym9070115

Authors: Dingjiang Huang Xiangxiang Li Shunchang Yu

In this paper we make a Lie symmetry analysis of a generalized nonlinear beam equation with both second-order and fourth-order wave terms, which is extended from the classical beam equation arising in the historical events of travelling wave behavior in the Golden Gate Bridge in San Francisco. We perform a complete Lie symmetry group classification by using the equivalence transformation group theory for the equation under consideration. Lie symmetry reductions of a nonlinear beam-like equation which are singled out from the classification results are investigated. Some classes of exact solutions, including solitary wave solutions, triangular periodic wave solutions and rational solutions of the nonlinear beam-like equations are constructed by means of the reductions and symbolic computation.

]]>Symmetry doi: 10.3390/sym9070114

Authors: Hyoseok Yoon Se-Ho Park Kyung-Taek Lee Jung Park Anind Dey SeungJun Kim

Wearable devices are being explored and investigated as a promising computing platform as well as a source of personal big data for the post smartphone era. To deal with a series of rapidly developed wearable prototypes, a well-structured strategy is required to assess the prototypes at various development stages. In this paper, we first design and develop variants of advanced wearable user interface prototypes, including joystick-embedded, potentiometer-embedded, motion-gesture and contactless infrared user interfaces for rapidly assessing hands-on user experience of potential futuristic user interfaces. To achieve this goal systematically, we propose a conceptual test framework and present a case study of using the proposed framework in an iterative cyclic process to prototype, test, analyze, and refine the wearable user interface prototypes. We attempt to improve the usability of the user interface prototypes by integrating initial user feedback into the leading phase of the test framework. In the following phase of the test framework, we track signs of improvements through the overall results of usability assessments, task workload assessments and user experience evaluation of the prototypes. The presented comprehensive and in-depth case study demonstrates that the iterative approach employed by the test framework was effective in assessing and enhancing the prototypes, as well as gaining insights on potential applications and establishing practical guidelines for effective and usable wearable user interface development.

]]>Symmetry doi: 10.3390/sym9070113

Authors: Hui-Fei Lin Chi-Hua Chen

An intelligent tour service system including an augmented reality (AR) tour-sharing Application (APP) and a query-answering server was developed in this study to promote tourist attractions involving local Hakka culture in Thailand. Subsequently, use of this APP to navigate Hakka culture tourist attractions in Thailand was observed. The novel random neural networks (RNNs) were proposed to obtain query-answering services, and the practical experimental results showed that the accuracy of RNNs was 99.51%. This study also integrated the Technology Acceptance Model with Uses and Gratifications Theory to predict the gratification, usage intention, and user attitudes toward marketed attractions of the AR tour-sharing APP. A questionnaire survey was conducted, and 446 valid questionnaires were returned. The following results were obtained: (a) self-presentation and perceived usefulness (PU) directly influenced gratification; (b) perceived entertainment indirectly influenced gratification through perceived ease of use and PU, and information sharing indirectly influenced gratification through PU; and (c) gratification was significantly and positively related to usage intention and attitude toward attractions. Based on these results, suggestions that new technology marketing can be used to promote causes other than Hakka tourist attractions established in Thailand can be contrived. For example, the tour-sharing APP developed in this study could be applied to emphasize the characteristics of Thai Hakka culture; users’ fondness for self-presentation and information sharing can be used for word-of-mouth marketing to attract additional visitors. In addition, this research provides a reference for enterprises and marketers regarding the use of AR tour-sharing APPs to market tourist attractions, and also for future related studies.

]]>Symmetry doi: 10.3390/sym9070112

Authors: Jing Li Guoqing He Peibiao Zhao

In this paper, we study submanifolds in a Riemannian manifold with a semi-symmetric non-metric connection. We prove that the induced connection on a submanifold is also semi-symmetric non-metric connection. We consider the total geodesicness and minimality of a submanifold with respect to the semi-symmetric non-metric connection. We obtain the Gauss, Cadazzi, and Ricci equations for submanifolds with respect to the semi-symmetric non-metric connection.

]]>Symmetry doi: 10.3390/sym9070111

Authors: Zebo Fang Jun Ye

Existing intuitionistic linguistic variables can describe the linguistic information of both the truth/membership and falsity/non-membership degrees, but it cannot represent the indeterminate and inconsistent linguistic information. To deal with the issue, this paper originally proposes the concept of a linguistic neutrosophic number (LNN), which is characterized independently by the truth, indeterminacy, and falsity linguistic variables. Then, we define the basic operational laws of LNNs and the score and accuracy functions of LNN for comparing LNNs. Next, we develop an LNN-weighted arithmetic averaging (LNNWAA) operator and an LNN-weighted geometric averaging (LNNWGA) operator to aggregate LNN information and investigate their properties. Further, a multiple attribute group decision-making method based on the proposed LNNWAA or LNNWGA operator is established under LNN environment. Finally, an illustrative example about selecting problems of investment alternatives is presented to demonstrate the application and effectiveness of the developed approach.

]]>Symmetry doi: 10.3390/sym9070109

Authors: Xinhua Liu Yao Shi Jing Xu

In order to improve the response performance of a proportion integration differentiation (PID) controller for magnetorheological fluids (MRF) brake and to reduce the braking fluctuation rate, an improved fruit fly optimization algorithm for PID controller parameters tuning of MRF brake is proposed. A data acquisition system for MRF brake is designed and the transfer function of MRF brake is identified. Moreover, an improved fruit fly optimization algorithm (IFOA) through integration of PID control strategy and cloud model algorithm is proposed to design a PID controller for MRF brake. Finally, the simulation and experiment are carried out. The results show that IFOA, with a faster response output and no overshoot, is superior to the conventional PID and fruit fly optimization algorithm (FOA) PID controller.

]]>Symmetry doi: 10.3390/sym9070110

Authors: Linbo Zhai Hua Wang

This paper studies cooperative spectrum sensing based on crowdsourcing in cognitive radio networks. Since intelligent mobile users such as smartphones and tablets can sense the wireless spectrum, channel sensing tasks can be assigned to these mobile users. This is referred to as the crowdsourcing method. However, there may be some malicious mobile users that send false sensing reports deliberately, for their own purposes. False sensing reports will influence decisions about channel state. Therefore, it is necessary to classify mobile users in order to distinguish malicious users. According to the sensing reports, mobile users should not just be divided into two classes (honest and malicious). There are two reasons for this: on the one hand, honest users in different positions may have different sensing outcomes, as shadowing, multi-path fading, and other issues may influence the sensing results; on the other hand, there may be more than one type of malicious users, acting differently in the network. Therefore, it is necessary to classify mobile users into more than two classes. Due to the lack of prior information of the number of user classes, this paper casts the problem of mobile user classification as a dynamic clustering problem that is NP-hard. The paper uses the interdistance-to-intradistance ratio of clusters as the fitness function, and aims to maximize the fitness function. To cast this optimization problem, this paper proposes a distributed algorithm for user classification in order to obtain bounded close-to-optimal solutions, and analyzes the approximation ratio of the proposed algorithm. Simulations show the distributed algorithm achieves higher performance than other algorithms.

]]>Symmetry doi: 10.3390/sym9070108

Authors: Sungju Lee Taikyeong Jeong

The goal of this paper is to compare and analyze the forecasting performance of two artificial neural network models (i.e., MLP (multi-layer perceptron) and DNN (deep neural network)), and to conduct an experimental investigation by data flow, not economic flow. In this paper, we investigate beyond the scope of simple predictions, and conduct research based on the merits and data of each model, so that we can predict and forecast the most efficient outcomes based on analytical methodology with fewer errors. In particular, we focus on identifying two models of neural networks (NN), a multi-layer perceptron (i.e., MLP) model and an excellent model between the neural network (i.e., DNN) model. At this time, predictability and accuracy were found to be superior in the DNN model, and in the MLP model, it was found to be highly correlated and accessible. The major purpose of this study is to analyze the performance of MLP and DNN through a practical approach based on an artificial neural network stock forecasting method. Although we do not limit S&amp;P (i.e., Standard&amp;Poor’s 500 index) to escape other regional exits in order to see the proper flow of capital, we first measured S&amp;P data for 100 months (i.e., 407 weeks) and found out the following facts: First, the traditional artificial neural network (ANN) model, according to the specificity of each model and depending on the depth of the layer, shows the model of the prediction well and is sensitive to the index data; Second, comparing the two models, the DNN model showed better accuracy in terms of data accessibility and prediction accuracy than MLP, and the error rate was also shown in the weekly and monthly data; Third, the difference in the prediction accuracy of each model is not statistically significant. However, these results are correlated with each other, and are considered robust because there are few error rates, thanks to the accessibility to various other prediction accuracy measurement methodologies.

]]>Symmetry doi: 10.3390/sym9070107

Authors: Yibing Li Xueying Diao Qianhui Dong Chunrui Tang

In this paper, we focus on the interference management in the cognitive radio (CR) network comprised of multiple primary users (PUs) and multiple secondary users (SUs). Firstly, two interference alignment (IA) schemes are proposed to mitigate the interference among PUs. The first one is an interference rank minimization (IRM) scheme, which aims to minimize the rank of the joint interference matrix via alternating between the forward and reverse communication links. Considering the overhead of information exchanged between the transmitters and receivers in the IRM scheme, we further develop an interference subspace distance minimization (ISDM) scheme which runs at the transmitters only. The ISDM scheme focuses on aligning the subspaces spanned by interference with an aligned subspace introduced in this paper. For the secondary network, though IRM and ISDM mitigate the received interference at secondary receivers, they make no attempt to eliminate the interference from SUs to PUs. To address this, we improve the IRM and ISDM schemes by putting a rank constraint into their optimizations, where the rank constraint forces the ranks of the interference matrices from SUs to PUs to be zero. Simulation results validate the effectiveness of the proposed schemes in terms of the average sum rate.

]]>Symmetry doi: 10.3390/sym9070106

Authors: Ru-xin Nie Jian-qiang Wang Hong-yu Zhang

As one of the promising renewable energy resources, solar-wind energy has increasingly become a regional engine in leading the economy and raising competitiveness. Selecting a solar-wind power station location can contribute to efficient utilization of resource and instruct long-term development of socio-economy. Since the selection procedure consists of several location alternatives and many influential criteria factors, the selection can be recognized as a multiple criteria decision-making (MCDM) problem. To better express multiple uncertainty information during the selection procedure, fuzzy set theory is introduced to manage that issue. Interval neutrosophic sets (INSs), which are characterized by truth-membership, indeterminacy-membership and falsity-membership functions in the interval numbers (INs) form, are feasible in modeling more uncertainty of reality. In this paper, a newly extended weighted aggregated sum product assessment (WASPAS) technique, which involves novel three procedures, is utilized to handle MCDM issues under INSs environment. Some modifications are conducted in the extended method comparing with the classical WASPAS method. The most obvious improvement of the extended method relies on that it can generate more realistic criteria weight information by an objective and subjective integrated criteria weight determination method. A case study concerning solar-wind power station location selection is implemented to demonstrate the applicability and rationality of the proposed method in practice. Its validity and feasibility are further verified by a sensitivity analysis and a comparative analysis. These analyses effectively reveal that the extended WASPAS technique can well match the reality and appropriately handle the solar-wind power station location selection problem.

]]>Symmetry doi: 10.3390/sym9070105

Authors: Chi-Lun Lo Chi-Hua Chen Ta-Sheng Kuan Kuen-Rong Lo Hsun-Jung Cho

This study proposes a fuel consumption estimation system and method with lower cost. On-board units can report vehicle speed, and user devices can send fuel information to a data analysis server. Then the data analysis server can use the proposed fuel consumption estimation method to estimate the fuel consumption based on driver behaviours without fuel sensors for cost savings. The proposed fuel consumption estimation method is designed based on a genetic algorithm which can generate gene sequences and use crossover and mutation for retrieving an adaptable gene sequence. The adaptable gene sequence can be applied as the set of fuel consumption in accordance with the pattern of driver behaviour. The practical experimental results indicated that the accuracy of the proposed fuel consumption estimation method was about 95.87%.

]]>Symmetry doi: 10.3390/sym9070104

Authors: Qiang Guo Guoqing Ruan Yanping Liao

This paper considers the underdetermined blind separation of multiple input multiple output (MIMO) radar signals that are insufficiently sparse in both time and frequency domains under noisy conditions, while traditional algorithms are usually applied in the ideal sparse environment. An effective separation method based on single source point (SSP) identification and time-frequency smoothed l 0 norm (TF-SL0) is proposed. Firstly, a preprocessing step of the moving average filter and a novel argument-based time-frequency SSPs detection are employed to improve the signal-to-noise ratio and signal sparsity of the observed signals, respectively. Then, the mixing matrix is obtained by using clustering algorithms. Secondly, to obtain the optimal solution of underdetermined sparse component analysis, the smoothed l 0 norm (SL0) is introduced to preliminarily achieve signal separation in the time-frequency domain. Finally, time-frequency ridge estimation is proposed to jointly enhance the reconstruction accuracy of the MIMO radar signals, and the time domain waveforms are recovered by the model of the signals. Simulations illustrate the validity of the method and show that the proposed method outperforms the traditional methods in source separation, especially in the non-cooperative electromagnetic case where the prior information is unknown.

]]>Symmetry doi: 10.3390/sym9070103

Authors: Yunna Wu Chuanbo Xu Haobo Zhang Jianwei Gao

We propose a new class of aggregation operator based on utility function and apply them to group decision-making problem. First of all, based on an optimal deviation model, a new operator called the interval generalized ordered weighted utility multiple averaging (IGOWUMA) operator is proposed, it incorporates the risk attitude of decision-makers (DMs) in the aggregation process. Some desirable properties of the IGOWUMA operator are studied afterward. Subsequently, under the hyperbolic absolute risk aversion (HARA) utility function, another new operator named as interval generalized ordered weighted hyperbolic absolute risk aversion utility multiple averaging-HARA (IGOWUMA-HARA) operator is also defined. Then, we discuss its families and find that it includes a wide range of aggregation operators. To determine the weights of the IGOWUMA-HARA operator, a preemptive nonlinear objective programming model is constructed, which can determine a uniform weighting vector to guarantee the uniform standard comparison between the alternatives and measure their fair competition under the condition of valid comparison between various alternatives. Moreover, a new approach for group decision-making is developed based on the IGOWUMA-HARA operator. Finally, a comparison analysis is carried out to illustrate the superiority of the proposed method and the result implies that our operator is superior to the existing operator.

]]>Symmetry doi: 10.3390/sym9070101

Authors: Jihun Kim Jonghee Youn

Thanks to the development of Internet of Things (IoT) technologies, wearable markets have been growing rapidly. Smartwatches can be said to be the most representative product in wearable markets, and involve various hardware technologies in order to overcome the limitations of small hardware. Motion recognition sensors are a representative example of those hardware technologies. However, smartwatches and motion recognition sensors that can be worn by users may pose security threats of password pattern leakage. In the present paper, passwords are inferred through experiments to obtain password patterns inputted by users using motion recognition sensors, and verification of the results and the accuracy of the results is shown.

]]>Symmetry doi: 10.3390/sym9070102

Authors: Kwan Lee Hyung Hong Kang Park

The application of user emotion recognition for fear is expanding in various fields, including the quantitative evaluation of horror movies, dramas, advertisements, games, and the monitoring of emergency situations in convenience stores (i.e., a clerk threatened by a robber), in addition to criminal psychology. Most of the existing methods for the recognition of fear involve referring to a single physiological signal or recognizing circumstances in which users feel fear by selecting the most informative one among multiple physiological signals. However, the level of accuracy as well as the credibility of these study methods is low. Therefore, in this study, data with high credibility were obtained using non-intrusive multimodal sensors of near-infrared and far-infrared light cameras and selected based on t-tests and Cohen’s d analysis considering the symmetrical characteristics of face and facial feature points. The selected data were then combined into a fuzzy system using the input and output membership functions of symmetrical shape to ultimately derive a new method that can quantitatively show the level of a user’s fear. The proposed method is designed to enhance conventional subjective evaluation (SE) by fuzzy system based on multi-modalities. By using four objective features except for SE and combining these four features into a fuzzy system, our system can produce an accurate level of fear without being affected by the physical, psychological, or fatigue condition of the participants in SE. After conducting a study on 20 subjects of various races and genders, the results indicate that the new method suggested in this study has a higher level of credibility for the recognition of fear than the methods used in previous studies.

]]>Symmetry doi: 10.3390/sym9070100

Authors: Fei Hao Doo-Soon Park Zheng Pei

In social networking analysis, there exists a fundamental problem called maximal cliques enumeration(MCE), which has been extensively investigated in many fields, including social networks, biological science, etc. As a matter of fact, the formation principle of maximal cliques that can help us to speed up the detection of maximal cliques from social networks is often ignored by most existing research works. Aiming to exploit the formation of maximal cliques in social networks, this paper pioneers a creative research issue on the detection of bases of maximal cliques in social networks. We propose a formal concept analysis-based approach for detecting the bases of maximal cliques and detection theorem. It is believed that our work can provide a new research solution and direction for future topological structure analysis in various complex networking systems.

]]>Symmetry doi: 10.3390/sym9070099

Authors: Gisela Kaplan

The neural processes of bird song and song development have become a model for research relevant to human acquisition of language, but in fact, very few avian species have been tested for lateralization of the way in which their audio-vocal system is engaged in perception, motor output and cognition. Moreover, the models that have been developed have been premised on birds with strong vocal dimorphism, with a tendency to species with complex social and/or monomorphic song systems. The Australian magpie (Gymnorhina tibicen) is an excellent model for the study of communication and vocal plasticity with a sophisticated behavioural repertoire, and some of its expression depends on functional asymmetry. This paper summarizes research on vocal mechanisms and presents field-work results of behavior in the Australian magpie. For the first time, evidence is presented and discussed about lateralized behaviour in one of the foremost songbirds in response to specific and specialized auditory and visual experiences under natural conditions. It presents the first example of auditory lateralization evident in the birds’ natural environment by describing an extractive foraging event that has not been described previously in any avian species. It also discusses the first example of auditory behavioral asymmetry in a songbird tested under natural conditions.

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