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

A q-rung orthopair fuzzy set (q-ROFS) provides a significant mechanism for managing symmetrical aspects in real life circumstances. The renowned distinguishing feature of q-ROFS is that the sum of the qth powers to each membership degree (MD) and non-membership degree (NMD) is less than or equal 1, and therefore the comprehensive uncertain space for q-ROF information is broader. Numerous researchers have suggested several aggregation operators based on q-ROFSs. In order to discuss prioritized relationship in the criterion and a smooth approximation of q-ROF information, we introduced q-rung orthopair fuzzy Einstein prioritized weighted averaging (q-ROFEPWA) operator and q-rung orthopair fuzzy Einstein prioritized weighted geometric (q-ROFEPWG) operator. Additionally, we presented a multi-criteria group decision making (MCGDM) technique based on q-rung orthopair fuzzy Einstein prioritized aggregation operators. These operators can evaluate the possible symmetric roles of the criterion that express the real phenomena of the problem. In order to investigate characteristic of suggested operators regarding the symmetry of attributes and their symmetrical roles under q-ROF information, we presented an application of Einstein prioritized aggregation operators. Finally, by comparing it with some other established representative MCGDM models, an illustrative example is provided to check the feasibility, efficiency and supremacy of the proposed technique. Full article

In the process of composite placement, irregularity and asymmetry pressure fluctuation will affect the density and evenness of composite products, which lead to the inconsistency of interfacial strength and fiber volume fraction. The dynamic performance of placement pressure systems will be affected by external disturbance, mechanism friction and measurement noise. In this paper, an adaptive sliding mode control (ASMC) strategy based on disturbance observer (DOB) is proposed. The disturbance observer is introduced to estimate the equivalent disturbance torque, and the estimation error is compensated by the switching term of sliding mode control. The adaptive method is used to ensure that the switching gain is not overestimated, and then the Lyapunov function is used to verify the stability of the closed-loop control system. The experimental results and simulation analysis show that ASMC-DOB has high control accuracy and good robustness. At the same time, the designed algorithm can effectively reduce the void content of composite products. Full article

Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research. Full article

Khon Kaen District is in the central, north-east part of Thailand and is being developed to handle the country’s growth. Khon Kaen District is undertaking the project of building a light rail as a facility for the people. Consequently, one of the problems is ensuring adequate parking for people using the light rail service. In general, the symmetry concept naturally used in decision making to finding an optimal solution for decision and optimization problems. In this paper, multi-criteria decision analysis (MCDA) and multi-objective decision making (MODM) were used to solve the parking site selection problem, which made the decision easier. This paper proposed an analytic hierarchy process (AHP) technique, combined with the geographical information system (GIS), to evaluate the weight of the criteria used in the analysis and find potential parking solutions. Furthermore, this paper proposed the application of a linguistic technique with fuzzy TOPSIS methods to analyze the appropriateness of parking site selections from potential candidates to support use of the light rail. The results of the MCDA show that the most suitable parking lot location is along the light rail and closest to the business area. The results of the fuzzy TOPSIS method, both positive and negative ideal decisions, can help inform decision makers in selecting which candidate site is optimal for parking. Full article

Social media users, including organizations, often struggle to acquire the maximum number of responses from other users, but predicting the responses that a post will receive before publication is highly desirable. Previous studies have analyzed why a given tweet may become more popular than others, and have used a variety of models trained to predict the response that a given tweet will receive. The present research addresses the prediction of response measures available on Twitter, including likes, replies and retweets. Data from a single publisher, the official US Navy Twitter account, were used to develop a feature-based model derived from structured tweet-related data. Most importantly, a deep learning feature extraction approach for analyzing unstructured tweet text was applied. A classification task with three classes, representing low, moderate and high responses to tweets, was defined and addressed using four machine learning classifiers. All proposed models were symmetrically trained in a fivefold cross-validation regime using various feature configurations, which allowed for the methodically sound comparison of prediction approaches. The best models achieved F1 scores of 0.655. Our study also used SHapley Additive exPlanations (SHAP) to demonstrate limitations in the research on explainable AI methods involving Deep Learning Language Modeling in NLP. We conclude that model performance can be significantly improved by leveraging additional information from the images and links included in tweets. Full article

We introduce a new integral method for Quantum Key Distribution to perform sifting, reconciliation and amplification processes to establish a cryptographic key through the use of binary matrices called frames which are capable to increase quadratically the secret key rate. Since the eavesdropper has no control on Bob’s double matching detection events, our protocol is not vulnerable to the Intercept and Resend (IR) attack nor the Photon Number Splitting (PNS) attack. The method can be implemented with the usual optical Bennett–Brassard ( $BB84$ ) equipment allowing strong pulses in the quantum regime. Full article

We investigate the relation between the number of passes made by a football team and the number of goals. We analyze the 380 matches of a complete season of the Spanish national league “LaLiga" (2018/2019). We observe how the number of scored goals is positively correlated with the number of passes made by a team. In this way, teams on the top (bottom) of the ranking at the end of the season make more (less) passes than the rest of the teams. However, we observe a strong asymmetry when the analysis is made depending on the part of the match. Interestingly, fewer passes are made in the second half of a match, while, at the same time, more goals are scored. This paradox appears in the majority of teams, and it is independent of the number of passes made. These results confirm that goals in the first half of matches are more “costly” in terms of passes than those scored in second halves. Full article

This paper proposes a separation model adopting gated nested U-Net (GNU-Net) architecture, which is essentially a deeply supervised symmetric encoder–decoder network that can generate full-resolution feature maps. Through a series of nested skip pathways, it can reduce the semantic gap between the feature maps of encoder and decoder subnetworks. In the GNU-Net architecture, only the backbone not including nested part is applied with gated linear units (GLUs) instead of conventional convolutional networks. The outputs of GNU-Net are further fed into a time-frequency (T-F) mask layer to generate two masks of singing voice and accompaniment. Then, those two estimated masks along with the magnitude and phase spectra of mixture can be transformed into time-domain signals. We explored two types of T-F mask layer, discriminative training network and difference mask layer. The experiment results show the latter to be better. We evaluated our proposed model by comparing with three models, and also with ideal T-F masks. The results demonstrate that our proposed model outperforms compared models, and it’s performance comes near to ideal ratio mask (IRM). More importantly, our proposed model can output separated singing voice and accompaniment simultaneously, while the three compared models can only separate one source with trained model. Full article

This article researched the security and application of smart locks in Internet of Things environments in the domain of computer and engineer science and symmetry. Smart locks bring much convenience for users. However, most smart lock systems are cloud-based and it is problematic managing and enforcing the permissions of an authorized device if the device is offline. Moreover, most smart lock systems lack fine-grained access control and cascading removal of permissions. In this paper, we leverage attribute-based access control mechanisms to manage the access of visitors with different identities. We use identity-based encryption to verify the identity of the visitor. In our proposed system, the administrator uses the policy set in the smart lock to implement access control on the device side, which reduces the dependence of access control on the server. We set attributes such as role, time, date, and location to have fine-grained control over access to different permissions and roles that might appear in the house. And the scheme provides the cascading delete function while providing the group access function. Our solution considers multiple roles in the home as well as hierarchical management issues, and improves the applicability of the smart lock system in complex residential and commercial situations. In the experimental section, we show that our system can be applied to premises with many different inhabitant identities. Full article

The near degeneracy of the neutrino masses—a mass symmetry—allows condensed neutrino objects that may be the Dark Matter everybody is looking for. If the KATRIN terrestrial experiment has a neutrino mass signal, it will contradict the analysis of the Planck Satellite Consortium reduction of their raw cosmological microwave data. Using Condensed Neutrino Objects as the Dark Matter along with Coma Galaxy Cluster data, we predict that KATRIN will indeed see a neutrino mass signal. If this physics drama unfolds, there will be profound implications for cosmology, which are discussed in this paper. Full article

The study aimed to determine the natural radioactivity levels of ²²⁶Ra, ²³²Th, and ⁴⁰K by the Gamma-Ray spectrometry method, and radiological hazard parameters of the plutonic rocks in the Western and Central Sakarya Zone and to analyze the data using multivariate statistical methods. The average radiological values of samples were determined as ⁴⁰K (1295.3 Bq kg⁻¹) > ²³²Th (132.1 Bq kg⁻¹) > ²²⁶Ra (119.7 Bq kg⁻¹). According to the skewness values of the distributions of the examined radionuclides, ²²⁶Ra (2.1) and ²³²Th (0.7) seemed to be positively right-skewed while ⁴⁰K (−0.2) had a negatively right-skewed histogram. On the other hand, the following kurtosis values were calculated for the distributions: ²²⁶Ra (5.8 > 3), ²³²Th (−0.7), and ⁴⁰K (−0.8). Kolmogorov–Smirnov and Shapiro–Wilk tests were applied to the data to test their normality. Therefore, Spearman’s correlation coefficient method was performed. The radionuclides of ²²⁶Ra and ²³²Th were found to have a positive correlation with radiological hazard parameters of the samples. 2 (two)-related factors identified, and the cumulative value was calculated to be 98.7% on the basis of the Scree Plot. According to the hierarchical cluster analysis, the samples that are grouped with those from Camlik region are prominent. The average radioactivity values of Camlik, Sogukpinar, Karacabey, and Sogut (except for ²³²Th) regions were detected to be higher than the world averages while the value of ⁴⁰K was also found to be higher than the average values of various countries in the world. Full article

The purpose of this study is to produce a landslide susceptibility map of Southeastern Helong City, Jilin Province, Northeastern China. According to the geological hazard survey (1:50,000) project of Helong city, a total of 83 landslides were mapped in the study area. The slope unit, which is classified based on the curvature watershed method, is selected as the mapping unit. Based on field investigations and previous studies, three groups of influencing Factors—Lithological factors, topographic factors, and geological environment factors (including ten influencing factors)—are selected as the influencing factors. Artificial neural networks (ANN’s) and support vector machines (SVM’s) are introduced to build the landslide susceptibility model. Five-fold cross-validation, the receiver operating characteristic curve, and statistical parameters are used to optimize model. The results show that the SVM model is the optimal model. The landslide susceptibility maps produced using the SVM model are classified into five grades—very high, high, moderate, low, and very low—and the areas of the five grades were 127.43, 151.60, 198.77, 491.19, and 506.91 km², respectively. The very high and high susceptibility areas included 79.52% of the total landslides, demonstrating that the landslide susceptibility map produced in this paper is reasonable. Consequently, this study can serve as a guide for landslide prevention and for future land planning in the southeast of Helong city. Full article

The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal. Full article

This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case of long-term analysis, a Detrended Fluctuation Analysis (DFA) approach is applied in order to estimate large-scale RVR time series similarities. The short-term persistence analysis of low-visibility events is evaluated by means of a Markov chain analysis of the binary time series associated with low-visibility events. We finally discuss an hourly short-term prediction of low-visibility events, using different approaches, some of them coming from the persistence analysis through Markov chain models, and others based on Machine Learning (ML) techniques. We show that a Mixture of Experts approach involving persistence-based methods and Machine Learning techniques provides the best results in this prediction problem. Full article

Nowadays, data security is becoming an emerging and challenging issue due to the growth in web-connected devices and significant data generation from information and communication technology (ICT) platforms. Many existing types of research from industries and academic fields have presented their methodologies for supporting defense against security threats. However, these existing approaches have failed to deal with security challenges in next-generation ICT systems due to the changing behaviors of security threats and zero-day attacks, including advanced persistent threat (APT), ransomware, and supply chain attacks. The symmetry-adapted machine-learning approach can support an effective way to deal with the dynamic nature of security attacks by the extraction and analysis of data to identify hidden patterns of data. It offers the identification of unknown and new attack patterns by extracting hidden data patterns in next-generation ICT systems. Therefore, we accepted twelve articles for this Special Issue that explore the deployment of symmetry-adapted machine learning for information security in various application areas. These areas include malware classification, intrusion detection systems, image watermarking, color image watermarking, battlefield target aggregation behavior recognition models, Internet Protocol (IP) cameras, Internet of Things (IoT) security, service function chains, indoor positioning systems, and cryptoanalysis. Full article

In this paper, we use q-derivative operator to define a new class of q-starlike functions associated with k-Fibonacci numbers. This newly defined class is a subclass of class $\mathcal{A}$ of normalized analytic functions, where class $\mathcal{A}$ is invariant (or symmetric) under rotations. For this function class we obtain an upper bound of the third Hankel determinant. Full article

The present investigation is directed towards synthesis of zinc oxide (ZnO) nanoparticles and steady blending with soybean biodiesel (SBME25) to improve the fuel properties of SBME25 and enhance the overall characteristics of a variable compression ratio diesel engine. The soybean biodiesel (SBME) was prepared using the transesterification reaction. Numerous characterization tests were carried out to ascertain the shape and size of zinc oxide nanoparticles. The synthesized asymmetric ZnO nanoparticles were dispersed in SBME25 at three dosage levels (25, 50, and 75 ppm) with sodium dodecyl benzene sulphonate (SDBS) surfactant using the ultrasonication process. The quantified physicochemical properties of all the fuels blends were in symmetry with the American society for testing and materials (ASTM) standards. Nanofuel blends demonstrated enhanced fuel properties compared with SBME25. The engine was operated at two different compression ratios (18.5 and 21.5) and a comparison was made, and best fuel blend and compression ratio (CR) were selected. Fuel blend SBME25ZnO50 and compression ratio (CR) of 21.5 illustrated an overall enhancement in engine characteristics. For SBME25ZnO50 and CR 21.5 fuel blend, brake thermal efficiency (BTE) increased by 23.2%, brake specific fuel consumption (BSFC) were reduced by 26.66%, and hydrocarbon (HC), CO, smoke, and CO₂ emissions were reduced by 32.234%, 28.21% 22.55% and 21.66%, respectively; in addition, the heat release rate (HRR) and mean gas temperature (MGT) improved, and ignition delay (ID) was reduced. In contrast, the NOx emissions increased for all the nanofuel blends due to greater supply of oxygen and increase in the temperature of the combustion chamber. At a CR of 18.5, a similar trend was observed, while the values of engine characteristics were lower compared with CR of 21.5. The properties of nanofuel blend SBME25ZnO50 were in symmetry and comparable to the diesel fuel. Full article

The well-known construction scheme to define a partial order on a vector space is to use a proper convex cone. Applying this idea to the gyrovector space we construct the partial order, called a gyro-order. We also give several inequalities of gyrolines and cogyrolines in terms of the gyro-order. Full article

We present surprisingly simple closed-form solutions for electric fields and electric potentials at arbitrary position $\left(x,y\right)$ within a plane crossed by infinitely long line charges at regularly repeating positions using angular or elliptic functions with complex arguments. The lattice sums for the electric-field components and the electric potentials could be exactly solved, and the duality symmetry of trigonometric and lemniscate functions occurred in some solutions. The results may have relevance in calculating field configurations with rectangular boundary conditions. Several series related to Gauss’s constant are presented, established either as corollary results or via parallel investigations conducted in the spirit of experimental mathematics. Full article

The fundamental groups and homotopy decompositions of algebraic topology have applications in systems involving symmetry breaking with topological excitations. The main aim of this paper is to analyze the properties of homotopy decompositions in quotient topological spaces depending on the connectedness of the space and the fundamental groups. This paper presents constructions and analysis of two varieties of homotopy decompositions depending on the variations in topological connectedness of decomposed subspaces. The proposed homotopy decomposition considers connected fundamental groups, where the homotopy equivalences are relaxed and the homeomorphisms between the fundamental groups are maintained. It is considered that one fundamental group is strictly homotopy equivalent to a set of 1-spheres on a plane and as a result it is homotopy rigid. The other fundamental group is topologically homeomorphic to the first one within the connected space and it is not homotopy rigid. The homotopy decompositions are analyzed in quotient topological spaces, where the base space and the quotient space are separable topological spaces. In specific cases, the decomposed quotient space symmetrically extends Sierpinski space with respect to origin. The connectedness of fundamental groups in the topological space is maintained by open curve embeddings without enforcing the conditions of homotopy classes on it. The extended decomposed quotient topological space preserves the trivial group structure of Sierpinski space. Full article

A plethora of higher order iterative methods, involving derivatives in algorithms, are available in the literature for finding multiple roots. Contrary to this fact, the higher order methods without derivatives in the iteration are difficult to construct, and hence, such methods are almost non-existent. This motivated us to explore a derivative-free iterative scheme with optimal fourth order convergence. The applicability of the new scheme is shown by testing on different functions, which illustrates the excellent convergence. Moreover, the comparison of the performance shows that the new technique is a good competitor to existing optimal fourth order Newton-like techniques. Full article

Concrete is a non-Newtonian fluid which is a counterexample of Jeffrey fluid. The flow of Jeffrey fluid is considered containing nanostructures of zinc oxide in this study. The flow of the nanofluid is modeled in terms of partial fractional differential equations via Atangana–Baleanu (AB) fractional derivative approach and then solved using the integral transformation. Specifically, the applications are discussed in the field of concrete and cement industry. The variations in heat transfer rate and skin friction have been observed for different values of volume fractions of nanoparticles. The results show that by adding 4% $ZnO$ nanoparticles increase skin friction up to 15%, ultimately enhancing the adhesion capacity of concrete. Moreover, $ZnO$ increase the density of concrete, minimizing the pores in the concrete and consequently increasing the strength of concrete. The solutions are simplified to the corresponding solutions of the integer ordered model of Jeffrey-nanofluid. Applications of this work can be found in construction engineering and management such as buildings, roads, tunnels, bridges, airports, railroads, dams, and utilities. Full article

In classical finance theory, cognitive bias does not play any role in predicting returns. With the development of the economy, the classical theory gradually finds it difficult to offset the irrational demand through arbitrage. Due to the rise of behavioral economics, how to allocate stock portfolios in the highly subjective environment is an unavoidable problem. Considering the decision heterogeneity between the rational market and the irrational one, the mean-variance (MV) method was improved in the construction of a market bias index for stock portfolio allocation, which we called EMACB (exponential moving average of cognitive bias)-variance method. Besides, due to the lack of related research, we introduced a measure of aggregate investor cognitive bias by adopting the state-space model. Finally, the proposed method was applied in an investment allocation example to prove its feasibility, and its advantages were emphasized by a comparison with another relevant approach. Full article

The main focus of this research is on a comprehensive analysis of robust dissipativity issues pertaining to a class of uncertain stochastic generalized neural network (USGNN) models in the presence of time-varying delays and Markovian jumping parameters (MJPs). In real-world environments, most practical systems are subject to uncertainties. As a result, we take the norm-bounded parameter uncertainties, as well as stochastic disturbances into consideration in our study. To address the task, we formulate the appropriate Lyapunov–Krasovskii functional (LKF), and through the use of effective integral inequalities, simplified linear matrix inequality (LMI) based sufficient conditions are derived. We validate the feasible solutions through numerical examples using MATLAB software. The simulation results are analyzed and discussed, which positively indicate the feasibility and effectiveness of the obtained theoretical findings. Full article

The authors have reviewed a wide production of scientific articles dealing with the evolution of the concept of convexity and its various applications, and based on this they have detected the relationship that can be established between trapezoidal inequalities, generalized convex functions, and special functions, in particular with the so-called Raina function, which generalizes other better known ones such as the hypergeometric function and the Mittag–Leffler function. The authors approach this situation by studying the Hermite–Hadamard inequality, establishing a useful identity using Raina’s fractional integral operator in the setting of $\varphi$ -convex functions, obtaining some integral inequalities connected with the right-hand side of Hermite–Hadamard-type inequalities for Raina’s fractional integrals. Various special cases have been identified. Full article

The initial value problem for the third order delay differential equation in a Hilbert space with an unbounded operator is investigated. The absolute stable three-step difference scheme of a first order of accuracy is constructed and analyzed. This difference scheme is built on the Taylor’s decomposition method on three and two points. The theorem on the stability of the presented difference scheme is proven. In practice, stability estimates for the solutions of three-step difference schemes for different types of delay partial differential equations are obtained. Finally, in order to ensure the coincidence between experimental and theoretical results and to clarify how efficient the proposed scheme is, some numerical experiments are tested. Full article

There is an opinion in professional literature that edge-strands in β-sheet are critical to the processes of amyloid transformation. Propagation of fibrillar forms mainly takes place on the basis of β-sheet type interactions. In many proteins, the edge strands represent only a partially matched form to the β-sheet. Therefore, the edge-strand takes slightly distorted forms. The assessment of the level of arrangement can be carried out based on studying the secondary structure as well as the structure of the hydrophobic core. For this purpose, a fuzzy oil drop model was used to determine the contribution of each fragment with a specific secondary structure to the construction of the system being the effect of a certain synergy, which results in the construction of a hydrophobic core. Studying the participation of β-sheets edge fragments in the hydrophobic core construction is the subject of the current analysis. Statuses of these edge fragments in β-sheets in ferredoxin-like folds are treated as factors that disturb the symmetry of the system. Full article

In this paper, by utilizing the resolvent operator theory, the stochastic analysis method and Picard type iterative technique, we first investigate the existence as well as the uniqueness of mild solutions for a class of $\alpha \in (1,2)$ -order Riemann–Liouville fractional stochastic evolution equations of Sobolev type in abstract spaces. Then the symmetrical technique is used to deal with the $\alpha \in (1,2)$ -order Caputo fractional stochastic evolution equations of Sobolev type in abstract spaces. Two examples are given as applications to the obtained results. Full article

The paper uses Kane’s formalism to study two degrees of freedom (DOF) mechanisms with elastic elements = employed in a wind water pump. This formalism represents an alternative, in our opinion, that is simpler and more economical to Lagrange’s equation, used mainly by researchers in this type of application. In the problems where the finite element method (FEM) is applied, Kane’s equations were not used at all. The automated computation causes it to be reconsidered in the case of mechanical systems with a high DOF. Analyzing the planar transmission mechanism, these equations were applied for the study of an elastic element. An analysis was then made of the results obtained for this type of water pump. The matrices coefficients of the obtained equations were symmetric or skew-symmetric. Full article

Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today’s high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI. Full article