bluuebunny/crossref_metadata_2025 huggingface dataset:
Development Status and Future Strategies of Networked Distribution System
Authors: Kim Woo-Hyun , Chae Woo-Kyu , Lee Hyeon-Myeong , No Hyun-Woo , Kim Dong-Sub
Published: September 2022

In response to the escalation of environmental issues including global warming due to reckless development, the energy industry has been partaking in measures to improve sustainability of the environment. As an effort to target decarbonization, thermal power generation using fossil fuels is being minimized whilst the generation of renewable energy is gradually being increased. In addition, research on the structure of power distribution systems is steadily on the rise to improve efficiency of energy supply and system reliability. Amongst existing distribution systems, high expectations have been focused on the networked distribution system (NDS) due to multiplex advantages the system possesses. The NDS, a power system of multiple interconnected distribution lines which allow uninterrupted supply of both power and communication, holds the potential of increasing renewable energy capacity, reducing construction of new feeders, expanding electric transport infrastructure capacity, and improving facility utilization rate. In this paper, technologies for the construction of NDS are introduced and strategies to increase future application of the NDS are discussed. Through this, both the validity of the NDS and the direction of the power industry is to be presented.
Development of the SUNRA Tool to Improve Regional and Local Sustainability of the Transportation Sector
Authors: Anderson-Sköld Yvonne , Afridi Muhammad Amjad , Nordin Lina , Patricio João , Lindgren Åsa , Johansson Carl-Martin , Olofsson Alexandra , Andersson Angelica , Erlingsson Sigurdur
Published: September 2022

To fulfil the global sustainable development goals (SDGs), achieving sustainable development is becoming urgent, not least in the transportation sector. In response to this, the sustainability framework Sustainability National Road Administrations (SUNRA) was developed to contribute to improving the sustainability performance of national road administrations across Europe. In the present study, the framework has been tested, applied and further developed to be applicable for target setting and follow-up at the project level at both the Swedish Transport Administration (STA) and at municipal levels. The aim was a framework relevant for investment, re-investments, maintenance and operation projects and also to make it more user applicable. The study also investigated how the framework can contribute to sustainability, identified drivers and barriers for applying the framework and examined whether the framework can be applied and adapted to projects of different complexities. The adaptations and developments were done in collaboration between researchers and practitioners. The results show that the framework could easily be used and adapted for investment, re-investment, maintenance and operation projects in the planning stage, as well as for small municipal establishments, construction or reconstruction of residential areas and frequent maintenance. The framework contributes to increased awareness on sustainability, and it provides a common structure and transparency on how infrastructure project goals/targets are set and fulfilled. The framework can also be applied to follow the fulfilment of the goals/targets and thereby adapt the project to better fulfil the goals. Identified barriers include the lack of obligations and lack of experience in using sustainability frameworks.
Verify the Validity of Guidance Sign in Buildings: A New Method Based on Mixed Reality with Eye Tracking Device
Authors: Jiang Fan , Ding Ning , Shi Jiguang , Fan Zhenyu
Published: September 2022

Evacuation signs are one of the key factors which influence the building evacuation efficiency and evacuees’ route choice behaviors. It is important to test the effectiveness of evacuation signs, and an eye movement test was verified to be a good way. In this paper, both reality and MR (Mixed-Reality) evacuation experiments were carried out to collect the eye movement data of participants, such as gaze position, fixation duration, and number of gazes. In the reality experiment, participants took part in an emergency evacuation at a T-junction corridor with the glass-type eye-tracking device. In order to collect eye movement data in the MR experiment, the eye tracking device was integrated into the Microsoft HoloLens device (MR-ET for short). In either reality or MR experiments, each participant took part in five sub-experiments with five different types of evacuation signs. It was found that (1) the evacuation signs with a black background, green foreground and flashing lights have a better effect in guidance; (2) the flashing lights can better attract the attention of the participants, and enhanced the participants’ understanding of the signs. (3) An X-mark will hinder participants’ understanding of the signs and increase their reaction time. According to the results of the experiments, it is also found that the eye-tracking test in an MR environment is also a good method to test the effectiveness of the evacuation signs, and such a method can also be used in building safety design without damaging a wall or changing the reality environment of a building.
Modeling Turkish Households’ Climate Change-Related Behaviors: Theory of Planned Behavior Approach
Authors: Pouya Ahmad Samim , Niyaz Özge Can
Published: September 2022

Global environmental problems are both the cause and outcome of human actions. Even though families contribute significantly to the problem, little is known about the reasons for household climate change behavior. Prior research has shown that household intentions and behavior play an essential role in climate change adaptation and mitigation. The goal of this exploratory study was to see how climate change-related factors influenced climate change-related intentions and behaviors. In terms of climate change, Turkey is a vulnerable country in Mediterranean Europe. As a result, the goal of this study is to apply the Theory of Planned Behavior to simulate Turkish households’ climate change-related behavior. Using a random sampling method, an online self-reported questionnaire of 400 Turkish households assessed the impact of practices to adapt and mitigate climate change. Within the context of the Theory of Planned Behavior, Structural Equation Modeling was used to examine household attitudes and behaviors about climate change. The findings imply that household intentions are important predictors of climate change-related behavior in Turkey. In addition, subjective norms and perceived behavioral control influence the goals of Turkish households. As a result, efforts should be undertaken to provide households with the subjective and perceptual abilities and tools they need to manage their climate-related activities.
Progress toward Circularity of Polyester and Cotton Textiles
Authors: Wang Siyan , Salmon Sonja
Published: September 2022

Millions of tons of textile waste are landfilled or incinerated in the world every year due to insufficient recycle value streams and the complex composition of textile end products. The goal of this review is to highlight pathways for simplifying and separating textile wastes into valuable raw material streams that will promote their recovery and conversion to useful products. The discussion focuses on advances in sorting, separation, decolorization and conversion of polyester and cotton, the two most common textile fibers. Sorting processes are gaining automation using spectroscopic methods that detect chemical composition differences between materials to divide them into categories. Separation, through dissolving or degrading, makes it possible to deconstruct blended textiles and purify polymers, monomers and co-products. Waste cotton can produce high quality regenerated cellulose fibers, cellulose nanocrystals (CNCs) or biofuels. Waste polyester can produce colored yarns or can be chemically converted to its starting monomers for the recreation of virgin polymer as a complete closed loop. The current strategies for decolorization are presented. Life cycle assessment (LCA) studies found that recycling polyester/cotton blended fabrics for subsequent uses is more sustainable than incineration, and research on producing biomass-based poly-ester also offers feasible avenues for improving textile sustainability and promoting circular processing.
Computational Investigation of a NASICON-Type Solid Electrolyte Material LiGe2(PO4)3
Authors: Kuganathan Navaratnarajah , Rex Kobiny Antony , Iyngaran Poobalasuntharam
Published: September 2022

Phosphate-based electrolyte materials are of great interest in the field of Li-ion batteries due to their rigid structural integrity. LiGe2(PO4)3 is a NASICON-type phosphate material with high thermal and electrochemical stability. Computational simulation techniques were employed to study the defects, diffusion, and dopant properties of LiGe2(PO4)3. Furthermore, the reaction energies for the formation of LiGe2(PO4)3 and the incorporation energies for the insertion of additional Li into this material were calculated. The calculations revealed that the Li Frenkel is the lowest-energy defect. The second most favorable defect is the Ge-P anti-site defect cluster. A low Li migration energy of 0.44 eV implies high Li ionic conductivity. The most favorable isovalent dopants on the Li and Ge sites are Na and Si, respectively. The formation of Li interstitials and oxygen vacancies can be facilitated through the doping of Ga on the Ge site. The doping of Ga slightly enhances the Li ionic conductivity. Li incorporation (up to four Li) is thermodynamically feasible.
Multipurpose Cloud-Based Compiler Based on Microservice Architecture and Container Orchestration
Authors: Heidari Sayed , Paznikov Alexey
Published: September 2022

Compilation often takes a long time, especially for large projects or when identifying better optimization options. Currently, compilers are mainly installed on local machines and used as standalone software. Despite the availability of several online compilers, they do not offer an efficient all-in-one package for private account management, command line interface (CLI), code advisors, and optimization techniques. Today, the widespread usage of Software as a Service (SaaS) is ever-growing, and compilers are not an exception. In this paper, we describe a symmetric approach to compilation and how to compile code on distributed systems. Although some improvements in cloud compilers have been made, it is possible to harness the potential of the most-modern technologies and architecture patterns toward designing efficient, in-cloud compilers. In this paper, we propose an architecture design of a cloud-based compiler that is fully compatible with orchestration technologies, such as Kubernetes, providing a higher level of scalability, reliability, security, and maintainability. Microservice architecture alongside containerization and orchestration technologies assist us in making a scalable system that provides a high level of availability. We propose this architecture so that the system can handle a higher workload as it receives a large number of compilation requests per second. Distributed compilation is a prominent benefit of this approach, as each phase of the compilation can be executed in a separate server, which supplies a kind of workload mitigation to the whole system. In other words, we propose a new perspective for an intelligent way of advisor, error detection, and optimization of compilers. We also propose an implementation example of the developed architecture. Finally, we analyze the results from an experimental implementation, proving that we can compile code from more than 100k requests concurrently on a cloud cluster with one master node and three worker nodes.
Highly Sensitive Terahertz Dielectric Sensor for Liquid Crystal
Authors: Li Xianping , Zheng Guanghong , Zhang Guozhen , Yang Jun , Hu Minggang , Li Jian , Li Ying , Lu Hongbo , Yin Zhiping
Published: September 2022

This paper presents the design and process of two highly sensitive sensors working in the terahertz band. The sensors comprise the quartz substrate, medium, reflection plate, and metal resonant layer with a symmetrical single-slot patch array. The devices help study the electrically induced permittivity of two liquid crystals in different frequency bands and at different voltages, and the experimental data verify that both liquid crystals have a large birefringence. Based on experimental results, the sensitivity of the fabricated sensor is 47.03 GHz/RIU in the frequency range 90–140 GHz. Similarly, the other fabricated sensor has a sensitivity of 112.47 GHz/RIU in the frequency range 325–500 GHz. The results show that both sensors have superior sensing properties and potential applications in biological and chemical liquid sensing.
An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers
Authors: Guo Qihang , Yin Zhenyu , Wang Pingxin
Published: September 2022

Most of data set can be represented in an asymmetric matrix. How to mine the uncertain information from the matrix is the primary task of data processing. As a typical unsupervised learning method, three-way k-means clustering algorithm uses core region and fringe region to represent clusters, which can effectively deal with the problem of inaccurate decision-making caused by inaccurate information or insufficient data. However, same with k-means algorithm, three-way k-means also has the problems that the clustering results are dependent on the random selection of clustering centers and easy to fall into the problem of local optimization. In order to solve this problem, this paper presents an improved three-way k-means algorithm by integrating ant colony algorithm and three-way k-means. Through using the random probability selection strategy and the positive and negative feedback mechanism of pheromone in ant colony algorithm, the sensitivity of the three k-means clustering algorithms to the initial clustering center is optimized through continuous updating iterations, so as to avoid the clustering results easily falling into local optimization. Dynamically adjust the weights of the core domain and the boundary domain to avoid the influence of artificially set parameters on the clustering results. The experiments on UCI data sets show that the proposed algorithm can improve the performances of three-way k-means clustering results and is effective in revealing cluster structures.
Some Generalizations of (∇∇)Δ–Gronwall–Bellman–Pachpatte Dynamic Inequalities on Time Scales with Application
Authors: El-Deeb Ahmed A. , El-Bary Alaa A. , Awrejcewicz Jan
Published: September 2022

As a new usage of Leibniz integral rule on time scales, we proved some new extensions of dynamic Gronwall–Pachpatte-type inequalities on time scales. Our results extend some existing results in the literature. Some integral and discrete inequalities are obtained as special cases of the main results. The inequalities proved here can be used in the analysis as handy tools to study the stability, boundedness, existence, uniqueness and oscillation behavior for some kinds of partial dynamic equations on time scales. Symmetry plays an essential role in determining the correct methods to solve dynamic inequalities.
Development of a Quality-Based Model for Software Architecture Optimization: A Case Study of Monolith and Microservice Architectures
Authors: Milić Miloš , Makajić-Nikolić Dragana
Published: September 2022

Various architectures can be applied in software design. The aim of this research is to examine a typical implementation of Jakarta EE monolithic and microservice software architectures in the context of software quality attributes. Software quality standards are used to define quality models, as well as quality characteristics and sub-characteristics, i.e., software quality attributes. This paper evaluates monolithic and microservice architectures in the context of Coupling, Testability, Security, Complexity, Deployability, and Availability quality attributes. The performed examinations yielded a quality-based mixed integer goal programming mathematical model for software architecture optimization. The model incorporates various software metrics and considers their maximal, minimal or targeted values, as well as upper and lower deviations. The objective is the sum of all deviations, which should be minimal. Considering the presented model, a solution which incorporated multiple monoliths and microservices was defined. This way, the internal structure of the software is defined in a consistent and symmetrical context, while the external software behavior remains unchanged. In addition, an intersection point of monolithic and microservice software architectures, where software metrics obtain the same values, was introduced. Within the intersection point, either one of the architectures can be applied. With the exception of some metrics, an increase in the number of features leads to a value increase of software metrics in microservice software architecture, whilst these values are constant in monolithic software architecture. An increase in the number of features indicated a quality attribute’s importance for the software system should be examined and an appropriate architecture should be selected accordingly. Finally, practical recommendations regarding software architectures in terms of software quality were given. Since each software system needs to meet non-functional in addition to functional requirements, a quality-driven software engineering can be established.
Attribute Reduction Based on Lift and Random Sampling
Authors: Chen Qing , Xu Taihua , Chen Jianjun
Published: September 2022

As one of the key topics in the development of neighborhood rough set, attribute reduction has attracted extensive attentions because of its practicability and interpretability for dimension reduction or feature selection. Although the random sampling strategy has been introduced in attribute reduction to avoid overfitting, uncontrollable sampling may still affect the efficiency of search reduct. By utilizing inherent characteristics of each label, Multi-label learning with Label specIfic FeaTures (Lift) algorithm can improve the performance of mathematical modeling. Therefore, here, it is attempted to use Lift algorithm to guide the sampling for reduce the uncontrollability of sampling. In this paper, an attribute reduction algorithm based on Lift and random sampling called ARLRS is proposed, which aims to improve the efficiency of searching reduct. Firstly, Lift algorithm is used to choose the samples from the dataset as the members of the first group, then the reduct of the first group is calculated. Secondly, random sampling strategy is used to divide the rest of samples into groups which have symmetry structure. Finally, the reducts are calculated group-by-group, which is guided by the maintenance of the reducts’ classification performance. Comparing with other 5 attribute reduction strategies based on rough set theory over 17 University of California Irvine (UCI) datasets, experimental results show that: (1) ARLRS algorithm can significantly reduce the time consumption of searching reduct; (2) the reduct derived from ARLRS algorithm can provide satisfying performance in classification tasks.
Proof-of-Useful-Work: BlockChain Mining by Solving Real-Life Optimization Problems
Authors: Todorović Milan , Matijević Luka , Ramljak Dušan , Davidović Tatjana , Urošević Dragan , Jakšić Krüger Tatjana , Jovanović Đorđe
Published: September 2022

Blockchains (BCs) are distributed database systems, popular for their innovative, unsupervised maintenance process. They use a so-called consensus protocol to prevent inference by any third party of absolute trust. Security, privacy, consistency, and energy consumption have been identified as the main issues involved in BC maintenance. According to the recent literature, some of these issues can be formulated as combinatorial optimization (CO) problems, and this fact motivated us to consider incorporating CO approaches into a BC. In this paper, we propose the new combinatorial optimization consensus protocol (COCP) based on the proof-of-useful-work (PoUW) concept that assumes solving instances of real-life CO problems. Due to the complexity of the underlying CO problems, we have developed various types of heuristic methods, which are utilized in the COCP. Most of these methods are problem-dependent stochastic heuristic or metaheuristic methods. As is the case with the majority of consensus protocols, PoUW exhibits the property of asymmetry. It is difficult to find a solution for the considered CO problem; however, once a solution is found, its verification is straightforward. We present here a BC framework combining the two above-mentioned fields of research: BC and CO. This framework consists of improvements aiming towards developing the COCP of the PoUW type. The main advantage of this consensus protocol is the efficient utilization of computing resources (by exploring them for finding solutions of real-life CO problem instances), and the provision of a broad range of incentives for the various BC participants. We enumerate the potential benefits of the COCP with respect to its practical impacts and savings in power consumption, describing in detail an illustrative example based on part of the real-life BC network. In addition, we identify several challenges that should be resolved in order to implement a useful, secure, and efficient PoUW consensus protocol.
Weakly Supervised Object Detection with Symmetry Context
Authors: Gu Xinyu , Zhang Qian , Lu Zheng
Published: September 2022

Recently, weakly supervised object detection (WSOD) with image-level annotation has attracted great attention in the field of computer vision. The problem is often formulated as multiple instance learning in the existing studies, which are often trapped by discriminative object parts and fail to localize the object boundary precisely. In this work, we alleviate this problem by exploiting contextual information that may potentially increase object localization accuracy. Specifically, we propose novel context proposal mining strategies and a Symmetry Context Module to leverage surrounding contextual information of precomputed region proposals. Both naive and Gaussian-based context proposal mining methods are adopted to yield informative context proposals symmetrically surrounding region proposals. Then mined context proposals are fed into our Symmetry Context Module to encourage the model to select proposals that contain the whole object, rather than the most discriminative object parts. Experimental results show that the mean Average Precision (mAP) of the proposed method achieves 52.4% on the PASCAL VOC 2007 dataset, outperforming the state-of-the-art methods and demonstrating its effectiveness for weakly supervised object detection.
Correction Algorithm of Tennis Dynamic Image Serving Path Based on Symmetric Algorithm
Authors: Li Liangming , Yang Aihua
Published: September 2022

The existing target tracking algorithm of the tennis dynamic image serving path cannot correct the serving path in time according to the physical characteristics of the target, resulting in poor correction accuracy and stability. To this end, this paper proposes correction algorithm of tennis dynamic image serving path based on symmetric algorithm. Dynamic images are obtained using the visual acquisition model. On this basis, the contour of the moving target is obtained by the symmetric algorithm, and the complete moving target is obtained by the background difference method. The physical properties of moving objects are analyzed through a tennis serve visual model. The path correction model based on physical features is used to construct the relevant model so as to realize the path correction of the moving target. The experimental results show that the highest accuracy of the algorithm for tennis net and bottom line is 92.88% and 93.10%, respectively, and the average image correction accuracy is 95%. The tracking accuracy of service paths in complex backgrounds is 95%. These data show that the proposed algorithm has high correction accuracy and stability.
Dual-Time-Scale Sliding Mode Control for Surface-Mounted Permanent Magnet Synchronous Motors
Authors: Che Zhiyuan , Yu Haitao , Mobayen Saleh , Ali Murad , Yang Chunyu , El-Sousy Fayez F. M.
Published: September 2022

The permanent magnet synchronous motors (PMSMs) as the completely symmetrical three-phase machines, which are usually driven by symmetrical voltage signals. Unfortunately, a PMSM system usually suffers from the different lumped disturbances, such as internal parametric perturbations and external load torques, the speed regulation problem should be addressed within the different operation situations. Characterizing by the current variation speed of the motor winding is much faster than that of the mechanical dynamic velocity, a dual-time-scale sliding mode control (SMC) method for the surface-mounted PMSMs is proposed in this paper. Firstly, the mathematical model of PMSMs is established in the two-phase synchronous rotating orthogonal reference coordinate system, and the slow and fast variation subsystems are obtained based on the quasi-steady-state theory. Secondly, a tracking differentiator (TD)-based and exponential reaching law-based sliding mode controllers are individually designed within dual-time-scale, respectively. As a result, the eventual SMC strategy is presented, and the stability of control system is analyzed by applying the Lyapunov stability theory. The main contribution of this study is to present an alternative control framework for the PMSMs servo system, where the dual-time-scale characteristic is involved, and thus a non-cascade control structure that different from the traditional drive strategy is proposed in the motor community. Finally, the model of whole system is built and carried out on the simulation platform. Research results demonstrate that the presented servo control system can accurately track the reference angle velocity signal, while the strong robustness and fast response performance are guaranteed in the presence of external disturbances. In addition, the three-phase current transient response values are completely symmetrical with the rapid adjustment characteristic.
Symmetry or Asymmetry? Complex Problems and Solutions by Computational Intelligence and Soft Computing
Authors: Kóczy László T.
Published: September 2022

What is the role of symmetry in the seemingly far away topics of solving complex applied problems by approaches offered by Soft Computing and Computational Intelligence [...]
Attribute Network Representation Learning with Dual Autoencoders
Authors: Wang Jinghong , Zhou Zhixia , Li Bi , Wu Mancai
Published: September 2022

The purpose of attribute network representation learning is to learn the low-dimensional dense vector representation of nodes by combining structure and attribute information. The current network representation learning methods have insufficient interaction with structure when learning attribute information, and the structure and attribute information cannot be well integrated. In this paper, we propose an attribute network representation learning method for dual-channel autoencoder. One channel is for the network structure, and adopting the multi-hop attention mechanism is used to capture the node’s high-order neighborhood information and calculate the neighborhood weight; The other channel is for the node attribute information, and a low-pass Laplace filter is designed to iteratively obtain the attribute information in the neighborhood of the node. The dual-channel autoencoder ensures the learning of structure and attribute information respectively. The adaptive fusion module is constructed in this method to increase the acquisition of important information through the consistency and difference constraints of two kinds of information. The method trains encoders by supervising the joint reconstruction of loss functions of two autoencoders. Based on the node clustering task on four authentic open data sets, and compared with eight network representation learning algorithms in clustering accuracy, standardized mutual information and running time of some algorithms, the experimental results show that the proposed method is superior and reasonable.
Infinitely Many Solutions for the Discrete Boundary Value Problems of the Kirchhoff Type
Authors: Zhang Weihua , Zhou Zhan
Published: September 2022

In this paper, we study the existence and multiplicity of solutions for the discrete Dirichlet boundary value problem of the Kirchhoff type, which has a symmetric structure. By using the critical point theory, we establish the existence of infinitely many solutions under appropriate assumptions on the nonlinear term. Moreover, we obtain the existence of infinitely many positive solutions via the strong maximum principle. Finally, we take two examples to verify our results.
Dynamic Hardy–Copson-Type Inequalities via (γ,a)-Nabla-Conformable Derivatives on Time Scales
Authors: El-Deeb Ahmed A. , Makharesh Samer D. , Awrejcewicz Jan , Agarwal Ravi P.
Published: September 2022

We prove new Hardy–Copson-type (γ,a)-nabla fractional dynamic inequalities on time scales. Our results are proven by using Keller’s chain rule, the integration by parts formula, and the dynamic Hölder inequality on time scales. When γ=1, then we obtain some well-known time-scale inequalities due to Hardy. As special cases, we obtain new continuous and discrete inequalities. Symmetry plays an essential role in determining the correct methods to solve dynamic inequalities.
A High-Performance Compound Control Method for a Three-Axis Inertially Stabilized Platform under Multiple Disturbances
Authors: Lei Xusheng , Fu Fa , Wang Rui
Published: September 2022

Symmetry is presented in the frame structure, modeling, and disturbance analysis of the three-axis inertially stabilized platform (ISP), which affects the control performance of the ISP. To realize high-performance control for the ISP, a nonlinear dynamic model based on the geographic coordinates and a compound control method based on the adaptive extended state observer (ESO) and adaptive back-stepping integral sliding mode control (SMC) are proposed. The nonlinear dynamic model based on geographic coordinates could avoid the degradation of measurement and control performance due to complex coordinate transformations. An adaptive ESO (AESO) has been developed to estimate the unknown disturbances of ISP. With the information from the ISP system, the adaptive bandwidth of AESO can deal with the peaking phenomenon without introducing excessive noise. Furthermore, based on the integral sliding mode, the adaptation laws of parameter uncertainty and disturbance estimation compensation have been developed for the back-stepping integral SMC method, which can reduce the estimation burden and improve the disturbance estimation accuracy of AESO. The asymptotic stability of the compound control method has been proven by the Lyapunov stability theory. Through a series of simulations and experiments, the effectiveness of the compound method is validated.
Analytical Decomposition of Transition Flux to Cycle Durations via Integration of Transition Times
Authors: Hou Ruizheng
Published: September 2022

Rigorous methods of decomposing kinetic networks to cycles are available, but the solutions usually contain entangled transition rates, which are difficult to analyze. This study proposes a new method of decomposing net transition flux to cycle durations, and the duration of each cycle is an integration of the transition times along the cycle. The method provides a series of neat dependences from the basic kinetic variables to the final flux, which support direct analysis based on the formulas. An assisting transformation diagram from symmetric conductivity to asymmetric conductivity is provided, which largely simplifies the application of the method. The method is likely a useful analytical tool for many studies relevant to kinetics and networks. Applications of the method shall provide new kinetic and thermodynamic information for the studied system.
Performance Analysis on the Small-Scale Reusable Launch Vehicle
Authors: Gong Zheng , Wang Zian , Yang Chengchuan , Li Zhengxue , Dai Mingzhe , Zhang Chengxi
Published: September 2022

According to the symmetrical characteristics of a new type of Reusable Launch Vehicle (RLV) in the recovery phase, we studied the basic aerodynamic model data of Starship and the aerodynamic data with rudder deflection, and the causes of its aerodynamic coefficients are expounded. At the same time, we analyzed its stability and maneuverability. According to the flying quality requirements, the lateral-directional model of Starship in the return phase at a high angle of attack is analyzed. Finally, we analyzed the lateral heading stability and control deviation of Starship by using the criterion and nonlinear open-loop simulations. The results show that the Starship has pitching and rolling stability, but it only has heading stability in some ranges of angle of attack, and there is no heading stability at a conventional large angle of attack. At the same time, after modal analysis and comparison of flight quality, it can be seen that the longitudinal long-period model of the starship degenerates into a real root and it is stable and convergent. The lateral heading roll mode is at level 2 flight quality, the helical mode is at level 1 flight quality, and the Dutch roll mode diverges, which needs to be stabilized and controlled later.
Study of a Viscous ΛWDM Model: Near-Equilibrium Condition, Entropy Production, and Cosmological Constraints
Authors: Cruz Norman , González Esteban , Jovel Jose
Published: September 2022

Extensions to a ΛDM model have been explored in order to face current tensions that occur within its framework, which encompasses broadening the nature of the dark matter (DM) component to include warmness and a non-perfect fluid description. In this paper, we investigated the late-time cosmological evolution of an exact solution recently found in the literature, which describes a viscous warm ΛDM model (ΛWDM) with a DM component that obeys a polytropic equation of state (EoS), which experiences dissipative effects with a bulk viscosity proportional to its energy density, with proportionality constant ξ0. This solution has the particularity of having a very similar behavior to the ΛCDM model for small values of ξ0, evolving also to a de Sitter type expansion in the very far future. We explore firstly the thermodynamic consistences of this solution in the framework of Eckart’s theory of non-perfect fluids, focusing on the fulfillment of the two following conditions: (i) the near-equilibrium condition and (ii) the positiveness of the entropy production. We explore the range of parameters of the model that allow to fulfill these two conditions at the same time, finding that a viscous WDM component is compatible with both ones, being in this sense, a viable model from the thermodynamic point of view. Furthermore, we constrained the free parameters of the model with the observational data coming from supernovae Ia (SNe Ia) and the observational Hubble parameter data (OHD), using these thermodynamics analyses to define the best priors for the cosmological parameters related to the warmness and the dissipation of the DM, showing that this viscous ΛWDM model can describe the combined SNe Ia+OHD data in the same way as the ΛCDM model. The cosmological constraint at 3σ CL gives us an upper limit on the bulk viscous constant of order ξ0∼106 Pa·s, which is in agreement with some previous investigations. Our results support that the inclusion of a dissipative WDM, as an extension of the standard cosmological model, leads to a both thermodynamically consistent and properly fitted cosmological evolution.
(Δ∇)∇-Pachpatte Dynamic Inequalities Associated with Leibniz Integral Rule on Time Scales with Applications
Authors: El-Deeb Ahmed A. , Baleanu Dumitru , Awrejcewicz Jan
Published: September 2022

We prove some new dynamic inequalities of the Gronwall–Bellman–Pachpatte type on time scales. Our results can be used in analyses as useful tools for some types of partial dynamic equations on time scales and in their applications in environmental phenomena and physical and engineering sciences that are described by partial differential equations.
High Performance Parallel Pseudorandom Number Generator on Cellular Automata
Authors: Levina Alla , Mukhamedjanov Daniyar , Bogaevskiy Danil , Lyakhov Pavel , Valueva Maria , Kaplun Dmitrii
Published: September 2022

Nowadays, the practice of developing algorithms to maintain the confidentiality of data shows that there is a lack of some features, such as velocity, predictability, etc. Generating pseudorandom numbers is one such problem that lies in the basement of many algorithms, even in hardware microprograms. An unreliable generator can cause cyberattacks on it, despite the security in the upper layers. At the same time, the algorithm should be fast enough to provide uninterrupted circuit work for the entire system. The paper presents a new algorithm generating pseudorandom numbers on cellular automata, which is not only fast and easy-repeating, but unpredictable enough and can be used in cryptographic systems. One of the main tasks of pseudorandom number generators (PRNG) is to present a high level of nonlinearity, or as it can also be named, asymmetry. Using the National Institute of Standards and Technology (NIST) statistical test suite for random number generators and pseudorandom number generators, it is shown that the presented algorithm is more than three times superior to the state-of-the-art methods and algorithms in terms of p-value. A high level of the presented algorithm’s parallelization allows for implementation effectively on calculators with parallel structure. Central Processing Unit (CPU)-based architecture, Field-Programmable Gate Array (FPGA)-based architecture, Compute Unified Device Architecture (CUDA)-based architecture of PRNG and different PRNG implementations are presented to confirm the high performance of the proposed solution.
Image Encryption Based on Local Fractional Derivative Complex Logistic Map
Authors: Natiq Hayder , Al-Saidi Nadia M. G. , Obaiys Suzan J. , Mahdi Mohammed Najah , Farhan Alaa Kadhim
Published: September 2022

Local fractional calculus (fractal calculus) plays a crucial role in applications, especially in computer sciences and engineering. One of these applications appears in the theory of chaos. Therefore, this paper studies the dynamics of a fractal complex logistic map and then employs this map to generate chaotic sequences for a new symmetric image encryption algorithm. Firstly, we derive the fractional complex logistic map and investigate its dynamics by determining its equilibria, geometric properties, and chaotic behavior. Secondly, the fractional chaotic sequences of the proposed map are employed to scramble and alter image pixels to increase resistance to decryption attacks. The output findings indicate that the proposed algorithm based on fractional complex logistic maps could effectively encrypt various kinds of images. Furthermore, it has better security performance than several existing algorithms.
Liouville Theory for Fully Analytic Studies of Transverse Beam Dynamics in Laser-Plasma Ion Accelerators
Authors: Curcio Alessandro , Apiñaniz Aginako Jon Imanol , Cebriano Ramírez Teresa , Ehret Michael , Kebladj Berkahoum , Morabito Antonia , Pérez Delgado Alberto , Salgado López Carlos , Volpe Luca , Gatti Giancarlo
Published: September 2022

The exact solution of the Liouville equation expressed in terms of exponential operators can describe the phase space evolution of particle beams in transport lines. In this paper, we generalize the solution of the above equation for the case of beam losses induced by apertures and for particle beams with large spreads in the momentum space. We discuss the applicability of such approach to ion beams produced by high-intensity lasers interacting with critical plasmas, based on the comparison between theoretical findings and measurements.
Symmetrization in the Calculation Pipeline of Gauss Function-Based Modeling of Hydrophobicity in Protein Structures
Authors: Banach Mateusz
Published: September 2022

In this paper, we show, discuss, and compare the effects of symmetrization in two calculation subroutines of the Fuzzy Oil Drop model, a coarse-grained model of density of hydrophobicity in proteins. In the FOD model, an input structure is enclosed in an axis-aligned ellipsoid called a drop. Two profiles of hydrophobicity are then calculated for its residues: theoretical (based on the 3D Gauss function) and observed (based on pairwise hydrophobic interactions). Condition of the hydrophobic core is revealed by comparing those profiles through relative entropy, while analysis of their local differences allows, in particular, determination of the starting location for the search for protein–protein and protein–ligand interaction areas. Here, we improve the baseline workflow of the FOD model by introducing symmetry to the hydrophobicity profile comparison and ellipsoid bounding procedures. In the first modification (FOD–JS), Kullback–Leibler divergence is enhanced with its Jensen–Shannon variant. In the second modification (FOD-PCA), the molecule is optimally aligned with the axes of the coordinate system via principal component analysis, and the size of its drop is determined by the standard deviation of all its effective atoms, making it less susceptible to structural outliers. Tests on several molecules with various shapes and functions confirm that the proposed modifications improve the accuracy, robustness, speed, and usability of Gauss function-based modeling of the density of hydrophobicity in protein structures.
Stability of Boundary Value Discrete Fractional Hybrid Equation of Second Type with Application to Heat Transfer with Fins
Authors: Shammakh Wafa , Selvam A. George Maria , Dhakshinamoorthy Vignesh , Alzabut Jehad
Published: September 2022

The development in the qualitative theory of fractional differential equations is accompanied by discrete analog which has been studied intensively in recent past. Suitable fixed point theorem is to be selected to study the boundary value discrete fractional equations due to the properties exhibited by fractional difference operators. This article aims at investigating the stability results in the sense of Hyers and Ulam with application of Mittag–Leffler function hybrid fractional order difference equation of second type. The symmetric structure of the operators defined in this article is vital in establishing the existence results by using Krasnoselkii’s fixed point theorem. Banach contraction mapping principle and Krasnoselkii’s fixed point theorem are employed to establish the uniqueness and existence results for solution of fractional order discrete equation. A problem on heat transfer with fins is provided as an application to considered hybrid type fractional order difference equation and the stability results are demonstrated with simulations.
A Review on Some Linear Positive Operators Defined on Triangles
Authors: Cătinaş Teodora
Published: September 2022

We consider results regarding Bernstein and Cheney–Sharma-type operators that interpolate functions defined on triangles with straight and curved sides and we introduce a new Cheney–Sharma-type operator for the triangle with one curved side, highlighting the symmetry between the methods. We present some properties of the operators, their products and Boolean sums and some results regarding the remainders of the corresponding approximation formulas, using modulus of continuity and Peano’s theorem. Additionally, we consider some numerical examples to show the approximation properties of the given operators.
Optimal Derivative-Free One-Point Algorithms for Computing Multiple Zeros of Nonlinear Equations
Authors: Kumar Sunil , Bhagwan Jai , Jäntschi Lorentz
Published: September 2022

In this paper, we describe iterative derivative-free algorithms for multiple roots of a nonlinear equation. Many researchers have evaluated the multiple roots of a nonlinear equation using the first- or second-order derivative of functions. However, calculating the function’s derivative at each iteration is laborious. So, taking this as motivation, we develop second-order algorithms without using the derivatives. The convergence analysis is first carried out for particular values of multiple roots before coming to a general conclusion. According to the Kung–Traub hypothesis, the new algorithms will have optimal convergence since only two functions need to be evaluated at every step. The order of convergence is investigated using Taylor’s series expansion. Moreover, the applicability and comparisons with existing methods are demonstrated on three real-life problems (e.g., Kepler’s, Van der Waals, and continuous-stirred tank reactor problems) and three standard academic problems that contain the root clustering and complex root problems. Finally, we see from the computational outcomes that our approaches use the least amount of processing time compared with the ones already in use. This effectively displays the theoretical conclusions of this study.
A Two-Step Iteration Method for Vertical Linear Complementarity Problems
Authors: Song Yunlin , Zheng Hua , Lu Xiaoping , Vong Seak-Weng
Published: September 2022

In this paper, a two-step iteration method was established which can be viewed as a generalisation of the existing modulus-based methods for vertical linear complementarity problems. The convergence analysis of the proposed method is presented, which can enlarge the convergence domain of the parameter matrix compared to the recent results. Numerical examples show that the proposed method is efficient with the two-step technique and confirm the improvement of the theoretical results.
Research on Solving Nonlinear Problem of Ball and Beam System by Introducing Detail-Reward Function
Authors: Yao Shixuan , Liu Xiaochen , Zhang Yinghui , Cui Ze
Published: September 2022

As a complex nonlinear system, the first-order incremental relationship between the state variables of the beam and ball system (BABS) is asymmetric in the definition domain of the variables, and the characteristics of the system do not satisfy the superposition theorem. Studying the balance control of the BABS can help to better grasp the relevant characteristics of the nonlinear system. In this paper, the deep reinforcement learning method is used to study the BABS based on a visual sensor. First, the detail-reward function is designed by observing the control details of the system, and the rationality of the function is proved based on Q-function; secondly, considering and comparing the applicability of image processing methods in ball coordinate location, an intelligent location algorithm is proposed, and the location effects between the algorithms are compared and analyzed; then, combining the nonlinear theory and LQR theory, a reinforcement learning policy model is proposed to linearize near the equilibrium point, which significantly improves the control effect. Finally, experiments are designed to verify the effectiveness of the above methods in the control system. The experimental results show that the design scheme can be effectively applied to the control system of the BABS. It is verified that the introduction of detail-reward mechanism into a deep reinforcement learning algorithm can significantly reduce the complexity of the nonlinear control system and iterative algorithm, and effectively solve nonlinear control problems.