bluuebunny/crossref_metadata_2025 huggingface dataset:
The Dynamics of a Continuous Newton-like Method
Authors: Singh Manoj K. , Argyros Ioannis K.
Published: October 2022

The objective of the current work is to invent and introduce the continuous version of Newton’s method. This scheme is used to establish some interesting properties with examples. We have plotted the fractal pattern graphs for a Newton-like method and a Damped Newton-like method in the discrete case and hence we have introduced a new concept of streamline for the continuous version of the Newton-like method. The graph and streamlines of these patterns are in agreement with numerical results and describe the convergence and stability of the proposed method to different roots when the Newton method fails.
Extended Multi-Step Jarratt-like Schemes of High Order for Equations and Systems
Authors: Argyros Ioannis K. , Argyros Chirstopher , Argyros Michael , Ceballos Johan , González Daniel
Published: October 2022

The local convergence analysis of multi-step, high-order Jarratt-like schemes is extended for solving Banach space valued systems of equations using the derivative instead of up to the ninth derivative as in previous works. Our idea expands the usage of the scheme in cases not considered earlier and can also be utilized in other schemes, too. Experiments test the theoretical results.
Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem
Authors: Wen Changsheng , Jia Heming , Wu Di , Rao Honghua , Li Shanglong , Liu Qingxin , Abualigah Laith
Published: October 2022

Remora Optimization Algorithm (ROA) is a metaheuristic optimization algorithm, proposed in 2021, which simulates the parasitic attachment, experiential attack, and host feeding behavior of remora in the ocean. However, the performance of ROA is not very good. Considering the habits of the remora that rely on the host to find food, and in order to improve the performance of the ROA, we designed a new host-switching mechanism. By adding new a host-switching mechanism, joint opposite selection, and restart strategy, a modified remora optimization algorithm (MROA) is proposed. We use 23 standard benchmark and CEC2020 functions to test the performance of MROA and compare them with eight state-of-art optimization algorithms. The experimental results show that MROA has better-optimized performance and robustness. Finally, the ability of MROA to solve practical problems is demonstrated by five classical engineering problems.
The Construction of Models of Rough Surfaces’ Interaction: Markov’s Approach
Authors: Gavrilov Konstantin , Rozhdestvenskii Yuriy , Umurzakov Ildar
Published: October 2022

This article shows that the change of rough surfaces in the way of their contact interaction can be analyzed using the theory of Markov’s processes. In the framework of existing models, this problem cannot be solved. In this article, the idea of reducing to Markov’s model is shown on a simple discrete scheme, which is then generalized. The approach was applied to the analysis of the friction process, to the fatigue failure mode, in which the surface element changes after multiple contacts, possibly many millions. The Kolmogorov-Feller’s equations for the model of this regime were presented and the model of the influence of lubrication is offered. A calculated example of estimating the evolution of surfaces separated by a lubricant layer is given. Additionally, the technical characteristics as functions of the friction path and the load were evaluated.
Modeling and Solving for Multi-Satellite Cooperative Task Allocation Problem Based on Genetic Programming Method
Authors: Qi Weihua , Yang Wenyuan , Xing Lining , Yao Feng
Published: October 2022

The past decade has seen an increase in the number of satellites in orbit and in highly dynamic satellite requests, making the control by ground stations inefficient. The traditional management composed of ground planning with separate onboard execution is seriously lagging in response to dynamically incoming tasks. To meet the demand for the real-time response to emergent events, a multi-autonomous-satellite system with a central-distributed collaborative architecture was formulated by an integer programming model. Based on the structure, evolutionary rules were proposed to solve this problem by the use of sequence solution construction and a constructed heuristic method based on gene expression programming evolution. First, the features of the problem are extracted based on domain knowledge, then, the problem-solving rules are evolved by gene expression programming. The simulation results reflect that the evolutionary rule completely surpasses the three types of heuristic rules with adaptive mechanisms and achieves a solution effect close to meta-heuristic algorithms with a reasonably fast solving speed.
Improvement of an Adaptive Robot Control by Particle Swarm Optimization-Based Model Identification
Authors: Issa Hazem , Tar József K.
Published: October 2022

Model-based controllers suffer from the effects of modeling imprecisions. The analytical form of the available model often contains only approximate parameters and can be physically incomplete. The consequences of these effects can be compensated by adaptive techniques and by the improvement of the available model. Lyapunov function-based classic methods, which assume exact analytical model forms, guarantee asymptotic stability by cautious and slow parameter tuning. Fixed point iteration-based adaptive controllers can work without the exact model form but immediately yield precise trajectory tracking. They neither identify nor improve the parameters of the available model. However, any amendment of the model can improve the controller’s operation by affecting its range and speed of convergence. It is shown that even very primitive, fast, and simple versions of evolutionary computation-based methods can produce considerable improvement in their operation. Particle swarm optimization (PSO) is an attractive, efficient, and simple tool for model improvement. In this paper, a PSO-based model approximation technique was investigated for use in the control of a three degrees of freedom PUMA-type robot arm via numerical simulations. A fixed point iteration (FPI)-based adaptive controller was used for tracking a nominal trajectory while the PSO attempted to refine the model. It was found that the refined model still had few errors, the effects of which could not be completely neglected in the model-based control. The best practical solution seems to be the application of the same adaptive control with the use of the more precise, PSO-improved model. Apart from a preliminary study, the first attempt to combine PSO with FPI is presented here.
Performance Analysis of BDS-3 FCB Estimated by Reference Station Networks over a Long Time
Authors: Qi Ke , Dang Yamin , Xu Changhui , Gu Shouzhou
Published: October 2022

The stability and validity of the BDS-3 precise point positioning ambiguity solution (PPP-AR) is becoming more and more important along with the development of BDS-3 orbit and clock products over long durations. Satellite phase fractional cycle biases (FCBs) are key in PPP-AR, so it is important to ensure the validity and stability of FCBs over a long duration. In this study, we analyzed the validity and stability of BDS-3 phase FCBs by estimating them. The BDS-3 FCB experiments showed that BDS-3 FCBs have the same stability as GPS/GAL/BDS-2. BDS-3 widelane (WL) FCBs also have stable characteristics and the maximal fluctuation value of WL FCBs was found to be 0.2 cycles in a month. BDS-3 narrowlane (NL) FCBs were found to be unstable and the maximal fluctuation value of NL FCBs was more than 0.25 cycles over one day. Analyzing the posteriori residual errors of BDS-3 WL and NL ambiguities showed that the BDS-3 FCBs had the same accuracy as GPS/GAL/BDS-2. However, the ambiguity-fixed rate of BDS-3 was about 70%, which was less than GPS/GAL/BDS-2 in PPP-AR experiments. For this reason, we analyzed the quality of data and the accuracy of orbit and clock products by using different analysis center products. The results showed that the low accuracy of the BDS-3 orbit and clock products was the main reason for the low-ambiguity fixed rate.
Some Results on Measures of Interaction among Risks
Authors: Fan Yiting , Fang Rui
Published: October 2022

It has become a common understanding that financial risk can spread rapidly from one institution to another, and the stressful status of one institution may finally result in a systemic crisis. One popular method to assess and quantify the risk of contagion is employing the co-risk measures and risk contribution measures. It is interesting and important to understand how the underlining dependence structure and magnitude of random risks jointly affect systemic risk measures. In this paper, we mainly focus on the conditional value-at-risk, conditional expected shortfall, the delta conditional value-at-risk, and the delta conditional expected shortfall. Existing studies mainly focus on the situation with two random risks, and this paper makes some contributions by considering the scenario with possibly more than two random risks. By employing the tools of stochastic order, positive dependence concepts and arrangement monotonicity, several results concerning the usual stochastic order, increasing convex order, dispersive order and excess wealth order are presented. Concisely speaking, it is found that for a large enough stress level, a larger random risk tends to lead to a more severe systemic risk. We also performed some Monte Carlo experiments as illustrations for the theoretical findings.
A Matrix Approach to the Modeling and Analysis of Network Evolutionary Games with Mixed Strategy Updating Rules
Authors: Gui Yalin , Du Chengyuan , Gao Lixin
Published: October 2022

So far, most studies on networked evolutionary game have focused on a single update rule. This paper investigated the seeking of the Nash Equilibrium and strategy consensus of the evolutionary networked game with mixed updating rules. First, we construct the algebraic formulation for the dynamics of the evolutionary networked game by using the semi-tensor product method. Second, based on the algebraic form, the dynamic behavior of networked evolutionary games is discussed, and an algorithm to seek the Nash equilibrium is proposed. Third, we investigate the strategy consensus problem for a special networked evolutionary game. Finally, some illustrative examples are given to verify our conclusions.
Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm
Authors: Abdelhamid Abdelaziz A. , El-Kenawy El-Sayed M. , Khodadadi Nima , Mirjalili Seyedali , Khafaga Doaa Sami , Alharbi Amal H. , Ibrahim Abdelhameed , Eid Marwa M. , Saber Mohamed
Published: October 2022

The world is still trying to recover from the devastation caused by the wide spread of COVID-19, and now the monkeypox virus threatens becoming a worldwide pandemic. Although the monkeypox virus is not as lethal or infectious as COVID-19, numerous countries report new cases daily. Thus, it is not surprising that necessary precautions have not been taken, and it will not be surprising if another worldwide pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application may be implemented to diagnose monkeypox as it invades the human skin. An image can be acquired and utilized to further diagnose the condition. In this paper, two algorithms are proposed for improving the classification accuracy of monkeypox images. The proposed algorithms are based on transfer learning for feature extraction and meta-heuristic optimization for feature selection and optimization of the parameters of a multi-layer neural network. The GoogleNet deep network is adopted for feature extraction, and the utilized meta-heuristic optimization algorithms are the Al-Biruni Earth radius algorithm, the sine cosine algorithm, and the particle swarm optimization algorithm. Based on these algorithms, a new binary hybrid algorithm is proposed for feature selection, along with a new hybrid algorithm for optimizing the parameters of the neural network. To evaluate the proposed algorithms, a publicly available dataset is employed. The assessment of the proposed optimization of feature selection for monkeypox classification was performed in terms of ten evaluation criteria. In addition, a set of statistical tests was conducted to measure the effectiveness, significance, and robustness of the proposed algorithms. The results achieved confirm the superiority and effectiveness of the proposed methods compared to other optimization methods. The average classification accuracy was 98.8%.
Numerical Analysis of Building Cooling Using New Passive Downdraught Evaporative Tower Configuration in an Arid Climate
Authors: Alshenaifi Mohammad Abdullah , Mesloub Abdelhakim , Hassen Walid , Abuhussain Mohammed Awad , Kolsi Lioua
Published: October 2022

Building energy consumption in hot arid climates is dominated by air conditioning use. Therefore, using passive cooling methods could reduce this demand, improve resource efficiency, and decrease carbon emissions. In this study, an innovative configuration of a passive downdraught evaporative cooling (PDEC) tower is investigated numerically. The governing equations are solved using the finite element method (FEM), and the effects of inlet velocity (0.5 m·s−1 ≤ uin ≤ 3 m·s−1) and temperature (35 °C ≤ Tin ≤ 45 °C) on the fluid structure, temperature field, and relative humidity are studied for three cases related to the position of the air outlet. The flow is considered as turbulent, and the building walls and the tower are assumed to be thermally well insulated. The PDEC tower is equipped with two vertical isotropic saturated porous layers. The results revealed that the inlet velocity and temperature play an essential role in the quality of the indoor temperature. In fact, the temperature can be reduced by about 7 degrees, and the relative humidity can be enhanced by 9% for lower inlet velocities.
The ASR Post-Processor Performance Challenges of BackTranScription (BTS): Data-Centric and Model-Centric Approaches
Authors: Park Chanjun , Seo Jaehyung , Lee Seolhwa , Lee Chanhee , Lim Heuiseok
Published: October 2022

Training an automatic speech recognition (ASR) post-processor based on sequence-to-sequence (S2S) requires a parallel pair (e.g., speech recognition result and human post-edited sentence) to construct the dataset, which demands a great amount of human labor. BackTransScription (BTS) proposes a data-building method to mitigate the limitations of the existing S2S based ASR post-processors, which can automatically generate vast amounts of training datasets, reducing time and cost in data construction. Despite the emergence of this novel approach, the BTS-based ASR post-processor still has research challenges and is mostly untested in diverse approaches. In this study, we highlight these challenges through detailed experiments by analyzing the data-centric approach (i.e., controlling the amount of data without model alteration) and the model-centric approach (i.e., model modification). In other words, we attempt to point out problems with the current trend of research pursuing a model-centric approach and alert against ignoring the importance of the data. Our experiment results show that the data-centric approach outperformed the model-centric approach by +11.69, +17.64, and +19.02 in the F1-score, BLEU, and GLEU tests.
A Survey on Deep Transfer Learning and Beyond
Authors: Yu Fuchao , Xiu Xianchao , Li Yunhui
Published: October 2022

Deep transfer learning (DTL), which incorporates new ideas from deep neural networks into transfer learning (TL), has achieved excellent success in computer vision, text classification, behavior recognition, and natural language processing. As a branch of machine learning, DTL applies end-to-end learning to overcome the drawback of traditional machine learning that regards each dataset individually. Although some valuable and impressive general surveys exist on TL, special attention and recent advances in DTL are lacking. In this survey, we first review more than 50 representative approaches of DTL in the last decade and systematically summarize them into four categories. In particular, we further divide each category into subcategories according to models, functions, and operation objects. In addition, we discuss recent advances in TL in other fields and unsupervised TL. Finally, we provide some possible and exciting future research directions.
Multi-User Massive MIMO System with Adaptive Antenna Grouping for Beyond 5G Communication Network
Authors: Chopra Shakti Raj , Gupta Akhil , Tanwar Sudeep , Safirescu Calin Ovidiu , Mihaltan Traian Candin , Sharma Ravi
Published: October 2022

Error-correcting codes with limited errors and higher spectral efficiency are the main concern for wireless communications. In the current situation, research is increasing daily to satisfy the growing demand for users with improved QoS. Adaptive Antenna Grouping (AAG) with a multilevel space–time trellis coding scheme in the Multi-User Massive MIMO system is the better option to provide flexible data transfer speeds, encoding gains, and gain in diversity with improved spectral efficiency and low decoding complexity, including the power optimization by reduced SNR at the same Symbol Error Rate/Frame Error Rate (SER/FER). The prior aim of maintaining spectral efficiency is achieved by using Massive MIMO. This paper presents the AAG according to the channel state information in the Massive MIMO scenario. The impact of the proposed model on standard ITU-R M.2135 scenarios is also demonstrated in this paper.
A Study on the Dynamic Evolution and Regional Differences of Public Capital and Return to Capital in China
Authors: Wang Zhihong , Tian Wenjia
Published: October 2022

Public capital plays a key role in national economic production in developing countries, and it has become the focus of factor reform in China. This paper aims at studying the dynamic characteristics of China’s public capital and the return rate. To this end, this paper inventories the public capital stock from 1978 to 2017; constructs a two-sector model to derive the necessary condition for optimal investment related to the return rate, and empirically tests it; estimates the output shares of the two sectors and calculates the rate of return to capital; and illustrates the dynamic evolution of public capital and the return rate from the time and regional dimensions. The study shows that, first, public capital has grown significantly. The portion of public capital held in the form of state-holding is increasing year by year. However, the proportion of public capital to total capital has decreased from 81% to 35%. Second, the growth of public capital has effectively balanced the gap of capital between regions. Third, the rate of return to capital has been decreasing, with two sectors’ return rates having converged in 1981 and in 2010–2013. This paper measures the stock of public capital and its return rate, which is beneficial for formulating the policy of public capital in economic production and distribution, as well as exploring the path to achieving common prosperity.
Solutions for Multitime Reaction–Diffusion PDE
Authors: Ghiu Cristian , Udriste Constantin
Published: October 2022

A previous paper by our research group introduced the nonlinear multitime reaction–diffusion PDE (with oblique derivative) as a generalized version of the single-time model. This paper states and uses some hypotheses that allow the finding of some important explicit families of the exact solutions for multitime reaction–diffusion PDEs of any dimension that have a multitemporal directional derivative term. Some direct methods for determining the exact solutions of nonlinear PDEs from mathematical physics are presented. In the single-time case, our methods present many advantages in comparison with other known approaches. Particularly, we obtained classes of ODEs and classes of PDEs whose solutions generate solutions of the multitime reaction–diffusion PDE.
Multitask Emergency Logistics Planning under Multimodal Transportation
Authors: Liu Hongbin , Song Guopeng , Liu Tianyu , Guo Bo
Published: October 2022

Multitask emergency logistics planning is a complex optimization problem in practice. When a disaster occurs, relief materials or rescue teams should be dispatched to destinations as soon as possible. In a nutshell, the problem can be described as an optimization of multipoint-to-multipoint transportation delivery problem in a given multimodal traffic network. In this study, a multimodal traffic network is considered for emergency logistics transportation planning, and a mixed-integer programming (MIP) formulation is proposed to model the problem. In order to solve this model, we propose a two-layer solution method. The inner layer is to manage the single-task route recommendation, for which we develop a shortest-path algorithm with the multimodal traffic network. Here, the optimal substructure of the algorithm and its time complexity are presented. With the route of each task calculated by the single-task solver, a general optimization algorithm based on improved particle swarm optimization (PSO) is proposed at the outer layer to coordinate the execution of each task constrained by the limited transportation capacity, so as to derive solutions for multi-commodity emergency logistics planning. Extensive computational results show that the proposed method can find solutions of good quality in reasonable time. Meanwhile, through the sensitivity analysis of the algorithm, we find the appropriate parameters for general optimization algorithm to solve the problem proposed in this paper. The proposed approach is effective and practical for solving multitask emergency logistics planning problem under multimodal transportation, which can find a satisfactory solution in an acceptable time.
Machine Learning Models for Predicting Romanian Farmers’ Purchase of Crop Insurance
Authors: Mare Codruţa , Manaţe Daniela , Mureşan Gabriela-Mihaela , Dragoş Simona Laura , Dragoş Cristian Mihai , Purcel Alexandra-Anca
Published: October 2022

Considering the large size of the agricultural sector in Romania, increasing the crop insurance adoption rate and identifying the factors that drive adoption can present a real interest in the Romanian market. The main objective of this research was to identify the performance of machine learning (ML) models in predicting Romanian farmers’ purchase of crop insurance based on crop-level and farmer-level characteristics. The data set used contains 721 responses to a survey administered to Romanian farmers in September 2021, and includes both characteristics related to the crop as well as farmer-level socio-demographic attributes, perception about risk, perception about insurers and knowledge about agricultural insurance. Various ML algorithms have been implemented, and among the approaches developed, the Multi-Layer Perceptron Classifier (MLP) and the Linear Support Vector Classifier (SVC) outperform the other algorithms in terms of overall accuracy. Tree-based ensembles were used to identify the most prominent features, which included the farmer’s general perception of risk, their likelihood of engaging in risky behaviour, as well as their level of knowledge about crop insurance. The models implemented in this study could be a useful tool for insurers and policymakers for predicting potential crop insurance ownership.
Blockchain and Deep Learning-Based Fault Detection Framework for Electric Vehicles
Authors: Trivedi Mihir , Kakkar Riya , Gupta Rajesh , Agrawal Smita , Tanwar Sudeep , Niculescu Violeta-Carolina , Raboaca Maria Simona , Alqahtani Fayez , Saad Aldosary , Tolba Amr
Published: October 2022

The gradual transition from a traditional transportation system to an intelligent transportation system (ITS) has paved the way to preserve green environments in metro cities. Moreover, electric vehicles (EVs) seem to be beneficial choices for traveling purposes due to their low charging costs, low energy consumption, and reduced greenhouse gas emission. However, a single failure in an EV’s intrinsic components can worsen travel experiences due to poor charging infrastructure. As a result, we propose a deep learning and blockchain-based EV fault detection framework to identify various types of faults, such as air tire pressure, temperature, and battery faults in vehicles. Furthermore, we employed a 5G wireless network with an interplanetary file system (IPFS) protocol to execute the fault detection data transactions with high scalability and reliability for EVs. Initially, we utilized a convolutional neural network (CNN) and a long-short term memory (LSTM) model to deal with air tire pressure fault, anomaly detection for temperature fault, and battery fault detection for EVs to predict the presence of faulty data, which ensure safer journeys for users. Furthermore, the incorporated IPFS and blockchain network ensure highly secure, cost-efficient, and reliable EV fault detection. Finally, the performance evaluation for EV fault detection has been simulated, considering several performance metrics, such as accuracy, loss, and the state-of-health (SoH) prediction curve for various types of identified faults. The simulation results of EV fault detection have been estimated at an accuracy of 70% for air tire pressure fault, anomaly detection of the temperature fault, and battery fault detection, with R2 scores of 0.874 and 0.9375.
Automotive Sector Financial Performance Dynamic Model: Europe vs. Asia Case Study
Authors: Ionescu Romeo-Victor , Zlati Monica-Laura , Antohi Valentin-Marian , Dincă Marius-Sorin
Published: October 2022

The current geo-political context brings to light new challenges to the smooth functioning of the global automotive trade, both through the economic boycott of Russian units and the intensified transition to the green economy. The main objective of the research is to quantify the financial efficiency of the global automotive industry in order to determine a general dynamic performance model and quantify the impact of external regional factors on the performance of economic entities in the automotive sector. The current objectives of the study are identifying recent asset developments in the industry, the main performance models in the literature, designing a global financial performance model and other regional dynamic models, validation of these models and dissemination of the model results and proposals. The used methods are of an empirical nature, namely, the literature study, with the authors aiming to identify the main performance models promoted by specialists in the field. We use qualitative-analytical and forecasting methods for dynamic performance modelling, using information from the 2010–2021 financial reports of major car manufacturers. The results of the study highlight the need for performance in relation to the influence of regional factors and performance leaders by economic and financial chapters. The results are useful for both managers of economic entities and supra-regional decision makers in order to establish economic development strategies and policies in view of the transition to the green economy and in the current geopolitical context.
An Approximation Method to Compute Highly Oscillatory Singular Fredholm Integro-Differential Equations
Authors: SAIRA  , Ma Wen-Xiu
Published: October 2022

This paper appertains the presentation of a Clenshaw–Curtis rule to evaluate highly oscillatory Fredholm integro-differential equations (FIDEs) with Cauchy and weak singularities. To calculate the singular integral, the unknown function approximated by an interpolation polynomial is rewritten as a Taylor series expansion. A system of linear equations of FIDEs obtained by using equally spaced points as collocation points is solved to obtain the unknown function. The proposed method attains higher accuracy rates, which are proven by error analysis and some numerical examples as well.
An Unchanged Basis Function and Preserving Accuracy Crank–Nicolson Finite Element Reduced-Dimension Method for Symmetric Tempered Fractional Diffusion Equation
Authors: Yang Xiaoyong , Luo Zhendong
Published: October 2022

We herein mainly employ a proper orthogonal decomposition (POD) to study the reduced dimension of unknown solution coefficient vectors in the Crank–Nicolson finite element (FE) (CNFE) method for the symmetric tempered fractional diffusion equation so that we can build the reduced-dimension recursive CNFE (RDRCNFE) method. In this case, the RDRCNFE method keeps the same basic functions and accuracy as the CNFE method. Especially, we adopt the matrix analysis to discuss the stability and convergence of RDRCNFE solutions, resulting in the very laconic theoretical analysis. We also use some numerical simulations to confirm the correctness of theoretical results.
Deep Learning Cascaded Feature Selection Framework for Breast Cancer Classification: Hybrid CNN with Univariate-Based Approach
Authors: Samee Nagwan Abdel , Atteia Ghada , Meshoul Souham , Al-antari Mugahed A. , Kadah Yasser M.
Published: October 2022

With the help of machine learning, many of the problems that have plagued mammography in the past have been solved. Effective prediction models need many normal and tumor samples. For medical applications such as breast cancer diagnosis framework, it is difficult to gather labeled training data and construct effective learning frameworks. Transfer learning is an emerging strategy that has recently been used to tackle the scarcity of medical data by transferring pre-trained convolutional network knowledge into the medical domain. Despite the well reputation of the transfer learning based on the pre-trained Convolutional Neural Networks (CNN) for medical imaging, several hurdles still exist to achieve a prominent breast cancer classification performance. In this paper, we attempt to solve the Feature Dimensionality Curse (FDC) problem of the deep features that are derived from the transfer learning pre-trained CNNs. Such a problem is raised due to the high space dimensionality of the extracted deep features with respect to the small size of the available medical data samples. Therefore, a novel deep learning cascaded feature selection framework is proposed based on the pre-trained deep convolutional networks as well as the univariate-based paradigm. Deep learning models of AlexNet, VGG, and GoogleNet are randomly selected and used to extract the shallow and deep features from the INbreast mammograms, whereas the univariate strategy helps to overcome the dimensionality curse and multicollinearity issues for the extracted features. The optimized key features via the univariate approach are statistically significant (p-value ≤ 0.05) and have good capability to efficiently train the classification models. Using such optimal features, the proposed framework could achieve a promising evaluation performance in terms of 98.50% accuracy, 98.06% sensitivity, 98.99% specificity, and 98.98% precision. Such performance seems to be beneficial to develop a practical and reliable computer-aided diagnosis (CAD) framework for breast cancer classification.
A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market
Authors: Das Asit Kumar , Mishra Debahuti , Das Kaberi , Mohanty Arup Kumar , Mohammed Mazin Abed , Al-Waisy Alaa S. , Kadry Seifedine , Kim Jungeun
Published: October 2022

In the Forex market, trend trading, where trend traders identify trends and attempt to capture gains through the analysis of an asset’s momentum in a particular direction, is a great way to profit from market movement. When the price of currency is moving in one either of the direction such as; up or down, it is known as trends. This trend analysis helps traders and investors find low risk entry points or exit points until the trend reverses. In this paper, empirical trade and trend analysis results are suggested by two-phase experimentations. First, considering the blended learning paradigm and wide use of deep-learning methodologies, the variants of long-short-term-memory (LSTM) networks such as Vanilla-LSTM, Stacked-LSTM, Bidirectional-LSTM, CNN-LSTM, and Conv-LSTM are used to build effective investing trading systems for both short-term and long-term timeframes. Then, a deep network-based system used to obtain the trends (up trends and down trends) of the predicted closing price of the currency pairs is proposed based on the best fit predictive networks measured using a few performance measures and Friedman’s non-parametric tests. The observed trends are compared and validated with a few readily available technical indicators such as average directional index (ADX), rate of change (ROC), momentum, commodity channel index (CCI), and moving average convergence divergence (MACD). The predictive ability of the proposed strategy for trend analysis can be summarized as follows: (a) with respect to the previous day for short-term predictions, AUD:INR achieves 99.7265% and GBP:INR achieves 99.6582% for long-term predictions; (b) considering the trend analysis strategy with respect to the determinant day, AUD:INR achieves 98.2906% for short-term predictive days and USD:INR achieves an accuracy of trend forecasting with 96.0342%. The significant outcome of this article is the proposed trend forecasting methodology. An attempt has been made to provide an environment to understand the average, maximum, and minimum unit up and/or downs observed during trend forecasting. In turn, this deep learning-based strategy will help investors and traders to comprehend the entry and exit points of this financial market.
Group Decision Making for Product Innovation Based on PZB Model in Fuzzy Environment: A Case from New-Energy Storage Innovation Design
Authors: Shi Jiawei , Zhou Yan
Published: October 2022

According to the World Economic Forum, countries and regions should steer their energy systems toward cheaper, safer, and more sustainable energy sources, and move away from their reliance on traditional energy sources. With this trend, it is significant that new-energy battery enterprises should not only maintain their current installed product, but also attract more consumers. Due to the differences in customers, there are different requirements for the products. Thus, this paper chooses new-energy storage product innovation design as the object, and proposes a novel multiagent group decision-making method based on QFD and PZB models in a fuzzy environment. Firstly, extensively collected multiagent (consumer and designer) requirements are transformed into specific functions through an extended multiagent QFD with HFLTS, and the relationship coefficients are derived. Afterward, different design schemes for functional components are evaluated according to the concept of the PZB model. Then, the satisfaction degree interval is calculated for each partial design. On the basis of these indicators, a multiagent multi-objective optimization model is established. Afterward, solving he model through NSGA-II quickly generates the most suitable product innovation design scheme. Lastly, the feasibility and superiority of proposed method are illustrated through innovation design for a new-energy storage battery.
Sufficient Conditions for the Existence and Uniqueness of Minimizers for Variational Problems under Uncertainty
Authors: Verma Mansi , Chen Chuei Yee , Kılıçman Adem , Ibragimov Gafurjan , Lim Fong Peng
Published: October 2022

Fuzzy variational problems have received significant attention over the past decade due to a number of successful applications in fields such as optimal control theory and image segmentation. Current literature on fuzzy variational problems focuses on the necessary optimality conditions for finding the extrema, which have been studied under several differentiability conditions. In this study, we establish the sufficient conditions for the existence of minimizers for fuzzy variational problems under a weaker notion of convexity, namely preinvexity and Buckley–Feuring differentiability. We further discuss their application in a cost minimization problem.
Frictional Energy Dissipation in Partial Slip Contacts of Axisymmetric Power-Law Graded Elastic Solids under Oscillating Tangential Loads: Effect of the Geometry and the In-Depth Grading
Authors: Wilhayn Josefine , Heß Markus
Published: October 2022

Due to the rapid development of additive manufacturing, a growing number of components in mechanical engineering are made of functionally graded materials. Compared to conventional materials, they exhibit improved properties in terms of strength, thermal, wear or corrosion resistance. However, because of the varying material properties, especially the type of in-depth grading of Young’s modulus, the solution of contact problems including the frequently encountered tangential fretting becomes significantly more difficult. The present work is intended to contribute to this context. The partial-slip contact of axisymmetric, power-law graded elastic solids under classical loading by a constant normal force and an oscillating tangential force is investigated both numerically and analytically. For this purpose, a fictitious equivalent contact model in the mathematical space of the Abel transform is used since it simplifies the solution procedure considerably without being an approximation. For different axisymmetric shaped solids and various elastic inhomogeneities (types of in-depth grading), the hysteresis loops are numerically generated and the corresponding dissipated frictional energies per cycle are determined. Moreover, a closed-form analytical solution for the dissipated energy is derived, which is applicable for a breadth class of axisymmetric shapes and elastic inhomogeneities. The famous solution of Mindlin et al. emerges as a special case.
Research on the Priority of Service Quality Index for Online English Teaching during the COVID-19 Pandemic: Dual Perspective
Authors: Ma Yu-Yu , Chen Jwu-Jenq , Lin Chia-Liang
Published: October 2022

Online English education has become a very common way of educating and learning during the coronavirus pandemic. However, the weight analysis index for the service quality survey of the online English education industry remains a research gap during this period. Thus, this research implemented the analytic network process (ANP) to analyse the index, weight and ranking of online English teaching based on the service quality (SERVQUAL) questionnaire and compare the differences between the dual perspectives of service providers and consumers. Interestingly, this research found that the dimension of responsiveness was considered the most important by service providers. However, consumers deemed the dimension of assurance to be the most significant. Meanwhile, this study discovered that consumers paid more attention to reassurance and safety when they faced problems and transaction procedures during the coronavirus pandemic. In addition, this research found that dimensions utilised to evaluate the quality of online education service are similar whether in the COVID-19 epidemic or prior to the coronavirus pandemic. Thus, it has a certain reference value for evaluating the service quality of online English teaching through the use of dimensions and index weights in the SERVQUAL scale during the coronavirus pandemic. Finally, the findings of this research revealed weights of dimensions and indicators, thereby providing suggestions for maintaining good service quality within online English teaching during the COVID-19 pandemic.
The Shortest-Edge Duplication of Triangles
Authors: Padrón Miguel Ángel , Perdomo Francisco , Plaza Ángel , Suárez José Pablo
Published: October 2022

We introduce a new triangle transformation, the shortest-edge (SE) duplication, as a natural way of mesh derefinement suitable to those meshes obtained by iterative application of longest-edge bisection refinement. Metric properties of the SE duplication of a triangle in the region of normalised triangles endowed with the Poincare hyperbolic metric are studied. The self-improvement of this transformation is easily proven, as well as the minimum angle condition. We give a lower bound for the maximum of the smallest angles of the triangles produced by the iterative SE duplication α=π6. This bound does not depend on the shape of the initial triangle.
An Extended Weibull Regression for Censored Data: Application for COVID-19 in Campinas, Brazil
Authors: Rodrigues Gabriela M. , Ortega Edwin M. M. , Cordeiro Gauss M. , Vila Roberto
Published: October 2022

This work aims to study the factors that increase the risk of death of hospitalized patients diagnosed with COVID-19 through the odd log-logistic regression model for censored data with two systematic components, as well as provide new mathematical properties of this distribution. To achieve this, a dataset of individuals residing in the city of Campinas (Brazil) was used and simulations were performed to investigate the accuracy of the maximum likelihood estimators in the proposed regression model. The provided properties, such as stochastic representation, identifiability, and moments, among others, can help future research since they provide important information about the distribution structure. The simulation results revealed the consistency of the estimates for different censoring percentages and show that the empirical distribution of the modified deviance residuals converge to the standard normal distribution. The proposed model proved to be efficient in identifying the determinant variables for the survival of the individuals in this study, which can help to find more opportune treatments and medical interventions. Therefore, the new model can be considered an interesting alternative for future works that evaluate censored lifetimes.
An Optimized Discrete Dragonfly Algorithm Tackling the Low Exploitation Problem for Solving TSP
Authors: Emambocus Bibi Aamirah Shafaa , Jasser Muhammed Basheer , Amphawan Angela , Mohamed Ali Wagdy
Published: October 2022

Optimization problems are prevalent in almost all areas and hence optimization algorithms are crucial for a myriad of real-world applications. Deterministic optimization algorithms tend to be computationally costly and time-consuming. Hence, heuristic and metaheuristic algorithms are more favoured as they provide near-optimal solutions in an acceptable amount of time. Swarm intelligence algorithms are being increasingly used for optimization problems owing to their simplicity and good performance. The Dragonfly Algorithm (DA) is one which is inspired by the swarming behaviours of dragonflies, and it has been proven to have a superior performance than other algorithms in multiple applications. Hence, it is worth considering its application to the traveling salesman problem which is a predominant discrete optimization problem. The original DA is only suitable for solving continuous optimization problems and, although there is a binary version of the algorithm, it is not easily adapted for solving discrete optimization problems like TSP. We have previously proposed a discrete adapted DA algorithm suitable for TSP. However, it has low effectiveness, and it has not been used for large TSP problems. In this paper, we propose an optimized discrete adapted DA by using the steepest ascent hill climbing algorithm as a local search. The algorithm is applied to a TSP problem modelling a package delivery system in the Kuala Lumpur area and to benchmark TSP problems, and it is found to have a higher effectiveness than the discrete adapted DA and some other swarm intelligence algorithms. It also has a higher efficiency than the discrete adapted DA.
Recursive Convex Model for Optimal Power Flow Solution in Monopolar DC Networks
Authors: Montoya Oscar Danilo , Zishan Farhad , Giral-Ramírez Diego Armando
Published: October 2022

This paper presents a new optimal power flow (OPF) formulation for monopolar DC networks using a recursive convex representation. The hyperbolic relation between the voltages and power at each constant power terminal (generator or demand) is represented as a linear constraint for the demand nodes and generators. To reach the solution for the OPF problem a recursive evaluation of the model that determines the voltage variables at the iteration t+1 (vt+1) by using the information of the voltages at the iteration t (vt) is proposed. To finish the recursive solution process of the OPF problem via the convex relaxation, the difference between the voltage magnitudes in two consecutive iterations less than the predefined tolerance is considered as a stopping criterion. The numerical results in the 85-bus grid demonstrate that the proposed recursive convex model can solve the classical power flow problem in monopolar DC networks, and it also solves the OPF problem efficiently with a reduced convergence error when compared with semidefinite programming and combinatorial optimization methods. In addition, the proposed approach can deal with radial and meshed monopolar DC networks without modifications in its formulation. All the numerical implementations were in the MATLAB programming environment and the convex models were solved with the CVX and the Gurobi solver.
Mathematical Modelling of Diffusion Flows in Two-Phase Stratified Bodies with Randomly Disposed Layers of Stochastically Set Thickness
Authors: Chernukha Olha , Chuchvara Anastasiia , Bilushchak Yurii , Pukach Petro , Kryvinska Natalia
Published: October 2022

The work is dedicated to mathematical modelling of random diffusion flows of admixture particles in a two-phase stratified strip with stochastic disposition of phases and random thickness of inclusion-layers. The study of such models are especially important during the creation of composite layered materials, in the research of the transmission properties of filters, and in the prediction of the spread of pollutants in the environment. Within the model we consider one case of uniform distribution of coordinates of upper boundaries of the layers of which the body is made up and two more cases, i.e., of uniform and triangular distributions of the inclusion thickness. The initial-boundary value problems of diffusion are formulated for flux functions; the boundary conditions at one of the body’s surfaces are set for flux and, at the other boundary, the conditions are given for admixture concentration; the initial condition being concerned with zero and non-zero constant initial concentrations. An equivalent integro-differential equation is constructed. Its solution is found in terms of Neumann series. For the first time it was obtained calculation formulae for diffusion flux averaged over the ensemble of phase configurations and over the inclusion thickness. It allowed to investigate the dependence of averaged diffusion fluxes on the medium’s characteristics on the basis of the developed software. The simulation of averaged fluxes of admixture in multilayered Fe−Cu and αFe−Ni materials is made. Comparative analysis of solutions, depending on the stage of averaging procedure over thickness, is carried out. It is shown that for some values of parameters the stage of averaging procedure over thickness has almost no effect on the diffusion flow value.
Two Linearized Schemes for One-Dimensional Time and Space Fractional Differential Equations
Authors: Orlov Victor N. , Elsayed Asmaa M. , Mahmoud Elsayed I.
Published: October 2022

This paper investigates the solution to one-dimensional fractional differential equations with two types of fractional derivative operators of orders in the range of (1,2). Two linearized schemes of the numerical method are constructed. The considered FDEs are equivalently transformed by the Riemann–Liouville integral into their integro-partial differential problems to reduce the requirement for smoothness in time. The analysis of stability and convergence is rigorously discussed. Finally, numerical experiments are described, which confirm the obtained theoretical analysis.