bluuebunny/crossref_metadata_2025 huggingface dataset:
MBD-Based Machining Feature Recognition and Process Route Optimization
Authors: Ding Shuhui , Guo Zhongyuan , Wang Bin , Wang Haixia , Ma Fai
Published: October 2022

Machining feature recognition is considered the key connecting technique to the integration of Computer-Aided Design (CAD) and Computer-Aided Process Planning (CAPP), and decision-making of the part processing scheme and the optimization of process route can effectively improve the processing efficiency and reduce the cost of product machining cost. At present, for the recognition of machining features in CAD models, there is a lack of a systematic method to consider process information (such as tolerance and roughness) and an effective process route optimization method to plan part processing procedures. Here we represent a novel model processing feature recognition method, and, on the basis of feature processing plan decision, realize the optimization of the process route. On the basis of a building model Attributed Adjacency Graph (AAG) based on model geometry, topology, and process information, we propose an AAG decomposition and reconstruction method based on Decomposed Base Surface (DBS) and Joint Base Surface (JBS) as well as the recognition of model machining features through Attributed Adjacency Matrix-based (AAM) feature matching. The feature machining scheme decision method based on fuzzy comprehensive evaluation is adopted, and the decision is realized by calculating the comprehensive evaluation index. Finally, the Machining Element Directed Graph (MEDGraph) is established based on the constraint relationship between Machining Elements (MEs). The improved topological sorting algorithm lists the topological sequences of all MEs. The evaluation function is constructed with the processing cost or efficiency as the optimization objective to obtain the optimal process route. Our research provides a new method for model machining feature recognition and process route optimization. Applications of the proposed approach are provided to validate the method by case study.
Data Analysis for Trajectory Generation for a Robot Manipulator Using Data from a 2D Industrial Laser
Authors: Gomes Diogo , Alvarez Mariano , Brancalião Laiany , Carneiro Jorge , Gonçalves Gil , Costa Paulo , Gonçalves José , Pinto Vítor H.
Published: October 2022

Nowadays, the automation of factory floors is necessary for extensive manufacturing processes to meet the ever-increasing competitiveness of current markets. The technological advances applied to the digital platforms have led many businesses to automate their manufacturing processes, introducing robotic manipulators collaborating with human operators to achieve new productivity, manufacturing quality, and safety levels. However, regardless of the amount of optimization implemented, some quality problems may be introduced in production lines with many products being designed and produced. This project proposes a solution for feature extraction that can be applied to automatic shape- and position-detection using a 2-dimension (2D) industrial laser to extract 3-dimension (3D) data where the movement of the item adds the third dimension through the laser’s beam. The main goal is data acquisition and analysis. This analysis will later lead to the generation of trajectories for a robotic manipulator. The results of this application proved reliable given their small measurement error values of a maximum of 2 mm.
Two-Step Calcination-Method-Derived Al-Substituted W-Type SrYb Hexaferrites: Their Microstructural, Spectral, and Magnetic Properties
Authors: Yang Yujie
Published: October 2022

W-type hexaferrites were discovered in the 1950s and are of interest for their potential applications. In this context, many researchers have conducted studies on the partial substitution of Fe sites in order to modify their electric and magnetic properties. In this study, W-type SrYb hexaferrites using Al3+ as substitutes for Fe3+ sites with the nominal composition Sr0.85Yb0.15Zn1.5Co0.5AlxFe16−xO27 (0.00 ≤ x ≤ 1.25) were successfully synthesized via the two-step calcination method. The microstructures, spectral bands of characteristic functional groups, morphologies, and magnetic parameters of the prepared samples were characterized using XRD, FTIR, SEM, EDX, and VSM. The XRD results showed that, compared with the standard patterns for the W-type hexaferrite, the W-type SrYb hexaferrites with the Al content (x) of 0.00 ≤ x ≤ 1.25 were a single-W-type hexaferrite phase. SEM images showed the flakes and hexagonal grains of W-type hexaferrites with various Al content (x). The saturation magnetization (Ms) and magneton number (nB) decreased with Al content (x) from 0.00 to 1.25. The remanent magnetization (Mr) and coercivity (Hc) decreased with Al content (x) from 0.00 to 0.25. Additionally, when the Al content (x) ≥ 0.25, Mr and Hc increased with the increase in the Al content (x). The magnetic anisotropy field (Ha) and first anisotropy constant (K1) increased with the Al content (x) increasing from 0.00 to 1.25. Al-substituted W-type SrYb hexaferrites with soft magnetic behavior, high Ms, and lower Hc may be used as microwave-absorbing materials.
Exploring the Polarization of Light in Ferrofluids with Mueller Matrices
Authors: Tufaile Alberto , Tufaile Adriana Pedrosa Biscaia
Published: October 2022

We studied the polarization of light in a thin film of ferrofluid subjected to a magnetic field using the Mueller matrix formalism. By observing the results of some experiments, we relate the observed light patterns with Stokes vectors that can be operated by Mueller matrices, which represent the magnetic field applied to the sample. We observed that the changes in the dichroism of this system can be monitored along the sample, allowing for the visualization of magneto-optical effects mainly for linear polarized light, and the effects of circular polarized light are related to birefringence.
Entropic Statistics: Concept, Estimation, and Application in Machine Learning and Knowledge Extraction
Authors: Zhang Jialin
Published: October 2022

The demands for machine learning and knowledge extraction methods have been booming due to the unprecedented surge in data volume and data quality. Nevertheless, challenges arise amid the emerging data complexity as significant chunks of information and knowledge lie within the non-ordinal realm of data. To address the challenges, researchers developed considerable machine learning and knowledge extraction methods regarding various domain-specific challenges. To characterize and extract information from non-ordinal data, all the developed methods pointed to the subject of Information Theory, established following Shannon’s landmark paper in 1948. This article reviews recent developments in entropic statistics, including estimation of Shannon’s entropy and its functionals (such as mutual information and Kullback–Leibler divergence), concepts of entropic basis, generalized Shannon’s entropy (and its functionals), and their estimations and potential applications in machine learning and knowledge extraction. With the knowledge of recent development in entropic statistics, researchers can customize existing machine learning and knowledge extraction methods for better performance or develop new approaches to address emerging domain-specific challenges.
Prospective Neural Network Model for Seismic Precursory Signal Detection in Geomagnetic Field Records
Authors: Petrescu Laura , Moldovan Iren-Adelina
Published: October 2022

We designed a convolutional neural network application to detect seismic precursors in geomagnetic field records. Earthquakes are among the most destructive natural hazards on Earth, yet their short-term forecasting has not been achieved. Stress loading in dry rocks can generate electric currents that cause short-term changes to the geomagnetic field, yielding theoretically detectable pre-earthquake electromagnetic emissions. We propose a CNN model that scans windows of geomagnetic data streams and self-updates using nearby earthquakes as labels, under strict detectability criteria. We show how this model can be applied in three key seismotectonic settings, where geomagnetic observatories are optimally located in high-seismicity-rate epicentral areas. CNNs require large datasets to be able to accurately label seismic precursors, so we expect the model to improve as more data become available with time. At present, there is no synthetic data generator for this kind of application, so artificial data augmentation is not yet possible. However, this deep learning model serves to illustrate its potential usage in earthquake forecasting in a systematic and unbiased way. Our method can be prospectively applied to any kind of three-component dataset that may be physically connected to seismogenic processes at a given depth.
Prior Knowledge-Based Causal Inference Algorithms and Their Applications for China COVID-19 Analysis
Authors: Li Haifeng , Hai Mo , Tang Wenxun
Published: October 2022

Causal inference has become an important research direction in the field of computing. Traditional methods have mainly used Bayesian networks to discover the causal effects between variables. These methods have limitations, namely, on the one hand, the computing cost is expensive if one wants to achieve accurate results, i.e., exponential growth along with the number of variables. On the other hand, the accuracy is not good enough if one tries to reduce the computing cost. In this study, we use prior knowledge iteration or time series trend fitting between causal variables to resolve the limitations and discover bidirectional causal edges between the variables. Subsequently, we obtain real causal graphs, thus establishing a more accurate causal model for the evaluation and calculation of causal effects. We present two new algorithms, namely, the PC+ algorithm and the DCM algorithm. The PC+ algorithm is used to address the problem of the traditional PC algorithm, which needs to enumerate all Markov equivalence classes at a high computational cost or with immediate output of non-directional causal edges. In the PC+ algorithm, the causal tendency among some variables was analyzed via partial exhaustive analysis. By fixing the relatively certain causality as prior knowledge, a causal graph of higher accuracy is the final output at a low running cost. The DCM algorithm uses the d-separation strategy to improve the traditional CCM algorithm, which can only handle the pairwise fitting of variables, and thus identify the indirect causality as the direct one. By using the d-separation strategy, our DCM algorithm achieves higher accuracy while following the basic criteria of Bayesian networks. In this study, we evaluate the proposed algorithms based on the COVID-19 pandemic with experimental and theoretical analysis. The experimental results show that our improved algorithms are effective and efficient. Compared to the exponential cost of the PC algorithm, the time complexity of the PC+ algorithm is reduced to a linear level. Moreover, the accuracies of the PC+ algorithm and DCM algorithm are improved to different degrees; specifically, the accuracy of the PC+ algorithm reaches 91%, much higher than the 33% of the PC algorithm.
Deep Learning Approaches to Automatic Chronic Venous Disease Classification
Authors: Barulina Marina , Sanbaev Askhat , Okunkov Sergey , Ulitin Ivan , Okoneshnikov Ivan
Published: October 2022

Chronic venous disease (CVD) occurs in a substantial proportion of the world’s population. If the onset of CVD looks like a cosmetic defect, over time, it might be transformed into serious problems that will require surgical intervention. The aim of this work is to use deep learning (DL) methods for automatic classification of the stage of CVD for self-diagnosis of a patient by using the image of the patient’s legs. The images of legs with CVD required for DL algorithms were collected from open Internet resources using the developed algorithms. For image preprocessing, the binary classification problem “legs–no legs” was solved based on Resnet50 with accuracy of 0.998. The application of this filter made it possible to collect a dataset of 11,118 good-quality leg images with various stages of CVD. For classification of various stages of CVD according to the CEAP classification, the multi-classification problem was set and resolved by using four neural networks with completely different architectures: Resnet50 and transformers such as data-efficient image transformers (DeiT) and a custom vision transformer (vit-base-patch16-224 and vit-base-patch16-384). The model based on DeiT without any tuning showed better results than the model based on Resnet50 did (precision = 0.770 (DeiT) and 0.615 (Resnet50)). vit-base-patch16-384 showed the best results (precision = 0.79). To demonstrate the results of the work, a Telegram bot was developed, in which fully functioning DL algorithms were implemented. This bot allowed evaluating the condition of the patient’s legs with fairly good accuracy of CVD classification.
Teaching Probabilistic Graphical Models with OpenMarkov
Authors: Díez Francisco Javier , Arias Manuel , Pérez-Martín Jorge , Luque Manuel
Published: October 2022

OpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries. In this paper we explain how to use it as a pedagogical tool to teach the main concepts of Bayesian networks and influence diagrams, such as conditional dependence and independence, d-separation, Markov blankets, explaining away, optimal policies, expected utilities, etc., and some inference algorithms: logic sampling, likelihood weighting, and arc reversal. The facilities for learning Bayesian networks interactively can be used to illustrate step by step the performance of the two basic algorithms: search-and-score and PC.
Sentence-CROBI: A Simple Cross-Bi-Encoder-Based Neural Network Architecture for Paraphrase Identification
Authors: Ortiz-Barajas Jesus-German , Bel-Enguix Gemma , Gómez-Adorno Helena
Published: October 2022

Since the rise of Transformer networks and large language models, cross-encoders have become the dominant architecture for various Natural Language Processing tasks. When dealing with sentence pairs, they can exploit the relationships between those pairs. On the other hand, bi-encoders can obtain a vector given a single sentence and are used in tasks such as textual similarity or information retrieval due to their low computational cost; however, their performance is inferior to that of cross-encoders. In this paper, we present Sentence-CROBI, an architecture that combines cross-encoders and bi-encoders to obtain a global representation of sentence pairs. We evaluated the proposed architecture in the paraphrase identification task using the Microsoft Research Paraphrase Corpus, the Quora Question Pairs dataset, and the PAWS-Wiki dataset. Our model obtains competitive results compared with the state-of-the-art by using model ensembles and a simple model configuration. These results demonstrate that a simple architecture that combines sentence pair and single-sentence representations without using complex pre-training or fine-tuning algorithms is a viable alternative for sentence pair tasks.
A Hybrid Competitive Evolutionary Neural Network Optimization Algorithm for a Regression Problem in Chemical Engineering
Authors: Gavrilescu Marius , Floria Sabina-Adriana , Leon Florin , Curteanu Silvia
Published: October 2022

Neural networks have demonstrated their usefulness for solving complex regression problems in circumstances where alternative methods do not provide satisfactory results. Finding a good neural network model is a time-consuming task that involves searching through a complex multidimensional hyperparameter and weight space in order to find the values that provide optimal convergence. We propose a novel neural network optimizer that leverages the advantages of both an improved evolutionary competitive algorithm and gradient-based backpropagation. The method consists of a modified, hybrid variant of the Imperialist Competitive Algorithm (ICA). We analyze multiple strategies for initialization, assimilation, revolution, and competition, in order to find the combination of ICA steps that provides optimal convergence and enhance the algorithm by incorporating a backpropagation step in the ICA loop, which, together with a self-adaptive hyperparameter adjustment strategy, significantly improves on the original algorithm. The resulting hybrid method is used to optimize a neural network to solve a complex problem in the field of chemical engineering: the synthesis and swelling behavior of the semi- and interpenetrated multicomponent crosslinked structures of hydrogels, with the goal of predicting the yield in a crosslinked polymer and the swelling degree based on several reaction-related input parameters. We show that our approach has better performance than other biologically inspired optimization algorithms and generates regression models capable of making predictions that are better correlated with the desired outputs.
Some Congruences for the Coefficients of Rogers–Ramanujan Type Identities
Authors: Gupta Vasudha , Rana Meenakshi
Published: October 2022

We examine a few mathematical characteristics of Rogers–Ramanujan type identities as a follow-up work. Recently authors interpreted Rogers–Ramanujan type identities combinatorially using signed color partitions. In the present study, we discovered several congruences for the coefficients of powers of q that are in arithmetic progressions modulo powers of 2 and 3.
A Weighted Surrogate Model for Spatio-Temporal Dynamics with Multiple Time Spans: Applications for the Pollutant Concentration of the Bai River
Authors: Huan Yue , Tian Yubin , Wang Dianpeng
Published: October 2022

Simulations are often used to investigate the flow structures and system dynamics of complex natural phenomena and systems, which are significantly harder to obtain from experiments or theoretical analyses. Surrogate models are employed to mimic the results of simulations by reducing computational costs. In order to reduce the amount of computational time consumed, a novel framework for building efficient surrogate models is proposed in this work. The novelty lies in that the new framework runs simulations using the different simulation time spans for different inputs and builds a comprehensive surrogate model through the fusion of non-homogeneous spatio-temporal data by integrating the temporal and spatial correlations in parametric space. This differs from the existing works in the literature, which only consider the situation of spatio-temporal data with a consistent time span during simulations under different inputs. Some simulation studies and real data analysis concerning the pollution of the river in the Sichuan Province of China are used to demonstrate the superior performance of the proposed methods.
Variable-Speed Wind Turbines for Grid Frequency Support: A Systematic Literature Review
Authors: Bhowon Aksher , Abo-Al-Ez Khaled M. , Adonis Marco
Published: October 2022

As the finite nature of non-renewable energy resources is realised and climate change concerns become more prevalent, the need to shift to more sustainable forms of energy such as the adoption of renewable energy has seen an increase. More specifically, wind energy conversion systems (WECS) have become increasingly important as a contribution to grid frequency support, to maintain power at the nominal frequency and mitigate power failures or supply shortages against demand. Therefore, limiting deviations in frequency is imperative and, thus, the control methods of WECS are called to be investigated. The systematic literature review methodology was used and aimed at investigating these control methods used by WECS, more specifically variable-speed wind turbines (VSWT), in supporting grid frequency as well as the limitations of such methods. The paper identifies these to be de-loading, energy storage systems and emulated inertial response. Further classification of these is presented regarding these control methods, which are supported by literature within period of 2015–2022. The literature indicated a persistent interest in this field; however, a few limitations of VSWTS were identified. The emulated inertial response, specifically using a droop control-based frequency support scheme, was the primary means of providing frequency support. This systematic literature review may be limited by the number of papers selected for the study. Results and conclusions will not only be useful for WECS development but also in assisting with the security of the transmission grid’s frequency stability. Future work will focus on further studying the limitations of WECS providing frequency support.
Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network
Authors: Liu Sihang , Tremblais Benoît , Carre Phillippe , Zhou Nanrun , Wu Jianhua
Published: October 2022

A new image reconstruction (IR) algorithm from multiscale interest points in the discrete wavelet transform (DWT) domain was proposed based on a modified conditional generative adversarial network (CGAN). The proposed IR-DWT-CGAN model generally integrated a DWT module, an interest point extraction module, an inverse DWT module, and a CGAN. First, the image was transformed using the DWT to provide multi-resolution wavelet analysis. Then, the multiscale maxima points were treated as interest points and extracted in the DWT domain. The generator was a U-net structure to reconstruct the original image from a very coarse version of the image obtained from the inverse DWT of the interest points. The discriminator network was a fully convolutional network, which was used to distinguish the restored image from the real one. The experimental results on three public datasets showed that the proposed IR-DWT-CGAN model had an average increase of 2.9% in the mean structural similarity, an average decrease of 39.6% in the relative dimensionless global error in synthesis, and an average decrease of 48% in the root-mean-square error compared with several other state-of-the-art methods. Therefore, the proposed IR-DWT-CGAN model is feasible and effective for image reconstruction with multiscale interest points.
ConvFaceNeXt: Lightweight Networks for Face Recognition
Authors: Hoo Seng Chun , Ibrahim Haidi , Suandi Shahrel Azmin
Published: October 2022

The current lightweight face recognition models need improvement in terms of floating point operations (FLOPs), parameters, and model size. Motivated by ConvNeXt and MobileFaceNet, a family of lightweight face recognition models known as ConvFaceNeXt is introduced to overcome the shortcomings listed above. ConvFaceNeXt has three main parts, which are the stem, bottleneck, and embedding partitions. Unlike ConvNeXt, which applies the revamped inverted bottleneck dubbed the ConvNeXt block in a large ResNet-50 model, the ConvFaceNeXt family is designed as lightweight models. The enhanced ConvNeXt (ECN) block is proposed as the main building block for ConvFaceNeXt. The ECN block contributes significantly to lowering the FLOP count. In addition to the typical downsampling approach using convolution with a kernel size of three, a patchify strategy utilizing a kernel size of two is also implemented as an alternative for the ConvFaceNeXt family. The purpose of adopting the patchify strategy is to reduce the computational complexity further. Moreover, blocks with the same output dimension in the bottleneck partition are added together for better feature correlation. Based on the experimental results, the proposed ConvFaceNeXt model achieves competitive or even better results when compared with previous lightweight face recognition models, on top of a significantly lower FLOP count, parameters, and model size.
Quantitizing Qualitative Data from Semi-Structured Interviews: A Methodological Contribution in the Context of Public Policy Decision-Making
Authors: Banha Francisco , Flores Adão , Coelho Luís Serra
Published: October 2022

This paper presents a methodology involving the transformation and conversion of qualitative data gathered from open, semi-structured interviews into quantitative data—a process known as quantitizing. In the process of analysing the factors behind the different levels of success in the implementation of entrepreneurship education programs in two case studies, we came up with a challenge that became the research question for this paper: “How can we best extract, organize and communicate insights from a vast amount of qualitative information?” To answer it, we developed a methodology involving codifying, labelling, attributing a score and creating indicators/indexes and a matrix of influence. This allowed us to extract more insights than would be possible with a mere qualitative approach (e.g., we were able to rank 53 categories in two dimensions, which would have been impossible based only on the qualitative data, given the high number of pairwise comparisons: 1378). While any work in the social sciences will always keep some degree of subjectivity, by providing an example of quantitizing qualitative information from interviews, we hope to contribute to the expansion of the toolbox in mixed methods research, social sciences and mathematics and encourage further applications of this type of approach.
Modeling Synchronization Risk among Sustainable Exchange Trade Funds: A Statistical and Network Analysis Approach
Authors: Magner Nicolás , Lavín Jaime F. , Valle Mauricio A.
Published: October 2022

We evaluate the environment, society, and corporate governance rating (ESG rating) contribution from a new perspective; the highest ESG rating mitigates the impact of unexpected change in the implied volatility on the systemic stock market risk. For this purpose, we use exchange-traded funds (ETF) classified by their ESG rating into quartiles to estimate the synchronization as a proxy by systemic risk. Then, for each ETF quartile, we study the effect of the implied volatility over the synchronization. Our study is the first to model sustainable ETFs’ synchronization by combining econometric modeling and network methods, including 100 ETFs representing 80% of the global ETF market size between 2013 and 2021. First, we find that a higher ESG rating mitigates the effect of implied volatility over ETF synchronization. Surprisingly, the effect is the opposite in the case of ETFs with lower ESG ratings, where an increase in the volatility expectation increases the synchronization. Our study depicts the effect of sustainable ETFs on lessening the systemic risk due to returns synchronization, this being a novel contribution of this asset class. Finally, this paper offers extensions to deepen the contribution of other asset classes of ETFs in terms of their synchronization behavior and impact on risk management and financial performance.
Competitive Pricing for Multiple Market Segments Considering Consumers’ Willingness to Pay
Authors: Pérez Juan , López-Ospina Héctor
Published: October 2022

Defining prices and in which consumers’ segments to put the company’s efforts within competitive markets selling bundles is challenging. On the one hand, methodologies focused on competition are usually appropriate for analyzing market dynamics but not for helping decision makers in specific tasks regarding pricing. On the other hand, simplistic cost-oriented methods may fail to capture consumer behavior. We see these characteristics in such markets as telecommunications, retail, and financial service providers, among others. We propose a framework to support pricing decisions for products with multiple attributes in competitive markets, considering consumers’ willingness to pay and multiple segments. The proposed model is a nonlinear profit maximization probabilistic problem. We represent the demands for products and services through a multinomial logit model and then include consumers’ maximum willingness to pay through soft constraints within the demand function. Since the profit function is non-concave, we deal with the nonlinearity and the multiple optima to solve the model through an equivalent nonlinear model and a particle swarm optimization (PSO) heuristic. This setting allows us to find the prices that achieve equilibrium for the game among the firms that maximize their profits. Including the features shown, our approach enables decision makers to set prices optimally. Estimating the parameters needed to run our model requires more effort than traditional multinomial approaches. Nevertheless, we show that it is essential to include these aspects because the optimal prices are different from those obtained with more simplified models that do not have them. Additionally, there are well-established methodologies available to estimate those parameters. Both the determination of the first-order optimality conditions and the PSO implementation allow to find equilibria, quantify the effect of the consumers’ maximum willingness to pay, and assess the competition’s relevance. As complementary material, we analyze a case from a Chilean telecommunications company and show the results regarding price decisions and market share effects. According to our literature review, these aspects have not been handled and quantified jointly, as we do to support pricing.
Industrial Demand-Side Management by Means of Differential Evolution Considering Energy Price and Labour Cost
Authors: Niccolai Alessandro , Taje Gaia Gianna , Mosca Davide , Trombello Fabrizio , Ogliari Emanuele
Published: October 2022

In the context of the high dependency on fossil fuels, the strong efforts aiming to shift towards a more sustainable world are having significant economic and political impacts. The electricity market is now encouraging prosumers to consume their own production, and thus reduce grid exchanges. Self-consumption can be increased using storage systems or rescheduling the loads. This effort involves not only residential prosumers but also industrial ones. The rescheduling process is an optimisation problem that can be effectively solved with evolutionary algorithms (EAs). In this paper, a specific procedure for bridging demand-side management from the theoretical application to a practical industrial scenario was introduced. In particular, the toroidal correction was used in the differential evolution with the aim of preventing the local minima worsening the effectiveness of the proposed method. Moreover, to achieve reasonable solutions, two different cost contributions have been considered: the energy cost and the labour cost. The method was tested on real data from a historical textile factory, Ratti S.p.A. Due to the nature of the loads, the design variables were the starting time of the 30 shiftable loads. The application of this procedure achieves a reduction in the total cost of approximately 99,500 EUR/year.
A Quantum-Based Chameleon Swarm for Feature Selection
Authors: Elaziz Mohamed Abd , Ahmadein Mahmoud , Ataya Sabbah , Alsaleh Naser , Forestiero Agostino , Elsheikh Ammar H.
Published: October 2022

The Internet of Things is widely used, which results in the collection of enormous amounts of data with numerous redundant, irrelevant, and noisy features. In addition, many of these features need to be managed. Consequently, developing an effective feature selection (FS) strategy becomes a difficult goal. Many FS techniques, based on bioinspired metaheuristic methods, have been developed to tackle this problem. However, these methods still suffer from limitations; so, in this paper, we developed an alternative FS technique, based on integrating operators of the chameleon swarm algorithm (Cham) with the quantum-based optimization (QBO) technique. With the use of eighteen datasets from various real-world applications, we proposed that QCham is investigated and compared to well-known FS methods. The comparisons demonstrate the benefits of including a QBO operator in the Cham because the proposed QCham can efficiently and accurately detect the most crucial features. Whereas the QCham achieves nearly 92.6%, with CPU time(s) nearly 1.7 overall the tested datasets. This indicates the advantages of QCham among comparative algorithms and high efficiency of integrating the QBO with the operators of Cham algorithm that used to enhance the process of balancing between exploration and exploitation.
Non-Smooth Dynamic Behaviors as well as the Generation Mechanisms in a Modified Filippov-Type Chua’s Circuit with a Low-Frequency External Excitation
Authors: Han Hongfang , Li Shaolong , Bi Qinsheng
Published: October 2022

The main purpose of this paper is to study point-cycle type bistability as well as induced periodic bursting oscillations by taking a modified Filippov-type Chua’s circuit system with a low-frequency external excitation as an example. Two different kinds of bistable structures in the fast subsystem are obtained via conventional bifurcation analyses; meanwhile, nonconventional bifurcations are also employed to explain the nonsmooth structures in the bistability. In the following numerical investigations, dynamic evolutions of the full system are presented by regarding the excitation amplitude and frequency as analysis parameters. As a consequence, we can find that the classification method for periodic bursting oscillations in smooth systems is not completely applicable when nonconventional bifurcations such as the sliding bifurcations and persistence bifurcation are involved; in addition, it should be pointed out that the emergence of the bursting oscillation does not completely depend on bifurcations under the point-cycle bistable structure in this paper. It is predicted that there may be other unrevealed slow–fast transition mechanisms worthy of further study.
The Efficiency of National Innovation Systems in Post-Soviet Countries: DEA-Based Approach
Authors: Ratner Svetlana V. , Balashova Svetlana A. , Lychev Andrey V.
Published: October 2022

The efficiency of the national innovation system (NIS) is widely considered to be the most important factor of innovation-based economic growth. Using the wide spectrum of different metrics for measuring the efficiency of NIS, modern studies focus mainly on high-income or upper-middle-income countries, while the effectiveness of the NIS in post-Soviet countries has not been studied enough. The post-socialist transformation has led to different models of economic development in these countries, which can be divided into three groups: a group with developed European institutions, a group with a focus on the European path of development, and, finally, a group of countries with an economic model of “state capitalism”. These models formed the trajectory of innovative development. The main purpose of this study is to compare the performance of NIS in post-Soviet countries and to find out whether differences between development institutions can help explain differences in the performance of NIS. The study applies the DEA methodology and considers NISs as homogeneous economic agents, which transform the same types of inputs (knowledge gained using human and financial resources) into the same types of positive outcomes (innovative products and services). The results of a study conducted on data for the period 2011–2018 show that there is no evidence to support the hypothesis that EU institutions or the type of economic model of the country directly relate to the effectiveness of the NIS. The example of Kazakhstan shows that NIS can be effective, even with strong state intervention in the economy. Taken together, the results of the paper suggest that the structure of R&D expenditures by sources of funding and types of research plays an important role in the formation of effective NIS.
New Approach to Split Variational Inclusion Issues through a Three-Step Iterative Process
Authors: Bejenaru Andreea , Postolache Mihai
Published: October 2022

Split variational inclusions are revealed as a large class of problems that includes several other pre-existing split-type issues: split feasibility, split zeroes problems, split variational inequalities and so on. This makes them not only a rich direction of theoretical study but also one with important and varied practical applications: large dimensional linear systems, optimization, signal reconstruction, boundary value problems and others. In this paper, the existing algorithmic tools are complemented by a new procedure based on a three-step iterative process. The resulting approximating sequence is proved to be weakly convergent toward a solution. The operation mode of the new algorithm is tracked in connection with mixed optimization–feasibility and mixed linear–feasibility systems. Standard polynomiographic techniques are applied for a comparative visual analysis of the convergence behavior.
Further Results on Robust Output-Feedback Dissipative Control of Markovian Jump Fuzzy Systems with Model Uncertainties
Authors: Nguyen Thanh Binh , Song Hyoung-Kyu
Published: October 2022

This paper investigates an improved criterion to synthesize dissipative observer-based controllers for Markovian jump fuzzy systems under model uncertainties. Since fuzzy-basis functions include some immeasurable state variable or uncertain parameters, there are differences in the fuzzy-basis functions between controller and plant, which is a mismatched phenomenon. This work presents the first attempt for applying double-fuzzy summation-based Lyapunov functions for the observer-based control scheme of the Markov jump fuzzy system regarding the mismatched phenomenon. To be specific, the dissipative conditions are formulated in terms of uncertain parameterized bilinear matrix inequalities. Based on the improved relaxation techniques, a linear-matrix-inequality (LMI)-based algorithm is proposed in the framework of sequence linear programming matrix method. The obtained observer-based controller ensures that the closed-loop system is stochastically stable, and the dissipative performances produce less conservative results compared to preceding works via two numerical examples.
Exploring Information and Communication Technologies as Driving Forces in Hotel SMEs Performance: Influence of Corporate Social Responsibility
Authors: Santos-Jaén José Manuel , León-Gómez Ana , Ruiz-Palomo Daniel , García-Lopera Francisca , Valls Martínez María del Carmen
Published: October 2022

This paper aims to analyze whether Corporate Social Responsibility (CSR) can be considered as a mediator variable on the relationship among Information and Communication Technologies (ICT) adoption and SMEs performance in the hotel industry due to the lack of general consensus on the direction of this relationship. Furthermore, future expectations about the economic cycle might be a determining factor in business decision-making. Therefore, we also analyze whether these expectations influence hotel managers to adopt CSR strategies and, indeed, influences on the hotel’s performance. To this end, partial least squares structural equation modeling (PLS-SEM) is applied to a sample of 117 Spanish hotel SMEs. These results have practical implications in considering ICT adoption as a source of competitive advantage that will facilitate the implementation of CSR practices in hotels, thus improving firm performance.
Numerical Solution of Finite Kuramoto Model with Time-Dependent Coupling Strength: Addressing Synchronization Events of Nature
Authors: Khatiwada Dharma Raj
Published: October 2022

The synchronization of an ensemble of oscillators is a phenomenon present in systems of different fields, ranging from social to physical and biological systems. This phenomenon is often described mathematically by the Kuramoto model, which assumes oscillators of fixed natural frequencies connected by an equal and uniform coupling strength, with an analytical solution possible only for an infinite number of oscillators. However, most real-life synchronization systems consist of a finite number of oscillators and are often perturbed by external fields. This paper accommodates the perturbation using a time-dependent coupling strength K(t) in the form of a sinusoidal function and a step function using 32 oscillators that serve as a representative of finite oscillators. The temporal evolution of order parameter r(t) and phases θj(t), key indicators of synchronization, are compared between the uniform and time-dependent cases. The identical trends observed in the two cases give an important indication that the synchrony persists even under the influence of external factors, something very plausible in the context of real-life synchronization events. The occasional boosting of coupling strength is also enough to keep the assembly of oscillators in a synchronized state persistently.
A Review of Intelligent Connected Vehicle Cooperative Driving Development
Authors: Wang Biyao , Han Yi , Wang Siyu , Tian Di , Cai Mengjiao , Liu Ming , Wang Lujia
Published: October 2022

With the development and progress of information technology, especially V2X technology, the research focus of intelligent vehicles gradually shifted from single-vehicle control to multi-vehicle control, and the cooperative control system of intelligent connected vehicles became an important topic of development. In order to track the research progress of intelligent connected vehicle cooperative driving systems in recent years, this paper discusses the current research of intelligent connected vehicle cooperative driving systems with vehicles, infrastructure, and test sites, and analyzes the current development status, development trend, and development limitations of each object. Based on the analysis results of relevant references of the cooperative control algorithm, this paper expounds on vehicle collaborative queue control, vehicle collaborative decision making, and vehicle collaborative positioning. In the case of taking the infrastructure as the object, this paper expounds the communication security, communication delay, and communication optimization algorithm of the vehicle terminal and the road terminal of intelligent connected vehicles. In the case of taking the test site as the object, this paper expounds the development process and research status of the real vehicle road test platform, virtual test platform, test method, and evaluation mechanism, and analyzes the problems existing in the intelligent connected vehicle test environment. Finally, the future development trend and limitations of intelligent networked vehicle collaborative control system are discussed. This paper summarizes the intelligent connected car collaborative control system, and puts forward the next problems to be solved and the direction of further exploration. The research results can provide a reference for the cooperative driving of intelligent vehicles.
A Modified Inverse Iteration Method for Computing the Symmetric Tridiagonal Eigenvectors
Authors: Chu Wei , Zhao Yao , Yuan Hua
Published: October 2022

This paper presents a novel method for computing the symmetric tridiagonal eigenvectors, which is the modification of the widely used Inverse Iteration method. We construct the corresponding algorithm by a new one-step iteration method, a new reorthogonalization method with the general Q iteration and a significant modification when calculating severely clustered eigenvectors. The numerical results show that this method is competitive with other existing methods, especially when computing part eigenvectors or severely clustered ones.
Application of Uncertain AHP Method in Analyzing Travel Time Belief Reliability in Transportation Network
Authors: Yang Yi , Gu Jiaying , Huang Siyu , Wen Meilin , Qin Yong
Published: October 2022

Because predictions of transportation system reliability can provide useful information for intelligent transportation systems (ITS), evaluation of them might be viewed as a beneficial activity for reducing traffic congestion. This evaluation procedure could include some alternatives and criteria in a discrete decision space. To handle this evaluation process in an uncertain environment, a novel uncertain multi-criteria decision-making (MCDM) method is put forward in this paper. Considering the validity of uncertainty theory as a measure of epistemic uncertainty, we first introduce it into analytic hierarchy process (AHP) and provide the whole calculation procedure of the approach. The proposed approach is employed to evaluate regional travel time belief reliability in a case study. Additionally, a comparison is performed between the results of uncertain AHP and other MCDM methods to examine the efficiency of this method. These analyses show that uncertainty theory is particularly suited to be employed combination with the AHP method.
Asymptotic Behavior of Three Connected Stochastic Delay Neoclassical Growth Systems Using Spectral Technique
Authors: Ali Ishtiaq , Khan Sami Ullah
Published: October 2022

In this study, we consider a nonlinear system of three connected delay differential neoclassical growth models along with stochastic effect and additive white noise, which is influenced by stochastic perturbation. We derived the conditions for positive equilibria, stability and positive solutions of the stochastic system. It is observed that when a constant delay reaches a certain threshold for the steady state, the asymptotic stability is lost, and the Hopf bifurcation occurs. In the case of the finite domain, the three connected, delayed systems will not collapse to infinity but will be bounded ultimately. A Legendre spectral collocation method is used for the numerical simulations. Moreover, a comparison of a stochastic delayed system with a deterministic delayed system is also provided. Some numerical test problems are presented to illustrate the effectiveness of the theoretical results. Numerical results further illustrate the obtained stability regions and behavior of stable and unstable solutions of the proposed system.
Role of Glucose Risk Factors on Human Breast Cancer: A Nonlinear Dynamical Model Evaluation
Authors: Alblowy Abeer Hamdan , Maan Normah , Alharbi Sana Abdulkream
Published: October 2022

Understanding of the glucose risk factors-mediated mechanism in human breast cancer remains challenging. In this perception, for the first time, we proposed a complex nonlinear dynamical model that may provide a basic insight into the mechanism of breast cancer for the patient with existing glucose risk factors. The impact of glucose risk factors on the cancer cells’ population is evaluated using the formulated analytical model. The dynamical features of the cancer cells are described by a system of ordinary differential equations. Furthermore, the Routh–Hurwitz stability criterion is used to analyze the dynamical equilibrium of the cells’ population. The occurrence of zero bifurcation as well as two and three of the Jacobian matrix are obtained based on the sums of principal minors of order one. The glucose risk factors are exploited as the bifurcation parameters (acted as necessary and sufficient conditions) to detect the Hopf bifurcation. The presence of excess glucose in the body is found to affect negatively the breast cancer cells’ dynamics, stimulating chaos in the normal and tumor cells and thus drastically deteriorating the efficiency of the human immune system. The theoretical results are validated using the numerical simulations. It is concluded that the present findings may be beneficial for the future breast cancer therapeutic drug delivery and cure.