bluuebunny/crossref_metadata_2025 huggingface dataset:
2-{9-(10-Bromoanthracenyl)}-1,3-dihydro-1H-[d]-1,3,2-benzodiazaborole
Authors: Sithebe Siphamandla
Published: January 2023

In this paper, a synthesis and characterization of a novel three coordinate 2-{9-(10-Bromoanthracenyl)}-1,3-dihydro-1H-[d]-1,3,2-benzodiazaborole from a cyclo-condensation reaction of o-phenylenediamine and 10-bromoanthracene-9-boronic acid is described. The desired product was obtained in 0.343 g (92% yields) with its structure characterized by 1H, 11B, 13C NMR, HRMS and FT-IR.
Fault Detection, Diagnostics, and Treatment in Automated Manufacturing Systems Using Internet of Things and Colored Petri Nets
Authors: Kaid Husam , Al-Ahmari Abdulrahman , N. Alqahtani Khaled
Published: January 2023

Internet of things (IoT) applications, which include environmental sensors and control of automated manufacturing systems (AMS), are growing at a rapid rate. In terms of hardware and software designs, communication protocols, and/or manufacturers, IoT devices can be extremely heterogeneous. Therefore, when these devices are interconnected to create a complicated system, it can be very difficult to detect and fix any failures. This paper proposes a new reliability design methodology using “colored resource-oriented Petri nets” (CROPNs) and IoT to identify significant reliability metrics in AMS, which can assist in accurate diagnosis, prognosis, and resulting automated repair to enhance the adaptability of IoT devices within complicated cyber-physical systems (CPSs). First, a CROPN is constructed to state “sufficient and necessary conditions” for the liveness of the CROPN under resource failures and deadlocks. Then, a “fault diagnosis and treatment” technique is presented, which combines the resulting network with IoT to guarantee the reliability of the CROPN. In addition, a GPenSIM tool is used to verify, validate, and analyze the reliability of the IoT-based CROPN. Comparing the results to those found in the literature shows that they are structurally simpler and more effective in solving the deadlock issue and modeling AMS reliability.
Grey Wolf Particle Swarm Optimized Pump–Motor Servo System Constant Speed Control Strategy
Authors: Lu Shengdong , Wang Hui , Zhao Guochao , Zhou Guoqiang
Published: January 2023

Aiming to solve the problems of poor dynamic response characteristics and the weak anti-jamming capability of the conventional proportional–integral–derivative (PID) controlled pump-motor servo system (PMSS) under the actual working environment, this study created a brand new hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithm to determine the best parameters of the PID controller for the PMSS speed control to make the PMSS achieve a constant speed control. We developed a GWOPSO-PID controller and compared it with a conventional PID controller, GWO-PID, PSO-PID, and GA-PID. In comparison to the other four control methods, the simulation and experimental results demonstrate that the designed GWOPSO-PID control had better dynamic response characteristics, with its rise times being reduced by 78.6%, 64.7%, 67.1%, and 41.5%, respectively. Additionally, the system under the GWOPSO-PID control exhibits a good stability and robustness even in the face of different load circumstances, with decreases in the re-equilibration times of 59.6%, 23.4%, 53.2%, and 41.9%, respectively, with a significantly improved immunity to disturbances.
Measuring Methods of Radius of Curvature and Tread Circle-Fitting Studies for Railway Wheel Profiles
Authors: Gao Chunfu , Bao Siyuan , Zhou Chongqiu , Sun Jianfeng , He Xinsheng
Published: January 2023

A railway wheel profile consists of short arcs with complex radii of curvature, and wheel wear leads to changes in the profile’s radius of curvature that ultimately affects the dynamic performance of the train. To track the evolution of in-service wheel profile curves, the radii of curvature of new foundry wheel profiles need to be measured. This study proposes a series of algorithms and calculation methods for measuring the radius of curvature of wheel profiles. Firstly, the curvature was estimated with the U-chord method, and the segment points were located. Secondly, the discrete derivative method and Two-Arcs Tangency Constrain (TATC) method were used to calculate the radius of curvature and the fitting circle radius, respectively. The experimental results of the three types of profiles showed that the wheel profile curves were precisely divided according to the estimated curvature method and that the maximum errors of the calculated results compared with standard values by the discrete derivative method and TATC method were 2.50% and 0.42%, respectively. Furthermore, the two measurement methods’ performances and repeated experiments were used to analyze the uncertainty.
An LWPR-Based Method for Intelligent Lower-Limb Prosthesis Control by Learning the Dynamic Model in Real Time
Authors: Liu Yi , An Honglei , Ma Hongxu , Wei Qing
Published: January 2023

A significant number of people in the world suffer from limb losses, while prosthesis is the hopeful way to help the amputees back to normal life. Recently, the most popular control method used in intelligent prosthesis is FSM-IC (finite state machine with impedance control), which requires a significant amount of manual parameter adjustments to achieve a good model compensation in a discrete way. Taking the lower-limb prosthesis as the research object, this paper applies an LWPR (locally weighted projection regression) model to learn the dynamic model of a prosthesis in real time in order to achieve a better model compensation in a continuous way and propose scientific experimental schemes to verify the control method. First, the basic control framework of lower-limb prosthesis is given. Then, the control law is derived on the basis of model building and LWPR’s addition. Finally, the proper experimental schemes are designed to carry out the control method effectively in a safe way. The experimental results show that the control law with the LWPR model can greatly improve the tracking performance during the swing phase and obtain rather good compliance during the stance phase. Moreover, the results also indicate that the LWPR model can approximate the dynamic model online. This method is hoped to be extended to more applications and fields.
Influence of Concentration of Thiol-Substituted Poly(dimethylsiloxane)s on the Properties, Phases, and Swelling Behaviors of Their Crosslinked Disulfides
Authors: Beaupre Danielle M. , Goroncy Alexander K. , Weiss Richard G.
Published: January 2023

A simple, efficient procedure has been employed to effect intra- and inter-chain crosslinking of two commercially available thiolated poly(dimethylsiloxane) copolymers (T-PDMS) with 4–6% or 13–17% of mercaptopropyl side-chains. The thiol functional groups were converted to disulfides (D-PDMS) in chloroform solutions of I2. Importantly, the conditions employed avoid over-oxidation to other types of sulfur-containing species, and the concentration of T-PDMS during the crosslinking reaction dictated the rheological properties and liquid or solid nature of the D-PDMS. The procedure for obtaining the crosslinked copolymers is simpler than other approaches in the literature used to crosslink polysiloxane backbones and to modulate their properties. By changing the concentration of T-PDMS during the treatment with I2, the degree of intra- and inter-chain crosslinking can be controlled (as assessed qualitatively by the solid or liquid nature of the products and their viscoelastic properties). For each of the T-PDMS materials, there is a concentration threshold, above which products are solids, and below which they are oils. Liquid and solid materials were characterized using 1H and 13C solution-state and 13C solid-state NMR spectroscopy, respectively. They indicate greater than 90% conversion of thiols to disulfides in the presence of excess I2. The rheological behavior of the liquid products, solvent swelling ability of solid products, and the thermal stability of the reactants and products are described. Furthermore, the solid products exhibit some of the highest swelling values reported in the literature for poly(dimethylsiloxane) (PDMS) materials. As assessed by thermal gravimetric analyses, the disulfide-crosslinked materials are more stable thermally than the corresponding thiols.
Possible Formation Mechanism of Lunar Hematite
Authors: Fu Yue , Wang Huizi , Zhang Jiang , Chen Jian , Shi Quanqi , Yue Chao , Lin Honglei , Guo Ruilong , Tian Anmin , Xiao Chao , Shang Wensai
Published: January 2023

Hematite, a ferric mineral with diagnostic features in the visible and infrared spectral range, has recently been discovered in the polar regions of the Moon by the Chandrayaan-1 Moon Mineralogy Mapper (M3). The oxygen involving the oxidization process producing lunar hematite is supposed to originate from the Earth’s upper atmosphere, and hematite with different ages may have preserved information on the oxygen evolution of the Earth’s atmosphere in the past billions of years. The discovery of lunar hematite may provide insight into the understanding of the oxidation products on the Moon and other airless bodies. In this work, we analyze hematite abundance distribution in the lunar polar regions, showing that the content of hematite on the lunar surface increases with latitude, and is positively correlated with surface water abundance. We suggest that the latitude dependence of hematite is derived from the latitude dependence of water, which indicates that water may play an essential role in the formation of hematite. The correlation between hematite and the optical maturity parameter (OMAT) was analyzed and a significant positive correlation was observed, which suggests that the hematite in the polar regions is the result of gradual and persistent oxidation reactions. In addition, based on the analysis of oxygen particles in the Earth wind, it was found that O+ and O2+ are much more abundant, suggesting that low-energy O+ or O2+ ions escaping from the upper atmosphere of the Earth may play a crucial role in the formation of hematite in the lunar polar regions.
A Trustworthy Healthcare Management Framework Using Amalgamation of AI and Blockchain Network
Authors: Jadav Dhairya , Jadav Nilesh Kumar , Gupta Rajesh , Tanwar Sudeep , Alfarraj Osama , Tolba Amr , Raboaca Maria Simona , Marina Verdes
Published: January 2023

Over the last few decades, the healthcare industry has continuously grown, with hundreds of thousands of patients obtaining treatment remotely using smart devices. Data security becomes a prime concern with such a massive increase in the number of patients. Numerous attacks on healthcare data have recently been identified that can put the patient’s identity at stake. For example, the private data of millions of patients have been published online, posing a severe risk to patients’ data privacy. However, with the advent of Industry 4.0, medical practitioners can digitally assess the patient’s condition and administer prompt prescriptions. However, wearable devices are also vulnerable to numerous security threats, such as session hijacking, data manipulation, and spoofing attacks. Attackers can tamper with the patient’s wearable device and relays the tampered data to the concerned doctor. This can put the patient’s life at high risk. Since blockchain is a transparent and immutable decentralized system, it can be utilized for securely storing patient’s wearable data. Artificial Intelligence (AI), on the other hand, utilizes different machine learning techniques to classify malicious data from an oncoming stream of patient’s wearable data. An amalgamation of these two technologies would make the possibility of tampering the patient’s data extremely difficult. To mitigate the aforementioned issues, this paper proposes a blockchain and AI-envisioned secure and trusted framework (HEART). Here, Long-Short Term Model (LSTM) is used to classify wearable devices as malicious or non-malicious. Then, we design a smart contract that allows only of those patients’ data having a wearable device to be classified as non-malicious to the public blockchain network. This information is then accessible to all involved in the patient’s care. We then evaluate the HEART’s performance considering various evaluation metrics such as accuracy, recall, precision, scalability, and network latency. On the training and testing sets, the model achieves accuracies of 93% and 92.92%, respectively.
Investigation of the Stress-Strain State of a Rectangular Plate after a Temperature Shock
Authors: Sedelnikov A. V. , Orlov D. I. , Serdakova V. V. , Nikolaeva A. S.
Published: January 2023

In this paper, the temperature shock phenomenon is considered. This phenomenon occurs during the operation of engineering structures on Earth and in outer space. A rectangular plate has been selected as a structural element exposed to temperature shock. It has a rigidly sealed edge and three free edges. A one-dimensional third initial boundary value problem of thermal conductivity was posed and solved to study the stress–strain state of the plate. Fourier’s law was used to solve this problem, taking into account the inertial term, since the temperature shock is a fairly fast-dynamic phenomenon. It was believed that all the thermophysical properties of the plate are constant and do not depend on its temperature. As a result, the temperature field of the plate was obtained after the temperature shock. This temperature field generates temperature stresses inside the plate, which lead to temperature deformations. To determine these deformations, the initial boundary value problem of thermoelasticity was posed and solved in this work. The Sophie Germain equation was used while solving this problem. To describe the plate, the theory of flexible plates was used, taking into account the stresses in the middle surface of the plate. Next, the accuracy of analytical solutions for the points displacement of a homogeneous plate subjected to a temperature shock was investigated. The temperature field was constructed using a numerical simulation. Functions of the displacement vector components were obtained using approximate analytical solutions. The accuracy of approximate analytical solutions for the components of the plate points deformation vector was estimated. The obtained results allow us to describe the stress–strain state of the plate after the temperature shock. The results of this work can be used in the design of engineering structures for both terrestrial and space purposes in terms of stability calculations and the implementation of deformation constraints.
An Aggregation-Based Algebraic Multigrid Method with Deflation Techniques and Modified Generic Factored Approximate Sparse Inverses
Authors: Natsiou Anastasia A. , Gravvanis George A. , Filelis-Papadopoulos Christos K. , Giannoutakis Konstantinos M.
Published: January 2023

In this paper, we examine deflation-based algebraic multigrid methods for solving large systems of linear equations. Aggregation of the unknown terms is applied for coarsening, while deflation techniques are proposed for improving the rate of convergence. More specifically, the V-cycle strategy is adopted, in which, at each iteration, the solution is computed by initially decomposing it utilizing two complementary subspaces. The approximate solution is formed by combining the solution obtained using multigrids and deflation. In order to improve performance and convergence behavior, the proposed scheme was coupled with the Modified Generic Factored Approximate Sparse Inverse preconditioner. Furthermore, a parallel version of the multigrid scheme is proposed for multicore parallel systems, improving the performance of the techniques. Finally, characteristic model problems are solved to demonstrate the applicability of the proposed schemes, while numerical results are given.
The Relative Distance Prediction of Transmembrane Protein Surface Residue Based on Improved Residual Networks
Authors: Chen Qiufen , Guo Yuanzhao , Jiang Jiuhong , Qu Jing , Zhang Li , Wang Han
Published: January 2023

(1) Background: Transmembrane proteins (TMPs) act as gateways connecting the intra- and extra-biomembrane environments, exchanging material and signals crossing the biofilm. Relevant evidence shows that corresponding interactions mostly happen on the TMPs’ surface. Therefore, knowledge of the relative distance among surface residues is critically helpful in discovering the potential local structural characters and setting the foundation for the protein’s interaction with other molecules. However, the prediction of fine-grained distances among residues with sequences remains challenging; (2) Methods: In this study, we proposed a deep-learning method called TMP-SurResD, which capitalized on the combination of the Residual Block (RB) and Squeeze-and-Excitation (SE) for simultaneously predicting the relative distance of functional surface residues based on sequences’ information; (3) Results: The comprehensive evaluation demonstrated that TMP-SurResD could successfully capture the relative distance between residues, with a Pearson Correlation Coefficient (PCC) of 0.7105 and 0.6999 on the validation and independent sets, respectively. In addition, TMP-SurResD outperformed other methods when applied to TMPs surface residue contact prediction, and the maximum Matthews Correlation Coefficient (MCC) reached 0.602 by setting a threshold to the predicted distance of 10; (4) Conclusions: TMP-SurResD can serve as a useful tool in supporting a sequence-based local structural feature construction and exploring the function and biological mechanisms of structure determination in TMPs, which can thus significantly facilitate the research direction of molecular drug action, target design, and disease treatment.
Investigation of the Effect of the Voltage Drop and Cable Length on the Success of Starting the Line-Start Permanent Magnet Motor in the Drive of a Centrifugal Pump Unit
Authors: Paramonov Aleksey , Oshurbekov Safarbek , Kazakbaev Vadim , Prakht Vladimir , Dmitrievskii Vladimir
Published: January 2023

The use of Line-Start Permanent Magnet Synchronous Motors (LSPMSM) improves the efficiency of conventional direct-on-line electric motor-driven fluid machinery such as pumps and fans. Such motors have increased efficiency compared to induction motors and do not have an excitation winding compared to classical synchronous motors with an excitation winding. However, LSPMSMs have difficulty in starting mechanisms with a high moment of inertia. This problem can be exacerbated by a reduced supply network voltage and a voltage drop on the cable. This article investigates the transients during the startup of an industrial centrifugal pump with a line-start permanent magnet synchronous motor. The simulation results showed that when the voltage on the motor terminals is reduced by 10%, the synchronization is delayed. The use of the cable also leads to a reduction in the voltage at the motor terminals in a steady state, but the time synchronization delay is more significant than that with a corresponding reduction in the supply voltage. The considered simulation example shows that the line-start permanent magnet synchronous motor has no problems with starting the pumping unit, even with a reduced supply voltage. The conclusions of this paper support a wider use of energy-efficient electric motors and can be used when selecting an electric motor to drive a centrifugal pump.
Public Debt, Governance, and Growth in Developing Countries: An Application of Quantile via Moments
Authors: Musa Kazi , Sohag Kazi , Said Jamaliah , Ghapar Farha , Ali Norli
Published: January 2023

Developing countries often encounter budget deficits by taking loans from internal and external sources. The effectiveness of public debt has been a long debate in the seminal and empirical literature. In this study, we investigate the effectiveness of public debt on economic growth, incorporating the role of governance in 44 developing countries. In doing so, we applied the Quantile Via Moments approach to analyze heterogeneous panel data ranging 1990–2000 considering the scale and location properties under different economic circumstances. Our results show that public debt impedes economic growth in all quantiles. Our empirical finding corroborates our proposition that in the presence of good governance, public debt promotes economic growth in the medium to higher quantiles. The empirical findings of this study confirm that governance is far more important in promoting economic growth.
Deep Successive Convex Approximation for Image Super-Resolution
Authors: Li Xiaohui , Wang Jinpeng , Liu Xinbo
Published: January 2023

Image super-resolution (SR), as one of the classic image processing issues, has attracted increasing attention from researchers. As a highly ill-conditioned, non-convex optimization issue, it is difficult for image SR to restore a high-resolution (HR) image from a given low-resolution (LR) instance. Recent researchers have tended to regard image SR as a regression task and to design an end-to-end convolutional neural network (CNN) to predict the pixels directly, which lacks inherent theoretical analysis and limits the effectiveness of the restoration. In this paper, we analyze image SR from an optimization perspective and develop a deep successive convex approximation network (SCANet) for generating HR images. Specifically, we divide non-convex optimization into several convex LASSO sub-problems and use CNN to adaptively learn the parameters. To boost network representation, we use residual feature aggregation (RFA) blocks and devise a spatial and channel attention (SACA) mechanism to improve the restoration capacity. The experimental results show that the proposed SCANet can restore HR images more effectively than other works. Specifically, SCANet achieves higher PSNR/SSIM results and generates more satisfying textures.
Effective Incomplete Multi-View Clustering via Low-Rank Graph Tensor Completion
Authors: Yu Jinshi , Duan Qi , Huang Haonan , He Shude , Zou Tao
Published: January 2023

In the past decade, multi-view clustering has received a lot of attention due to the popularity of multi-view data. However, not all samples can be observed from every view due to some unavoidable factors, resulting in the incomplete multi-view clustering (IMC) problem. Up until now, most efforts for the IMC problem have been made on the learning of consensus representations or graphs, while many missing views are ignored, making it impossible to capture the information hidden in the missing view. To overcome this drawback, we first analyzed the low-rank relationship existing inside each graph and among all graphs, and then propose a novel method for the IMC problem via low-rank graph tensor completion. Specifically, we first stack all similarity graphs into a third-order graph tensor and then exploit the low-rank relationship from each mode using the matrix nuclear norm. In this way, the connection hidden between the missing and available instances can be recovered. The consensus representation can be learned from all completed graphs via multi-view spectral clustering. To obtain the optimal multi-view clustering result, incomplete graph recovery and consensus representation learning are integrated into a joint framework for optimization. Extensive experimental results on several incomplete multi-view datasets demonstrate that the proposed method can obtain a better clustering performance in comparison with state-of-the-art incomplete multi-view clustering methods.
On Cauchy Problems of Caputo Fractional Differential Inclusion with an Application to Fractional Non-Smooth Systems
Authors: Yu Jimin , Zhao Zeming , Shao Yabin
Published: January 2023

In this innovative study, we investigate the properties of existence and uniqueness of solutions to initial value problem of Caputo fractional differential inclusion. In the study of existence problems, we considered the case of convex and non-convex multivalued maps. We obtained the existence results for both cases by means of the appropriate fixed point theorem. Furthermore, the uniqueness corresponding to both cases was also determined. Finally, we took a non-smooth system, the modified Murali–Lakshmanan–Chua (MLC) fractional-order circuit system, as an example to verify its existence and uniqueness conditions, and through several sets of simulation results, we discuss the implications.
Unification of Chowla’s Problem and Maillet–Demyanenko Determinants
Authors: Wang Nianliang , Chakraborty Kalyan , Kanemitsu Shigeru
Published: January 2023

Chowla’s (inverse) problem (CP) is to mean a proof of linear independence of cotangent-like values from non-vanishing of L(1,χ)=∑n=1∞χ(n)n. On the other hand, we refer to determinant expressions for the (relative) class number of a cyclotomic field as the Maillet–Demyanenko determinants (MD). Our aim is to develop the theory of discrete Fourier transforms (DFT) with parity and to unify Chowla’s problem and Maillet–Demyanenko determinants (CPMD) as different-looking expressions of the relative class number via the Dedekind determinant and the base change formula.
Quantum Computing in Insurance Capital Modelling
Authors: Tamturk Muhsin
Published: January 2023

This paper proposes a quantum computing approach for insurance capital modelling. Using an open-source software development kit, Qiskit, an algorithm for working on a superconducting type IBM quantum computer is developed and implemented to predict the capital of insurance companies in the classical surplus process. With the fundamental properties of quantum mechanics, Dirac notation and Feynman’s path calculation are shown. Furthermore, custom quantum insurance premium and claim gates are investigated in order to build a quantum circuit with respect to initial reserve, premium and claim amounts. Some numerical results are presented and discussed at the end of the paper.
Reduced Clustering Method Based on the Inversion Formula Density Estimation
Authors: Lukauskas Mantas , Ruzgas Tomas
Published: January 2023

Unsupervised learning is one type of machine learning with an exceptionally high number of applications in various fields. The most popular and best-known group of unsupervised machine learning methods is clustering methods. The main goal of clustering is to find hidden relationships between individual observations. There is great interest in different density estimation methods, especially when there are outliers in the data. Density estimation also can be applied to data clustering methods. This paper presents the extension to the clustering method based on the modified inversion formula density estimation to solve previous method limitations. This new method’s extension works within higher dimensions (d > 15) cases, which was the limitation of the previous method. More than 20 data sets are used in comparative data analysis to prove the effectiveness of the developed method improvement. The results showed that the new method extension positively affects the data clustering results. The new reduced clustering method, based on the modified inversion formula density estimation, outperforms popular data clustering methods on test data sets. In cases when the accuracy is not the best, the data clustering accuracy is close to the best models’ obtained accuracies. Lower dimensionality data were used to compare the standard clustering based on the inversion formula density estimation method with the extended method. The new modification method has better results than the standard method in all cases, which confirmed the hypothesis about the new method’s positive impact on clustering results.
Complex Dynamical Sampling Mechanism for the Random Pulse Circulation Model and Its Application
Authors: Tang Lin , Shi Kaibo , Yu Songke
Published: January 2023

The fast multi-pulse spectrum is a spectrum acquisition method that obtains an average pulse amplitude in a dynamic window, which improves the energy resolution by sharpening peaks in the acquired spectra, but produces the counting loss. Owing to the counting loss problem, a counting rate multiplication method based on uniform sampling, also called the pulse circulation method, is presented in this paper. Based on the theory of mathematical statistics and uniform sampling, this method adopted a dynamic sample pool to update the pulse amplitude sample in real time. Random numbers from the uniform distribution were sampled from the sample pool, and the sampled results were stored in the random pulse circulator so that the pulse amplitude information used for spectrum generation was uniformly expanded. In the experiment section, the obtained spectrum was analyzed to verify the multiplication effect of the pulse circulation method on the counting rate and the compensation effect of the fast multi-pulse spectrum algorithm on the counting rate loss. The results indicated that the characteristic peaks of each element in the X-ray spectrogram obtained by the pulse circulation method could realize counting rate multiplication uniformly, and the multiplication ratio of every element was approximately equal. This is of great significance for obtaining an accurate X-ray fluorescence spectrum.
High-Dimensional Consistencies of KOO Methods for the Selection of Variables in Multivariate Linear Regression Models with Covariance Structures
Authors: Fujikoshi Yasunori , Sakurai Tetsuro
Published: January 2023

In this paper, we consider the high-dimensional consistencies of KOO methods for selecting response variables in multivariate linear regression with covariance structures. Here, the covariance structures are considered as (1) independent covariance structure with the same variance, (2) independent covariance structure with different variances, and (3) uniform covariance structure. A sufficient condition for model selection consistency is obtained using a KOO method under a high-dimensional asymptotic framework, such that sample size n, the number p of response variables, and the number k of explanatory variables are large, as in p/n→c1∈(0,1) and k/n→c2∈[0,1), where c1+c2<1.
Fixed-Switching-Frequency Modulated Model Predictive Control for Islanded AC Microgrid Applications
Authors: Villalón Ariel , Muñoz Carlos , Muñoz Javier , Rivera Marco
Published: January 2023

In this paper, a fixed-switching-frequency modulated model predictive control (M2PC) is established for a two-level three-phase voltage source inverter (VSI) working in an islanded AC microgrid. These small-scale power systems are composed by two or more VSIs which interface DGs, controlling the voltage amplitude and frequency in the system, and simultaneously sharing the load active and reactive power. Generally, these operational characteristics are achieved using hierarchical linear control loops, but with challenging limitations such as slow transient reaction to disturbances and high proneness to be affected by parameter modifications. Model predictive control may solve these issues. Nevertheless, the most used and developed predictive control scheme, the finite-set model predictive control (FS-MPC), presents the drawback of having the harmonic spectrum spread over all the frequencies. This brings issues with coupling between the different hierarchical control levels of the whole microgrid system, and eventually, when designing the filters for main-grid connection. This paper aims to solve these issues by developing the fixed-switching-frequency M2PC working with higher-level control loops for operation in an islanded AC microgrid. These advantages are proved in an AC microgrid configuration where methodology for paralleling multiple M2PC-regulated VSIs is described, with rapid transient response, inherent stability, and fully decentralised operation of individual VSIs, achieving proper load power sharing, eliminating circular currents, and proper waveforms for output currents and capacitor voltages. All these achievements have been confirmed via simulation and experimental verification.
Spatial Autocorrelation of Global Stock Exchanges Using Functional Areal Spatial Principal Component Analysis
Authors: Khoo Tzung Hsuen , Pathmanathan Dharini , Dabo-Niang Sophie
Published: January 2023

This work focuses on functional data presenting spatial dependence. The spatial autocorrelation of stock exchange returns for 71 stock exchanges from 69 countries was investigated using the functional Moran’s I statistic, classical principal component analysis (PCA) and functional areal spatial principal component analysis (FASPCA). This work focuses on the period where the 2015–2016 global market sell-off occurred and proved the existence of spatial autocorrelation among the stock exchanges studied. The stock exchange return data were converted into functional data before performing the classical PCA and FASPCA. Results from the Monte Carlo test of the functional Moran’s I statistics show that the 2015–2016 global market sell-off had a great impact on the spatial autocorrelation of stock exchanges. Principal components from FASPCA show positive spatial autocorrelation in the stock exchanges. Regional clusters were formed before, after and during the 2015–2016 global market sell-off period. This work explored the existence of positive spatial autocorrelation in global stock exchanges and showed that FASPCA is a useful tool in exploring spatial dependency in complex spatial data.
Statistical Method Based Waveform Optimization in Collocated MIMO Radar Systems
Authors: Hassan Ahmad Kamal , Al-Saggaf Ubaid M. , Moinuddin Muhammad , Alshoubaki Mohamed K.
Published: January 2023

Multiple-input multiple-output (MIMO) radar has acquired considerable attention as it offers an additional degree of freedom which results in performance gains when contrasted with the regular single antenna element radar system. Waveform optimization in MIMO radar is essential as it can offer tremendous improvements in target detection which are quantified in terms of reductions in the symbol error rate and improvements in target detection probability. In this work, we foster a strategy for the optimization of transmitter and receiver waveform in a collocated MIMO radar by only considering the second order statistics, thus relaxing the information of the instantaneous target states. Our contributions are primarily two-fold. First, we find a closed-form expression of the outage probability of an unknown target under clutter environment. For this prospect, we model the signal-to-interference-plus-noise ratio in a canonical quadratic structure, and then utilize the modern residue theory approach to characterize the distribution function. Secondly, we propose constrained and unconstrained optimization problems for the reduction in outage probability using algorithmic techniques such as interior-point, sequential-quadratic programming, and the active-set method for the optimization of the transmitter and receiver waveform. We also provide simulated re-enactments to validate our hypothetical deductions.
Topological Structure and Existence of Solutions Set for q-Fractional Differential Inclusion in Banach Space
Authors: Rezaiguia Ali , Hassan Taher S.
Published: January 2023

In this work, we concentrate on the existence of the solutions set of the following problem cDqασ(t)∈F(t,σ(t),cDqασ(t)),t∈I=[0,T]σ0=σ0∈E, as well as its topological structure in Banach space E. By transforming the problem posed into a fixed point problem, we provide the necessary conditions for the existence and compactness of solutions set. Finally, we present an example as an illustration of main results.
Appearance-Based Gaze Estimation Method Using Static Transformer Temporal Differential Network
Authors: Li Yujie , Huang Longzhao , Chen Jiahui , Wang Xiwen , Tan Benying
Published: January 2023

Gaze behavior is important and non-invasive human–computer interaction information that plays an important role in many fields—including skills transfer, psychology, and human–computer interaction. Recently, improving the performance of appearance-based gaze estimation, using deep learning techniques, has attracted increasing attention: however, several key problems in these deep-learning-based gaze estimation methods remain. Firstly, the feature fusion stage is not fully considered: existing methods simply concatenate the different obtained features into one feature, without considering their internal relationship. Secondly, dynamic features can be difficult to learn, because of the unstable extraction process of ambiguously defined dynamic features. In this study, we propose a novel method to consider feature fusion and dynamic feature extraction problems. We propose the static transformer module (STM), which uses a multi-head self-attention mechanism to fuse fine-grained eye features and coarse-grained facial features. Additionally, we propose an innovative recurrent neural network (RNN) cell—that is, the temporal differential module (TDM)—which can be used to extract dynamic features. We integrated the STM and the TDM into the static transformer with a temporal differential network (STTDN). We evaluated the STTDN performance, using two publicly available datasets (MPIIFaceGaze and Eyediap), and demonstrated the effectiveness of the STM and the TDM. Our results show that the proposed STTDN outperformed state-of-the-art methods, including that of Eyediap (by 2.9%).
Optimal Decision of Dynamic Bed Allocation and Patient Admission with Buffer Wards during an Epidemic
Authors: Wang Chengliang , Yang Feifei , Li Quan-Lin
Published: January 2023

To effectively prevent patients from nosocomial cross-infection and secondary infections, buffer wards for screening infectious patients who cannot be detected due to the incubation period are established in public hospitals in addition to isolation wards and general wards. In this paper, we consider two control mechanisms for three types of wards and patients: one is the dynamic bed allocation to balance the resource utilization among isolation, buffer, and general wards; the other is to effectively control the admission of arriving patients according to the evolution process of the epidemic to reduce mortality for COVID-19, emergency, and elective patients. Taking the COVID-19 pandemic as an example, we first develop a mixed-integer programming (MIP) model to study the joint optimization problem for dynamic bed allocation and patient admission control. Then, we propose a biogeography-based optimization for dynamic bed and patient admission (BBO-DBPA) algorithm to obtain the optimal decision scheme. Furthermore, some numerical experiments are presented to discuss the optimal decision scheme and provide some sensitivity analysis. Finally, the performance of the proposed optimal policy is discussed in comparison with the other different benchmark policies. The results show that adopting the dynamic bed allocation and admission control policy could significantly reduce the total operating cost during an epidemic. The findings can give some decision support for hospital managers in avoiding nosocomial cross-infection, improving bed utilization, and overall patient survival during an epidemic.
Optimal Designs for Antoine’s Equation: Compound Criteria and Multi-Objective Designs via Genetic Algorithms
Authors: de la Calle-Arroyo Carlos , González-Fernández Miguel A. , Rodríguez-Aragón Licesio J.
Published: January 2023

Antoine’s Equation is commonly used to explain the relationship between vapour pressure and temperature for substances of industrial interest. This paper sets out a combined strategy to obtain optimal designs for the Antoine Equation for D- and I-optimisation criteria and different variance structures for the response. Optimal designs strongly depend not only on the criterion but also on the response’s variance, and their efficiency can be strongly affected by a lack of foresight in this selection. Our approach determines compound and multi-objective designs for both criteria and variance structures using a genetic algorithm. This strategy provides a backup for the experimenter providing high efficiencies under both assumptions and for both criteria. One of the conclusions of this work is that the differences produced by using the compound design strategy versus the multi-objective one are very small.
UVCS: Unit Virtual Coordinate System for UAV Intra-Swarm Routing in GPS-Denied Environment
Authors: Gaidamaka Yuliya , Samouylov Konstantin
Published: January 2023

Wireless ad hoc networks are the solution for providing network connectivity in challenging environments with a lack or absence of infrastructure. Data transmission in such networks typically adopts geographic routing protocols, which use geographic coordinates as addresses of network devices. However, geographic coordinates are not always obtainable, as traditional localization systems (GNSS, Wi-Fi, terrestrial infrastructure) might not be available due to signal loss. In this paper, we propose a method that assigns virtual coordinates to network nodes, which can be used as input for geographic routing protocols. The numerical results demonstrate the high topological similarity between the physical and the virtual network. Our method exhibits convergence advantages over conventional approaches and outperforms them in terms of the total number of discovered paths.
When Less Is More: Understanding the Adoption of a Minimalist Lifestyle Using the Theory of Planned Behavior
Authors: Druică Elena , Ianole-Călin Rodica , Puiu Andreea-Ionela
Published: January 2023

Minimalism is a promising approach that supports consumers’ shift towards sustainable behaviors, with the perks of increasing emotional well-being. To understand which socio-psychological factors and intrinsic values determine the adoption of a minimalist lifestyle, we employ an extended framework of the theory of planned behavior to investigate the drivers behind the adoption intention. We test, through a partial least squares path modelling analysis, a structural model that depicts: (i) the influences of value orientations (altruistic, bioshperic, and egoistic) on attitudes; and (ii) the influence of attitudes, subjective norms, and perceived behavioral control on intention. The results indicate positive effects for all examined relationships, with effect sizes highlighting that attitudes and altruistic values should be prioritized in practical interventions that support a sustainable behavior. Surprisingly, we find a positive effect also for egoistic values, suggestive of the need for future cross-cultural research on minimalism and sustainability in Central and Eastern Europe.
The Bitcoin Halving Cycle Volatility Dynamics and Safe Haven-Hedge Properties: A MSGARCH Approach
Authors: Chan Jireh Yi-Le , Phoong Seuk Wai , Phoong Seuk Yen , Cheng Wai Khuen , Chen Yen-Lin
Published: January 2023

This paper introduces a unique perspective towards Bitcoin safe haven and hedge properties through the Bitcoin halving cycle. The Bitcoin halving cycle suggests that Bitcoin price movement follows specific sequences, and Bitcoin price movement is independent of other assets. This has significant implications for Bitcoin properties, encompassing its risk profile, volatility dynamics, safe haven properties, and hedge properties. Bitcoin’s institutional and industrial adoption gained traction in 2021, while recent studies suggest that gold lost its safe haven properties against the S&P500 in 2021 amid signs of funds flowing out of gold into Bitcoin. Amid multiple forces at play (COVID-19, halving cycle, institutional adoption), the potential existence of regime changes should be considered when examining volatility dynamics. Therefore, the objective of this study is twofold. The first objective is to examine gold and Bitcoin safe haven and hedge properties against three US stock indices before and after the stock market selloff in March 2020. The second objective is to examine the potential regime changes and the symmetric properties of the Bitcoin volatility profile during the halving cycle. The Markov Switching GARCH model was used in this study to elucidate regime changes in the GARCH volatility dynamics of Bitcoin and its halving cycle. Results show that gold did not exhibit safe haven and hedge properties against three US stock indices after the COVID-19 outbreak, while Bitcoin did not exhibit safe haven or hedge properties against the US stock market indices before or after the COVID-19 pandemic market crash. Furthermore, this study also found that the regime changes are associated with low and high volatility periods rather than specific stages of a Bitcoin halving cycle and are asymmetric. Bitcoin may yet exhibit safe haven and hedge properties as, at the time of writing, these properties may manifest through sustained adoption growth.
Dynamic Analysis and FPGA Implementation of a New, Simple 5D Memristive Hyperchaotic Sprott-C System
Authors: Yu Fei , Zhang Wuxiong , Xiao Xiaoli , Yao Wei , Cai Shuo , Zhang Jin , Wang Chunhua , Li Yi
Published: January 2023

In this paper, we first present a simple seven-term 4D hyperchaotic system based on the classical Sprott-C 3D chaotic system. This novel system is inspired by the simple 4D hyperchaotic system based on Sprott-B proposed by A. T. Sheet (2022). We discuss the phenomenon of premature divergence brought about by the improper choice of coupling parameters in that paper and describe the basic properties of the new system with phase diagrams, Lyapunov exponential spectra and bifurcation diagrams. Then, we find that the dynamical behaviors of the system suffer from the limitation of the control parameters and cannot represent the process of motion in detail. To improve the system, we expand the dimensionality and add the control parameters and memristors. A 5D memristive hyperchaotic system with hidden attractors is proposed, and the basic dynamical properties of the system, such as its dissipation, equilibrium point, stability, Lyapunov exponential spectra and bifurcation diagram, are analyzed. Finally, the hardware circuits of the 4D Sprott-C system and the 5D memristive hyperchaotic system were realized by a field programmable gate array (FPGA) and verified by an experiment. The experimental results are consistent with the numerical simulation results obtained in MATLAB, which demonstrates the feasibility and potential of the system.
Many-Objectives Optimization: A Machine Learning Approach for Reducing the Number of Objectives
Authors: Gaspar-Cunha António , Costa Paulo , Monaco Francisco , Delbem Alexandre
Published: January 2023

Solving real-world multi-objective optimization problems using Multi-Objective Optimization Algorithms becomes difficult when the number of objectives is high since the types of algorithms generally used to solve these problems are based on the concept of non-dominance, which ceases to work as the number of objectives grows. This problem is known as the curse of dimensionality. Simultaneously, the existence of many objectives, a characteristic of practical optimization problems, makes choosing a solution to the problem very difficult. Different approaches are being used in the literature to reduce the number of objectives required for optimization. This work aims to propose a machine learning methodology, designated by FS-OPA, to tackle this problem. The proposed methodology was assessed using DTLZ benchmarks problems suggested in the literature and compared with similar algorithms, showing a good performance. In the end, the methodology was applied to a difficult real problem in polymer processing, showing its effectiveness. The algorithm proposed has some advantages when compared with a similar algorithm in the literature based on machine learning (NL-MVU-PCA), namely, the possibility for establishing variable–variable and objective–variable relations (not only objective–objective), and the elimination of the need to define/chose a kernel neither to optimize algorithm parameters. The collaboration with the DM(s) allows for the obtainment of explainable solutions.