bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of the Effect of Rope Cutter on Water Flow behind Ship Propellers Based on CFD Analysis
Authors: Nyongesa Antony John , Pham Van Chien , Yoon Sung Hwan , Kwon Woo-Seok , Kim Jun-Soo , Ngo Duy Nam , Choi Jae-Hyuk , Sul Young-Yun , Lee Won-Ju
Published: April 2022

Small vessels operating in coastal waters are susceptible to propeller failure because of the entanglement of marine debris. Secondary accidents such as the injury of divers may also occur when removing entangling material. Rope cutters are devices used to prevent marine litter from entangling the propeller of small ships. However, installing rope cutters on propeller shafts might affect the working of the propeller. In this study, three-dimensional simulations were performed to investigate the effect of a rope cutter on flow characteristics behind the propeller. The Computational fluid dynamics (CFD) models were validated by particle image velocimetry (PIV) experiments performed in a rope cutter performance testing tank. The study results showed that the installation of a rope cutter on the propeller shaft led to an insignificant reduction in water flow velocity magnitude behind the propeller. Additionally, the effects of the rope cutter on the reductions of thrust (0.87%) and torque (0.76%) of the propeller were also negligible. However, it is very interesting to note that rope cutter installation resulted in a lower vortex formation, leading to a significant reduction in the turbulence intensity behind the propeller by 27.12%, 37.50%, and 47.29% at 100, 150, and 200 rpm propeller speed, respectively. Based on the study results, it can be concluded that rope cutters help to reduce propeller entanglements without significantly affecting the propeller’s working.
Extending NMR Quantum Computation Systems by Employing Compounds with Several Heavy Metals as Qubits
Authors: Lino Jéssica Boreli dos Reis , Gonçalves Mateus Aquino , Sauer Stephan P. A. , Ramalho Teodorico Castro
Published: April 2022

Nuclear magnetic resonance (NMR) is a spectroscopic method that can be applied to several areas. Currently, this technique is also being used as an experimental quantum simulator, where nuclear spins are employed as quantum bits or qubits. The present work is devoted to studying heavy metal complexes as possible candidates to act as qubit molecules. Nuclei such 113Cd, 199Hg, 125Te, and 77Se assembled with the most common employed nuclei in NMR-QIP implementations (1H, 13C, 19F, 29Si, and 31P) could potentially be used in heteronuclear systems for NMR-QIP implementations. Hence, aiming to contribute to the development of future scalable heteronuclear spin systems, we specially designed four complexes, based on the auspicious qubit systems proposed in our previous work, which will be explored by quantum chemical calculations of their NMR parameters and proposed as suitable qubit molecules. Chemical shifts and spin–spin coupling constants in four complexes were examined using the spin–orbit zeroth-order regular approximation (ZORA) at the density functional theory (DFT) level, as well as the relaxation parameters (T1 and T2). Examining the required spectral properties of NMR-QIP, all the designed complexes were found to be promising candidates for qubit molecules.
Yetter–Drinfeld Modules for Group-Cograded Hopf Quasigroups
Authors: Liu Huili , Yang Tao , Zhu Lingli
Published: April 2022

Let H be a crossed group-cograded Hopf quasigroup. We first introduce the notion of p-Yetter–Drinfeld quasimodule over H. If the antipode of H is bijective, we show that the category YDQ(H) of Yetter–Drinfeld quasimodules over H is a crossed category, and the subcategory YD(H) of Yetter–Drinfeld modules is a braided crossed category.
Vasicek Quantile and Mean Regression Models for Bounded Data: New Formulation, Mathematical Derivations, and Numerical Applications
Authors: Mazucheli Josmar , Alves Bruna , Korkmaz Mustafa Ç. , Leiva Víctor
Published: April 2022

The Vasicek distribution is a two-parameter probability model with bounded support on the open unit interval. This distribution allows for different and flexible shapes and plays an important role in many statistical applications, especially for modeling default rates in the field of finance. Although its probability density function resembles some well-known distributions, such as the beta and Kumaraswamy models, the Vasicek distribution has not been considered to analyze data on the unit interval, especially when we have, in addition to a response variable, one or more covariates. In this paper, we propose to estimate quantiles or means, conditional on covariates, assuming that the response variable is Vasicek distributed. Through appropriate link functions, two Vasicek regression models for data on the unit interval are formulated: one considers a quantile parameterization and another one its original parameterization. Monte Carlo simulations are provided to assess the statistical properties of the maximum likelihood estimators, as well as the coverage probability. An R package developed by the authors, named vasicekreg, makes available the results of the present investigation. Applications with two real data sets are conducted for illustrative purposes: in one of them, the unit Vasicek quantile regression outperforms the models based on the Johnson-SB, Kumaraswamy, unit-logistic, and unit-Weibull distributions, whereas in the second one, the unit Vasicek mean regression outperforms the fits obtained by the beta and simplex distributions. Our investigation suggests that unit Vasicek quantile and mean regressions can be of practical usage as alternatives to some well-known models for analyzing data on the unit interval.
Approaching the Pareto Front in a Biobjective Bus Route Design Problem Dealing with Routing Cost and Individuals’ Walking Distance by Using a Novel Evolutionary Algorithm
Authors: Calvete Herminia I. , Galé Carmen , Iranzo José A.
Published: April 2022

This paper addresses a biobjective bus routing problem that pays attention to both the routing cost and the total distance walked by the individuals to reach their assigned pickup point. These two objectives are conflicting. Generally, the less the individuals walk, the more the number of visited pickup points and so the more the routing cost. In addition, the problem deals with finding the set of pickup points visited among the set of potential locations, identifying the set of individuals assigned to each visited pickup point, and designing the bus routes. Taking into account the highly combinatorial nature of the problem, an evolutionary algorithm is proposed to approach the associated Pareto front. Its main novelties are twofold. The first is the way in which the chromosomes are encoded since they only provide information about the number of routes and the visited pickup points. The second novelty lies in the procedure to construct a feasible solution from the chromosome, which involves a heuristic and several local search procedures to improve both objective functions. Computational experiments are carried out to check the performance of the algorithm in terms of the quality of the Pareto front yielded.
Boundary Controller Design for a Class of Horizontal Belt Transmission System with Boundary Vibration Constraint
Authors: Sun Runhuan , Tang Li , Liu Yanjun
Published: April 2022

In this paper, the problem of transverse vibration suppression of a belt system moving in the horizontal direction is investigated. This system is characterized by the boundary vibration constraint and is affected by external disturbances. For it, we introduced a logarithmic function in the candidate term of the Lyapunov function and used a symbolic function in the controller to compensate for the effects of boundary vibration constraints and boundary disturbances, respectively. In order to better achieve the control objective, we designed a boundary control scheme. The state feedback boundary controller was designed using the boundary signals of the system when they can be available directly. Considering the presence of noise in the practical system, some system signals cannot be measured accurately. Therefore, a high-gain observer was introduced to estimate these signals, and an output feedback boundary controller was designed. Finally, the simulation example showed that both controllers guarantee effective suppression of the transverse vibration of the system without violating the boundary vibration constraints.
Can a Restaurant Benefit from Joining an Online Take-Out Platform?
Authors: Zhang Peng , Ju Sisi , Huang Hongfu
Published: April 2022

In this paper, we study a restaurant’s take-out model choice and the coordination of an online take-out supply chain. To this end, we first derive the restaurant’s optimal price and/or platform’s commission rate under the restaurant’s three possible take-out models: do not provide online take-out service (NTO model), provide take-out service by joining an online take-out platform (TOF model), or provide online take-out service by itself (TOH model). We investigate the restaurant’s optimal take-out model choice. We then derive the optimal decisions of price and the take-out model under centralization, and study the online take-out supply chain coordination problem. We find that, first, the restaurant may not always benefit from providing online take-out service. It will be beneficial only if the incremental demand generated by take-out service is high. Second, under the centralized supply chain, the TOF model is always better than the TOH model. Meanwhile, when the incremental demand is high, the restaurant should choose the TOF model; otherwise, the NTO model is better. Third, we find that the restaurant’s take-out price and model choice decisions under a decentralized supply chain are both inconsistent with that under the centralized supply chain. Last, we design a sales reward contract which can achieve the price and model choice coordination as well as win-win outcomes for all supply chain members.
Minimum Zagreb Eccentricity Indices of Two-Mode Network with Applications in Boiling Point and Benzenoid Hydrocarbons
Authors: Khabyah Ali Al , Zaman Shahid , Koam Ali N. A. , Ahmad Ali , Ullah Asad
Published: April 2022

A two-mode network is a type of network in which nodes can be divided into two sets in such a way that links can be established between different types of nodes. The relationship between two separate sets of entities can be modeled as a bipartite network. In computer networks data is transmitted in form of packets between source to destination. Such packet-switched networks rely on routing protocols to select the best path. Configurations of these protocols depends on the network acquirements; that is why one routing protocol might be efficient for one network and may be inefficient for a other. Because some protocols deal with hop-count (number of nodes in the path) while others deal with distance vector. This paper investigates the minimum transmission in two-mode networks. Based on some parameters, we obtained the minimum transmission between the class of all connected n-nodes in bipartite networks. These parameters are helpful to modify or change the path of a given network. Furthermore, by using least squares fit, we discussed some numerical results of the regression model of the boiling point in benzenoid hydrocarbons. The results show that the correlation of the boiling point in benzenoid hydrocarbons of the first Zagreb eccentricity index gives better result as compare to the correlation of second Zagreb eccentricity index. In case of a connected network, the first Zagreb eccentricity index ξ1(ℵ) is defined as the sum of the square of eccentricities of the nodes, and the second Zagreb eccentricity index ξ2(ℵ) is defined as the sum of the product of eccentricities of the adjacent nodes. This article deals with the minimum transmission with respect to ξi(ℵ), for i=1,2 among all n-node extremal bipartite networks with given matching number, diameter, node connectivity and link connectivity.
The Impact of Mobility on Shopping Preferences during the COVID-19 Pandemic: The Evidence from the Slovak Republic
Authors: Harantová Veronika , Kalašová Alica , Skřivánek Kubíková Simona , Mazanec Jaroslav , Jordová Radomíra
Published: April 2022

The COVID-19 global pandemic has affected normal human behaviour in day-to-day activities. As a result of various restrictions, people have significantly changed their shopping and mobility to limit the spread of the pandemic. This article aims to determine the association between consumers’ shopping preferences and the frequency of selected daily activities during and before the COVID-19 pandemic using correspondence analysis. The total sample consists of 407 respondents from Slovakia. The data are obtained from an online questionnaire divided into several sections such as socio-demographic factors, shopping preferences, and frequency of selected activities per week. The results show that there is an association between consumers’ preference for shopping in supermarkets and the frequency of family visits per week during the pandemic, among other factors. These findings follow up on previous studies on the consequences of changing mobility as a result of the global crisis.
The Analysis of Fintech Risks in China: Based on Fuzzy Models
Authors: Pi Tianlei , Hu Haoxuan , Lu Jingyi , Chen Xue
Published: April 2022

Fintech has not changed the nature and risk attributes of financial business. Its openness, interoperability, and other characteristics trigger the concealment, infectivity, universality and sudden characteristics of financial risk more obviously, and the potential systemic risk more complexly. This paper adopts fuzzy set analysis to conduct a comprehensive review of Fintech risk. It is found that the technology risk, moral hazard and legal risk, with a weight of up to 80%, are the dominant factors affecting the Fintech risk, while other prominent credit risk, market risk and operational risk in the traditional financial field account for a small proportion, but they still cannot be ignored. The research of this paper enriches the relevant research on the quantification of Fintech risk, and helps to strengthen risk prevention from the aspect of enhancing the safety of Fintech infrastructure, improving the legal system, sound the regulatory framework and strengthening industry self-discipline.
Transposition Regular AG-Groupoids and Their Decomposition Theorems
Authors: Du Yudan , Zhang Xiaohong , An Xiaogang
Published: April 2022

In this paper, we introduce transposition regularity into AG-groupoids, and a variety of transposition regular AG-groupoids (L1/R1/LR, L2/R2/L3/R3-groupoids) are obtained. Their properties and structures are discussed by their decomposition theorems: (1) L1/R1-transposition regular AG-groupoids are equivalent to each other, and they can be decomposed into the union of disjoint Abelian subgroups; (2) L1/R1-transposition regular AG-groupoids are LR-transposition regular AG-groupoids, and an example is given to illustrate that not every LR-transposition regular AG-groupoid is an L1/R1-transposition regular AG-groupoid; (3) an AG-groupoid is an L1/R1-transposition regular AG-groupoid if it is an LR-transposition regular AG-groupoid satisfying a certain condition; (4) strong L2/R3-transposition regular AG-groupoids are equivalent to each other, and they are union of disjoint Abelian subgroups; (5) strong L3/R2-transposition regular AG-groupoids are equivalent to each other and they can be decomposed into union of disjoint AG subgroups. Their relations are discussed. Finally, we introduce various transposition regular AG-groupoid semigroups and discuss the relationships among them and the commutative Clifford semigroup as well as the Abelian group.
Military Applications of Machine Learning: A Bibliometric Perspective
Authors: Galán José Javier , Carrasco Ramón Alberto , LaTorre Antonio
Published: April 2022

The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.
Refinement Mappings Related to Hermite-Hadamard Type Inequalities for GA-Convex Function
Authors: Latif Muhammad Amer , Kalsoom Humaira , Khan Zareen A. , Al-moneef Areej A.
Published: April 2022

In this paper, we present some new refinement mappings associated with the Hermite–Hadamard type inequalities that are constructed for GA-convex mappings. Our investigation of the mappings leads to the discovery of several interesting features as well as the development of some inequalities for the Hermite–Hadamard type inequalities, which have already been established for GA-convex functions, as well as refining the relationship between the middle, rightmost, and leftmost elements of the function. Some applications to special means of positive real numbers are also given.
Observer-Based PID Control Strategy for the Stabilization of Delayed High Order Systems with up to Three Unstable Poles
Authors: Cruz-Díaz César , del Muro-Cuéllar Basilio , Duchén-Sánchez Gonzalo , Márquez-Rubio Juan Francisco , Velasco-Villa Martín
Published: April 2022

In this paper, a new method to manage the stabilization and control problems of n-dimensional linear systems plus dead time, which includes one, two, or three unstable poles, is proposed. The control methodology proposed in this work is an Observer-based Proportional-Integral-Derivative (PID) strategy, where an observer and a PID controller are used to relocate the original unstable open-loop poles to stabilize the resultant closed-loop system. The observer provides an adequate estimation of the delayed-free variables and the PID uses the delay-free variables estimated by the proposed observer. Also, step-tracking is achieved in the overall control scheme. Necessary and sufficient conditions are presented to ensure closed-loop stability based on the open loop parameters of the system. The observer-based PID strategy considers five to seven constant parameters to obtain a stable closed-loop system. A general procedure to implement the proposed control strategy is presented and its performance is evaluated by means of numerical simulations.
The Existence and Multiplicity of Homoclinic Solutions for a Fractional Discrete p−Laplacian Equation
Authors: Wu Yong , Tahar Bouali , Rafik Guefaifia , Rahmoune Abita , Yang Libo
Published: April 2022

In this study, we investigate the existence and multiplicity of solutions for a fractional discrete p−Laplacian equation on Z. Under suitable hypotheses on the potential function V and the nonlinearity f, with the aid of Ekeland’s variational principle, via mountain pass lemma, we obtain that this equation exists at least two nonnegative and nontrivial homoclinic solutions when the real parameter λ>0 is large enough.
Analytic Approximation for American Straddle Options
Authors: Goard Joanna , AbaOud Mohammed
Published: April 2022

This paper looks at adapting a recent approach found in the literature for pricing short-term American options to price American straddle options with two free boundaries. We provide a series solution in which explicit formulas for the coefficients are given. Hence, no complicated, recursive systems or nonlinear integral equations need to be solved, and the method efficiently provides fast solutions. We also compare the method with a numerical method and find that it gives very accurate prices not only for the option value, but also for the critical stock prices.
DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph
Authors: Fang Hui , Chen Chongcheng , Long Yunfei , Xu Ge , Xiao Yongqiang
Published: April 2022

In the era of information explosion, it is difficult for people to obtain their desired information effectively. In tourism, a travel recommender system based on big travel data has been developing rapidly over the last decade. However, most work focuses on click logs, visit history, or ratings, and dynamic prediction is absent. As a result, there are significant gaps in both dataset and recommender models. To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system. We provided two linked data sets, namely, interaction sequence and dialogue data sets. The usage of the former data set was done to fully explore the static preference characteristics of users based on it, while the latter identified the dynamics changes in user preference from it. Then, we proposed and evaluated BERT-based baseline models for the travel conversational recommender system and compared them with several representative non-conversational and conversational recommender system models. Extensive experiments demonstrated the effectiveness and robustness of our approach regarding conversational recommendation tasks. Our work can extend the scope of the travel conversational recommender system and our annotated data can also facilitate related research.
Application of Logistic Regression Model to Assess the Impact of Smartwatch on Improving Road Traffic Safety: A Driving Simulator Study
Authors: Pešić Duško , Pešić Dalibor , Trifunović Aleksandar , Čičević Svetlana
Published: April 2022

Speeding is one of the most relevant risk behaviours for serious and fatal road traffic accidents, particularly among young drivers, being the cause of approximately every third road traffic accident. Due to this background, many road traffic safety campaigns are aimed at reducing speeding among young drivers. However, the effects of campaigns aimed at complying with speed limits for young drivers have significantly fewer effects than other campaigns. For these reasons, an experimental study was conducted to examine how young drivers react to the speeding campaign, which was shown to them on a smartwatch while driving in a driving simulator. Speeding results were compared for three scenarios: no campaign, a billboard campaign and a smartwatch campaign. The experiment involved 102 participants with an average age of 21 years. The results showed that participants were six times more likely to comply with the speed limit if a campaign was shown on a smartwatch than when shown on billboards.
How Resilient Is the U.S. Economy to Foreign Disturbances?
Authors: Oyekola Olayinka
Published: April 2022

We assess the relative importance of domestic and foreign disturbances in explaining fluctuations in key macroeconomic variables and find that both types of shocks are equally important. We reach this conclusion within a constructed two-sector open economy DSGE model context, where we isolate the relative contributions of each group of disturbances to post-WWII U.S. business cycles. Our approach is to apply the indirect inference method to test the model’s fit against a four-equation VAR(1) of output, real exchange rate, energy use, and consumption. Our main result is that foreign disturbances are pivotal to driving movements in these home variables; accounting for 38% of the variability in aggregate output, 73% of the variation in the real exchange rate, 45% of the variance of energy use, and 84% of the volatility of consumption. Further, foreign disturbances are also identified to be crucial for some other home macroeconomic variables, explaining larger fractions in changes to investment, labour hours, and real interest rate. However, the U.S. economy appears to be resilient to foreign disturbances with respect to certain macroeconomic variables; in particular, exports, imports, real wages, and domestic absorption.
Confidence-Interval-Based Fuzzy Testing for the Lifetime Performance Index of Electronic Product
Authors: Yu Chun-Min , Chen Kuen-Suan , Hsu Ting-Hsin
Published: April 2022

When the lifetime of an electronic component does not reach the required level, it can be enhanced by means of the paralleling current sharing backup system or the redundant backup system. The lifetime of the redundant backup system is the sum of lifetimes of all electronic components, which is the maximum of all the electronic components’ lifetimes, compared with the lifetime of the parallel current sharing backup system. For the purpose of enhancing products’ reliability, electronic goods are usually designed with spare electronic components. If it is assumed that there are m−1 redundant backup components for each electronic product, then the lifetime of the electronic product will be distributed as a Gamma distribution with two parameters—m and λ, where λ is the mean for each lifetime of each electronic component. According to numerous studies, the sample size is not large, as it takes a long time to test the lifetime of an electronic product, and enterprises consider cost and timeliness. This paper concerns the performance index of the lifetime of the electronic product. Therefore, based on the confidence interval, this paper aims to develop a fuzzy testing model. As this model can integrate past data and expert experience, the testing accuracy can be retained despite small-sized samples. In fact, through adopting the testing model proposed by this paper, companies can make precise and intelligent decisions instantly with the use of small-sized samples to grasp the opportunities for improvement.
A New Intelligent Dynamic Control Method for a Class of Stochastic Nonlinear Systems
Authors: Huang Haifeng , Shirkhani Mohammadamin , Tavoosi Jafar , Mahmoud Omar
Published: April 2022

This paper presents a new method for a comprehensive stabilization and backstepping control system design for a class of stochastic nonlinear systems. These types of systems are so abundant in practice that the control system designer must assume that random noise with a definite probability distribution affects the dynamics and observations of state variables. Stochastic control is intended to determine the time course of control variables so that the control target is achievable even with minimal cost. Since the mathematical equations of stochastic nonlinear systems are not always constant, not every model-based controller can be accurate. Therefore, in this paper, a type-3 fuzzy neural network is used to estimate the parameters of the backstepping control method. In the simulation, the proposed method is compared with the Type-1 fuzzy and RBFN methods. Results clearly show that the proposed method has a very good performance and can be used for any system in this class.
A Fuzzy Improvement Testing Model of Bank APP Performance
Authors: Chen Tian , Hsu Ting-Hsin , Chen Kuen-Suan , Yang Chun-Ming
Published: April 2022

Numerous studies have pointed out that the issue of global warming is getting increasingly more serious. Therefore, the concepts of circular economy (CE) and sharing economy have been more and more valued by enterprises and governments. With the gradual popularization and maturity of the Internet of Things (IoT), various smart APP platforms have sprung up rapidly. For example, the fuzzy evaluation model of bank APP performance was proposed in such an environment, aiming to improve the APP service performance by means of evaluating, analyzing, improving, and enhancing customers’ satisfaction with their use of APPs, and increasing the number of users of APPs. Since the follow-up of the article did not mention the improved testing model used to verify the improvement effect, this paper then proposed a fuzzy two-tailed testing model with two indices before and after the improvement based on the confidence interval to verify whether the improvement has had a significant effect. This complete bank APP fuzzy performance evaluation, analysis, and improvement model measured the bank APP operation performance using customer time intervals, so the data collection time was short. Not only can it meet enterprises’ need for rapid response and grasp the opportunity for improvement to achieve the effect of energy-saving and carbon reduction, but it also can satisfy enterprises’ requirement to pursue fast and accurate decision-making. Furthermore, the fuzzy two-tailed test proposed by this paper was based on the confidence interval, which can reduce the risk of misjudgment caused by sampling error. Plenty of studies have indicated that the designs based on confidence intervals can integrate expert experience and past data so that the accuracy of testing can be maintained in the case of small-sized samples.
Chaos Embed Marine Predator (CMPA) Algorithm for Feature Selection
Authors: Alrasheedi Adel Fahad , Alnowibet Khalid Abdulaziz , Saxena Akash , Sallam Karam M. , Mohamed Ali Wagdy
Published: April 2022

Data mining applications are growing with the availability of large data; sometimes, handling large data is also a typical task. Segregation of the data for extracting useful information is inevitable for designing modern technologies. Considering this fact, the work proposes a chaos embed marine predator algorithm (CMPA) for feature selection. The optimization routine is designed with the aim of maximizing the classification accuracy with the optimal number of features selected. The well-known benchmark data sets have been chosen for validating the performance of the proposed algorithm. A comparative analysis of the performance with some well-known algorithms advocates the applicability of the proposed algorithm. Further, the analysis has been extended to some of the well-known chaotic algorithms; first, the binary versions of these algorithms are developed and then the comparative analysis of the performance has been conducted on the basis of mean features selected, classification accuracy obtained and fitness function values. Statistical significance tests have also been conducted to establish the significance of the proposed algorithm.
On the Best Ulam Constant of the Linear Differential Operator with Constant Coefficients
Authors: Baias Alina Ramona , Popa Dorian
Published: April 2022

The linear differential operator with constant coefficients D(y)=y(n)+a1y(n−1)+…+any,y∈Cn(R,X) acting in a Banach space X is Ulam stable if and only if its characteristic equation has no roots on the imaginary axis. We prove that if the characteristic equation of D has distinct roots rk satisfying Rerk>0,1≤k≤n, then the best Ulam constant of D is KD=1|V|∫0∞|∑k=1n(−1)kVke−rkx|dx, where V=V(r1,r2,…,rn) and Vk=V(r1,…,rk−1,rk+1,…,rn),1≤k≤n, are Vandermonde determinants.
Non-Markovian Inverse Hawkes Processes
Authors: Seol Youngsoo
Published: April 2022

Hawkes processes are a class of self-exciting point processes with a clustering effect whose jump rate is determined by its past history. They are generally regarded as continuous-time processes and have been widely applied in a number of fields, such as insurance, finance, queueing, and statistics. The Hawkes model is generally non-Markovian because its future development depends on the timing of past events. However, it can be Markovian under certain circumstances. If the exciting function is an exponential function or a sum of exponential functions, the model can be Markovian with a generator of the model. In contrast to the general Hawkes processes, the inverse Hawkes process has some specific features and self-excitation indicates severity. Inverse Markovian Hawkes processes were introduced by Seol, who studied some asymptotic behaviors. An extended version of inverse Markovian Hawkes processes was also studied by Seol. With this paper, we propose a non-Markovian inverse Hawkes process, which is a more general inverse Hawkes process that features several existing models of self-exciting processes. In particular, we established both the law of large numbers (LLN) and Central limit theorems (CLT) for a newly considered non-Markovian inverse Hawkes process.
Three-Legged Compliant Parallel Mechanisms: Fundamental Design Criteria to Achieve Fully Decoupled Motion Characteristics and a State-of-the-Art Review
Authors: Pham Minh Tuan , Yeo Song Huat , Teo Tat Joo
Published: April 2022

A three-legged compliant parallel mechanism (3L-CPM) achieves fully decoupled motions when its theoretical 6 × 6 stiffness/compliance matrix is a diagonal matrix, which only contains diagonal components, while all non-diagonal components are zeros. Because the motion decoupling capability of 3L-CPMs is essential in the precision engineering field, this paper presents the fundamental criteria for designing 3L-CPMs with fully decoupled motions, regardless of degrees-of-freedom and the types of flexure element. The 6 × 6 stiffness matrix of a general 3L-CPM is derived based on the orientation of each flexure element, e.g., thin/slender beam and notch hinge, etc., and its relative position to the moving platform. Based on an analytical solution, several requirements for the flexure elements were identified and needed to be satisfied in order to design a 3L-CPM with a diagonal stiffness/compliance matrix. In addition, the developed design criteria were used to analyze the decoupled-motion capability of some existing 3L-CPM designs and shown to provide insight into the motion characteristics of any 3L-CPM.
On the Autocorrelation Function of 1/f Noises
Authors: Carpena Pedro , Coronado Ana V.
Published: April 2022

The outputs of many real-world complex dynamical systems are time series characterized by power-law correlations and fractal properties. The first proposed model for such time series comprised fractional Gaussian noise (fGn), defined by an autocorrelation function C(k) with asymptotic power-law behavior, and a complicated power spectrum S(f) with power-law behavior in the small frequency region linked to the power-law behavior of C(k). This connection suggested the use of simpler models for power-law correlated time series: time series with power spectra of the form S(f)∼1/fβ, i.e., with power-law behavior in the entire frequency range and not only near f=0 as fGn. This type of time series, known as 1/fβ noises or simply 1/f noises, can be simulated using the Fourier filtering method and has become a standard model for power-law correlated time series with a wide range of applications. However, despite the simplicity of the power spectrum of 1/fβ noises and of the known relationship between the power-law exponents of S(f) and C(k), to our knowledge, an explicit expression of C(k) for 1/fβ noises has not been previously published. In this work, we provide an analytical derivation of C(k) for 1/fβ noises, and we show the validity of our results by comparing them with the numerical results obtained from synthetically generated 1/fβ time series. We also present two applications of our results: First, we compare the autocorrelation functions of fGn and 1/fβ noises that, despite exhibiting similar power-law behavior, present some clear differences for anticorrelated cases. Secondly, we obtain the exact analytical expression of the Fluctuation Analysis algorithm when applied to 1/fβ noises.
The Impact of COVID-19 on the Connectedness of Stock Index in ASEAN+3 Economies
Authors: Aziz Mukhriz Izraf Azman , Ahmad Norzalina , Zichu Jin , Nor Safwan Mohd
Published: April 2022

This paper explores the impact of the COVID-19 pandemic on the connectedness of stock indexes in the group of developed and emerging economies known as the ASEAN+3. We derived our empirical findings from the Diebold and Yilmaz (DY12) and Baruník and Křehlík (BK18) spillover methods, using daily data from 10 May 2005 to 24 February 2021. We show that the COVID-19 pandemic has had a bigger impact on the return and volatilities of ASEAN+3 stock markets than previous economic turmoil, such as the 2008 global financial crisis and the 2009–2012 European debt crisis. Using a frequency domain methodology, we find evidence that return spillovers mostly occur in the short-term, while volatility connectedness is more pronounced in the long-term. The Singapore stock market primarily acts the as top transmitter in returns and volatilities, whereas Vietnam has become the top receiver of shocks in returns. We also demonstrate that it is possible to replicate the frequency-domain connectedness measures of BK18 with a DY12 methodology. Using a series decomposed with a wavelet-based approach, we find that the total spillover indices for short-, medium-, and long-term frequencies computed with the DY12 approach are comparable to the within connectedness indices of BK18. Our results have important policy implications for investors, regulators, and policy makers.
Geometric Algebra Applied to Multiphase Electrical Circuits in Mixed Time–Frequency Domain by Means of Hypercomplex Hilbert Transform
Authors: Montoya Francisco G. , Baños Raúl , Alcayde Alfredo , Arrabal-Campos Francisco M. , Roldán-Pérez Javier
Published: April 2022

In this paper, power flows in electrical circuits are modelled in a mixed time-frequency domain by using geometric algebra and the Hilbert transform for the first time. The use of this mathematical framework overcomes some of the limitations of some of the existing methodologies, in which the so-called “active current” may not lead to the lowest Root Mean Square (RMS) current under distorted supply or unbalanced load. Moreover, this current may contain higher levels of harmonic distortion compared to the supply voltage. The proposed method can be used for sinusoidal and non-sinusoidal power supplies, non-linear loads and single- and multi-phase electrical circuits, and it provides meaningful engineering results with a compact formulation. It can also serve as an advanced tool for developing algorithms in the power electronics field. Several examples have been included to verify the validity of the proposed theory.
Reconfiguration of Foodbank Network Logistics to Cope with a Sudden Disaster
Authors: Ogazón Esteban , Smith Neale R. , Ruiz Angel
Published: April 2022

Foodbank networks provide adequate infrastructure and perform logistics activities to supply food to people in need on a day-to-day basis. However, in the case of a sudden event, such as a natural disaster, they must reconfigure themselves to quickly and fairly satisfy the needs of the affected people, despite the rapid changes in supply and demand, as much as possible. In contrast to most of the studies in the humanitarian logistics literature, which have focused on aid distribution—the downstream part of the supply chain—this paper extends the field of view upstream, explicitly considering supply (or, in the case of foodbanks, donors). To this end, we compare several network design strategies in order to assess the potential benefits of centralized decisions in a context where, in practice, there exists no formal protocol to support bank coordination. We propose a mathematical formulation for the design of such logistics processes, including collection, transshipment, and aid distribution, over a network of foodbanks inspired by the real case of Bancos de Alimentos de México (BAMX). The case considers several categories of food and encompasses restrictions on their mixture to ensure the nutritional quality of the delivered food, distinct from other models in the literature. Finally, we assess the differences in the strategies through the use of effectiveness and efficiency performance metrics.
The Generalized Alpha Exponent Power Family of Distributions: Properties and Applications
Authors: Hussain Sajid , Rashid Muhammad Sajid , Ul Hassan Mahmood , Ahmed Rashid
Published: April 2022

Here, a new method is recommended to characterize a new continuous distribution class, named the generalized alpha exponent power family of distributions (GAEPFDs). A particular sub-model is presented for exemplifying the objective. The basic statistical properties, such as ordinary moments, the probability weighted moments, mode, quantile, order statistics, entropy measures, and moment generating functions, etc., were explored. To gauge the GAEPPRD parameters, the ML technique was utilized. The estimator behaviour was studied by a Monte Carlo simulation study (MCSS). The effectiveness of GAEPFDs was demonstrated observationally through lifetime data. The applications show that GAEPFDs can offer preferable results over other competitive models.
A Medical Endoscope Image Enhancement Method Based on Improved Weighted Guided Filtering
Authors: Zhang Guo , Lin Jinzhao , Cao Enling , Pang Yu , Sun Weiwei
Published: April 2022

In clinical surgery, the quality of endoscopic images is degraded by noise. Blood, illumination changes, specular reflection, smoke, and other factors contribute to noise, which reduces the quality of an image in an occluded area, affects doctors’ judgment, prolongs the operation duration, and increases the operation risk. In this study, we proposed an improved weighted guided filtering algorithm to enhance endoscopic image tissue. An unsharp mask algorithm and an improved weighted guided filter were used to enhance vessel details and contours in endoscopic images. The scheme of the entire endoscopic image processing, which included detail enhancement, contrast enhancement, brightness enhancement, and highlight area removal, is presented. Compared with other algorithms, the proposed algorithm maintained edges and reduced halos efficiently, and its effectiveness was demonstrated using experiments. The peak signal-to-noise ratio and structural similarity of endoscopic images obtained using the proposed algorithm were the highest. The foreground–background detail variance–background variance improved. The proposed algorithm had a strong ability to suppress noise and could maintain the structure of original endoscopic images, which improved the details of tissue blood vessels. The findings of this study can provide guidelines for developing endoscopy devices.
On the Stabilization through Linear Output Feedback of a Class of Linear Hybrid Time-Varying Systems with Coupled Continuous/Discrete and Delayed Dynamics with Eventually Unbounded Delay
Authors: De la Sen Manuel
Published: April 2022

This research studies a class of linear, hybrid, time-varying, continuous time-systems with time-varying delayed dynamics and non-necessarily bounded, time-varying, time-differentiable delay. The considered class of systems also involves a contribution to the whole delayed dynamics with respect to the last preceding sampled values of the solution according to a prefixed constant sampling period. Such systems are also subject to linear output-feedback time-varying control, which picks-up combined information on the output at the current time instant, the delayed one, and its discretized value at the preceding sampling instant. Closed-loop asymptotic stabilization is addressed through the analysis of two “ad hoc” Krasovskii–Lyapunov-type functional candidates, which involve quadratic forms of the state solution at the current time instant together with an integral-type contribution of the state solution along a time-varying previous time interval associated with the time-varying delay. An analytic method is proposed to synthesize the stabilizing output-feedback time-varying controller from the solution of an associated algebraic system, which has the objective of tracking prescribed suited reference closed-loop dynamics. If this is not possible—in the event that the mentioned algebraic system is not compatible—then a best approximation of such targeted closed-loop dynamics is made in an error-norm sense minimization. Sufficiency-type conditions for asymptotic stability of the closed-loop system are also derived based on the two mentioned Krasovskii–Lyapunov functional candidates, which involve evaluations of the contributions of the delay-free and delayed dynamics.
On \({\mathbb{BID}}\)-Cone b-Metric Spaces over Banach Algebras: New Topological Properties and Fixed Point Theorems
Authors: Huang Huaping , Du Wei-Shih , Chen Jen-Yuan
Published: April 2022

In this paper, we introduce the concepts of an inferior idempotent cone and a BID-cone b-metric space over Banach algebra. We establish some new existence theorems and fixed point theorems in the setting of complete BID-cone b-metric spaces over Banach algebra. Some fundamental questions and examples are also given.
Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain
Authors: Monje Leticia , Carrasco Ramón A. , Rosado Carlos , Sánchez-Montañés Manuel
Published: April 2022

Time series forecasting of passenger demand is crucial for optimal planning of limited resources. For smart cities, passenger transport in urban areas is an increasingly important problem, because the construction of infrastructure is not the solution and the use of public transport should be encouraged. One of the most sophisticated techniques for time series forecasting is Long Short Term Memory (LSTM) neural networks. These deep learning models are very powerful for time series forecasting but are not interpretable by humans (black-box models). Our goal was to develop a predictive and linguistically interpretable model, useful for decision making using large volumes of data from different sources. Our case study was one of the most demanded bus lines of Madrid. We obtained an interpretable model from the LSTM neural network using a surrogate model and the 2-tuple fuzzy linguistic model, which improves the linguistic interpretability of the generated Explainable Artificial Intelligent (XAI) model without losing precision.
Interaction Behaviours between Soliton and Cnoidal Periodic Waves for Nonlocal Complex Modified Korteweg–de Vries Equation
Authors: Peng Junda , Ren Bo , Shen Shoufeng , Wang Guofang
Published: April 2022

The reverse space-time nonlocal complex modified Kortewewg–de Vries (mKdV) equation is investigated by using the consistent tanh expansion (CTE) method. According to the CTE method, a nonauto-Bäcklund transformation theorem of nonlocal complex mKdV is obtained. The interactions between one kink soliton and other different nonlinear excitations are constructed via the nonauto-Bäcklund transformation theorem. By selecting cnoidal periodic waves, the interaction between one kink soliton and the cnoidal periodic waves is derived. The specific Jacobi function-type solution and graphs of its analysis are provided in this paper.