bluuebunny/crossref_metadata_2025 huggingface dataset:
Shielding Properties of Epoxy Matrix Composites Reinforced with MgO Micro- and Nanoparticles
Authors: Sayyed M. I. , Yasmin Sabina , Almousa Nouf , Elsafi Mohamed
Published: September 2022

The aim of the current study is to investigate the impact of introducing micro- and nanoparticle MgO as a filler into epoxy resin on the radiation shielding abilities of the prepared samples. To this end, we performed a gamma-radiation spectroscopy experiment with the help of an HPGe detector and Am-241, Cs-137, and Co-60 sources. We evaluated the particle size effect (PSE) and detected the maximum PSE value with the addition of 50 wt% MgO particles, indicating that nanoparticle MgO was more successful in shielding against incoming radiation than microparticle MgO. We compared the half-value layer (HVL) for the samples with 10 wt%, 20 wt%, and 30 wt % micro-MgO and nano-MgO and found that the HVL values were lower for the nanoparticle samples than for the microparticles samples, confirming that smaller particle sizes enhanced the shielding ability of the samples against radiation. The MFP results showed that epoxy matrices containing micro-MgO, for all investigated energies, resulted in higher MFP values that those containing nano-MgO.
Design, Analysis and Experimental Research of Dual-Tendon-Driven Underactuated Gripper
Authors: Zhang Yunzhi , Xia Dingkun , Lu Qinghua , Zhang Qinghua , Wei Huiling , Chen Weilin
Published: September 2022

To improve the adaptive clamping performance of traditional single-tendon-driven underactuated grippers for grasping multiple categories of objects, a novel dual-tendon-driven underactuated gripper is proposed in this paper. First, two independent tendons with different winding paths are designed in the gripper to realize the changeable resultant moment of the end knuckle rotating joint and the movement sequences of gripper knuckles driven by different tendons are analysed too. Then, some kinematic analysis and dynamical simulations are carried out to verify the validation of the knuckle structure and dual-tendon winding path design. At last, a prototype of the novel gripper is manufactured and some grasping experiments are carried out on multiple categories of objects, with different sizes and shapes. The experimental results show that all the objects can be clamped tightly. Compared with the traditional single-tendon-driven gripper, the novel one can achieve a more flexible grasping operation and a larger end clamping force, which are more suitable for the adaptive grasping requirements of robotic automatic sorting.
Bearing Fault Diagnosis with Variable Speed Based on Fractional Hierarchical Range Entropy and Hunter–Prey Optimization Algorithm–Optimized Random Forest
Authors: Ma Jie , Liu Fangming
Published: September 2022

It is difficult for rolling bearings to realize high-precision fault diagnosis with variable speed. To obtain the features of variable speed fault signal effectively and complete the classification work of high accuracy, robust local mean decomposition (RLMD), fractional hierarchical range entropy (FrHRE), hunter–prey optimization algorithm (HPO) and random forest (RF) are combined. Then the paper advances a model for fault diagnosis based on RLMD, FrHRE and HPO-RF. Firstly, RLMD is selected to reconstruct the signal to eliminate some noise interference in this paper. Secondly, FrHRE is chosen to extract the useful feature. Next step, HPO is used to optimize the important parameters of RF and enhance RF’s classification ability. Finally, these obtained features are imported into the optimized RFmodel to achieve the classification. The experimental data is provided by University of Ottawa. The experiment analysis demonstrates that the proposed method performs very well in classification.
Singularity Analysis and Geometric Optimization of a 6-DOF Parallel Robot for SILS
Authors: Pisla Doina , Birlescu Iosif , Crisan Nicolae , Pusca Alexandru , Andras Iulia , Tucan Paul , Radu Corina , Gherman Bogdan , Vaida Calin
Published: September 2022

The paper presents the singularity analysis and the geometric optimization of a 6-DOF (Degrees of Freedom) parallel robot for SILS (Single-Incision Laparoscopic Surgery). Based on a defined set of input/output constraint equations, the singularities of the parallel robotic system are determined and geometrically interpreted. Then, the geometric parameters (e.g., the lengths of the mechanism links) for the 6-DOF parallel robot for SILS are optimized such that the robotic system complies with an operational workspace defined in correlation with the SILS task. A numerical analysis of the singularities showed that the operational workspace is singularity free. Furthermore, numerical simulations validate the parallel robot for the next developing stages (e.g., designing and prototyping stages).
Optimized Trapezoidal Acceleration Profiles for Minimum Settling Time of the Load Velocity
Authors: Cusimano Giancarlo
Published: September 2022

This paper deals with a method for generating symmetrical trapezoidal acceleration profiles for the motor of a vibrating system in rest-to-velocity motion. The aim was to significantly reduce the acceleration time and residual load vibration of lightly damped systems. Under undamped conditions, the analytical values of the jerk time are found to be in relation to the estimated natural frequency and the minimum value of the acceleration time is provided, also taking into account a limit value for motor acceleration. The analysis of the sensitive curves allows the designer to understand the magnitude of the residual vibration generated by an incorrect estimate of the natural frequency. Numerical simulations, with a closed-chain controlled motor and a zero or very small structural damping coefficient of the oscillating system, confirm the validity of the proposed method.
A Review of Path-Planning Approaches for Multiple Mobile Robots
Authors: Lin Shiwei , Liu Ang , Wang Jianguo , Kong Xiaoying
Published: September 2022

Numerous path-planning studies have been conducted in past decades due to the challenges of obtaining optimal solutions. This paper reviews multi-robot path-planning approaches and decision-making strategies and presents the path-planning algorithms for various types of robots, including aerial, ground, and underwater robots. The multi-robot path-planning approaches have been classified as classical approaches, heuristic algorithms, bio-inspired techniques, and artificial intelligence approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. The decision-making strategies mainly consist of centralized and decentralized approaches. The trend of the decision-making system is to move towards a decentralized planner. Finally, the new challenge in multi-robot path planning is proposed as fault tolerance, which is important for real-time operations.
Optimization of Centrifugal Pump Impeller for Pumping Viscous Fluids Using Direct Design Optimization Technique
Authors: Kim Bubryur , Siddique Mohammed Hamid , Samad Abdus , Hu Gang , Lee Dong-Eun
Published: September 2022

Pumping viscous fluids using centrifugal pumps in the subsea industry is very common. The pump performance degrades drastically when the viscosity of fluids increases, which ultimately gives rise to the installation and oil production cost. Their design optimization can lead to a significant improvement in their performance. Therefore, this study presented the effect of impeller geometry on pumping fluid viscosity through impeller design optimization. Here, pump operation is simulated numerically by solving the Reynolds-averaged Navier-Stokes (RANS) equations at different flowrates. Experimental testing is also performed using the same oils, for numerical validation. Artificial neural-network-assisted multiobjective optimization was performed with two independent design parameters; wrap angle and splitter blade length of impeller, with head and input power as objective functions. Wrap angle and splitter blade length, both significantly affect pump performance while pumping viscous oils; as the oil viscosity increases, increasing splitter length and decreasing wrap angle improve the head significantly.
Parameterized Instantaneous Frequency Estimation Method for Vibration Signal with Nonlinear Frequency Modulation
Authors: Huang Yuexin , Zhang Qiukun , Zhong Jianfeng , Chen Zhixiong , Zhong Shuncong
Published: September 2022

The vibration signal from the rotatory machinery condition monitoring under time-varying speed is usually amplitude-modulated (AM) and frequency-modulated (FM). It is important to efficiently and accurately estimate the instantaneous frequency (IF) of the vibration signal. In this paper, a novel parameterized IF estimation method is proposed. The method employs a high-order polynomial function to approximate the nonlinear IF and subsequently constructs overdetermined systems of linear equations by calculating the Fourier transform of the derivative of the signal. The IF can be estimated by using least squares estimation to solve the equations. The proposed method has high computational efficiency because it can obtain the estimation of IF over a period of time simultaneously; it differs from traditional time-frequency analysis methods that need to calculate the IF at each point in the time axis. It is demonstrated that the proposed method is not only particularly powerful for the nonlinear FM mono-component signal but also applicable to the multi-component signal constructed by multiple harmonics. The numerical simulation validates the effectiveness of the proposed method, and the experiment’s results show that the method is suitable for the IF estimation of the vibration signal from the varying-speed rotor system.
Cross-Linked Polymeric Gels and Nanocomposites: New Materials and Phenomena Enabling Technological Applications
Authors: Barbero Cesar A. , Martínez María V. , Acevedo Diego F. , Molina María A. , Rivarola Claudia R.
Published: September 2022

Cross-linked gels are synthesized by homo- and copolymerization of functionalized acrylamides. The gels swell in aqueous solution, and some of them (e.g., poly(N-isopropylacrylamide (PNIPAM)) also in organic solvents of low polarity (e.g., dichloromethane), making the gels amphiphilic materials. Nanocomposites can be made by dispersing nanoparticles (metallic, graphene, nanotubes, and conducting polymers) inside the gels. Additionally, true semi-interpenetrated networks of polyaniline (PANI) inside PNIPAM gels can be prepared by swelling the gel in true solutions of PANI in NMP. PNIPAM-based nanocomposites show a lower critical solution temperature (LCST) transition of the gel matrix, which can be reached by thermal heating or absorption of electromagnetic radiation (light, microwaves, radiofrequency) in the conductive nanomaterials. The characteristic properties (swelling degree and rate, LCST, solute partition, mass transport, hydrophilicity, biocompatibility) can be tuned by changing the functional groups in the copolymers and/or the other components in the nanocomposite. Mass transport and mechanical properties can be adjusted by forming materials with macro- (nanoporous and macroporous), micro- (microgels, thin films, Pickering emulsions), or nano- (nanogels, stabilized nanoparticles) sized features. The material properties are used to produce technological applications: sensors, actuators, controlled release, biological cell scaffolds and surfaces, antimicrobial, carriers of bioactive substances, and matrixes to immobilize enzymes and yeast cells.
Automated Immunomagnetic Enrichment and Optomicrofluidic Detection to Isolate Breast Cancer Cells: A Proof-of-Concept towards PoC Therapeutic Decision-Making
Authors: Stiefel Janis , Baßler Michael , Wittek Jörn , Freese Christian
Published: September 2022

In breast cancer research, immunomagnetic enrichment of circulating tumor cells (CTCs) from body fluids has impressively evolved over the last decades. However, there is growing interest in further singularizing these pre-enriched rare cells to decrease signal-to-noise ratio for downstream molecular analysis, e.g., to distinguish between hormone receptor-associated tumor subtypes. This can be done by a combinatory principle to link magnetic cell separation with flow cytometry and single cell dispensing. We have recently introduced an automated benchtop platform with a microfluidic disposable cartridge to immunomagnetically enrich, fluorescence-based detect and dispense single cells from biological samples. Herein, we showcase the fine-tuning of microfluidic cell isolation in dependency of bead-binding on the cell surface. We implemented a gating function for the cytometer subunit of the benchtop platform to selectively dispense cells instead of autofluorescent objects. Finally, we developed a simplified qPCR protocol without RNA purification targeting breast cancer-relevant progesterone and estrogen receptor, Muc-1, Her-2, EpCAM and CXCR4 transcripts. In conclusion, the presented results markedly demonstrate a future diagnostic and therapy-accompanying semi-automated workflow using immunomagnetic enrichment, fluorescence-based isolation and dispensing of circulating tumor cells to achieve tumor subtyping by means of rapid, simple and immediate molecular biological examination of single cells.
Inertial Tseng Method for Solving the Variational Inequality Problem and Monotone Inclusion Problem in Real Hilbert Space
Authors: Husain Shamshad , Tom Mohammed Ahmed Osman , Khairoowala Mubashshir U. , Furkan Mohd , Khan Faizan Ahmad
Published: September 2022

The main aim of this research is to introduce and investigate an inertial Tseng iterative method to approximate a common solution for the variational inequality problem for γ-inverse strongly monotone mapping and monotone inclusion problem in real Hilbert spaces. We establish a strong convergence theorem for our suggested iterative method to approximate a common solution for our proposed problems under some certain mild conditions. Furthermore, we deduce a consequence from the main convergence result. Finally, a numerical experiment is presented to demonstrate the effectiveness of the iterative method. The method and methodology described in this paper extend and unify previously published findings in this field.
A Numerical Algorithm for Self-Learning Model Predictive Control in Servo Systems
Authors: Yang Hengzhan , Xi Dian , Weng Xu , Qian Fucai , Tan Bo
Published: September 2022

Model predictive control (MPC) is one of the most effective methods of dealing with constrained control problems. Nevertheless, the uncertainty of the control system poses many problems in its performance optimization. For high-precision servo systems, friction is typically the main factor in uncertainty affecting the accuracy of the system. Our work focuses on stochastic systems with unknown parameters and proposes a model predictive control strategy with machine learning characteristics that utilizes pre-estimated information to reduce uncertainty. Within this model, the parameters are obtained using the estimator. The uncertainty caused by the parameter estimation error in the system is parameterized, serving as a learning control component to reduce future uncertainty. Then, the estimated parameters and the current state of the system are used to predict the future p-step state. The control sequence is calculated under the MPC’s rolling optimization mechanism. After the system output is obtained, the new parameter value at the next moment is re-estimated. Finally, MPC is carried out to realize the dual rolling optimization mechanism. In general, the proposed strategy optimizes the control objective while reducing the system uncertainty of the future parameter and achieving better system performance. Simulation results demonstrate the effectiveness of the algorithm.
Predicting Perceptual Decision-Making Errors Using EEG and Machine Learning
Authors: Batmanova Alisa , Kuc Alexander , Maksimenko Vladimir , Savosenkov Andrey , Grigorev Nikita , Gordleeva Susanna , Kazantsev Victor , Korchagin Sergey , Hramov Alexander
Published: September 2022

We trained an artificial neural network (ANN) to distinguish between correct and erroneous responses in the perceptual decision-making task using 32 EEG channels. The ANN input took the form of a 2D matrix where the vertical dimension reflected the number of EEG channels and the horizontal one—to the number of time samples. We focused on distinguishing the responses before their behavioural manifestation; therefore, we utilized EEG segments preceding the behavioural response. To deal with the 2D input data, ANN included a convolutional procedure transforming a 2D matrix into the 1D feature vector. We introduced three types of convolution, including 1D convolutions along the x- and y-axes and a 2D convolution along both axes. As a result, the F1-score for erroneous responses was above 88%, which confirmed the model’s ability to predict perceptual decision-making errors using EEG. Finally, we discussed the limitations of our approach and its potential use in the brain-computer interfaces to predict and prevent human errors in critical situations.
Optimal Purchasing Decisions with Supplier Default in Portfolio Procurement
Authors: Liu Xiaoqing , Luo Gongli , Xu Xinsheng
Published: September 2022

As global public health events and regional conflicts have greater influence on supply chains nowadays, supplier default in procurement becomes more and more common in practice. However, there is less research on portfolio procurement purchasing decisions in the case of fixed-term contract supplier default. This paper focuses on the optimal purchasing decision of buyers by using a combination of fixed-term contracts and spot transactions, which is a beneficial extension of the classical newsvendor model. When supplier default is not considered, the optimal purchase quantity in the fixed-term contract is first obtained, which maximizes the buyer’s expected profits. Research shows that supplier default has an important impact on the optimal purchasing decision making in portfolio procurement. The optimal purchase quantity of the buyer in the fixed-term contract decreases with the increase in the default rate of the contract supplier, which implies that the default from the contract supplier inhibits a larger purchase quantity in the fixed-term contract. In addition, it is proved that the buyer’s expected profits from portfolio procurement increases with the decrease in the contract supplier’s default rate. Finally, numerical experiments and sensitivity analysis are conducted to prove the result, and some management opinions on the optimal decision-making in portfolio procurement with fixed-term contracts and spot transactions are put forward.
Entropy Analysis of Sutterby Nanofluid Flow over a Riga Sheet with Gyrotactic Microorganisms and Cattaneo–Christov Double Diffusion
Authors: Faizan Muhammad , Ali Farhan , Loganathan Karuppusamy , Zaib Aurang , Reddy Ch Achi , Abdelsalam Sara I.
Published: September 2022

In this article, a Riga plate is exhibited with an electric magnetization actuator consisting of permanent magnets and electrodes assembled alternatively. This exhibition produces electromagnetic hydrodynamic phenomena over a fluid flow. A new study model is formed with the Sutterby nanofluid flow through the Riga plate, which is crucial to the structure of several industrial and entering advancements, including thermal nuclear reactors, flow metres and nuclear reactor design. This article addresses the entropy analysis of Sutterby nanofluid flow over the Riga plate. The Cattaneo–Christov heat and mass flux were used to examine the behaviour of heat and mass relaxation time. The bioconvective motile microorganisms and nanoparticles are taken into consideration. The system of equations for the current flow problems is converted from a highly non-linear partial system to an ordinary system through an appropriate transformation. The effect of the obtained variables on velocity, temperature, concentration and motile microorganism distributions are elaborated through the plots in detail. Further, the velocity distribution is enhanced for a greater Deborah number value and it is reduced for a higher Reynolds number for the two cases of pseudoplastic and dilatant flows. Microorganism distribution decreases with the increased magnitude of Peclet number, Bioconvection Lewis number and microorganism concentration difference number. Two types of graphical outputs are presented for the Sutterby fluid parameter (β = −2.5, β = 2.5). Finally, the validation of the present model is achieved with the previously available literature.
Boundary Value Problem of Space-Time Fractional Advection Diffusion Equation
Authors: Mahmoud Elsayed I. , Aleroev Temirkhan S.
Published: September 2022

In this article, the analytical and numerical solution of a one-dimensional space-time fractional advection diffusion equation is presented. The separation of variables method is used to carry out the analytical solution, the basis of the system eigenfunction and their corresponding eigenvalue for basic equation is determined, and the numerical solution is based on constructing the Crank-Nicolson finite difference scheme of the equivalent partial integro-differential equations. The convergence and unconditional stability of the solution are investigated. Finally, the numerical and analytical experiments are given to verify the theoretical analysis.
Finite-Time Extended State Observer-Based Fixed-Time Attitude Control for Hypersonic Vehicles
Authors: Zhao Jiaqi , Feng Dongzhu , Cui Jiashan , Wang Xin
Published: September 2022

A finite-time extended, state-observer-based, fixed-time backstepping control algorithm was designed for hypersonic flight vehicles. To enhance the robustness of the controller, two novel finite-time extended state observers were introduced to compensate for the negative effects of lumped disturbances such as uncertainties and external disturbances. Two hyperbolic sine tracking differentiators were used to approximate the derivatives of the virtual control signals and guidance commands, thereby alleviating the computational burden associated with traditional backstepping control. Furthermore, a fixed-time backstepping attitude controller was used to guarantee that the tracking errors converged to a small neighbor of the origin in fixed time. According to the simulation results, the proposed controller outperformed a fixed-time sliding mode disturbance, observer-based, finite-time backstepping controller in terms of the tracking precision and convergence rate. Moreover, the proposed controller was noted to be robust in simulations involving lumped disturbances.
Tilting and Cotilting in Functor Categories
Authors: Wang Junfu , Zhao Tiwei
Published: September 2022

In this paper, we introduce the notion of n-tilting (resp. n-cotilting) objects in functor categories and give some characterizations of n-tilting objects and n-tilting classes (resp. n-cotilting objects and n-cotilting classes).
Spectra of Complemented Triangulation Graphs
Authors: Wei Jia , Wang Jing
Published: September 2022

The complemented triangulation graph of a graph G, denoted by CT(G), is defined as the graph obtained from G by adding, for each edge uv of G, a new vertex whose neighbours are the vertices of G other than u and v. In this paper, we first obtain the A-spectra, the L-spectra, and the Q-spectra of the complemented triangulation graphs of regular graphs. By using the results, we construct infinitely many pairs of A-cospectral graphs, L-cospectral graphs, and Q-cospectral graphs. We also obtain the number of spanning trees and the Kirchhoff index of the complemented triangulation graphs of regular graphs.
VID2META: Complementing Android Programming Screencasts with Code Elements and GUIs
Authors: Alahmadi Mohammad D.
Published: September 2022

The complexity of software projects and the rapid technological evolution make it such that developers often need additional help and knowledge to tackle their daily tasks. For this purpose, they often refer to online resources, which are easy to access and contain a wealth of information in various formats. Programming screencasts hosted on platforms such as YouTube are one such online resource that has seen a growth in popularity and adoption over the past decade. These screencasts usually have some metadata such as a title, a short description, and a set of tags that should describe what the main concepts captured in the video are. Unfortunately, metadata are often generic and do not contain detailed information about the code showcased in the tutorial, such as the API calls or graphical user interface (GUI) elements employed, which could lead to developers missing useful tutorials. Having a quick overview of the main code elements and GUIs used in a video tutorial can be very helpful for developers looking for code examples involving specific API calls, or looking to design applications with a specific GUI in mind. The aim is to make this information easily available to developers, and propose VID2META, a technique that automatically extracts Java import statements, class names, method information, GUI elements, and GUI screens from videos and makes them available to developers as metadata. VID2META is currently designed to work with Android screencasts. It analyzes video frames using a combination of computer vision, deep learning, optical character recognition, and heuristic-based approaches to identify the needed information in a frame, extract it, and present it to the developer. VID2META has been evaluated in an empirical study on 70 Android programming videos collected from YouTube. The results revealed that VID2META can accurately detect and extract Java and GUI elements from Android programming videos with an average accuracy of 90%.
Predictability of Population Fluctuations
Authors: Crespo-Miguel Rodrigo , Cao-García Francisco J.
Published: September 2022

Population dynamics is affected by environmental fluctuations (such as climate variations), which have a characteristic correlation time. Strikingly, the time scale of predictability can be larger for the population dynamics than for the underlying environmental fluctuations. Here, we present a general mechanism leading to this increase in predictability. We considered colored environmental fluctuation acting on a population close to equilibrium. In this framework, we derived the temporal auto and cross-correlation functions for the environmental and population fluctuations. We found a general correlation time hierarchy led by the environmental-population correlation time, closely followed by the population autocorrelation time. The increased predictability of the population fluctuations arises as an increase in its autocorrelation and cross-correlation times. These increases are enhanced by the slow damping of the population fluctuations, which has an integrative effect on the impact of correlated environmental fluctuations. Therefore, population fluctuation predictability is enhanced when the damping time of the population fluctuation is larger than the environmental fluctuations. This general mechanism can be quite frequent in nature, and it largely increases the perspectives of making reliable predictions of population fluctuations.
Spatial Outlier Accommodation Using a Spatial Variance Shift Outlier Model
Authors: Baba Ali Mohammed , Midi Habshah , Abd Rahman Nur Haizum
Published: September 2022

Outlier detection has been a long-debated subject among researchers due to its effect on model fitting. Spatial outlier detection has received considerable attention in the recent past. On the other hand, outlier accommodation, particularly in spatial applications, retains vital information about the model. It is pertinent to develop a method that is capable of accommodating detected spatial outliers in a fashion that retains vital information in the spatial models. In this paper, we formulate the variance shift outlier model (SVSOM) in the spatial regression as a robust spatial model using restricted maximum likelihood (REML) and use weights based on the detected outliers in the model. The spatial outliers are accommodated via a revised model for the outlier observations with the help of the SVSOM. Simulation results show that the SVSOM, based on the detected spatial outliers is more efficient than the general spatial model (GSM). The findings of this study also reveal that contamination in the residuals and x variable have little effect on the parameter estimates of the SVSOM, and that outliers in the y variable are always detectable. Asymptotic distribution of the squared spatial prediction residuals are obtained to confirm the outlyingness of an observation. The merit of or proposed SVSOM for the detection and accommodating outliers is also confirmed using artificial and COVID-19 data sets.
A New Type-3 Fuzzy Predictive Approach for Mobile Robots
Authors: Hua Guoxin , Wang Fei , Zhang Jianhui , Alattas Khalid A. , Mohammadzadeh Ardashir , The Vu Mai
Published: September 2022

Wheeled mobile robots are widely used for their excellent mobility and high maneuverability. The intelligentization of mobile robots to perform autonomous movement is one of the favorite topics in the robotic field. This paper proposes a new intelligent path-following scheme for mobile robots. A secure path is designed by the chaotic systems and an unknown switching mechanism. The proposed controller is constructed by the type-3 (T3) fuzzy logic systems (FLSs) and a predictive compensator. The T3-FLSs are taught to obtain good accuracy. In addition, the stability is mathematically investigated and guaranteed by the designed compensator. The simulations show that the case-study robot follows the planned secure path well and resists uncertainties.
Numerical Simulation of Single Droplet Impingement upon Dynamic Liquid Film Obliquely
Authors: Yang Shanshan , Zeng Quanyuan , Zhang Xiaohua , Dong Chunzhu , Guan Ling
Published: September 2022

To better understand the application of droplet impingement in industry and agriculture, in this paper, the coupled level set and volume of fluid (CLSVOF) method is applied to study droplet oblique impact on a dynamic liquid film. The conclusions are the following: the downstream crown height increases and then decreases as the impact angle increases, whereas upstream crown height and spreading length decrease significantly; moreover, the spreading length and upstream crown height increase with the increase of film velocity, while the downstream crown height decreases instead. The increase of gas density inhibits both upstream and downstream crowns. When the fluid viscosity decreases or the impact velocity increases, the crown height increases significantly, which easily leads to crown rupture or droplet splash. The increase in impact velocity leads to an increase in spreading length; however, viscosity has almost no effect on the spreading length.
Short Tasks for Scaffolding Computational Thinking by the Global Bebras Challenge
Authors: Dagiene Valentina , Dolgopolovas Vladimiras
Published: September 2022

The short task methodology enhances the Bebras constructive environment, and provides an emotional context that triggers the convolution of initially biased mental models and corresponding emotional reactions into an unbiased set of conceptual models for informatics education. This provides the motivation of our research–to explore the process of pedagogical design of short informatics concept-based tasks from the standpoint of mindset formation, which allows one to build conceptual models for CT education. The aim of the research is to gain a conceptual understanding of what a short task is in the context of the global Bebras Challenge initiative. We explore the principles which should underlie the pedagogical design of short tasks for informatics education that scaffold CT. Exploration of a number of practical examples of the Bebras short tasks is the background of our research methodology. The results include an analysis of the structure of short tasks, focusing on the interaction of mental models, conceptual models, and heuristics inherent in the task design. The discussion provides a comprehensive insight into the issues of the short tasks in relation to CT and the Bebras environment. We conclude with recommendations for organizing an effective pedagogical design of a short task.
BlockCrime: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety
Authors: Patel Dev , Sanghvi Harshil , Jadav Nilesh Kumar , Gupta Rajesh , Tanwar Sudeep , Florea Bogdan Cristian , Taralunga Dragos Daniel , Altameem Ahmed , Altameem Torki , Sharma Ravi
Published: September 2022

Detecting malicious activity in advance has become increasingly important for public safety, economic stability, and national security. However, the disparity in living standards incites the minds of certain undesirable members of society to commit crimes, which may disrupt society’s stability and mental calm. Breakthroughs in deep learning (DL) make it feasible to address such challenges and construct a complete intelligent framework that automatically detects such malicious behaviors. Motivated by this, we propose a convolutional neural network (CNN)-based Xception model, i.e., BlockCrime, to detect crimes and improve public safety. Furthermore, we integrate blockchain technology to securely store the detected crime scene locations and alert the nearest law enforcement authorities. Due to the scarcity of the dataset, transfer learning has been preferred, in which a CNN-based Xception model is used. The redesigned Xception architecture is evaluated against various assessment measures, including accuracy, F1 score, precision, and recall, where it outperforms existing CNN architectures in terms of train accuracy, i.e., 96.57%.
A Unified Local-Semilocal Convergence Analysis of Efficient Higher Order Iterative Methods in Banach Spaces
Authors: Sharma Janak Raj , Singh Harmandeep , Argyros Ioannis K.
Published: September 2022

To deal with the estimation of the locally unique solutions of nonlinear systems in Banach spaces, the local as well as semilocal convergence analysis is established for two higher order iterative methods. The given methods do not involve the computation of derivatives of an order higher than one. However, the convergence analysis was carried out in earlier studies by using the assumptions on the higher order derivatives as well. Such types of assumptions limit the applicability of techniques. In this regard, the convergence analysis is developed in the present study by imposing the conditions on first order derivatives only. The central idea for the local analysis is to estimate the bounds on convergence domain as well as the error approximations of the iterates along with the formulation of sufficient conditions for the uniqueness of the solution. Based on the choice of initial estimate in the given domain, the semilocal analysis is established, which ensures the convergence of iterates to a unique solution in that domain. Further, some applied problems are tested to certify the theoretical deductions.
Research on b Value Estimation Based on Apparent Amplitude-Frequency Distribution in Rock Acoustic Emission Tests
Authors: Chen Daolong , Xia Changgen , Liu Huini , Liu Xiling , Du Kun
Published: September 2022

The rock acoustic emission (AE) technique has often been used to study rock destruction properties and has also been considered an important measure for simulating earthquake foreshock sequences. Among them, the AE b value is an essential parameter for the size distribution characteristics and probabilistic hazard analysis of rock fractures. Variations in b values obtained in rock AE tests and earthquakes are often compared to establish analogies in the damage process and precursory analysis. Nevertheless, because the amplitudes measured on the sample boundary by an acoustic sensor (apparent amplitude) are often used to estimate the b value, which cannot descript the source size distribution, it is necessary to develop a method to obtain the size distribution characteristics of the real source from the apparent amplitude in doubly truncated distribution. In this study, we obtain AE apparent amplitudes by applying an attenuation operator to source amplitudes generated by a computer with an underlying exponential distribution and then use these simulated apparent amplitudes to perform a comparative analysis of various b value estimation methods that are used in earthquakes and propose an optimal b value estimation procedure for rock AE tests through apparent amplitudes. To further verify the reliability of the newly proposed procedure, a b value characteristics analysis was carried out on a non-explosive expansion agent rock AE test and transparent refractive index experiment with red sandstone, marble, granite, and limestone. The results indicate that mineral grains of different sizes and compositions and different types of discontinuities of rock specimens determine the rock fracture characteristics, as well as the b value. The dynamic b values decreased linearly during the loading process, which confirms that variations in the b value also depend on the stress. These results indicate that the newly proposed procedure for estimating the b value in rock AE tests based on apparent amplitudes has high reliability.
A Study of Learning Issues in Feedforward Neural Networks
Authors: Teso-Fz-Betoño Adrian , Zulueta Ekaitz , Cabezas-Olivenza Mireya , Teso-Fz-Betoño Daniel , Fernandez-Gamiz Unai
Published: September 2022

When training a feedforward stochastic gradient descendent trained neural network, there is a possibility of not learning a batch of patterns correctly that causes the network to fail in the predictions in the areas adjacent to those patterns. This problem has usually been resolved by directly adding more complexity to the network, normally by increasing the number of learning layers, which means it will be heavier to run on the workstation. In this paper, the properties and the effect of the patterns on the network are analysed and two main reasons why the patterns are not learned correctly are distinguished: the disappearance of the Jacobian gradient on the processing layers of the network and the opposite direction of the gradient of those patterns. A simplified experiment has been carried out on a simple neural network and the errors appearing during and after training have been monitored. Taking into account the data obtained, the initial hypothesis of causes seems to be correct. Finally, some corrections to the network are proposed with the aim of solving those training issues and to be able to offer a sufficiently correct prediction, in order to increase the complexity of the network as little as possible.
Wave Dispersion Analysis of Functionally Graded GPLs-Reinforced Sandwich Piezoelectromagnetic Plates with a Honeycomb Core
Authors: Sobhy Mohammed , Al Mukahal Fatemah H. H.
Published: September 2022

This paper studies wave propagation in a new structure composed of three layers. The upper and lower layers are made of a piezoelectromagnetic material reinforced with graphene platelets (GPLs) that may be uniformly disseminated or continuously varied throughout the thickness of the layers. To produce a lighter plate, the core layer is assumed to comprise honeycomb structures. The smart nanocomposite plate is exposed to external electric and magnetic potentials. The effective elastic modulus of the face layers of the sandwich plate is evaluated based on Halpin-Tsai model. Whereas, the mixture rule is utilized to calculate mass density, Poisson’s ratio and electric and magnetic properties of both upper and lower layers of the sandwich plate. The governing motion equations of the lightweight sandwich plate are obtained by refined higher-order shear deformation plate theory and Hamilton’s principle. These equations are solved analytically to obtain wave dispersion relations. Impacts of the geometry of plates, GPLs weight fraction, GPLs distribution patterns, piezoelectric properties, external electric voltage and external magnetic potential on the wave frequency and phase velocity of the GPLs lightweight plates are discussed in detail.
Preimage Problem Inspired by the F-Transform
Authors: Janeček Jiří , Perfilieva Irina
Published: September 2022

In this article, we focus on discrete data processing. We propose to use the concept of closeness, which is less restrictive than a metric, to describe a certain relationship between objects. We establish a fuzzy partition of a given set of objects in a way that admits a closeness space to emerge. The fuzzy (F-) transform is a tool that maps objects with common characteristics to the same discrete image—the direct F-transform. We are interested in the inverse (preimage) problem: How can we describe the class of all functions mapped onto the same direct F-transform? In this manuscript, we focus on this preimage problem, formulated accordingly. Its solution is presented from three different points of view and shows which functions belong to the same class determined by a given image (by the direct F-transform). Conditions under which a solution to the preimage problem is given by the inverse F-transform over the same fuzzy partition, or by transforming a given image using a new system of basic functions, are formulated. The developed theory contributes to a better understanding of ill-posed problems that are typical for machine learning. The appendix contains illustrative numerical examples.
Rain Rendering and Construction of Rain Vehicle Color-24 Dataset
Authors: Hu Mingdi , Wang Chenrui , Yang Jingbing , Wu Yi , Fan Jiulun , Jing Bingyi
Published: September 2022

The fine identification of vehicle color can assist in criminal investigation or intelligent traffic management law enforcement. Since almost all vehicle-color datasets that are used to train models are collected in good weather, the existing vehicle-color recognition algorithms typically show poor performance for outdoor visual tasks. In this paper we construct a new RainVehicleColor-24 dataset by rain-image rendering using PS technology and a SyRaGAN algorithm based on the VehicleColor-24 dataset. The dataset contains a total of 40,300 rain images with 125 different rain patterns, which can be used to train deep neural networks for specific vehicle-color recognition tasks. Experiments show that the vehicle-color recognition algorithms trained on the new dataset RainVehicleColor-24 improve accuracy to around 72% and 90% on rainy and sunny days, respectively. The code is available at humingdi2005@github.com.
Recovering a Space-Dependent Source Term in the Fractional Diffusion Equation with the Riemann–Liouville Derivative
Authors: Liu Songshu
Published: September 2022

This research determines an unknown source term in the fractional diffusion equation with the Riemann–Liouville derivative. This problem is ill-posed. Conditional stability for the inverse source problem can be given. Further, a fractional Tikhonov regularization method was applied to regularize the inverse source problem. In the theoretical results, we propose a priori and a posteriori regularization parameter choice rules and obtain the convergence estimates.
Prediction of Surface Roughness in Turning Applying the Model of Nonlinear Oscillator with Complex Deflection
Authors: Horváth Richárd , Cveticanin Livija , Ninkov Ivona
Published: September 2022

This paper deals with prediction of the roughness of a cutting surface in the turning process, applying the vibration data of the system. A new type of dynamic model for a workpiece-cutting tool system, appropriate for vibration simulation, is developed. The workpiece is modelled as a mass-spring system with nonlinear elastic property. The cutting tool acts on the workpiece with the cutting force which causes strong in-plane vibration. Based on the experimentally measured values, the cutting force is analytically described as the function of feed ratio and cutting speed. The mathematical model of the vibrating system is a non-homogenous strong nonlinear differential equation with complex function. A new approximate solution for the nonlinear equation is derived and analytic description of vibration is obtained. The solution depends on parameters of the excitation force, velocity of rotation and nonlinear properties of the system. Increasing the feed ratio at a constant velocity of the working piece, the frequency of vibration decreases and the amplitude of vibration increases; increasing the velocity of working piece for constant feed ratio causes an increase of the frequency and a decrease of the amplitude of vibration. Experiments demonstrate that the analytical solution of the nonlinear vibration model in turning process is in direct correlation with the cutting surface roughness. The predicted surface roughness is approximately (1–2) × 10−3 times smaller than the amplitude of vibration of the nonlinear model considered in this paper.
Clustering of Dark Patterns in the User Interfaces of Websites and Online Trading Portals (E-Commerce)
Authors: Nazarov Dmitry , Baimukhambetov Yerkebulan
Published: September 2022

dark patterns in the interfaces of users using sites and portals of online trading affect their behavior by companies that own digital resources. The authors propose to implement the detection of dark patterns on sites in user interfaces using cluster analysis algorithms using two methods for clustering many dark patterns in application interfaces: hierarchical and k-means. The complexity of the implementation lies in the lack of datasets that formalize dark patterns in user interfaces. The authors conducted a study and identified signs of dark patterns based on the use of Nelsen’s antisymmetric principles. The article proposes a technique for assessing dark patterns using linguistic variables and their further interval numerical assessment for implementing cluster data analysis. The last part of the article contains an analysis of two clustering algorithms and an analysis of the methods and procedures for applying them to clustering data according to previously selected features in the RStudio environment. We also gave a characteristic for each resulting cluster.
Self-Similar Solutions of a Gravitating Dark Fluid
Authors: Barna Imre Ferenc , Pocsai Mihály András , Barnaföldi Gergely Gábor
Published: September 2022

In this paper, a fluid model is presented which contains the general linear equation of state including the gravitation term. The obtained spherical symmetric Euler equation and the continuity equations were investigated with the Sedov-type time-dependent self-similar ansatz which is capable of describing physically relevant diffusive and disperse solutions. The result of the space and time-dependent fluid density and radial velocity fields are presented and analyzed. Additionally, the role of the initial velocity on the kinetic and total energy densities of the fluid is discussed. This leads to a model, which can be considered as a simple model for a dark-fluid.
On One- and Two-Dimensional α–Stancu–Schurer–Kantorovich Operators and Their Approximation Properties
Authors: Heshamuddin Md. , Rao Nadeem , Lamichhane Bishnu P. , Kiliçman Adem , Ayman-Mursaleen Mohammad
Published: September 2022

The goal of this research article is to introduce a sequence of α–Stancu–Schurer–Kantorovich operators. We calculate moments and central moments and find the order of approximation with the aid of modulus of continuity. A Voronovskaja-type approximation result is also proven. Next, error analysis and convergence of the operators for certain functions are presented numerically and graphically. Furthermore, two-dimensional α–Stancu–Schurer–Kantorovich operators are constructed and their rate of convergence, graphical representation of approximation and numerical error estimates are presented.