bluuebunny/crossref_metadata_2025 huggingface dataset:
Simultaneous Features of CC Heat Flux on Dusty Ternary Nanofluid (Graphene + Tungsten Oxide + Zirconium Oxide) through a Magnetic Field with Slippery Condition
Authors: Souayeh Basma
Published: January 2023

The purpose of this work is to offer a unique theoretical ternary nanofluid (graphene/tungsten oxide/zirconium oxide) framework for better heat transfer. This model describes how to create better heat conduction than a hybrid nanofluid. Three different nanostructures with different chemical and physical bonds are suspended in water to create the ternary nanofluid (graphene/tungsten oxide/zirconium oxide). Toxic substances are broken down, the air is purified, and other devices are cooled thanks to the synergy of these nanoparticles. The properties of ternary nanofluids are discussed in this article, including their thermal conductivity, specific heat capacitance, viscosity, and density. In addition, heat transport phenomena are explained by the Cattaneo–Christov (CC) heat flow theory. In the modeling of the physical phenomena under investigation, the impacts of thermal nonlinear radiation and velocity slip are considered. By using the right transformations, flow-generating PDEs are converted into nonlinear ordinary differential equations. The parameters’ impacts on the velocity and temperature fields are analyzed in detail. The modeled problem is graphically handled in MATLAB using a numerical technique (BVP4c). Graphical representations of the important factors affecting temperature and velocity fields are illustrated through graphs. The findings disclose that the performance of ternary nanofluid phase heat transfer is improved compared to dusty phase performance. Furthermore, the magnetic parameter and the velocity slip parameter both experience a slowing-down effect of their respective velocities.
A New Incommensurate Fractional-Order COVID 19: Modelling and Dynamical Analysis
Authors: Al-Husban Abdallah , Djenina Noureddine , Saadeh Rania , Ouannas Adel , Grassi Giuseppe
Published: January 2023

Nowadays, a lot of research papers are concentrating on the diffusion dynamics of infectious diseases, especially the most recent one: COVID-19. The primary goal of this work is to explore the stability analysis of a new version of the SEIR model formulated with incommensurate fractional-order derivatives. In particular, several existence and uniqueness results of the solution of the proposed model are derived by means of the Picard–Lindelöf method. Several stability analysis results related to the disease-free equilibrium of the model are reported in light of computing the so-called basic reproduction number, as well as in view of utilising a certain Lyapunov function. In conclusion, various numerical simulations are performed to confirm the theoretical findings.
Improving the Robustness of Variable Selection and Predictive Performance of Regularized Generalized Linear Models and Cox Proportional Hazard Models
Authors: Hong Feng , Tian Lu , Devanarayan Viswanath
Published: January 2023

High-dimensional data applications often entail the use of various statistical and machine-learning algorithms to identify an optimal signature based on biomarkers and other patient characteristics that predicts the desired clinical outcome in biomedical research. Both the composition and predictive performance of such biomarker signatures are critical in various biomedical research applications. In the presence of a large number of features, however, a conventional regression analysis approach fails to yield a good prediction model. A widely used remedy is to introduce regularization in fitting the relevant regression model. In particular, a L1 penalty on the regression coefficients is extremely useful, and very efficient numerical algorithms have been developed for fitting such models with different types of responses. This L1-based regularization tends to generate a parsimonious prediction model with promising prediction performance, i.e., feature selection is achieved along with construction of the prediction model. The variable selection, and hence the composition of the signature, as well as the prediction performance of the model depend on the choice of the penalty parameter used in the L1 regularization. The penalty parameter is often chosen by K-fold cross-validation. However, such an algorithm tends to be unstable and may yield very different choices of the penalty parameter across multiple runs on the same dataset. In addition, the predictive performance estimates from the internal cross-validation procedure in this algorithm tend to be inflated. In this paper, we propose a Monte Carlo approach to improve the robustness of regularization parameter selection, along with an additional cross-validation wrapper for objectively evaluating the predictive performance of the final model. We demonstrate the improvements via simulations and illustrate the application via a real dataset.
Column-Type Prediction for Web Tables Powered by Knowledge Base and Text
Authors: Wu Junyi , Ye Chen , Zhi Haoshi , Jiang Shihao
Published: January 2023

Web tables are essential for applications such as data analysis. However, web tables are often incomplete and short of some critical information, which makes it challenging to understand the web table content. Automatically predicting column types for tables without metadata is significant for dealing with various tables from the Internet. This paper proposes a CNN-Text method to deal with this task, which fuses CNN prediction and voting processes. We present data augmentation and synthetic column generation approaches to improve the CNN’s performance and use extracted text to get better predictions. The experimental result shows that CNN-Text outperforms the baseline methods, demonstrating that CNN-Text is well qualified for the table column type prediction.
Dynamic Image Representation in a Spiking Neural Network Supplied by Astrocytes
Authors: Stasenko Sergey V. , Kazantsev Victor B.
Published: January 2023

The mathematical model of the spiking neural network (SNN) supplied by astrocytes is investigated. The astrocytes are a specific type of brain cells which are not electrically excitable but induce chemical modulations of neuronal firing. We analyze how the astrocytes influence images encoded in the form of the dynamic spiking pattern of the SNN. Serving at a much slower time scale, the astrocytic network interacting with the spiking neurons can remarkably enhance the image representation quality. The spiking dynamics are affected by noise distorting the information image. We demonstrate that the activation of astrocytes can significantly suppress noise influence, improving the dynamic image representation by the SNN.
Classifying Cardiac Arrhythmia from ECG Signal Using 1D CNN Deep Learning Model
Authors: Ahmed Adel A. , Ali Waleed , Abdullah Talal A. A. , Malebary Sharaf J.
Published: January 2023

Blood circulation depends critically on electrical activation, where any disturbance in the orderly pattern of the heart’s propagating wave of excitation can lead to arrhythmias. Diagnosis of arrhythmias using electrocardiograms (ECG) is widely used because they are a fast, inexpensive, and non-invasive tool. However, the randomness of arrhythmic events and the susceptibility of ECGs to noise leads to misdiagnosis of arrhythmias. In addition, manually diagnosing cardiac arrhythmias using ECG data is time-intensive and error-prone. With better training, deep learning (DL) could be a better alternative for fast and automatic classification. The present study introduces a novel deep learning architecture, specifically a one-dimensional convolutional neural network (1D-CNN), for the classification of cardiac arrhythmias. The model was trained and validated with real and noise-attenuated ECG signals from the MIT-BIH dataset. The main aim is to address the limitations of traditional electrocardiograms (ECG) in the diagnosis of arrhythmias, which can be affected by noise and randomness of events, leading to misdiagnosis and errors. To evaluate the model performance, the confusion matrix is used to calculate the model accuracy, precision, recall, f1 score, average and AUC-ROC. The experiment results demonstrate that the proposed model achieved outstanding performance, with 1.00 and 0.99 accuracies in the training and testing datasets, respectively, and can be a fast and automatic alternative for the diagnosis of arrhythmias.
Optimization-Based Energy Disaggregation: A Constrained Multi-Objective Approach
Authors: Park Jeewon , Ajani Oladayo S. , Mallipeddi Rammohan
Published: January 2023

Recently, optimization-based energy disaggregation (ED) algorithms have been gaining significance due to their capability to perform disaggregation with minimal information compared to the pattern-based ED algorithms, which demand large amounts of data for training. However, the performances of optimization-based ED algorithms depend on the problem formulation that includes an objective function(s) and/or constraints. In the literature, ED has been formulated as a constrained single-objective problem or an unconstrained multi-objective problem considering disaggregation error, sparsity of state switching, on/off switching, etc. In this work, the ED problem is formulated as a constrained multi-objective problem (CMOP), where the constraints related to the operational characteristics of the devices are included. In addition, the formulated CMOP is solved using a constrained multi-objective evolutionary algorithm (CMOEA). The performance of the proposed formulation is compared with those of three high-performing ED formulations in the literature based on the appliance-level and overall indicators. The results show that the proposed formulation improves both appliance-level and overall ED results.
Bounds and Maxima for the Workload in a Multiclass Orbit Queue
Authors: Morozov Evsey V. , Peshkova Irina V. , Rumyantsev Alexander S.
Published: January 2023

In this research, a single-server M-class retrial queueing system (orbit queue) with constant retrial rates and Poisson inputs is considered. The main purpose is to construct the upper and lower bounds of the stationary workload in this system expressed via the stationary workloads in the classical M/G/1 systems where the service time has M-component mixture distributions. This analysis is applied to establish the extreme behaviour of stationary workload in the retrial system with Pareto service-time distributions for all classes.
Joint Discrete Approximation of Analytic Functions by Shifts of the Riemann Zeta-Function Twisted by Gram Points
Authors: Laurinčikas Antanas
Published: January 2023

Let θ(t) denote the increment of the argument of the product π−s/2Γ(s/2) along the segment connecting the points s=1/2 and s=1/2+it, and tn denote the solution of the equation θ(t)=(n−1)π, n=0,1,…. The numbers tn are called the Gram points. In this paper, we consider the approximation of a collection of analytic functions by shifts in the Riemann zeta-function (ζ(s+itkα1),…,ζ(s+itkαr)), k=0,1,…, where α1,…,αr are different positive numbers not exceeding 1. We prove that the set of such shifts approximating a given collection of analytic functions has a positive lower density. For the proof, a discrete limit theorem on weak convergence of probability measures in the space of analytic functions is applied.
Hotelling T2 Control Chart for Detecting Changes in Mortality Models Based on Machine-Learning Decision Tree
Authors: Rakhmawan Suryo Adi , Omar M. Hafidz , Riaz Muhammad , Abbas Nasir
Published: January 2023

Mortality modelling is a practical method for the government and various fields to obtain a picture of mortality up to a specific age for a particular year. However, some information on the phenomenon may remain in the residual vector and be unrevealed from the models. We handle this issue by employing a multivariate control chart to discover substantial cohort changes in mortality behavior that the models still need to address. The Hotelling T2 control chart is applied to the externally studentized deviance model, which is already optimized using a machine-learning decision tree. This study shows a mortality model with the lowest MSE, MAPE, and deviance, by accomplishing simulations in various countries. In addition, the model that is more sensitive in detecting signals on the control chart is singled out so that we can perform a decomposition to determine the attributes of death in the specific outlying age group in a particular year. The case study in the decomposition uses data from the country Saudi Arabia. The overall results demonstrate that our method of processing and producing mortality models with machine learning can be a solution for developing countries or countries with limited mortality data to produce accurate predictions through monitoring control charts.
A Color Image Encryption Algorithm Based on Hash Table, Hilbert Curve and Hyper-Chaotic Synchronization
Authors: Wang Xiaoyuan , Zhang Xinrui , Gao Meng , Tian Yuanze , Wang Chunhua , Iu Herbert Ho-Ching
Published: January 2023

Chaotic systems, especially hyper-chaotic systems are suitable for digital image encryption because of their complex properties such as pseudo randomness and extreme sensitivity. This paper proposes a new color image encryption algorithm based on a hyper-chaotic system constructed by a tri-valued memristor. The encryption process is based on the structure of permutation-diffusion, and the transmission of key information is realized through hyper-chaotic synchronization technology. In this design, the hash value of the plaintext image is used to generate the initial key the permutation sequence with the Hash table structure based on the hyper-chaotic sequence is used to implement pixel-level and bit-level permutation operations. Hilbert curves combining with the ciphertext feedback mechanism are applied to complete the diffusion operation. A series of experimental analyses have been applied to measure the novel algorithm, and the results show that the scheme has excellent encryption performance and can resist a variety of attacks. This method can be applied in secure image communication fields.
Optimization of the 24-Bit Fixed-Point Format for the Laplacian Source
Authors: Perić Zoran , Dinčić Milan
Published: January 2023

The 32-bit floating-point (FP32) binary format, commonly used for data representation in computers, introduces high complexity, requiring powerful and expensive hardware for data processing and high energy consumption, hence being unsuitable for implementation on sensor nodes, edge devices, and other devices with limited hardware resources. Therefore, it is often necessary to use binary formats of lower complexity than FP32. This paper proposes the usage of the 24-bit fixed-point format that will reduce the complexity in two ways, by decreasing the number of bits and by the fact that the fixed-point format has significantly less complexity than the floating-point format. The paper optimizes the 24-bit fixed-point format and examines its performance for data with the Laplacian distribution, exploiting the analogy between fixed-point binary representation and uniform quantization. Firstly, the optimization of the 24-bit uniform quantizer is performed by deriving two new closed-form formulas for a very accurate calculation of its maximal amplitude. Then, the 24-bit fixed-point format is optimized by optimization of its key parameter and by proposing two adaptation procedures, with the aim to obtain the same performance as of the optimal uniform quantizer in a wide range of variance of input data. It is shown that the proposed 24-bit fixed-point format achieves for 18.425 dB higher performance than the floating-point format with the same number of bits while being less complex.
Viability Selection at Linked Sites
Authors: Eldon Bjarki
Published: January 2023

Evolutionary ecology may be described as explaining ecology through evolution and vice versa, but one may also view it as an integration of the two fields, where one takes the view that ecology and evolution are inseparable, and one can only begin to understand the biology of organisms by synthesizing the two fields. An example of such a synthesis is the biology of high fecundity and the associated concept of sweepstakes reproduction, or skewed individual recruitment success. As an illustration, we consider selection at linked sites under various dominance and epistasis mechanisms in a diploid population evolving according to random sweepstakes and experiencing recurrent bottlenecks. Using simulations, we give a few examples of the impact of the stated elements on selection. We show that depending on the dominance mechanisms, random sweepstakes can shorten the time to fixation (conditional on fixation) of the fit type at all sites. Bottlenecks tend to increase the fixation time, with random sweepstakes counteracting the effects of bottlenecks on the fixation time. Understanding the effect of random sweepstakes, recurrent bottlenecks, dominance mechanisms and epistasis on the fate of selectively advantageous mutations may help with explaining genetic diversity in natural highly fecund populations possibly evolving under sweepstakes reproduction.
A Multilevel Heterogeneous ADMM Algorithm for Elliptic Optimal Control Problems with L1-Control Cost
Authors: Chen Xiaotong , Song Xiaoliang , Chen Zixuan , Xu Lijun
Published: January 2023

In this paper, elliptic optimal control problems with L1-control cost and box constraints on the control are considered. To numerically solve the optimal control problems, we use the First optimize, then discretize approach. We focus on the inexact alternating direction method of multipliers (iADMM) and employ the standard piecewise linear finite element approach to discretize the subproblems in each iteration. However, in general, solving the subproblems is expensive, especially when the discretization is at a fine level. Motivated by the efficiency of the multigrid method for solving large-scale problems, we combine the multigrid strategy with the iADMM algorithm. Instead of fixing the mesh size before the computation process, we propose the strategy of gradually refining the grid. Moreover, to overcome the difficulty whereby the L1-norm does not have a decoupled form, we apply nodal quadrature formulas to approximately discretize the L1-norm and L2-norm. Based on these strategies, an efficient multilevel heterogeneous ADMM (mhADMM) algorithm is proposed. The total error of the mhADMM consists of two parts: the discretization error resulting from the finite-element discretization and the iteration error resulting from solving the discretized subproblems. Both errors can be regarded as the error of inexactly solving infinite-dimensional subproblems. Thus, the mhADMM can be regarded as the iADMM in function space. Furthermore, theoretical results on the global convergence, as well as the iteration complexity results o(1/k) for the mhADMM, are given. Numerical results show the efficiency of the mhADMM algorithm.
Phase-Space Modeling and Control of Robots in the Screw Theory Framework Using Geometric Algebra
Authors: Medrano-Hermosillo Jesús Alfonso , Lozoya-Ponce Ricardo , Rodriguez-Mata Abraham Efraím , Baray-Arana Rogelio
Published: January 2023

The following paper talks about the dynamic modeling and control of robot manipulators using Hamilton’s equations in the screw theory framework. The difference between the proposed work with diverse methods in the literature is the ease of obtaining the laws of control directly with screws and co-screws, which is considered modern robotics by diverse authors. In addition, geometric algebra (GA) is introduced as a simple and iterative tool to obtain screws and co-screws. On the other hand, such as the controllers, the Hamiltonian equations of motion (in the phase space) are developed using co-screws and screws, which is a novel approach to compute the dynamic equations for robots. Regarding the controllers, two laws of control are designed to ensure the error’s convergence to zero. The controllers are computed using the traditional feedback linearization and the sliding mode control theory. The first one is easy to program and the second theory provides robustness for matched disturbances. On the other hand, to prove the stability of the closed loop system, different Lyapunov functions are computed with co-screws and screws to guarantee its convergence to zero. Finally, diverse simulations are illustrated to show a comparison of the designed controllers with the most famous approaches.
Research of the Solutions Proximity of Linearized and Nonlinear Problems of the Biogeochemical Process Dynamics in Coastal Systems
Authors: Sukhinov Alexander , Belova Yulia , Panasenko Natalia , Sidoryakina Valentina
Published: January 2023

The article considers a non-stationary three-dimensional spatial mathematical model of biological kinetics and geochemical processes with nonlinear coefficients and source functions. Often, the step of analytical study in models of this kind is skipped. The purpose of this work is to fill this gap, which will allow for the application of numerical modeling methods to a model of biogeochemical cycles and a computational experiment that adequately reflects reality. For this model, an initial-boundary value problem is posed and its linearization is carried out; for all the desired functions, their final spatial distributions for the previous time step are used. As a result, a chain of initial-boundary value problems is obtained, connected by initial–final data at each step of the time grid. To obtain inequalities that guarantee the convergence of solutions of a chain of linearized problems to the solution of the original nonlinear problems, the energy method, Gauss’s theorem, Green’s formula, and Poincaré’s inequality are used. The scientific novelty of this work lies in the proof of the convergence of solutions of a chain of linearized problems to the solution of the original nonlinear problems in the norm of the Hilbert space L2 as the time step τ tends to zero at the rate O(τ).
Common Fixed Point of Two L2 Operators with Convergence Analysis and Application
Authors: Calineata Cristina , Ciobanescu Cristian , Turcanu Teodor
Published: January 2023

This article introduces a new numerical algorithm for approximating the solution of the common fixed point problem for two operators defined on CAT(0) spaces, belonging to the class L2, which was very recently introduced. The main results refer to Δ and strong convergence of the sequence generated by the new algorithm. A distinct feature of the adopted approach is the use of equivalent sequences.
Parameter Estimation and Hypothesis Testing of The Bivariate Polynomial Ordinal Logistic Regression Model
Authors: Rifada Marisa , Ratnasari Vita , Purhadi Purhadi
Published: January 2023

Logistic regression is one of statistical methods that used to analyze the correlation between categorical response variables and predictor variables which are categorical or continuous. Many studies on logistic regression have been carried out by assuming that the predictor variable and its logit link function have a linear relationship. However, in several cases it was found that the relationship was not always linear, but could be quadratic, cubic, or in the form of other curves, so that the assumption of linearity was incorrect. Therefore, this study will develop a bivariate polynomial ordinal logistic regression (BPOLR) model which is an extension of ordinal logistic regression, with two correlated response variables in which the relationship between the continuous predictor variable and its logit is modeled as a polynomial form. There are commonly two correlated response variables in scientific research. In this study, each response variable used consisted of three categories. This study aims to obtain parameter estimators of the BPOLR model using the maximum likelihood estimation (MLE) method, obtain test statistics of parameters using the maximum likelihood ratio test (MLRT) method, and obtain algorithms of estimating and hypothesis testing for parameters of the BPOLR model. The results of the first partial derivatives are not closed-form, thus, a numerical optimization such as the Berndt–Hall–Hall–Hausman (BHHH) method is needed to obtain the maximum likelihood estimator. The distribution statistically test is followed the Chi-square limit distribution, asymptotically.
Deterioration Control Decision Support System for the Retailer during Availability of Trade Credit and Shortages
Authors: Jani Mrudul Y. , Patel Heta A. , Bhadoriya Amrita , Chaudhari Urmila , Abbas Mohamed , Alqahtani Malak S.
Published: January 2023

The deterioration rate is a significant aspect of perishable goods. Since perishable items will always deteriorate, there are effective methods for reducing the rate of deterioration. Furthermore, in the existing inventory control literature, the deterioration rate is often viewed as an exogenous component. Keeping this problem in mind, this article develops the perishable inventory control system from the retailer’s perspective in which: (i) the deterioration rate is a controllable factor and suggests a new fresh quality technology (FQT) indicator, (ii) demand is determined by the perishable product’s quality, that is controlled by its rate of deterioration, (iii) the credit duration is predefined, and (iv) shortages are expected. The key goal is to demonstrate that there is an ideal quantity of the order that minimizes the retailer’s overall cost in terms of cycle time and deterioration rate. Finally, theoretical results are validated by solving two numerical illustrations and conducting a sensitivity analysis of the main factors resulting from the following managerial implications: (i) if the range of deterioration is between zero and one then the retailer should invest in the preservation factor to preserve the perishable product and if greater than one the retailer should not invest in the preservation factor, (ii) credit period significantly reduces the total cost. Hence, this trade credit strategy is more beneficial to the model.
Two-Step Cluster Analysis of Passenger Mobility Segmentation during the COVID-19 Pandemic
Authors: Harantová Veronika , Mazanec Jaroslav , Štefancová Vladimíra , Mašek Jaroslav , Foltýnová Hana Brůhová
Published: January 2023

In this paper, we analyse the specific behaviour of passengers in personal transport commuting to work or school during the COVID-19 pandemic, based on a sample of respondents from two countries. We classified the commuters based on a two-step cluster analysis into groups showing the same characteristics. Data were obtained from an online survey, and the total sample size consists of 2000 respondents. We used five input variables, dividing the total sample into five clusters using a two-step cluster analysis. We observed significant differences between gender, status, and car ownership when using public transport, cars, and other alternative means of transportation for commuting to work and school. We also examined differences between individual groups with the same socioeconomic and socio-demographic factors. In total, the respondents were classified into five clusters, and the results indicate that there are differences between gender and status. We found that ownership of a prepaid card for public transport and social status are the most important factors, as they reach a significance level of 100%, unlike compared to other factors with importance ranging from 60 to 80%. Moreover, the results demonstrate that prepaid cards are preferred mainly by female students. Understanding these factors can help in planning transport policy by knowing the habits of users.
Analytical Description of the Diffusion in a Cellular Automaton with the Margolus Neighbourhood in Terms of the Two-Dimensional Markov Chain
Authors: Kulagin Anton E. , Shapovalov Alexander V.
Published: January 2023

The one-parameter two-dimensional cellular automaton with the Margolus neighbourhood is analyzed based on considering the projection of the stochastic movements of a single particle. Introducing the auxiliary random variable associated with the direction of the movement, we reduce the problem under consideration to the study of a two-dimensional Markov chain. The master equation for the probability distribution is derived and solved exactly using the probability-generating function method. The probability distribution is expressed analytically in terms of Jacobi polynomials. The moments of the obtained solution allowed us to derive the exact analytical formula for the parametric dependence of the diffusion coefficient in the two-dimensional cellular automaton with the Margolus neighbourhood. Our analytic results agree with earlier empirical results of other authors and refine them. The results are of interest for the modelling two-dimensional diffusion using cellular automata especially for the multicomponent problem.
Identification of Cross-Country Fault with High Impedance Syndrome in Transmission Line Using Tunable Q Wavelet Transform
Authors: Sinha Pampa , Paul Kaushik , Saiprakash Chidurala , Abdelaziz Almoataz Y. , Omar Ahmed I. , Su Chun-Lien , Elsisi Mahmoud
Published: January 2023

The transmission lines of an electricity system are susceptible to a wide range of unusual fault conditions. The transmission line, the longest part of the electricity grid, sometimes passes through wooded areas. Storms, cyclones, and poor vegetation management (including tree cutting) increase the risk of cross-country faults (CCFs) and high-impedance fault (HIF) syndrome in these regions. Recognizing and classifying CCFs associated with HIF syndrome is the most challenging part of the project. This study extracted signal characteristics associated with CCF and HIF syndrome using the Tunable Q Wavelet Transform (TQWT). An adaptive tunable Q-factor wavelet transform (TQWT) based feature-extraction approach for CCHIF fault signals with high impact, short response period, and broad resonance frequency bandwidth was presented. In the first part, the time–frequency distribution of the vibration signal is used to determine the distinctive frequency range. Adaptive optimal matching of the impact characteristic components in the vibration signal was achieved by optimizing the number of decomposition layers, quality factor, and redundancy of TQWT based on the characteristic frequency band. In the last, the TQWT inverse transform was utilized to recreate the best sub-band to boost its weak impact characteristics. The effectiveness of the approach is confirmed by simulation and experimental findings in signal processing. The best decomposition level for signature features that can be extracted has been decided by Minimum Description length (MDL). The IEEE 39-bus system is used to test the suggested approach with reactor switching and the Ferranti effect.
On Some Expansion Formulas for Products of Jacobi’s Theta Functions
Authors: Zhai Hong-Cun , Cao Jian , Arjika Sama
Published: January 2023

In this paper, we establish several expansion formulas for products of the Jacobi theta functions. As applications, we derive some expressions of the powers of (q;q)∞ by using these expansion formulas.
A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model
Authors: Zaheer Shahzad , Anjum Nadeem , Hussain Saddam , Algarni Abeer D. , Iqbal Jawaid , Bourouis Sami , Ullah Syed Sajid
Published: January 2023

Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.
Existence of Self-Excited and Hidden Attractors in the Modified Autonomous Van Der Pol-Duffing Systems
Authors: Matouk A. E. , Abdelhameed T. N. , Almutairi D. K. , Abdelkawy M. A. , Herzallah M. A. E.
Published: January 2023

This study investigates the multistability phenomenon and coexisting attractors in the modified Autonomous Van der Pol-Duffing (MAVPD) system and its fractional-order form. The analytical conditions for existence of periodic solutions in the integer-order system via Hopf bifurcation are discussed. In addition, conditions for approximating the solutions of the fractional version to periodic solutions are obtained via the Hopf bifurcation theory in fractional-order systems. Moreover, the technique for hidden attractors localization in the integer-order MAVPD is provided. Therefore, motivated by the previous discussion, the appearances of self-excited and hidden attractors are explained in the integer- and fractional-order MAVPD systems. Phase transition of quasi-periodic hidden attractors between the integer- and fractional-order MAVPD systems is observed. Throughout this study, the existence of complex dynamics is also justified using some effective numerical measures such as Lyapunov exponents, bifurcation diagrams and basin sets of attraction.
Recent Advances in Breast Cancer Diagnosis, Treatment, Psychology, Management, and Reconstruction
Authors: Den Jennifer , Sisti Andrea
Published: January 2023

Breast cancer is the second most common cancer in women after skin cancer [...]
Epstein–Barr Virus-Positive Cutaneous and Systemic Plasmacytosis with TAFRO Syndrome-like Symptoms Successfully Treated with Rituximab
Authors: Kakiuchi Seiji , Akiyama Hiroaki , Harima Isamu , Takagi Ikumi , Rikitake Junpei , Kozuki Yoko , Inaba Mayumi , Fujiwara Hiroshi , Kurose Nozomu , Yamada Sohsuke , Masaki Yasufumi
Published: January 2023

Histopathologic findings in the lymph nodes of patients with thrombocytopenia, anasarca, fever, reticulin fibrosis, renal dysfunction, and organomegaly (TAFRO) syndrome are similar to those of idiopathic multicentric Castleman’s disease (iMCD), but TAFRO syndrome is different from iMCD in how it can progress rapidly and be fatal. These patients present scarce lymphadenopathy and low immunoglobulin levels. We present a case of cutaneous and systemic plasmacytosis (C/SP) that caused TAFRO syndrome-like symptoms which were successfully treated with rituximab. A 67-year-old woman presented with fever and a pruritic skin rash. Numerous plasma cells were observed in the peripheral blood and imaging revealed organomegaly, anasarca, and generalized lymphadenopathy. Subsequently, she rapidly developed thrombocytopenia as well as renal and heart failure. She tested positive for the Epstein–Barr virus (EBV), elevated immunoglobulins, and C/SP, which are also atypical for TAFRO syndrome, thereby complicating the diagnosis. However, after using the Japanese TAFRO Syndrome Research Group diagnostic criteria, we promptly administered rituximab to treat the C/SP with TAFRO-like symptoms and saved her life. Finally, histopathological observations of the lymph node biopsy helped confirm EBV-positive hypervascular-type iMCD. Therefore, diagnosing TAFRO-like syndromes based on the Japanese diagnostic criteria and following the associated treatment even without a confirmed diagnosis is crucial to improving the patient outcomes.
Numerical Simulation of Bubble Size Distribution in Single Snorkel Furnace (SSF) with Population Balance Model (PBM)
Authors: Qi Fengsheng , Ye Nan , Liu Zhongqiu , Cheung Sherman C. P. , Li Baokuan
Published: January 2023

Single Snorkel Furnace (SSF) vacuum refining furnace is a novel external refining equipment for high clean steel production. RH is a molten steel refining technology developed by Rheinstahl-Heraeus company. Compared with the traditional RH furnace, the SSF furnace has the advantages of a simple structure, high refining efficiency, and low production cost. However, because the upward flow and the downward flow are in a single snorkel, the flow phenomenon is more complex than that in the RH device. Therefore, the gas–liquid two-phase flow law in SSF furnaces plays an important role in improving equipment efficiency and accurate control. In addition, the evolution and movement behavior of bubbles have an important influence on the two-phase flow. In this study, the Population Balance Model (PBM) model is employed to study the bubble properties, taking into account the effect of bubble coalescence and breakup on the flow field. The simulation results with this model are consistent with the experimental values, and the comparison with the results of the model without the PBM is revealed to be closer with less error. The results show that with the PBM model the flow field is more homogeneously distributed, the flow velocity is more stable, and the area distribution of the upward flow and downward flow in the snorkel is more symmetrical. In the case of this study, as the fluid level rises, the bubble diameter will increase due to the decrease in hydrostatic pressure.
Cost Estimation Tool for Metallic Parts Made by Casting: A Case Study
Authors: Iraola-Arregui Itziar , Ben Youcef Hicham , Trabadelo Vera
Published: January 2023

This work presents a mathematical tool to estimate the cost of fabricating pumps by traditional casting. The drivers that most affect the cost of the process are identified in each step of the procedure, i.e., material, labour, energy, tools and general expenses associated with any industrial activity. The mathematical expressions used for the calculation of the different cost drivers are presented and used to estimate the cost of fabricating the different parts composing a radial pump in a small Moroccan foundry. Therefore, the input data of the equations carefully consider the economic factors of this country. However, the developed tool is versatile and can be adapted to describe the casting process in other countries with different economies just by changing the input data accordingly. It is observed that the influence of the drivers in the final cost of the pump is very dependent on the number of pumps to be produced. In any case, the cost estimation tool presented in this work will allow for the optimization of the yield of the casting process rendering the involved company more reliable. The tool provides an idea of the implications of changing production factors, allowing the foundry to give accurate and fast responses without compromising its profits.
Granite Pluton at the Panasqueira Tungsten Deposit, Portugal: Genetic Implications as Revealed from New Geochemical Data
Authors: Breiter Karel , Ďurišová Jana , Korbelová Zuzana , Vašinová Galiová Michaela , Hložková Michaela
Published: January 2023

Core samples recovered from exploration boreholes and granite/greisen outcrops at the Panasqueira world-class tungsten deposit in central Portugal were subjected to chemical analyses and petrographic studies. We present a geochemical dataset and the trace element compositions of quartz and micas from a large part of the unexposed Panasqueira granitic pluton. Our data suggest that the hidden granite body is more complicated than previously believed. It consists of a flat cupola of porphyritic granite with only traces of mineralization at Rio and a steep stock of greisenized leucogranite surrounded by a swarm of flat quartz–muscovite veins rich in wolframite between Barroca Grande and Panasqueira. The contents of W (Sn, Nb, Ta) in muscovite markedly drop at a transition from the unmineralized greisen body to quartz veins. The W deposit was formed in three principal stages: (1) intrusion of porphyritic two-mica granite accompanied with local near-contact greisenization and uncommon quartz–wolframite veinlets; (2) intrusion of a more strongly fractionated leucogranite and formation of the cupola and apophyses; (3) circulation of hydrothermal fluids from deeper parts of the granite body into the cupola, greisenization, hydraulic fracturing and opening of flat structures in and outside the cupola and formation of ore veins.
Mineral Processing Techniques Dedicated to the Recycling of River Sediments to Produce Raw Materials for Construction Sector
Authors: Henry Mathieu , Haouche Laurence , Lemière Bruno
Published: January 2023

Dredged river sediments produce a huge volume of mineral materials, which could be incorporated into building materials. Considering the raw sediment preparation, mineral processing techniques fit perfectly to this purpose. This work describes two procedures to prepare river sediments, according to the final beneficial use. The first is a dry procedure of deagglomeration to prepare river sediments with the aim of being incorporated into a concrete formulation to build a bicycle path. A large amount of deagglomerated sediment was prepared, requiring upscaling of the deagglomeration process. Successive steps of sieving and roll crushing were used to obtain deagglomerated sediments. To use it as raw material to produce pozzolanic materials and lightweight aggregates, a second procedure consisting of a wet classification at 63 µm was carried out. Steps of wet sieving, followed by hydrocycloning and screw classifying, were used to prepare several silt fractions under 63 µm.
Zinc(II) Carboxylate Coordination Polymers with Versatile Applications
Authors: Vasile Scaeteanu Gina , Maxim Catalin , Badea Mihaela , Olar Rodica
Published: January 2023

This review considers the applications of Zn(II) carboxylate-based coordination polymers (Zn-CBCPs), such as sensors, catalysts, species with potential in infections and cancers treatment, as well as storage and drug-carrier materials. The nature of organic luminophores, especially both the rigid carboxylate and the ancillary N-donor bridging ligand, together with the alignment in Zn-CBCPs and their intermolecular interaction modulate the luminescence properties and allow the sensing of a variety of inorganic and organic pollutants. The ability of Zn(II) to act as a good Lewis acid allowed the involvement of Zn-CBCPs either in dye elimination from wastewater through photocatalysis or in pathogenic microorganism or tumor inhibition. In addition, the pores developed inside of the network provided the possibility for some species to store gaseous or liquid molecules, as well as to deliver some drugs for improved treatment.
Reliability Characteristics of Metal-Insulator-Semiconductor Capacitors with Low-Dielectric-Constant Materials
Authors: Cheng Yi-Lung , Peng Wei-Fan , Huang Chi-Jia , Chen Giin-Shan , Fang Jau-Shiung
Published: January 2023

In this study, the reliability characteristics of metal-insulator-semiconductor (MIS) capacitor structures with low-dielectric-constant (low-k) materials have been investigated in terms of metal gate area and geometry and thickness of dielectric film effects. Two low-k materials, dense and porous low-k films, were used. Experimental results indicated that the porous low-k films had shorter breakdown times, lower Weibull slope parameters and electric field acceleration factors, and weaker thickness-dependence breakdowns compared to the dense low-k films. Additionally, a larger derivation in dielectric breakdown projection model and a single Weilbull plot of the breakdown time distributions from various areas merging was observed. This study also pointed out that the porous low-k film in the irregular-shaped metal gate MIS capacitor had a larger dielectric breakdown time than that in the square- and circle-shaped samples, which violates the trend of the sustained electric field. As a result, another breakdown mechanism exists in the irregular-shaped sample, which is required to explore in the future work.
Bulk Photovoltaic Current Mechanisms in All-Inorganic Perovskite Multiferroic Materials
Authors: Chen Jiazheng , Ma Guobin , Gong Boxiang , Deng Chaoyong , Zhang Min , Guo Kaixin , Cui Ruirui , Wu Yunkai , Lv Menglan , Wang Xu
Published: January 2023

After the discovery of bulk photovoltaic effect more than half a century ago, ferro-electrical and magneto-optical experiments have provided insights into various related topics, revealing above bandgap open voltages and non-central symmetrical current mechanisms. However, the nature of the photon-generated carriers responses and their microscopic mechanisms remain unclear. Here, all-inorganic perovskite Bi0.85Gd0.15Fe1−xMnxO3 thin films were prepared by a sol-gel process and the effects of Gd and Mn co-doped bismuth ferrites on their microtopography, grain boundries, multiferroic, and optical properties were studied. We discovered a simple “proof of principle” type new method that by one-step measuring the leakage current, one can demonstrate the value of photo generated current being the sum of ballistic current and shift current, which are combined to form the so-called bulk photovoltaic current, and can be related to the prototype intrinsic properties such as magneto-optical coupling and ferroelectric polarization. This result has significant potential influence on design principles for engineering multiferroic optoelectronic devices and future photovoltaic industry development.
Amphiphilic Bowl-Shaped Janus Particles Prepared via Thiol–Ene Click Reaction for Effective Oil–Water Separation
Authors: Qi Xian , Du Yaxian , Zhang Ziqiang , Zhang Xu
Published: January 2023

Janus particles for oil–water separation have attracted widespread attention in recent years. Herein, we prepared a bowl-shaped Janus particle that could rapidly separate oil and water through a thiol–ene click reaction and selective etching. Firstly, snowman-like composite microspheres based on silica and mercaptopropyl polysilsesquioxane (SiO2@MPSQ) were prepared by a hydrolytic condensation reaction and phase separation, and the effects of the rotational speed and molar ratios on their microscopic morphologies were investigated. Subsequently, bowl-shaped Janus particles with convex hydrophilic and concave oleophilic surfaces were prepared via a thiol–ene click reaction followed by HF etching. Our amphiphilic bowl-shaped Janus particles could remarkably separate micro-sized oil droplets from an n-heptane–water emulsion with a separation efficiency of >98% within 300 s. Based on the experimental and theoretical results, we proposed the underlying mechanism for the coalescence of oil droplets upon the addition of the amphiphilic bowl-shaped Janus particles.