bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mean-field dynamics of infinite-dimensional particle systems: global shear versus random local forcing
Authors: Agoritsas Elisabeth 
Published: March 2021

Abstract
In infinite dimensions, many-body systems of pairwise interacting particles provide exact analytical benchmarks for the features of amorphous materials, such as the stress–strain curve of glasses under quasistatic shear. Here, instead of global shear, we consider an alternative driving protocol, as recently introduced by Morse et al 2020 (arXiv:2009.07706), which consists of randomly assigning a constant local displacement on each particle, with a finite spatial correlation length. We show that, in the infinite-dimensional limit, the mean-field dynamics under such a random forcing are strictly equivalent to those under global shear, upon a simple rescaling of the accumulated strain. Moreover, the scaling factor is essentially given by the variance of the relative local displacements of interacting pairs of particles, which encodes the presence of a finite spatial correlation. In this framework, global shear is simply a special case of a much broader family of local forcing, which can be explored by tuning its spatial correlations. We discuss the specific implications for the quasistatic driving of glasses—initially prepared at a replica-symmetric equilibrium—and how the corresponding ‘stress–strain’-like curves and elastic moduli can be rescaled onto their quasistatic-shear counterparts. These results hint at a unifying framework for establishing rigorous analogies, at the mean-field level, between different driven disordered systems.
Large deviations for Markov processes with stochastic resetting: analysis via the empirical density and flows or via excursions between resets
Authors: Monthus Cécile 
Published: March 2021

Abstract
Markov processes with stochastic resetting towards the origin generically converge towards non-equilibrium steady-states. Long dynamical trajectories can be thus analyzed via the large deviations at level 2.5 for the joint probability of the empirical density and the empirical flows, or via the large deviations of semi-Markov processes for the empirical density of excursions between consecutive resets. The large deviations properties of general time-additive observables involving the position and the increments of the dynamical trajectory are then analyzed in terms of the appropriate Markov tilted processes and of the corresponding conditioned processes obtained via the generalization of Doob’s h-transform. This general formalism is described in detail for the three possible frameworks, namely discrete-time/discrete-space Markov chains, continuous-time/discrete-space Markov jump processes and continuous-time/continuous-space diffusion processes, and is illustrated with explicit results for the Sisyphus random walk and its variants, when the reset probabilities or reset rates are space-dependent.
Statistical Analysis of Wind Speed Data and Assessment of Wind Power Density Using Weibull Distribution Function (Case Study: Four Regions in Iraq)
Authors: Jabbar R. I. 
Published: March 2021

Abstract
Assessment of accurate wind power potential is very essential before planning to implement wind power conversion system in a particular site. In order to determine the wind power potential, a statistical analysis of wind speed measurements is required. The main objective of this paper is to analyse the daily measured wind speed data at the height of 10 m and 50 m over the ground for four regions (Al-Rutba, Sinjar, Al-Qa’im and Al-Bayji) in Iraq using the Weibull distribution function. The wind data that is used in this research is collected from NASA surface meteorology. The Weibull parameters for the individual month have also been computed. This statistical analysis is programmed in MATLAB environment to evaluate the monthly and yearly wind power density of the selected areas. In addition, the wind power classes for the four locations will be determined. The results explain the months that have maximum power wind generation in the four regions. According to the calculated yearly wind power density, it can be concluded that the selected areas are between marginal to fair for wind power generation.
Radix-p Multiple Valued Logic Function Simplification using Higher Radix Representation
Authors: Al-Wardi Zaid 
Published: March 2021

Abstract
Conventional Boolean logic, which is based on radix-2 binary logic, is no longer suitable to represent complex digital systems. As digital systems continue to grow in complexity, more compact representations become essential to describe these systems. One such possibility is to use multiple-valued logic, with radices larder than two. Many non-conventional technologies are considered as candidates to replace conventional semiconductor based switching circuit technology for computational systems such. Interestingly, some of these technologies are inherently multiple valued. Multiple-valued logic design and optimization methodologies are not yet elaborated, as compared to the well-established binary logic design and optimization and testing scheme. In this paper we are proposing an algorithm, with which functions to describe digital systems are specified and simplified using Multiple valued logic. Representing a digital system with different radices come out with different equivalent specifications for that system. A designer can choose the most suitable specification for his design constrains. The influence of radix increase on the complexity of the algorithm and the resulting specification is also demonstrated and discussed in this paper.
A Novel Kite Cross Hexagonal Search Algorithm for Fast Block Motion Estimation
Authors: Hamood Tareq Zaid , Abdulmunem Matheel Emaduldeen 
Published: March 2021

Abstract
The performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to results obtained in this paper, KCHS needs less search time than others algorithms and gives very acceptable performance quality.
Right Truncated of Mixture Topp-Leone with Exponential Distribution
Authors: Hashim Hassaneen Abd Aljabar , Al-Khafaji Kareema Abed Al-kadim 
Published: March 2021

Abstract
In this paper the truncated distribution from the right was found for our distribution resulting from the mixing of two Topp Leone with the exponential distributions. The CDF and PDF are derived, as are the survival, moment and hazard functions. Two methods for estimating the parameters were derived, namely the maximum likelihood method and the moment method, as well as in the data analysis an algorithm was constructed and applied in the MATLAB.
On (gαφ, - Regularisation of English Letters Partition
Authors: Alabaaweee Alaa I. , Mahmood Ammar S. 
Published: March 2021

Abstract
Any partition it has two characteristics, the first is called regular and the second is regularisation, both of which were introduced by James in 1976. Through this research, we will try to present a new type that greatly serves the proposed model of English letters that was presented by Mahmood and Mahmood in 2019.
A Computational Method for Nonlinear Fredholm Integro-Differential Equations Using Haar Wavelet Collocation Points
Authors: Swaidan Waleeda , Ali Haleema S. 
Published: March 2021

Abstract
Haar wavelet collocation points method is developed to the computational solution for nonlinear Fredholm integral and integro-differential equations on interval [0, tf ] using Leibnitz-Haar wavelet collocation points method. Essential principle is transmutation of the integral equation to equivalent higher order differential equation together with initial conditions. The transmutation is carried out using the Leibniz law. Haar wavelet collocation points and its operational matrix is employed to transform the higher order differential equation to a set of algebraic equations, then resolving these equations usage MATLAB program to calculate the demanded Haar coefficients. The computational results of the proposed approach is presented in four problems and make a simulation against the accurate solution. In addition, Error analysis is exhibited the proficiency of the proposed technique and when Haar wavelet resolutions increases the results are close to the accurate solutions.
Human Face Identification using Moments and Transformations
Authors: Hashim Asaad Noori , Taher Jannah Raad 
Published: March 2021

Abstract
Face recognition one of the most promising techniques such that the importance of biometric user identification is increasing every day, several methods have been suggested to perform it such as classification, deep learning, statistical, moments, etc., this work describes different approaches to develop biometric technique, based on the moments and transformation. The proposed method for face recognition is based on Legendre moments and singular value decomposition for feature extraction. Local approaches by dividing images into several blocks (overlapped blocks and non-overlapped) have been adopted to gain the best recognition rate, as well as normalization using the Z-score method, which has been used. The outcomes experimental showed that the suggested system is effective, it has been tested using ORL face image databases with 10 cases and achieved a recognition rate from 85.27-100%, also, applied on FEI Brazil face database with 10 cases and achieved a recognition rate from to 79.77-100%.
Asymptotic Regional Boundary Gradient Observability in Diffusion Neumann Problem
Authors: Al-Janabi Asmaa N. , Jaafar Zainab A. 
Published: March 2021

Abstract
The principle objective of this paper is to determine the concept of asymptotic regional boundary gradient observability to the case of diffusion system with Neumann boundary conditions. More precisely, for linear distributed parameter diffusion systems, we show that the sensors characterizations allow to reconstruct regional boundary gradient state in a boundary target subregion of the considered systems domain. Moreover, various interesting results associated with the selection of sensors are explained in specific and customized position situations respect to the asymptotic regional boundary gradient observability notion to be realized. Finally, we was clarified the existence of a dynamical systems that is not asymptotic boundary gradient observable in whole the boundary domain, but it may be performed on a part of boundary domain.
An Analytical Appraisal for Supervised Classifiers’ Performance on Facial Expression Recognition Based on Relief-F Feature Selection
Authors: Abdulrazaq Maiwan B. , Mahmood Mayyadah R. , Zeebaree Subhi R. M. , Abdulwahab Mohammad H. , Zebari Rizgar R. , Sallow Amira B. 
Published: March 2021

Abstract
Face expression recognition technology is one of the most recently developed fields in machine learning and has profoundly helped its users through forensic, security, and biometric applications. Many researchers and program developers have allocated their time and energy to figure out various techniques which would add to the technology’s functionality and accuracy. Face expression recognition is a complicated computational process in which is implemented via analyzing changes in facial traits that follow different emotional reactions. This paper endeavors to inspect accuracy ratio of six classifiers based on Relief-F feature selection method, relying on the utilization of the minimum quantity of attributes. The classifiers in which the paper attempts to inspect are Multi-Layer Perceptron, Random Forest, Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Radial Basis Function. The experiment illustrates that K-Nearest Neighbor is the most accurate classifier with the total accuracy ratio of 94.93% amongst the rest when applied on CK+ Dataset.
On θ-Continuity in Micro Topological Spaces
Authors: Jassim Rana H. , Rasheed Reem O. , Faris Hairan I. 
Published: March 2021

Abstract
Every year different types of topological spaces are introduced by many topologists. Nowadays available topologies are supra topology, ideal topology, bitopology, fuzzy topology, Fine topology, nano topology and so on. Nano topology introduced by Thivagar, using this nano topology we introduced micro topology and also study the concepts of θ - micro open sets and θ – micro continuous and some of their properties are investigated.
Estimate Probability Distribution of Monthly Maximum Daily Rainfall of Iraq
Authors: Nassif Wedyan G. , Al-Taai Osama T. , Mohammed Ali J. , AL-Shamarti Hasanain K. A. 
Published: March 2021

Abstract
Precipitation is very important to humankind, as it represents a pure source of water beside the earth. Therefore, many researchers are interested in rain. In this study, the monthly data for the amount of rainfall for fourteen stations (Baghdad, Basra, Diwania, Kirkuk, Smaw, Tikirit, Rutb, Mousl, Alialgarbi, talafer, Kut, samara, Hali, and Nasiriya) in Iraq was collected from the General Authority for Meteorology and Seismic Monitoring, the maximum understanding of precipitation behavior, was tested the data according to tests Kolmogorov-Smirnov. Anderson, Chi-Squared, The 20 probability distributions were applied to find the appropriate distribution of this data. Was found that the Mosul station has the largest amount of rain from other stations and that this data follows the distribution Log-Gamma Which is considered the best distribution according to the tests, while Basra station takes to distribute Burr (4p), while, Rutba Station takes logistical distribution while Baghdad Log-Pearson 3 Station, despite the maximum monthly precipitation, Baghdad has more statistics than Rutba.
The Relationships Between Quantum Logic Maps, and T-norms on D-posets via Probabilistic Space Operations
Authors: Al-Adilee Ahmed , Al-Mousawi Mustafa G. 
Published: March 2021

Abstract
In this paper, We propose several constructions of quantum logic maps, and T-norms on d-poset. Afterwards, we examine the relationships of those constructions to the probability space operations. We have presented several relations, proofs, and examples of each structure.
Predicting Autism Spectrum Disorder (ASD) for Toddlers and Children Using Data Mining Techniques
Authors: Musa Rasool Azeem , Manaa Mehdi Ebady , Abdul-Majeed Ghassan 
Published: March 2021

Abstract
Autism Spectrum Disorder (ASD) is a contemporary disease that has recently spread among toddlers and children. Many researchers have been interested to determine the features of the autism. However, this kind of studies is costly in term of the gathering information from several sources. In this paper, we introduced and applied a novel and early prediction techniques based on the using of data mining and machine learning tools. It is difficult to determine the features of any autism ages. In this paper, we used data mining predication techniques which play an integral role to predict the symptoms of autism for any age group. The data of this study is AQ-10 dataset which are involved for toddlers and children. The results present a superior performance for ASD classification. Random forest, Decision Tree, Support Vector Machine, and Naive Bayes the accuracy of 1.0 with the features selected by correlation technique.
Evaluation and Realization of Memristor emulator spice
Authors: Ahmed Zahraa Khaled , Taha Faris Hassan 
Published: March 2021

Abstract
The fourth missing element of electrical device now are attractive in many applications, memristor is one of the elements which the resistance depends on the historical memory of the current flowing. In this paper, a memristor device were realized, implemented and evaluated using PSPICE package. Where the HP laboratories are manufactured and tests the performance. The relationship between voltage and current characteristics has been measured in different frequencies and voltage to the memristor. The results obtained are compatible with the designed micromodel with the expected performance.
Survey: Convolution Neural networks in Object Detection
Authors: Hakim Heba , Fadhil Ali 
Published: March 2021

Abstract
In latest years, deep neural networks were observed to be the most influential among all innovations in the computer vision field, generating remarkable performance on image classification. Convolution neural networks (CNNs) are considering as an interesting tool for studying vision of biological because this category of artificial vision systems shows the capabilities of visual recognition similar to those of human observers. By improving the recognition performance of these models, it appears that they become more effective in prediction. Recent benchmarks have shown that deep CNNs are excellent approaches for object recognition and detection. In this paper, we are focusing on the core building blocks of convolution neural networks architecture. Different object detection methods that utilize convolution neural networks are discussed and compared. On the other hand, there is a simple summary of the common CNNs architectures.
The impact of Routing Protocols and topologies on IOT Based Systems
Authors: Hameed Saif Saad , Saleh Hadeel Mohammed 
Published: March 2021

Abstract
Routing protocols is a mechanism of detecting best route path from single node to multiple nodes or signal node in order to transfer data between network and it specify how routing techniques in routers communicate between routers to accomplish communication tasks. A simulation was done to compare some protocols such as ““RIP””, ““OSPF”” and “EIGRB’ to determine the best routing protocol at a specific given network topology for IoT applications. The main problem there is a different routing protocols was developed each has its performance among different IoT applications. As well a simulation was required to detect the perfect routing protocol required for IoT applications. In this project a simulation of “IoT” network that serve the “IoT” applications using different routing protocols such as ““RIP””, ““OSPF”’ and “EIGRB” and an evaluation to the performance of each routing technique was to decide which is the best routing for “IoT” applications. Opnet modular was used to implement the simulation and it is found that the ““IGRP”” has the fastest router convergence among the three protocols we are testing.
Evaluating Facebook success in Iraq: An extension of the DeLone and McLean’s model of information systems success (ISS)
Authors: Al-Azawei Ahmed , Al-Azawi Raad 
Published: March 2021

AbstractWith the continuous improvement in Internet and World Wide Web (WWW) technologies, people are increasingly using social network sites (SNS) such as Facebook. SNS has many different implications for individuals, societies, and organizations. Hence, it is important to evaluate the success of such technologies from the users’ point of view. This research represents one of the first empirical evaluations of Facebook success based on an extended Delone and Mclean’s information systems success (ISS) model in Iraq. The proposed model includes eight constructs namely, information quality, system quality, service quality, intrinsic motivation, perceived interaction, Facebook use, user satisfaction, and net benefits. The validity and reliability of the research instrument used to collect the research data was confirmed first. Accordingly, the partial least squares-structural equation modeling technique (PLS-SEM) was performed on data gathered from 369 Facebook users. This method is useful in identifying the association between different variables as well as the prediction ability of independent on dependent constructs. The findings show that nine out of thirteen hypothesized associations were upheld. These outcomes provide significant implications for both practice and theory.
On Soft Closure Function in Soft Ideal Spaces
Authors: Abbas Sameer Annon , Hussain Ahmed Hadi , Salman Abbas Musleh , Hussein Nabeel Ali 
Published: March 2021

Abstract
As a result of evolution in science by researchers and scientists led to quantum leaps in several different fields of science, including medicine, engineering, and chemistry, physics and other Humanities and social as this evolution based on evolution in various branches of mathematics which is the infrastructure to develop the rest of science. Where dentition of (Soft Set) came to solve many problems facing humans. This is because of researcher Molodtsov in 1999 [1]. Where the definition of soft sets was based universal set, Ex
represent the all parameters of X, also the function from Ex
to P(X). The motivation behind this paper is to present the thought of soft ideal in soft set theory. The idea of soft closure is likewise presented. These ideas are talked about with a view to discover unprecedented soft topologies from the first one The fundamental structure, particularly a reason for such produced soft topologies likewise considered here. At long last, the idea of soft closure compatibility with soft topologies is presented and some proportional conditions. As well as the concept of soft Ψ
Γ
-Operator and study of the most significant properties.
Design of Cloud Computing for Educational Centers Using Private Cloud Computing: A Case Study
Authors: Ouda Ghazwan K. , Yas Qahtan M. 
Published: March 2021

Abstract
Cloud computing is becoming a popular approach to solve many serious problematic issues since its introduction in 2000. Cloud computing includes data storage in addition to software and hardware sharing via using network infrastructure, computers, and other resources. However, the educational centers are still considered conventional and limited in terms of their existing hardware infrastructure. A case study of cloud computing deployment in the University of Diyala-ICC (Internet and Computer Center)-will be used to illustrate the situation. This study aims to develop a basic cloud-based design for improving the efficiency of ICC laboratories, as an educational center in the University of Diyala to make it a study and research center of cloud computing for computer science students in the university. The proposed design is built via using “Software as a Service” (SaaS) model. Cloud computing in ICC laboratories is designed using Private Cloud Computing. The proposed design provided flexibility to ICC and allows to improve the capabilities of computer network and helps in managing their resources easily.
Proposed APs Distribution Optimization Algorithm: Indoor Coverage Solution
Authors: Rawaa Akram M. , Al-Nakkash Aseel H. , Salim Omar Nameer Mohammed , AlAbdullah Ali A. S 
Published: March 2021

Abstract
Recently, Wireless systems for indoor environments have become very important and popular. With increasing the need for mobile devices like laptops, tabs and smartphones, the optimization of WLAN coverage became very necessary to provide efficient communication with less cost. This optimization is verified by decreasing number of WiFi Access Point coverage. A multi objectives algorithm is proposed in this paper to optimize WiFi coverage in indoor environment using MatLab software. The proposed algorithm employs (BPSO) to determine the optimal number and locations for WiFi APs distribution based on predefined received power thresholds value. it has been found that the proposed algorithm is flexible using weighted multi-fitness functions. In addition, conducting site survey in real environment verifies the reliability of the proposed algorithm. Applying APD-CS algorithm in the adopted real environment increase the coverage by 64.6% and the average received power 7 dBm at Pth equal to-55 dBm.