bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples
Authors: Thong Zhonghui , Tan Jolena Ying Ying , Loo Eileen Shuzhen , Phua Yu Wei , Chan Xavier Liang Shun , Syn Christopher Kiu-Choong 
Published: January 2021

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.
Prevalence of COVID-19 Among Children and Adolescents While Easing Lockdown Restrictions in Cologne, North Rhine-Westphalia, Germany
Authors: Körner Robert Walter , Weber Lutz Thorsten 
Published: January 2021

Abstract
Background In Germany, widespread full closures of schools and day care facilities were part of lockdown measures to control the spread of coronavirus disease 2019 (COVID-19). In the state of North Rhine-Westphalia closures took place on March 16, 2020 and were gradually eased from end of April 2020 until beginning of June 2020.
Objective This study aims to evaluate the prevalence of COVID-19 among children and adolescents during the reopening period of schools and day care facilities in Cologne, North Rhine-Westphalia, Germany. It further depicts medical history and results of physical examinations of pediatric patients undergoing a test for severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2).
Methods Testing for SARS-CoV-2 was carried out by a naso- and / or oropharyngeal swab by local pediatricians at the time of presentation. Samples were analyzed by real-time reverse transcription polymerase chain reaction (RT-PCR). Medical history and physical examination results were retrospectively analyzed.
Results 525 children and adolescents presented mainly with mild upper respiratory tract infections. Three patients were diagnosed with COVID-19. Their medical history and examination results did not stand out from the other patients.
Conclusion A precautious stepwise opening of schools and day care facilities was not associated with the occurrence of a relevant prevalence of COVID-19 among children and adolescents. However, a low general prevalence of COVID-19 at the end of the observation period has to be taken into account. Systematic testing might enable adjusted regulations in favor of full closures, especially in the light of increasing evidence for pediatric patients constituting a low-risk group for COVID-19.
Oxidative Desymmetrization of Isoindolines Realized by tert-Butyl Nitrite (TBN) Initiated Radical sp3 C–H Activation Relay (CHAR)
Authors: Yuan Yu , Jia Xiaodong , Sun Zheng , Shao Yu , Zhang Shuwei , Zhang Yuxian 
Published: January 2021

AbstractAn oxidative desymmetrization of isoindolines was realized by TBN initiated radical sp3 C–H activation relay (CHAR), providing a series of ω-hydroxylactams in high yields. This reaction exhibits broad substrate scope and functional group tolerance, and even N-alkyl iso­indolines can be well tolerated. The mechanistic study shows that the C–H bond oxidation, dioxygen trapping and intramolecular 1,5-H shift might be the key steps to achieve the oxidative desymmetrization.
On the design of the URL
Authors: Pemberton Steven 
Published: January 2021

Abstract
Notations can affect the way we think, and how we operate;
consider as a simple example the difference between Roman Numerals
and Arabic Numerals: Arabic Numerals allow us not only to more
easily represent numbers, but also simplify calculations and the
manipulation of numbers.
One of the innovations of the World Wide Web was the URL. In the
last 30 years URLs have become a ubiquitous element of everyday
life, so present that we scarcely even grant them a second thought.
And yet they are a designed artefact: there is nothing natural about
their structure – each part is there as part of a design.
This paper looks at the design issues behind the URL, what a URL
is meant to represent, how it relates to the resources it
identifies, and its relationship with representational state
transfer (REST) and the protocols that REST is predicated on. We
consider, with hindsight, to what extent the design could have been
improved.
KF-Swoosh: An Efficient Spark-Based Entity Resolution Algorithm for BigData
Authors: Alami L. , Aassem Y. , Hafidi I. 
Published: January 2021

Abstract
Entity matching (EM), which is, the task of identifying records that refer to the same entity, is a critical task when constructing data warehouses. This task is often very expensive at the running time because data must be compared in pairs. This problem becomes more important when dealing with large-scale data. We propose a new parallel algorithm that divides the data using K-Medoid algorithm implemented with Spark framework. The computational experiments are done and show that we can improve the solution of a set of instances in a reduced execution time.
The Classification of the Environmental Indicators using ELECTRE TRI Method for Loukkos Basin in Morocco
Authors: Aziz Layla , Achki Samira , Chalh Ridouane. 
Published: January 2021

Abstract
Multi-Criteria Analysis (MCA) has found many applications in both technical and research sectors. MCA is a way to break the problem into more practicable elements, to permit data and decisions to be judgements to support the elements, and make the right decision. The aim of this paper is to analyze, compare, and make decisions of various current, and future scenarios of different quantifiable indicators for different considerations and various socio-economic aspects. Furthermore, this analysis is used to improve or at least to preserve the environment and natural resources in the basin. In this study an application by a real data set is made, these data are evaluated and extracted from classified satellite images of Loukkos basin, the classification of this satellite images regroups several classes of the data set such as agglomeration, dams, watercourses, croplands, bare soils and forests … etc. In reality, these data come from different sources like watershed information system (drinking water supply, irrigation system), transportation infrastructures (roads, dams), natural resources (Water, soils, and vegetation), human activities (agriculture, urbanization, and industry) and different socio-economic factors (demography). The main objective of this work is sorting the environmental indicators using the ELECTRE TRI tool, where ten alternatives are considered. We focus the classification of the real data set into the altitude, and the combined surface area factors. The obtained results prove that the classification is stable and the multi-criteria approach ELECTRE-TRI is suitable to a better sorting of the environmental indicators for the Loukkos Basin located in Morocco.
Meshless approach based on MLS with additional constraints for large deformation analysis
Authors: El Kadmiri R. , Belaasilia Y. , Timesli A. , Kadiri M. S. 
Published: January 2021

AbstractIn the present work, we are interested to develop a meshless approach, based on the strong form MLS approximation with additional constraints, to solve the nonlinear elastic and elsto-plastic problems for regular and irregular distribution of points. We adopt a plastic behavior law based on the total deformation theory, which is very convenient when the physical nonlinearity is more important than the effect of irreversible process and the loading history. In plasticity, one encounters discontinuities of rigidity where the application of asymptotic developments seems difficult or impossible. To apply the Taylor series expansion, regularization methods have been adapted. The strong form MLS approximation allows us to avoid the inconvenient of the numerical integration, while the asymptotic developments help us to reduce the computation cost observed in the incremental law of plasticity and the iterative methods. For irregular points distribution, we can get an ill-posed least squares problems due to a singular moment matrix of MLS approximation. To avoid this difficulty, we propose a modified MLS approximation by introducing additional constraints which allows to increase the error functional used in the derivation of the shape functions.
An overview of the different methods for optimizing the virtual resources placement in the Cloud Computing
Authors: Ndayikengurukiye Aristide , Ez-Zahout Abderrahmane , Omary Fouzia 
Published: January 2021

Abstract
The emergence of Cloud Computing has driven users of new technologies to adopt it. This is made possible by the used technologies, namely the virtualization, which is the key element in Cloud Computing. The management and orchestration of virtual resources (the virtual machine placement and VNFs) in the Cloud remains a complex task, which, recently, attracted the interest of many researchers. This paper provides an overview of the techniques adopted to optimize the virtual machines placement and the virtual networks functions. Focused on different resources (CPU, memory, bandwidth and storage)applied in a virtualized environment. These techniques always target several approaches including the improvement of the QoS defined in the service level agreements (SLA), the good management of the energy used by physical resources, the allocation of resources and the management tasks in the data center.
Application of empirical analysis to underground mine a study case In morocco
Authors: Ghazdali O. , Tagma T. , Rziki S. , Moustadraf J. 
Published: January 2021

Abstract
The OUMJRANE mine located in the eastern Anti-Atlas in Morocco, is operating underground mining in various sedimentary rocks from sandstones to shales. During operation, several instabilities are recorded in the underground galleries. The present study represents an empirical characterization based on the classification index of the rock mass: RMR (Rock Mass Rating), Q (Tunnel Quality Index) of the stability of the excavation. The input data used for this analysis were based on fracturing surveys, core logging, and laboratory tests. The results of this study show that the rock mass is of low quality.
Big-Parallel-ETL: New ETL for Multidimensional NoSQL Graph Oriented Data
Authors: Soussi Nassima 
Published: January 2021

Abstract
The quantitative explosion of digital data derived from social networks, smart devices, IoT sensors, etc is eventuated by the Big Data concept considered as a very important aspect in the performance improvement of traditional decision-making systems since it reveals serious challenges to be addressed. Therefore, the main purpose of this research paper is the integration of NoSQL Graph-oriented Data into Data Warehouse to deal with Big Data challenges especially with the absence of similar approaches to the best of our knowledge. In this paper, we propose a new approach called Big-Parallel-ETL that aims to adapt the classical ETL process (Extract-Transform-Load) with Big Data technologies to accelerate data handling based on the famous MapReduce concept characterized by its efficient parallel processing feature. Our solution proposes a set of detailed Algorithms based on several rules able to conceive rapidly and efficiently the target multidimensional structure (dimensions and facts) from the NoSQL Graph oriented database.
Congestion Control of Multi-layer Cellular Networks based on Modeling of Transmit Power
Authors: Dai Jiahao , Chen Yongqun , Chen Yucheng , Meng Aodong 
Published: January 2021

Abstract
The explosive growth of network traffic and the limited storage node resources make the existing network architecture bear great pressure, and network congestion appears in work and life from time to time. In order to ensure the timeliness and effectiveness of the network, we propose a congestion control method based on transmission power. Different from previous work, we built a multi-layer cellular network consisting of multiple macro stations, small base stations and mobile terminals. On this basis, we establish a congestion control model based on the mixed queuing model of service window and calculate the input/output rate and congestion. Finally, we use simulation to verify the effectiveness of the congestion model. The network builder can use our model to set the appropriate power of macro stations and small base stations to ensure network stability.
Modeling and Intelligent Identification of Axis Orbit for Rotating Machinery Based on the Convolution Neural Networks
Authors: He Xiaofeng , Liu Xiaofeng , Lu Xiulian , He Lipeng , Ma Yunxiang , Guo Sheng , Yang Tao 
Published: January 2021

Abstract
Based on the high dimensional complex feature recognition capability of convolutional neural networks (CNN), this paper proposes a method for orbit intelligent identification of rotating machinery based on CNN. In this method, the orbit is purified based on the frequency-domain filtering algorithm. Then an algorithm for converting the vibration signal to the orbit matrix is proposed to construct the input matrix of CNN. Finally the classification model of the CNN is established to realize the automatic identification of the orbit of rotating machines. Case studies show that the proposed method has high identification accuracy over 85% on experimental data and good universality over 73% in field data identification.
Avalanche Dynamics of Multi-Information Propagation Modelling
Authors: Wang Jingjing , Xu Yizhan , Tang Ming 
Published: January 2021

AbstractWith the rapid development of social network information technology, some information can spread in a wide range while others are difficult to diffuse and quickly vanish. Previous researches have carried out some empirical analysis and theoretical methods on the information dissemination such as news and rumours, but few of them study multiple information dissemination process based on empirical data. Here, we study the online true and false information diffusion based on a Twitter dataset. We analyse their difference on size, depth, annual generation and annual transmission, and find that the spreading ability of false information is significantly stronger than that of true information. Then, we propose a novel multi-information propagation model, which considers the probability of information generation and transmission. With limited attention of users, different true and false information will compete for attention and ultimately lead to the heterogeneity of information size. Extensive numerical simulations demonstrate that the competition of these two information will transit from insufficient state to sufficient state, which will introduce the criticality of the information dissemination system. Furthermore, we study the impact of information generation and transmission probability to the spreading ability, and find that they can significantly affect the distribution of information size, which is consistent with the empirical results. This work helps to understand the dissemination characteristics of the true and false information on online social networks and would promote further studies on the critical phenomenon of information dissemination.
Detection of Common Foreign Objects on Power Grid Lines Based on Faster R-CNN Algorithm and Data Augmentation Method
Authors: Zu Guangxin , Wu Guoliang , Zhang Chong , He Xing 
Published: January 2021

Abstract
Foreign objects hanging on the electrical equipment are big threats to the safety of the power grid. In this paper, a high-precision object detection model designed for common foreign objects in power grid based on Faster R-CNN algorithm is proposed. The data augmentation methods such as rotation, adjusting brightness and colour saturation, adding Gaussian noise are carried out to expand the data in the training set. The accuracy rate and the recall rate of the model reach 93.09% and 94.33% respectively. The object detection model trained by the training set with data augmentation has achieved better detection results.
Multi-objective Task Scheduling Algorithm Based on Harmony Search for Grid Microservice Optimization
Authors: Xie Yuqing , Wang Yuan , Jiang Ying , Peng Zirong , Wang Yongli 
Published: January 2021

Abstract
In order to solve the multi-objective task scheduling optimization problem of data platform microservice combination, this paper proposes an improved harmony search algorithm MODRHS, which fuses the multi-objective optimization NSGA-II algorithm, and improve its elite selection strategy by a circular elimination method, and strengthens the uniformity of the non-dominated solutions at first layer and improves the algorithm’s Pareto optimization ability. At the same time, it describes the mathematical expression of the Dockers model and service optimization goals of the data platform microservice scheduling. The improved harmony search algorithm is applied to practical problems. The performance test of MODRHS is carried out through standard test functions and real data sets. Compared with other classic multi-objective optimization algorithms, the convergence and diversity of the MODRHS algorithm have been effectively improved.
Algorithm Modeling of Smile Intensity Estimation Using a Spatial Attention Convolutional Neural Network
Authors: Luo Zhenzhen , Sha Zongyu , Jin Chen , Xu Ning 
Published: January 2021

Abstract
Smile intensity estimation is a challenging task as it required subtle feature extraction, self-adapted weighted model and classifier. complexity of the problem domains, and problems on fine-grained image recognition are some of the issues related to intensity estimation. In this study, we designed a self-weighted deep convolutional neural network architecture for smiles intensity estimation using graphics processing unit. In the case of using only CK+ smile images, the accuracy of the model is also higher than that of the latest technology. Our model achieved better accuracy by just using CK+ smile images than state-of-the-art techniques. Visualizations of learned features at various layers and their deconvolutions are also presented for understanding the learning process.
Simulation and Derivative Analysis of Operation Effect of Electricity Spot Market
Authors: Tang Yao , Xiao Yanwei , Wang Gaoqin 
Published: January 2021

Abstract
Under the background of global power market reform, the construction of China’s electricity spot market is a hot spot in China. In order to ensure the safe and stable operation of the power system, it is necessary to carry out detailed simulation before the reform. In this paper, a simulation platform is developed to fit the characteristics of the spot market, and a set of evaluation indexes that can be used to quantify the market operation effect is designed. Based on the data of typical summer and winter load days in an area of East China, the operation of the spot market is simulated. The results show that the index can reasonably evaluate the operation effect of the market in a variety of scenarios.
Research on Interior Design Optimization based on Virtual Reality Technology
Authors: Zhu Zhongcui , Du Yan 
Published: January 2021

Abstract
The professional knowledge structure system of interior design pays attention to the continuous improvement of the comprehensive quality of interior designers. Interior designers should not only master the traditional design skills, but also master the most advanced and cutting-edge new technologies, so as to apply them to the practical projects of interior design. Virtual reality technology based on building digitization is being applied in various types of high-end indoor and outdoor construction projects, which builds a real bridge between designers and users and greatly enriches the interior space art. Research on the application of virtual reality technology in the field of interior design will guide the development of interior design industry better, more scientifically and systematically.
Numerical Analysis and Optimization Application Trend of Deepfake Video Inspection Technology
Authors: Xia Jiyu , Hua Man 
Published: January 2021

Abstract
Science and technology can be a double-edged sword. Novel digital technologies not only allow people to enjoy more digital achievements, but also make it increasingly difficult to distinguish the authenticity of digital information. This review begins with a brief introduction of the deepfake technology, followed by an explanation of the possible hazards of deepfake, and finally, an overview of the detection technology and research status of deepfake.
Chemical and mechanical bases of coating application in vibration technological systems
Authors: Ivanov V V , Popov S I , Dontsov N S , Marchenko Ju V , Ryzhkin N V , Oleynikova Ju A 
Published: January 2021

Abstract
The article presents the study of vibrational chemico-mechanical coatings. The energy model of coating formation in vibration technological systems is proposed. The classification is developed depending on the energy capacity of the system (the nature and intensity of the processes occurring between the base and the coating). The advantages of vibrational chemical-mechanical coatings over traditional coating and the scope of their practical application are shown. Comparative trials of vibrational chemico-mechanical coatings (Vamp) formed using this vibratory equipment and coatings obtained by conventional methods (electroplating, manual application have demonstrated a number of undeniable advantages and quality indicators of vibrational chemico-mechanical coatings.
A greedy approach for mutual exclusivity analysis in cancer study
Authors: Fang Hongyan , Zhang Zeyu , Zhou Yinsheng , Jin Lishuai , Yang Yaning 
Published: January 2021

Summary
The main challenge in cancer genomics is to distinguish the driver genes from passenger or neutral genes. Cancer genomes exhibit extensive mutational heterogeneity that no two genomes contain exactly the same somatic mutations. Such mutual exclusivity (ME) of mutations has been observed in cancer data and is associated with functional pathways. Analysis of ME patterns may provide useful clues to driver genes or pathways and may suggest novel understandings of cancer progression. In this article, we consider a probabilistic, generative model of ME, and propose a powerful and greedy algorithm to select the mutual exclusivity gene sets. The greedy method includes a pre-selection procedure and a stepwise forward algorithm which can significantly reduce computation time. Power calculations suggest that the new method is efficient and powerful for one ME set or multiple ME sets with overlapping genes. We illustrate this approach by analysis of the whole-exome sequencing data of cancer types from TCGA.