bluuebunny/crossref_metadata_2025_split huggingface dataset:
Computer Prediction Model for Equipment Maintenance Using Cloud Computing and Secure Data-sharing
Authors: Pan Kun , Jiang Yuchen 
Published: December 2021

Abstract
A With the popularization of automation in the industrial field, productivity has been greatly improved. However, manufacturing corporations are facing a data tsunami which brings new challenges to predictive maintenance (PdM). In recent years, many approaches and architecture for predictive maintenance have been proposed to solve some of these problems to varying degrees. This paper introduces a general framework based on the Internet of Things, cloud computing and big data analytics for PdM of industrial equipment. In this framework, smart sensors are installed on the device to obtain electrical data, which is then encrypted and uploaded to the cloud platform to predict the health condition by deep learning methods. Several working instances including feature selection, feature fusion, and Remaining Useful Life (RUL) prediction are provided. The effectiveness of the proposed methods is demonstrated by real-world cases.
Research on prediction method of S-wave velocity based on deep learning
Authors: Yang Guojie , Wang Shuhua 
Published: December 2021

Abstract
Aiming at the s-wave velocity prediction problem, based on the analysis of the advantages and disadvantages of the empirical formula method and the rock physics modeling method, combined with the s-wave velocity prediction principle, the deep learning method is introduced, and a deep learning-based logging s-wave velocity prediction method is proposed. This method uses a deep neural network algorithm to establish a nonlinear mapping relationship between reservoir parameters (acoustic time difference, density, neutron porosity, shale content, porosity) and s-wave velocity, and then applies it to the s-wave velocity prediction at the well point. Starting from the relationship between p-wave and s-wave velocity, the study explained the feasibility of applying deep learning technology to s-wave prediction and the principle of sample selection, and finally established a reliable s-wave prediction model. The model was applied to s-wave velocity prediction in different research areas, and the results show that the s-wave velocity prediction technology based on deep learning can effectively improve the accuracy and efficiency of s-wave velocity prediction, and has the characteristics of a wide range of applications. It can provide reliable s-wave data for pre-stack AVO analysis and pre-stack inversion, so it has high practical application value and certain promotion significance.
Algorithm analysis based on machine learning in alarm information of metering system
Authors: Ma Yuhan 
Published: December 2021

Abstract
Due to the huge scale of the metering warning system and the wide variety of warning information, manual identification can no longer meet the real-time monitoring needs of modern power grids. Through the use of machine learning, data mining technology, linear regression method, naive Bayes method, automatic classification analysis of alarm information from the massive alarm information. Construct an SVM prediction model to improve the accuracy of alarm information analysis and improve the effectiveness of power grid monitoring and management. The experimental results show that it can provide support for the normal operation of the grid metering system.
Research on Big data Security privacy Protection based on cloud Computing
Authors: Xu Tongtong , Shi Lei 
Published: December 2021

Abstract
Cloud computing is a new way of computing and storage. Users do not need to master professional skills, but can enjoy convenient network services as long as they pay according to their own needs. When we use cloud services, we need to upload data to cloud servers. As the cloud is an open environment, it is easy for attackers to use cloud computing to conduct excessive computational analysis on big data, which is bound to infringe on others’ privacy. In this process, we inevitably face the challenge of data security. How to ensure data privacy security in the cloud environment has become an urgent problem to be solved. This paper studies the big data security privacy protection based on cloud computing platform. This paper starts from two aspects: implicit security mechanism and display security mechanism (encryption mechanism), so as to protect the security privacy of cloud big data platform in data storage and data computing processing.
Study of Chinese spam filtering Based on Improved Naive Bayesian Classification Algorithm
Authors: Zuo Kaiying 
Published: December 2021

Abstract
Spam is a growing threat to mobile communications. This paper puts forward some mitigation technologies, including white list and blacklist, challenge response and content-based filtering. However, none are perfect and it makes sense to use an algorithm with higher accuracy for classification. Bayesian classification method shows high accuracy in spam processing, so it has attracted extensive attention. In this paper, a Bayesian classification method based on annealing evolution algorithm is introduced into Chinese spam filtering to improve the accuracy of classification. Our simulation results show that the algorithm has better performance in spam filtering.
Research on the Development and Application of Computer Data Mining Technology
Authors: Fu Zhe 
Published: December 2021

Abstract
With the rapid development of computer technology, computer data mining technology has been widely used in various fields, and it has also promoted tremendous progress in society as a whole. As the economic and social development will inevitably promote the continuous skyrocketing of data in various fields, how to find useful information in the infinite data will inevitably require computer data mining technology. The article will analyze the development and application of computer data mining technology.
RETRACTED: Application of Keras neural network in the era of big data
Authors: Wang Junqi 
Published: December 2021

Abstract
The experiment uses crawler tools to obtain data, and the data is preprocessed to find missing values and eliminate invalid data, meanwhile, the model is constructed by information entropy and ID3 algorithm so as to select the desired amount of features, and then basic modeling and data filtering is performed to train and evaluate the model for the first time, finally, in order to get a more ideal model, this experiment The optimal model is obtained by changing the number of hidden layers and neurons of the neural network to build a high-level neural network API neural network model written by pure python - Keras neural network model. The results show that when the model defines a 2-layer neural network and the number of neurons in the hidden layer is fourteen, the accuracy of the model is the highest, and the accuracy of the test set is as high as ninety-one percent.
Research on numerical simulation and intelligent computing of vehicle wading driving by smooth particle hydrodynamics method
Authors: Zhang Wei , Jiao Peigang , Hou Qinzhong 
Published: December 2021

Abstract
The method based on Smooth Particle Hydrodynamics (SPH) is a meshless method which is widely used at present. Its advantage is that it can effectively improve the mesh distortion when finite element is used to deal with large deformation, and its particle characteristics are suitable to deal with the simulation problem of fluid. Based on the actual vehicle wading test site and the actual parameters of the vehicle, combined with the actual situation and theoretical basis, the SPH method is used for numerical simulation analysis of the vehicle wading problem. By comparing the simulation process with the actual water changes during wading, the feasibility of using SPH method in vehicle wading application is proved. In the simulation process of vehicle wading driving, under the condition of constant water level, by setting different wading speeds of vehicle, the flow law and change mechanism of water free surface are analyzed, which are of great significance in theoretical research and engineering application research.
Spatial resolution enhancement of rotational-radar subsurface datasets using combined processing method
Authors: McDonald Thomas , Robinson Mark , Tian GuiYun 
Published: December 2021

Abstract
Effective visualisation of railway tunnel subsurface features (e.g. voids, utilities) provides critical insight into structural health and underpins planning of essential targeted predictive maintenance. Subsurface visualisation here utilises a rotating ground penetrating radar antenna system for 360° point cloud data capture. This technology has been constructed by our industry partner Railview Ltd, and requires the development of complimentary signal processing algorithms to improve feature localisation. The main novelty of this work is extension of Shrestha and Arai’s Combined Processing Method (CPM) to 360° Ground Penetrating Radar (360GPR) datasets, for first-time application in the context of railway tunnel structural health inspection. Initial experimental acquisition of a sample rotational transect for CPM enhancement is achieved by scanning a test section of tunnel sidewall - featuring predefined target geometry - with the rotating antenna. Next, frequency data separately undergo Inverse Fast Fourier Transform (IFFT) and Multiple Signal Classification (MUSIC) processing to recover temporal responses. Numerical implementation steps are explicitly provided for both MUSIC and two associated spatial smoothing algorithms, addressing an identified information deficit in the field. Described IFFT amplitude is combined with high spatial resolution of MUSIC via the CPM relation. Finally, temporal responses are compared qualitatively and quantitatively, evidencing the significant enhancement capabilities of CPM.
Methodological Bases of Optimal Load Distribution at a Thermal Power Plant with a Complex Composition of Equipment, Taking into Account Market Requirements
Authors: Arakelyan E K , Andryushin A V , Yagupova Y Y , Neklyudov A V , Mezin S V 
Published: December 2021

Abstract
The features of a mathematical model for optimizing the distribution of heat and electricity at a large thermal power plant with a complex composition of equipment as part of traditional heating units and a heating CCGT are considered. The selection and justification of optimization criteria at different stages of preparation and entry of the station to the electricity and capacity market is given. The disadvantages of the previously proposed optimal distribution algorithms are analyzed in relation to thermal power plants with a complex composition of equipment and with a complex scheme for the supply of electricity and heat. A method and algorithm for solving the problem are proposed based on the equivalence of the CHP equipment and the decomposition of the problem taking into account the schemes of electricity and heat output. The description of mathematical optimization methods is given, taking into account the peculiarities of the CCGT operating modes at reduced loads. The requirements for information support when integrating the developed algorithm into the application software of the automated process control system based on the PTC are given.
Seeing the unseen—the iceberg phenomenon in the first months of the COVID19 pandemic
Authors: Anghel Dragos-Victor , Alexandru Anghel loan Tudor 
Published: December 2021

Abstract
We analyze the evolution of the COVID19 infections in the first months of the pandemics and show that the basic compartmental SIR model cannot explain the data, some characteristic time series being by more than an order of magnitude different from the fit function over significant parts of the documented time interval. To correct this large discrepancy, we amend the SIR model by assuming that there is a relatively large population that is infected but was not tested and confirmed. This assumption qualitatively changes the fitting possibilities of the model and, despite its simplicity, in most cases the time series can be well reproduced. The observed dynamic is only due to the transitions between two infected compartments, which are the unconfirmed infected and the confirmed infected, and the rate of closing the cases (by recovery or death) in the confirmed infected compartment. We also discuss some relevant extensions of this model, to improve the interpretation and the fitting of the data. These findings qualitatively and quantitatively evidences the “iceberg phenomenon” in epistemology.
A general one-dimensional traffic model for motion of molecular motors on microtubules of variable length
Authors: Aldrich E. , Reed B. , Stoleriu L. , Mazilu D.A. , Mazilu I. 
Published: December 2021

Abstract
We present a traffic model inspired by the motion of molecular motors along microtubules, represented by particles moving along a one-dimensional track of variable length. As the particles move unidirectionally along the track, several processes can occur: particles already on the track can move to the next open site, additional particles can attach at unoccupied sites, or particles on the track can detach. We study the model using mean-field theory and Monte Carlo simulations, with a focus on the steady-state properties and the time evolution of the particle density and particle currents. For a specific range of parameters, the model captures the microtubule instability observed experimentally and reported in the literature. This model is versatile and can be modified to represent traffic in a variety of biological systems.
A one-dimensional modified TASEP model on a track of variable length: analytical and computational results
Authors: Reed B. , Aldrich E. , Stoleriu L. , Mazilu D.A. , Mazilu I. 
Published: December 2021

Abstract
We present analytical solutions and Monte Carlo simulation results for a one-dimensional modified TASEP model inspired by the interplay between molecular motors and their cellular tracks of variable lengths, known as microtubules. Our TASEP model incorporates rules for changes in the length of the track based on the occupation of the first two sites. Using mean-field theory, we derive analytical results for the particle densities and particle currents and compare them with Monte Carlo simulations. These results show the limited range of mean-field methods for models with localized high correlation between particles. The variability in length adds to the complexity of the model, leading to emergent features for the evolution of particle densities and particle currents compared to the traditional TASEP model.
On stability boundary of the flow of an acid solution through a chemically active porous medium
Authors: Plavnik Rinat , Zavialov Ivan , Konyukhov Andrey , Izvekov Oleg , Negodyaev Sergey 
Published: December 2021

Abstract
It is known that during the flow, if the displacing fluid can chemically react with the components of porous medium and with the release of a gas phase, then such a flow regime can be unstable. During this process, pressure fluctuations can be observed, and the displacing fluid will move in “waves”. In the course of our research, a simple mathematical model was proposed that provides a qualitative explanation of the reasons for the emergence of such a phenomenon; laboratory modeling was carried out, and the criterion of the “waves” formation was found, depending on the concentration of chemically active components. The proposed model can predict the emergence of the wave instabilities in a laboratory experiment, which will allow to carry out a future experiment on a larger scale.
Scaling laws and phase space analysis of a geomagnetic domino model
Authors: Peqini K , Prenga D , Osmanaj R 
Published: December 2021

Abstract
The geomagnetic field is among the most striking features of the Earth. By far the most important ingredient of it is generate in the fluid conductive outer core and it is known as the main field. It is characterized by a strong dipolar component as measured on the Earth’s surface. It is well established the fact that the dipolar component has reversed polarity many times, a phenomenon dubbed as dipolar field reversal (DFR). There have been proposed numerous models focused on describing the statistical features of the occurrence of such phenomena. One of them is the domino model, a simple toy model that despite its simplicity displays a very rich dynamic. This model incorporates several aspects of the outer core dynamics like the effect of rotation of Earth, the appearance of convective columns which create their own magnetic field, etc. In this paper we analyse the phase space of parameters of the model and identify several regimes. The two main regimes are the polarity changing one and the regime where the polarity remains the same. Also, we draw some scaling laws that characterize the relationship between the parameters and the mean time between reversals (mtr), the main output of the model.
The Extension of the Physical and Stochastic Problems to Space-Time-Fractional Differential Equations
Authors: Abdel-Rehim E.A. 
Published: December 2021

Abstract
The fractional calculus gains wide applications nowadays in all fields. The implementation of the fractional differential operators on the partial differential equations make it more reality. The space-time-fractional differential equations mathematically model physical, biological, medical, etc., and their solutions explain the real life problems more than the classical partial differential equations. Some new published papers on this field made many treatments and approximations to the fractional differential operators making them loose their physical and mathematical meanings. In this paper, I answer the question: why do we need the fractional operators?. I give brief notes on some important fractional differential operators and their Grünwald-Letnikov schemes. I implement the Caputo time fractional operator and the Riesz-Feller operator on some physical and stochastic problems. I give some numerical results to some physical models to show the efficiency of the Grünwald-Letnikov scheme and its shifted formulae.

MSC 2010: Primary 26A33, Secondary 45K05, 60J60, 44A10, 42A38, 60G50, 65N06, 47G30,80-99
Statistical approaches to the problem of homogeneous melting of solids in the microcanonical ensemble
Authors: Olguín-Arias Vivianne , Davis Sergio , Gutiérrez Gonzalo 
Published: December 2021

Abstract
Melting is a common phenomenon in our daily life, and although it is understood in thermodynamic (macroscopic) terms, the transition itself has eluded a description from the point of view of microscopic dynamics. While there are studies of metastable states in classical spin Hamiltonians, cellular automata, glassy systems and other models, the statistical mechanical description of the microcanonical superheated solid state is lacking.
Our work is oriented to the study of the melting process of superheated solids, which is believed to be caused by thermal vacancies in the crystal or by the occupation of interstitial sites. When the crystal reaches a critical temperature, it becomes unstable and a collective self-diffusion process is triggered. These studies are often observed in a microcanonical environment, revealing long-range correlations due to collective effects, and from theoretical models using random walks over periodic lattices. Our results suggest that the cooperative motion made possible by the presence of vacancy-interstitial pairs (Frenkel pairs) above the melting temperature can induce long-range effective interatomic forces even beyond the neighboring fourth layer. From microcanonical simulations it is also known that an ideal crystal needs a random waiting time until the solid phase collapses. Regarding this, our results also point towards a description of these waiting times using a statistical model in which there is a positive quantity X that accumulates from zero in incremental steps, until it exceeds a threshold value.
On the asymptotic stability of advection-diffusion equations of mass transport in a bubble column bioreactor
Authors: Lecca Paola , Re Angela 
Published: December 2021

Abstract
This study presents an asymptotic stability analysis of a model of a bioreactor converting carbon monoxide (CO) gas into ethanol through a C. autoethanogenum biocatalyst. The configuration is a bubble column reactor with co-current gas-liquid flows where gas feed is introduced by a gas distributor placed at the bottom of the column. A pure culture of C. autoethanogenum is subsequently injected at the bottom of the column; therein, cells are dispersed in the liquid and consume the dissolved gas and release by-products such as ethanol and acetic acid. Cellular growth and byproduct secretion are affected by spatially varying dissolved gas concentrations due to advection-diffusion mass transports which are induced by the effect of the injection pressure and gravitational force. The model accounts for four species representing the biomass, the CO substrate in the liquid phase, and two by-products - ethanol and acetic acid. Substrate dynamics is described by an advection-diffusion equation.
We investigate the asymptotic stability of the biomass dynamics that is a requirement for the system’s controllability, i.e. for the possibility to steer a dynamical system from an arbitrary initial state to an arbitrary final state using a set of controls. The concept of stability of the controls is extremely relevant to controllability since almost every workable control system is designed to be stable. If a control system is not stable, it is usually of no use in practice in industrial processes. In the case of a bioreactor, the control is the biomass and controllability is the possibility of modulating through this control the ethanol production. We present a test for asymptotic stability, based on the analysis of the properties of the dynamic function defining its role as storage function.
The leaking soft stadium
Authors: Espinoza Ortiz J. S. , Lagos-Monaco R.E. 
Published: December 2021

Abstract
We soften the non zero y-boundary on a Bunimovich like quarter-stadium. The smoothing procedure is performed via an exponent monomial potential, the system becomes partially reflective, preserving the particle’s translation and rotational motion. By increasing the exponent value, the stadium’s boundaries become rigid and the system’s dynamics reaches a chaotic regime. We set a leaking soft stadium family by opening a limited region located at some place of its basis’s boundary, throughout which the particles can leak out. This work is an extension of our recently reported paper on this matter. We chase the particle’s trajectory and focus on the stadium transient behavior by means of the statistical analysis of the survival probability on the marginal orbits that never leave the system, the so called bouncing ball orbits. We compare these family orbits with the billiard’s transient chaos orbits.