bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phase-amplitude coupling between low-frequency scalp EEG and high-frequency intracranial EEG during working memory task
Authors: Ye Huanpeng , Li Guangye , Sheng Xinjun , Zhu Xiangyang 
Published: April 2022

Abstract

Objective. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown. To address this, we investigated the phase-amplitude coupling (PAC) phenomenon between the low-frequency band of scalp EEG and the high-gamma band of iEEG. Approach. We analyzed a simultaneous iEEG and scalp EEG dataset acquired under a verbal working memory paradigm from nine epilepsy subjects. The PAC values between pairs of scalp EEG channel and identified iEEG channel were explored. After identifying the frequency combinations and electrode locations that generated the most significant PAC values, we compared the PAC values of different task periods (encoding, maintenance, and retrieval) and memory loads. Main results. We demonstrated that the amplitude of high-gamma activities in the entorhinal cortex, hippocampus, and amygdala was correlated to the delta or theta phase at scalp locations such as Cz and Pz. In particular, the frequency bin that generated the maximum PAC value centered at 3.16–3.84 Hz for the phase and 50–85 Hz for the amplitude. Moreover, our results showed that PAC values for the retrieval period were significantly higher than those of the encoding and maintenance periods, and the PAC was also influenced by the memory load. Significance. This is the first human simultaneous iEEG and scalp EEG study demonstrating that the amplitude of iEEG high-gamma components is associated with the phase of low-frequency components in scalp EEG. These findings enhance our understanding of multiscale neural interactions during working memory, and meanwhile, provide a new perspective to estimate intracranial high-frequency features with non-invasive neural recordings.
Early Estimation of Daily Reference Evapotranspiration Using Machine Learning Techniques for Efficient Management of Irrigation Water
Authors: Chouaib El Hachimi , Salwa Belaqziz , Saïd Khabba , Abdelghani Chehbouni 
Published: April 2022

Abstract
Assessing the right amount of water needs for a specific crop is a key task for farmers and agronomists to achieve efficient and optimal irrigation scheduling, and then an optimal crop yield. To this end, the reference evapotranspiration (ET0) was developed. It represents the atmospheric evaporation demand, and therefore an important variable for irrigation management. In this regard, several methods such as the FAO’s Penman-Monteith and Hargreaves have been used to model and estimate ET0. These methods use climatic parameters data for calculation procedures such as solar net radiation (Rn), saturation vapour pressure(es), and min-max air temperatures or a combination of them. In this paper, we investigated two proposed data-driven methods to predict ET0 values in a semi-arid region in Morocco. The first approach is based on forecasting techniques and the second one uses end-to-end modeling of ET0 based on meteorological data and machine learning models. The feature selection and engineering results show that solar global radiation (Rg) and mean air temperature (Ta) have a significance of more than 87% as relevant predictors features for the ET0. We then used them as input to machine learning regression models. Regression evaluation metrics showed that The XGboost regressor model performs well in both cross-validation with R2=0.93 in the first fold, and in hold-out validation with R2=0.92 and RMSE=0.55. As a final step, we compared the univariate time series forecasting of ET0 using the Facebook Prophet model versus the machine learning modeling method that we proposed. As goodness-of-fit measures, forecasting using machine learning modeling of ET0 showed better results in terms of both R2 and RMSE.
DeepFake Video Detection through Facial Sparse Optical Flow based Light CNN
Authors: Fang Shuya , Wang Shucheng , Ye Rongjun 
Published: April 2022

Abstract
DeepFake detection has become an attractive research topic with tremendous growth of interests recently. However, existing DeepFake detection studies spare no effort to improve accuracy or Area Under Curve metric, regardless of computing costs. In this work, the tradeoff between result accuracy and computing resources is taken into consideration. A facial sparse optical flow method is proposed to extract spatio-temporal features representing facial expression incoherence, which helps to distinguish fake videos and real videos. The features fed into a light CNN model are highly compact and low-dimensional. The proposed method has an amazing small amount of parameters with high training speed and low usage of GPU memory. The low resource requirement makes it possible to port to embedded development platform.
Research on Knowledge Transfer on Multilayer Networks Based on Link Prediction Algorithm
Authors: Li Shouwei , Wang Yihan 
Published: April 2022

Abstract
Multi-layered networks have great advantages in portraying the multi-attributes of links and can describe complex real-life systems better. Link prediction and knowledge transfer in complex networks have been extensively studied, but link prediction and knowledge transfer on hierarchical networks are less of a concern. Based on the definition of hierarchical network, a random walk model including link prediction and knowledge transfer is proposed. The link prediction method is proposed from the structural similarity and knowledge compatibility, and then the knowledge transfer rules are proposed. This paper also proposes the evaluation indicators for link prediction and knowledge transfer. The experimental results by using real hierarchical networks show that the link prediction has obtained better results and the complexity has been reduced; the knowledge transfer efficiency has been improved. This study has important reference value for the development of multi-layer network theory.
Attention-Based Dual-Branch Cascade Network for Multi-Label Image Recognition
Authors: Li Xingyu 
Published: April 2022

Abstract
Multi-label image recognition is a practical task which aims to predict all concerned objects in an image, and the correlation between labels is considered as the key to solve multilabel problem. Previous approaches focus on exploring and exploiting the underlying relations between labels, by which the performance of most labels is improved significantly. But small object and label tail problems are ignored, and negatively influences the overall results. For ameliorating above problems, we propose a unified deep neural network named Attention-Based Dual-Branch Cascade Network (ADC Net), which contains Main Branch and Auxiliary Branch. ADC Net cascades to predict labels, and obtains the final result by element-wise adding. Attention modules in each branch contribute to recognizing small objects. Top-Down-Attention Module (TDAM) utilizes the preceding prediction map to guide the Auxiliary Branch. Besides, expanding training dataset is applied for learning the feature representation of tail labels. Our proposed methods are evaluated on two benchmark datasets: MS-COCO and VOC PASCAL 2007 datasets, and achieves state-of-the-art performance. Results of small objects, tail labels and visualization also prove the effectiveness of our method.
Speech Fatigue Detection Based on Deep Learning
Authors: Chen Shuxi , Sun Yiyang , Zhang Haifei , Liu Qinqin , Lv Xingqin , Mei Xiangxiang 
Published: April 2022

Abstract
Medically speaking, moderate fatigue is the embodiment of workload, and excessive fatigue will cause adverse effects on the human body. Therefore, real-time monitoring of human fatigue is of great significance. In order to detect fatigue in real time and conveniently, a speech fatigue detection method based on deep learning is proposed in this paper. The main work of this paper is to screen the useful speech segments of the fatigue corpus created in the previous work; Secondly, the speech signal is preprocessed and the analog speech signal is digitized; Thirdly, deep learning can effectively reflect the phonetic features of fatigue information; Finally, the fatigue degree is classified and recognized by classifier. The best classification and recognition rate of speech fatigue detection based on BLSTM network recommended by the author can reach 92.7%, which realizes the effective recognition of sports fatigue.
Data Collector Design for Vibration Analysis by Raspberry pi 3B Embedded System Means for Industrial Applications
Authors: Cárdenas C , Sandoval C , Rincón A , Galván D , Téllez H 
Published: April 2022

Abstract
Currently, the industry is linked to technological advances, and the maintenance area plays an important role in this regard. Good practices are being refined, and predictive techniques have been implemented within the maintenance plan. These techniques allow decisions to be based on made equipment condition, which optimizes costs. One of these techniques is the mechanical vibrations analysis, applied commonly rotating equipment, where is common the situation of expensive measuring equipment. For this reason, this report provides a problem basic solution, through the economical measuring equipment design for industrial applications.
Enhancement of Active Swimming near Fluid Interfaces
Authors: Cartwright Avriel , Du Jian 
Published: April 2022

Abstract
Microorganisms often move through heterogeneous fluid medium composed of multiple materials with very different properties. Biological locomotions are significantly influenced by the physical compositions and rheology of the fluidic environment. Some micro-swimmers are able to exploit nearby deformable interfaces to enhance their speed. Through computational simulations, we investigate the movement of a finite-length undulatory swimmer near interfaces within a viscous two-fluid media. Our results show that significant speed-ups can be obtained only if the active swimmer has a large body elasticity.
Modelling of Aggregates using Aperiodic Space-Filling and Physical Simulation
Authors: Gan X , Ito T , Takeshima Y , Kikuchi T 
Published: April 2022

Abstract
In this paper, we will explain to you how to procedural generate realistic aggregates. The program requires two or more 3D models, and the number of elements and friction values. There are two steps: the first requires user-input elements and a provided shape and fills the defined shape with non-overlapping elements; the second, together with user-input friction data, is a physical simulation to generate a more realistic pile.
Iterative Method of Finding All Thermophysical Parameters of a Two-Layer Soil
Authors: Rysbaiuly B , Mukhametkaliyeva N E 
Published: April 2022

Abstract
The paper develops methods for finding the thermophysical parameters of a two-layer soil. The difference scheme for the equation of quasi-linear thermal conductivity is taken as the basis for the study. Two-layer complexes of containers have been created, the side faces of which are thermally insulated. Measurement work was carried out to obtain values at the two end borders, the environment and at the contact boundary of the two containers. This circumstance makes it possible to solve the inverse coefficient problem in each container independently of each other. We have developed methods for finding all the thermophysical parameters of the soils in both containers. A rational method of choosing the damping coefficient is also proposed, which provides an indicative rate of convergence of the approximate value of the functional to zero. Computational experiments were carried out on the basis of the developed methods and measured data. The results of which show the viability of the developed iterative methods.
The Positive Role of the Partial Discharge Monitoring System in the Partial Discharge Detection of GIS Equipment
Authors: Chen Jiangtian , Wei Dongliang , Wang Zhi , Liu Congcong , Shao Weitao , Shao Weiling 
Published: April 2022

Abstract
When gas insulated metal enclosed switchgear and controlgear (GIS) has internal discharge, some faults are difficult to find during inspections due to the enclosed metal casing. This paper introduces the event of the floating potential discharge inside the GIS equipment of a 500kV substation found by GIS on-line monitoring system. Through on-site ultra-high frequency detection and precise positioning of the oscilloscope to determine the position of the discharge source at a certain switch, combined with the disassembly of the equipment, it is determined that the equipment defect is the discharge of the floating electrode of the switch fork.The results show that the on-line monitoring of GIS partial discharge has an important reference role in discovering the internal discharge of equipment during the test interval, and the monitoring and maintenance of GIS partial discharge on-line monitoring system should be strengthened in daily work.
Simulation Research on Flow Boiling and Heat Transfer of Micro-channel for Electronic Cooling
Authors: Yuan Xudong , Zhao Panpan , Huang Jing , Wu Junfeng , Zhou Dao , Wang Rujin , Hu Jisun , Qu Boyi 
Published: April 2022

Abstract
Micro-channel heat exchanger is a popular device for the electronic cooling. Heat transfer of flow boiling in micro-channel is a research hotspot. For the convenience of the theoretical research on micro-channel, a high efficiency simulation model on flow boiling and heat transfer of micro-channel was established. In the simulation research, the influence rules of operating conditions and thermal properties of refrigerants (working fluids) on the flow boiling were studied; the distribution rules along flow direction of characteristic parameters such as vapor quality, Heat Transfer Coefficient (HTC), pressure drop and bottom wall temperature of micro-channel T
w were analyzed; the key factors on the distribution of characteristic parameters were proposed. The characteristic parameters based on the refrigerants R134a and R1234ze(E) in micro-channel were compared and researched. According to the simulation results, HTC and pressure drop of R134a are larger and smaller than those of R1234ze(E) respectively, the uniformities of HTC and T
w of R134a are all better than those of R1234ze(E) respectively.
Research on Safety of Inner Spherical Surface Machining on A--C Double-Turntable Five-axis Machine Tool
Authors: Li Tiren , Wang Qi , Zhao Gengbo , Sun Jiangong , Zhang Yanhua 
Published: April 2022

Abstract
This article discusses and verifies the accessibility and safety of internal spherical parts in A-C double turntable five-axis machine tools. The inner spherical surface can be machined completely by controlling the tool axis vector. The theoretical calculations determine the safe height range of the fixture to avoid collision between the tool and the workpiece and the Y-axis overrange. The correctness and reliability of the calculation are verified by machining simulation and actual machining of typical inner spherical parts.
Simulation and Statistical Analysis on the Transport Process of Salt Water Mass from the North Branch in the Yangtze River Estuary
Authors: Wu Dean , Wang Yigang , Shao Yuchen , Hu Guodong 
Published: April 2022

Abstract
Based on the analysis of measured data, a two-dimensional hydrodynamic Salinity Transport Model in the Yangtze River estuary is established by using Mike21 model. This model is used to study the average daily distribution and changing patterns of the saline water mass in the South Branch that are from the North Branch while only the saltwater intrusion from the North Branch is considered. It determines the intrusion process of saline water mass from the North Branch and the area that are affected by saline water intrusion. In addition, through the research of salt water mass core moving down and salinity variation in the south branch channel, the flow pattern and mechanism of salt water mass in the south branch channel are revealed. The saltwater intrusion from the North Branch has a great impact on the vertical, horizontal and vertical salinity fields of the Yangtze River estuary. The variation trend of statistical variance, deviation and standard deviation of the half month time series of salinity at each point along the route is basically the same, showing a gradual decreasing trend towards the downstream. Those show that the salinity dispersion is larger in the upstream estuary and lower outside the estuary. Combined with the salinity kurtosis and skewness, the deviation degree and direction of the salinity distribution at each point along the route can be judged, which can reflect the comprehensive process of runoff, tidal current and topography. Therefore, the kurtosis and skewness statistics can effectively describe the law of transport and dynamic characteristics of intrusion saline water masses in the Yangtze River Estuary.
Development of an Anti-radon Coating for Underground Engineering
Authors: Pan Liangliang , Qin Guoxiu , Wang Qimin , Li Fan , Li Weizhe 
Published: April 2022

Abstract
To satisfy the requirements of underground engineering applications, an anti-radon coating was developed by selecting suitable modified polymer emulsions, element materials, pigments, fillers, additives, etc. Experimental results on radon mitigation performance show that for a coating thickness of 2.0 mm, the radon mitigation efficiency reaches 95.1%. In addition, an anti-radon coating has a very low content of toxic substances and excellent water resistance and durability. Using the coating in an engineering application showed that the coating has the advantages of fast drying speed, no peculiar smell and excellent radon mitigation performance, where 2-3 coats produce good radon mitigation in damp and sealed underground engineering environments.
Blind Write Protocol Throughput
Authors: Herman Nanna Suryana , Anshar Khairul , Andono Pulung Nurtantio 
Published: April 2022

Abstract
Blind Write Protocol allows more transactions to be executed at the same time. It is achieved by removing the locking phase to any entity while the write operation is performed. To have a fair throughput comparison with the Locking Protocol then this paper introduces three different approaches using Blind Write Protocol that can preserve consistency. The throughputs are presented and compared with Locking Protocol. We found that preserving consistency using Blind Write Protocol needs more operations. As a result, it consumes more resources and makes the response times much slower compared to Locking Protocol. If Blind Write Protocol ignores consistency, then it shows better response time compared to Locking Protocol. The Blind Write Protocol aims to achieve availability rather than consistency.
Development and Implementation of a Model in MATLAB Simulink Responsible for the Analysis of Sensor Data via the CAN Bus
Authors: Elert Szymon 
Published: April 2022

Abstract
The paper presents a modelling of the communication system between the TI F28379D microcontroller and the sensors responsible for measuring the flight parameters of the missile. Data acquisition in aviation and their detailed interpretation are very important for the correct operation of the system. One of these devices is the AHRS module (Attitude and Heading Reference System) which was used for the test. Communication between devices is carried out via the CAN bus. The MATLAB Simulink environment was used to create the model, which made it possible to design the program and then implement the generated code on the processor. The article presents how the individual tasks have been performed by the designed controller. Then an interface was created to allow quick change of basic parameters without in-depth interference with the program. The developed program can be used as one of the autopilot subsystems and used for real-time tests.
Detect Denial of Service Attack (DoS) in MANETs Partition Scenario Using Puzzle Map Method
Authors: Alsumayt Albandari 
Published: April 2022

Abstract
The usage of the Internet increases significantly especially regards COVID-19. The lifestyle relies of the networks and the Internet to do all tasks such as job, meetings, and education. The mobile ad hoc network (MANET) is a type of wireless network that nodes can communicate without the occurrence of an administration point. Nodes in MANET can join and leave the network frequently, so the scenario of networks merging and partitioning can occur. In this study, the detection of Denial of Service (DoS) attack which degrades the performance of the network will be illustrated in the situation of network partitioning. The detection will be based on MrDR method and puzzle map method, the puzzle concept will help to complete the network partitioning and avoid the DoS attack. The proposed method is simulated using NS2 and the results shows the effectiveness of the puzzle map method to increase the performance of network during the partitioning scenario.
Research on Identification Method of Fragile Component of Power Grid Considering Both Structure and State
Authors: Yang Xuetao , Song Dunwen , Liu Kaixin , Li Haifeng , Jin Tao , Chen Yong 
Published: April 2022

Abstract
Whether in the power grid planning, design or operation stage, the identification of fragile component in the power system is always one of the core issues of power grid security analysis. In order to realize the accurate positioning of fragile lines, firstly, considering the overall distribution characteristics and local fluctuation characteristics of the power flow under the disturbance of the breaking, a composite entropy evaluation model of fragile components based on the distribution entropy and transfer entropy of the power flow is proposed. Secondly, the power flow betweenness evaluation method based on the power transmission transfer distribution factor is used to measure the transmission contribution and carrying capacity of the branch in the power grid. Through normalization, a comprehensive evaluation index for vulnerable branches is established. Aiming at the proposed identification method, the IEEE10 machine 39-node calculation example and comparison with other methods show that the method in this paper can more comprehensively and accurately identify the fragile branch of the power grid.