bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison study of Surface-enhanced Raman spectroscopy substrates
Authors: Khnykina K A , Baranov M A , Babaev A A , Dubavik A Y , Fedorov A V , Baranov A V , Bogdanov K V 
Published: September 2021

Abstract
Different parameters are used when comparing SERS substrates in the research of most scientific groups. These parameters are more often aimed at comparing theory and experiment. We described experimental models for comparison and explain their applicability. In this work, a reaction mixture volume fixing technique was chosen when comparing different substrates. The intensity distribution maps with an area of 2025 μm2(9X9 points) were recorded and described. We successfully used the parameters average intensity of intensity distribution maps and percentage gate of intensity distribution maps for comparison of enhancing substrate. We have observed uniform enhancing across the entire surface of self-assembled plasmonic film. A comparative study has shown that the self-assembled plasmonic film sample has high enhancing factors and good reproducibility of factors over the studying sample surface. Average intensity of dropcasted colloidal gold nanoparticles (Au-NPs) sample is equal to 1860 counts and percentage gate of intensity data equals 125%. Average intensity of self-assembling plasmonic film is equal to 112000 counts and percentage gate equals 1,67%.
A set of multifunctional equipment for deposition of colloidal photonic crystal films and monolayers
Authors: Dyubanov V A , Ibragimov A R , Panfilova E V 
Published: September 2021

Abstract
It is impossible to obtain colloidal photonic crystal film of given properties without special equipment. In order to minimize the influence of external disturbing influences such devices should be automated. To obtain large-area or hetero structures it is rational to combine in one universal device equipment for the implementation of several depositional methods. In this paper the authors present two automated devices for both special and universal purposes. They can be used to obtain films and monolayers by vertical lifting of the substrate from the colloidal suspension, vertical deposition by pumping out the suspension, electrophoresis and Langmuir-Blodgett technique and its combinations.
Smart-UPS with a priority-based load management system
Authors: Bharti Prince , Singh Abhijeet , Singh Pushpendra 
Published: September 2021

Abstract
In this paper, a cost-effective Smart-UPS has been developed which will perform load shedding on a priority basis. This smart UPS will include an accessory installed along with the conventional UPS. This paper relates to the field of managing power distribution. In particular, it pertains to a system and method for power distribution among devices, based on priority. The objective of the present disclosure is to efficient supply to distribute power among requirement of devices, to provide cost efficient power management and to enhance output of the devices with limited power back up.
The designed project will switch off lower priority loads according to Battery percentage to minimize the load suffered by the battery. The loads connected to supply load are divided into three sections, i.e. high priority (HP), medium priority (MP) and low priority (LP) loads. The project involves the development of a smart UPS system which will automatically switch off/on the high priority (HP) loads, medium priority (MP) and low priority (LP) loads (specified during installation of the UPS as specified by the user).
IoT based wastewater spillage detection system
Authors: Patel Rutvik , Prajapati Jay , Dave Meha , Joshi Ishwariy , Rathod Jagdish M 
Published: September 2021

Abstract
Internet of Things (IoT) is a system of interrelated devices including computers, processors, machines, or objects capable of transferring data over a network with no object-object, object-system interactions. In India, one of the most influential problems observed is the overflowing drains in the sewage system which becomes more problematic during the monsoon seasons when the authorities are unaware of the overflowing drains. It becomes unhygienic for the nearby residents and also causes water-logging which leads to breeding of pests. Our solution to this problem is an IoT system which instantly notifies the municipal authorities about the overflowing drains via email or notification at the city control center, also the citizens will get updates via social media or a mobile application. This system mainly comprises a low-powered IoT based portable device to be attached below the manhole cover. It leverages the LoRa technology to transmit the sensor data. Also, the frame of the manhole will be using solar power to charge the device. The proposed system makes the management and monitoring of the underground drainage system easier and hence saves the lives of hundreds of manual scavengers who die each year while cleaning the drainage systems.
Setup Design for the Production of Methane Gas from Kitchen Waste
Authors: Kumar Satyam , Hussain Fahd , Trivedi Shefali 
Published: September 2021

Abstract
Generation of kitchen waste is increasing day by day because of raise in population as well as change in socio-cultural habits. Most of these wastes can be decomposed by the action of microorganism in a short span of time because they are biodegradable substances. E.g. Food waste, vegetable peals, fruit pulp etc. If these materials are dumped in soil or kept in open, it undergoes microbial changes & releases Carbon dioxide, Methane, Ammonia & Hydrogen sulphide which can lead to air pollution and foul smell. If these gasses are trapped properly, it can be used as a source of energy and can save environment on the same go. In the present paper, work has been done on a setup for conversion of kitchen waste into energy which will be useful for college canteen & mess, and at places where a large amount of waste are generated on daily basis. The setup design for the production of methane gas has been done using Solidworks software (2016 version) and stress analysis of the methane gas collector has also been carried out. This setup also consists of flow measuring sensors which can measure the amount of gas formed & used, in a certain period of time. This setup will be connected via mobile app for real-time data and can be used anytime for monitoring of gases and for research purpose as well.
The Analysis Of Scattering For Transverse Electric By Perfectly Conducting Connected Strips At Node
Authors: Vimal  
Published: September 2021

Abstract
This paper analyzes the scattering for transverse electric (TE) incidence by perfectly conducting connected strips. The configuration of the problem has multi-strips connected at a node. The proposed method is current-based hybrid method which reducing the number of unknowns. In the proposed procedure very few expansion functions are required to achieve the result. Hence this is very efficient method. The proposed method based on Galerkin scheme using expansion functions.
Comparison of Various Load Frequency Control Schemes in Restructured Power System Environment
Authors: Kumar Nagendra , Kumar Jitendra , Chaurasia Ravi , Sharma Himanshu 
Published: September 2021

Abstract
This paper presents comparison of performances of secondary controllers like Integral (I), Proportional-Integral (PI), Integral-Derivative (ID), Proportional-Integral-Derivative (PID) and Integral-Double Derivative (IDD) as load frequency control scheme. The controllers have been tested on two equal areas thermal power system with non-reheat turbines in the presence of nonlinearity i.e. generation rate constraint (GRC) of 3% per minute. The disco participation matrix (DPM) is considered for various power transactions between Genco and Discos in both areas. A well-known optimization algorithm i.e. genetic algorithm (GA) is used to determine the optimal parameters of different controllers. Three cases i.e. load perturbation in one area only, load perturbation in both areas and more demand asked by Disco than the contract have been considered to check the response of controllers. Comparative analysis of the results obtained for all three cases show that Proportional-Integral-Derivative (PID) controller provides better performance than other controllers in terms of settling time..
BLDC Drive for EV Application
Authors: Srinivasan Shiva Srenivasan , Lakshanasri N.R.S , Setty Sahana M , Dubey Piyush , Singh Anant , Senthilmurugan S. 
Published: September 2021

Abstract
The purpose of this paper is to evaluate the efficiency of a BLDC motor in regenerative braking mode. The mode of operation of the BLDC motor using its torque-slip characteristics is taken into consideration to evaluate its performance. The simulation, characteristics and the tabular column are all got from the MATLAB simulation. The paper ends with a suggestion of using supercapacitors to compensate for the energy lost while the motor is in regenerative mode. The purpose of the supercapacitor has also been discussed to show the usefulness on the inclusion of the component. The motor and the supercapacitor compliment each other to increase the efficiency of the electrical vehicle.
The Q-MBS Operators in a linear positive operator Voronovskaya style asymptotic formula
Authors: Sharma Rupa Rani , Mishra R.K. , Garg Sudesh Kumar 
Published: September 2021

AbstractThe paper reveals the development theory of modified Beta Stancu operators. Incorporate Q derivatives and get critical moments from another Q-Beta generator. There is also a simple module inference for Q operators. Primary moments, Voronovskaya method asymptotic process, and error calculations applying these characteristics are produced for these operators. We used Q integral properties to verify the recurrence theorem for Q analog moments of the updated Operators. We still have an asymptotic method. After the day, We examined all such Q operators’ updating to help estimates.
Performance evaluation of coconut shell originated activated carbon as an adsorbent on the paint factory effluent treatment
Authors: Vishali S. , Sandhiya J. , Divya  , Aarthi K. , Abhilash  
Published: September 2021

Abstract
The current study was aimed to evaluate the ability of the activated carbon prepared from coconut shell in the decolorization of paint factory effluent (PFE). The studies were performed in the fixed bed adsorption column by optimizing the design variables viz., initial effluent concentration, bed height and low rate. The experimental observations were used to make breakthrough curves and were validated using Adams – Bohart model and Yoon-Nelson model. The results recommended that the encapsulated activated carbon of coconut shell origin showcased its capacity to perform in the process of paint factory effluents decolorization as an adsorbent.
Fractographic investigation of Low Cycle Fatigue behaviour of IN718 coated with NaCl at 550°C
Authors: Kumar Mukesh 
Published: September 2021

Abstract
Nickel base super alloys due to their superior properties are used in different parts of gas turbines, in jet engines, and as well as in marine application from 250°C to 650°C. At such high temperature sulphur and vanadium as impurities in fuel oil get oxidised in SO2 and V2O5. Further, sulphur oxide reacts with NaCl to form Na2SO4. These salts cause high temperature corrosion which causes stress corrosion cracking of engine components of marine gas turbine. Therefore, LCF resistance of the material becomes an important consideration in design of turbine. Fatigue samples both coated with NaCl salt and uncoated samples were tested in low cycle fatigue. The fatigue tested samples were analyzed under Scanning Electron Microscope (SEM). Since micrographs analysis is an important tool with the testing of materials to evaluate the various properties of the materials. In present study micrographs analysis helped to a great extent for evaluation the Low cycle fatigue behaviour of Nickel base super alloy INCONAL 718 (IN718) at high temperature which is 550°C.
A Matching Algorithm T-AKAZE for Image Recognition of Hydroelectric Equipment Failure
Authors: Xu Jiayu , Ji Xiu , Wang Yan 
Published: September 2021

Abstract
Aiming at the shortcomings of the current image-based fault identification algorithm for hydropower station equipment, such as low efficiency and poor accuracy, an improved image matching algorithm based on AKAZE, T-AKAZE was proposed. In this paper, AKAZE algorithm is firstly used to obtain image feature points. Then, the improved algorithm T-AKAZE is used for coarse matching of feature points. On the basis of Tanimoto coarse matching, RANSAC algorithm is used for fine matching to improve the algorithm accuracy. In order to verify the matching effect of the improved algorithm, ORB algorithm, AKAZE algorithm and ORB+Tanimoto algorithm were compared and analyzed. The experimental results show that the proposed algorithm has good robustness for image matching of different scales and similarity, and its timeliness also meets the application requirements, which provides convenience for the staff to identify the fault of hydropower station equipment.
Research on the Emotional Polarity Classification of Barrage Texts
Authors: Su Qian , Tan Wenan , Zhang Lu 
Published: September 2021

Abstract
With the widespread application of multimedia technology, video barrage has become an important form of expressing opinions and emotions. The classification of Chinese barrage texts according to emotions is of great significance for mining users’ emotions. However, the large number of Internet buzzwords in the barrage texts brings certain difficulties to sentiment analysis. Therefore, in order to address the ambiguity problem of Internet buzzwords in the barrage texts and effectively classifying the video barrage texts, the BERT pre-training language model and Support Vector Machine are combined to classify the barrage texts of positive and negative emotions. In this paper, the word vectors of the barrage texts are firstly generated by the BERT model. The SVM model is used as a classifier, and Particle Swarm Optimization is introduced to optimize the parameters of the SVM model. This method is compared with the traditional methods on the barrage data sets of different video types. The experimental results show that the precision, recall and F1-score of the proposed method are higher than those of the traditional models.
A Classification Method for Network Traffic Based on Semi-supervised Approach
Authors: Liu Yun , Zhu Zhiqiang , Zhong Pengfei 
Published: September 2021

Abstract
Traffic data encryption has been a trend in most Internet applications, and traditional protocol filtering based on fixed port and traffic classification based on Deep Packet Inspection are unable to Identify encrypted traffic. Recently, the traffic classification method based on the statistical characteristics of network traffic, which can solve the problem of encrypting data or user privacy protection, has been widely discussed. However, the traditional supervised learning method requires manual marking of a large amount of network traffic data, which is tedious and time-consuming. In this paper, a improved semi-supervised traffic classification framework based on BIRCH clustering method is proposed, and through experiments, the proposed algorithm, supervised learning algorithm and classical semi-supervised traffic classification algorithm are analyzed and compared. The results show that the algorithm proposed in this paper has higher overall accuracy and classification accuracy, and the algorithm can increase the accuracy on traffic classification.
Tracing Knowledge of Student based on Academic Knowledge with Machine Learning and Deep Learning
Authors: Hu Jiayu , Li Mingyi , Mao Huimin , Liu Cheng 
Published: September 2021

Abstract
Because the number of students not attending school is expanding at an alarming pace, and because of the COVID-19 epidemic, 102 countries have implemented nationwide closures to conduct local shut-downs and temporarily close schools. This slowed down learning possibilities and intellectual growth even more. Every country’s equity disparities may widen. As a result, we must restructure our educational system so that students can gain correct knowledge and teachers can track how much each student has learned. As a result, Machine Learning and Deep Learning are the most effective solutions for this type of problem. As a result, we’re releasing a method for tackling this challenge using Tabnet, Transformers, LGBM, and a variety of other machine learning approaches for student knowledge tracing.
Batch scheduling with incompatible job families and common due date
Authors: Liu Lili 
Published: September 2021

Abstract
This paper explores incompatible batch scheduling with common due date on a single batch processing machine. The problem of minimizing the number of tardy jobs is proved to be NP-hard with respect to id-encoding, and pseudo-polynomial time algorithms for minimizing the number of tardy jobs and the weighted number of tardy jobs are presented, respectively.
Pedestrian age recognition method based on gait deep learning
Authors: Song Tao , Yang Xin , Wang Yulin , Lei Yu , Liu Guanting 
Published: September 2021

Abstract
A pedestrian age recognition method based on gait information and dynamic weight convolutional networks is proposed to solve the problem that face age recognition needs a close positive perspective and frontal angle which limits its application scenarios. Firstly, the method takes gait energy image as input and the gait energy image is divided into head, upper body and lower body. Then, the global and local features of gait are extracted by global local convolutional networks. Secondly, the multi-layer residual expansion networks is constructed to optimize the gait energy image, and the average pooling is used to replace the fully convolutional networks for age classification. In order to solve the problem of sample imbalance in different age groups, the dynamic weight cross-entropy loss function is utilized as the network loss function to constrain the parameter updating, so as to avoid the recognition results inclining to the class with more samples. Experimental results on OULP age dataset demonstrate the effectiveness of the proposed approach.
Comparative Study on Numerical Simulation of Thorax Blunt Impact Based on LS-DYNA Software Technology
Authors: Chen Chaoming , Zhao Fadong , Bao Hongtao , Zhuang Hongwei 
Published: September 2021

Abstract
In order to better predict the risk of blunt impact on human thorax, the LS-DYNA software was used to reproduce the launch conditions of the postmortem human specimens(PMHS) experiment of the Wayne State University research group, numerical simulations of thorax blunt impact were carried out in combination with the human body model AM50 and Hybird III 50th Dummy model. The projectiles of impact test were respectively 37mm in diameter, 100mm in length, 140g in mass, with an impact velocity of 20m/s and 40m/s, and 37mm in diameter, 28.5mm in length, 30g in weight, with an impact velocity of 60m/s. The numerical response of thoracic compression was obtained by simulation experiment, which was verified by comparison with the experimental data of PMHS. The displacement curves of the thoracic compression of the two models were both in the response corridors of PMHS by the simulation experiment, and the differences between two displacement curves were analyzed. The viscous standard of Dummy under three launch conditions was calculated to be within the boundary of VCmax in PMHS experiment, which provided a certain theoretical reference for Dummy as a subsequent experimental research object.
The Research Progress of the Deep Hybrid Model in the Field of Text Classification
Authors: Yang Chunfeng , Wu Qiang , Lu Jiajia , Chen Huiyu 
Published: September 2021

Abstract
The traditional deep convolution neural network model cannot extract the context information effectively in dealing with complex long text data sets, and it is difficult to obtain the deep semantic information of the text. The deep hybrid neural network can optimize and improve the local feature extraction ability of CNN model while preserving the ability of local feature extraction. It has achieved good performance on complex data sets, so it has been paid more and more attention by researchers. Firstly, this article sorts out the current mainstream text classification data sets. Secondly, the model of the hybrid neural network based on the convolutional neural network construction is as follows: the improvement of the CNN model; technology fusion based on CNN model and the CNN-based model mixing three categories carry out analysis and sorting. Finally summarize the current problems in the text classification field, and look forward to future development and research.
Ant Colony Algorithm for Time Optimal Load Balancing Scheduling Problem Strategy
Authors: Tong Yuxiang , Li Xiaohui 
Published: September 2021

Abstract
In order to optimize the task execution efficiency of FPGA high-performance network clusters, this paper proposes a time-optimal load balancing scheduling problem algorithm and initialization strategy. Taking the task completion time as the optimization direction, the load balancing scheduling problem is abstracted as solving the placement matrix problem. On this basis, a node selection strategy using critical matrix is proposed, and the algorithm design is completed based on the ant colony algorithm. Aiming at the problem of low efficiency of random initialization, this paper proposes two ant colony initialization methods, which are ranked greedy initialization strategy and polling decentralized initialization strategy. Experiments show that the algorithm and initialization strategy designed in this paper can adapt to different computing scales, and the two initialization strategies can achieve a 15%∼55% improvement in the iterative convergence times and load balancing compared to random initialization.
Research on Cell Counting Method Based on Flood Fill Algorithm
Authors: Ning Zile , Wang Shuo , Li Xiao , Yu Yuanhua , Gao Le 
Published: September 2021

Abstract
Cell counting has a wide range of applications in cytology research and clinical practice. Cell counts are widely used in the early diagnosis and treatment of serious diseases and the screening of effective drugs in the later stage. The method of studying cell counts is of great significance. This paper proposes a method for cell counting based on the highlight area of the cell center as a live cell marker. This method uses algorithms bilateral filtering to smooth noise in the original image; after adaptive threshold segmentation, an algorithm based on flood filling is used. Accurate segmentation of the highlighted area in the center of the cell; finally, counting is achieved through the connected domain labeling algorithm. The experimental results show that the accuracy of the cell counting method proposed in this paper is above 98%, which verifies the feasibility and practicability of the method proposed in this paper.