bluuebunny/crossref_metadata_2025 huggingface dataset:
The Research on the Complexity of 1T-TaS2 at Ultra-low Temperatures
Authors: Han Tianxue
Published: January 2022

Graphene, as a successfully industrialized two-dimensional material, has greatly promoted the development of other two-dimensional materials, such as transition metal dichalcogenide (TMDs). 1T-TaS2 is a classical TMDs material, which presents metallicity at high temperature. It undergoes a variety of charge density wave (CDW) phase transitions during the temperature declining process, and presents insulating properties at low temperature. During the temperature rise period, 1T-TaS2 goes through a phase transition, from an energy band insulator to Mott insulator, followed by an insulation-metal phase transition. The complexity of 1T-TaS2 phase diagram encourages researchers to conduct extensive research on it. This paper, via means of resistance, magnetic susceptibility and other technical methods, finds out that the ultra-low temperature of 1T-TaS2 suggests additional complexity. In addition, with the angle resolved photoemission spectroscopy (ARPES) technique of in-situ alkali metal evaporation, this paper proposes that the 1T-TaS2 ultra-low temperature ground state may exist a combination of state and surface state. Our findings provide more experimental evidence for the physical mechanism of this system.
Research on the Synthesis and Performance of a New Type of Neutral Polymer Bonding Agent
Authors: Song Junming
Published: January 2022

In order to improve the mechanical properties of nitrate ester plasticized polyether (NEPE) propellants, 3-allylic hydantoin was synthesized by hydantoin, potassium hydroxide and 3-bromopropene, and then a new type of intermediate polymer bonding agent (NPBA) was synthesized by 3-allylic hydantoin, acrylonitrile, hydroxyethyl acrylate and dimethylaminoethyl methacrylate. At the same time, two traditional neutral polymer bonding agents were synthesized for comparative study. Through the contact angle test, the bond performance prediction shows that: compared with the two traditional bond agents, the bond work between the new bond agent and oxidant (ammonium nitrate, ammonium perchlorate) is greater, indicating that the bond between the new bond agent and oxidant is stronger.
High Performance P-Type Perovskite Lagao3 Thin Film Field Effect Transistor Prepared by Solution-Processed
Authors: Chen Qian
Published: January 2022

Metal oxide semiconductor (MOS) is essential to compose high-performance electronic devices, however, the investigation on p-type MOS is relatively rare compared with its n-type counterpart. In this work, LaGaO3 thin films with superior p-type conductivity have been prepared via a facile solution process. Moreover, we have implemented Al2O3 and SiO2 as the dielectric of the p-channel LaGaO3 thin film transistors (TFTs) annealed at different temperatures. Particularly, the LaGaO3/Al2O3 TFTs annealed at 700 °C exhibit an ultrahigh hole mobility of 12.4 cm2V-1s-1, Under the same conditions, LaGaO3/Al2O3 thin film transistor is two orders of magnitude higher than LaGaO3/SiO2 thin film transistor. The advanced p-type characteristics of the LaGaO3 thin film, along with its facile low-cost fabrication process can shed new light on future design of high-performance complementary MOS circuit with other optimized facile-integrated dielectrics.
Research on Rheology and Micro-properties of Modified Room Temperature Biological Asphalt
Authors: Shi Yang , Wang Wen , Guo Liujie , Li Jiusu
Published: January 2022

In order to explore the rheological property and principles of modified room temperature biological asphalt made with petroleum asphalt, vegetable asphalt, and unsaturated fatty acids as raw materials, waste rubber powder as modifier, and calcium hydroxide powder as curing agent, this paper compared and tested the rheological properties and the original petroleum asphalt by using DSR and BBR, and the micro-properties of the asphalt were studied by using SEM. The PG classification of modified room temperature biological asphalt has been upgraded from PG58-28 of the original petroleum asphalt to PG82-28. Compared with the original petroleum asphalt, the high temperature rheological property of the modified room temperature biological asphalt has been greatly improved. Its low temperature rheological property is equivalent to the original one, but the possibility of cracking is lower. SEM test showed that the components of the modified room temperature biological asphalt are well combined. The calcium hydroxide curing agent reacts with fatty acid and the rubber particles cross link with each other to form a mesh package in the asphalt, which provides strength for the modified temperature biologic asphalt at room temperature.
Analysis on the Operation Methods of Rail Welding and Postweld Heat Treatment in the Track Change Overhaul of Existing Railway Lines
Authors: Feng Qi , Song Hongtu
Published: January 2022

Based on the analysis of operation methods of rail welding and postweld heat treatment in the track change overhaul of existing railway lines at home and abroad, this paper puts forward the existing problems in the operation methods of off-line welding and on-line welding in China, and puts forward the solutions to the existing problems. When this operation mode is used for on-line welding, it can reduce the quality risk in the heat treatment process of low-temperature locking joint; in case of broken track and urgent repair of broken track, this method can eliminate secondary shunting and facilitate the recovery of the line as soon as possible.
The Effect of Sugar Content for the Wettability of Superhydrophobic Aluminum Substrate
Authors: Lv Shuwei , Zhang Xinming , Yang Xiaodong , Zhai Ying
Published: January 2022

A chemical etching technique is used to prepare a superhydrophobic surface with a honeycomb rough structure on the aluminum surface. Use SEM, Optical contact angle meter and Surface tension detector to characterize the etched aluminum substrate. After the 8th etching, the surface of the sample showed the morphology of micro/nano-scale honeycomb pores and protrusions, and the water contact angle (WCA) is 135°. After being modified with octadecanethiol methanol solution, WCA is 153.1°. After modification, the contact angle of the sample surface decreases with the increase of the glucose solution concentration. When the glucose solution concentration reaches 1000 mg/L, the superhydrophobicity is lost.
Seismic Permanent Deformation Analysis for Gravel Dams in High Altitude Meizoseismal Areas
Authors: He Junjie , Guo Yonggang
Published: January 2022

It is commonly believed that the permanent deformation is introduced by the meizoseismal impacts of embankment dams, which is impossible to be reinstated and will further endanger the safety and normal use thereof. In this study, a three-dimensional finite element model of the dam have been established with the equivalent nodal force approach to calculate the permanent deformation of the dams under seismic protection. It was indicated by the results that the acceleration response of dams was not intense in the meizoseismal areas and the vertical seismic permanent deformation mainly occurred at the top of the dams, of which the collapse rate is less than 1% with small lateral and horizontal seismic permanent deformation. Moreover, the dam profile has been indicated with inward shrinkage upon the seismic permanent deformation, which is beneficial to the stability of the dam slopes. However, seismic measures are required to improve the seismic performance of the dam area because of the large acceleration and permanent seismic deformation at the dam tops. The research results provide a decision basis for seismic hazard assessment and reinforcement solutions of similar dams.
Test and Analysis of TIG-MIG Hybrid Welding Are
Authors: Gou Weijie , Wang Lihong
Published: January 2022

A high-speed camera is used to observe the arc starting and arc stabilization process of the TIG-MIG hybrid welding system. Paschen’s law is used to analyze the path of TIG welding arc breakdown under the condition of the conductive channel provided by the MIG welding arc, and the arc starting process of the double arc hybrid welding is determined. The study found that when the electrode spacing is less than 8.5 mm, two molten pools can form a common molten pool after arc initiation of MIG welding; when the spacing is 10 mm, the two molten pools after arc initiation form a “8” shape; When the distance is 12 mm, there is a low temperature zone between the two arcs, which is separated.
Preparation Method of Iron Oxyhydroxide Nanosheets for Treating Organic Dyes
Authors: Lian Weijie , Ma Lan , Chen Bo , Zhai Yuxin , Wen Yibo
Published: January 2022

Considering the promoted purification requirement for industry sewage, the discharge of azo dye wastewater will have a serious impact on the environment, iron oxyhydroxide nanosheets were designed and prepared, and the purification effects and application conditions were investigated in this work. We have used its structural advantages to perform adsorption tests on Congo red (CR). It found that it has very excellent properties. It has strong adsorption capacity for Congo red under the condition of low dosage and no adjustment of PH. By employing iron oxyhydroxide nanosheets with optimized molar ratio of reducing agent to Fe, it was found that the adsorption efficiency increase in the removal percentage for CR from industry sewage could be achieved at the large specific surface area and pore volume of B-Fe Nanosheets.
Analysis of Mechanical Characteristics of Impeller of Spray Duster Based on ANSYS Workbench
Authors: Sun Kunpeng , Yang Lihong , Li Jicheng
Published: January 2022

The spray dustless machine is an important environmental protection equipment for harnessing haze. The booster impeller of the spray dustless machine is one of the decisive factors of the booster capacity. The stability of the blade directly determines the reliability of the spray duster. In this paper, ANSYS Workbench is used to analyze the mechanical characteristics of a certain type of spray dustless blade. The results show that: under the rated condition, the maximum equivalent stress of the impeller is 55.6Mpa, which is far less than the allowable stress of the impeller material 415Mpa, the maximum deformation of the circumferential position at the bottom of the blade is 1.2mm, and other deformation positions are mainly the outer edge of the blade, which can be optimized later. The interference frequency is far away from the vibration frequency of the first two modes, so resonance will not occur.
Effects of Gas Composition in Producing Carbon Nanomaterials by Plasma Enhanced Chemical Vapor Deposition
Authors: Yang Chen
Published: January 2022

Carbon nanomaterials are becoming new materials which can be used in many fields including transistors, sensors, displays, hydrogen storage, capacitors, catalyst supporters and so on. PECVD is an important method for producing carbon nanomaterials. However, it remains a challenge to control the final production of the PECVD process. This paper tries to figure out the gas factors influencing the growth of different carbon nanomaterials. This paper begins with a brief introduction of PECVD and the growth regime of carbon nanowalls and carbon nanotubes. It discusses how the Argon, carbon source, hydrogen and other gas affect the growth of CNMs. The paper ends with a discussion on the practical influence of confirming the function of each gas.
Comparative Analysis of Lithium Iron Phosphate Battery and Ternary Lithium Battery
Authors: Su Yuhao
Published: January 2022

This article analyses the lithium iron phosphate battery and the ternary lithium battery. With the development of new energy vehicles, people are discussing more and more about the batteries of electric vehicles. Nowadays, electric vehicles mainly use the lithium iron phosphate battery and the ternary lithium battery as energy sources. Existing research and articles have given the current performance of the two batteries but have not systematically compared the two batteries with more details. This article introduces the basic principles, cathode structure, and standard preparation methods of the two batteries by summarizing and discussing existing data and research. The article discusses the two types of batteries and concludes the advantages and disadvantages of the two batteries at the present stage. This article aims to help readers have a more comprehensive understanding of the basic information of the two batteries at this stage and provide theoretical guidance for future research on batteries for electric vehicles.
Effect of irradiation with Ar ions on properties of proton conductors on the base of LaScO3
Authors: Khromushin I V , Aksenova T I , Kislitsin S B
Published: January 2022

The effect of irradiation with low-energy Ar ions on the structure and conducting properties of lanthanum scandate synthesized by acceptor doping and creation of a lanthanum deficit has been studied.
Formation tantalum coating on NiTi surface by magnetron sputtering technique
Authors: Larionov A S , Dikov A S , Zhakanbaev E A
Published: January 2022

The materials of research the structure and morphology of the tantalum coating on the surface of the NiTi alloy in this work presents. Tantalum was deposited by the DC - magnetron sputtering method. The influence of the energy supplied to the magnetron on the formation of the Ta-coating is shown.Changes in the structure of the coating at different stages of its formation are shown.It was found that with a significant decrease in the energy supplied to the magnetron, tantalum is deposited more uniformly. Besides the gradient of mechanical stresses in the coating is reduced.
Radiation-doped SiC*/Si heterostructure formation and defects evolution
Authors: Chepurnov V I , Dolgopolov M V , Gurskaya A V , Puzyrnaya G V , Elkhimov D E
Published: January 2022

The authors consider heterostructures of silicon carbide obtained during endotaxy on silicon substrates. The question is raised in connection with the description of the endotaxy process itself at the structural level. Authors focus on the technological aspects of the formation of a stable β-SiC/Si heterostructure by endotaxy in relation to the evolution of point defects of various nature and their probable association models with the participation of a radionuclide impurity at the micro-alloying level: 1) the growth of the SiC*/Si thin layer with C-14 atoms in the doping process; 2) physical properties of defects formation; 3) some interface between properties and efficiency.
Dependence of the nature of the Holstein polaron motion in a polynucleotide chain subjected to a constant electric field on the initial polaron state and the parameters of the chain
Authors: Korshunova A.N. , Lakhno V.D.
Published: January 2022

In this work, we consider the motion of a polaron in a polynucleotide Holstein molecular chain in a constant electric field. It is shown that the character of the polaron motion in the chain depends not only on the chosen parameters of the chain, but also on the initial distribution of the charge along the chain. It is shown that for a small set value of the electric field intensity and for fixed values of the chain parameters, changing only the initial distribution of the charge in the chain, it is possible to observe either a uniform movement of the charge along the chain, or an oscillatory mode of charge movement.
Holstein polaron at fixed temperature: influence of the chain length
Authors: Fialko Nadezhda , Olshevets Maxim , Lakhno Victor D.
Published: January 2022

Based on the semiclassical Holstein model, the dynamics of a quantum particle in one-dimensional molecular chain with a trapping site is modeled. Numerical simulation is used to investigate the dynamics of a polaron in a chain with small random Langevin-like perturbations which imitate the thermostat. Parameter values are chosen such that the polaron energy at the trapping site is much greater than the energy of temperature fluctuations. Results of modeling demonstrate that temperature decay of a polaron depends on the chain length even at very low temperatures.
Comparison of various machine learning approaches uses in heart ailments prediction
Authors: Gupta Gunjan , Adarsh U , Subba Reddy N V , Ashwath Rao B
Published: January 2022

Heart disease has been the leading cause of a huge number of deaths in recent years. As a result, an accurate and feasible system is required to diagnose this disease early to provide better treatment. Advances in machine learning have the potential to enhance healthcare access. Given the importance of a crucial organ like the heart, medical professionals and physicians have made it a priority to forecast heart failure-related events in clinical practice, nevertheless, forecasting heart failure-related events in clinical practice has generally failed to achieve high accuracy. The objective here is to demonstrate how machine learning may be used to solve the problem. By analyzing hundreds of healthcare data and other semantics, machine learning algorithms can analyze related cases with diseases and health conditions. Here a demonstration of how to load the data, generate predictions through different models from patient data is shown. The metrics are then compared for a better understanding of their function and what impact can be inferred from them.
Design of a Flexible Microstrip Antenna for BAN Applications
Authors: Sharma Avi , Kumar Pramod
Published: January 2022

A flexible microstrip antenna is a compact antenna that can be coupled with the skin. However, such antennas require to be coupled with an intermediate matching liquid medium which makes the antenna bulky, complicated, and expensive. Body area network devices are wearable wireless devices/sensors that are used to get the information of a patient’s health in terms of physiological changes irrespective of location. A flexible layer made of Polyethylene is chosen as the substrate and a copper patch is levied upon it. This substrate layer lies in between two adhesive layers (GIL GML 1000).In this paper, flexible antennas are designed and simulated for Body area networks (BANs). The S11 parameter, VSWR value, Gain, and the radiation pattern of the antennas are compared. The polyethylene substrate is highly flexible and lightweight; therefore it would be an ideal material to be used as the substrate of the required antenna.
An prediction of Healthy Diet required to Ease the recovery from Covid-19 using the approach of Machine Learning.
Authors: Maria Colaco Rencita , Shreya  , Subba Reddy N V , Acharya U Dinesh
Published: January 2022

Global terror that has shaken the world named, COVID-19 virus has taken away huge number of lives. According to the research there are lot of recovery cases also. Most important thing to survive from this disease is having good immunity. Everyone does not have same level of immunity. One main factor on which immunity depends is having a healthy diet. If the routine of having healthy diet is maintained, then the immunity to fight against this virus increases. It is much required that people need to be informed about having an healthy diet. Using the dataset of healthy dietary and using various machine learning algorithms we can determine what type of diet one person needs to have. By using algorithms like Random Forest, KNN, logistic regression and Support Vector Machines we can determine the type of diet and probability of recovery. The dataset required for analysis needs to have all the information regarding the diet. Based on the dataset the prediction is taken place by using Decision Tree algorithm. This method of finding the appropriate diet of a particular person based on amount of Sugar level, Blood Pressure and BMI can be the most useful research in this pandemic time.
Predicting Mental Health Illness using Machine Learning Algorithms
Authors: Vaishnavi Konda , Nikhitha Kamath U , Ashwath Rao B , Subba Reddy N V
Published: January 2022

Early detection of mental health issues allows specialists to treat them more effectively and it improves patient’s quality of life. Mental health is about one’s psychological, emotional, and social well-being. It affects the way how one thinks, feels, and acts. Mental health is very important at every stage of life, from childhood and adolescence through adulthood. This study identified five machine learning techniques and assessed their accuracy in identifying mental health issues using several accuracy criteria. The five machine learning techniques are Logistic Regression, K-NN Classifier, Decision Tree Classifier, Random Forest, and Stacking. We have compared these techniques and implemented them and also obtained the most accurate one in Stacking technique based with an accuracy of prediction 81.75%.
Modeling of Supervised Machine Learning using Mechanism of Quantum Computing.
Authors: Nivelkar Mukta , Bhirud S. G.
Published: January 2022

Mechanism of quantum computing helps to propose several task of machine learning in quantum technology. Quantum computing is enriched with quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. Qubit is sole of quantum technology and help to use quantum mechanism for several tasks. Tasks which are non-computable by classical machine can be solved by quantum technology and these tasks are classically hard to compute and categorised as complex computations. Machine learning on classical models is very well set but it has more computational requirements based on complex and high-volume data processing. Supervised machine learning modelling using quantum computing deals with feature selection, parameter encoding and parameterized circuit formation. This paper highlights on integration of quantum computation and machine learning which will make sense on quantum machine learning modeling. Modelling of quantum parameterized circuit, Quantum feature set design and implementation for sample data is discussed. Supervised machine learning using quantum mechanism such as superposition and entanglement are articulated. Quantum machine learning helps to enhance the various classical machine learning methods for better analysis and prediction using complex measurement.
Next frame prediction using ConvLSTM
Authors: Desai Padmashree , Sujatha C , Chakraborty Saumyajit , Ansuman Saurav , Bhandari Sanika , Kardiguddi Sharan
Published: January 2022

Intelligent decision-making systems require the potential for forecasting, foreseeing, and reasoning about future events. The issue of video frame prediction has aroused a lot of attention due to its usefulness in many computer vision applications such as autonomous vehicles and robots. Recent deep learning advances have significantly improved video prediction performance. Nevertheless, as top-performing systems attempt to foresee even more future frames, their predictions become increasingly foggy. We developed a method for predicting a future frame based on a series of prior frames that services the Convolutional Long-Short Term Memory (ConvLSTM) model. The input video is segmented into frames, fed to the ConvLSTM model to extract the features and forecast a future frame which can be beneficial in a variety of applications. We have used two metrics to measure the quality of the predicted frame: structural similarity index (SSIM) and perceptual distance, which help in understanding the difference between the actual frame and the predicted frame. The UCF101 data set is used for testing and training in the project. It is a data collection of realistic action videos taken from YouTube with 101 action categories for action detection. The ConvLSTM model is trained and tested for 24 categories from this dataset and a future frame is predicted which yields satisfactory results. We obtained SSIM as 0.95 and perceptual similarity as 24.28 for our system. The suggested work’s results are also compared to those of state-of-the-art approaches, which are shown to be superior.
Fake News Detection from Online media using Machine learning Classifiers
Authors: Pandey Shalini , Prabhakaran Sankeerthi , Subba Reddy N V , Acharya Dinesh
Published: January 2022

With the advancement in technology, the consumption of news has shifted from Print media to social media. The convenience and accessibility are major factors that have contributed to this shift in consumption of the news. However, this change has bought upon a new challenge in the form of “Fake news” being spread with not much supervision available on the net. In this paper, this challenge has been addressed through a Machine learning concept. The algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naïve Bayes and Logistic regression Classifiers to identify the fake news from real ones in a given dataset and also have increased the efficiency of these algorithms by pre-processing the data to handle the imbalanced data more appropriately. Additionally, comparison of the working of these classifiers is presented along with the results. The model proposed has achieved an accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for Decision Tree and 89.33% for SVM in our experiment.
Model Checking M2M and Centralised IOT authentication Protocols.
Authors: Rekha H , Siddappa M.
Published: January 2022

It is very difficult to develop a perfect security protocol for communication over the IoT network and developing a reliable authentication protocol requires a detailed understanding of cryptography. To ensure the reliability of security protocols of IoT, the validation method is not a good choice because of its several disadvantages and limitations. To prove the high reliability of Cryptographic Security Protocols(CSP) for IoT networks, the functional correctness of security protocols must be proved secure mathematically. Using the Formal Verification technique we can prove the functional correctness of IoT security protocols by providing the proofs mathematically. In this work, The CoAP Machine to Machine authentication protocol and centralied IoT network Authentication Protocol RADIUS is formally verified using the well-known verification technique known as model checking technique and we have used the Scyther model checker for the verification of security properties of the respective protocols. The abstract protocol models of the IoT authentication protocols were specified in the security protocol description language and the security requirements of the authentication protocols were specified as claim events.
Design and Simulation of Waveguide Bragg Grating based Temperature Sensor in COMSOL
Authors: Dhavamani Vigneshwar , Chakraborty Srijani , Ramya S , Nandi Somesh
Published: January 2022

With the advancements in the domain of photonics and optical sensors, Fibre Bragg Grating (FBG) sensors, owing to their increased advantages, have been researched widely and have proved to be useful in sensing applications. Moreover, the advent of Photonic Integrated Circuits (PICs) demands the incorporation of optical sensing in waveguides, which can be integrated on silicon photonic chips. In this paper, the design of a sub-micron range Waveguide Bragg Grating (WBG) based temperature sensor with high peak reflectivity and thermal sensitivity is proposed. The flexibility of COMSOL Multiphysics software is explored to simulate the sensor and the results are verified with the analytical values calculated using MATLAB. The simulation is carried out for the proposed design having 16000 gratings and a corresponding peak reflectivity of 0.953 is obtained. A thermal sensitivity of 80 pm/K is achieved, which is approximately eight times better than that of FBG based sensor.
An evaluation of approaches for enhancing inductive learning with a transductive view
Authors: Lokesh Kumar T N , Das Bhaskarjyoti
Published: January 2022

Availability of enough labeled data is a challenge for most inductive learners who try to generalize based on limited labeled dataset. A traditional semi-supervised approach for the same problem attempts to approach it by methods such as wrapping multiple inductive learners on derived pseudo-labels, unsupervised feature extraction or suitable modification of the objective function. In this work, a simple approach is adopted whereby an inductive learner is enhanced by suitably enabling it with a transductive view of the data. The experiments, though conducted on a small dataset, successfully provide few insights i.e. transductive view benefits an inductive learner, a transductive view that considers both attribute and relations is more effective than one that considers either attributes or relations and graph convolution based embedding algorithms effectively captures the information from transductive views compared to popular knowledge embedding approaches.
Design and Implementation of Power-Efficient FSM based UART
Authors: Kamath Akshatha , Mendez Tanya , Ramya S , Nayak Subramanya G
Published: January 2022

The remarkable innovations in technology are driven mainly by the high-speed data communication requirements of the modern generation. The Universal Asynchronous Receiver Transmitter (UART) is one of the most sought-after communication protocols. This work mainly focuses on implementing and analysing the UART for data communication. The Finite State Machine (FSM) implements the baud rate generator, transmitter, and receiver modules. Cadence NCSIM was utilized for simulation, and Cadence RTL Compiler was used during synthesis using the 45 nm and 90 nm General Process Design Kit (GPDK) library files. The baud rate of 9600 bps and 50 MHz clock frequency was used to design UART. The increased speed and complexity of the VLSI chip designs has resulted in a significant increase in power consumption. The comparative analysis of power and delay for different clock periods shows an improvement in the total power and the Power Delay Product (PDP) with increasing clock periods. Better results were observed using 45 nm in comparison to the 90 nm library.
Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction
Authors: Lekshmy H O , Panickar Dhanyalaxmi , Harikumar Sandhya
Published: January 2022

Epilepsy is a common neurological disease that affects more than 2 percent of the population globally. An imbalance in brain electrical activities causes unpredictable seizures, which eventually leads to epilepsy. Neurostimulators have the power to intervene in advance and avoid the occurrence of seizures. Its efficiency can be increased with the help of heuristics like advanced seizure prediction. Early identification of preictal state will help easy activation of neurostimulator on time. This research concentrates on the performance analysis of various machine learning algorithms on recorded EEG data. Through this study, we aim to find the best model, which can be used to create an ensemble model for better learning. This involves modeling and simulation of classical machine learning technique like Logistic regression, Naive Bayes model, K nearest neighbors Random Forest, and deep learning techniques like an Artificial neural network, Convolutional neural networks, Long short term memory, and Autoencoders. In this analysis, Random Forest and Long Short-Term Memory performed well among all models in terms of sensitivity and specificity.
Optimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL
Authors: Mishra Sthitprajna , Prasad Ganthia Bibhu , Sridharan Abel , Rajakumar P , Padmapriya D. , Kaliappan S.
Published: January 2022

The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners for making important decisions on unit commitments, energy trading, system security & reliability and optimal reserve capacity. The objective is to produce a desktop version of personal computer based complete expert system which can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes study of load forecasting in smart grid, detailed study of architecture and configuration of Artificial Neural Network(ANN), Mathematical modeling and implementation of ANN using MATLAB and Detailed study of load forecasting using back propagation algorithm.
Sensorless Speed Contorl of Doubly-Fed Induction Machine Using Reactive Power Based MRAS
Authors: Kumar Samal Sidhartha , Jena Smrutisikha , Ganthia Bibhu Prasad , Kaliappan S. , Sudhakar M. , Sriram Kalyan S. K.
Published: January 2022

A sensorless speed control method for doubly-fed induction machine (DFIM) operating with constant frequency but in variable speed mode is presented in this project work. The control method is based on rotor speed estimation technique by a reactive power model reference adaptive system (MRAS) observer. The presented technique does not depend on any kind of flux evaluation and also independent to the resistance variation of either stator or rotor. The MRAS observer has a capacity for speed catching operation. PI controller is designed and also optimized using algorithm for better dynamic behaviour of the machine. MATLAB Simulink model and the simulation results are shown to check the effectiveness of the observer and also of the controller.
Comparison of Classification Models Based on Deep learning on COVID-19 Chest X-Rays
Authors: Mane Pallavi R , Shenoy Rajat , Prabhu Ghanashyama
Published: January 2022

COVID -19, is a deadly, dangerous and contagious disease caused by the novel corona virus. It is very important to detect COVID-19 infection accurately as quickly as possible to avoid the spreading. Deep learning methods can significantly improve the efficiency and accuracy of reading Chest X-Rays (CXRs). The existing Deep learning models with further fine tune provide cost effective, rapid, and better classification results. This paper tries to deploy well studied AI tools with modification on X-ray images to classify COVID 19. This research performs five experiments to classify COVID-19 CXRs from Normal and Viral Pneumonia CXRs using Convolutional Neural Networks (CNN). Four experiments were performed on state-of-the-art pre-trained models using transfer learning and one experiment was performed using a CNN designed from scratch. Dataset used for the experiments consists of chest X-Ray images from the Kaggle dataset and other publicly accessible sources. The data was split into three parts while 90% retained for training the models, 5% each was used in validation and testing of the constructed models. The four transfer learning models used were Inception, Xception, ResNet, and VGG19, that resulted in the test accuracies of 93.07%, 94.8%, 67.5%, and 91.1% respectively and our CNN model resulted in 94.6%.
RF and microwave diagnostics for compact plasma traps and possible perspectives for fusion devices
Authors: Torrisi G. , Naselli E. , Di Donato L. , Mauro G.S. , Mazzaglia M. , Mishra B. , Pidatella A. , Sorbello G. , Mascali D.
Published: January 2022

Plasma diagnostics is a topic having a great impact on R&D in compact ion sources as well as in large fusion reactors. Towards this aim, non-invasive microwave diagnostics approaches, such as interferometric, polarimetric and microwave imaging profilometric techniques can allow obtaining volumetric, line-integrated or even space-resolved information about plasma electron density. Special probes can be also designed and implemented in order to characterize external and/or self-generated radio-waves in the plasmas. In particular, the design, construction and operation of a K-band microwave interferometry/polarimetry setup based on the Frequency-Modulated Continuous-Wave (FMCW) method at INFN-LNS will be described: this tool provides reliable measurements of the plasma density even in the extreme unfavorable wavelength-to-plasma scale ratio in plasma-based ion sources. A “frequency sweep” and a post-processing filtering method (for interferometry and polarimetry, respectively) were used to filter out the multipath contributions or cavity induced depolarizations in the detected signals. Besides this, the use of the aforementioned RF plasma-immersed probes will also be discussed, which allow measuring local E-fields and fast temporal response in order to characterize turbulent (through kinetic instabilities, cyclotron maser emission, etc.) vs. stable plasma regimes. An analysis based on wavelet transform applied to measurements of plasma radio self-emission in B-minimum and simple mirror traps will be presented. These tools and methods have the potential to be applied to plasma machines both in compact traps and large-size reactors with a proper scaling.
Fabrication of 3D scaffolds based on fully biobased unsaturated polyester resins by microstereo-lithography
Authors: Gonçalves Filipa A M M , Fonseca Ana C , Cordeiro Rosemeyre , Piedade Ana P , Faneca Henrique , Serra Arménio , Coelho Jorge F J
Published: January 2022

Additive Manufacturing (AM) technologies are an effective route to fabricate tailor made scaffolds for tissue engineering (TE) and regenerative medicine, with microstereo-lithography (µSLA) being one of the most promising techniques to produce high quality 3D structures. Here, we report the crosslinking studies of fully biobased unsaturated polyesters (UPs) with 2-hydroxyethyl methacrylate (HEMA) as the unsaturated monomer (UM), using thermal and µSLA crosslinking processes. The resulting resins were fully characterized in terms of their thermal and mechanical properties. Determination of gel content, water contact angle, topography and morphology analysis by atomic force microscopy and scanning electron microscopy were also performed. The results show that the developed UP resins (UPRs) have promising properties for µSLA.In vitrocytotoxicity assays performed with 3T3-L1 cell lines showed that the untreated scaffolds exhibited a maximum cellular viability around 60%, which was attributed to the acidic nature of the UPRs. The treatment of the UPRs and scaffolds with ethanol (EtOH) improved the cellular viability to 100%. The data presented in this manuscript contribute to improve the performance of biobased UPs in AM.
Mean exit time in irregularly-shaped annular and composite disc domains
Authors: Carr Elliot J , VandenHeuvel Daniel J , Wilson Joshua M , Simpson Matthew J
Published: January 2022

Calculating the mean exit time (MET) for models of diffusion is a classical problem in statistical physics, with various applications in biophysics, economics and heat and mass transfer. While many exact results for MET are known for diffusion in simple geometries involving homogeneous materials, calculating MET for diffusion in realistic geometries involving heterogeneous materials is typically limited to repeated stochastic simulations or numerical solutions of the associated boundary value problem (BVP). In this work we derive exact solutions for the MET in irregular annular domains, including some applications where diffusion occurs in heterogenous media. These solutions are obtained by taking the exact results for MET in an annulus, and then constructing various perturbation solutions to account for the irregular geometries involved. These solutions, with a range of boundary conditions, are implemented symbolically and compare very well with averaged data from repeated stochastic simulations and with numerical solutions of the associated BVP. Software to implement the exact solutions is available on GitHub.
Conditions for realizing one-point interactions from a multi-layer structure model
Authors: Zolotaryuk Alexander V , Zolotaryuk Yaroslav
Published: January 2022

A heterostructure composed of N parallel homogeneous layers is studied in the limit as their widths l
1, …, l

N
shrink to zero. The problem is investigated in one dimension and the piecewise constant potential in the Schrödinger equation is given by the strengths V
1, …, V

N
as functions of l
1, …, l

N
, respectively. The key point is the derivation of the conditions on the functions V
1(l
1), …, V

N
(l

N
) for realizing a family of one-point interactions as l
1, …, l

N
tend to zero along available paths in the N-dimensional space. The existence of equations for a squeezed structure, the solution of which determines the system parameter values, under which the non-zero tunneling of quantum particles through a multi-layer structure occurs, is shown to exist and depend on the paths. This tunneling appears as a result of an appropriate cancellation of divergences.
Raman spectra of twisted bilayer graphene close to the magic angle
Authors: Barbosa Tiago C , Gadelha Andreij C , Ohlberg Douglas A A , Watanabe Kenji , Taniguchi Takashi , Medeiros-Ribeiro Gilberto , Jorio Ado , Campos Leonardo C
Published: January 2022

In this work, we study the Raman spectra of twisted bilayer graphene samples as a function of their twist-angles (θ), ranging from 0.03° to 3.40°, where local θ are determined by analysis of their associated moiré superlattices, as imaged by scanning microwave impedance microscopy. Three standard excitation laser lines are used (457, 532, and 633 nm wavelengths), and the main Raman active graphene bands (G and 2D) are considered. Our results reveal that electron–phonon interaction influences the G band’s linewidth close to the magic angle regardless of laser excitation wavelength. Also, the 2D band lineshape in the θ < 1° regime is dictated by crystal lattice and depends on both the Bernal (AB and BA) stacking bilayer graphene and strain soliton regions (SP) (Gadelha et al 2021 Nature
590 405–9). We propose a geometrical model to explain the 2D lineshape variations, and from it, we estimate the SP width when moving towards the magic angle.
Fabrication of superhydrophilic surfaces for long time preservation on 316 l stainless steel by ultraviolet laser etching
Authors: Pan Chunyang , Xu Changfeng , Zhou Jun
Published: January 2022

Due to the good biocompatibility, 316 l stainless steel is widely used in the manufacture of medical instruments and human implants. The super hydrophilic 316 l steel surface is used for reducing friction and adhesion. By choosing appropriate laser process parameters 316 l steel surfaces with super-hydrophilic were obtained. The effects of laser process parameters including repeat frequency, pulse width, scanning speed, and the number of scanning were investigated to find the relationship between surface microstructure and wetting ability. To investigate the super-hydrophilic maintenance time on the textured surface, the textured surfaces were preserved in ambident air, distilled water, and absolute ethanol. The results showed that by choosing appropriate laser process parameters surface with super-hydrophilicity can be maintained for 30 d.
Intense green luminescence in Mn-doped gallogermanate nanostructured glass
Authors: Xu Xiaoyun , Xing Yingying , Yang Ziyue
Published: January 2022

Mn-activated optical materials have been demonstrated to be promising for various applications such as light-emitting device, tunable lasers and smart sensors. Most of the candidates belong to the single crystals and crystalline powders, and few reports involve about the glass-derived materials. This letter reports the fabrication of active Mn-doped nanostructured glass sample via control of the crystallization of a typical gallogermanate glass. LiGa5O8 and LiGaGeO4 can precipitate after heat-treatment, and Mn centers selectively incorporate into the crystalline phase. Importantly, the luminescence can be switched from red to green colour and the intensity can be enhanced simultaneously. Furthermore, the luminescence properties can be tuned via control of the phase of the nanostructured glass.
Choice and the Placebo Effect: A Meta-analysis
Authors: Tang Biya , Barnes Kirsten , Geers Andrew , Livesey Evan , Colagiuri Ben
Published: January 2022

Background

Choice has been proposed as a method of enhancing placebo effects. However, there have been no attempts to systematically evaluate the magnitude, reliability, and moderators of the influence of choice on the placebo effect.

Purpose

To estimate the effect size of choice on the placebo effect and identify any moderators of this effect.

Methods

Web of Science, PsycINFO, EMBASE, and PubMed were systematically searched from inception to May 2021 for studies comparing placebo treatment with any form of choice over its administration (e.g., type, timing) to placebo treatment without choice, on any health-related outcome. Random-effects meta-analysis was then used to estimate the effect size associated with the influence of choice on the placebo effect. Meta-regression was subsequently employed to determine the moderating effect of factors such as type of choice, frequency of choice, and size of the placebo effect without choice.

Results

Fifteen independent studies (N = 1,506) assessing a range of conditions, including pain, discomfort, sleep difficulty, and anxiety, met inclusion criteria. Meta-analysis revealed that choice did significantly enhance the placebo effect (Hedges’ g = 0.298). Size of the placebo effect without choice was the only reliable moderator of this effect, whereby a greater effect of choice was associated with smaller placebo effects without choice.

Conclusions

Treatment choice can effectively facilitate the placebo effect, but this effect appears more pronounced in contexts where the placebo effect without choice is weaker. Because most evidence to date is experimental, translational studies are needed to test whether providing choice in clinical scenarios where placebo effects are weaker may help boost the placebo effect and thereby improve patient outcomes.
Associations Between Parents’ Health and Social Control Behaviors and Their Adolescent’s Self-Efficacy and Health Behaviors: Insights From the Family Life, Activity, Sun, Health, and Eating (FLASHE) survey
Authors: Nakamura Monique S , Huelsnitz Chloe O , Rothman Alexander J , Simpson Jeffry A
Published: January 2022

Background

Parents can influence their children to live healthier lifestyles by modeling healthy behaviors and/or trying to persuade their children to engage in healthier activities. Adolescents and their parents tend to have similar eating and exercise patterns, but less is known about the simultaneous influence of parent’s health behavior and social control on adolescents’ self-efficacy and health behaviors, including whether their effect is moderated by parenting style.

Purpose

We examine the degree to which parents’ social control and health behaviors are associated with their adolescent’s self-efficacy and health behaviors, including whether parenting styles moderate these associations.

Method

We analyzed data from the Family Life, Activity, Sun, Health, and Eating project.

Results

We found that parents’ own health behaviors are positively and strongly associated with their adolescent’s health behaviors across four domains: fruit/vegetable consumption, junk food consumption, physical activity, and nonacademic screen time. We found positive, moderate-to-strong associations between parents’ use of social control and their adolescents’ fruit/vegetable and junk food consumption, small negative associations with screen time, and no associations with physical activity. The effects of social control for junk food consumption and screen time, however, depended on parents’ own behavior in those domains. Parent responsiveness moderated the relation between parents’ social control and their adolescent children’s self-efficacy and health behaviors.

Conclusions

The health behaviors parents model and their social control efforts are associated with their adolescents’ beliefs and behavior. Efforts to leverage parents as sources of influence must consider the context in which influence is enacted.