bluuebunny/crossref_metadata_2025 huggingface dataset:
A comparative study on radiation reliability of composite channel InP high electron mobility transistors*
Authors: Zhang Jia-Jia , Ding Peng , Jin Ya-Nan , Meng Sheng-Hao , Zhao Xiang-Qian , Hu Yan-Fei , Zhong Ying-Hui , Jin Zhi
Published: February 2021

This paper proposes a reasonable radiation-resistant composite channel structure for InP HEMTs. The simulation results show that the composite channel structure has excellent electrical properties due to increased modulation doping efficiency and carrier confinement. Moreover, the direct current (DC) and radio frequency (RF) characteristics and their reliability between the single channel structure and the composite channel structure after 75-keV proton irradiation are compared in detail. The results show that the composite channel structure has excellent radiation tolerance. Mechanism analysis demonstrates that the composite channel structure weakens the carrier removal effect. This phenomenon can account for the increase of native carrier and the decrease of defect capture rate.
Trigger mechanism of PDSOI NMOS devices for ESD protection operating under elevated temperatures*
Authors: Wang Jia-Xin , Li Xiao-Jing , Zhao Fa-Zhan , Zeng Chuan-Bin , Li Duo-Li , Gao Lin-Chun , Li Jiang-Jiang , Li Bo , Han Zheng-Sheng , Luo Jia-Jun
Published: February 2021

Trigger characteristics of electrostatic discharge (ESD) protecting devices operating under various ambient temperatures ranging from 30 °C to 195 °C are investigated. The studied ESD protecting devices are the H-gate NMOS transistors fabricated witha 0.18-μm partially depleted silicon-on-insulator (PDSOI) technology. The measurements are conducted by using a transmission line pulse (TLP) test system. The different temperature-dependent trigger characteristics of grounded-gate (GGNMOS) mode and the gate-triggered (GTNMOS) mode are analyzed in detail. The underlying physical mechanisms related to the effect of temperature on the first breakdown voltage V
T1 are investigated through the assist of technology computer-aided design (TCAD) simulation.
Introducing the general condition for an operator in curved space to be unitary
Authors: Mehdi Jafari Matehkolaee
Published: February 2021

We investigate the general condition for an operator to be unitary. This condition is introduced according to the definition of the position operator in curved space. In a particular case, we discuss the concept of translation operator in curved space followed by its relation with an anti-Hermitian generator. Also we introduce a universal formula for adjoint of an arbitrary linear operator. Our procedure in this paper is totally different from others, as we explore a general approach based only on the algebra of the operators. Our approach is only discussed for the translation operators in one-dimensional space and not for general operators.
An extended scale-free network evolution model based on global coupling motif embedding
Authors: Wang Difei , Jian Lirong , Duan Linbo , Xue Chenyan
Published: February 2021

The BA scale-free network evolution model assumes that one node enters at every unit time, which does not adequately reflect team entries that usually occur during the evolution of many practical networks, i.e. the phenomenon of motif embedment. Unfortunately, there are no specific studies on how the motif embedment mechanism affects the degree distribution of networks. In order to solve this problem, an extended scale-free network evolution model with global coupling motif embedment and with the motif size obeying an arbitrary discrete probability distribution (called the MEEBA model for short) is formulated with the help of the ‘motif’ concept. Using the Markov chain method, the accurate analytical expression of the network degree distribution of the MEEBA model is obtained and the correctness of the equation is verified through comparisons with numerical simulation results. The study results show that the right tail of the degree distribution of the MEEBA model still has a power-law behavior, while its left tail reflects the horse-head-like shapes of the degree distribution of many real networks. Furthermore, the power-law exponent and the horse-head shapes are both related to the distribution of the motif size. When the motif size follows a one-point distribution, the network degree distribution of the MEEBA model degenerates into a two-parameter Waring-like distribution and its horse-head shapes disappear. In particular, if the expectation of the one-point distribution is 1, the MEEBA model further degenerates to the BA model. Finally, the practicality and effectiveness of the MEEBA model are verified through a case study.
Overview of the I Scientific Conference on Advances in Science, Engineering and Digital Education – ASEDU-2020
Authors: Kovalev Igor V , Voroshilova Anna A
Published: February 2021

The overview describes the main directions and results of the I International Scientific Conference ASEDU-2020 held in Krasnoyarsk in October 8-9, 2020. It gives the details about the participants and the proceedings.
Some aspects of using mixed reality technologies in the training of physics teachers
Authors: Zakharov P V , Kataeva A S , Kochkin A S , Eremin A M , Markidonov A V
Published: February 2021

The introduction of modern technologies in the learning process at school is an important task, both for the education system as a whole and for an individual teacher. The ability to work with new teaching aids allows the teacher to interest students in their subject. One of these tools is virtual and mixed reality headsets. The emphasis is on mixed reality, although the two are often closely related. The article provides a brief overview of works devoted to the use of mixed reality in teaching physics, a study of the readiness of students of a pedagogical university to use this technology in their future professional activities is carried out. The questionnaire for expert assessment of mixed reality developments has been modified, and a survey of experts in this field has been conducted. The results of the research may be of interest to teachers of various specialties, senior students and graduate students in the field of physics teaching methods.
Numerical modelling of the process of implementing media educational projects using GERT networks
Authors: Dorrer A G , Dorrer M G , Kasyanova E V , Zyryanov A A
Published: February 2021

The purpose of this work was to test the applicability of the methods for work networks based on the stochastic GERT networks analysing to the analysis and forecasting of the executing similar projects processes. The experimental object was the data on the implementation of media education projects by students of the general education lyceum. The processes were modelled using the graphical BPMN methodology. For numerical prediction, we used formulas and algorithms for constructing and analysing stochastic GERT networks. The GERT network, built on the basis of data analysis of the implemented media educational projects, made it possible to calculate the probability distribution density of the time of the media project execution, as well as to calculate the first central moments of the distribution law-significant in practice indicators of the average execution time of a media project, taking into account loops and cycles, as well as an indicator of the variance of the project execution time, which makes it possible to estimate the spread of the execution time of the same type of media projects. The result obtained allows us to speak about the applicability of the GERT-model for predicting the numerical indicators of models of business processes for executing projects of the same type as an alternative to statistical simulation.
Info-cognitive technologies in language education
Authors: Dugarova T Ts , Filippovich Yu N , Bulanova M N , Bagramyan E R , Aigumova Z I , Dugarov T-Zh Ts
Published: February 2021

Digitalization is global modern challenge leading to digital transformations changing of all society institutes and structures including education. This article presents rationale for the necessity of info-cognitive technologies introduction in native language-related education on the methodological bases of cognitive and cross-cultural approaches. Realization experience of cognizer idea which is an info-cognitive technology of language-culture native speaker mind verbal modeling is represented. Informational system includes the researcher’s web interface, a responder and data bases having total information. An associative inquest for created system testing was carried out; the research was done in the Buryat Republic (Russia). Buryats are Mongolian language ethnic group remained traditional life, values-based attitude to family/nation, nature-made objects. The state of the Buryat language is rated as critical; it was added to the list of endangered world languages by UNESCO. Students aged 17-20 years old took part in this research. There were about 105 stimulus words. They found out specific linguistic consciousness and values-based orientations of native speakers, preservation of respondent’s national language code/mentality which shows the necessity of their consideration in the process of native language studying.
The application of artificial intelligence and Big Data analytics in personalized learning
Authors: Magomadov V S
Published: February 2021

The concept of personalized learning is not something new in the field of education but with the advent of artificial intelligence and Big Data analytics, new ways of implementing it are being discovered. This paper is focused on these new ways of its application through these technologies. First of all, it explains what personalized learning is and how it differs from traditional teaching methods. Then, it focuses on the artificial intelligence component of personalized learning, and how it can be utilized to make this type of education more effective. Furthermore, the paper discusses the role of Big Data in personalized learning, and how this data should be dealt with appropriately. Finally, the paper discusses some of the arguments against these trends in education.
Magnetocaloric properties of multicomponent Laves phase compounds and their composites
Authors: Ćwik J , Koshkid’ko Yu , Nenkov K , Kolchugina N
Published: February 2021

Heat capacity measurements have been performed for multicomponent (Ho0.9Er0.1)1-xGdxCo2 compounds with x = 0.05, 0.1, and 0.15. The isothermal magnetic entropy change, ΔS
mag, allowing the estimation of the magnetocaloric effect, was determined based on the heat capacity measurements in magnetic fields up to 2 T. A numerical method, with the magnetic entropy change of individual (Ho0.9Er0.1)1-xGdxCo2 compounds, was used to calculate the optimal molar composition of the constituents and the resulting change of the isothermal magnetic entropy of composite, ΔS
comp. The results show that proposed composite can be considered as a refrigerant material in magnetic refrigerators performing an Ericsson cycle in a temperature range of 90-130 K.
Binder and polymeric-ceramic material filled with nanosized Al2O3
Authors: Osipchik V S , Egorov A S , Anokhin A S , Olikhova Yu V , Osipchik V V
Published: February 2021

The production of polymer-ceramic materials with high technological and mechanical properties requires the development of effective binders. The article considers the influence of modifiers on the curing process of polymethylsiloxane resin. It is shown that the introduction of modifiers helps to reduce the viscosity of the resin, and in the presence of a hardener, it makes it possible to obtain a binder with a low viscosity and an acceptable gelation time. Polymer-ceramic material based on the developed binder and nanoscale plasma-chemical Al2O3 powder has high mechanical properties, reduced shrinkage and porosity.
Research on a novel gesture recognition device
Authors: Huang Jian
Published: February 2021

In order to effectively detect the input gesture information, a new type of capacitive sensing technology, which is of a low power consumption, low cost but high resolution non-contact sensing technology, is adopted. When our finger gets close to the metal plate, it will cause the change of the capacitance value of the metal plate to the ground. That value can be converted into 28-bit binary data for output. The microprocessor is equipped with the IIC interface for acquisition and processing of the output data. Because a variety of gestures produce different capacitance values, their output varies. After many times of training and machine learning, different gestures can be identified. The test results show the rate of different gestures recognition is over 99%, which can be applicable in human-computer interaction equipment.
A method of filtering bad text in network based on topic sensitive words tagging
Authors: Xiong Jianying
Published: February 2021

Bad information filtering is conducive to creating a healthy network. A two-level filtering method based on topic and sensitive words is proposed. In the first stage, the network text is filtered by using thesaurus, by setting the weight of different topics. In the second stage, according to the frequency, position and sensitivity of sensitive words, the value of bad tendency is obtained by weighting the web text. Finally, taking the text set recognition of bad financial publicity content in the network as an example, the result proves that it can improve the efficiency and accuracy of filtering of bad investment information.
Comparative advantage analysis of production of jasmine tea in China
Authors: Wu Qinyao , Yang Jiangfan
Published: February 2021

Based on the analysis of the evolution of the production layout of jasmine tea since the founding of new China, this paper further explores the heterogeneity of the comparative advantage in the main producing areas of jasmine tea from four aspects: comparative advantage index of efficiency, comparative advantage index of scale, comprehensive comparative advantage index and comparative advantage index of development. The research shows that except for Fujian, the traditional producing areas of jasmine tea have lost their comparative advantages, such as Zhejiang and Guangdong. Although the emerging producing areas, led by Guangxi, have absolute advantages in terms of comprehensive comparative advantages, but have no advantages in terms of the development advantage index. Therefore, it is necessary for all parties to focus on the key technical issues in order to achieve the sustainable and healthy development of the jasmine tea industry. Based on the above research results, it is proposed initiative and economic new normal opportunities should be seized to improve the international competitiveness and influence of China’s jasmine tea industry under the background of “One Belt And One Road”. To improve effective supply, promote tea consumption to promote economic growth and other suggestions.
Indoor intelligent driving scene-based traffic signal data set
Authors: Fu En , Pan Weiguo , Pan Feng , Yang Qing
Published: February 2021

In recent years, with the rapid development of intelligent driving technology, vision based traffic signal target detection is one of the urgent problems to be solved; especially with the extensive application of deep learning technology, the traffic signal data set is more and more important. Due to the variety of traffic signals, there is no data set that can cover all categories at home and abroad. This paper presents a traffic signal data set based on indoor traffic scene. The currently disclosed data category includes 22 categories, including traffic signs and traffic lights. Simultaneously, this paper also uses a two-stage deep learning method to conduct experiments. In order to facilitate other researchers to conduct further research on the disclosed data set in this paper.
Evolution of dynamics of omni-channel pre-sale and spot competition based on consumer heterogeneity
Authors: Li Peiqin
Published: February 2021

The paper constructs the pre-sale competition model, the pre-sale and spot hybrid competition model, and the pure spot competition model under the influence of the pre-sale promotion efforts. In the analysis of complete pre-sale competition model, only when the homogeneity of consumers is high, retailers will launch full pre-sale competition. If the pre-sale competition is to be launched, the greater the difference in the response of consumers’ demand to the pre-sale promotion, the higher the requirement for the homogeneity of consumers is. In the hybrid competition model of presale and spot, it is found that the optimal price equilibrium solution to full pre-sale competition is always less than the optimal price equilibrium solution of both pre-sale market and spot market. In the pure spot market competition model, the conditions are given for breaking the hybrid competition equilibrium between pre-sale and spot market, so as to realize the pure spot market competition. Finally, the paper finds the consumer heterogeneity interval optimized by the above three models through numerical analysis. And when heterogeneity of consumers in a medium range, enterprises 1 need more significant effort level to achieve the established earnings rise, on the other hand, when a consumer heterogeneity, within the range of low or high level interval, enterprise 1 only takes significant promotional effort in general level, the rise of yield can be achieved.
Data analysis of relationship between equity concentration and innovation investment
Authors: Xu Qian , Lin Hongzhen
Published: February 2021

Based on the relevant data of A-share listed high-tech enterprises in Shanghai and Shenzhen in 2014-2018, the relationship between ownership structure and innovation input is empirically studied according to the principal-agent theory and Information asymmetry theory, and combined with the intermediary effect model, further analysis of internal control in the relationship between the intermediary role. It is found that the degree of equity concentration has a negative correlation with innovation input, but has a positive correlation with internal control. The degree of equity concentration can weaken the negative influence on innovation input through the positive influence on internal control, it can be seen that internal control plays an intermediary role in the relationship between ownership concentration and innovation input. The research results can provide enlightenment for high-tech enterprises to optimize the ownership structure, improve the effectiveness of internal control, enhance innovation input and enhance the competitive advantage of science and technology.
Study on rms integrated simulation modeling of multi-agent warship propulsion system
Authors: Qian Chao , Bao Jingyuan , Tang Yong
Published: February 2021

As the key design characteristics of warship, reliability, maintainability, and supportability (RMS) are crucial to the successful completion of warship missions. To enrich the RMS design and analysis methods of warship equipment, this paper focuses on the voyage of warship propulsion system, and builds a theoretical model of RMS integrated assessment system for warship on the basis of multi-agent technology, so as to indicate how successfulness of the mission warship propulsion system is dynamically related to equipment and supporting resources. Furthermore, Anylogic is used as a platform to develop a RMS simulation system for warship propulsion system in the mission of voyage. By changing the RMS parameters of warship propulsion system and repeating the simulation of voyage, important factors are identified for the improvement of probability of mission completion success, which is of great military significance.
Research on operation strategy of new energy microgrid theory model containing biomass energy
Authors: Wang Tang , Li Fei , Ge Liangquan , Zeng Guoqiang , Zhang Qingxian , Gu Yi
Published: February 2021

In order to alleviate the energy crisis and improve environmental quality, rational use of renewable energy has become a development trend. This paper proposes a research on the new energy micro-grid operation strategy, and selects the appropriate power generation method according to the development status of each distributed energy in the microgrid. Specially establish the biomass power generation, photovoltaic power generation, wind power generation and storage battery models of the micro-grid, and infer the power generation output characteristics according to the power generation characteristics of each module. A brief analysis of the micro-grid model containing biomass power generation established in the article is carried out, and the basic operation strategy of the micro-grid is proposed, which provides a theoretical basis for the development of the micro-grid in the future.
High level verification of the VFAT3 ASIC for CMS GEM detectors
Authors: Petrow H. , Aspell P. , Ciaglia D. , Robertis G. De , Licciulli F. , Loddo F. , Tuuva T.
Published: February 2021

A front-end readout chip VFAT3 was designed for the muon detector gas electron multipliers (GEM). GEMs were installed at the Compact Muon Solenoid (CMS) experiment of the Large Hadron Collider (LHC) at CERN for the high luminosity upgrade. The design of the VFAT3 uses 790 analog and 172 digital blocks which are highly integrated, thus it is crucial to ensure that the different blocks work together and the chip works as a whole. Mixed signal simulation methods were used to verify the high level functionality. Trigger latencies of 125, 150, 175 and 225 ns were found for front-end peaking times of 25, 50, 75 and 100 ns, respectively. The maximum trigger rate for reading out standard data packets was found to be 1.7 MHz. Results of the VFAT3 high level verification are presented and the simulation methods described.
Research on Simulation and Calculation of Temperature Field and Pressure of High Voltage Transformer
Authors: Fan Houyang , Wu Jian , Fei Xuan , Zhang Peng , Yang Xinchi , Liu Bin
Published: February 2021

In order to ensure the normal and safe operation of high-voltage transformers, this paper uses oil-immersed current transformers as an example to use finite element analysis to simulate the internal temperature field of high-voltage transformers when a fault occurs, and obtain the temperature distribution of the whole body under several typical fault types. Through theoretical calculations, the pressure change at the expander on the top of the high-voltage transformer is obtained. The measured data is compared with the simulation calculation results, and the state of the high-voltage transformer at the time of failure is analyzed, and the safe and controllable temperature and pressure range of the high-voltage transformer are obtained.
Resource allocation method based on massive MIMO NOMA MEC on distribution communication network
Authors: Li Da , Qin Ningli , Li Bo , Jing Xiaosong , Du Changyu , Wan Chunyi
Published: February 2021

The current smart distribution network has problems of slow fault detection and low efficiency. This paper reasonably derives the expression of delay from two aspects of transmission time and calculation time based on technologies such as massive multiple input multiple output, non-orthogonal multiple access, and mobile edge computing (MIMO-NOMA-MEC). In addition, a multi-objective iterative algorithm is designed to minimize the delay by jointly optimizing the transmission power, task allocation coefficient, and the computing resources of the MEC server. Finally, the simulation results show that the processing delay of the system gradually decreases as the number of antennas increases. In addition, the processing delay of the system can still be below 10ms under the extremely bad situation that there are many devices currently requesting services.
The Development Research on the Construction Waste Resource Industry in PPP Pattern
Authors: Lu Shiyu
Published: February 2021

This paper first describes the development of construction waste recycling industry and PPP pattern, then discusses the related technologies of construction waste recycling industry, and summarizes the related risks of the combination of the two. Finally, it puts forward some corresponding policy suggestions.
Research on influence of excavation parameters of shield tunnel based on ABAQUS
Authors: Liu Yang , Fu Xin , Yang Minghu , Tang Zhendong , Chen Lu , Liu Yuancai
Published: February 2021

At present, the mainstream method of urban subway construction is shield construction. What cannot be avoided in shield construction is the settlement and displacement of the ground surface, and the tunneling pressure is closely related to the grouting pressure and the settlement of the soil. For the overlapping tunnel shield construction, this paper takes the shield construction of the overlapped tunnel section of Kunming Rail transit Line 6 as the research object. Using ABAQUS finite element software to simulate overlap shield tunnel construction, the research of shield tunneling parameters and the relationship between the surface displacement of soil. The results show that reasonable selection of excavation parameters can well control ground settlement.
Performance Analysis of Activation Functions for Wheat Crop Yield Prediction
Authors: Bhojani Shital H. , Bhatt Nirav
Published: February 2021

Activation function (AF) plays a vital role in the neural network. This research study focuses on the performance evaluation of the various AFs in multilayer perceptron (MLP) algorithm for the wheat crop yield prediction at the regional level. Nowadays researchers are publishing the new AFs and also proposing the improvement in existing AFs for more accurate results. Sigmoid AF is the default function used in the MLP algorithm. In this research we have applied the various AFs in MLP using the WEKA java libraries and performance analysis has been carried out for the selected districts of the Gujarat state. Based on the mean absolute percentage error (MAPE) performance measure output is discussed. Therefore scientists and researchers can select the appropriate AF to improve the prediction accuracy and hence improve the performance of the network as per the research data.

Neural Network, Activation Function (AF), MLP, Yield Prediction
Regression Model for Prediction of Epidemic Outbreaks
Authors: Shashank Punna Sai Teja , Vaibhavi Akkaladevi , Vaishnavi Jenega , Jabbar M.A
Published: February 2021

Epidemic diseases are contagious diseases that can spread into the entire nation if the contagion measurement had reached the outbreak level and there is a chance to wipe out the entire population. So, there must be a way to track these epidemics to take counter measurements to restrict its spreading. There are many ways to track these epidemics. Nowadays, machine learning is applied in almost all the fields since data is generating rapidly from various sources. The data which is useful for tracking epidemics is also very high around us. Various machine learning techniques can be implemented to find out the status of epidemics. Regression is a machine learning algorithm that is used to find the relationship between a dependent variable and an independent variable. For fitting a regression line, the most common method is the method of least-squares. In this method, the best-fitting line is calculated for the observed data such that the sum of the squares of the vertical deviations from each data point to the line is minimum. In this paper, we proposed a Regression-based prediction system that is used to predict the status of an epidemic in a region. Linear regression is used for this purpose to predict the number of deaths in a region on a specific date. Various libraries of python like NumPy, pandas, matplotlib, and sci-kit-learn are used to develop the model.
A Survey of Machine Learning Techniques Used on Indian Stock Market
Authors: Vohra Archit A. , Tanna Paresh J.
Published: February 2021

Stock market provides nonlinear time series data that has been difficult for making any kind of predictions. Since the advent of various Machine Learning techniques lot of research is covered in this area. Researches have mostly been carried out using Supervised Machine Learning algorithms on predicting the price of a particular stock and also on predicting the stock index movement. The studies also cover various unsupervised machine learning algorithms as well as some statistical methods used in the stock price prediction. Study has been covered on generating portfolio of stocks that maximizes the profit with minimum risk. Social Media today generates a large amount of data and has a significant impact on the buy/sell decision made by experts. Specifically, research has been carried out on how tweets and news have an impact on stock price movement. Recent researches focus more on neural networks, deep learning and hybrid models. This study tries to identify the different approaches used by researchers in order to improve the accuracy of machine learning algorithms on the India Stock Market. The survey emphasizes that all aspects related to stock market need to be considered before making any kind of prediction.
Survey on: Applications of Smart Wearable Technology in Health Insurance
Authors: Shah Apeksha , Ahirrao Swati , Phansalkar Shraddha , Kotecha Ketan
Published: February 2021

Humanity is considered as the most intelligent species on the earth and are health reactive. Today, due to increase in sluggish behavior there is reduction in physical activity and that leads to health problems. To overcome this cause, wearable technology helps human being to get updates about physical health behavior e.g. heart rate, blood pressure, steps count, BMI, calories and many more. Wearables available in market helps to measure all kind of metrics related to health. The aim of this research is to study and analyze the research to check whether wearable technologies with augmented smart AI solutions help to predict health status and its outcome. Leveraging Artificial Intelligence (AI) and wearable technology could help health insurance to cover the risks associated with the uncertainties in the health, increased life span and high cost of treatments. In this study, we first discuss the works where wearable technology and AI can create value in the field of healthcare and importantly discuss the use case of leveraging the insurance domain with a smart health-monitoring service for the customers. Although, there are inhibitions in implementing a smart and fitness monitoring system in health-insurance sector like privacy concerns and hesitations towards sharing the data, health-insurance sector can look at this use-case as an incentivization to the fitness conscious clients.
Real Time Emotion Detection of Humans Using Mini-Xception Algorithm
Authors: Fatima Syed Aley , Kumar Ashwani , Raoof Syed Saba
Published: February 2021

In the past few decades there has been operating analysis on emotion expression recognition due to the great intra-class deviation also it is still challenging. Maximum number of research work performs the best on controlled datasets (i.e., small datasets with less features), whereas it fails to operate well and it’s still challenging on datasets varies variations in images and even in partial faces. In modern years, many works have introduced an end-to-end plan for emotion expression recognition, utilizing deep learning models. Although emotion recognition is a great task, there still seems to be a huge area for development. In this paper, we developed a mini-Xception based on Xception and Convolution Neural Network (CNN), which is easy to concentrate on great parts like the face, and conclude important improvements to earlier works. We validated our model by creating a real-time vision system which accomplishes the tasks of face detection, and emotion classification simultaneously in one blended step using our proposed mini-Xception architecture. We still utilize a visualization technique that is ready to detect important face sectors because recognizing various emotions, based on the classifier’s output. For experimental analysis we had used FER-2013 dataset and results manifest that the proposed method can efficiently perform all the tasks like detection and classification with seven different emotions using with Mini-Xception algorithm and achieved accuracy around 95.60%.
AutoAssociative Memory for Face Recognition using High-Dimensional Octonion Data
Authors: Salvadi Shoba Rani , Rao D. Nagendra , Vatsal S
Published: February 2021

Uniqueness or individuality of an individual face is the representation of one’s identity. Recent research in color image processing focusing on octonion algebra, which is the generalization of complex and quaternion algebra. In this paper, the face of an individual is used for identification making automatically. For this, an autoassociative memory is used for the recognition of images or videos with 8-dimensions. Octonion algebra is not commutative and not associative but it is an alternate algebra for representing the phase and angle of images effectively. The prospective experiments are conducted on color images for image reconstruction, denoising and efficiently retrieving blurred images in true color images.
RETRACTED: Detecting Sybil Attack In Wireless Sensor Networks Using Machine Learning Algorithms
Authors: Mounica Mandala , Vijayasaraswathi R. , Vasavi R.
Published: February 2021

In sensitive areas such as battlefields, a Wireless Sensor Network (WSN) is especially in military and civilian applications and it is of utmost importance to develop security in these networks. In various respects, this can improve the quality of life. But to be used for protection reasons in multiple situations such as implementation. There is a high risk of being exposed to multiple viruses and hacking attacks. Unauthorized APs for information protection needs to be detected. Any malicious attacks against these networks, Like Sybil Attack leads to breach of security by enacting as a node that illegitimately declares several false identities at the same time. This misleads valid nodes, and they presume each of those identifiers as actual independent nodes by accident. Thus we proposed a machine learning model to detect Sybil attack in network where rawtraffic data has been collected and is used identify authorized and unauthorized APs in an integrated wired/wireless environment.
Layered double hydroxides intercalated with methyl orange as a controlled-release corrosion inhibitor for iron in chloride media
Authors: Bakhtaoui N , Benali O , Mazarío E , Recio Francisco J , Herrasti P
Published: February 2021

In this study, the corrosion inhibition properties of nanocontainer-type layered double hydroxide (LDH) are evaluated on iron that is immersed in a 3.5% NaCl aqueous solution. LDH ZnAl-NO3 was synthesized via coprecipitation. The material presents satisfactory crystallinity with a Zn/Al ratio of 2:1. Methyl orange (MO) has been added into the synthesis process by exchange with nitrate ions and/or by adsorption of MO onto LDH surfaces (LDH-MO). Iron was immersed in solutions with various concentrations of LDH and LDH-MO ranged 1–6 gl−1, and the corrosion inhibition properties were investigated using linear sweep votammetry, electrochemical impedance spectroscopy (EIS), and SEM. Based on pitting potential studies, LDH has demonstrated inhibition of the pitting corrosion process, and the optimal concentration was identified as 2 gl−1. The presence of MO in LDH provides excellent anticorrosive properties with a mixed inhibition mechanism. The corrosion potential of LDH-MO presents more noble values and exchange current densities that are one order of magnitude less than those of the bare iron after 72 h of immersion in a 3.5% NaCl aqueous solution. EIS results corroborated that the corrosion resistance increased when 2 gl−1 of LDH-MO was in solution. SEM images support the anticorrosive behaviour of the LDH-MO.
Species delimitation and hybridization history of a hazel species complex
Authors: Lu Zhiqiang , Sun Yongshuai , Li Ying , Yang Yongzhi , Wang Gaini , Liu Jianquan
Published: February 2021

Background and Aims

Hybridization increases species adaptation and biodiversity but also obscures species boundaries. In this study, species delimitation and hybridization history were examined within one Chinese hazel species complex (Corylus chinensis–Corylus fargesii). Two species including four varieties have already been described for this complex, with overlapping distributions.

Methods

A total of 322 trees from 44 populations of these four varieties across their ranges were sampled for morphological and molecular analyses. Climatic datasets based on 108 geographical locations were used to evaluate their niche differentiations. Flowering phenology was also observed for two co-occurring species or varieties.

Key Results

Four statistically different phenotypic clusters were revealed, but these clusters were highly inconsistent with the traditional taxonomic groups. All the clusters showed statistically distinct niches, with complete or partial geographical isolation. Only two clusters displayed a distributional overlap, but they had distinct flowering phenologies at the site where they co-occurred. Population-level evidence based on the genotypes of ten simple sequence repeat loci supported four phenotypic clusters. In addition, one cluster was shown to have an admixed genetic composition derived from the other three clusters through repeated historical hybridizations.

Conclusions

Based on our new evidence, it is better to treat the four clusters identified here as four independent species. One of them was shown to have an admixed genetic composition derived from the other three through repeated historical hybridizations. This study highlights the importance of applying integrative and statistical methods to infer species delimitations and hybridization history. Such a protocol should be adopted widely for future taxonomic studies.
Identification of the kinanthraquinone biosynthetic gene cluster by expression of an atypical response regulator
Authors: Takao Risa , Sakai Katsuyuki , Koshino Hiroyuki , Osada Hiroyuki , Takahashi Shunji
Published: February 2021

Recent advances in genome sequencing have revealed a variety of secondary metabolite biosynthetic gene clusters in actinomycetes. Understanding the biosynthetic mechanism controlling secondary metabolite production is important for utilizing these gene clusters. In this study, we focused on the kinanthraquinone biosynthetic gene cluster, which has not been identified yet in Streptomyces sp. SN-593. Based on chemical structure, 5 type II polyketide synthase gene clusters were listed from the genome sequence of Streptomyces sp. SN-593. Among them, a candidate gene cluster was selected by comparing the gene organization with grincamycin, which is synthesized through an intermediate similar to kinanthraquinone. We initially utilized a BAC library for subcloning the kiq gene cluster, performed heterologous expression in Streptomyces lividans TK23, and identified the production of kinanthraquinone and kinanthraquinone B. We also found that heterologous expression of kiqA, which belongs to the DNA-binding response regulator OmpR family, dramatically enhanced the production of kinanthraquinones.
LncRNA SNHG12 downregulates RAGE to attenuate hypoxia-reoxygenation-induced apoptosis in H9c2 cells
Authors: Lu Ping , Xiao Shihui , Chen Shaoze , Fu Youlin , Zhang Peng , Yao Yaner , Chen Feng
Published: February 2021

Ischemia-reperfusion (I/R) injury causes cardiac dysfunction through several mechanisms including the irregular expression of some long noncoding RNA. However, the role of SNHG12 in myocardial I/R injury remains unclear. Here, we found the increase of the SNHG12 level in hypoxia-reoxygenation (H/R)-injured-H9c2 cells. SNHG12 silencing enhanced the apoptosis of H/R-injured H9c2 cells, while SNHG12 overexpression relieved the cardiomyocyte apoptosis induced by H/R stimulation. Additionally, the suppression of SNHG12 significantly boosted the H/R-induced expression and the production of TNF-α, IL-6, and IL-1β, as well as the activation of NF-κB, which were fully reversed after overexpression of SNHG12. Mechanistically, SNHG12 adversely regulated the production of receptor for advanced glycation end products (RAGE) in H/R-stimulated H9c2 cells. Antibody blocking of RAGE alleviated the apoptosis of H/R-injured H9c2 cells. Collectively, we have determined a valuable mechanism by which the high level of SNHG12 contributes to H9c2 cells against H/R injury through the reduction of RAGE expression.
Purification and characterization of cycloisomaltotetraose-forming glucanotransferases from Agreia sp. D1110 and Microbacterium trichothecenolyticum D2006
Authors: Fujita Akihiro , Kawashima Akira , Mitsukawa Yuuki , Kitagawa Noriaki , Watanabe Hikaru , Mori Tetsuya , Nishimoto Tomoyuki , Aga Hajime , Ushio Shimpei
Published: February 2021

Glucanotransferases that can synthesize cyclo-{→6)-α-d-Glcp-(1→6)-α-d-Glcp-(1→6)-α-d-Glcp-(1→6)-α-d-Glcp-(1→} (CI4) from dextran were purified to homogeneity from the culture supernatant of Agreia sp. D1110 and Microbacterium trichothecenolyticum D2006. The molecular mass of both enzymes was estimated to be 86 kDa by SDS-PAGE. The glucanotransferase, named CI4-forming enzyme, from Agreia sp. exhibited the highest activity at pH 6.0 and 40 °C. The enzyme was stable on the pH range of 4.6-9.9 and up to 40 °C. On the other hand, the enzyme from M. trichothecenolyticum exhibited the highest activity at pH 5.7 and 40 °C. The enzyme was stable on the pH range of 5.0-6.9 and up to 35 °C. Both enzymes catalyzed 4 reactions, namely, intramolecular α-1,6-transglycosylation (cyclization), intermolecular α-1,6-transglycosylation, hydrolysis of CI4, and coupling reaction. Furthermore, the CI4-forming enzyme produced CI4 from α-1,6-linked glucan synthesized from starch by 6-α-glucosyltransferase. These findings will enable the production of CI4 from starch.
Knockdown of p62/sequestosome enhances ginsenoside Rh2-induced apoptosis in cervical cancer HeLa cells with no effect on autophagy
Authors: Bian Shuai , Zhao Yue , Li Fangyu , Lu Shuyan , Yang Song , Liu Meichen , Wang Siming , Zhao Daqing , Zhang Wei , Wang Jiawen
Published: February 2021

p62/sequestosome is a multifunctional adaptor protein that participates in a wide variety of cellular processes. 20(S)-Ginsenoside Rh2 (G-Rh2) has various biological effects, including anticancer activity. We found that G-Rh2 can induce apoptosis and autophagy in HeLa cells. G-Rh2 significantly enhanced the transcriptional level of p62. A siRNA was constructed to knock down p62 and assess its effect on apoptosis induced by G-Rh2. p62 protein levels were successfully downregulated in cells transfected with the p62-specific siRNA. Silencing of p62 further decreased cell viability while also enhancing cell apoptosis, reactive oxygen species generation, the ratio of Bax to Bcl-2, and the cleavage of PARP. p62 knockdown decreased expression levels of Nrf2. Moreover, silencing of p62 had no significant effect on autophagy induced by G-Rh2. These results suggest that combining G-Rh2 treatment with inhibition of p62 may be a potential treatment strategy for cervical cancer.
MMRFBiolinks: an R-package for integrating and analyzing MMRF-CoMMpass data
Authors: Settino Marzia , Cannataro Mario
Published: February 2021

In order to understand the mechanisms underlying the onset and the drug responses in multiple myeloma (MM), the second most frequent hematological cancer, the use of appropriate bioinformatic tools for integrative analysis of publicly available genomic data is required. We present MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and from the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. The package provides several methods for integrative analysis (array–array intensity correlation, Kaplan–Meier survival analysis) and visualization (response to treatments plot) of MMRF data, for performing an easily comprehensible analysis workflow. MMRFBiolinks extends the TCGABiolinks package by providing 13 new functions to analyze MMRF-CoMMpass data: six dealing with MMRF-RG data and seven with NCI-GDC data. As validation of the tool, we present two cases studies for searching, downloading and analyzing MMRF data. The former presents a workflow for identifying genes involved in survival depending on treatment. The latter presents an analysis workflow for analyzing the Best Overall (BO) response through correlation plots between the BO Response with respect to treatments, time, duration of treatment and annotated variants, as well as through Kaplan–Meier survival curves. The case studies demonstrate how MMRFBiolinks is able of overcoming the limitations of the analysis tools available at NCI-GDC and MMRF-RG, facilitating and making more comprehensive the retrieval, downloading and analysis of MMRF data.
NPI-GNN: Predicting ncRNA–protein interactions with deep graph neural networks
Authors: Shen Zi-Ang , Luo Tao , Zhou Yuan-Ke , Yu Han , Du Pu-Feng
Published: February 2021

Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA–protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative ways. In this work, we collected five benchmarking datasets for predicting NPIs. Based on these datasets, we evaluated and compared the prediction performances of existing machine-learning based methods. Graph neural network (GNN) is a recently developed deep learning algorithm for link predictions on complex networks, which has never been applied in predicting NPIs. We constructed a GNN-based method, which is called Noncoding RNA–Protein Interaction prediction using Graph Neural Networks (NPI-GNN), to predict NPIs. The NPI-GNN method achieved comparable performance with state-of-the-art methods in a 5-fold cross-validation. In addition, it is capable of predicting novel interactions based on network information and sequence information. We also found that insufficient sequence information does not affect the NPI-GNN prediction performance much, which makes NPI-GNN more robust than other methods. As far as we can tell, NPI-GNN is the first end-to-end GNN predictor for predicting NPIs. All benchmarking datasets in this work and all source codes of the NPI-GNN method have been deposited with documents in a GitHub repo (https://github.com/AshuiRUA/NPI-GNN).
Social organization of otters in relation to their ecology
Authors: Lélias Marie-Loup , Lemasson Alban , Lodé Thierry
Published: February 2021

Otter species are known to fluctuate intraspecifically from a solitary lifestyle to group-living arrangements. By examining what is known about habitat use and foraging style in otters of 13 different species, based on 93 studied sites, we assessed (1) the relationship between social habits and preferred habitats, (2) the relationship between species and prey preferences, and (3) the effect of predator avoidance on their social organization in order to assess the socio-ecological factors influencing otters. Females remain the core of their social stability. We show the major influence of habitats and feeding strategies (i.e. socio-ecology) of otters. The different species of solitary otters most often inhabit linear environments, such as freshwater ecosystems or wave-exposed marine coasts, and their habitat is often subject to disturbances that fragment their functional continuity. Social otters are more often found in extensive habitats with high plant cover, regular food resources and in areas with large predators compared to solitary species. The maintenance of regular resources and the fact that the main trophic resources are replenished rapidly might be determining factors driving sociality. Group-living and bachelor congregations among otters can also respond to pressure from large predators. This suggests that foraging, habitat use and the presence of large predators may be the drivers of sociality in otters. We conclude that most otters have a greater social potential than previously assumed, which is confirmed by their various vocalizations recently described.