bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Source Rock Quality in Chang-7 Member of Major Oil Fields in Ordos Basin
Authors: Li Tianyi
Published: January 2022

Petroleum is an important natural resource for human. The demand for petroleum in the next several decades will continue to be high, which requires the exploitation of more petroleum sources. Chang-7 member of the Ordos basin is proven to be the richest among all members, and this research took a focus on making a comparison between the source rock quality of Chang-7 member in four major oil fields in the basin, to provide a simple view of priority oil fields for exploitation in the future. The research analyzed the source rock quality of four oil fields from the TOC, A%, rock type, R0, sedimentary environment, and kerogen type, and found a superiority of source rock quality of Maling and Zhidan oil field in multiple dimensions, mainly triggered by their sedimentary environment. The research concluded that the oil fields that was once in the central-southern region of the basin contains a slightly better source rock quality among the chosen fields, which is related to a more stable sedimentary environment in the early Triassic.
Analysis of Hydraulic Fracture Morphology and Propagation under Different Perforation Azimuth Angles
Authors: Shen Zhongshan , Xue Hui , Bai Zhiqiang
Published: January 2022

Perforation azimuth has an important influence on the nucleation, propagation path and morphology of hydraulic fractures. In this paper, the true triaxial hydraulic fracturing simulation experimental system is used to analyze the hydraulic fracture morphology and propagation path under different perforation azimuth angles. With the increase of the azimuth angle of perforation, the stable fracture propagation pressure of the fracturing sample also increases. When the azimuth angle of perforation is 0°, the propagation pressure is about 18 MPa, and when the azimuth angle of perforation is 90°, the propagation pressure is about 26.5 MPa, increasing by nearly 47.22%.
(Nickel) Substrate Nano-ZnO Dye-sensitized Solar Cell Preparation and Performance Research
Authors: Cao Ping
Published: January 2022

With the rapid growth and development of the world and global economy, human material and living standards continue to improve and improve, people’s demand for renewable energy has become more and more, and many traditional energy sources, such as oil, natural gas, coal, etc., due to their limited storage capacity, serious environmental pollution during mining, and many other reasons, no longer fully meet the human demand for sustainable use of renewable energy in our era. Therefore, the development of a clean industrial energy is gradually becoming an important solution for contemporary Chinese enterprises to solve the problem of clean industrial energy utilization. Solar cells are an inexhaustible clean and green energy. It has great research and development and application value. It has attracted extensive research attention from social scientists. Among them, solar energy dye-sensitized battery solar plastic cells mainly have The dye process is simple, the cost of the preparation process is low, the conversion efficiency is high, and the large-scale production can be achieved throughout the year. Therefore, it has gradually become a hot spot in my country’s solar power battery technology research in recent years. This article aims to study the preparation and performance of (nickel) substrate nano-ZnO dye-sensitized solar cells. The optimal conditions for the synthesis of ZnO and the influence of different reaction conditions on the growth of ZnO were explored, and the growth mechanism of ZnO was speculated. The photoelectric synthesis catalytic conditions of the microspheres agglomerated between different sizes of ZnO are explored, and the direct influence of the small dye-sensitized micro-solar synthesis cells currently used in the synthesis on the synthesis of small dye-sensitized micro-solar cells is directly affected. Laboratory research results show that in order to use more reactive dyes for dye adsorption in order to improve the efficiency of the conversion using dye sensitizers and solar fuel cells, the dye film needs to pass through ZnO and be adsorbed by the dye at the same time. The morphological structure should not be too dense and the film should not be too thick; at the same time, the absorption of the dye film should be proportional to the overall coating thickness of the film. Therefore, the overall thickness of the film should be moderate when used in ZnO.
The Concept, Project and Current Status of Virtual Power Plant: A Review
Authors: Zhang Jiatong
Published: January 2022

Due to technological advancements in recent years, distributed energy resources (DER) applications have become more prevalent in households and businesses, including various renewable energy applications. While the virtual power plant (VPP) can integrate energy storage, flexible loads and DER, etc., it can support the power grid operating stability and security. Therefore, more and more researchers give their attention to VPP and advise on their optimization. This paper states the VPP concept from other researchers’ studies and provides a detailed explanation. Meanwhile, some typical VPP projects worldwide are also presented. In addition, some potential challenges and future development advice in the VPP studies are also presented.
Progress in Preparation and Research of Water Electrolysis Catalyst for Transition Metal Phosphide
Authors: Sun Miguang , Gu Jiajun
Published: January 2022

Confronted with growing energy crisis and environmental challenges, water electrolysis for hydrogen production can provide high-density, clean and renewable energy, but limited by sluggish kinetics of two half reaction, anodic oxygen evolution reaction (OER) and cathodic hydrogen evolution reaction(HER). Noble-metal-based electrocatalysts can decrease overpotential and accelerate kinetics dramatically, but limited by its scarcity and high cost. Transitional metal catalysts are abundant, low cost and have potential to become excellent catalyst due to unique electronic structure. Beginning from basic principle of electrocatalysis, this paper focuses on the synthesis method of transitional metal phosphide (TMP), and further discusses modification methods of TMP, including phase tuning, element doping/alloying, interfacial/structural engineering and three-dimensional architecture. Finally, the challenges of TMP are analyzed and future research focuses are prospected.
Analysis of Influence of 1000MW Unit Boiler Side Wind, Side Wind and Solar Radiation on Direct Air Cooling System
Authors: Xu Junfeng , Wang Taoying , Xue Zhipeng
Published: January 2022

In the operation of direct air-cooled units, there are many factors that affect the safety and economy of the unit. The “hot air recirculation” and “backfill” phenomena caused by lateral wind, the influence of high ambient wind on the heat transfer performance of the air cooling unit near the windshield wall, and the “hot air recirculation” phenomenon caused by the back wind of the furnace, these may affect the direct air cooling unit. Safe and economical operation. Through carrying out on-site test research, collecting the actual operating conditions of the unit operation, the organic combination of laboratory simulation data and actual data is provided to provide a strong basis for the development of corresponding technologies.
GdxFe3-xO4 nanoparticles with silane shell as potential theranostic agent for cancer treatment
Authors: Zibert Alexandr V. , Lissovskaya Lana I. , Korolkov Ilya V. , Zdorovets Maxim V.
Published: January 2022

In this study of GdxFe3-xO4 compound core silane shell nanoparticles method is represented.Samples were analyzed with SEM, EDA and FTIR spectroscopy. Further attaching of carborane compounds is discussed. Products can be used in biomedical applications for MRI imaging and drug delivery for NCT, so can be considered as potential theranostic agents for cancer treatment.
Peculiarities of stress corrosion fracture of sensitized and neutron irradiated chromium-nickel austenitic steel
Authors: Yarovchuk A V , Dikov A S , Tsay K V
Published: January 2022

The results of SEM studies of fracture surfaces for the 12Cr18Ni9 austenitic steel ruptured under a fixedtensile load in FeCl3 water solutionand in air are presented. The samples of austenized, sensitized at 650° and irradiated with neutrons (to 1020n/cm2) steel were examined. It was shown thatirradiation hardening and sensitizing annealing increased the susceptibility of steel to intergranular cracking in corrosive solution. Structural features of formation of the strain-induced α’-martensite and its reinforcing effect on fracture in various environments are discussed.
Titanium beryllide as an alternative to beryllium in nuclear and thermonuclear engineering, capabilities of UMP JSC in the technology development and beryllides products manufacture
Authors: Frants Ye , Borsuk A , Vechkutov A , Zenkov K , Zorin B , Kylyshkanov M , Podoinikov M , Udartsev S , Vladimirov P , Gaisin R
Published: January 2022

For many decades, beryllium has been used as a structural element in nuclear installations as a moderator / breeder of fast neutrons. The consequence of neutron irradiation is a significant production of gas products in the form of helium and tritium, which leads to swelling and loss of strength properties of beryllium reflectors. The relatively low melting point of beryllium also imposes restrictions on the high-limit temperature regimes of the reactor core. As an alternative to pure beryllium, it is necessary to consider intermetallic compounds based on it, in particular titanium beryllide. Preliminary studies on the thermal desorption of helium and tritium from titanium beryllide have shown that this material has a much lower retention tendency and a lower release temperature. The higher melting point of titanium beryllide compared to pure beryllium is also an advantageous characteristic.Over the past years, UMP JSC, thanks to its research in this area, has achieved significant success in the development of technology for obtaining intermetallic billets and articles based on titanium and chromium beryllides. As a technology demonstrator, prototypes of structural elements of a helium-cooled blanket breeder module of the projected DEMO reactor were made by order of the Karlsruhe Institute of Technology, Germany.The advantages of titanium beryllide, as well as the success achieved in the production of billets and products from it, open up opportunities for a more extensive study of the nuclear, physical and mechanical properties of this material with the possibility of further use in nuclear technology, including thermonuclear reactors, and in high-temperature instrumentation.
Dynamic properties on 99Mo adsorption and 99mTc elution with alumina columns
Authors: Fujita Y , Seki M , Sano T , Fujihara Y , Suzuki T , Yoshinaga H , Hori J , Suematsu H , Tsuchiya K
Published: January 2022

The Mo adsorption/99mTc elution properties of alumina used as Mo adsorbents are examined for the development of 99Mo/99mTc generators using 99Mo produced by the (n, γ) method. MoO3 is irradiated by the Kyoto University Research Reactor (KUR). The alumina columns are filled with three types of alumina made from different raw materials. In this paper, elongated tubes are used as columns and the properties under Mo dynamic adsorption conditions on alumina are compared with those under static adsorption conditions. The results obtained suggest that the 99Mo/99mTc ratio specified by the Minimum Requirements for Radiopharmaceuticals of Japan (MRRP) is greatly affected by the method of adsorbing Mo on alumina.
Experimental device design justification for radiation resistance tests of single-mode optical fibers and FBG-based sensors at the IVG.1M reactor
Authors: Gnyrya V , Gordienko Yu , Surayev A , Baklanova Yu , Kashaykin P , Britskiy V , Kenzhina I , Vityuk G , Tulubayev Ye , Bochkov V
Published: January 2022

One of the most important stages in the development of an experimental device is to carry out a series of computational studies to substantiate the compliance of device design with the objectives of the experiment, such as the choice of test modes and the study of standard and hypothetical emergency modes of its operation. Result of these studies is the neutron-physical, thermal, strength and hydrodynamic characteristics of the structural elements of the device and working bodies. During this work, a series of neutron calculations was conducted using the MCNP6 code and thermal-physical calculations using the ANSYS software package of two configurations of the experimental device. A feature of the calculated studies is the presence of specific requirements for the thermal state of the experimental device sleeve. Namely, ensuring a predetermined temperature gradient between its ribs, which should not exceed 4°K during the reactor tests.
Measurement of the spatial-energy distribution of neutrons in the irradiation channel of the critical facility
Authors: Aitkulov M T , Dyussambayev D S , Romanova N K , Gizatulin Sh H , Shaimerdenov A A , Bugybay Zh T , Kisselyov K S , Beisebayev A O
Published: January 2022

One of the basic installations of the Republican State Enterprise “Institute of Nuclear Physics” of the Ministry of Energy of the Republic of Kazakhstan is a critical assembly, which is a zero-power reactor. Desalinated water and beryllium serve as moderators and neutrons reflectors. The energy spectrum of neutrons in the core is thermal. The main purpose and area of application is the modeling and study of the neutronic characteristics of the cores of water-moderated research reactors of various types.

The paper presents the results of experimental measurements of the spatial-energy distribution of neutrons in the dry, central channel of the critical assembly. Measurements of the neutron flux were carried out using activation foils for three energy groups of neutrons: thermal, epithermal, and fast.

The measured thermal neutrons flux in the irradiation channel is ~ 3·108 cm‒2s‒1, and fast neutrons flux (with energies above 0.7 MeV) is ~ 8·108 cm‒2s‒1. The fraction of thermal neutrons in the integral flux was 0.23%, and the fraction of fast neutrons was 0.62%. In the axial distribution of thermal and fast neutrons, the maximum value of the neutron flux is 50 mm below the midplane of the core.
Calculation of radiation dose enhancement by gadolinium compounds for radiation therapy
Authors: Abdullaeva G.A. , Kulabdullaev G.A. , Kim A.A. , Nebesny A.F. , Yuldashev D.O.
Published: January 2022

In this study, we evaluate the features of dose enhancement with Gd contrast agent (Magnevist). Due to the increased relaxation time and high atomic number (z=64) Gd can be used in radiation therapy as a radiosensitizer. To perform a quantitative evaluation of the radiosensitization effect is determined a parameter called the dose enhancement factor - DEF. The DEF values were calculated based on the analysis of the mass absorption coefficients for gadolinium and biological tissue. An increase in DEF is observed when the radiation energy is higher than the K-shell ionization energy of Gd atoms. For the presence of 20315 ppm Gd contrast agent in biological tissue the dose enrichment factor is maximum DEF = 4.12 at photon irradiation energy 60 keV. Also, based on calculations for photon irradiation sources considered high degrees of dose enhancement occur for Am-241, Yb-196, and 100 kVp X-ray tube.
Prediction of Lung Cancer using Ensemble Classifiers
Authors: Ashwin Shanbhag G , Anurag Prabhu K , Subba Reddy N V , Ashwath Rao B
Published: January 2022

Carcinoma detection from CT scan images is extremely necessary for numerous diagnostic and healing applications. Because of the excessive amount of information in CT scan images and blurred boundaries, tumor segmentation and class are extremely laborious. The intention is to categorize carcinoma into benign and malignant categories. In MR pictures, the number of facts is a lot for interpreting and evaluating manually. Over the previous few years, carcinoma detection in CT has grown to be a rising evaluation space in the area of the scientific imaging system. Correct detection of length and site of lung cancer performs a vital position in the designation of carcinoma. In this paper, we introduce a novel carcinoma detection methodology that helps in predicting the carcinoma from the CT scanned images. The methodology has 4 different stages, pre-processing the image data, segmentation, extracting features, and classification stage to categorize the benign and malignant. This work makes use of extraordinary models for detecting carcinoma in a CT test via way of means of constructing an ensemble classifier. Techniques proposed in the paper helped us achieve an accuracy of 85% using Ensemble-Classifier which showcases that model has the capability of predicting the malignant cases correctly. The ensemble classifier consists of 5 machine learning models like SVM, LR, MLP, decision tree, and KNN. The inevitable parameters like accuracy, recall, and precision is calculated to determine the accurate results of the classifier.
Image and pattern reconstruction using differentiable plasticity
Authors: Priyanka Jigalur , Prasad B G
Published: January 2022

The brain is a substantial boon to humankind that adapts nature accordingly. The brain can learn and unlearn based on the situation. This singularity of human learning led to the research creating models using Artificial Intelligence (AI) to incorporate the brain’s behavior. The investigation opened up many new approaches to study AI with neural networks by adding new techniques to imitate the human brain’s functionalities. Many models can learn from experience like Recurrent Neural Network(RNN’s), Long Short Term Memory (LSTM) with the fixed network size. This paper describes the simple method of creating the model which will behave similar to the biological brain and recreates its differentiable plasticity to adopt the features of neural network connection. It also shows that applying plasticity and the Hebbian plastic connection rule can result in optimization in RNN. This new approach of reconstruction of images based on plastic neural network experiments shows that the above novel approach gives more optimized results than the traditionally used RNN techniques. In this paper, a proposal is made where models can memorize and reconstruct unseen sets of images by solving recurrent networks using plasticity rules.
Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers
Authors: Sai Krishna Reddy V , Meghana P , Subba Reddy N V , Ashwath Rao B
Published: January 2022

Machine Learning is an application of Artificial Intelligence where the method begins with observations on data. In the medical field, it is very important to make a correct decision within less time while treating a patient. Here ML techniques play a major role in predicting the disease by considering the vast amount of data that is produced by the healthcare field. In India, heart disease is the major cause of death. According to WHO, it can predict and prevent stroke by timely actions. In this paper, the study is useful to predict cardiovascular disease with better accuracy by applying ML techniques like Decision Tree and Naïve Bayes and also with the help of risk factors. The dataset that we considered is the Heart Failure Dataset which consists of 13 attributes. In the process of analyzing the performance of techniques, the collected data should be pre-processed. Later, it should follow by feature selection and reduction.
Scale-Aware Cascading for Semantic Segmentation
Authors: Ahmed Ali Salim , Prasad B G
Published: January 2022

Semantic segmentation is an important technology commonly used in medical imaging, autonomous driving vehicles, and backgrounds for virtual meetings. Scale Aware approaches have become the standard when it comes to the semantic segmentation domain of Machine Learning. Multiple image scales are passed through the network allowing the result to use the regular CNN layers such as max-pooling as well as convolution layers. Also, a cascading hierarchy of attention has been shown to improve the accuracy of models for such segmentation tasks. The combination of both these approaches has been shown to greatly improve the accuracy of such models. A side effect of using the cascading approach is that the model turns out to use less memory in comparison to previous approaches. Auto-labelling engines are also helpful in generalizing the model further. The cityscapes dataset used here is a useful data bank as it consists of a myriad of situations where the model can be trained and tested on. This paper presents the tested results of such a segmentation model and incremental modifications to the model pipeline to understand and improve upon the existing architecture.
Data Clustering using Genomic Analysis in Graph model
Authors: Chithambarathanu M , Ganesh D R
Published: January 2022

In the event that the data is addressed as a diagram, wherein the hubs are devices and the hyperlinks establish associations among devices then a bunch might be defined as an associated perspective; i.e., a gathering of devices that are identified with each other, yet that don’t have any association with objects outside the gathering. Bunching is an essential test in the quality examination. This ponders monster impact genetic field. Thusly in the current system, the various genomic assessments are scattered in various dispersed structures. In our proposed work, we endeavour to develop a normal data base for genomic and proteomic assessment using diagram grouping.
A comparative study using supervised learning for anomaly detection in network traffic
Authors: Garg R , Mukherjee S
Published: January 2022

A user connects to hundreds of remote networks daily, some of which can be corrupted by malicious sources. To overcome this problem, a variety of Network Intrusion Detection systems are built, which aim to detect harmful networks before they establish a connection with the user’s local system. This paper focuses on proposing a model for Anomaly based Network Intrusion Detection systems (NIDS), by performing comparisons of various Supervised Learning Algorithms on metric of their accuracy. Two datasets were used and analysed, each having different properties in terms of the volume of data they contain and their use cases. Feature engineering was done to retrieve the most optimum features of both the datasets and only the top 25% best features were used to build the models – a smaller subset of features not only aids in decreasing the capital required to collect the data but also gets rid of redundant and noisy information. Two different splicing methods were used to train the data and each method showed different trends on the ML models.
A foundational model to spot indications of generalized anxiety disorder and assist mental well being
Authors: Revanthsai Puttaparthi , Sai Maadhurya N , Dhruthi Varna Konijeti Sai , Priya B. K.
Published: January 2022

Mental health, a subject that carries equal importance as physical health, has always remained in the limelight with least medical assistance sought, often leading not only to difficulty in the carrying out day-to-day activities but also to death by suicide. The number has begun to peak since the onset of the pandemic and thus needs to be addressed. Early detection and treatment play an extremely important role in treatment of mental illness. Though the period of recovery is gradual, constant screening and a follow up is sufficient enough in aiding the path back to change in lifestyle. Generalized Anxiety Disorder is one of the most common of mental illnesses among a never-ending list in the 21st century’s fast paced world and is often neglected, but curable if detected in early stages and treated. With mental well-being gaining importance and people moving forward to seek help, a technology to help them in their betterment would be benefiting. A design that is not only user-friendly but also cost effective can come handy to enable simultaneous monitoring.
Depression and Suicide Analysis Using Machine Learning and NLP
Authors: Jain Pratyaksh , Ram Srinivas Karthik , Vichare Abhishek
Published: January 2022

Depression is a common type of mental illness that can impair performance and lead to suicide ideation or attempts. Traditional techniques used by mental health experts can assist in determining an individual’s type of depression. Machine learning and NLP were used to understand how to predict posts that indicate depression in people and their accuracy. For this work, we have used a dataset from reddit. Reddit is an ideal destination to use as a supplement to the traditional public health system because of its punctuality in exchanging ideas, versatility in presenting emotions, as well as compatibility to use medical terms. We examined the comments and posts about suicidal ideation. We used NLP to gain a better understanding of interdisciplinary fields which are related to suicide. We discovered two help groups for depression and suicidal thoughts: r/depression and r/SuicideWatch. The famous “SuicideWatch” subreddit is commonly used by people who have thoughts of suicide and gives significant signals for suicidal behavior. A brief scan through the articles discloses that the subreddits are legitimate online spots to seek assistance and provide honest text data about people’s mental state. We have used multiple ML algorithms such as Naïve Bayes, SVM. To address the research problem, we have considered two subreddits that provided us with appropriate information to track people at risk. We achieved results of 77.29 % accuracy and 0.77 f1-score of Logistic Regression, 74.35 % accuracy and 0.74 f1-score of Naïve Bayes, 77.120% accuracy and 0.77 f1-score of Support Vector Machine, 77.298% accuracy, and 0.77 f1-score of Random Forest.
On the solar dynamics from radio signal communication by recent solar probes
Authors: Banerjee Abhijit , Bhattacharya Rina
Published: January 2022

The very inquisition of the humanity always remains about its parent star of this planetary system. Scientists across the world are always egger to investigate the details of the phenomenon of the solar flares and coronal mass ejections (CMEs). There are some fundamental mysteries related to the solar coronal heating along with the acceleration of the solar wind and energetic particles. In this context we have discussed on the solar radio signal data obtained from the Parker Solar Probe (PSP) mission of National Aeronautics and Space Administration (NASA), USA in course of its journey towards the Sun and the very recent data of Solar and Heliospheric Observatory (SOHO) space probe of European Space Agency (ESA) and NASA. In this work the simultaneous and periodical analysis of the data from the SOHO and PSP will light into the delicate features of the near and far Earth observations on the solar coronal mass ejections related dynamics and that reveals some interesting facts in relation to the solar magnetic field.
Mental Health Assist and Diagnosis Conversational Interface using Logistic Regression Model for Emotion and Sentiment Analysis
Authors: Moulya S , Pragathi T R
Published: January 2022

The aim of this work was to create a fully functional AI-ML based conversational agent that behaves like a real time therapist which analyses the user’s emotion at every step and provides appropriate responses and feedback. AI chatbots, although fairly new to the domain of mental health, can help in destigmatizing seeking help, and are more easily accessible to everyone, at any time. Chatbots provide an effective way to communicate with a user and offer helpful emotional support in a more economical way. While making regular psychiatric visits often require a fixed duration/appointment which can be time consuming and is restricted to a fraction of the day, the proposed chatbot can keep track of your health on the go at any time. The application will have a self-healing kit suggesting various exercises, both mental and physical that the user may implement in his day-to-day life. The study below goes into further detail on the major insinuations for future chatbot agent design and assessment
Performance Improvement in E-Gun Deposited SiOx- Based RRAM Device by Switching Material Thickness Reduction
Authors: Roy Sourav , Maikap Siddheswar
Published: January 2022

A performance improvement by reduction in switching material thickness in a e-gun deposited SiOx based resistive switching memory device was investigated. Reduction in thickness cause thinner filamentary path formation during ON-state by controlling the vacancy defects. Thinner filament cause lowering of operation current from 500 μA to 100 μA and also improves the reset current (from >400 μA to <100 μA). Switching material thickness reduction also cause the forming free ability in the device. All these electrical parametric improvements enhance the device reliability performances. The device show >200 dc endurance, >3-hour data retention and >1000 P/E endurance with 100 ns pulses.
Protall (An Intelligent, multi-sensor, comprehensive obstacle avoidance system for automobiles and UAVs)
Authors: Dhelia Manav , Chaughule Saurabh , Choraria Amit , Hariharan Arjun , Manohara Pai M M
Published: January 2022

In this era of Artificial Intelligence and Automation, manufacturing and testing of self-driving cars and autonomous delivery of parcels to the desired location with the help of UAVs have a considerable amount of growth in the industry. This advancement in technology also raises safety issues due to the failure of sensors to detect the object or sometimes because of the dynamic environment. Protall (Protect-all) is an integrated solution for UAV and automobile vehicles to provide ultimate safety to both itself and its surroundings. With the strategic sensor integration and its intelligent processing, it aims to produce controlled output and hence, ensures to prevent any possible failures from occurring. The system constantly reacts to the environment and maintains comprehensive interaction with the user thus, enabling it to handle any dynamic situation and hence, it emerges as an optimal solution for Vehicles and UAVs.
Analysis of Total Harmonic Distortion and implementation of Inverter Fault Diagnosis using Artificial Neural Network
Authors: Manjunath T G , Vikramathithan A C , Girish H
Published: January 2022

As power electronics devices dependability is very significant to guarantee Multi Level Inverter (MLI) systems stable functioning, it is imperative to identify and position faults as promptly as possible. Due to the fault occurrences, the Total Harmonic Distortion (THD) on the system gets a hit. In this perspective, to improve fault diagnosis accuracy and efficient working of a Cascaded Multi level Inverter System (CHMLIS), a quick and accurate fault diagnosis strategy with an optimized training algorithm using Artificial Neural Network (ANN) is presented. Also, Total Harmonic Distortion (THD) is analyzed for each switch Fault simulated using MATLAB/Simulink and the results are presented. Results shows the efficacy of Algorithm in identifying the fault. The auxiliary cell is replaced while the fault occurs in the main cell thus making the uninterrupted working of the Multi-Level Inverter (MLI) in the Induction Motor Drive (IMD).
Real time Face Detection and Optimal Face Mapping for Online Classes
Authors: Archana MCP , Nitish CK , Harikumar Sandhya
Published: January 2022

The main objective of this paper is to provide a web-based tool for identifying faces in a real-time environment, such as Online Classes. Face recognition in real-time is now a fascinating field with an ever-increasing challenge such as light variations, occlusion, variation in facial expressions, etc. During the current pandemic scenario of COVID-19, the demand for online classrooms has rapidly increased. This has escalated the need for a real-time, economic, simple, and convenient way to track the attendance of the students in a live classroom. This paper addresses the aforementioned issue by proposing a real-time online attendance system. Two alternative face recognition algorithms are perceived in order to develop the tool for realtime face detection and recognition with improved accuracy. The algorithms adopted are Local Binary Pattern Histogram(LBPH) and Convolutional Neural Network (CNN) for face recognition as well as Haar cascade classifier with boosting for face detection. Experimental results show that CNN with an accuracy of 95% is better in this context than LBPH that yields an accuracy of 78%.
Automatic Remote Farm Irrigation System with WSN and Weather Forecasting
Authors: Sai Kilaru Aditya , Madishetty Prem , Vardhan Naidu Yamala Harsha , Giriraja C V
Published: January 2022

The paper showcases the system used for automating agriculture using wireless sensor network (WSN) and weather prediction. WSN, is more efficient than IoT as it avoids connecting all the sensor nodes directly to Internet, thus reducing the traffic over Internet and energy consumption of the sensor network. The system consists of a clustered tree topology to increase the range of operation, connectivity and easily connect new nodes dynamically. The sensor nodes being the leaves, local gateways being the branches and the global gateway being the root node. The system is implemented using cost effective micro-controllers, robust communication modules and reliable data showcasing platforms. Our implementation uses weather prediction to minimize the water needed for irrigation. Thereby minimizing cost and increasing efficient usage of resources.
A Review on Classifications of Tracking Systems in Augmented Reality
Authors: Bhakar Suman , Pallavi Bhatt Devershi , Singh Dhaka Vijaypal , Kumar Sarma Yogender
Published: January 2022

Augmented Reality (AR) is now becoming an exceptional technology that offers a new world. Users to enable their senses to feel, listen and see the surroundings in different and better ways witness a unique experience. AR technology is used to superimpose the real view of the user with the virtual scenes. This research work illustrates the core details of AR, its definition, history, and development process. In addition, discusses ideas having various approaches during utilization of AR frameworks along with glyph tracking system. Current applications of AR technology completely depend on the proper utilization of AR frameworks.
Preparation and antibacterial properties of an AgBr@SiO2/GelMA composite hydrogel
Authors: Li Bo , Li Hong , Yang Haocheng , Shu Yue , Li Kejiang , Chen Ke , Xiao Wenqian , Liao Xiaoling
Published: January 2022

Pure gelatin hydrogels lack antibacterial function and have poor mechanical properties, which restrict their application in wound dressings. In this study, nanosized silver bromide-doped mesoporous silica (AgBr@SiO2) microspheres with hollow structures were prepared by a modified Stober method. The novel microspheres can not only release silver ions to treat bacteria but also release drugs to treat skin wound. Furthermore, AgBr@SiO2 microspheres were modified with propyl methacrylate, incorporated into methacrylated gelatin (GelMA), and crosslinked by UV light to prepare AgBr@SiO2/GelMA dressings consisting of composite hydrogels. The results showed that the AgBr@SiO2 microspheres could enhance the mechanical properties of the hydrogels. With the increase in the AgBr@SiO2 concentration from 0.5 to 1 mg ml−1, the dressings demonstrated effective antimicrobial activity against both Staphylococcus aureus and Escherichia coli. Furthermore, full-thickness skin wounds in vivo wound healing studies with Sprague–Dawley rats were evaluated. When treated with AgBr@SiO2/GelMA containing 1 mg ml−1 AgBr@SiO2, only 15% of the wound area left on day 10. Histology results also showed the epidermal and dermal layers were better organized. These results suggest that AgBr@SiO2/GelMA-based dressing materials could be promising candidates for wound dressings.
Satellite evidence of canopy-height dependence of forest drought resistance in southwestern China
Authors: Xu Peipei , Fang Wei , Zhou Tao , Li Hu , Zhao Xiang , Berman Spencer , Zhang Ting , Yi Chuixiang
Published: January 2022

The frequency and intensity of drought events are increasing with warming climate, which has resulted in worldwide forest mortality. Previous studies have reached a general consensus on the size-dependency of forest resistance to drought, but further understanding at a local scale remains ambiguous with conflicting evidence. In this study, we assessed the impact of canopy height on forest drought resistance in the broadleaf deciduous forest of southwestern China for the 2010 extreme drought event using linear regression and a random forest (RF) model. Drought condition was quantified with standardized precipitation evapotranspiration index (SPEI) and drought resistance was measured with the ratio of normalized difference vegetation index during (i.e. 2010) and before (i.e. 2009) the drought. At the regional scale we found that (a) drought resistance of taller canopies (30 m and up) declined drastically more than that of canopies with lower height under extreme drought (SPEI < −2); (b) RF model showed that the importance of canopy height increased from 17.08% to 20.05% with the increase of drought intensities from no drought to extreme drought. Our results suggest that canopy structure plays a significant role in forest resistance to extreme drought, which has a broad range of implications in forest modeling and resource management.
Multipole Born series approach to light scattering by Mie-resonant nanoparticle structures
Authors: Ustimenko Nikita A , Kornovan Danil F , Baryshnikova Kseniia V , Evlyukhin Andrey B , Petrov Mihail I
Published: January 2022

Optical response of Mie-resonant nanoparticles can be modeled either by full-wave numerical simulations or by the widely used analytical coupled multipole method (CMM). However, an analytical solution in the framework of CMM can be obtained only in a limited number of cases. In this paper, a modification of the CMM in the framework of the Born series and its applicability for the simulation of light scattering by finite nanosphere structures, maintaining both dipole and quadrupole resonances, are investigated. The Born approximation simplifies an analytical analysis of various systems and helps shed light on physical processes ongoing in that systems. Using Mie theory and Green’s functions approach, we analytically formulate the rigorous coupled dipole-quadrupole equations and their solution in the different-order Born approximations. We analyze in detail the resonant scattering by dielectric nanosphere structures such as dimer and ring to obtain the convergence conditions of the Born series and investigate the influence of the physical characteristics such as absorption in particles, type of multipole resonance, and geometry of ensemble on the convergence of Born series and its accuracy.
Hybrid surface characterisation of intra thin-walled Ti6Al4V surfaces produced by laser powder bed fusion technology
Authors: S Anand Kumar , Kushwaha Ajay , B K Nagesha , Barad Sanjay
Published: January 2022

The proposed work investigates the hybrid surface characterisation of intra thin-walled Ti6Al4V surfaces fabricated using laser powder bed fusion technology. The thin-walled samples were characterised using scanning electron microscopy and Opto-digital microscopy techniques. The fractal dimensional analysis was performed using ImageJ software integrated with an open-source MultiFrac plug-in. The surface microscopy analysis revealed satellites powder particles, partially melted powder particles, spherical balling, and pores on the thin-walled surface. The fractal dimension establishes a correlation between the surface roughness values. The surface areal surface parameters analysis suggested variation along the build direction of the thin-walled Ti6Al4V sample. The development of sharp peaks and thus higher Ra, Sku and Ssk values were found along the build direction of the intra thin-walled samples. Therefore, the combination of areal surface topography analysis and fractal dimension approach can be a promising methodology towards surface characterisation of additively manufactured complex thin-walled surfaces.
Development of Al-SiC composite material from rice husk and its parametric assessment
Authors: Baghel Madhusudan , Krishna C M , Suresh S
Published: January 2022

In this research work, the development of Al-SiC composite material from rice husk and its parametric assessment is done using a CNC milling machine. They are further surface characterized, and mechanical properties such as BET surface area, SEM-EDX, and XRD, fracture toughness, tensile, and bending strength are studied. The machinability of the components is investigated for selected values of input-output parameters. Three castings, each with different particulate reinforcement combinations, are made with aluminum alloy (6061) using the stir casting method. BET surface area of extracted silica and Al-SiC composite material was found 374 m2 g−1 and 150 m2 g−1, respectively. From results of BET surface area revealed that silica obtained from rice husk is more heterogeneous with a large surface area. A heterogeneous surface with larger pores was found through SEM images. XRD diffraction peaks show changes of amorphous silica into crystallinity in the composite material. The results also indicate that fracture toughness is very good at low temperatures and good machinability on CNC milling machines makes it suitable for aerospace applications.
GIS-based research on climate suitable region of Loquat in Lishui, Zhejiang province of China
Authors: Jiang Yanmin , Huang Anning , Wu Haomin , Zhang Xindan
Published: January 2022

To develop a climatic suitability index and conduct the cultivation division of loquat in Lishui, Zhejiang province of China, we introduced the multi-indicator comprehensive risk assessment method to combine with the hazard factor model, necessary climatic elements during the growing season of loquat and geographic information elements. Results show that the annual active accumulated temperature (rainfall) over most Lishui is more than 4500 °C (1600mm). The two climatic factors above can well meet the needs of loquat growth. The frozen injury days over most Lishui during the young fruit period of loquat are more than 10.0 days, which are higher than those during the flowering period. The annual mean number of continuous overcast rain occurrences is less than 4.5. The climatic mean annual occurrence of persistent abnormal high temperature weather is less than 1. Overall, the most suitable area for the cultivation of loquat mainly located over the relatively flat areas such as river valleys and basins, especially the banks of the Oujiang River in Qingtian and Xiaoxi River valley in Jingning. The excellent combinations of light, heat and water with relatively few meteorological disasters just like frozen injury, continuous rain and high temperature provide a good climatic conditions of the high-quality of loquat planting.
The DEMS-DOSS study: validating a delirium monitoring tool in hospitalised older adults
Authors: Montgomery Amy , Todd Jo-Anne , Jones Cindy , Koroitamana June , Grealish Laurie , Wand Anne , Billett Stephen , Teodorczuk Andrew
Published: January 2022

Objective

to evaluate the sensitivity, specificity and test–retest reliability of the Delirium Early Monitoring System-Delirium Observation Screening Scale (DEMS-DOSS).

Design

prospective diagnostic accuracy study of a convenience sample of admitted older adults with DEMS-DOSS and reference standard assessments.

Setting

60-bed aged care precinct at a metropolitan hospital in Sydney, Australia.

Participants

156 patients (aged ≥65 years old) were recruited to participate between April 2018 and March 2020. One hundred participants were included in the analysis.

Measurements

Participants were scored on the DEMS-DOSS. Trained senior aged care nurses conducted a standardised clinical interview based on the Diagnostic and Statistical Manual of Mental Disorder (DSM)-IV delirium criteria, within two hours of DEMS-DOSS completion. The senior aged care nurse undertaking the DSM-IV interview was blinded to the results of the DEMS-DOSS.

Results

Participants’ mean age was 84 (SD ±7.3) years and 39% (n = 39) had a documented diagnosis of dementia. Delirium was detected in 38% (n = 38) according to the reference standard. The DEMS-DOSS had a sensitivity of 76.3% and a specificity of 75.8% for delirium. The area under the receiver operating characteristics curve for delirium was 0.76. The test–retest reliability of the DEMS-DOSS was found to be high (r = 0.915).

Conclusion

DEMS-DOSS is a sensitive and specific tool to assist with monitoring new onset and established delirium in hospitalised older adults. Further studies are required to evaluate the impact of the monitoring tool on health outcomes.
TNFR2 pathways are fully active in cancer regulatory T cells
Authors: Okuzono Yuumi , Muraki Yo , Sato Shuji
Published: January 2022

Tumor necrosis factor receptor 2 (TNFR2), a membrane-bound tumor necrosis factor receptor expressed by regulatory T cells (Tregs), participates in Treg proliferation. Although a specific TNFR2 pathway has been reported, the signaling mechanism has not been completely elucidated. This study sought to clarify TNFR2 signaling in human Tregs using amplicon sequencing and single-cell RNA sequencing to assess Tregs treated with a TNFR2 agonist antibody. Pathway enrichment analysis based on differentially expressed genes highlighted tumor necrosis factor α signaling via nuclear factor kappa B, interleukin-2 signal transducer and activator of transcription 5 signaling, interferon-γ response, and cell proliferation-related pathways in Tregs after TNFR2 activation. TNFR2-high Treg-focused analysis found that these pathways were fully activated in cancer Tregs, showing high TNFR2 expression. Collectively, these findings suggest that TNFR2 orchestrates multiple pathways in cancer Tregs, which could help cancer cells escape immune surveillance, making TNFR2 signaling a potential anticancer therapy target.
Expectations of the timing and intensity of a stimulus propagate to the auditory periphery through the medial olivocochlear reflex
Authors: Otsuka Sho , Nakagawa Seiji , Furukawa Shigeto
Published: January 2022

Expectations concerning the timing of a stimulus enhance attention at the time at which the event occurs, which confers significant sensory and behavioral benefits. Herein, we show that temporal expectations modulate even the sensory transduction in the auditory periphery via the descending pathway. We measured the medial olivocochlear reflex (MOCR), a sound-activated efferent feedback that controls outer hair cell motility and optimizes the dynamic range of the sensory system. MOCR was noninvasively assessed using otoacoustic emissions. We found that the MOCR was enhanced by a visual cue presented at a fixed interval before a sound but was unaffected if the interval was changing between trials. The MOCR was also observed to be stronger when the learned timing expectation matched with the timing of the sound but remained unvaried when these two factors did not match. This implies that the MOCR can be voluntarily controlled in a stimulus- and goal-directed manner. Moreover, we found that the MOCR was enhanced by the expectation of a strong but not a weak, sound intensity. This asymmetrical enhancement could facilitate antimasking and noise protective effects without disrupting the detection of faint signals. Therefore, the descending pathway conveys temporal and intensity expectations to modulate auditory processing.