bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the impact of large-diameter shield construction adjacent to the side of the pile foundation
Authors: Ma Wenbin , Liu Junyan , Zhang Liangliang
Published: February 2021

In the process of urban subway construction, tunnel construction will inevitably cross the bridge pile foundation and disturb the existing bridge structure. In view of the unstable impact of tunnel construction on the bridge, this paper relies on a tunnel project in Guangzhou and uses MADIS GTS/NX three-dimensional finite element analysis software to reinforce and protect the presence or absence of isolated piles, and simulate the side of the shield tunnel through pile foundation construction according to different working conditions, and analyze the impact of construction on bridge and stratum deformation. The results show that the use of the isolation pile reinforcement protection plan with φ0.8m@1m and the pile length 50m effectively controls the displacement of the ground and the bridge, and also verifies the effectiveness of the reinforcement plan.
Research on dissipative energy of dynamic splitting tensile failure of carbon nanofibers reinforced concrete
Authors: Xia Wei , Xu Jinyu , Nie Liangxue , Wang Zhihang , Huang Zhe , Liu Gaojie
Published: February 2021

The dynamic splitting tensile tests of carbon nanofibers reinforced concrete (CNFC) with fiber volume content of 0%, 0.1%, 0.2%, 0.3% and 0.5% were carried out respectively by split Hopkinson pressure bar (SHPB) test device. Based on the internal relationship between the dynamic splitting tensile dissipative energy and the change rate of incident wave energy, the dissipative energy properties of CNFC were analyzed. The results show that: the total energy of incident wave, reflected wave and transmitted wave changes in a similar way with time, which is a cumulative process, with the increase of the change rate of incident wave energy, the dissipative energy of concrete also increases gradually, and the two basically show a linear growth trend; the addition of CNFs has a significant promoting effect on the dissipative energy of concrete, with the increase of the content of CNFs, the enhancement effect of CNFs on the dissipative energy shows a trend of first strengthening and then weakening, with the optimal content of 0.3%.
Economic analysis of prefabricated buildings based on value engineering analysis
Authors: Fang Jiayue , Qi Yuan
Published: February 2021

Combined with the value engineering theory, this paper compares the project’s value of output at different time by calculating the incremental cost and incremental benefit of the prefabricated building and the cast-in-place building. The calculations show that all aspects of prefab building are improving over time, but government subsidies have yet to have a significant effect on the overall value output.
Analysis of Stress Path in the Whole Process of Foundation Pit Excavation and Heavy Lifting
Authors: Cao Ying , Liu Yan , Du Changyan
Published: February 2021

Based on a foundation pit in Jinan City, this paper uses ABAQUS to conduct numerical simulations based on actual construction on site. Considering the rotation of the main stress axis, the corresponding stress path and discharge law are obtained in different affecting areas. The results show that whether inside or outside the pit, the horizontal and vertical stresses decrease with the excavation of the foundation pit, but the amount of soil unloading inside the pit is much greater than outside the pit; the passive zone

p
¯

in the pit keeps decreasing, and

q
¯

keeps increasing. The range of rotation angle of large principal stress is 0∼89.39°, the transition zone outside the pit and the active zone

p
¯

keep decreasing, while

q
¯

is almost unchanged, the change range is 0∼44.78°, and all rotate clockwise. When a crane load is applied to the side of the pit, it will have a greater impact on the soil elements in the active zone directly below the load outside the pit. The principal stress rotation angle will rotate counterclockwise, which is about 1/3 of the final deflection angle. The impact is relatively small.
Creep of CFRP confined reinforced concrete members under large eccentric compression
Authors: Cao Jian , Pan Xinpeng , Zeng Jingqi , Zeng Lingyun
Published: February 2021

Based on the multiaxial stress statetheory of concrete under and B3 model for calculating concrete creep, combined with the mechanical characteristics of reinforced concrete members under large eccentric compression, considering the influence of member deflection, reinforcement configuration and hoop force, the creep model of CFRP confined reinforced concrete members under long-term load is established. Compared with the experimental results, the theoretical calculation results are in good agreement with the experimental values. It has a certain guiding significance to the actual engineering structure.
Strength Model of Unsaturated Clays with Adsorptive Effects
Authors: Kong Xiaoang , Zhao Binbin , Liu Yi , Ou Wenhao , Yang Zhi
Published: February 2021

The interactions between liquid phase and solid matrix in unsaturated clays can be divided into capillary effects and physicochemical effects according to their formation mechanism. The physicochemical effects (that is, the adsorption effects) become significant at low saturation degrees. However, current strength models for unsaturated soils almost are established on the basis of capillary mechanism, and the contribution of adsorption effects to soil strength is neglected. A binary-medium shear strength model is firstly proposed, involving the capillary and adsorption effects. The unsaturated clay is assumed to consist of two ideal parts, i.e., the ideal capillarity part and the ideal adsorption part, and then the corresponding ideal strength formulas are established. Furthermore, a participation function is used to reflect the degrees of capillary effects and adsorption effects. Finally, predictions are performed on the shear strength of unsaturated clays. After comparing predicted results and exiting test results, it is shown that the proposed model can well describe the strength of unsaturated clays.
Analysis of the impact difference caused by the shape and the falling posture when the single coal-rock impacting the top coal caving hydraulic support tail beam
Authors: Xin Zhengyuan , Yang Yang , Xin Fengwen
Published: February 2021

To investigate the difference in responses of the tail beam of the top coal caving hydraulic support caused by different shapes and falling postures of coal-rock particles and study the vibrational signal changes, we conducted a dynamic analysis of single particle impact on the tail beam. First, formula of the contact force generated by the particle striking the tail beam slope was derived using Hertz theory. Then we used the finite element analysis software Abaqus to simulate the impact of different shapes of coal-rock particles on the tail beam. The results show the contact force generated by the coal-rock particles with different shapes and same mass impact the tail beam differently. The larger the radius of curvature of the particle contact point, the greater the contact force generated and the greater the vibration generated by the tail beam. Therefore, when the vibration signal of the tail beam is detected and used as the basis for judging the coal-rock particles, attention should be paid to the vibration difference because of the particle shape to prevent misjudgment.
Convolution Neural Network based Rain Noise Removal for Real Time Application
Authors: Sowjanya A. , Kumar Sandeep , Swaroop K. Sonali
Published: February 2021

Image quality is affected by rain streaks that have an adverse effect on the applications which involves the ideas of surveillance, automatic path navigation. Hence it is important to process the information present in the images affected by water droplets caused by the rain or the mist. So this forms the basic need to identify the problem which does not have any mathematical correlation about the same. This research tries to work on the methodology which will denoise the criteria based on Convolutional neural network (CNN). CNN modelling helps in finding out the connections between the noise input image and rain streak. The proposed method uses synthetic database available publicly.
RETRACTED: Artery-Vein Detection using Neural Network in Retina Images
Authors: Swaroop K. Sonali , Kumar Sandeep , Sowjanya A.
Published: February 2021

Retinal veins segmentation play a vital role in the life because of entire world moves toward the digital words. The paper presents an approach to identify the blood vessels from the optically affected areas of an eye. This method is proposed as the manual extraction of vessels from the fundus image is a very tedious task also it takes higher amount of time. Therefore, the paper mainly focuses on the developing an approach for vessel extraction. The methodology reportedly excels in terms of accuracy also being simple it finds many applications in image diagnosis for predicting various pathological diseases opathalmologically. It can create a new perspective in disease detection in a more accurate way. The input images are initially preprocessed; next the features are extracted from the images. The extracted images form the training set and the testing process is performed using the proposed methodology. Finally, the proposed work achieved 96.8% accuracy on benchmark datasets.
Computational Effective Multimodal Medical Image Fusion in NSCT Domain
Authors: Sathish Kumar G.A.E. , Devanna H.
Published: February 2021

Multimodal Medical Image Fusion provides comprehensive data in recent medical image diagnosis applications. This paper presents a novel Multimodal Medical Image fusion framework based on the Angular Consistency (AC) and Sub Band Adaptive Filtering (SAF). In the proposed model, the Non-subsampled Contourlet Transform (NSSCT) decomposes the source image pair into high frequency (HF) and low frequency (LF) sub bands. The LF bands are integrated with Angular consistency rule that can fuse the finer details which are very much helpful in medical image diagnosis. Further the HF bands are processed through sub band adaptive filtering such that the redundant information is filtered out and only significant information is preserved. At last the fused sub bands are processed for Inverse Non-subsampled Contourlet Transform to produce a fused image. Experimental evaluation is accomplished through a real time image data set and the performance is measured through Edge Based Similarity index, Local Quality Index and Computational time. The obtained results show that the proposed system achieves a satisfactory results compared to the conventional methods.
A Novel Error Free Dimensional Projection Coding
Authors: Rafi Shaik Mohammed , Basavaraj G D , Fairooz Shaik
Published: February 2021

In this paper, dimensional coding of projection has been developed with the hardware design for RS algorithms. The result of simulation of Memory Interface Unit for basic and dimensional projection coding with scale and Implemented Video Interface module using VHDL and Target on to the FPGA implemented (XCV300-bg432-6). It provided operating speed of 732.5 MHz, power consumption of 1249 BEL and 128.3 mW in FPGA.
Design of Deterministic Sparse Fractal Array with Haferman Counter-Diagonal Fractal Tapering Technique for Modern Wireless Systems
Authors: Sankar Ponnapalli V. A. , Praveena A , Vinod Babu P
Published: February 2021

Deterministic fractal structure is a repetitive geometry based structure having applications in various regions of science and engineering. Fractal antennas and array antennas are artistic structures and they are finding much attention in recent years owing to their feasible properties. A few deterministic fractal arrays owing to the fractal dimensional design methodology are thinned arrays, but the great concern for these types of array antennas is the great number of antennas present at higher level iteration and as well as expansion levels. The present research work discusses the feasibility of reducing antenna elements at higher expansion and iteration levels of fractal arrays with a new type of tapering technique named as Haferman counter-diagonal fractal tapering. Different types of fractal arrays created using a sub array of concentric elliptical ring design methodology have been considered for the application proposed tapering technique. Owing to the proposed technique, nearly 40% to 50% of the antenna elements thinned while maintaining fully populated array factor properties. The proposed technique can simplify the design complexity of fractal arrays and reduce the designing cost.
Ontology Based Website for Job Posting and Searching
Authors: Kumar N. Anil , Ramu K
Published: February 2021

Searching for the jobs remains a tedious work. Whenever we search for job we getting some results which are not very relevant. So, we have to manually search through those results and find some suitable job for which we can apply. Many times, we miss out some job postings because these posting do not appear in the results. The same problem exists for recruiters also. When recruiters search for some specialization there are not getting the same results because the keywords used for searching are different from the keywords used in their resumes. So, to solve this problem we use ontology to post job and to search the jobs.
Indian agriculture GDP and non performing assets: A regression model
Authors: Jethwani Bhoomika , Dave Devasya , Ali Tamanna , Phansalkar Shraddha , Ahhirao Swati
Published: February 2021

Agriculture is one of India’s crucial sectors in terms of its contribution to employment and the country’s (Gross Domestic Product) GDP. It has primarily emerged as an essential - growing sector in the global economy since independence. [21]However, the non-realization of the reasonable price for agricultural crop production leads to the introduction of loan waivers, which impact the credit culture and weaken the farming economy and growth. The presented work aims to perform exploratory data analytics on the GDP data in agriculture public domain by performing feature engineering on the factors affecting the agricultural GDP using the data for the period 1961 to 2019. It further builds a multi-linear prediction model to forecast the Agriculture Sector’s economic performance in terms of GDP and NPAs generated by the Agricultural Sector using Machine Learning Techniques. Keywords: Multiple Linear Regression, Ordinary least Squared, Agricultural GDP, Non-Performing assets, Indian Economy.
A survey on Pose Estimation using Deep Convolutional Neural Networks
Authors: Patel Manisha , Kalani Nilesh
Published: February 2021

Human pose estimation is a technique, which identifies the human body’s landmarks in images and videos. Human pose estimation can be divided into single person pose estimation and multi-person pose estimation, also an estimated human poses in crowded places as well as in videos. Depends upon the application such as activity recognition, Animation, Sports, Augmented reality, etc., Pose estimation output can be in 2D or 3D coordinate format. 3D pose estimation is estimated considering joint angles in 2D. Some challenges like small and barely visible joints, strong articulations, occlusions, clothing, and lighting changes increase difficulty in estimating pose. Remarkable progress has gained in the field of human pose estimation using Deep learning-based CNN models. In this paper, we compare and summarized various deep learning models for pose estimation of a single person and multi-person.
Reconstruction of Gene Regulatory Network using Bayesian Network
Authors: Bhola Abhishek , Mittal Sandeep
Published: February 2021

Gene Regulatory Networks (GRN) are complicated govern systems, that refers to the intercommunication of genes and it also helps to understand and visualize all the biological interconnections. Reconstruction of GRN helps in finding out various hidden facts that can be helpful in solving various complexities related to living life, curing many diseases and inventing new drugs for the humans. There are number of computational methods available in literature for reconstructing GRNs. In this paper, a method is proposed for reconstructing GRN i.e. based upon Bayesian network. The mentioned method in this paper is considered as one of the most capable method for reconstructing GRN. The proposed method functions in two stages. In its first stage, there is two level clustering is employed to diminish the size of given dataset. In its second stage, Bayesian Network will be utilized to reconstruct the network for the sub-clusters that are gained from the first stage. The proposed method is also validated and evaluated using different parameters.
Intelligent Travel Route Suggestion System Based on Pattern of Travel And Difficulties
Authors: Pulmamidi Nikhitha , Aluvalu Rajanikanth , Uma Maheswari V
Published: February 2021

People travel a lot to new places for career, business, education, travel, health and for various other reasons. Travellers always search to find shortest and safest route to reach the destination. They majorly consider distance, modes of transport and safety as major parameters. Various algorithms, mechanisms are proposed earlier to serve the purpose. However existing algorithms, mechanisms suffer from various shortcomings like unable to analyze the suggestions and filtering the database. The existing models use collaborative or content filtering algorithms. These methods do not collect the past user experience. The main objective of this paper is to help the travellers from their source to target from a database with best possible way. Proposed method provides the information from the travellers and problems faced by them during their travel. Based on the reported issues and experiences, new path will be enrooted to the destination. In the proposed model C4.5 decision tree algorithm is used to collect data from previous user experience, minimum redundancy maximum relevance (MRMR) and features selection algorithm is used to reduce execution time and increase system accuracy. The experimental outcome of this paper is to improve the system accuracy and performance.
VMATE – Intelligent E-Learning Management System for Educational Institutions
Authors: Anjali Pudiyadatha , Harshita Nunnabhatla , Akash M.D.N. , Vinith Patlolla , Prasada Reddy G.S
Published: February 2021

The world is facing a huge health crisis which is demanding to reorganize the way it functions from square one. The spread of the pandemic, the shutdown of education institutions, and the shift to online learning were so fast that it barely gave any time to consider the difficulties faced by students. This is a great opportunity where educational institutions will shift to blended learning; hence it will become a new way of learning where there will be a need to find new ways to deliver the best content by using learning management systems. Therefore, the virtual learning world will create a great impact on the current education system due to the diverse effect of the epidemic. Hence different organizations are tremendously advancing their technology, and it will become more crucial over time. VMATE –Intelligent E-Learning Management System provides an interface for learners, which is of great significance and practical value for the improvement of learning and teaching quality. The use of Computers and the Internet has introduced technological conditions for teachers and students who can make good use of online information provided and communicating with others. This can be used by institutions and for different environments, where they can have computer-based instructions, blended-learning, and increased quality and quantity learning. Besides some basic management functions, the system focuses on assignment grading, assessment for various learning courses which makes it suitable to provide personalized grading and detailed feedback.
A Multifarious Diagnosis of Breast Cancer Using Mammogram Images – Systematic Review
Authors: Swapna M. , Hegde Nagaratna
Published: February 2021

Breast Cancer is most common disease in worldwide leads to high rate in mortality. Detection of symptoms at early stage is difficult to identify the breast cancer. Diagnosis through General Medical Examination cannot detect the disease. This paper represents various common methods to detect the Breast Cancer and detection of tumors using mammogram images with Artificial Intelligence (AI). Algorithms helps Images in preprocessed using various feature extraction methods and classification algorithms to predict the class of tumors is benign or malignant. This article insight the recent approaches used in detection of tumors and stage of breast cancer.
A Neural Network trained to perform as an Imprecise 4:2 Compressor using Machine Learning Algorithm
Authors: Maddisetti Lavanya , Senapati Ranjan K. , Ravindra JVR
Published: February 2021

In many fields, such as speech recognition, image processing, Internet of Things (IoT) systems, arithmetic circuits, etc., artificial neural networks have become a popular solution for a wide range of problems. In this research work, a 4:2 compressor-based arithmetic circuit is considered and an approximation technique called probabilistic pruning is applied to it. This proposed compressor is compared with various existing approximate 4:2 compressors. Training of a neural network as proposed inexact 4:2 compressor is performed using a supervised machine learning algorithm. The accuracy values of the proposed compressor are acceptable at architectural and neural network levels. The absolute difference between the train set and the predicted test set accuracy values obtained is very less (1.73%) when compared with the other (inexact 4:2 compressors) trained deep neural network models.
WSN Smart Irrigation System and Weather Report System
Authors: Srinidhi J. Akhila , Aasish A. , Kumar N. Kishore , Ramakrishnaiah T.
Published: February 2021

This paper proposes a cost-effective system that can be used by farmers easily. In these modern times also, we find farmers going to their fields to check the water level and to see that no cattle are present near the field. Even if there are cattle he can’t do anything at that moment. So, we proposed a system where he can be notified if there is any animal or if the water content is low on his registered mobile number. In this system, an irrigation-based Wireless Sensor Network (WSN) using Raspberry Pi and the solar panel is implemented, Raspberry Pi being used as Coordinator node and Arduino as an end node, nRF24L01 as a transceiver. The sensor input values such as soil moisture level, PIR; the temperature is collected by the Arduino and is wirelessly sent to the Raspberry Pi. The water pump can be turned on based on sensor values using the Raspberry Pi web server and those values are also posted on the webpage. For temperature and humidity detection, we used the DHT11 sensor and the solar panel is used as an additional source of power. A PIR sensor is used to check if there are any animals present near the field.
Visualization of Real World Enterprise Data using Python Django Framework
Authors: Vamsi K. Manikanta , Lokesh P. , Reddy K. Neha , Swetha P.
Published: February 2021

In the meantime, startup companies are rising more and more. Companies want to track their prominence and their advancement. Basically, the whole data need to be found and stored in database. They need to check their company’s turnout-catastrophe ratio and their expansion. The company obliged to know how compact it is, with different companies. Since the data is in huge amount, estimating the data is almost difficult. Soon these terms, physically reckoning may become annoyed. Considering the plot, we have a proposal termed visualization. Visualization is the graphical depiction of data in the pattern of graphs, tabulations, charts by which we could know about company widening. Using data visualization, we could form an opinion on company’s stand/rate. So to evolve and to develop web pages, the automations we used are html, css for the data analysis, python modules are used. Few of them are pandas, seaborn, matplotlib, plotly. Implementing the data visualization using IDE’s like jupyter, Sublime Text makes the effort transparent. But integrating web pages with python modules will be the challenging load. So to carry out this, we use DJANGO Framework to visualize the data. DJANGO affords frontend, back-end and database This Data visualization is intended to evolve web pages and scrutinize the data.
Improved accuracy of pest detection using augmentation approach with Faster R-CNN
Authors: Patel Deven , Bhatt Nirav
Published: February 2021

In agriculture, Pests are decreasing agricultural productivity. Identifying a pest is a challenging process and subject to expert opinion. Nowadays, lots of work carried out for automatic pest detection. It becomes possible because of emerging Deep Learning’s object detection architectures. This paper shows the multi-class pest detection using Faster R-CNN architecture and compared the performance results of image augmentation with focused on the accuracy performance along with small dataset. We have used Horizontal Flip and 90 Degree Rotation augmentation parameters for solving class imbalance problem. We found that trained pest detection model with augmentation options can perform better with an accuracy of 91.02% using Faster R-CNN architecture.
Usage of time series forecasting model in Supply chain sales prediction
Authors: Raiyani A , Lathigara A , Mehta H
Published: February 2021

This paper representing a study of supply chain operation data that was used on 100 different store items from 10 stores using 5 years history of sales through open sources contest to compare the performance of time-series forecasting model mainly, decomposition, Auto-Regressive Integrated Moving Average(ARIMA), Prophet, Box-Cox transformation. Here data is collected from 2013 to 2018 were used in real-time transaction at different store, initially model was applied on 2013 to 2017 data and based on the that predicted for 2018 then again cross checked with actual 2018 with proceed predicted data of 2018. To improve the performance and evaluation of the supply chain management system, scrutiny 3 metrices that will help to make decision on the model selection. The accuracy of the Machine learning model in forecasting future sales of supply chain store. Although the result on comparison indicates that there is no single method gives better and superior result. But present study indicates that prophet and ARIMA hybrid model gives better result compare to individual model.
Tyre Pressure Monitoring System
Authors: Abbi Amogha , Ramakrishnaiah T.
Published: February 2021

The Tyre Pressure Monitoring System which is used today indicates only whether the air pressure in any of the tyres is below a particular level of safety. This avoidable signal has made it possible for the statistical death rate to come up to 650 deaths per year. This project displays values through continuous monitoring. Therefore, this research proposed a method to cumulate the various factors of the tyre and to display it to the driver for a safer experience. The proposed TPMS has an electronic device unit that is attached to the tyre and transmits the values of the pressure, temperature, and the wheel alignment to the unit to be placed inside the vehicle, observable to the driver. This unit includes pressure sensors, microcontrollers, RF transmission, and batteries. An alert is introduced which indicates if the pressure exceeds the maximum or minimum safe pressure level. Several experiments have been carried out to analyze the proposed system. The integrated TPMS has proven to be an effective alternative to maintain the tyres and aims to improve the comfort and safety of the driver.
Multi Variant Handwritten Telugu Character Recognition Using Transfer Learning
Authors: Ganji Tejasree , Velpuru Muni Sekhar , Dugyala Raman
Published: February 2021

Optical Character Recognition (OCR) has become one of the most important techniques in computer vision, given that it can easily obtain information from various images. However, existing OCR techniques cannot recognition Telugu literature characters (Handwritten Golusu Kattu writing) due to a lack of datasets and trained deep Convolutional Neural Networks (CNN). Since the Kakatiya Empire (12th to 14th century) the glorious era of Telugu literature spread across the region. Thereupon, several handwritten documents consist of ancient knowledge, health care tips, wealth information, and several land records written in Telugu Golusukattu writing. Therefore, getting that information has become a major problem because of a lack of expertise in Golusukattu writing skills in skills. In order to solve the above problem, we are proposing deep learning aided-OCR for Telugu literature.
A literature survey on recurrent attention learning for text classification
Authors: Mariyam Ayesha , Basha SK Althaf Hussain , Raju S Viswanadha
Published: February 2021

Witha rapid rise of complex data every year needs more enrichment in machine learning methods to provide vigorous and accurate data classification. Deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory(LSTM) have accomplished to obtain better results in the domain of computer vision, object recognition, speech recognition and natural language processing compared to traditional machine learning algorithms. This paper mainly discusses about the blending of attention mechanism with various deep learning models for text classification which improves the performance of text classification task.
An Approach to detect fault text in articles
Authors: Reddy Pakala Prahasit , Prasheela Yempati , Reddy Avula Uday Kumar , Aluvalu Rajanikanth
Published: February 2021

The problem of Fault text (that contains fake news) has spread enormously in recent years. Social media is the major source for this type of news because it is easily and freely accessible to everyone and this type of news can be shared easily and rapidly, this enabled the widespread of news, so there is a need to detect this type of news. This news will mislead the users creating chaos and unrest. So the detection has become mandatory to avoid chaos and to maintain the pleasant news. We use Machine Learning Algorithms and basic Natural Language Process techniques to detect this type of news. In this work, Machine Learning Algorithms are used to parse the article and extract the summary from the article, this summary is used to predict the credibility. A passive-aggressive classifier and multinomial naive Bayes algorithms are used to train the model. A web app is created using the Flask API. One can put the URL in the web page to detect the news as “FAKE” or “REAL”.
5:3 compressor based neural network with LM Algorithm in Multiplier as Application
Authors: Charan Lanka Sai , Reddy M Vaishnavi , Reddy N Pooja , Ravindra J V R
Published: February 2021

In conventional approach for VLSI, implementing large numbers of operations in parallel is possible with NN. The fundamental task of a neural network hardware is not dependent on the implementation technology and are quite constructive in simulating the digital circuits. These NN representations can be incorporated in various applications where in the behaviour of these circuits are essential to get a solution for discrete problems. Therefore, supervised learning algorithms are used to train the computer software. One such algorithm is the L-M method which is one of the powerful and widely used approaches. This paper accounts to the designing and training of a neural network model as computing technique in 5:3 compressor with Lavenberg-Marquardt Algorithm. The performance parameters are significantly remarkable for real time implementation, are presented in the paper.
Green synthesis of Co-doped ZnO via the accumulation of cobalt ion onto Eichhornia crassipes plant tissue and the photocatalytic degradation efficiency under visible light
Authors: Zelekew Osman Ahmed , Aragaw Setegn Geta , Sabir Fedlu Kedir , Andoshe Dinsefa Mensur , Duma Alemayehu Dubale , Kuo Dong-Hau , Chen Xiaoyun , Desissa Temesgen D. , Tesfamariam Belay Berehane , Feyisa Gebisa Bekele , Abdullah Hairus , Bekele Eneyew Tilahun , Aga Fekadu Gochole
Published: February 2021

Nowadays, water pollution is a major concern to the globe. For this reason, various research works has been done to access pure water thereby minimizing the effect of pollutants. In this work, the cobalt doped ZnO (Co-doped ZnO) via the accumulation of cobalt ion onto Eichhornia crassipes plant tissue for different days and combined with zinc precursor was synthesized. The resulting catalyst powder samples were characterized by Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), X-Ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and Ultraviolet–vis (UV–vis) spectroscopy, and microwave plasma atomic emission spectrometer (MP-AES). The catalysts were also tested for the photocatalytic degradation of methylene blue (MB) in the presence of H2O2 under visible light irradiation. The best catalytic activity was gained by the 8th-days accumulation of cobalt ion onto the Eichhornia crassipes plant tissue and 99.6% of the dye was degraded within 45 min. However, 69.6, 65.7, 73.6, and 94.8% of MB dye was degraded by 1, 2, 4, and 6 days accumulations. Hence, removal of toxic heavy metal by using Eichhornia crassipes plant and recycling in the wastewater treatment gain is highly appreciated. Moreover, the Co-doped ZnO photocatalysts could enhance the photocatalytic activities due to suppressing of the electron and hole recombination and the porosity of the catalysts resulted from the Eichhornia crassipes plant after calcination.
Storage and separation of methane and carbon dioxide using platinum- decorated activated carbons treated with ammonia
Authors: Aboud Mohamed F Aly , ALOthman Zeid A , Bagabas Abdulaziz A
Published: February 2021

Activated carbon (AC) was treated with ammonia for nitrogen doping and then was further anchored with platinum nanoparticles through ultrasound-assisted impregnation method. Methane (CH4) and carbon dioxide (CO2) storage of the pristine and modified activated carbon were determined at ambient temperature. All the samples showed high storage capacities with preference towards carbon dioxide storage, which would be favorable for both energy and environmental applications. Ammonia treatment slightly improved the storage of both CH4 and CO2 for all the samples, which could be attributed to the little improvement in the micropore properties upon nitrogen doping. Platinum decoration had an opposite influence on both CH4 and CO2 storage, which might be due to the absence of metal encouraging storage mechanism such as gas dissociation or reconstruction on platinum’s surface in addition to the harmful effect of metal pore-blocking and higher density of metal particles.
Electro-brush plating Ni-Graphene oxide composite coating enhanced corrosion resistance of fluoride ion
Authors: Xu Pengyuan , Shen Xiaochao , Xin Gang
Published: February 2021

Ni-Graphene Oxide(GO) composite coating was prepared on Q235 steel plate by electro-brush plating technique to reduce the corrosion of fluoride ion. In this study, SEM, XRD and Raman tests were performed to analyze the morphologies and structures of the coatings, then electrochemical characterization was evaluated to determine the corrosion resistance. The results showed that GO provided more nucleation sites, and the surface of coatings looked more uniform and much denser because GO and Ni atoms were co-deposited on coating surface. The composite coating showed a better corrosion resistance than the pure nickel and there was an optimal addition amount of GO (Ni-GO/0.75 coating). In detail, the corrosion voltage increased from −0.61187 V to −0.52542 V, and the corrosion current correspondingly decreased from 26.6 μA·cm−2 to 6.8 μA·cm−2, correspondingly. The charge transfer resistance was also increased from 558.6 Ω to 1769.0 Ω. Moreover, the immersion testing demonstrated that the weight loss of Ni-GO/0.75 composite coating was only 22.2% of the pure nickel. In addition, XPS spectra indicated that the fluoride ions in the corrosive medium preferentially bonded with nickel atoms, preventing the medium from entering the active area. In conclusion, Ni-GO composite coating exhibited excellent corrosion resistance to fluoride ion due to the impermeability and high conductivity of GO.
Monte Carlo calculated detector-specific correction factors for Elekta radiosurgery cones
Authors: Renil Mon P S , Meena Devi V N , Bhasi Saju , Nair Sneha S
Published: February 2021

A radiation field is considered small if its dimension is lower than the range of secondary electrons and the collimating devices partially occlude the source. Different detector types, such as unshielded diodes, diamond detectors, and small-volume ion chambers, are used for small-field measurements. Although the active volumes of these detectors are small, their non-water equivalent materials cause response variations. Herein, we aim to calculate the correction factors for our clinical detectors, EDGE detector (Sun Nuclear), 60017 diode (PTW), and CC01 ion chamber (IBA), for stereotactic radiosurgery cones of diameters of 5–15 mm in an Elekta Synergy linear accelerator using a Monte Carlo simulation. An Elekta Synergy linear accelerator treatment head was simulated using BEAMnrc Monte Carlo code as per the manufacturer specification. All three detectors were simulated as per the manufacturer specification. Three EGSnrc user codes were used for the detector simulation based on the detector geometry. The Monte Carlo model of the treatment head was validated against the measured data for a standard field size of 10 × 10 cm2. The off-axis profile, percentage depth dose, and tissue phantom ratio

TPR

10

20

were verified in the validation procedure. The measured and Monte Carlo calculated relative output factors (ROFs) were not consistent. In a 5 mm field size, EDGE diode overestimated the ROF by 7.06%, and 60017 diode to 4.611%. In a 7.5 mm field size, the variations were 4.295% and 3.691% for EDGE and 60017 diodes, respectively. CC01 ion chamber under-responded up to 10% because of its low-density active volume. The maximum corrections were obtained in the smallest field size, which were 0.939(0.007), 0.962(0.006), and 1.117(0.008) for EDGE, PTW T60017, and CC01 detectors, respectively. After applying the Monte Carlo calculated correction factor to the measured ROF, it became consistent with the Monte Carlo calculated ROF.
Nanoscale differentiation of surfaces and cores for olivine phosphate particles—a key characteristic of practical battery materials
Authors: Zhang Y , Alarco J A , Khosravi M , Mackinnon I D R
Published: February 2021

We provide a review of our recent studies on the surface chemistries and electronic structures of olivine phosphate cathode materials LiMPO4 (M = Mn, Fe, Co, Ni). Li-depletion and mixed oxidation for the transition metal ions have been detected on particle surfaces, across the family of metal phosphate cathode materials. The effects of surface Li-depletion on optical properties and electronic band structures are discussed. LiFePO4 doped with metals or ions that are reported in the literature as beneficial for rate capability enhancement show more pronounced surface Li-depletion and mixed oxidation compared to undoped material. This outcome, among others, indicates that the dopant resides predominantly on, and influences, the surface of cathode materials.
Retention of activity and secondary structure of hyperthermophilic laccase adsorbed on carbon black
Authors: Tamaki Takanori , Sugiyama Tomoharu , Oshiba Yuhei , Yamaguchi Takeo
Published: February 2021

Further increases in the current density of biofuel cells are partly limited by the deactivation of enzymes upon adsorption on hydrophobic carbon materials. A hyperthermophilic enzyme, hyperthermophilic laccase, was employed in the present study and the change in the activities and secondary structures upon adsorption on carbon black (CB) were evaluated by the oxidation rate of 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonate) and by Fourier transform infrared spectroscopy, respectively, in comparison with the mesophilic enzymes, laccase fromTrametes versicolor(denoted as mesophilic laccase), and glucose oxidase (GOx). Hyperthermophilic laccase retained its specific activities and secondary structures upon adsorption on CB compared with the other two enzymes mesophilic laccase and GOx.
Experimental determination of Li diffusivity in LLZO using isotopic exchange and FIB-SIMS
Authors: Brugge Rowena H , Chater Richard J , Kilner John A , Aguadero Ainara
Published: February 2021

Current lithium ion battery technology makes use of flammable liquid electrolytes and so the development of solid ceramic electrolytes for the next generation of all-solid-state batteries can offer a safer alternative. However, the lithium diffusion behaviour in these solid electrolytes is not yet well characterised, despite the importance of this information for optimising cell performance. Similarly, the transport properties at the metal anode interface are critically important, but not well understood. We propose a methodology for obtaining lithium diffusion coefficients of bulk solid ceramic garnet-type Li7La3Zr2O12 (LLZO) electrolytes by coupling dense pellets with isotopically labelled lithium metal, followed by analysis with focused-ion-beam secondary ion mass spectrometry. We report room temperature lithium diffusivities of 2–8 × 10−13 m2 s−1 for doped LLZO using an estimate of the lithium diffusion length in good agreement with electrochemical impedance spectroscopy. Simultaneous detection of positive and negative secondary ion species by SIMS enables correlation of layered interfaces consisting of metallic lithium, corrosion/surface degradation products and bulk LLZO during depth profiling. Charging of the ceramic during ion sputtering is investigated and shown to have a minimal effect on the obtained lithium isotopic fractions in the current setup. Additionally, the effect of the presence of corrosion products at the surface of garnets as a result of air-exposure is investigated. This method could be extended to any Li-metal stable solid electrolyte, or with a reactive solid electrolyte coupled with a stable interlayer. As such, this work sets the basis of a methodology for further quantitative diffusion analyses for Li-conducting solid ceramic electrolytes and their interfaces with electrodes, as used in both solid-state lithium batteries and hybrid systems coupling solid and liquid electrolytes.
A Broad Safety Assessment of the 9-Valent Human Papillomavirus Vaccine
Authors: Yih W Katherine , Kulldorff Martin , Dashevsky Inna , Maro Judith C
Published: February 2021

Parents indicate that safety is their top concern about human papillomavirus (HPV) vaccination. A data-mining method not requiring prespecification of health outcome(s) or postexposure period(s) of potentially increased risk can be used to identify possible associations between an exposure and any of thousands of medically attended health outcomes; this method was applied to data on the 9-valent HPV vaccine (HPV9) to detect potential safety problems. Data on 9- to 26-year-olds who had received HPV9 vaccine between November 4, 2016, and August 5, 2018, inclusive, were extracted from the MarketScan database and analyzed for statistically significant clustering of incident diagnoses within the hierarchy of diagnoses coded using the International Classification of Diseases and temporally within the 1 year after vaccination, using the self-controlled tree-temporal scan statistic and TreeScan software. Only 56 days of postvaccination enrollment was required; subsequent follow-up was censored at disenrollment. Multiple testing was adjusted for. The analysis included 493,089 doses of HPV9. Almost all signals resulted from temporal confounding, not unexpected with a 1-year follow-up period. The only plausible signals were for nonspecific adverse events (e.g., injection-site reactions, headache) on days 1–2 after vaccination, with attributable risks as low as 1 per 100,000 vaccinees. Considering the broad scope of the evaluation and the high statistical power, the findings of no specific serious adverse events should provide reassurance about this vaccine’s safety.
Association Between Nocturnal Blood Pressure Dipping and Chronic Kidney Disease Among Patients With Controlled Office Blood Pressure
Authors: Cho So Mi J , Lee Hokyou , Yoo Tae-Hyun , Jhee Jong Hyun , Park Sungha , Kim Hyeon Chang
Published: February 2021

BACKGROUND

Although abnormal blood pressure (BP) patterns are associated with adverse cardiorenal outcomes, their associations are yet unquantified by nocturnal dipping status. We examined the association of nocturnal BP dipping pattern with albuminuria and kidney function among participants with controlled hypertension without prior advanced kidney disease.

METHODS

Ambulatory BP (ABP) measurements were collected from 995 middle-aged, cardiology clinic patients with controlled office BP (OBP) (<140/90 mm Hg). The magnitude of dipping was calculated as the difference between daytime and nighttime systolic BP (SBP) divided by daytime SBP. Accordingly, the participants were categorized as extreme-dipper (≥20%), dipper (10% to <20%), non-dipper (0% to <10%), or reverse-dipper (<0%). We analyzed the cross-sectional associations of dipping with albuminuria (urine albumin-to-creatinine ratio ≥30 mg/g) and decreased estimated glomerular filtration rate (<60 ml/min/1.73 m2), adjusting for OBP/ABP, antihypertensive class, body mass index, total cholesterol, fasting glucose, socioeconomic status, and health behavior.

RESULTS

The participants (mean age 60.2 years; 52.9% male) consisted of 13.5% extreme-dippers, 43.1% dippers, 34.7% non-dippers, and 8.7% reverse-dippers. In reference to dippers, odds ratios [95% confidence interval] for albuminuria were 1.73 [1.04–2.60] in reverse-dippers, 1.67 [1.20–2.32] in non-dippers, and 0.62 [0.38–1.04] in extreme-dippers. Likewise, abnormal dipping profile was associated with decreased kidney function: reverse-dipping, 2.02 [1.06–3.84]; non-dipping, 1.98 [1.07–3.08]; extreme-dipping, 0.69 [0.20–1.17]. The associations persisted among participants with more conservatively controlled OBP (<130/80 mm Hg).

CONCLUSIONS

Monitoring diurnal and nocturnal BP may identify chronic kidney disease otherwise overlooked based on OBP.