bluuebunny/crossref_metadata_2025_split huggingface dataset:
ICT for Cyber Security in Business
Authors: Hristev R , Veselinova M 
Published: March 2021

Abstract
In recent years, the financial value of the stored information repeatedly exceeds the one of the equipment which is needed for its storage. Therefore, the process of business development needs the implementation of up-to-date ICT for data security from cyber-attacks. The scalability of IT infrastructures in this direction leads to development of malware creators. Thus, file servers become inefficient and give away to private clouds. Attacks against data stored on file servers and workstations are becoming more widespread. Standard security methods (for example, backup) are not always secure enough and can also be the target of a cyber-attack if not properly managed. In addition, information from the time of the last backup to the time of attack will be lost. The paper aims to research and propose approaches which can be used for recovery of the information arrays. We consider some software for implementation of private cloud which correspond to the ISO 27001 standard. The methods for accessing data will be analyzed and the techniques by which clouds deal with some of cyber-attacks will be addressed.
A Cost-Efficient Proof-of-Stake-Voting Based Auditable Blockchain e-Voting System
Authors: Sharma Trishie , Rama Krishna C , Bahga Arshdeep 
Published: March 2021

Abstract
In recent years, blockchain-based systems have emerged powerfully and led to the development of several new and improved applications in terms of increased security, trust, accountability and reliability. One important trust-based application is electronic voting system which still lacks in providing good throughput while also reducing the costs in voting given the deployment of state-of-the-art technologies. So, there is a need to imply an efficient consensus protocol to impart cost-efficiency as well as better performance of a blockchain-based electronic voting system. This paper focuses on boosting up the cost-efficiency in the voting process, while also being auditable, thus, strengthening the trust of voters on the government and lessening the overall costs incurred in voting. In this paper, Proof-of-Stake-Voting is used as consensus protocol which is observed to give a throughput of 4-5K transactions per second. Then its performance is compared with the other existing consensus protocols such as Proof-of-Work and Proof-of-Authority in terms of gas consumption, gas cost, and throughput. It has been shown through the results that there is a significant difference in the total gas cost, with Proof-of-Stake-Voting protocol consuming 3.1 x 10−7 Ethers (ETH), as compared to the Proof-of-Work, taking 5.42536 x 10−4 ETH and Proof-of-Authority protocols taking 2.49759 x 10−4 ETH.
A Computational Model to Automate the Design of Reinforced Concrete Tee Beam Girder Bridge Using Python Ecosystem
Authors: Jain Utkarsh , Sharma Mahima , Tumrate Charanjeet Singh 
Published: March 2021

Abstract
The design of tee beams girder bridge members such as deck slab, main girder, and cross girder is automated by using a computation model based on the python ecosystem. The slab, main girder, cross girder section, and reinforcement detailing are generated by using the purposed computational model. The strength and serviceability checks are as per the Indian Road Congress: 112-2011, Code of Practice for Concrete Road Bridges. The model takes the design parameters of dead and live load from Indian Road Congress 5 and 6 as an input and provides structural detailing of elements involved in the tee beam girder bridge system. The validation of the model architect is performed by comparing it with the solved illustration as per the Indian Road Congress norms. The section sizes and the steel requirement is calculated efficiently and with accuracy thus the time required to generate the structural drawings has reduced drastically. Moreover, with help of the purposed model, a more intensive database of tee beam girder bridges including bridge bearings, piers and foundation can be modelled towards the automation of complete reinforced concrete bridge design as per the Indian Road Congress.
Statistical Analysis of Influencing Design Parameters for Two Way Slabs
Authors: Sancheti Gaurav , Raitka Hinshal , Bhatnagar Vaibhav , Prasad Jagdish 
Published: March 2021

Abstract
The slab is a fundamental component of any building that are used as floors and roofs. It is, most commonly, made up of reinforced concrete. Slabs are the structural elements whose thickness is significantly less than their other dimensions. Slabs can have different shapes and conditions of support. It may be a solid, waffle-type or a ribbed slab. They are categorized as one way and two-way slab according to their load-carrying nature. In this paper, a comprehensive statistical analysis of factors that influence the design parameters of slab is presented. It is found that there is low correlation between that there is high but negative correlation found between Effective Depth Required and spacing_x and Inputs variable is able to explain well only two variables Effective_depth require and Spacing_x. The effect of magnitude of different input variables on output variables and interaction of combination of different input variables on output variables for design of Two-Way Slab is also performed.
Real Estate Cost Estimation Through Data Mining Techniques
Authors: Khare Sandali , Gourisaria Mahendra Kumar , Harshvardhan GM , Joardar Subhankar , Singh Vijander 
Published: March 2021

Abstract
Real estate is one of the most fast-paced and emerging industries today. Nowadays everyone wants to be the owner of their house rather than live on rent. Therefore, people are very cautious in searching for the most suitable house. Different people have a different budgets and so varies their desire. This paper draws attention to the house rate predictions based on different objectives like financial status and expectations of non-house holders. It consists of two prediction sets, one with all the available features required for buying the house and the other with a few selected features. It involves varying machine learning regression techniques like linear regression, polynomial regression, decision tree, and random forest. Here, all the above techniques are compared, and it is found that polynomial regression with all the features gives the best results.
Problems of Analyzing Socio-Political Content of Internet Resources Based on Neural Network Technologies
Authors: Rogachev A F 
Published: March 2021

Abstract
Ensuring information security requires identifying undesirable information content of Internet resources. Semantic and lexicological diversity of Internet content requires improvement of methods of neural network analysis of natural language texts (NLP). The problem is complicated by the presence of” information garbage”, which is a specific information noise that complicates the task of classifying texts. Well-known NLP technologies using artificial neural networks (ANN) include substantiation of the structure and construction of a subject-oriented database of text data bodies, frequency analysis and construction of dictionaries. To identify semantic content and latent threats, a dense vector representation of the analyzed texts in a multidimensional space (embedding) is justified. The authors substantiate a modified NLP approach to identifying sociocultural and cyber threats, contained in the information content of Internet resources. To justify and research the ANN architecture and hyperparameters focused on the socio-political content under study, the ANN family was built in Python 3. The ANN architecture included combinations of fully connected, convolutional, and/or recurrent layers. The number of neurons of the recognizing fully connected layer with the “softmax [[CHECK_DOUBLEQUOT_ENT]] activation function (or sigmoid in multiple classification) was taken by the number of classes marked in the text corpus.
Opinion Classification of Product Reviews Using Naïve Bayes, Logistic Regression and Sentiwordnet: Challenges and Survey
Authors: Dadhich A , Thankachan B 
Published: March 2021

Abstract
In recent years, automated opinion classification has evolved as one of the most demanding area in natural language processing. Many such systems have been implemented and developed for the summarization and classification of text and reviews of online products. There are many data sources and domains which sells the online products, such as Amazon, Flipkart, Snapdeal etc. In the same direction, this paper is intended to present a detailed review and comparative analysis of various existing sentiment analysis algorithms especially for the Amazon products, which have worked upon the supervised learning techniques called Naïve Bayes, logistic regression and SentiWordNet. Various key parameters and aspects of such a comparative tour are the use of feature reduction method, sentiment polarity, dataset domain and sources, product name, data set size and classifier. Further this paper includes the discussion on their accuracy results; additional results including important findings; and needs, challenges and limitations. Lastly, the performance of these algorithms is evaluated by comparing the % usage of the key parameters.
Structuring Rich Agriculture using Pervasive Computing
Authors: Bhatnagar Anurag , Bhatnagar Nikhar , Kumar Pradeep 
Published: March 2021

Abstract
Now a day almost all humans carry gadgets which are smaller in size but has maximum utilization in term of functionality i.e., mobile or smart watches. Cloud Computing is proven one of the best hope for farmers for better cultivation of crops by using digital compilation. In almost every year, more than 40% downfall in the cultivation has observed due to unfavourable conditions for instance changes in the climate, the storage of water and shortage of labour due to increased urbanized population. The solution of this problem turn onto the form of a new technology named ‘eplant’. With the minimized input the best possible crops production for the best health is the focus of this technique. Thus the term “Pervasive Computing” came into existence. “Pervasive Computing” is also known as “Ubiquitous Computing”. This paper is about introducing this new technique in farming. Data sensors are used to provide all necessary resources for better crop development through and different compatible actuators are also used as per the environmental conditions. The role of sensors is the collection of the data about soil, the climate and for specific plants conditions. In order to minimize the hard work and to maximize the profit of the farmer this technique provided desired parameters by different calculations.
A Cluster Based Classification for Imbalanced Data Using SMOTE
Authors: Tripathi Rajesh Kumar , Raja Linesh , Kumar Ankit , Dadheech Pankaj , Kumar Abhishek , Nachappa M N 
Published: March 2021

Abstract
There is tremendous upturn in data repositories because of data generation by various organizations like government, cooperates, health caring in large amounts. Large amount of data is being produced, processed, collected, and analysed online. So there comes a requirement to transform this data into valuable information. This process of extracting the knowledge from large amount of data is referred as data mining. The proposed hybrid approach can be checked on different classifiers like Naïve Bayes, Random forest classifier etc. In proposed methodology we find that SMOTE algorithm which used K-nearest neighbour algorithm is limited to some minority class instances for creating synthetic samples, which sometimes leads to over fitting, so an effective oversampling approach can be developed.
Using Improved MPPT Charge Controller Improvement Functioning Efficiency of Power Grid Connected Solar Photovoltaic System
Authors: Dua Surendra Singh , Sharma Ruchi , Patidar Raghavendra 
Published: March 2021

Abstract
In recent decades, the world’s energy demand is closely linked to the rise of manufacturing, transport and the media. Today, large amount of the power is provided by non-renewable energy including coal, natural gas, oil and uranium. Pace of regeneration of energy is very sluggish, which adds to the possibility of short-term depletion. This paper focuses on power grid-connected photovoltaic systems. The primary objective is to regulating power pumped into the grid from solar panels and improving the configuration and regulation of inverters as used to supply electricity to the grid with optimum efficiency and according to networking requests as an interface between the grid and photovoltaic systems. The infused power management not only contains the aggressive force control but also the reactive power control. An algorithm is proposed for the design of a simple and robust grid-connected inverter power. It is based on the strategy for digital power. The proposed work analyses and improves the constraints of the VSCs’ Voltage Source Converters as converters for supplying active and reactive power into the grid, ensuring the best possible connection from the solar panels, ensuring effective operation of the inverter, increasing the current injection into the grid power and harmonic levels.
Acridone and acridinium constructs with red-shifted emission
Authors: Tikhomirova Anastasiia A , Swift Kerry M , Haack Richard A , Macdonald Patrick J , Hershberger Stefan J , Tetin Sergey Y 
Published: March 2021

Abstract
Acridinium 9-carboxylic acid derivatives have been extensively used as chemiluminescent labels in diagnostic assays. Triggering acridinium with basic hydrogen peroxide produces a highly strained dioxetanone intermediate, which converts into an acridone in an electronically excited state and emits light at 420–440 nm. Here, we introduce a novel acridinium-fluorescein construct emitting at 530 nm, in which fluorescein is covalently attached to the acridinium N-10 nitrogen via a propyl sulfonamide linker. To characterize the spectral properties of the acridinium-fluorescein chemiluminophores, we synthesized the analogous acridone-fluorescein constructs. Both acridinium and acridone were linked to either 5- or 6-carboxyfluorescein and independently synthesized as individual structural isomers. Using fluorescent acridone-fluorophore tandems, we investigated and optimized the diluent composition to prevent dye aggregation. As monomolecular species, the acridone isomers demonstrated similar absorption, excitation, and emission spectra, as well as the expected fluorescence lifetimes and molecular brightness. Chemical triggering of acridinium-fluorescein tandems, as well as direct excitation of their acridone-fluorescein analogs, resulted in a nearly complete energy transfer from acridone to fluorescein. Acridone-based dyes can be studied with steady-state spectroscopy. Thus, they will serve as useful tools for structure and solvent optimizations, as well as for studying chemiluminescent energy transfer mechanisms in related acridinium-fluorophore tandems. Direct investigations of the light-emitting molecules generated in the acridinium chemiluminescent reaction empower further development of chemiluminescent labels with red-shifted emission. As illustrated by the two-color HIV model immunoassay, such labels can find immediate applications for multicolor detection in clinical diagnostic assays.
Effects of roasting and NH4Cl catalysis on the direct electrotransformation products from an AlCl3 solution
Authors: Han Xiuxiu , Zhang Ting-an , Lv Guozhi , Pan Xijuan , Fu Daxue 
Published: March 2021

Abstract
Contemporary processes for extracting alumina from fly ash involve various disadvantages, including high-energy consumption, heavy slag discharge, and severe equipment corrosion, so a new method for extracting alumina via electrotransformation was developed in this work. This novel method reduced slag discharge, enabled recycling, and produced no acid or alkali waste. The theoretical solution pH associated with Al(OH)3 precipitation and the actual solution pH during electrotransformation were also compared herein. Differential thermogravimetric analysis was performed for the direct electrotransformation products generated with and without NH4Cl as a catalyst; the activation energy and reaction order of these reactions revealed large fluctuations. The effects of the roasting temperature on the phase, morphology, D(50), and D(90) of the roasting products were also studied. The results indicated that the solution reached the theoretical pH (4.7) of complete Al(OH)3 precipitation in a short time when NH4Cl was implemented as a catalyst. Additionally, the D(50) and D(90) of the roasting products were nearly ten times higher when NH4Cl was used, relative to when it was not, reaching 227.1 and 519.6 μm, respectively. The activation energies of each reaction in the electrotransformation and roasting process were also reduced in the presence of the NH4Cl catalyst. Between 100 °C–250 °C, the physical adsorption of water occurred, and some of the structural water of the electrotransformation products generated with NH4Cl were removed in an endothermic reaction (reaction order, n1 = 0.43; activation energy, E1 = 2.13J mol−1). At temperatures between 250 °C–500 °C, the nitrogen-containing substances were removed in another endothermic reaction (reaction order, n2 = 0.4697; activation energy, E2 = 5.316J mol−1). Increasing the roasting temperature was beneficial for improving the crystal form of the roasting products, and the reduced electrotransformation products appeared more compact after roasting.
Boston Naming Test: Lose the Noose
Authors: Eloi Janelle M , Lee Jennifer , Pollock Erica N , Tayim Fadi M , Holcomb Matthew J , Hirst Rayna B , Tocco Carly , Towns Stephanie J , Lichtenstein Jonathan D , Roth Robert M 
Published: March 2021

Abstract

Objective
Administering the noose item of the Boston Naming Test (BNT) has been questioned given the cultural, historical, and emotional salience of the noose in American culture. In response, some have modified the BNT by skipping/removing this item and giving the point as if the examinee responded correctly. It is unknown, however, whether modifying standardized administration and scoring in this manner affects clinical interpretation. In the present study, we examined the prevalence of noose item failure, whether demographic and clinical characteristics differed between those who responded correctly versus failed the item, and whether giving a point to those who failed affected clinical interpretation.

Method
Participants included a mixed clinical sample of 762 adults, ages 18–88 years, seen for neuropsychological evaluation at one of five sites within the USA.

Results
Those who failed the item (13.78%) were more likely to be female, non-White, and have primary diagnoses of major neurocognitive disorder and epilepsy. Noose item failure was associated with lower BNT total score, fewer years of education and lower intellectual functioning, expressive vocabulary, and single word reading. Giving a point to those who failed the item resulted in descriptor category change for 17.1%, primarily for patients with poor overall BNT performance.

Conclusions
Only a small percentage of patients fail the noose item, but adding a point for these has an impact on score interpretation. Factors associated with poorer overall performance on the BNT, rather than specific difficulty with the noose item, likely account for the findings.
Event-specific quantitative polymerase chain reaction methods for detection of double-herbicide-resistant genetically modified corn MON 87419 based on the 3′-junction of the insertion site
Authors: Long Likun , Yan Wei , Li Congcong , Dong Liming , Liu Na , Xing Zhenjuan , Li Feiwu 
Published: March 2021

ABSTRACT
MON 87419 was one of the new transgenic corn events developed in US with the trait of herbicide resistance to both dicamba and glyphosate. To monitor unintended release of genetically modified organism in the future, as well as to meet GM-labeling requirements, it is requisite to develop a reliable method for the detection and quantification of MON 87419, an event-specific primer pair was designed to amplify the 3′-junction site between the endogenous genome sequence and the transferred DNA of GM event MON 87419, amplicons of desired size were produced by qualitative polymerase chain reaction (PCR) assay. For the validation of this quantitative method, the mixed samples containing 10%, 1%, and 0.1% MON 87419 ingredient were quantified. The precisions were expressed as relative standard deviations, deviated by 7.87%, 12.94%, and 19.98%, respectively. These results clearly demonstrate that the PCR methods we developed herein can be used for event-specific quantitative testing of the double-herbicide-resistant corn MON 87419.
Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients
Authors: Mahmud S M Hasan , Al-Mustanjid Md , Akter Farzana , Rahman Md Shazzadur , Ahmed Kawsar , Rahman Md Habibur , Chen Wenyu , Moni Mohammad Ali 
Published: March 2021

Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein–protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors–genes interaction, protein–drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.