bluuebunny/crossref_metadata_2025_split huggingface dataset:
Partnership between science, education and business to train young professionals in the conditions of digitalization
Authors: Klimova S P , Kondykov A V 
Published: March 2021

Abstract
This paper puts forward the problem that it is necessary to train personnel that meet the modern requirements of highly dynamic developing agriculture in the conditions of digitalization of the country in the post-pandemic period. The relevance of the research topic is explained by the fact that the agricultural labor market is experiencing a shortage of young highly qualified specialists with knowledge of IT technologies in digital agriculture. At present, a modern young professional must meet the conjuncture-forming selection criteria in the agricultural labor market and have a risk-oriented thinking. It is necessary to update the structure of the formation of an effective mechanism for training highly qualified personnel on the basis of industry universities in the region. The aim of the research is to analyze the challenges facing the agricultural university in training young professionals for agriculture as a branch of the digital economy of Russia. Objectives: to develop a model for developing partnerships and promoting the employment of young professionals in a post-pandemic economy to develop optimal options for covering the personnel deficit in the agricultural sector; to form practical recommendations for regulating the labor market for young professionals. The solution of these tasks will allow training highly qualified young specialists who have all the necessary competencies and are able to meet the requirements of the modern agricultural labor market.
Handwritten Character Recognition of Kannada Language Using Convolutional Neural Networks and Transfer Learning
Authors: Parikshith H , Naga Rajath S M , Shwetha D , Sindhu C M , Ravi P 
Published: March 2021

Abstract
Recognition of Kannada Handwritten Characters in recent times is one among the active research fields of study. It is one of the challenging topics in the pattern recognition field due vocabulary in large scale, complicated structural hierarchy and different people have different handwriting styles. In this study, we put forward a technique for recognition of characters in kannada which are handwritten based on convolutional neural networks and transfer learning. We have first pre-processed each character to remove noise, cropped each character image and resize the images. After the pre-processing the enhanced pixel values in the image helps in training of the neural network for an efficient classification of characters to their respective classes. We have considered vowels, consonants and numerals of Kannada language which are handwritten. Even though some of the characters in kannada language have similarities in structures our model is capable of classifying the character to their correct respective class. We have also used transfer learning to further improve the model so that any new style of handwritten character can be correctly predicted. Our method is efficient in neural network approach and it achieves competitive performance on the rest of the other traditional recognition methods in the literature.
A Blockchain-based framework for Agriculture subsidy disbursement
Authors: Bakare Savita , Shinde Suneel C , Hubballi Roopa , Hebbale Giridhar , Joshi Vinayak 
Published: March 2021

Abstract
Agriculture is the primary livelihood factor in the growth of India’s economy. One of the significant challenges faced by the Government of India in the agriculture sector is to increase the delivery of subsidies by re-engineering the existing process. One of the solutions initiated by the government of India on 1st January 2013 with the highest priority is called a Direct Cash transfer scheme under the Direct Benefit Transfer (DBT) scheme. It involves crediting subsidies (cash/payments) directly to the farmer’s account. However, the major problem associated with this scheme is that the government subsidies do not reach all the eligible farmers due to corruption, delay, intermediaries, and other reasons. The present direct cash transfers system’s major drawbacks such as (key loopholes are) lack of proper auditing, inability to track subsidy, and inadequate digital banking infrastructure especially in rural areas. In this regard, the disbursement of direct cash transfer scheme is addressed benefitting the farmers directly via Blockchain Smart Contracts technology thereby ensuring transparency, de-duplication, reduced delays, and reducing instances of fraud in the existing government subsidy delivery system. We aim to develop a blockchain-based smart contracts prototype model called a single-window approach for automating farmer’s subsidies and eliminating middlemen. The automated model will be fast, transparent, and immutable by reducing the complexities faced by Indian farmers.
Multi-Agents Based Coordinated Scheduling in Hospitals
Authors: Dhiviya Rose J. , Christalin Nelson S. 
Published: March 2021

Abstract
Patient Scheduling is the process of scheduling the patients by creating an optimal schedule for the patients in using the resources in hospitals. The real time scheduling with various constraints like emergency situations like accident case makes this problem complex. The manually scheduling algorithms and the traditional approaches needs more coordination, time and manpower to achieve its efficiency. Agents are a natural choice because they are known to represent a distributed environment well and they are capable of coordinating with each other in dynamic environment. The architectural components like Belief that maintains the internal state of the system, Desires that represents the motivational state of the agent and Intentions that helps to deliberate the state of the goal make this autonomous system adaptable and fault tolerant. In the proposed system each resources and patient is constructed to be software agents, which has the ability to coordinate among them. The optimal schedule is calculated in minimal time using the Genetic Algorithm (GA), an efficient search-based optimization technique. When a patient arrives, he/she is served by a common agent where the priority, resource requirement, processing time, and sequence are assigned to each patient. Hence in this highly dynamic domain optimal schedule generation with minimal time and hardware/software requirements will provide the customers satisfaction with their visit to the hospitals.
RETRACTED: A Hybrid Clustering Based Approach To Extract Drug Elements Which Causes Side Effects
Authors: Vijayan Alpha , Chandrasekar B.S 
Published: March 2021

Abstract
Prescription manifestations or drug reactions are an imperative and complex test. Medications are synthetic substances for treating illnesses, however, may actuate unfriendly responses or results. Medication revelation is tedious and works concentrated, and applicant drugs experience the ill effects of likely side. Heaps of affirmed drugs were removed from the market due to sudden results. Since drug results are an incredible worry of the general wellbeing, the recognizable proof of medication results assists with diminishing dangers in drug revelation. With the expansion of medication information, scientists gathered data about endorsed sedates, and recognize possible symptoms of new applicant drugs. Medication disappointments because of unexpected unfriendly impacts at clinical preliminaries present wellbeing hazards for the members and lead to generous monetary misfortunes. Side effect forecast calculations can possibly direct the medication configuration measure. Drug utilized in remedy relies upon the harmony between anticipated favourable circumstances and possible threats.
Extracting information for failure prediction from intermittent data
Authors: Kesavan Balakrishna S , Mahamuni Amol B 
Published: March 2021

Abstract
The edge devices used for edge analytics usually record data intermittently. So, it is hard to extract patterns of device failures, using common temporal approaches, like Long Short-Term Memory, or multivariate models, like boosted trees. Survival analysis can produce actionable insights in such scenarios.
Data analysis of a match using soccer-log
Authors: Srinath R , Devarakonda NagaSwetha , Biradar Arun , Biradar Shilpa 
Published: March 2021

Abstract
Applying Machine learning, data analytics etc have proven a greater improvement in decision making in all levels. A special attention has been made in the field of sports. As a whole, team improvement is a main concerned in all directions. The goal of any team would be to win every match they play. This can happen only if there is an excellent team efforts i.e. good coordination between players. We propose Sports Analysis an approach for better performance day by day. In this paper we talk about acquiring the details of a match and presenting team statistics.
RETRACTED: A Comparative Analysis of Pre-Processing Time in Summary of Hindi Language using Stanza and Spacy
Authors: Kumar Atul , Katiyar Vinodani , Kumar Pankaj 
Published: March 2021

Abstract
Text summarization is a method or say way for converting large texts into smaller ones by keeping all important points of the larger text as it is in the smaller ones and giving the output in the modified form called summary of original text document. This task is very difficult for humans as it required large amount of rigorous analysis of the document. In this proposed paper we are comparing pre-processing time for two tools of natural language processing one is STANZA and the other is SPACY both are based on modern technologies and are examine for HINDI language processing. In this paper we are preforming a comparative study of both the tools on the bases of their pre-processing time for processing HINDI language.
Now a days, a lot of text summarizer tools are present in the environment and we are keen to use them but here the point of this paper comes into light that how we know which Summarizer is fast enough to get us the same accuracy.
RETRACTED: Indoor human occupancy detection using Machine Learning classification algorithms & their comparison
Authors: Giri Disha , Shreya Sakshi , Kumari Pushpanjali , Yadav Richa 
Published: March 2021

Abstract
This paper presents a method to determine the occupancy in a room with the help of datasets whose data was collected from different sensors and using different ML algorithms. Features representing the occupancy level and the relative changes are taken from different sensors: Humidity sensors, light sensors,temperature sensors carbon dioxide (CO2) sensors. Different classification algorithms which are used for detection of occupancy are: Naive Bayes, classification via regression, random forest, simple logistic, multiclass classification, decision table. The result of the classifier can be classified in the class called - binary-class (which refers to the presence or absence of the person).
Effects of direct current bias on nucleation density of superhard boron-rich boron carbide films made by microwave plasma chemical vapor deposition
Authors: Ramkorun Bhavesh , Chakrabarty Kallol , Catledge Shane A 
Published: March 2021

Abstract
We report bias enhanced nucleation and growth of boron-rich deposits through systematic study of the effect of a negative direct current substrate bias during microwave plasma chemical vapor deposition. The current flowing through a silicon substrate with an applied bias of −250 V was investigated for a feedgas containing fixed hydrogen (H2) flow rate but with varying argon (Ar) flow rates for 1330, 2670, and 4000 Pa chamber pressure. For 1330 and 2670 Pa, the bias current goes through a maximum with increasing Ar flow rate. This maximum current also corresponds to a peak in substrate temperature. However, at 4000 Pa, no maximum in bias current or substrate temperature is observed for the range of argon flow rates tested. Using these results, substrate bias pre-treatment experiments were performed at 1330 Pa in an Ar/H2 plasma, yielding the maximum bias current. Nucleation density of boron deposits were measured after subsequent exposure to B2H6 in H2 plasma and found to be a factor of 200 times higher than when no bias and no Ar was used. Experiments were repeated at 2670 and 4000 Pa (fixed bias voltage and Ar flow rate) in order to test the effect of chamber pressure on the nucleation density. Compared to 4000 Pa, we find nearly 7 times higher boron nucleation densities for both 1330 and 2670 Pa when the substrate was negatively biased in the Ar/H2 plasma. Results are explained by incorporating measurements of plasma optical emission and by use of heterogeneous nucleation theory. The optimal conditions at 1330 Pa for nucleation were used to grow boron-rich amorphous films with measured hardness as high as 58 GPa, well above the 40 GPa threshold for superhardness.
Detection of atmospheric pressure plasma-induced removal of fingerprints via analysis of histograms obtained by imaging ellipsometry
Authors: Koulouris N A , Tasche D , Scheglov A , Mrotzek J , Gerhard C , Viöl W 
Published: March 2021

Abstract
In this publication we report on the suitability of imaging ellipsometry for the semi-quantitative investigation of a contactless removal of human fingerprints from surfaces by an atmospheric pressure plasma tretament. Special attention is paid to the impact of the applied plasma on the complex mixture of biological substances of a fingerprint. For this purpose, the cleaning effect of an argon plasma at two different electrical powers is investigated. By using imaging ellipsometry as a analysis method it could be shown that the cleaning efficiency increases with increasing electrical power and plasma treatment time. In addition, measurements were made by means of x-ray photoelectron spectroscopy (XPS) in order to characterize the chemical composition of surface-adherent contamination and its plasma-induced modifications. It was found that especially the amount of organic impurities is reduced in the course of plasma treatment.
Evidence of warm core structure maintenance over land: a case study analysis of cyclone Kelvin
Authors: Shepherd J M , Thomas A M , Santanello J A , Lawston-Parker P , Basara J 
Published: March 2021

Abstract
Cyclone Kelvin made landfall in northwest Australia as a category 1 storm on 18 February 2018. After landfall, tropical cyclones typically weaken due to the exhaustion of a moisture supply. Cyclone Kelvin continued to intensify with winds gusting to 100 knots and significant flooding well inland. A distinct eye feature also formed well after landfall. An analysis of meteorological satellite, MERRA2 reanalysis, and Global Data Assimilation System (GDAS) data was employed in a case study of Cyclone Kelvin. Using a cyclone phase diagram, it is confirmed that a warm core structure was maintained as Cyclone Kelvin continued to move inland, even through a period of brief inland intensification.
Modulation of global sea surface temperature on tropical cyclone rapid intensification frequency
Authors: Hong Jiacheng , Wu Qiaoyan 
Published: March 2021

Abstract
Rapid intensification (RI; ≥30 knots within 24 h) is an essential characteristic of a CAT 4–5 hurricane (≥115 knots). Between 1980 and 2018, interannual variability in CAT 4–5 hurricanes was strongly correlated with RI, both globally and in individual basins. The annual number of tropical cyclone RI events is a useful measure of global intense CAT 4–5 hurricanes. Here, singular value decomposition analysis was applied to sea surface temperature and the number of RI events throughout the world as calculated from best track data. The first two modes explained ~70% of the total covariance. The mode that dominated variability in global RI occurrence was primarily modulated by the El Niño southern oscillation and the Pacific decadal oscillation. The first mode indicated decreased RI activity in the North Atlantic and increased RI activity in the North Pacific except between 10°–30° N and 125°–135° E, and the corresponding sea surface temperature pattern matched the positive El Niño southern oscillation and Pacific decadal oscillation phases. For the second mode, RI occurrence increased in most parts of the active RI region though with some exceptions, and this was linked with global warming. These results suggest that interannul variability in TC RI events and the associated CAT 4–5 hurricanes are modulated by climate variability in the global scale.
Investigation on hot forging strategy for 5CrNiMoV via high-throughput experiment and machine learning
Authors: Wang Yongshan , Sun Zhiren , Hu Zhiqiang , Wang Kaikun 
Published: March 2021

Abstract
Hot deformation conditions have important influence on the final properties of 5CrNiMoV steel. Based on the developed high-throughput forging equipment, a combined method of high-throughput simulation and machine learning was put forward to efficiently explore the best deformation conditions for 5CrNiMoV steel. A dataset containing 960 sets of data was established, describing the average grain size, damage, and dynamic recrystallization volume fraction of samples, strain rates and temperatures. The RFR (Random Forest Regression) model was trained and used to predict the optimal hot deformation conditions of 5CrNiMoV steel. Based on the searching space and the screening strategies, the optimal hot deformation conditions of 5CrNiMoV at different strains was successfully achieved. The results show that the designed strategy could be used to improve the research efficiency for better production processes and provide a certain theoretical reference for further experimental verification.
Code Review as a Simple Trick to Enhance Reproducibility, Accelerate Learning, and Improve the Quality of Your Team’s Research
Authors: Vable Anusha M , Diehl Scott F , Glymour M Maria 
Published: March 2021

Abstract
Programming for data wrangling and statistical analysis is an essential technical tool of modern epidemiology, yet many epidemiologists receive limited formal training in strategies to optimize the quality of our code. In complex projects, coding mistakes are easy to make, even for skilled practitioners. Such mistakes can lead to invalid research claims that reduce the credibility of the field. Code review is a straightforward technique used by the software industry to reduce the likelihood of coding bugs. The systematic implementation of code review in epidemiologic research projects could not only improve science but also decrease stress, accelerate learning, contribute to team building, and codify best practices. In the present article, we argue for the importance of code review and provide some recommendations for successful implementation for 1) the research laboratory, 2) the code author (the initial programmer), and 3) the code reviewer. We outline a feasible strategy for implementation of code review, though other successful implementation processes are possible to accommodate the resources and workflows of different research groups, including other practices to improve code quality. Code review isn’t always glamorous, but it is critically important for science and reproducibility. Humans are fallible; that’s why we need code review.
Favorable Crisscrossing Pattern With Polydioxanone: Barbed Thread Lifting in Constructing Fibrous Architecture
Authors: Song Jennifer Kim , Chang Jiyeon , Cho Kae Won , Choi Chang Yong 
Published: March 2021

Abstract

Background
The longevity of polydioxanone (PDO)-barbed lifting threads remains controversial.

Objectives
The authors sought to assess the longevity extension effect of a crisscross implantation pattern in PDO-barbed thread lifting.

Methods
To acquire the desired outcome in PDO-barbed thread lifting, the authors suggested a paradigm shift to incorporate biochemical factors in enforcing the physico-mechanical lift. A nude mouse model was employed to evaluate their theory to compare the conventional fan-shaped protocols in barbed thread lifting with an architectural construction of intersections of fibrous capsule in a crisscross pattern. Three fragments of monofilament PDO-barbed–lifting threads were implanted in the dorsal skin of 12 nude mice. The pattern of implantation was fan-shaped in the control group and crisscross in the experimental group. Tissue specimens containing tangential areas of threads were harvested, fixed, and paraffin-embedded. Samples were horizontally cut and histologically analyzed employing hematoxylin and eosin, Massons’ Trichrome, and Sirius red staining. Fibrotic areas and the width of fibrosis from the thread were also analyzed.

Results
Fibrous capsulations around the barbed area of the PDO-barbed lifting threads were threefold greater than those around the barb-free areas of the threads. In the crisscross implantation pattern, width and density of the fibrotic areas were fivefold greater than those of the fan-shaped areas. Induction of fibrous capsules around the PDO-barbed thread was markedly condensed in the crisscross areas.

Conclusions
This study provides the basis for a more logical implantation pattern in PDO-barbed lifting threads for facial rejuvenation. By generating controlled multiple crisscross patterns, we can create more intense fibrogenesis, reduce tension applied on each barbed thread, and, therefore, extend the longevity of the result.

Level of Evidence: 4
VCFShark: how to squeeze a VCF file
Authors: Deorowicz Sebastian , Danek Agnieszka , Kokot Marek 
Published: March 2021

Abstract

Summary
Variant Call Format (VCF) files with results of sequencing projects take a lot of space. We propose the VCFShark, which is able to compress VCF files up to an order of magnitude better than the de facto standards (gzipped VCF and BCF). The advantage over competitors is the greatest when compressing VCF files containing large amounts of genotype data. The processing speeds up to 100 MB/s and main memory requirements lower than 30 GB allow to use our tool at typical workstations even for large datasets.

Availability and implementation
https://github.com/refresh-bio/vcfshark.

Supplementary information
Supplementary data are available at Bioinformatics online.
OMAmer: tree-driven and alignment-free protein assignment to subfamilies outperforms closest sequence approaches
Authors: Rossier Victor , Warwick Vesztrocy Alex , Robinson-Rechavi Marc , Dessimoz Christophe 
Published: March 2021

Abstract

Motivation
Assigning new sequences to known protein families and subfamilies is a prerequisite for many functional, comparative and evolutionary genomics analyses. Such assignment is commonly achieved by looking for the closest sequence in a reference database, using a method such as BLAST. However, ignoring the gene phylogeny can be misleading because a query sequence does not necessarily belong to the same subfamily as its closest sequence. For example, a hemoglobin which branched out prior to the hemoglobin alpha/beta duplication could be closest to a hemoglobin alpha or beta sequence, whereas it is neither. To overcome this problem, phylogeny-driven tools have emerged but rely on gene trees, whose inference is computationally expensive.

Results
Here, we first show that in multiple animal and plant datasets, 18–62% of assignments by closest sequence are misassigned, typically to an over-specific subfamily. Then, we introduce OMAmer, a novel alignment-free protein subfamily assignment method, which limits over-specific subfamily assignments and is suited to phylogenomic databases with thousands of genomes. OMAmer is based on an innovative method using evolutionarily informed k-mers for alignment-free mapping to ancestral protein subfamilies. Whilst able to reject non-homologous family-level assignments, we show that OMAmer provides better and quicker subfamily-level assignments than approaches relying on the closest sequence, whether inferred exactly by Smith-Waterman or by the fast heuristic DIAMOND.

Availabilityand implementation
OMAmer is available from the Python Package Index (as omamer), with the source code and a precomputed database available at https://github.com/DessimozLab/omamer.

Supplementary information
Supplementary data are available at Bioinformatics online.