bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Experimental Performance Analysis on Robotics Process Automation (RPA) With Open Source OCR Engines: Microsoft Ocr And Google Tesseract OCR
Authors: Malathi T , Selvamuthukumaran D , Diwaan Chandar C S , Niranjan V , Swashthika A K 
Published: February 2021

Abstract
Robotic Process Automation (RPA) plays an important role in the solution for automating human digital interaction in various software domains like IT, Business Strategy and so on. Optical Character Recognition(OCR) superimposes subtitled characters on an image.Here we use two Open source OCR engines, Google Tesseract OCR - It literally makes use of the open source Tesseract OCR Engine[1]. Microsoft OCR - This is another open source OCR engine accessible in the Robotics Process Automation tool, UiPath[1]. The technique of optical character recognition (OCR) has been used to transform printed text into editable text. In various applications, OCR is a very useful and common technique. OCR precision can be dependent on algorithms for text preprocessing and segmentation. Often because of different scale, design, orientation, complex picture context, etc., it is difficult to retrieve text from the image. We begin this paper with an introduction to the Optical Character Recognition (OCR) process, in this paper, we propose a research analysis to estimate the accuracy of two open source OCR engines with Robotics Process Automation(RPA). These RPA processes are created using the UiPath tool and our results compare the OCR engines performance during the execution with the workflow and save the results to our local storage or on cloud storage if cloud support enabled and here we used local storage for purpose, and the result analysis and error analysis was evaluated by manual process which also included excel formulas to calculate the accuracy of the extracted string with the original string.
Application of value stream mapping using simulation tool in manufacturing assemble line: A case study
Authors: Latha B.M , Raghavendra N V , Ramesh Jangam 
Published: February 2021

Abstract
Value stream mapping is one of the most convenient graphic tools in the manufacturing industry to eliminate wastes. This study has been carried out for the heat sink part assembly line of a manufacturing unit. The basic objective was to employ computational tools for carrying out value stream mapping and to make improvements. In the first stage, value stream mapping was done for the existing assemble line with details such as cycle time, setup time, number of employees, etc., to identify bottlenecks. Future value stream mapping was carried out using a software tool. Simulation is employed for identifying the real-time production bottlenecks; this in turn leads to improvement in productivity. This proposed method can be implemented for any manufacturing industry to eliminate non-value-added activities (NVA). The study demonstrates that the use of appropriate computational tools ensure standard methodology for a complex problem as well as produces quick results.
Optimization of process parameters in Electro Chemical Discharge Machining of silica glass through Analysis of Means
Authors: Bellubbi Sadashiv , Hipparagi Maheshwar A , Naik Ravindra , Sathisha N 
Published: February 2021

Abstract
In recent years, the Electro Chemical Discharge Machining (ECDM) process has advanced as a substitute for machining of conductive and non-conductive materials. Optimization of the process parameters plays a crucial role in improving the quality of the processed components. The machining of microchannel in silica glass has been successfully attempted in present work by considering Analysis of Means (ANOM) of ECDM process parameters. The experiments were planned using Taguchi’s L9 orthogonal array with applied voltage, stand-off distance (SOD), electrolyte concentration (EC) and pulse on time (TON) as process parameters. The Material Removal Rate (MRR), Machining Depth (MD) and Overcut (OC) were observed as output characteristics. The ANOM study revealed that the error between experimental and predicted values of MRR, MD and OC are 4.22%, 1.92% and 3.46% respectively. Stand-off distance was found to be the most significant factor followed by pulse on time, applied voltage and electrolyte concentration from ANOVA. Confirmation test was performed by setting the optimum combination of process parameters and the predicted values from regression analysis exhibited a good agreement with experimental results.
Effect of Fuel Intake Temperature on Performance and Emission of Diesel Engine
Authors: Vijay V S , Gonsalvis Joseph 
Published: February 2021

Abstract
Heating diesel fuel before injecting into the engine cylinder is the technique employed in this experiment with the primary objective of improving combustion and decreasing emission. This results in gasification of diesel when injected into the engine cylinder leading to better mixing, evaporation and combustion of diesel fuel. This also results in decrease of physical delay period. Experiments are done at standard injection timing with preheating done in the interval of 100°C, 175°C and 250°C. At each temperature, readings are taken at no load, 25%, 50% and 75% of full load. It is observed that preheating results in decreased emission of CO and HC at all the loads. At 50% and 75% load CO and HC decreased by about 35% and 50% respectively. NOx is also increased showing that combustion of diesel fuel is improved. Maximum brake thermal efficiency (BTE) and minimum brake specific fuel consumption (BSFC) is obtained at preheating temperature of 100°C at 75% load condition. With increase in load condition, performance is found to be better at higher preheating temperature. With increase in preheating temperature, performance and emission is better and efficient. Performance improvement is better at 50% load than at no load and 25% load.
Mechanical Characterization Of Mmt Nano Clay/Epoxy/ Basalt Fiber Composites
Authors: Mutalikdesai Susilendra , Vani R , Bhandari Arpan , Aishwarya Jain Y 
Published: February 2021

AbstractThe importance of composite fibers have been rapidly increasing since the inception of its discovery, in almost every engineering field. They possess beneficial properties such as low weight, high strength, highly resistive towards corrosion which makes them unique. The objective of present study is to fabricate composite using epoxy as polymer and basalt fiber as reinforcement and to investigate the influence of dispersing MMT Nano clay (Montmorillonite Nano-clay) in polymer. Conventional hand lay-up technique was used for the fabrication of composite laminate. The bi-directional basalt fiber was used as reinforcement and epoxy was taken as matrix material. Mechanical Characterization of MMT Nano clay/epoxy/basalt fiber composites was carried out for tests such as tensile, flexural and impact test, the results showed that the laminate had comprehensible properties, concluding usage of composite material is more beneficial and cost effective than conventional materials in many ways.
Transient Heat Transfer Analysis Of Dimpled Rod
Authors: Kaldgi Abdul Rajak , Buradi Abdul Rajak , Vishwanath K C , Sreenivasalu Reddy N. , Muneer Rayid , Avvad Mohammed , Chandrasekhar A , Afzal Asif , Ahamed Saleel C 
Published: February 2021

Abstract
The considerable heat transfer increase that the dimple causes has led to an interest in its technology recently, with pressure drop penalties lesser than other heat augmentation types. The increase in heat transfer using dimples is based on the fact that the scrubbing of cooling fluid occurs within the dimple and intensifies the delay of flow separation over the surface. The researchers have used different dimple-shaped geometries from all the research studied, such as triangular, ellipsoidal, circular, square, from which the ellipsoidal shape offers better results than other shapes because of prior vortex development. The present work mainly aims to study the dimpled specimen’s transient properties and determine their influence on heat transfer.
A Melanoma Skin Cancer Detection Using Machine Learning Technique: Support Vector Machine
Authors: Banasode Praveen , Patil Minal , Ammanagi Nikhil 
Published: February 2021

Abstract
In this paper proposed is an easy way to detect the disease and help us to know before something turns out to be serious. The aim of this work is to detect skin cancer. People can get to know what skin disease they are having and what all precaution and measures to be taken at an early stage and it will help in treating the disease successfully. The major causes of skin cancer are air pollution, UV radiation, unhealthy life style etc. The concept of machine learning will be used to determine the disease and help us to detect the result. The most commonly used classification algorithms is support vector machine (SVM). First we are analyzing the skin image then converting the images into BGR-Gray and BGR-HSV for the computer to understand and enable it to read binary codes. The result of this study will help doctors to treat disease at the initial stage and further aggravation can be avoided.
Analysis and Predicting Results of IPL T20 Matches
Authors: Banasode Praveen , Patil Minal , SupriyaVerma  
Published: February 2021

Abstract
The craze of Indian Premier League (IPL) is always there in millions of people including Indian and our work is about the analysis of the data and prediction of the IPL matches. IPL Data Analysis is all about the analysing the data that is al- ready present in data set using data science, machine learning and python. This is an application design for the purpose of analysing the data by fetching the attribute from the data set and predicting the future of the match and as well as of the players. This will help in the selection of the IPL team that the team should perform good and win the match. Prediction is done for anything like which player will play well in tomorrow’s match, which team will win toss and even match etc. The prediction can be done with the help of the analysis on that data set collected and by displaying proper data that is useful for the future prediction. The algorithms have given accuracy over 95%.
Cloud Data Security Model Using Modified Decoy Technique In Fog Computing For E-Healthcare
Authors: Maragathavalli Dr. P. , Atchaya S. , Kaliyaperumal N. , Saranya S. 
Published: February 2021

Abstract
In recent years, the cloud provides the facility of storing the possible files to a remote database which can be retrieved on demand. Healthcare cloud infrastructure is used to store the medical records which in turn help in managing and tracking the records of the user. The primary care providers use Electronic Medical Record (EMR) for diagnosing and evaluating medical data. Big data analytics is an important technology in enormous business areas which includes banking, social media, machine sensor data and medical welfare. Healthcare cloud provides various storage facilities such as Google drive, dropbox business and one drive in which security issues like data theft attacks to be considered as the serious security breaches of the cloud. In this paper, a modified decoy technique is proposed in order to ensure more security for the user’s Medical Big Data (MBD). The tri-party authentication key uses key agreement protocol which is modelled by bilinear pairing cryptography. The proposed system detects the attacker and sends the information such as IP address, access time and date to the user. It also provides the facility to block the account from further access. Using the triple-des algorithm in bilinear pairing the parameters like throughput is increased and computational complexity is decreased.
An ensemble classification based approach for breast cancer prediction
Authors: Mahesh Vijayalakshmi G V , Mohan Kumar M 
Published: February 2021

Abstract
Breast cancer is the second major reason for deaths in women. Early detection of the breast cancer and receiving the appropriate treatment can reduce the death rates as survival becomes hard in the higher stages of the tumor growth. Application of machine learning in healthcare play a key role in aiding the clinical experts to detect the disease at a early stage and perform precise assessment. This paper proposes a pattern recognition methodology that uses breast cancer biomarkers as the attributes and ensemble classification approach for accurately detecting the presence of cancer. The proposed method was evaluated for the samples of the breast cancer Coimbra dataset by fusing the decisions of naive Baye’s, radial basis function neural network and linear discriminant analysis classifiers based on majority voting rule. The experimental results demonstrated the enhanced performance of the system with fusion of classifiers as compared to the single classifiers.
The enhanced catalytic activity of Cu/SAPO-34 by ion exchange method for selective catalytic reduction of nitric oxide
Authors: Wang Dapeng , Li Zhaokun , Song Chengwen 
Published: February 2021

Abstract
The Cu/SAPO-34 catalysts were prepared via the ion exchange process for achieving improved catalytic activity. Effects of various parameters including copper ions sources, copper ions loadings and ion exchange temperature on catalyst performance of the Cu/SAPO-34 catalysts were investigated. The results showed that the Cu/SAPO-34 catalysts roughly maintained similar cubic-like morphology and crystalline structure with the SAPO-34 catalyst, and achieved enhanced catalytic activity. Among various copper ion sources, the Cu/SAPO-34 catalyst using Cu(CH3COO)2 as copper ion source demonstrated high nitric oxide conversion rate. Increasing the copper ion loadings, the nitric oxide conversion rate of the Cu/SAPO-34 catalysts achieved significant improvement. However, when the copper ion loadings exceeded 0.01 mol, the nitric oxide conversion rate began to decline. The catalytic activity of the Cu/SAPO-34 also closely depended on ion exchange temperature. The nitric oxide conversion rate of the Cu/SAPO-34 catalyst showed a trend of first increasing and then decreasing with the improvement of ion exchange temperature, and the recommended ion exchange temperature was 60 °C. Hydrothermal aging treatment further confirmed the good stability of the Cu/SAPO-34 catalyst. Moreover, kinetic investigation was carried out, which was in agreement with the SCR results.
Safety and efficacy of very short dual antiplatelet therapy followed by P2Y12 inhibitor monotherapy in older patients undergoing percutaneous coronary intervention: meta-analysis of randomised controlled trials
Authors: Roule Vincent , Lemaitre Adrien , Pommier Wilhelm , Bignon Mathieu , Sabatier Rémi , Blanchart Katrien , Beygui Farzin 
Published: February 2021

Abstract

Background
older patients undergoing percutaneous coronary intervention (PCI) represent a growing population sharing both a high ischemic and bleeding risk. Dual antiplatelet therapy (DAPT) reduces the incidence of thrombotic events but exposes patients to an increased risk of bleeding and subsequent mortality. Its optimal duration after PCI remains unclear.

Objective
to assess the impact of short-duration DAPT on both bleeding and ischemic events in the specific population of older patients undergoing PCI.

Methods
we performed a meta-analysis of randomised controlled trials comparing the safety and efficacy of standard versus very short duration (≤ 3 months, followed by P2Y12 inhibitor monotherapy) DAPT after PCI with a drug-eluting stent in older patients.

Results
four studies, representing 8,961 older patients, were finally included. Compared with standard duration, short-duration DAPT was associated with similar rates of major bleeding (relative risks, RR 0.70 [0.47; 1.05]) and the composite efficacy endpoint (RR 0.85 [0.63; 1.14]). There was a high level of heterogeneity between the studies (I2 = 68%) regarding major bleeding.

Conclusion
our meta-analysis suggests that short DAPT may be a valid option in older patients after PCI but it also highlights the need for specific studies in such patients on optimal duration of antiplatelet therapy.
Mold’s Dominion: Science, Empire, and Capitalism in a Globalizing World
Authors: Sasges Gerard 
Published: February 2021

Abstract
Today, Jōkichi Takamine and Albert Calmette are known for their contribution to the history of medicine. Yet their early careers centered not on medicine, but on biotechnology, and on parallel projects to bring a food processing technology used across Asia to new audiences around the world. In the end, they both succeeded, but in very different ways. Takamine would make his fortune with “Taka-Diastase,” a patent digestive aid once popular around the world; Calmette would help develop the “Amylo Process” of industrial brewing and distilling used as the basis of exploitative alcohol monopolies in colonial Indochina and Formosa. Understanding how a single technology could take on two radically different forms provides a unique opportunity to explore entanglements of science, empire, and capitalism in a globalizing world. Empires and capitalist enterprises combined to send mold along pathways that both reflected and transcended imperial boundaries. And as it traveled, mold was remade by—and in turn helped to remake—the political economies it encountered. Today, when the products of mold technologies are part of our daily lives, it’s tempting to see globalization in terms of seamless, uniform, and seemingly inevitable outcomes. This paper focuses attention instead on alternative paths, contingent and deeply inequitable processes, and the way science, empire, and capitalism produced the modern world.
Opioids and the Risk of Fracture: A Self-Controlled Case Series Study in the Clinical Practice Research Datalink
Authors: Peach Emily J , Pearce Fiona A , Gibson Jack , Cooper Andrew J , Chen Li-Chia , Knaggs Roger D 
Published: February 2021

Abstract
Self-controlled study designs can be used to assess the association between exposures and acute outcomes while controlling for important confounders. Using routinely collected health data, a self-controlled case series design was used to investigate the association between opioid use and bone fractures in 2008–2017 among adults registered in the United Kingdom Clinical Practice Research Datalink. The relative incidence of fracture was estimated, comparing periods when these adults were exposed and unexposed to opioids, adjusted for time-varying confounders. Of 539,369 people prescribed opioids, 67,622 sustained fractures and were included in this study. The risk of fracture was significantly increased when the patient was exposed to opioids, with an adjusted incidence rate ratio of 3.93 (95% confidence interval (CI): 3.82, 4.04). Fracture risk was greatest in the first week of starting opioid use (adjusted incidence rate ratio: 7.81, 95% CI: 7.40, 8.25) and declined with increasing duration of use. Restarting opioid use after a gap in exposure significantly increased fracture risk (adjusted incidence rate ratio: 5.05, 95% CI: 4.83, 5.29) when compared with nonuse. These findings highlight the importance of raising awareness of fractures among patients at opioid initiation and demonstrate the utility of self-controlled methods for pharmacoepidemiologic research.
The Promise and Pitfalls of Environmental DNA and RNA Approaches for the Monitoring of Human and Animal Pathogens from Aquatic Sources
Authors: Farrell Jessica A , Whitmore Liam , Duffy David J 
Published: February 2021

Abstract
Novel forensics-inspired molecular approaches have revolutionized species detection in the wild and are particularly useful for tracing endangered or invasive species. These new environmental DNA or RNA (eDNA or eRNA)–based techniques are now being applied to human and animal pathogen surveillance, particularly in aquatic environments. They allow better disease monitoring (presence or absence and geographical spread) and understanding of pathogen occurrence and transmission, benefitting species conservation and, more recently, our understanding of the COVID-19 global human pandemic. In the present article, we summarize the benefits of eDNA-based monitoring, highlighted by two case studies: The first is a fibropapillomatosis tumor-associated herpesvirus (chelonid herpesvirus 5) driving a sea turtle panzootic, and the second relates to eRNA-based detection of the SARS-CoV-2 coronavirus driving the COVID-19 human pandemic. The limitations of eDNA- or eRNA-based approaches are also summarized, and future directions and recommendations of the field are discussed. Continuous eDNA- or eRNA-based monitoring programs can potentially improve human and animal health by predicting disease outbreaks in advance, facilitating proactive rather than reactive responses.
A Connectivity-Based Psychometric Prediction Framework for Brain–Behavior Relationship Studies
Authors: Wu Jianxiao , Eickhoff Simon B , Hoffstaedter Felix , Patil Kaustubh R , Schwender Holger , Yeo B T Thomas , Genon Sarah 
Published: February 2021

AbstractThe recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions’ connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain–behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions’ connectivity profiles. We first illustrate two main applications: 1) single brain region’s predictive power for a range of psychometric variables and 2) single psychometric variable’s predictive power variation across brain region. We compare the patterns of brain–behavior provided by these approaches to the brain–behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain–behavior relationships, as well as the unique insight into brain–behavior relationships offered by this approach.
Road traffic noise and cardiovascular disease risk factors in UK Biobank
Authors: Kupcikova Zuzana , Fecht Daniela , Ramakrishnan Rema , Clark Charlotte , Cai Yutong Samuel 
Published: February 2021

Abstract

Aims
The aim of this study was to investigate the cross-sectional associations of modelled residential road traffic noise with cardiovascular disease risk factors [systolic (SBP) and diastolic blood pressure (DBP), C-reactive protein, triglycerides, glycated haemoglobin, and self-reported hypertension] in UK Biobank.

Methods and results
The UK Biobank recruited 502 651 individuals aged 40–69 years across the UK during 2006–10. Road traffic noise (Lden and Lnight) exposure for 2009 was estimated at baseline address using a simplified version of the Common Noise Assessment Methods model. We used multivariable linear and logistic regression models, adjusting for age, sex, body mass index (BMI), smoking, alcohol intake, area- and individual-level deprivation, season of blood draw, length of time at residence, and nitrogen dioxide (main model), in an analytical sample size of over 370 000 participants. Exposure to road-traffic Lden &gt;65 dB[A], as compared to ≤55 dB[A], was associated with 0.77% [95% confidence interval (CI) 0.60%, 0.95%], 0.49% (95% CI 0.32%, 0.65%), 0.79% (95% CI 0.11%, 1.47%), and 0.12% (95% CI −0.04%, 0.28%) higher SBP, DBP, triglycerides, and glycated haemoglobin, respectively. Removing BMI from the main model yielded significant positive associations with all five markers with elevated percent changes. The associations with SBP or DBP did not appear to be impacted by hypertension medication while a positive association with prevalent self-reported hypertension was seen in the non-medicated group who exposed to a Lden level of 60–65 dB[A] (odds ratio 1.07, 95% CI 1.00, 1.15).

Conclusion
Exposure to road traffic noise &gt;65 dB[A], independent of nitrogen dioxide, was associated with small but adverse changes in blood pressure and cardiovascular biochemistry.