bluuebunny/crossref_metadata_2025_split huggingface dataset:
Uncertain association between maximal fat oxidation during exercise and cardiometabolic risk factors in healthy sedentary adults
Authors: Amaro‐Gahete Francisco J. , Sanchez‐Delgado Guillermo , Jurado‐Fasoli Lucas , Ruiz Jonatan R. 
Published: March 2021

ABSTRACTThe present work examines the relationships between maximal fat oxidation during a graded exercise test (MFO), the intensity of exercise that elicits MFO (Fatmax), and traditional cardiometabolic risk factors in healthy, sedentary adults. A total of 119 (81 women) young, sedentary adults (22.1 ± 2.2 years old), and 71 (37 women) middle‐aged, sedentary adults (53.4 ± 4.9 years old) participated in the current study. Systolic and diastolic blood pressures were determined following standard procedures. Plasma glucose, insulin, total cholesterol, high‐density lipoprotein cholesterol and triglycerides were determined in a fasted state and the homeostatic model assessment of insulin resistance index and low‐density lipoprotein cholesterol levels subsequently calculated. A sex and age group‐specific cardiometabolic risk Z‐score was also calculated for each subject based on waist circumference, systolic and diastolic blood pressure, plasma glucose, high‐density lipoprotein cholesterol and triglycerides. MFO and Fatmax were determined using a walking graded exercise test using indirect calorimetry. No clear association was seen of MFO and Fatmax with any cardiometabolic risk factor (all P≥0.05), except for a weak, inverse association between Fatmax and the fatty liver index (P=0.027). Similarly, neither MFO nor Fatmax was apparently associated with the cardiometabolic risk Z‐score (all P≥0.05). The current findings suggest an uncertain association of MFO and Fatmax during a graded exercise test with the cardiometabolic profile of healthy, sedentary adults.
Highlights
The study of the physiological mechanisms that trigger the onset of metabolic disorders has received considerable attention in recent years, with changes in MFO and Fatmax being highlighted as a potential key factor.
This work shows that MFO and Fatmax during a graded exercise test are not associated with the cardiometabolic profile in sedentary, healthy adults.
Further studies are needed to elucidate which other physiological disorders are related to cardiometabolic risk.
Toroidal dipole response in the individual silicon hollow cylinder under radially polarized beam excitation
Authors: Xu Jiawei , Fan Haihua , Dai Qiaofeng , Liu Haiying , Lan Sheng 
Published: March 2021

Abstract
The toroidal dipole (TD) has attracted growing attention due to its unique properties. Here, we propose and demonstrate almost pure TD resonance in the visible region in a silicon hollow cylinder. The enhanced optical coupling to TD resonance is implemented using a focused radially polarized beam illumination matching, well-designed individual silicon nanostructure resonator. The polarization of the longitudinal electric field in the silicon hollow cylinder that breaks space-inversion symmetry is critical to the formation of enhanced TD resonance. Additionally, the pure TD resonance can be achieved in a wide spectral range by tuning the geometrical parameters of the structure. The proposed pure TD resonator may provide potential applications in the local enhancement of electromagnetic fields and the design of all-dielectric nanoantennas.
Irreversible bonding techniques for the fabrication of a leakage-free printed circuit board-based lab-on-chip in microfluidic platforms—a review
Authors: Ali@Hasim Norshah Rizal , Ahaitouf Ali , Abdullah Mohd Zaid 
Published: March 2021

Abstract
Lab-on-chip (LOC) is recognised as one of the most affordable solutions for integrating electronics and fluidics devices. In this field, bonding plays a vital role because it provides the means for attaching multiple components onto a substrate, transforming them into a microfluidic circuit. Bonding is an integral step, especially when designing a device that is free from leakage and eventual clogging. A comprehensive review of the latest irreversible bonding technologies is discussed in this paper, in which the focus is on the layered microfluidic systems with large sensor arrays. This review covers microfluidic devices fabricated from a rigid-type glass–fibre-printed circuit board and a thermoplastic flexible printed circuit with 186 references whose development date back three decades ago. The bonding techniques are organised into the following four groups: (a) adhesive bonding, (b) thermal and solvent bonding, (c) surface modification and dry bonding and (d) photoresist groups. Other techniques are available beyond these groupings, but they can be classified into the nearest group to facilitate the discussion. This paper will benefit researchers and practitioners aiming to develop polymer-based LOC devices.
Defect, temperature, and strain effects on lattice heat conductivity of egg-tray graphene
Authors: Sun Zhihui , Zhang Jie , Wang Hanxiao , Pan Guangshan , Wang Tianhua , Xie Lu , Peng Qing 
Published: March 2021

Abstract
Micro and nano devices generally have the characteristics of high performance and compact size, so their own heat transfer and heat dissipation problems are becoming more and more serious. Therefore, it is necessary to clarify the heat transport mechanism in the micro–nano structure by analyzing the heat transport properties of nanomaterials, and then control the thermal conductivity of nanodevices. We have investigated the lattice heat transfer of egg-tray graphene using non-equilibrium molecular dynamics simulations. Three structures (I, II and III) are studied according to the number of hexagons as 10, 16, and 56 respectively. The increases of lattice thermal conductivity with an increase of length in sub-microns implies the large mean free path of phonons in egg-tray graphene, similar as that of graphene. The large-size-limit thermal conductivity is 43, 45, and 60 W m−1 K−1 for I, II, and III respectively, much smaller than that of graphene (393 W m−1 K−1) in our model. The thermal conductivity decreases with an increase of strain, as well as temperature. The heat transfer performance of structure-II is sensitive to both phonon modes and phonon quantities in compression, while in tension it is determined only by the phonon modes. Our results may be useful in thermal conductivity engineering and heat transfer management in egg-tray graphene.
GaN-based tunnel junctions and optoelectronic devices grown by metal-organic vapor-phase epitaxy
Authors: Takeuchi Tetsuya , Kamiyama Satoshi , Iwaya Motoaki , Akasaki Isamu 
Published: March 2021

Abstract
This paper mainly describes the status and prospects of GaN-based tunnel junctions grown by metal-organic vapor-phase epitaxy. GaN-based tunnel junctions are expected to offer an alternative structure for hole injection in various optoelectronic devices, simultaneously providing additional features, such as electrical contacts between cascaded devices, current confinement, simple device fabrication processes, and novel controllability in band engineering. After reviewing the role of tunnel junctions and the history of the development of GaN-based tunnel junctions, the development details of GaInN, GaN, and AlGaN tunnel junctions are separately summarized, including those grown by molecular beam epitaxy. Various optoelectronic devices utilizing GaN-based tunnel junctions are reviewed from the viewpoint of device characteristics.
Organic passivation of Al0.5Ga0.5N epilayers using self-assembled monolayer of Zn(II) porphyrin for improved solar-blind photodetector performance
Authors: Kaushik Shuchi , Naik Tejas R , Ravikanth M , Liao Che-Hao , Li Xiaohang , Rao V Ramgopal , Singh R 
Published: March 2021

Abstract
We report on the passivation of surface states of Al0.5Ga0.5N epilayers by employing self-assembled monolayers (SAMs) of organic molecules, which led to a significant improvement in the performance of Al0.5Ga0.5N based solar-blind photodetector. The formation of SAM of meso-(5-hydroxyphenyl)-10,15,20-tri(p-tolyl) porphyrin (ZnTPP(OH)) on the surface of Al0.5Ga0.5N was probed by contact angle measurement, x-ray photoelectron spectroscopy, and atomic force microscopy. The successful passivation of surface states was confirmed by Kelvin probe force microscopy as a significant decrease in the surface potential of Al0.5Ga0.5N by ∼280 mV was observed. The inference was supported by a four-fold increase in the photoluminescence intensity of the near-band edge emission peak upon passivation. As a result, the dark current of the as-fabricated solar-blind photodetector reduced by two orders of magnitude, without compromising with the magnitude of the photo current at 270 nm. The role of SAM was evident in improving the performance of the photodetector as the peak value of photo-to-dark current ratio enhanced by ∼36 times. The peak responsivity of the photodetector increased from 1.6 to 2.2 mA W−1 at 10 V. The significant reduction in the dark current and enhancement in the responsivity led to an improvement in the specific detectivity by ∼10 times. Additionally, the response speed of the photodetector was found to improve significantly from 4 to 0.5 s.
Neural partial differential equations for chaotic systems
Authors: Gelbrecht Maximilian , Boers Niklas , Kurths Jürgen 
Published: March 2021

Abstract
When predicting complex systems one typically relies on differential equation which can often be incomplete, missing unknown influences or higher order effects. By augmenting the equations with artificial neural networks we can compensate these deficiencies. We show that this can be used to predict paradigmatic, high-dimensional chaotic partial differential equations even when only short and incomplete datasets are available. The forecast horizon for these high dimensional systems is about an order of magnitude larger than the length of the training data.
Spin precession as a new window into disformal scalar fields
Authors: Brax Philippe , Davis Anne-Christine , Melville Scott , Wong Leong Khim 
Published: March 2021

Abstract
We launch a first investigation into how a light scalar field coupled both conformally and disformally to matter influences the evolution of spinning point-like bodies. Working directly at the level of the equations of motion, we derive novel spin-orbit and spin-spin effects accurate to leading order in a nonrelativistic and weak-field expansion. Crucially, unlike the spin-independent effects induced by the disformal coupling, which have been shown to vanish in circular binaries due to rotational symmetry, the spin-dependent effects we study here persist even in the limit of zero eccentricity, and so provide a new and qualitatively distinct way of probing these kinds of interactions. To illustrate their potential, we confront our predictions with
spin-precession measurements from the Gravity Probe B experiment and
find that the resulting constraint improves upon existing bounds from
perihelion precession by over 5 orders of magnitude. Our results therefore establish spin effects as a promising window into the disformally coupled dark sector.
COVID-19 on spot detection as a service (COSDaaS) – A cloud-based pandemic service approach by means of a smart screening device for mass screening to minimize the spread of infection efficiently.
Authors: Biswas Himadri , Sarkar Vaskar , Sarddar Debabrata , Bandyopadhyay Anjan 
Published: March 2021

Abstract
The planet is now suffering from the fever of Corona Virus Disease 2019 (COVID-19) since December 2019, a human health fatality, education and the global economy, and we don’t know how to cope with this deadly environment and what will happen in the immediate future. First thing is stop transmitting this pandemic disease as a cross-infection so long as there is no discovered antidote to cope with this worrying situation. As a result, a certain number of citizens in all countries must be considered for a mandatory test that may not be available by all individuals or may not be supported by the government due to the availability and expense of the test kit or any other testing tool or procedures that require time to install and use it for community use. Here we have proposed a smart screening device focused on a new cloud-oriented platform “COVID-19 on spot detection as a service (COSDaaS)” – for early screening of infected populations, deploying at low cost in various heavy public transition areas with less effort to protect non-infected people from cross-infection.
Increasing robustness of deep neural network models against adversarial attacks
Authors: Bhagwat Pankaj D , Shingare Pratibha P 
Published: March 2021

Abstract
In Autonomous driving detecting correct object is important, further studies proved that by adding small pattern above object can also lead intentional fooling of network. Small intentional changes in the input can significantly distort output of a deep neural network model. This makes the machine learned model vulnerable to these small changes in images. Hence, these models have wide scope of failure. If we are able to tackle these intentional attacks it will help to make system more robust.
In this project, we have combined multiple techniques used for defending against adversarial attacks. First technique is Adversarial training which include modifying training dataset, second technique is pre-processing input data before applying it to deep learned model and Third technique randomly selects image pre-processing technique. Third method is aimed to distract attacker who know the method used in pre-processing by randomly selecting from multiple methods in image pre-processing.
We will measure robustness of deep learned model in terms of Accuracy on Designed system and previous deep learned model. Test samples and adversarial images generated from dataset will be used for testing on deep learned model.
Among all methods which we have combined, Adversarial training proved best method to defend against white box attack. If we would have used strong defences in Random selection of image transformation then the system could have performed much better. However, Random selection have done its work of confusing the attacker by selecting random transformations.
Containing COVID-19 Pandemic using Community Detection
Authors: Prabhu Shreekanth M. , Subramanyam Natarajan , Girdhar Rhythm 
Published: March 2021

Abstract
Diseases such as COVID-19 that quickly spread through social contact where Infections remain undetected for long pose new kinds of challenges. Traditional epidemic models when applied to COVID-19 not only give highly alarmist predictions but also fail to indicate any way to target interventions. The models also do not factor social structure/context/dynamics.. To address this challenge we propose a novel Social Infection Analysis Model explain the intuition behind it and a methodology that factors social structure while performing the analysis. SIAM is particularly beneficial as standard epidemic models are dependent on wide-spread testing. Our methodology includes (i) modelling society/localities using social network paradigm and analysis at macro level (ii) detecting communities and (iii) analysis of infection spread within a community over time. To arrive at an outlook for infection spread within communities we make use of the NK Model for solving complex, long term problems coupled with agent based modeling for member behavior. We make use of NetLogo to run simulations. We intend to validate our research in Indian context.
Analysis of Self-Similarity, Memory and Variation in Growth Rate of COVID-19 Cases in Some Major Impacted Countries
Authors: Samadder Swetadri , Ghosh Koushik 
Published: March 2021

Abstract
In the present work investigation of self-similarity as well as scaling analysis have been performed over daily number of new confirmed cases of COVID-19 in some major impacted countries viz. USA, Brazil, India, Russia, Spain, UK, Italy, Germany and France from their respective dates of first report of COVID-19 till June 30, 2020. To reduce uncertainty and irregular fluctuations in these present time series seven-point moving averages are taken and the entire analysis has been further performed over these seven-point moving average data. Scale invariance and self-similarity or self affinity manifests the fractal nature. For these time series, investigations of fractal nature have been performed by means of Higuchi method and corresponding fractal dimensions have been obtained. Also scaling analysis has been applied to understand the nature of the memory in these by means of Hurst exponent. Next, on the basis of these seven-point moving average data cumulative profiles of confirmed cases of COVID-19 for these countries have been generated. An effort has been made to understand the initial exponential growths in them. Study does not yield constant growth rates of infection; rather it shows time dependent profiles with variable functional representations at different windows of time for all these countries. Finally an effort has been made to predict the scenario for certain upcoming days for all the nine countries considered within a pre-assigned tolerance level continuing with the last obtained exponential growth rates. Results are not persistent in most of the cases. This might point towards a difficult scenario of prediction of future impacts in these countries.
Performance measurement model for ranking of educational institutes: a fuzzy reasoning approach
Authors: Dam Mrinmoy , Majumder Debasish , Bhattacharjee Rupak , Santra Shyam Sundar 
Published: March 2021

Abstract
Ranking of educational institutes starts gaining a significant attention among all the stakeholders to know the comparative status of the institutes. This paper presents a comprehensive ranking framework based on fuzzy logic using multiple criteria proposed in National institutional ranking framework (NIRF). The proposed model presents a hierarchical structure of metrics proposed by NIRF to measure the performance attributes and then the overall performance of the institute. Mamdani fuzzy reasoning approach has been adopted to address the inexact causal relationship between the leading (input) and the lagging (output) metrics. Finally, the behaviour of the responses produced by the proposed model has been analyzed and also a comparative study has been conducted between National institutional ranking framework (NIRF) and rankings with proposed rankings system for various institutes.
Knowledge Mining in Spreading Pattern of COVID-19 and Its Impact on India Economy
Authors: Shaw Rohit , Mishra Madhusmita , Jena Amrut Ranjan 
Published: March 2021

Abstract
Multidimensional data sources require identification of proper pattern to get knowledge. Knowledge mining helps to design an information system in the process of decision making. It helps to extract, structure, and organize knowledge from a database. In this paper, an attempt is made in the direction of knowledge mining to analyse and visualize spreading pattern of COVID-19 and its impact on India economy. The economy trend before and after COVID-19 is analyzed and forecasting is done for near future of Indian economy.
SMS Spam Detection Using Machine Learning
Authors: Gupta Suparna Das , Saha Soumyabrata , Das Suman Kumar 
Published: March 2021

Abstract
In the modern world where digitization is everywhere, SMS has become one of the most vital forms of communications, unlike other chatting-based messaging systems like Facebook, WhatsApp etc, SMS does not require active inter n et conne ct ion a t all. As we all know that Hackers / Spammer tries to intrude in Mobile Computing Device, and SMS support for mobile devices had become vulnerable, as attacker tries to intrude to the system by sending unwanted link, with which on clicking those link the attacker can gain remote access over the mobile computing device. So, to identify those messages Authors have developed a system which will identify such malicious messages and will identify whether or not the message is SPAM or HAM (malicious or not malicious). Authors have created a dictionary using the TF-IDF Vectorizer algorithm, which will include all the features of words a SPAM SMS possess, based on content of message and referring to this dictionary the system will be classifying the SMS as spam or ham.
IoT Based Scanner For Corona Detection
Authors: Biswas Pratnadeep , Ganguly Kankana , Chatterjee Sumanta 
Published: March 2021

Abstract
Human civilisation is observing a very strange and serious time fighting an invisible disastrous enemy, the NOVEL COVID -19 corona virus. Initially observed in the Wuhan province of China, its symptoms range from mild illness to pneumonia. Here we design an IoT based device which can detect the subsistence of this virus presumably based on certain framework of body components. In this context we summarized the latest research process of epidemiology, pathogenesis, clinical sensing and discussed some scientific advancement to combat the epidemic novel COVID -19. This paper proposes a design of an IoT based device which can detect the subsistence of this virus presumably based on certain framework of body components. Presence of corona virus in human body may be detected by measuring an individual’s body temperature with the help of temperature sensors. RQ or respiratory quotient will be used as another determining principle to detect corona virus. Analysis of blood samples also tells us the difference between an infected and a normal sample. From analysing the blood sample we can easily determine the total leukocyte count whereas an infected person can have an abnormal leukocyte count. We can access the data over the wifi and over the internet. Some parts of the device like the breathalyzer will be sanitized automatically and injection needles will be loaded automatically after one use and is convenient for proper detection as it analyses the three components of a. So commercially it would be economically reliable product.
Optimization of low frequency noise for dielectric modulated TFET for bio sensing applications
Authors: Bhattacherjee S. , Roy S. , Chakroborty G. , Ray U. 
Published: March 2021

Abstract
In this paper first time we are working on the low frequency noise analysis of dielectric modulated TFET biosensors. Our work is based on simulation technique using SILVACO ATLAS. We used different dielectrics in order to detect biomolecules. Moreover we also observed the drain current power spectral density relevant to low frequency noise of the biosensor. Our results show that noise has a significant role and it depends on the insulating layers of the dielectric modulated biosensor. Thus the application of suitable dielectric material can effectively improve the optimum performance of the dielectric modulated biosensor.
COVID-19 Monitoring Application for better human life
Authors: Majumder Annwesha Banerjee , Pal Aditya , Ojha Abhinav , Majumder Sourav , Sanki Debasish 
Published: March 2021

Abstract
COVID-19 –a panic –a threat to human has changed the total world’s normal flow of life. In this disease patient having mild cough and cold but if not identified at early stage and properly treated the patient would be died. So early state diagnosis is very crucial in this virus infection. In this paper a monitoring system model has been proposed with automated instant messaging, alarm generating and call facility. As we know COVID-19 patient has certain kind of symptoms like-high temperature, cold cough, loss of sense of smell, low oxygen saturation level along with that presence of diabetes, renal infection, pneumonia, hypertension, cardiovascular disease and obesity will influence the risk more. The application will measure oxygen saturation level, blood pressure and taking input for symptoms related questions and identifying the risk will either provide suggestions to patients and if oxygen saturation level is less then will send automated message to local COVID control units, police station and hospitals for arrangement of beds so the process for migrating the patient would be easy.