bluuebunny/crossref_metadata_2025 huggingface dataset:
Photoelectron spectra of early 3d-transition metal dioxide molecular anions from GW calculations
Authors: Rezaei Meisam , Öğüt Serdar
Published: March 2021

Photoelectron spectra of early 3d-transition metal dioxide anions, ScO2−, TiO2−, VO2−, CrO2−, and MnO2−, are calculated using semilocal and hybrid density functional theory (DFT) and many-body perturbation theory within the GW approximation using one-shot perturbative and eigenvalue self-consistent formalisms. Different levels of theory are compared with each other and with available photoelectron spectra. We show that one-shot GW with a PBE0 starting point (G0W0@PBE0) consistently provides very good agreement for all experimentally measured binding energies (within 0.1 eV–0.2 eV or less). We attribute this to the success of PBE0 in mitigating self-interaction error and providing good quasiparticle wave functions, which renders a first-order perturbative GW correction effective. One-shot GW calculations with a Perdew–Burke–Ernzerhof (PBE) starting point do poorly in predicting electron removal energies by underbinding orbitals with typical errors near 1.5 eV. A higher exact exchange amount of 50% in the DFT starting point of one-shot GW does not provide very good agreement with experiment by overbinding orbitals with typical errors near 0.5 eV. While not as accurate as G0W0@PBE0, the G-only eigenvalue self-consistent GW scheme with W fixed to the PBE level provides a reasonably predictive level of theory (typical errors near 0.3 eV) to describe photoelectron spectra of these 3d-transition metal dioxide anions. Adding eigenvalue self-consistency also in W, on the other hand, worsens the agreement with experiment overall. Our findings on the performance of various GW methods are discussed in the context of our previous studies on other transition metal oxide molecular systems.
Prepped for PrEP? Acceptability, continuation and adherence among men who have sex with men and transgender women enrolled as part of Vietnam's first pre-exposure prophylaxis program
Authors: Green Kimberly Elizabeth , Nguyen Long Hoang , Phan Huong Thu Thi , Vu Bao Ngoc , Tran Minh Hung , Ngo Huu Van , Tieu Van Thi Thu , Van Hung , Le Thanh Minh , Do Khang Quang , Nguyen Phong Anh , Ngo Trang Minh , Doan Anh Hong , Bui Diep Thi Ngoc , Nguyen Trang Nguyen Nhu , Hang Lan Thi Xuan , Tran Tham Thi , Luong Binh Quoc
Published: March 2021

Background
HIV prevalence among men who have sex with men (MSM) and transgender women (TGW) in Vietnam is high, whereas coverage of effective HIV prevention services has been inadequate. Studies have measured MSM and TGW demand for pre-exposure prophylaxis (PrEP) services, which led to the design of the first ever PrEP program in Vietnam, Prepped for PrEP (P4P). Methods: In March 2017, PrEP services were offered in Ho Chi Minh City as part of the P4P demonstration project, enabling same-day enrolment in three key population (KP)-led clinics and four public clinics. P4P aimed to assess acceptability and feasibility of PrEP services through calculating the rate of PrEP enrolment over time, and quarterly measures of continuation and adherence over an 18-month period. Results: A total of 1069 MSM and 62 TGW were enrolled in P4P. Average monthly PrEP enrolment among MSM increased five-fold from the first 3 months (March–June 2017) to the last 3 months of active enrolment (March–June 2018), whereas for TGW, no increased trend in PrEP enrolment per quarter was seen. Self-reported PrEP adherence was >90% at all time points among MSM, but varied from 11.1% to 88.9% among TGW. PrEP continuation was calculated at months 3, 6, 9, 12, 15 and 18. For MSM, it was 88.7% at month 3, 68.8% at month 12 and 46.6% at month 18, whereas for TGW, it was 87.1%, 54.8% and 52.8%, respectively. Multivariable regression identified that MSM with lower-than-average income (adjusted odds ratio (aOR) 2.38 (95% confidence interval (CI): 1.59–3.54), P = 0.000), aged >30 years (aOR 2.03 (95% CI: 1.30–3.40), P = 0.007) and with an increasing number of sex partners (aOR: 1.06 (1.01–1.11), P = 0.011) had greater odds of remaining on PrEP. For TGW, being aged >30 years was associated with continuing on PrEP (aOR 5.62 (95% CI: 1.05–29.9), P = 0.043). Conclusions: We found PrEP to be highly acceptable among MSM and moderately acceptable among TGW. Continuation rates were relatively high for the first roll-out of PrEP; however, those aged ≤30 years were much more likely to discontinue services. Scaling-up PrEP through differentiated and community-led and engaged PrEP service delivery will be key to effectively increase access and uptake over the next 5 years.
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

The 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Specialized covid-19 detection techniques with machine learning
Authors: Chatterjee Parag , Biswas Mainak , Das Arnab Kumar
Published: March 2021

COVID-19 has been declared as a pandemic in over 200 countries of the world.COVID-19 is an infectious disease that is primarily caused by severe acute respiratory syndrome coronavirus 2((SARS-CoV-2). According to the latest figures by the world health organization, the number of confirmed cases for the COVID-19 pandemic worldwide is more than 20 million worldwide and the number of fatalities reported is over 700,000. It has been found from several studies that medical imaging coupled with machine learning methods holds great promise in the detection and follow-up of the COVID-19 disease due to the enhanced accuracy in results of the experiments performed by the researchers. Machine Learning (ML)-based solutions can be used to simultaneously analyze multiple input computed tomography (CT) images of chest and lungs. A large number of papers have been published that show the application of machine learning methods in successful detection of the COVID-19 disease. Such applications demonstrate the suitability of feature prediction, identification of involved risks and therefore managing and intercepting the outbreak of such diseases. This paper describes some of the techniques in machine learning that can be used detection of COVID-19 disease.
EFFECTIVE HEALTH SCREENING TO PREVENT INFECTION AND CONTROL THE SPREADING OF COVID-19
Authors: Bag S , Sikdar S , Ganguly K , Banerjee S , Lahiri P
Published: March 2021

Health screening or examinations are of paramount importance from medical perspective as prevention is better than cure. In current pandemic situation, effective health screening is essential to detect COVID-19 in its early stage even when there have been no symptoms or signs of such disease. To prevent the transmission of such contagious infection, source control measures should be implemented for everyone in the facility, regardless of symptoms. Recent literatures stated that infected people having no symptoms also likely to perform a crucial role in the spread of corona virus in the society. To restrict the transmitting of such disease in community, it is imperative to identify the CORONA positive patient at each and every entry point of the market places, railway stations, airport premises, hospital buildings etc. and isolate them in an efficient manner. Rapid qualitative detection of such disease is made through body temperature measurement with IR thermal gun and RT-PCR test. Detection of COVID- 19 positive patient early means getting immediate treatment, restrict & control the transmission of such disease within the facility and limit the exposures for other patients and healthcare personnel.
Biomechanical Comparison of A Novel Dynamic Cervical Implant With Conventional Implant: A Finite Element Study
Authors: Samanta S , Paul T , Mondal D , Biswas Dr. J K
Published: March 2021

Ball-socket pair is generally used for designing implants for total disk replacement (TDR) surgery on cervical spine. Although, there is exists an issue of wear which reduces implant’s life as well as success rate. To overcome this issue, without compromising stability, a new design of dynamic cervical implant (DCI) is introduced. In this present study, biomechanical performances of a conventional Prodisc-C and the proposed DCI were compared using finite element (FE) analysis. CT scan data was used to develop three-dimensional model of cervical spine (C4–C7), and the implants were inserted at C5-C6 segment. The FE model with Prodisc-C showed an increase in range of motion (ROM) at the implanted segment and decrease in adjacent segment, while the proposed DCI design showed convenient results in term of ROM compared to intact spine model, and lower risk of stress shielding at the adjacent bone.
Atomic chain of carbon atoms: Smallest negative differential resistance device
Authors: Bhowmick Rinki , Chattopadhyaya Mausumi , Chakraborty Jit , Maity Swarnendu , Basu Arnab , Koley Sayantanu , Roy Anusweta , Sen Sabyasachi
Published: March 2021

In the present work, we report giant negative differential resistance action in probably the simplest molecular nanoelectronic device comprising of carbon chain placed between two ferromagnetic electrodes named as graphitic carbon nitride (g-C4N3). The negative differential resistance action is spin polarized and only evident at spin down channel. Spin polarized quantum transport studies using Keldysh non-equilibrium Green's function based density functional theory reports very high negative differential resistance over the bias range of ±0.1 V to ±0.3 V. This symmetric negative differential resistancec feature has been explained by an analysis of transmission spectrum across the Fermi energy level and Molecular Projected Self-Consistent Hamiltonian states (MPSH) of the system. Role of in-phase and out of phase electron waves in ensuring negative differential resistance feature has been justified through transmission pathways of the system. The simplicity of the molecular system added with robust spin polarized negative differential action added with experimental relevance certainly establishes the uniqueness of the device in respect of modern spintronic research.
Design and Development of IoT based Smart Security System in Covid19 Situation
Authors: Das Sudip , Dhar Abhishek , Pal Biswamoy , Biswas Papun , Gayen Pritam Kumar , Majumder Subhojyoti
Published: March 2021

In COVID19 pandemic situation social distancing and proper thermal screening is very much essential for restrict the spreading of virus. In security purpose human beings directly contacts with several known and unknown persons that is tremendously thoughtful matter. That problem will be overcome by a prototype device which can detect the temperature of the body and the collected data will be uploaded to the server as well as to the predefined consumer mobile number thorough IoT system. This system very much fruitful for office, factory, school even multi stored building security system. Instantly all related data regarding body temperature, image capturing, coming from area, area details scenario during any pandemic situation will be detect to the consumer mobile or computer. Using this it can keep a record to the number of visitors along with their temperature of the body. And in this current situation using this we can avoid human touch and the chances of getting affected will be less and a huge number of people can save their life. Solar powered IoT based smart security system will be automatically notified to the consumer when visitor come to meet in their premises. It will also give an alert if the visitor coming from containment zone or highly affected area.
Design and Development of Fire Fighting System Using Hot Redundant PLC in COVID Situation
Authors: Das Sudip , Dhar Abhishek , Das Abakash , Gayen Pritam Kumar , Pal Biswamoy , Biswas Papun
Published: March 2021

A firefighting system is the most foremost part of any industry and buildings as its aim to protect property, human and industrial equipment. In any pandemic situation like COVID19 it is utmost requirement to increase reliability of all important instruments which are directly involve to human life. In case of fire fighter fight against fire, this required time and skilled man power. Under these pandemic situation chances of infection spreading in firefighters and social distancing will not maintain during this situation. Only one way to resolve this problem is by implementation of fully automated reliable system. In this system we are trying to automate the total system by using hot redundant PLC which is fail safe. It consists of mainly three basic parts - fire storage tank, fire pumps and automated distribution system. Fire storage tank contains the water that used against fire. Fire pumps helps to fill the tank and automated distribution system will be activated during fire and instantly distribute water. There are three pumps one is main electrical pump, second one is diesel backup pump and last one is jockey pump.In redundant configuration there are two PLC acting simultaneously and the entire network component and power supply also has back up so that it will never fail. If one of the PLC fails during operation then second PLC automatically take care of the system within few millisecond. Similarly if power supply failure then UPS supply is also there for uninterrupted power supply.
Second-order Nonlinear Differential Equations: Oscillation Tests and Applications
Authors: Ghosh Tanusri , Santra Shyam S. , Bhattacharjee Rupak , Majumder Debasish
Published: March 2021

The 2nd order Differential Equations (DEs) are applied to analyze various phenomena in physics and it is extended to engineering. Specifically 2nd order linear DEs are having a big role in this field. We already observed this working rule in moving systems like a vertical spring attached with a mass. Another example is an electric circuit with a resistor, an inductor, and a capacitor in series connection. These models are applicable to approximate more complex situations like bond structure between atoms or molecules. They are generally patterned as vibrating springs which is explained by the same DEs. Qualitative theory analysis of DEs is an active area of investigation in theory and application point of view. For instance, oscillatory behavior of 2nd order DEs have many applications in the research related to distributed networks where in high-speed computations lossless transmission lines evolve. These lossless transmission lines are employed to connect switching circuits. Recently the study the functional DEs of neutral type is an active area of research. Many researchers are working in the field of oscillation theory and studying only sufficient conditions. Merely few are considering the necessary and sufficient conditions. We have attempted to set up the necessary and sufficient conditions for oscillation of solutions of 2nd order nonlinear neutral DEs. Mathematics Subject Classification (2010): 34K.
Evaluating student performance based on bloom’s taxonomy levels
Authors: Prasad G. N. R.
Published: March 2021

Knowing the performance of the student in online classes is very difficult. Staff are taking online classes and conducting objective type examinations. It is very difficult to know at which level student is performing better and which is not. Blooms taxonomy is a technique developed by Dr. Benjamin Bloom in the year of 1956. Understudy appraisal is a critical bit of educating and is done through the procedure of examinations and readiness of test question papers has reliably involved scheme. Bloom's taxonomy is a lot of three progressive models used to group instructive learning goals into dimensions of multifaceted nature and explicitness. The cognitive domain list has been the primary focus of most traditional education and is frequently used to structure curriculum learning objectives, assessments and activities. Bloom's taxonomy is a device that can help human services educators expand the profundity of their students' learning.[3] The test with the taxonomy is creating assessments that measure every one of the six levels. There are 19 sorts of psychological procedures that can be grouped into six noteworthy classifications: recall, comprehend, apply, break down, assess, and make. There are four noteworthy classifications of learning: verifiable, applied, procedural, and meta cognitive. Instances of computer based appraisals of critical thinking are given dependent on the assessment of the subjective outcomes of youngsters' cooperation in an after-school computer club. In this paper, an objective question paper have been given using six levels of Bloom's taxonomy and asked students to write the test using online and evaluates the results of student performance[1].
Conventional Education & Online Education in Engineering: A Case Study
Authors: Bhattacharyya Sucharita , Thander Anup Kumar
Published: March 2021

Online education is gaining ground since last few decades all over the world including India. But so far it is utilised mainly in various alternative courses or knowledge-enhancing courses beyond regular curriculum which learners could opt as per their convenience and interest using internet access, particularly if the situations in Indian universities are considered. But under COVID-19 pandemic situation, the utility and use of online education is suddenly multiplied and extended and is made inclusive within regular university semester courses also. Undergraduate engineering is one of the very prominent areas to mention. Accordingly, in the present study, the authors tried to show that in the last even semester, how the online education served the basic subjective needs of engineering students so far as their learning is concerned. For that purpose, a students' feedback data based on the online teaching provided is analysed in detail, which are taken from one of the standard engineering colleges of West Bengal. Corresponding results are analysed for end semester examination conducted online and is compared finally with relevant data obtained from evaluation of conventional education for some interesting discussion.
Non-rigid medical image registration using multi-scale residual deep fully convolutional networks
Authors: Yang T.J. , Tang Q. , Li L. , Bai X.H.
Published: March 2021

Medical image registration technology is important in medical image processing, which can establish correspondence between complex medical images and ensure the comparability of image data. Since the deep convolutional neural network model is prone to degradation and the convolution kernel has limited detection range during training, a Multi-scale residual full convolutional network (MS-ResFCN) model is proposed for unsupervised non-rigid medical image registration tasks. The model introduces a residual structure in the Fully Convolutional Network (FCN) model to ensure effective and stable training. At the same time, a hierarchical multi-scale convolution kernel is constructed within a single convolutional layer of the residual structure, which enhances the nonlinear mapping ability of the network. The local texture information and the contextual information of the image are synchronously extracted and fused to enrich the diversity of features. The experiment results on LPBA40 data sets show that the MS-ResFCN model can effectively eliminate the degradation phenomenon of the deep network during the training process and extract multi-scale features, which achieves better feature representation ability and registration accuracy.
Therapeutic angiogenesis using zinc oxide nanoflowers for the treatment of hind limb ischemia in a rat model
Authors: Barui Ayan Kumar , Nethi Susheel Kumar , Basuthakur Papia , Jhelum Priya , Bollu Vishnu Sravan , Reddy Bommana Raghunath , Chakravarty Sumana , Patra Chitta Ranjan
Published: March 2021

Critical limb ischemia (CLI) is a severe type of peripheral artery disease (PAD) which occurs due to an inadequate supply of blood to the limb extremities. Patients with CLI often suffer from extreme cramping pain, impaired wound healing, immobility, cardiovascular complications, amputation of the affected limb and even death. The conventional therapy for treating CLI includes surgical revascularization as well as restoration of angiogenesis using growth factor therapy. However, surgical revascularization is only suitable for a small percentage of CLI patients and is associated with a high perioperative mortality rate. The use of growth factors is also limited in terms of their poor therapeutic angiogenic potential, as observed in earlier clinical studies which could be attributed to their poor bio-availability and non-specificity issues. Therefore, to overcome the aforesaid disadvantages of conventional strategies there is an urgent need for the advancement of new alternative therapeutic biomaterials to treat CLI. In the past few decades, various research groups, including ours, have been involved in developing different pro-angiogenic nanomaterials. Among these, zinc oxide nanoflowers (ZONFs), established in our laboratory, are considered one of the more potent nanoparticles for inducing therapeutic angiogenesis. In our earlier studies we showed that ZONFs promote angiogenesis by inducing the formation of reactive oxygen species and nitric oxide (NO) as well as activating Akt/MAPK/eNOS cell signaling pathways in endothelial cells. Recently, we have also reported the therapeutic potential of ZONFs to treat cerebral ischemia through their neuritogenic and neuroprotective properties, exploiting angio-neural cross-talk. Considering the excellent pro-angiogenic properties of ZONFs and the importance of revascularization for the treatment of CLI, in the present study we comprehensively explore the therapeutic potential of ZONFs in a rat hind limb ischemia model (established by ligating the hind limb femoral artery), an animal model that mimics CLI in humans. The behavioral studies, laser Doppler perfusion imaging, histopathology and immunofluorescence as well as estimation of serum NO level showed that the administration of ZONFs could ameliorate ischemia in rats at a faster rate by promoting therapeutic angiogenesis to the ischemic sites. Altogether, the present study offers an alternative nanomedicine approach employing ZONFs for the treatment of PADs.
The 2019/2020 mega-fires exposed Australian ecosystems to an unprecedented extent of high-severity fire
Authors: Collins Luke , Bradstock Ross A , Clarke Hamish , Clarke Michael F , Nolan Rachael H , Penman Trent D
Published: March 2021

Extreme fire seasons characterised by very large ‘mega-fires’ have demonstrably increased area burnt across forested regions globally. However, the effect of extreme fire seasons on fire severity, a measure of fire impacts on ecosystems, remains unclear. Very large wildfires burnt an unprecedented area of temperate forest, woodland and shrubland across south-eastern Australia in 2019/2020, providing an opportunity to examine the impact of extreme fires on fire severity patterns. We developed an atlas of wildfire severity across south-eastern Australia between 1988 and 2020 to test (a) whether the 2019/2020 fire season was more severe than previous fire seasons, and (b) if the proportion of high-severity fire within the burn extent (HSp) increases with wildfire size and annual area burnt. We demonstrate that the 2019/2020 wildfires in south-eastern Australia were generally greater in extent but not proportionally more severe than previous fires, owing to constant scaling between HSp and annual fire extent across the dominant dry-forest communities. However, HSp did increase with increasing annual fire extent across wet-forests and the less-common rainforest and woodland communities. The absolute area of high-severity fire in 2019/2020 (∼1.8 M ha) was larger than previously seen, accounting for ∼44% of the area burnt by high-severity fire over the past 33 years. Our results demonstrate that extreme fire seasons are a rare but defining feature of fire regimes across forested regions, owing to the disproportionate influence of mega-fires on area burnt.