Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Passive Rectifier Circuits for ModularSolid State Transformer Applications

Solid state transformers have a major role to play in grid integration of renewables in smart grid networks. Unidirectional, step up solid state transformers are state of the art in wind energy systems and solar PV parks. Modular SST topologies have gained significant impetus in recent research. Many research works have focussed on resonant tank converter topologies for modular SST applications. However, passive rectifier part remained uninvestigated and hence only secondary side rectifier and associated HF transformer is investigated in this work. In this work, three rectifier configurations are considered. Steigerwald's constant is derived for each independently and it is proved that irrespective of rectifier configuration, ac equivalent resistance remains unchanged for similar voltage and power ratings. Thus design methodology remains unchanged. Simulation results are analysed and various losses are estimated for full load operation. High frequency transformer design for minimum footprint and loss minimization are discussed. Further, it is concluded that bridge rectifier configuration yields minimum power losses.
AI and IoT Solutions for Tackling COVID-19 Pandemic

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), has become an unprecedented public health crisis. To tackle this crisis in an effective way different computational solutions involving artificial intelligence and machine learning have been propounded by researchers across the world. Artificial Intelligence has changed the landscape of the healthcare industry and is being used by many corporations and governments around the world to tackle health care issues and hence, it finds applications in these troubling times as well. The internet specially google scholar scoured for relevant and accurate applications of machine learning and deep learning in solving the issues of this pandemic. The different applications include diagnosis, mortality rate prediction, vaccine development, drug development, sentiment analysis regarding COVID-19 comments and misinformation detection. A systematic study presents the best working models in the respective field.
An Improvised Analysis in the Parameter of a Conventional Microstrip Patch Antenna for 5G Communication

In this proposed work, an improvised analysis in the parameter of a conventional Microstrip Patch Antenna for 5G is reviwed. In this design of the antenna is modified for the better gain and return loss with the best possible result using simulation software (HFSS-19.2). The performance of the antenna has been measured and compared to analyze in terms of gain, the return loss, radiation pattern and bandwidth at 28 GHz operating frequency.
A Real-Time-Capable Closed-Form Multi-Objective Redundancy Resolution Scheme for Seven-DoF Serial Manipulators

Using a closed-form inverse kinematics solution for motion planning has many advantages compared to traditional numerical approaches, most notably much faster computation times and better suitability for real-time applications. Steady progress has been made to develop an analytic inverse kinematics solution for seven degrees of freedom (DoF) manipulators without joint offsets. Redundancy resolutions have been proposed to push the individual joint angles away from their limits. However, no further objectives are considered in these closed-form solutions. We propose a multi-objective real-time optimization approach that is able to minimize joint velocities and accelerations, while still avoiding joint limits. A closed-form solution to the optimization is derived, which only requires a single intuitive tuning parameter. We provide a free and open-source software implementation of the full kinematics algorithm that covers the complete solution space. The null-space motion resulting from our method is evaluated, achieving faster motion times and smoother arm motions along Cartesian paths in comparison to the state-of-the-art approach. Finally, experimental results with a KUKA iiwa robot are reported.
Electrical and Data-Retention Characteristics of Two-Terminal Thyristor Random Access Memory

Two-terminal (2-T) thyristor random access memory (TRAM) based on nanoscale cross-point vertical array is investigated in terms of lengths and doping concentrations of storage regions for long data retention time (<italic>T</italic><sub>ret</sub>). For high device scalability and low program voltage (<italic>V</italic><sub>P</sub>), lengths of the storage regions are determined by the sum of depletion widths of N- and P-storage regions. When doping concentrations of two storage regions are equal to each other at 10<sup>18</sup> cm<sup>−3</sup>, 2-T TRAM exhibits the longest <italic>T</italic><sub>ret</sub> of 100 ms and the lowest impact ionization of the device can suppress various reliability issues such as hot carrier injection and junction degradation. Although <italic>T</italic><sub>ret</sub> of 2-T TRAM can be reduced from 100 ms to 1.5 ms due to decreased read voltage with operating temperature rising from 300 K to 360 K, <italic>T</italic><sub>ret</sub> can be further improved to >10 s by applying standby voltage (<italic>V</italic><sub>standby</sub>). The effective way to set minimum <italic>V</italic><sub>standby</sub> is presented using the <italic>I</italic><sub>A</sub>-<italic>V</italic><sub>A</sub> characteristics with 1000-s fall time. Moreover, the optimal <italic>V</italic><sub>standby</sub> is set to 0.60 V by considering disturbance in array operation. Consequently, the proposed design and operation guidelines can provide a pathway to realize nanoscale 2-T TRAM for capacitor-less 4F<sup>2</sup> 1T DRAM technology.
An Automated Design Environment for CT Incremental Sigma-Delta ADCs

In this paper a comprehensive design environment for continuous-time incremental Sigma-Delta ADCs is described. In the state of the art, the system-level design of incremental Sigma-Delta ADCs is based on the same methods as for their free-running counterparts, neglecting the ADCs specific behavior caused by the reset and the reconstruction filter. Moreover, the incremental ADCs performance is usually estimated with time-domain calculations based on a discrete-time prototype, neglecting major non-idealities. This work presents an automated design tool which directly accounts for the reset and the reconstruction filter. It is based on the framework provided by www.sigma-delta.de and allows therefore to design the ADC in the continuous-time domain, giving the designer a comprehensive overview of the performance and behavior of the ADC. It is shown that the tool allows to account and compensate for non-idealities such as excess-loop-delay, finite GBW and finite DC gain. A comparison with the state-of-the-art design process further proves the reliability of the tool for general usage.
Physical Layer Security of NOMA with Decode-and-Forward Relaying in Underlay CR Network

This paper investigates the physical layer security of non-orthogonal multiple access (NOMA) with decode-and-forward (DF) relays in underlay cognitive radio (CR) network. Secondary transmitter communicates with legitimate users via the aid of relays, while the eavesdroppers try to intercept the message. To safeguard legitimate communications against eavesdropping, the joint of artificial-noise (AN) and relay selection (RS) is applied. We derive the secrecy outage probability (SOP) and the secrecy throughput to evaluate the secrecy performance. In addition, the asymptotic floor of SOP is derived. Numerical results verify that cooperative NOMA in underlay CR (CRNOMA) significantly outperforms the conventional orthogonal multiple access (OMA) in underlay CR network (CROMA) in terms of the secrecy outage performance. Furthermore, the effect of some critical parameters on the secrecy performance has been studied.
An Imperative Diagnostic Model for Predicting CHD using Deep Learning

Coronary heart disease (CHD) is one of the leading cause of an increase in the mortality rate. Various factors are influencing to estimate the presence of CHD in an individual biologically. We made inferences with these influencing factors and their peculiarity by analyzing with a various set of algorithms which help in obtaining a precisive decision support system for the presence of CHD in an individual. We considered a set of linear models (Logistic Regression, Linear Discriminant Analysis), Naïve Bayes, Classification and Regression Trees (CART), Support Vector Machines (SVM). K-Nearest Neighbor (k-NN) for k=1 to 21. Next, we have considered a set of ensemble models. Furtherly, we computed a Multi-Layer Perceptron (MLP) and a Deep Neural Network to evaluate the performance through a deep learning approach. So, with this analysis, we found a linear model (Logistic Regression) tend to give on par results both in the case of Cleveland as well as Framingham dataset. This analysis led to design an intuitive approach for CHD classification and would give insights on how to use them in the medical research field.
Performance Characteristics of Virtualized GPUs for Deep Learning

As deep learning techniques and algorithms become more and more common in scientific workflows, HPC centers are grappling with how best to provide GPU resources and support deep learning workloads. One novel method of deployment is to virtualize GPU resources allowing for multiple VM instances to have logically distinct virtual GPUs (vGPUs) on a shared physical GPU. However, there are many operational and performance implications to consider before deploying a vGPU service in an HPC center. In this paper, we investigate the performance characteristics of vGPUs for both traditional HPC workloads and for deep learning training and inference workloads. Using NVIDIA’s vDWS virtualization software, we perform a series of HPC and deep learning benchmarks on both non-virtualized (bare metal) and vGPUs of various sizes and configurations. We report on several of the challenges we discovered in deploying and operating a variety of virtualized instance sizes and configurations. We find that the overhead of virtualization on HPC workloads is generally < 10%, and can vary considerably for deep learning, depending on the task.
Radio Frequency Identification Based Attendance System

This paper presents the implementation of RFID based attendance system with the help of Radio-frequency Identification technology, database management system, and Django. This paper addresses the design flow, working of RFID based system, and the benefits it provides over the traditional paper-based attendance system. The benefits of using database management and Django are discussed along with all the technical difficulties faced and the solutions applied to solve the problems. The working of the model prepared is successfully demonstrated. With the help of minor advancements, the future scope of this system is also discussed.
A density functional theory for polymers: Application to hard chain–hard sphere mixtures in slitlike pores

A density functional theory for polymer fluids is derived. The form of the theory is identical to a continuum self‐consistent‐field theory but we show that with the correct ‘‘mean field,’’ the theory is exact. We apply the theory to a polymer fluid made up of tangential hard spheres and borrow from a successful density functional theory for simple hard spheres to obtain a nonlocal functional expression for this mean field. The latter depends only upon the monomer density. This approximation is used to treat the problem of a mixture of such polymers and simple hard spheres, contained in slitlike pores. Comparison with simulations show that the theory, though simple in structure, and containing a single adjustable parameter, is surprisingly accurate.
Study of HCl and Secco Defect Etching for Characterization of Thick sSOI

In order to continue improving strained silicon-on-insulator (sSOI) crystal quality, high-resolution defect monitoring needs to be developed and implemented for further defects reduction. The study presented in this paper evaluates and compares two techniques for revealing crystal defects in sSOI wafers produced by the Smart-Cut Technology. Two different etching techniques, based on the use of gaseous HCl in an epitaxy reactor or of a diluted Secco wet etching solution, were compared on their ability to delineate various defects. Both techniques should provide the required defects density resolution for analysis of thin and thick strained silicon layers. For the sake of analysis and discussion, samples with a high defects density were chosen in order to simplify the quantitative comparison (within the resolution of optical microscopy). We have observed a difference in etching selectivity between the two techniques. After a statistical comparison of defects delineated by the two techniques (in terms of threading dislocations, areal densities, and planar defect linear densities), we have demonstrated complementarities rather than a direct correlation between the HCl and Secco etch. The HCl etch seems quite suitable for revealing threading dislocations through etch pits and shows a higher sensitivity for pit delineation compared to Secco (difference corresponding to a factor of 10 in defects density). Meanwhile, the Secco chemical etching appears more appropriate to highlight the planar defects (linear density between 600 and 1000 cm -1 ). Moreover, a minimum etched thickness for sSOI defect revelation has been determined for both techniques (between 26 and 36 nm for HCl and between 20 and 30 nm for Secco for a 600 A thick starting layer). Because the Secco etch is particularly sensitive for the delineation of various types of defects (isolated etch pits and planar defects), it is now used by us as a quality control method for SOI and sSOI. Thanks to it sSOI process improvements are tracked and current and next generations of this product evaluated.
BASIC CONCEPTS IN INFORMATION RETRIEVAL PROCESS

Information retrieval is a continuous process of selection of problem, appropriate keywords, information sources, retrieval techniques and finally evaluates the information retrieved. Due to the advances in research in the field of information technology results in the development of efficient retrieval techniques. By using these retrieval techniques, user can easily retrieve their needed information's effectively and efficiently. Information retrieval techniques can reduce the risk of information overload to a certain context
SerumSialic Acid and Sialyltransferaseas Monitorsof Tumor Burdenin MalignantMelanomaPatient&

phin have proven particularly useful (13, 16). Although these markers may be helpful in diagnosis, they have far greater meritas monitorsof tumor burden in followingdiseaserecur rence or response to treatment (13, 16, 25). It is important that when used solely as monitors of tumor burden, markers need not be tumor specific. However, most of the clinically validated markers are relatively tumor specific and thereby clinically restricted to a limited spectrum of tumor cell types. Tumor markers with relatively broad specificity need to be identified and evaluated for use in the many other forms of human cancer. Sialic acid (N-acetylneuraminic acid) is one such marker. The rationale for evaluating sialic acid as a possible tumor marker arose from experimental findings that an increased density of this carbohydrate is frequently found at the malignant cell surface in both animal models and humans (18, 31). In tissue culture experiments, tumor-related sialylglycoproteins are readily detected free in spent medium and might therefore be detectable in the serum of cancer patients as well (1 , 12). Similar observations have been made for sialyltransferase, the enzyme responsible for the linking of sialic acid to polysaccha ride chains (2, 35). Although there are remarkably few detailed studies, serum elevations of either sialic acid or sialyltransfer ase activity have been independently reported in patients har boring a variety of neoplasms (2, 4, 6, 10, 11, 19, 21 , 22). In addition to more detailed investigation, the relationship of these 2 markers in the same sera would be of interest. The objectives of this study were to extend our previous findings on sialic acid in a different and larger group of malig nant melanoma patients, to evaluate the use of sialyltransferase activity as a marker for tumor burden in malignant melanoma, and to investigate the relationship between sialic acid and sialyltransferase in the same individual patients' sera.
Amphotropic ionic liquid crystals with low order parameters

Methyl-tri-n-decylphosphonium salts (1P10A, where the counterion A is NO 3 - or the hydrate of bromide, Br - .H 2 O) have been shown to exhibit unique liquid-crystalline properties as neat compounds and with high concentrations (up to 20 wt %) of a small organic molecule, acetonitrile. These results indicate dual thermotropic-lyotropic (i.e., amphotropic) behavior. Optical micrographs of salts with even small amounts of acetonitrile are oily streak patterns. The low-angle peaks in X-ray diffractograms characteristic of the lamellar spacings in the smectic A phases of the neat salts are broadened but do not change their positions appreciably as the concentration of added acetonitrile is increased. The liquid-crystalline phases of both salts become oriented in strong magnetic fields. As a result, NMR experiments with the nitrate salt exhibited only one type of orientation for acetonitrile at concentrations up to ca. 10 wt %. However, two types of orientations with opposite signs of the order parameter were detected in the Br - . H 2 O salt at < 1 wt % acetonitrile. The acetonitrile proton line width decreases by a factor of 2 between 0.7 and 13 wt % in the nitrate salt. The dual thermotropic-lyotropic behavior and relatively low order parameter of the solute suggest that these amphiphilic salts might be useful for structural studies using NMR spectroscopy.
Effect of Tungsten on Synthesis of Multiwalled Carbon Nanotubes Using Cobalt as Catalyst

The effect of W and W2C on the synthesis of multi-walled carbon nanotubes (MWCNTs) by hot-filament-assisted chemical vapor deposition (HFCVD) using Co particles as a catalyst was examined. W or W2C was vacuum-evaporated onto Co particles supported by zeolite and used as a catalyst in CNT synthesis. Methanol was used as a carbon source. Synthesis of CNTs was carried out at about 400°C. The density of the MWCNTs synthesized using only Co as a catalyst was the lowest, and was sequentially higher when using Co combined with W (Co + W) and Co combined with W2C (Co + W2C). The mechanism of the enhancement of CNT growth upon the addition of W or W2C was considered.
Phosphorus Availability and Early Corn Growth Response in Soil Amended with Turkey Manure Ash

Incinerating turkey manure is a new option in the USA to generate renewable energy and to eliminate environmental problems associated with manure stockpiling. Incineration produces turkey manure ash (TMA) with a nutrient content of 43 g phosphorus (P) kg−1 and 100 g potassium (K) kg−1. We conducted a greenhouse pot study using a low P (6 mg kg−1) and high K (121 mg kg−1) soil/sand mixture with a 7.0 pH to evaluate early growth response of corn (Zea mays L.) to TMA. A control and five rates based on P (5.6, 10.9, 16.5, 21.9, and 27.2 mg kg−1) and respective K contents in TMA were compared with triple-superphosphate and potassium chloride fertilizer. Plant height and stalk thickness at 24 and 31 days after emergence (DAE) were greatest with the fertilizer, but no differences were detected at the final sampling (52 DAE). Regardless of nutrient source, plant biomass increased with P rate. Because of faster initial plant development, corn dry matter 52 DAE was 15 to 20% greater with fertilizer than with TMA. Corn tissue P concentration was greater with TMA than with fertilizer, but P uptake was similar. Tissue micronutrient concentrations were greatest for the control. Bray 1 P appeared to extract excessive amounts of P in TMA-amended soil, whereas soil P levels with the Olsen extractant provided an estimate of plant-available P that was consistent with plant response. Based on this first approximation, we conclude that TMA is a potential source of P for field crops. Field studies are required to determine recommended application rates.
Childhood cognitive skills trajectories and suicide by mid-adulthood: an investigation of the 1958 British Birth Cohort. Psychological

Background : Poor cognitive abilities and low are suicide attempts and suicide mortality. However, knowledge of how this association develops across the life-course is limited. Our study aims to establish whether individuals who died by suicide by mid-adulthood are distinguishable by their child-to-adolescence cognitive trajectories. Methods : Participants were from the 1958 British Birth Cohort and were assessed for academic performance at ages 7, 11, and 16 and intelligence at 11 years. Suicides occurring by September 2012 were identified from linked national death certificates. We compared mean mathematics and reading abilities and rate of change across 7 to 16 years for individuals who died by suicide versus those still alive, with and without adjustment for potential early-life confounding factors. Analyses were based on 14,505 participants. Results : Fifty-five participants (48 males) had died by suicide by age 54 years. While males who died by suicide did not differ from participants still alive in reading scores at age 7 (effect size [g]=-0.04, p =0.759), their reading scores had a less steep improvement up to age 16 compared to other particpants. Adjustments for early-life confounding factors explained these differences. A similar pattern was observed for mathematics scores. There was no difference between individuals who died by suicide versus participants still alive on intelligence at 11 years. Conclusions : While no differences in tests of academic performance and IQ were observed, individuals who died by suicide had a less steep improvement in reading abilities over time compared to same-age peers.
CFD Investigation of Hydrodynamic and Heat Transfer Phenomena around Trilobe Particles in Hydrocracking Reactor

In this work, computational fluid dynamics (CFD) has been employed to compute local convection heat transfer coefficient (h) that is the key parameter in calculation of heat transfer rate between the particle and fluids in packed bed reactors. In addition, the relation between Reynolds number and Nusselt number for spherical and trilobe catalyst particles have been investigated. Moreover, the parameters of Ranz-Marshall (R-M) correlation have been estimated in order to use it for trilobe catalyst particle. The heat transfer coefficients of the spherical and trilobe particles were compared and the effect of particle shape and configuration on heat transfer rate has been investigated. Eulerian-Eulerian approach was employed in order to investigate gas-liquid hydrodynamic especially liquid film formation around trilobe particles. The effects of liquid film around a trilobe particle and liquid volume fraction on heat transfer coefficient have also been studied. The CFD simulation results indicate that increasing inlet liquid volume fraction raises the liquid film thickness around the particles leading to reduction of heat transfer coefficient. In addition, the results revealed that flow field and temperature profiles around the particles became more complicated as a result of liquid film formation and gas-liquid interactions.