bluuebunny/crossref_metadata_2025_split huggingface dataset:
Noise Resilient distributed Voltage and Frequency Control for Low Voltage Islanded Microgrid
Authors: Shrivastava S , Sahu J , Sitharthan R 
Published: October 2020

Abstract
Motivated by advancement in distributed control schemes of microgrid (MG) network, this paper presents a noise resilient secondary control scheme for low voltage (LV) islanded MG network. For a LV MG the grid lines are highly resistive, hence conventional droop control results in poor stability and power sharing. A combination of droop-boost primary control with a noise resilient secondary distributed control is presented in this work. The presented control scheme achieves accurate power sharing and voltage and frequency restoration under noisy communication links for LV MG network. The communication links used for information sharing in secondary control are subjected to noisy environment; therefore the data received is corrupted with noise. The presented control scheme counteracts the noise effect for LV MG network. The performance of presented control scheme is evaluated by simulating a 4-bus MG test system in MATLAB/SimPowerSystem environment under different test scenarios. The obtained results envisage that the presented control scheme restores the voltage/frequency and provide accurate active power sharing in short time under load perturbation, varying noise parameters, and plug and play operating conditions.
Generation of energy from Solar and its protections
Authors: Rajkumar A. , Nagendran J. , Subramaniyan J. , Jai Ganesh R. 
Published: October 2020

Abstract
In this article, a seven-level inverter powered by solar has been proposed to achieve a sinusoidal output voltage with high efficiency and enhanced power quality. This system consists of active inverter and flipped condenser clamping. It gives output voltage level of 2/3. By connecting the switched condenser branch in the front or back end series to the inverter output a seven-level output voltage wave shape can be obtained with ample non-work switching states to match the voltage of switching condensers. The consequence of the proposed method is validated using MATLAB SIMULINK for simulation. Comparing with conventional seven-level inverter topology, the proposed design used a minimal number of switches using a basic modulation process. The demand for electricity that is so high in India might be a key driver for the use of renewable power as an option for energy requirements. To satisfy this need, this article introduces a solar smart flower layout and then modeled using the resources provided in the solid function system. And use a set Photovoltaic panels harvest additional power mostly during 12 pm to 2:00 Pm in Nigeria which resulted in less renewable energy. Consequently, there is a need to increase the energy consumption of PV solar panel by developing a solar tracker cannot be overlooked. Photovoltaic cells must be aligned with the sun in order to just get optimum energy.
The microcontroller is being used to power the motor dependent on information gathered from the LDRs. The findings of this research have clearly demonstrated that the monitoring solar panel generates more electricity compared to a conventional panel. The location of solar panel including voltage was going to monitor by using IoT. Light Dependent Resistors LDRs are being used to detect the strength of lighting and thus the PV solar panel is appropriately addressed to control maximum energy. The system uses motors to regulate the amount of the solar panel. The microcontroller is being used to power the motor centered on signals obtained from the LDRs. The outcome of this work has demonstrated clearly that the monitoring solar panel generates more power compared to a conventional panel. The location of solar panel and volts was going to monitor through using IoT.
Analyzing the performance of Cascaded H-Bridge Multilevel Inverter with three phase multiwinding transformer and single phase supply
Authors: Kathirvel P. , Karpagam V. , Vijayakumar K. , Venkatesh A. 
Published: October 2020

Abstract
A multi-level inverter type cascaded H-bridge design with a different harmonic reduction technique is provided. The proposed design includes the DC source, hybrid cascaded multilevel inverter, transformer and load circuit. For a hybrid, multi-level inverter, the 7-level stepped form of the wave is achieved similar to the sinus wave. The transformer supplies the seven-tier hybrid inverter with cascading into the load. The 5th and 7th harmonics were efficiently extracted with the algorithm from the line current. The simulation and research was carried out to show how the system proposed works. The observed harmonic detail results, the THD are approximately 12.27% only. The 3rd level harmonics magnitude is 5.2% and 5th level harmonics is only 8.2%. Simulation results from SIMULINK/MATLAB software and hardware experimental results are tabulated. The results are explained with graphically.
Comparative Study of Recurrent and Non Recurrent Neural Network Based Approach for Modeling of PEM Fuel Cell Powered Electric Vehicle
Authors: Gomathi K , Karthik M , Usha S 
Published: October 2020

AbstractIn this paper, a non recurrent Focused Time Delay Neural Network (FTDN) and a recurrent nonlinear autoregressive network with exogenous inputs (NARX) Neural Network are employed as a black box prediction model for substituting the complex conventional model of PEM 5kW Proton Exchange Membrane (PEM) Fuel Cell system. A comparative assessment is performed between the recurrent and non recurrent neural network on certain performance measures to identify an optimal network for modeling the PEM Fuel Cell System in PEM Fuel Cell powered electric vehicle application. From the simulation result, it is observed that the recurrent NARX network is showed excellent prediction ability in terms of minimizing the Mean Square Error (MSE) value with faster convergence. The optimized network is tested with intermittent data for examining its adaptability and validated with experimental benchmark data for proving its reliability. Hence the optimum network is integrated with converters and vehicle dynamic system to develop a fuel cell based electric vehicle system. The performance of the proposed vehicle is tested with US06 drive cycle pattern for justifying its reliability.
Experimental analysis of PQ parameter estimation of VFD drives
Authors: Dutta Nabanita , Kaliannan Palanisamy , Subramaniam Umashankar 
Published: October 2020

AbstractControlling the AC motor speed and torque changing voltage and frequency for electrical and mechanical system the adjustable speed drive system is required which is known as Variable Frequency Drive. The usage of VFD is gradually increasing every day. The 25% of the world’s energy is consumed by the motor load for which the usage of VFD is responsible. The cost of VFD can be reduced and size also reduced to help the system more compatible within a very short span of space. It is based on semiconductor topologies, driver circuit, control circuit and both software and hardware devices are also used. Now power quality is the important role plays for the energy efficiency of the system. The researchers are working on various issues of power quality like voltage sag, swell, system unbalance etc. which are required to be analysed for the improvement of the efficiency of the system. In this research the VFD based induction motor faces various power quality issues which are described. The paper is concentrating on various power quality issues which affects the performance of VFD drives through various parameters. The various power quality parameters are predicted for the change in loadings with the help of MATLAB curve fitting tool. With different load variations voltage and current unbalance have been measured. The process has been done though experimental setup with the help of power quality analyser Fluke 302 and both the controller technique like VVC+ and V/F methods have been used.
Seamless Engineering Process to Adapt IEC 61850 Standard for Substation Automation Through Requirements Specification
Authors: Khan S U , Kalam A 
Published: October 2020

Abstract
Whatever demographic one belongs to in the technology adaption life cycle, for a seamless implementation of IEC 61850 Standard it is required to answer the key concern related to extent of change. This paper provides guidelines on the evaluation of amount of change by going through the requirement specification process, interface evaluation, and configuration tools. All this information is presented in a process form which makes it easier to quantify the inputs for organizational change management.
IoT and Renewable Energy systems based Connected Chair for Health Monitoring
Authors: Dhayalini K. , Priyadarshini S.A. , Abirami M. 
Published: October 2020

Abstract
Health monitoring of a system involve in various activities done in order to make the system operating condition feasible with certain constrains such as current systems states which includes repair and maintenance of the observations. Remote health monitoring is a process in which patients with numerous conditions such a heart disease or diabetics are monitored based on the latest technology. Internet of things and renewable energy sources are the brain of this work based smart wheel chair for special people for autonomous movement and also gathering person’s health information using smart sensors displayed in webpage. For this innovative work a digital single board computer for gathering sensor information and also transfer the status of the persons through inbuilt Wi-Fi, and also the smart chair movement dependent upon user’s voice, which the sceneries are recognized by connected camera to raspberry pi. Renewable energy system such as PV array or wind energy systems can be replaced for supply of the entire health monitoring system and connected chair system.
Visualization and Classification of Diseases using Deep Learning Based Convolution Neural Network
Authors: Janeera D.A , Amudhavalli P. , Sherubha P , Sasirekha S.P , Anantha Christu Raj P. , Sitharthan R 
Published: October 2020

AbstractIn the contemporary phase of big data, data visualization is one of the challenging segment of the discovery process. For a classifier, the primary goal is to identify the hidden levels of big data. The performance of classifiers depends on the feature space, number of classes and size of the data. To improve the reliability, efficiency and accuracy of the classifiers, new algorithms are required for analysis. This paper enables classification and visualization of information on diseases using a Deep Learning based Convolution Neural Network classifier. For feature selection and handling massive data in analysis of multivariate data is performed using particle swarm optimization (PSO) and principal component analysis (PCA) techniques. Real-world datasets are utilized for demonstration of the proposed learning algorithm. Deep learning classifiers are scientifically higher and offers better performance when compared to other classifiers according to the comparative study.
Estimation of Lifetime and Reliability of Input Rectifier in Variable Frequency Drives
Authors: Ramesh P , Govarthanan R , Palanisamy K , Paramasivam S 
Published: October 2020

Abstract
This research paper investigates the causes of power module failures in front-end rectifier of variable frequency drive (VFD) and their lifetime estimation. The front-end rectifier plays an important role in the electrical system for energy conversion. However, from the industrial survey it is found that they are sensitive to harsh operating conditions and are prone to damage. Hence, it becomes inevitable to analyse the causes of their failure and lifetime. Due to prolonged usage, sudden variations in supply and load, the power modules will be subjected to stresses such as overvoltage, voltage variations due to sag, phase unbalance, over current, temperature, harmonics and pre-distortions. The major causes of failure are due to several stresses such as solder joint break, bond wire lift-off, cracks in power modules are discussed. In this paper, the lifetime of the power module is estimated for a given mission profile data. The junction temperature profile is obtained from the VFD Front-end rectifier simulation model. The number of temperature cycles from the temperature profile is estimated using cycle counting methods. Lifetime estimation and damage modelling is done using Coffin-Manson rule and Palmgren Miner rules. All these modelling and simulations are done for a specific VFD power size using MATLAB and PLECS.
A new crystal plasticity modeling of uniaxial ratcheting behavior for face-centered cubic 6061 aluminum alloy
Authors: Dong Yawei , He Xu , Zhang Zhiyong 
Published: October 2020

Abstract
The uniaxial ratcheting behavior of face-centered cubic 6061 aluminum alloy is investigated theoretically based on the crystal plasticity theory. In this model, a simplified flow rule is adopted for convenient engineering application, and the classical Kocks-Mecking-Estrin isotropic hardening rule related to dislocation density is adopted to describe the cyclic hardening characteristic of the material. The classical Armstrong-Frederic kinematic hardening rule is modified associated with cumulative slip to predict the ratcheting behavior more accurately. The single crystal version of this model is expanded to metal polycrystalline using a simplified explicit scale transition rule. The capability of the proposed model to capture the uniaxial ratcheting response of metal polycrystalline is verified by comparing the predictions with the corresponding experimental results of 6061 aluminum alloy. The evolution of uniaxial ratcheting of 6061 aluminum alloy can be reasonably predicted by the proposed model, and the capability to simulate uniaxial ratcheting behavior in inter-granular scale is qualitatively discussed as well.
Shape-programmable and healable materials and devices using thermo- and photo-responsive vitrimer
Authors: Kuang Xiao , Mu Quanyi , Roach Devin J , Qi H Jerry 
Published: October 2020

Abstract
Shape morphing materials have been extensively studied to control the formation of sophisticated three-dimensional (3D) structures and devices for a broad range of applications. Various methods, including the buckling of pre-strained bilayer composites, stimuli-responsive shape-shifting of shape memory polymers, and hydrogels, have been previously employed to transform 2D sheets to 3D structures and devices. However, the residual stress locked in these shape-shifting structures will drive them to gradually revert to their original layouts upon the removal of external stimuli or constrains. Here, we report a multistimuli-responsive vitrimer (m-vitrimer) bearing thermal- and photo-reversible disulfide bonds as shape programmable and healable materials for functional 3D devices. The mechanical properties and thermomechanical properties of vitrimer were tuned by altering the disulfide content and catalyst loading. Heat and light exposure induces effective stress relaxation and network rearrangement, enabling material shape programming and healing. We demonstrate that printed flexible smart electronics are fabricated using the m-vitrimer as a matrix and printed conductive silver nanoparticles as conductive wire. The printed electronics possess good electro-mechanical properties, strong interfacial bonding, and thermal- and photo-responsive shape programming. Moreover, the m-vitrimer can be healed upon damage by heat and light, which partially restores silver conductivity and protect the electronics from further damage. The converging of multi-stimuli-responsive polymers and printed electronics for functional 3D devices have the potential of finding broad applications in smart and morphing electronics, biomedical devices, and 4D printing.
A novel estimator of the interaction matrix in Graphical Gaussian Model of omics data using the entropy of non-equilibrium systems
Authors: Borzou Ahmad , Sadygov Rovshan G 
Published: October 2020

Abstract

Motivation
Inferring the direct relationships between biomolecules from omics datasets is essential for the understanding of biological and disease mechanisms. Gaussian Graphical Model (GGM) provides a fairly simple and accurate representation of these interactions. However, estimation of the associated interaction matrix using data is challenging due to a high number of measured molecules and a low number of samples.

Results
In this article, we use the thermodynamic entropy of the non-equilibrium system of molecules and the data-driven constraints among their expressions to derive an analytic formula for the interaction matrix of Gaussian models. Through a data simulation, we show that our method returns an improved estimation of the interaction matrix. Also, using the developed method, we estimate the interaction matrix associated with plasma proteome and construct the corresponding GGM and show that known NAFLD-related proteins like ADIPOQ, APOC, APOE, DPP4, CAT, GC, HP, CETP, SERPINA1, COLA1, PIGR, IGHD, SAA1 and FCGBP are among the top 15% most interacting proteins of the dataset.

Availability and implementation
The supplementary materials can be found in the following URL: http://dynamic-proteome.utmb.edu/PrecisionMatrixEstimater/PrecisionMatrixEstimater.aspx.

Supplementary information
Supplementary data are available at Bioinformatics online.
Double trouble: combined cardiovascular effects of particulate matter exposure and coronavirus disease 2019
Authors: Tanwar Vineeta , Adelstein Jeremy M , Wold Loren E 
Published: October 2020

Abstract
The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly grown into a pandemic. According to initial reports, the lungs were thought to be the primary target, but recent case studies have shown its reach can extend to other organs including the heart and blood vessels. The severity of cardiac complications of COVID-19 depends on multiple underlying factors, with air pollutant exposure being one of them, as reported by several recent studies. Airborne particulate matter (PM) attracts heightened attention due to its implication in various diseases, especially respiratory and cardiovascular diseases. Inhaled PM not only carries microorganisms inside the body but also elicits local and systemic inflammatory responses resulting in altering the host’s immunity and increasing susceptibility to infection. Previous and recent studies have documented that PM acts as a ‘carrier’ for the virus and aids in spreading viral infections. This review presents the mechanisms and effects of viral entry and how pollution can potentially modulate pathophysiological processes in the heart. We aimed to concisely summarize studies examining cardiovascular outcomes in COVID-19 patients and postulate on how PM can influence these outcomes. We have also reviewed evidence on the use of renin–angiotensin system inhibitors, namely angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, in patients with COVID-19. The interplay of pollution and SARS-CoV-2 is essential to understanding the effects of accentuated cardiovascular effects of COVID-19 and deserves in-depth experimental investigations.
Hospital organization and thoracic oncological patient management during the coronavirus disease-2019 outbreak: a brief report from a nationwide survey of the Italian Society of Thoracic Surgery
Authors: Viggiano Domenico , Lococo Filippo , Dell’Amore Andrea , Crisci Roberto , Torre Massimo , Rea Federico , Muriana Giovanni ,   Italian Thoracic Surgery Society (SICT) Task Force on COVID-19 Pandemic
Published: October 2020

Abstract
The current coronavirus disease 2019 (COVID-19) pandemic has made us aware of the weaknesses and often the inadequacies of our current technologies and practices and has presented us with a huge challenge: to reorganize the way we work and sometimes even think, in order to ensure the safety of our patients. The Italian Society of Thoracic Surgery has launched various initiatives in response to the COVID-19 pandemic, aimed at facilitating the exchange of information, strategies and personal experiences between institutions. This article presents the results of a survey amongst all Italian thoracic surgery units accredited to SICT, with the aim of providing a glimpse of the current working conditions in these units, and an understanding of the impact of COVID-19 on their daily activities and patient care.
A Fast Analytical Method for Determining Synthetic Cathinones in Oral Fluid by Liquid Chromatography–Tandem Mass Spectrometry
Authors: Pascual-Caro Sergi , Borrull Francesc , Calull Marta , Aguilar Carme 
Published: October 2020

AbstractIn this paper, we present a method for simultaneously determining 11 synthetic cathinones in oral fluid (OF) by liquid chromatography–tandem mass spectrometry. Synthetic cathinones, a wide variety of which are available on the market, are constantly evolving. It is therefore important to provide efficient methods for determining cathinones in different matrices. A common matrix for detecting recent drug intake is OF, which can easily be collected using one of numerous commercial devices. Most methods aimed at determining drugs in biological samples such as OF require labor-intensive and time-consuming sample-preparation steps. However, the pretreatment of complex samples is often a challenge in the development of a method. For this reason, in this paper, we present a simple, easy-to-handle alternative that uses a Salivette® device and pretreats the sample in the same device. Matrix-matched calibration curves were used to cover the concentration range at which these substances are usually present in the OF from drug consumers. The method detection limits ranged from 0.003 to 0.03 ng/g, and the method quantification limits were set at 0.075 ng/g. This is a simple, rapid and sensitive method with good potential for determining recent drug consumption in OF.
Post-mTBI Pain Interference in a U.S. Military Population: A Chronic Effects of Neurotrauma Consortium Study
Authors: Khokhar Bilal R , Lindberg Megan A , Walker William C 
Published: October 2020

ABSTRACT

Introduction
Chronic pain is a significant problem for service members and veterans with mild traumatic brain injury (mTBI). While the root cause of pain is not clearly understood, comorbidities may contribute to how their pain disrupts their functional status, a construct termed “pain interference.” The purpose of this study is to examine the associations between mTBI, other comorbidities, and pain interference.

Materials and Methods
The sample comprised participants with mTBI(s) from The Chronic Effects of Neurotrauma Consortium multicenter observational study. Potential concussive events were identified using a modified Ohio State University traumatic brain injury (TBI) Identification interview and then further with a structured interview. Pain interference was measured with the TBI quality-of-life pain interference score, which was categorized into insignificant, moderate, and high pain interference. Comorbidities of interest included anxiety, depression, post-traumatic stress disorder, insomnia, and arthritis. Multivariable relationships were analyzed using logistic regression.

Results
The analysis sample included 346 participants with mTBI(s). In adjusted analysis, those with high pain interference were more likely to have history of ≥ 3 TBIs (odds ratio (OR) 3.1, 95% confidence interval [CI] 1.4, 6.9) and to have clinical levels of post-traumatic stress disorder (OR 5.4, 95% CI 1.9, 15.7), depression (OR 2.5, 95% CI, 1.0, 6.1), anxiety (OR 4.9, 95% CI, 2.0, 11.7), and sleep disturbances (OR 6.1, 95% CI 2.0, 19.0) versus those with insignificant pain interference.

Conclusion
These results identify clinical features of veterans and service members with mTBI(s) who are at highest risk for pain-related disability. These findings also demonstrate the need to consider mental health and sleep problems in their pain evaluation and treatment approach.