Development of a whole genome sequencing method for Tick-borne encephalitis virus in low viraemic samples

Due to the rapid increase in Tick-Borne Encephalitis (TBE) viral infections in Norway and in the Western Europe, attentions have been drawn to develop rapid screening and whole genome sequencing methods directly from the tick samples. This would facilitate the identification of Tick-Borne Encephalitis virus (TBEV) endemic area, to predict the neuroinvasive ability and the disease severity of the TBEV natural foci. In this study, the optimization of RT-PCR, PCR and the whole genome sequencing with Oxford Nanopore technology (ONT) were carried out using tenfold dilutions of total RNA extract of Vero E6 cell culture sample incubated with TBEV-Hochosterwitz strain (NCBI Acc. No. MT311861). Total of 172 tick samples, including nymph pools of ten nymphs per pool (76) and adult males (50) and adult females (46) were collected from Larvik, Norway and separately subjected to total RNA extractions and evaluated for TBEV using two separate semiquantitative real-time PCR techniques. Whole genome amplifications and Oxford Nanopore sequencing and reference-based mapping were carried out for the TBEV positive tick RNA samples against the best optimized primer schemes. Phylogenetic tree analysis was carried out for the TBEV genome sequence fragments to identify the evolutionary closest TBEV subtype. In addition, TBEV positive tick species were identified using 18S-rRNA sequences found in Oxford Nanopore reads by phylogenetic tree analysis. TBEV positive five nymph tick pools, five female and three male adult ticks were identified and the prevalence of TBEV at the study site found to be 0.68 ± 0.30%, 10.90% and 6.00%, respectively. Primer schemes {[(2E+2Q)+(2F+2R)] and [(G+S)+(H+T)]} were identified as suitable for the whole genome amplification of TBEV directly form the tick samples. The maximum average sequencing breadth over 20 reads and the sequencing coverage were fell below the 13.6% and 50.56%, respectively in the reconstructed TBEV genomes. All the TBEV genomic sequence fragments shown to have close evolutionary relationship to the TBEV-European subtype and the TBEV positive ticks were identified as Ixodes ricinus. The optimized primer schemes exhibited whole genome amplification ability over the low viraemic TBEV positive tick samples and having sequencing gaps in the reconstructed genomes. Further optimizations are necessary to improve the quality of the reconstructed TBEV genomes directly from the tick samples and which will lead to improve the accuracy in the predictions of the neuroinvasive ability of the TBEV strains

Novel Energy Management System for a DC MicroGrid

This paper presents a design and simulation of a rule based energy management system for a dc MicroGrid that considers a cost function to reflect the battery degradation and that relates to the actual battery parameters.The derivation of the battery cost function and the utilization of that to ensure an optimum utilization of the battery energy storage were presented. The detailed description of the algorithms used to implement the EMS was presented. Simulation on PSCAD/EMTDC software was used to demonstrate the operation of the EMS both under grid connected and islanded modes. Further, the inertia support provided by the super-capacitor to avoid the collapse of the dc link of the MicroGrid was demonstrated

Experimental and Analytical Study of Masonry Subjected to Uniaxial Cyclic Compression

Structural evaluation of masonry against dynamic seismic actions invariably requires appropriate cyclic compression constitutive models. However, not many research studies have been dedicated to date to investigate the cyclic compression behaviour of masonry. Therefore, series of experimental investigation followed by analytical model verification were employed in this research to better understand the cyclic compression characteristics of masonry. Twelve masonry wallettes were experimentally tested under cyclic compression loading with different unit-to-mortar assemblies, which are commonly found in masonry structures. The experimental results indicated that the cyclic compression behaviour is greatly influenced by the masonry compressive strength and deformation properties. Thereafter, the ability of five literature analytical models to predict the masonry structural response under cyclic compression loading was investigated. The advantages and limitations of these models are presented and discussed, and the most appropriate analytical model to define the cyclic compression characteristics of masonry has been evaluated and reported. The suggested analytical model is shown to predict the cyclic compression characteristics of different masonry assemblies such as the envelop response, the stiffness degradation, the plastic strain history of the unloading and reloading stages

Modeling Electric Vehicle Charging Station Behavior Using Multiagent System

Agent-based models(ABMs) are a type of simulation in which a large number of self-sufficient agents interact in a way that combines stochastic and deterministic behavior. Recently, there have been reestablished interests in utilizing multiagent systems (MASs) to get more granular data relating to specific conditions. MESA is an ABM framework for Python. It enables users to quickly develop ABMs with built-in core components, view them with a browser-based interface, and evaluate their findings with Python’s data analysis capabilities. This chapter depicts an ABM of a photovoltaic (PV)-powered electric vehicle (EV) charging station in a university car park modeled using MESA. The goal is to determine the preliminary requirements for PV-powered EV charging stations, which would result in increased PV and cost benefits

Thirteen-level inverter for photovoltaic applications

With the recent cost reduction and efficiency improvement of solar photovoltaic (PV) cells, there is a growing interest towards PV systems in different applications. One promising application is solar PV powered electric vehicles. When they are moving on roads, the whole or some parts of the PV system might be shaded by trees, high buildings, etc.; which result in non-uniform insolation conditions. As a remedial measure, this paper presents a development of a cascaded multi-level inverter based PV system for electric vehicle applications. The basic architecture and switching of the converter switches are described. A laboratory prototype of the proposed architecture was implemented using MOSFETs and harmonic performance under different shading conditions was evaluated. It was found, that under shaded conditions, the 3rd harmonic content can increase and that it depends on the number of modules shaded and the loading condition. The shading performance, losses and power utilization of the cascaded multi-level inverter are compared with that of a conventional Pulse Width Modulated (PWM) inverter architecture. The proposed inverter shows better immunity for shading than a PWM inverter. Furthermore, it was found that the switching losses of the proposed inverter are one 10th to one 20th of that of a PWM inverter. Additionally, by properly selecting the switches, it is also possible to reduce the conduction losses compared to that of a PWM inverter. Even though the power utilization is compromised at full insolation, the power utilization performance of the proposed inverter is superior under shading conditions, thus ideally suited for the selected application. As the modular nature of the proposed inverter allows cascading of more H-bridges with fewer cells, the harmonic, shading, loss and power utilization performance of the proposed inverter can be enhanced with more number of steps in the output waveform

IMU-based Modularized Wearable Device for Human Motion Classification

Human motion analysis is used in many different fields and applications. Currently, existing systems either focus on one single limb or one single class of movements. Many proposed systems are designed to be used in an indoor controlled environment and must possess good technical know-how to operate. To improve mobility, a less restrictive, modularized, and simple Inertial Measurement units based system is proposed that can be worn separately and combined. This allows the user to measure singular limb movements separately and also monitor whole body movements over a prolonged period at any given time while not restricted to a controlled environment. For proper analysis, data is conditioned and pre-processed through possible five stages namely power-based, clustering index-based, Kalman filtering, distance-measure-based, and PCA-based dimension reduction. Different combinations of the above stages are analyzed using machine learning algorithms for selected case studies namely hand gesture recognition and environment and shoe parameter-based walking pattern analysis to validate the performance capability of the proposed wearable device and multi-stage algorithms. The results of the case studies show that distance-measure-based and PCA-based dimension reduction will significantly improve human motion identification accuracy. This is further improved with the introduction of the Kalman filter. An LSTM neural network is proposed as an alternate classifier and the results indicate that it is a robust classifier for human motion recognition. As the results indicate, the proposed wearable device architecture and multi-stage algorithms are cable of distinguishing between subtle human limb movements making it a viable tool for human motion analysis.Comment: 10 pages, 12 figures, 28 reference

Holistic interpretation of public scenes using computer vision and temporal graphs to identify social distancing violations

Social distancing measures are proposed as the primary strategy to curb the spread of the COVID-19 pandemic. Therefore, identifying situations where these protocols are violated has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically interpret the information in CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are evaluated in a range of scenarios, and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics, with a system performance of 76% accuracy

A sensitivity matrix approach using two-stage optimization for voltage regulation of LV networks with high PV penetration

The occurrence of voltage violations is a major deterrent for absorbing more rooftop solar power into smart Low-Voltage Distribution Grids (LVDGs). Recent studies have focused on decentralized control methods to solve this problem due to the high computational time in performing load flows in centralized control techniques. To address this issue, a novel sensitivity matrix was developed to estimate the voltages of the network by replacing load flow simulations. In this paper, a Centralized Active, Reactive Power Management System (CARPMS) is proposed to optimally utilize the reactive power capability of smart Photovoltaic (PV) inverters with minimal active power curtailment to mitigate the voltage violation problem. The developed sensitivity matrix is able to reduce the time consumed by 55.1% compared to load flow simulations, enabling near-real-time control optimization. Given the large solution space of power systems, a novel two-stage optimization is proposed, where the solution space is narrowed down by a Feasible Region Search (FRS) step, followed by Particle Swarm Optimization (PSO). The failure of standalone PSO to converge to a feasible solution for 34% of the scenarios evaluated further validates the necessity of the two-stage optimization using FRS. The performance of the proposed methodology was analysed in comparison to the load flow method to demonstrate the accuracy and the capability of the optimization algorithm to mitigate voltage violations in near-real time. The deviations of the mean voltages of the proposed methodology from the load flow method were: 6.5×10−3 p.u for reactive power control using Q-injection, 1.02×10−2 p.u for reactive power control using Q-absorption, and 0 p.u for active power curtailment case

Holistic interpretation of public scenes using computer vision and temporal graphs to identify social distancing violations

Social distancing measures are proposed as the primary strategy to curb the spread of the COVID-19 pandemic. Therefore, identifying situations where these protocols are violated has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically interpret the information in CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are evaluated in a range of scenarios, and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics, with a system performance of 76% accuracy

A novel ultrasound technique to detect early chronic kidney disease [version 2; referees: 2 approved]

Chronic kidney disease (CKD) of unknown etiology is recognized as a major public health challenge and a leading cause of morbidity and mortality in the dry zone in Sri Lanka. CKD is asymptomatic and are diagnosed only in late stages. Evidence points to strong correlation between progression of CKD and kidney fibrosis. Several biochemical markers of renal fibrosis have been associated with progression of CKD. However, no marker is able to predict CKD consistently and accurately before being detected with traditional clinical tests (serum creatinine, and cystatin C, urine albumin or protein, and ultrasound scanning). In this paper, we hypothesize that fibrosis in the kidney, and therefore the severity of the disease, is reflected in the frequency spectrum of the scattered ultrasound from the kidney. We present a design of a simple ultrasound system, and a set of clinical and laboratory studies to identify spectral characteristics of the scattered ultrasound wave from the kidney that correlates with CKD. We believe that spectral parameters identified in these studies can be used to detect and stratify CKD at an earlier stage than what is possible with current markers of CKD