Beltrami-like fields created by baroclinic effect in two-fluid plasmas

A theory of two-dimensional plasma evolution with Beltrami-like flow and field due to baroclinic effect has been presented. Particular solution of the nonlinear two-fluid equations is obtained. This simple model can explain the generation of magnetic field without assuming the presence of a seed in the system. Coupled field and flow naturally grow together. The theory has been applied to estimate B-field in laser-induced plasmas and the result is in good agreement with experimental values.Comment: 3 page

Electrodynamics of Black Holes in STU Supergravity

External magnetic fields can probe the composite structure of black holes in string theory. With this motivation we study magnetised four-charge black holes in the STU model, a consistent truncation of maximally supersymmetric supergravity with four types of electromagnetic fields. We employ solution generating techniques to obtain Melvin backgrounds, and black holes in these backgrounds. For an initially electrically charged static black hole immersed in magnetic fields, we calculate the resultant angular momenta and analyse their global structure. Examples are given for which the ergoregion does not extend to infinity. We calculate magnetic moments and gyromagnetic ratios via Larmor's formula. Our results are consistent with earlier special cases. A scaling limit and associated subtracted geometry in a single surviving magnetic field is shown to lift to $AdS_3\times S^2$. Magnetizing magnetically charged black holes give static solutions with conical singularities representing strings or struts holding the black holes against magnetic forces. In some cases it is possible to balance these magnetic forces.Comment: 31 page

Modeling and Experimental Investigation of Energy Management for Hybrid Electric Vehicle based on Variable Structure Control Strategy

The current study presents real-time modeling and non-linear controllers-based energy management system (EMS) for multi-energy hybrid Electric Vehicle (EV), where a detailed physics-based dynamic vehicle model has been considered. The main objective of the paper is to regulate the power flow, stabilize DC voltage for an EV driven by a brushless DC motor, and ensure effective power sharing in a hybrid electric system under complex driving circumstances. The approach is based on tracking the reference battery current by backstepping sliding mode control for optimal power distribution. Subsequently, Integral Sliding Mode Control based on barrier function (NBS-ISMC), and Fractional Order Terminal Sliding Mode Control (FOTSMC) are implemented to control the switching operation of converters for Photovoltaic (PV) and Ultra-capacitor (UC), respectively. User-defined and practical standard drive cycles are selected to test the effectiveness of proposed reference current controllers

Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach

Analysis of Device-to-Device Communication over Double-Generalized Gamma Channels

In this paper, performance of a device-to-device (D2D) communication system is analyzed over double-generalized Gamma (dGG) fading channels. The dGG is a generic distribution for modeling double-scattering fading conditions. Co-channel interference (CCI) caused by various wireless devices in the system is also considered. The CCI fading channel is assumed to be Nakagami distributed. Analytical expressions for important statistical metrics, i.e. probability density function (PDF) and cumulative distribution function (CDF) of signal-to-interference ratio (SIR), are presented. Based on these statistical parameters, expressions for the outage probability, channel capacity and symbol error rate (SER) of the D2D communication system are presented. The performance of D2D system is then discussed and analyzed with the help of numerical results with arbitrary channel fading, path-loss and interference conditions

Analysis of D2D Communication System Over κ-μ Shadowed Fading Channel

Outage performance of a device-to-device (D2D) communication system in the presence of co-channel interference (CCI) is analyzed in this paper. Channels for the D2D and CCI signals are assumed to be κ-μ shadowed faded. Maximal ratio combining (MRC) and selection combining (SC) techniques are considered to combat fading conditions. Characteristic function (CF) expression of the D2D system in the presence of CCI is presented. Outage probability and success probability expressions are presented for the MRC and SC schemes. These outage probability and success probability expressions are functions of various CCI, path-loss and channel fading parameters. With the help of numerical results, effects of CCI on the performance of D2D communication system under different channel fading and path-loss conditions are discussed

Cost estimation alongside a multi-regional, multi-country randomized trial of antenatal ultrasound in five low-and-middle-income countries

Background: Improving maternal health has been a primary goal of international health agencies for many years, with the aim of reducing maternal and child deaths and improving access to antenatal care (ANC) services, particularly in low-and-middle-income countries (LMICs). Health interventions with these aims have received more attention from a clinical effectiveness perspective than for cost impact and economic efficiency.Methods: We collected data on resource use and costs as part of a large, multi-country study assessing the use of routine antenatal screening ultrasound (US) with the aim of considering the implications for economic efficiency. We assessed typical antenatal outpatient and hospital-based (facility) care for pregnant women, in general, with selective complication-related data collection in women participating in a large maternal health registry and clinical trial in five LMICs. We estimated average costs from a facility/health system perspective for outpatient and inpatient services. We converted all country-level currency cost estimates to 2015 United States dollars (USD). We compared average costs across countries for ANC visits, deliveries, higher-risk pregnancies, and complications, and conducted sensitivity analyses.Results: Our study included sites in five countries representing different regions. Overall, the relative cost of individual ANC and delivery-related healthcare use was consistent among countries, generally corresponding to country-specific income levels. ANC outpatient visit cost estimates per patient among countries ranged from 15 to 30 USD, based on average counts for visits with and without US. Estimates for antenatal screening US visits were more costly than non-US visits. Costs associated with higher-risk pregnancies were influenced by rates of hospital delivery by cesarean section (mean per person delivery cost estimate range: 25-65 USD).Conclusions: Despite substantial differences among countries in infrastructures and health system capacity, there were similarities in resource allocation, delivery location, and country-level challenges. Overall, there was no clear suggestion that adding antenatal screening US would result in either major cost savings or major cost increases. However, antenatal screening US would have higher training and maintenance costs. Given the lack of clinical effectiveness evidence and greater resource constraints of LMICs, it is unlikely that introducing antenatal screening US would be economically efficient in these settings--on the demand side (i.e., patients) or supply side (i.e., healthcare providers).Trial registration: Trial number: NCT01990625 (First posted: November 21, 2013 on https://clinicaltrials.gov )