A framework of L-HC and AM-MKF for accurate harmonic supportive control schemes

In this paper, an enhanced optimal control technique based on adaptive Maximize-M Kalman filter (AM-MKF) is used. To maximize power extraction from solar PV (Photovoltaic) panel, a learning-based hill climbing (L-HC) algorithm is implemented for a grid integrated solar PV system. For the testing, a three-phase system configuration based on 2-stage topology, and the deployed load on a common connection point (CCP) are considered. The L-HC MPPT algorithm is the modified version of HC (Hill Climbing) algorithm, where issues like, oscillation in steady-state condition and, slow response during dynamic change condition are mitigated. The AM-MKF is an advanced version of KF (Kalman Filter), where for optimal estimation in KF, an AM-M (Adaptive Maximize-M) concept is integrated. The key objective of the novel control strategy is to extract maximum power from the solar panel and to meet the demand of the load. After satisfying the load demand, the rest power is transferred to the grid. However, in the nighttime, the system is used for reactive power support, which mode of operation is known as a DSTATCOM (Distribution Static Compensator). The capability of developed control strategies, is proven through testing on a prototype. During experimentation, different adverse grid conditions, unbalanced load situation and variable solar insolation are considered. In these situations, the satisfactory performances of control techniques prove the effectiveness of the developed control strategy

On the assessment of cyber risks and attack surfaces in a real-time co-simulation cybersecurity testbed for inverter-based microgrids

The integration of variable distributed generations (DGs) and loads in microgrids (MGs) has made the reliance on communication systems inevitable for information exchange in both control and protection architectures to enhance the overall system reliability, resiliency and sustainability. This communication backbone in turn also exposes MGs to potential malicious cyber attacks. To study these vulnerabilities and impacts of various cyber attacks, testbeds play a crucial role in managing their complexity. This research work presents a detailed study of the development of a real-time co-simulation testbed for inverter-based MGs. It consists of a OP5700 real-time simulator, which is used to emulate both the physical and cyber layer of an AC MG in real time through HYPERSIM software; and SEL-3530 Real-Time Automation Controller (RTAC) hardware configured with ACSELERATOR RTAC SEL-5033 software. A human–machine interface (HMI) is used for local/remote monitoring and control. The creation and management of HMI is carried out in ACSELERATOR Diagram Builder SEL-5035 software. Furthermore, communication protocols such as Modbus, sampled measured values (SMVs), generic object-oriented substation event (GOOSE) and distributed network protocol 3 (DNP3) on an Ethernet-based interface were established, which map the interaction among the corresponding nodes of cyber-physical layers and also synchronizes data transmission between the systems. The testbed not only provides a real-time co-simulation environment for the validation of the control and protection algorithms but also extends to the verification of various detection and mitigation algorithms. Moreover, an attack scenario is also presented to demonstrate the ability of the testbed. Finally, challenges and future research directions are recognized and discussed

Community Awareness of HPV Screening and Vaccination in Odisha

Introduction. A number of new technologies including cervical cancer screening and vaccination have introduced new tools in the fight against cervical cancer. Methods. This study was set in Odisha, India, at the Acharya Harihar Regional Cancer Center and study research infrastructure at the Asian Institute of Public Health. IRB approvals were obtained and a research assistant recruited 286 women aged 18–49 years, who provided informed consent and completed a survey tool. Data were entered into EpiData software and statistical analysis was conducted. Results. 76.3% women participants were married, 45.5% had sexual debut at age 21 or greater, 60.5% used contraception, 12.2% reported having a Pap smear in the past, and 4.9% reported having prior genital warts. Most, 68.8% had never heard of HPV and 11.9% were aware that HPV is the main cause of cervical cancer. 82.9% women thought that vaccinations prevent disease, and 74.8% said they make the decision to vaccinate their children. Conclusion. The Odisha community demonstrated a low level of knowledge about cervical cancer prevention, accepted vaccinations in the prevention of disease and screening, and identified mothers/guardians as the key family contacts

Leaky least logarithmic absolute difference based control algorithm and learning based InC MPPT technique for grid integrated PV system

This paper introduces a novel leaky least logarithmic absolute difference (LLLAD)-based control algorithm and a learning-based incremental conductance (LIC) maximum power point tracking algorithm for a grid-integrated solar photovoltaic (PV) system. Here, a three-phase topology of the grid-integrated PV system is implemented, with the nonlinear/linear loads. The proposed LIC technique is an improved form of an incremental conductance (InC) algorithm, where inherent problems of the traditional InC technique, such as steady-state oscillations, slow dynamic responses, and fixed-step-size issues, are successfully mitigated. The prime objective of the proposed LLLAD control is to meet the active power requirement of the loads from the generated solar PV power, and after satisfying the load demand, the excess power is supplied to the grid. However, when the generated solar power is less than the load demand, then LLLAD meets the load by taking extra required power from the grid. During these power management processes, on the grid side, the power quality is maintained. During daytime, the proposed control technique provides load balancing, power factor correction, and harmonic filtering. Moreover, when solar irradiation is zero, then the dc-link capacitor and a voltage-source converter act as a distribution static compensator, which enhances the utilization factor of the system. The proposed techniques are modeled, and their performances are verified experimentally on a developed prototype in solar insolation variation conditions, unbalanced loading, and in different grid disturbances such as over- and undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc. Test results have met the objectives of the proposed paper, and parameters are under the permissible limit, according to the IEEE-519 standard

Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019 (COVID-19) has raised a real threat to human lives owing to infection on lung cells and human respiratory systems. It is a daunting task for the researchers to find suitable infection patterns on lung CT images for automated diagnosis of COVID-19. A novel integrated semi-supervised shallow neural network framework comprising a Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) for automatic segmentation of lung CT images followed by Fully Connected (FC) layers, is proposed in this article. The proposed PQISNet model is aimed at providing fully automated segmentation of lung CT slices without incorporating pre-trained convolutional neural network based models. A parallel trinity of layered structure of quantum bits are interconnected using an N-connected second order neighborhood-based topology in the suggested PQIS-Net architecture for segmentation of lung CT slices with wide variations of local intensities. A random patch-based classification on PQIS-Net segmented slices is incorporated at the classification layers of the suggested semi-supervised shallow neural network framework. Intensive experiments have been conducted using three publicly available data sets, one for purely segmentation task and the other two for classification (COVID-19 diagnosis). The experimental outcome on segmentation of CT slices using self-supervised PQIS-Net and the diagnosis efficiency (Accuracy, Precision and AUC) of the integrated semi-supervised shallow framework is found to be promising. The proposed model is also found to be superior than the best state of the art techniques and pre-trained convolutional neural network-based models, specially in COVID-19 and Mycoplasma Pneumonia (MP) screening

From menarche to menopause: A population-based assessment of water, sanitation, and hygiene risk factors for reproductive tract infection symptoms over life stages in rural girls and women in India.

Women face greater challenges than men in accessing water, sanitation, and hygiene (WASH) resources to address their daily needs, and may respond to these challenges by adopting unsafe practices that increase the risk of reproductive tract infections (RTIs). WASH practices may change as women transition through socially-defined life stage experiences, like marriage and pregnancy. Thus, the relationship between WASH practices and RTIs might vary across female reproductive life stages. This cross-sectional study assessed the relationship between WASH exposures and self-reported RTI symptoms in 3,952 girls and women from two rural districts in India, and tested whether social exposures represented by reproductive life stage was an effect modifier of associations. In fully adjusted models, RTI symptoms were less common in women using a latrine without water for defecation versus open defecation (Odds Ratio (OR) = 0.69; Confidence Interval (CI) = 0.48, 0.98) and those walking shorter distances to a bathing location (OR = 0.79, CI = 0.63, 0.99), but there was no association between using a latrine with a water source and RTIs versus open defecation (OR = 1.09; CI = 0.69, 1.72). Unexpectedly, RTI symptoms were more common for women bathing daily with soap (OR = 6.55, CI = 3.60, 11.94) and for women washing their hands after defecation with soap (OR = 10.27; CI = 5.53, 19.08) or ash/soil/mud (OR = 6.02; CI = 3.07, 11.77) versus water only or no hand washing. WASH practices of girls and women varied across reproductive life stages, but the associations between WASH practices and RTI symptoms were not moderated by or confounded by life stage status. This study provides new evidence that WASH access and practices are associated with self-reported reproductive tract infection symptoms in rural Indian girls and women from different reproductive life stages. However, the counterintuitive directions of effect for soap use highlights that causality and mechanisms of effect cannot be inferred from this study design. Future research is needed to understand whether improvements in water and sanitation access could improve the practice of safe hygiene behaviors and reduce the global burden of RTIs in women