Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks

The current COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With more and more people getting affected during the second wave, the hospitals were over-burdened, running out of supplies and oxygen. In this scenario, prediction of the number of COVID-19 cases beforehand might have helped in the better utilization of limited resources and supplies. This manuscript deals with the prediction of new COVID-19 cases, new deaths and total active cases for multiple days in advance. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models that are pre-trained on the data from four different countries (United States of America, Brazil, Spain and Bangladesh) and are fine-tuned or retrained on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then give a multiple days ahead predictions using recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the combination of different models. This method with two countries, Spain and Brazil, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models.Comment: 8 pages, 7 figure

Hybrid automotive in-vehicle networks

The design of automotive in-vehicle networks is influenced by several factors like bandwidth, real-time properties, reliability and cost. This has led to a number of protocols and communication standards like CAN, MOST, FlexRay and more recently the use of Ethernet. In the future, wireless in-vehicle communication might also become a possibility. In all of these cases, often hybrid schemes such as the combination of time-triggered (TT) and event-triggered (ET) paradigms have been considered to be useful. Thus, hybrid protocols like FlexRay and TTEthernet, offering advantages of TT and ET communications, are becoming more popular. However, until now the hybrid nature of the protocols has not been exploited in application design. In this paper, we will discuss design strategies for automotive control applications that exploit the hybrid nature of the underlying communication architecture on which they are mapped. Towards this, we will consider a mix of time- and event-triggered schemes as well as a combination of reliable and unreliable communication. Correspondingly, we will show how appropriate abstractions of these hybrid schemes could be lifted to the application design stage

Exploiting system dynamics for resource-efficient automotive CPS design

Automotive embedded systems are safety-critical, while being highly cost-sensitive at the same time. The former requires resource dimensioning that accounts for the worst case, even if such a case occurs infrequently, while this is in conflict with the latter requirement. In order to manage both of these aspects at the same time, one research direction being explored is to dynamically assign a mixture of resources based on needs and priorities of different tasks. Along this direction, in this paper we show that by properly modeling the physical dynamics of the systems that an automotive control software interacts with, it is possible to better save resources while still guaranteeing safety properties. Towards this, we focus on a distributed controller implementation that uses an automotive FlexRay bus. Our approach combines techniques from timing/schedulability analysis and control theory and shows the significance of synergistically combining the cyber component and physical processes in the cyber-physical systems (CPS) design paradigm

Multi-objective co-optimization of FlexRay-based distributed control systems

\u3cp\u3eRecently, research on control and architecture co- design has been drawing increasingly more attention. This is because these techniques integrate the design of the controllers and the architecture and explore the characteristics on both sides to achieve more efficient design of embedded control systems. However, there still exist several challenges like the large design space and inadequate trade-off opportunities for different objectives like control performance and resource utilization. In this paper, we propose a co-optimization approach for FlexRay-based distributed control systems, that synthesizes both the controllers and the task and communication schedules. This approach exploits some FlexRay protocol specific characteristics to reduce the complexity of the whole optimization problem. This is done by employing a customized control design and a nested two-layered optimization technique. Therefore, compared to existing methods, the proposed approach is more scalable. It also allows multi-objective optimization taking into account both the overall control performance and the bus resource utilization. This approach generates a Pareto front representing the trade-offs between these two, which allows the engineers to make suitable design choices.\u3c/p\u3

Exploiting system dynamics for resource-efficient automotive CPS design

Automotive embedded systems are safety-critical, while being highly cost-sensitive at the same time. The former requires resource dimensioning that accounts for the worst case, even if such a case occurs infrequently, while this is in conflict with the latter requirement. In order to manage both of these aspects at the same time, one research direction being explored is to dynamically assign a mixture of resources based on needs and priorities of different tasks. Along this direction, in this paper we show that by properly modeling the physical dynamics of the systems that an automotive control software interacts with, it is possible to better save resources while still guaranteeing safety properties. Towards this, we focus on a distributed controller implementation that uses an automotive FlexRay bus. Our approach combines techniques from timing/schedulability analysis and control theory and shows the significance of synergistically combining the cyber component and physical processes in the cyber-physical systems (CPS) design paradigm

Screening of ethnomedicinal plants of diverse culture for antiviral potentials

474-481Since time immemorial Ethnomedicinal plants have been used for diverse ailments including infectious diseases. There is an increasing need for new anti-infective molecules, particularly from the plants used in ethnomedicinal practices, as the treatment of infectious diseases with the antimicrobial drugs frequently develops drug-resistance microbes. Herpes simplex virus type 1 (HSV-1) and type 2 (HSV-2) causes a variety of diseases including herpes labiles, keratoconjunctivitis, encephalitis, herpes genitalis, and the lifelong latent infections in sensory nerve ganglia. Till date there is no effective anti-HSV vaccine, and the available drugs used against HSV infections have limited efficacy with frequent development of drug-resistant viruses. Here we have evaluated the anti-HSV potential of nine selected ethnomedicinal plant extracts of different families, traditionally used by diverse communities against skin, intestinal and sexual ailments, using wild-type and clinical isolates of HSV-1. The cytotoxicity of the extracts was determined on Vero cell by MTT assay; while the antiviral activity was screened by cytopathic effect reduction, MTT assay and plaque reduction assay. Interestingly the extracts of Dillenia indica, Odina wodier and Moringa oleifera<span style="mso-bidi-font-style: italic"> exhibited significant antiviral activity against HSV-1 at non-cytotoxic concentrations; while the extracts of Morus alba and Butea monosperma showed antiviral activity at higher concentrations