A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels

Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable k-model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity. Crown

Gaussian Process-aided Transfer Learning for Probabilistic Load Forecasting against Anomalous Events

A probabilistic load forecasting method that can deal with sudden load pattern changes caused by abnormal events such as COVID-19 is proposed in this paper. The deep residual network (ResNet) is first applied to extract the load pattern for the normal period from historical data. When an abnormal event occurs, a Gaussian Process (GP) with a composite kernel is utilized to adapt to the changes on load pattern by estimating the forecasting residual of the ResNet. The designed kernel enables the proposed method to adapt rapidly to changes in the load pattern and effectively quantify the uncertainties caused by the abnormal event using a few training samples. Comparative tests with state-of-the-art point and probabilistic forecasting methods demonstrate the effectiveness of the proposed method. IEEE

CdcSegNet: Automatic COVID-19 Infection Segmentation from CT Images

It has been more than two years since the outbreak of COVID-19, which has spread to almost every corner of the world and killed a great number of people. Rapid detection and screening have become an important means of controlling the spread of COVID-19. Segmentation of COVID-19 infected tissue from computed tomography (CT) images of a patient’s lungs can provide clinicians with important information to quantify and diagnose COVID-19. However, the accuracy of medical image segmentation is seriously affected by such factors as the low contrast between the infected tissue and the edge of the surrounding environment, the large variation of the infected tissue and the lack of labeling data. Therefore, a deep learning model called CdcSegNet to accurately segment lung lesions from CT images infected by COVID-19 is proposed. In our method, transfer learning is introduced to solve the problem of lack of annotation data, and three modules, i.e., continuous dilated convolution module (CDC), parallel dual attention module (PDA) and additional multi-core pooling layer (AMP) are innovatively proposed to solve the problem of fuzzy segmentation boundary and to segment effectively infected tissues. Extensive experiments and comparison studies are made, and demonstrate that our model CdcSegNet has high accuracy in COVID-19 segmentation, and is superior to the state-of-the-art models in terms of DICE, SEN, SPE, PPV, and VOE. IEEE

Analysis of Pre- and during COVID-19 Mixed Load Models on Unbalanced Radial Distribution System Using a New Metaphor-Less Rao Optimization

An unbalanced electrical distribution system (DS) with radial construction and passive nature suffers from significant power loss. The unstable load demand and poor voltage profile resulted from insufficient reactive power in the DS. This research implements a unique Rao algorithm without metaphors for the optimal allocation of multiple distributed generation (DG) and distribution static compensators (DSTATCOM). For the appropriate sizing and placement of the device, the active power loss, reactive power loss, minimum value of voltage, and voltage stability index are evaluated as a multiobjective optimization to assess the device's impact on the 25-bus unbalanced radial distribution system. Various load models, including residential, commercial, industrial, battery charging, and other dispersed loads, were integrated to develop a mixed load model for examining electrical distribution systems. The impact of unpredictable loading conditions resulting from the COVID-19 pandemic lockdown on DS is examined. The investigation studied the role of DG and DSTATCOM (DGDST) penetration in the electrical distribution system for variations in different load types and demand oscillations under the critical emergency conditions of COVID-19. The simulation results produced for the mixed load model during the COVID-19 scenario demonstrate the proposed method's efficacy with distinct cases of DG and DSTATCOM allocation by lowering power loss with an enhanced voltage profile to create a robust and flexible distribution network. Copyright © 2023 Jitendra Singh Bhadoriya et al.

Impact of Lockdown on Operation of Distribution System with Renewable Energy Sources and D-STATCOM

This paper provides an effective method for optimal sizing and allocation of DGs & D-STATCOM placement to minimize the actual power losses and improve voltage profile in RDS (Radial Distribution System) with incorporating effect of load growth & load modelling. The technique's legitimacy is tried on the standard IEEE 33-bus RDS by performing load flow analysis after compensating the candidate bus. The outcomes acquired are contrasted with and without the Solar Photovoltaic Panel based DG (PVDG), Wind Turbine based DG (WTDG) and D-STATCOM for minimum actual power loss. Further the changes in the operational circumstances of PVDG and WTDG as well as D-STATCOM, are also investigated in order to fulfil the shifting load profile while preserving the voltage constraint and minimizing real power loss owing to the COVID-19 pandemic. The variation in operational setting and the power supplied to the grid for compensating the coal-based generation during the lockdown, pan-India lights off event and Unlock 1 are also studied in the paper. © 2022 IEEE.

Smoothed Bernstein Online Aggregation for Short-Term Load Forecasting in IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm

We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based on a novel online forecast combination of multiple point prediction models. It contains four steps: i) data cleaning and preprocessing, ii) a new holiday adjustment procedure, iii) training of individual forecasting models, iv) forecast combination by smoothed Bernstein Online Aggregation (BOA). The approach is flexible and can quickly adjust to new energy system situations as they occurred during and after COVID-19 shutdowns. The ensemble of individual prediction models ranges from simple time series models to sophisticated models like generalized additive models (GAMs) and high-dimensional linear models estimated by lasso. They incorporate autoregressive, calendar, and weather effects efficiently. All steps contain novel concepts that contribute to the excellent forecasting performance of the proposed method. It is especially true for the holiday adjustment procedure and the fully adaptive smoothed BOA approach. Author

Human Mobility-Based Features to Analyse the Impact of COVID-19 on Power System Operation of Ireland

COVID-19 non-pharmaceutical interventions (NPIs) are changing human mobility patterns;however, the effects on power systems remain unclear. Previous loads and timings along with weather features are often used in literature as input features in load forecasting, but these may be insufficient during COVID-19. As a result, this paper proposes an analytical framework to assess the impact of COVID-19 on power system operation as well as day-ahead electricity prices in Ireland. To improve peak demand forecasting during pandemics, we incorporate mobility, NPIs, and COVID-19 cases as complementary input features and representative of human behaviour changes. By defining different combinations of these explanatory features, several Machine Learning (ML) algorithms are applied and their performance is compared with the baseline scenario currently used in the literature. Using SHapley Additive Explanations (SHAP), we interpret the best performing model, Light Gradient Boosted Machine, to determine the influence of each feature on the predicted outcomes. We discover that typical load forecasting features still influence ML outcomes the most, but mobility-related changes are also significant. Our finding shows that NPIs impact human behaviour and electricity consumption during times of crisis and can be used in the context of load forecasting to assist policymakers and energy distributors. Author