Investigating the role of non-pharmaceutical interventions (NPI) during pandemics: COVID-19 as an example.

Background And Objective: The coronavirus pandemic, with a wide range of clinical manifestations, is considered a serious emergency in increasing anxiety for vulnerable groups of young people such as students. The purpose of this study is to look into how COVID-19 affects depression and anxiety in students at Damghan University. It also aims to determine how non-pharmaceutical intervention (NPI) education affects COVID-19 anxiety and related aspects.

Investigating DNA damage caused by COVID-19 and influenza in post COVID-19.

The SARS-CoV-2 virus (termed COVID-19) was responsible for over 34 million global deaths. Although the COVID-19 pandemic has subsided, infection by emerging mutant variants of SARS-CoV-2 poses a continuing threat to public health. COVID-19 infection has been associated with the development of cytokine storm syndrome, hypercoagulability, immunological dysregulation and direct viral invasion of organs, and the long-term consequences for the health of COVID-19 survivors are currently unknown.

Investigating the relationship between lymphocyte cells apoptosis and DNA damage and oxidative stress and therapeutic and clinical outcomes of COVID-19 elderly patients.

Background: While COVID-19 has been controlled and deaths have decreased, the long-term consequences of COVID-19 remain a challenge we face today. This study was conducted to determine the relationship between the apoptosis of lymphocyte cells with DNA damage and oxidative stress and the therapeutic and clinical outcomes of elderly patients with COVID-19.

Methods: This study was conducted from April 2020 to May 2021 (the period of severe attacks of the epidemic peak of COVID-19) and September 2022 (the post-COVID-19 period).

An approach to detect and predict epileptic seizures with high accuracy using convolutional neural networks and single-lead-ECG signal.

One of the epileptic patients' challenges is to detect the time of seizures and the possibility of predicting. This research aims to provide an algorithm based on deep learning to detect and predict the time of seizure from one to two minutes before its occurrence. The proposed Convolutional Neural Network (CNN) can detect and predict the occurrence of focal epilepsy seizures through single-lead-ECG signal processing instead of using EEG signals.

Ultra-thin broadband solar absorber based on stadium-shaped silicon nanowire arrays.

This paper investigates how the dimensions and arrangements of stadium silicon nanowires (NWs) affect their absorption properties. Compared to other NWs, the structure proposed here has a simple geometry, while its absorption rate is comparable to that of very complex structures. It is shown that changing the cross-section of NW from circular (or rectangular) to a stadium shape leads to change in the position and the number of absorption modes of the NW.

Design of two compact waveguide display systems utilizing metasurface gratings as couplers.

We propose two approaches to design compact head mount display (HMD) systems employing metasurface gratings. In the first approach, we design and simulate a monocular optical waveguide display by applying crystalline-silicon elliptical-shaped metasurface arrays as couplers on a right trapezoid waveguide to achieve large field of view (FOV) horizontally. As such, we achieve a FOV as large as 80° that is approximately 80% higher than the FOV in traditional waveguide systems based on diffractive gratings.

Calibration time reduction through local activities estimation in motor imagery-based brain-computer interfaces.

Objective: One of the main limitations for the practical use of brain-computer interfaces (BCI) is the calibration phase. Several methods have been suggested for the truncating of this undesirable time, including various variants of the popular CSP method. In this study, we cope with the problem, using local activities estimation (LAE).