ABSTRACT
Nanofiber‐based products are widely used in the fields of public health, air/water filtration, energy storage, etc. The demand for nonwoven products is rapidly increasing especially after COVID‐19 pandemic. Electrospinning is the most popular technology to produce nanofiber‐based products from various kinds of materials in bench and commercial scales. While centrifugal spinning and electro‐centrifugal spinning are considered to be the other two well‐known technologies to fabricate nanofibers. However, their developments are restricted mainly due to the unnormalized spinning devices and spinning principles. High solution concentration and high production efficiency are the two main strengths of centrifugal spinning, but beaded fibers can be formed easily due to air perturbation or device vibration. Electro‐centrifugal spinning is formed by introducing a high voltage electrostatic field into the centrifugal spinning system, which suppresses the formation of beaded fibers and results in producing elegant nanofibers. It is believed that electrospinning can be replaced by electro‐centrifugal spinning in some specific application areas. This article gives an overview on the existing devices and the crucial processing parameters of these nanofiber technologies, also constructive suggestions are proposed to facilitate the development of centrifugal and electro‐centrifugal spinning.
ABSTRACT
Nanofiber‐based products are widely used in the fields of public health, air/water filtration, energy storage, etc. The demand for nonwoven products is rapidly increasing especially after COVID‐19 pandemic. Electrospinning is the most popular technology to produce nanofiber‐based products from various kinds of materials in bench and commercial scales. While centrifugal spinning and electro‐centrifugal spinning are considered to be the other two well‐known technologies to fabricate nanofibers. However, their developments are restricted mainly due to the unnormalized spinning devices and spinning principles. High solution concentration and high production efficiency are the two main strengths of centrifugal spinning, but beaded fibers can be formed easily due to air perturbation or device vibration. Electro‐centrifugal spinning is formed by introducing a high voltage electrostatic field into the centrifugal spinning system, which suppresses the formation of beaded fibers and results in producing elegant nanofibers. It is believed that electrospinning can be replaced by electro‐centrifugal spinning in some specific application areas. This article gives an overview on the existing devices and the crucial processing parameters of these nanofiber technologies, also constructive suggestions are proposed to facilitate the development of centrifugal and electro‐centrifugal spinning. [ FROM AUTHOR]
ABSTRACT
Increasingly prevalent respiratory infectious diseases (e.g., COVID-19) have posed severe threats to public health. Viruses including coronavirus, influenza, and so on can cause respiratory infections. A pandemic may potentially emerge owing to the worldwide spread of the virus through persistent human-to-human transmission. However, transmission pathways may vary; respiratory droplets or airborne virus-carrying particles can have a key role in transmitting infections to humans. In conjunction with social distancing, hand cleanliness, and other preventative measures, the use of face masks is considered to be another scientific approach to combat ubiquitous coronavirus. Different types of face masks are produced using a range of materials (e.g., polypropylene, polyacrylonitrile, polycarbonate, polyurethane, polystyrene, polyester and polyethylene) and manufacturing techniques (woven, knitted, and non-woven) that provide different levels of protection to the users. However, the efficacy and proper disposal/management of the used face masks, particularly the ones made of non-biodegradable polymers, pose great environmental concerns. This review compiles the recent advancements of face masks, covering their requirements, materials and techniques used, efficacy, challenges, risks, and sustainability towards further enhancement of the quality and performance of face masks.
ABSTRACT
Amid growing debate between scientists and policymakers on the trade-off between public safety and reviving economy during the COVID-19 pandemic, the government of Bangladesh decided to relax the countrywide lockdown restrictions from the beginning of June 2020. Instead, the Ministry of Public Affairs officials have declared some parts of the capital city and a few other districts as red zones or high-risk areas based on the number of people infected in the late June 2020. Nonetheless, the COVID-19 infection rate had been increasing in almost every other part of the country. Ironically, rather than ensuring rapid tests and isolation of COVID-19 patients, from the beginning of July 2020, the Directorate General of Health Services restrained the maximum number of tests per laboratory. Thus, the health experts have raised the question of whether the government is heading toward achieving herd immunity instead of containing the COVID-19 pandemic. In this paper, the dynamics of the pandemic due to SARS-CoV-2 in Bangladesh is analyzed with integrated the Unscented Kalman Filter (UKF) in the SIRD model. We demonstrate that the herd immunity threshold can be reduced to 31% than that of 60% by considering age group cluster analysis resulting in a total of 53.0 million susceptible populations. With the data of COVID-19 cases till January, 2021, the time-varying reproduction numbers are used to explain the nature of the pandemic. Based on the estimations of active, severe and critical cases, we discuss a set of policy recommendations to improve the current pandemic control methods in Bangladesh.
ABSTRACT
The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (<=5%), moderate (>5% and <=50%), and high (>50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.
ABSTRACT
As one of the most densely populated countries in the world, Bangladesh has been trying to contain the impact of a pandemic like coronavirus disease 2019 (COVID-19) since March, 2020. Although government announced an array of restricted measures to slow down the diffusion in the beginning of the pandemic, the lockdown has been lifted gradually by reopening all the industries, markets and offices with a notable exception of educational institutes. As the physical geography of Bangladesh is highly variable across the largest delta, the population of different regions and their lifestyle also differ in the country. Thus, to get the real scenario of the current pandemic and a possible second wave of COVID-19 transmission across Bangladesh, it is essential to analyze the transmission dynamics over the individual districts. In this paper, we propose to integrate the Unscented Kalman Filter (UKF) with classic SIRD model to explain the epidemic evolution of individual districts in the country. We show that UKF-SIRD model results in a robust prediction of the transmission dynamics for 1–4 months. Then we apply the robust UKF-SIRD model over different regions in Bangladesh to estimates the course of the epidemic. Our analysis demonstrates that in addition to the densely populated areas, industrial areas and popular tourist spots will be in the risk of higher COVID-19 transmission if a second wave of COVID-19 occurs in the country. In the light of these outcomes, we also provide a set of suggestions to contain the future pandemic in Bangladesh. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Modern Physics C: Computational Physics & Physical Computation is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
The Susceptible, Infected and Recover (SIR) model is a very simple model to estimate the dynamics of an epidemic. In the current pandemic due to Covid-19, the SIR model has been used to estimate the dynamics of infection for various infected countries. Numerical solutions are used to obtain the value of parameters for the SIR model. The maximum and minimum basic reproduction number (14.5 and 2.3) are predicted to be in Turkey and China, respectively.