An Evaluation of Offsite Construction Recoveries after the Pandemic: The Case of the Southeast Asian Region

Offsite construction (OSC) in Southeast Asia is facing many challenges due to the COVID-19 pandemic. Despite its importance, there is scant research on this topic in the region. This study aims to review OSC and identify determinants of success and their correlation with success after the pandemic in the Southeast Asian region. This paper follows a sequential exploratory mixed methodology: (1) reviewing statistical data relevant to construction in four countries, (2) carrying out a configurative literature review and documentary research, (3) eliciting responses from in-depth interviews consisting of two phases, (4) collecting written consultancies, and (5) capturing experts' opinions in four countries through a questionnaire survey. The study uses three statistical techniques to determine the correlations between factors: ordinal logistic regression with factors input, a generalized linear model with ordinal logistic response and covariates, and Spearman's correlations. Based on 55 variables and 459 responses from 4 countries, the statistical calculations identify the 42 most significant pairs of relationships between determinants and success, from which the follow-up priorities involve economic differentiation, productivity in the factory setting, reducing uncertainty over the program, and minimizing disturbances in the vicinity of the site under post-COVID-19 controls. This empirical research generates three outcomes: (1) filling the gap of OSC review in the region, (2) offering topical solutions for the construction industry after COVID-19, and (3) helping four countries derive economic benefits from OSC.

Detecting Fake News Conspiracies with Multitask and Prompt-Based Learning

In this paper, we present our participation to the MediaEval-2021 challenge on fake news detection about coronavirus related Tweets. It consists in three subtasks that can be seen as multi-labels classification problems we solved with transformer-based models. We show that each task can be solved independantly with mutiple monotasks models or jointly with an unique multitasks model. Moreover, we propose a prompt-based model that has been finetuned to generate classifications from a pre-trained model based on DistilGPT-2. Our experimental results show the multitask model to be the best to solve the three tasks. Copyright 2021 for this paper by its authors.

Robust Face Mask Detection Using Local Binary Pattern and Deep Learning

The ongoing COVID-19 has caused a great amount of serious troubles for people around the world. Even though vaccination has been proven to be safe and highly effective against the COVID-19, it is far away to prevent thoroughly the spread of the disease and truly halt the pandemic. Therefore, we need to apply additional methods aside from vaccine injection, such as keeping the distance between people and always using the face masks during the ordinary conversations, in efforts to further reduce the COVID-19 contagion rate. To implement such methods, this research aims to investigate an efficient approach to detect and warn people that they should wear mask whenever they go to public places. Our proposed system studies the benefits of Local Binary Pattern (LBP) and deep learning model to provide accurate face mask detection and classification system. After comprehensive testing, we found that our system provided the detection rate up to 90% with the Kaggle, Face-Mask-Net, and our own datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Low-Cost High-Accuracy Thermal Camera Using Off-the-shelf Hardware Devices

The Covid-19 pandemic is spreading worldwide, the demand for controlling and screening fever is increasing very rapidly. Some traditional methods like using thermometers or medical checking cannot handle many people in crowded areas such as airports, train stations, or buildings. Some thermal imaging systems are used to control people's ins and outs. These systems help many governments decrease and prevent viruses' spread by screening human temperature based on skin radiation. Despite all of the advantages this technology has had, this system's very high cost is one reason to prevent some organizations from applying it despite the high potential of image processing, artificial intelligence, and the Internet of Things. This paper will present a system combining a low-cost thermal camera and a standard RGB camera for screening fever combined with attendance checking. The experimental results show that this proposed system can be applied in many areas, from universities to buildings, with the minimum cost and acceptable performance. © 2021 IEEE.

Lipid-Based Nanoparticles in the Clinic and Clinical Trials: From Cancer Nanomedicine to COVID-19 Vaccines

COVID-19 vaccines have been developed with unprecedented speed which would not have been possible without decades of fundamental research on delivery nanotechnology. Lipid-based nanoparticles have played a pivotal role in the successes of COVID-19 vaccines and many other nanomedicines, such as Doxil^R and Onpattro^R, and have therefore been considered as the frontrunner in nanoscale drug delivery systems. In this review, we aim to highlight the progress in the development of these lipid nanoparticles for various applications, ranging from cancer nanomedicines to COVID-19 vaccines. The lipid-based nanoparticles discussed in this review are liposomes, niosomes, transfersomes, solid lipid nanoparticles, and nanostructured lipid carriers. We particularly focus on the innovations that have obtained regulatory approval or that are in clinical trials. We also discuss the physicochemical properties required for specific applications, highlight the differences in requirements for the delivery of different cargos, and introduce current challenges that need further development. This review serves as a useful guideline for designing new lipid nanoparticles for both preventative and therapeutic vaccines including immunotherapies.