2023
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Publication Open Access COVID-19 symptom identification using Deep Learning and hardware emulated systems(Elsevier, 2023-06-28) Liyanarachchi, R; Wijekoon, J; Premathilaka, M; Vidhanaarachchi, SThe COVID-19 pandemic disrupted regular global activities in every possible way. This pandemic, caused by the transmission of the infectious Coronavirus, is characterized by main symptoms such as fever, fatigue, cough, and loss of smell. A current key focus of the scientific community is to develop automated methods that can effectively identify COVID-19 patients and are also adaptable for foreseen future virus outbreaks. To classify COVID-19 suspects, it is required to use contactless automatic measurements of more than one symptom. This study explores the effectiveness of using Deep Learning combined with a hardware-emulated system to identify COVID-19 patients in Sri Lanka based on two main symptoms: cough and shortness of breath. To achieve this, a Convolutional Neural Network (CNN) based on Transfer Learning was employed to analyze and compare the features of a COVID-19 cough with other types of coughs. Real-time video footage was captured using a FLIR C2 thermal camera and a web camera and subsequently processed using OpenCV image processing algorithms. The objective was to detect the nasal cavities in the video frames and measure the breath cycles per minute, thereby identifying instances of shortness of breath. The proposed method was first tested on crowd-sourced datasets (Coswara, Coughvid, ESC-50, and a dataset from Kaggle) obtained online. It was then applied and verified using a dataset obtained from local hospitals in Sri Lanka. The accuracy of the developed methodologies in diagnosing cough resemblance and recognizing shortness of breath was found to be 94% and 95%, respectively.Publication Open Access Minimizing Liability of the COVID-19 Pandemic on Construction Contracts—A Structural Equation Model for Risk Mitigation of Force Majeure Impacts(MDPI, 2023-01) Chadee, A. A; Gallage, S; Martin, H. H; Rathnayake, U; Ray, I; Kumar, B; Sihag, PA pandemic is a force majeure event, and contracting parties can invoke conditions under force majeure to minimize liability for unforeseen, uncontrollable, and unavoidable circumstances. This study develops a conceptual model to assist in the management of delays and cost overruns due to force majeure events arising from the construction sector in Small Island Developing States (SIDS). A critical case study analysis of past epidemics and pandemics was conducted to develop a survey questionnaire for administration to construction professionals in Trinidad and Tobago. Based on the empirical data of 65 construction professionals, the structural equation model shows that there are strong causal effects from the implications of COVID-19 and force majeure events, which in turn have a dire impact on the construction industry. The leading implication of COVID-19 is the drastic increases in the cost of materials. Also, granting an extension of time to contractors was the main risk variable under the force majeure conditions. From the results, the measurement model verifies that events under force majeure and its perceived implications strongly influence the construction industry, and proposes that force majeure contractual clauses require explicit treatment of the periodic reoccurrence of pandemics to avoid conflicts among contracting parties. This research explores and builds on new avenues from the latest COVID-19 scholarship to better understand existing impacts on the construction industry, and consequently add to the novel body of knowledge on the implications of pandemics on construction contracts. Overall, this research provides a risk-guidance framework for construction professionals and academia to mitigate unforeseen, uncontrollable, and unavoidable risks on construction projects