Managing risk in commercial property development projects during the COVID-19 pandemic: Evidence from China

The COVID-19 pandemic is the biggest global health crisis in years. China is the first market primarily affected by the COVID-19 pandemic, with unprecedented lockdown measures bringing real estate and other economic activities to a standstill. This study has two objectives: (1) to identify the risks critical to the risk management of commercial real estate (CRE) development projects based on the project life cycle stages and (2) to identify the stages most affected by the COVID-19 pandemic and the risk factors at different stages. Three rounds of the Delphi study were conducted with nine experts involved in the construction project. The findings indicate that the construction, lease and sale phases are prone to significant risks. Additionally, the analytic hierarchy process (AHP) identified ‘health and safety risk' as the most critical risk factor during the construction phase and ‘marketing and payback risk' as the most critical risk factor during the lease and sale phase. This study enhanced the effectiveness of risk management practices for implementing CRE development projects in China.

Anxiety and associated factors among Vietnamese students during COVID-19 pandemic: A cross-sectional study.

Mental health care for students in general, particularly anxiety, is a significant problem that needs more attention, especially during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to describe the prevalence of anxiety and examine the associated factors among students during the COVID-19 pandemic in Vietnam. A cross-sectional study was conducted from August to September 2021 among 5730 students. An online survey was used to collect sociodemographic information, and the generalized anxiety disorder questionnaire (GAD-7) was used to assess anxiety symptoms among Vietnamese students. Results showed that the prevalence of anxiety among study participants was 16.2% (95% confidence interval [CI]: 15.3%-17.2%). Factors related to anxiety among students were gender, type of housemate, COVID-19 exposure/infection status, vaccination status, health status, academic performance, and social relationships during the COVID-19 pandemic. A significant number of students experienced anxiety during COVID-19, and this rate was related to several factors. Psychological interventions are required to support students during and after the COVID-19 pandemic and other health crises. Further studies are required to confirm our findings.

Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning.

The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.

The Russia-Ukraine Conflict: Its Implications for the Global Food Supply Chains.

Food is one of the most traded goods, and the conflict in Ukraine, one of the European breadbaskets, has triggered a significant additional disruption in the global food supply chains after the COVID-19 impact. The disruption to food output, supply chains, availability, and affordability could have a long-standing impact. As a result, the availability and supply of a wide range of food raw materials and finished food products are under threat, and global markets have seen recent increases in food prices. Furthermore, the Russian-Ukrainian conflict has adversely affected food supply chains, with significant effects on production, sourcing, manufacturing, processing, logistics, and significant shifts in demand between nations reliant on imports from Ukraine. This paper aims to analyze the impacts of the conflict between Russia and Ukraine on the effectiveness and responsiveness of the global food supply chains. A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, including grey literature, was deployed to investigate six key areas of the food supply chains that would be impacted most due to the ongoing war. Findings include solutions and strategies to mitigate supply chain impacts such as alternative food raw materials, suppliers and supply chain partners supported by technological innovations to ensure food safety and quality in warlike situations.

Codesign of Food System and Circular Economy Approaches for the Development of Livestock Feeds from Insect Larvae.

Processes that utilise low-value wastes and convert them to high-value food ingredients systemically add value across commercial operations. Current common disposal options include use as animal feed, anaerobic digestion, composting, incineration, and the worst-case options of landfill and wastewater disposal. The pressure is acute with food manufacturers needing to align with the UN Sustainable Development Goals and reach targets of zero waste to landfill. This research identifies black soldier fly larvae as a bioreactor that converts most food waste into high-value feed materials. Production of larvae and the regulatory framework for their use as animal feed is being assessed in several nations. The requirement to understand the availability of feedstocks for larvae production and the capability to establish feedstock supply chains was tested in this study using geographical information system and life cycle assessment methodologies, providing new research insights for resource utilisation in a circular economy.