Blockchain technology for agricultural supply chains during the COVID-19 pandemic: Benefits and cleaner solutions.

This study aims to investigate blockchain technology for agricultural supply chains during the COVID-19 pandemic. Benefits and solutions are identified for the smooth conduction of agricultural supply chains during COVID-19 using blockchain. This study uses interviews with agricultural companies operating in Pakistan. The findings discover the seven most commonly shared benefits of applying blockchain technology, four major challenges, and promising solutions. About 100% of the respondents mentioned blockchain as a solution for tracking the shipment during COVID-19, data retrieval and data management, product and transaction frauds, and an Inflexible international supply chain. Roughly 75% of the respondents mentioned the challenge of lack of data retrieval and data management and the Inflexible international supply chain in COVID-19 besides their solutions. This study can expand existing knowledge related to agricultural supply chains. The experiences shared in this study can serve as lessons for practitioners to adopt the blockchain technology for performing agricultural supply chain during pandemic situations such as COVID-19.

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

Covid-19 shock: Development of strategic management framework for global energy.

Energy resources are vital for the economic development of any nation, and they are currently recognised as an essential commodity for human beings. Many countries are facing various levels up to severe energy crisis due to limited natural resources, coupled with the Covid-19 pandemic. This crisis can lead to the shutdown or restriction of many industrial units, limited energy access, exacerbating unemployment, simultaneous impacts on people's lives. The main reason for these problems is the increasing gap between energy supply and demand, logistics, financial issues, as well as ineffective strategic planning issues. Different countries have different visions, missions, and strategies for energy management. Integrated strategic management is requisite for managing global energy. This study aims to develop a strategic management framework that can be used as a methodology for policymakers to analyse, plan, implement, and evaluate the energy strategy globally. A conceptual research method that relies on examining the related literature is applied to develop the framework. The present study yielded two main observations: 1) The identification of key concepts to consider in designing the strategic management framework for global energy, and 2) A strategic management framework that integrates the scope, process, important components, and steps to manage global energy strategies. This framework would contribute to providing a standard procedure to manage energy strategies for policymakers at the global, regional, national, state, city, district, and sector levels.

Simulation and Improvement of Patients' Workflow in Heart Clinics during COVID-19 Pandemic Using Timed Coloured Petri Nets.

The COVID-19 epidemic has spread across the world within months and creates multiple challenges for healthcare providers. Patients with cardiovascular disease represent a vulnerable population when suffering from COVID-19. Most hospitals have been facing difficulties in the treatment of COVID-19 patients, and there is a need to minimise patient flow time so that staff health is less endangered, and more patients can be treated. This article shows how to use simulation techniques to prepare hospitals for a virus outbreak. The initial simulation of the current processes of the heart clinic first identified the bottlenecks. It confirmed that the current workflow is not optimal for COVID-19 patients; therefore, to reduce waiting time, three optimisation scenarios are proposed. In the best situation, the discrete-event simulation of the second scenario led to a 62.3% reduction in patient waiting time. This is one of the few studies that show how hospitals can use workflow modelling using timed coloured Petri nets to manage healthcare systems in practice. This technique would be valuable in these challenging times as the health of staff, and other patients are at risk from the nosocomial transmission.

Value chain mapping of the water and sewage treatment to contribute to sustainability.

Inaccessible hygiene water sources and sanitation are one of the sustainability issues that need to be solved. An attempt to solve this problem is to change the conventional system used in the water supply and sewage treatment to sustainable water and waste management. To transform the system, companies initially need to map their business value chain. However, this process is often not receiving a full attention by the organisation. In an academic perspective, there are limited studies that map the value chains of water and waste systems. To overcome this limitation, the present study aims to map the value chain processes of the water and wastewater utility companies towards a sustainability solution. A review of related studies is used to conduct this study. Khuzestan Urban Water and Sewage company in Iran has been selected as case studies. The mapping results indicate a lack of sustainability integration in a water management system that leads to ineffective and inefficient water management. Value chain mapping process is significant for practitioners, particularly in the water and sewage companies, as a starting point for transforming their conventional water management systems towards sustainability. Experts at the company stated that value chain mapping as part of value chain analysis enables organisations to increase operational efficiency and eliminate waste by 57%.