Computational Drug Design Strategies for Fighting the COVID-19 Pandemic.

The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.

Smart Sensors and Smart Data for Precision Agriculture: A Review.

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.

Toxic Release Damage Distance Assessment Based on the Short-Cut Method: A Case Study for the Transport of Chlorine and Hydrochloric Acid in Densely Urbanized Areas in the Mediterranean Region.

The transportation of dangerous goods by road is the most accident-prone mode of transportation, even if accidents involving road transportation of dangerous goods are considered as a Low Probability and High Consequence event (LPHC event). However, several dangerous goods are transported by road networks, such as petroleum products and chemicals, which can generate major dangerous consequences such as spills, explosions, fires, or toxic clouds. In this context, this article presents a method to calculate and quickly quantify the sizes of impact zones characterized by high lethality and irreversible injuries to people in the case of a hazardous materials transport accident. This method is used as a module for the analysis of the consequences of different potential accident scenarios, for the Web-GIS platform proposed by LOSE+LAB, that implements appropriate ICT tools and systems for monitoring the flow of goods that would enable a continuous monitoring system at the cross-border level and transmit data and information to the territory actors involved in the management of dangerous goods according to the ADR standard. The proposed method provides the user with a visualization of the possible outcomes of an event by reproducing the impact area for different accident scenarios, which can provide quick maps of the hazard and represents a decision support system for territorial governance in terms of intervention and response protocols for emergency management in the cases of dangerous goods accidents.