We propose palps, a Process Algebra with Locations for Population Systems. palps allows us to produce spatially-explicit individualbased ecological models and to reason about their behavior. palps has two abstraction levels: At the first level, we may define the behavior of an individual of a population and, at the second level, we may specify a system as the collection of individuals of various species located in space. In palps, the individuals move through their life cycle while changing their location and interact with each other in various ways such as predation, infection or mating. Furthermore, we propose a translation of a subset of palps into the probabilistic model checker prism. We illustrate our framework via models of dispersal in metapopulations and by applying prism on palps models for verifying temporal logic properties and conducting reachability and steady-state analysis.

Agricultural activity has always been threatened by the presence of pests and diseases that prevent the proper development of crops and negatively affect the economy of farmers. One of these pests is Coffee Leaf Rust (CLR), which is a fungal epidemic disease that affects coffee trees and causes massive defoliation. As an example, this disease has been affecting coffee trees in Colombia (the third largest producer of coffee worldwide) since the 1980s, leading to devastating losses between 70% and 80% of the harvest. Failure to detect pathogens at an early stage can result in infestations that cause massive destruction of plantations and significantly damage the commercial value of the products. The most common way to detect this disease is by walking through the crop and performing a human visual inspection. As a result of this problem, different research studies have proven that technological methods can help to identify these pathogens. Our contribution is an experiment that includes a CLR development stage diagnostic model in the Coffea arabica, Caturra variety, scale crop through the technological integration of remote sensing (through drone capable multispectral cameras), wireless sensor networks (multisensor approach), and Deep Learning (DL) techniques. Our diagnostic model achieved an F1-score of 0.775. The analysis of the results revealed a p-value of 0.231, which indicated that the difference between the disease diagnosis made employing a visual inspection and through the proposed technological integration was not statistically significant. The above shows that both methods were significantly similar to diagnose the disease.

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

Most interactive scenarios are based on informal specifications, so that it is not possible to formally verify properties of such systems. We advocate the need for a general and formal model aiming at ensuring safe executions of interactive multimedia scenarios. Interactive scores (is) is a formalism based on temporal constraints to describe interactive scenarios. We propose new semantics for is based on timed event structures (TES). With such a semantics, we can specify more properties of the system, in particular, properties about execution traces, which are difficult to specify as constraints. We also present an operational semantics of is based on the non-deterministic timed concurrent constraint calculus and we relate such a semantics to the TES semantics. With the operational semantics, we can describe the behaviour of scores whose timed object durations can be arbitrary integer intervals.