A retrospective on ASPires—An advanced system for the prevention and early detection of forest fires

2021 ◽  
pp. 100456
Author(s):  
Peter Peinl
2021 ◽  
Vol 11 (5) ◽  
pp. 209
Author(s):  
Olga Arias-Gundín ◽  
Ana García Llamazares

(1) Background: The response to intervention (RtI) model makes possible the early detection of reading problems and early intervention for students at risk. The purpose of this study is to analyze the effective measures that identify struggling readers and the most effective practices of the RtI model in reading in Primary Education. (2) Method: A systematic review of the literature published between 2010 and 2020 was performed, analyzing in the 31 selected articles, the identification and monitoring methods and the interventions at the different tiers of the RtI model. (3) Results: There are different methods to identify struggling readers, and there is no consensus on the matter. There are also many differences in the implementation of the different tiers of the RtI model; however, its effectiveness is demonstrated. (4) Conclusions: The implementation of the RtI model in a flexible way adapted to the circumstances of each moment, and can be considered as a highly effective resource in the prevention and early detection of reading learning problems.


2021 ◽  
Vol 21 (2) ◽  
pp. 152-166
Author(s):  
Patricia Da Rosa ◽  
Lori Koenecke ◽  
Laura Gudgeon ◽  
Whitney Keller ◽  
Wei Gu

Automatic environmental monitoring is a field that encompasses several scientific practices for the assessment of risks that may negatively impact a given environment, such as the forest. A forest is a natural environment that hosts various forms of plant and animal life, so preserving the forest is a top priority. To this end, the authors of this paper will focus on the development of an intelligent system for the early detection of forest fires, based on an IoT solution. This latter will thus facilitate the exploitation of the functionalities offered by the Cloud and mobile applications. Detecting and predicting forest fires with accuracy is a difficult task that requires machine learning and an in-depth analysis of environmental conditions. This leads the authors to adopt the forward neural network algorithm by highlighting its contribution through real experiments, performed on the prototype developed in this paper.


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