Simulation of Teacher Interventions in a Training Course of Mathematics Teacher Educators

This work aimed to characterize simulated teacher interventions to manage a didactic configuration in which students solve problem situations using an ICT resource. We also aimed to identify patterns of interaction between the teacher and the students that make it possible to characterize styles of interaction and assess both their interactional and their ecological suitability. To do this, we used the tools of the Onto-semiotic Approach to Mathematical Cognition and Instruction to analyze the level of reflection of 102 educators of preservice teachers of mathematics, about hypothetical problem resolutions of their students and the types of interactions simulated by them to manage the students’ learning. The educators were participants of a Teacher Training of the Teacher Specialization in Education and ICT in Argentina. The results show that there are three levels of reflection and four types of interaction (maieutic, falsificationist, dogmatic and paternalistic). We did not find a high interactional suitability in their proposals of dialogues, and participants do not consider aspects of the ecological suitability, such as the adaptation to the official curriculum. We conclude that the type of interventions that teacher educators propose is closely related to their level of reflection about the reasons why their students respond to problems in one manner or another.

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies have systematically tested this assumption. We aimed to test the assumption for specialty crops in Denmark. First, we mapped suitability for 41 specialty crops using machine learning. Then, we compared the predicted land suitabilities with the mechanistic model ECOCROP (Ecological Crop Requirements). The results showed that there was little agreement between the suitabilities based on machine learning and ECOCROP. Therefore, we argue that the methods represent different phenomena, which we label as socioeconomic suitability and ecological suitability, respectively. In most cases, machine learning predicts socioeconomic suitability, but the ambiguity of the term land suitability can lead to misinterpretation. Therefore, we highlight the need for increasing awareness of this distinction as a way forward for agricultural land suitability assessment.

Dengue in Western Uganda: a prospective cohort of children presenting with undifferentiated febrile illness

Background The spatial distribution and burden of dengue in sub-Saharan Africa remains highly uncertain, despite high levels of ecological suitability. The goal of this study was to describe the epidemiology of dengue among a cohort of febrile children presenting to outpatient facilities located in areas of western Uganda with differing levels of urbanicity and malaria transmission intensity. Methods Eligible children were first screened for malaria using rapid diagnostic tests. Children with a negative malaria result were tested for dengue using a combination NS1/IgM/IgG rapid test (SD Bioline Dengue Duo). Confirmatory testing by RT-PCR was performed in a subset of participants. Antigen-capture ELISA was performed to estimate seroprevalence. Results Only 6 of 1416 (0.42%) children had a positive dengue rapid test, while none of the RT-PCR results were positive. ELISA testing demonstrated reactive IgG antibodies in 28 (2.2%) participants with the highest prevalence seen at the urban site in Mbarara (19 of 392, 4.9%, p < 0.001). Conclusions Overall, these findings suggest that dengue, while present, is an uncommon cause of non-malarial, pediatric febrile illness in western Uganda. Further investigation into the eocological factors that sustain low-level transmission in urban settings are urgently needed to reduce the risk of epidemics.

Intelligent urban ecological suitability system based on pattern recognition

Today, with the rapid development of urbanization, the ecological and environmental problems of the city have become increasingly serious and have become the focus of the world. The most important issue facing the majority of ecological workers is how to apply the theory of ecology to solve today’s problems. The various environmental problems faced in urban life and the sustainable development of the city’s ecological civilization. How ecological planning is used to coordinate the relationship between people and the natural environment and natural resources is increasingly gaining attention and expanding the range and scope of its applications. However, as an ecological suitability analysis based on ecological planning, many analytical methods and systems are still being explored and developed due to the geographical complexity and factor diversity involved. In recent years, with the rapid development of computer hardware and software technology, pattern recognition has received more and more attention, pattern recognition and image processing technology has become more and more perfect, and has been successfully applied in more and more fields. This thesis begins to focus on the urban ecological suitability content based on pattern recognition technology and image processing. The main contents of this thesis include: introducing the background of urban ecological suitability and the status quo of ecological suitability analysis and existing research methods. According to the structure of the urban ecosystem and the national standards for the construction of ecological systems and ecological cities, an indicator system for ecological suitability evaluation is established. A pattern recognition system and common pattern recognition and image processing methods are introduced. Based on some common evaluation methods and models, the pattern recognition technology theory and image processing technology are introduced into the urban ecological suitability analysis. Based on the image system theory and vector projection principle, the ecological suitability analysis is established. Associated projection model. The model considers the evaluation sample and the quality standards at each level as vectors, and respectively projects the same vector ideal. Based on establishing the ecological suitability evaluation index system and standards, the ecological suitability was evaluated by using the model.