Digital teaching materials and their relationship with the metacognitive skills of students in primary education

Metacognition is a construct that is noteworthy for its relationship with the prediction and enhancement of student performance. It is of interest in education, as well as in the field of cognitive psychology, because it contributes to competencies, such as learning to learn and the understanding of information. This study conducted research at a state school in the Community of Madrid (Spain) with a sample of 130 students in Grade 3 of their primary education (8 years old). The research involved the use of a digital teaching platform called Smile and Learn, as the feedback included in the digital activities may have an effect on students' metacognition. We analyzed the implementation of the intelligent platform at school and the activities most commonly engaged in. The Junior Metacognitive Awareness Inventory (Jr. MAI) was the measuring instrument chosen for the external evaluation of metacognition. The study's results show a higher use of logic and spatial activities. A relationship is observed between the use of digital exercises that have specific feedback and work on logic and visuospatial skills with metacognitive knowledge. We discuss our findings surrounding educational implications, metacognition assessment, and recommendations for improvements of the digital materials.This research was funded by Community of Madrid ‘Industrial PhD grants’, under project number IND2017/SOC-7874

ProLogis Park Sant Boi: una de las primeras grandes actuaciones de drenaje urbano sostenible en España

Repaso a una de las primeras grandes actuaciones de drenaje sostenible en España. Hace más de 20 años (1998) el grupo inmobiliario internacional ProLogis desarrolló la planificación del ProLogis Park Sant Boi (cerca de Barcelona). Se describen las características de la problemática planteada por la urbanización del sector y cómo fue resuelta en aquellos momentos embrionarios de las técnicas SUDS. Además, una vez construido en 2001, superó con éxito la prueba que generaron las fuertes lluvias de noviembre del 2002 que provocaron inundaciones en otros sectores próximos.Postprint (published version

Comparison of Errors Produced by ABA and ITC Methods for the Estimation of Forest Inventory Attributes at Stand and Tree Level in Pinus radiata Plantations in Chile

Airborne laser scanning (ALS) technology is fully implemented in forest resource assessment processes, providing highly accurate and spatially continuous results throughout the area of interest, thus reducing inventory costs when compared with traditional sampling inventories. Several approaches have been employed to estimate forest parameters using ALS data, such as the Area-Based Approach (ABA) and Individual Tree Crown (ITC). These two methodologies use different information processing and field data collection approaches; thus, it is important to have a selection criterion for the method to be used based on the expected results and admissible errors. The objective of this study was to compare the prediction errors of forest inventory attributes in the functioning of ABA and ITC approaches. A plantation of 500 ha of Pinus radiata (400–600 trees ha−1) in Chile was selected; a forest inventory was conducted using the ABA and ITC methods and the accuracy of both methods was analyzed. The ITC models performed better than the ABA models at low tree densities for all forest inventory attributes (15% MAPE in tree density—N—and 11% in volume—V). There was no significant difference in precision regarding the volume and basal area (G) estimations at medium densities, although ITC obtained better results for density and dominant height (Ho). At high densities, ABA performed better for all the attributes except for height (6.5% MAPE in N, 8.7% in G, and 8.9% in V). Our results showed that the precision of forest inventories based on ALS data can be adjusted depending on tree density to optimize the selected approach (ABA and ITC), thus reducing the inventory costs. Hence, field efforts can be greatly decreased while achieving better prediction accuracies

Prevalence of Depression and Related Factors among Patients with Chronic Disease during the COVID-19 Pandemic: A Systematic Review and Meta-Analysis

The management of chronic diseases in the midst of the COVID-19 pandemic is especially challenging, and reducing potential psychological harm is essential. This review aims to determine the prevalence of depression during the COVID-19 pandemic in patients with chronic disease, and to characterize the impacts of related factors. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The meta-analysis was performed using StatsDirect software. The review identified 33 articles with a total of 50,905 patients with chronic diseases. Four meta-analyses were performed to estimate the prevalence of depression. In diabetic patients, the prevalence ranged from 17% (95% CI = 7–31) (PHQ-9) to 33% (95% CI = 16–51) (PHQ-8); in obese patients, the prevalence was 48% (95% CI = 26–71); and in hypertensive patients, the prevalence was 18% (95% CI = 13–24). The factors significantly associated with depression were female sex, being single, deterioration in the clinical parameters of diabetes, a decrease in self-care behavior, reduced physical activity and sleep time and fear of contagion. The COVID-19 pandemic has significantly increased levels of depression among persons with chronic disease. Pandemics and other emergency events have a major impact on mental health, so early psychological interventions and health management policies are needed to reinforce chronic patients’ physical and mental health

An exploratory analysis of the implementation and use of an intelligent platform for learning in primary education

Smile and Learn is an intelligent platform with more than 4500 educational activities for children aged 3-12. The digital material developed covers all courses of primary education and most of the subjects with the different topic-related worlds with activities in the field of logics and mathematics, science, linguistics and tales, visual-spatial and cognitive skills, emotional intelligence, arts, and multiplayer games. This kind of material supports active learning and new pedagogical models for teachers to use in their lessons. The purpose of this paper is to explore the usage of the platform in three pilot groups schools from different regions of Spain, outlining future directions in the design of such digital materials. Usage is assessed via descriptive analysis and variance analysis, with data collected from users interacting with the intelligent platform. The results show a high use of STEM (Science, Technology, Engineering and Maths) activities among all the activities that could be chosen. Cross-curricular activities are also used. Continuation in the development of such materials is concluded necessary, focusing integration of different fields, accentuating games over quizzes, and the value of teacher training for improving their use in schools.This research was funded by Community of Madrid 'Industrial PhD grants' under project number IND2017/SOC-7874

Thinning Effect of C Sequestration along an Elevation Gradient of Mediterranean Pinus spp. Plantations

Forests are key elements in mitigating the effects of climate change due to the fact of their carbon sequestration capacity. Forest management can be oriented to optimise the carbon sequestration capacity of forest stands, in line with other productive objectives and the generation of ecosystem services. This research aimed to determine whether thinning treatments have a positive influence on the growth patterns of some of the main Mediterranean pine species and, therefore, on their Carbon (C) fixation capacity, both in terms of living biomass and soil organic carbon. The results obtained show that C sequestration capacity (biomass and SOC) increased at higher thinning intensities due to the induced alterations in tree growth patterns. We observed almost a 1.5-fold increase in P. nigra and P. sylvestris, respectively, and over a two-fold increase in P. pinaster under heavy thinning treatments; SOC stocks were affected by the intensity of the thinning treatments. These results can contribute to improving silvicultural practices aimed at C sequestration in forest plantations located in dry areas of the Mediterranean

Assessment of the Carbon Stock in Pine Plantations in Southern Spain through ALS Data and K-Nearest Neighbor Algorithm Based Models

Accurate estimation of forest biomass to enable the mapping of forest C stocks over large areas is of considerable interest nowadays. Airborne laser scanning (ALS) systems bring a new perspective to forest inventories and subsequent biomass estimation. The objective of this research was to combine growth models used to update old inventory data to a reference year, low-density ALS data, and k-nearest neighbor (kNN) algorithm Random Forest to conduct biomass inventories aimed at estimating the C sequestration capacity in large Pinus plantations. We obtained a C stock in biomass (Wt-S) of 12.57 Mg·ha−1, ranging significantly from 19.93 Mg·ha−1 for P. halepensis to 49.05 Mg·ha−1 for P. nigra, and a soil organic C stock of the composite soil samples (0–40 cm) ranging from 20.41 Mg·ha−1 in P. sylvestris to 37.32 Mg·ha−1 in P. halepensis. When generalizing these data to the whole area, we obtained an overall C-stock value of 48.01 MgC·ha−1, ranging from 23.96 MgC·ha−1 for P. halepensis to 58.09 MgC·ha−1 for P. nigra. Considering the mean value of the on-site C stock, the study area sustains 1,289,604 Mg per hectare (corresponding to 4,732,869 Mg CO2), with a net increase of 4.79 Mg·ha−1·year−1. Such C cartography can help forest managers to improve forest silviculture with regard to C sequestration and, thus, climate change mitigation

Bayesian Inference of Gene Expression

Omics techniques have changed the way we depict the molecular features of a cell. The integrative and quantitative analysis of omics data raises unprecedented expectations for understanding biological systems on a global scale. However, its inherently noisy nature, together with limited knowledge of potential sources of variation impacting health and disease, require the use of proper mathematical and computational methods for its analysis and integration. Bayesian inference of probabilistic models allows propagation of the uncertainty from the experimental data to our beliefs of the model parameters, allowing us to appropriately answer complex biological questions. In this chapter, we build probabilistic models of gene expression from RNA-seq data and make inference about their parameters using Bayesian methods. We present models of increasing complexity, from the quantification of a single gene expression to differential gene expression for a whole transcriptome, comparing them to the available tools for analysis of gene expression data. We provide Stan scripts that introduce the reader into the implementation of Bayesian statistics for omics data. The rationale that we apply for transcriptomics data may be easily extended to model the particularities of other omics data and to integrate the different regulatory layers.FS-C received support from the Spanish Ministerio de Economía y Competitividad [grant no. RTI2018-102084-B-I00]; EL-P received support from the Spanish Ministerio de Economía y Competitividad (RTI2018-096961-B-I00), from the European Union (CardioNeT-ITN-289600 and CardioNext-ITN-608027) and the Spanish Carlos III Institute of Health (RD12/0042/066); M.A.d.P received support from the Spanish Ministerio de Economía y Competitividad (SAF2017-83130-R) and from the European Union Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement nº 641639 BIOPOL-ITN-641639; V.J-J. received an ESR contract from BIOPOL-ITN-641639). M.A.d.P is member of the Tec4Bio consortium (ref. S2018/NMT4443). The CNIC is supported by MCIU and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence [MCIU award SEV-2015-0505].S

Assessment of the effects of digital educational material on executive function performance

The Learning Analytics system of the Smile and Learn platform recorded the students'use during class. According to the usage analysis, the results obtained show preference of using activities from Logic and Spatial worlds. In the external analysis of the effect of the learning material, the results record a significant effect using activities in Logic and Spatial worlds with the Gray Trails task, which involves spatial perception, processing speed, and working memory, among others. A second analysis to contrast the results with a post hoc design approaches relationships among executive functions as involved in tasks like Gray Trails, Interference, and Ring Tasks within the usage of Spatial and Logic activities. The need for further research to improve these materials for enhanced learning and the extrapolation of training from executive functions to other tasks is discussed. Likewise, limitations of the implementation and design of these materials are pointed out.This study is part of the project IND2017/SOC-7874 for assessment and improve the digital material design, funded by the Community of Madrid by 'Industrial Ph.D' grants

Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation