Percepção de aprendizagem on-line na disciplina de biomateriais / Perception of online learning in the biomaterials course

Com as universidades fechadas e a implementação do Ensino Remoto Emergencial, as atividades curriculares ocorreram através de plataformas digitais. O objetivo do presente trabalho foi avaliar a percepção de aprendizagem on-line na disciplina de Biomateriais da Faculdade de Odontologia da Universidade Federal Fluminense no período da pandemia. O questionário COLLES (Constructivist OnLine Learning Environment Survey) foi enviado individualmente por e-mail aos cinquenta alunos, apresentando 24 declarações divididas em seis quesitos: relevância, reflexão crítica, interatividade, apoio dos tutores, apoio entre os colegas e compreensão; e para cada declaração cinco opções de resposta: quase sempre, frequentemente, algumas vezes, raramente e quase nunca. Quarenta e um alunos responderam. A soma das médias obtidas em quase sempre e frequentemente foi de 87,2% para relevância, 70% para reflexão crítica, 33,9% para interatividade, 47,6% para apoio dos tutores, 44,2% para apoio dos colegas e 89,5% para compreensão. Concluiu-se que a relevância, a reflexão crítica e a compreensão apresentaram melhores resultados, enquanto a interatividade, o apoio entre os colegas e o apoio dos tutores demonstraram necessidade de aprimoramento. E apesar das limitações do ERE, a avaliação positiva dos alunos evidenciou esta modalidade de educação on-line como uma solução plausível.

With universities closed and the implementation of Emergency Remote Teaching, curricular activities took place through digital platforms. The objective of this study was to assess the perception of online learning in the Biomaterials course at the Dental School of the Federal Fluminense University during the pandemic. The COLLES questionnaire (Constructivist OnLine Learning Environment Survey) was individually sent via email to fifty students, presenting 24 statements divided into six aspects: relevance, critical reflection, interactivity, tutor support, peer support, and comprehension. For each statement, there were five response options: almost always, often, sometimes, rarely, and almost never. Forty-one students responded. The sum of the averages obtained for almost always and often was 87.2% for relevance, 70% for critical reflection, 33.9% for interactivity, 47.6% for tutor support, 44.2% for peer support, and 89.5% for comprehension. It was concluded that relevance, critical reflection, and comprehension showed better results, while interactivity, peer support, and tutor support demonstrated a need for improvement. Despite the limitations of Emergency Remote Teaching, the positive evaluation from the students highlighted this mode of online education as a plausible solution.

Advances and Challenges Associated with Low-Cost Pulse Oximeters in Home Care Programs: A Review.

Oximeters have significantly evolved since their invention and are essential for monitoring chronic diseases in home care. However, commercial models can present an economic barrier. Therefore, we conducted a review of the use of low-cost pulse oximeters in the home care of patients with respiratory diseases. Our review included studies addressing oxygen saturation and heart rate monitoring in adults, focusing on the use of portable devices. Our search identified advances in vital signs monitoring that could provide accessible solutions for non-clinical settings. Although there are challenges related to clinical validation and accuracy, these oximeters may improve medical care, particularly in resource-limited areas. As a result, the accessibility of these devices opens up new possibilities for patients with chronic respiratory diseases in home care, enabling regular self-monitoring and increasing control over their health.

Geo-Sensing-Based Analysis of Urban Heat Island in the Metropolitan Area of Merida, Mexico.

Urban Heat Islands are a major environmental and public health concern, causing temperature increase in urban areas. This study used satellite imagery and machine learning to analyze the spatial and temporal patterns of land surface temperature distribution in the Metropolitan Area of Merida (MAM), Mexico, from 2001 to 2021. The results show that land surface temperature has increased in the MAM over the study period, while the urban footprint has expanded. The study also found a high correlation (r> 0.8) between changes in land surface temperature and land cover classes (urbanization/deforestation). If the current urbanization trend continues, the difference between the land surface temperature of the MAM and its surroundings is expected to reach 3.12 °C ± 1.11 °C by the year 2030. Hence, the findings of this study suggest that the Urban Heat Island effect is a growing problem in the MAM and highlight the importance of satellite imagery and machine learning for monitoring and developing mitigation strategies.

Towards sustainable coastal management: aerial imagery and deep learning for high-resolution Sargassum mapping.

The massive arrival of pelagic Sargassum on the coasts of several countries of the Atlantic Ocean began in 2011 and to date continues to generate social and environmental challenges for the region. Therefore, knowing the distribution and quantity of Sargassum in the ocean, coasts, and beaches is necessary to understand the phenomenon and develop protocols for its management, use, and final disposal. In this context, the present study proposes a methodology to calculate the area Sargassum occupies on beaches in square meters, based on the semantic segmentation of aerial images using the pix2pix architecture. For training and testing the algorithm, a unique dataset was built from scratch, consisting of 15,268 aerial images segmented into three classes. The images correspond to beaches in the cities of Mahahual and Puerto Morelos, located in Quintana Roo, Mexico. To analyze the results the fß-score metric was used. The results for the Sargassum class indicate that there is a balance between false positives and false negatives, with a slight bias towards false negatives, which means that the algorithm tends to underestimate the Sargassum pixels in the images. To know the confidence intervals within which the algorithm performs better, the results of the f0.5-score metric were resampled by bootstrapping considering all classes and considering only the Sargassum class. From the above, we found that the algorithm offers better performance when segmenting Sargassum images on the sand. From the results, maps showing the Sargassum coverage area along the beach were designed to complement the previous ones and provide insight into the field of study.

Adaptation of the normative rating procedure for the International Affective Picture System to a remote format.

BACKGROUND: The Self-Assessment Manikin (SAM), a pictorial scale for the measurement of pleasure and arousal dimensions of emotions, is one of the most applied tools in the emotion research field. OBJECTIVE: We present a detailed description of a remote method to collect affective ratings in response to pictures by using the SAM scale. METHODS: To empirically validate our remote method, we conducted a study using a digitized version of the SAM scale and delivered online didactic instructions that followed the normative rating procedure for the International Affective Picture System (IAPS) to the participants. We presented 70 pictures from the IAPS and an additional set of 22 food pictures to the participants. RESULTS: We found strong correlations between the ratings of IAPS pictures obtained in our sample and those reported by North American and Brazilian participants in previous in-person studies that applied the same pictures and methodology. We were also able to obtain an additional standardized set of food pictures. CONCLUSION: The protocols described here may be useful for researchers interested in collecting remotely valid and reliable affecting ratings.

Spatiotemporal Modeling of Aedes aegypti Risk: Enhancing Dengue Virus Control through Meteorological and Remote Sensing Data in French Guiana.

French Guiana lacks a dedicated model for developing an early warning system tailored to its entomological contexts. We employed a spatiotemporal modeling approach to predict the risk of Aedes aegypti larvae presence in local households in French Guiana. The model integrated field data on larvae, environmental data obtained from very high-spatial-resolution Pleiades imagery, and meteorological data collected from September 2011 to February 2013 in an urban area of French Guiana. The identified environmental and meteorological factors were used to generate dynamic maps with high spatial and temporal resolution. The study collected larval data from 261 different surveyed houses, with each house being surveyed between one and three times. Of the observations, 41% were positive for the presence of Aedes aegypti larvae. We modeled the Aedes larvae risk within a radius of 50 to 200 m around houses using six explanatory variables and extrapolated the findings to other urban municipalities during the 2020 dengue epidemic in French Guiana. This study highlights the potential of spatiotemporal modeling approaches to predict and monitor the evolution of vector-borne disease transmission risk, representing a major opportunity to monitor the evolution of vector risk and provide valuable information for public health authorities.

Spatial analysis to identify unauthorized municipal solid waste disposal sites in rural areas of southern Mexico.

The management of solid waste in rural areas of developing countries faces significant challenges due to economic constraints and irregular human settlements. These factors often lead to the creation of unauthorized disposal sites, which pose risks to human health, ecosystems and the economy. Remote sensing and geographic information system techniques provide a means to understand the complex issues associated with inadequate municipal solid waste (MSW) disposal. This study aimed to identify unauthorized disposal sites in the rural areas of southern Quintana Roo, Mexico, by examining land surface temperature (LST) and vegetation indices as potential indicators of unauthorized final disposal sites (FDSs). The findings reveal that 13% of the study areas have a high, moderate or low probability of hosting unauthorized disposal sites. Additionally, 3 authorized final disposal sites (FDSs) were confirmed, and 20 unauthorized sites were identified. LST and the normalized difference vegetation index were effective in detecting unauthorized sites, as these areas exhibited higher temperatures and less vigorous vegetation compared to adjacent areas. The results provide valuable insights into the issues associated with inadequate waste disposal in rural areas and offer information that can help optimize MSW management and mitigate its environmental and health impacts.

Quality assurance of serologic testing for Chagas disease in a primary care setting of rural Paraguay.

The diagnosis of Chagas disease mostly relies on the use of multiple serologic tests that are often unavailable in many of the remote settings where the disease is highly prevalent. In the Teniente Irala Fernández Municipality, in central Paraguay, efforts have been made to increase the diagnostic capabilities of specific rural health centres, but no quality assurance of the results produced has been performed. We comparatively analysed the results obtained with 300 samples tested using a commercial rapid diagnostic test (RDT) and enzyme linked immunosorbent assays (ELISA) at the laboratory of the Teniente Irala Fernández Health Center (CSTIF) with those generated upon repeating the tests at an independent well-equipped research laboratory (CEDIC). A subgroup of 52 samples were further tested at Paraguay's Central Public Health Laboratory (LCSP) by means of a different technique to evaluate the diagnostic performance of the tests carried out at CSTIF. We observed an excellent agreement between the ELISA results obtained at CSTIF and CEDIC (kappa coefficients between 0.85 and 0.93 for every kit evaluated), and an overall good performance of the tests carried out at CSTIF. However, the sensitivity of one kit was lower at CSTIF (81.3 %) than at CEDIC (100 %). The individual use of an RDT to detect the infection at CSTIF showed a similar sensitivity to that obtained combining it to an ELISA test (92.3% vs 88.5, p = 1). Nonetheless, the generalizability of this result is yet limited and will require of further studies.

Analysis of environmental variables and deforestation in the amazon using logistical regression models.

In this study, we applied a multivariate logistic regression model to identify deforested areas and evaluate the current effects on environmental variables in the Brazilian state of Rondônia, located in the southwestern Amazon region using data from the MODIS/Terra sensor. The variables albedo, temperature, evapotranspiration, vegetation index, and gross primary productivity were analyzed from 2000 to 2022, with surface type data from the PRODES project as the dependent variable. The accuracy of the models was evaluated by the parameters area under the curve (AUC), pseudo R2, and Akaike information criterion, in addition to statistical tests. The results indicated that deforested areas had higher albedo (25%) and higher surface temperatures (3.2 °C) compared to forested areas. There was a significant reduction of the EVI (16%), indicating water stress, and a decrease in GPP (18%) and ETr (23%) due to the loss of plant biomass. The most precise model (91.6%) included only surface temperature and albedo, providing important information about the environmental impacts of deforestation in humid tropical regions.

Unraveling impacts on carbon, water and energy exchange of Pinus plantations in South American temperate ecosystems.

Tree plantations are expanding in southern South America and their effects on ecosystem services, particularly climate regulation, are still not well understood. Here, we used remote sensing techniques and a paired design to analyze ≈33,000 ha of Pinus plantations along a broad geographical and environmental gradient (26-43° South latitude, 54-72° West longitude). Radiation interception, surface temperature, evapotranspiration, and albedo were assessed both in tree plantations stands and in adjacent uncultivated areas. Additionally, the climatic impact of tree plantations was quantified by analyzing changes in atmospheric radiative forcing and its carbon (C) equivalent. Tree plantations intercepted more radiation when replacing steppes, grasslands, and shrublands but not when replacing forests. The control exerted on radiation interception by precipitation decreased in both space and time after tree plantation. Furthermore, evapotranspiration notably increased in tree plantations. The lower albedo of tree plantations compared to uncultivated adjacent areas induces global warming through the biophysical pathway. Thus, the climate benefits of afforestation through C sequestration can be counteracted by 18 to 83 % due to albedo changes. It is necessary to fully consider the biophysical effects and water footprint of tree plantations in public policies that promote them, as well as in international carbon accounting mechanisms.

Mediating role of digital skills and mobile self-efficacy in the stress and academic engagement of Peruvian university students in postpandemic virtual environments.

BACKGROUND: Remote education emerged as an option during the COVID-19 pandemic; however, this modality continues to be used by various universities around the world in the postpandemic context. The aim of this study was to determine the mediating role of digital skills and mobile self-efficacy in the influence of stress on the academic engagement of Peruvian university students during remote teaching by COVID-19 using structural equation modeling (SEM). METHOD: This study involved 1,468 students from nine public and private universities in northern Peru who had undergraduate and graduate distance learning programs. RESULTS: The results showed that stress negatively influenced academic engagement (ß=-0.107*) and digital skills (ß=-0.328***). In addition, digital skills (ß = 0.470**) and mobile self-efficacy (ß = 0.684***) positively influence academic engagement. Similarly, digital skills mediate the relationship between stress and academic engagement (ß=-0.154**), and both variables act as sequential mediators in this relationship (ß=-0.348***). CONCLUSION: This study provides a deeper understanding of the factors that influence academic engagement during Remote education and lays the groundwork for the development of interventions and training programs tailored to hybrid learning contexts that promote the well-being and academic success of college students in postpandemic times.

Effects of urbanization on cloud-to-ground lightning strike frequency: a global perspective.

Urbanization tends to increase local lightning frequency (i.e. the 'lightning enhancement' effect). Despite many urban areas showing lightning enhancement, the prevalence of these effects is unknown and the drivers underlying these patterns are poorly quantified. We conducted a global assessment of cloud-to-ground lightning flashes (lightning strikes) across 349 cities to evaluate how the likelihood and magnitude of lightning enhancement vary with geography, climate, air pollution, topography and urban development. The likelihood of exhibiting lightning enhancement increased with higher temperature and precipitation in urban areas relative to their natural surroundings (i.e. urban heat islands and elevated urban precipitation), higher regional lightning strike frequency, greater distance to water bodies and lower elevations. Lightning enhancement was stronger in cities with conspicuous heat islands and elevated urban precipitation effects, higher lightning strike frequency, larger urban areas and lower latitudes. The particularly strong effects of elevated urban temperature and precipitation indicate that these are dominant mechanisms by which cities cause local lightning enhancement.

Sensing Classroom Temperature, Relative Humidity, Illuminance, CO2, and Noise: An Integral Solution Based on an IoT Device for Dense Deployments.

Maintaining optimal Indoor Environmental Quality (IEQ) requires continuous measurement of certain variables. To this end, ASHRAE and BPIE recommend that at least the following areas of interest be considered when measuring IEQ: thermal comfort, illuminance, indoor air quality, and noise. At this time, it is not common to find an IoT device that is suitable for dense deployments in schools, university campuses, hospitals, and office buildings, among others, that measures variables in all of the above areas of interest. This paper presents a solution to the problem previously outlined by proposing an IoT device that measures variables across all of the aforementioned areas of interest. Moreover, in a radio frequency network with a tree-like structure of IoT devices, this device is able to assume the roles of sensor and hub node, sensor and router node, and only sensor node. The experimental results are satisfactory, and the detailed system design ensures the replicability of the device. Furthermore, the theoretical analysis paves the way for high scalability.

Exploratory Analysis Using Deep Learning for Water-Body Segmentation of Peru's High-Mountain Remote Sensing Images.

High-mountain water bodies represent critical components of their ecosystems, serving as vital freshwater reservoirs, environmental regulators, and sentinels of climate change. To understand the environmental dynamics of these regions, comprehensive analyses of lakes across spatial and temporal scales are necessary. While remote sensing offers a powerful tool for lake monitoring, applications in high-mountain terrain present unique challenges. The Ancash and Cuzco regions of the Peruvian Andes exemplify these challenges. These regions harbor numerous high-mountain lakes, which are crucial for fresh water supply and environmental regulation. This paper presents an exploratory examination of remote sensing techniques for lake monitoring in the Ancash and Cuzco regions of the Peruvian Andes. The study compares three deep learning models for lake segmentation: the well-established DeepWaterMapV2 and WatNet models and the adapted WaterSegDiff model, which is based on a combination of diffusion and transformation mechanisms specifically conditioned for lake segmentation. In addition, the Normalized Difference Water Index (NDWI) with Otsu thresholding is used for comparison purposes. To capture lakes across these regions, a new dataset was created with Landsat-8 multispectral imagery (bands 2-7) from 2013 to 2023. Quantitative and qualitative analyses were performed using metrics such as Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and F1 Score. The results achieved indicate equivalent performance of DeepWaterMapV2 and WatNet encoder-decoder architectures, achieving adequate lake segmentation despite the challenging geographical and atmospheric conditions inherent in high-mountain environments. In the qualitative analysis, the behavior of the WaterSegDiff model was considered promising for the proposed application. Considering that WatNet is less computationally complex, with 3.4 million parameters, this architecture becomes the most pertinent to implement. Additionally, a detailed temporal analysis of Lake Singrenacocha in the Vilcanota Mountains was conducted, pointing out the more significant behavior of the WatNet model.

GOES ABI-derived hailstorm polygons and tracking dataset for Brazil.

This paper describes a dataset of convective systems (CSs) associated with hailstorms over Brazil tracked using GOES-16 Advanced Baseline Imager (ABI) measurements and the Tracking and Analysis of Thunderstorms (TATHU) tool. The dataset spans from June 5, 2018, to September 30, 2023, providing five-year period of storm activity. CSs were detected and tracked using the ABI's clean IR window brightness temperature at 10.3 µm, projected on a 2 km x 2 km Lat-Lon WGS84 grid. Systems were identified using a brightness temperature (BT) threshold of 235 K, conducive to detecting convective clusters with larger area and excluding smaller or non-convective cells such as groups of thin Cirrus clouds. Each detected CS was treated as an object, containing geographic boundaries and raster statistics such as BT's mean, minimum, standard deviation, and count of data points within the CS polygon, which serves as proxy for size estimates. The life cycle of each system was tracked based on a 10 % overlap area criterion, ensuring continuity, unless disrupted by dissociative or associative events. Then, the tracked CSs were filtered for intersections in space and time with verified ground reports of hail, from the Prevots group. The matches were then exported to a database with SpatiaLite enabled data format to facilitate spatial data queries and analyses. This database is structured to support advanced research in severe weather events, in particular hailfall. This setting allows for extensive temporal and spatial analyses of convective systems, making it useful for meteorologists, climate scientists, and researchers in related fields . The inclusion of detailed tracking information and raster statistics offers potential for diverse applications, including climate model validation, weather prediction enhancements, and studies on the climatological impact of severe weather phenomena in Brazil.

Carbon footprints of tailings dams' disasters: A study in the Brumadinho region (Brazil).

Tailings dams' breaks are environmental disasters with direct and intense degradation of soil. This study analyzed the impacts of B1 tailings dam rupture occurred in the Ribeirão Ferro-Carvão watershed (Brumadinho, Brazil) in January 25, 2019. Soil organic carbon (SOC) approached environmental degradation. The analysis encompassed wetlands (high-SOC pools) located in the so-called Zones of Decreasing Destructive Capacity (DCZ5 to DCZ1) defined along the Ferro-Carvão's stream bed and banks after the disaster. Remote sensed water indices were extracted from Landsat 8 and Sentinel-2 satellite images spanning the 2017-2021 period and used to distinguish the wetlands from other land covers. The annual SOC was extracted from the MapBiomas repository inside and outside the DCZs in the same period, and assessed in the field in 2023. Before the dam collapse, the DCZs maintained stable levels of SOC, while afterwards they decreased substantially reaching minimum values in 2023. The reductions were abrupt: for example, in the DCZ3 the decrease was from 51.28 ton/ha in 2017 to 4.19 ton/ha in 2023. Besides, the SOC increased from DCZs located near to DCZs located farther from the dam site, a result attributed to differences in the percentages of clay and silt in the tailings, which also increased in the same direction. The Ferro-Carvão stream watershed as whole also experienced a slight reduction in the average SOC levels after the dam collapse, from nearly 43 ton/ha in 2017 to 38 ton/ha in 2021. This result was attributed to land use changes related with the management of tailings, namely opening of accesses to remove them from the stream valley, creation of spaces for temporary deposits, among others. Overall, the study highlighted the footprints of tailings dams' accidents on SOC, which affect not only the areas impacted with the mudflow but systemically the surrounding watersheds. This is noteworthy.

Teleassessment using five times sit-to-stand test in older adults with and without Parkinson's disease: Guidelines and barriers.

BACKGROUND AND PURPOSE: Assessing lower limb strength, balance, and fall risk are crucial components of rehabilitation, especially for the older adult population. With the growing interest in telehealth, teleassessment has been investigated as an alternative when in-person assessments are not possible. The Five Times Sit-to-Stand test (5TSTS) provides a quick measure of balance during chair transfers, muscle power, endurance, and the hability to change and maintain body position, and is highly recommended by guidelines. However, the literature is unclear about the viability and safety of teleassessment using the 5TSTS in older adults with and without Parkinson's disease (PD). This study aimed to evaluate the reliability of teleassessment using the 5TSTS and to determine its feasibility and safety for older adults with and without PD. METHODS: This cross-sectional study included older adults with and without PD who were evaluated remotely through a videoconference platform. To ensure effective and comprehensive instructions for the test, we developed a guideline called OMPEPE (an acronym for: Objective; Materials; Position-Start; Execution; Position-End; Environment). We assessed the 5TSTS intra- and inter-rater reliability by comparing scores obtained from the same examiner and from different examiners, respectively. Participants and examiners completed online surveys to provide information about feasibility and safety. RESULTS: Twelve older adults with PD and 17 older adults without PD were included in this study (mean ages 69.0 and 67.6 years, respectively). Based on the participants' perspectives and the absence of adverse effects, teleassessment using the 5TSTS is feasible and safe for older adults with and without PD. Excellent intra- and inter-rater reliability (intraclass correlation coefficient >0.90) was found for all measurements of the 5TSTS. DISCUSSION: This study demonstrated the feasibility, safety, and reliability of teleassessment using the 5TSTS. The guidelines developed may help health professionals minimize barriers and safely conduct an online assessment that includes a physical test such as the 5TSTS in older adults with or without PD. In addition to addressing technological barriers, the OMPEPE guideline might ensure the optimal execution of evaluations. CONCLUSION: Teleassessment using the 5TSTS for older adults with and without PD is feasible and safe. Both synchronous (i.e., live) and asynchronous (i.e., recorded) online 5TSTS tests demonstrate excellent intra- and inter-rate reliability.

[Cost-effectiveness of digital care and face-to-face care in the first-level of care]. / Costo-efectividad de la atención digital y la atención presencial en el primer nivel.

Background: The medical care paradigm is face-to-face; however, technological development has led to the digital modality. Objective: To determine cost-effectiveness of digital care and face-to-face care at the first level of care. Material and methods: Cost-effectiveness study. 2 groups were integrated: the digital service and the face-to-face service. The sample size was calculated with the percentage formula for 2 groups, and the result was 217 per group. The effectiveness was evaluated in 3 dimensions: the satisfaction of the patient, of the doctor and of the medical assistant. In all 3 cases the Visual analogue scale was used. The cost corresponded to the fixed unit cost estimated with the technique of times and movements adjusted for the duration of care. The statistical analysis included averages, percentages and cost-effectiveness ratio. Results: The cost of digital attention is $343.83 and face-to-face attention is $171.91 (all estimated in Mexican pesos). From the patient's perspective, the effectiveness in digital care is $9.47 and in face-to-face is $9.25. The cost to reach effectiveness of 10 in face-to-face care is $185.85 and in digital care $363.20. From the physician's perspective, the cost to achieve effectiveness of 10 is $419.13 in digital care and $184.52 in face-to-face care. From the perspective of the medical assistant, to achieve effectiveness of 10, the cost in digital care is $468.43 and in face-to-face $179.83. Conclusions: Currently, the best cost-effectiveness ratio corresponds to face-to-face care; however, digital care is an option that will have to evolve.

Introducción: el paradigma de atención médica es presencial; sin embargo, el desarrollo tecnológico ha propiciado la modalidad digital. Objetivo: determinar el costo-efectividad de la atención digital y la atención presencial en primer nivel. Material y métodos: estudio de costo-efectividad. Se integraron 2 grupos: el de atención digital y el de atención presencial. El tamaño de muestra se calculó con la fórmula de porcentajes para 2 grupos y el resultado fue 217 por grupo. La efectividad se evaluó en 3 dimensiones: la satisfacción del paciente, del médico y de la asistente médica. En los 3 casos se utilizó la Escala visual analógica. El costo correspondió al costo unitario fijo estimado con la técnica de tiempos y movimientos ajustado por la duración de la atención. El análisis estadístico incluyó promedios, porcentajes y relación costo-efectividad. Resultados: el costo de la atención digital es $343.83 y la atención presencial $171.91. Desde la perspectiva del paciente, la efectividad en atención digital es 9.47 y en presencial 9.25. El costo para alcanzar una efectividad de 10 en la atención presencial es $185.85 y en la atención digital $363.20. Desde la perspectiva del médico, el costo para alcanzar una efectividad de 10 es $419.13 en la atención digital y $184.52 en la atención presencial. Desde la perspectiva de la asistente médica, para alcanzar una efectividad de 10 el costo en la atención digital es $468.43 y en la presencial $179.83. Conclusiones: actualmente la mejor relación costo-efectividad corresponde a la atención presencial; sin embargo, la atención digital es una opción que tendrá que evolucionar.

The Evolution of Active Learning in Response to the Pandemic: The Role of Technology.

Active learning has consistently played a significant role in education. Through interactive tasks, group projects, and a variety of engaging activities, students are encouraged to forge connections with the subject matter. However, the pandemic has necessitated that educators adapt and refine their active learning techniques to accommodate the online environment. This has resulted in stimulating innovations in the field, encompassing virtual simulations, online collaboration tools, and interactive multimedia. The COVID-19 pandemic has rapidly transformed the landscape of teaching and learning, particularly in higher education. One of the most prominent shifts has been the widespread adoption of active learning techniques, which have increased student engagement and fostered deeper learning experiences. In this chapter, we examine the evolution of active learning during the pandemic, emphasizing its advantages and challenges. Furthermore, we delve into the role of advances in artificial intelligence and their potential to enhance the effectiveness of active learning approaches. As we once focused on leveraging the opportunities of remote teaching, we must now shift our attention to harnessing the power of AI responsibly and ethically to benefit our students. Drawing from our expertise in educational innovation, we provide insights and recommendations for educators aiming to maximize the benefits of active learning in the post-pandemic era.

Monitoring agricultural drought using different indices based on remote sensing data in the Brazilian biomes of Cerrado and Atlantic Forest.

Several remote sensing indices have been used to monitor droughts, mainly in semi-arid regions with limited coverage by meteorological stations. The objective of this study was to estimate and monitor agricultural drought conditions in the Jequitinhonha Valley region, located in the Brazilian biomes of the Cerrado and Atlantic Forest, from 2001 to 2021, using vegetation indices and the meteorological drought index from remote sensing data. Linear regression was applied to analyze drought trends and Pearson's correlation coefficient was applied to evaluate the relationship between vegetation indices and climatic conditions in agricultural areas using the Standardized Precipitation Index. The results revealed divergences in the occurrences of regional droughts, predominantly covering mild to moderate drought conditions. Analysis spatial of drought trends revealed a decreasing pattern, indicating an increase in drought in the Middle and Low Jequitinhonha sub-regions. On the other hand, a reduction in drought was observed in the High Jequitinhonha region. Notably, the Vegetation Condition Index demonstrated the most robust correlation with the Standardized Precipitation Index, with R values ​​greater than 0.5 in all subregions of the study area. This index showed a strong association with precipitation, proving its suitability for monitoring agricultural drought in heterogeneous areas and with different climatic attributes. The use of remote sensing technology made it possible to detect regional variations in the spatio-temporal patterns of drought in the Jequitinhonha Valley. This vision helps in the implementation of personalized strategies and public policies, taking into account the particularities of each area, in order to mitigate the negative impacts of drought on agricultural activities in the region.