Dataset of tomato plants growth observations obtained from multiple sources in a production-like setting

This dataset contains observations of tomato growth in a production-like setting, at research greenhouses. Two plants in each of three growth cycles were continuously monitored and pictures were taken every other day from above and from a side view while a weighting system was used to record changes in weight of plant and water in the substrate. Other plants in the environment were subjected to destructive analysis in general every two weeks to quantify aspects of growth that required destructive measurements, such as dry weight and plant leaf area, and these records are also included in the dataset, including the scans of digitized leaves. Plant samples destined to destructive measurements also had their pictures taken before removal. In total, 618 photos of monitored and removed plants were annotated, and masks of leaf, fruit and mature fruit are also provided. The dataset also includes measurements of photosynthetically active radiation and air temperature recorded inside the greenhouses by two sets of different sensors during the growth cycles. The dataset allows for applications regarding growth monitoring and computer vision tasks

CARDIOVASCULAR EFFECTS OF FREE OR COMPLEXED LINALOOL WITH Β-CYCLODEXTRIN: A FOCUS FOR ANTIHYPERTENSIVE ACTION

Investigating the cardiovascular effects of natural compounds, such as linalool, has aroused interest due to the potential impact on cardiovascular health. Linalool, a component present in several essential oils, has demonstrated promising pharmacological properties, including antihypertensive activity. However, its bioavailability and efficacy can be influenced by complexation with β-cyclodextrin, a strategy frequently used to improve the solubility and stability of bioactive substances. This study aimed to carry out a systematic review of the literature, exploring the cardiovascular effects of free linalool and linalool complexed with β-cyclodextrin. Objective: To investigate the cardiovascular effects of free linalool and linalool complexed with β-cyclodextrin, with emphasis on the antihypertensive action, through a systematic review of the literature. Methodology: The review was conducted according to PRISMA guidelines. The PubMed, Scielo and Web of Science databases were consulted, using the descriptors "linalool", "β-cyclodextrin", "cardiovascular effects", "antihypertensive" and "complexation". The inclusion criteria covered studies published in the last 10 years, focusing on in vivo experiments, clinical trials and systematic reviews. Articles unrelated to the topic, duplicate studies and those without peer review were excluded. Results: They revealed that linalool, when complexed with β-cyclodextrin, presented greater bioavailability and stability, enhancing its antihypertensive action. In vivo studies have demonstrated a significant reduction in blood pressure in hypertensive animal models. Furthermore, the complexation positively influenced the gastrointestinal absorption of linalool. These findings suggest that the complexed formulation may represent an effective approach to improving the cardiovascular effects of linalool. Conclusion: The systematic review highlights the relevance of complexing linalool to β-cyclodextrin as a strategy to enhance its antihypertensive effects. Understanding these mechanisms can contribute to the development of more effective pharmaceutical formulations in the management of hypertension, promoting advances in cardiovascular therapy.Investigating the cardiovascular effects of natural compounds, such as linalool, has aroused interest due to the potential impact on cardiovascular health. Linalool, a component present in several essential oils, has demonstrated promising pharmacological properties, including antihypertensive activity. However, its bioavailability and efficacy can be influenced by complexation with β-cyclodextrin, a strategy frequently used to improve the solubility and stability of bioactive substances. This study aimed to carry out a systematic review of the literature, exploring the cardiovascular effects of free linalool and linalool complexed with β-cyclodextrin. Objective: To investigate the cardiovascular effects of free linalool and linalool complexed with β-cyclodextrin, with emphasis on the antihypertensive action, through a systematic review of the literature. Methodology: The review was conducted according to PRISMA guidelines. The PubMed, Scielo and Web of Science databases were consulted, using the descriptors "linalool", "β-cyclodextrin", "cardiovascular effects", "antihypertensive" and "complexation". The inclusion criteria covered studies published in the last 10 years, focusing on in vivo experiments, clinical trials and systematic reviews. Articles unrelated to the topic, duplicate studies and those without peer review were excluded. Results: They revealed that linalool, when complexed with β-cyclodextrin, presented greater bioavailability and stability, enhancing its antihypertensive action. In vivo studies have demonstrated a significant reduction in blood pressure in hypertensive animal models. Furthermore, the complexation positively influenced the gastrointestinal absorption of linalool. These findings suggest that the complexed formulation may represent an effective approach to improving the cardiovascular effects of linalool. Conclusion: The systematic review highlights the relevance of complexing linalool to β-cyclodextrin as a strategy to enhance its antihypertensive effects. Understanding these mechanisms can contribute to the development of more effective pharmaceutical formulations in the management of hypertension, promoting advances in cardiovascular therapy

Sugarcane yield estimation from Sentinel-2A satellite imagery and Random Forest machine learning algorithms

A cana-de-açúcar é uma das culturas mais importantes para a economia brasileira, por isso, técnicas de aprendizado de máquina são utilizadas como importantes ferramentas de estimativa da produtividade. O objetivo deste trabalho foi criar modelos empíricos utilizando dados agronômicos, climáticos e de imagens de satélite, a partir do algoritmo Random Forest, para estimar a produtividade da cana-de-açúcar antes da colheita, no estado de São Paulo (SP). Para isso, foram utilizadas imagens Sentinel-2A; dados agronômicos; balanço hídrico da cultura e dados climáticos. Para selecionar as variáveis preditoras mais importantes foram criados modelos de estimativa de produtividade com três conjuntos de dados de uma usina: i) o primeiro conjunto de dados utilizou as variáveis agronômicas, climáticas, o balanço hídrico da cultura, índices de vegetação e bandas espectrais; ii) no segundo conjunto de dados, as variáveis fortemente correlacionadas foram removidas; e iii) o terceiro conjunto de dados foi criado com base na seleção de varáveis mais importantes pelo índice de Gini. Os modelos criados com o conjuntos de dados i, ii, iii apresentaram R2 entre 0,77 e 0,8, RMSE entre 8,2 e 8,6 ton ha-1, MAE entre 4,9 e 5,26 ton ha-1 e d-Willmott entre 0,93 e 0,94, sendo o melhor modelo com o conjunto de dados iii. As variáveis mais relevantes para estimar a produtividade da cana-de-açúcar foram o estágio de corte, o déficit hídrico, os índices NDVIRE e CIRE, além das bandas Red-edge, NIR-8A e SWIR1. A seleção das variáveis importantes reduziu a dimensionalidade dos dados e melhorou o desempenho do modelo. Após a identificação das variáveis preditoras mais importantes, foram criados três modelos operacionais para aplicação em escala regional, com 70% de dados para treino e 30% para teste. Para isso, foram utilizados dados de 3 usinas localizadas no estado de SP. O Modelo I (geral) considerou os dados de todas as usinas para treino e teste; o Modelo II foi similar ao I para o treino, porém foi testado em cada uma das usinas de forma separada; para o Modelo III o treinamento e teste foi feito com base em dois ciclos de produção da cana de açúcar (cana-planta e cana-soca). O Modelo I apresentou R2 igual a 0,72 enquanto os R2 do Modelo II ficaram entre 0,60 e 0,78, o RMSE para o Modelo I foi igual a 11,7 ton ha-1 enquanto o Modelo II de 8,62 a 15,56 ton ha-1, rRMSE foi igual a 16,5% para o Modelo I e 12,4 a 21,6%, para o Modelo II. O Modelo III apresentou R2 maior que 0,61, e RMSE entre 9,6 e 13,5 ton ha-1. Quando se comparou o rendimento médio com os erros RMSE, obtém-se um melhor desempenho para o modelo III com rRMSE inferior a 15,3%. A utilização do Random Forest para a criação de modelos globais para estimativa da cana-de-açúcar no estado de São Paulo mostrou-se promissora quando calibrado com três usinas e, separados em ciclos de produção da cana-de-açúcar (cana-planta e cana-soca).Sugarcane is a very important crop for the Brazilian economy, so machine learning techniques are being used as an important tool to improve yield estimation. This study aimed to create an empirical model using agronomic, climatic, and satellite images, by Random Forest algorithm, to estimated sugarcane yield before the harvest, in São Paulo state (SP). We used radiometric bands and vegetation indices from Sentinel-2 images; agronomic data; crop water balance and climatic data. To select the most important variables it were builted yield estimation models based on three datasets from one mill: i) the first dataset used agronomic data, climatic data, crop water balance, and remote sensing data); ii) in the second dataset, the most strongly correlated variables were removed; and iii) the third dataset was created with the variables selected by feature selection using the Gini index. The models created with the datasets i, ii, and iii showed R2 from 0.77 to 0.8, RMSE from 8.2 to 8.6 ton ha-1, MAE from 4.9 to 5.26 ton ha-1 and d-Willmott from 0.93 to 0.94, where the best result was using dataset 3 (iii). The most relevant variables to estimated sugarcane productivity were number of harvests, water deficit, NDRE and CIRE vegetation indices and Red-edge, NIR-8A and SWIR1 bands. The variable selection reduced the dimensionality of the data and improved the models\' performance. After the selection of the most important predictor variables, it was created three operational models for application on the regional scale, using 70% of data to train and 30% to test. For this, we used data from three mills located in SP. The Model I (general) considered data from all mills for training and testing; Model II was similar to I for training, however, it was tested in each mill independently; for Model III the training and testing were made based on two groups of the sugarcane production cycles (plant cane and sugarcane ratoons). The results for Model I showed R2 equal to 0.72 while the R2 of Model II were between 0.60 and 0.78, RMSE for Model I was equal to 11.7 ton ha-1 while Model II from 8.62 to 15.56 ton ha-1, rRMSE was equal to 16.5% for Model I and 12.4 to 21.6%, for Model II. Model III showed R2 greater than 0.61, and RMSE between 9.6 and 13.5 ton ha-1. When average yield was compared with RMSE errors, better performance is obtained for Model III with rRMSE less than 15.3%.The use of Random Forest to create general models for sugarcane yield estimation in the state of Sao Paulo showed promise when calibrated with three mills and, separated by sugarcane production cycles

Avaliação de variáveis agronômicas e imagens do satélite Sentinel-2 para estimativa de produtividade da cana-de-açúcar a partir do algoritmo Random Forest

Gestão tecnológica : proceedings / coordenação [de] Marcelo Prado... [et al.]. - Lisboa : Universidade Lusíada, 2022

Models of Support for Caregivers and Patients with the Post-COVID-19 Condition: A Scoping Review

Background: In December 2019, an outbreak of the coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in the city of Wuhan, China. On 30 January 2020, the World Health Organization declared the outbreak a public health emergency of international concern. In October 2021, with the advancement of the disease, the World Health Organization defined the post-COVID-19 condition. The post-COVID-19 condition occurs in individuals with a history of probable or confirmed infection with SARS-CoV-2, usually 3 months after the onset of the disease. The chronicity of COVID-19 has increased the importance of recognizing caregivers and their needs. Methods: We conducted a scoping review following international guidelines to map the models of support for caregivers and patients with the post-COVID-19 condition. The searches were conducted in electronic databases and the grey literature. The Population, Concept, and Context framework was used: Population: patients with the post-COVID-19 condition and caregivers; Concept: models of caregiver and patient support; and Context: post-COVID-19 condition. A total of 3258 records were identified through the electronic search, and 20 articles were included in the final sample. Results: The studies approached existing guidelines and health policies for post-COVID-19 condition patients and support services for patients and home caregivers such as telerehabilitation, multidisciplinary care, hybrid models of care, and follow-up services. Only one study specifically addressed the home caregivers of patients with this clinical condition. Conclusions: The review indicates that strategies such as telerehabilitation are effective for training and monitoring the patient–family dyad, but the conditions of access and digital literacy must be considered

Astrophysics with the Laser Interferometer Space Antenna

submitted to Living Reviews In RelativityLaser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy as it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and other space-based instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed: ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help make progress in the different areas. New research avenues that LISA itself, or its joint exploitation with studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe

Astrophysics with the Laser Interferometer Space Antenna

submitted to Living Reviews In RelativityLaser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy as it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and other space-based instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed: ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help make progress in the different areas. New research avenues that LISA itself, or its joint exploitation with studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe

Astrophysics with the Laser Interferometer Space Antenna

submitted to Living Reviews In RelativityLaser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy as it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and other space-based instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed: ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help make progress in the different areas. New research avenues that LISA itself, or its joint exploitation with studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe

Astrophysics with the Laser Interferometer Space Antenna

submitted to Living Reviews In RelativityLaser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy as it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and other space-based instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed: ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help make progress in the different areas. New research avenues that LISA itself, or its joint exploitation with studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe