Integration of a mobile autonomous robot in a surveillance multi-agent system

This dissertation aims to guarantee the integration of a mobile autonomous robot equipped with many sensors in a multi-agent distributed and georeferenced surveillance system. The integration of a mobile autonomous robot in this system leads to new features that will be available to clients of surveillance system may use. These features may be of two types: using the robot as an agent that will act in the environment or by using the robot as a mobile set of sensors. As an agent in the system, the robot can move to certain locations when alerts are received, in order to acknowledge the underlying events or take to action in order to assist in resolving this event. As a sensor platform in the system, it is possible to access information that is read from the sensors of the robot and access complementary measurements to the ones taken by other sensors in the multi-agent system. To integrate this mobile robot in an effective way it is necessary to extend the current multi-agent system architecture to make the connection between the two systems and to integrate the functionalities provided by the robot into the multi-agent system

Yield and quality of cherry tomato fruits in hydroponic cultivation

Because of the food and industrial importance of tomato, it holds great significance, and is one of the most produced species using the hydroponic cultivation systems. The objective of this study was to evaluate the effects of different concentrations of nutrient solution on the production and quality of cherry tomatoes (Lycopersicon esculentum ‘Samambaia’) grown in a hydroponic system in protected conditions. The experiment was conducted in pots filled with coconut fiber substrate using a randomized complete block design with four replications and six plants per plot. Five concentrations of nutrients were evaluated (50, 75, 100, 125, and 150% of the standard nutrient solution); the solutions produced the following electrical conductivities: 1.8, 2.0, 2.6, 3.4, and 3.9 dS m-1, respectively. At 90 days after transplanting, the tomato fruits were harvested, at which time the production variables and post-harvest quality of mature fruits were determined. The best production and post-harvest quality indexes of cherry tomatoes (‘Samambaia’) were found when using 111% of the standard nutrient solution, corresponding to the concentrations of 9.44, 2.44, 2.22, 6.44, 4.11, 2.44, and 2.78 mmolc L-1, of NO3-, NH4+, P, K, Ca, Mg, and S, respectively; and 66.6, 55.5, 14.4, 1.89, 0.56, and 0.44 mmolc L-1, of Fe, B, Mn, Zn, Cu, and Mo, respectively. Nutrient solutions with electrical conductivity above 2.89 dS m-1 severely reduced the fruit yield of cherry tomatoes

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Supplementary Table S1 from Mitochondrial Metabolism Drives Low-density Lipoprotein-induced Breast Cancer Cell Migration

List of primers used for measurements of gene expression levels and mtDNA content by quantitative real-time PCR.</p

Supplementary Table S2 from Mitochondrial Metabolism Drives Low-density Lipoprotein-induced Breast Cancer Cell Migration

Fatty acid profile in relative content (%). Qualitative analysis was calculated by dividing each raw area by the sum of total raw areas. Dash (‘-‘) represents lipid species that were not identified in the sample. Legend: CTR (control), ETO (etomoxir, 100 µM).</p

Supplementary Figure S4 from Mitochondrial Metabolism Drives Low-density Lipoprotein-induced Breast Cancer Cell Migration

Lipid exposure induces metabolic and bioenergetic dependencies in breast cancer cells. Related to Fig. 4</p

Resumos em andamento - Arquitetura e Urbanismo

Resumos em andamento - Arquitetura e Urbanism