Parametric investigation of flow-sound interaction mechanism of circular cylinders in cross-flow

Flow-excited acoustic resonance in heat exchangers has been an ongoing issue for the past century. The main challenge in this issue, is in the actual prediction of the resonance occurrence. This is due to the complexity of the flow-sound interaction mechanism that takes place between the packed cylinders. Most of the research lately has therefore shifted focus to simpler geometries that resemble the same mechanisms of flow-sound interaction found in actual heat-exchangers. The research presented hereafter summarizes an extensive experimental parametric work performed on multiple simple configurations such as single, tandem and side-by-side cylinders in cross-flow. The main objective of the research is to identify the critical parameters that should be included in the damping criteria to reliably predict the occurrence of acoustic resonance in tube bundles. Special attention is given to the geometrical characteristics of the duct (i.e. cross-sectional area) and how they affect the acoustic resonance. To achieve this; more than one hundred experiments have been performed in three different wind-tunnels of different cross-sectional areas. The research is motivated by the fact that most of the criteria developed to date, fail to predict the destructive phenomena of acoustic resonance in 30-40% of the cases

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

Multicomponent Reactions for Multitargeted Compounds for Alzheimer`s Disease

International audienc

Propargylamine-derived multi-target directed ligands for Alzheimer’s disease therapy

International audienc

Synthesis and Friedlander reactions of 5-amino-4-cyano-1,3-oxazoles

The synthesis of 2-substituted 5-amino-4-cyano-1,3-oxazoles (1-4, 6-11) and the Friedländer-type reaction of compounds 1, 3, 4 is described. Compounds 13-17 are tacrine (18) analogues provided by the Friedländer reaction. The anti-cholinesterase activity of compounds 13, 14, 16 and 17 has been investigated.Centro de Estudos de Ciências Farmacêuticas (CECF) e Centro de Patogénese Molecular (CPM