Urban and rural population growth in a spatial panel of municipalities

Urban and rural population growth in a spatial panel of municipalities. Regional Studies. Using Bayesian posterior model probabilities and data pertaining to 3659 Brazilian minimum comparable areas (MCAs) over the period 1970-2010, two theoretical settings of population growth dynamics resulting in two spatial econometric specifications in combination with a wide range of potential neighbourhood matrices are tested against each other. The best performing combination counts five determinants producing significant long-term spatial spillover effects. Ignoring these spillovers, as many previous population growth studies have done, is shown to underestimate their impact and thus the effectiveness of policy measures acting on these determinants

GERAÇÃO E COMPOSIÇÃO GRAVIMÉTRICA DE RESÍDUOS SÓLIDOS URBANOS EM MUNICÍPIOS DO SEMIÁRIDO DE ALAGOAS

The generation of solid urban waste (MSW) has grown significantly in recent decades, mainly due to the increase in population and urban areas. Thus, it is necessary to know the quantity and composition of these materials, especially in small municipalities, where the management of this waste is still deficient. In this sense, the present study aims to analyze the production of solid urban waste and its gravimetry in eight municipalities in the sertão of Alagoas. As a source of data, the State Solid Waste Plan of the State of Alagoas - Sertão Region and economic information from the Brazilian Institute of Geography and Statistics were used. A Principal Component Analysis (PCA) was employed. The municipalities were characterized by a low urban population and low GDP per capita values. High variability was observed for the daily generation of MSW, with the predominance of the organic fraction, followed by plastic. On the other hand, low percentages of metals were observed. The ACP explained 69.2% of the original variance of the data in the first two axes and evidenced the strong association between economic aspects and the gravimetric composition of the residues. In view of the large amount of organic material present in the residues of the study area, actions for the use and proper disposal of this fraction, such as composting, should be encouraged.A geração de resíduos sólidos urbanos (RSU) tem crescido de forma expressiva nas últimas décadas, principalmente devido ao aumento populacional e das áreas urbanas. Dessa forma, torna-se necessário conhecer a quantidade e a composição desses materiais, principalmente em pequenos municípios, onde a gestão desses resíduos ainda é deficitária. Nesse sentido, o presente estudo objetiva analisar a produção de resíduos sólidos urbanos e a sua gravimetria em oito municípios do sertão de Alagoas. Como fonte de dados, utilizou-se o Plano  Estadual  de  Resíduos  Sólidos  do  Estado  de Alagoas – Região Sertão e informações econômicas do Instituto Brasileiro de Geografia e Estatística. Uma Análise de Componentes Principais (ACP) foi empregada. Os municípios se caracterizaram por apresentar baixa população urbana e baixos valores de PIB per capita. Foi observada elevada variabilidade para geração diária de RSU, com a predominância da fração orgânica, seguida pelo plástico. Em contrapartida, observou-se baixos percentuais de metais.  A  ACP explicou  69.2%  da  variância  original  dos  dados  nos  dois primeiros  eixos e evidenciou a forte associação entre aspectos econômicos e a composição gravimétrica dos resíduos. Visto a grande quantidade de material orgânico presente nos resíduos da área de estudo, ações de aproveitamento e destinação adequada dessa fração, como a compostagem, devem ser incentivadas

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

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

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements