The Kormendy relation of cluster galaxies in PPS regions

We study a sample of 936 early-type galaxies located in 48 low-z regular galaxy clusters with $M_{200}\geq 10^{14}~ M_\odot$ at $z< 0.1$. We examine variations in the Kormendy relation (KR) according to their location in the projected phase space (PPS) of the clusters. We have used a combination of Bayesian statistical methods to identify possible differences between the fitted relations. Our results indicate that the overall KR is better fitted when we take into account the information about PPS regions. We also find that objects with time since infall $\geq 6.5$ Gyr have a significant statistical difference of the KR coefficients relative to objects that are more recent in the cluster environment. We show that giant central ellipticals are responsible for tilting the KR relation towards smaller slopes. These galaxies present a late growth probably due to cumulative preprocessing during infall, plus cannibalism and accretion of smaller stripped objects near the center of the clusters.Comment: 8 pages, 8 figures, appendix, published in MNRAS. arXiv admin note: text overlap with arXiv:2302.0428

Local Power as the Basis of the Understanding of the Federative Pact

The research aimed to describe the existing problems in the relationship between City, State and Federal Government, through the Brazilian Federative Pact, mainly for municipalities with population of less than 50,000 inhabitants. The research is structured from a qualitative perspective. The theoretical framework was built from the local power of the discussion based on the understanding of the federal pact and local interest and the municipality in Brazil. The paper argues that the federal pact is little debated, discussed, much less questioned by society in general, it only strengthens the lack of a legal and institutional framework for coordination and cooperation among federal entities in the country, which results in public policy fragmented the territory and without direction, causing waste of public resources

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics

EM TEMPOS GLOBAIS, UM “NOVO” LOCAL: a Ford na Bahia

O artigo analisa a dinâmica da Região Metropolitana de Salvador (RMS) a partir da implantação da Ford, discutindo a perspectiva do ‘lugar’ (a periferia metropolitana), dentro de uma relação assimétrica com os negócios globais na era da flexibilidade. O texto caracteriza o complexo Ford de Camaçari a partir da reestruturação produtiva e das mudanças na organização e funcionamento dos territórios e, na segunda parte, seus impactos sobre a periferia metropolitana de Salvador. Na conclusão demonstra que as mesmas circunstâncias que permitiram a vinda da montadora para Camaçari constrangem as ambições originais de melhor equacionamento entre crescimento econômico e progresso social: a flexibilidade dos novos arranjos, que tornam os espaços periféricos estratégicos, compromete o “enraizamento” do investimento; a “produção enxuta”, exígua de emprego e diligente na sua precarização, inibe os benefícios sociais. PALAVRAS CHAVE: reestruturação produtiva, mercado de trabalho, indústria automobilística, periferia metropolitana, segregação socioespacial. IN GLOBAL TIMES, A “NEW” PLACE: Ford in Bahia Ângela Franco This paper makes an analysis of the dynamics of the Metropolitan Area of Salvador (in Portuguese, RMS) starting from the implantation of Ford, discussing the perspective of the ‘local’ (the metropolitan periphery), inside of an asymmetrical relationship with global businesses in the age of flexibility. The Ford Automotive Compound is caracterized in the first part of the paper from its productive reestructuring and changes in the organization and work of territories, and, in the second part, from its impact on the the metropolitan periphery from Salvador. In its conclusion it demonstrates that the same circumstances that allowed the arrival of the automotive maker in Camaçari constrain the original ambitions of better ratio between economical growth and social progress: the flexibility of the new automotive production methods, making peripheric spaces strategic, compromises on the permanence of the investments; and the “streamlined production”, easy on job production and hard on job flexibilization inhibit social benefits. KEYWORDS: productive restructuring, job market, automobile industry, metropolitan periphery, socioespatial segregation. EN PERIODE DE MONDIALISATION, UN “NOUVEAU” LOCAL: Ford à Bahia Ângela Franco Cet article traite de l’analyse de la dynamique de la Région Métropolitaine de Salvador (RMS), à partir de l’implantation de l’usine Ford. On y discute de la perspective du “lieu” (la périphérie métropolitaine), dans une relation asymétrique avec les affaires globales à une époque de flexibilité. On y caractérise le complexe Ford de Camaçari à partir de la restructuration productive et des changements dans l’organisation et le fonctionnement des territoires. Ses impacts sur la périphérie métropolitaine de Salvador sont présentés dans la deuxième partie. En conclusion, on y démontre que ce sont les mêmes circonstances qui ont permis l’arrivée de l’usine de montage à Camaçari qui représentent une contrainte pour les ambitions qui, à l’origine, voulaient atteindre une meilleure équation entre la croissance économique et le progrès social. La flexibilité de ces nouveaux arrangements, qui rendent les espaces périphériques stratégiques, compromet “l’enracinement” des investissements, la “production exiguë”, l’exiguïté des emplois et la diligence dans leur précarisation, elle inhibe les avantages sociaux. MOTS-CLÉS: restructuration productive, marché du travail, industrie automobile, périphérie métropolitaine, ségrégation sociale et spatiale. Publicação Online do Caderno CRH: http://www.cadernocrh.ufba.b

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