PERCEPÇÃO AMBIENTAL DE ALUNOS DAS “SALAS DE INCLUSÃO” NA ESCOLA LICEU PARAIBANO, JOÃO PESSOA – PB, NO CONTEXTO DO PARADIGMA DA EDUCAÇÃO INCLUSIVA

http://dx.doi.org/10.5902/2236130812951This article intends to provoke a reflection about the paradigm of the inclusive education, by studying the conception of environment debated in the school Liceu Paraibano in João Pessoa – PB. This research was developed only to try to produce a short characterization of the environmental perception of the public school students, although it became a controversial debate about the problematic of the educational inclusion of the  individuals with special needs in regular teaching. The inclusion process must have understood as a political and social posture on behalf of special education, and it has been increasing in school atmosphere, and it is aimed at attain egalitarian relationships for everybody. However this research came across the real context of these “inclusion classrooms”. Realize one that to avoid that someone is in risk of being excluded of a classroom context, another individual is excluded from the teaching and learning process, passing by disadvantage, selectivity and prejudice situations.http://dx.doi.org/10.5902/2236130812951O presente artigo tem como objetivo provocar uma reflexão sobre o paradigma da educação inclusiva, a partir do estudo da concepção de meio ambiente levantada na escola Liceu Paraibano em João Pessoa - PB. Esta pesquisa foi desenvolvida com o intuito de realizar uma caracterização da percepção ambiental dos alunos, porém tornou-se um polêmico debate sobre a problemática da inclusão pedagógica de portadores de necessidades especiais no ensino regular. O processo de inclusão deve ser entendido como um posicionamento político e social em prol da educação especial, e que vem crescendo no ambiente escolar, de forma a alcançar relações mais igualitárias para todos. Porém, a pesquisa se deparou com o contexto real dessas “salas de aula de inclusão”. Percebeu-se que para se evitar que alguém esteja em risco de ser excluído de um dado contexto na sala de aula, acaba excluindo outro sujeito do processo de ensino-aprendizagem, transcorrendo de toda forma em situações de desvantagem, seletividade e preconceitos

Integração party-granlog: interpretação abstrata aplicada a paralelização de programas em lógica

Este trabalho apresenta a integração dos modelos ParTy e Granlog. O ParTy (Parallel Types Analyzer) é um interpretador abstrato de tipos. O Granlog (Granularity Analyzer for LOGic programming) é um analisador automático de granulosidade na Programação em Lógica. O modelo Granlog necessita da análise estática de modos, tipos, medidas e dependências dos argumentos de um programa Prolog. Atualmente a análise de modos, tipos e medidas não é automática, o programador adiciona estas informações ao código fonte. A análise estática pode ser realizada através da técnica de Interpretação Abstrata. Esta técnica simula a execução de um programa segundo um domínio abstrato obtendo informações sobre o seu comportamento. O ParTy realiza a interpretação abstrata de tipos para os argumentos de um programa Prolog. A integração ParTy-Granlog torna automática a análise de tipos no modelo Granlog.Eje: TeoríaRed de Universidades con Carreras en Informática (RedUNCI

Integração party-granlog: interpretação abstrata aplicada a paralelização de programas em lógica

Este trabalho apresenta a integração dos modelos ParTy e Granlog. O ParTy (Parallel Types Analyzer) é um interpretador abstrato de tipos. O Granlog (Granularity Analyzer for LOGic programming) é um analisador automático de granulosidade na Programação em Lógica. O modelo Granlog necessita da análise estática de modos, tipos, medidas e dependências dos argumentos de um programa Prolog. Atualmente a análise de modos, tipos e medidas não é automática, o programador adiciona estas informações ao código fonte. A análise estática pode ser realizada através da técnica de Interpretação Abstrata. Esta técnica simula a execução de um programa segundo um domínio abstrato obtendo informações sobre o seu comportamento. O ParTy realiza a interpretação abstrata de tipos para os argumentos de um programa Prolog. A integração ParTy-Granlog torna automática a análise de tipos no modelo Granlog.Eje: TeoríaRed de Universidades con Carreras en Informática (RedUNCI

PCH: a preservação do patrimônio cultural e natural como política regional e urbana

TThis paper analyzes the roots of the Historical Cities Program (PCH), its creation in 1973, and deactivation in early 1980s. The program was rooted in the insertion of IPHAN in international cooperation network, the ideas of its articulator, Arch. Renato Soeiro, and military government objectives, which included the Northeast development through cultural tourism. The presence of northeastern and northerners people in ministries and higher ranks of the military government has contributed to its creation. After 36 years, Recife replace the leadership of Rio de Janeiro on heritage policy, strengthening local groups and setting off a struggle for control within IPHAN. Without support from the Secretariat of Planning and General Coordination of the Presidency of the Republic, the PCH started to be dismounted by the government of General João Figueiredo in 1979, after a new cultural policy to create a popular base for the "gradual and controlled opening policy." Alongside this ideological shift, the regionalist dispute between groups of Recife and Rio de Janeiro was intensified and the PCH progressively dismounted.Este texto analisa as raízes do Programa das Cidades Históricas, sua criação em 1973 e sua desativação no início da década de 1980. Suas raízes estariam na inserção do Iphan na rede de cooperação internacional, no ideário de seu principal articulador, o arquiteto Renato Soeiro, e no objetivo do governo militar de promover o desenvolvimento da região Nordeste. Para sua criação, teria contribuído a presença de nordestinos e nortistas em ministérios e altos postos do governo militar. Após 36 anos da criação do Sphan, o protagonismo das ações sobre o patrimônio seria transferido do Rio de Janeiro para o Recife, fortalecendo grupos locais e dando início a uma disputa pelo controle do Iphan. Sem contar mais com o apoio da Secretaria de Planejamento e Coordenação Geral da Presidência da República, o PCH começa a ser desativado em 1979, no governo do Gal. João Figueiredo, com uma nova política cultural destinada a criar uma base popular para a "abertura política gradual e controlada". Paralelamente a essa mudança ideológica, se acirra a disputa entre os grupos do Recife e do Rio de Janeiro e se dá a progressiva desativação do PCH

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

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For schoolaged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesit

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