Timber is a commodity produced in Brazil that is relevant in the economic scenario; however, its environmental impact, due to the expansion of exploration areas, has generated concern about deforestation and consequent environmental changes, especially in the Amazon region. In this investigation, three instrumental analytical methodologies were tested for the authentication of native timber from the Amazon based on its physical-chemical characterization. The combined use of spectrometric techniques such as X-ray Fluorescence (XRF), as well as analysis of organic compounds by Raman (ER) and Infrared (IR) Spectroscopy proved to be efficient for use in forensic metrology, providing data that can be used in the identification of native timber, composing an alternative methodology for monitoring the problems faced in the Amazon region (deforestation, burning and illegal invasions). In addition, the methodology established in this study can be applied to other regions of Brazil, which also produce this type of commodity and face the same problems.

Abstract After the interruption of the Territories of Citizenship (TC) Program by the Brazilian federal government, Bahia was one of the few Brazilian states that maintained the territorial approach, the “Territories of Identity”. The objective of this study is to evaluate the effect of territorial management for the TC-Bahia (treatment group) in comparison to TC-Brazil (control group). A Rural Development Index for Family Agriculture (RDI-FA) was developed for two time periods to calculate its evolution for each group. The results showed that the RDI-FA had a significantly higher evolution for TC-Bahia in relation to TC-Brazil (p-value < 0.01). In ten years, TC-Bahia index increased by 0.07 while the rest of TC-Brazil grew by 0.04. We concluded that institutionalization and improvements of Bahian territorial approach has benefited family farmers, especially in the institutional dimension of territorial heritage.

São Pedro/SP é um município do Estado de São Paulo, com cerca de 35 mil habitantes. Com o status de Estância Turística, localiza-se a 30 km do município de Piracicaba/SP, sede da Escola Superior de Agricultura “Luiz de Queiroz”, da Universidade de São Paulo. Desde 1989, os agricultores familiares do bairro do Alto da Serra de São Pedro/SP receberam apoio de trabalhos de Assistência Técnica e Extensão Rural do Departamento de Economia, Administração e Sociologia Rural da ESALQ. A partir disso, a agricultura familiar se desenvolveu e o município foi objeto de estudos de Pós-Graduação, bem como de pesquisas de estudantes franceses da AgroParis Tech. Atualmente, São Pedro/SP tem se destacado como um polo de atração para residência, tendo superado as projeções de crescimento populacional do Censo e recebido aumento nos repasses de verbas federais. Atualmente, a dinamização da proteção ambiental, do turismo rural e a valorização da arte e da cultura têm se tornado a marca do município. Esses fenômenos envolvendo o município fizeram-nos questionar se São Pedro/SP poderia ser entendido como um exemplo das ruralidades contemporâneas das quais tratam Maria de Nazareth Baudel Wanderley, Maria José Carneiro e Angela Duarte Damasceno Ferreira. Nesse sentido, neste artigo, procuramos lançar um olhar sobre este município, questionando-nos em que medida os caminhos do desenvolvimento de São Pedro/SP configuram um processo de urbanização ou exemplo de uma nova ruralidade.

Protein-based nanoparticles have garnered significant interest for their potential in theranostic applications, including cancer treatment and nuclear medicine. Plasma proteins are particularly appealing due to their ability to bypass the rapid clearance typically associated with synthetic particles. Human serum albumin (HSA), for example, is already utilized in diagnostic procedures such as lymphoscintigraphy and sentinel lymph node detection and in cancer treatments where it delivers therapeutic agents in nanoparticulate form. Among the various techniques for synthesizing protein nanoparticles, ionizing radiation stands out for its ability to maintain good size control and preserve the protein's three-dimensional structure. Factors such as the reaction precursor, pH, protein concentration, presence of a stabilizer, and irradiation dose can all influence the size and shape of the resulting nanoparticles. This study sought to identify a correlation between the size of albumin nanoparticles and reaction parameters, including protein concentrations, the ionic content of the buffer solution, and the e-beam irradiation dose. We conducted different synthesis methods with varying BSA concentrations, using two different buffers and varying radiation doses. The resulting nanoparticles were evaluated using Dynamic Light Scattering. The size data were statistically analyzed using SAS Studio, SAS JMP, and WEKA software to identify correlations among the synthesis variables. Our observations primarily revealed that lower protein concentrations consistently resulted in smaller particles. Additionally, using Tris-HCl buffer as a medium led to a more proportional growth of particles with increases in concentration and irradiation dose. These findings suggest that Tris buffer is more suitable for BSA nanoparticle synthesis, as increased albumin concentration and radiation dose resulted in more consistent size control.

Este artigo tem o objetivo de apresentar uma metodologia de avaliação de impacto da gestão territorial nos Territórios da Cidadania (TC) do estado da Bahia, diante do abandono desta ação pública no restante do país. Para tanto, um Índice de Desenvolvimento Rural Sustentável (IDRS) foi elaborado como forma de avaliar o progresso de indicadores sociais, econômicos e ambientais da agricultura familiar. Os grupos de análise selecionados para desenvolver essa investigação são os TC do estado da Bahia em comparação com os TC do restante do Brasil. Com esse procedimento, espera-se colaborar com o desenvolvimento de futuros estudos quantitativos de avaliação de impacto de políticas públicas, especialmente para o meio rural, favorecendo a inclusão da agricultura familiar nas estratégias de desenvolvimento do país.

Background: Brazil has consolidated a relevant position in the world market, being the largest exporter and second producer of beef. Genetics, feeding system, geographic origin and climate influence the multielement profile of beef. The feasibility of combining classification algorithms with major and trace elements was evaluated as a tool for authentication of beef cuts.Methods: Animals of Angus, Nelore and Wagyu crossbreeds, raised in a vertically integrated system, were sampled at the slaughterhouse for chuck steak, rump cap and sirloin steak. Supervised learning algorithms i.e. Classification and Regression Tree (CART), Multilayer Perceptron (MLP), Naive Bayes (NB), Random Forest (RF) and Sequential Minimal Optimization (SMO) were used to build classification models based on the multielement profile of beef determined by neutron activation analysis.Results: Br, Co, Cs, Fe, K, Na, Rb, Se and Zn were determined in the beef samples. The classification accuracy values obtained for the beef cuts were 96% (MLP), 95% (SMO), 91% (RF), 86% (NB) and 70% (CART).Conclusion: The Multilayer Perceptron algorithm provided the best classification performance towards authentication of beef cuts on basis of major and trace element mass fractions.

Resumo Após a paralisação do programa federal Territórios da Cidadania (TC), a Bahia foi um dos poucos estados brasileiros que manteve a abordagem territorial, por meio dos Territórios de Identidade. Assim, o objetivo do estudo é avaliar o efeito da gestão territorial para os TC do estado da Bahia (grupo tratamento) em comparação com os demais TC do Brasil (grupo controle). Para tanto, um Índice de Desenvolvimento Rural da Agricultura Familiar (IDR-AF) foi desenvolvido para dois períodos, permitindo calcular a sua evolução para os dois grupos. Os resultados evidenciaram uma evolução significativamente superior do IDR-AF nos TC da Bahia em comparação com os TC do Brasil (p-valor < 0,01). Em dez anos, o índice da Bahia cresceu em 0,07, enquanto que o restante do Brasil progrediu em 0,04. Concluiu-se que a institucionalização e os aprimoramentos da abordagem territorial baiana beneficiaram a agricultura familiar, especialmente na dimensão institucional do patrimônio territorial.

Este artigo investigou o comprometimento das disciplinas dos currículos de Ciências Agrárias (Engenharia Agronômica e Engenharia Florestal), Gestão Ambiental e Administração da Escola Superior de Agricultura "Luiz de Queiroz" (ESALQ - USP), com a formação acadêmica do extensionista rural, sob a perspectiva da Lei de ATER, nº 12.188/2010. Trata-se de estudo interdisciplinar entre a área das ciências ambientais, suas distintas dimensões (política, social, histórica e cultural) e o campo da educação. A hipótese básica deste estudo é que a cultura institucional da ESALQ, pautada pela sua própria história e atrelada àquela da fundação da Universidade de São Paulo (USP), influencia a construção de uma estrutura curricular com forte fundamentação produtivista e elitista, propiciando um viés formativo para os futuros agentes da assistência técnica e extensão rural, que poderão atuar tanto na esfera pública quanto no particular. Por meio de um processo metodológico idiossincrático entre a pesquisa bibliográfica e a teoria da Análise Crítica do Discurso (ACD) foram encontrados elementos de capital cultural inerentes ao contexto histórico e sociopolítico da USP e da ESALQ, que influenciam formas específicas de conhecimento no delineamento dos respectivos currículos.

The species, variety and geographic origin of coffee directly influence the characteristics of the coffee beans and, consequently, the quality of the beverage. The added economic value that these features bring to the product has boosted the use of non-designative tools for authentication purposes. In this work, the feasibility of implementing a traceability system for Arabica coffee by country of origin was investigated using quality attributes and supervised machine learning algorithms: Multilayer Perceptron (MLP), Random Forest (RF), Random Tree (RT) and Sequential Minimal Optimization (SMO). We use an available database containing quality parameters for coffee beans produced in 15 countries, including the largest exporters and importers. Overall, Ethiopia, Kenya and Uganda had the highest coffee quality index (Total Cup Points). Differences between countries were found with 99% confidence using Robusta Multivariate Data Science with original data and 98% accuracy using Bootstrapping resampling method and Supervised Machine Learning algorithms. The model obtained by RF provided the best classification accuracy. The most important attributes to discriminate Arabica coffee by country of origin, in descending order, were body, moisture, total cup points, cupper points, acidity, aftertaste, flavor, aroma, balance, sweetness and uniformity. The coffee variety proved to be a promising variable to increase accuracy and can be incorporated among the quality attributes for classification and grading of coffee beans.

Cat food samples from commercial brands available in the Brazilian market were characterized by neutron activation analysis. The multielemental profiles were evaluated according to animal age, feed category and main ingredient. Statistically significant differences were observed between feed category (standard, premium and super premium) and main ingredient (animal and vegetable). Machine learning algorithms were effective in discriminating feed category (78.6% accuracy) and main ingredient (78.7% accuracy), indicating that the multielemental profile can be used for identification and traceability of cat food.

Dietary supplements and agricultural byproducts were characterized by neutron activation analysis. The nutritional potential of supplements was evaluated according to alternative and commercial categories, using analysis of variance and cluster analysis, and recommended dietary intake for children. The results indicated statistically significant differences between both categories for the elements Cs, K, Na, and Rb. For the nutritional elements Ca, Co, Fe, K, Na, and Zn, the categories were similar in cluster analysis. The similarity between elemental profiles of alternative supplements and agricultural byproducts was calculated using a dissimilarity matrix, showing that rice and wheat are the predominant ingredients.

High performance animals of Angus, Nelore and Wagyu cattle breeds raised in a vertically integrated system were selected for this study. A comprehensive elemental profile of tail hair was obtained by neutron activation analysis and triple quadrupole inductively coupled plasma mass spectrometry, including Al, As, Ba, Br, Ca, Cd, Co, Cr, Cs, Cu, Fe, K, La, Na, Mg, Mn, Mo, P, Pb, Rb, Sc, Se, Sr, V and Zn. The results indicated that tail hair can be an useful tool for monitoring nutritional status of beef cattle.

Mass spectrometry-based techniques have been used to study the chemical profile of honeys to authenticate entomological, botanical and geographical origins. Sample preparation is a crucial step of the analysis to obtaining reliable data and minimizing interference owing to matrix effects. The present work studied the best sample digestion procedure for elemental analysis of Brazilian honeys from Tetragonisca angustula (Jataí) and Apis mellifera sp (Apis) by triple quadrupole inductively coupled plasma mass spectrometry (TQ-ICP-MS). A central composite design with 2² factorial and 3 center points considering different volumes of HNO3 and H2O2 was investigated. There was no statistically significant influence of the amounts of HNO3 and H2O2 on the recoveries of Ag, Al, As, Ba, Be, Ca, Cd, Ce, Co, Cr, Cs, Cu, K, La, Mg, Mn, Na, Ni, Pb, Rb, Se, Sr, Th, U, V and Zn mass fractions. Machine learning algorithms (Multilayer Perceptron, Random Forest and Support Vector Machine) allowed discriminating entomological origin of honeys based on chemical profile with a classification accuracy of 99%.

The growing awareness of the environmental impact of beef production is greatly influencing the consumption decision.Beef production is strongly criticized due to the remarkable environmental impact of this activity, associated with problems of deforestation, water consumption, global warming, and climate change.Despite this, livestock food products play an important role in food security, accounting for 33% of global protein consumption.Enhancing the transparency of the beef production chain is essential to increase consumer perception about its origin, safety for consumption, environmental and human aspects.A study was undertaken to assess if beef samples from different producing countries can be distinguished from another on basis of their contents of chemical elements.Beef samples from some of the top world exporters, Brazil (1 st ), Australia (2 nd ), Argentina (5 th ), Uruguay (8 th ), and Paraguay (9 th ), were analyzed by neutron activation analysis for multi-element determination.Five machine learning algorithms, Classification and Regression Tree (CART), Multilayer Perceptron (MLP), Naive Bayes (NB), Random Forest (RF), and Sequential Minimal Optimization (SMO), were used to analyze the measurement results and classify the beef producing countries.MLP model provided the best classification performance, with an accuracy of 100%, 98%, 98%, 96%, and 82% respectively for Paraguay, Uruguay, Australia, Argentina, and Brazil.Reducing the number of classes (each country against the remaining countries), the accuracy achieved for the Brazilian beef samples was improved to 94% without changing the performance for other countries.Multi-element compositional data and machine learning algorithms allowed for discriminating beef producing countries, providing an outlook of becoming a valuable tool for geographical origin traceability and transparency.

1.Introdução Prevista faz décadas, a pandemia não nos pegou de surpresa, mas nem por isso melhor preparados. Dadas as políticas públicas de contenção de gastos, os sistemas de saúde de quase todos os

Brazilian livestock with a herd of more than 215 million animals is distributed over a vast area of 160 million hectares, leading the country to the first position in the world beef exports and second in beef production and consumption. Animals risen in the biomes Amazônia, Caatinga, Cerrado, Pampa and Pantanal were selected for this study. Beef samples were analyzed for their elemental content by neutron activation analysis and classified according to their origin by three machine learning algorithms (Multilayer Perceptron, Random Forest and Classification and Regression Tree). Significant differences (p < 0.0001) were observed between the beef elemental content from the different biomes for all multivariate contrasts using NPMANOVA. The highest classification performance was obtained for the biomes Amazônia and Caatinga using Multilayer Perceptron. Results showed the feasibility of combining trace element content and machine learning approaches for the Brazilian beef traceability.

The objective of this study was to evaluate temporal variability of rare earth elements (REE) in soils of citrus agroecosystems. Instrumental neutron activation analysis was applied for measuring La, Ce, Nd, Sm, Eu, Tb, Yb, Lu and Sc. Sampling was performed in four citrus farms, with Valencia variety (Citrus sinensis L. Osbeck) grafted onto ‘Rangpur’ lime (Citrus limonia Osbeck), comprising both organic and conventional production systems in the Borborema region, São Paulo State, Brazil. There was slight temporal variability of some REE in different farms, however no clear positive or negative trend could be observed along the 3 years.

This case study used exploratory and descriptive research to look into how stakeholders involved in the organization and practice of adventure races in Brazil perceive impacts related to this outdoor activity. Additionally, questions were posed about whether such impacts have been taken into consideration when planning these sporting events. Finally, the research aimed to understand why racers and adventure race organizers choose a certain time of year and venue to partake and organize a race: whether for more logistical purposes or also considering conservation. Online surveys were set up to target adventure race organizers, racers, and national park managers. Overall, there seems to be very little knowledge among racers and race organizers about social and environmental impacts associated with adventure races. This has led to the organization of events with very few or no specific concerns to the environment. Moreover, racers and adventure race organizers seem to perceive certain ecological issues—i.e., erosion—as challenges to the sport and not a problem to be addressed or avoided. National park managers were the group surveyed with more knowledge about the negative impacts adventure races might have on the environment.

Neutron activation analysis and data mining techniques were combined for assessing the mineral composition of diets commonly used to feed beef cattle in Brazil. Among twenty chemical elements determined, Br, Ca, Cs, La, Sc, Se, Sr, Th and Zn showed statistically significant differences between the two cattle diets studied. Chi square indicated that Cs, Se and Sc provided better diets discrimination. The highest classification performances using these elements were achieved for multilayer perceptron and sequential minimal optimization with prediction accuracy of 100%.