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Malaria is the most important vector-borne disease in the world and a challenge for control programs. In Brazil, 99% of cases occur in the Amazon region. In the extra-Amazonian region, a non-endemic area, epidemiological surveillance focuses on imported malaria and on autochthonous outbreaks, including cases with mild symptoms and low parasitemia acquired in the Atlantic Forest biome. In this scenario, cases are likely to be underreported, since submicroscopic parasitemias are not detected by thick blood smear, considered the reference test. Molecular tests are more sensitive, detecting asymptomatic individuals and mixed infections. The aim of this study was to propose a more efficient alternative to detect asymptomatic individuals living in areas of low malaria endemicity, as they are reservoirs of Plasmodium that maintain transmission locally. In total, 955 blood samples from residents of 16 municipalities with autochthonous malaria outbreaks in the Sao Paulo State were analyzed; 371 samples were collected in EDTA tubes and 584 in filter paper. All samples were initially screened by a genus-specific qPCR targeting ssrRNA genes (limit of detection of 1 parasite/µL). Then, positive samples were subjected to a nested PCR targeting ssrRNA and dihydrofolate reductase-thymidylate synthase genes (limit of detection of 10 parasites/µL) to determine Plasmodium species. The results showed a statistically significant difference (K = 0.049; p < 0.0001) between microscopy positivity (6.9%) and qPCR (22.9%) for EDTA-blood samples. Conversely, for samples collected in filter paper, no statistical difference was observed, with 2.6% positivity by thick blood smear and 3.1% for qPCR (K = 0.036; p = 0.7). Samples positive by qPCR were assayed by a species-specific nested PCR that was in turn positive in 26% of samples (16 P. vivax and 4 P. malariae). The results showed that molecular protocols applied to blood samples from residents in areas with autochthonous transmission of malaria were useful to detect asymptomatic patients who act as a source of transmission. The results showed that the genus-specific qPCR was useful for screening positives, with the subsequent identification of species by nested PCR. Additional improvements, such as standardization of blood plotting on filter paper and a more sensitive protocol for species determination, are essential. The qPCR-based algorithm for screening positives followed by nested PCR will contribute to more efficient control of malaria transmission, offering faster and more sensitive tools to detect asymptomatic Plasmodium reservoirs. [ABSTRACT FROM AUTHOR]

The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge's operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil. [ABSTRACT FROM AUTHOR]

This paper concerns the cinematic representation of São Paulo's Elevado João Goulart, a 3.5km elevated expressway close to the historic centre of the city, popularly known as the Minhocão. Built during the boom period of Brazil's dictatorship, the Minhocão opened to traffic in January 1971. Controversial at the beginning of its existence, it has been partially rehabilitated, a process in which film has been important. The paper describes three general modes of the Minhocão's depiction on film: in the first it represents a generalised fear of the modern city, in the second it is a normal part of the landscape, and in the third it is a form of aesthetic occupation. Key films discussed include Kiss of the Spider Woman (1985), directed by Hector Babenco (1985) and the documentary Elevado 3.5 (Maíra Bühler, Paulo Pastorelo and João Sodré 2006). The paper responds to Sophia's Landscapes of Care theme by showing how over time film can, over time, help rehabilitate seemingly irredeemable infrastructure. [ABSTRACT FROM AUTHOR]

Background: Dengue, Zika, and chikungunya, whose viruses are transmitted mainly by Aedes aegypti, significantly impact human health worldwide. Despite the recent development of promising vaccines against the dengue virus, controlling these arbovirus diseases still depends on mosquito surveillance and control. Nonetheless, several studies have shown that these measures are not sufficiently effective or ineffective. Identifying higher-risk areas in a municipality and directing control efforts towards them could improve it. One tool for this is the premise condition index (PCI); however, its measure requires visiting all buildings. We propose a novel approach capable of predicting the PCI based on facade street-level images, which we call PCINet. Methodology: Our study was conducted in Campinas, a one million-inhabitant city in São Paulo, Brazil. We surveyed 200 blocks, visited their buildings, and measured the three traditional PCI components (building and backyard conditions and shading), the facade conditions (taking pictures of them), and other characteristics. We trained a deep neural network with the pictures taken, creating a computational model that can predict buildings' conditions based on the view of their facades. We evaluated PCINet in a scenario emulating a real large-scale situation, where the model could be deployed to automatically monitor four regions of Campinas to identify risk areas. Principal findings: PCINet produced reasonable results in differentiating the facade condition into three levels, and it is a scalable strategy to triage large areas. The entire process can be automated through data collection from facade data sources and inferences through PCINet. The facade conditions correlated highly with the building and backyard conditions and reasonably well with shading and backyard conditions. The use of street-level images and PCINet could help to optimize Ae. aegypti surveillance and control, reducing the number of in-person visits necessary to identify buildings, blocks, and neighborhoods at higher risk from mosquito and arbovirus diseases. Author summary: The strategies to control Ae. aegypti require intensive work and considerable financial resources, are time-consuming, and are commonly affected by operational problems requiring urgent improvement. The PCI is a good tool for identifying higher-risk areas; however, its measure requires a high amount of human and material resources, and the aforementioned issues remain. In this paper, we propose a novel approach capable of predicting the PCI of buildings based on street-level images. This first work combines deep learning-based methods with street-level data to predict facade conditions. Considering the good results obtained with PCINet and the good correlations of facade conditions with PCI components, we could use this methodology to classify building conditions without visiting them physically. With this, we intend to overcome the high cost of identifying high-risk areas. Although we have a long road ahead, our results show that PCINet could help to optimize Ae. aegypti and arbovirus surveillance and control, reducing the number of in-person visits necessary to identify buildings or areas at risk. [ABSTRACT FROM AUTHOR]

In the present study, a methodology to calculate lidar ratios for distinct cirrus clouds has been implemented for a site located in the Southern Hemisphere. The cirrus cloud lidar data processing has been developed to consider a large cloud variability with the final aim of cirrus cloud monitoring through a robust retrieval process. Among the many features lidar systems can extract for cirrus detection, we highlight: cloud geometrical information and extinction-to-backscatter ratio (also called lidar ratio - LR). LR's can, in general, provide important information on cirrus cloud microphysics due to the presence of ice crystals and their properties such as shape, size, composition and orientation of particles and their effect on LR values. Conditions for LR calculations and their resulting uncertainty have been improved as their analysis requires identifying cirrus cloud stationary periods through the use of a specific statistical approach well-established in the literature and employed here with good results, allowing for the study of specific cases with multi-layer cirrus cloud occurrence. The results from the measurements taken in the region of the Metropolitan City of São Paulo - MSP have been used to implement and test the methodology developed herein. In addition to the geometrical parameters extracted, improved values of LR's were calculated and showed significantly different values for the different layers inspected, varying between 19± 01 sr and 74± 13sr. This large value interval allowed us to indirectly verify the presence of different ice crystal sizes and shapes and those associated with different air mass sources for the cirrus cloud formation [ABSTRACT FROM AUTHOR]

Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the Brazilian state of São Paulo, contaminated sites (CSs) constitute threats to health, environment and socioeconomic situation of populations. Over the past two decades, the Environmental Agency of São Paulo (CETESB) has monitored these known CSs. This paper discusses the produced dataset through digitising the CETESB reports and making them accessible to the public in English. The dataset reports on qualitative aspects of contamination within the registered sites (e.g., contamination type and spread) and their management status. The data was extracted from CETESB reports using a machine-learning computer vision algorithm. It comprises two components: an optical character recognition (OCR) engine for text extraction and a convolutional neural network (CNN) image classifier to identify checked boxes. The digitisation was followed by harmonisation and quality assurance processes to ensure the consistency and validity of the data. Making this dataset accessible will allow future work on predictive analysis and decision-making and will inform the required policy-making to improve the management of the CSs in Brazil. [ABSTRACT FROM AUTHOR]

This paper presents an overview for the Special Issue of Clean Technology and Environmental Policy journal (CTEP), and it includes accepted papers from: 5th South East European Conferences on Sustainable Development of Energy, Water and Environment Systems (SDEWES) held from May 22–26, 2022, in Vlore, Albania. 3rd Latin American Conferences on Sustainable Development of Energy, Water and Environment Systems (SDEWES) held from July 24–28, 2022, in Sao Paulo, Brazil. 17th Conferences on Sustainable Development of Energy, Water and Environment Systems (SDEWES) held from November 6–10, 2022, in Paphos, Cyprus. Considering CTEP's policy of high-quality research papers, guest editors have invited 35 research articles, presented at the SDEWES 2022 conference. After a vigorous review process, 13 papers have been accepted for publication in this special issue. All 13 accepted papers are briefly presented in this overview together with a wider view that presents research efforts within the SDEWES community published through previous SDEWES special issues. [ABSTRACT FROM AUTHOR]

In this paper, five commonly used software tools to estimate solar radiation in the urban context (GRASS GIS, ArcGIS, SimStadt, CitySim and Ladybug) are run on the same test site and are compared in terms of input data requirements, usability, and accuracy of the results. Spatial and weather data have been collected for an area located in the Brazilian city of São Paulo, in the district of Santana. The test area surrounds a weather station, for which meteorological data of the last 15 years have been collected and used as ground truth when analysing and comparing the simulation results. In terms of spatial data, raster- and vector-based models of the study area have been generated in order to comply with the different input requirements. More specifically, in the case of the vector-based tools (SimStadt, CitySim and Ladybug), a common 3D model based on CityGML and containing buildings, vegetation (trees) and terrain has been generated and used as a common urban model. The paper presents the findings and discusses the results not only from a numerical point of view, but also from the perspective of the overall usability of the software in terms of data requirements, simulation time and task automatisation. [ABSTRACT FROM AUTHOR]

The article informs that Sao Paulo, Brazil-based FuturaGene PLC, forestry company will share yield-enhancement traits used in woody crops with a nonprofit research institute to improve the flexibility of staple crops grown in arid and semiarid regions of Asia, Africa and Brazil.

The article presents the highlights of the 2004 International Drying Symposium in São Paulo City, Brazil. A graph showing the number of participants by their country of origin is given. A total of 430 abstracts were submitted while 316 manuscripts were received. A total of 25 reviewers work in the reviewing process.

This paper presents a study conducted in São Paulo, Brazil, where the planetary boundary layer height (PBLH) was determined using ceilometer data and the wavelet covariance transform method. The retrieved PBLH values were subsequently compared with the concentrations of CO2 and CH4 measured at three distinct experimental sites in the city. The period of study was July 2021. This study also included a comparison between ceilometer data and lidar data, which demonstrated the favorable applicability of the ceilometer data for PBLH estimation. An examination of the correlation between changes in average CO2 concentrations and PBLH values revealed stronger correlations for the IAG and UNICID stations, with correlation coefficients (ρ) of approximately −0.86 and −0.85, respectively, in contrast to the Pico do Jaraguá station, which exhibited a lower correlation coefficient of −0.42. When assessing changes in CH4 concentrations against variations in PBL height, the retrieved correlation coefficients were approximately −0.78 for IAG, −0.66 for UNICID, and −0.38 for Pico do Jaraguá. The results indicated that CO2/CH4 concentrations are negatively correlated with PBL heights, with CO2 concentrations showing more significant correlation than CH4. Additionally, among the three measurement stations, IAG measurements displayed the most substantial correlation. The results from this study contribute to the understanding of the relationship between PBLH and greenhouse gas concentrations, emphasizing the potential of remote sensing systems like ceilometers in monitoring and studying atmospheric processes. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the information literacies used by community representatives when engaging with participatory budgeting in São Paulo City, Brazil. Using questions established from context-setting interviews with stakeholders, a focus group was held in 2019 with eight participative councillors, with in situ interpretation, resulting in a translated transcript of the discussion. Thematic analysis was used to understand information issues faced by community representatives in relation to past research. It was found that the community representatives face informational barriers to their engagement with participatory budgeting, in (a) learning about their role (b) understanding the information needs of the communities served and (c) gathering and sharing information about local issues with stakeholders. These findings allow the refining of CILIP's definition of information literacy (IL) for citizenship and provide the basis for proposing a model for the IL of community representatives. It is also proposed that future IL research could further develop the role of digitally-enabled place and community in shaping the landscape of literacy and the role of hyperlocal representation. Additionally, the role of translation in cross-lingual IL research is considered. [ABSTRACT FROM AUTHOR]

Purpose: A sustainable transportation system should represent a win-win situation: minimizing transport's impact on the environment and reducing natural disasters' effects on transportation. A well-distributed set of rain gauges is crucial for monitoring services in smart cities. However, those services should consider the uncertainties about the registers of rainfall impacts. In this paper, the authors present a case study of optimal rain gauge location based on an actual database of rainfall events with impacts on urban mobility in the city of Sao Paulo (Brazil). Design/methodology/approach: This paper presents a maximal covering location formulation and proposes a robustness analysis considering spatial location perturbations. Findings: In this case study, the robustness of the objective function is above 99.99%. The robustness for the number of covered demand points is 88.93%, and the frequency associated with every candidate is between 11.71% and 69.49%. Originality/value: Incorporating spatial uncertainties on coverage problems is essential to provide stakeholders more realistic supporting tools and to draw different possible scenarios. [ABSTRACT FROM AUTHOR]

The coronavirus (COVID-19) is a disease declared a global pandemic that threatens the whole world. Since then, research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease. Several researchers have focused on using the potential of Artificial Intelligence (AI) techniques in disease diagnosis to diagnose and detect the coronavirus. This paper developed deep learning (DL) and machine learning (ML) -based models using laboratory findings to diagnose COVID-19. Six different methods are used in this study: Knearest neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) as a machine learning method, and Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long-term memory (LSTM) as DL methods. These approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo, Brazil. This data consists of 5644 laboratory results from different patients, with 10% being Covid-19 positive cases. The dataset includes 18 attributes that characterizeCOVID-19. We used accuracy, f1-score, recall and precision to evaluate the different developed systems. The obtained results confirmed these approaches' effectiveness in identifying COVID-19, However, ML-based classifiers couldn't performup to the standards achieved byDL-basedmodels. Among all, NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56% and 85% accuracies, respectively. Besides these, DL models attained better performance as CNN, DNN and LSTM secured more than 90% accuracies. The LTSM outperformed all by achieving an accuracy of 96.78% and an F1-score of 96.58%. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hierarchical Bayesian approach for modeling the evolution of the 7-day moving average for the number of deaths due to COVID-19 in a country, state or city. The proposed approach is based on a Gaussian process regression model. The main advantage of this model is that it assumes that a nonlinear function f used for modeling the observed data is an unknown random parameter in opposite to usual approaches that set up f as being a known mathematical function. This assumption allows the development of a Bayesian approach with a Gaussian process prior over f. In order to estimate the parameters of interest, we develop an MCMC algorithm based on the Metropolis-within-Gibbs sampling algorithm. We also present a procedure for making predictions. The proposed method is illustrated in a case study, in which, we model the 7-day moving average for the number of deaths recorded in the state of São Paulo, Brazil. Results obtained show that the proposed method is very effective in modeling and predicting the values of the 7-day moving average. [ABSTRACT FROM AUTHOR]

Public transport is essential to provide urban mobility, and cities need to offer a fast, comfortable, secure, and low-pollutant public transport mode to attract passengers and reduce private car use. Despite metros, trains, and light rail systems being desirable, the cost of implementation and low flexibility make bus services the best option for many cities. One solution for improving bus service systems is the implementation of bus rapid transit systems that are composed of buses running in segregated lines with pre-payment fares and level boarding platforms in stations. However, the main challenge of bus systems is the use of engines fueled by diesel, which is extremely polluted. One possible solution is the use of battery buses; but are they really feasible regarding daily operation cost? In this paper, we investigate the adoption of battery buses in comparison to diesel engines in a BRT system using municipal data from São Paulo, Brazil, as a reference. We collected operational data from the Sao Paulo city government transport agency and data from the literature to produce an analytic hierarchy process (AHP) model, which allowed us to compare both systems. The AHP model considered a triple-bottom-line perspective using social, economic, and environmental impact criteria. The result showed that the initial costs of acquisition can be a barrier, but in the long run, cities adopting battery buses in their BRT system can benefit from the effects of reduction in gas emissions and longer lifetimes of electric bus components. Moreover, the results showed that the adoption of battery buses in BRT systems depends on local government subsidies being implemented. [ABSTRACT FROM AUTHOR]

Goal: This research aim is to answer the question: which Maturity Model (MM) is suitable to evaluate LC implantation in Brazilian Small-Medium Sized Enterprises (SME) of construction. Design / Methodology / Approach: A literature review was carried out to identify MM related to LC. Then, a case study was conducted applying selected MM in one SME located in São Paulo country town state. The data collection was carried out from April to August of 2018 with professionals of the construction company. A content analysis was used to lead the suited MM through the case study outcomes. Results: The case study results were analyzed and guide to the suitable MM for this scenario. Improvements to the MM and complementary tools for companies starting on LC implementation are discussed. It was verified that the elected MM has many advantages and can be used in different Brazilian SME of construction. Limitations of the investigation: The MM assessment cover just one project and the content analysis classification criteria embrace this project issues and the experience provide by this application. Practical implications: This paper point out the suitable MM to be used by construction practitioners in Brazilian construction projects that are starting lean implementation an SME seeking to meet the gap of elect one model to fill this goal and spread lean philosophy in this scenario. Originality / Value: This paper contributes to the LC body of knowledge discussing the individuality of Brazilian SME construction projects and the challenge of choosing the MM that fit these companies, leading to method selection criteria that support this issue. [ABSTRACT FROM AUTHOR]

The transition to remote learning in the context of COVID-19 led to dramatic setbacks in education. Is the return to in-person classes sufficient to eliminate these losses eventually? We study this question using data from the universe of secondary students in São Paulo State, Brazil. We estimate the causal medium-run impacts of the length of exposure to remote learning during the pandemic through a triple-differences strategy, which contrasts changes in educational outcomes across municipalities and grades that resumed in-person classes earlier (already by Q4/2020) or only in 2021. We find that relative learning losses from longer exposure to remote learning did not fade out over time--attesting that school reopening was at the same time key but not enough to mitigate accumulated learning losses in face of persistence. Using observational and experimental variation in local responses across 645 municipalities, we further document that remedial educational policies in the aftermath of the pandemic boosted learning recovery. [ABSTRACT FROM AUTHOR]

This paper presents a multi-temporal comparison of high-resolution 3D digital models from two urban areas susceptible to landslides in three time periods. The study areas belong to the São Paulo landslide risk mapping database and are named "CEU Paz" (CP) and "Parque Santa Madalena I" (PSM). For each area, a lidar digital surface model (DSM) (2017) and two structure-from-motion multi-view stereo DSMs (2019 and 2022) built from drone imagery were combined using raster algebra to generate three digital surface models of differences (DoDs). The DoDs were able to highlight changes in vegetation cover and buildings, which are important characteristics for evaluating geological risks in an urban context. Still, they were unable to highlight changes in the ground surface. The results demonstrate that the method greatly supports monitoring, allowing for greater detail and ease of detecting large-scale changes. Even with promising results, this technique should be understood as one more tool for mapping risk areas without replacing fieldwork. [ABSTRACT FROM AUTHOR]

Glyphosate is the most widely used herbicide for weed control in citrus orchards in Brazil; therefore, it is likely that several species have gained resistance to this herbicide and that more than one resistant species can be found in the same orchard. The objective was to identify weeds resistant to glyphosate in citrus orchards from different regions of the São Paulo State (SP) and determine how many resistant species are present within the same orchard. Seeds of Amaranthus deflexus, A. hybridus, Bidens pilosa, Chloris elata, Conyza bonariensis, Digitaria insularis, Solanum Americanum, and Tridax procumbens, which, as reported by growers, are suspected to be resistant to glyphosate, were collected from plants that survived the last application of this herbicide (>720 g of acid equivalent [ae] ha–1) in sweet orange and Tahiti acid lime orchards. Based on dose–response and shikimic acid accumulation assays, all populations of A. deflexus, A. hybridus, B. pilosa, and T. procumbens were sensitive to glyphosate. However, populations of B. pilosa from the Olimpia region (R-NS, R-PT and R-OdA) showed signs of resistance based on plant mortality rates by 50% within a population (LD50 = 355–460 g ae ha−1). All populations of C. bonariensis, C. elata, and D. insularis were resistant to glyphosate, presenting resistance ratios from 1.9 to 27.6 and low shikimate accumulation rates. Solanum americanum also showed resistance, with resistance ratios ranging from 4.3 to 25.4. Most of the citrus orchards sampled presented the occurrence of more than one species resistant to glyphosate: Nossa Senhora—one species; Olhos D'agua and Passatempo—two species; Araras—four species; and Cordeiropolis and Mogi-Mirim—up to five species. The results reported in this paper provide evidence of multiple species in citrus orchards from São Paulo that have exhibited resistance to glyphosate. This underscores the difficulties in managing glyphosate-resistant weeds which are prevalent throughout the country, such as C. bonariensis and D. insularis. The presence of these resistant species further complicates the control of susceptible species that may also develop resistance. In addition, the glyphosate resistance of S. americanum was identified for the first time. [ABSTRACT FROM AUTHOR]

In recent decades, the use of regression models with random effects has made great progress. Among these models' attractions is the flexibility to analyze correlated data. In various situations, the distribution of the response variable presents asymmetry or bimodality. In these cases, it is possible to use the normal regression with random effect at the intercept. In light of these contexts, i.e. the desire to analyze correlated data in the presence of bimodality or asymmetry, in this paper we propose a regression model with random effect at the intercept based onthe generalized inverse Gaussian distribution model with correlated data. The maximum likelihood is adopted to estimate the parameters and various simulations are performed for correlated data. A type of residuals for the new regression is proposed whose empirical distribution is close to normal. The versatility of the new regression is demonstrated by estimating the average price per hectare of bare land in 10 municipalities in the state of São Paulo (Brazil). In this context, various databases are constantly emerging, requiring flexible modeling. Thus, it is likely to be of interest to data analysts, and can make a good contribution to the statistical literature. [ABSTRACT FROM AUTHOR]

Copyright of Borealis: An International Journal of Hispanic Linguistics is the property of Nordic Council for Reindeer Husbandry Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Characterizing food systems, i.e., describing their organizational features, can help to generate a better understanding of the structural vulnerabilities that constrain transitions towards sustainable food security. However, their characterization across different economic contexts remains challenging. In this paper, by linking key concepts from research on food regimes, food system vulnerabilities and responsible innovation, we aim to characterize food systems in a developing and a developed economy to identify their shared vulnerabilities. We applied a case study design to characterize food production, processing and distribution in the province of Québec (Canada) and in the state of São Paulo (Brazil). In both cases, the processing and distribution stages have higher economic predominance when compared to the agricultural production stage. Furthermore, we observed concentration in a few activities in both food systems, with a shared focus on export-oriented supply chains. Vulnerabilities in both food systems include: (1) increased interdependence because some supply chains are export-oriented or depend on foreign labor and are, therefore, exposed to external risks; (2) concentration in a few activities, which threatens present and future local food diversity and (3) unequal power relations, making small and medium players vulnerable to decisions made by big players. The characterization developed in this study shows that the two food systems are mainly pursuing economic goals, following the institutional logics of the neoliberal food regime, which are not necessarily aligned with food security goals. It also exposes the presence of characteristics of 'responsibility' that may eventually help overcome food systems' vulnerabilities and support transitions toward sustainability. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Estudos Urbanos e Regionais is the property of Associacao Nacional de Pos-Graduacao e Pesquisa em Planejamento Urbano e Regional and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper presents a feasibility study for hybrid system operation of a hydroelectric power plant and a floating photovoltaic plant. Using the database of government agencies, it established the daily production of electrical energy and the corresponding water flow of the Ilha Solteira hydroelectric power plant in São Paulo, Brazil. The PVsyst software simulated the potential of 480 MWp of a floating photovoltaic plant in the hydroelectric power plant lake. The MATLAB software performed the mathematical modeling, analyzing 12 scenarios of weather conditions for hybrid electricity generation at the hydroelectric power plant and the floating photovoltaic plant. The data obtained in the analyzed scenarios show an average monthly reduction of 6% in hydroelectric power generation and 7% in the volume of water in the turbine, allowing the generation of electric energy from a floating photovoltaic plant, improving the reserves of waterenergy stock, reducing the production of greenhouse gases, and avoiding the emission of 55,000 tCO2/yr. The financial evaluation shows a cost of US$ 0.73/Wp, and 13 years for the floating photovoltaic plant system to start producing a profit. Yet the Floating Photovoltaic is advantageous because it shares the power transmission system of the hydroelectric plant, and it is not necessary to acquire large land areas. [ABSTRACT FROM AUTHOR]

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 and 579 KCN4) from Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of São Paulo, São Paulo in Brazil and 1531 eyes (Healthy = 400, KCN1 = 378, KCN2 = 285, KCN3 = 200, KCN4 = 88) from Department of Ophthalmology, Jichi Medical University, Tochigi in Japan and used several accuracy metrics including Precision, Recall, F-Score, and Purity. We compared the proposed method with three other standard unsupervised algorithms including k-means, Kmedoids, and Spectral cluster. Based on two independent datasets, the proposed model outperformed the other algorithms, and thus could provide improved identification of the corneal status of the patients with keratoconus. [ABSTRACT FROM AUTHOR]

This paper explores the scope for upscaling and transnational transfer of participatory landslide risk management strategies for informal settlements in Latin America. Drawing on lessons from transdisciplinary action-research in three neighbourhoods in Medellín, Colombia, and one in São Paulo, Brazil, the paper discusses how bottom-up approaches were developed to co-produce landslide risk management in both Global South cities, in a way that optimises the collaboration between communities and relevant governmental bodies. The analysis focuses on mitigation and 'agreement-seeking', from the perspectives of scale, power and knowledge, which help understand the parallels between co-production of landslide risk management and co-production of urban services. Two key conclusions are that landslide risk management should be built into neighbourhood upgrading and management, and that both community and the state have stronger roles to play in landslide risk management from their respective capacities. The paper also reflects on the role of academia in enabling co-production of landslide risk management through engaging with local communities. [ABSTRACT FROM AUTHOR]

Over the last few decades, and more prominently currently, many countries have launched and reinforced campaigns to reduce CO2 emissions from all human activities and, in the area of energy, promote energy generating technologies from low carbon, renewable sources, especially wind and solar. In recent years, this promotion of renewables can be seen in statistics as well as an extraordinary increase in plants using renewable sources. There is more activity surrounding the use of small devices installed close to consumers, such as small wind turbines (SWT). In cities, the best places to install SWT are tall buildings. The Institute of Energy and Environment (IEE-USP) has installed a 1.8 kW SWT on the University of São Paulo campus in São Paulo, Brazil. Even with low-magnitude winds at the site, the SWT installation was carried out to serve as a didactic apparatus and demonstration initiative of wind energy generation connected directly to the University's electric grid, which already has other embedded renewable sources installed, namely photovoltaic and biogas plants. The turbine was placed on the roof of the existing High Voltage Laboratory building, leading to an operating height of 35 m. This paper presents previous local wind data measurements using a Lidar system, annual energy yield estimation calculations, and measurements, also bringing all implementation details. It reports and analyzes the operation and energy production data from three full operational years, from 2018 to 2020, discussing and concluding with further improvements of SWT from technical and economic aspects. [ABSTRACT FROM AUTHOR]

Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To support the decision-making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications, considering the Brazilian scenario. Three artificial intelligence techniques were implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The database used was the one provided by the website Oceans14, containing the history of Fundamental Indicators and the history of Quotations, considering BOVESPA (São Paulo State Stock Exchange). The results of the different algorithms were compared to each other using the following metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%. [ABSTRACT FROM AUTHOR]

The concept of institutional interplay (i.e., the interaction between institutions) is critical if the challenges to multilevel governance are to be better understood and addressed. Drawing on the literature on institutional interplay, this paper develops an analytical approach to examine challenges to multilevel coastal governance. São Paulo Macrometropolitan region (MMP, in Portuguese) is used to ground the empirical analysis. The macrometropolitan is one of the largest urban areas in the Southern Hemisphere; it houses the Metropolitan Region of São Paulo—one of the six most significant in the world. The MMP concentrates critical infrastructure, such as ports, airports, and roads, and considerable knowledge, technology, and innovation hubs. The coastal governance in the MMP area characterizes a highly connected multilevel system formed by 60 organizations (7 international, 29 national, 11 state, and 13 local). These comprised a complex environment featuring a great deal of fragmentation, and, consequently, jurisdictional and functional gaps and overlaps. This case was best described in terms of normative, functional, and political interplay. Interplay management has the potential to improve governance on the cross-level interactions among scale-dependent institutions of the MMP, enhancing synergies, and minimizing tensions among the institutions analyzed. This includes fostering cognitive interaction (i.e., promoting interinstitutional learning and assistance, and enhancing synergy) between institutions with complementary and/or similar objectives. Ultimately, interplay management may reduce fragmentation, improve compliance and monitoring, and increase cost-effectiveness. The findings from this paper may prove useful to other jurisdictions where pressing environmental issues involve multiple governance levels and interacting institutions. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, Brazilian urban peripheries have been severely affected both by the spread of the virus and by social, political, and economical dynamics, raising concerns about the psychological wellbeing and mental health of the population living in these areas. The pandemic broke out in a context of reduced public spending in social and health policies as well as in a process of erosion of social rights, fostering processes of exclusion and highlighting the association between austerity, the increase in poverty and inequality as well as in health and mental health problems indicators. This article presents the results of a qualitative participatory research that investigated subjective experiences in a peripheral neighborhood of São Paulo, Brazil, aiming to understand how contextual dynamics played a role in shaping mental health experiences during the COVID-19 pandemic. Amultidisciplinary team of researchers worked closely with local volunteers trained to provide emotional support calls to neighbors of the community who signed up for the project. This article presents three ethnographic cases of women who had their routines strongly affected by the suspension of public and social protection services for the containment of the SARS-CoV-2 pandemic, leading to psychological suffering due to the increased demand of "domestic circuits of care". We argue that within a context of austerity, the pandemic was remarkably harsh in urban peripheries and, specifically, for women with caring responsibilities. In addition to highlighting the pervasive "social protection gap", the cases presented in this paper also reveals the unequal dynamics of the social reproduction work in several layers, which falls mainly on women's shoulders. The "crisis of care", proposed by gender and feminist scholars, can contribute to understanding the psychological outcomes of the COVID-19 pandemic for these women. [ABSTRACT FROM AUTHOR]

Objective: To investigate the association between ultra-processed food (UPF) consumption and internalising symptoms (IS) among adolescents.Design: It is a cross-sectional study. Paper-pencil survey was completed in classroom with information on UPF consumption, IS and selected covariates. IS were assessed with the Internalizing Symptoms sub-scale from the Social Behaviour Questionnaire (IS-SBQ). UPF was evaluated with a FFQ extracted from the Brazilian National School Health Survey. Crude and adjusted association between UPF and IS was investigated with structural equation models.Setting: São Paulo, SP, Brazil.Participants: A total of 2680 students, Mage = 14·85; (95 % CI 14·81, 14·88).Results: UPF consumption was associated with higher scores in IS in the crude (β = 0·14; P < 0·001) and adjusted (β = 0·12; P < 0·001) models. The higher the consumption of UPF, the higher is the IS score. The following variables were associated with a lower risk of UPF consumption: male sex, public school and having more meals with parents. The change in the magnitude of the standardised score was almost negligible, but the model was significantly improved with the inclusion of covariates.Conclusions: Our results provide evidence about the positive association between UPF consumption and IS among adolescents. The association, despite its low magnitude, remained significant after adjusting for potential confounders. These results are relevant considering the increase in UPF consumption worldwide and in low- and middle-income countries. Also, our study emphasises the importance of a healthy diet with a reduction in UPF consumption among adolescents. [ABSTRACT FROM AUTHOR]

Favelas are the most common type of informal settlements found in Brazil. The Housing Secretariat, City Hall, Sao Paulo, has conducted surveys using Unmanned Aerial Vehicles (UAVs) for the favelas to facilitate the slum upgrading projects and has taken the initiative to create a digital twin of the slum areas. This study illustrates the feasibility of developing a methodological workflow to create a digital twin by automatic 3D building reconstruction in slums from the UAV point cloud and the 2D building footprints with a continuous link for updating the building semantic information. This study focuses on facilitating data integration and updating the semantic information into the 3D model to provide additional information about the individual buildings in the slums. The assessments concluded that the proposed workflow is suitable for creating digital twins for slums based on the UAV and 2D cadastral data. However, the 3D slum model had a few limitations, which are discussed in this paper. [ABSTRACT FROM AUTHOR]

The novel coronavirus illness (COVID-19) outbreak, which began in a seafood market in Wuhan, Hubei Province, China, in mid-December 2019, has spread to almost all countries, territories, and places throughout the world. And since the fault in diagnosis of a disease causes a psychological impact, this was very much visible in the spread of COVID-19. This research aims to address this issue by providing a better solution for diagnosis of the COVID-19 disease. The paper also addresses a very important issue of having less data for disease prediction models by elaborating on data handling techniques. Thus, special focus has been given on data processing and handling, with an aim to develop an improved machine learning model for diagnosis of COVID-19. Random Forest (RF), Decision tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support vector machine, and Deep Neural network (DNN) models are developed using the Hospital Israelita Albert Einstein (in São Paulo, Brazil) dataset to diagnose COVID-19. The dataset is pre-processed and distributed DT is applied to rank the features. Data augmentation has been applied to generate datasets for improving classification accuracy. The DNN model dominates overall techniques giving the highest accuracy of 96.99%, recall of 96.98%, and precision of 96.94%, which is better than or comparable to other research work. All the algorithms are implemented in a distributed environment on the Spark platform. [ABSTRACT FROM AUTHOR]

The traditional approach to characterize extreme rainfall events is based on using past observed data to fit a probability distribution, which is used to associate a certain probability of occurrence to a rainfall magnitude, under a stationary assumption. Observed series, however, may exhibit trends and abrupt changes, resulting from the natural hydro climatic variability and the climate change context, which are not usually considered in the stationary modelling procedure and which may affect the characterization of extreme events. In this paper, it was evaluated the presence of these temporal changes in rainfall series and their impact to estimate design quantiles, using rainfall data of 317 stations, located in São Paulo State, Brazil. The results showed that extreme rainfall events are changing in the state. Low-return period events (up to 10 years) are intensifying. Moderate- and high-return period events, on the other hand, did not present a clear spatial pattern. Trends and abrupt changes may significantly influence the estimation of design quantiles. The presence of the former in rainfall series exhibited more influence on low-return period events, showing, however, no clear relationship with high-return period design quantiles. The presence of the latter seems to influence the characterization process, affecting estimated quantiles of all evaluated return periods. Such findings highlight the need to consider the presence of trends and abrupt changes and their influence on design quantiles for a more reliable characterization. [ABSTRACT FROM AUTHOR]

Background: There is growing recognition of the health sector’s potential role in addressing domestic violence (DV) against women. Although Brazil has a comprehensive policy framework on violence against women (VAW), implementation has been slow and incomplete in primary healthcare (PHC), and little is known about the implementation challenges. This paper aims to assess the readiness of two PHC clinics in urban Brazil to integrate an intervention to strengthen their DV response. Methods: We conducted 20 semi-structured interviews with health managers and health providers; a document analysis of VAW and DV policies from São Paulo and Brazil; and 2 structured facility observations. Data were analysed using thematic analysis. Results: Findings from our readiness assessment revealed gaps in both current policy and practice needing to be addressed, particularly with regards to governance and leadership, health service organisation and health workforce. DV received less political recognition, being perceived as a lower priority compared to other health issues. Lack of clear guidance from the central and municipal levels emerged as a crucial factor that weakened DV policy implementation both by providers and managers. Furthermore, responses to DV lost visibility, as they were diluted within generic violence responses. The organizational structure of the PHC system in São Paulo, which prioritised the number of consultations and household visits as the main performance indicators, was an additional difficulty in legitimising healthcare providers’ time to address DV. Individual-level challenges reported by providers included lack of time and knowledge of how to respond, as well as fears of dealing with DV. Conclusion: Assessing readiness is critical because it helps to evaluate what services and infrastructure are already in place, also identifying obstacles that may hinder adaptation and integration of an intervention to strengthen the response to DV before implementation. [ABSTRACT FROM AUTHOR]

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This paper presents the study of the execution speed influence on displacements of soil-nailed structures with a vertical face in urban areas, intended as the basements of buildings. Fifteen structures in the Metropolitan Region of São Paulo, Brazil, were evaluated. The main objective was to demonstrate the evolution of the construction technique, making its application possible in conditions considered by the technical community to be unfeasible due to the inevitable resulting displacements. The studied structures ranged from 6.8 to 22.7 m high. Soil-nailed walls were instrumented with displacement gauges (deflectometers) positioned near the top, which monitored the structures' horizontal displacements. The execution time of each work was evaluated in terms of the sectoral excavation advancement, which was 3, 4, or 5 days, as well as the production in square meters per day. The results showed works that were less displaceable than the estimates proposed in the literature. The impact of execution speed also was evidenced, showing that the opening interval of new work fronts resulted in significant differences, with displacements ranging from 20 to 23 mm, from 10 to 15 mm, and from 0 to 6 mm, respectively. Two equations proposed to predict horizontal displacement based on the retaining structure height and daily work productivity are presented. The study can enable works that may be considered impractical if analyzed from the displacement premises of the literature; moreover, this study disseminates design and execution standards to obtain less-displaceable works. [ABSTRACT FROM AUTHOR]

Copyright of Desenvolvimento e Meio Ambiente is the property of Universidade Federal do Parana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: A new algorithm that uses self-organizing maps and entropy calculus to generate a tree structure based on XAI software principles. Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been aimed at increasing the hit rates of algorithms. However, when the problem is restricted to applications in healthcare, besides the concern with performance, it is also necessary to design algorithms whose results are understandable by the specialists responsible for making the decisions. Among the problems in the field of medicine, a current focus is related to COVID-19: AI algorithms may contribute to early diagnosis. Among the available COVID-19 data, the blood test is a typical procedure performed when the patient seeks the hospital, and its use in the diagnosis allows reducing the need for other diagnostic tests that can impact the detection time and add to costs. In this work, we propose using self-organizing map (SOM) to discover attributes in blood test examinations that are relevant for COVID-19 diagnosis. We applied SOM and an entropy calculation in the definition of a hierarchical, semi-supervised and explainable model named TESSOM (tree-based entropy-structured self-organizing maps), in which the main feature is enhancing the investigation of groups of cases with high levels of class overlap, as far as the diagnostic outcome is concerned. Framing the TESSOM algorithm in the context of explainable artificial intelligence (XAI) makes it possible to explain the results to an expert in a simplified way. It is demonstrated in the paper that the use of the TESSOM algorithm to identify attributes of blood tests can help with the identification of COVID-19 cases. It providing a performance increase in 1.489% in multiple scenarios when analyzing 2207 cases from three hospitals in the state of São Paulo, Brazil. This work is a starting point for researchers to identify relevant attributes of blood tests for COVID-19 and to support the diagnosis of other diseases. [ABSTRACT FROM AUTHOR]

Background: Outdoor air pollution is increasingly recognised as a key threat to population health globally, with particularly high risks for urban residents. In this study, we assessed the association between residential nitrogen dioxide (NO2) exposure and children's cognitive and behavioural development using data from São Paulo Brazil, one of the largest urban agglomerations in the world. Methods: We used data from the São Paulo Western Region Birth Cohort, a longitudinal cohort study aiming to examine determinants as well as long-term implications of early childhood development. Cross-sectional data from the 72-month follow-up was analysed. Data on NO2 concentration in the study area was collected at 80 locations in 2019, and land use regression modelling was used to estimate annual NO2 concentration at children's homes. Associations between predicted NO2 exposure and children's cognitive development as well as children's behavioural problems were estimated using linear regression models adjusted for an extensive set of confounders. All results were expressed per 10 μg/m3 increase in NO2. Results: 1143 children were included in the analysis. We found no association between NO2 and children's cognitive development (beta -0.05, 95% CI [-0.20; 0.10]) or behavioural problems (beta 0.02, 95% CI [-0.80; 0.12]). Conclusion: No association between child cognition or child behaviour and NO2 was found in this cross-sectional analysis. Further research will be necessary to understand the extent to which these null results reflect a true absence of association or other statistical, biological or adaptive factors not addressed in this paper. [ABSTRACT FROM AUTHOR]

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Background: The shortage and high turnover of physicians is a recurrent problem in health care systems; this is especially harmful to the expansion and full operation of primary health care (PHC). The aim of this paper is to analyze incidence and associated factors with physician turnover in primary health care services in the East Zone of São Paulo City.Methods: This is a retrospective cohort study of 1378 physicians over a 15 years' time period based on physicians' administrative records from two distinct secondary databases. Physicians' individual characteristics were analyzed including graduation and specialization. Survival analysis techniques such Kaplan-Meier and Cox Regression were used to analyze the termination of contract.Results: One thousand three hundred seventy-eight physicians were included in the study of which 130 [9.4%(CI95 8.0-11.1%)] remained in the PHC services. The mean and median time until the occurrence of the physician leaving the service was 2.14 years (CI95% 1.98-2.29 years) and 1.17 years [(CI95% 1.05-1.28 years)]. The probability of contract interruption was 45% in the first year and 68% in the second year. Independent factors associated with TEC were identified: workload of 40 h/week HR = 1.71 [(CI95% 1.4-2.09), p < 0.001]; initial salary ≤1052 BGI HR = 1.87 [(CI95 1.64-2.15), p < 0.001]; time since graduation ≤2 years HR =1.36 [(CI95 1.18-1.56), p < 0.001]; and the conclusion of residency in up to 3 years after leaving the service HR = 1.69 [(CI95 1.40-2.04), p < 0.001].Conclusions: The time of employment of the physician in PHC was relatively short, with a high probability of TEC in the first year. Modifiable factors such as working hours, starting salary, time since graduation from medical school and need to enter in a residency program were associated with TEC. In pointing out that modifiable factors are responsible for long term employment or the end of contract of physicians in PHC services of the Unified Health System in the periphery of a metropolitan area, the study provides support for the planning, implementation and management of policies and strategies aimed at attracting and retaining physicians in suburban, priority or underserved regions. [ABSTRACT FROM AUTHOR]

Copyright of Confluenze. Rivista di Studi Iberoamericani is the property of Universita di Bologna, Dipartimento di Lingue, Letterature e Culture Moderne and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Sociedade e Estado is the property of Sociedade e Estado and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The purpose of this study is to evaluate the microfoundations of student entrepreneurship, a cornerstone of innovation ecosystems. To this end, this paper assesses how perceived university support for entrepreneurship influences entrepreneurial characteristics and intentions in students enrolled at Amazonas and São Paulo State Universities. Design/methodology/approach: A quantitative approach based on multivariate data analysis using confirmatory factor analysis and structural equation modeling was applied to a sample of 420 respondents. Findings: Results indicate that the university environment positively influences entrepreneurial behavior and intention in students. Nonetheless, further integration between academia and external dimensions of the ecosystems is necessary to drive more intense entrepreneurial activity in students. The educational contexts of Amazonas and São Paulo present significant differences in the relationship between entrepreneurial characteristics and entrepreneurial intention with a stronger influence found for Amazonas. This finding suggests a relative lack of propensity of students from São Paulo to engage in entrepreneurial venturing. Research limitations/implications: The main limitations involve the use of non-probabilistic sampling procedures and students' heterogeneity in terms of academic seniority. Practical implications: This research offers guidance for policies targeting the generation of entrepreneurial activity in universities embedded in developing countries' innovation ecosystems and facing distinct levels of socioeconomic development. Originality/value: This research presents a novel analysis of the microfoundations driving student entrepreneurship within different educational contexts in a developing country. Results highlight the necessary conditions for universities to foster entrepreneurial activity and, incidentally, feed innovation ecosystems with entrepreneurial talent. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Estudos Urbanos e Regionais is the property of Associacao Nacional de Pos-Graduacao e Pesquisa em Planejamento Urbano e Regional and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)