The evolution of universities' relations with the business sector in Brazil: What national publications between 1980 and 2012 reveal

ABSTRACT This paper addresses the changes in university-industry relations in Brazil regarding innovation activities. It is based on a survey of articles published in major national journals or presented at the most relevant Brazilian and regional conferences, between 1980 and 2012. The year 1980 was chosen due to the creation of the Technological Innovation Offices (NITs), which was the first government initiative to encourage knowledge transfer from universities to companies; the second was the Innovation Act of 2004. Our assumption was that after the Act the number of academic papers on this subject would increase, bringing new ideas and propositions of models to enhance this relationship. The methodology employed a qualitative, exploratory approach, using bibliographical research and a bibliometric analysis of 247 papers. Literature review of international studies shows the discussion of problems and suggestions for improvements, while in Brazil there is still a debate on whether this collaboration should occur, and if this is a legitimate role for the university. Despite the numerical growth, the content analysis showed few papers on new configurations and procedures for partnership management. We conclude that university-industry relations are not a regular and totally accepted process in Brazilian public universities, which reflect an ideological bias against cooperation with firms.

A review of evolution of oviposition guilds in the Bruchidae (Coleoptera)

Three guilds of bruchid beetles oviposit on seeds at different times and in different ways, i. e., in these guilds some species only oviposit on fruits while on the plant (Guild A), other species only oviposit on seeds exposed in fruits while still on the plant (Guild B) and some only oviposit on seeds once they are exposed on the substrate (Guild C). It has been established that one plant species may be oviposited upon by all three guilds, some only by two guilds and some by only one guild. Before and after the inception of this concept many papers have been published that seem to establish that early oviposition behavior of bruchids was probably onto fruits where they burrowed through the fruit wall and fed on seeds (Guild A). Then, as evolution of the fruits developed for dispersal of seeds and possible escape from bruchid predation, bruchids developed to feed in seeds in various other ways (Guilds B and C). Our data show that about 78% of extant bruchids oviposit on fruits, and the other 22% with behavior of Guilds B and C. A review of these papers and new data on oviposition guilds and bruchid evolution are presented and discussed here.

Dung beetle (Coleoptera, Scarabaeidae) assemblage of a highly fragmented landscape of Atlantic forest: from small to the largest fragments of northeastern Brazilian region

Human activities in tropical forests are the main causes of forest fragmentation. According to historical factor in deforestation processes, forest remnants exhibit different sizes and shapes. The aim of the present study was to evaluate the dung beetle assemblage on fragments of different degree of sizes. Sampling was performed during rainy and dry season of 2010 in six fragments of Atlantic forest, using pitfall traps baited with excrement and carrion. Also, we used two larger fragments as control. We used General Linear Models to determine whether the fragments presented distinguished dung beetle abundance and richness. Analysis of Similarities and Non-Metric Multidimensional Scaling were used to determine whether the dung beetle assemblage was grouped according to species composition. A total of 3352 individuals were collected and 19 species were identified in the six fragments sampled. Dung beetle abundance exhibited a shift according to fragment size; however, richness did not change among fragments evaluated. Also, fragments sampled and the two controls exhibited distinct species composition. The distinction on abundance of dung beetles among fragments may be related to different amount of resource available in each one. It is likely that the dung beetle richness did not distinguish among the different fragments due to the even distribution of the mammal communities in these patches, and consequent equal dung diversity. We conclude that larger fragments encompass higher abundance of dung beetle and distinct species. However, for a clearer understanding of effects of fragmentation on dung beetles in Atlantic forest, studies evaluating narrower variations of larger fragments should be conducted.

Magnetic susceptibility to identify landscape segments on a detailed scale in the region of Jaboticabal, São Paulo, Brazil

The agricultural potential is generally assessed and managed based on a one-dimensional vision of the soil profile, however, the increased appreciation of sustainable production has stimulated studies on faster and more accurate evaluation techniques and methods of the agricultural potential on detailed scales. The objective of this study was to investigate the possibility of using soil magnetic susceptibility for the identification of landscape segments on a detailed scale in the region of Jaboticabal, São Paulo State. The studied area has two slope curvatures: linear and concave, subdivided into three landscape segments: upper slope (US, concave), middle slope (MS, linear) and lower slope (LS, linear). In each of these segments, 20 points were randomly sampled from a database with 207 samples forming a regular grid installed in each landscape segment. The soil physical and chemical properties, CO2 emissions (FCO2) and magnetic susceptibility (MS) of the samples were evaluated represented by: magnetic susceptibility of air-dried fine earth (MS ADFE), magnetic susceptibility of the total sand fraction (MS TS) and magnetic susceptibility of the clay fraction (MS Cl) in the 0.00 - 0.15 m layer. The principal component analysis showed that MS is an important property that can be used to identify landscape segments, because the correlation of this property within the first principal component was high. The hierarchical cluster analysis method identified two groups based on the variables selected by principal component analysis; of the six selected variables, three were related to magnetic susceptibility. The landscape segments were differentiated similarly by the principal component analysis and by the cluster analysis using only the properties with higher discriminatory power. The cluster analysis of MS ADFE, MS TS and MS Cl allowed the formation of three groups that agree with the segment division established in the field. The grouping by cluster analysis indicated MS as a tool that could facilitate the identification of landscape segments and enable the mapping of more homogeneous areas at similar locations.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.