Aplicação do modelo Tucker-3 para a análise da biodegradação de diesel

Tucker-3 model offers several advantages for analysis of environmental data but its interpretation is still challenging. A Tucker-3 model was applied to a biodegradation experiment involving a large number of overlapped chromatographic peaks and a temporal variation. The Tucker-3 model allowed the data to be decomposed in two processes: evaporation and biodegradation. The results suggest that linear hydrocarbons were those biodegraded first and demonstrate that the data analysis can be simplified by interpreting the elements of the core array. The approach discussed in this work can be applied in similar problems involving multi-way data in other areas of chemistry.

Metabolômica, uma abordagem otimizada para exploração da biodiversidade brasileira: estado da arte, perspectivas e desafios

Brazilian biodiversity is a colossal source of secondary metabolites with remarkable structural features, which are valuable in further biodiscovery studies. In order to fully understand the relations and interactions of a living system with its surroundings, efforts in natural product chemistry are directed toward the challenge of detecting and identifying all the molecular components present in complex samples. It is plausible that this endeavor was born out of recent technological sophistication in secondary metabolite identification with sensitive spectroscopic instruments (MS and NMR) and higher resolving power of chromatographic systems, which allow a decrease in the amount of required sample and time to acquire data. Nevertheless, the escalation of data acquired in these analyses must be sorted with statistical and multi-way tools in order to select key information. Chromatography is also of paramount importance, more so when selected compounds need to be isolated for further investigation. However, in the course of pursuing a "greener" environment, new policies, with an aim to decrease the use of energy and solvents, are being developed and incorporated into analytical methods. Metabolomics could be an effective tool to answer questions on how living organisms in our huge biodiversity work and interact with their surroundings while also being strategic to the development of high value bio-derived products, such as phytotherapeutics and nutraceuticals. The incorporation of proper phytotherapeutics in the so-called Brazilian Unified Health System is considered an important factor for the urgent improvement and expansion of the Brazilian national health system. Furthermore, this approach could have a positive impact on the international interest toward scientific research developed in Brazil as well as the development of high value bio-derived products, which appear as an interesting economic opportunity in national and global markets. Thus, this study attempts to highlight the recent advances in analytical tools used in detection of secondary metabolites, which can be useful as bioproducts. It also emphasizes the potential avenues to be explored in Brazilian biodiversity, known for its rich chemical diversity.

A network society communicative model for optimizing the Refugee Status Determination (RSD) procedures

This article recommends a new way to improve Refugee Status Determination (RSD) procedures by proposing a network society communicative model based on active involvement and dialogue among all implementing partners. This model, named after proposals from Castells, Habermas, Apel, Chimni, and Betts, would be mediated by the United Nations High Commissioner for Refugees (UNHCR), whose role would be modeled after that of the International Committee of the Red Cross (ICRC) practice.

Dynamic network data envelopment analysis for university hospitals evaluation

ABSTRACT OBJECTIVE To develop an assessment tool to evaluate the efficiency of federal university general hospitals. METHODS Data envelopment analysis, a linear programming technique, creates a best practice frontier by comparing observed production given the amount of resources used. The model is output-oriented and considers variable returns to scale. Network data envelopment analysis considers link variables belonging to more than one dimension (in the model, medical residents, adjusted admissions, and research projects). Dynamic network data envelopment analysis uses carry-over variables (in the model, financing budget) to analyze frontier shift in subsequent years. Data were gathered from the information system of the Brazilian Ministry of Education (MEC), 2010-2013. RESULTS The mean scores for health care, teaching and research over the period were 58.0%, 86.0%, and 61.0%, respectively. In 2012, the best performance year, for all units to reach the frontier it would be necessary to have a mean increase of 65.0% in outpatient visits; 34.0% in admissions; 12.0% in undergraduate students; 13.0% in multi-professional residents; 48.0% in graduate students; 7.0% in research projects; besides a decrease of 9.0% in medical residents. In the same year, an increase of 0.9% in financing budget would be necessary to improve the care output frontier. In the dynamic evaluation, there was progress in teaching efficiency, oscillation in medical care and no variation in research. CONCLUSIONS The proposed model generates public health planning and programming parameters by estimating efficiency scores and making projections to reach the best practice frontier.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Espectrometria de absorção atômica: o caminho para determinações multi-elementares

This paper present an overview of way covered for the spectrometry of atomic absorption (AAS), tracing a line of the historical events in its development and its establishment as a multielement technique. Additionally, the efforts carried by through several researchers in the search for the instrumental evolution, the advances, advantages, limitations, and trends of this approach are related. Several works focusing its analytical applications are cited employing simultaneous multielement determination by flame (FAAS) and/or graphite furnace (GF AAS), and fast sequential multielement determination using FAAS are reported in the present review.

Non-Linear Unsteady Aerodynamic Response Approximation Using Multi-Layer Functionals

Non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. A recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. For a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (FIR) neural networks. Identification of an appropriate FIR neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. The present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. The training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. Application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. The approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of Mach numbers in the transonic regime.

Signaled two-way avoidance learning using electrical stimulation of the inferior colliculus as negative reinforcement: effects of visual and auditory cues as warning stimuli

The inferior colliculus is a primary relay for the processing of auditory information in the brainstem. The inferior colliculus is also part of the so-called brain aversion system as animals learn to switch off the electrical stimulation of this structure. The purpose of the present study was to determine whether associative learning occurs between aversion induced by electrical stimulation of the inferior colliculus and visual and auditory warning stimuli. Rats implanted with electrodes into the central nucleus of the inferior colliculus were placed inside an open-field and thresholds for the escape response to electrical stimulation of the inferior colliculus were determined. The rats were then placed inside a shuttle-box and submitted to a two-way avoidance paradigm. Electrical stimulation of the inferior colliculus at the escape threshold (98.12 ± 6.15 (A, peak-to-peak) was used as negative reinforcement and light or tone as the warning stimulus. Each session consisted of 50 trials and was divided into two segments of 25 trials in order to determine the learning rate of the animals during the sessions. The rats learned to avoid the inferior colliculus stimulation when light was used as the warning stimulus (13.25 ± 0.60 s and 8.63 ± 0.93 s for latencies and 12.5 ± 2.04 and 19.62 ± 1.65 for frequencies in the first and second halves of the sessions, respectively, P<0.01 in both cases). No significant changes in latencies (14.75 ± 1.63 and 12.75 ± 1.44 s) or frequencies of responses (8.75 ± 1.20 and 11.25 ± 1.13) were seen when tone was used as the warning stimulus (P>0.05 in both cases). Taken together, the present results suggest that rats learn to avoid the inferior colliculus stimulation when light is used as the warning stimulus. However, this learning process does not occur when the neutral stimulus used is an acoustic one. Electrical stimulation of the inferior colliculus may disturb the signal transmission of the stimulus to be conditioned from the inferior colliculus to higher brain structures such as amygdala