Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model

One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model.

Evolution of coauthorship networks: worldwide scientific production on leishmaniasis

Introduction Collaboration is one of the defining features of contemporary scientific research, and it is particularly important with regard to neglected diseases that primarily affect developing countries. Methods The present study has identified publications on leishmaniasis in the Medline database from 1945 to 2010, analyzing them according to bibliometric indicators and statistics from social network analysis. Examining aspects such as scientific production, diachronic evolution, and collaboration and configuration of the research groups in the field, we have considered the different types of Leishmania studied and the institutional affiliation and nationality of the authors. Results Seven-hundred and thirty-five authors participate in 154 prominent research clusters or groups. Although the most predominant and consolidated collaborations are characterized by members from the same country studying the same type of Leishmania, there are also notable links between authors from different countries or who study different clinical strains of the disease. Brazil took the lead in this research, with numerous Brazilian researchers heading different clusters in the center of the collaboration network. Investigators from the USA, India, and European countries, such as France, Spain, the United Kingdom, and Italy, also stand out within the network. Conclusions Research should be fostered in countries such as Bangladesh, Nepal, Sudan, and Ethiopia, where there is a high prevalence of different forms of the disease but limited research development with reference authors integrated into the collaboration networks.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

CLASSIFICATION OF FOREST ROADS AND DETERMINATION OF ROUTE USING GEOGRAPHIC INFORMATION SYSTEM

ABSTRACT Geographic Information System (GIS) is an indispensable software tool in forest planning. In forestry transportation, GIS can manage the data on the road network and solve some problems in transportation, such as route planning. Therefore, the aim of this study was to determine the pattern of the road network and define transport routes using GIS technology. The present research was conducted in a forestry company in the state of Minas Gerais, Brazil. The criteria used to classify the pattern of forest roads were horizontal and vertical geometry, and pavement type. In order to determine transport routes, a data Analysis Model Network was created in ArcGIS using an Extension Network Analyst, allowing finding a route shorter in distance and faster. The results showed a predominance of horizontal geometry classes average (3) and bad (4), indicating presence of winding roads. In the case of vertical geometry criterion, the class of highly mountainous relief (4) possessed the greatest extent of roads. Regarding the type of pavement, the occurrence of secondary coating was higher (75%), followed by primary coating (20%) and asphalt pavement (5%). The best route was the one that allowed the transport vehicle travel in a higher specific speed as a function of road pattern found in the study.

Avaliação do uso de métodos quimiométricos em análise de solos

One of the major interests in soil analysis is the evaluation of its chemical, physical and biological parameters, which are indicators of soil quality (the most important is the organic matter). Besides there is a great interest in the study of humic substances and on the assessment of pollutants, such as pesticides and heavy metals, in soils. Chemometrics is a powerful tool to deal with these problems and can help soil researchers to extract much more information from their data. In spite of this, the presence of these kinds of strategies in the literature has obtained projection only recently. The utilization of chemometric methods in soil analysis is evaluated in this article. The applications will be divided in four parts (with emphasis in the first two): (i) descriptive and exploratory methods based on Principal Component Analysis (PCA); (ii) multivariate calibration methods (MLR, PCR and PLS); (iii) methods such as Evolving Factor Analysis and SIMPLISMA; and (iv) artificial intelligence methods, such as Artificial Neural Networks.

Redes neurais e suas aplicações em calibração multivariada

Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special characteristics for model complex systems. The basic principles of two types of neural networks, the multi-layer perceptrons and radial basis functions, are introduced, as well as, a pruning approach to architecture optimization. Two analytical applications based on near infrared spectroscopy are presented, the first one for determination of nitrogen content in wheat leaves using multi-layer perceptrons networks and second one for determination of BRIX in sugar cane juices using radial basis functions networks.

Ensuring dynamic strategic fit of firms that compete globally in alliances and networks: proposing the Global SNA - Strategic Network Analysis - framework

In order to sustain their competitive advantage in the current increasingly globalized and turbulent context, more and more firms are competing globally in alliances and networks that oblige them to adopt new managerial paradigms and tools. However, their strategic analyses rarely take into account the strategic implications of these alliances and networks, considering their global relational characteristics, admittedly because of a lack of adequate tools to do so. This paper contributes to research that seeks to fill this gap by proposing the Global Strategic Network Analysis - SNA - framework. Its purpose is to help firms that compete globally in alliances and networks to carry out their strategic assessments and decision-making with a view to ensuring dynamic strategic fit from both a global and relational perspective.

Network dynamics in scientific knowledge acquisition: an analysis in three public universities in the state of Bahia

This paper aims to cast some light on the dynamics of knowledge networks in developing countries by analyzing the scientific production of the largest university in the Northeast of Brazil and its influence on some of the remaining regional research institutions in the state of Bahia. Using a methodology test to be employed in a larger project, the Universidade Federal da Bahia (UFBA) (Federal University of Bahia), the Universidade do Estado da Bahia (Uneb) (State of Bahia University) and the Universidade Estadual de Santa Cruz (Uesc)'s (Santa Cruz State University) scientific productions are discussed in one of their most traditionally expressive sectors in academic production - namely, the field of chemistry, using social network analysis of co-authorship networks to investigate the existence of small world phenomena and the importance of these phenomena in research performance in these three universities. The results already obtained through this research bring to light data of considerable interest concerning the scientific production in unconsolidated research universities. It shows the important participation of the UFBA network in the composition of the other two public universities research networks, indicating a possible occurrence of small world phenomena in the UFBA and Uesc networks, as well as the importance of individual researchers in consolidating research networks in peripheral universities. The article also hints that the methodology employed appears to be adequate insofar as scientific production may be used as a proxy for scientific knowledge.

Transduction motif analysis of gastric cancer based on a human signaling network

To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR,MAPK14, BCL2L1, KRT18,PTPN6, CASP3, TGFBR2,AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

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.

Scientific authorships and collaboration network analysis on Chagas disease: papers indexed in PubMed (1940-2009)

Chagas disease is a chronic, tropical, parasitic disease, endemic throughout Latin America. The large-scale migration of populations has increased the geographic distribution of the disease and cases have been observed in many other countries around the world. To strengthen the critical mass of knowledge generated in different countries, it is essential to promote cooperative and translational research initiatives. We analyzed authorship of scientific documents on Chagas disease indexed in the Medline database from 1940 to 2009. Bibliometrics was used to analyze the evolution of collaboration patterns. A Social Network Analysis was carried out to identify the main research groups in the area by applying clustering methods. We then analyzed 13,989 papers produced by 21,350 authors. Collaboration among authors dramatically increased over the study period, reaching an average of 6.2 authors per paper in the last five-year period. Applying a threshold of collaboration of five or more papers signed in co-authorship, we identified 148 consolidated research groups made up of 1,750 authors. The Chagas disease network identified constitutes a "small world," characterized by a high degree of clustering and a notably high number of Brazilian researchers.

Network Meta-analysis to Synthesize Evidence for Decision Making in Cardiovascular Research

Abstract Clinical decision-making requires synthesis of evidence from literature reviews focused on a specific theme. Evidence synthesis is performed with qualitative assessments and systematic reviews of randomized clinical trials, typically covering statistical pooling with pairwise meta-analyses. These methods include adjusted indirect comparison meta-analysis, network meta-analysis, and mixed-treatment comparison. These tools allow synthesis of evidence and comparison of effectiveness in cardiovascular research.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Harvesting and wood transport planning with SNAP III program (Scheduling and Network Analysis Program) in a pine plantation in Southeast Brazil

The objective of this study was to verify the potential of SNAP III (Scheduling and Network Analysis Program) as a support tool for harvesting and wood transport planning in Brazil harvesting subsystem definition and establishment of a compatible route were assessed. Initially, machine operational and production costs were determined in seven subsystems for the study area, and quality indexes, construction and maintenance costs of forest roads were obtained and used as SNAP III program input data. The results showed, that three categories of forest road occurrence were observed in the study area: main, secondary and tertiary which, based on quality index, allowed a medium vehicle speed of about 41, 30 and 24 km/hours and a construction cost of about US$ 5,084.30, US$ 2,275.28 and US$ 1,650.00/km, respectively. The SNAP III program used as a support tool for the planning, was found to have a high potential tool in the harvesting and wood transport planning. The program was capable of defining efficiently, the harvesting subsystem on technical and economical basis, the best wood transport route and the forest road to be used in each period of the horizon planning.