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.

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.

The international implications of the Chinese model of development in the Global South: Asian Consensus as a network power

This paper analyzes People's Republic of China (PRC) economic and political ascendance in the 21st century focusing on the evolution of the sui generis economic development model and its significances of the evolution of relationship between China and the developing countries in the peripheral "Global South." The objective of this article is to analyze the relationship between China and the Global South (Africa and South America) in the 21st century, characterized as a new Center-periphery global network power based on trade and investment that we call as "Asian Consensus."

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.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

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.

Distinction of males of the Lutzomyia intermedia (Lutz & Neiva, 1912) species complex by ratios between dimensions and by an artificial neural network (Diptera: Psychodidae, Phlebotominae)

The females of the two species of the Lutzomyia intermedia complex can be easily distinguished, but the males of each species are quite similar. The ratios between the extra-genital and the genital structures of L. neivai are larger than those of L. intermedia s. s., according to ANOVA. An artificial neural network was trained with a set of 300 examples, randomly taken from a sample of 358 individuals. The input vectors consisted of several ratios between some structures of each insect. The model was tested on the remaining 58 insects, 56 of which (96.6%) were correctly identified. This ratio of success can be considered remarkable if one takes into account the difficulty of attaining comparable results using traditional statistical techniques.

Sentinel network for monitoring in vitro susceptibility of Plasmodium falciparum to antimalarial drugs in Colombia: a proof of concept

Drug resistance is one of the principal obstacles blocking worldwide malaria control. In Colombia, malaria remains a major public health concern and drug-resistant parasites have been reported. In vitro drug susceptibility assays are a useful tool for monitoring the emergence and spread of drug-resistant Plasmodium falciparum. The present study was conducted as a proof of concept for an antimalarial drug resistance surveillance network based on in vitro susceptibility testing in Colombia. Sentinel laboratories were set up in three malaria endemic areas. The enzyme linked immunosorbent assay-histidine rich protein 2 and schizont maturation methods were used to assess the susceptibility of fresh P. falciparum isolates to six antimalarial drugs. This study demonstrates that an antimalarial drug resistance surveillance network based on in vitro methods is feasible in the field with the participation of a research institute, local health institutions and universities. It could also serve as a model for a regional surveillance network. Preliminary susceptibility results showed widespread chloroquine resistance, which was consistent with previous reports for the Pacific region. However, high susceptibility to dihydroartemisinin and lumefantrine compounds, currently used for treatment in the country, was also reported. The implementation process identified critical points and opportunities for the improvement of network sustainability strategies.

The network social support experience of people involved in home care

Objective: To build a theoretical model to configure the network social support experience of people involved in home care. Method: A quantitative approach research, utilizing the Grounded Theory method. The simultaneous data collection and analysis allowed the interpretation of the phenomenon meaning The network social support of people involved in home care. Results: The population passive posture in building their well-being was highlighted. The need of a shared responsibility between the involved parts, population and State is recognized. Conclusion: It is suggested for nurses to be stimulated to amplify home care to attend the demands of caregivers; and to elaborate new studies with different populations, to validate or complement the proposed theoretical model.


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.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.

Silting in the dense reservoir network of the Pereira de Miranda catchment

This study aims to analyze the impacts of the reservoir network within Pereira de Miranda - CE catchment (also called Pentecoste) over sediment transport and storage capacity of the system. The survey of the "damming" was carried out using satellite images. We identified 502 erosion units, derived from overlaying maps of the Universal Soil Loss Equation parameters, which allowed the estimation of localized erosion in the basin and identification of areas potentially generating sediment. In order to estimate silting in Pentecoste reservoir, different system structure scenarios were considered. An average erosion rate of 59 t ha-1year-1 was estimated. According to the model, the silting of Pentecoste reservoir may vary from 1.1 to 2.6% per decade, depending on the scenario considered. It is also observed that the reservoirs upstream can retain up to 58% of the sediment that would reach the Pentecoste reservoir. Very small reservoirs with a capacity of up to 100,000 m³, although representing only 1.83% of the system water availability, are able to retain almost 8% of total sediment produced.

Biochemical adaptations of mammalian hibernation: exploring squirrels as a perspective model for naturally induced reversible insulin resistance

An important disease among human metabolic disorders is type 2 diabetes mellitus. This disorder involves multiple physiological defects that result from high blood glucose content and eventually lead to the onset of insulin resistance. The combination of insulin resistance, increased glucose production, and decreased insulin secretion creates a diabetic metabolic environment that leads to a lifetime of management. Appropriate models are critical for the success of research. As such, a unique model providing insight into the mechanisms of reversible insulin resistance is mammalian hibernation. Hibernators, such as ground squirrels and bats, are excellent examples of animals exhibiting reversible insulin resistance, for which a rapid increase in body weight is required prior to entry into dormancy. Hibernator studies have shown differential regulation of specific molecular pathways involved in reversible resistance to insulin. The present review focuses on this growing area of research and the molecular mechanisms that regulate glucose homeostasis, and explores the roles of the Akt signaling pathway during hibernation. Here, we propose a link between hibernation, a well-documented response to periods of environmental stress, and reversible insulin resistance, potentially facilitated by key alterations in the Akt signaling network, PPAR-γ/PGC-1α regulation, and non-coding RNA expression. Coincidentally, many of the same pathways are frequently found to be dysregulated during insulin resistance in human type 2 diabetes. Hence, the molecular networks that may regulate reversible insulin resistance in hibernating mammals represent a novel approach by providing insight into medical treatment of insulin resistance in humans.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

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.