Modeling Worldwide Highway Networks

This letter addresses the problem of modeling the highway systems of different countries by using complex networks formalism. More specifically, we compare two traditional geographical models with a modified geometrical network model where paths, rather than edges, are incorporated at each step between the origin and destination nodes. Optimal configurations of parameters are obtained for each model and used in the comparison. The highway networks of Brazil, the US and England are considered and shown to be properly modeled by the modified geographical model. The Brazilian highway network yielded small deviations that are potentially accountable by specific developing and sociogeographic features of that country.Comment: 5 pages, 3 figures, 1 tabl

Comparison of Algorithms for Baseline Correction of LIBS Spectra for Quantifying Total Carbon in Brazilian Soils

LIBS is a promising and versatile technique for multi-element analysis that usually takes less than a minute and requires minimal sample preparation and no reagents. Despite the recent advances in elemental quantification, the LIBS still faces issues regarding the baseline produced by background radiation, which adds non-linear interference to the emission lines. In order to create a calibration model to quantify elements using LIBS spectra, the baseline has to be properly corrected. In this paper, we compared the performance of three filters to remove random noise and five methods to correct the baseline of LIBS spectra for the quantification of total carbon in soil samples. All combinations of filters and methods were tested, and their parameters were optimized to result in the best correlation between the corrected spectra and the carbon content in a training sample set. Then all combinations with the optimized parameters were compared with a separate test sample set. A combination of Savitzky-Golay filter and 4S Peak Filling method resulted in the best correction: Pearson's correlation coefficient of 0.93 with root mean square error of 0.21. The result was better than using a linear regression model with the carbon emission line 193.04 nm (correlation of 0.91 with error of 0.26). The procedure proposed here opens a new possibility to correct the baseline of LIBS spectra and to create multivariate methods based on the a given spectral range.Comment: 13 pages, 5 figure

Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications

The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches and in applications to real-world problems. Complex networks have fared extremely well in both of these aspects, with their sound theoretical basis developed over the years and with a variety of applications. In this survey, we analyze the applications of complex networks to real-world problems and data, with emphasis in representation, analysis and modeling, after an introduction to the main concepts and models. A diversity of phenomena are surveyed, which may be classified into no less than 22 areas, providing a clear indication of the impact of the field of complex networks.Comment: 103 pages, 3 figures and 7 tables. A working manuscript, suggestions are welcome

Sampling effect on the topological properties of complex networks

Muitos sistemas complexos naturais ou construídos pelos seres humanos podem ser representados por redes complexas, uma teoria que une o estudo de grafos com a mecânica estatística. Esse tipo de representação, porém, pode ser comprometido pela maneira como os dados são obtidos. Em geral, os dados utilizados para representar tais sistemas nem sempre são precisos ou completos e correspondem a apenas amostras pequenas de redes maiores, como é o caso da teia mundial (WWW). Dessa forma, mesmo que as amostras sejam grandes, as suas propriedades são diretamente afetadas pela maneira como elas são obtidas e podem não corresponder com as de suas respectivas redes originais. Por exemplo, a amostragem mais utilizada para captura de roteadores da Internet, se empregada em redes aleatórias, tende a obter redes sem escala como resultado. Em contrapartida, amostras de redes sem escala não têm garantia de preservar essa estrutura. Por causa desses e outros problemas que possam ocorrer na amostragem das redes, é muito importante avaliar a variação das propriedades das redes a ruídos (para saber quais variam menos, sendo, portanto, mais adequadas para caracterizar redes com problemas de amostragem) e os efeitos da amostragem na caracterização, classificação e análise de redes complexas (pois redes amostradas podem não corresponder ao sistemas dos quais foram obtidas, tornando os resultados incorretos). Neste trabalho, foi investigada a influência de três tipos de perturbação (ruído): adição, remoção e troca aleatória de conexões nas propriedades de redes complexas, e as mais apropriadas para caracterizar redes amostradas foram identificadas. Além disso, foram definidas duas novas estruturas em redes complexas: árvores de borda e cadeias de vértices. A ocorrência dessas estruturas em redes mal amostradas tende a ser alta, indicando que existe uma relação com redes parcialmente amostradas. Para verificar tal hipótese, foi investigada a presença de cadeias de vértices em redes gradativamente amostradas por caminhadas aleatórias.Several natural or human made complex systems can be represented by complex networks a theory which integrates the study of graphs with statistical mechanics. This kind of representation, however, can be biased by the way in which the data is obtained. In general, the data used to represent such systems is not always accurate, as in the case of theWorldWideWeb (WWW). Therefore, even if the sampled networks are large, their properties are directly affected by the way in which they were obtained and may not correspond to those of their respective original networks. For instance, the most used sampling methodology for capturing routers of the Internet, if performed on random networks, tends to obtain scale-free networks as results. On the other hand, sampled scale-free networks are not guaranteed to have this property. Because of these and other problems which may occur during the network sampling, it is very important to evaluate the variation of the network properties with respect to noise (in order to know which of them have less variation, being therefore more suitable for the characterization of networks with sampling problems) and the effect of sampling in the characterization, classification, and analysis of complex networks. In this work, we investigated the effect of three types of perturbations (noise), namely, edge addition, removal, and rewiring on the respectively estimated complex network properties, and the most suitable properties to characterize sampled networks were identified. Furthermore, two novel structures in complex networks were defined, namely, border trees and chains of vertices, which are possibly related to sampling. The occurrence of these structures in poorly-sampled networks was found to be high, implying a relation with partially sampled networks. In order to investigate such a hypothesis, the presence of chains of vertices was investigated in networks which were gradually sampled by random walks

An object oriented tool for load monitoring in parallel systems.

Este trabalho apresenta uma ferramenta orientada ao objeto para o monitoramento de cargas em sistemas paralelos. O desenvolvimento desta ferramenta surgiu com o intuito de facilitar a programação paralela em sistemas distribuídos como NOWs, Networks of Workstations , e Grids computacionais, pois este tipo de programação é bem mais difícil do que a seqüencial e, por isso, desestimula novos programadores a desenvolver aplicações paralelas. Dentre as razões que tornam a programação paralela difícil destaca-se o balanceamento de cargas em que se quer maximizar a utilização dos recursos computacionais do sistema distribuído. Outro motivo para o programador de aplicações paralelas se preocupar com balanceamento de cargas é o desempenho, que é drasticamente afetado com o desequilíbrio de cargas do sistema. Com relação ao tempo em que as decisões de rebalanceamento de cargas são tomadas, os algoritmos de distribuição de cargas podem ser estáticos, realizados em tempo de compilação, ou dinâmicos, efetuados em tempo de execução. Embora o algoritmo estático não gere sobrecarga em tempo de execução na distribuição de carga, o dinâmico é a melhor escolha, pois se adapta bem em qualquer situação. Assim, o sistema de monitoramento de cargas surge como uma ferramenta de auxílio ao programador que deseje implementar algoritmos de balanceamento dinâmico de cargas nas suas aplicações paralelas, provendo informações de como os recursos computacionais do sistema distribuído estão sendo utilizados.This work presents an object oriented tool for load monitoring in parallel systems. This tool was developed with intention to easy the parallel programming in distributed systems like NOWs (Networks of Workstations) and Computational Grids, because this type of programming is more difficult than the sequential and, therefore, it does not stimulate new programmers to develop parallel softwares. One of the most important reasons why parallel programming is difficult is the worry about load balancing where the purpose is to maximize the use of the computational resources of the distributed system. Another reason for the programmer of parallel softwares to worry about load balancing is the performance, which is drastically affected with the load imbalance of the system. With respect to the time where the decisions of load balancing are made, the load distribution algorithms can be static, done at compilation time, or dynamic, done at execution time. Although the static algorithm does not generate overhead at execution time, the dynamic one is a better choice, because it adapts well to any situation. Thus, the monitoring system appears as a tool to aid the programmer who desires to implement dynamic load balancing algorithms in his or her parallel softwares, providing information on how the computational resources of the distributed system are being used

An object oriented tool for load monitoring in parallel systems.

Este trabalho apresenta uma ferramenta orientada ao objeto para o monitoramento de cargas em sistemas paralelos. O desenvolvimento desta ferramenta surgiu com o intuito de facilitar a programação paralela em sistemas distribuídos como NOWs, Networks of Workstations , e Grids computacionais, pois este tipo de programação é bem mais difícil do que a seqüencial e, por isso, desestimula novos programadores a desenvolver aplicações paralelas. Dentre as razões que tornam a programação paralela difícil destaca-se o balanceamento de cargas em que se quer maximizar a utilização dos recursos computacionais do sistema distribuído. Outro motivo para o programador de aplicações paralelas se preocupar com balanceamento de cargas é o desempenho, que é drasticamente afetado com o desequilíbrio de cargas do sistema. Com relação ao tempo em que as decisões de rebalanceamento de cargas são tomadas, os algoritmos de distribuição de cargas podem ser estáticos, realizados em tempo de compilação, ou dinâmicos, efetuados em tempo de execução. Embora o algoritmo estático não gere sobrecarga em tempo de execução na distribuição de carga, o dinâmico é a melhor escolha, pois se adapta bem em qualquer situação. Assim, o sistema de monitoramento de cargas surge como uma ferramenta de auxílio ao programador que deseje implementar algoritmos de balanceamento dinâmico de cargas nas suas aplicações paralelas, provendo informações de como os recursos computacionais do sistema distribuído estão sendo utilizados.This work presents an object oriented tool for load monitoring in parallel systems. This tool was developed with intention to easy the parallel programming in distributed systems like NOWs (Networks of Workstations) and Computational Grids, because this type of programming is more difficult than the sequential and, therefore, it does not stimulate new programmers to develop parallel softwares. One of the most important reasons why parallel programming is difficult is the worry about load balancing where the purpose is to maximize the use of the computational resources of the distributed system. Another reason for the programmer of parallel softwares to worry about load balancing is the performance, which is drastically affected with the load imbalance of the system. With respect to the time where the decisions of load balancing are made, the load distribution algorithms can be static, done at compilation time, or dynamic, done at execution time. Although the static algorithm does not generate overhead at execution time, the dynamic one is a better choice, because it adapts well to any situation. Thus, the monitoring system appears as a tool to aid the programmer who desires to implement dynamic load balancing algorithms in his or her parallel softwares, providing information on how the computational resources of the distributed system are being used

Sampling effect on the topological properties of complex networks

Muitos sistemas complexos naturais ou construídos pelos seres humanos podem ser representados por redes complexas, uma teoria que une o estudo de grafos com a mecânica estatística. Esse tipo de representação, porém, pode ser comprometido pela maneira como os dados são obtidos. Em geral, os dados utilizados para representar tais sistemas nem sempre são precisos ou completos e correspondem a apenas amostras pequenas de redes maiores, como é o caso da teia mundial (WWW). Dessa forma, mesmo que as amostras sejam grandes, as suas propriedades são diretamente afetadas pela maneira como elas são obtidas e podem não corresponder com as de suas respectivas redes originais. Por exemplo, a amostragem mais utilizada para captura de roteadores da Internet, se empregada em redes aleatórias, tende a obter redes sem escala como resultado. Em contrapartida, amostras de redes sem escala não têm garantia de preservar essa estrutura. Por causa desses e outros problemas que possam ocorrer na amostragem das redes, é muito importante avaliar a variação das propriedades das redes a ruídos (para saber quais variam menos, sendo, portanto, mais adequadas para caracterizar redes com problemas de amostragem) e os efeitos da amostragem na caracterização, classificação e análise de redes complexas (pois redes amostradas podem não corresponder ao sistemas dos quais foram obtidas, tornando os resultados incorretos). Neste trabalho, foi investigada a influência de três tipos de perturbação (ruído): adição, remoção e troca aleatória de conexões nas propriedades de redes complexas, e as mais apropriadas para caracterizar redes amostradas foram identificadas. Além disso, foram definidas duas novas estruturas em redes complexas: árvores de borda e cadeias de vértices. A ocorrência dessas estruturas em redes mal amostradas tende a ser alta, indicando que existe uma relação com redes parcialmente amostradas. Para verificar tal hipótese, foi investigada a presença de cadeias de vértices em redes gradativamente amostradas por caminhadas aleatórias.Several natural or human made complex systems can be represented by complex networks a theory which integrates the study of graphs with statistical mechanics. This kind of representation, however, can be biased by the way in which the data is obtained. In general, the data used to represent such systems is not always accurate, as in the case of theWorldWideWeb (WWW). Therefore, even if the sampled networks are large, their properties are directly affected by the way in which they were obtained and may not correspond to those of their respective original networks. For instance, the most used sampling methodology for capturing routers of the Internet, if performed on random networks, tends to obtain scale-free networks as results. On the other hand, sampled scale-free networks are not guaranteed to have this property. Because of these and other problems which may occur during the network sampling, it is very important to evaluate the variation of the network properties with respect to noise (in order to know which of them have less variation, being therefore more suitable for the characterization of networks with sampling problems) and the effect of sampling in the characterization, classification, and analysis of complex networks. In this work, we investigated the effect of three types of perturbations (noise), namely, edge addition, removal, and rewiring on the respectively estimated complex network properties, and the most suitable properties to characterize sampled networks were identified. Furthermore, two novel structures in complex networks were defined, namely, border trees and chains of vertices, which are possibly related to sampling. The occurrence of these structures in poorly-sampled networks was found to be high, implying a relation with partially sampled networks. In order to investigate such a hypothesis, the presence of chains of vertices was investigated in networks which were gradually sampled by random walks

Physical and chemical matrix effects in soil carbon quantification using laser-induced breakdown spectroscopy

Advanced field methods of carbon (C) analysis should now be capable of providing repetitive, sequential measurements for the evaluation of spatial and temporal variation at a scale that was previously unfeasible. Some spectroscopy techniques, such as laser-induced breakdown spectroscopy (LIBS), have portable features that may potentially lead to clean and rapid alternative approaches for this purpose. The goal of this study was to quantify the C content of soils with different textures and with high iron and aluminum concentrations using LIBS. LIBS emission spectra from soil pellets were captured, and the C content was estimated (emission line of C (I) at 193.03 nm) after spectral offset and aluminum spectral interference correction. This technique is highly portable and could be ideal for providing the soil C content in a heterogeneous experiment. Dry combustion was used as a reference method, and for calibration a conventional linear model was evaluated based on soil textural classes. The correlation between reference and LIBS values showed r = 0.86 for medium-textured soils and r = 0.93 for fine-textured soils. The data showed that better correlation and lower error (14%) values were found for the fine-textured LIBS model. The limit of detection (LOD) was found to be 0.32% for medium-textured soils and 0.13% for fine-textured soils. The results indicated that LIBS quantification can be affected by the texture and chemical composition of soil. Signal treatment was shown to be very important for mitigation of these interferences and to improve quantification.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

DataSheet_1_Laser-induced breakdown spectroscopy as an analytical tool for total carbon quantification in tropical and subtropical soils: evaluation of calibration algorithms.pdf

The demand for efficient, accurate, and cost-effective methods of measuring soil carbon (C) in agriculture is growing. Traditional approaches are time consuming and expensive, highlighting the need for alternatives. This study tackles the challenge of utilizing laser-induced breakdown spectroscopy (LIBS) as a more economical method while managing its potential accuracy issues due to physical–chemical matrix effects. A set of 1,019 soil samples from 11 Brazilian farms was analyzed using various univariate and multivariate calibration strategies. The artificial neural network (ANN) demonstrated the best performance with the lowest root mean square error of prediction (RMSEP) of 0.48 wt% C, a 28% reduction compared to the following best calibration method (matrix-matching calibration – MMC inverse regression and multiple linear regression – MLR at 0.67 wt% C). Furthermore, the study revealed a strong correlation between total C determined by LIBS and the elemental CHNS analyzer for soils samples in nine farms (R² ≥ 0.73). The proposed method offers a reliable, rapid, and cost-efficient means of measuring total soil C content, showing that LIBS and ANN modeling can significantly reduce errors compared to other calibration methods. This research fills the knowledge gap in utilizing LIBS for soil C measurement in agriculture, potentially benefiting producers and the soil C credit market. Specific recommendations include further exploration of ANN modeling for broader applications, ensuring that agricultural soil management becomes more accessible and efficient.</p