Marcia Helena Moreira Paiva

MoCalc: A new graphical user interface for molecular calculations

A new computer program called MoCalc (Molecular Calculations) has been designed to help the computational chemistry practitioner in the task of performing and analyzing molecular calculations. MoCalc is a graphical user interface for the MO calculation programs Gamess and Mopac, and uses Rasmol and Babel for molecule display and file conversion, respectively. In its initial version, MoCalc can execute the following operations: (a) create and handle Gamess and Mopac input files; (b) import any kind of molecular geometry supported by Babel and paste it as Cartesian, internal, or Gaussian‐type coordinates on the input file; (c) convert Gamess and Mopac output files to inputs of both programs; (d) edit and validate the keywords that control the Gamess and Mopac calculation procedure; (e) display the input (Mopac) and output (Gamess and Mopac) molecular geometries; (f) run single or multiple (batch) calculations, either interactively or in background; (g) automatically open the output files as soon as the calculation finishes; (h) extract results from the output files, such as energy, charges, dipole, population analysis, wave function, bond orders, and valence analysis, and display them in spreadsheets; (i) calculate reactivity indices derived from the frontier orbital theory and the root‐mean‐square (rms) deviation of input and output geometries. All the results generated by MoCalc can be promptly transferred to text editors and electronic spreadsheets, which facilitate a detailed subsequent analysis and the publication of the results. MoCalc can also perform graphical and numerical comparative analysis of the some results when more than one output file is loaded. The program was coded in Visual Basic and runs in Windows 95/98/NT4/ME/2000/XP environments.

Twin Datacenter Interconnection Topology

The authors propose an interconnection topology suited to server-centric networks based on Twin graphs. The topology exploits graph theory to cost-effectively improve network scalability and resilience. The authors discuss datacenter network topology by comparing Twin interconnection topologies to different datacenter topologies (that is, Fat Tree, BCube, DCell, and Hypercube). Their results show that Twin topologies have a lower link cost than previous datacenter topologies, are scalable with fine granularity, and are efficient, fault tolerant, and resilient owing to their features, which enable traffic balancing under both normal and faulty operating conditions.

Telecommunication Technologies for Smart Grids: Total Cost Optimization

Abstract What makes a smart grid really smart is its ability to exchange information among thousands of devices spread over a vast geographic area. However, it is important to build reliable and secure communication networks with a different data capacity and different transmission technologies. In this chapter, we will introduce the basic concepts, requirements, and challenges of such a heterogeneous network. The design of cost-effective smart grid communication networks based on the total cost optimization problem is presented considering optical fiber communications, power line communication, and wireless systems.

Feature selection for optical network design via a new mutual information estimator

Abstract An efficient design of optical networks is a complex challenge that requires knowledge of the desired performance trends. Such knowledge would have a potential impact on an expert system to this end, for instance, would help identify reliable topological parameters to characterize the desired behavior of the network. Feature selection from information theory is widely explored in many areas of expert and intelligent systems, and it is a suitable technique to choose such parameters. In optical networks, many signals are carried along the same fiber, each one with its wavelength. A possible desired performance is the minimal usage of different wavelengths, which can be influenced by many topological parameters established in the network design. However, it is difficult to determine the dependence between topological parameters and the number of wavelengths, because this latter addresses an NP-hard problem. We perform a comprehensive literature review to find topological metrics that are easier to compute and apply feature selection using a new mutual information estimator. Based on coincidence detection, this estimator is lightweight and easy-to-use and allows measuring the relevance between discrete and continuous features, without discretization nor estimating probability density functions. For this purpose, tests are performed using 315 topological parameters from graph theory and complex networks, in 15 real-world optical networks and 2.2 million random topologies that mimic real-world ones. The topological parameters are ranked based on its mutual information values, obtaining a set of the most influential for explaining the wavelength requirements. Among these parameters, as a result, the method highlights the ones derived from the edge betweenness. Moreover, some parameters proposed by the literature do not perform as expected. The results of this study can serve as a basis for new expert systems to design and expansion of optical networks, driven by the most relevant topological parameters.

A complex network analysis of the Brazilian Power Test System

This paper presents a complex network analysis of the Brazilian Power Test System (BPTS), which is based on the real Brazilian electric power system. The aim is to model the BPTS cases via graphs, characterize them according to existing complex network models, compute several invariants from graph and complex networks, and analyze them with respect to these invariants, in order to obtain useful information for the application. The analysis of these metrics applied to the electrical power system can help us to identify and solve problems such as network overload, catastrophic failures and blackouts. Each invariant leads to different critical nodes. Thus, we could look for the best combination of invariants and weights to analyze power system networks.