The Optimal Virtual Path Design of ATM Networks

This paper studies the optimal configuration of virtual paths in ATM networks. A linear model that minimizes the maximum flow density of links is proposed for the optimal virtual path configuration. The advantage of the modeling is that traffic loads are distributed evenly over entire network. An algorithm based on the multi-commodity approach is proposed to solve the problem. Examples are given to show that this algorithm solves the optimal VP assignment problem very quickly and efficiently. Since only single source-destination linear programming subproblems need to be solved in our approach, this algorithm can be applied to large-scale networks.published_or_final_versio

Automatic Speaker Recognition System in Adverse Conditions — Implication of Noise and Reverberation on System Performance

Speaker recognition has been developed and evolved over the past few decades into a supposedly mature technique. Existing methods typically utilize robust features extracted from clean speech. In real-world applications, especially security and forensics related ones, reliability of recognition becomes crucial, meanwhile limited speech samples and adverse acoustic conditions, most notably noise and reverberation, impose further complications. This paper is presented from a study into the behavior of typical speaker recognition systems in adverse retrieval phases. Following a brief review, a speaker recognition system was implemented using the MSR Identity Toolbox by Microsoft. Validation tests were carried out with clean speech and the speech contaminated by noise and/or reverberation of varying degrees. The image source method was adopted to take into account real acoustic conditions in the spaces. Statistical relationships between recognition accuracy and signal to noise ratios or reverberation times have therefore been established. Results show noise and reverberation can, to different extents, degrade the performance of recognition. Both reverberation time and direct to reverberation ratio can affect recognition accuracy. The findings may be used to estimate the accuracy of speaker recognition and further determine the likelihood a particular speaker

Consistency of cruise data of the CARINA database in the Atlantic sector of the Southern Ocean

Initially a North Atlantic project, the CARINA carbon synthesis was extended to include the Southern Ocean. Carbon and relevant hydrographic and geochemical ancillary data from cruises all across the Arctic Mediterranean Seas, Atlantic and Southern Ocean were released to the public and merged into a new database as part of the CARINA synthesis effort. Of a total of 188 cruises, 37 cruises are part of the Southern Ocean, including 11 from the Atlantic sector. The variables from all Southern Ocean cruises, including dissolved inorganic carbon (TCO2), total alkalinity, oxygen, nitrate, phosphate and silicate, were examined for cruise-to-cruise consistency in one collective effort. Seawater pH and chlorofluorocarbons (CFCs) are also part of the database, but the pH quality control (QC) is described in another Earth System Science Data publication, while the complexity of the Southern Ocean physics and biogeochemistry prevented a proper QC analysis of the CFCs. The area-specific procedures of quality control, including crossover analysis between stations and inversion analysis of all crossover data (i.e. secondary QC), are briefly described here for the Atlantic sector of the Southern Ocean. Data from an existing, quality controlled database (GLODAP) were used as a reference for our computations – however, the reference data were included into the analysis without applying the recommended GLODAP adjustments so the corrections could be independently verified. The outcome of this effort is an internally consistent, high-quality carbon data set for all cruises, including the reference cruises. The suggested corrections by the inversion analysis were allowed to vary within a fixed envelope, thus accounting for natural variability. The percentage of cruises adjusted ranged from 31% (for nitrate) to 54% (for phosphate) depending on the variable

In vitro resistance of gram-negative enteric bacilli from wound infections to honey

Letter to the Edito

Advanced Fault Section Estimation System for Power Networks Based on Hybrid Fuzzy System and Radial Basis Function Neural Network

Abstract Although the radial basis function neural network (RBF NN) offers a potential solution for fault section estimation (FSE) in power networks, it has to be totally retrained for the case of power network topology change or power network expansion and cannot provide any explanations for its diagnosis results due to the blackbox nature of the neural network. In this paper, the functional equivalence between RBF NN and fuzzy system (FS) is built up for FSE problem throughout the neural network training process. Furthermore, based on this point, a novel retraining strategy is presented for RBF NN, which can extract the unchanged knowledge from the original RBF NN and then insert the knowledge back to the new RBF NN about the changing part of the power network in the case of network topology change or expansion. The retraining strategy has been implemented and tested in a 4-bus power system. The simulation results show that the advanced FSE system with hybrid FS and RBF NN works successfully and efficiently in power networks.published_or_final_versio

Multi-Layer Cyber-Physical Security and Resilience for Smart Grid

The smart grid is a large-scale complex system that integrates communication technologies with the physical layer operation of the energy systems. Security and resilience mechanisms by design are important to provide guarantee operations for the system. This chapter provides a layered perspective of the smart grid security and discusses game and decision theory as a tool to model the interactions among system components and the interaction between attackers and the system. We discuss game-theoretic applications and challenges in the design of cross-layer robust and resilient controller, secure network routing protocol at the data communication and networking layers, and the challenges of the information security at the management layer of the grid. The chapter will discuss the future directions of using game-theoretic tools in addressing multi-layer security issues in the smart grid.Comment: 16 page

Strict protected areas are essential for the conservation of larger and threatened mammals in a priority region of the Brazilian Cerrado

ssessing protected area (PA) effectiveness is key to ensure the objectives of habitat protection are being achieved. There is strong evidence that legal protection reduces loss of natural vegetation, but biodiversity loss can still happen without significant changes in vegetation cover. Here we use data from a specifically designed camera trap survey to conduct a counterfactual assessment of PA effectiveness at safeguarding local biodiversity in the Brazilian Cerrado. We surveyed the mammal community in 517 locations at the Sertão Veredas-Peruaçu mosaic, distributed across five strict PAs (264 survey sites in five arrays) and two multiple-use PAs with low management levels (253 survey sites in four arrays). We adopted a multi-species occupancy framework to analyse our dataset while also controlling for confounding factors not directly related to protection. Of the 21 species assessed, nine had higher occupancy in strict PAs, one had higher occupancy in multiple-use PAs, and ten did not respond to protection level. Site species richness was nearly twice as large in areas under stricter protection, with even greater differences for species richness of globally threatened and larger mammals (>15 kg). Overall we demonstrated that the strict PAs surveyed support higher mammal diversity than similar areas under less restrictive management, with a particular strong effect on larger and threatened species. Given that strict PAs cover only 3% of the Cerrado, our results suggest that expanding the area under strict protection is likely to benefit iconic species of the Brazilian savanna, such as the maned wolf and giant anteater

Cross‐scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins

Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity to climate variability and climate change is comparable for impact models designed for either scale. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climate change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a better reproduction of reference conditions. However, the sensitivity of the two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases, but have distinct differences in other cases, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability. Whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models calibrated and validated against observed discharge should be used