Validation of the CDC Biofilm Reactor as a Dynamic Model for Assessment of Encrustation Formation on Urological Device Materials

Contemporary medical science is reliant upon the rational selection and utilization of devices, and therefore, an increasing need has developed for in vitro systems aimed at replicating the conditions to which urological devices will be subjected to during their use in vivo. We report the development and validation of a novel continuous flow encrustation model based on the commercially available CDC biofilm reactor. Proteus mirabilis-induced encrustation formation on test biomaterial sections under varying experimental parameters was analyzed by X-ray diffraction, infrared- and Raman spectroscopy and by scanning electron microscopy. The model system produced encrusted deposits similar to those observed in archived clinical samples. Results obtained for the system are highly reproducible with encrustation being rapidly deposited on test biomaterial sections. This model will have utility in the rapid screening of encrustation behavior of biomaterials for use in urological applications. (C) 2010 Wiley Periodicals. Inc. J Biomed Mater Res Part B: Appl Biomater 93B: 128-140, 2010

Dynamic global security-aware synthesis using System-C

A dynamic global security-aware synthesis flow using the SystemC language is presented. SystemC security models are first specified at the system or behavioural level using a library of SystemC behavioural descriptions which provide for the reuse and extension of security modules. At the core of the system is incorporated a global security-aware scheduling algorithm which allows for scheduling to a mixture of components of varying security level. The output from the scheduler is translated into annotated nets which are subsequently passed to allocation, optimisation and mapping tools for mapping into circuits. The synthesised circuits incorporate asynchronous secure power-balanced and fault-protected components. Results show that the approach offers robust implementations and efficient security/area trade-offs leading to significant improvements in turnover.

Measurement-based Method for Wind Farm Power System Oscillations Monitoring

The global increase in the penetration of renewable energy is pushing electrical power systems into uncharted territory, especially in terms of transient and dynamic stability. In particular, the greater penetration of wind generation in European power networks is, at times, displacing a significant capacity of conventional synchronous generation with fixed-speed induction generation and now more commonly, doubly fed induction generators. The impact of such changes in the generation mix requires careful monitoring to assess the impact on transient and dynamic stability. This study presents a measurement-based method for the early detection of power system oscillations, with consideration of mode damping, in order to raise alarms and develop strategies to actively improve power system dynamic stability and security. A method is developed based on wavelet-based support vector data description (SVDD) to detect oscillation modes in wind farm output power, which may excite dynamic instabilities in the wider system. The wavelet transform is used as a filter to identify oscillations in frequency bands, whereas the SVDD method is used to extract dominant features from different scales and generate an assessment boundary according to the extracted features. Poorly damped oscillations of a large magnitude, or that are resonant, can be alarmed to the system operator, to reduce the risk of system instability. The proposed method is exemplified using measured data from a chosen wind farm site.

Gender Classification via Lips: Static and Dynamic Features

Automatic gender classification has many security and commercial applications. Various modalities have been investigated for gender classification with face-based classification being the most popular. In some real-world scenarios the face may be partially occluded. In these circumstances a classification based on individual parts of the face known as local features must be adopted. We investigate gender classification using lip movements. We show for the first time that important gender specific information can be obtained from the way in which a person moves their lips during speech. Furthermore our study indicates that the lip dynamics during speech provide greater gender discriminative information than simply lip appearance. We also show that the lip dynamics and appearance contain complementary gender information such that a model which captures both traits gives the highest overall classification result. We use Discrete Cosine Transform based features and Gaussian Mixture Modelling to model lip appearance and dynamics and employ the XM2VTS database for our experiments. Our experiments show that a model which captures lip dynamics along with appearance can improve gender classification rates by between 16-21% compared to models of only lip appearance.

Assessment of rolling resistance models in discrete element simulations

Particulate systems are of interest in many disciplines. They are often investigated using the discrete element method because of its capability to investigate particulate systems at the individual particle scale. To model the interaction between two particles and between a particle and a boundary, conventional discrete element models use springs and dampers in both the normal and tangential directions. The significance of particle rotation has been highlighted in both numerical studies and physical experiments. Several researchers have attempted to incorporate a rotational torque to account for the rolling resistance or rolling friction by developing different models. This paper presents a review of the commonly used models for rolling resistance and proposes a more general model. These models are classified into four categories according to their key characteristics. The robustness of these models in reproducing rolling resistance effects arising from different physical situations was assessed by using several benchmarking test cases. The proposed model can be seen to be more general and suitable for modelling problems involving both dynamic and pseudo-static regimes. An example simulation of the formation of a 2D sandpile is also shown. For simplicity, all formulations and examples are presented in 2D form, though the general conclusions are also applicable to 3D systems.

Measurement-based Method for Wind Farm Power System Oscillation Monitoring

The global increase in the penetration of renewable energy is pushing electrical power systems into uncharted territory, especially in terms of transient and dynamic stability. In particular, the greater penetration of wind generation in European power networks is, at times, displacing a significant capacity of conventional synchronous generation with fixed-speed induction generation and now more commonly, doubly-fed induction generators. The impact of such changes in the generation mix requires careful monitoring to assess the impact on transient and dynamic stability. This paper presents a measurement based method for the early detection of power system oscillations, with attention to mode damping, in order to raise alarms and develop strategies to actively improve power system dynamic stability and security. A method is developed based on wavelet transform and support vector data description (SVDD) to detect oscillation modes in wind farm output power, which may excite dynamic instabilities in the wider system. The wavelet transform is used as a filter to identify oscillations in different frequency bands, while SVDD is used to extract dominant features from different scales and generate an assessment boundary according to the extracted features. Poorly damped oscillations of a large magnitude or that are resonant can be alarmed to the system operator, to reduce the risk of system instability. Method evaluation is exemplified used real data from a chosen wind farm.

Efficiency assessment of electricity power generation on Ireland’s Single Electricity Market by Data Envelopment Analysis

The efficiency of generation plants is an important measure for evaluating the operating performance. The objective of this paper is to evaluate electricity power generation by conducting an All-Island-Generator-Efficiency-Study (AIGES) for the Republic of Ireland and Northern Ireland by utilising a Data Envelopment Analysis (DEA) approach. An operational performance efficiency index is defined and pursued for the year 2008. The economic activities of electricity generation units/plants examined in this paper are characterized by numerous input and output indicators. Constant returns to scale (CRS) and variable returns to scale (VRS) type DEA models are employed in the analysis. Also a slacks based analysis indicates the level of inefficiency for each variable examined. The findings from this study provide a general ranking and evaluation but also facilitate various interesting efficiency comparisons between generators by fuel type.

Phytoremediation assessment of Gomphrena globosa and Zinnia elegans grown in arsenic-contaminated hydroponic conditions as a safe and feasible alternative to be applied in arsenic-contaminated soils of the Bengal Delta

Several agricultural fields show high contents of arsenic because of irrigation with arsenic- contaminated groundwater. Vegetables accumulate arse- nic in their edible parts when grown in contaminated soils. Polluted vegetables are one of the main sources of arsenic in the food chain, especially for people living in rural arsenic endemic villages of India and Bangladesh. The aim of this study was to assess the feasibility of floriculture in the crop rotation system of arsenic en- demic areas of the Bengal Delta. The effects of different arsenic concentrations (0, 0.5, 1.0, and 2.0 mg As L−1) and types of flowering plant (Gomphrena globosa and Zinnia elegans) on plant growth and arsenic accumula- tion were studied under hydroponic conditions. Total arsenic was quantified using atomic absorption spec- trometer with hydride generation (HG-AAS). Arsenic was mainly accumulated in the roots (72 %), followed by leaves (12 %), stems (10 %), and flowers (<1 %). The flowering plants studied did not show as high phytoremediation capacities as other wild species, suchas ferns. However, they behaved as arsenic tolerant plants and grew and bloomed well, without showing any phytotoxic signs. This study proves that floriculture could be included within the crop rotation system in arsenic-contaminated agricultural soils, in order to im- prove food safety and also food security by increasing farmer’s revenue.

Differential evolution combined with clonal selection for dynamic economic dispatch

Dynamic economic load dispatch (DELD) is one of the most important steps in power system operation. Various optimisation algorithms for solving the problem have been developed; however, due to the non-convex characteristics and large dimensionality of the problem, it is necessary to explore new methods to further improve the dispatch results and minimise the costs. This article proposes a hybrid differential evolution (DE) algorithm, namely clonal selection-based differential evolution (CSDE), to solve the problem. CSDE is an artificial intelligence technique that can be applied to complex optimisation problems which are for example nonlinear, large scale, non-convex and discontinuous. This hybrid algorithm combines the clonal selection algorithm (CSA) as the local search technique to update the best individual in the population, which enhances the diversity of the solutions and prevents premature convergence in DE. Furthermore, we investigate four mutation operations which are used in CSA as the hyper-mutation operations. Finally, an efficient solution repair method is designed for DELD to satisfy the complicated equality and inequality constraints of the power system to guarantee the feasibility of the solutions. Two benchmark power systems are used to evaluate the performance of the proposed method. The experimental results show that the proposed CSDE/best/1 approach significantly outperforms nine other variants of CSDE and DE, as well as most other published methods, in terms of the quality of the solution and the convergence characteristics.

Who Knows What's Going On? Mapping New Security Landscapes in Contemporary Espionage Fiction

This article explores whether or to what extent the contemporary espionage novel is able to map and interrogate transformations in the post-9/11security environment. It asks how well a form or genre of writing, typically handcuffed to the machinations and demands of the Cold War and state sovereignty, is able to adapt to a new security environment characterized by strategies of “risk assessment” and “resilience-building” and by modes or regimes of power not reducible to, or wholly controlled by, the state. In doing so, it thinks about the capacities of this type of fiction for “resisting” the formations of power it wants to make visible and is partly complicit with.

Assessment of the double-skin façade passive thermal buffer effect.

Double Skin Façades (DSFs) are becoming increasingly popular architecture for commercial office buildings. Although DSFs are widely accepted to have the capacity to offer significant passive benefits and enable low energy building performance, there remains a paucity of knowledge with regard to their operation. Identification of the most determinant architectural parameters of DSFs is the focus of ongoing research. This paper presents an experimental and simulation study of a DSF installed on a commercial building in Dublin, Ireland. The DSF is south facing and acts to buffer the building from winter heat losses, but risks enhancing over-heating on sunny days. The façade is extensively monitored during winter months. Computational Fluid Dynamic (CFD) models are used to simulate the convective operation of the DSF. This research concludes DSFs as suited for passive, low energy architecture in temperature climates such as Ireland but identifies issues requiring attention in DSF design.

An efficient and robust case sorting for algorithm for transient stability assessment

An efficient and robust case sorting algorithm based on Extended Equal Area Criterion (EEAC) is proposed in this paper for power system transient stability assessment (TSA). The time-varying degree of an equivalent image system can be deduced by comparing the analysis results of Static EEAC (SEEAC) and Dynamic EEAC (DEEAC), the former of which neglects all time-varying factors while the latter partially considers the time-varying factors. Case sorting rules according to their transient stability severity are set combining the time-varying degree and fault messages. Then a case sorting algorithm is designed with the “OR” logic among multiple rules, based on which each case can be identified into one of the following five categories, namely stable, suspected stable, marginal, suspected unstable and unstable. The performance of this algorithm is verified by studying 1652 contingency cases from 9 real Chinese provincial power systems under various operating conditions. It is shown that desirable classification accuracy can be achieved for all the contingency cases at the cost of very little extra computational burden and only 9.81% of the whole cases need to carry out further detailed calculation in rigorous on-line TSA conditions.

Assessment Of various sensors for structural health monitoring for bridge weigh-in-motion (B-WIM).

In recent years, Structural Health Monitoring (SHM) systems have been developed to monitor bridge deterioration, assess real load levels and hence extend bridge life and safety. A road bridge is only safe if the stresses caused by the passing vehicles are less than the capacity of the bridge to resist them. Conventional SHM systems can be used to improve knowledge of the bridges capacity to resist stresses but generally give no information on the causes of any increase in stresses (based on measuring strain). The concept of in Bridge Weigh-in-Motion (B-WIM) is to establish axle loads, without interruption to traffic flow, by using strain sensors at a bridge soffit and subsequently converting the data to real time axle loads or stresses. Recent studies have shown it would be most beneficial to develop a portable system which can be easily attached to existing and new bridge structures for a specified monitoring period. The sensors could then be left in place while the data acquisition can be moved for various other sites. Therefore it is necessary to find accurate sensors capable of capturing peak strains under dynamic load and suitable methods for attaching these strain sensors to existing and new bridge structures. Additionally, it is important to ensure accurate strain transfer between concrete and steel, the adhesives layer and the strain sensor. This paper describes research investigating the suitably of using various sensors for the monitoring of concrete structures under dynamic vehicle load. Electrical resistance strain (ERS) gauges, vibrating wire (VW) gauges and fibre optic sensors (FOS) are commonly used for SHM. A comparative study will be carried out to select a suitable sensor for a bridge Weigh in Motion System. This study will look at fixing methods, durability, scanning rate and accuracy range. Finite element modeling is used to predict the strains which are then validated in laboratory trials.