Energy use in controlled temperature marine aquaculture

Temperature control offers benefits in land‐based marine aquaculture: stock growth rates may be enhanced by heating in winter; mortality rates in summer may be reduced by cooling. However, if the plant is not well designed, temperature control may create very large energy demands. This paper describes the application of temperature control in abalone aquaculture in New Zealand, with a focus on energy considerations in plant design. An abalone farm using a semi‐closed water conditioning system is used as a case study for which an energy model, based on a heat pump system, is developed. The model is used to determine the impact of plant design and tank conditions on the economics of the operation. Copyright © 2002 John Wiley & Sons, Ltd.     

Optimal synthesis of tunable elastic wave-guides

Topology optimization, or control in the coefficients of partial differential equations, has been successfully utilized for designing wave-guides with precisely tailored functionalities. For many applications it would be desirable to have the possibility of drastically altering the wave-guiding properties of a device “on the fly,” in a controllable manner as an influence of some external input. This would enable wave-guides with highly non-linear input-output mappings, such as for example controllable wave switches.In this paper, we propose using finite elastic pre-straining for the purpose of tuning a wave-guide. In order to systematically formulate and solve the wave-guide synthesis problems we utilize mathematical programming methods in conjunction with topology optimization for parametrizing the design space. The resulting extremal problem is, from a practical point, equivalent to finding an optimal subdivision of a given control volume into two disjoint subsets occupied by two different materials, normally resulting in a highly heterogeneous elastic body with desired wave-guiding functionalities in the original and finitely deformed configurations.The proposed methodology is illustrated with numerical examples.     

The effect of Pseudomonas aeruginosa infection on clinical parameters in steady-state bronchiectasis

STUDY OBJECTIVE: To investigate the effect of Pseudomonas aeruginosa infection on clinical parameters in Chinese patients with noncystic fibrosis and steady-state bronchiectasis. DESIGN: Prospective, cross-sectional clinicomicrobiological study with informed consent. SETTING: Consecutive outpatient recruitment from a specialist bronchiectasis respiratory clinic. PATIENTS: Outpatients (n = 100; 62 women; 55.1+/-16.7 years old; FEV1/FVC 1.4+/-0.7/2.1+/-0.9 L), who had stable respiratory symptoms for more than 3 weeks. MEASUREMENTS AND RESULTS: Respiratory pathogens isolated from the sputum were: Pseudomonas aeruginosa (33), Haemophilus influenzae (10), Moraxella catarrhalis (2), other Gram-negative bacilli (5), Streptococcus pneumoniae (6), Staphylococcus aureus (5), mycobacteria (3), and yeast (1). Clinical parameters in patients with positive isolation of P aeruginosa were compared with those without the organism in the sputum culture (non-P aeruginosa). In the P aeruginosa group, the FEV1/FVC ratio and sputum volume were lower (p < 0.005) and higher (p < 0.0001), respectively, than those of the non-P aeruginosa group. The FEV1/FVC ratio (< 60%) and sputum volume (grading > 5) were independently associated with a positive sputum isolation of P aeruginosa with odds ratios of 3.1 (confidence interval [CI] 1.2 to 8.4; p < 0.01) and 4.7 (CI 1.6 to 13.3; p < 0.001), respectively. CONCLUSIONS: P aeruginosa is the predominant respiratory pathogen isolated in the sputum of Chinese patients with steady-state bronchiectasis, and its isolation is associated with high sputum output (> or = 75th quartile) and moderately severe airflow obstruction (FEV1/FVC < 60%).     

Structural stability of single-layered LaNi4.25Al0.75 film and its electrochemical hydrogen-storage properties

AB5-based hydrogen storage thin fdms （LaNi4.25Al0.75）, deposited on Cu substrate by dc magnetron sputtering were investigated in this study. X-ray diffraction （XRD） revealed that the microstructure of the layer was in crystal form. SEM and AFM analyses proved that the film appeared to be rather rough with numerous randomly sized pores of approximately 15-40 in nm diameter. Structural stability of the film was examined by the combined analyses of DSC, XRD, and SEM, which indicated that this film maintained its structural stability below 500 K or so, and a network structure was observed on the film after being heated at 700 K for 30 min. Electrochemical hydrogen-storage properties of the films were investigated by simulated battery tests. It was found that single-layered LaNi4.25A10.75 film exhibited electrochemical hydrogen-storage properties similar to typical AB5 alloys in bulk, and the maximum discharge capacity of the film was about 220 mAh/g. After 20 charge/discharge cycles, small needle-shaped aluminium oxide was formed on some fractions of the film surface.     

Determining Geographic Vulnerabilities Using a Novel Impact Based Resilience Metric

Various natural and man-made disasters as well as major political events (like riots) have increased the importance of understanding geographic failures and how correlated failures impact networks. Since mission critical networks are overlaid as virtual networks over a physical network infrastructure forming multilayer networks, there is an increasing need for methods to analyze multilayer networks for geographic vulnerabilities. In this paper, we present a novel impact-based resilience metric. Our new metric uses ideas borrowed from performability to combine network impact with state probability to calculate a new metric called Network Impact Resilience. The idea is that the highest impact to the mission of a network should drive its resilience metric. Furthermore, we present a state space analysis method that analyzes multilayer networks for geographic vulnerabilities. To demonstrate the methods, the inability to provision a given number of upper layer services is used as the criteria for network failure. Mapping techniques for multilayer network states are presented. Simplifying geographic state mapping techniques to reduce enumeration costs are also presented and tested. Finally, these techniques are tested on networks of varying sizes.     

The Malta cistern mapping project: Underwater robot mapping and localization within ancient tunnel systems

This paper documents the development of an underwater robot system enabled with several mapping and localization techniques applied to a particular archaeological expedition. The goal of the expedition was to explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted as water storage systems for fortresses, private homes, and churches. Such cisterns often consisted of several connected chambers, still containing water. A sonar‐equipped remotely operated vehicle (ROV) was deployed into these cisterns to obtain both video footage and sonar range measurements. Six different mapping and localization techniques were employed, including (1) sonar image mosaics using stationary sonar scans, (2) sonar image mosaics using stationary sonar scans with Smart Tether position data, (3) simultaneous localization and mapping (SLAM) while the vehicle was in motion, (4) SLAM using stationary sonar scans, (5) localization using previously created maps, and (6) SLAM while the vehicle was in motion with Smart Tether position data. Top‐down‐view maps of 22 different cisterns were successfully constructed. It is estimated that the cisterns were built as far back as 300 B.C., and few records of their size, shape, and connectivity existed before the expedition. © 2010 Wiley Periodicals, Inc.     

Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.     

Service bid comparisons by fuzzy ranking in open railway market timetabling

In an open railway access market, the Infrastructure Provider (IP), upon the receipts of service bids from the Train Service Providers (TSPs), assigns track access rights according to its own business objectives and the merits of the bids; and produces the train service timetable through negotiations. In practice, IP chooses to negotiate with the TSPs one by one in such a sequence that IP optimizes its objectives. The TSP bids are usually very complicated, containing a large number of parameters in different natures. It is a difficult task even for an expert to give a priority sequence for negotiations from the contents of the bids. This study proposes the application of fuzzy ranking method to compare and prioritize the TSP bids in order to produce a negotiation sequence. The results of this study allow investigations on the behaviors of the stakeholders in bid preparation and negotiation, as well as evaluation of service quality in the open railway market.     

A mixture of generalized latent variable models for mixed mode and heterogeneous data

In the behavioral, biomedical, and social-psychological sciences, mixed data types such as continuous, ordinal, count, and nominal are common. Subpopulations also often exist and contribute to heterogeneity in the data. In this paper, we propose a mixture of generalized latent variable models (GLVMs) to handle mixed types of heterogeneous data. Different link functions are specified to model data of multiple types. A Bayesian approach, together with the Markov chain Monte Carlo (MCMC) method, is used to conduct the analysis. A modified DIC is used for model selection of mixture components in the GLVMs. A simulation study shows that our proposed methodology performs satisfactorily. An application of mixture GLVM to a data set from the National Longitudinal Surveys of Youth (NLSY) is presented.