Links between type E botulism outbreaks, lake levels, and surface water temperatures in Lake Michigan, 1963-2008

Relationships between large-scale environmental factors and the incidence of type E avian botulism outbreaks in Lake Michigan were examined from 1963 to 2008. Avian botulism outbreaks most frequently occurred in years with low mean annual water levels, and lake levels were significantly lower in outbreak years than in non-outbreak years. Mean surface water temperatures in northern Lake Michigan during the period when type E outbreaks tend to occur (July through September) were significantly higher in outbreak years than in non-outbreak years. Trends in fish populations did not strongly correlate with botulism outbreaks, although botulism outbreaks in the 1960s coincided with high alewife abundance, and recent botulism outbreaks coincided with rapidly increasing round goby abundance. Botulism outbreaks occurred cyclically, and the frequency of outbreaks did not increase over the period of record. Climate change scenarios for the Great Lakes predict lower water levels and warmer water temperatures. As a consequence, the frequency and magnitude of type E botulism outbreaks in the Great Lakes may increase.     

PREACO: A fast ant colony optimization for codebook generation

This paper presents an effective and efficient method for speeding up ant colony optimization (ACO) in solving the codebook generation problem. The proposed method is inspired by the fact that many computations during the convergence process of ant-based algorithms are essentially redundant and thus can be eliminated to boost their convergence speed, especially for large and complex problems. To evaluate the performance of the proposed method, we compare it with several state-of-the-art metaheuristic algorithms. Our simulation results indicate that the proposed method can significantly reduce the computation time of ACO-based algorithms evaluated in this paper while at the same time providing results that match or outperform those ACO by itself can provide.     

Experience with model-based performance, reliability, and adaptability assessment of a complex industrial architecture

In this paper, we report on our experience with the application of validated models to assess performance, reliability, and adaptability of a complex mission critical system that is being developed to dynamically monitor and control the position of an oil-drilling platform. We present real-time modeling results that show that all tasks are schedulable. We performed stochastic analysis of the distribution of task execution time as a function of the number of system interfaces. We report on the variability of task execution times for the expected system configurations. In addition, we have executed a system library for an important task inside the performance model simulator. We report on the measured algorithm convergence as a function of the number of vessel thrusters. We have also studied the system architecture adaptability by comparing the documented system architecture and the implemented source code. We report on the adaptability findings and the recommendations we were able to provide to the system’s architect. Finally, we have developed models of hardware and software reliability. We report on hardware and software reliability results based on the evaluation of the system architecture.     

Remote flood monitoring system based on plastic optical fibres and wireless motes

This paper summarises an effort in the development of a remote flood monitoring system based on plastic optical fibre (POF) sensors and a wireless mote network. The wireless mote, comprising of a network of MICA2DOT™ units, was used as a platform to monitor and record the signal from the POF sensors and transmit this information to a base station wirelessly. A prototype of the integrated wireless POF sensor unit has been constructed, rendering it possible to deploy the autonomous unit remotely at multiple monitoring points as required. A flood monitoring simulation was carried out in a 24 m × 10 m × 0.9 m wave basin where four of these wireless optical fibre mote sensors were used to detect the rising water level in the basin. The novelty of the work lies in the successful integration of the wireless platform to a POF-based liquid level sensor and the subsequent demonstration of the prototype of the system for the purposes of flood monitoring applications.The sensing principle of the POF sensor developed here is well-known and is based on the loss of total internal reflection of the optical signal as the sensor probe comes in contact with the liquid. Compared to optical fibre-based sensors reported previously in the literature, the probe profile used in this study differs in terms of its simplicity in design, while exhibiting an excellent signal intensity loss ratio without the need for additional attachments to the probe such as optical prisms. The tests carried out showed that the POF sensor is capable of detecting a variety of fluids. Exhibiting good signal stability, the sensor also detects the liquid level reliably when the liquid rises or falls to the predetermined level. The responsiveness of the optical fibre sensor was evaluated by simulating different rates at which the liquid rises by immersing the sensor tip into the liquid and vice-versa at various speeds ranging from 1 mm/min to 500 mm/min.     

Dynamic composition and optimization of Web services

Process-based composition of Web services has recently gained significant momentum for the implementation of inter-organizational business collaborations. In this approach, individual Web services are choreographed into composite Web services whose integration logics are expressed as composition schema. In this paper, we present a goal-directed composition framework to support on-demand business processes. Composition schemas are generated incrementally by a rule inference mechanism based on a set of domain-specific business rules enriched with contextual information. In situations where multiple composition schemas can achieve the same goal, we must first select the best composition schema, wherein the best schema is selected based on the combination of its estimated execution quality and schema quality. By coupling the dynamic schema creation and quality-driven selection strategy in one single framework, we ensure that the generated composite service comply with business rules when being adapted and optimized.     

An expert system for fault diagnosis in internal combustion engines using probability neural network

An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.     

An Interoperable Grid Information System for Integrated Resource Monitoring Based on Virtual Organizations

In many Grid infrastructures different kinds of information services are in use, which utilize different incompatible data structures and interfaces to encode and provide their data. Homogeneous monitoring of these infrastructures with the monitoring data being accessible everywhere independently of the middleware which provided it, is the basis for a consistent status reporting on the Grids’ resources and services. Thus, interoperability or interoperation between the different information services in a heterogeneous Grid infrastructure is required. Monitoring data must contain the identity of the affected Virtual Organization (VO) so that it can be related to the resources and services the VO has allocated to enable VO-specific information provision. This paper describes a distributed architecture for an interoperable information service, which combines data unification and categorization with policies for VO membership, VO resource management and data transformations. This service builds the basis for an integrated and interoperating monitoring of Grids, which provide their data to more than one VO and utilize heterogeneous information services.     

Evaluation of a type 1 diabetes serum cohort by SELDI-TOF MS protein profiling

Proteomics analysis of serum from patients with type 1 diabetes (T1D) may lead to novel biomarkers for prediction of disease and for patient monitoring. However, the serum proteome is highly sensitive to sample processing and before proteomics biomarker research serum cohorts should preferably be examined for potential bias between sample groups. SELDI‐TOF MS protein profiling was used for preliminary evaluation of a biological‐bank with 766 serum samples from 270 patients with T1D, collected at 18 different paediatric centers representing 15 countries in Europe and Japan over 2 years (2000–2002). Samples collected 1 (n = 270), 6 (n = 248), and 12 (n = 248) months after T1D diagnosis were grouped across centers and compared. The serum protein profiles varied with collection site and day of analysis; however, markers of sample processing were not systematically different between samples collected at different times after diagnosis. Three members of the apolipoprotein family increased with time in patient serum collected 1, 6, and 12 months after diagnosis (ANOVA, p<0.001). These results support the use of this serum cohort for further proteomic studies and illustrate the potential of high‐throughput MALDI/SELDI‐TOF MS protein profiling for evaluation of serum cohorts before proteomics biomarker research.     

3D Semantic Scene Completion: A Survey

International Journal of Computer Vision - Semantic scene completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input. In the last years...     

3D strain mapping in rocks using digital volumetric speckle photography technique

Speckle photography technique is a versatile displacement/deformation mapping technique that can be applied to almost any material. It has its genesis in the laser speckle interferometry technique whereby the natural speckles created by illuminating an optically rough surface using a coherent laser beam are used as displacement gauging elements. It evolves into the white light speckle photography technique whereby a random pattern of any type is used as a displacement measurement transducer. With the advent of digital cameras and ubiquitous usage of computers, the digital version of the technique is developed. Up to now, the technique is essentially limited to 2D applications. Recently, we extended the technique into the 3D domain by using the volumetric imaging capabilities of CT or MRI. In this paper, we apply this technique to measuring the internal deformation of rocks. It so happens that certain rocks have natural density variation at various places such that when imaged by CT these impurities can be treated as 3D speckles. The elements of volumetric speckle photography technique are as follows. A reference volumetric image of the rock is recorded by a micro-CT scan and stored as a reference. Under load, the deformed CT image of the rock is also recorded. Both volume images are divided into subsets of certain voxel arrays. Each corresponding pair of the subsets is “compared” via a two-step 3D Fourier transform analysis. The result is a 3D map of displacement vectors representing the collective displacement experienced by all the speckles within the subset of voxels. The strain distribution of the entire rock specimen can then be calculated using appropriate displacement strain relations. The application of this technique to strain mapping of red sandstone and argillite rocks is presented.