This paper proposes a novel method for using the service (field failure) data of consumer vehicles to estimate customer perception. To achieve this, relevant variables are extracted from the vehicle service data and provided as input to the proposed algorithm which then comes up with an optimized mathematical model for predicting the Customer Satisfaction Index or CSI. The methodology is then extended in a way that allows comparison of the CSIs of two or more vehicle models, thus providing a measure of the market's perceived quality of a vehicle model relative to another. Validation against the Consumer Reports data shows that customer experiences and their consequent response in surveys are indeed a reflection of the numbers the service data provides. However, it is argued that the proposed model is more generic than the Consumer Reports because: (1) it doesn't rely on consumer surveys and (2) it can be used to assess individual consumer level satisfaction.

This paper describes the grid-oriented storage (GOS) architecture and its implementations. A GOS-specific file system (GOS-FS), the single-purpose intent of a GOS OS, and secure interfaces via grid security infrastructure (GSI) motivate and enable this new architecture. As an FTP server, GOS with a slimmed OS, with a total volume of around 150 MB, outperforms the standard GridFTP by 20-40 percent. As a file server, GOS-FS acts as a network/grid interface, enabling a user to perform searches and access resources without downloading them locally. In the real-world tests between Cambridge and Beijing, where the transfer distance is 10,000 km, the multistreamed GOS-FS file opening/saving resulted in a remarkable performance increase of about 2-25 times, compared to the single-streamed network file system (NFSv4). GOS is expected to be a variant of or successor to the well-used network-attached storage (NAS) and/or storage area network (SAN) products in the grid era

Decomposing a complex computational problem into sub-problems, which are computationally simpler to solve individually and which can be combined to produce a solution to the full problem, can efficiently lead to compact and general solutions. Modular neural networks represent one of the ways in which this divide-and-conquer strategy can be implemented. Here we present a co-evolutionary model which is used to design and optimize modular neural networks with task-specific modules. The model consists of two populations. The first population consists of a pool of modules and the second population synthesizes complete systems by drawing elements from the pool of modules. Modules represent a part of the solution, which co-operates with others in the module population to form a complete solution. With the help of two artificial supervised learning tasks created by mixing two sub-tasks we demonstrate that if a particular task decomposition is better in terms of performance on the overall task, it can be evolved using this co-evolutionary model.