The measures weighted stratum and weighted compactness on the weighted digraph-based models of the hypermedia and navigation

This study focuses on carrying out weighted digraph-based mathematical construction of hypermedia and user navigation on hypermedia and making a presentation of “weighted” forms of known stratum and compactness. In the instructional Web design researches, the structural analysis of navigation on the hypermedia is important in modeling the behaviors that the learners perform while using this kind of media. Therefore, new conceptions of the presented usage of new measures towards this goal are also discussed within the scope of our study.     

A real options approach for evaluation and justification of a hospital information system

Nowadays healthcare organizations globally recognize the importance of investing in information technologies to improve the quality of care delivery and reduce costs. The key drivers of healthcare sector such as continuously improving healthcare standards and insurance systems have introduced new requirements for hospitals, which in return provided a solid ground for decision-makers to consider implementing hospital information systems that are customized and improved versions of enterprise resource planning (ERP) systems designed according to the needs of the healthcare sector. The conventional discounted cash flow methods ignore the value of managerial and strategic flexibility inherent in these investments, which is crucial for justification of the investment decision. This study introduces a real options-based methodology which overcomes the limitations of traditional valuation methods and enables decision-makers to value an ERP system investment incorporating multiple options. The option valuation model developed in this study extends the binomial lattice framework to model a hospital information system (HIS) investment opportunity with compound options. The potential application of the proposed model is illustrated through evaluation of a real-world HIS investment.     

Robust online detection of pipeline corrosion from range data

We present the Finite-Window Robust Sequential Estimator for the detection and analysis of corrosion in range images of gas pipelines. This statistically robust, real-time technique estimates the pipeline surface range function in the presence of noise, surface deviations, and changes in the underlying model. Deviations from the robust surface fit, corresponding to statistical outliers, represent potential areas of corrosion. Because the algorithm estimates surface parameters over a finite, sliding window of data, it can track moderately high-order surfaces using lower order models. The system is consistent, objective, and non-destructive and can be used with the pipeline in service. Received: 7 September 1999 / Accepted: 2 November 2000     

The State of the Art in Mobile Graphics Research

High-quality computer graphics let mobile-device users access more compelling content. Still, the devices' limitations and requirements differ substantially from those of a PC. This survey of mobile graphics research describes current solutions in terms of specialized hardware (including 3D displays), rendering and transmission, visualization, and user interfaces.     

Digital bundling

Increasingly, we see that firms offer many items on information-intensive channels and the Internet. Especially with low-marginal-cost digital goods, bundling may be a beneficial strategy. Different bundles may help customers become more discriminating and maximize profits. However, the marketplace evidence provides mixed observation of bundling rigorously pursued. In this study, we provide a general framework to analyze when and how bundling may be beneficial. We compare and contrast the firm and customer characteristics on bundling strategy. We analyze when individual sales can be beneficial. We find that when costs do not increase relative to the bundle valuation, firms find it beneficial to limit the number of bundles offered in the market. A low (zero) marginal cost firm benefits from offering just one big bundle. Such a firm obtains a higher profit compared to a firm that offers many bundles. With high marginal costs, the number of bundles increases with increasing customer valuation and/or heterogeneity. When a firm offers all the bundles, prices and profit increase as customer heterogeneity and valuation increase. When the number of offered items is high, individual sale (unbundling) may be preferred over bundling and bundling becomes an inferior strategy. Interestingly, a firm may benefit from unbundling the items when customers have higher valuations.     

Discovering subproblem prioritization rules for shifting bottleneck algorithms

Shifting bottleneck (SB) algorithms have been successful in solving most real-sized scheduling problems. However, it is known that under certain conditions, solution quality degrades because of subproblem interactions. Changing the order in which subproblems are solved greatly affects the solution quality. We demonstrate that for some class of problems it is possible to induce production (IF–THEN) rules that determine the subproblem solution order and lead to good quality solutions, matching the performance of the SB algorithm in most other instances.     

Prediction of rotation capacity of wide flange beams using neural networks

This study proposes Neural Networks (NN) as a new approach for the estimation and explicit formulation of available rotation capacity of wide flange beams. Rotation capacity is an important phenomenon which determines the plastic behaviour of steel structures. Thus the database for the NN training is directly based on extensive experimental results from literature. The results of the NN approach are compared with numerical results obtained by a specialized computer. Available rotation capacity is also introduced in a closed form solution based on the proposed NN model. The proposed NN method is seen to be more accurate than numerical results, practical and fast compared to FE models.     

Exact algorithms for the joint object placement and request routing problem in content distribution networks

This paper describes two exact algorithms for the joint problem of object placement and request routing in a content distribution network (CDN). A CDN is a technology used to efficiently distribute electronic content throughout an existing Internet Protocol network. The problem consists of replicating content on the proxy servers and routing the requests for the content to a suitable proxy server in a CDN such that the total cost of distribution is minimized. An upper bound on end-to-end object transfer time is also taken into account. The problem is formulated as a nonlinear integer programming formulation which is linearized in three different ways. Two algorithms, one based on Benders decomposition and the other based on Lagrangean relaxation and decomposition, are described for the solution of the problem. Computational experiments are conducted by comparing the proposed linearizations and the two algorithms on randomly generated Internet topologies.     

Learning goal hierarchies from structured observations and expert annotations

We describe a relational learning by observation framework that automatically creates cognitive agent programs that model expert task performance in complex dynamic domains. Our framework uses observed behavior and goal annotations of an expert as the primary input, interprets them in the context of background knowledge, and returns an agent program that behaves similar to the expert. We map the problem of creating an agent program on to multiple learning problems that can be represented in a “supervised concept learning’’ setting. The acquired procedural knowledge is partitioned into a hierarchy of goals and represented with first order rules. Using an inductive logic programming (ILP) learning component allows our framework to naturally combine structured behavior observations, parametric and hierarchical goal annotations, and complex background knowledge. To deal with the large domains we consider, we have developed an efficient mechanism for storing and retrieving structured behavior data. We have tested our approach using artificially created examples and behavior observation traces generated by AI agents. We evaluate the learned rules by comparing them to hand-coded rules. Editor: Rui Camacho