Non-Euclidian geographic routing in wireless networks

Greedy geographic routing is attractive for large multi-hop wireless networks because of its simple and distributed operation. However, it may easily result in dead ends or hotspots when routing in a network with obstacles (regions without sufficient connectivity to forward messages). In this paper, we propose a distributed routing algorithm that combines greedy geographic routing with two non-Euclidian distance metrics, chosen so as to provide load balanced routing around obstacles and hotspots. The first metric, Local Shortest Path, is used to achieve high probability of progress, while the second metric, Weighted Distance Gain, is used to select a desirable node among those that provide progress. The proposed Load Balanced Local Shortest Path (LBLSP) routing algorithm provides loop freedom, guarantees delivery when a path exists, is able to efficiently route around obstacles, and provides good load balancing.     

Raman microspectroscopy of a working elastohydrodynamic contact

The design and operation of two closely-related pieces of apparatus which allow the acquisition of Raman spectra from lubricant fluids ‘in lubro’ in an elastohydrodynamic contact are reported. Spectroscopic sensitivity was not sufficient to allow a detailed structural interpretation of the spectra under these extreme conditions. However, the systems have been used in a novel way to obtain profiles and maps of the pressure distribution, as experienced by the fluid in the working elastohydrodynamic contact.     

Sample size determination strategies for normal tolerance intervals using historical data

Statistical tolerance intervals are often used during design verification or process validation in diverse applications, such as the manufacturing of medical devices, the construction of nuclear reactors, and the development of protective armor for the military. Like other statistical problems, the determination of a minimum required sample size when using tolerance intervals commonly arises. Under the Faulkenberry-Weeks approach for sample size determination of parametric tolerance intervals, the user must specify two quantities—typically set to rule-of-thumb values—that characterize the desired precision of the tolerance interval. Practical applications of sample size determination for tolerance intervals often have historical data that one expects to closely follow the distribution of the future data to be collected. Moreover, such data are typically required to meet specification limits. We provide a strategy for specifying the precision quantities in the Faulkenberry-Weeks approach that utilizes both historical data and the required specification limits. Our strategy is motivated by a sampling plan problem for the manufacturing of a certain medical device that requires calculation of normal tolerance intervals. Both classical and Bayesian normal tolerance intervals are considered. Additional numerical studies are provided to demonstrate the general applicability of our strategy for setting the precision quantities.     

A memory-based NFA regular expression match engine for signature-based intrusion detection

Signature-based intrusion detection is required to inspect network traffic at wire-speed. Matching packet payloads against patterns specified with regular expression is a computation intensive task. Hence, the design of hardware accelerator to speed up regular expression matching has been an active research area. A systematic approach to detect regular expression is based on finite automaton. The space-time trade-off between deterministic finite automaton (DFA) and non-deterministic finite automaton (NFA) is well-known. DFA can offer constant throughput but it may suffer from the state explosion problem. Hence, implementation of DFA for large pattern sets on embedded device with limited on-chip memory may not be viable. NFA requires linear space but the throughput can be very low. Implementations of NFA with hardwired circuits can overcome the speed deficiency by exploiting the massive parallelism offered by dedicated hardware circuitries, but this approach does not support efficient dynamic updates. In this paper, we shall present a memory-based architecture for the implementation of NFA to speed up regular expression matching for signature-based intrusion detection. The proposed method supports dynamic updates and offers constant throughput so that it can be used to supplement the existing DFA-based methods in handling large pattern sets.     

Critical Success Factors for Stakeholder Management: Construction Practitioners’ Perspectives

Although various factors have been identified as important for accomplishing successful stakeholder management, few studies appear to have undertaken a comparative analysis of practitioners’ views on the relative importance of critical success factors (CSFs) for stakeholder management in construction projects. In an attempt to fill this research gap, a questionnaire survey was conducted in Hong Kong to collect the opinions of construction practitioners regarding the relative importance of CSFs for stakeholder management. Findings from this study show that all 15 selected CSFs are regarded as critical by most respondents for the success of stakeholder management in construction projects. The factor regarding social responsibilities is considered most important for managing stakeholders. Although correlations between CSFs and types of projects and organizations were statistically significant, these were not particularly strong. Also, even though there is a general consensus on the rankings of the CSFs among different respondents, the detailed pairwise comparisons actually show the existence of a few differences in perceptions on the relative importance of the CSFs. Therefore, the working priorities of project managers for managing stakeholders are context specific, depending on the nature, client sector, and cost of the project, and also on their organizations and management levels in the organization. These findings should help project managers become more aware of their responsibilities and the relative importance of issues for management stakeholders.     

Enhancing the diversity of conversational collaborative recommendations: a comparison

In conversational collaborative recommender systems, user feedback influences the recommendations. We report mechanisms for enhancing the diversity of the recommendations made by collaborative recommenders. We focus on techniques for increasing diversity that rely on collaborative data only. In our experiments, we compare different mechanisms and show that, if recommendations are diverse, users find target items in many fewer recommendation cycles.