We focus on the implications of movement, landscape variables, and spatial heterogeneity for food web dynamics. Movements of nutrients, detritus, prey, and consumers among habitats are ubiquitous in diverse biomes and can strongly influence population, consumer-resource, food web, and community dynamics. Nutrient and detrital subsidies usually increase primary and secondary productivity, both directly and indirectly. Prey subsidies, by movement of either prey or predators, usually enhance predator abundance beyond what local resources can support. Top-down effects occur when spatially subsidized consumers affect local resources by suppressing key resources and occasionally by initiating trophic cascades. Effects on community dynamics vary with the relative amount of input, the trophic roles of the mobile and recipient entities, and the local food web structure. Landscape variables such as the perimeter/area ratio of the focal habitat, permeability of habitat boundaries, and relative productivity of trophically connected habitats affect the degree and importance of spatial subsidization.

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Recommender system research has gained popularity recently because many businesses are willing to pay for a way to predict future user opinions. Such knowledge could simplify decision-making, improve customer satisfaction, and increase sales. We focus on the recommendation accuracy of memory-based collaborative filtering recommender systems and propose a novel input generation algorithm that helps identify a small group of relevant ratings. Any combination algorithm can be used to generate a recommendation from such ratings. We attempt to improve the quality of these ratings through recursive sorting. Finally, we demonstrate the effectiveness of our approach on the Netflix dataset, a popular, large, and extremely sparse collection of movie ratings.

Hierarchical data structures are important for many computing and information science disciplines including data mining, terrain modeling, and image analysis. There are many specialized hierarchical data management systems, but they are not always available. Alternatively, relational databases are far more common and offer superior reliability, scalability, and performance. However, relational databases cannot natively store and manage hierarchical data. Labeling schemes resolve this issue by labeling all nodes with alphanumeric strings that can be safely stored and retrieved from a database. One such scheme uses prime numbers for its labeling purposes, however the performance and space utilization of this method are not optimal. We propose a more efficient and compact version of this approach.

The increasing frequency and malevolence of online security threats require that we consider new approaches to this problem. The existing literature focuses on the Web security problem from the server-side perspective. In contrast, we explore it from the client-side, considering the major types of threats. After a short threat summary, we discuss related research and existing countermeasures. We then examine intuitive human-oriented trust models and posit a flexible, multilayer framework to facilitate automated client-side decision making. The proposed suggestions are not intrusive and do not require advanced technical knowledge from end users.