A framework to integrate software behavior into dynamic probabilistic risk assessment

Software plays an increasingly important role in modern safety-critical systems. Although, research has been done to integrate software into the classical probabilistic risk assessment (PRA) framework, current PRA practice overwhelmingly neglects the contribution of software to system risk. Dynamic probabilistic risk assessment (DPRA) is considered to be the next generation of PRA techniques. DPRA is a set of methods and techniques in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. The fact remains, however, that modeling software for use in the DPRA framework is also quite complex and very little has been done to address the question directly and comprehensively. This paper develops a methodology to integrate software contributions in the DPRA environment. The framework includes a software representation, and an approach to incorporate the software representation into the DPRA environment SimPRA. The software representation is based on multi-level objects and the paper also proposes a framework to simulate the multi-level objects in the simulation-based DPRA environment. This is a new methodology to address the state explosion problem in the DPRA environment. This study is the first systematic effort to integrate software risk contributions into DPRA environments.     

Review: Modified Atmosphere Packaging and its effects on the microbiological quality and safety of produce

Modified Atmosphere Packaging and related technologies are increasingly used to extend shelf-life of fresh produce. This paper reviews the effect of such technology on the spoilage microbiological flora and food-borne pathogens which may be present in produce and also on the organoleptic properties of the product.     

The Simon effect occurs relative to the direction of an attention shift.

We investigated whether the Simon effect depends on the orienting of attention. In Experiment 1, participants were required to execute left-right discriminative responses to 2 patterns that were presented to the left or right of fixation. The 2 patterns were similar, and the discrimination was difficult. A letter at fixation signaled whether the current trial was a catch trial. The results showed a reversal of the Simon effect. That is, spatially noncorresponding responses were faster than spatially corresponding responses. In Experiment 2, the discrimination of the relevant stimulus attribute was easy. In Experiment 3, the discrimination of the relevant stimulus attribute was difficult, but the stimulus exposure time was long. In either experiment, the regular Simon effect was reinstated. In Experiment 4, the letter that signaled a catch trial appeared to the left or right of the imperative stimulus. The Simon effect occurred relative to the position of the letter. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Learning-based dynamic coupling of discrete and continuous trackers

We present a data-driven dynamic coupling between discrete and continuous methods for tracking objects of high dofs, which overcomes the limitations of previous techniques. In our approach, two trackers work in parallel, and the coupling between them is based on the tracking error. We use a model-based continuous method to achieve accurate results and, in cases of failure, we re-initialize the model using our discrete tracker. This method maintains the accuracy of a more tightly coupled system, while increasing its efficiency. At any given frame, our discrete tracker uses the current and several previous frames to search into a database for the best matching solution. For improved robustness, object configuration sequences, rather than single configurations, are stored in the database. We apply our framework to the problem of 3D hand tracking from image sequences and the discrimination between fingerspelling and continuous signs in American Sign Language.     

Analysis of a master-slave architecture for distributed evolutionary computations.

This paper introduces a new mathematical model of the master-slave architecture for distributed evolutionary computations (EC). This model is validated using a concrete implementation based on the Distributed BEAGLE C++ framework. Results show that contrary to (current) popular belief, master-slave architectures are able to scale well over local area networks of workstations using off-the-shelf networking equipment. The main properties of the master-slave are also compared with those of the more mainstream island-model.     

Self-disclosure and liking: A meta-analytic review.

Self-disclosure plays a central role in the development and maintenance of relationships. One way that researchers have explored these processes is by studying the links between self-disclosure and liking. Using meta-analytic procedures, the present work sought to clarify and review this literature by evaluating the evidence for 3 distinct disclosure-liking effects. Significant disclosure-liking relations were found for each effect: (1) People who engage in intimate disclosures tend to be liked more than people who disclose at lower levels, (2) people disclose more to those whom they initially like, and (3) people like others as a result of having disclosed to them. In addition, the relation between disclosure and liking was moderated by a number of variables, including study paradigm, type of disclosure, and gender of the discloser. Taken together, these results suggest that various disclosure-liking effects can be integrated and viewed as operating together within a dynamic interpersonal system. Implications for theory development are discussed, and avenues for future research are suggested. (PsycINFO Database Record (c) 2010 APA, all rights reserved)