Evaluating the design of flexible manufacturing systems

Evaluating the design of flexible manufacturing systems is complex. Developing a measure of performance useful for evaluating alternate designs continues to be interesting. Here, total productivity of the system is proposed as an appropriate measure. Specification of parameters based upon strategic considerations for this measure are discussed. Finally, the usefulness of the measure is demonstrated through an example.     

Moral reasoning and moral concerns: An alternative to Gilligan's gender based hypothesis.

Studied the differences in moral orientation in terms of gender specific modes of reasoning, in 2 experiments. In Exp 1, 32 Ss (aged 24–55 yrs) were read the "Heinz dilemma," and asked 3 questions regarding it. Deliberative and justificatory strategies were identified on the basis of the presence or absence of the higher level knowledge structures. In Exp 2, 40 undergraduates were presented with "Michael and Kohlberg Heinz dilemma," followed by a set of relevant questions. Male Ss preferred to apply a norm or rule in their solutions, while females rejected the application of a norm and sought alternative solutions. This was replicated in Exp 2, but the pattern was reversed with female Ss preferring to apply a norm. Males were divided in their use of either strategy indicating that although, genders differed in their judgments as to which norms or rules to apply; once adopted, norms and rules were used in similar ways. (French abstract) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Randomization in individual choice behavior.

There is ample evidence that people cannot generate random series when instructed to do so. Rather, they produce sequences with too few symmetries and long runs and too many alternations among events. The authors propose a psychological theory to account for these findings, which assumes that subjects generate nonrandom sequences that locally represent theoretical random series subject to a constraint on their short-term memory. Closed-form expressions are then derived for the major statistics that have been used to test for deviations from randomness. Results from 3 experiments with 2 and 3 equiprobable alternatives support the model on both the individual and group levels. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Fooling anti-virus systems

Anti-virus (AV) products and other fingerprint-based systems are typical of the applications used to enforce security. AV products are probably the first step that most organizations take to protect their systems and enhance security. Many small to medium organizations rely almost entirely on AV products.     

A Maximum Entropy Approach for Collaborative Filtering

Collaborative filtering (CF) involves predicting the preferences of a user for a set of items given partial knowledge of the user's preferences for other items, while leveraging a database of profiles for other users. CF has applications e.g. in predicting Web sites a person will visit and in recommending products. Fundamentally, CF is a pattern recognition task, but a formidable one, often involving a huge feature space, a large data set, and many missing features. Even more daunting is the fact that a CF inference engine must be capable of predicting any (user-selected) items, given any available set of partial knowledge on the user's other preferences. In other words, the model must be designed to solve any of a huge (combinatoric) set of possible inference tasks. CF techniques include memory-based, classification-based, and statistical modelling approaches. Among these, modelling approaches scale best with large data sets and are the most adept at handling missing features. The disadvantage of these methods lies in the statistical assumptions (e.g. feature independence), which may be unjustified. To address this shortcoming we propose a new model-based CF method, based on the maximum entropy principle. For the MS Web application, the new method is demonstrated to outperform a number of CF approaches, including naive Bayes and latent variable (cluster) models, support vector machines (SVMs), and the (Pearson) correlation method.     

Butyltin Species and Inorganic Tin in Water and Sediment of the Detroit and St. Clair Rivers

Water and sediment samples from 29 locations in the Detroit and St. Clair rivers were analyzed for the highly toxic tri-n-butyltin (Bu₃Sn⁺) species and for the less toxic di-n-butyltin (Bu₂Sn²⁺) and n-butyltin (BuSn³⁺) species and inorganic tin. In general, locations sampled in the St. Clair River were less contaminated with butyltin species than those in the Detroit River. Inorganic tin and BuSn³⁺ were detected in over 90% of all subsurface water samples, while Bu₂Sn²⁺ and Bu₃Sn⁺ were detected in 45 and 28% of the same samples, respectively. The highest concentration ofBu₃Sn⁺ in subsurface water, 5.9 × 10^?10 mol Sn/L, was at the mouth of the Ecorse River, a tributary of the Detroit River. The three butyltin species and inorganic tin were also detected in 23–46% of all sediment samples. The highest concentrations of Bu₃Sn⁺ in sediment were found close to the mouths of the River Rouge, another tributary of the Detroit River, and the Ecorse River, and were 6.2 × I0^–7 and 1.7 × I0^–7 mol Sn/kg dry weight, respectively, for the top 2 cm of sediment.