Contextual cues aid recovery from interruption: The role of associative activation.

A series of experiments introduced interruptions to the execution phase of simple Tower of London problems and found that the opportunity for preparation before the break in task reduced the time cost at resumption. Retrieval of the suspended goal was facilitated when participants were given the opportunity to encode retrieval cues during an "interruption lag" (the brief time before engaging in the interrupting task) but was impeded when these visual cues were subsequently altered following interruption. The results provide useful support for the goal-activation model (E. M. Altmann & G. J. Trafton, 2002), which assumes that context--at the points of both goal suspension and goal retrieval--is critical to efficient interruption recovery. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Edible oil evaluation by room odor tests: A preliminary report

Room odors developed on heating edible fats in open vessels were evaluated and characterized by a 20 member odor panel. Edible fats tested were: special soybean salad and cooking oils, hydrogenated soybean oil and some commercial salad and cooking oils. Factors were investigated that affect reliability and reproducibility of the test and the acuity of the panel members. The effects of fry temperature and size of sample were investigated. The method has been applied to a study of hydrogenated and unhydrogenated soybean oil samples. Presented at the AOCS Meeting, Chicago, September 1970. No. Market. Nutr. Res. Div., ARS, USDA.     

Long term storage of soybean and cottonseed salad oils

Commercially prepared and packaged soybean and cottonseed salad oils from several different processors were evaluated periodically during storage for 12 months. Partially hydrogenated and winterized soybean oils, as well as unhydrogenated soybean salad oils, were stored in bottles and cans at 78 and 100 F. Control samples of all oils were held at 0 F during the entire test. Some lots in bottles and cans were packaged under nitrogen to improve storage stability. Agreement was good between organoleptic and oxidative evaluation of aged oils. After 26 weeks of storage at 100 F, the flavor of partially hydrogenated-winterized oils packaged under nitrogen showed a minimum loss. These same oils did not exhibit much, if any, reduction in their oxidative stability as indicated by storage peroxide values (active oxygen method). Soybean oil not protected with nitrogen demonstrated progressive flavor deterioration at 100 F. After 10 weeks of storage, the deterioration became marked and the flavor score was below 5. From limited observations, bottled oils appear to have a better stability than oils packaged in screw-cap tin cans. Hydrogenated oils packaged under nitrogen in cans had good oxidative stability, but some lowering of the flavor score was observed. Nonhydrogenated soybean oils packaged in tin cans not under nitrogen exhibited the most rapid flavor deterioration of all lots of oil investigated. Presented in part at the AOCS meeting, New York, October 1968 ARS, USDA     

Dual Verbal Elicitation: The Complementary Use of Concurrent and Retrospective Reporting Within a Usability Test

Verbal protocols are the primary tool for understanding users' task-solving behaviors during usability testing. A qualitative study that examined the utility of combining a concurrent and retrospective think-aloud within the same usability test is described. The results indicate that although there was significant overlap between the types of utterances produced during each think-aloud, the retrospective phase produced more verbalizations that were relevant to usability analysis, for example, helpful self-assessments of performance, yielding insights into the impact of encountered difficulties. However, a small number of less desirable utterance types emerged: hypothesising, rationalizing, and forgetting. When used together, both methods contributed to an understanding of usability issues; the concurrent phase yielded more usability issues overall, and the retrospective data improved the understanding of these by (a) reinforcement: users highlighted the impact of an issue on their experience, (b) elaboration: users would provide causal explanations of encountered difficulties, and (c) context: users provided information about the product's context of use.     

Protein profiling of low-density lipoprotein from obese subjects

Although obesity and high levels of low-density lipoprotein (LDL) are well-known risk factors for cardiovascular disease, the precise role(s) of different LDL constituents in obesity has not been explored. In the present study, we compared the LDL proteome of healthy control adults (body mass index<25) and obese subjects (body mass index>30). LDL was isolated by density-gradient ultracentrifugation and proteins were separated with 2-D PAGE, quantified, and identified by peptide mass fingerprinting using MALDI-TOF MS. A new LDL-associated protein was identified as transthyretin and found to be significantly more abundant in LDL from the obese subjects. In addition, LDL from the obese subjects contained relatively more α(1) -antitrypsin, apo J, apo C-II, than LDL from controls, and also more of an acidic isoform (pI/Mr; 5.2/23 100) of apo A-I. On the other hand, the relative amounts of apo A-IV and the major isoform of apo A-I (pI/Mr; 5.3/23 100) were significantly less in LDL from the obese subjects. Apo E was less and non-sialylated apo C-III more abundant in LDL from obese men than control men, while there were no such differences between LDL from obese and control women. These findings illustrate that obesity is not only associated with increased LDL-cholesterol levels but also with alterations in the LDL protein composition. The presence of transthyretin in LDL from obese subjects may reflect over-nutrition and affect the lipid metabolism in obesity.     

Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy

Computational Economics - Arbitrage opportunity exploration is important to ensure the profitability of statistical arbitrage. Prior studies that concentrate on cointegration model and other...     

Feature Extraction Using Laplacian Maximum Margin Criterion

Feature extraction by Maximum Margin Criterion (MMC) can more efficiently calculate the discriminant vectors than LDA, by avoiding calculation of the inverse within-class scatter matrix. But MMC ignores the local structures of samples. In this paper, we develop a novel criterion to address this issue, namely Laplacian Maximum Margin Criterion (Laplacian MMC). We define the total Laplacian matrix, within-class Laplacian matrix and between-class Laplacian matrix by using the similar weight of samples to capture the scatter information. Laplacian MMC based feature extraction gets the discriminant vectors by maximizing the difference between between-class laplacian matrix and within-class laplacian matrix. Experiments on FERET and AR face databases show that Laplacian MMC works well.     

Effects of attitudes and behaviours on learning mathematics with computer tools

This mixed-methods study investigates the effects of student attitudes and behaviours on the outcomes of learning mathematics with computer tools. A computer tool was used to help students develop the mathematical concept of function. In the whole sample (N = 521), student attitudes could account for a 3.4 point difference in test scores between individuals on a 10-point scale. General attitude towards mathematics positively predicted test scores. However, more able students who were well-disposed towards mathematical computer tools achieved lower scores. Self-reported behaviours were unrelated to test scores. Detailed observation of a small number of students (N = 8) revealed that positive attitudes towards mathematics and mathematical computer tools augmented exhibited learning behaviours, and that both a positive attitude to mathematical computer tools and exhibited learning behaviours benefited tool mastery. Although tool mastery and test scores are intimately related, reflective processes appear to mediate this relationship. Promoting learning with mathematical computer tools needs to take several factors into account, including improving student attitudes, raising levels of learning behaviours, and giving sufficient opportunity for constructing new mathematical knowledge within meaningful mathematical discourse.     

SedLog: A shareware program for drawing graphic logs and log data manipulation

SedLog is a free multi-platform software package for creating graphic sediment logs providing an intuitive graphical user interface. The graphic sediment logs generated by SedLog can be exported as PDF, Scalable Vector Graphics (SVG), or JPEG for use by other drawing applications or for publications. Log data can be imported and exported in Comma Separated Values (CSV) format. The logs can also be printed to any paper size the user wants. Zoom In, Zoom Out, Fit page, Fit Height and Fit Width facilities are also provided to enable the user to customise the workspace size.     

Creating corroborated crisis reports from social media data through formal concept analysis

During a crisis citizens reach for their smart phones to report, comment and explore information surrounding the crisis. These actions often involve social media and this data forms a large repository of real-time, crisis related information. Law enforcement agencies and other first responders see this information as having untapped potential. That is, it has the capacity extend their situational awareness beyond the scope of a usual command and control centre. Despite this potential, the sheer volume, the speed at which it arrives, and unstructured nature of social media means that making sense of this data is not a trivial task and one that is not yet satisfactorily solved; both in crisis management and beyond. Therefore we propose a multi-stage process to extract meaning from this data that will provide relevant and near real-time information to command and control to assist in decision support. This process begins with the capture of real-time social media data, the development of specific LEA and crisis focused taxonomies for categorisation and entity extraction, the application of formal concept analysis for aggregation and corroboration and the presentation of this data via map-based and other visualisations. We demonstrate that this novel use of formal concept analysis in combination with context-based entity extraction has the potential to inform law enforcement and/or humanitarian responders about on-going crisis events using social media data in the context of the 2015 Nepal earthquake.