Spaceborne memory organization Interim report

Associative memory applications in unmanned space vehicle

Social Determinants of Smoke Exposure During Pregnancy: Findings From Waves 1 & 2 of the Population Assessment of Tobacco and Health (PATH) Study

Maternal smoking during pregnancy (MSDP) and secondhand smoke (SHS) exposure are associated with a myriad of negative health effects for both mother and child. However, less is known regarding social determinants for SHS exposure, which may differ from those of maternal smoking during pregnancy (MSDP). To identify social determinants for SHS exposure only, MSDP only, and MSDP and SHS exposure, data were obtained from all pregnant women (18–54 years; N = 726) in waves 1 and 2 of the Population Assessment of Tobacco and Health Study (2014–2015). Multiple logistic regressions were conducted using SAS 9.4. Smoke exposure during pregnancy was common; 23.0% reported SHS exposure only, 6.1% reported MSDP only, and 11.8% reported both SHS exposure and MSDP. Results demonstrate that relationships between smoke exposure during pregnancy and social determinants vary by type of exposure. Women at risk for any smoke exposure during pregnancy include those who are unmarried and allow the use of combustible tobacco products within the home. Those who are at higher risk for SHS exposure include those who are younger in age, and those who are earlier in their pregnancy. Those who are at higher risk for maternal smoking include those with fair/poor mental health status and those who believe that others\u27 view tobacco use more positively. These results suggest the need for implementing more comprehensive policies that promote smoke-free environments. Implementing these strategies have the potential to improve maternal and fetal health outcomes associated with tobacco smoke exposure

Predicting Corporate Bankruptcy: A Re-examination of the Altman's Z Score models in the service industry.

The Altman models have been widely cited in both trade publications, finance and accounting textbooks. The models are seen as providing the basis for predicting financial distress of a company, a tool to assist in credit evaluation, an internal control guideline for management and an aid in portfolio selection for investors. Edward I Altman is internationally renowned as an expert on corporate bankruptcy and credit risk analysis. He is one of the main researchers that formed the foundation for other academics in the field of Corporate Bankruptcy Prediction. Altman came up with his first model in 1968 which was called 5 Variable Z Score model which was used for the manufacturing industry in the USA. After a few years he came up with another model in 1983 which was called the 4 Variable Z score Model. The reason behind the 4 variable model was to apply it to the private firm industry. Both models came up with successful Bankruptcy prediction results with an accuracy of up to 95% for both models at a year prior to bankruptcy and around 70% prediction accuracy 5 years prior to Bankruptcy. The aim of this research is to re-examine the Altman models to find out if they can still be used in recent periods, to confirm if they can be used within the Service industry rather than the Manufacturing or Private firm industry and to find out which of the model performs better at predicting bankruptcy in the Service Industry. The author finds that both models can predict a year prior to Bankruptcy in the Service Industry at an accuracy rate of 82.3% for the 5 Variable Model and 81.5% for the 4 Variable model. Both models decreased to around 70% accurate prediction 5 years prior to Bankruptcy which is quite consistent with the literature. This means that after applying Altman’s models to the service industry, both have Underperformed Altman’s original results. This is discussed further in the study and possible reasons given for the lower accuracy rate. These results confirm that Altman’s models can be used in recent periods and it can be applied to the service industry. In terms of performance of the different models in the Service Industry, the results indicate that it depends on which year the prediction is being used for. If it is a Year prior or 5 years prior, the 5 Variable model is recommended and if it is 2, 3 or 4 years prior, the 4 Variable model is recommended. The study concludes by proposing some recommendations for further research which can further explain the models underperformance in the service industry and add more insight to the current literature on Corporate Bankruptcy prediction

Spaceborne memory organization, phase 1 Final report

Application of associative memories to data processing for future space vehicle

Computational modelling of double focus in American English

This study investigated how double focus in English statements and questions can be computationally modelled. PENTAtrainer2 was used to learn syllable-sized multi-functional targets from a corpus of 1960 English utterances, with controlled variations in lexical stress, focus, modality, and sentence length. The results showed that the learned targets could generate F0 contours close to the original. In particular, the asymmetry in the interaction between focus and modality was effectively simulated

A creative exchange” for enterprise and employability -

In the Artificial Bee Colony (ABC) algorithm, the employed bee and the onlooker bee phase involve updating the candidate solutions by changing a value in one dimension, dubbed one-dimension update process. For some problems which the number of dimensions is very high, the one-dimension update process can cause the solution quality and convergence speed drop. This paper proposes a new algorithm, using reinforcement learning for solution updating in ABC algorithm, called R-ABC. After updating a solution by an employed bee, the new solution results in positive or negative reinforcement applied to the solution dimensions in the onlooker bee phase. Positive reinforcement is given when the candidate solution from the employed bee phase provides a better fitness value. The more often a dimension provides a better fitness value when changed, the higher the value of update becomes in the onlooker bee phase. Conversely, negative reinforcement is given when the candidate solution does not provide a better fitness value. The performance of the proposed algorithm is assessed on eight basic numerical benchmark functions in four categories with 100, 500, 700, and 900 dimensions, seven CEC2005's shifted functions with 100, 500, 700, and 900 dimensions, and six CEC2014's hybrid functions with 100 dimensions. The results show that the proposed algorithm provides solutions which are significantly better than all other algorithms for all tested dimensions on basic benchmark functions. The number of solutions provided by the R-ABC algorithm which are significantly better than those of other algorithms increases when the number of dimensions increases on the CEC2005's shifted functions. The R-ABC algorithm is at least comparable to the state-of-the-art ABC variants on the CEC2014's hybrid functions

Acoustic Cues to Perceived Prominence Levels:Evidence from German Spontaneous Speech

The iambic-trochaic law (ITL) states that a louder sound signals the beginning of a group, while a longer sound signals its end. Although the ITL has been empirically supported in experiments with a variety of stimuli, it is not clear whether it is due to universal cognitive mechanisms or the outcome of language-specific prosodic properties. We tested the law with speakers of English, Greek and Korean who heard sequences of tones varied in duration and/or intensity. The results revealed neither significant differences among languages nor a strong bias shared by speakers of all languages. Significantly, listeners� grouping preferences were influenced by the duration of the inter-stimulus interval (ISI), with longer ISI resulting in stronger trochaic preferences, indicating that specific experimental conditions may be responsible for differences in listener responses across experiments testing the ITL