Individual differences among young children in Logo environments

Computers are becoming an integral part of the classroom and home environments of children. Yet, there are unanswered questions concerning their effects on young children's learning. Conceptualized within the framework of Piagetian theory and the educational philosophy provided by Papert, this study is a longitudinal investigation of the qualitative changes shown by fourteen preschool children in the development of programming knowledge about Logo over a period of 8 months. Based on their performance, children were grouped into three groups: (1) those who are sophisticated Logo learners; (2) those who function well in Logo environments under support and continuous direction from the Logo teacher; and (3) children who do not function well in Logo environments. In the second part of the study, an investigation of individual difference variables such as field dependent-independent cognitive styles, mathematical ability, creativity, computer and computer-related experiences within the home and outside the home, indicated that only the variable computer-related experiences within the home and outside the home was important in differentiating between the three groups. The findings of the study do not support the claims made by Papert about learning in Logo environments and suggest that Logo learning environments for preschool children be designed by taking into consideration developmental levels of children and individual differences among children. Furthermore, a new set of concepts and paradigms for studying learning in Logo environments needs to be formulated.     

Lexical effects on the phonetic categorization of speech: The role of acoustic structure.

This study examines the extent to which acoustic parameters contribute to lexical effects on the phonetic categorization of speech. Experiment 1 was designed to replicate previous findings. Two test continua were created varying in voice onset time. Results of both identification and reaction time (RT) range data showed an effect of lexical status at the phonetic boundary, but only in the slowest RT ranges, suggesting that lexical effects on phonetic categorization are postperceptual. Experiment 2 explored whether the lexical effect would emerge when the stimulus continua more nearly approximated the parameter values of natural speech. Both identification and RT range data indicated that the lexical effect disappeared. These results suggest that without attention to the acoustic structure of the stimuli, the role of top–down processing in phonetic categorization may be overemphasized. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The composition of red cell membrane phospholipids in Canadian Inuit consuming a diet high in marine mammals

A study of the fatty acid composition of red cell phosphatidylcholine and phosphatidylethanolamine and serum cholesterol was undertaken in 185 Canadian Inuit (age 2 months-82 years). Samples from 24 Canadian men and women (21–50 years) living in Vancouver were also analyzed as a reference for the Inuit in this age range. Dietary survey of the Inuit community (325 Inuit) demonstrated a diet based on traditional foods in which the principal source of n−3 fatty acid was marine mammal flesh (mean intake: 164 g/person/day) rather than fish (mean intake: 13 g/person/day). Compared to the Vancouver samples, the Inuit phosphatidylethanolamine had higher 20∶5n−3 and 22∶6n−3 and lower 20∶4n−6, but similar 18∶2n−6 levels. The level of 20∶5n−3 was higher and 20∶4n−6 was lower in the Inuit than in the Vancouver red cell phosphatidylcholine. Despite these differences in percentage content of C20 and C22 n−6 and n−3 fatty acids, the mean chain length and unsaturation index of the Inuit and Vancouver red cell phosphatidylcholine and phosphatidylethanolamine were very similar. Serum cholesterol concentration showed no sex difference within the Inuit, and no difference from Vancouver men and women of similar age. The analyses suggest that the fatty acid composition of the Inuit red cell phospholipids are primarily a reflection of their diet-fat composition.     

SECOL: a semantic environment based on social media to support organisational learning

Organisational learning (OL) helps companies to significantly improve their processes through the reuse of experiences, making knowledge accessible to the whole organisation. However, establishing learning in software development companies is not a trivial task, since it is an area in which processes and knowledge are usually hidden inside the employees’ mind. Generally, employees prefer to look for knowledge via Internet search engines rather than using the knowledge produced inside the company. Hence, we explored how better organising content produced within the company may minimise this problem. We investigated how a semantic collaborative environment, titled semantic collaborative environment for organisational learning (SECOL), based on social software, learning objects (LOs), and units of learning (UL) may assist to improve OL for software development companies. We defined an approach to generate LOs and UL from social software’s content used by companies. The environment was implemented based on ontologies in order to represent and organise acquired knowledge. Furthermore, an experiment was conducted using qualitative data analysis. The results indicated that the use of the environment is appropriate to improve OL in software development teams and the use of SECOL is efficient, particularly in order to acquire new knowledge, assisting the promotion of the use of organisational patterns and minimising repeated solutions.     

Evaluating MT for massive open online courses

This article reports a multifaceted comparison between statistical and neural machine translation (MT) systems that were developed for translation of data from massive open online courses (MOOCs). The study uses four language pairs: English to German, Greek, Portuguese, and Russian. Translation quality is evaluated using automatic metrics and human evaluation, carried out by professional translators. Results show that neural MT is preferred in side-by-side ranking, and is found to contain fewer overall errors. Results are less clear-cut for some error categories, and for temporal and technical post-editing effort. In addition, results are reported based on sentence length, showing advantages and disadvantages depending on the particular language pair and MT paradigm.     

Effect of moisture‐ingress on adhesion energy in a metal oxide‐polymer system

This work quantifies the damage caused by moisture in a metal coating system under extreme weathering conditions, using Variable Radius Roll Adhesion Test (VaRRAT). Interfacial toughness (adhesion energy) between the metal oxide and the polymer in painted steel panels, studied by using VaRRAT, is observed to fall with increasing temperature and time of exposure to moisture. Possible cause for irreversible loss in adhesion energy in the paint system is attributed to the sorption of free water at the metal oxide–polymer interface. Different failure responses were observed in two different paint–metal systems. Adsorption or diffusion in the Henry's mode is rate controlling in green paints as indicated by the low activation energy of 12 kJ mol^?1. The white samples showed a high activation energy of 30 kJ mol^?1, indicating a mixed process of diffusion as well as chemical to be rate determining. Different paint/binder ratios are responsible for the different responses of these samples. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci, 2006     

Some volatile constituents of female dog urine

The volatile compounds from female beagle urine, across the state of estrus, were examined by headspace gas chromatography and mass spectrometry. The major constituents identified were methyl propyl sulfide, methyl butyl sulfide, and acetone. Nine minor constituents, including trimethyl amine and five disulfides, were identified. Two of the unidentified minor constituents may possibly be associated with the state of estrus.     

Specifying and computing preferred plans

In this paper, we address the problem of specifying and computing preferred plans using rich, qualitative, user preferences. We propose a logical language for specifying preferences over the evolution of states and actions associated with a plan. We provide a semantics for our first-order preference language in the situation calculus, and prove that progression of our preference formulae preserves this semantics. This leads to the development of PPlan, a bounded best-first search planner that computes preferred plans. Our preference language is amenable to integration with many existing planners, and beyond planning, can be used to support a diversity of dynamical reasoning tasks that employ preferences.     

John McCarthy's legacy

This special issue is dedicated to John McCarthy, founding father of Artificial Intelligence. It contains a collection of recent contributions to the field of knowledge representation and reasoning, a field that McCarthy founded and that has been a main focus of his research during the last half century. In this introductory article, we survey some of McCarthy's major contributions to the field of knowledge representation and reasoning, and situate the papers in this special issue in the context of McCarthy's previous work.