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The aim of this study was to assess the accuracy of Cameriere's methods on dental age estimation in the northern Chinese population. A sample of orthopantomographs of 785 healthy children (397 girls and 388 boys) aged between 5 and 15 years was collected. The seven left permanent mandibular teeth were evaluated with Cameriere's method. The sample was split into a training set to develop a Chinese-specific prediction formula and a test set to validate this novel developed formula. Following the training dataset study, the variables gender (g), x 3 (canine teeth), x 4 (first premolar), x 7 (second molar), N 0, and the first-order interaction between s and N 0 contributed significantly to the fit, yielding the following linear regression formula: Age = 10.202 + 0.826 g - 4.068x 3 - 1.536x 4 - 1.959x 7 + 0.536 N 0 - 0.219 s [Symbol: see text] N 0, where g is a variable, 1 for boys and 0 for girls. The equation explained 91.2 % (R (2) = 0.912) of the total deviance. By analyzing the test dataset, the accuracy of the European formula and Chinese formula was determined by the difference between the estimated dental age (DA) and chronological age (CA). The European formula verified on the collected Chinese children underestimated chronological age with a mean difference of around -0.23 year, while the Chinese formula underestimated the chronological age with a mean difference of -0.04 year. Significant differences in mean differences in years (DA - CA) and absolute difference (AD) between the Chinese-specific prediction formula and Cameriere's European formula were observed. In conclusion, a Chinese-specific prediction formula based on a large Chinese reference sample could ameliorate the age prediction accuracy in the age group of children.

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How do speakers learn the social meaning of different linguistic variants, and what factors influence how likely a particular social-linguistic association is to be learned? It has been argued that the social meaning of more salient variants should be learned faster, and that learners' preexisting experience of a variant will influence its salience. In this paper, we report two artificiallanguage-learning experiments investigating this. Each experiment involved two language-learning stages followed by a test. The first stage introduced the artificial language and trained participants in it, while the second stage added a simple social context using images of cartoon aliens. The first learning stage was intended to establish participants' experience with the artificial language in general and with the distribution of linguistic variants in particular. The second stage, in which linguistic stimuli were accompanied by images of particular aliens, was intended to simulate the acquisition of linguistic variants in a social context. In our first experiment, we manipulated whether a particular linguistic variant, associated with one species of alien in the second learning phase, had been encountered in the first learning phase. In the second experiment, we manipulated whether the variant had been encountered in the same grammatical context. In both cases we predicted that the unexpectedness of a new variant or a new grammatical context for an old variant would increase the variant's salience and facilitate the learning of its social meaning. This is what we found, although in the second experiment, the effect was driven by better learners. Our results suggest that unexpectedness increases the salience of variants and makes their social distribution easier to learn, deepening our understanding of the role of individual language experience in the acquisition of sociolinguistic meaning.

This article presents preliminary results indicating that speakers have a different pitch range when they speak a foreign language compared to the pitch variation that occurs when they speak their native language. To this end, a learner corpus with French and German speakers was analyzed. Results suggest that speakers indeed produce a smaller pitch range in the respective L2. This is true for both groups of native speakers. A possible explanation for this finding is that speakers are less confident in their productions, therefore, they concentrate more on segments and words and subsequently refrain from realizing pitch range more native-like. For language teaching, the results suggest that learners should be trained extensively on the more pronounced use of pitch in the foreign language.

In vehicular ad hoc networks (VANETs), network topology and communication links frequently change due to the high mobility of vehicles. Key challenges include how to shorten transmission delays and increase the stability of transmissions. When establishing routing paths, most research focuses on detecting traffic and selecting roads with higher vehicle densities in order to transmit packets, thus avoiding carry-and-forward scenarios and decreasing transmission delays; however, such approaches may not obtain accurate real-time traffic densities by periodically monitoring each road because vehicle densities change so rapidly. In this paper, we propose a novel routing information system called the machine learning-assisted route selection (MARS) system to estimate necessary information for routing protocols. In MARS, road information is maintained in roadside units with the help of machine learning. We use machine learning to predict the moves of vehicles and then choose some suitable routing paths with better transmission capacity to transmit packets. Further, MARS can help to decide the forwarding direction between two RSUs according to the predicted location of the destination and the estimated transmission delays in both forwarding directions. Our proposed system can provide in-time routing information for VANETs and greatly enhance network performance.

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