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In Qassimi Arabic, the emphatic segments /tˤ, sˤ, ðˤ, rˤ/ spread their emphasis feature to the neighboring vowels turning the front feature of the vowels /ɪ,æ,æː/ into back. However, the front vowels /iː,eː/ are not affected by this process and consequently maintain their front feature in the adjacency of the emphatic segments. In this work, we provide a theoretical analysis of Emphasis Spread in the dialect of Qassimi Arabic within the underspecification theory. It has been concluded that the vowels /ɪ,æ,æː/ are underlying underspecified for the back feature, whereas the vowels /iː,eː/ are underlyingly specified for the back feature. The emphatic segments spread their secondary feature [Dorso-Pharyngeal] to the adjacent underspecified vowels and thus make them back. However, they fail to spread their secondary feature to the vowels /iː,eː/ because they have an underlying back feature. Therefore, unlike many other phonological theories, the underspecification theory can provide a more straightforward and precise analysis of Emphasis Spread. As a result, that would allow us to account for the various effects of emphatics examined in the different Arabic dialects, on the one hand, or, more broadly, any similar assimilation process in other languages, on the other.

Adaptive Façades (AFs) have proven to be effective as a building envelope that can enhance energy efficiency and thermal comfort. However, evaluating the performance of these AFs using the current building performance simulation (BPS) tools is complex, time-consuming, and computationally intensive. These limitations can be overcome by using a machine learning (ML) model as a method to assess the AF system efficiently during the early design stage. This study presents an alternative approach using an Artificial Neural Network (ANN) model that can predict the hourly cooling loads of AF in significantly less time compared to BPS. To construct the model, a generative parametric simulation of office tower spaces with an AF shading system were simulated in terms of energy consumption using Honeybee add-on in Grasshopper which are linked to EnergyPlus for training the ANN model. The prediction results showed a highly accurate model that can estimate cooling loads within seconds.

Arab and non-Arab English as a foreign language (EFL) students continue to have difficulty pronouncing English vowels accurately. To examine this, our study analyzes how male and female Saudi EFL students pronounce English monophthongs when compared to native speakers assessed in previous research. Gender-related variations between male and female Arab English speakers are also explored. Formant frequencies (F1 and F2) are employed to evaluate vowel quality, with vowel duration measured to investigate vowel length. Learners’ pronunciations of English words containing vowels of interest are used to collect data. Five male and five female EFL learners produced English monophthongs in the /hVd/ context. We then compare the results with previous data on native English speakers and conduct acoustic analysis. Regarding duration, male non-native English speakers’ data are compared with previous results for male native speakers, revealing that the vowels of Saudi learners are shorter than those of native English speakers, and those of non-native men are longer than those of non-native women. Moreover, the low vowels produced by Saudi and native men are longer than their non-low vowels. Regarding vowel quality, men produce lower vowels than native speakers. Women, however, produce lower and more front vowels than native women. Statistically, this study reveals significant differences between male and female Saudi EFL learners in producing English vowels. Saudi men’s vowel space is more centralized than Saudi women’s space. Both men and women overlap low vowels. Saudi learners’ mispronunciations of English vowels indicate that L1 interference is not the only cause of mispronunciations.