A new longitudinal diary study of a child (E) learning American English reveals two patterns of segmental neutralization: velar fronting, in which /k/ and /g/ are realized as [t] and [d], and lateral gliding, in which /l/ is realized as [j]. Both phenomena are restricted to prosodically strong positions, affecting only consonants in word-initial position or in the onsets of stressed syllables. An explanation for positional velar fronting that combines phonetic and grammatical considerations is proposed to account for the occurrence of the effect in children but not adults: the greater gestural magnitude of prosodically strong onsets in English interacts with the anatomy of the young child's vocal tract to produce coronalization of prosodically strong velars. E extended the resulting pattern to lateral gliding, which developed later and has similar grammatical conditioning but less direct phonetic motivation.

Henry R. Kahane was born on November 2, 1902, in Berlin, Germany, ‘an Austrian in the German world’ (Kahane 1992b:40). He was raised in the intellectually stimulating world of theatre and literature and it was there that he began his formal academic life with Literaturwissenschaft, gradually moving into Romance linguistics. Henry was drawn to linguistics, ‘attracted by the magnetic personality of Ernst Gamillscheg, a trail-blazing genius, who, up to his death in 1971, was inexhaustible in linguistic themes and explanations and, despite political flings, of an incorruptible professional objectivity’ (ibid.). Gamillscheg was to have a multifold impact on Henry’s life. It was in this professor’s seminar, in 1927, that Henry met Renée, née Toole, of Irish ancestry born in Cephalonia, Ionian Island. It was through Renée’s influence that Henry developed his interest in Mediterranean studies. Two other European scholars who also left an indelible impact on Henry in relating language and history were Max Leopold Wagner (1880-1962) and Gerhard Rohlfs (1892-1986).

Slotted blade technology is a passive flow control strategy that can effectively suppress the boundary layer separation within compressors. To reduce the iteration time of the traditional Design-Experiment-Design method, this study innovatively proposes a fast and universal three-dimensional design method for the slotted blade technology, enabling slot modeling completion within 1 s. Furthermore, combined with machine learning (ML), the mapping relationships between eight design parameters and two key aerodynamic performances – compressor design point efficiency ($\eta$DE) and stator total pressure recovery coefficient at the near-stall point ($\sigma ^{*}_{NS}$) – were pioneeringly established. In this study, the prediction performances of six models were compared: one-dimensional convolutional neural network (1D-CNN), random forest (RF), support vector regression (SVR), Gaussian process regression (GPR), multi-layer perceptron (MLP) and long short-term memory network (LSTM). The results indicate that 1D-CNN achieves the highest prediction accuracy: for the $\eta$DE, the mean absolute error (MAE) and coefficient of determination (R2) are 0.041 and 0.987, respectively; for the $\sigma ^{*}_{NS}$, the MAE and R² are 0.479 × 10−3 and 0.955, respectively. Notably, the computational time of the 1D-CNN model is 99.11% less than that of the computational fluid dynamics (CFD). The Shapley Additive exPlanations (SHAP) method was employed to reveal the effects of design parameters on the compressor aerodynamic performance. Notably, the slot outlet axial position (Zout) exerts the most significant influence on the $\eta$DE, while the slot outlet radial position close to the casing (R1_out) has the strongest impact on the $\sigma ^{*}_{NS}$. This study provides theoretical support and valuable references for the intelligent design of slotted blade technology.