Functional expansions of temporal adverbs and discursive connectives

This paper examines the synchronic competition and diachronic substitution of three Latin temporal expressions: tum, tunc (‘at that time’, ‘then’), and later dumque (originally, ‘while-and’), and its Old Italian outcome dunque (‘then’). Besides providing a new path of development and a new etymology for Italian dunque, we describe in detail the steps by which these forms gradually replaced one another and examine the factors at play in their renewal, showing that such forms all display a similar inference-driven functional expansion from propositional to discourse-organizational meanings. However, their subsequent development led to a functional similarity that is only partial, as is often the case in semantic–pragmatic cycles. While discussing the nature of this cycle, we focus on the speaker’s role in this type of change, which in our view can be summarized in the speaker’s cyclical application of recurrent functional principles: phonetic efficiency, analogy, and regularity in semantic change.

Four-Valued Logics BD and DM4: Expansions

The paper discusses functional properties of some four-valued logics which are the expansions of four-valued Belnap’s logic DM4. At first, we consider the logics with two designated values, and then logics defined by matrices having the same underlying algebra, but with a different choice of designated values, i.e. with one designated value. In the preceding literature both approaches were developed independently. Moreover, we present the lattices of the functional expansions of DM4.

Comparative Dynamic Control of SynRM Servodrive Continuously Variable Transmission System Using Blend Amend Recurrent Gegenbauer-Functional-Expansions Neural Network Control and Altered Artificial Bee Colony Optimization

In comparison control performance with more complex and nonlinear control methods, the classical linear controller is poor because of the nonlinear uncertainty action that the continuously variable transmission (CVT) system is operated by the synchronous reluctance motor (SynRM). Owing to good learning skill online, a blend amended recurrent Gegenbauer-functional-expansions neural network (NN) control system was developed to return to the nonlinear uncertainties behavior. The blend amended recurrent Gegenbauer-functional-expansions NN control system can fulfill overseer control, amended recurrent Gegenbauer-functional-expansions NN control with an adaptive dharma, and recompensed control with a reckoned dharma. In addition, according to the Lyapunov stability theorem, the adaptive dharma in the amended recurrent Gegenbauer-functional-expansions NN and the reckoned dharma of the recompensed controller are established. Furthermore, an altered artificial bee colony optimization (ABCO) yields two varied learning rates for two parameters to find two optimal values, which helped improve convergence. Finally, the experimental results with various comparisons are demonstrated to confirm that the proposed control system can result in better control performance.