On quotients of holomorphic functions in the disc with boundary regularity conditions

In this paper we give characterizations of those holomorphic functions in the unit disc in the complex plane that can be written as a quotient of functions in A(D), A°°(D) or Al(D) with a nonvanishing denominator in D. As a consequence we prove that if f E Al (D) does not vanish in D, then there exists g E Al (D) which has the same zero set as f in D and such that fg E A(D)

Division and extension in weighted Bergman-Sobolev spaces

Let D be a bounded strictly pseudoconvex domain of Cn with C 8 boundary and Y = {z; u1(z) = ... = ul(z) = 0} a holomorphic submanifold in the neighbourhood of D', of codimension l and transversal to the boundary of D. In this work we give a decomposition formula f = u1f1 + ... + ulfl for functions f of the Bergman-Sobolev space vanishing on M = Y n D. Also we give necessary and sufficient conditions on a set of holomorphic functions {fa}|a|=m on M, so that there exists a holomorphic function in the Bergman-Sobolev space such that Daf |M = fa for all |a| = m

Computational Design in Mechanical Engineering Applications via CFD: Uncertainty Quantification and Optimisation

Engineering practice is nowadays inconceivable without the presence of computational tools. Within this context, Computational Fluid Dynamics (CFD) are an essential tool for fluid-based machine design, such as heat exchangers, turbines, cooling processes or aerodynamic performance of vehicles. Among the simulation capabilities of modern softwares, Reynolds-Averaged Navier Stokes (RANS) simulations are the most popular industrial approach, due to the decent computation elapsed times and accuracy for a vast range of applications. However, some engineering applications that simulate complex flows may exhibit certain discrepancies as a consequence of neglected sources of uncertainty. The effect of uncertainty can be even increased when the effect of different sources of inaccuracy are combined in the simulation. Once a reliable computational model is achieved, further designs can be explored. One advantage of CFD is that prototyping costs can be reduced by performing optimisation via simulation. This allows to obtain a large number of data at lower cost than experimental testing. Thus, such data can be further used to train Machine Learning algorithms that may improve or speed up the optimisation process. In this presentation, the aforesaid concepts will be shown. Different examples of uncertainty propagation in CFD simulations of engineering applications will be illustrated. Finally, a successful case of Machine Learning aided optimisation of a mechanical micro heat exchanger/mixer will be presented. This research is supported by the UMA18-FEDERJA-184 funding.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech Investigación subvencionada por: contrato UMA18-FEDERJA-184 y Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI - Junta de Andalucía

Relational cohesion between users and smart voice assistants

Purpose This study aims to examine users’ affective relationships with smart voice assistants (SVAs) and aims to analyze how these relationships explain user engagement behaviors toward the brands of SVAs. Drawing on relational cohesion theory, it proposes that cohesion between users and SVAs influences brand engagement behaviors, that is, continuing purchasing other products of the brand, providing knowledge to the brand and referring the brand. Design/methodology/approach Data from a survey of 717 US regular SVA users confirm the validity of the measurement scales and provide the input for the covariance-based structural equation modeling. Findings The results demonstrate that frequent user-SVA interactions evoke positive emotions, which encourage cohesive relationships. Pleasured-satisfaction and interest emerge as strong emotions. Moreover, relational cohesion between users and SVAs promotes engagement with the brand of the assistant. Originality/value This paper applies an interpersonal approach in a context that, to date, has been examined from a predominantly technological perspective. It shows that users develop positive emotions toward smart technologies through their interactions, and establishes the importance of building affective relationships. To the best of the authors’ knowledge, this is the first study to analyze cohesion between users and smart technologies and to examine the effect of this cohesion on user engagement with the brand

Dimethylformamide Impurities as Propylene Polymerization Inhibito

This research study examined how the use of dimethylformamide (DMF) as an inhibitor af fects the propylene polymerization process when using a Ziegler–Natta catalyst. Several experiments were carried out using TiCl4/MgCl2 as a catalyst, aluminum trialkyl as a cocatalyst, and different amounts of DMF. Then, we analyzed how DMF influences other aspects of the process, such as catalyst activity, molecular weight, and the number of branches in the polymer chains obtained, using experimental and computational methods. The results revealed that as the DMF/Ti ratio increases, the catalyst activity decreases. From a concentration of 5.11 ppm of DMF, a decrease in catalyst activity was observed, ranging from 45 TM/Kg to 44 TM/Kg. When the DMF concentration was increased to 40.23 ppm, the catalyst activity decreased to 43 TM/Kg, and with 75.32 ppm, it dropped even further to 39 TM/Kg. The highest concentration of DMF evaluated, 89.92 ppm, resulted in a catalyst productivity of 36.5 TM/Kg and lost productivity of 22%. In addition, significant changes in the polymer’s melt flow index (MFI) were noted as the DMF concentration increased. When 89.92 ppm of DMF was added, the MFI loss was 75%, indicating a higher flowability of the poly mer. In this study, it was found that dimethylformamide (DMF) exhibits a strong affinity for the titanium center of a Ziegler–Natta (ZN) catalyst, with an adsorption energy (Ead) of approximately −46.157 kcal/mol, indicating a robust interaction. This affinity is significantly higher compared to propylene, which has an Ead of approximately −5.2 kcal/mol. The study also revealed that the energy gap between the highest occupied molecular orbital (HOMO) of DMF and the lowest unoccupied molecular orbital (SOMO) of the Ziegler–Natta (ZN) catalyst is energetically favorable, with a value of approximately 0.311 eV.Universidad Tecnológica de Bolivar, Universidad de Cartagena, Universidad de la Cost