Conceptual Metaphor of Eliot’s Waste Land Versus Al-Sayyab’s Rain Song

Of the rhetorical tools, metaphor still has insufficient interest, primarily as a crosscultural phenomenon though it is an attractive and vivid area, so it should be studied and highlighted (Suhadi, 2018) and (Barton, 2017). This comparative study investigated the conceptual metaphor in modern Arabic versus English poetry with reference to Al-Sayyab and T. S. Eliot as two poles of modern poetry in Arabic and English. This study tried to shed light on the frequency of the conceptual metaphors in Al-Sayyab’s The Rain Song versus Eliot’s The Waste Land. Besides, it aimed to explore the similarities and differences between the two poems in using the CMT orientational ’Up’ and ’Down’ strategy. However, to accomplish its aims, this study adopted Lakoff and Jonson’s Conceptual Metaphor Theory ’CMT’ (1980); this theory asserted that metaphor is an inborn mental system in which we understand a certain concept in terms of another by drawing a logical mapping between the source domain and the target one. Finally, the study found that modern poetry was wealthy of conceptual metaphors. It also discovered that The Rain Song involved 65.29% conceptual metaphors of its total lines, so it exceeded The Waste Land which comprised only 39.40%. Furthermore, the study revealed that the two poems were generally pessimistic in which the ’Down’ domain exceeded the ’Up’ one in each poem. Also, it detected that Eliot was more pessimistic than Al Sayyab who was more optimistic.     Keywords: conceptual metaphor, orientational metaphor, pessimistic, ’Up and Down’ strateg

Analysis of power losses and Lifetime for the inverter in electric Vehicles using variable voltage Control and variable switching Frequency modified pwm

With the increasing demand for reduced emissions and improved fuel economy, the automakers are focusing on the development of electric vehicles (EVs). The performance requirements for EVs includes high driving range and long life of its components. The power converters are among the most stressed and less reliable EV drivetrain components. Hence, improving the lifetime of the power converters is essential for the success of EV adoption. The lifetime of the power converters can be improved by reducing thermal stress of the power devices, which represents the main cause of failure. Since the temperature and power losses of the power device are proportional, thermal stress can be reduced by minimizing the power losses. In addition to the lifetime improvement, minimizing the power losses of the power converters can extend the EV range since the power demand under a given loading conditions is reduced. In this regard, this thesis aims to study the impact of an existing power loss reduction technique known as variable dc-bus voltage control (VVC) on the inverter lifetime. In addition, it proposes a new pulse width modulation (PWM) strategy called variable switching frequency modified PWM (VSF-MPWM) for three-phase two level voltage source inverter. The VSF-MPWM aims to minimize the inverter power losses, but without sacrificing the output current quality. In order to study the impact of the VVC on the inverter lifetime, a lifetime estimation method is first presented. This method uses the Artemis urban and US06 driving cycles in order to obtain the thermal loading, and consequently the lifetime consumption of the inverter power devices. Then, the VSF-MPWM is proposed, which minimizes the switching loss by clamping any of the three-phase legs at the phase current peak and by reducing the number of commutations through variable switching frequency. However, in order to achieve an acceptable current quality, the proposed VSF-MPWM controls both the clamping period and the switching frequency according to the current quality constraints of the conventional PWM strategy. The impact of the VVC on the inverter lifetime and the performance of the proposed VSF-MPWM on the inverter power losses and current quality are investigated through MATLAB Simulink. The lifetime analysis reveals that the VVC has the ability to improve the lifetime of the inverter by a factor of 5.06 and 3.43 under Artemis urban and US06 driving cycles, respectively, compared to the conventional constant dc-bus voltage control (CVC). On the other hand, the simulation result shows that the proposed VSF-MPWM can save up to 35.4 % and 23.8 % of switching and power losses, respectively, compared to the conventional PWM. Meanwhile, the VSF-MPWM can obtain the same output current quality as that of the conventional PWM

Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project no. (IFKSUOR3–057-3).Peer reviewedPublisher PD

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, inducing non-negligible errors. In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models. It consists in decomposing the dynamics into two components: a physical component accounting for the dynamics for which we have some prior knowledge, and a data-driven component accounting for errors of the physical model. The learning problem is carefully formulated such that the physical model explains as much of the data as possible, while the data-driven component only describes information that cannot be captured by the physical model, no more, no less. This not only provides the existence and uniqueness for this decomposition, but also ensures interpretability and benefits generalization. Experiments made on three important use cases, each representative of a different family of phenomena, i.e. reaction-diffusion equations, wave equations and the non-linear damped pendulum, show that APHYNITY can efficiently leverage approximate physical models to accurately forecast the evolution of the system and correctly identify relevant physical parameters

APHYN-EP: Physics-based deep learning framework to learn and forecast cardiac electrophysiology dynamics

International audienceBiophysically detailed mathematical modeling of cardiac electrophysiology is often computationally demanding, for example, when solving problems for various patient pathological conditions. Furthermore, it is still difficult to reduce the discrepancy between the output of idealized mathematical models and clinical measurements, which are usually noisy. In this paper, we propose a fast physics-based deep learning framework to learn cardiac electrophysiology dynamics from data. This novel framework has two components, decomposing the dynamics into a physical term and a data-driven term, respectively. This construction allows the framework to learn from data of different complexity. Using 0D in silico data, we demonstrate that this framework can reproduce the complex dynamics of transmembrane potential even in presence of noise in the data. Additionally, using ex vivo 0D optical mapping data of action potential, we show the ability of our framework to identify the relevant physical parameters for different heart regions

The mediating role of customer awareness to enhance the relationship between using social media tools and post-purchase behavior upon electrical devices buyers in Jordan

This study examined the effect of social media on post-purchase behavior on electrical device buyers in Jordan. Drawing on resource-based and knowledge-based previous studies, the mediating effects of customer awareness were also tested. Data were collected from 385 participants from the segment targeted group of customers in Jordan, and hypotheses were tested through partial least squares structural equation modeling using Smart PLS 4.0. The results showed that the mediating role of customer awareness influences enhancing the relationship between social media and promotion mix on the one hand, and post-purchase behavior (exit, voice, and loyalty) on the other hand. Our findings contribute to the existing literature by explaining and strengthening this relationship, which is also referred to as the black box through the mediation of customer awareness. Marketers should recognize the importance of this relationship to develop modern promotional tools in multiple social media to positively enhance the customers’ post-purchase behavior by giving them a competitive advantage