Operational performance of a PV generator feeding DC shunt and induction motors with MPPT

This paper presented the transient and operational behavior of a parallel Combination of DC Shunt Motor and IM fed by a photovoltaic generator at different solar irradiance levels. The maximum power point of current/voltage (I/V) characteristic of the PV generator was achieved for different solar intensities, by utilizing an open circuit voltage method. The nonlinear operational behavior of (I/V) characteristics of the PV generator at various solar intensities and the magnetization curve of the ferromagnetic material of the DC shunt motor were both modeled by high order polynomial mathematical expressions. The study investigated the response of the system at different solar irradiance levels and changing the torque loads for both motors and then following step change in solar intensity levels with fixed loading torques for both motors. All numerical simulations were executed using MATLAB software

Safety out of control: dopamine and defence

We enjoy a sophisticated understanding of how animals learn to predict appetitive outcomes and direct their behaviour accordingly. This encompasses well-defined learning algorithms and details of how these might be implemented in the brain. Dopamine has played an important part in this unfolding story, appearing to embody a learning signal for predicting rewards and stamping in useful actions, while also being a modulator of behavioural vigour. By contrast, although choosing correct actions and executing them vigorously in the face of adversity is at least as important, our understanding of learning and behaviour in aversive settings is less well developed. We examine aversive processing through the medium of the role of dopamine and targets such as D2 receptors in the striatum. We consider critical factors such as the degree of control that an animal believes it exerts over key aspects of its environment, the distinction between 'better' and 'good' actual or predicted future states, and the potential requirement for a particular form of opponent to dopamine to ensure proper calibration of state values

The Digital Divide and its Impact on Quality of Education at Jordanian Private Universities Case Study: Al-Ahliyya Amman University

The study aimed to identify the Impact of digital divide in its dimensions (Technological dimension, Knowledge dimension, and Legislation &amp; laws dimension) on the quality of education (University presidency's commitment to quality, Academic reputation and published scientific research) at Al-Ahliyya Amman University.To achieve this goal, the researcher used the descriptive and analytical approach, the study tool for collecting information and data was a questionnaire distributed to all employees, whose number is (630).The study questions and hypotheses were analyzed and tested through the Statistical Package for Social Sciences (SPSS) program. This study has found many results, the most important is:The digital divide in its combined dimensions has a statistically significant impact on the quality of education at Al-Ahliyya Amman University. It concluded with many recommendations, the most important is that a strategic plan be drawn up for universities to develop its infrastructure, improve and develop it continuously in order to enable digital access and bridge the digital divide among researchers and academics.</jats:p

Correction: Saadeh et al. Recent Advances in the Synthesis and Biological Activity of 8-Hydroxyquinolines. Molecules 2020, 25, 4321

The author wishes to make the following correction to this paper [...]</jats:p

A novel approach for detecting deep fake videos using graph neural network

Abstract Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs

Correction: Saadeh et al. Recent Advances in the Synthesis and Biological Activity of 8-Hydroxyquinolines. <i>Molecules</i> 2020, <i>25</i>, 4321

The author wishes to make the following correction to this paper [...