Cairo University

About: Cairo University is a education organization based out in Giza, Egypt. It is known for research contribution in the topics: Population & Medicine. The organization has 33532 authors who have published 55581 publications receiving 792654 citations. The organization is also known as: Fuad I University & King Fuad I University.

Closure to “Optimal Protection Coordination for Meshed Distribution Systems With DG Using Dual Setting Directional Over-Current Relays”

Abstract: The authors would like to sincerely thank T. Aghdam et al. for their thoughtful discussion on our paper. They raise a valid, important and excellent point where the backup scheme of dual setting relays will not be fully coordinated properly. As such, one will experience, as stated by the discussers, for a fault at F1, R5 forward operation operating before R3 reverse operation. As suggested by the discussers, a possible solution would be to add (2) and this would yield both dual setting and conventional relay results the same and thus no improvement in operating times can be achieved by utilizing dual setting relays. The issue of possible loss of selecitivity but for definite time relays was reported in [1] and to resolve this issue, a communication link was used to block the operation of upstream relays [1] . This same approach can be applied to the dual settings relays and thus, another solution to resolve the dual setting selectivity problem would be to utilize a low bandwidth communication between relays which can then realize the full advantages of dual setting relays. The reported results in the paper can be achieved with proper protection coordination by amending dual setting relays with a low bandwidth communication that is utilized such that R1, once it detects an increase in current, would send a signal to block the unwanted operation of forward operating relays that are affected by that fault (for example, for a fault at F1, R1 forward operation sends a signal to block R5 forward operation and R5 forward operation sends a signal to block the forward operation of R4). This would apply to all relays in the system. Once again, we thank the reviewers for their careful review of the paper.

Modeling Perceived Influences on Journalism: Evidence from a Cross-National Survey of Journalists

Abstract: Surveying 1,700 journalists from seventeen countries, this study investigates perceived influences on news work. Analysis reveals a dimensional structure of six distinct domains—political, economic, organizational, professional, and procedural influences, as well as reference groups. Across countries, these six dimensions build up a hierarchical structure where organizational, professional, and procedural influences are perceived as more powerful limits to journalists' work than political and economic influences.

Application of the recurrent multilayer perceptron in modeling complex process dynamics

Abstract: A nonlinear dynamic model is developed for a process system, namely a heat exchanger, using the recurrent multilayer perceptron network as the underlying model structure. The perceptron is a dynamic neural network, which appears effective in the input-output modeling of complex process systems. Dynamic gradient descent learning is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over a static learning algorithm used to train the same network. In developing the empirical process model the effects of actuator, process, and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response of various testing sets. Extensive model validation studies with signals that are encountered in the operation of the process system modeled, that is steps and ramps, indicate that the empirical model can substantially generalize operational transients, including accurate prediction of instabilities not in the training set. However, the accuracy of the model beyond these operational transients has not been investigated. Furthermore, online learning is necessary during some transients and for tracking slowly varying process dynamics. Neural networks based empirical models in some cases appear to provide a serious alternative to first principles models. >

ARHGDIA mutations cause nephrotic syndrome via defective RHO GTPase signaling

Abstract: Nephrotic syndrome (NS) is divided into steroid-sensitive (SSNS) and -resistant (SRNS) variants. SRNS causes end-stage kidney disease, which cannot be cured. While the disease mechanisms of NS are not well understood, genetic mapping studies suggest a multitude of unknown single-gene causes. We combined homozygosity mapping with whole-exome resequencing and identified an ARHGDIA mutation that causes SRNS. We demonstrated that ARHGDIA is in a complex with RHO GTPases and is prominently expressed in podocytes of rat glomeruli. ARHGDIA mutations (R120X and G173V) from individuals with SRNS abrogated interaction with RHO GTPases and increased active GTP-bound RAC1 and CDC42, but not RHOA, indicating that RAC1 and CDC42 are more relevant to the pathogenesis of this SRNS variant than RHOA. Moreover, the mutations enhanced migration of cultured human podocytes; however, enhanced migration was reversed by treatment with RAC1 inhibitors. The nephrotic phenotype was recapitulated in arhgdia-deficient zebrafish. RAC1 inhibitors were partially effective in ameliorating arhgdia-associated defects. These findings identify a single-gene cause of NS and reveal that RHO GTPase signaling is a pathogenic mediator of SRNS.