Speaking and Silence as Means of Resistance in Alifa Rifaat\u27s \u3cem\u3eDistant View of a Minaret\u3c/em\u3e and \u3cem\u3eBahiyya\u27s Eyes\u3c/em\u3e

This study aims at investigating the dilemma of creating a counter discourse that speaks against the dominant androcentric one in Alifa Rifaat’s fiction. The study explores the characterization of the protagonists of two short stories: “Distant View of a Minaret” and “Bahiyya’s Eyes,” culled from Rifaat’s collection Distant View of a Minaret and Other Short Stories (1983). These stories present two different paradigms of resistance that the female protagonists use, which are speaking and silence. The study argues that both speaking and silence are attempts to heal women’s cyclic trauma, as they are means of representing women’s experience and oppression over time. The protagonists’ response to the hegemonic discourse in the two stories is carnivalesque because the use of language (or its absence) aims at deconstructing the phallogocentric discourse and establishing a new one. Accordingly, Rifaat uses two narrative points of view in each story to express the protagonists’ new discourses. Speaking and silence, thus, are not to be judged according to the symbolic discourse of men; instead they are placed in the purview of the Discourse of the Hysteric, which is regarded as an arena of resistance for women

Design of GCSC Stabilizing Controller for Damping Low Frequency Oscillations

This paper presents a systematic procedure for modeling and simulation of a power system equipped with FACTS type Gate Controlled Series Compensator (GCSC) based stabilizer controller. Single Machine Infinite Bus (SMIB) power system was investigated for evaluation of GCSC stabilizing controller for enhancing the overall dynamic system performance. PSO algorithm is employed to compute the optimal parameters of damping controller. Eigenvalues of system under various operating condition and nonlinear time domain simulation is employed to verify the effectiveness and robustness of GCSC stabilizing controller in damping low frequency oscillations (LFO) modes

Artificial lift selection methods in conventional and unconventional wells: a summary and review from old techniques to machine learning applications.

Artificial lift (AL) selection is an important process in enhancing oil and gas production from reservoirs. This article explores the old and current states of AL selection in conventional and unconventional wells, identifying the challenges faced in the process. The role of various factors such as production and reservoir data and economic and environmental considerations is highlighted. The article also examines the use of machine learning (ML) techniques in the AL selection process, emphasising their potential to increase the accuracy of selection and reduce data analysis time. The findings of this article provide valuable insights for researchers and practitioners in the oil and gas industry, as well as for those interested in the development of AL selection methods

A summary of artificial lift failure, remedies and run life improvements in conventional and unconventional wells.

Artificial lift (AL) systems are crucial for enhancing oil and gas production from reservoirs. However, the failure of these systems can lead to significant losses in production and revenue. This paper explores the different types of AL failures and the causes behind them. The article discusses the traditional methods of identifying and mitigating these failures and highlights the need for new designs and technologies to improve the run life of AL systems. Advances in AL system design and materials, as well as new methods for monitoring and predicting failures using data analytics and machine learning techniques, have been discussed. The findings of this work provide valuable insights for researchers and practitioners in the development of more reliable and efficient AL systems

Feature selection using information gain for improved structural-based alert correlation

Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset

Prediction Model for Construction Cost and Duration in Jordan

Risk is mitigated in the course of reliable prediction. A probabilistic model is proposed to predict the risk effects on time and cost of construction projects. Project managers and consultants can use the model in estimating project cost and duration based on historic data. Statistical regression models and sample tests are developed using real data of 140 projects. The research objective is to develop a model to predict project cost and duration based on historic data of similar projects. The model result can be used by project managers in the planning phase to validate the schedule critical path time and project budget. Research methodology is steered per the following progression: i) Conduct nonparametric test for project cost and time performance. ii) Develop generic multiple-regression models to predict project cost and duration using historic performance data. iii) The percent prediction error is statistically analyzed; and found to be substantial; thus, iv) Custom multiple regression models are developed for each project type to obtain statistically reliable results. In conclusion, the 95% point estimation of error margin= ±0.035%. Therefore, at a probability of 95%, the proposed model predicts the project cost and duration with a precision of ±0.035% of the mean cost and time

A UV-Spectrophotmetric Chemometric Method for the Simultaneous Determination of Sulfadoxine and Pyrimethamine in Tablets

In the present study, a simple, inexpensive, precise and accurate uv-spectrophotometric method based on chemometrics, has been developed for the simultaneous determination of sulfadoxine and pyrimethamine in tablet formulation. The % recoveries obtained were 99.7% ± 0.9 and 101.5% ± 0.8 for sulfadoxine and pyrimethamine, respectively. The developed method has been compared to USP-HPLC method with regard to accuracy and precision. The calculated F-ratio and the (t) statistics indicate that there is no significant difference at 5% level with regard to precision and accuracy between the proposed and the USP methods. Moreover, the developed method is simple, cost-effective, and less time-consuming. Accordingly, it can be used advantageously in routine quality control of sulfadoxine and pyrimethamine in tablet formulation