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Beyond Pink and Plastic: A Critical Analysis of Barbie (2023) Through the Lens of Gender Performativity, Intersectionality, and Diverse Cultural Perspectives
Get PDFThis paper critically examines Barbie (2023), directed by Greta Gerwig, through the frameworks of Judith Butler’s gender performativity and Kimberlé Crenshaw’s intersectionality. By analyzing qualitative data from interviews with Egyptian university students, the study explores how the film challenges traditional gender norms while addressing its polarizing reception globally. While Barbie (2023) succeeds in sparking conversations about gender, identity, and societal expectations, its Western-centric narrative limits its resonance with non-Western audiences, particularly in the Middle East. The findings reveal a dichotomy: the film effectively critiques systemic inequalities and empowers women but simultaneously overlooks intersectional and diverse lived experiences. This analysis highlights the need for feminist media to bridge cultural divides and engage authentically with global audiences. The study contributes to feminist media criticism by addressing the gap in research on Barbie (2023) and underscoring the importance of inclusivity and representation in feminist narratives
Sensor Data Fusion For Air Quality Monitoring
Get PDFSince traditional air quality monitoring methods often rely on geographically sparse and costly air quality monitoring stations, image-based air quality method- ologies are recently offering a compelling alternative that utilizes images from sources like satellites, traffic cameras, and even smartphones to monitor pollution levels by using estimation models, image-processing techniques, and deep-learning models. In this thesis, we first conduct a systematic review, in which we categorize and discuss the existing literature work. Moreover, we introduce a novel, multi- modal dataset designed to address the limitations of existing datasets, which are restricted in size, geographical coverage, and fixed-scene imagery, impeding the generalization of existing deep-learning prediction models. Our AirFusion dataset comprises 9,411 images paired with synchronized meteorological and geospatial readings, collected by a portable commercial air quality sensor, from 179 diverse locations. lastly, we introduce AirFusionNet, which leverages transfer learning of the pre-trained ResNet50 and use an attention mechanism to extract features from both image and numerical data to predict five key air quality parameters: PM1, PM2.5, PM10, temperature, and humidity. Our analysis of AirFusionNet estab- lishes baseline results on this challenging dataset. Our model achieves an RMSE of 10.44, 11.56, 13.18, 2.98, and 9.02 for PM1, PM2.5, PM10, temperature, and humidity, respectively, on the filtered day dataset and achieves RMSE of 9.08, 9.75, 10.69, 2.63, and 8.49 for the same parameters respectively, on the filtered combined day-night dataset. These results establish a new baseline for outdoor air quality prediction, demonstrating good performance for normal pollution levels. However, during high pollution events the RMSE increases, suggesting the need for more high-pollution data samples to improve model performance
Perceptions of Faculty on Trauma-Informed Practices at the American University in Cairo
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Trauma-informed education is gaining momentum in higher education as a framework that seeks to create learning environments that are safe, inclusive, and responsive to the emotional and psychological needs of students—particularly those who have experienced trauma. This qualitative study, situated at the American University in Cairo (AUC), explores how faculty perceive, understand, and implement trauma-informed practices in a diverse university context that includes refugee and displaced students. Data were collected through semi-structured interviews with seventeen faculty members from various disciplines and incorporating an autoethnographic reflection. An expert interview was conducted with Dr. Mays Imad, a leading scholar in trauma-informed education, whose insights helped contextualize the findings and guided the interpretation of faculty responses. A thematic analysis approach, combining both inductive and deductive coding revealed that while many faculty members demonstrated deep empathy and a commitment to student well-being, their understanding of trauma and trauma-informed practices varied widely and is often shaped by personal experience rather than formal training. Faculty reported emotional challenges, institutional gaps, and a strong interest in professional development. The study emphasizes the urgent need for culturally grounded training, institutional policies, and psychosocial support systems. It concludes by recommending that AUC take the lead in promoting trauma-informed education in the region, positioning itself as a model for equity, resilience, and inclusive teaching in higher education
The Impact of COVID -19 on Digital Teaching Practices: The Case of Faculty at the American University in Cairo
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This study aims to analyze the impact of COVID-19 on digital teaching practices of some AUC faculty in different disciplines. In addition, the study aims to address lessons learned from the COVID-19 experience of the chosen faculty sample and what digital literacies they gained from this experience that they are still using currently in their face-to-face (F2F) classes. The study analyzed the data using both the Community of Inquiry Model (CoI) which highlights three elements in an online course that foster student engagement incorporating emotional presence and the PICRAT framework (PIC) (passive, interactive, creative) which highlights the student’s relationship with technology in a particular educational scenario while (RAT) (replacement, amplification, transformation) explains technology’s impact on a teacher’s previous practice as well as understanding faculty’s digital literacies in the lens of Belshaw’s eight essential elements of digital literacy.
Keywords: COVID-19 impact on higher education, emergency remote teaching, emergency remote education, COVID-19 challenges, the difference between ERT and online learning, digital literac
Study of Capital and Operation Costs for Reverse Osmosis Desalination Plants
Get PDFDesalination is a progressively common solution to supply freshwater in numerous parts of the globe where this resource falls short. Among all desalination techniques, seawater reverse osmosis (SWRO) is the most globally prevalent technology. Evaluating a desalination technology\u27s cost-effectiveness is essential to ensure a well-designed plant can be implemented successfully and within an acceptable budget. In the Egyptian context, where water scarcity heightens the urgency of effective resource allocation, economic indicators such as capital expenditure (CAPEX) and operational expenditure (OPEX) are pivotal in shaping policy and planning decisions. Adopting this integrated financial perspective ensures that desalination projects are not only economically viable within budgetary limits but also sustainable in terms of long-term operational efficiency. Egypt aims to launch several desalination projects with a total planned capacity of 8.85 million m³/day by 2050, with 3.35 million m³/day targeted by 2025 as the first phase of this program. Furthermore, to address the water scarcity, Egypt plans to draw in strategic investors to develop, construct, and operate desalination plants. Cost overruns are a common challenge encountered during the implementation and operation phases of desalination projects in Egypt. This research aims to develop a capital expenditure (CAPEX) estimation tool to support the feasibility study stages of seawater reverse osmosis (SWRO) desalination projects, while also analyzing CAPEX and operational expenditure (OPEX) data from existing plants to provide data-driven insights for investors and planners. The methodology involves identifying key cost components in desalination plants and determining the five most influential cost drivers: membranes, energy recovery devices (ERDs), civil works, intake systems, and pumps. For components such as membranes, pumps, and ERDs, cost estimation models were developed using deterministic equations and correlations with plant-scale parameters including membrane count, pressure vessel configuration, number of trains, and ERD flow rates. In contrast, site-specific elements like civil works and intake systems were handled using user inputs and simple regression models based on available cost data. A detailed cost analysis was conducted on selected SWRO plants for which reliable data was accessible, accompanied by sensitivity analyses to assess the impact of critical variables on total costs. A set of charts was produced to visualize cost trends, highlight key cost components, and compare plant-level variations. The resulting estimation tool offers a simplified and rapid approach for CAPEX forecasting in early project planning and contributes to more informed decision making in Egypt’s ongoing desalination expansion
Contractor Prequalification Using Support Vector Machines
No full textContractors frequently experience financial difficulties, which has a substantial negative impact on the economy and causes large disruptions and losses to project stakeholders. This paper demonstrates a method for predicting financial distress using support vector machines (SVM). The objective of this work is to increase the forecasting accuracy and examine how macroeconomic factors and financial ratios affect the financial status of the contractor. Eight financial ratios and six macroeconomic factors were chosen as the input variables to this model. Macroeconomic variables and financial ratios are both present in the first dataset, but financial ratios alone are present in the second dataset. Both datasets are compared to determine the significance of macroeconomic variables. In this work, three kernel SVM function techniques—linear, polynomial, and radial basis (RBF)—were employed on both datasets. In comparison with the other techniques, the linear kernel function generated the highest accuracy (87.27%). Additionally, the dataset that included macroeconomic factors and financial ratios produced improved performance measures in comparison with the dataset containing financial ratios only. As a result, this framework is a useful tool for forecasting the financial health of building companies
Analysis of plasmonic nanoparticles effects on the performance of perovskite solar cells through surface recombination and short-circuiting behaviors
No full textPlasmonic photovoltaics integrate nanoparticles into the active layer to enhance power absorption. However a gap exists between simulated and experimental IV characteristics. Fabrication studies have attributed the issues to fabrication resolution, and recombination with no detailed step-by-step characterization. To address this issue, the paper presents a comprehensive optical and electrical study of a new plasmonic crescent nanoparticle (CNP). These particles serve as a near-field confinement source to enhance the efficiency of perovskite TiO2-MAPbI3-Spiro solar cells. The proposed design demonstrates that an optimized structure with polarization-dependent multiple modes can offer broad-spectrum absorption across both the visible and near-infrared spectra, resulting in a 15% improvement in the total absorption. The notably high stability of absorption with respect to parameter variation is a remarkable key factor. Employing Charge Transport (CHARGE) solver, the electrical characterization of the proposed plasmonic device is performed in a step-by-step procedure using three different models to characterize the sources of efficiency degradation The ohmic contact reduces quantum efficiency by 11%. Moreover, when surface recombination is considered, the degradation increases significantly to 54%, which matches the experimental studies in the literature. The paper also suggests incorporating a passivation layer which demonstrates its impact in enhancing the quantum efficiency from 18.2% to 22.2%
Revealing Students’ and Teachers’ Perceptions of Challenges and Strategies in Arabic Reading Comprehension
Get PDFThis thesis investigates the perceptions of Arabic as a Foreign Language (AFL) students and teachers regarding the influence of eight textual features on reading comprehension. These features are: vowelization, word patterns, roots, vocabulary, case endings, word order, punctuation marks, and text organization. It also investigates reading comprehension strategies employed by both groups to overcome comprehension challenges associated with these features. Data were collected from 102 AFL students and 26 AFL teachers through questionnaires, and semi-structured interviews were conducted with 6 students and 11 teachers. Results revealed that students generally rated the influence of these features more highly than teachers. The disparity could be attributed to the text types students read independently (authentic texts) compared to those used in classroom settings (textbooks, adapted materials). All coping strategies were rated by students as either ‘usually’ or ‘sometimes’ employed with reference material use and bottom decoding skills emerging as most frequently top employed. Teachers reported frequently utilizing comprehension checks, emphasizing bottom-up decoding skills, and incorporating pre-reading strategies to help students overcome challenges associated with textual features. Qualitative data, through interviews, provided valuable insights into the specific challenges faced by students and the strategies employed by students and teachers based on their actual experiences. A deeper understanding of these perceptions highlights the importance of conducting students’ needs analysis and adapting instruction to foster learners\u27 autonomy
A Framework to Harmonize Legal Provisions in Infrastructure PPP Projects in Egypt Funded by Multilateral Development Banks
Get PDFOver the last decade, Egypt has witnessed an increase in the number of Public-Private Partnership (PPP) projects, specifically in infrastructure, as a result of the growing interest and popularity of various entities participating in such projects. Public infrastructure systems are no longer thought to be exclusively provided by the government; instead, the private sector’s contribution in the provision of such services is increasing. However, not every PPP program has been equally effective or has made the optimum use of planning and decision-making. The possibility of a project failure as a result of poor risk allocation is increased, if the potential dispute causing factors are not taken into account, and correspondingly acted upon. This research aims to develop a framework for best practices focusing on the alignment of legal provisions in Infrastructure projects funded by Multilateral Development Banks, aiding all project entities to address their diversified purposes, to optimize each entity’s objectives and resolve conflicts up front, with consideration to legal aspects. This was accomplished by addressing the three main contractual relationships; concession agreement, lending agreement, and engineering, procurement and construction (EPC) agreement, focusing on the most crucial contract provisions that have the potential of causing conflicts between parties, namely; (1) governing law and (2) dispute resolution in both the concession and EPC agreements of five real-world case studies in addition to expert interviews. Multilateral Development Banks were found to have relatively high preference in regards to the choice of English law as the governing law of lending and EPC agreements. It can be concluded that having different governing laws in the agreements would not affect the success of the project, However, the selection of the legal provisions to be applied is crucial. Moreover, in regards to dispute resolution, conflicts cause delays, which lead to higher costs. Therefore, the effectiveness of the dispute settlement process is important in luring Multilateral Development Banks and private investors to collaborate with the government
Identification of Novel Argonaute Proteins Using a Metagenomic Mining Approach
Get PDFGene editing is one of the most promising tools in science. It enables precise modifications of an organism\u27s genetic material. Metagenomics is considered a powerful tool that unlocks the broad genetic potential found in uncultured microbial communities. Exploring the genetic diversity of uncultured microbial communities helps identify novel functional proteins with unique properties and make the best use of these diverse microbial ecosystems.
We developed and employed a metagenomic-based approach to mine more than 1000 metagenomes for prokaryotic argonaute proteins (pAgos), a potential gene editing machinery encoded in bacterial and archaeal genomes. Our workflow involved strict quality control, sequence assembly, taxonomic classification, profiling and analysis, and annotation. We then identified the presence of key domains such as PIWI and MID. Our methodology ensured effective screening and identification of these proteins across metagenomes from 25 different ecosystems. We analyzed 1,011 publicly available metagenomic datasets from which we identified 905 putative pAgos across these diverse environments by building a custom Hidden Markov Model (HMM) profile. Our analysis identified proteins with substantial diversity and considerable abundance in diverse microbial environments and included novel variants with unique functional characteristics. Our work highlights the power of metagenomics to discover novel functional proteins and presents promising applications for gene editing