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Digital Replica for Trustworthy Cooperative Autonomous Vehicles
No full textTrust in an automated system can be defined as confidence in a vehicle\u27s reliability, safety, and predictability, which is essential for the acceptance and widespread adoption of fully autonomous vehicles (FAVs); without it, users might disengage from using autonomous vehicles or reject the technology altogether. Most of the previous research has focused on trust from an ego vehicle perspective.
However, next-generation vehicles are becoming more autonomous and connected, relying on vehicle-to-vehicle technology and vehicle-to-infrastructure technology with no human intervention. Hence, trust becomes more complex and fragile as multiple agents interact with each other, and it might become harder to establish due to unexpected inter-vehicle coordination, diminishing human control, limited system transparency, and generalization effects where users might transfer their trust or distrust from one system to another. In such a complex environment, trust has to exceed ego vehicles and take into account other autonomous vehicles and their coordination abilities.
This thesis hypothesizes that providing the user with more information regarding the environmental state, including information about ego vehicle intentions, other autonomous vehicles\u27 intentions, cooperation agreements, and road conditions, can foster trust not only toward ego vehicles but also toward other cooperative robotic road users. This can be reached by visualizing vehicle-to-everything information through augmented reality interfaces.
To test this, a within-subjects experiment was conducted in a Virtual Reality (VR) environment. A customized DReyeVR simulator was utilized to develop a digital replica, which enables the simultaneous mimicking of vehicle behavior, algorithms, and user interface within a VR setup. Participants experienced three interfaces: (A) no transparency, (B) system-level transparency showing Only the ego vehicle\u27s intentions, and (C) environment-level transparency displaying cooperation intention, planned path by other vehicles, and infrastructure information. The results indicate that the interface that offered environment-level transparency at the cost of a higher mental effort enhanced trust in the ego vehicle and cooperating fully autonomous vehicles (FAVs). These results provide insight for designing interfaces for cooperative autonomous vehicles, fostering trust toward other cooperative agents, and preventing users\u27 disengagement from the technology
The Association Between Acceptance of Illness and Quality of Life Among Rheumatoid Arthritis Patients in Egypt
Full text linkRheumatoid arthritis is a common connective tissue autoimmune disease that has multidimensional impact on the patients’ daily functioning and quality of life. Due to the long- lasting incurable nature of RA, psychosocial adaptation to living with it becomes inevitable. Literature indicates that acceptance of illness as an appraisal of the disease and personal attitude towards it is related to better quality of life. The present study aims at investigating the relationship between disease acceptance and quality of life among rheumatoid arthritis patients in Egypt. This study also investigates the association of selected socio-demographic and disease related variables with quality of life and disease acceptance. A total of 125 patients participated in the study by answering a self-report questionnaire that included the WHOQoL-BREF and the acceptance of illness scale (AIS) as well as questions on socio-demographic and disease related data. Results indicate a positive correlation between acceptance of illness scores and the participants’ scores on all domains of quality of life. Results also highlight the educational level of participants as an important socio-demographic factor in the process of accepting and adapting to RA. Findings of this study accentuate the importance of integrating acceptance-based interventions in psychological care services for RA patients
Skin Microbiome Dynamics and Strain-Level Dysbiosis in Acne Vulgaris: Evidence from Diverse Egyptian Populations
Full text linkBackground: Acne vulgaris, a prevalent chronic inflammatory disease, is a multifactorial disease comprising microbiological, immunological, and environmental factors. Recent findings highlight the importance of the skin microbiome, in particular the diversity and strain variability of Cutibacterium acnes, in the pathogenesis of acne. The Middle East and North Africa exhibit unique genetic and environmental traits; however, there is an absence of data concerning acne-related skin microbiome signatures in this region.
Objectives: We aimed to characterize the facial skin microbiome of Egyptian acne patients and healthy controls, identify microbial signatures associated with the severity of acne, and compare skin microbial composition between urban and rural living.
Methods: Cheek skin swabs were obtained from 45 acne patients (both urban and rural) and 25 healthy controls. Using Illumina NovaSeq, the V3-V4 region of the 16S rRNA gene was sequenced after extracting DNA. QIIME2 was used for investigating the structure of microbial communities. This included alpha and beta diversity, differential abundance testing, and predictive functional profiling.
Results: Cutibacterium (66.3%) and Staphylococcus (16.1%) made up most of the skin microbiome. The relative abundance of C. acnes was not significantly different in healthy controls compared to acne patients across severity groups. In contrast, moderate-to-severe acne was associated with significantly higher alpha diversity compared to controls and mild acne. Based on a differential abundance analysis, specific C. acnes phylotype IA1 amplicon sequence variants (ASVs) were depleted in patients with moderate-to-severe acne, and in individuals who lived in rural compared to urban environments. Patients with acne in rural areas had higher alpha diversity and a distinct community composition. This was shown by an increase in environmental taxa like Kocuria and Peptostreptococcaceae and a decrease in Ralstonia and other taxa. Functional predictions revealed that urban samples were enriched in amino acid pathways. In contrast, rural samples showed enrichment in both amino acid and lipid metabolism pathways but had reduced energy metabolism pathways.
Conclusions: Alterations in skin microbial composition and a depletion of specific C. acnes IA1 strains in association with acne severity are observed in the Egyptian population, rather than a global increase in C. acnes abundance. Environmental drivers such as urbanization play a key role in modulating skin microbiome diversity and can even alter the taxonomic and functional profile of skin microbial communities. These findings support a model in which acne pathogenesis involves both the loss of beneficial commensal C. acnes strains and the emergence of pathogenic variants. Emerging therapies should aim to restore microbial diversity and specifically target pathogenic strains, rather than broadly reducing C. acnes populations. Rural data suggest environmental microbial exposure can reshape the adult skin microbiome, supporting topical supplementation to enhance skin health
Predictive Maintenance of Wind Turbines: Remaining Useful Life Estimation Using a BPSO-Optimized Attention-Based CNN-BiLSTM Approach
No full textThe increasing demand for renewable energy technologies, particularly wind energy, has made it essential to develop an effective maintenance strategy that ensures the reliability and efficiency of wind turbines. Traditional corrective and preventive maintenance approaches are not reliable enough since unplanned downtime and high maintenance costs are often associated with them. Hence, this study developed a Predictive Maintenance (PdM) framework for wind turbines by estimating the Remaining Useful Life (RUL) of critical wind turbine components using a hybrid Deep Learning (DL) model. The study proposes a hybrid model that combines the strengths of Convolutional Neural Networks (CNN) in extracting spatial correlations and Bidirectional Long Short-Term Memory (Bi-LSTM) networks in capturing temporal dependencies. To novelize this approach, several features were added; an attention mechanism is utilized to ensure that the most informative features are given greater weight. Additionally, feature selection was applied using Binary Particle Swarm Optimization (BPSO), where only the most essential features were selected as input to the CNN-BiLSTM model. This helps reduce computational complexity and decreases the risk of overfitting, as the SCADA dataset may contain irrelevant features. The proposed solution also utilizes Bayesian optimization to select specific hyperparameters, including the number of filters in the convolutional layer and the activation functions used in the dense layer of the fully connected layer.
The proposed solution followed a structured pipeline, which starts with preprocessing and cleaning the high-dimensional SCADA dataset by removing outliers, normalizing the data, creating RUL labels, and generating sequential data segments. The next step involves PSO-optimized feature selection, followed by the development of the CNN-BiLSTM model architecture and the tuning, training, and evaluation of the model hyperparameters. However, to be able to evaluate the model performance fairly, it was compared to baseline models. Hence, three additional DL models were developed: a standalone CNN, a standalone BiLSTM, and a hybrid CNN-BiLSTM model without an attention mechanism. Each of these models will be tested twice: firstly, using all features provided in the original SCADA dataset, and secondly, using only the features selected by the PSO optimizer. The evaluation metrics used were the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²). The proposed method outperformed the other three models in both scenarios. The attention-based CNN-BiLSTM model with feature selection achieved an average RMSE of 56 timesteps (560 minutes or 9.3 hours). This means the model can estimate the RUL with a mean deviation of 9.3 hours. Compared to the standalone CNN model, which had a mean deviation of 13.9 hours, this is a 44% improvement. It also shows a 71% improvement over the BiLSTM model (30.47 hours) and a 33% improvement compared to the CNN-BiLSTM model (14.14 hours). In addition, MAE and MAPE were reduced by 36.9% and 11.4%, respectively, compared to the base CNN. The model also achieved the highest R² score of 0.9970, which indicates the best fit. These improvements done to the RUL estimation models will help in the decision-making process of when a wind turbine should be maintained.
Although the results of the proposed model are promising, it can be further improved by increasing the PSO population and iterations, as well as the number of calls in the Bayesian optimizer. However, the values were selected due to computational limitations, which did not allow a higher number of populations, iterations, and calls. This research contributes by integrating PSO-optimized feature selection with an attention-based CNN-BiLSTM, achieving up to a 71% improvement over baseline models and providing a computationally efficient PdM framework for wind turbines. This provides an efficient solution for maintaining wind turbines, making them a more reliable source of clean energy
Digital Payment Adoption and Firm Performance in Egypt: An Empirical Study from the Digital Transformation Era in MENA
No full textThis study investigates the relationship between digital payment adoption and firm-level innovation and revenue performance in Egypt, using microdata from the Survey of Enterprise Digitization published by the Economic Research Forum (ERF). Digital transformation, specifically payments, has accelerated across emerging economies, yet empirical evidence on its firm-level impacts in the Middle East and North Africa (MENA) remains limited. The study is framed through the Technology-Environment-Organization (TOE) framework and is highly incorporated with the Solow Growth Model through indirect technological growth inputs in the Solow residual. To address this gap, the study employs probit and ordered logit models to examine whether firms that adopt digital payments—either alone or in combination with e-commerce—exhibit higher probabilities of introducing innovation and achieving higher revenue levels. Results show that digital payment adoption is positively and significantly associated with both innovation and revenue performance, even after controlling for firm size, age, sector, workforce characteristics, and regional factors. Marginal effects further confirm that firms using both digital payments and e-commerce exhibit the largest performance gains. The findings stress the importance of expanding digital and financial infrastructure, supporting Micro, Small, and Medium Enterprise (MSMEs) digitalization, strengthening fintech ecosystems, and improving national data collection systems to enable evidence-based policy design in Egypt’s evolving digital economy
The Affective Politics of Labelling in Egypt Post-2013
No full textThe process and consequences of labelling groups and individuals has been a central concern for sociology and anthropology present in the writings of those considered founders of the disciplines, through development of cultural studies, criminology, and, in more recent times considerations of affect. However, little has been written about these processes and their social and political consequences outside of western contexts. This thesis focuses on how labels have been created, acquired meaning, and shaped the conditions of social and political life in Egypt since 2013. It explores the mechanisms of being labeled, named, and categorized within shifting political and cultural discourses. Through an ethnographically grounded and affectively attuned approach, this research examines the subtle mechanisms through which labels operate, circulate in public spheres, and define modes of belonging and exclusion. This study focuses on how labels are portrayed in multiple sites such as political discourse, pop culture, and the everyday
Fly Ash Proppant Developed for Hydraulic Fracturing
Full text linkProppants are critical components in hydraulic fracturing, used to maintain fracture conductivity and enhance reservoir stimulation. In high-pressure and harsh reservoir environments, the use of high-strength proppants is essential to ensure well performance. Conventional proppants, being sand, ceramic, and sintered bauxite can be expensive, and their use often requires costly fracture fluids. This study investigates the possibility of utalizing fly ash geopolymers as a sustainable alternative that is also cost-effective for proppants manufacturing. Fly ash, a by-product from the combustion of coal and other materials, was explored as a base material for proppants, utilizing an alkaline solution as an activator and binder. The research aims to develop fly ash-based proppants that meet the stringent mechanical, environmental, and cost performance standards required in hydraulic fracturing applications. The fly ash proppant samples were subjected to a variety of tests to evaluate their physical and mechanical properties. Density measurements showed that the fly ash-based proppants exhibited significantly lower densities (ranging from 1.09 to 1.36 g/cm³) compared to conventional materials like sand (1.5–1.6 g/cm³) and sintered bauxite (3.5 g/cm³). The lower density of the fly ash proppants contributes to improved buoyancy, enhancing their transport within fracturing fluids and facilitating more effective proppant placement in fractures. This characteristic aligns with the goal of achieving a lightweight proppant that requires less viscous fracture fluid, thereby reducing overall costs.
The proppant samples were also tested for their environmental durability, with exposure to high temperatures, pressures, acidic, saline, and crude oil environments. The results revealed notable variations in the proppants\u27 performance based on their mix designs. The B20W25 mix design demonstrated the best durability, exhibiting minimal surface erosion, cracking, and weight loss across various environmental conditions. In contrast, the B25W25 mix showed significant degradation, particularly under high-pressure and high-temperature conditions, highlighting the critical role of mix design in ensuring proppant performance. The B22W25 mix, while performing moderately well, was found to be more susceptible to high-alkalinity conditions compared to the B20W25 mix. Compressive strength testing revealed that the B20W25 mix exhibited the highest strength among the fly ash-based samples, with a compressive strength of 8144 KPa (1181.19 psi). The B25W25 mix, however, demonstrated lower compressive strength and failed more readily under environmental stresses. These results highlight the critical role of optimizing binder content and the water-to-binder ratio in improving the strength and durability of fly ash-based proppants. Additionally, the study compared the performance of volcanic ash-based geopolymers, which outperformed the fly ash-based samples in compressive strength, with the B10W30 volcanic ash mix reaching a compressive strength of 13,988 KPa (2028.79 psi), suggesting volcanic ash as a promising alternative material for proppant development. Incorporating sand into fly ash-based geopolymer mixes was also investigated, showing that moderate sand content (25%) improved compressive strength, while higher sand content (50%) led to a reduction in strength due to increased porosity. The study highlights the importance of carefully balancing binder and filler content to optimize the mechanical properties of fly ash-based proppants. Overall, the findings of this research suggest that fly ash-based proppants hold significant potential as a cost-effective, sustainable alternative to conventional materials used in hydraulic fracturing. Despite their lower compressive strength compared to traditional proppants, fly ash-based proppants offer advantages such as reduced material costs, enhanced buoyancy, and environmental sustainability. Future research should focus on optimizing the mix designs, binder-to-filler ratios, and further improving the mechanical properties of fly ash-based proppants to meet the demanding requirements of high-pressure, high-temperature, and chemically aggressive environments typical in deep-well hydraulic fracturing applications
Gendered Harms in Armed Conflict: International Legal Responses to the Gendered Effects of the War in Sudan
Full text linkThis thesis interrogates how international law conceptualizes, prosecutes, and ultimately limits the understanding of sexual violence in contexts of war and displacement. Drawing on feminist legal theory, postcolonial critique, and ethnographic fieldwork with Sudanese women displaced in Egypt, it questions the dominant legal framing of conflict-related sexual violence (CRSV) as episodic, exceptional, and individualized. Instead, it reveals sexual violence as structural, continuous, and embedded in the everyday realities of racialized, gendered, and colonial harm.
Through ethnographical narratives, the study exposes how survivors\u27 experiences often exceed the legibility frameworks of humanitarian and legal institutions, which prioritize spectacular, forensic evidence over slow, atmospheric, or “ordinary” forms of violence. It critiques international law\u27s reliance on individualized liability, including direct perpetration, forensic verification, and intent, as well as its failure to account for collective, institutional, and infrastructural forms of complicity. By engaging critically with different doctrines of international law, the thesis demonstrates how international legal mechanisms obscure the cultural, militarized, and bureaucratic conditions that normalize sexual violence.
Ultimately, the thesis offers a critical reflection on justice, one that centers recognition, ethical responsibility, and structural transformation. It advocates for a feminist legal praxis that listens beyond what the law can codify, values narrative, and prioritizes survivors’ lived knowledge over carceral performance
CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications
Full text linkMental health applications are increasingly leveraging intelligent systems to sup- port psychological well-being, yet preserving user privacy remains a major concern. This thesis presents CogniVault, a secure ecosystem for cognitive distortion data. The framework includes Cognify, a mobile journaling application that detects cog- nitive distortions in user-written journal entries using a locally deployed machine learning model. Cognitive distortions are maladaptive thought patterns such as catastrophizing or personalization, which the app identifies to provide therapeu- tic insights. To ensure privacy-preserving data analytics, CogniVault includes the design and implementation of a hybrid security architecture, PRISM-HDI, that combines Paillier Homomorphic Encryption (HE), Differential Privacy (DP), and Immutability. User data, including mood scores and detected cognitive distortion labels, are encrypted on-device using Paillier HE and sent to a secure backend where encrypted aggregation is performed. After decryption, noise is added ac- cording to differential privacy guarantees to protect individual user contributions before presenting insights to therapists and other users. These insights include label and mood distribution across patients and per-user temporal trends. To protect the safety of stored data on the cloud, we implement blockchain inspired immutability to ensure data is tamper-proof. We evaluate the system’s usabil- ity, encryption overhead, and security resilience to demonstrate its feasibility for real-world deployment. Our results show that privacy can be preserved without significantly affecting performance or sacrificing the utility of mental health ana- lytics, paving the way for responsible AI in mental health care
Exploring the Perspectives of AFL Teachers on Teacher Development Needs, with Reference to the Level of Professional Experience
Full text linkThis thesis explores the perceptions of teachers of Arabic as a Foreign Language (AFL) regarding their training needs, with an attempt to examine whether those needs might vary according to the level of teaching experience. It also examines the views and reflections of AFL teachers about their training experiences; highlighting perceived strengths, weaknesses, and preferred training formats. The teaching competence examined in this study are based on the Cambridge Framework (n.d.) and the British Council CPD (1) Framework (2016). The key areas surveyed are Learning and the Learner, Teaching and Assessment, Arabic Language in the Classroom, Arabic Language Knowledge and Ability, Professional Development and Professional Values, Integrating Technology. Each of these areas comprises a range of sub-competences. Data were collected online from 76 AFL teachers through a questionnaire which was provided in Arabic and English versions and which had quantitative and descriptive sections. Of the total respondents, 57 completed the Arabic version, while 19 completed the English version. Additionally, semi-structured interviews were conducted with volunteer participants to discuss their responses more deeply. Results revealed that across the main domains explored by the questionnaire, training for teaching and assessment was the highest on demand, where training for Arabic knowledge and Ability ranked the lowest. The level of professional experience did not appear to have a significant impact on the choices of respondents, with the exception of Arabic Knowledge and Ability, where early-career teachers reported a significantly higher perceived need for training compared to senior teachers. Perceived weaknesses of teacher development programs included the over-use of theoretical, nonpractical, traditional, and sometimes outdated content, insufficient attention to trainees\u27 individual needs and learning preferences, and a limited focus on fostering teachers’ own knowledge of the Arabic language. Regarding the strengths, participants expressed appreciation for teacher development programs that focus on teaching methods and strategies, offer opportunities for teaching practice, integrate technology and AI, incorporate trainee feedback, and encourage trainees to share their expertise with each other.
(1) Continuous Professional Developmen