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All Journal Journal of Tecnologia Quantica Techno Agriculturae Studium of Research Journal of Humanities Research Sustainability Journal Markcount Finance Journal of World Future Medicine, Health and Nursing
Mona Abdallah
Alexandria University
Agriculture, Biological Sciences & Forestry Humanities Physics

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 1 
Quantum Simulation for Studying High-Temperature Superconductors Yasser Sayed; Ahmed Hossam; Mona Abdallah
Journal of Tecnologia Quantica Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v1i6.1701

Abstract

High-temperature superconductors are a very interesting phenomenon because they can operate at much higher temperatures compared to conventional superconductors. However, the mechanism underlying superconductivity at high temperatures is still not fully understood. This study aims to study the properties of high-temperature superconductors through quantum simulations to identify factors that affect the critical temperature and phase stability of superconductors. The method used is quantum simulation using the Monte Carlo technique to model electron-interaction and magnetic fluctuations in various high-temperature superconducting materials, such as cuprates and iron-based superconductors. The results showed that strong electron interactions and optimal crystal structure played an important role in achieving high critical temperatures, while strong magnetic fluctuations could disrupt the stability of Cooper pairs and lower critical temperatures. This research contributes to a deeper understanding of the role of electron-interaction and magnetic fluctuations in high-temperature superconductivity, as well as opening up opportunities to design new materials with higher critical temperatures. The limitations of this study lie in the complexity of the system being studied, which requires large computing resources. Further research can be focused on the development of more efficient simulation algorithms and the application of physical experiments to validate the simulation results.
AN AUTOMATED FEED MANAGEMENT SYSTEM FOR HIGH-DENSITY CATFISH AQUACULTURE USING ACOUSTIC SENSORS AND MACHINE LEARNING Mai Kamal; Mona Abdallah; Tamer Youssef
Techno Agriculturae Studium of Research Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i6.2962

Abstract

The rapid expansion of high-density catfish aquaculture has increased the demand for efficient and precise feed management systems to optimize growth performance, reduce feed waste, and maintain water quality. Conventional feeding practices largely depend on fixed schedules and visual estimation, which often result in overfeeding or underfeeding, leading to increased production costs and environmental degradation. Recent advances in sensing technologies and artificial intelligence offer new opportunities to transform aquaculture management through data-driven and automated approaches. The purpose of this study is to develop and evaluate an automated feed management system for high-density catfish aquaculture by integrating acoustic sensors and machine learning algorithms. The system aims to accurately detect feeding activity and dynamically regulate feed delivery based on real-time fish behavior. This study employed a research and development design combined with experimental field testing. Acoustic sensors were deployed in catfish ponds to capture underwater sound patterns associated with feeding behavior. The collected acoustic data were processed using machine learning models to classify feeding intensity and determine optimal feeding duration. System performance was evaluated through accuracy testing, feed efficiency analysis, and comparative assessment against conventional feeding methods. The results show that the proposed system successfully identified feeding activity with high classification accuracy and significantly reduced feed waste compared to manual feeding practices. Feed conversion ratios improved, and water quality indicators remained more stable due to reduced excess feed accumulation. In conclusion, the automated feed management system demonstrates strong potential as an intelligent aquaculture solution for high-density catfish farming. By integrating acoustic sensing and machine learning, the system enhances feeding precision, supports sustainable aquaculture practices, and contributes to increased productivity and environmental efficiency.
Digital Philology and Manuscript Sustainability: A Semantic Annotation Model for Classical Arabic Texts Surip Stanislaus; Mona Abdallah
Journal of Humanities Research Sustainability Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jhrs.v2i3.2304

Abstract

Background. The sustainability of classical Arabic manuscripts is often confined to digitization efforts that focus solely on image preservation and limited text markup. These approaches do not fully address the semantic richness and epistemological structure inherent in Islamic intellectual heritage. Purpose. This study aims to develop a semantic annotation model tailored for classical Arabic texts to support digital philology and enrich manuscript sustainability through machine-readable and concept-linked interpretations. Method. Using a developmental qualitative research design, three classical manuscripts were annotated semantically using a custom-built model based on RDF and Islamic ontology. The model was evaluated by domain experts in philology and computational linguistics, focusing on four criteria: semantic accuracy, contextual relevance, interoperability, and usability. Results. The model achieved annotation accuracy above 91% across all manuscripts. Experts rated semantic precision (4.7/5) and contextual relevance (4.6/5) as its strongest aspects. The system successfully mapped technical terms and logical concepts within classical texts and linked them across manuscripts. A case study demonstrated the model’s effectiveness in identifying relationships between epistemological terms and enabling thematic exploration. Conclusion. This semantic annotation model advances digital philology by enabling structured, concept-based analysis of classical Arabic texts. It bridges computational methods with Islamic textual traditions and opens new pathways for collaborative, sustainable, and meaningful engagement with manuscript heritage.
Ethical AI in Financial Decision-Making: Balancing Innovation, Regulation, and Social Justice Shohib Muslim; Ahmed Hossam; Mona Abdallah; Farida Akbarina
Journal Markcount Finance Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jmf.v3i3.2714

Abstract

The integration of Artificial Intelligence (AI) in financial decision-making has revolutionized the sector, offering unprecedented speed and efficiency. However, the increasing reliance on AI systems has raised concerns regarding ethical implications, particularly in terms of fairness, transparency, and accountability. This study explores the intersection of ethical AI, financial decision-making, and social justice, emphasizing the need to balance technological innovation with regulatory oversight and societal impact. The research aims to assess how AI-driven financial decisions align with ethical principles and the role of regulation in ensuring equitable outcomes. A mixed-methods approach was employed, combining a qualitative review of existing literature on AI ethics in finance with quantitative analysis of AI algorithms in decision-making processes within financial institutions. The findings suggest that while AI has the potential to enhance financial decision-making, there is a significant gap in the ethical regulation of AI systems. The study identifies key challenges in ensuring transparency and fairness, particularly in automated lending and investment decisions. It concludes that a comprehensive regulatory framework is essential for balancing innovation with ethical standards, ensuring that AI serves the public good while mitigating the risk of biased decision-making. The findings underscore the importance of social justice considerations in the deployment of AI in financial systems.
Technology-Based Care Model: Building a Sustainable Nursing System in Disadvantaged Areas Rizka Adela Fatsena; Dina Ahmed; Mona Abdallah; Siska Indrayani
Journal of World Future Medicine, Health and Nursing Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v3i2.1910

Abstract

In disadvantaged areas, limited access to healthcare services poses significant challenges to achieving equitable health outcomes. The integration of technology in healthcare systems has been proposed as a solution to bridge gaps in access and quality, particularly in nursing care. This research explores the effectiveness of a technology-based care model in building a sustainable nursing system in underserved regions. The study utilizes a mixed-methods approach, combining quantitative data on health outcomes with qualitative insights from healthcare providers and patients. The findings indicate that technology-based interventions, such as telemedicine, mobile health applications, and remote patient monitoring, led to a 20% improvement in patient outcomes and a 30% reduction in healthcare delivery costs. Additionally, healthcare providers reported increased efficiency and job satisfaction due to the support offered by technology in monitoring patient conditions and facilitating remote consultations. The study concludes that the adoption of technology-driven care models can significantly enhance nursing systems in disadvantaged areas by improving accessibility, efficiency, and sustainability of care. However, challenges related to infrastructure, training, and data security must be addressed to ensure the success of such models. Future research should focus on scaling these models to other resource-limited settings and assessing their long-term impact.
Co-Authors Ahmed Hossam Dina Ahmed Farida Akbarina Mai Kamal Rizka Adela Fatsena Shohib Muslim Siska Indrayani Surip Stanislaus Tamer Youssef Yasser Sayed