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Energy, Exergy, Economic, and Environmental Analyses (4E) of Geothermal Power Plant With Double Flash System for Power and Heat Production
Get PDFGeothermal energy is a reliable and sustainable renewable energy source due to its continuous availability and eliminating the need for energy storage systems. Among various types of geothermal power plants, double flash (DF) geothermal plants are among the most widely utilized. This paper presents a comprehensive thermodynamic analysis of a DF geothermal power plant, integrating energy, exergy, economic, and exergoenvironmental (4E) evaluations. The study examines the influence of key parameters, including the high-pressure separator and geothermal production well temperature, on the system performance. The results indicate that the expansion valve of the high pressure separator exhibits the highest exergy destruction rate (EDR) followed by the steam trubine, while both high- and low-pressure separators experience no exergy destruction. The energy and exergy efficiencies are found to be 13.3% and 51.23%, respectively. The condensation heat rate is obtained around 46.551 MW, suggesting potential use for district heating applications. Additionally, the findings demonstrate that increasing the geofluid source temperature and the pressure of high-pressure separator 1 lead to a decrease in the unit exergy cost, but an increase in overall cost rate, highlighting important trade-offs for optimizing plant performance
Impacts of AI Usage Ethically on Students
No full textThis study explores the impact of ethical artificial intelligence (AI) usage on university students\u27 academic experiences, performance, and knowledge acquisition. Conducted at Zayed University in the UAE, this cross-sectional study assesses student perceptions of ethical AI practices, including fairness, transparency, and responsibility, using a conceptual framework adapted from prior research. Findings reveal that ethical AI practices, such as the responsible use of adaptive learning systems and AI-driven feedback mechanisms, significantly enhance students\u27 academic outcomes while addressing issues like plagiarism and over-reliance on AI. However, challenges such as biases, reliability, and context understanding in AI tools highlight the need for enhanced development and guidelines. By integrating ethical considerations and reflecting on these challenges, this study underscores the importance of balancing AI’s transformative potential with responsible implementation to foster equitable and effective learning environments. Recommendations for educators, students, and developers include promoting ethical AI usage, developing reliable systems, and enhancing awareness of privacy concerns to maximize AI’s benefits in education
Ultrasound-Assisted Dispersion of Metal Nanoparticles on Clay for Reduction of Organic Pollutants and Antimicrobial Activities
No full textWater pollution by persistent dyes and bacteria has become one of the major environmental concerns. One of the most widely used strategies is the use of supported metal nanoparticles (MNPs) to remove a wide variety of dyes. This work concerns the dispersion of MNPs (M = Ag, Zn, and Cu) on kaolin clay using ultrasonic irradiation. The resulting solids were used as catalysts to reduce methylene blue (MB), methyl orange (MO), and orange G (OG) dyes in a simple and binary system. The obtained results showed that ultrasonic irradiation produced a good dispersion of MNPs with ultrafine sizes. According to XPS and TEM analysis, the MNPs (M = Ag, Cu, and Zn) were well formed. Catalytic tests showed that AgNPs-modified kaolin (K-Ag) was the most efficient compared with other catalysts modified by ZnNPs and CuNPs. In all tests, the K-Ag catalyst was more efficient with MB dye than with azo dyes. It was shown that the reduction of dyes is influenced by the concentration of the starting reactants, the mass of the catalyst, and the nature of the dye used. The rate constants were calculated to be 83.10−4 and 24.10−4 s−1 for MB and MO dyes, respectively. For the system containing a mixture of dyes, the K-Ag catalyst was more selective with MB dye. The reuse of the K-Ag catalyst showed good results without losing its performance. Antibacterial applications showed that K-Cu material was the most efficient overall bacteria
Foundations of deep learning and large language models in cybersecurity
No full textThe integration of deep learning (DL) and large language models (LLMs) has significantly advanced the field of cybersecurity, offering innovative approaches to threat detection, anomaly identification, and secure communication. Deep learning techniques, such as neural networks and reinforcement learning, have demonstrated robust capabilities in detecting previously unknown threats by learning patterns from vast amounts ofcybersecurity data. Similarly,LLMs, particularly transformers, have revolutionized natural language processing tasks, enabling effective vulnerability analysis, malware classification, and phishing detection. This chapter explores the foundational concepts of deep learning and LLMs, highlighting their applications and challenges within the cybersecurity landscape. Additionally, it discusses the synergy between these technologies, focusing on how they complement traditional cybersecurity measures and drive the evolution of intelligent defense mechanisms
Bridging the Education-Employment Gap: Barriers to Employment and Well-Being Among Unemployed Individuals in Abu Dhabi
Get PDFUnemployment remains a critical socio-economic challenge with profound implications for individual wellbeing. This study examines the self-perceived barriers to employment and their impact on key well-being determinants, including life satisfaction, happiness, mental health (where higher scores indicate worse outcomes), subjective health, family relations, friend relations, and the ability to make ends meet (ATMEM), among unemployed individuals in Abu Dhabi. Using data from a comprehensive survey (QoL-5), the analysis reveals significant differences in well-being outcomes across demographic factors such as gender, age, education level, and head of household status. Notably, barriers such as lack of social networks, job competition, and education-related challenges emerged as the most impactful, with Lack of social network strongly associated with worse mental health and lower social connections. Heads of households and mid-life respondents experienced greater vulnerabilities, reporting poorer mental health and financial strain. Conversely, younger and non-head respondents exhibited better mental well-being and stronger social relationships. These findings highlight the complex interplay between self-perceived barriers, demographic factors, and well-being dimensions. The study underscores the importance of targeted interventions to address unemployment\u27s psychological and social challenges and foster a more inclusive labor market in Abu Dhabi
Wearing the Veil in the Web: Transformations of Social Norms and Everyday Practices in the Digital Sphere Within the MENA Region
Get PDFIn the digital era, the meaning of different kinds of veil, including the hijab, undertakes significant transformations due to the interaction between laws, norms, everyday practice, and digital technologies. At the same time, the digital sphere intensifies the manifestation of diverse cultural expressions, including veiling practices, leading to new trends, meanings, and layers of complexity. Through the lens of the semiotic of culture, this contribution aims to explore how these practices, traditionally associated with certain notions of modesty and religious identity, are now subject to new translations and dynamic reinterpretations within digital spaces. The digital sphere appears as a semiotic continuum where the veiling practices are negotiated, affecting their perception, and creating hybrid cultural identities. Furthermore, these digital interactions influence the relationship between laws and social norms surrounding veiling practices. The various veiled forms of female dress-code move across semiospheres, where they are revalorized and often recontextualized, challenging established norms and suggesting a new paradigm for the study of these conventions. In this sense, such digital interactions prompt a reassessment of regulatory frameworks and cultural expressions. Drawing on Lotman’s theory of cultures, these dynamics tend to be seen as cultural translation processes across semiotic boundaries. By exploring these dynamics through the semiotic analysis of a corpus of digital representations of the veiling practices within the MENA region, the research will contribute to a deeper understanding of the cultural adaptations that are leading to negotiated identities, offering a perspective on how the evolving nature of cultural symbols translates into legal frameworks and societal values
Comparative analysis of leading artificial intelligence chatbots in the context of entrepreneurship
Get PDFArtificial intelligence (AI) chatbots show remarkable abilities across applications. Despite a growing literature, their capability in the field of entrepreneurship is not fully understood. The aim of this study is to empirically evaluate and compare capabilities of five major AI chatbots—GPT-3.5, GPT-4, Gemini 1.0, Llama 2, and Claude—in the context of entrepreneurship theory, using a benchmark entrepreneurship test. In particular, the performance of the chatbots on a set of multiple-choice questions, short-answer questions, and essay questions related to entrepreneurship is assessed. The results indicate that GPT-4 delivers the strongest overall performance. Meanwhile, Llama 2 offers precise responses with a significantly lower word count compared to the GPT models. Although chatbots do not always provide correct or precise answers to questions or complex prompts, they still prove to be valuable analytical tools for entrepreneurs. While the study offers compelling insights into chatbots’ grasp of entrepreneurship concepts, the findings are somewhat limited by the scarce availability of data
Cyber threat intelligence for smart grids using knowledge graphs, digital twins, and hybrid machine learning in SCADA networks
Get PDFIn the SCADA (Supervisory Control and Data Acquisition) network of a smart grid, the network switch is connected to multiple Intelligent Electronic Devices (IEDs) that are based on protective relays. False-Data Injection Attacks (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attacks (SRA) are three types of cyber-attacks on SCADA networks, resulting in single-line-to-ground (SLG) fault, IED-relay failure, and circuit-breaker open issues occur. The existing cyber threat intelligence (CTI) approaches of grids are unable to provide visualization of cyber-attacking grid effects. To understand the full effect of the attacks, there is a need for a knowledge-graph method-based digital-twin cyber-attack visualization approach in SCADA networks, which is missing in existing SCADA systems. This study presents a novel “Digital-twin and Machine Learning-based SCADA Cyber Threat Intelligence (DT-ML-SCADA-CTI)” approach, which utilizes an innovative algorithm to visualize and predict the effects of cyber-attacks, including FDIA, RTCI, and SRA, on SCADA systems. The process begins with data transformation to generate cyber-attack grid data, which is then analyzed for attack prediction using machine learning models such as Extra-Trees, XGBoost, Random Forest, Bootstrap Aggregating, and Logistic Regression. To further enhance the analysis, a directed-graph (DiGraph) algorithm is applied to create a knowledge-graph-based digital twin, allowing for a deeper understanding of how these cyber-attacks impact SCADA operations. The comparison with existing models demonstrates the superiority of the proposed approach, as it offers a more detailed and clearer digital-twin representation of cyber-attack effects. This enhanced visualization provides deeper insights into attack dynamics and significantly improves predictive accuracy, showcasing the effectiveness of the proposed method in understanding and mitigating cyber threats
Temporary Migrant Workers in Transnational Space: Understanding Class Ambiguity
Get PDFThis paper has two main objectives. First, it delves into the complex dynamics of social inequality among migrant workers who, being transnational, often face a dual or multiple class and status positions depending on their physical location, and their ethno-national, and gender backgrounds. Their class position becomes uncertain, fluid, and unstable. Drawing comparisons with their sedentary counterparts, the ambiguity of their class positions in the home and host countries becomes apparent. Migrant workers, often driven by economic necessity or geopolitical factors, face unique challenges that distinguish their experiences from those in settled communities. By examining the multifaceted dimensions of social inequality, this paper aims to shed light on the distinct characteristics and implications of inequality within migrant populations while also highlighting key differences from their sedentary counterparts in their societies of origin. Second, this paper argues that the concept of class, as used in mainstream sociology, falls short when applied to transnational migrants. Yet, the concept of class has not outlived its utility, despite the challenges the case of migrant laborers poses. Conceptual innovation is needed for tackling the subject of class in the context of transnationalism, which this paper strives to provide by interrelating social class with occupational status and exploring their effects on transnational migrant workers
A comparative study of machine learning and deep learning models in binary and multiclass classification for intrusion detection systems
Get PDFNetwork infrastructure evolution has significantly expanded the attack surface, leading to increasingly complex and sophisticated cybersecurity threats. Traditional rule-based intrusion detection systems (IDS) often fail to detect emerging attack vectors, prompting the need for intelligent, data-driven approaches. This study evaluates and compares the performance of machine learning (ML) and deep learning (DL) models for network intrusion detection. Two publicly available datasets were utilized: a binary-labeled software-defined networking (SDN) dataset and a multiclass industrial control system dataset based on the IEC 60870-5-104 protocol. Preprocessing steps included normalization, label encoding, and a 70:10:20 train-validation-test split. Seven models, Random Forest, Decision Tree, K-Nearest Neighbors, XGBoost, Convolutional Neural Network, Gated Recurrent Unit, and Long Short-Term Memory, were trained and evaluated using precision, recall, and F1-score. The Random Forest model achieved the highest F1-score of 93.57 % on the IEC 60870-5-104 dataset, while XGBoost attained a near-perfect F1-score of 99.97 % on the SDN dataset. These results outperform comparable models in the literature and offer practical insights for selecting effective IDS solutions based on classification type and dataset structure