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Item Development, performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using deep learning(Springer Nature, 2025-12-01) Elwakeel, Abdallah Elshawadfy; Elden, Abdallah Zein; Ahmed, Saad F.; Issa, Sali; Li, Changyou; Ali, Khaled Abdeen Mousa; Hanafy, Waleed M.; Ali, Guma; Alzahrani, Fawaz; Fathy, AtefSugarcane is a vital global crop, serving as a primary source of sugar, biofuel, and renewable energy. Advancements in harvesting are critical to meeting rising demand, enhancing profitability, and supporting eco-friendly agricultural practices in the sugarcane sector. Based on the current challenges of sugarcane harvesting in developed countries, the current study aimed to develop a semiautomatic whole-stalk sugarcane harvester (SWSH) for harvesting two rows of sugarcane stalks at a time and to be front-mounted on a classic four-wheel agricultural tractor. Then performance evaluation and prediction of optimal operational conditions for a double-row sugarcane harvester using Feedforward Neural Network (FNN) and Deep Neural Network (DNN) at different levels of forward speeds (3, 3.5, 4.5, and 5 km/h), row spacing (71, 78.89, and 88.75 cm), cutting heights (0, 2, and 4 cm), and numbers of knives (2 and 4) of the cutting systems. The obtained results showed that the cutting efficiency of the developed SWSH reached 100%. Where the higher cutting efficiency was observed at a cutting height equal to zero (ground level), forward speed of 3 km/hand row spacing of 71 cm using both 2 and 4 knives. The minimum total operating cost of the developed SWSH was about 4.42 USD/ha, and it was detected when using a forward speed of 4.5 km/h, row spacing of 88.75 cm, a cutting height of 4 cm, and two knives only on the cutting disk. Furthermore, at a row spacing of 88.75 cm, the maximum field capacity of the developed SWSH was 0.554 ha/h, observed at a forward speed of 4.5 km/h.Item Performance evaluation of a triple-sided solar dryer in terms of energy-exergy analysis, sustainable indicators and CFD simulation during drying tilapia fish strips(Springer Nature, 2025-11-17) Ghanem, Tarek Hussien M.; Oraiath, Awad Ali Tayoush; Nsasrat, Loai S.; Ali, Guma; Younis, Omar Shahat; Eldin, Abdalla Zain; Elmolakab, Khaled Mohamad; Alhumedi, M.; Ahmed, Atef Fathy; Tantawy, Aml Abubakr; Elwakeel, Abdallah ElshawadfyFixed flat-plate solar collectors suffer from low energy efficiency during mornings and evenings due to suboptimal solar incidence angles, reducing thermal output. While tracking systems improve efficiency by following the sun’s path, their high initial costs, mechanical complexity, and need for advanced control systems limit widespread adoption. These drawbacks demonstrate the importance of cost-effective, efficient alternatives that balance performance and simplicity in solar thermal applications. Thus, a triple-sided solar dryer (TSSD) integrated with intelligent airflow gating was developed to overcome these issues. This study evaluates the performance of a TSSD for drying tilapia strips at three thicknesses (4, 8, and 12 mm) using computational fluid dynamics (CFD), energy-exergy analysis, and sustainability indicators. According to the CFD simulations, they were employed to analyze airflow patterns, temperature distribution, and velocity profiles inside the TSSC and drying room (DR) during a day from 8 a.m. to 5 p.m. Additionally, the CFD was used to estimate the highest air temperature inside the drying to choose the appropriate speed of the air exhaust fan. The simulation analysis indicated that the highest air temperatures were 188.67, 124.4, and 96.51 °C, at three corresponding air velocities of the exhaust fan (1.0, 1.5, and 2.0 m/s), respectively, under a solar intensity of 872 W/m². Where the best velocity of the air exhaust fan was 2 m/s, it provided a uniform drying temperature of 96.51 °C, at solar noon (less than 100 °C). On the other hand, the energy-exergy analysis and sustainable indicators were estimated over two consecutive drying days (8 a.m.–5 p.m.) to assess the thermal behavior of the TSSC and TSSD. The energy analysis showed that the TSSC attained a maximum input energy of 1752.72 W and a useful energy of 810.31 W. Its energy efficiencies ranged from 40.79% to 57.21%. Meanwhile, the maximum drying efficiency was 8.19%, 8.51%, and 8.46% for tilapia strip thicknesses of 4, 8, and 12 mm, respectively. Furthermore, the exergy efficiency ranged from 7.28% to 32.83% (TSSC) and from 66.5% to 87.19% (DR). Additionally, sustainability indicators, such as improvement potential (IP) ranging from 1.19 to 7.22 W, waste exergy ratio (WER) between 0.67 and 0.93, and sustainability index (SI) from 1.08 to 1.49, showed that the system is both environmentally friendly and effective in its operations. The results show that the TSSD is an effective, eco-friendly, and affordable option compared to traditional solar drying systems, providing the best heat performance, better energy-exergy efficiency, and less harm to the environment for drying tilapia.Item An Editorial vision for civil engineering: navigating intelligence and innovation(Mesopotamian Academic Press, 2025-11-01) Mijwil, Maad M.; Adamopoulos, Ioannis; Ali, GumaThe integration of Artificial Intelligence in civil engineering has seen a major advancement over the applications of primitive data-driven models, reaching advanced hybrid physics-informed models. This evolution represents a significant change of paradigm from the basic predictive analytics to what is now called "structural cognition." But in this cutting-edge paradigm, the evolutionary learning of AI beyond the structural prediction is further developed, with the ability to learn underlying causal relationships in complicated engineering systems. This not only allows them to diagnose potential problems, but also to proactively propose well-targeted remedial measures, giving engineers a greater understanding of the situation as well as better, better-informed decision-making with respect to infrastructure sustainability and safety [1], [2]. A critical element to this evolution is the emergence of "perceptive infrastructure." This idea is further greatly supported by similar inventions like wavelet-diffusion architectures, and it is very useful in the design of the most robust vision-based monitoring systems. The systems are especially well-suited to demanding environments, such as low-light and other bad weather conditions where conventional surveillance techniques tend to break down. By equipping infrastructure with the ability to sense and make real-time sense of their environment with high fidelity, these technologies are ushering in a new era of self-sensing, self-reporting and even self-predicting civil assets, heading toward constant intelligent self-diagnosis [1]. Figure 1 illustrates artificial intelligence tools in civil engineering.Item The impact of microplastics on global public health, distribution, and contamination: a systematic review and meta-analysis(Springer Nature, 2025-10-27) Adamopoulos, Ioannis; Valamontes, Antonios; Karantonis, John T.; Syrou, Niki; Mpourazanis, George; Tsirkas, Panagiotis; Mpourazanis, Pantelis; Ali, Guma; Mijwil, Maad M.; Tornjanski, Vesna; Frantzana, Aikaterini; Vogiatzis, Romanos; Dounias, George; Lamnisos, DemetrisPurpose: Microplastics (MPs), defined as plastic particles smaller than 5 mm, are an emerging environmental contaminant of concern due to their widespread presence in air, water, food, and human tissue. Although the evidence has identified MPs in the bloodstream, placentas, and excrement, suggesting chronic internal exposure, MPs' health outcomes and biological mechanisms remain inadequate. Methods: This systematic review and meta-analysis follow PRISMA 2020 guidelines, and the Coherence risk of bias tools to evaluate MPs' exposure pathways and health risks. A comprehensive search across PubMed, Scopus, Web of Science, Embase, Cochrane, and other databases identified 2441 initial records. 78 studies met the inclusion criteria. Ingestion, inhalation, and dermal contact pathways were analysed, and observational and toxicological studies were assessed. Results: Ingestion accounted for ~ 74% of MP exposure pathways, followed by inhalation (22%) and dermal absorption (4%). MPs were detected in 33–65% of human faecal and blood samples across national surveys. Meta-analysis-random-effects models and test of overall effect size z = 1.18, p-value = 0,24 were used to account for inter-study variance. Homogeneity: Q = 1.16 and Heterogeneity: H-squared = 1,00. Conclusions: Animal studies suggest MPs induce oxidative stress, inflammation, immune disruption, and endocrine effects. However, causality in human populations remains unverified due to inconsistent study designs and exposure quantification methods. Subgroup analysis revealed stronger associations with reproductive, gastrointestinal, and neurotoxic markers in rodent models. MPs exposure is recognised as a ubiquitous and potentially hazardous public health concern. This study underscores the pressing need for long-term studies and effective public risk strategies to mitigate human exposure and safeguard the ecosystem.Item Post-quantum secure blockchain-based federated learning framework for enhancing smart grid security.(the University of Information Technology and Communications (UoITC), 2025-10-10) Mijwil, Maad M.; Ali, Guma; Kabiito, Simon Peter; Dhoska, Klodian; Adamopoulos, IoannisEmerging technologies have accelerated the digitalization of smart grids, improving demand-side management, sustainability, and operational efficiency. The attack surface is widened by this interconnection, though, leaving vital smart grid data and systems vulnerable to online attacks. Single points of failure, privacy violations, and a lack of robustness against sophisticated attacks persist in centralized data processing. Traditional cryptographic techniques are further threatened by the development of quantum computing, which raises significant security risks for smart grids. With a focus on post-quantum cryptography (PQC) resilience, this study examines 206 peer-reviewed research articles on blockchain-based federated learning (BFL) in smart grids that were published between January 2023 and July 2025. It assesses the advantages, limitations, and compromises of the current BFL models in this field. The paper suggests a unique post-quantum secure BFL (PQS-BFL) framework that integrates federated learning (FL), lightweight PQC protocols, and a scalable blockchain architecture to solve the vulnerabilities that have been uncovered. This design enables decentralized, private, and impenetrable cooperation among grid nodes. The results demonstrate that the system mitigates quantum-resilient attacks and inference threats while improving data integrity, key management, and secure model aggregation. A path for creating safe, scalable PQS-BFL solutions for upcoming smart energy systems is provided in the paper's conclusion, along with an overview of the main research issues. This study shows that using PQC, blockchain, and FL to secure next-generation smart grids is both feasible and important.Item Fusion of blockchain, IoT, artificial intelligence, and robotics for efficient waste management in smart cities(Association of Talent under Liberty in Technology, 2025-08-09) Ali, Guma; Asiku, Denis; Mijwil, Maad M.; Adamopoulos, Ioannis; Dudek, MarekRapid urbanization and population growth are accelerating waste generation in cities worldwide, posing serious environmental and socio-economic challenges. Traditional waste management systems, often centralized and infrastructure-deficient, struggle with inefficiencies, unscheduled collection, and a lack of real-time data. These limitations hinder progress toward smart and sustainable urban environments. Blockchain, the Internet of Things (IoT), Artificial Intelligence (AI), and Robotics are reshaping waste collection, sorting, and recycling. This review examines how these technologies integrate to create secure, efficient, and sustainable waste management in smart cities. An analysis of 184 studies published between January 2022 and July 2025 reveals key shortcomings in conventional waste management systems and showcases the benefits of smart waste management solutions. The results showed that cities are already using IoT-enabled smart bins, AI-driven route optimization, Blockchain for waste tracking, and robotic sorting. However, challenges such as data privacy concerns, limited Blockchain scalability, system interoperability gaps, sensor reliability issues, and high computational demands limit broader adoption. The review outlines future research priorities, including AI-powered waste forecasting, swarm robotics, real-time edge computing, and enhanced cybersecurity. By providing a roadmap for technological innovation and integration, this study supports policymakers, urban planners, and industry leaders in developing intelligent, cost-effective, and environmentally resilient waste management systems.Item Blockchain and federated learning in edge-fog-cloud computing environments for smart logistics(Mesopotamian Academic Press, 2025-07-22) Ali, Guma; Adebo, Thomas; Mijwil, Maad M.; Al-Mahzoum, Kholoud; Sallam, Malik; Salau, Ayodeji Olalekan; Adamopoulos, Ioannis; Bala, Indu; Al-jubori, Aseed Yaseen RashidThe rapid growth of smart logistics, driven by IoT devices and data-intensive applications, necessitates secure, scalable, and efficient computing frameworks. As the edge-fog-cloud (EFC) paradigm supports real-time data processing, it faces significant security threats and attacks, including privacy risks, data breaches, and unauthorized access. To address these security threats and attacks, blockchain and federated learning (FL) have gained popularity as potential solutions in EFC computing environments for smart logistics. This survey reviews the current landscape in EFC computing environments for smart logistics, highlighting the existing benefits and challenges identified in 134 research studies published between January 2023 and June 2025. The applications of blockchain and FL demonstrate their ability to enhance data security and privacy, improve real-time tracking and monitoring, and ensure inventory and supply chain optimization. Although these technologies offer promising solutions, challenges such as scalability issues, data quality, interoperability and standardization hinder their effective implementation. The survey suggests future research directions focused on developing advanced blockchain and FL, integrating emerging technologies, developing policies and regulations, fostering collaborative research, and ensuring cross-industry adoption and interoperability. Integrating blockchain and FL within the EFC model offers a transformative path toward building secure, intelligent, and resilient logistics systems.Item Influence of physical shape and salting on tomato drying performance using mixed mode solar and open-air methods in semi-cloudy weather(Springer Nature, 2025-07-20) Elwakeel, Abdallah Elshawadfy; Ali, Guma; Eldin, Abdalla Zain; Alsebiey, Mohamed Mahmoud; Tantawy, Aml Abubakr; AL-Harbi, Mohammad S.; Ahmed, Atef Fathy; Metwally, Khaled A.SD Solar drying is increasingly recognized as a sustainable and energy-efficient solution for preserving agricultural products, offering a practical alternative to fossil fuel-dependent methods and traditional open sun drying (OSD). However, its overall performance is highly influenced by environmental variability and system design. This study provides a detailed evaluation of a newly developed direct solar dryer (DDSD) for tomato dehydration, conducted under real and fluctuating climatic conditions in Aswan, Egypt, from February 22 to 27, 2025. During the trial period, solar irradiance ranged widely from 88 to 826 W/m2 due to intermittent cloud cover, while ambient temperatures fluctuated between 22 and 34 °C—conditions representative of actual field environments. Tomato samples were prepared in three physical forms—halves, quarters, and 6 mm slices—and subjected to two pretreatment methods (salted and unsalted) to assess their effects on drying kinetics. The DDSD demonstrated significantly better performance than OSD, reducing drying durations by 25–39.6%. The most efficient results were achieved for salted 6 mm slices, which dried in just 9 h—substantially faster than the 29 h for unsalted halves in DDSD and 48 h in OSD. These samples also exhibited the highest effective moisture diffusivity (Deff) (5.92 × 10⁻⁹ m2/s), reflecting enhanced internal moisture transport. Among 12 drying models evaluated, the Logistic model most accurately described the drying behavior in the DDSD, with an excellent statistical fit (R2 = 0.999524, χ2 = 6.74 × 10⁻5, RMSE = 0.006868). Economically, the DDSD, integrated with a photovoltaic (PV) system, required a modest initial investment of $520 and achieved a payback period of just 1.82 years for salted slices due to faster processing and increased throughput. From an environmental perspective, the system is projected to offset approximately 105.68 metric tons of CO₂ emissions over a 20-year lifespan, with an energy payback time of only 1.10 years and potential revenue of $1321.04 from carbon credits. These findings underscore the DDSD’s potential as a cost-effective, environmentally sustainable, and technically efficient solution for agricultural drying in solar-rich regions.Item Enhancing cybersecurity in smart education with deep learning and computer vision: A Survey.(Mesopotamian Academic Press, 2025-06-26) Ali, Guma; Aziku, Samuel; Mijwil, Maad M.; Al-Mahzoum, Kholoud; Sallam, Malik; Salau, Ayodeji Olalekan; Bala, Indu; Dhoska, Klodian; Melekoglu, EnginThe rapid digital transformation of education, driven by the widespread adoption of smart devices and online platforms, has ushered in the era of smart education. While this shift enhances learning experiences, it also introduces significant cybersecurity risks that threaten the confidentiality, integrity, and availability of educational resources, student data, and institutional systems. This survey examines how deep learning (DL) and computer vision (CV) techniques can enhance cybersecurity in smart education environments. By reviewing 202 peer-reviewed research papers published between January 2022 and June 2025 across leading publishers such as ACM Digital Library, Frontiers, Wiley Online Library, IGI Global, Nature, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, Taylor & Francis, Sage, BMC, and Google Scholar, the study explores the integration of these advanced technologies to address emerging threats. It highlights the use of DL in intrusion detection, anomaly detection, and biometric authentication to protect digital learning platforms. It also examines how CV techniques, such as facial recognition, behavioral analysis, and emotion detection, enhance security and foster adaptive learning environments. The survey also addresses key challenges, including data quality, model interpretability, computational costs, and ethical considerations. By identifying research gaps and proposing future directions, this survey offers valuable insights for researchers, educators, and policymakers aiming to develop robust, scalable, and ethical AI-driven cybersecurity solutions for smart education.Item Spectral realization of the nontrivial zeros of the Riemann Zeta function via a Hermitian operator framework(Mesopotamian Academic Press, 2025-05-18) Valamontes, Antonios; Adamopoulos, Ioannis; Ali, GumaWe present a spectral construction of a Hermitian operator whose spectrum coincides exactly with the imaginary parts of the nontrivial zeros of the Riemann zeta function The operator, denoted H∞, is defined on a discrete geometric space modeled by a 20-vertex dodecahedral graph, incorporating a discrete Laplacian, an entropy-based coherence potential, and a prime-indexed infinite-order algebraic term derived from Infinity Algebra. We show that H∞ is self-adjoint, spectrally complete, and compatible with the analytic continuation and functional symmetry of ζ(s). A spectral determinant constructed from its eigenvalues matches the Hadamard product representation of ζ 1 + it , and no extraneous roots appear off the critical line. Numerical approximations from a truncated version of the operator validate this correspondence. The construction yields a functional-analytic framework that supports a spectral- theoretic resolution of the Riemann HypothesisItem Model predictive control for quad active bridge DC-DC converter for more electric aircraft applications(Springer Nature, 2025-04-21) Adam, Ahmed Hamed Ahmed; Chen, Jiawei; Xu, Minghan; Kamel, Salah; Ali, GumaThe isolated multi-port converters quad-active bridge (QAB) presents a unique opportunity to connect multiple sources and loads operating at different power and voltage levels, offering galvanic isolation and shared magnetics as advantages. However, the high number of modulation variables, dynamic response, and overall modeling complexity of QAB converters pose challenges to controller design. Traditional linear controllers often struggle with voltage overshooting and undershooting under abrupt load changes and exhibit limited dynamic performance and coupling among different ports. To address these challenges, this paper introduces a moving discretized control set-model predictive control (MDCS-MPC) strategy for QAB converters. The developed approach predicts phase shift values through the converter model, ensuring fast dynamic performance and eliminating steady-state errors in control variables. The prediction model’s embedded circuit parameters and operating modes enhance performance across various power and terminal voltage ranges. An adaptive step is implemented for quick transitions, significantly reducing computational demands. These analytical findings and the MDCS-MPC strategy are verified through Matlab simulation results and experimental results obtained from the Hardware-in-the-Loop (HIL) real-time Typhoon 602 platform. Both experimental and simulation results demonstrate the effectiveness of the developed strategy, showing superior dynamic response, robustness, and reduced computational requirements. Furthermore, the voltage achieves a very fast dynamic response and exhibits no significant voltage overshoot or undershoot.Item Improved quadratic interpolation optimizer for stochastic short-term hydrothermal scheduling with integration of solar PV and wind power(Springer Nature, 2025-04-02) Khan, Noor Habib; Wang, Yong; Jamal, Raheela; Ebeed, Mohamed; Kamel, Salah; Ali, Guma; Jurado, Francisco; Youssef, Abdel-RaheemThe quadratic interpolation optimization (QIO) introduces a novel approach inspired by the generalized quadratic interpolation (GQI) with dual mechanisms. Initially, QIO employs GQI in its exploration strategy, updating populations based on two randomly selected individuals. Subsequently, it incorporates another exploration strategy, updating populations based on the best solution and two randomly selected individuals. Despite QIO’s effectiveness in numerous optimization tasks, it exhibits limitations when addressing highly nonlinear and multidimensional problems, such as stagnation, susceptibility to local optima, low diversity, and premature convergence. In this study, we propose three enhancement strategies to refine traditional QIO, aiming to bolster its exploration and exploitation capabilities through Weibull flight motion, chaotic mutation, and PDO mechanisms. The resultant improved QIO (IQIO) is then applied to solve the short-term hydrothermal scheduling (STHS) problem, considering system uncertainties and the potential installation of PV and wind turbine generation units to reduce fuel costs and emissions. The STHS is solved with considering the system constraints including water discharge and reservoir storage, the generated powers by the hydro and thermal units as well as balanced powers. The dependent constraints are handled using weighted summation method. The efficacy of the proposed IQIO is demonstrated using the CEC 2022 test suite, and the obtained results are benchmarked against various competitive optimization methods. Statistical analysis of the results confirms a notable enhancement in the original QIO’s performance upon applying the suggested IQIO. Furthermore, the inclusion of renewable generation units by IQIO yields maximum reductions of 23.73% in costs and 45.50% in emissions, underscoring its potential for sustainable energy management.Item Integration of artificial intelligence, blockchain, and quantum cryptography for securing the Industrial Internet of Things (IIoT): Recent advancements and future trends(Mesopotamian Academic Press, 2025-03-27) Ali, Guma; Aziku, Samue; Kabiito, Simon Peter; Morish, Zaward; Adebo, Thomas; Wamusi, Robert; Asiku, Denis; Sallam, Malik; Mijwil, Maad M.; Ayad, Jenan; Salau, Ayodeji Olalekan; Dhoska, KlodianThe swift growth of the Industrial Internet of Things (IIoT) offers tremendous potential to boost productivity, facilitate real-time decision-making, and automate procedures in various industries. However, as industries increasingly adopt IIoT, they face paramount data security, privacy, and system integrity challenges. Artificial intelligence (AI), Blockchain, and quantum cryptography are gaining significant attention as solutions to address these challenges. This paper comprehensively surveys advanced technologies and their potential applications for securing IIoT ecosystems. It reviews findings from 196 sources, including peer-reviewed journal articles, conference papers, books, book chapters, reports, and websites published between 2021 and 2025. The survey draws insights from leading platforms like Springer Nature, ACM Digital Library, Frontiers, Wiley Online Library, Taylor & Francis, IGI Global, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar. This paper explores AI-driven approaches to anomaly detection, predictive maintenance, and adaptive security mechanisms, demonstrating how machine learning (ML) and deep learning (DL) can identify and mitigate threats instantly. It also examines Blockchain technology, emphasizing its decentralized nature, immutability, and ability to secure data sharing and authentication within IIoT networks. The paper discusses quantum cryptography, which utilizes quantum mechanics for theoretically unbreakable encryption, ensuring secure communications in highly sensitive industrial environments. The integration of these technologies is analyzed to create a multi-layered defense against cyber threats, highlighting challenges in scalability, interoperability, and computational overhead. Finally, the paper reviews the current research, limitations and challenges, and future directions for securing IIoT with these advanced technologies. This survey offers valuable insights to researchers, engineers, and industry practitioners working to secure the expanding IIoT infrastructure.Item Deep learning-based neural network modeling for economic performance prediction: An empirical study on Iraq(Peninsula Publishing Press, 2025-02-20) Shaker, Atheel Sabih; Ali, Guma; Wamusi, Robert; Habib, HassanThis study investigates the application of deep learning-based neural networks for predicting Iraq’s economic performance. Traditional econometric models impose restrictive assumptions that limit their predictive accuracy, especially in volatile economic environments. To overcome these limitations, we propose an artificial neural network (ANN) model trained on six key macroeconomic indicators: Gross Domestic Product (GDP), inflation rate, unemployment rate, exchange rate, trade volume, and government spending. The dataset spans from 2000 to 2023, sourced from authoritative economic institutions. The methodology incorporates feature scaling, hyperparameter tuning, and backpropagation optimization to minimize mean squared error (MSE) and enhance generalization performance. The model is validated through cross-validation and out-of-sample testing. Descriptive statistical analysis highlights the variability of macroeconomic indicators, while the ANN model effectively captures nonlinear dependencies. The results indicate that GDP and government spending are the most influential factors in economic performance prediction, while unemployment rate and exchange rate exhibit lower predictive significance. The model demonstrates superior accuracy compared to traditional regression-based approaches, with minimal error in both training and testing phases. This research contributes to the empirical literature on machine learning in economic forecasting by presenting a robust alternative to conventional predictive models. The findings provide policymakers with valuable insights for designing data-driven economic policies. Future work should explore hybrid models integrating deep learning with traditional econometrics to improve interpretability while maintaining predictive power.Item A Survey on artificial intelligence and blockchain applications in cybersecurity for smart cities(2025-01-10) Asiku, Denis; Adebo, Thomas; Wamusi, Robert; Aziku, Samuel; Kabiito, Simon Peter; Zaward, Morish; Sallam, Malik; Ali, Guma; Mijwil, Maad M.Smart cities rapidly evolve into transformative ecosystems where advanced technologies work together to improve urban living. These interconnected environments use emerging technologies to offer efficient services and sustainable solutions for urban challenges. As these systems become more complex, their vulnerability to cybersecurity threats also increases. Integrating artificial intelligence (AI) and Blockchain technologies to address these challenges presents promising solutions that ensure secure and resilient infrastructures. This study provides a comprehensive survey of integrating AI, Blockchain, cybersecurity, and smart city technologies based on an analysis of peer-reviewed journals, conference proceedings, book chapters, and websites. Seven independent researchers reviewed relevant literature published between January 2021 and December 2024 using ACM Digital Library, Wiley Online Library, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, IGI Global, and Google Scholar. The study explores how AI can enhance threat detection, anomaly detection, and predictive analytics, enabling real-time responses to cyber threats. It examines various AI methodologies, including machine learning and deep learning, to identify vulnerabilities and prevent attacks. It discusses the role of Blockchain in securing data integrity, improving transparency, and providing decentralized control over sensitive information. Blockchain’s tamper-proof ledger and smart contract capabilities offer innovative solutions for identity management, secure transactions, and data sharing among smart city stakeholders. The study also highlights how combining AI and Blockchain can create robust cybersecurity frameworks, enhancing resilience against emerging threats. The survey concludes by outlining future research directions and offering recommendations for policymakers, urban planners, and cybersecurity professionals. This study identifies emerging trends and applications for enhancing the security and resilience of smart cities through innovative technological solutions. The survey provides valuable insights for researchers and practitioners who aim to utilize AI and Blockchain to improve smart city cybersecurity.Item Advancing green hydrogen production in Algeria with opportunities and challenges for future directions(Springer Nature, 2025-02-14) Benchenina, Yacine; Zemmit, Abderrahim; Bouzaki, Mohammed Moustafa; Loukriz, Abdelouadoud; Elsayed, Salah K.; Alzaed, Ali; Ghoneim, Sherif S. M.Green hydrogen represents a sustainable energy solution capable of supporting the global shift away from fossil fuels. In Algeria, with its abundant solar resources, this potential is significant. However, challenges related to water resource management and the energy cost of production limit large-scale implementation. Addressing these issues is crucial for effectively harnessing Algeria’s renewable energy potential. This study conducts an in-depth analysis leveraging advanced simulation tools like HOMER Pro to compare photovoltaic (PV) productivity and hydrogen yields in Algerian regions. The study identifies both desert regions and non-desert areas for their potential, employing innovative methods such as seawater electrolysis and wastewater utilization for sustainable water sourcing. The potential integration of hydrogen fuel cells into microgrids is also explored for enhanced energy stability and storage. The findings reveal that desert regions, such as Tamanrasset and Adrar, exhibit the highest photovoltaic electricity productivity, generating 33.5 GWh/year and 32.9 GWh/year, respectively. This translates into green hydrogen production capacities of 679 tons/year and 668 tons/year. Meanwhile, northern regions like Tlemcen and Skikda demonstrate substantial potential, producing 29 GWh/year and 26.6 GWh/year of solar electricity, which results in green hydrogen production outputs of 589 tons/year and 539 tons/year, respectively. This underscores Algeria’s ability to leverage solar energy across diverse regions. The study highlights that while desert regions exhibit high solar and hydrogen production, northern areas provide a strategic advantage due to their proximity to European markets. Algeria’s existing infrastructure supports efficient export to European markets, offering a strategic advantage in green hydrogen trade.Item Quality of indoor air in educational institutions and adverse public health in Europe: A scoping review.(Modestum, 2025-03-01) Adamopoulos, Ioannis Pantelis; Syrou, Niki Fotios; Mijwil, Maad; Thapa, Pramila; Ali, Guma; Dávid, Lóránt DénesIndoor air quality (IAQ) at educational institutions has emerged as an important public health issue, affecting the health and cognitive performance of school-aged children, students, and faculty alike. This scoping review study seeks to investigate and synthesize current literature on the factors influencing the current state of research on IAQ in educational institutions and its implications for public health. The methodology of this study is the scoping review with the guidelines of preferred reporting items for extension reviews. The technique thoroughly investigated peer-reviewed journals, international organizations, government reports, and case studies on IAQ in educational contexts. Using keywords such as IAQ; educational institutions; public health; Europe, and “adverse health outcomes,” the study’s inclusion and exclusion criteria, as well as the criteria use of quality assessments. The results show that poor IAQ is linked to various public health problems, including respiratory issues and cognitive impairments, especially among vulnerable groups like children and teachers. Inadequate ventilation, volatile organic compounds, mold growth, and external contaminants are all common causes of poor IAQ. Monitoring and management measures are required to improve IAQ in Educational Institutions, encouraging students’ health and academic performance. Policy implications are also important for interdisciplinary approaches addressing this public health concern.Item Chinese generative AI models challenge western AI in clinical chemistry MCQs: A Benchmarking follow-up study on AI use in health education(Mesopotamian Press, 2025-02-08) Sallam, Malik; Al-Mahzoum, Kholoud; Eid, Huda; Al-Salahat, Khaled; Sallam, Mohammed; Ali, Guma; Mijwil, Maad M.Background: The emergence of Chinese generative AI (genAI) models, such as DeepSeek and Qwen, has introduced strong competition to Western genAI models. These advancements hold significant potential in healthcare education. However, benchmarking the performance of genAI models in specialized medical disciplines is crucial to assess their strengths and limitations. This study builds on prior research evaluating ChatGPT (GPT-3.5 and GPT-4), Bing, and Bard against human postgraduate students in Medical Laboratory Sciences, now incorporating DeepSeek and Qwen to assess their effectiveness in Clinical Chemistry Multiple-Choice Questions (MCQs). Methods: This study followed the METRICS framework for genAI-based healthcare evaluations, assessing six models using 60 Clinical Chemistry MCQs previously administered to 20 MSc students. The facility index and Bloom’s taxonomy classification were used to benchmark performance. GenAI models included DeepSeek-V3, Qwen 2.5-Max, ChatGPT-4, ChatGPT-3.5, Microsoft Bing, and Google Bard, evaluated in a controlled, non-interactive environment using standardized prompts. Results: The evaluated genAI models showed varying accuracy across Bloom’s taxonomy levels. DeepSeek-V3 (0.92) and ChatGPT-4 (1.00) outperformed humans (0.74) in the Remember category, while Qwen 2.5-Max (0.94) and ChatGPT-4 (0.94) surpassed human performance (0.61) in the Understand category. ChatGPT-4 (+23.25%, p < 0.001), DeepSeek-V3 (+18.25%, p = 0.001), and Qwen 2.5-Max (+18.25%, p = 0.001) significantly outperformed human students. Decision tree analysis identified cognitive category as the strongest predictor of genAI accuracy (p < 0.001), with Chinese AI models performing comparably to ChatGPT-4 in lower-order tasks but exhibiting lower accuracy in higher-order domains. Conclusions: The findings highlighted the growing capabilities of Chinese genAI models in healthcare education, proving that DeepSeek and Qwen can compete with, and in some areas outperform, Western genAI models. However, their relative weakness in higher-order reasoning raises concerns about their ability to fully replace human cognitive processes in clinical decision-making. As genAI becomes increasingly integrated into health education, concerns regarding academic integrity, genAI dependence, and the validity of MCQ-based assessments must be addressed. The study underscores the need for a re-evaluation of medical assessment strategies, ensuring that students develop critical thinking skills rather than relying on genAI for knowledge retrieval.Item High performance medicine: Involving artificial intelligence models in enhancing medical laws and medical negligence matters A Case study of Act, 2009 (Act 792) in Ghana(Peninsula Publishing Press, 2025-01-10) Mensah, George Benneh; Mijwil, Maad M.; Abotaleb, Mostafa; Ali, Guma; Dutta, Pushan KumarThis paper examines Ghana's Interpretation Act, 2009 for applicability in AI medical negligence cases. Doctrinal analysis focuses on causation and liability apportionment provisions. Findings reveal opacity and distributed responsibility issues in attributing algorithm harm via "but-for" and related tests. However, contributory liability and proportionality stipulations provide means for an equitable remedy. Recommendations include codifying AI accountability through updated laws and jurisprudence, plus transparency requirements for medical AI approvals. Ensuring current law dynamically governs emerging technologies remains vital for public welfare. The analysis aims to spur policy adaptations, balancing innovation with adequate causation tests and flexible liability rules for AI medical harms.Item Explainable AI for healthcare: training healthcare workers to use artificial intelligence techniques to reduce medical negligence in Ghana’s Public Health Act, 2012 (Act 851)(Peninsula Publishing Press, 2025-01-10) Mensah, George Benneh; Mijwil, Maad M.; Abotaleb, Mostafa; Ali, Guma; Awwad, Emad Mahrous; Dutta, Pushan Kumar; Mzili, Toufik; Eid, Marwa M.This analysis examines whether Ghana’s Public Health Act, 2012 (Act 851) imposes adequate legal responsibilities on healthcare facilities concerning personnel training on artificial intelligence (AI) systems and implementation of medical negligence reduction measures. Through an evaluative review of Act 851 provisions on staff qualifications, technology deployment, quality care, safety planning, and risk management benchmarks relative to precedents in Ghana and other countries, critical gaps in binding regulations to incentivize organizational capacity building for mitigating errors, hazards and liabilities from substandard practices were identified. Key recommendations include amending Act 851 to mandate credentialing assurance frameworks, clinical audits, risk assessment models and transparency requirements around reporting quality indicators. Strengthening policy directives will compel internal monitoring, governance, and accountability among healthcare facilities as multilayered negligence prevention strategies. Scientific contributions highlight deficiencies in Ghana’s health legislation regarding contemporary challenges like AI adoption risks and propose legal reforms to modernize regulations to support safer, responsible healthcare delivery nationwide.