Experimental analysis of the workability of copper matrix composites enhanced with MoS2 and TiO2 particles Mohamed Kchaou, Velayudham Subbian, Sujin Jose Arul, Hariharan Nair Sandeep, Faisal Khaled Aldawood, Mohammed Alquraish International Journal of Materials Research, 2026 This study investigated the workability of copper-based hybrid composites. The copper composites with enhanced mechanical properties were fabricated using powder metallurgy to be reinforced with titanium dioxide (TiO 2 ) and molybdenum disulfide (MoS 2 ). Triaxial stress state conditions were used to evaluate the performance of the copper composites, such as true axial strain and stress, true hoop stress, true mean stress, true effective stress, strain hardening index, strength coefficient, and instantaneous strain hardening from the measurement obtained using cold upset testing. The relationships between various stresses and stress–strain ratios were plotted and analyzed. Results showed that the increased reinforcement contents in the copper matrix enhanced the mechanical properties of the copper composites, especially the true axial, the true hoop, and the true effective stresses. Furthermore, the combination of 5 wt.% of TiO 2 and 4 wt.% of MoS 2 in the copper matrix was identified as the optimum composition for the best workability.
Precision Pediatric Cancer Nanomedicine: Advancing Personalized Nano Therapies to Reduce Non-Communicable Diseases Through AI-Driven 3D-Printed Drugs Neeraj Choudhary, Dinesh Kumar, Thakur Jyoti, Bhupendra Prajapati, Mohamed Kchaou, Thomas Webster, Md Faiyazuddin International Journal of Nanomedicine, 2026 Pediatric cancers (PC) require treatments that maintain cure rates while minimizing long-term toxicity and non-communicable diseases. Yet, conventional dosing, adult-oriented formulations, and high treatment burden remain major limitations in childhood cancer care. This review synthesizes the current evidence using artificial intelligence (AI) and 3D nano printing as emerging tools to support personalized pediatric oncology. A structured literature search of PubMed, Scopus, Web of Science, and Google Scholar (2005-2024) identified English-language studies related to pediatric cancer, nanomedicine (NM), 3D printing, precision dosing, and pharmacogenomics, and relevant findings were organized by cancer type, clinical application, and potential impact on toxicity, adherence, and survivorship. Across leukemia, neuroblastoma, brain tumors, bone sarcoma, and lymphoma, AI-supported platforms were found to improve individualized chemotherapy exposure, anticipate toxicity based on clinical or pharmacogenomic markers, and assist clinicians towards modifying early treatment. At the same time, 3D nano printing enabled child-friendly medicines, multi-drug polypills, and controlled-release formulations that reduced dosing errors and improved treatment adherence. Early hospital-based experience with Bayesian therapeutic drug monitoring and on-demand pediatric drug printing suggested high feasibility for real clinical settings. Overall, AI-guided dosing and nano-printed formulations enhanced precision, lowering acute and late toxicities that support healthier long-term outcomes in children with cancer, particularly when linked to disease-specific needs. Further multicenter pediatric studies, regulatory development, and expansion of hospital 3D printing capacity are recommended to enable safe and equitable translation of these technologies into routine clinical care.
Artificial Intelligence and Machine Learning in Tribology: Selected Case Studies and Overall Potential Raj Shah, Rudy Jaramillo, Garvin Thomas, Thohid Rayhan, Nayem Hossain, Mohamed Kchaou, Francisco J. Profito, Andreas Rosenkranz Advanced Engineering Materials, 2025 Artificial intelligence (AI) and machine learning (ML) have been the subjects of increased interest in recent years due to their benefits across several fields. One sector that can benefit from these tools is the tribology industry, with an emphasis on friction and wear prediction. This industry hopes to train and utilize AI algorithms to classify equipment life status and forecast component failure, mainly using supervised and unsupervised learning approaches. This article examines some of the methods that have been used to accomplish this, such as condition monitoring for predictions in material selection, lubrication performance, and lubricant formulation. Furthermore, AI and ML can support the determination of tribological characteristics of engineering systems, allowing for a better fundamental understanding of friction, wear, and lubrication mechanisms. Moreover, the study also finds that the continued use of AI and ML requires access to findable, accessible, interoperable, and reusable data to ensure the integrity of the prediction tools. The advances of AI and ML methods in tribology show considerable promise, providing more accurate and extensible predictions than traditional approaches.
Industry 5.0 adaptation for disability-inclusive healthcare: A review of emergent and AI technologies for assistive digital health Mohamed Kchaou, Yamuna Munusamy, Khalid Ayed Alharthi, Akram Fadhl Al-mahmodi Digital Health, 2025 Industry 5.0 is reshaping healthcare through human-centric design, sustainability, and advanced technologies. However, there is limited insight into how these innovations address the specific needs of people with disabilities. This review aims to examine the role of emerging and AI-driven technologies in enabling disability-inclusive digital healthcare solutions. A comprehensive scoping review was conducted, focusing on studies published in recent years on Industry 5.0 technologies applied to disability-inclusive digital healthcare pathways. Key technologies reviewed include collaborative robotics, virtual reality, telemedicine, and human-centered artificial intelligence. Relevant case studies and ethical considerations were also analysed. The analysis highlighted that Industry 5.0 technologies show promise in enhancing diagnostic accuracy, personalization, and accessibility for people with disabilities. Applications include remote assessments, assistive tools, and adaptive interfaces that improve diagnostic processes. Despite this progress, integration of these technologies remains fragmented, and challenges such as ethical concerns, regulatory barriers, and inclusive design persist. This review uniquely synthesizes these technologies within the framework of Industry 5.0, offering a broader perspective than prior single-technology reviews and proposing a roadmap for the successful implementation that incorporates training, regulatory alignment, interdisciplinary collaboration, social-economic barriers, real-world evidence, and inclusivity across disability types. As conclusion, Industry 5.0 holds significant promise for advancing disability-inclusive digital healthcare. Realizing this potential, however, requires coordinated efforts to address integration gaps, strengthen ethical and regulatory frameworks, and embed user-centered co-design principles. Future research should focus on more developing inclusive, and sustainable diagnostic solutions aligned with Industry 5.0 principles.
Development of wind turbines for urban environment using innovative design thinking methodology Claudia V. Campos Rubio, Mohamed Kchaou, Paulo Eustáquio de Faria, Juan C. Campos Rubio, Faris Alqurashi Journal of Engineering Research Kuwait, 2025 This work focuses mainly on wind energy usage as a renewable source for energy generation in urban environments, where the variability of air current flow is a challenge. One of the main objectives to be achieved in this study is to develop small vertical wind turbines. Design thinking methodology was used to innovative ideas development using the application of concepts from rapid prototyping (additive manufacturing). As a target object to idealize an innovative product, was the providing of a mobile equipment recharging system, using the air current generated by the urban public transport vehicles. As a result, a modular system of small power generation was produced, tested, and validated. The mini-turbine with the best performance in power generation in wind flows lower than 4 m/s was the VAWT, which achieved the smallest difference, 18 % greater. Therefore, it was demonstrated that the installation of small wind turbines in an urbanized area requires a minimum of wind measurements at the exact location to validate the innovative design.
Robust Autism Spectrum Disorder Screening Based on Facial Images (For Disability Diagnosis): A Domain-Adaptive Deep Ensemble Approach Mohammad Shafiul Alam, Muhammad Mahbubur Rashid, Ahmad Jazlan, Md Eshrat E. Alahi, Mohamed Kchaou, Khalid Ayed B. Alharthi Diagnostics, 2025 Background/Objectives: Artificial intelligence (AI) is revolutionising healthcare for people with disabilities, including those with autism spectrum disorder (ASD), in the era of advanced technology. This work explicitly addresses the challenges posed by inconsistent data from various sources by developing and evaluating a robust deep ensemble learning system for the accurate and reliable classification of autism spectrum disorder (ASD) based on facial images. Methods: We created a system that learns from two publicly accessible datasets of ASD images (Kaggle and YTUIA), each with unique demographics and image characteristics. Utilising a weighted ensemble strategy (FPPR), our innovative ASD-UANet ensemble combines the Xception and ResNet50V2 models to maximise model contributions. This methodology underwent extensive testing on a range of groups stratified by age and gender, including a critical assessment of an unseen, real-time dataset (UIFID) to determine how well it generalised to new domains. Results: The performance of the ASD-UANet ensemble was consistently better. It significantly outperformed individual transfer learning models (e.g., Xception alone on T1+T2 yielded an accuracy of 83%), achieving an impressive 96.0% accuracy and an AUC of 0.990 on the combined-domain dataset (T1+T2). Notably, the ASD-UANet ensemble demonstrated strong generalisation on the unseen real-time dataset (T3), achieving 90.6% accuracy and an AUC of 0.930. This demonstrates how well it generalises to new data distributions. Conclusions: Our findings demonstrate significant potential for widespread, equitable, and clinically beneficial ASD screening using this promising, reasonably priced, and non-invasive method. This study establishes the foundation for more precise diagnoses and greater inclusion for people with autism spectrum disorder (ASD) by integrating methods for diverse data and combining deep learning models.
Adaptive Smart Materials in Architecture: Enhancing Durability and Sustainability in Modern Construction Aiswarya Kallayil, Jigar Patadiya, Balasubramanian Kandasubramanian, Aleksey Adamtsevich, Mohamed Kchaou, Faisal Khaled Aldawood ACS Omega, 2025 High Resolution Image Download MS PowerPoint Slide Adaptive materials in civil construction offer superior performance and high potential for 4D printing integration. Smart materials exhibit rapid response times, precise sensor applications, and real-time durability monitoring, with phase-changing materials improving thermal efficiency by 30% and self-sensing concrete detecting microstrains as low as 10 με. These materials enhance fracture toughness (50% increase), corrosion resistance (40% improvement), and fire stability (up to 1200 °C). Smart bricks incorporate phase-change materials, glazing systems, and recyclable composites, with some embedding electrodes achieving conductivity of 10 –3 S/m for strain sensing. Additive manufacturing (AM) reduces material waste by 50%, enhances design flexibility (90%), and lowers the carbon footprint (40–60%). This review examines communication protocols such as Zigbee, LoRaWAN, and 5G, which enable real-time data transfer and processing. However, embedded systems in smart bricks may be vulnerable to cyber-attacks and data breaches. Ensuring security involves encryption methods, blockchain technology, intrusion detection systems to protect data integrity and network reliability, 3D-printed smart bricks, categorizing materials, and fabrication mechanisms. Integrating AM with smart materials fosters resilient, energy-efficient construction, essential for sustainable urbanization.
Multiphysical analysis of nanoparticles and their effects on plants Mohammad Asaduzzaman Chowdhury, Md Bengir Ahmed Shuvho, Md Imran Hossain, Md Osman Ali, Mohamed Kchaou, Atiqur Rahman, Nilufa Yeasmin, Abdus Sabur Khan, Md Azizur Rahman, M. Mofijur Biotechnology and Applied Biochemistry, 2021
3D-Printed Objects for Multipurpose Applications Nayem Hossain, Mohammad Asaduzzaman Chowdhury, Md. Bengir Ahmed Shuvho, Mohammod Abul Kashem, Mohamed Kchaou Journal of Materials Engineering and Performance, 2021
