KL Vasudev¹, Manish Pandey², Jaan H. Pu^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.7, pp. 1545-1569, 2025, DOI:10.32604/fdmp.2025.065289 - 31 July 2025

Abstract This study presents a comparative analysis of optimisation strategies for designing hull shapes of Autonomous Underwater Vehicles (AUVs), paying special attention to drag, lift-to-drag ratio, and delivered power. A fully integrated optimisation framework is developed accordingly, combining a single-objective Genetic Algorithm (GA) for design parameter generation, Computer-Aided Geometric Design (CAGD) for the creation of hull geometries and associated fluid domains, and a Reynolds-Averaged Navier–Stokes (RANS) solver for evaluating hydrodynamic performance metrics. This unified approach eliminates manual intervention, enabling automated determination of optimal hull configurations. Three distinct optimisation problems are addressed using the proposed methodology. First,… More >

Madeline Keane¹, Natalia Wojciechowska², Lindsay Zumwalt^1,*, Emilie Sandfeld³, Alejandra Dominguez¹, Jason Wang², Anish Ray²

Oncology Research, Vol.33, No.8, pp. 1895-1908, 2025, DOI:10.32604/or.2025.065547 - 18 July 2025

Abstract Background: Precision medicine is an emerging approach for treating pediatric cancer due to its ability to target tumor-specific genetic drivers rather than provide broad and aggressive treatments. The study aimed to outline the establishment and impact of a Precision Medicine Clinic (PMC) in the setting of pediatric oncology, with the objective of offering targeted treatment options within the institution and creating a scalable model for adoption by other healthcare systems to achieve a wider impact. Methods: Recognizing this need for an individualized approach to treating patients, Cook Children’s Medical Center (CCMC) established a multidisciplinary molecular… More >

Issah Zabsonre Alhassan^1,2,*, Gaddafi Abdul-Salaam¹, Michael Asante¹, Yaw Marfo Missah¹, Alimatu Sadia Shirazu¹

Journal of Cyber Security, Vol.7, pp. 165-196, 2025, DOI:10.32604/jcs.2025.063529 - 07 July 2025

Abstract Pseudorandom number generators (PRNGs) are foundational to modern cryptography, yet existing approaches face critical trade-offs between cryptographic security, computational efficiency, and adaptability to emerging threats. Traditional PRNGs (e.g., Mersenne Twister, LCG) remain widely used in low-security applications despite vulnerabilities to predictability attacks, while machine learning (ML)-driven and chaos-based alternatives struggle to balance statistical robustness with practical deployability. This study systematically evaluates traditional, chaos-based, and ML-driven PRNGs to identify design principles for next-generation systems capable of meeting the demands of high-security environment like blockchain and IoT. Using a framework that quantifies cryptographic robustness (via NIST SP… More >

Thaer Thaher^1,*, Majdi Mafarja², Muhammed Saffarini³, Abdul Hakim H. M. Mohamed⁴, Ayman A. El-Saleh⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2615-2673, 2025, DOI:10.32604/cmes.2025.064857 - 30 June 2025

Abstract Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks, sunglasses, and other obstructions. Addressing this issue is crucial for applications such as surveillance, biometric authentication, and human-computer interaction. This paper provides a comprehensive review of face detection techniques developed to handle occluded faces. Studies are categorized into four main approaches: feature-based, machine learning-based, deep learning-based, and hybrid methods. We analyzed state-of-the-art studies within each category, examining their methodologies, strengths, and limitations based on widely used benchmark datasets, highlighting their adaptability to partial and severe occlusions. The review… More >

Amine Assouguem^1,*, Saoussan Annemer^2,3, Mohammed Kara⁴, Abderrahim Lazraq⁵

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1631-1668, 2025, DOI:10.32604/phyton.2025.065750 - 27 June 2025

Abstract Unlike primary metabolites, secondary metabolites serve critical ecological functions, including plant protection, stress tolerance, and symbiosis. This review focuses on extracting, separating, and identifying the major classes of secondary metabolites, including alkaloids, terpenoids, phenolics, glycosides, saponins, and coumarins. It describes optimized methods regarding plant selection, extraction by solvents, and purification of the metabolites, highlighting the latest advancements in chromatographic and spectroscopic techniques. The review also describes some of the most important problems, such as the instability of the compounds or diversity of the structures, and discusses emerging technologies that solve these issues. Moreover, it examines More >

Etan Eigner^1,*, Yacov Reisman², Nicola Fazza¹, Ameer Nsair¹, Valentin Shabataev¹, Ariel Zisman¹

Canadian Journal of Urology, Vol.32, No.3, pp. 229-235, 2025, DOI:10.32604/cju.2025.063332 - 27 June 2025

Abstract Inflatable penile prosthesis (IPP) implantation is the gold standard treatment for patients with erectile dysfunction who are refractory to medical therapy. The standard placement of the reservoir in the space of Retzius (SOR) may be contraindicated in patients with prior pelvic or abdominal surgery due to altered anatomy and increased risk of complications. This has led to the development of alternative ectopic reservoir placement techniques. In this narrative review, we summarize the literature on various ectopic reservoir approaches, including low and high submuscular placements, submuscular techniques with counter incisions or transfascial fixation, midline submuscular placement, More >

Yu Zhou¹, Eneko Osaba², Xiao Zhang^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 237-242, 2025, DOI:10.32604/cmc.2025.066663 - 09 June 2025

Abstract This article has no abstract. More >

Amrutanshu Panigrahi¹, Abhilash Pati¹, Bibhuprasad Sahu², Ashis Kumar Pati³, Subrata Chowdhury⁴, Khursheed Aurangzeb^5,*, Nadeem Javaid⁶, Sheraz Aslam^7,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1633-1657, 2025, DOI:10.32604/cmc.2025.063562 - 09 June 2025

Abstract The integration of IoT and Deep Learning (DL) has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management (PHM). Electrocardiograms (ECGs) are widely used for cardiovascular disease (CVD) diagnosis, but fluctuating signal patterns make classification challenging. Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations. With this motivation, the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis. Deep Transfer Learning (DTL) techniques extract features, followed by feature fusion to eliminate redundancy… More >

Vidivelli Soundararajan^*, Manikandan Ramachandran^*, Srivatsan Vinodh Kumar

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5259-5277, 2025, DOI:10.32604/cmc.2025.063059 - 19 May 2025

Abstract Many applications, including security systems, medical diagnostics, and human-computer interfaces, depend on eye gaze recognition. However, due to factors including individual variations, occlusions, and shifting illumination conditions, real-world scenarios continue to provide difficulties for accurate and consistent eye gaze recognition. This work is aimed at investigating the potential benefits of employing transfer learning to improve eye gaze detection ability and efficiency. Transfer learning is the process of fine-tuning pre-trained models on smaller, domain-specific datasets after they have been trained on larger datasets. We study several transfer learning algorithms and evaluate their effectiveness on eye gaze… More >

Muhammad Hameed Siddiqi^1,*, Menwa Alshammeri¹, Jawad Khan^2,*, Muhammad Faheem Khan³, Asfandyar Khan⁴, Madallah Alruwaili¹, Yousef Alhwaiti¹, Saad Alanazi¹, Irshad Ahmad⁵

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5345-5371, 2025, DOI:10.32604/cmc.2025.062340 - 19 May 2025

Abstract As legal cases grow in complexity and volume worldwide, integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus. This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain. The proposed framework comprises three core modules: legal feature extraction, semantic similarity assessment, and verdict recommendation. For legal feature extraction, a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts. Semantic similarity between cases is evaluated using a hybrid method that… More >