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 >

Zeyang Zhou^1,*, Zachary James Ryan¹, Utkarsh Sharma², Tran Tien Anh³, Shashi Mehrotra⁴, Angelo Greco⁵, Jason West⁶, Mukesh Prasad^1,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4795-4813, 2025, DOI:10.32604/cmc.2025.060291 - 19 May 2025

Abstract Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced… More >

Baker M. Ayyash¹, Yen Chuan Chen², Ahmad Sallehuddin², Ziyad M. Hijazi^1,*

Congenital Heart Disease, Vol.20, No.2, pp. 143-179, 2025, DOI:10.32604/chd.2025.064599 - 30 April 2025

Abstract Pediatric heart valve replacement (PHVR) remains a challenging procedure due to the unique anatomical and physiological characteristics of children, including growth and development, as well as the long-term need for durable valve function. This review provides an overview of both surgical and transcatheter options for aortic, mitral, pulmonary, and tricuspid valve replacements in pediatric patients, highlighting the indications, outcomes, and advancements in technology and technique. Surgical valve replacement traditionally involves the implantation of biological or mechanical prosthetic valves, with biological valves being preferred in children to reduce the need for lifelong anticoagulation therapy. However, the… More >

Ilesanmi Afolabi Daniyan^1,*, Rumbidzai Muvunzi², Festus Fameso³, Julius Ndambuki³, Williams Kupolati³, Jacques Snyman³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1623-1648, 2025, DOI:10.32604/cmc.2025.062092 - 16 April 2025

Abstract This study evaluates the Fuzzy Analytical Hierarchy Process (FAHP) as a multi-criteria decision (MCD) support tool for selecting appropriate additive manufacturing (AM) techniques that align with cleaner production and environmental sustainability. The FAHP model was validated using an example of the production of aircraft components (specifically fuselage) employing AM technologies such as Wire Arc Additive Manufacturing (WAAM), laser powder bed fusion (L-PBF), Binder Jetting (BJ), Selective Laser Sintering (SLS), and Laser Metal Deposition (LMD). The selection criteria prioritized eco-friendly manufacturing considerations, including the quality and properties of the final product (e.g., surface finish, high strength,… More >

Sarah M. Kamel^1,*, Mai A. Fadel², Lamiaa Elrefaei^1,3, Shimaa I. Hassan^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 373-411, 2025, DOI:10.32604/cmes.2025.062837 - 11 April 2025

Abstract Visual question answering (VQA) is a multimodal task, involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer. In this paper, we propose a VQA system intended to answer yes/no questions about real-world images, in Arabic. To support a robust VQA system, we work in two directions: (1) Using deep neural networks to semantically represent the given image and question in a fine-grained manner, namely ResNet-152 and Gated Recurrent Units (GRU). (2) Studying the role of the utilized multimodal bilinear… More >