Rabindranath Bera^1,#, Yotaro Ochi^2,3, Ying-Jung Huang¹, Ming-Chung Kuo^1,4, Kenichi Yoshida⁵, Seishi Ogawa², Lee-Yung Shih^1,4,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070259 - 30 December 2025

Abstract Background: Breakpoint Cluster Region-Abelson (BCR::ABL1) fusion protein is essential in the pathogenesis of chronic myeloid leukemia (CML); however, the chronic-to-blast phase transformation remains elusive. We identified novel kinesin light chain 2 (KLC2) mutations in CML-myeloid blast phase patients. We aimed to examine the functional role of KLC2 mutations in leukemogenesis. Methods: To evaluate the biological role of KLC2 mutants (MT) in CML cells, we expressed KLC2-MT in different human CML cell lines harboring BCR::ABL1 and performed immunoblot, immunofluorescence, cell proliferation, differentiation, and apoptosis; Tyrosine kinase inhibitor (TKI)-drug activities; and clonogenic assays for in vitro functional analyses. We co-expressed KLC2-MT… More >

Zhongrui Jing¹, Hongzhang Yang^1,*, Jiangpu Guo²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.067759 - 09 December 2025

Abstract Hard disk drives (HDDs) serve as the primary storage devices in modern data centers. Once a failure occurs, it often leads to severe data loss, significantly degrading the reliability of storage systems. Numerous studies have proposed machine learning-based HDD failure prediction models. However, the Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes differ across HDD manufacturers. We define hard drives of the same brand and model as homogeneous HDD groups, and those from different brands or models as heterogeneous HDD groups. In practical engineering scenarios, a data center is often composed of a heterogeneous population of… More >

Ghadah Naif Alwakid¹, Samabia Tehsin^2,*, Mamoona Humayun^3,*, Asad Farooq², Ibrahim Alrashdi¹, Amjad Alsirhani¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162 - 10 November 2025

Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… More >

Mohamed Ezz¹, Meshrif Alruily^1,*, Ayman Mohamed Mostafa^2,*, Alaa S. Alaerjan¹, Bader Aldughayfiq², Hisham Allahem², Abdulaziz Shehab²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.063189 - 10 November 2025

Abstract Automated essay scoring (AES) systems have gained significant importance in educational settings, offering a scalable, efficient, and objective method for evaluating student essays. However, developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology, diglossia, and the scarcity of annotated datasets. This paper presents a hybrid approach to Arabic AES by combining text-based, vector-based, and embedding-based similarity measures to improve essay scoring accuracy while minimizing the training data required. Using a large Arabic essay dataset categorized into thematic groups, the study conducted four experiments to evaluate the impact of feature selection,… More >

Taras Shatylko^1,*, Ruslan Safiullin¹, Safar Gamidov^1,2, Tatiana Ivanets¹, Ramazan Mammaev², Kanan Guluzade², Ilia Rodin³, Gennadiy Sukhikh¹

Canadian Journal of Urology, Vol.32, No.6, pp. 561-568, 2025, DOI:10.32604/cju.2025.064572 - 30 December 2025

Abstract Background: Some patients with prostate cancer have elevated gonadotropin levels. It is unknown, however, whether this condition directly influences carcinogenesis in the prostate. It is also unknown whether any specific hormone levels are useful to predict aggressive disease. The potential role of luteinizing hormone (LH) and follicle-stimulating hormone (FSH) in prostate physiology is widely discussed. The study aimed to evaluate whether patients with this endocrine pattern have different outcomes following radical prostatectomy. Methods: This was a prospective cohort study of consecutive patients undergoing robot-assisted radical prostatectomy at the Andrology and Urology Department, National Medical Research… More >

Miljenko Lapaine^*

Revue Internationale de Géomatique, Vol.34, pp. 793-808, 2025, DOI:10.32604/rig.2025.066916 - 06 November 2025

Abstract The paper investigates the properties of the Lambert equivalent azimuthal projection, which is often used in normal aspect in atlases for maps of the northern and southern hemispheres. The field of research is theoretical in nature and assumes a mastery of mathematics because it deals with map projections. The transverse aspect is commonly used for eastern and western hemisphere atlas maps. In addition, the Hammer projection was created from the transverse aspect of that projection. Therefore, if we want to get to know the Hammer projection better, we must first investigate the Lambert equivalent azimuthal… More >

Yifan Li^*, Congzhe Zhu, Rong Gao^*, Bin Yang

Energy Engineering, Vol.122, No.11, pp. 4523-4539, 2025, DOI:10.32604/ee.2025.068480 - 27 October 2025

Abstract The local overheating issue is a serious threat to the safe operation of data centers (DCs). The chip-level liquid cooling with pool boiling is expected to solve this problem. The effect of nano configuration and surface wettability on the boiling characteristics of copper surfaces is studied using molecular dynamics (MD) simulation. The argon is chosen as the coolant, and the wall temperature is 300 K. The main findings and innovations are as follows. (1) Compared to the smooth surface and fin surface, the cylindrical nano cavity obtains the superior boiling performance with earlier onset of… More > Graphic Abstract

Sheeba Razzaq^1,*, Majid Iqbal Khan²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5377-5398, 2025, DOI:10.32604/cmc.2025.068539 - 23 October 2025

Abstract Multi-Object Tracking (MOT) represents a fundamental but computationally demanding task in computer vision, with particular challenges arising in occluded and densely populated environments. While contemporary tracking systems have demonstrated considerable progress, persistent limitations—notably frequent occlusion-induced identity switches and tracking inaccuracies—continue to impede reliable real-world deployment. This work introduces an advanced tracking framework that enhances association robustness through a two-stage matching paradigm combining spatial and appearance features. Proposed framework employs: (1) a Height Modulated and Scale Adaptive Spatial Intersection-over-Union (HMSIoU) metric for improved spatial correspondence estimation across variable object scales and partial occlusions; (2) a feature More >

Thi Phuong Thao Nguyen¹, Tho Thong Nguyen¹, Huu Quynh Nguyen², Tien Duc Nguyen³, Chu Kien Nguyen¹, Nguyen Giap Cu^4,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4899-4920, 2025, DOI:10.32604/cmc.2025.067707 - 23 October 2025

Abstract Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches, leveraging the relational structure between image regions to improve accuracy. This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network (CNN) features with graph convolutional network (GCN) learning, leveraging superpixel-based image representations. The proposed framework initiates the process by segmenting input images into significant superpixels, reducing computational complexity while preserving essential spatial structures. A pre-trained CNN backbone extracts both global and local features from these superpixels, capturing critical texture and shape information. These features are structured into a… More >

Lakshmi Alekhya Jandhyam^1,*, Ragupathy Rengaswamy¹, Narayana Satyala²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3679-3714, 2025, DOI:10.32604/cmes.2025.068941 - 30 September 2025

Abstract Human Activity Recognition (HAR) has become increasingly critical in civic surveillance, medical care monitoring, and institutional protection. Current deep learning-based approaches often suffer from excessive computational complexity, limited generalizability under varying conditions, and compromised real-time performance. To counter these, this paper introduces an Active Learning-aided Heuristic Deep Spatio-Textural Ensemble Learning (ALH-DSEL) framework. The model initially identifies keyframes from the surveillance videos with a Multi-Constraint Active Learning (MCAL) approach, with features extracted from DenseNet121. The frames are then segmented employing an optimized Fuzzy C-Means clustering algorithm with Firefly to identify areas of interest. A deep ensemble More >