Zeeshan Ali Haider¹, Inam Ullah^2,*, Ahmad Abu Shareha³, Rashid Nasimov⁴, Sufyan Ali Memon^5,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071042

Abstract The advent of sixth-generation (6G) networks introduces unprecedented challenges in achieving seamless connectivity, ultra-low latency, and efficient resource management in highly dynamic environments. Although fifth-generation (5G) networks transformed mobile broadband and machine-type communications at massive scales, their properties of scaling, interference management, and latency remain a limitation in dense high mobility settings. To overcome these limitations, artificial intelligence (AI) and unmanned aerial vehicles (UAVs) have emerged as potential solutions to develop versatile, dynamic, and energy-efficient communication systems. The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning (CoRL) to manage an autonomous network.… More >

Wen-Tsai Sung¹, Indra Griha Tofik Isa^2,3, Sung-Jung Hsiao^4,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070922

Abstract Mango is a plant with high economic value in the agricultural industry; thus, it is necessary to maximize the productivity performance of the mango plant, which can be done by implementing artificial intelligence. In this study, a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential, so that it becomes an early detection warning system that has an impact on increasing agricultural productivity. The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules, namely the C2S module. The C2S module consists of three sub-modules such as the… More >

Xin Wen^1,2,3, Siru Chen¹, Kechen Song^2,3,4,*, Han Yu^2,3,*, Xingjie Li^2,3, Ling Zhong¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070906

Abstract With the growing demand for higher product quality in manufacturing, X-ray non-destructive testing has found widespread application not only in industrial quality control but also in a wide range of industrial applications, owing to its unique capability to penetrate materials and reveal both internal and surface defects. This paper presents a systematic review of recent advances and current applications of X-ray-based defect detection in industrial components. It begins with an overview of the fundamental principles of X-ray imaging and typical inspection workflows, followed by a review of classical image processing methods for defect detection, segmentation,… More >

Vi Hoai Nam¹, Chu Thi Minh Hue², Dang Van Anh^1,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070605

Abstract Unmanned Aerial Vehicles (UAVs) have become integral components in smart city infrastructures, supporting applications such as emergency response, surveillance, and data collection. However, the high mobility and dynamic topology of Flying Ad Hoc Networks (FANETs) present significant challenges for maintaining reliable, low-latency communication. Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable. To overcome these limitations, this paper proposes an improved routing protocol based on reinforcement learning. This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware. The proposed method optimizes the selection of… More >

Thierry Mugenzi, Cahit Perkgoz^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070381

Abstract Missing data presents a crucial challenge in data analysis, especially in high-dimensional datasets, where missing data often leads to biased conclusions and degraded model performance. In this study, we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision. The proposed loss combines (i) a guided, masked mean squared error focusing on missing entries; (ii) a noise-aware regularization term to improve resilience against data corruption; and (iii) a variance penalty to encourage expressive yet stable reconstructions. We evaluate the proposed model across four missingness mechanisms, such as Missing… More >

Jinglu Chen¹, Mengpan Chen², Wenhao Zhang^2,*, Huihui Ren², Daniel Dajun Zeng^1,2

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069331

Abstract Temporal knowledge graph completion (TKGC), which merges temporal information into traditional static knowledge graph completion (SKGC), has garnered increasing attention recently. Among numerous emerging approaches, translation-based embedding models constitute a prominent approach in TKGC research. However, existing translation-based methods typically incorporate timestamps into entities or relations, rather than utilizing them independently. This practice fails to fully exploit the rich semantics inherent in temporal information, thereby weakening the expressive capability of models. To address this limitation, we propose embedding timestamps, like entities and relations, in one or more dedicated semantic spaces. After projecting all embeddings into… More >

Juan Guerrero-Martín^*, Eladio Estella-Nonay, Margarita Bachiller-Mayoral, Mariano Rincón

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.066083

Abstract Neuropsychological tests, such as the Rey-Osterrieth complex figure (ROCF) test, help detect mild cognitive impairment (MCI) in adults by assessing cognitive abilities such as planning, organization, and memory. Furthermore, they are inexpensive and minimally invasive, making them excellent tools for early screening. In this paper, we propose the use of image analysis models to characterize the relationship between an individual’s ROCF drawing and their cognitive state. This task is usually framed as a classification problem and is solved using deep learning models, due to their success in the last decade. In order to achieve good… More >

Canadian Journal of Urology, DOI:10.32604/cju.2025.073515

Abstract This article has no abstract. More >

Abdulbari Bener^1,2,*, Ünsal Veli Üstündağ³, Emir Barışık⁴, Cem Cahit Barışık⁵

Canadian Journal of Urology, DOI:10.32604/cju.2025.067184

Abstract Objectives: The evidence remains insufficient and controversial for evaluating modifiable parameters—such as vitamin D, calcium, ferritin, and uric acid—as preclinical biomarkers to contribute to the prevention and early diagnosis of prostate cancer, a disease with a prevalence of up to 10%–20% in men over 50 and strongly associated with environmental factors including diet (high in fat and red meat), obesity, physical inactivity, and carcinogen exposure. This study aims to investigate the potential biomarker role of vitamin D, calcium, ferritin, and uric acids in reducing the risk of prostate cancer (PCa). Methods: The case-control design was… More >

Hong Wu, Yang Liu^*

BIOCELL, DOI:10.32604/biocell.2025.069527

Abstract Background: Acute pancreatitis (AP) is a serious gastrointestinal disorder. Tetramethylpyrazine (TMP), a bioactive alkaloid extracted from Ligusticum chuanxiong, exhibits various pharmacological effects, but its protective mechanisms against AP remain unclear. This study aimed to investigate the protective effects and underlying mechanisms of TMP in AP. Methods: The study utilized Cerulein (CER) to model pancreatitis across experimental systems. Cellular responses were characterized through functional assays (CCK-8 viability, EdU proliferation, Transwell migration, flow cytometric apoptosis, Fluo-3/AM calcium imaging) and inflammatory profiling (ELISA for trypsin, CRP, TNF-α, IL-1β, IL-6). Autophagy was monitored via mRFP-GFP-LC3 flux and LysoTracker staining, with… More > Graphic Abstract