Kirubavathi Ganapathiyappan¹, Chahana Ravikumar¹, Raghul Alagunachimuthu Ranganayaki¹, Ayman Altameem², Ateeq Ur Rehman^3,*, Ahmad Almogren^4,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072840

Abstract Android smartphones have become an integral part of our daily lives, becoming targets for ransomware attacks. Such attacks encrypt user information and ask for payment to recover it. Conventional detection mechanisms, such as signature-based and heuristic techniques, often fail to detect new and polymorphic ransomware samples. To address this challenge, we employed various ensemble classifiers, such as Random Forest, Gradient Boosting, Bagging, and AutoML models. We aimed to showcase how AutoML can automate processes such as model selection, feature engineering, and hyperparameter optimization, to minimize manual effort while ensuring or enhancing performance compared to traditional… More >

Yingkai Ge, Jiasheng Zhang, Jiale Zhang, Zhenguo Ma, Yu Liu, Lihua Wang^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072799

Abstract With the continuous development of artificial intelligence and computer vision technology, numerous deep learning-based lane line detection methods have emerged. DeepLabv3+, as a classic semantic segmentation model, has found widespread application in the field of lane line detection. However, the accuracy of lane line segmentation is often compromised by factors such as changes in lighting conditions, occlusions, and wear and tear on the lane lines. Additionally, DeepLabv3+ suffers from high memory consumption and challenges in deployment on embedded platforms. To address these issues, this paper proposes a lane line detection method for complex road scenes… More >

Rayeesa Mehmood^*, Sergei Koltcov, Anton Surkov, Vera Ignatenko

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072735

Abstract Activation pruning reduces neural network complexity by eliminating low-importance neuron activations, yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally expensive and typically requires exhaustive search. We introduce a thermodynamics-inspired framework that treats activation distributions as energy-filtered physical systems and employs the free energy of activations as a principled evaluation metric. Phase-transition–like phenomena in the free-energy profile—such as extrema, inflection points, and curvature changes—yield reliable estimates of the critical pruning threshold, providing a theoretically grounded means of predicting sharp accuracy degradation. To further enhance efficiency, we propose a renormalized free energy technique that More >

Juhwan Kim¹, Gwanghyun Jo², Dongsik Jo^1,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072664

Abstract In recent years, three-dimensional reconstruction technologies that employ multiple cameras have continued to evolve significantly, enabling remote collaboration among users in extended Reality (XR) environments. In addition, methods for deploying multiple cameras for motion capture of users (e.g., performers) are widely used in computer graphics. As the need to minimize and optimize the number of cameras grows to reduce costs, various technologies and research approaches focused on Optimal Camera Placement (OCP) are continually being proposed. However, as most existing studies assume homogeneous camera setups, there is a growing demand for studies on heterogeneous camera setups.… More >

Chung-Che Tsai^1,#, Chih-Hung Lin^2,#, Katherine Lin^3,4, Jia Hong Hubert Chen^4,5, Ying Jie Celia Chen^4,5, Ilyssa Ting-Ying Chang^3,4, Hsu-Hung Chang⁶, Jin-Yin Chang⁷, Tin-Yi Chu⁸, Po-Chih Hsu^4,8,*, Chan-Yen Kuo^8,*

BIOCELL, DOI:10.32604/biocell.2025.073698

Abstract Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, most commonly driven by chronic hepatitis B virus (HBV) infection. The HBV X protein (HBx) plays a central role in hepatocarcinogenesis by regulating transcription, signal transduction, epigenetic modification, and interactions with noncoding RNAs. This review summarizes current advances in HBx-mediated signaling pathways and mutation-specific functions, highlighting its potential as a prognostic biomarker and therapeutic target, and providing insights for future strategies in HCC treatment and HBV eradication. Activation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), cAMP response element binding protein/activating transcription factor More > Graphic Abstract

KAWALJIT KAUR^*

BIOCELL, DOI:10.32604/biocell.2025.073340

Abstract Diffuse intrinsic pontine glioma (DIPG) is a pediatric brainstem tumor with a very poor prognosis, characterized by immunosuppressive tumor microenvironment (TME) that limits immune infiltration, including a significant reduction in circulating natural killer (NK) cells. This drop in NK cell levels and activity may promote tumor growth and immune evasion, making NK cells a promising target for immunotherapy. NK cells can attack and eliminate DIPG tumor cells, including glioma stem cells, while counteracting certain immune evasion strategies. Although the DIPG microenvironment and blood-brain barrier present challenges, NK cell-based therapies have shown encouraging tumor control and… More >

ANNA PAWłOWSKA-ŁACHUT^*, DOROTA SUSZCZYK, IWONA WERTEL

BIOCELL, DOI:10.32604/biocell.2025.072104

Abstract Ovarian cancer (OC) remains the most lethal gynecological malignancy, and it is characterized by high heterogeneity, early metastatic dissemination, and frequent recurrence within 12–18 months after primary therapy. Despite progress in clinical management and drug development, the mortality rate remains high, and the biological drivers of OC aggressiveness are not fully understood. A major contributor to therapeutic resistance and disease progression is the ovarian tumor microenvironment (TME), which supports tumor growth and immune evasion. Its complexity poses significant challenges to the development of effective therapies. Current treatments, especially in advanced or recurrent stages, have limited… More >

Jalees Ur Rahman¹, Muhammad Hanif¹, Usman Haider^2,*, Saeed Mian Qaisar^3,*, Sarra Ayouni⁴

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071251

Abstract Deep neural networks have achieved excellent classification results on several computer vision benchmarks. This has led to the popularity of machine learning as a service, where trained algorithms are hosted on the cloud and inference can be obtained on real-world data. In most applications, it is important to compress the vision data due to the enormous bandwidth and memory requirements. Video codecs exploit spatial and temporal correlations to achieve high compression ratios, but they are computationally expensive. This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos. However, contrary… More >

Wei Chen^*, Yuanteng Zhao, Yue Liang

FDMP-Fluid Dynamics & Materials Processing, DOI:10.32604/fdmp.2025.073778

Abstract In deep underground engineering, geological disposal of nuclear waste, and geothermal development, the granite–mortar interface represents a critical weak zone that strongly influences sealing performance under high-temperature conditions. While previous studies have primarily focused on single materials, the dynamic evolution of interface permeability under thermal loading remains insufficiently understood. In this study, time-resolved gas permeability measurements under thermal cycling (20°C → 150°C → 20°C) were conducted, complemented by multi-scale microstructural characterization, to investigate the nonlinear evolution of permeability. Experimental results indicate that interface permeability at room temperature is approximately one order of magnitude higher than… More >

Yang Dou¹, Hao Qin¹, Yuzhi Zhang^1,2, Ning Wang¹, Haiqing Liu^3,4, Wanli Yang^1,2,4,*

FDMP-Fluid Dynamics & Materials Processing, DOI:10.32604/fdmp.2025.070082

Abstract In recent years, tuned liquid dampers (TLDs) have emerged as a focal point of research due to their remarkable potential for structural vibration mitigation. Yet, progress in this field remains constrained by an incomplete understanding of the fundamental mechanisms governing sloshing-induced loads in liquid-filled containers. Aqueducts present a distinctive case, as the capacity of their contained water to function effectively as a TLD remains uncertain. To address this gap, the present study investigates the generation mechanisms of sloshing loads under non-resonant cases through a two-dimensional (2D) computational fluid dynamics (CFD) model developed in ANSYS Fluent.… More >