VINOTHKUMAR VEERASAMY¹, VEERAVARMAL VEERAN², SIDDAVARAM NAGINI^1,3,*

Oncology Research, Vol.33, No.8, pp. 1835-1860, 2025, DOI:10.32604/or.2025.064010 - 18 July 2025

Abstract The phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathway is one of the most frequently dysregulated signaling networks in oral squamous cell carcinoma (OSCC). Although the tumor microenvironment (TME) and epigenetic modifiers are recognized to play a pivotal role in aberrant activation of the PI3K/AKT pathway in OSCC, the available evidence is fragmentary and a comprehensive analysis is warranted. This review evaluates the intricate mechanisms by which various components of the TME facilitate proliferation, apoptosis evasion, invasion, migration, angiogenesis, metastasis, as well as therapy resistance in OSCC through activation of PI3K/AKT signalling. The review has also More >

Gaofeng Pan^1,2,3, Jiali Li^1,2, Weijie Sun⁴, Jiayu He^1,2, Maoying Fu³, Yufeng Gao^1,2,*

Oncology Research, Vol.33, No.8, pp. 1991-2011, 2025, DOI:10.32604/or.2025.063993 - 18 July 2025

Abstract Aims: The aim of this study is to develop a prognostic model for hepatocellular carcinoma (HCC) using stemness-related genes (SRGs), while also pinpointing and validating pivotal genes associated with this process. Methods: Utilizing the TCGA and ICGC database, a prognostic stemness-related scores (SRS) for HCC through a combination of WGCNA and machine learning. Bioinformatics analysis evaluated tumor immune infiltration characteristics and drug sensitivity in different SRS subgroups, identifying the key gene TOMM40L. qRT-PCR and IHC were employed to detect the expression level of TOMM40 L. Kaplan-Meier survival analysis assessed the prognostic value of TOMM40L in… More >

Kailai Zhou^*, Weikang Xia, Jiatai Wang

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3217-3240, 2025, DOI:10.32604/cmc.2025.065334 - 03 July 2025

Abstract Ride-hailing (e.g., DiDi and Uber) has become an important tool for modern urban mobility. To improve the utilization efficiency of ride-hailing vehicles, a novel query method, called Approachable k-nearest neighbor (A-kNN), has recently been proposed in the industry. Unlike traditional kNN queries, A-kNN considers not only the road network distance but also the availability status of vehicles. In this context, even vehicles with passengers can still be considered potential candidates for dispatch if their destinations are near the requester’s location. The V-Tree-based query method, due to its structural characteristics, is capable of efficiently finding k-nearest moving objects within… More >

Pengfei Wang^1,2,3, Jiwu Sun², Lu Lu^1,4, Hongchen Li¹, Hongzhe Liu², Cheng Xu², Yongqiang Liu^1,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2883-2903, 2025, DOI:10.32604/cmc.2025.065267 - 03 July 2025

Abstract Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms. Traditional methods often struggle to address issues such as image blurring, dynamic noise interference, and variations in target scale. Conventional neural network (CNN)-based target detection approaches face notable limitations in such adverse weather scenarios, primarily due to the fixed geometric sampling structures that hinder adaptability to complex backgrounds and dynamically changing object appearances. To address these challenges, this paper proposes an optimized YOLOv9 model incorporating an improved deformable convolutional network (DCN) enhanced with a multi-scale dilated attention (MSDA) mechanism. Specifically,… More >

Jie He, Dongmei Zhao, Tao Liu^*, Qingfeng Zou, Jian’an Xie

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2733-2749, 2025, DOI:10.32604/cmc.2025.065205 - 03 July 2025

Abstract Robot navigation in complex crowd service scenarios, such as medical logistics and commercial guidance, requires a dynamic balance between safety and efficiency, while the traditional fixed reward mechanism lacks environmental adaptability and struggles to adapt to the variability of crowd density and pedestrian motion patterns. This paper proposes a navigation method that integrates spatiotemporal risk field modeling and adaptive reward optimization, aiming to improve the robot’s decision-making ability in diverse crowd scenarios through dynamic risk assessment and nonlinear weight adjustment. We construct a spatiotemporal risk field model based on a Gaussian kernel function by combining… More >

Shitharth Selvarajan^1,2,3,*, Hariprasath Manoharan⁴, Taher Al-Shehari⁵, Nasser A Alsadhan⁶, Subhav Singh^7,8

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3541-3557, 2025, DOI:10.32604/cmc.2025.064394 - 03 July 2025

Abstract This research investigates the application of digital images in military contexts by utilizing analytical equations to augment human visual capabilities. A comparable filter is used to improve the visual quality of the photographs by reducing truncations in the existing images. Furthermore, the collected images undergo processing using histogram gradients and a flexible threshold value that may be adjusted in specific situations. Thus, it is possible to reduce the occurrence of overlapping circumstances in collective picture characteristics by substituting grey-scale photos with colorized factors. The proposed method offers additional robust feature representations by imposing a limiting More >

Sehun Lee, Taehoon Kim, Junho Ahn^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3623-3648, 2025, DOI:10.32604/cmc.2025.063985 - 03 July 2025

Abstract This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multi-dimensional vision identification technology adapted to the situation in large indoor and underground spaces. With the expansion of large shopping malls and underground urban spaces (UUS), there is an increasing need for new technologies that can quickly identify complex indoor structures and changes such as relocation, remodeling, and construction for the safety and management of citizens through the provision of the up-to-date indoor 3D site maps. The proposed algorithm utilizes data collected by an unmanned robot to create a 3D site… More >

Chao Li^1,3,#, Quanzhi Feng^1,3,#, Caichang Ding^2,*, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3605-3621, 2025, DOI:10.32604/cmc.2025.063703 - 03 July 2025

Abstract The increasing adoption of unmanned aerial vehicles (UAVs) in urban low-altitude logistics systems, particularly for time-sensitive applications like parcel delivery and supply distribution, necessitates sophisticated coordination mechanisms to optimize operational efficiency. However, the limited capability of UAVs to extract state-action information in complex environments poses significant challenges to achieving effective cooperation in dynamic and uncertain scenarios. To address this, we presents an Improved Multi-Agent Hybrid Attention Critic (IMAHAC) framework that advances multi-agent deep reinforcement learning (MADRL) through two key innovations. Firstly, a Temporal Difference Error and Time-based Prioritized Experience Replay (TT-PER) mechanism that dynamically adjusts… More >

Omar Alnajar^*, Ahmed Barnawi

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3861-3897, 2025, DOI:10.32604/cmes.2025.065833 - 30 June 2025

Abstract The Tactile Internet of Things (TIoT) promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems. Yet TIoT’s stringent requirements for ultra-low latency, high reliability, and robust privacy present significant challenges. Conventional centralized Federated Learning (FL) architectures struggle with latency and privacy constraints, while fully distributed FL (DFL) faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous. To address these limitations, we propose a Clustered Distributed Federated Learning (CDFL) architecture tailored for a 6G-enabled TIoT environment. Clients are grouped into clusters based on… More >

Walaa N. Ismail^1,2,#,*, Fathimathul Rajeena P. P.^3,#, Mona A. S. Ali^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3709-3741, 2025, DOI:10.32604/cmes.2025.065564 - 30 June 2025

Abstract Early detection of Alzheimer’s disease (AD) is crucial, particularly in resource-constrained medical settings. This study introduces an optimized deep learning framework that conceptualizes neural networks as computational “sensors” for neurodegenerative diagnosis, incorporating feature selection, selective layer unfreezing, pruning, and algorithmic optimization. An enhanced lightweight hybrid DenseNet201 model is proposed, integrating layer pruning strategies for feature selection and bioinspired optimization techniques, including Genetic Algorithm (GA) and Harris Hawks Optimization (HHO), for hyperparameter tuning. Layer pruning helps identify and eliminate less significant features, while model parameter optimization further enhances performance by fine-tuning critical hyperparameters, improving convergence speed,… More >