Fankun Meng^1,2,3, Yuyang Liu^1,2,*, Xiaohua Liu⁴, Chenlong Duan^1,2, Yuhui Zhou^1,2,3

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.075865 - 06 February 2026

Abstract Carbonate gas reservoirs are often characterized by strong heterogeneity, complex inter-well connectivity, extensive edge or bottom water, and unbalanced production, challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior. To address these issues within a unified, data-driven framework, this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx, and is applicable to a wide range of reservoir types. The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support. The governing equations are solved using a Newton–Raphson scheme, while particle More > Graphic Abstract

Guangyu Xu^1,2, Yuxin Liu¹, Bo Yang¹, Siyu Lu^3,*, Chao Liu⁴, Junmin Lyu⁵, Wenfeng Zheng^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074938 - 29 January 2026

Abstract Soft-tissue motion introduces significant challenges in robotic teleoperation, especially in medical scenarios where precise target tracking is critical. Latency across sensing, computation, and actuation chains leads to degraded tracking performance, particularly around high-acceleration segments and trajectory inflection points. This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking. Three models—autoregressive (AR), long short-term memory (LSTM), and temporal convolutional network (TCN)—were implemented and evaluated on both synthetic and real datasets. By aligning the prediction horizon with the end-to-end system delay, we demonstrate that prediction-based compensation significantly reduces tracking errors. Among the models, TCN More >

Huda Talib Najm^1,2, Ahmed Sabah Al-Araji³, Nur Syazreen Ahmad^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071837 - 29 January 2026

Abstract Mobile service robots (MSRs) in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions, including model uncertainties and external disturbances. This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller (NFIDC) with a Feedback Radial Basis Function Neural Network (FRBFNN). The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1. The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.… More >

Junhui Xu¹, Qi Wang^1,*, Chichen Lin², Weijian Fan³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071661 - 12 January 2026

Abstract Social bots are automated programs designed to spread rumors and misinformation, posing significant threats to online security. Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots: a higher number of connected components weakens their collaborative capabilities, thereby reducing their proportion within the overall network. However, current social bot detection methods still make limited use of topological features. Furthermore, both graph neural network (GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations, and existing studies lack an effective… More >

Song Xu^1,2,*, Liang Xuan^1,2, Yifeng Li^1,2, Qiang Zhang^1,2

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070472 - 09 December 2025

Abstract The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts. In the industrial robot production scenarios, the 6D pose estimation of industrial parts mainly faces two challenges: one is the loss of information and interference caused by occlusion and stacking in the sorting scenario, the other is the difficulty of feature extraction due to the weak texture of industrial parts. To address the above problems, this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts, namely CB-PVNet. On the… More >

Wei Liu^1,*, Ruiyang Wang¹, Guangwei Liu²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070328 - 09 December 2025

Abstract Q-learning is a classical reinforcement learning method with broad applicability. It can respond effectively to environmental changes and provide flexible strategies, making it suitable for solving robot path-planning problems. However, Q-learning faces challenges in search and update efficiency. To address these issues, we propose an improved Q-learning (IQL) algorithm. We use an enhanced Ant Colony Optimization (ACO) algorithm to optimize Q-table initialization. We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma. The IQL algorithm is extensively tested in three grid environments of different scales. The results validate the… More >

Longfei Gao^*, Weidong Wang, Dieyun Ke

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068873 - 10 November 2025

Abstract At present, energy consumption is one of the main bottlenecks in autonomous mobile robot development. To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments, this paper proposes an Attention-Enhanced Dueling Deep Q-Network (AD-Dueling DQN), which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework. A multi-objective reward function, centered on energy efficiency, is designed to comprehensively consider path length, terrain slope, motion smoothness, and obstacle avoidance, enabling optimal low-energy trajectory generation in 3D space from the… More >

Rıdvan Yayla, Hakan Üçgün^*, Onur Ali Korkmaz

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4055-4087, 2025, DOI:10.32604/cmes.2025.072703 - 23 December 2025

Abstract Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems. Artificial intelligence enables real-time sensing, decision-making, and control on embedded platforms with improved efficiency. This study presents the design and implementation of an autonomous radio-controlled (RC) vehicle prototype capable of lane line detection, obstacle avoidance, and navigation through dynamic path planning. The system integrates image processing and ultrasonic sensing, utilizing Raspberry Pi for vision-based tasks and Arduino Nano for real-time control. Lane line detection is achieved through conventional image processing techniques, providing the basis for local path generation, while traffic sign classification employs a… More > Graphic Abstract

Miah A. Robinson, Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Khalid H. Abed^*

Journal on Artificial Intelligence, Vol.7, pp. 615-632, 2025, DOI:10.32604/jai.2025.073535 - 01 December 2025

Abstract Ensuring the safety of Artificial Intelligence (AI) is essential for providing dependable services, especially in various sectors such as the military, education, healthcare, and automotive industries. A highly effective method to boost the precision and performance of an AI agent involves multi-configuration training, followed by thorough evaluation in a specific setting to gauge performance outcomes. This research thoroughly investigates the design of three AI agents, each configured with a different number of hidden units. The first agent is equipped with 128 hidden units, the second with 256, and the third with 512, all utilizing the… More >

Sidra Naseem¹, Rashid Jahangir^1,*, Nazik Alturki², Faheem Shehzad³, Muhammad Sami Ullah⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2481-2501, 2025, DOI:10.32604/cmes.2025.072575 - 26 November 2025

Abstract Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies. While deep learning models have significantly advanced medical image analysis, challenges such as imbalanced datasets and redundant features persist. This study proposes a novel framework that customizes two deep learning models, NasNetMobile and ResNet50, by incorporating bottleneck architectures, named as NasNeck and ResNeck, to enhance feature extraction. The feature vectors are fused into a combined vector, which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power. The optimized feature vector is then classified using artificial… More >