Abdou Alzubaidi¹, Khalid Mahmud², Rashid Mehmood^2,*, Siddra Rana³, Mohammed Alkinidri⁴

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.4, 2026, DOI:10.32604/fdmp.2026.075928 - 07 May 2026

Abstract Flows over rotating disks are central to numerous engineering applications, including turbines, rotating sensors, and advanced cooling devices, where the incoming fluid often strikes the disk at an angle. This study examines magnetohydrodynamic (MHD) oblique slip flow toward a rotating disk, accounting for critical effects such as velocity slip, thermal slip and thermal radiation. In particular, the Cattaneo–Christov heat flux model is used to capture thermal relaxation phenomena, frequently overlooked in prior analyses, while employing a uniform transverse magnetic field to regulate both momentum and heat transfer. Using similarity transformations, the governing nonlinear equations are… More >

Tianzhe Jiao, Yuming Chen, Xiaoyue Feng, Chaopeng Guo, Jie Song^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080595 - 27 April 2026

Abstract Knowledge distillation bridges the performance gap between camera-based and LiDAR-based 3D detectors by leveraging the precise geometric information from LiDAR. However, cross-modal knowledge transfer remains challenging due to the inherent modality heterogeneity between LiDAR and camera data, which often leads to instability during training. In this work, we find that these instabilities are closely related to distribution mismatch in the cross-modal feature space and noisy teacher signals. To address this issue, we propose a novel distribution-aware cross-modal distillation framework, named DA-T3D. Specifically, we first explicitly model the LiDAR teacher’s Bird’s-Eye-View (BEV) feature distribution and use… More >

Ahmed Ibrahim Hassanin Mohamed^1,2,*, Nourhan M. Amin^2,3, Heba Elsaid Matter², Ibrahim F. Eldemary², Ahmed F. Oan²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079630 - 27 April 2026

Abstract The design of foundations for high-voltage electrical network lattice towers depends on reliable prediction of resistance to uplift and lateral forces. Because foundation works contribute substantially to the total project cost, a clear understanding of ultimate pullout capacity and the associated failure mechanism is required to support safe and economical design. This paper presents a three-dimensional finite element investigation using PLAXIS 3D to quantify the influence of soil type (pure sand and sand with 8% fines), footing dimensions ((3.5 × 7), (5 × 10), (7.5 × 15)), relative compaction RC are 92% and 100%, and… More >

Ovidijus Grigas^*, Rytis Maskeliūnas

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077324 - 27 April 2026

Abstract Early detection of Mild Cognitive Impairment (MCI) with FDG-PET is essential for timely Alzheimer’s disease intervention. However, PET image quality is limited by low spatial resolution, partial volume effects, and Poisson noise. Standard enhancement methods, such as Bilateral filtering or Contrast Limited Adaptive Histogram Equalization (CLAHE), can increase contrast but often introduce heavy noise or distort image texture, while deep learning methods may produce hallucinated structures. We propose a fully data-adaptive, non-learned 3D enhancement framework whose output is deterministic for a given input volume, that combines Laplacian-based local contrast modulation with a gradient-gated Difference-of-Gaussians (DoG)… More >

Rajanand Patnaik Narasipuram^1,*, Md M. Pasha², Suresh Badugu³, Saleha Tabassum⁴, Attuluri R.Vijay Babu⁵, Bharath Kumar N⁵, Amit Singh Tandon⁶

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.076542 - 27 April 2026

Abstract Supercapacitors are increasingly deployed as high power buffers in modern energy systems, yet their broader impact is constrained by limited energy density, fragmented testing practices, and incomplete understanding of lifecycle implications. This article presents a critical, method driven review based on a structured literature survey and explicit inclusion criteria, aggregating quantitative performance data for major electrode families (carbon materials, transition metal oxides, conducting polymers, biomass derived carbons, MXenes, and hybrid composites), electrolytes (aqueous, organic, ionic liquid, and gel/solid state), and device architectures (flexible, micro, solid state, lithium ion capacitors, and structural supercapacitors) under harmonized metrics… More > Graphic Abstract

Munro Matthew James^1,*, López Vásquez Clara Elena^1,2, Wickremesekera Agadha³, Chan Alex Ho Chuen¹, Gray Clint Lee^1,4,5,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.074958 - 22 April 2026

Abstract Objective: Meningioma is the most common primary brain tumour. Invasion into the brain is a diagnostic feature of grade II meningiomas and is associated with recurrence and poor prognosis. Mebendazole is a microtubule inhibitor typically prescribed as an anthelmintic. However, it has the potential to be repurposed for cancer treatment. Here, we aimed to assess the ability of mebendazole to inhibit meningioma cell invasion. Methods: Primary patient-derived meningioma cell lines were cultured as 3D spheroids and embedded in an extracellular matrix-like matrix as an in vitro model of invasion. Mebendazole-treated and untreated control spheroids were analysed… More >

Gangtao Han¹, Yurui Chen¹, Song Wang^1,*, Enqing Chen¹, Lingling Li², Gaofeng Pan³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077768 - 09 April 2026

Abstract The integrity of perception data transmitted over in-vehicle networks is important for the safety of autonomous driving. However, legacy protocols like the Controller Area Network (CAN) bus which lacks essential security features make In-Vehicle Networks (IVNs) vulnerable to data tampering attacks. Current research typically focuses on detecting the attack itself but ignores the information recovery from the missing data, leading to an unsafe autonomous driving system. To address the issue, we propose a 3D object recovery framework to recover the missing data caused by the tampering attack that occurred in in-vehicle networks. The proposed framework… More >

Yihao Kuang^1,2, Hong Zhang^1,2, Jiaqi Wang^1,2, Lingyu Jin^1,2, Bo Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076652 - 09 April 2026

Abstract 3D single object tracking (SOT) based on point clouds is a fundamental task for environmental perception in autonomous driving and dynamic scene understanding in robotics. Recent technological advancements in this field have significantly bolstered the environmental interaction capabilities of intelligent systems. This field faces persistent challenges, including feature degradation induced by point cloud sparsity, representation drift caused by non-rigid deformation, and occlusion in complex scenarios. Traditional appearance matching methods, particularly those relying on Siamese networks, are severely constrained by point cloud characteristics, often failing under rapid motions or structural ambiguities among similar objects. In response,… More >

Zhidong Wang, Yanping Lian^*, Mingjian Li, Jiawei Chen, Ruxin Gao

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078312 - 30 March 2026

Abstract Metal additive manufacturing (AM) technology has promising applications across many fields due to its near-net-shape advantages. The quality of the as-built component is closely linked to the temperature evolution during the metal AM process, which exhibits strong nonlinearities, localized high gradients, and rapid cooling rates. Therefore, real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality, which poses surprising challenges for numerical methods, as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations. In this study, we proposed an isothermal surface imaging and transfer… More >

Huda F. Al-Shahad^1,2, Razali Yaakob^1,*, Nurfadhlina Mohd Sharef¹, Hazlina Hamdan¹, Hasyma Abu Hassan³, Xiaoyi Jiang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.074109 - 30 March 2026

Abstract Currently, thyroid diseases are prevalent worldwide; therefore, it is necessary to develop techniques that help doctors improve their diagnostic skills for such diseases. In previous studies, 2-dimensional convolutional neural network (2D CNN) techniques were employed to classify thyroid nodules as benign and malignant without detecting the presence of thyroid nodules in the obtained ultrasound images. To address this issue, we propose a 3-dimensional convolutional neural network (3D CNN) for thyroid nodule detection. The proposed CNN exploits the 3D information and spatial features contained in ultrasound images and generates distinctive features during its training using multiple… More >