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

Faten S. Alamri¹, Adil Ali Saleem², Muhammad I. Khan³, Hafeez Ur Rehman Siddiqui², Amjad Rehman^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074679 - 29 January 2026

Abstract Motor imbalance is a critical failure mode in rotating machinery, potentially causing severe equipment damage if undetected. Traditional vibration-based diagnostic methods rely on direct sensor contact, leading to installation challenges and measurement artifacts that can compromise accuracy. This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar. A dataset of 1802 experimental trials was sourced, covering four imbalance levels (0, 10, 20, 30 g) across varying motor speeds (500–1500 rpm) and load torques (0–3 Nm). Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second… More >

Rebecca H. Maggs^*, Marcus J. Brookes, Kenneth S. Rankin

Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.069781 - 19 January 2026

Abstract Background: Photodynamic therapy (PDT) may eradicate residual malignant cells following sarcoma resection, through reactive oxygen species (ROS) mediated cytotoxicity, thus improve clinical outcomes. This study aims to assess the efficacy of 5-aminolevulinic acid (5-ALA) as a photosensitizer in combination with red light (RL) for PDT of bone sarcoma cells in vitro. Methods: Three bone sarcoma cell lines underwent treatment with 5-ALA and RL or sham-RL (SL). 5-ALA uptake was assessed using flow cytometry. Production of ROS was measured using CellROX Green staining and fluorescence microscopy. Cell viability was assessed using Cell Counting Kit-8 assays. Results: All cell… More >

Shuai Zhao, Zhiwei Zhang^*, Junkai Wang, Ye Yuan, Guoren Wang

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073315 - 12 January 2026

Abstract With the development of sharded blockchains, high cross-shard rates and load imbalance have emerged as major challenges. Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates. Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads. Meanwhile, because of the coupling between consensus and execution, a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution. However, we observe that transaction partitioning and subsequent consensus do not require… More >

Ahsan Habib Siam¹, Md. Ehsanul Haque¹, Fahmid Al Farid², Anindita Sutradhar³, Jia Uddin^4,*, Sarina Mansor^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069718 - 12 January 2026

Abstract As healthcare systems increasingly embrace digitalization, effective management of electronic health records (EHRs) has emerged as a critical priority, particularly in inpatient settings where data sensitivity and real-time access are paramount. Traditional EHR systems face significant challenges, including unauthorized access, data breaches, and inefficiencies in tracking follow-up appointments, which heighten the risk of misdiagnosis and medication errors. To address these issues, this research proposes a hybrid blockchain-based solution for securely managing EHRs, specifically designed as a framework for tracking inpatient follow-ups. By integrating QR code-enabled data access with a blockchain architecture, this innovative approach enhances… More >

Thierry Mugenzi, Cahit Perkgoz^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.070381 - 10 November 2025

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 >

Yeasul Kim¹, Chaeeun Won¹, Hwankuk Kim^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.069608 - 10 November 2025

Abstract With the increasing emphasis on personal information protection, encryption through security protocols has emerged as a critical requirement in data transmission and reception processes. Nevertheless, IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices, spanning a range of devices from non-encrypted ones to fully encrypted ones. Given the limited visibility into payloads in this context, this study investigates AI-based attack detection methods that leverage encrypted traffic metadata, eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices. Using the UNSW-NB15 and CICIoT-2023 dataset, encrypted and… More >

Abeer Alhuzali^1,*, Qamar Al-Qahtani¹, Asmaa Niyazi¹, Lama Alshehri¹, Fatemah Alharbi²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069491 - 10 November 2025

Abstract The surge in smishing attacks underscores the urgent need for robust, real-time detection systems powered by advanced deep learning models. This paper introduces PhishNet, a novel ensemble learning framework that integrates transformer-based models (RoBERTa) and large language models (LLMs) (GPT-OSS 120B, LLaMA3.3 70B, and Qwen3 32B) to enhance smishing detection performance significantly. To mitigate class imbalance, we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques. Our system employs a dual-layer voting mechanism: weighted majority voting among LLMs and a final ensemble vote to classify messages as ham, spam, or smishing. Experimental More >

Yafei Liu^1,2, Yuling Chen^1,2,*, Xuewei Wang³, Yuxiang Yang², Chaoyue Tan²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069152 - 10 November 2025

Abstract As blockchain technology rapidly evolves, smart contracts have seen widespread adoption in financial transactions and beyond. However, the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems. Although numerous detection techniques have been proposed, existing methods suffer from significant limitations, such as class imbalance and insufficient modeling of transaction-related semantic features. To address these challenges, this paper proposes an oversampling-based detection framework for Ponzi smart contracts. We enhance the Adaptive Synthetic Sampling (ADASYN) algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions. This enhancement facilitates the… More >

Amjad Rehman^1,*, Tanzila Saba¹, Mona M. Jamjoom², Shaha Al-Otaibi³, Muhammad I. Khan¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068958 - 10 November 2025

Abstract Modern intrusion detection systems (MIDS) face persistent challenges in coping with the rapid evolution of cyber threats, high-volume network traffic, and imbalanced datasets. Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively. This study introduces an advanced, explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets, which reflects real-world network behavior through a blend of normal and diverse attack classes. The methodology begins with sophisticated data preprocessing, incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions, ensuring standardized and model-ready inputs.… More >