Jun Zhe Tan¹, Rodney H. G. Tan^1,*, Nor Ashidi Mat Isa², Sew Sun Tiang¹, Chun Kit Ang¹, Kuo-Ping Lin^1,3,4, Wei Hong Lim^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2025.073821 - 26 February 2026

Abstract Accurate estimation of photovoltaic (PV) parameters is essential for optimizing solar module performance and enhancing resource efficiency in renewable energy systems. This study presents a process innovation by introducing, for the first time, the Triangulation Topology Aggregation Optimizer (TTAO) integrated with parallel computing to address PV parameter estimation challenges. The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets (KC200GT and R.T.C. France solar cells) and a real-world dataset (Poly70W solar module) under single-, double-, and triple-diode configurations. Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE More >

Myeong Ryeo Kim^1,*, Jae Rim Lee¹, Xiaohan Zhang², Kwang Won Jeong^1,*

Oncology Research, Vol.34, No.3, 2026, DOI:10.32604/or.2025.072592 - 24 February 2026

Abstract Objectives: Tamoxifen is a key drug that provides endocrine therapy for estrogen receptor (ER) α-positive breast cancer; however, resistance remains a significant clinical challenge. This study aims to investigate the molecular mechanisms of tamoxifen resistance in ERα-positive breast cancer, with particular focus on the role of SET Domain Containing 1A (SETD1A)-driven forkhead box A2 (FOXA2) as a key regulator of this resistance. Methods: FOXA2 expression and its regulation by SETD1A were assessed via (quantitative polymerase chain reaction), western blotting, transcriptome profiling, and chromatin immunoprecipitation analyses. The effects of FOXA2 on cell proliferation, migration, invasion, and cancer… More >

Liqiu Suo¹, Lin Xia¹, Yoona Chung¹, Eunchan Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.076329 - 10 February 2026

Abstract Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features. This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three layers: Basic, Conversion & Stability (efficiency and volatility across actions), and Advanced Interactions & Activity (cross-behavior synergies and intensity). Using real Taobao (Alibaba’s primary e-commerce platform) logs (57,976 records for 10,203 users; 25 November–03 December 2017), we conducted a hierarchical, layer-wise evaluation that holds data splits and hyperparameters fixed while varying only the feature set to quantify each layer’s marginal contribution. Across logistic regression (LR), decision… More >

Isam Bahaa Aldallal¹, Abdullahi Abdu Ibrahim^1,*, Saadaldeen Rashid Ahmed^2,3

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075212 - 10 February 2026

Abstract The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false More >

Sara Tehsin¹, Inzamam Mashood Nasir^1,*, Wiem Abdelbaki², Fadwa Alrowais³, Khalid A. Alattas⁴, Sultan Almutairi⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074416 - 10 February 2026

Abstract Objective: Deep learning is employed increasingly in Gastroenterology (GI) endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection. In the real world, implementation requires high accuracy, therapeutically relevant explanations, strong calibration, domain generalization, and efficiency. Current Convolutional Neural Network (CNN) and transformer models compromise border precision and global context, generate attention maps that fail to align with expert reasoning, deteriorate during cross-center changes, and exhibit inadequate calibration, hence diminishing clinical trust. Methods: HMA-DER is a hierarchical multi-attention architecture that uses dilation-enhanced residual blocks and an explainability-aware Cognitive Alignment Score (CAS) regularizer to directly align… More >

Ahmad Junaid¹, Abid Iqbal^2,*, Abuzar Khan¹, Ghassan Husnain^1,*, Abdul-Rahim Ahmad³, Mohammed Al-Naeem⁴

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073189 - 10 February 2026

Abstract Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness. This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail. It uses the NASA CMAPSS dataset, which has over 200,000 engine cycles from 260 engines. The process begins with systematic preprocessing, which includes imputation, outlier removal, scaling, and labelling of the remaining useful life. Dimensionality is reduced using a hybrid selection method that combines variance filtering, recursive elimination, and gradient-boosted importance scores, yielding a stable set of… More >

Chih-Chieh Chang^*, Khairul Izyan Bin Anuar, Yu-Hwa Liu

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073177 - 10 February 2026

Abstract Small datasets are often challenging due to their limited sample size. This research introduces a novel solution to these problems: average linkage virtual sample generation (ALVSG). ALVSG leverages the underlying data structure to create virtual samples, which can be used to augment the original dataset. The ALVSG process consists of two steps. First, an average-linkage clustering technique is applied to the dataset to create a dendrogram. The dendrogram represents the hierarchical structure of the dataset, with each merging operation regarded as a linkage. Next, the linkages are combined into an average-based dataset, which serves as… More >

Lijia Li, Shu Liu, Xiaoliang Huang^*, Zhilin Qi

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.073294 - 06 February 2026

Abstract Identifying geohazards such as landslides and methane leakage is crucial during gas extraction from natural gas hydrate (NGH) reservoirs, and understanding reservoir settlement behavior is central to this assessment. Horizontal wells can enlarge the pressure relief zone within the formation, improving single-well productivity, and are therefore considered a promising approach for NGH development. This study examines the settlement response of hydrate-bearing sediments during depressurization using horizontal wells. A fully coupled thermal, hydraulic, mechanical, and chemical (THMC) model with representative reservoir properties (Shenhu region in the South China Sea) is presented accordingly. The simulations show that More >

Bárbara Polo-Martín^*

Revue Internationale de Géomatique, Vol.35, pp. 53-78, 2026, DOI:10.32604/rig.2026.071069 - 05 February 2026

Abstract The expansion of transportation networks, including railways and ports, has been a major force driving urban growth, mobility, and socio-economic transformations since the Industrial Revolution. This study utilizes Historical Geographic Information Systems to examine the global evolution of transportation infrastructure, focusing on railways and ports, from 1880 to 2020. The dataset enables a multidimensional analysis of how transportation systems have shaped cities, influenced regional development, and helped to make possible sustainability efforts. By offering insights into transport accessibility, land-use changes, and economic connectivity, the study provides a robust empirical foundation for understanding long-term infrastructure dynamics. More >

Nuh Azginoglu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.075834 - 29 January 2026

Abstract Breast cancer screening programs rely heavily on mammography for early detection; however, diagnostic performance is strongly affected by inter-reader variability, breast density, and the limitations of conventional computer-aided detection systems. Recent advances in deep learning have enabled more robust and scalable solutions for large-scale screening, yet a systematic comparison of modern object detection architectures on nationally representative datasets remains limited. This study presents a comprehensive quantitative comparison of prominent deep learning–based object detection architectures for Artificial Intelligence-assisted mammography analysis using the MammosighTR dataset, developed within the Turkish National Breast Cancer Screening Program. The dataset comprises… More >