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 >

Van-Viet Nguyen¹, Huu-Khanh Nguyen², Kim-Son Nguyen¹, Thi Minh-Hue Luong¹, Duc-Quang Vu¹, Trung-Nghia Phung³, The-Vinh Nguyen^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075442 - 29 January 2026

Abstract It remains difficult to automate the creation and validation of Unified Modeling Language (UML) diagrams due to unstructured requirements, limited automated pipelines, and the lack of reliable evaluation methods. This study introduces a cohesive architecture that amalgamates requirement development, UML synthesis, and multimodal validation. First, LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements. Then, DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code. Using this dual-LLM pipeline, we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families. Rendering analysis showed that 89.5% of the generated diagrams compile correctly, while… More >

Shengkang Zhang¹, Yong Jin^2,*, Soon Poh Yap^1,*, Haoyun Fan¹, Shiyuan Li³, Ahmed El-Shafie⁴, Zainah Ibrahim¹, Amr El-Dieb⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075351 - 29 January 2026

Abstract Concrete-filled steel tubes (CFST) are widely utilized in civil engineering due to their superior load-bearing capacity, ductility, and seismic resistance. However, existing design codes, such as AISC and Eurocode 4, tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core. To address this limitation, this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer (PKO), a nature-inspired algorithm, to enhance the accuracy of shear strength prediction for CFST columns. Additionally, quantile regression is employed to construct prediction intervals for… More >

Bin Ou^1,2,3,4, Haoquan Chi^1,3, Xu’an Qian^1,3, Shuyan Fu^1,3, Zhirui Miao^1,3, Dingzhu Zhao^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.074757 - 29 January 2026

Abstract Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation. To address the challenges of complex monitoring data, the uneven spatial distribution of deformation, and the construction and optimization of a prediction model for deformation prediction, a multipoint ultrahigh arch dam deformation prediction model, namely, the CEEMDAN-KPCA-GSWOA-KELM, which is based on a clustering partition, is proposed. First, the monitoring data are preprocessed via variational mode decomposition (VMD) and wavelet denoising (WT), which effectively filters out noise and improves the signal-to-noise ratio of the data, providing high-quality input data for subsequent prediction… More > Graphic Abstract

Haoxiang Wen¹, Zhaoyang Wang¹, Zhonglin Ye^1,*, Haixing Zhao¹, Maosong Sun²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074410 - 29 January 2026

Abstract Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems. However, in existing research, multivariate data are often influenced by various factors during the data collection process, resulting in temporal misalignment or displacement. Due to these factors, the node representations carry substantial noise, which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance. Accordingly, this study proposes a novel multivariate anomaly detection model grounded in graph structure learning. Firstly, a recommendation strategy is employed to identify strongly coupled variable pairs, which are then used More >

Mohammad Shokouhifar^1,*, Fakhrosadat Fanian², Mehdi Hosseinzadeh^3,4,*, Aseel Smerat^5,6, Kamal M. Othman⁷, Abdulfattah Noorwali⁷, Esam Y. O. Zafar⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.073789 - 29 January 2026

Abstract Wireless Sensor Networks (WSNs) have become foundational in numerous real-world applications, ranging from environmental monitoring and industrial automation to healthcare systems and smart city development. As these networks continue to grow in scale and complexity, the need for energy-efficient, scalable, and robust communication protocols becomes more critical than ever. Metaheuristic algorithms have shown significant promise in addressing these challenges, offering flexible and effective solutions for optimizing WSN performance. Among them, the Grey Wolf Optimizer (GWO) algorithm has attracted growing attention due to its simplicity, fast convergence, and strong global search capabilities. Accordingly, this survey provides… More >

Xiaolu Wang^1,2, Haoyu Sun¹, Aiguo Wang¹, Xin Xia^3,*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073991 - 27 January 2026

Abstract To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell (PEMFC) performance degradation prediction, this study proposes a data augmentation-based model to predict PEMFC performance degradation. Firstly, an improved generative adversarial network (IGAN) with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples. Then, the IGAN is used to generate data with a distribution analogous to real data, thereby mitigating the insufficiency and imbalance of original PEMFC samples and providing the prediction model with training data rich More >

Luyao Liu^*, Xiao Liao, Yiqian Li, Shaofeng Zhang

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.072631 - 27 January 2026

Abstract Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions. In the context of carbon neutrality, the integration of data centers with renewable energy has become a prevailing trend. To advance the renewable energy integration in data centers, it is imperative to thoroughly explore the data centers’ operational flexibility. Computing workloads and refrigeration systems are recognized as two promising flexible resources for power regulation within data center micro-grids. This paper identifies and categorizes delay-tolerant computing workloads into three types (long-running non-interruptible, long-running interruptible, and short-running) and develops… More >

Girish Hariharan¹, Vinyas¹, Gowrishankar Mandya Chennegowda¹, Nitesh Kumar¹, Shiva Kumar¹, Deepak Doreswamy², Subraya Krishna Bhat^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073772 - 12 January 2026

Abstract This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite–epoxy (G–E) composites reinforced with cast iron (CI) filler particles. Hybrid G–E composite with added cast iron (CI) filler particles enhances stiffness, strength, and vibration damping, offering enhanced performance for vibration-sensitive engineering applications. Unlike conventional approaches, this work simultaneously employs Artificial Neural Networks (ANN) for high-accuracy property prediction and Response Surface Methodology (RSM) for in-depth analysis of factor interactions and optimization. A total of 24 experimental test data sets of varying input… More >