Navjeet Kaur¹, Ayush Mittal², Saad Alahmari^3,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079991 - 08 May 2026

Abstract Mobile fog computing must support latency-sensitive applications under dynamic user mobility and time-varying network conditions. Existing mobility-aware scheduling approaches are largely reactive and often ignore prediction uncertainty, resulting in service disruptions and inefficient task migration. This paper proposes an uncertainty-aware digital twin-based orchestration framework for proactive mobility-aware fog computing. The framework maintains real-time synchronized digital twins of users and fog nodes and integrates a hybrid Gated Recurrent Unit-Exponentially Weighted Moving Average (GRU-EWMA) mobility prediction model with fog-load forecasting to enable joint mobility- and load-aware decision-making. An entropy-based confidence mechanism is introduced to regulate proactive handover More >

Tian Niu, Kaizhao Zhang, Zhongxue Gan, Wenchao Ding^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079324 - 27 April 2026

Abstract Ensuring operational safety for autonomous vehicles is a critical challenge in modern engineering, particularly due to the intricate interactions among diverse traffic participants. Traditional approaches often treat planning and prediction as unidirectional processes, failing to capture the dynamic, game-theoretic nature of real-world traffic. In the context of Digital Twins, there is an urgent need for high-fidelity virtual representations that can model the continuous, bidirectional evolution of the ego vehicle and surrounding agents to support robust decision-making under uncertainty. To address these limitations, a novel framework named Planning by Simulation with mutual influence prediction is proposed,… 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

Chinmai Bhat^1,2, Mayur Jiyalal Prajapati², Yulius Shan Romario³, Wojciech Macek⁴, Maziar Ramezani⁵, Cho-Pei Jiang^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075197 - 09 April 2026

Abstract This study aims to develop a digital twin framework for fabricating automotive components through additive manufacturing (AM) technology. The framework comprises topology optimization (TO), finite element analysis (FEA), and fabrication analysis using Simufact Additive, which ensures the first-time-right fabrication of the component. Using TO-FEA, the component is designed with reduced overall weight without compromising the structural and functional performance. After the successful design of the component, it is analyzed for fabrication feasibility before undergoing the actual fabrication process. In the present study, an automotive flange fork is designed and fabricated through AM laser powder-bed fusion… More >

Milan Sága, Ján Minárik^*, Milan Vaško, Jaroslav Majko

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076734 - 30 March 2026

Abstract The presented study analyses the impact of hysteresis on the response of mechanical systems. The main objective is to determine how the hysteretic models influence the system behaviour and if they can be utilised to describe a damaged or a faulty system. The hysteretic models are able to describe various types of nonlinear behaviour that can reflect the wear or damage of the system components. The data obtained from these models can possibly serve as a basis for the advanced approaches, such as digital twin modelling and predictive maintenance. All the results presented in this… More >

Victor Leiva^1,2,*, Carlos Martin-Barreiro^3,*, Viviana Giampaoli⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.074800 - 26 February 2026

Abstract Mortality prediction in respiratory health is challenging, especially when using large-scale clinical datasets composed primarily of categorical variables. Traditional digital twin (DT) frameworks often rely on longitudinal or sensor-based data, which are not always available in public health contexts. In this article, we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset. This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System, including both COVID-19 and non-COVID-19 patients. Four classification models—extreme gradient boosting (XGBoost), logistic regression, random forest, and a deep neural… More >

Nur Afikah Juhari, Khairul Nizam Tahar^*

Revue Internationale de Géomatique, Vol.35, pp. 1-11, 2026, DOI:10.32604/rig.2026.070991 - 19 January 2026

Abstract The use of Unmanned Aerial Vehicles (UAVs) in photogrammetry has grown rapidly due to enhanced flight stability, high-resolution imaging, and advanced Structure from Motion (SfM) algorithms. This study investigates the potential of UAVs as a cost-effective alternative to Terrestrial Laser Scanners (TLS) for 3D building reconstruction. A 3D model of Bangunan Sarjana was generated in Agisoft Metashape Professional v.2.0.2 using 492 aerial images captured at flying altitudes of 40, 50, and 60 m. Ground control points were established using GNSS (RTK-VRS), and Total Station measurements were employed for accuracy validation. The results indicate that the 60 More >

Abdullah Alourani¹, Mehtab Alam^2,*, Ashraf Ali³, Ihtiram Raza Khan⁴, Chandra Kanta Samal²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-32, 2026, DOI:10.32604/cmc.2025.070161 - 10 November 2025

Abstract The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management. Earlier approaches have often advanced one dimension—such as Internet of Things (IoT)-based data acquisition, Artificial Intelligence (AI)-driven analytics, or digital twin visualization—without fully integrating these strands into a single operational loop. As a result, many existing solutions encounter bottlenecks in responsiveness, interoperability, and scalability, while also leaving concerns about data privacy unresolved. This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing, distributed intelligence, and simulation-based decision support. The… More >

Changyu Jeon, Younghoon Kim^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3723-3745, 2025, DOI:10.32604/cmes.2025.073030 - 23 December 2025

Abstract Accurate State-of-Health (SOH) prediction is critical for the safe and efficient operation of lithium-ion batteries (LiBs). However, conventional methods struggle with the highly nonlinear electrochemical dynamics and declining accuracy over long-horizon forecasting. To address these limitations, this study proposes CIPF-Informer, a novel digital twin framework that integrates the Informer architecture with Channel Independence (CI) and a Particle Filter (PF). The CI mechanism enhances robustness by decoupling multivariate state dependencies, while the PF captures the complex stochastic variations missed by purely deterministic models. The proposed framework was evaluated using the Massachusetts Institute of Technology (MIT) battery More >

Saeed Asadi¹, Hajar Kazemi Naeini¹, Delaram Hassanlou², Abolhassan Pishahang³, Saeid Aghasoleymani Najafabadi⁴, Abbas Sharifi⁵, Mohsen Ahmadi^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1259-1301, 2025, DOI:10.32604/cmes.2025.070528 - 26 November 2025

Abstract The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control.… More >