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 >

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 >

Chenjun Ni, Kuo Tian^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.012069

Abstract The development of large-scale, high-precision aerospace structures has imposed increasingly stringent requirements on mechanical response monitoring during ground testing. Aiming at the long-standing limitations of mechanical response monitoring for ground tests in terms of accuracy and real-time performance, this study introduces an intelligent structural strength monitoring method using a dynamically evolving digital twin model.
First, a reduced-order modeling method that accounts for actual test deviations is established. By jointly sampling deviation and loading information as variables, a reduced-order model with full-field mechanical responses as output is constructed, enabling rapid updates to reflect the real test conditions.… More >

J. S. V. Siva Kumar¹, Mahmad Mustafa², Sk. M. Unnisha Begum³, Badugu Suresh⁴, Rajanand Patnaik Narasipuram^5,*

Energy Engineering, Vol.122, No.10, pp. 3891-3904, 2025, DOI:10.32604/ee.2025.070052 - 30 September 2025

Abstract Electric vehicle (EV) monitoring systems commonly depend on IoT-based sensor measurements to track key performance parameters such as vehicle speed, state of charge (SoC), battery temperature, power consumption, motor RPM, and regenerative braking. While these systems enable real-time data acquisition, they are often hindered by sensor noise, communication delays, and measurement uncertainties, which compromise their reliability for critical decision-making. To overcome these limitations, this study introduces a comparative framework that integrates reference signals, a digital twin model emulating ideal system behavior, and real-time IoT measurements. The digital twin provides a predictive and noise-resilient representation of More >

Xinyu Yu¹, Lucheng Chen^2,3, Xingzhi Feng^2,4, Xiaoping Lu^2,4,*, Yuye Yang¹, You Shi^5,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3231-3252, 2025, DOI:10.32604/cmc.2025.066975 - 23 September 2025

Abstract This paper investigates mobility-aware online optimization for digital twin (DT)-assisted task execution in edge computing environments. In such systems, DTs, hosted on edge servers (ESs), require proactive migration to maintain proximity to their mobile physical twin (PT) counterparts. To minimize task response latency under a stringent energy consumption constraint, we jointly optimize three key components: the status data uploading frequency from the PT, the DT migration decisions, and the allocation of computational and communication resources. To address the asynchronous nature of these decisions, we propose a novel two-timescale mobility-aware online optimization (TMO) framework. The TMO… More >

Cecilia Castro¹, Victor Leiva^2,*, Franco Basso^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1481-1543, 2025, DOI:10.32604/cmes.2025.067992 - 31 August 2025

Abstract Metaverse technologies are increasingly promoted as game-changers in transport planning, connected-autonomous mobility, and immersive traveler services. However, the field lacks a systematic review of what has been achieved, where critical technical gaps remain, and where future deployments should be integrated. Using a transparent protocol-driven screening process, we reviewed 1589 records and retained 101 peer-reviewed journal and conference articles (2021–2025) that explicitly frame their contributions within a transport-oriented metaverse. Our review reveals a predominantly exploratory evidence base. Among the 101 studies reviewed, 17 (16.8%) apply fuzzy multi-criteria decision-making, 36 (35.6%) feature digital-twin visualizations or simulation-based testbeds,… More > Graphic Abstract

Diego Vergara^*, Antonio del Bosque, Pablo Fernández-Arias

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 41-64, 2025, DOI:10.32604/cmc.2025.067881 - 29 August 2025

Abstract Digital twins (DTs) are rapidly emerging as transformative tools in materials science and engineering, enabling real-time data integration, predictive modeling, and virtual testing. This study presents a systematic bibliometric review of 1106 peer-reviewed articles published in the last decade in Scopus and Web of Science. Using a five-stage methodology, the review examines publication trends, thematic areas, citation metrics, and keyword patterns. The results reveal exponential growth in scientific output, with Materials Theory, Computation, and Data Science as the most represented area. A thematic analysis of the most cited documents identifies four major research streams: foundational More >

Saeed Alzahrani¹, Nazar Hussain², Farah Mohammad^3,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4825-4849, 2025, DOI:10.32604/cmc.2025.066322 - 30 July 2025

Abstract Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients. This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling. By integrating real-time images, electronic health records, and genomic information, the system enables personalized simulations for disease progression modeling, treatment response prediction, and preventive care strategies. In dysarthric speech, which is characterized by articulation imprecision, temporal misalignments, and phoneme distortions, existing models struggle to capture these irregularities. Traditional approaches, often relying solely on audio features, fail to address… More >

Yu-Hsien Lin^*, Po-Cheng Chuang, Joyce Yi-Tzu Huang

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4907-4948, 2025, DOI:10.32604/cmc.2025.065995 - 30 July 2025

Abstract This study proposes an automatic control system for Autonomous Underwater Vehicle (AUV) docking, utilizing a digital twin (DT) environment based on the HoloOcean platform, which integrates six-degree-of-freedom (6-DOF) motion equations and hydrodynamic coefficients to create a realistic simulation. Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements, deep reinforcement learning (DRL) offers a promising alternative. In the positioning stage, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is employed for synchronized depth and heading control, which offers stable training, reduced overestimation… More >

Tony Thomas^*, Ravi Prakash, Soumya Pal

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4055-4104, 2025, DOI:10.32604/cmc.2025.064903 - 30 July 2025

Abstract The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time data collection, analysis, and personalized treatment through interconnected devices such as sensors and wearables. The integration of Digital Twins (DTs), the virtual replicas of physical components and processes, has also been found to be a game changer for the ever-evolving IoMT. However, these advancements in the healthcare domain come with significant cybersecurity challenges, exposing it to malicious attacks and several security threats. Intrusion Detection Systems (IDSs) serve as a critical defense mechanism, yet traditional IDS approaches often struggle with the complexity and scale… More >