A Digital Twin (DT) is an innovative area of computer science and technology research that generates an imitation of a realistic system, procedure, or object. The usage of DT technology in innovation across several industries has made it a vital and revolutionary tool. The CiteSpace tool can be utilised for carrying out a scientometric evaluation of the effectiveness of DT technologies in engineering. The current analysis used data acquired through the Scopus repository from 2014 to 2025. To highlight emerging areas of research across various engineering areas, it offers link–walkthrough, keyword co-occurrence and author co-citation analysis of networks. The report highlights the present state of DTs in engineering, their impact on numerous fields and the potential for collaborative research.

The Internet of Things (IoT) has transformed the industrial sector. This study presents a novel framework for real-time evaluation of service quality in residential gas pipeline networks. IoT devices collect critical operational and environmental data, which are processed through a fog-cloud architecture using Bayesian modeling. This enables the calculation of a comprehensive Quality of Service Measure (QSM) and a Service Quality Delivery Value (SQDV) to assess and interpret service performance. A two-level decision-tree model further supports decisions by regulators and end-users. The framework was validated using 73,462 service interaction records, showing significant improvements over existing approaches: reduced data delay (179.01 s), high classification performance (Specificity: 94.22%, Sensitivity: 92.78%, Precision: 93.15%), enhanced decision-making (Accuracy: 95.45%, Error Rate: 1.29%), improved reliability (93.69%), and robust system stability (73.25%).

Cyber twin technology, a successful branch of simulation modeling in business, is now being applied in the healthcare sector. An intelligent architecture inspired by cyber twins is proposed to explore the unique visual, behavioral, and physiological experiences of individuals with anxiety disorders while working in smart office environments. Temporal data mining is utilized for framing data granules, and quantum probability techniques are employed for anomaly detection within the framework. Additionally, a novel multilayer Convolutional Neural Network is introduced to predict a quantifiable Health Vulnerability Index. A smart alert system is included, capable of notifying caregivers of any identified health concerns, enabling timely assistance. To evaluate the effectiveness of the proposed strategy, it was tested on real-world data comprising 82,235 cases. The results demonstrate that the method excels in several key performance metrics: time efficiency (24.6 seconds), classification efficiency (Precision (92.77%), Specificity (92.43%), and Sensitivity (92.82%)), decision-making efficiency ( \(r^{2}=79%\) ), error rate (AAE 0.31%), and stability (75%), surpassing current state-of-the-art methodologies.

Advancements in the Internet of Things (IoT) paradigm have greatly improved the quality of services in the electricity industry through the integration of smart energy distribution and dependable electric devices. Conspicuously, the current research introduces a method for managing electricity consumption in smart residences using IoT-Fog technology, focusing on efficient energy allocation and real-time energy needs. The study specifically examines the effectiveness of electricity grid sub-stations in distributing energy using fog computing technology. By utilizing a quantum computing-assisted approach, optimal energy distribution is achieved by calculating a novel Electricity Usage Measure (EUM) based on actual energy usage patterns of smart homes. Furthermore, the Quantumized Neural Network (QiM-NN) technique is developed to forecast the electricity distribution over grid substations. For performance assessment, 4-month data are collected using four smart houses. Comparative analysis with existing data assessment techniques illustrates the effectiveness in terms of Temporal Delay (6.33 ms), Optimization Performance (Specificity (93.00%), Sensitivity (90.86%), Precision (96.66%), Coverage (96.66 %), Reliability (93.76%), and Stability (71%).

Internet of Things (IoT) technology backed by Artificial Intelligence (AI) techniques has been increasingly utilized for the realization of the Industry 4.0 vision. Conspicuously, this work provides a novel notion of the smart sports industry for provisioning efficient services in the sports arena. Specifically, an IoT-inspired framework has been proposed for real-time analysis of athlete performance. IoT data is utilized to quantify athlete performance in the terms of probability parameters of Probabilistic Measure of Performance (PMP) and Level of Performance Measure (LoPM). Moreover, a two-player game-theory-based mathematical framework has been presented for efficient decision modeling by the monitoring officials. The presented model is validated experimentally by deployment in District Sports Academy (DSA) for 60 days over four players. Based on the comparative analysis with state-of-the-art decision-modeling approaches, the proposed model acquired enhanced performance values in terms of Temporal Delay, Classification Efficiency, Statistical Efficacy, Correlation Analysis, and Reliability.