Transforming consumers’ IoT data into behavioral insights with AI-enabled Consumer Digital Twins (CDT) for marketing analytics

The relevance of the problem. In the post-war period, the issue of efficient organization of production and logistics of construction materials, in particular ready-mixed concrete, which is the basis for the restoration of residential and industrial infrastructure, is of particular relevance. Concrete plants face numerous logistical challenges, including order management, on-time delivery, transportation constraints, and volatile demand. In such conditions, the role of digital twins and intelligent decision support systems is growing. Digital twins allow modeling and optimizing production and logistics processes based on real data in near real time. Methodology. The study applies a systematic approach to the design of digital twins of concrete plant production and logistics using modern methods of simulation modeling, machine learning, and artificial intelligence. A modular information technology architecture has been developed, covering the physical IoT layer, data collection and storage layer, modeling and analytics layer, as well as integration with ERP, CRM, WMS, TMS, MES/EAM, and SCADA. Decision trees, random forests, and neural networks are used as machine learning models to analyze the efficiency of ready-mixed concrete logistics in real time. Results. A generalized model of a digital twin of a concrete enterprise is proposed, which allows combining production processes, order management, logistics, and analytics into a single intelligent information system. Machine learning models for analyzing the efficiency of logistics in real time, which take into account the state of the transport fleet, capacity utilization, delivery schedules, order priority, etc., are considered. Novelty. For the first time, a holistic information technology of a digital twin of the production and logistics system of a concrete industry enterprise is proposed, which integrates the technologies of the industrial Internet of Things, simulation multi-agent modeling, and machine learning methods. The integration of ML models with IoT data allows for automatic adjustment of production and logistics scenarios, unlike the existing order management system, IoT platform of concrete mixers, and dispatching system, which allows for an increase in the efficiency of predictive analytics of ready-mixed concrete logistics. Practical significance. The proposed approaches can be implemented in concrete enterprises of various sizes, both as part of the reconstruction of existing plants and in the construction of new mobile concrete plants. They provide increased forecasting accuracy, reduced costs, improved customer service, and reduced environmental impact through logistics optimization.

Background information: Real-time AI and Digital Twins are revolutionizing mental health with real-time stress detection. With improved precision in physiological, behavioral, and social IoT data, scalable, accurate, and privacy-preserving systems must be designed for mental health diagnoses. Objective: Improvement of AI and DTs to enhance the real-time stress detection accuracy, IoT data in enhancing the scalability, accuracy, and privacy of mental health diagnosis. Methods: real-time adaptive reinforcement learning, PCA, gradient boosting, and IoT data. During cross-validation of accuracy measures, Bayesian optimization is applied to hyperparameters to optimize for computational efficiency. Result: The framework reduced feature redundancy and data transmission costs by 35% and 30%, respectively, and obtained a correctness of 99.5% with a 98% F1-score. Conclusion: The AI-driven framework, with its use of real-time algorithms and IoT data, is revolutionizing stress detection and mental health evaluation internationally.

The industry use cases for the Digital Twin idea

Smart cities in the time of climate change and Covid‐19 need digital twins

Digital twins are becoming popular in a wide range of industries for the monitoring, control, and optimization of physical objects, processes, and systems. Its growing demand is related to its potential to improve efficiency, reduce costs and increase safety in different applications. As a result, a variety of approaches, modeling processes, technologies, and tools have been used to develop and deploy digital twins. The choice of which to use often depends on the specific application area, case study, available resources, and expertise. This paper explores the idea of using the digital twin concept applied to power wheelchair systems, to supervise and improve their operation and maintenance. In particular, it focuses on data flow and connectivity within the digital twin, proposing an IoT and cloud-based data exchange to enable efficient cyber-physical connection and easy data management. For this work, a small-scale prototype of a power wheelchair was built with some sensors and actuators interfaced with a microcontroller, and the data exchange with a ROS-based virtual entity was performed via cloud under the MQTT protocol.

An ontology-based methodology to establish city information model of digital twin city by merging BIM, GIS and IoT

The Role of IoT and Big Data in the Fifth Industrial Revolution highlights the shift from automation-driven Industry 4.0 to the human-centered, intelligent ecosystem of Industry 5.0. The book integrates perspectives from IoT, Big Data analytics, Artificial Intelligence, Digital Twins, and 5G-enabled IIoT to demonstrate how these innovations are transforming manufacturing, energy systems, healthcare, urban planning, and human-machine interaction. The chapters explore IoT architectures, smart fault detection systems, big data driven optimization, and industrial cybersecurity, while dedicated sections delve into Digital Twins across multiple sectors, advanced facial expression recognition for human machine interfaces, and IoT-based energy management solutions. Real-world case studies and practical insights illustrate how emerging technologies are enabling sustainable, efficient, and resilient industrial environments. By bridging theoretical foundations with applied advancements, this book serves as a critical resource for understanding the technological pillars and practical implications of the Fifth Industrial Revolution. Key Features -Explores foundational and emerging concepts in IoT, AI, Big Data, and Digital Twins. -Demonstrates real-world industrial applications through practical case studies. -Addresses security, sustainability, and ethical challenges in smart technology deployment. -Integrates cross-disciplinary perspectives spanning manufacturing, healthcare, energy, and urban systems.

This article takes a further step on the digitalization path in tunneling by implementing the concept of the digital twin and examining its potential at the three levels of real‐world integration: digital model, digital shadow, and digital twin (DT). It evaluates the current implementation of tunnel information modeling and its adoption in the infrastructure sector. The importance of structured and real‐time data synchronization through technologies such as IoT and Big Data is emphasized. It explores advancements from tunnel model to shadow to DT, emphasizing the importance of structured and data real‐time synchronization through technologies like IoT and Big Data. A comprehensive literature review highlights both technical and non‐technical barriers to the implementation of DT. Continuous improvement of DT, supported by advancements in data acquisition and analytical methods, is expected to significantly enhance tunnel construction. As a main focus, this article provides a framework for a centralized and comprehensive platform for all levels of tunnel twin development, leveraging Autodesk Platform Services. It concludes with a vision for the future, discusses emerging technologies advocating for a strategic approach to digital transformation in tunneling that leverages technological innovations for sustainable development and societal benefits.

<p>River basin management involves many issues including water resources, water ecology, water environment, and water disasters. Digital twins integrated with GIS, BIM and IoT are applied to river basin management, to establish digital twin data and model integration and visual expression methods, study the digital twin operation mechanism, build smart river basin twin, and design a digital twin-driven smart river basin platform. Taking the joint flood dispatching management and control of the reservoir group in the Duhe River Basin as an example. The application has proven that: compared with the problems of incomplete information, insufficient accuracy, lagging feedback and single expression in traditional basin database management or two-dimensional plane management, the digital twin-driven smart basin platform, on the basis of the integration and interaction of GIS data, BIM data and IoT data, and on the basis of data and model two-way drive, can realize simulation, decision making, optimization and visualization in external environment, and the control effect is better than traditional means. The research and practice of the platform can realize real-time monitoring, diagnosis, analysis, decision making and prediction for river basin management, providing a new solution for its intelligent operation, precise control and safe operation and maintenance.</p>

<p>River basin management involves many issues including water resources, water ecology, water environment, and water disasters. Digital twins integrated with GIS, BIM and IoT are applied to river basin management, to establish digital twin data and model integration and visual expression methods, study the digital twin operation mechanism, build smart river basin twin, and design a digital twin-driven smart river basin platform. Taking the joint flood dispatching management and control of the reservoir group in the Duhe River Basin as an example. The application has proven that: compared with the problems of incomplete information, insufficient accuracy, lagging feedback and single expression in traditional basin database management or two-dimensional plane management, the digital twin-driven smart basin platform, on the basis of the integration and interaction of GIS data, BIM data and IoT data, and on the basis of data and model two-way drive, can realize simulation, decision making, optimization and visualization in external environment, and the control effect is better than traditional means. The research and practice of the platform can realize real-time monitoring, diagnosis, analysis, decision making and prediction for river basin management, providing a new solution for its intelligent operation, precise control and safe operation and maintenance.</p>

The article offers a multi-perspective modelling approach for building a digital twin (DT) in smart agriculture. The proposed MultiDigiII methodology involves the development of conceptual models at different levels of abstraction, so that complex systems can be more fully represented in the conceptual model. In the development of complex systems, it is necessary to use more than one approach to modelling, and one, simplified conceptual model development is not enough to fully reflect the essence of the system. The MultiDigiII methodology is used in the development of the digital twin for smart agriculture. Agriculture is a very complex system to model due to various influencing changing natural conditions. Therefore, remote sensing in a dynamic environment can provide the missing spatial information needed for crop models to improve yield prediction. A more precise and appropriate model can be built based on remote sensing and IoT data. Building the digital twin by the application of a multi- perspective view is important to provide a comprehensive model, which includes not only an object from the field (for example, a potato) but also influencing obj ects. Such an approach allows getting a richer picture of processes and possible outcomes. The result of this study is the proposed MultiDigiII methodology for creating a digital twin and the results of a case study in the real-life situation of smart agriculture.

This paper describes the solution approaches for horizontal value creation using IoT and digital twin data. First, the various lifecycle stages that a product goes through are described. A classification of digital twins is then carried out. On this basis, the horizontal and vertical data integration is described, which forms the basis for future value creation.

The laboratory SmartTechLab as a key of experimental SMART manufacturing system based on Industry 4.0 concept is described in the paper. This laboratory focuses on four research fields: assembly, identification, digitalization and online monitoring. The laboratory is designed also to improve theoretical and practical knowledge of students and young researchers. The development of the assembly is due to use of modern industrial and collaborative robots on the assembly line, designed to move objects and assemble products. The field of identification is supported by RFID technology and two machine vision measuring stations. KEYENCE devices - industrial cameras and laser profilometer are used as machine vision technology. Digital twins, OPC server, HP server, IoT Data and Cloud platforms are the basic elements of digitalization research field. The quality control system in 3D printing for assessment of the product accuracy and surface quality is based on camera and laser displacement sensor.KeywordsSMART ManufacturingIndustry 4.0AutomationDigitalizationQuality control

The project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

Digital twins probe into food cooling and biochemical quality changes for reducing losses in refrigerated supply chains