The increasing prevalence of digital twins across various industries is attributed to their capacity for enhancing performance, minimizing downtime, and improving efficiency in the manufacturing process. These virtual models enable users to monitor and analyze the real-time behavior of physical assets, systems, or processes. However, the design of digital twins presents several challenges, such as excessive time costs, security risks, data accuracy, privacy concerns, and complexity in chosen components. This research paper introduces a comprehensive framework that effectively and flexibly addresses these challenges in digital twin development by employing S4 features, a Morphological Matrix, and Fuzzy TOPSIS. The proposed framework draws inspiration from methodologies utilized in product design and leverages a morphological matrix to identify and categorize diverse S4 features. These features are crucial in creating smart, sustainable, sensing, and socially impactful digital twins. Additionally, Fuzzy TOPSIS is employed to determine the optimal digital twin structure. The digital twin is divided into a physical and virtual model, interconnected and interactively communicated through a dedicated interface model. Furthermore, the framework integrates human knowledge into the digital twin structure, enhancing its robustness. When combined with a Large Natural Language Processing model, a comparative analysis of the digital twin structure is presented. A reconfigurable micromachine is used as a case study to validate the proposed approach. The results demonstrate the proposed framework’s applicability for developing digital twins within the manufacturing industry. The main contribution of this paper is the innovative integration of human expertise and generative AI in a digital twin design framework. This approach provides a structured method that aligns the solution closely with industry requirements and offers a scalable and adaptable design methodology validated through practical applications. This framework is particularly valuable when dealing with a high number of devices that are challenging for human experts to evaluate rapidly in digital twin design.

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station’s capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station’s use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station’s open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.

A smart city is a city that binds together technology, society, and government to enable the existence of a smart economy, smart mobility, smart environment, smart living, smart people, and smart governance in order to reduce the environmental impact of cities and improve life quality. The first step to achieve a fully connected smart city is to start with smaller modules such as smart homes and smart buildings with energy management systems. Buildings are responsible for a third of the total energy consumption; moreover, heating, ventilation, and air conditioning (HVAC) systems account for more than half of the residential energy consumption in the United States. Even though connected thermostats are widely available, they are not used as intended since most people do not have the expertise to control this device to reduce energy consumption. It is commonly set according to their thermal comfort needs; therefore, unnecessary energy consumption is often caused by wasteful behaviors and the estimated energy saving is not reached. Most studies in the thermal comfort domain to date have relied on simple activity diaries to estimate metabolic rate and fixed values of clothing parameters for strategies to set the connected thermostat’s setpoints because of the difficulty in tracking those variables. Therefore, this paper proposes a strategy to save energy by dynamically changing the setpoint of a connected thermostat by human activity recognition based on computer vision preserving the occupant’s thermal comfort. With the use of a depth sensor in conjunction with an RGB (Red–Green–Blue) camera, a methodology is proposed to eliminate the most common challenges in computer vision: background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance on human detection. Moreover, a Recurrent Neural Network (RNN) is implemented for human activity recognition (HAR) because of its data’s sequential characteristics, in combination with physiological parameters identification to estimate a dynamic metabolic rate. Finally, a strategy for dynamic setpoints based on the metabolic rate, predicted mean vote (PMV) parameter and the air temperature is simulated using EnergyPlus™ to evaluate the energy consumption in comparison with the expected energy consumption with fixed value setpoints. This work contributes with a strategy to reduce energy consumption up to 15% in buildings with connected thermostats from the successful implementation of the proposed method.