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This paper aims to describe a modelling and solving methodology of a (static converter–electric motor–control) system for its sizing by optimization, considering the dynamic thermal heating of the machine.

The electrical drive sizing model is composed of two simulators (electrical and thermal) that are co-simulated with a master−slave relationship for the time step management. The computation is stopped according to simulation criteria.

This paper details a methodology to represent and size an electrical drive using a multiphysics and multidynamics approach. The thermal simulator is the master and calls the electrical system simulator at a fixed exchange time step. The two simulators use a dedicated dynamic time solver with adaptive time step and event management. The simulation automatically stops on pre-established criteria, avoiding useless simulations.

This paper aims to present a generic methodology for the sizing by optimization of electrical drives with a multiphysics approach, so the precision and computation time highly depend on the modelling method of each components. A genetic multiobjective optimization algorithm is used.

The methodology can be applied to size electrical drives operating in a thermally limited zone. The power electronics converter and electrical machine can be easily adapted by modifying their sub-model, without impacting the global model and simulation principle.

The approach enables to compute a maximum operating duration before reaching thermal limits and to use it as an optimization constraint. These system considerations allow to over constrain the electrical machine, enabling to size a smaller machine while guaranteeing the same output performances.

The primary aim of this study is to identify and analyze the key digital transformation areas and determine their impact on technological unemployment in the textile sector. In addition, this study explores whether digital transformation contributes to neo-Luddism or Robot Breaking.

The advent of digital transformation has raised significant concerns, particularly concerning technological unemployment. This study focuses on conducting an analytical hierarchical process (AHP) analysis to determine the impact of different disciplines within digital transformation on technological unemployment. The investigation specifically delves into the ongoing transition to Industry 4.0 within the textile industry. This study uses a mixed-method approach, consisting of a literature review, bibliometric analysis, eight expert phenomenological interviews, and AHP.

This study revealed that artificial intelligence, machine learning and deep learning are the most crucial disciplines that will affect the concept of neo-Luddism. The fact that technological unemployment in the textile sector is examined with AHP Analysis makes this study unique.

This study contributes to Industry 4.0 literature by examining the nexus of technological unemployment in textile manufacturing and the emergence of neo-Luddism.

Express freight transportation is in rapid development currently. Owing to the higher speed of express freight train, the deformation of the bridge deck worsens the railway line condition under the action of wind and train moving load when the train runs over a long-span bridge. Besides, the blunt car body of vehicle has poor aerodynamic characteristics, bringing a greater challenge on the running stability in the crosswind.

In this study, the aerodynamic force coefficients of express freight vehicles on the bridge are measured by scale model wind tunnel test. The dynamic model of the train-long-span steel truss bridge coupling system is established, and the dynamic response as well as the running safety of vehicle are evaluated.

The results show that wind speed has a significant influence on running safety, which is mainly reflected in the over-limitation of wheel unloading rate. The wind speed limit decreases with train speed, and it reduces to 18.83 m/s when the train speed is 160 km/h.

This study deepens the theoretical understanding of the interaction between vehicles and bridges and proposes new methods for analyzing similar engineering problems. It also provides a new theoretical basis for the safety assessment of express freight trains.

This study aims to develop a methodology that extracts an architectural concept from a biological analogy that integrates forms and kinetic behavior to identify whether complex forms work better or simple forms with proper kinetic behavior for improving visual comfort and daylight performance.

The research employs a transdisciplinary approach using several methods consisting of a biomimetic functional-morphological approach, kinetic design strategy, case study comparison using algorithmic workflow and parametric simulation and inverse design, to develop an interactive kinetic façade with optimized daylight performance.

A key development is the introduction of a periodic interactive region (PIR), which draws inspiration from the butterfly wings' nanostructure. These findings challenge conventional perspectives on façade complexity, highlighting the efficacy of simpler shapes paired with appropriate kinetic behavior for improving visual comfort. The results show the façade with a simpler “Bookshelf” shape integrated with a tapered shape of the periodic interactive region, outperforms its more complex counterpart (Hyperbolic Paraboloid component) in terms of daylight performance and glare control, especially in southern orientations, ensuring occupant visual comfort by keeping cases in the imperceptible range while also delivering sufficient average spatial Daylight Autonomy of 89.07%, Useful Daylight Illuminance of 94.53% and Exceeded Useful Daylight Illuminance of 5.11%.

The investigation of kinetic façade studies reveals that precedent literature mostly focused on engineering and building physics aspects, leaving the architectural aspect underutilized during the development phase. Recent studies applied a biomimetic approach for involving the architectural elements besides the other aspects. While the biomimetic method has proven effective in meeting occupants' visual comfort needs, its emphasis has been primarily on the complex form which is difficult to apply within the kinetic façade development. This study can address two gaps: (1) the lack of an architectural aspect in the kinetic façade design specifically in the development of conceptual form and kinetic behavior dimensions and (2) exchanging the superficial biomimetic considerations with an in-depth investigation.

Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.

This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.

Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.

The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.

This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.

This research analyses challenges faced by users at various levels in planning and designing participatory simulation models of cities. It aims to identify issues that hinder experts from maximising the effectiveness of the SUMO tool. Additionally, evaluating current methods highlights their strengths and weaknesses, facilitating the use of participatory simulation advantages to address these issues. Finally, the presented case studies illustrate the diversity of user groups and emphasise the need for further development of blueprints.

In this research, action research was used to assess and improve a step-by-step guideline. The guideline's conceptual design is based on stakeholder analysis results from those involved in developing urban logistics scenarios and feedback from potential users. A two-round process of application and refinement was conducted to evaluate and enhance the guideline's initial version.

The guidelines still demand an advanced skill level in simulation modelling, rendering them less effective for the intended audience. However, they have proven beneficial in a simulation course for students, emphasising the importance of developing accurate conceptual models and the need for careful implementation.

This paper introduces a step-by-step guideline designed to tackle challenges in modelling urban logistics scenarios using SUMO simulation software. The guideline's effectiveness was tested and enhanced through experiments involving diverse groups of students, varying in their experience with simulation modelling. This approach demonstrates the guideline's applicability and adaptability across different skill levels.