In this paper advanced Sentiment Analysis techniques were applied to evaluate public opinions reported by rail users with respect to four major European railway companies, i.e., Trenitalia and Italo in Italy, SNCF in France and Renfe in Spain. Two powerful language models were used, RoBERTa and BERT, to analyze big amount of text data collected from a social platform dedicated to customers reviews, i.e., TrustPilot. Data concerning the four European railway companies were first collected and classified into subcategories related to different aspects of the railway sector, such as train punctuality, quality of on-board services, safety, etc. Then, the RoBERTa and BERT models were developed to understand context and nuances of natural language. This study provides a useful support for railways companies to promote strategies for improving their service.

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.

The search for the development of nanostructured materials has led to the study of the properties of their precursors. For the production of nanofibers by the electrospinning process, it is necessary to determine the rheological parameters of the precursor solutions. Since these properties can be influenced by the processing variables and chemical composition of the polymer, this study aims to elucidate the effect of the addition of vinyl monomers in the formulation of nanofibers based on polyacrylonitrile and to determine the optimal parameters for the production of the precursor polymer solution. The effects of temperature and addition of vinyl monomers were evaluated by rheometry, from the analysis of the variation of the viscosity of the solutions, and by microscopy, the morphology of the nanofibers produced. It was observed that the increase in the temperature used to produce the solutions improves the fibers’ properties. Still, there is a relationship between the time of exposure of the polymeric solution to the temperature and the homogeneity of the fibers, which cannot exceed 45 min. The addition of vinyl monomers, to produce PAN-PVA co-polymeric fibers, increases the conductivity and reduces the viscosity of the solutions, resulting in more refined and homogeneous fibers.

A significant percentage of any nation’s economy comes from the building industry, and its performance can impact overall economic growth and development. This paper aims to identify the similarities and differences between the construction sector (CS) of developed and developing economies in terms of size, growth, and contribution to the Gross domestic product (GDP) to understand the similarities and variances in the CS dynamics, trends, and challenges, and to inform policy decisions and investments through the literature review. The study also explores the factors that affect the CS’s performance in both types of economies, such as government policies, market conditions, and technological advancements. This paper concludes that the CS in developed economies is more established and technologically advanced, but there is still significant room for growth in developing economies. Moreover, a framework is proposed that could assist developing nations in opting for the construction economy. Further, the review emphasizes the significance of government policies and investments in infrastructure development to stimulate the CS’s growth and support overall economic development. The results of the study will assist in enhancing understanding of the CS’s potential in both developed and developing economies and support decision-making for policymakers, industry practitioners, and academicians.

National governments and academic higher education institutions continue to realign human resource development (HRD) strategies to address the gaps in HRD mandate. This study will investigate new and recalibrated skills that higher institutions (HEIs) professionals and the labor force produce to reconfigure curriculum development in tertiary education. The study extracts narrative from 6 curriculum developers, 3 HRD heads and h3 manpower organizations on the labor landscapes from different local and multinational industries from entry-level to mid-career ranges through case scenario-based interviews and focus group discussions to determine the skills around motivation, innovativeness, and adaptability and subsequently integrate strategic initiatives to reconfigure the compatibility of these skills from higher education institutions to post-pandemic industries. The findings reveal skills that can be managed at the individual level, e.g., self-motivation and adaptability as well as the need to emerge from the technological pressures by adapting to organizational and clientele demands. These human resource traits become the mantra of surviving and progressing in a landscape shaped by the pre- and post-pandemic setting and become the basis of HEI programs to match the needs of the labor force and the industries.