The increasing number of passengers and services using railways and the corresponding increase in rail use has caused the acceleration of rail wear and surface defects which makes rail defect identification an important issue for rail maintenance and monitoring to ensure safe and efficient operation. Traditional visual inspection methods for identifying rail defects are time-consuming, less accurate, and associated with human errors. Deep learning has been used to improve railway maintenance and monitoring tasks. This study aims to develop a structured model for detecting railway artifacts and defects by comparing different deep-learning models using ultrasonic image data. This research showed whether it is practical to identify rail indications using image classification and object detection techniques from ultrasonic data and which model performs better among the above-mentioned methods. The methodology includes data processing, labeling, and using different conventional neural networks to develop the model for both image classification and object detection. The results of CNNs for image classification, and YOLOv5 for object detection show 98%, and 99% accuracy respectively. These models can identify rail artifacts efficiently and accurately in real-life scenarios, which can improve automated railway infrastructure monitoring and maintenance.

The advent of Advanced Air Mobility (AAM) presents a transformative solution to multi-modal transportation. However, coordinating missions, as well as monitoring and controlling multiple Unmanned Aerial Vehicles (multi-UAV systems), remains a significant challenge. The adoption of digital twin (DT) technology has the potential to provide a viable solution. This study synthesizes insights from 145 publications across the UAV, traditional aviation, and manufacturing domains, retrieved from major scientific databases such as Scopus, IEEE Xplore, and ScienceDirect. The aim is to comprehensively analyze advancements in cyber–physical systems (CPS) and DT technologies, with a particular focus on the key aspects of UAV-based DT construction, including framework architecture, geometric modeling, physical modeling, behavioral modeling, rule modeling, and cyber–physical consistent modeling approaches. Additionally, the application of DTs in AAM scenarios is analyzed, and key challenges are identified. Finally, we provide insights into research directions to enhance the robustness and applicability of future AAM-based DT.

The rapidly evolving urbanization is generating unprecedented travel demand, notably in intercity travel. With increasing challenges in motorized traffic, innovative and sustainable transport modes are more than ever necessary. The Hyperloop system is an emerging transportation mode with the potential to change long–distance commutes, especially between cities. There is a need for, but also gap in, understanding this potentially emerging transport mode. This study aims at filling this gap by deploying a stated preference study in Germany, in which data was collected for 786 respondents with 5640 scenarios, to investigate the factors impacting users’ preferences towards Hyperloop systems. Models were developed to examine factors impacting the immediate Hyperloop adoption (in the first year of its implementation), but also the choice between Hyperloop and other long–distance travel modes, such as airplanes and high-speed trains. Results indicate that mode-related characteristics (travel time, travel cost, safety), individual characteristics (gender, income level, availability of a driving license, access to a car, familiarity with the Hyperloop system), the current satisfaction level with high-speed trains and airplanes, and personality traits (confidence, affinity to technology) are the most significant factors in the choice and early adoption of Hyperloop systems.

Heat and temperature is on the news of the air in the today's world. High temperatures in urban areas have a direct impact on human health and are associated with heat related problems and excess summer deaths. This study is carried out to know the trend of land surface temperature and assess the impact of land use intensity on temperature in the Chittagong City Corporation Area, Bangladesh. A general linear model was used to estimate surface temperatures using Landsat TM, ETM+ and OLI_TIRS are used in the year 1991, 2003 and 2015 the months of May. Land use and its environmental effects can be quantitatively defined by land-use intensity. In this study, land-use intensity metric was improved using a geographic mapping method. The results revealed the temperature increased when the land-use intensity increased via a hierarchical transition owing to forest land reductions of 26.25%, respectively; built-up land increased by 48.45%. The temperature increase was driven more by the external environmental degrades than by land use intensity changes. The temperature response to land-use intensity changes was more sensitive in low altitude areas than in high altitude areas.