Artificial intelligence (AI) is not only disrupting industries and businesses, particularly the ones have fallen behind the adoption, but also significantly impacting public life as well. This calls for government authorities pay attention to public opinions and sentiments towards AI. Nonetheless, there is limited knowledge on what the drivers behind the public perception of AI are. Bridging this gap is the rationale of this paper. As the methodological approach, the study conducts an online public perception survey with the residents of Sydney, Melbourne, and Brisbane, and explores the collected survey data through statistical analysis. The analysis reveals that: (a) the public is concerned of AI invading their privacy, but not much concerned of AI becoming more intelligent than humans; (b) the public trusts AI in their lifestyle, but the trust is lower for companies and government deploying AI; (c) the public appreciates the benefits of AI in urban services and disaster management; (d) depending on the local context, public perceptions vary; and (e) the drivers behind the public perception include gender, age, AI knowledge, and AI experience. The findings inform authorities in developing policies to minimise public concerns and maximise AI awareness.

Low emission logistics have become an expected and desired goal in all fields of transportation, particularly in the European Union. Heavy-duty trucks (HDTs) are significant producers of emissions and pollution in inland transports. Their role is significant, as in multimodal transport chains truck transportation is, in most cases, the only viable solution to connect hinterlands with ports. Diesel engines are the main power source of trucks and their emission efficiency is the key challenge in environmentally sound freight transportation. This review paper addresses the academic literature focusing on truck emissions. The paper relies on the preliminary hypothesis that simple single solutions are nonexistent and that there will be a collection of suggestions and solutions for improving the emission efficiency in trucks. The paper focuses on the technical properties, emission types, and fuel solutions used in freight logistics. Truck manufacturing, maintenance, and other indirect emissions like construction of road infrastructure have been excluded from this review.

Digitalization is frequently addressed in recent economic and social scientific literature. This paper applies a distinction to digital data (raw data) and digital technologies (including both software platforms and hardware solutions). The open data is defined as follows: it is publicly available and non-chargeable data (information content) that is machine readable. Open data enables software and application development for external partners and users. A common feature in open-data applications is location-based identification (e.g., real-time traffic monitoring). These include spatial map visualizations, and monitoring of traffic and modes of transport. This visualized information provides additional support for data-based decision-making and management as these study results indicate. This information is valuable particularly in the decisions concerning unconventional and sudden events. This research indicates that the most suitable data resources for opening include information related to port transport infrastructure. In terms of temporal monitoring, static road and rail data is currently the most potential alternative for open data in ports. The main reasons are that these data sources are already at least partly published. However, they are not always in open-data formats. Static data is also a grounded starting point because the technical requirements are much less demanding in comparison to real-time data-processing and management