The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy.

Accurate estimations and assessments of vehicle emissions can support decision-making processes. Current emission estimation tools involve several calculation methods that provide estimates of the exhaust components that result from driving on urban arterial roads. This is an important consideration, as the emissions generated have a direct impact on the health of pedestrians near the roads. In recent years, there has been an increase in the use of emission models, especially in combination with traffic simulator models. This is because it is very difficult to obtain an actual measurement of road emissions for all vehicles travelling along the analysed road section. This paper concerns a review of selected traffic simulations and the estimation of exhaust gas components models. The models presented have been aggregated into a group with respect to their scale of accuracy as micro, meso, and macro. This paper also presents an overview of selected works that combine both traffic and emission models. The presented literature review also emphasises the proper calibration process of simulation models as the most important factor in obtaining accurate estimates. This work also contains information and recommendations on modelling that may be helpful in selecting appropriate emission estimation tools to support decision-making processes for, e.g., road managers.

Road transport contributes to almost a quarter of carbon dioxide emissions in the EU. To analyze the exhaust emissions generated by vehicle flows, it is necessary to use specialized emission models, because it is infeasible to equip all vehicles on the road in the tested road sections with the Portable Emission Measurement System (PEMS). However, the currently used emission models may be inadequate to the investigated vehicle structure or may not be accurate due to the used macroscale. This state of affairs is especially related to full hybrid vehicles, since there are none of the microscale emission models that give estimated emissions values exclusively for this kind of drive system. Several automakers over the past decade have invested in hybrid vehicles with great opportunities to reduce costs through better design, learning, and economies of scale. In this work, the authors propose a methodology for creating a CO₂ emission model, which takes relatively little computational time, and the models created give viable results for full hybrid vehicles. The creation of an emission model is based on the review of the accuracy results of methods, such as linear, robust regression, fine, medium, coarse tree, linear, cubic support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and neural network (NNET). Particularly in the work, the best fit for the road input data for the CO₂ emission model creation was the GPR method. PEMS data was used, as well as model training data and model validation. The model resulting from this methodology can be used for the analysis of emissions from simulation tests, or they can be used for input parameters for speed, acceleration, and road gradient.

Universities play an essential role in preparing human resources for the industry of the future. By providing the proper knowledge, they can ensure that graduates will be able to adapt to the ever-changing industrial sector. However, to achieve this, the courses provided by academia must cover the current and future industrial needs by considering the trends in scientific research and emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC). This work presents the survey results conducted among academics to assess the current state of university courses, regarding the level of knowledge and skills provided to students about the Internet of Things, Artificial Intelligence, and Edge Computing. The novelty of the work is that (a) the research was carried out in several European countries, (b) the current curricula of universities from different countries were analyzed, and (c) the results present the teachers’ perspective. To conduct the research, the analysis of the relevant literature took place initially to explore the issues of the presented subject, which will increasingly concern the industry in the near future. Based on the literature review results and analysis of the universities’ curricula involved in this study, a questionnaire was prepared and shared with academics. The outcomes of the analysis reveal the areas that require more attention from scholars and possibly modernization of curricula.

Electric vehicles in a short time will make up the majority of the fleet of vehicles used in general. This state of affairs will generate huge sets of data, which can be further investigated. The paper presents a methodology for the analysis of electric vehicle data, with particular emphasis on the energy consumption parameter. The prepared database contains data for 123 electric vehicles for analysis. Data analysis was carried out in a Python environment with the use of the dabl API library. Presentation of the results was made on the basis of data classification for continuous and categorical features vs. target parameters. Additionally, a heatmap Pearson correlation coefficient was performed to correlate the energy consumption parameter with the other parameters studied. Through the data classification for the studied dataset, it can be concluded that there is no correlation against energy consumption for the parameter charging speed; in contrast, for the parameters range and maximum velocity, a positive correlation can be observed. The negative correlation with the parameter energy consumption is for the parameter acceleration to 100 km/h. The methodology presented to assess data from electric vehicles can be scalable for another dataset to prepare data for creating machine learning models, for example.

Road transport is the main source of pollution to the environment in urban areas; therefore, there is a need to accurately estimate the amount of exhaust gases emitted by motor vehicles. The development of systems for measuring emissions of exhaust gases caused the exit from stationary chassis dynamometers to real road test. This paper presents an analysis of emission data from the PEMS system for real driving cycles of various types of vehicles, complying with EURO2-EURO6 standards, fueled with petrol, LPG, and diesel in urban, rural, and motorway areas as well as detailing roundabouts. The results show that in the range of roundabouts, there is an increased emission of harmful exhaust components, such as CO₂, THC, CO, and NOx. Due to the specific traffic conditions that prevail at the roundabout (acceleration, braking, acceleration to a certain speed), the methodology for creating an exhaust emission model for this type of objects has been proposed. Statistical analysis of the received boosted regression tree models based on the coefficient of regression, root mean square error, and mean absolute error and based on the visual assessment of the results show that the obtained models are well represented by real data. The obtained results of emission calculations on roundabouts may be used to identify areas of increased emission of harmful exhaust components, as well as an introduction to prepare new roundabout design guidelines concerning emission data.