A Virtual Reality Laboratory for Blended Learning Education: Design, Implementation and Evaluation

Launched during the pandemic, the EU-funded JANUS project aimed to ensure the continuity of student workshops at universities using a virtual reality (VR) robotics laboratory. With the return to normality, the project has been redesigned to capitalise on the positive outcomes of the experience. The VR lab provides safe and unrestricted access to the labs and experiments with the machines, reducing the consequences of student mistakes and improving the user experience by allowing the experiment to be repeated from different angles, some of which are impossible to access in the real lab. In addition, integration with an interactive learning platform called “ViLLE” allows for continuous assessment of the learning experience. Self-evaluation of the material taught and learned can be integrated with the execution of the exercises that pave the way for Kaizen. Two VR workshops for the blended learning of robotics were developed during the JANUS project. Their evaluation reported favourable responses from the students whose learning performance was indirectly measured

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

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

Vehicle Emission Models and Traffic Simulators: A Review

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

Assessing Vehicle Emissions from a Multi-Lane to Turbo Roundabout Conversion Using a Microsimulation Tool

The development of urban strategies for the reduction of environmental impacts and decarbonization requires ongoing monitoring from the local scale and further deployment of actions to improve transport demand (user characteristics and modal choice) and supply (infrastructure and services). The analysis of pollution sources and the evaluation of possible scenarios are preliminary to the mitigation of impacts. In particular, the study of geometrical and functional characteristics of infrastructures through micro-simulation allows understanding of which schemes can support the reduction of emissions and guarantee high levels of service (LOS), reducing the problem of vehicular congestion in urban areas. The present work focuses on the small-scale analysis of vehicular traffic emissions at a multi-lane roundabout road intersection and the comparison of geometric schemes (current and design) and use with a turbo roundabout scheme as traffic volumes changes. These volumes have plummeted due to the current COVID-19 pandemic. The results show that the geometric-functional modification of the roundabout intersection from a multi-lane to a turbo-roundabout intersection allows a reduction of up to 30% of the emissions considering the current composition of the traffic fleet in the city of Rzeszow in Poland. The proposed comparative analysis methodology can contribute to the drafting of sustainable urban mobility plans (SUMPs) proposing a set of investments for new road works and considering a number of scenarios with interventions that can be implemented in the medium and long term that can provide the incentive to reduce road congestion and vehicular emissions

Investigation of Vehicular Pollutant Emissions at 4-Arm Intersections for the Improvement of Integrated Actions in the Sustainable Urban Mobility Plans (SUMPs)

Sustainable urban mobility planning is a strategic and integrated approach that aims to effectively address the complexities of urban transportation. Additionally, vehicle emissions are still a significant problem found in cities. Its greatest concentration involves intersections, as they have the highest number of stop-and-go operations, resulting in the highest engine load. Although electrification of vehicles is underway, the coming years and the energy crisis may cause the full transformation and fulfillment of the European Green Deal to be postponed. This state of affairs means that much effort should still go into possibly modifying the current infrastructure to make it more environmentally friendly. The article addresses the use of vertical road markings such as “stop”, “give way”, and also signal controllers signs, at four-arm X intersections. The modeling of intersection variants was carried out in the traffic microsimulation software VISSIM. The created model was calibrated according to real world data. The actual part of the work concerns the assumption of specific traffic flow scenarios, for which measurements of delay and emissions of harmful exhaust components such as NOx and PM10 were made. The results obtained can have practical application in proposals for creating unequal intersections. Based on the results, it can be concluded that below the traffic volume value of 1200 vehicles/h, an intersection can be considered with a yield sign and stop sign for two directions of traffic. However, for traffic volumes from 1200 vehicles/h to 2000 vehicles/h, an intersection with stop signs can be used for all traffic directions. The results may also provide some information on the location of the crosswalks and the improvement of strategies to be introduced into the SUMPs

Lubricity of Ethanol–Diesel Fuel Blends—Study with the Four-Ball Machine Method

Due to the increasing consumption of fuels in heavy industries, especially in road transportation, significant efforts are being made to increase the market participation of renewable fuels, including ethanol. In diesel engines, however, ethanol cannot be used as a pure fuel, primarily due to its very low cetane number and lubricity. For this reason, greater attention is being paid to blended fuels containing diesel and varying percentages of ethanol. Tests of lubricating properties carried out in accordance with the standard HFRR (high frequency reciprocating rig) method for ethanol–diesel fuel blends have long durations, which leads to ethanol evaporation and changes in the composition of the tested fuel sample under elevated temperatures. Therefore, this study presents an alternative lubricity assessment criterion based on the measurement of the scuffing load with a four-ball machine. Lubricity tests of blends of typical diesel fuel and ethanol, with ethanol volume fractions up to 14% (v/v), were conducted using a four-ball machine with a continuous increase of the load force of the friction node. In this method the lubrication criterion was the scuffing load of the tribosystem. The obtained results provided insights into the influence of the addition of ethanol to diesel fuel on lubricating properties, while limiting the ethanol evaporation process. The results also showed that an increase in the fraction of ethanol up to 14% (v/v) in diesel fuel resulted in a decrease in the scuffing load and a corresponding deterioration in the lubricating properties of the diesel–ethanol blend. For an ethanol volume fraction of 6–14%, the changes in the scuffing load were smaller than in ethanol volume fractions of 0–6%

The Development of CO₂ Instantaneous Emission Model of Full Hybrid Vehicle with the Use of Machine Learning Techniques

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

Assessment of Petrol and Natural Gas Vehicle Carbon Oxides Emissions in the Laboratory and On-Road Tests

The problem of global warming and the related climate change requires solutions to reduce greenhouse gas emissions, in particular CO2. As a result, newly manufactured cars consume less fuel and emit lower amounts of CO2. In terms of exhaust emissions and fuel consumption, old cars are significantly inferior to the more recent models. In Poland, for instance, the average age of passenger cars is approximately 13 years. Therefore, apart from developing new solutions in the cars produced today, it is important to focus on measures that enable the reduction in CO2 emissions in older vehicles. These methods include the adaptation of used cars to run on gaseous fuels. Natural gas is a hydrocarbon fuel that is particularly preferred in terms of CO2 emissions. The article presents the results of research of carbon oxides emission (CO, CO2) in the exhaust gas of a passenger car fueled by petrol and natural gas. The emissions were measured under the conditions of the New European Driving Cycle (NEDC) test and in real road tests. The test results confirm that compared to petrol, a CNG vehicle allows for a significant reduction in CO2 and CO emissions in a car that is several years old, especially in urban traffic conditions

Evaluation of the Effect of Chassis Dynamometer Load Setting on CO₂ Emissions and Energy Demand of a Full Hybrid Vehicle

Among the solutions that make it possible to reduce CO2 emissions in the transport sector, particularly in urban traffic conditions, are hybrid vehicles. The share of driving performed in electric mode for hybrid vehicles is highly dependent on motion resistance. There are different methods for determining the motion resistance function during chassis dynamometer testing, leading to different test results. Therefore, the main objective of this study was to determine the effect of the chassis dynamometer load function on the energy demand and CO2 emissions of a full-hybrid passenger car. Emissions tests according to the New European Driving Cycle (NEDC) were carried out on a chassis dynamometer for three different methods of determining the car’s resistance to motion. The study showed that adopting the motion resistance function according to different methods, results in differences in CO2 emissions up to about 35% for the entire cycle. Therefore, the authors suggest that in the case of tests carried out with chassis dynamometers, it is necessary to also provide information on the chassis dynamometer loading function adopted for the tests

Plan and Develop Advanced Knowledge and Skills for Future Industrial Employees in the Field of Artificial Intelligence, Internet of Things and Edge Computing

Knowledge and skills in the field of Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC) are more and more important for industry. Therefore, it is crucial to know what current students and future employees can offer to the industry. University students develop their knowledge and skills to support the industry in implementing modern technologies in the future. It can be expected that the first source of information for students will be lectures and other activities at the university. However, they may obtain knowledge from other sources. This article presents the results of research conducted among students assessing their own knowledge and skills in the field of IoT, AI, and EC. The research was preceded by an analysis of curricula at selected universities in terms of topics related to AI, IoT, and EC. Based on the results of the analysis, survey questions were prepared. The developed questionnaire was made available to students. The research sample for the survey participants was 563 students. The results obtained were analyzed. The results of the analysis show which issues are better known to students and which are worse. The information presented in this paper can be a source of information for the industry that can assess the competences that are or will be available on the labor market in the near future. Additionally, universities can obtain information on the areas in which there are competency gaps and which methods of teaching AI, IoT, and EC are better perceived by students