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This paper discusses the feasibility of data captured in a long-term Naturalistic Driving Study (NDS) for identification of vehicle dynamics. Driving data were captured for over a year. In this data capture, there was minimal effort to define or control everyday driving practices. While the use of real-world data for model parameter identification is a well-known method, NDS are commonly used to explore the behavior of drivers or to analyze real-world traffic situations. Data from NDS have not yet been used for the purpose of parameterizing vehicle dynamics models since everyday drives commonly do not reflect the full range of vehicle dynamics. This leads to the question if the data from an NDS contains the needed information to describe vehicle dynamics accurately. This paper shows that data captured from long-term everyday vehicle usage is sufficient to characterize vehicle dynamics models. It uses lateral vehicle dynamics as an example to show how the data quantity changes the model accuracy and robustness. There is a point where any further data capture produces redundancy and does not add to the overall information. The well-known single-track model serves as the modeling example which offers options to simply compare the derived model behavior with a reference.

To achieve the goals of the Paris Climate Agreement, decarbonization targets for the global automotive industry are required. We assess the quantity of light-duty vehicles (LDVs) with internal combustion engines (ICEs) that can be manufactured within the identified carbon budget and compare it with the current sales plans of the four largest automobile manufacturers—Volkswagen, General Motors, Toyota, and Hyundai/Kia—as representative of traditional car manufacturers. We first describe the quantification of a carbon budget for LDVs under the 1.5 °C target and a methodology for calculating the market shares that will allow different drive-train technologies to stay within it. The global LDV market for new sales and historic and future vehicle retirement rates are presented, together with assumptions for car usage (in passenger kilometres per year) and fuel efficiencies. We calculate the quantity of ICE LDVs that can be sold before the manufacture of ICEs must cease globally. We then compare this upper global limit with the current sales plans of car companies. The plans of the four manufacturers differ, but all considerably exceed the number of ICE vehicle sales required to meet the 1.5 °C target. This analysis does not forecast the development of the global LDV market, but assesses the gap between manufacturers’ intention and the requirement under a 1.5 °C pathway.

Agriculture 4.0 technologies have the potential to revolutionise both agricultural production and the way we educate the next generation of agricultural scientists. As many students enrolling in agricultural science education lack an agricultural background, it is difficult for them to grasp complex agricultural principles in an educational setting. Students without experience are thus at a disadvantage compared to others with prior knowledge. Technologies such as extended reality (XR) can be used as a tool to bridge the gap between theory and practice. With its ability to replicate real-world agricultural production principles within a classroom, XR can provide students with the necessary agricultural experience required within the industry. However, existing literature lacks comprehensive frameworks to guide educators on the implementation of this technology, which could lead to unsuccessful implementation. To ensure the successful implementation of XR in agricultural science education in higher education settings and to bridge the theory-practice gap, this article used the best-fit synthesis to develop a comprehensive framework from the existing literature. This framework can guide educators on the implementation of XR in higher education settings. The findings suggest that agriculture determinants, curriculum development, and facilitating conditions are three important concepts to consider for successful implementation. This XR-AgriEdu framework can be used by educators in agricultural science education to successfully implement XR.

Cybersickness is a global issue affecting users of immersive virtual reality. However, there is no agreement on the exact cause of cybersickness. Taking into consideration how it can differ greatly from one person to another, it makes it even more difficult to determine the exact cause or find a solution. Because cybersickness excludes so many prospective users, including healthcare professionals, from using immersive virtual reality as a learning tool, this research sought to find solutions in existing literature and construct a framework that can be used to prevent or minimise cybersickness during immersive virtual clinical simulation (CyPVICS). The Bestfit Framework by Carrol and authors were used to construct the CyPVICS framework. The process started by conducting two separate literature searchers using the BeHEMoTh (for models, theories, and frameworks) and SPIDER (for primary research articles) search techniques. Once the literature searches were completed the models, theories and framework were used to construct a priori framework. The models' theories and frameworks were analysed to determine aspects relevant to causes, reducing, eliminating, and detecting cybersickness. The priori framework was expanded by, first coding the findings of the primary research study into the existing aspects of the priori framework. Once coded the aspects that could not be coded were added in the relevant category, for example causes. After reviewing 1567 abstracts and titles as part of the BeHEMoTh search string,19 full text articles, a total of 15 papers containing models, theories, and frameworks, were used to construct the initial CyPVICS framework. Once the initial CyPVICS was created, a total 904 primary research studies (SPIDER) were evaluated, based on their titles and abstracts, of which 100 were reviewed in full text. In total, 67 articles were accepted and coded to expand the initial CyPVICS framework. This paper presents the CyPVICS framework for use, not only in health professions’ education, but also in other disciplines, since the incorporated models, theories, frameworks, and primary research studies were not specific to virtual clinical simulation.

In order to improve the knowledge on the influence of vehicle on the SPF (Sun Protection Factor), we determined in vitro the SPF from an oil-in-water (O/W) emulsion containing octylmethoxycinnamate (4%). We changed the oily phase composition. The in vitro determination of SPF is based on the physical determination of the reduction of the energy in the UV range, through a film of product which has previously been spread on an adequate substrate. About 30 mg were applied on roughened PMMA plates and the transmission measurements are carried out using a spectrophotometer equipped with integrating sphere. Eighteen oily ingredients were studied. Each one of them was incorporated in the amount of 20%. It is the lanolin which appears most effective SPF booster. Then oils come blackcurrant, argan, sweet almond, sesame, groundnut, carthame and palmotene.