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7 articles
Systematic Review 3 Feb 2026
Hugo González-González, Gemma Fernández-Caminero, Carmen-María Hernández-Lloret, Luis Espino-Díaz and José-Luis Álvarez-Castillo
Article 23 Dec 2025
Waleed Mugahed Al-Rahmi
Highlights of Sustainability
Volume 4 (2025), Issue 4, pp. 329–352
Volume 4 (2025), Issue 4, pp. 329–352
491 Views83 Downloads
Article 4 Dec 2025
Aivars Spilbergs, Biruta Dzērve, Sandra Ozoliņa, Gunta Innuse-Breidaka, Tatjana Mavrenko, Laima Čable, Agnese Vincēviča, Biruta Sloka, Ginta Tora and Kristīne Liepiņa
This study examines the primary risks associated with using generative artificial intelligence (GAI) in social science research and proposes a framework for higher education institutions to effectively manage these risks. As universities increasingly integrate GAI into
This study examines the primary risks associated with using generative artificial intelligence (GAI) in social science research and proposes a framework for higher education institutions to effectively manage these risks. As universities increasingly integrate GAI into teaching, research, and administration, concerns around intellectual property, academic integrity, data privacy, and ethical use have intensified. This paper explores the adequacy of current legal frameworks in addressing these challenges, drawing on recent legal analyses and institutional practices. Survey data reveal statistically significant differences in perceptions of the need for GAI guidelines based on respondents’ age, education level, field of study, research experience, and geographic region. The findings underscore the urgency of developing adaptive, risk-based policies that support responsible integration of GAI while safeguarding academic standards. The study concludes by proposing guiding principles for a dynamic legal framework that balances innovation with accountability. These recommendations aim to support sustainable and ethical GAI adoption in higher education institutions and contribute to the broader discourse on responsible AI governance in academia.
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Highlights of Sustainability
Volume 4 (2025), Issue 4, pp. 285–298
Volume 4 (2025), Issue 4, pp. 285–298
854 Views1294 Downloads
Article 26 Nov 2025
Kurt Orkun Aktaş, Ajda Zaim, Özlem Nur Aslantamer, Gözen Güner Aktaş and Hüseyin Emre Ilgın
Highlights of Sustainability
Volume 4 (2025), Issue 4, pp. 256–284
Volume 4 (2025), Issue 4, pp. 256–284
688 Views118 Downloads
Article 7 Mar 2025
Andreas Plesner, Allan P. Engsig-Karup and Hans True
Highlights of Vehicles
Volume 3 (2025), Issue 1, pp. 1–14
Volume 3 (2025), Issue 1, pp. 1–14
2284 Views499 Downloads
Article 1 Nov 2024
Francesco Scalamonti
Highlights of Sustainability
Volume 3 (2024), Issue 4, pp. 354–373
Volume 3 (2024), Issue 4, pp. 354–373
3777 Views4224 Downloads4 Citations
Article 26 Jul 2023
Maksym Diachuk and Said M. Easa
Highlights of Vehicles
Volume 1 (2023), Issue 1, pp. 29–53
Volume 1 (2023), Issue 1, pp. 29–53
3599 Views1031 Downloads2 Citations
Article 26 Jul 2023
Maksym Diachuk and Said M. Easa
The study aims at improving the technique of planning the autonomous vehicles’ (AV) speed mode based on a kinematic model with physical restrictions. A mathematical model relates the derivatives of kinematic parameters with ones of the
The study aims at improving the technique of planning the autonomous vehicles’ (AV) speed mode based on a kinematic model with physical restrictions. A mathematical model relates the derivatives of kinematic parameters with ones of the trajectory’s curvature. The inverse approach uses an expanded vehicle model considering the distribution of vertical reactions, wheels’ longitudinal reactions according to a drive type, and lateral forces ensuring motion stability. For analysis of the drive type, four options are proposed: front-wheel drive (FWD), rear-wheel drive (RWD), permanent engaged all-wheel drive (AWD), and 4-wheel drive with torque vectoring (4WD-TV). The optimization model is also built by the inverse scheme. The longitudinal speed’s higher derivatives are modeled by the finite element (FE) functions with nodal unknowns. The sequential integrations ensure the optimality and smoothness of the third derivative. The kinematic restrictions are supplemented by the tire-road critical slip states. Sequential quadratic programming (SQP) and the Gaussian N-point scheme for quadrature integration are used to minimize the objective function. The simulation results show a significant difference in the mode forecasts between four types of AV drives at the same initial conditions. This technique allows redistributing the traction forces strictly according to the wheels’ adhesion potentials and increases the optimization performance by about 40% compared to using the kinematic model based on the same technique without physical constrains.
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Highlights of Vehicles
Volume 1 (2023), Issue 1, pp. 29–53
Volume 1 (2023), Issue 1, pp. 29–53
3599 Views1031 Downloads2 Citations
Volume 5 (2026), Issue 1, pp. 104–115