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Achieving robust and efficient control of internal combustion engines is critical for meeting stringent environmental regulations and reducing greenhouse gas emissions. Natural gas engines, in particular, offer significant potential for cleaner energy but present unique challenges due to variations in fuel composition and combustion dynamics.This thesis investigates novel methods for improving com

In this paper, we discuss the pedagogical concepts of socialisation and uncoverage and the theoretical perspective of metanarratives on history and historical concepts as tools to integrate the students of the online Master’s programme Religious Roots of Europe into the practice of the historical study of religion. We describe how we worked in facilitating the typical learning processes and social

Objectives: Videofluoroscopic swallowing studies (VFSS) remain the gold standard for the instrumental assessment of oropharyngeal swallowing disorders alongside flexible endoscopic evaluation of swallowing (FEES), requiring a high standard of quality and correct implementation. The current best practice position statements aim to guide the clinical practice of VFSS in individuals experiencing swal

Background: The rapid advancement of medical technologies presents significant challenges for researchers and practitioners. While traditional clinical trials remain the gold standard, they are often limited by high costs, lengthy durations, and ethical constraints. In contrast, in-silico trials and digital twins have emerged not only as efficient and ethical alternatives but also as a complementa

Introduction: Evidence-based clinical practice guidelines drive optimal patient care and facilitate access to high-quality treatment. Creating guidelines for rare diseases such as haemophilia, where evidence does not often come from randomized controlled trials but from non-randomized and well-designed observational studies and real-world data, is challenging. The methodology used for assessing av

The Dirichlet–Neumann method is a common domain decomposition method for nonoverlapping domain decomposition and the method has been studied extensively for linear elliptic equations. However, for nonlinear elliptic equations, there are only convergence results for some specific cases in one spatial dimension. The aim of this manuscript is therefore to prove that the Dirichlet–Neumann method conve

The complex orography of the Tibetan plateau (TP) and the scarcity and uneven spatial distribution of meteorological stations present significant challenges in accurately estimating meteorological variables for hydrological simulations. This study aims to enhance the accuracy of daily precipitation and temperature interpolation for hydrological simulations in the Lhasa River Basin (LRB), particula

Mount Fuji, the iconic mountain of Japan, remains a young and frequently active volcano. However, despite these dangers, people have continuously lived in the landscapes surrounding Mount Fuji since the peopling of the Japanese Archipelago, approximately 38ka. In this paper we explore the geological history of Mount Fuji and use a landscape archaeology approach to reconstruct how Jomon communities

With Gaia Data Release 3 (DR3), new and improved astrometric, photometric, and spectroscopic measurements for 1.8 billion stars have become available. Alongside this wealth of new data, however, there are challenges in finding efficient and accurate computational methods for their analysis. In this paper, we explore the feasibility of using machine learning regression as a method of extracting bas

The application of sustainable aviation fuels (SAFs) in aviation industry has emerged as a key strategy for reducing the net carbon oxide (CO2) emissions while minimizing modifications to the current aircraft and engine systems. SAFs, which are derived from sustainable feedstocks through biological, thermal and chemical (or their combinations) conversion pathways, can be used in current aero engin

The robustness of deep neural networks is an increasingly essential issue as they become more and more prevalent in several real-world applications like autonomous vehicles. If traffic signs turn to adversarial examples, an autonomous vehicle will probably be misled and cause fatal accidents. To improve adversarial robustness, a new cost function for training convolutional neural recognition netwo