Over many decades, significant efforts have been made to improve the strength-elongation product of advanced high strength steels (AHSSs) by creating tailored multi-phase microstructures. Successive solid-state phase transformations for steels with a well selected chemical composition turned out to be the key instrument in the realisation of such microstructures. In this contribution, we first provide a brief review of the desired microstructures for Transformation-induced plasticity (TRIP), Carbide-free Bainitic (CFB), Quenching & Partitioning (Q&P) and Medium Manganese steels followed by comprehensive discussions on the phase transformations to be used in their creation. The implications for the steel composition to be selected are addressed too. As the presence of the right amount and type of metastable retained austenite (RA) is of crucial importance for the mechanical performance of these AHSSs, special attention is paid to the important role of successive solid-state phase transformations in creating the desired fraction and composition of RA by suitable element partitioning (in particular C and Mn). This critical partitioning not only takes place during final cooling (austenite decomposition) but also during the back transformation (austenite reversion) during reheating. This review aims to be more than just descriptive of the various findings, but to present them from a coherent thermodynamic / thermo-kinetic perspective, such that it provides the academic and industrial community with a rather complete conceptual and theoretical framework to accelerate the further development of this important class of steels. The detailed stepwise treatment makes the review relevant not only for experts but also metallurgists entering the field.

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The progress in technology development over the past decades, both with respect to software and hardware, offers the vision of automated vehicles as means of achieving zero fatalities in traffic. However, the promises of this new technology – an increase in road safety, traffic efficiency, and user comfort – can only be realized if this technology is smoothly introduced into the existing traffic system with all its complexities, constraints, and requirements. SHAPE- IT will contribute to this major undertaking by addressing research questions relevant for the development and introduction of automated vehicles in urban traffic scenarios. Previous research has pointed out several research areas that need more attention for a successful implementation and deployment of human-centred vehicle automation in urban environments.

In SHAPE-IT, for example, a better understanding of human behaviour and the underlying psychological mechanisms will lead to improved models of human behaviour that can help to predict the effects of automated systems on human behaviour already during system development. Such models can also be integrated into the algorithms of automated vehicles, enabling them to better understand the human interaction partners’ behaviours.

Further, the development of vehicle automation is much about technology (software and hardware), but the users will be humans and they will interact with humans both inside and outside of the vehicle. To be successful in the development of automated vehicles functionalities, research must be performed on a variety of aspects. Actually, a highly interdisciplinary team of researchers, bringing together expertise and background from various scientific fields related to traffic safety, human factors, human-machine interaction design and evaluation, automation, computational modelling, and artificial intelligence, is likely needed to consider the human-technology aspects of vehicle automation.

Accordingly, SHAPE-IT has recruited fifteen PhD candidates (Early Stage Researchers – ESRs), that work together to facilitate this integration of automated vehicles into complex urban traffic by performing research to support the development of transparent, cooperative, accepted, trustworthy, and safe automated vehicles. With their (and their supervisors’) different scientific background, the candidates bring different theoretical concepts and methodological approaches to the project. This interdisciplinarity of the project team offers the unique possibility for each PhD candidate to address research questions from a broad perspective – including theories and methodological approaches of other interrelated disciplines. This is the main reason why SHAPE-IT has been funded by the European Commission’s Marie Skłodowska-Curie Innovative Training Network (ITN) program that is aimed to train early state researchers in multidisciplinary aspects of research including transferable skills. With the unique scope of SHAPE-IT, including the human-vehicle perspective, considering different road-users (inside and outside of the vehicle), addressing for example trust, transparency, and safety, and including a wide range of methodological approaches, the project members can substantially contribute to the development and deployment of safe and appreciated vehicle automation in the cities of the future.

To achieve the goal of interdisciplinary research, it is necessary to provide the individual PhD candidate with a starting point, especially on the different and diverse methodological approaches of the different disciplines. The empirical, user-centred approach for the development and evaluation of innovative automated vehicle concepts is central to SHAPE- IT. This deliverable (D1.1 “Methodological Framework for Modelling and Empirical Approaches”) provides this starting point. That is, this document provides a broad overview of approaches and methodologies used and developed by the SHAPE-IT ESRs during their research. The SHAPE-IT PhD candidates, as well as other researchers and developers outside of SHAPE-IT, can use this document when searching for appropriate methodological approaches, or simply get a brief overview of research methodologies often employed in automated vehicle research.

The first chapter of the deliverable shortly describes the major methodological approaches to collect data relevant for investigating road user behaviour. Each subchapter describes one approach, ranging from naturalistic driving studies to controlled experiments in driving simulators, with the goal to provide the unfamiliar reader with a broad overview of the approach, including its scope, the type of data collected, and its limitations. Each subchapter ends with recommendations for further reading – literature that provide much more detail and examples.

The second chapter explains four different highly relevant tools for data collection, such as interviews, questionnaires, physiological measures, and as other current tools (the Wizard of Oz paradigm and Augmented and Virtual Reality). As in the first chapter this chapter provides the reader with information about advantages and disadvantages of the different tools and with proposed further readings.

The third chapter deals with computational models of human/agent interaction and presents in four subchapters different modelling approaches, ranging from models based on psychological mechanisms, rule-based and artificial intelligence models to simulation models of traffic interaction.

The fourth chapter is devoted to Requirements Engineering and the challenge of communicating knowledge (e.g., human factors) to developers of automated vehicles. When forming the SHAPE-IT proposal it was identified that there is a lack of communication of human factors knowledge about the highly technical development of automated vehicles. This is why it is highly important that the SHAPE-IT ESRs get training in requirement engineering. Regardless of the ESRs working in academia or industry after their studies it is important to learn how to communicate and disseminate the findings to engineers.

The deliverable ends with the chapter “Method Champions”. Here the expertise and association of the different PhD candidates with the different topics are made explicit to facilitate and encourage networking between PhDs with special expertise and those seeking support, especially with regards to methodological questions.@en

Rainfall is one of the most influential climatic factors on regional development and environment, and changes in rainfall intensity are of specific concern. In the Huaihe River Valley (HRV), heavy rainfall is a primary trigger of floods. However, the difference in the origin of moisture contributed to heavy rainfall and light rainfall is rarely studied and not entirely understood. This study analyzes the rainfall moisture sources in association with different categories of rainfall intensity over the HRV during 1980–2018 using the Water Accounting Model with ERA-Interim reanalysis and precipitation observations from China Meteorological Administration. The results show that the moisture for the HRV summer rainfall is mainly from terrestrial subregion (40%), the Indian Ocean (27%), the Pacific Ocean (25%), and the local HRV (8%). In addition, moisture sources differ substantially between light and heavy rainfall. Specifically, the local HRV contributes more moisture to light rainfall (12%) compared to heavy rainfall (4%), whereas the Indian Ocean contributes more to heavy rainfall (33%) than to light rainfall (20%). The grids located in the southern source region make higher contribution ratio in heavy rainfall than in light rainfall. These results suggest that moisture from distant oceanic areas, especially the Indian Ocean, plays a crucial role in intense summer rainfall, whereas moisture from the land sources covering local grids plays a dominant role in light rainfall in the HRV.

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Purpose To accelerate coronary MRI acquisitions with arbitrary undersampling patterns by using a novel reconstruction algorithm that applies coil self-consistency using subject-specific neural networks. Methods Self-consistent robust artificial-neural-networks for k-space interpolation (sRAKI) performs iterative parallel imaging reconstruction by enforcing self-consistency among coils. The approach bears similarity to SPIRiT, but extends the linear convolutions in SPIRiT to nonlinear interpolation using convolutional neural networks (CNNs). These CNNs are trained individually for each scan using the scan-specific autocalibrating signal (ACS) data. Reconstruction is performed by imposing the learned self-consistency and data-consistency, which enables sRAKI to support random undersampling patterns. Fully-sampled targeted right coronary artery MRI was acquired in six healthy subjects. The data were retrospectively undersampled, and reconstructed using SPIRiT, l₁-SPIRiT and sRAKI for acceleration rates of 2 to 5. Additionally, prospectively undersampled whole-heart coronary MRI was acquired to further evaluate reconstruction performance. Results sRAKI reduces noise amplification and blurring artifacts compared with SPIRiT and l₁-SPIRiT, especially at high acceleration rates in targeted coronary MRI. Quantitative analysis shows that sRAKI outperforms these techniques in terms of normalized mean-squared-error (~44% and ~21% over SPIRiT and ‘₁-SPIRiT at rate 5) and vessel sharpness (~10% and ~20% over SPIRiT and l₁-SPIRiT at rate 5). Whole-heart data shows the sharpest coronary arteries when resolved using sRAKI, with 11% and 15% improvement in vessel sharpness over SPIRiT and l₁-SPIRiT, respectively. Conclusion sRAKI is a database-free neural network-based reconstruction technique that may further accelerate coronary MRI with arbitrary undersampling patterns, while improving noise resilience over linear parallel imaging and image sharpness over l₁ regularization techniques.

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Precipitation on the Tibetan Plateau (TP) showed different spatial changes during 1979-2016, with an increasing trend over the northern Tibetan Plateau (NTP) and a slightly negative trend over the southern Tibetan Plateau (STP). The changes in precipitation moisture sources over the NTP and STP are investigated using the improved Water Accounting Model with an atmospheric reanalysis as well as observational precipitation and evaporation data. The results show the region in the northwest (region NW), ranging from the TP to Europe dominated by the westerlies, provides 38.9% of precipitation moisture for the NTP, and the region in the southeast (region SE), ranging from the TP to the Indian Ocean and Indochina dominated by the Asian monsoons, provides 51.4% of precipitation moisture for the STP. For the precipitation increase over the NTP, the SE and TP are the main contributors, contributing around 35.8% and 51.7% of the increase, respectively. The contributions from the SE and TP to the STP are, however, minor and insignificant. Meanwhile, the NW shows a negative trend of -4.2 ± 2.9mmyr ^-1 decade ^-1 (significant at the 0.01 level), which contributes to the negative precipitation trend over the STP. Results during the wet season indicate that moisture sources from the areas dominated by the Asian monsoons have contributed more precipitated moisture for the NTP, but not for the STP. Further analysis reveals that precipitated moisture originating from the Indian subcontinent has increased for the NTP while it has decreased for the STP during 1979-2016.

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The interaction between moving α/γ interfaces and interphase precipitated (IPd) carbides during the austenite (γ) to ferrite (α) and the ferrite (α) to austenite (γ) transformation has been systematically investigated through cyclic phase transformation experiments for a 0.1C-1.5Mn alloy containing 0.1 wt% Niobium (Nb) and its Nb-free counterpart. Shifts in the critical reaction temperatures during continuous heating and cooling are observed, which are attributed to the pinning force (PF) originating from the IPd carbides present. By applying the Gibbs energy balance (GEB) model to analyze experimental results, the PF was derived to be about 15 J/mol for the α→γ transformation and about 5 J/mol for the γ→α transformation, respectively, both of which are quite small compared to chemical driving force of phase transformations. Moreover, various modified Zener pinning equations have also been used to predict the PF, and it was found that these values are comparable with those obtained from experiments, which suggests that the classical Zener theory still has promising potential for carbide-interface interaction analysis.

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Kinetics of the austenite (γ) to ferrite (α) transformation and the reverse ferrite (α) to austenite (γ) transformation in a series of Fe-X (X = Ni, Mn and Co) binary alloys has been experimentally and theoretically investigated. A transition from partitioning to partitionless transformation has been predicted to occur during both the γ→α and α→γ transformations by a so called Gibbs Energy Balance (GEB) model, in which the chemical driving force is assumed to be equal to the energy dissipation due to interface friction and diffusion of X inside the migrating interfaces. The transition temperature is found to depend on the kind of X and its concentration, which is in good agreement with experimental results. The intrinsic mobility of the α/γ interface has been derived from the kinetic curves of both the γ→α and α→γ transformations in the investigated alloys, and its value seems to be marginally affected by the transformation direction and alloying elements.

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The transition temperature between upper bainite and lower bainite is calculated with an extended Gibbs energy balance model, which is able to quantitatively describe the evolution of carbon supersaturation within bainitic ferrite sheaves during the entire thickening process. The nucleation rate of intra-lath cementite precipitation on a dislocation is calculated based on of the degree of carbon supersaturation. Upper bainite and lower bainite are thus distinguished by the effective nucleation density and therefore a numerical criterion can be set to define the transition. The model is applied to Fe-xC-1Mn/2Mn/1Mo ternary alloys. Results show that the transition temperature increases with bulk carbon content at lower carbon concentration but decreases in the higher carbon region. This prediction agrees very well with the experimental observations in Mn and Mo alloyed systems. Moreover, the highest transition temperature and the carbon content at which it occurs in the Fe-xC-2Mn system are in good agreement with reported experimental data. The inverse V shaped character of the carbon concentration-transition temperature curve indicates two opposite physical mechanisms operating at the same time. An analysis is carried out to provide an explanation.@en