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Beyond human proxies: The roles and usefulness of large language models in user research for mobility service development
No full textUser research is an integral part of mobility service development. However, user research is resource-intensive, including the effort required to recruit participants, and facilitate data collection procedures. Consequently, large language models (LLM) have been applied in the domain, building on the notion of LLMs being able to provide human-like outputs and thus simulate human users. Despite the increasing interest, guidelines for LLM implementation in user research processes and for evaluating the usefulness of LLM incorporation remain scarce, hindering researchers and practitioners from leveraging them effectively. Therefore, with the aim of providing a structured understanding of LLM integration, we delineate four main roles LLMs can play in user research (i.e., replace, complement, improve, research), along with three facets (i.e., realism, novelty, effort) for evaluating their usefulness. Combining the roles and usefulness facets, we introduce a framework for how LLMs could be used and what are the conditions for realizing their benefits. Additionally, we describe a hypothetical user research path, applying the approach to the development of new mobility services. The framework presents how and why LLMs could be used in various user research stages, providing a structured lens that aids researchers and practitioners to critically consider when and how to incorporate LLMs, and to evaluate LLM output usefulness, while considering the risks for doing so from the perspective of successful service design. By adopting a pragmatic and critical approach, the paper contributes to research on mobility, as well as more broadly on the use of synthetic participants in design praxis.</p
Emulating a forest growth and productivity model with deep learning
No full textWe studied the possibility of replacing a complex forest growth and productivity model with a deep learning model with sufficient accuracy. We used three different neural network architectures for emulating the prediction task of the PREBASSO (Mäkelä 1997; Minunno et al. 2016) forest growth model: 1) Recurrent Neural Network (RNN) Encoder-decoder network, 2) RNN encoder network, and 3) Transformer encoder network. The PREBASSO forest growth model was used to produce 25-year predictions for forest variables: tree height, stem diameter, basal area, and the carbon balance variables: net primary production (NPP), gross primary production per tree layer (GPP), net ecosystem exchange (NEE) and gross growth (GGR) to train the machine learning models. The Finnish Forest Centre provided the data for 29 619 field inventory plots in continental Finland that were used as the initial state of the forest sites to be simulated. Climate data downloaded from Copernicus Climate Data Store were used to provide realistic climate scenarios. We emphasized the importance of low bias in long term predictions and set the goal for the emulator prediction relative bias to be within ±2%. The RNN encoder model produced the best results with the mean of the yearly bias values within the specified ±2% limit over the 25-year prediction period. The study shows that emulating the operation of analytical forest growth models is feasible using state-of-the-art machine learning methods and indicates the potential of using such emulators for producing long time span simulations for e.g. digital twins.</p
High-temperature corrosion of steels by nitridation in ammonia: Degradation mechanisms and comparison between steel grades
No full textAmmonia is an integral part of hydrogen economy and a viable carbon-free fuel for marine combustion engines. The combination of ammonia, high temperatures and long exposure times can cause nitridation corrosion in steels relevant for combustion engines, but limited research has been conducted on the topic. In this work, three steels, 34CrNiMo6 (low-alloy steel EN1.6582), X40CrSiMo10-2 (alloy steel EN1.4731), and 316plus (stainless steel EN1.4420), were exposed to gaseous ammonia atmospheres at 400 °C and 500 °C for up to 1000 h. The specimen surfaces were characterised by a variety of techniques, e.g., electron backscatter diffraction, glow-discharge optical emission spectroscopy, and micro-indentation, while the system thermodynamics was modelled with Thermo-Calc Software making use of compositional depth profile data. All materials underwent nitridation under the test conditions, and the formed nitride surface film was in most cases brittle, porous, and cracked, and typically tens of micrometres thick. In all investigated alloys, the structure, phase and elemental composition of the surface films were function of the alloying elements. For the studied stainless steel grade, the surface film compositions were dependent also on temperature, with a protective chromium nitride film being formed at 500 °C compared to an iron nitride film at 400 °C, in agreement with thermodynamics of nitride formation. The obtained results can be used to tailor the film composition in the desired direction. The study highlighted the importance of careful material selection for the conditions in ammonia combustion engines
Processing and performance of HVAF-sprayed Fe-based bulk metallic glass coatings:A sustainable alternative
No full textBulk metallic glasses (BMGs) are increasingly recognized as promising materials for surface engineering due to their exceptional hardness, corrosion resistance, and wear resistance, which are critical attributes for components operating in harsh environments. Fe-based BMGs offer a sustainable alternative to conventional coatings that often rely on toxic or scarce raw materials, thus aligning with global environmental and regulatory objectives. This study focuses on a newly developed Fe-based BMG alloy with enhanced glass-forming ability and processability for thermal spray applications. While BMGs exhibit outstanding properties, their limited ductility poses significant processing challenges, including propensity to cracking during deposition. To overcome this, the feasibility of producing dense Fe-based BMG coatings using High Velocity Air Fuel (HVAF) spraying was examined, aiming for superior wear and corrosion resistance. Initial trials resulted in coatings with severe cracking, delamination, and poor adhesion. However, systematic manipulation of spray parameters led to coatings with markedly reduced cracking, improved adhesion, and enhanced microstructural integrity. Comprehensive characterization included microstructure, amorphicity, and hardness examination, complemented by wear and corrosion performance assessments. The promising results represent a critical step toward establishing Fe-based BMGs as viable and environmentally friendly coating solutions via HVAF thermal spraying.</p
A Scots pine 4-coumarate:CoA ligase with high cinnamic acid affinity is the primary candidate for pinosylvin biosynthesis
No full textPinosylvin and its derivatives are defense-related stilbenoids that contribute to pathogen and decay resistance of Scots pine (Pinus sylvestris L.) heartwood. While most enzymes in the pinosylvin biosynthesis pathway in Scots pine have been identified, the enzyme responsible for activating cinnamic acid to cinnamoyl-CoA, an essential precursor for pinosylvin biosynthesis, has remained unknown. In this study, we explored Scots pine transcriptomic data to observe the expression profiles of the 4-coumarate:CoA ligase (4CL) genes and produced the enzymes in Nicotiana benthamiana to assay their kinetic properties. We identified and characterized four 4CL isoforms, one of which (Ps4CL2) was both co-expressed with pinosylvin biosynthesis genes under stilbene-inducing conditions and exhibited an unusually high affinity for cinnamic acid surpassing that of previously characterized cinnamate-activating 4CLs. Through site-directed mutagenesis and domain-swapping experiments with the closely related Ps4CL3, we showed that substrate preference in Ps4CL2 results from multiple interacting regions within the enzyme. Our findings establish Ps4CL2 as the prime candidate for cinnamate activation as the last missing enzyme for pinosylvin biosynthesis in Scots pine and provide insight into enzyme specialization within the 4CL family.</p
High-temperature corrosion of steels by nitridation in ammonia: Degradation mechanisms and comparison between steel grades
No full textAmmonia is an integral part of hydrogen economy and a viable carbon-free fuel for marine combustion engines. The combination of ammonia, high temperatures and long exposure times can cause nitridation corrosion in steels relevant for combustion engines, but limited research has been conducted on the topic. In this work, three steels, 34CrNiMo6 (low-alloy steel EN1.6582), X40CrSiMo10-2 (alloy steel EN1.4731), and 316plus (stainless steel EN1.4420), were exposed to gaseous ammonia atmospheres at 400 °C and 500 °C for up to 1000 h. The specimen surfaces were characterised by a variety of techniques, e.g., electron backscatter diffraction, glow-discharge optical emission spectroscopy, and micro-indentation, while the system thermodynamics was modelled with Thermo-Calc Software making use of compositional depth profile data. All materials underwent nitridation under the test conditions, and the formed nitride surface film was in most cases brittle, porous, and cracked, and typically tens of micrometres thick. In all investigated alloys, the structure, phase and elemental composition of the surface films were function of the alloying elements. For the studied stainless steel grade, the surface film compositions were dependent also on temperature, with a protective chromium nitride film being formed at 500 °C compared to an iron nitride film at 400 °C, in agreement with thermodynamics of nitride formation. The obtained results can be used to tailor the film composition in the desired direction. The study highlighted the importance of careful material selection for the conditions in ammonia combustion engines
IoT Service Orchestration in Edge-Cloud Continuum with 6G:A Review
No full textThe development of 6th-generation (6G) mobile networks brings advancements in wireless communications, including lower latency, higher data rates, and improved spectral efficiency, as well as enhanced facilitation for the use of Artificial Intelligence (AI) technologies, integrated communications, sensing, and renewable energy sources. To harness this potential, a scalable framework integrating edge and cloud computing is needed to provide a robust computing continuum for various IoT applications and services. This paper analyses the strengths and weaknesses of existing and emerging distributed computing frameworks—ranging from traditional centralized IoT architectures, through fog, edge, and local-edge architectures, to the full three-tier edge–cloud continuum—in terms of IoT service orchestration and diverse application requirements. We emphasize the latest evolutionary step, three-tier edge-cloud continuum, which aims to resolve the most significant limitations of the previous frameworks, recognized in the literature. It enables efficient IoT service orchestration by utilizing distributed resources and computation closer to end users, while taking full advantage of the centralized resources at data centers. We evaluate the maturity of these frameworks, considering factors such as scalability, resource efficiency, adaptability to changes, resource availability, security and privacy, as well as robustness and resilience. Overall, this study aims to serve as a roadmap for researchers, network architects, and industry stakeholders to make informed decisions on implementing the computing continuum in 6G networks.</p
Understanding column-formation in axial-SPPS thermal barrier coatings:Evolution of microstructure and role of bond coat roughness
No full textColumnar yttria-stabilized zirconia (YSZ) thermal barrier coatings (TBCs) are renowned for their exceptional resistance to thermal cyclic fatigue (TCF) and their consequent role in extending the service life of gas turbine components. Traditionally, such coatings have been produced by electron beam physical vapor deposition (EB-PVD) and, more recently, by suspension plasma spraying (SPS). Latest studies demonstrating the capability of the aqueous solution precursor route to fabricate columnar YSZ TBCs using axial plasma spraying indicate its considerable potential to overcome shortcomings associated with both EB-PVD and SPS methods. In this work, the microstructural evolution of axial solution precursor plasma-sprayed (SPPS) 8 wt% YSZ coatings is investigated, with emphasis on the role of bond coat roughness. In-flight particle generation and splat formation have been carefully examined to obtain insights into column development. Results show that a coarse bond coat surface promotes column initiation, whereas a much smoother surface favours vertical cracking, not quite leading to column formation. Careful collection of particles generated in flight confirmed their size to be predominantly in the 100–500 nm range (d50 ≈ 280 nm), leading to largely sub-micron splats (d50 ≈ 465 nm). It is postulated that such fine sizes are inadequate to rapidly roughen the growing surface for spontaneous column initiation, thereby making the initial bond coat roughness crucial in tailoring TBC microstructures. In this context, the concept of a certain ‘threshold roughness’ being necessary to trigger column formation is also proposed and needs further investigation.</p