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A way of presenting information in visual representations of technical systems influences the progress and the outcome of the engineering design process. Consequently, improving the means by and through which information is utilised during the process is one suggested approach to advancing engineering design. Engineers’ interaction with visual representations of technical systems is mainly visual and virtual. Although such interactions are cognitively complex, little is known about cognition (mental information processing) underlying the utilisation of design information during engineering design. To narrow the research gap, this study explores how visual representations of technical systems affect engineers’ brain activity while generating computer-aided design (CAD) models based on them. More precisely, the brain activity of 20 engineers is captured and analysed using electroencephalography (EEG) during the visuospatially-intensive design tasks of CAD modelling in two conditions; when technical systems are presented with orthographic and isometric projections in technical drawings. The results imply the sensitivity of engineers’ brain activity in CAD modelling to the visual representation from which a technical system is interpreted. In particular, significant differences are found in theta, alpha, and beta task-related power (TRP) over the cortex when interpreting the technical drawings and CAD modelling from them. Furthermore, the results reveal significant differences in theta and alpha TRP when considering the individual electrodes, the cortical hemispheres, and the cortical areas. In particular, theta TRP over the right hemisphere and the frontal area seems essential in distinguishing neurocognitive responses to the orthographic and isometric projections. As such, the conducted exploratory study sets the foundations for exploring engineers’ brain activity while performing visuospatially-intensive design tasks, whose segments are relatable to the aspects of visuospatial thinking. Future work will explore brain activity in other design activities that are highly visuospatial, with a larger sample size and an EEG device of a higher spatial resolution.

Facial aging is the most visible manifestation of aging. People desire to look younger than others of the same chronological age. Hence, perceived age is often used as a visible marker of aging, while biological age, often estimated by methylation markers, is used as an objective measure of age. Multiple epigenetics-based clocks have been developed for accurate estimation of general biological age and the age of specific organs, including the skin. However, it is not clear whether the epigenetic biomarkers (CpGs) used in these clocks are drivers of aging processes or consequences of aging. In this proof-of-concept study, we integrate data from GWAS on perceived facial aging and EWAS on CpGs measured in blood. By running EW Mendelian randomization, we identify hundreds of putative CpGs that are potentially causal to perceived facial aging with similar numbers of damaging markers that causally drive or accelerate facial aging and protective methylation markers that causally slow down or protect from aging. We further demonstrate that while candidate causal CpGs have little overlap with known epigenetics-based clocks, they affect genes or proteins with known functions in skin aging, such as skin pigmentation, elastin, and collagen levels. Overall, our results suggest that blood methylation markers reflect facial aging processes, and thus can be used to quantify skin aging and develop anti-aging solutions that target the root causes of aging.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that adversely affects maternal and offspring health. A variety of risk factors, such as BMI and age, have been associated with increased risks of gestational diabetes. However, in many cases, gestational diabetes occurs in healthy nulliparous women with no obvious risk factors. Emerging data suggest that the tendency to develop gestational diabetes has genetic and environmental components. Here we develop a polygenic risk score for GDM and investigate relationships between its genetic architecture and genetically constructed risk factors and biomarkers. Our results demonstrate that the polygenic risk score can be used as an early screening tool that identifies women at higher risk of GDM before its onset allowing comprehensive monitoring and preventative programs to mitigate the risks.

Preeclampsia and gestational hypertensive disorders (GHD) are common complications of pregnancy that adversely affect maternal and offspring health, often with long-term consequences. High BMI, advanced age, and pre-existing conditions are known risk factors for GHD. Yet, assessing a woman’s risk of GHD based on only these characteristics needs to be reevaluated in order to identify at-risk women, facilitate early diagnosis, and implement lifestyle recommendations. This study demonstrates that a risk score developed with machine learning from the case-control genetics dataset can be used as an early screening test for GHD. We further confirm BMI as a risk factor for GHD and investigate a relationship between GHD and genetically constructed anthropometric measures and biomarkers. Our results show that polygenic risk score can be used as an early screening tool that, together with other known risk factors and medical history, would assist in identifying women at higher risk of GHD before its onset to enable stratification of patients into low-risk and high-risk groups for monitoring and preventative programs to mitigate the risks.

This paper examines the potential of immersive virtual reality technology for design education. A quasi-experimental study has been conducted with 40 students of different expertise levels. The students analysed a design representation using one of the two visualisation technologies: immersive virtual reality (IVR) and non-immersive virtual reality (nIVR). The results show that the expertise in the used technology and the expertise in the design domain significantly affect design understanding. On the other hand, the effect of contextual expertise was not found significant. Spatial ability affected design understanding in nIVR but not in the IVR. Visualisation technology did not have an overall effect on understanding, but IVR helped students with lower expertise to understand specific aspects of a design better (e.g. rotation-based mechanisms). The study suggests that researchers and educators control the students’ expertise when assessing the effect of technology on design education. Overall, the results support the constructivist learning theory, as IVR can support context-dependent and context-independent understanding.

Studies revealed that, while collaborating, humans tend to synchronise on multiple levels (e.g., neurocognitive or physiological). Inter-brain synchrony has been linked to improved problem-solving, decision-making, and creativity. Nevertheless, studies on synchrony in design teams started to emerge only recently. This study contributes to this stream of research by utilising a computational model of a design team to explore the relationships between team cohesion, synchrony, and team performance. The experiments revealed a positive link between team cohesion level and the emergence of (cognitive) synchrony. Furthermore, cohesive teams were found to be more efficient, converging quicker and producing solutions at a higher rate. In addition, the diversity of the solutions generated by highly cohesive teams tends to increase over time. Teams in medium- and low-cohesive settings initially generate highly diverse solutions, but such diversity decreases as the simulation progresses. Finally, highly-cohesive teams were found to be prone to premature convergence.

The conventional prescriptive and descriptive models of design typically decompose the overall design process into elementary processes, such as analysis, synthesis, and evaluation. This study revisits some of the assumptions established by these models and investigates whether they can also be applied for modelling of problem-solution co-evolution patterns that appear during team conceptual design activities. The first set of assumptions concerns the relationship between performing analysis, synthesis, and evaluation and exploring the problem and solution space. The second set concerns the dominant sequences of analysis, synthesis, and evaluation, whereas the third set concerns the nature of transitions between the problem and solution space. The assumptions were empirically tested as part of a protocol analysis study of team ideation and concept review activities. Besides revealing inconsistencies in how analysis, synthesis, and evaluation are defined and interpreted across the literature, the study demonstrates co-evolution patterns, which cannot be described by the conventional models. It highlights the important role of analysis-synthesis cycles during both divergent and convergent activities, which is co-evolution and refinement, respectively. The findings are summarised in the form of a model of the increase in the number of new problem and solution entities as the conceptual design phase progresses, with implications for both design research and design education.

Automating modelling activities in computer-aided design (CAD) systems is no exception within design automation, one of the current research endeavours aiming to use and transform design-related data in design decision-making processes and the generation and evaluation facilitation of new design solutions. The paper explores the differences between CAD models based on their feature-based CAD modelling sequences that lead to the final models' design. The dataset collected and structured for the study contains more than 1400 CAD models clustered on two levels by using an unsupervised K-means clustering algorithm. The algorithm is performed on the number (total and unique) and the first-order Markov model transition matrices of the CAD modelling operations and their sequential order, respectively. Therefore, three and ten groups (clusters) of CAD models are obtained regarding the level of clustering. The results show that most of the obtained groups are specified by the dominant transition between particular modelling operations. In addition, the study also provides insight into the potential of using feature-based CAD modelling operations' sequences as a first step toward automating the user interaction with the CAD system.

Use of virtual reality (VR) is considered beneficial for reviewing 3D models throughout product design. However, research on its usability in the design field is still explorative, and previous studies are often contradictory regarding the usability of VR for 3D model review. This paper argues that the usability of VR should be assessed by analysing human factors such as spatial perception and taking into consideration the complexity of the reviewed product. Hence, a comparative evaluation study has been conducted to assess spatial perception in desktop interface-based and VR-based review of 3D models of products with different levels of complexity. The results show that participants in VR more could perceive the fit of user interface elements, and estimation of the model dimensions had a lower relative error than in desktop interface. It has been found that various sensory cues are used to perceive the model size and that the employed sensory cues depend on the level of complexity. Finally, it is proposed that differences between a desktop interface and VR for reviewing models are more evident when reviewing models of higher complexity levels.

When observing a design space expansion during teamwork, several studies found that cumulative solution-related issues' occurrence follows a linear trend. Such findings contradict the hypothesis of solution-related issues being characteristic for the later design stages. This work relies on agent-based simulations to explore the emerging patterns in design solution space expansion during teamwork. The results demonstrate trends that accord with the empirical findings, suggesting that a cognitive effort in solution space expansion remains constant throughout a design session. The collected data on agents' cognitive processes and solution space properties enabled additional insights, which led to the detection of four distinct regimes of design solution space expansion.