The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial properties, HMM enables an automatic inference on states in neurocognitive activation data that are highly likely to occur in concept generation. The states with a higher likelihood of occupancy show more activation in the brain regions from the executive control network, the default mode network, and the middle temporal cortex. Different activation patterns and transfers are associated with these states, linking to varying cognitive functions, for example, semantic processing, memory retrieval, executive control, and visual processing, that characterize possible transitions in cognition related to concept generation. HMM offers new insights into cognitive dynamics in design by uncovering the temporal and spatial patterns in neurocognition related to concept generation. Future research can explore new avenues of data analysis methods to investigate design neurocognition and provide a more detailed description of cognitive dynamics in design.

The research presented in this paper explores features of temporal design neurocognition by comparing regions of activation in the brain during concept generation. A total of 27 engineering graduate students used brainstorming, morphological analysis, and TRIZ to generate concepts to design problems. Students' brain activation in their prefrontal cortex (PFC) was measured using functional near-infrared spectroscopy (fNIRS). Temporal activations were compared between techniques. When using brainstorming and morphological analysis, highly activated regions are consistently situated in the medial and right part of the PFC over time. For both techniques, the temporal neuro-physiological patterns are similar. Cognitive functions associated to the medial and right part of the PFC suggest an association with divergent thinking and adaptative decision making. In contrast, highly activated regions over time when using TRIZ appear in the medial or the left part of the prefrontal cortex, usually associated with goal directed planning.

The research presented in this paper explores how engineering students cognitively manage concept generation and measures the effects of additional dimensions of sustainability on design cognition. Twelve first-year and eight senior engineering students generated solutions to 10 design problems. Half of the problems included additional dimensions of sustainability. The number of unique design solutions students developed and their neurocognitive activation were measured. Without additional requirements for sustainability, first-year students generated significantly more solutions than senior engineering students. First-year students recruited higher cortical activation in the brain region generally associated with cognitive flexibility, and divergent and convergent thinking. Senior engineering students recruited higher activation in the brain region generally associated with uncertainty processing and self-reflection. When additional dimensions of sustainability were present, first-year students produced fewer solutions. Senior engineering students generated a similar number of solutions. Senior engineering students required less cortical activation to generate a similar number of solutions. The varying patterns of cortical activation and different number of solutions between first-year and senior engineering students begin to highlight cognitive differences in how students manage and retrieve information in their brain during design. Students’ ability to manage complex requirements like sustainability may improve with education.

This paper presents the results of studying the brain activations of 30 engineering students when using three different design concept generation techniques: brainstorming, morphological analysis, and TRIZ. Changes in students’ brain activation in the prefrontal cortex were measured using functional near-infrared spectroscopy. The results are based on the area under the curve analysis of oxygenated hemodynamic response as well as an assessment of functional connectivity using Pearson’s correlation to compare students’ cognitive brain activations using these three different ideation techniques. The results indicate that brainstorming and morphological analysis demand more cognitive activation across the prefrontal cortex (PFC) compared to TRIZ. The highest cognitive activation when brainstorming and using morphological analysis is in the right dorsolateral PFC (DLPFC) and ventrolateral PFC. These regions are associated with divergent thinking and ill-defined problem-solving. TRIZ produces more cognitive activation in the left DLPFC. This region is associated with convergent thinking and making judgments. Morphological analysis and TRIZ also enable greater coordination (i.e., synchronized activation) between brain regions. These findings offer new evidence that structured techniques like TRIZ reduce cognitive activation, change patterns of activation and increase coordination between regions in the brain.

Parameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then used to simulate the wheat yield and evapotranspiration (ET) in the North China Plain (referred to as the Vcmax method). To evaluate its performance, the simulated yield and ET were compared with those derived by the leaf area index (LAI) method using the retrieved LAI from NDVI to drive the VIP model. The results showed that the Vcmax method performed better than the LAI method in highly productive fields, while the LAI method described the inter-annual variations of yield more favourably in fields with low productivity. Over the study area, average yield (4520 kg/ha) and seasonal ET (360 mm) simulated by the LAI method was slightly lower than those simulated using the Vcmax method (4730 kg/ha for yield and 372 mm for ET). Compared with the census data of yield, the relative root mean square error (RMSE) of grain yield with Vcmax method (0·17) was lower than that of the LAI method (0·20). In conclusion, the physical model with spatial Vcmax pattern from remote sensing is reliable for regional crop productivity prediction.