Background Diabetes affects many people across the world. Diabetes education plays a critical role in helping people with diabetes to perform diabetes self-management at home. Intervention We explored whether serious games can be used as a supplementary tool for diabetes education. Two online video games- the “Diabetic Dog” game and “Carb Counting with Lenny the Lion” game, were used for the study. Methods Fourteen patients with Type II diabetes were recruited from the Norman Diabetes Center by personal invitations. After initial training, the patients played the games a minimum of four times per week during the two-week study duration. Pre- and post-assessments of patients' diabetes knowledge and self-efficacy in diabetes self-management behaviors were performed using questionnaires, and an interview was conducted at the end to gauge the effectiveness of the game intervention. Results The results from the questionnaires show a general trend of improvement in patient diabetes knowledge and self-efficacy in diabetes self-management. A general trend of improvements in patients’ self-efficacy in controlling blood sugar level, handling abnormal blood sugar levels, taking insulin, and complying with a diabetes diet was observed. The interview results showed that the patients reinforced their diabetes knowledge and became more aware of their own lifestyles by playing diabetes educational games. They perceived the games as fun and easy to play. They also provided suggestions for the game design for diabetes education. Conclusion The study showed that serious game intervention had good potential to be a useful supplement to clinic-based diabetes education in improving patient diabetes knowledge and increasing patient self-efficacy in diabetes self-management behavior.

Nonlinear optical (NLO) crystals are the key materials in modern laser technology and science because of their intrinsic capability to convert the wavelength of the light source. The search for new NLO materials is still very active in both scientific and industrial communities. Machine learning (ML) becomes a powerful tool to explore new candidates of NLO materials and to reveal the underlying relationship between structures and properties. In this work, we have proposed multilevel features that are relevant to the atomic properties, the characters of fundamental structural groups, and the crystal structures to describe inorganic NLO crystals for machine learning. The first-level and second-level descriptors can be obtained based on chemical compositions of crystals without prior knowledge about crystal structures. Several ML classifiers have been optimized using a database that consists of hundreds of NLO crystals to identify the samples with desired birefringence (Δn) and second-order nonlinear coefficients (d ij ). In particular, almost all of the ML models that only involve the first-level and second-level features, called as the crystal-structure-free model, exhibit good classification performance. It is still far from perfect but suitable to act as a filter in the first step of high-throughput materials discovery. Using the optimized ML models, feature importance analyses and virtual screening processes have been performed to understand the relationship between the features and targeted properties and to extract the statistical pictures on elements and fundamental structural groups. Several unexplored crystals are also picked out as ML-proposed candidates, and three of them are suggested as new potential NLO materials based on further first-principle calculations. The present ML models are expected to accelerate the inverse design for new NLO crystals with desired properties.

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In order to help designers to acquire and apply knowledge, a kind of knowledge push technologies based on quality function knowledge deployment was proposed. During the whole lifecycle of product design, the knowledge requirement house of quality model could link up the required knowledge with design activities to help designers to track the required knowledge for realizing product innovation design. The hierarchical relationship between design tasks, designers, design activities, and required knowledge was analyzed, and the knowledge management system structure for product innovation design based on designers was put forward. Furthermore, knowledge acquisition method was analyzed. In view of the different requirements for knowledge in the different design stages, this paper proposed the mapping model between the process of product innovation design and required knowledge. Then the knowledge push model for product innovation design based on quality function knowledge deployment was established. Finally, a prototype system of computer-aided innovation design platform was developed to implement this knowledge push model, and through its application on the design case of space robot gripper, the practicability and validity of the prototype system were demonstrated.

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