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BackgroundPeople with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy's effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named "Sleep Catcher," that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i.MethodsThe application development process will be described-writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input.ResultsThe mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI.ConclusionThere is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

With the increase of thermal and mechanical load of engine,the higher standards were required for materials,and the more serious challenges had been taken for the design technology. Based on simulation results of several cylinder heads,which had been strengthened,a relatively optimal frame cylinder head was obtained. The load carrying structures,which were interdependent,were included by bolt holes,the fuel injector holes,stiffeners of the top ceiling and the fire face. The results of reliability tests about mechanical fatigue and thermal fatigue revealed the structure of the Al- alloyed cylinder head under peak pressure（ 20 MPa） was reasonable.

Objective: This study aimed to investigate the impact of different ultrasonic power and time pretreatments on the structures and gel properties of the flaxseed gum and soybean protein isolate composite gel induced by TG enzyme. Methods: The structural characteristics, molecular conformation, and micro-network structures of the composite gel under varying ultrasonic conditions were analyzed using a texture analyzer, laser particle size meter, circular dichroism (CD), fluorescence spectrum, and scanning electron microscope (SEM). Results: The 50% power ultrasound pretreatment for 10 min could significantly improve the hardness, adhesiveness, chewiness, cohesiveness and water-holding property of the composite gel, and reduce its adhesiveness. After 50% power ultrasound pretreatment for 10 min, the α-helix and β-turn content of composite gel proteins decreased, and the content of β-folding and irregular curls increased, and the fluorescence intensity increased, which was conducive to the improvement of the functional properties of the gel. The microstructure of the composite gel became ordered from disorder, the network density increased, the pore diameter became smaller, and the pore wall was thickened. While after increasing the ultrasound power to 80%, the gel properties fluctuated only slightly and the microstructure was destroyed. Conclusion: 50% power ultrasound pretreatment for 10 min can significantly improve the gel properties and network structure of TG enzyme cross-linked flaxseed gum-soybean protein isolate composite. [ABSTRACT FROM AUTHOR]