ABSTRACT
Breezes are a common source of renewable energy in the natural world. However, effectively harnessing breeze energy is challenging with conventional wind generators. These generators have a relatively high start-up wind speed requirement due to their large and steady rotational inertia. This study puts forth the idea of an autoregulatory driving arm (ADA), utilizing a stretchable arm for every wind cup and an elastic thread to provide adjustable rotational inertia and a low start-up speed. The self-adjustable rotational inertia of the harvester is achieved through coordinated interaction between the centrifugal and elastic forces. As the wind speed varies, the arm length of the wind cup automatically adjusts, thereby altering the rotational inertia of the harvester. This self-adjustment mechanism allows the harvester to optimize its performance and adapt to different wind conditions. By implementing the suggested ADA harvester, a low start-up speed of 1 m/s is achieved due to the small rotational inertia in its idle state. With the escalation of wind speed, the amplified centrifugal force leads to the elongation of the driving arms. When compared to a comparable harvester with a constant driving arm (CDA), the ADA harvester can generate more power thanks to this stretching effect. Additionally, the ADA harvester can operate for a longer time than the CDA harvester even after the wind has stopped. This extended operation time enables the ADA harvester to serve as a renewable power source for sensors and other devices in natural breeze environments. By efficiently utilizing and storing energy, the ADA harvester ensures a continuous and reliable power supply in such settings.
ABSTRACT
Breeze energy is a widely distributed renewable energy source in the natural world, but its efficient exploitation is very difficult. The conventional harvester with fixed arm length (HFA) has a relatively high start-up wind speed owing to its high and constant rotational inertia. Therefore, this paper proposes a harvester with a helix s-type vertical axis (HSVA) for achieving random energy capture in the natural breeze environment. The HSVA is constructed with two semi-circular buckets driven by the difference of the drag exerted, and the wind energy is transferred into mechanical energy. Firstly, as the wind speed changes, the HSVA harvester can match the random breeze to obtain highly efficient power. Compared with the HFA harvester, the power coefficient is significantly improved from 0.15 to 0.2 without additional equipment. Furthermore, it has more time for energy attenuation as the wind speeds dropped from strong to moderate. Moreover, the starting torque is also better than that of HFA harvester. Experiments showed that the HSVA harvester can improve power performance on the grounds of the wind speed ranging in 0.8-10.1 m/s, and that the star-up wind speed is 0.8 m/s and output peak power can reach 17.1 mW. In comparison with the HFA harvester, the HSVA harvester can obtain higher efficient power, requires lower startup speed and keeps energy longer under the same time. Additionally, as a distributed energy source, the HSVA harvester can provide a self-generating power supply to electronic sensors for monitoring the surrounding environment.
ABSTRACT
BACKGROUND: Improving the understandability of health information can significantly increase the cost-effectiveness and efficiency of health education programs for vulnerable populations. There is a pressing need to develop clinically informed computerized tools to enable rapid, reliable assessment of the linguistic understandability of specialized health and medical education resources. This paper fills a critical gap in current patient-oriented health resource development, which requires reliable and accurate evaluation instruments to increase the efficiency and cost-effectiveness of health education resource evaluation. OBJECTIVE: We aimed to translate internationally endorsed clinical guidelines to machine learning algorithms to facilitate the evaluation of the understandability of health resources for international students at Australian universities. METHODS: Based on international patient health resource assessment guidelines, we developed machine learning algorithms to predict the linguistic understandability of health texts for Australian college students (aged 25-30 years) from non-English speaking backgrounds. We compared extreme gradient boosting, random forest, neural networks, and C5.0 decision tree for automated health information understandability evaluation. The 5 machine learning models achieved statistically better results compared to the baseline logistic regression model. We also evaluated the impact of each linguistic feature on the performance of each of the 5 models. RESULTS: We found that information evidentness, relevance to educational purposes, and logical sequence were consistently more important than numeracy skills and medical knowledge when assessing the linguistic understandability of health education resources for international tertiary students with adequate English skills (International English Language Testing System mean score 6.5) and high health literacy (mean 16.5 in the Short Assessment of Health Literacy-English test). Our results challenge the traditional views that lack of medical knowledge and numerical skills constituted the barriers to the understanding of health educational materials. CONCLUSIONS: Machine learning algorithms were developed to predict health information understandability for international college students aged 25-30 years. Thirteen natural language features and 5 evaluation dimensions were identified and compared in terms of their impact on the performance of the models. Health information understandability varies according to the demographic profiles of the target readers, and for international tertiary students, improving health information evidentness, relevance, and logic is critical.
