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This study aimed to evaluate the levels of student acceptance and effectiveness of an AI-powered chatbot platform as a teaching tool in the undergraduate oral medicine and pathology curriculum compared to traditional teaching methods. Oral medicine and pathology case scenarios were developed with prompt engineering techniques on the GPT-4-powered CognitusAI web-based platform (Med2Lab), which utilised both a virtual patient and a virtual preceptor in conducting each case. The first phase of the study involved pilot testing to assess 50 students’ levels of acceptance of the platform and gather suggestions for improvement. The second phase was a comparative study, with 40 students in the intervention group undergoing case-based learning on the CognitusAI platform, and 40 in the control group undergoing a traditional tutorial of the same scenarios. The students took pre- and post-intervention assessments to evaluate knowledge levels. 92% of students felt confident to apply the knowledge gained on the platform, and 86% stated that it encouraged them to understand. Over 80% of students were more aware of their progress and areas for improvement after the module, and over 90% were interested in similar learning opportunities in the future. Students felt that the platform’s advantages included its strong clinical relevance, facilitation of active recall, personalised and non-judgemental feedback, and good engagement and interactiveness. In the comparative study, students in both the intervention and control groups showed significant score improvements after the teaching session (p < 0.001 for both). Although the difference in score improvements between the intervention and control group was not statistically significant (p = 0.062), the intervention group had significantly higher post-intervention test scores (p = 0.041) and a higher proportion of students with improved scores (p < 0.001). AI-powered chatbot platforms are well-received teaching tools and are equally effective and potentially superior to traditional teaching methods in enhancing students’ knowledge and understanding in oral medicine and pathology.

IntroductionClimate change significantly impacts sexual and reproductive health (SRH) attitudes and practices, yet large-scale quantitative analyses exploring these effects are limited. This study investigates the historical associations between climate change, specifically temperature and precipitation anomalies, and key SRH attitudes and practices including contraception use, fertility preferences, and contraceptive autonomy.MethodsUsing data from 74 IMPUMS-harmonized Demographic and Health Surveys merged with high-resolution climate data, we analyzed a sample of 820,746 non-pregnant, reproductive-aged women across 33 low- and middle-income countries from 2000 to 2016. Fixed-effect logistic regression models were employed to assess the association between climate anomalies and SRH attitudes and practices.ResultsPooled sample results indicate modest but significant associations globally: higher exposure to extreme heat in the year prior to survey administration was associated with lower odds of modern contraception use, lower odds of desire for children, and higher odds of contraceptive autonomy, while higher exposure to extreme precipitation was associated with lower odds of desire for children and higher odds of contraceptive autonomy. These associations were more pronounced when both temperature and precipitation anomalies occurred concurrently. Substantial demographic and geographic variability were observed, with mixed directionality and strength of association observed across countries and stronger associations observed among nulliparous women and younger respondents.DiscussionOur findings underscore the potential impact of climate change on SRH attitudes and practices, as well as SRH service delivery needs in the context of extreme heat and extreme precipitation, highlighting the importance of targeted, gender-responsive health interventions tailored to climate change-affected populations.

This paper presents methods for placing length sensors on a soft continuum robot joint as well as a novel configuration estimation method that drastically minimizes configuration estimation error. The methods utilized for placing sensors along the length of the joint include a single joint length sensor, sensors lined end-to-end, sensors that overlap according to a heuristic, and sensors that are placed by an optimization that we describe in this paper. The methods of configuration estimation include directly relating sensor length to a segment of the joint's angle, using an equal weighting of overlapping sensors that cover a joint segment, and using a weighted linear combination of all sensors on the continuum joint. The weights for the linear combination method are determined using robust linear regression. Using a kinematic simulation we show that placing three or more overlapping sensors and estimating the configuration with a linear combination of sensors resulted in a median error of 0.026% of the max range of motion or less. This is over a 500 times improvement as compared to using a single sensor to estimate the joint configuration. This error was computed across 80 simulated robots of different lengths and ranges of motion. We also found that the fully optimized sensor placement performed only marginally better than the placement of sensors according to the heuristic. This suggests that the use of a linear combination of sensors, with weights found using linear regression is more important than the placement of the overlapping sensors. Further, using the heuristic significantly simplifies the application of these techniques when designing for hardware.

In this work we present a novel, inductance-based system to measure and control the motion of bellows-driven continuum joints in soft robots. The sensing system relies on coils of wire wrapped around the minor diameters of each bellows on the joint. As the bellows extend, these coils of wire become more distant, decreasing their mutual inductance. Measuring this change in mutual inductance allows us to measure the motion of the joint. By dividing the sensing of the joint into two sections and measuring the motion of each section independently, we are able to measure the overall deformation of the joint with a piece-wise constant-curvature approximation. This technique allows us to measure lateral displacements that would be otherwise unobservable. When measuring bending, the inductance sensors measured the joint orientation with an RMS error of 1.1 °. The inductance sensors were also successfully used as feedback to control the orientation of the joint. The sensors proposed and tested in this work provided accurate motion feedback that would be difficult to achieve robustly with other sensors. This sensing system enables the creation of robust, self-sensing soft robots based on bellows-driven continuum joints.

A new scalable, modular hybrid solar power system is designed to generate electricity through two pathways, CPV and CSP, in order to better meet grid energy demands for reliable renewable energy. The key element, a transmissive, spectrum-splitting multi-junction solar module, is modeled and simulated to analyze its electrical, optical, and thermal properties. Optimized designs are proposed to deliver high efficiency visible light-to-electricity conversion while transmitting infrared light to a thermal receiver. Prospective challenges in the upcoming development and fabrication are discussed.