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Growing demands for cleaner energy sources lead to innovations that require investigations in solar energy harvesting. Though numerous organic and inorganic photovoltaic devices have been explored for the solar power conversion, achieving a high efficiency is still an open challenge for the researchers. In this context, an efficient, self-adjusting solar power panel coupled with low-cost and high reliability is of great significance and demand. In this study, we investigate the potential of Shape Memory Alloy (SMA) actuators for solar tracking applications. Three SMA configurations were considered containing one and up to three SMAs arranged in parallel. The temperature range for the displacement experiments was 40°–60°. Additionally, three levels of mass were used, namely, 500 g, 600 g, and 700 g. The displacement experiments revealed that the addition of more SMAs into the configuration provided a more consistent performance. The force experiment revealed that two-SMA configuration achieved 60% higher force production compared to the one-SMA configuration under the same conditions while the three-SMA configuration was 31% higher than in the two-SMA configuration and 110% compared to the one-SMA configuration. Additionally, the force hysteresis of the two-SMA setup was smaller and closer to that of single-SMA configuration. The two-SMA configuration force hysteresis exhibited a more linear trend as compared to that of the three-SMA configuration. The outcomes of this work highlight the potential of using SMAs as actuators in solar-powered applications and that optimization in terms of the needed number of SMAs is required to meet the displacement and/or force requirements.Article Highlights: SMA actuators show promise for efficient solar tracking, with higher temperatures leading to increased displacement. Configurations with two SMAs perform comparably to those with three SMAs, offering a simpler design while maintaining effectiveness. Linear force trends and reduced hysteresis in two SMA configurations suggest easier system design and optimization for solar tracking applications. [ABSTRACT FROM AUTHOR]

Aggression in children is frequent during the early years of childhood. Among children with psychiatric disorders in general, and autism in particular, challenging behaviours and aggression rates are higher. These can take on different forms, such as hitting, kicking, and throwing objects. Social robots that are able to detect undesirable interactions within its surroundings can be used to target such behaviours. In this study, we evaluate the performance of five machine learning techniques in characterizing five possible undesired interactions between a child and a social robot. We examine the effects of adding different combinations of raw data and extracted features acquired from two sensors on the performance and speed of prediction. Additionally, we evaluate the performance of the best developed model with children. Machine learning algorithms experiments showed that XGBoost achieved the best performance across all metrics (e.g., accuracy of 90%) and provided fast predictions (i.e., 0.004 s) for the test samples. Experiments with features showed that acceleration data were the most contributing factor on the prediction compared to gyroscope data and that combined data of raw and extracted features provided a better overall performance. Testing the best model with data acquired from children performing interactions with toys produced a promising performance for the shake and throw behaviours. The findings of this work can be used by social robot developers to address undesirable interactions in their robotic designs. [ABSTRACT FROM AUTHOR]

The quality of a companion robot's reaction is important to make it acceptable to the users and to sustain interactions. Furthermore, the robot's reaction can be used to train socially acceptable behaviors and to develop certain skills in both normally developing children and children with cognitive disabilities. In this study, we investigate the influence of reaction time in the emotional response of a robot when children display aggressive interactions toward it. Different interactions were considered, namely, pickup, shake, drop and throw. The robot produced responses as audible sounds, which were activated at three different reaction times, namely, 0.5 s, 1.0 s, and 1.5 s. The results for one of the tasks that involved shaking the robotic toys produced a significant difference between the timings tested. This could imply that producing a late response to an action (i.e. greater than 1.0 s) could negatively affect the children's comprehension of the intended message. Furthermore, the response should be comprehensible to provide a clear message to the user. The results imply that the designers of companion robotic toys need to consider an appropriate timing and clear modality for their robots' responses. [ABSTRACT FROM AUTHOR]

Social robots have gained a lot of attention recently as they have been reported to be effective in supporting therapeutic services for children with autism. However, children with autism may exhibit a multitude of challenging behaviors that could be harmful to themselves and to others around them. Furthermore, social robots are meant to be companions and to elicit certain social behaviors. Hence, the presence of a social robot during the occurrence of challenging behaviors might increase any potential harm. In this paper, we identified harmful scenarios that might emanate between a child and a social robot due to the manifestation of challenging behaviors. We then quantified the harm levels based on severity indices for one of the challenging behaviors (i.e. throwing of objects). Our results showed that the overall harm levels based on the selected severity indices are relatively low compared to their respective thresholds. However, our investigation of harm due to throwing of a small social robot to the head revealed that it could potentially cause tissue injuries, subconcussive or even concussive events in extreme cases. The existence of such behaviors must be accounted for and considered when developing interactive social robots to be deployed for children with autism. [ABSTRACT FROM AUTHOR]