Biomimetics

This study introduces an innovative Honey Badger Optimization (HBO) designed to address the Optimal Power Flow (OPF) challenge in electrical power systems. HBO is a unique population-based searching method inspired by the resourceful foraging behavior of honey badgers when hunting for food. In this algorithm, the dynamic search process of honey badgers, characterized by digging and honey-seeking tactics, is divided into two distinct stages, exploration and exploitation. The OPF problem is formulated with objectives including fuel cost minimization and voltage deviation reduction, alongside operational constraints such as generator limits, transformer settings, and line power flows. HBO is applied to the IEEE 30-bus test system, outperforming existing methods such as Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in both fuel cost reduction and voltage profile enhancement. Results indicate significant improvements in system performance, achieving 38.5% and 22.78% better voltage deviations compared to GWO and PSO, respectively. This demonstrates HBO’s efficacy as a robust optimization tool for modern power systems. In addition to the single-objective studies, a multi-objective OPF formulation was investigated to produce the complete Pareto front between fuel cost and voltage deviation objectives. The proposed HBO successfully generated a well-distributed set of trade-off solutions, revealing a clear conflict between economic efficiency and voltage quality. The Pareto analysis demonstrated HBO’s strong capability to balance these competing objectives, identify knee-point operating conditions, and provide flexible decision-making options for system operators.

In recent years, robots have started to be used in tasks involving human interaction. For this to be possible, humans must perceive robots as suitable interaction partners. This can be achieved by giving the robots an animate appearance. One of the methods that can be utilised to endow a robot with a lively appearance is giving it the ability to perform expressions on its own, that is, combining multimodal actions to convey information. However, this can become a challenge if the robot has to use gestures and speech simultaneously, as the non-verbal actions need to support the message communicated by the verbal component. In this manuscript, we present a system that, based on a robot’s utterances, predicts the corresponding gesture and synchronises it with the speech. A deep learning-based prediction model labels the robot’s speech with the types of expressions that should accompany it. Then, a rule-based synchronisation module connects different gestures to the correct parts of the speech. For this, we have tested two different approaches: (i) using a combination of recurrent neural networks and conditional random fields; and (ii) using transformer models. The results show that the proposed system can properly select co-speech gestures under the time constraints imposed by real-world interactions.

Background: This study examined the trends in restorative dental practice among 12-year-old children treated at a nationwide public health maintenance organization in Israel between 2016 and 2022, focusing on the use of amalgam versus composite resin restorations in permanent premolars and molars. Methods: Data were extracted from electronic health records of the second-largest public health organization in Israel, identifying children who underwent restorative treatments during the study period. Restoration rates were compared overall and stratified by gender, socioeconomic status, and number of surfaces restored. Statistical analysis was conducted using SPSS version 27, employing Levene’s test for equality of variances and Welch’s one-way ANOVA. Results: The results showed a statistically significant decline in amalgam use (p < 0.05) alongside a marked increase in composite resin restorations (p < 0.05), consistent across genders and socioeconomic groups. Notably, composite resins were increasingly selected for complex, multi-surface restorations (p < 0.05). Conclusions: These findings highlight a substantial shift in paediatric restorative practice in Israel, reflecting growing preference for composite resins likely influenced by patient demands and national dental reforms that eliminated financial barriers. The observed trend underscores the importance of continued monitoring of material selection to guide evidence-based practice in pediatric dentistry.