Evaluation of DNA-mediated electron transfer using a hole-trapping nucleobase under crowded conditions

The effects of a crowded environment on DNA-mediated electron transfer were evaluated using a pyrene-modified oligonucleotide containing a hole-trapping nucleobase in poly(ethylene glycol) mixed solutions. Rapid decompositions of hole-trapping bases...

On Safety and Time Efficiency Enhancement of Robot Navigation in Crowded Environment utilizing Deep Reinforcement Learning

<div>The evolution of social robots has increased with the advent of recent artificial intelligence techniques. Alongside humans, social robots play active roles in various household and industrial applications. However, the safety of humans becomes a significant concern when robots navigate in a complex and crowded environment. In literature, the safety of humans in relation to social robots has been addressed by various methods; however, most of these methods compromise the time efficiency of the robot. For robots, safety and time-efficiency are two contrast elements where one dominates the other. To strike a balance between them, a multi-reward formulation in the reinforcement learning framework is proposed, which improves the safety together with time-efficiency of the robot. The multi-reward formulation includes both positive and negative rewards that encourage and punish the robot, respectively. The proposed reward formulation is tested on state-of-the-art methods of multi-agent navigation. In addition, an ablation study is performed to evaluate the importance of individual rewards. Experimental results signify that the proposed approach balances the safety and the time-efficiency of the robot while navigating in a crowded environment.</div>

On Safety and Time Efficiency Enhancement of Robot Navigation in Crowded Environment utilizing Deep Reinforcement Learning

<div>The evolution of social robots has increased with the advent of recent artificial intelligence techniques. Alongside humans, social robots play active roles in various household and industrial applications. However, the safety of humans becomes a significant concern when robots navigate in a complex and crowded environment. In literature, the safety of humans in relation to social robots has been addressed by various methods; however, most of these methods compromise the time efficiency of the robot. For robots, safety and time-efficiency are two contrast elements where one dominates the other. To strike a balance between them, a multi-reward formulation in the reinforcement learning framework is proposed, which improves the safety together with time-efficiency of the robot. The multi-reward formulation includes both positive and negative rewards that encourage and punish the robot, respectively. The proposed reward formulation is tested on state-of-the-art methods of multi-agent navigation. In addition, an ablation study is performed to evaluate the importance of individual rewards. Experimental results signify that the proposed approach balances the safety and the time-efficiency of the robot while navigating in a crowded environment.</div>

Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs

In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consumption problems, which pose serious constraints on the flight duration and completion of energy-demanding tasks. The possibility of providing UAVs with advanced decision-making capabilities in an energy-effective way would be extremely beneficial. In this paper, we propose a practical solution to this problem that exploits deep learning on the edge. The developed system integrates an OpenMV microcontroller into a DJI Tello Micro Aerial Vehicle (MAV). The microcontroller hosts a set of machine learning-enabled inference tools that cooperate to control the navigation of the drone and complete a given mission objective. The goal of this approach is to leverage the new opportunistic features of TinyML through OpenMV including offline inference, low latency, energy efficiency, and data security. The approach is successfully validated on a practical application consisting of the onboard detection of people wearing protection masks in a crowded environment.

The Influence of Social Crowding on Consumer Creativity

The importance of consumer creativity is currently widely recognized, yet the examination of the influence of environmental elements on consumer creativity is still limited. Our research investigates the influence of social crowding on consumer creativity performance. While past research mainly focuses on extreme crowding conditions, our research examines the impact of a moderate level of social crowding, which is more commonly experienced in reality. From two lab experiments, our research shows that compared to consumers in crowded environments, consumers in uncrowded environments perform better on creativity tasks (e.g., designing promotion slogans and identifying solutions to problems). Furthermore, the effect of social crowding is mediated by approach motives. Consumers in an uncrowded (vs. crowded) environment are more likely to trigger approach-based motivation, which enhances their creativity performance. These findings extend our knowledge of social crowding and creativity and can help consumers and companies improve creativity performance in appropriate environments.