Indoor navigation robots, which have been developed using a robot operating system, typically use a direct current motor as a motion actuator. Their control algorithm is generally complex and requires the cooperation of sensors such as wheel encoders to correct errors. For this study, an autonomous navigation robot platform named Owlbot was designed, which is equipped with a stepping motor as a mobile actuator. In addition, a stepping motor control algorithm was developed using polynomial equations, which can effectively convert speed instructions to generate control signals for accurately operating the motor. Using 2D LiDAR and an inertial measurement unit as the primary sensors, simultaneous localization, mapping, and autonomous navigation are realised based on the particle filtering mapping algorithm. The experimental results show that Owlbot can effectively map the unknown environment and realise autonomous navigation through the proposed control algorithm, with a maximum movement error being smaller than 0.015 m.

This study proposes a solution for Active Simultaneous Localization and Mapping (Active SLAM) of robots in unknown indoor environments using a combination of Deep Deterministic Policy Gradient (DDPG) path planning and the Cartographer algorithm. To enhance the convergence speed of the DDPG network and minimize collisions with obstacles, we devised a unique reward function that integrates exploration and exploitation strategies. The exploration strategy allows the robot to achieve the shortest running time and movement trajectory, enabling efficient traversal of unmapped environments. Moreover, the exploitation strategy introduces active closed loops to enhance map accuracy. We conducted experiments using the simulation platform Gazebo to validate our proposed model. The experimental results demonstrate that our model surpasses other Active SLAM methods in exploring and mapping unknown environments, achieving significant grid completeness of 98.7%.

DNS over HTTPS (DoH) reduces the risk of privacy leakage of DNS queries, but it also provides a covert communication channel for malicious activities. In this paper, we propose a method for malicious encrypted traffic identification, which harnesses the advanced context comprehension of Large Language Model (LLM) and incorporates expert features to detect anomalies. The evaluation results show that the method proposed in this paper can not only identify common and emerging malicious DoH tunnel tools such as dns2tcp, iodine, and dnstt, but also identify weaponized DoH traffic within a real APT attack, with a recall of 0.9995.

Hospitalized adolescents often encounter difficulties in emotional communication due to the mismatch between their developmental needs and adult-centered clinical environments. While prior work has explored psychological and technological interventions, less is known about adolescents’ own experiences and acceptable forms of support. We present a qualitative study using questionnaires and semi-structured interviews to examine adolescents’ emotional communication in hospital settings. Our analysis reveals ambivalent social connection, indirect and mediated emotional communication, and adolescents’ expectations for emotional expression tools. Based on these findings, we discuss implications for designing low-intrusion emotional support tools that align with adolescents’ emotional needs in hospital settings.