This paper introduces a gesture-controlled conversational interface driven by a local AI model, aimed at improving accessibility and facilitating hands-free interaction within digital environments. The technology utilizes real-time hand gesture recognition via a typical laptop camera and connects with a local AI engine to produce customized learning materials. Users can peruse educational documents, obtain topic summaries, and generate automated quizzes with intuitive gestures, including lateral finger movements, a two-finger gesture, or an open palm, without the need for conventional input devices. Upon selection of a file, the AI model analyzes its whole content, producing a structured summary and a multiple-choice assessment, both of which are immediately saved for subsequent inspection. A unified set of gestures facilitates seamless navigating within the user interface and the opened documents. The system underwent testing with university students and faculty (n = 31), utilizing assessment measures such as gesture detection accuracy, command-response latency, and user satisfaction. The findings demonstrate that the system offers a seamless, hands-free user experience with significant potential for usage in accessibility, human–computer interaction, and intelligent interface design. This work advances the creation of multimodal AI-driven educational aids, providing a pragmatic framework for gesture-based document navigation and intelligent content enhancement.

Interactive teleoperation offers an intuitive pathway for human–robot interaction, yet many existing systems rely on dedicated sensors or wearable devices, limiting accessibility and scalability. This paper presents a vision-based teleoperation framework that enables real-time control of an articulated robotic arm (five joints plus a gripper actuator) using human hand tracking from a single, typical laptop camera. Hand pose and gesture information are extracted using a real-time landmark estimation pipeline, and a set of compact kinematic descriptors—palm position, apparent hand scale, wrist rotation, hand pitch, and pinch gesture—are mapped to robotic joint commands through a calibration-based control strategy. Commands are transmitted over a lightweight network interface to an embedded controller that executes synchronized servo actuation. To enhance stability and usability, temporal smoothing and rate-limited updates are employed to mitigate jitter while preserving responsiveness. In a human-in-the-loop evaluation with 42 participants, the system achieved an 88% success rate (37/42), with a completion time of 53.48 ± 18.51 s, a placement error of 6.73 ± 3.11 cm for successful trials (n = 37), and an ease-of-use score of 2.67 ± 1.20 on a 1–5 scale. Results indicate that the proposed approach enables feasible interactive teleoperation without specialized hardware, supporting its potential as a low-cost platform for robotic manipulation, education, and rapid prototyping.

A Type 22 Broadsword class of frigate uses a combined gas or gas (COGOG) propulsion system that utilizes four different gas turbines, depending on the mode of operation. Its twin ST40M cruise gas turbines are operational most frequently, and an increase in their efficiency would significantly impact fuel usage and ship range. This study evaluates the option to upgrade the cruise gas turbines to a combined gas turbine–electric and steam (COGES) system, which utilizes a steam Rankine cycle to recover energy from the exhaust gases of the gas turbine. An alternative system using hot air is also analyzed for performance comparison. The analysis contains calculations for the energy extraction and dimensions of heat exchangers, for the power extracted from the additional steam or air turbine and for pressure losses of the exhaust gas. Different configurations for the heat exchangers were investigated, as well as various parameters for the steam and hot air. The size and mass of the system were an important aspect of the analysis. It was concluded that an auxiliary system operating at a maximum steam pressure of 20 bar could generate an additional 771 kW of power while adding a mass of 7.4 tons to the frigate. These findings suggest that upgrading to a COGES system could cover the electricity needs of the entire ship and thus reduce the overall fuel consumption, resulting in lower operational costs and less emissions.