The role of Artificial Intelligence (AI) in laboratories is fundamentally changing. It is evolving from a passive data-processing tool into an active agent capable of autonomous planning, decision-making, and direct control of physical experiments. This transition from a “tool” to an “actor” represents a core, yet underrecognized, challenge for contemporary laboratory safety.
The rapid integration of Artificial Intelligence (AI), particularly generative models and large language models, is fundamentally reshaping contemporary academic research. While AI offers unprecedented capabilities for data analysis, automation, and knowledge discovery, its widespread adoption introduces significant ethical, methodological, and structural
The DISC personality framework, while widely adopted in applied settings, relies on a fixed rule-based classification method that may oversimplify individual behavioural profiles. This study explores whether machine learning can offer a more flexible, efficient, and accurate approach to DISC classification.
To design, implement, and evaluate a multilingual, voice-enabled Retrieval-Augmented Generation (RAG) chatbot that delivers personalized, clinically grounded information and answers patient questions about retinal detachment.
Medical molecular robotics can be seen as the integration of nanotechnology, robotic technology, imaging innovation, and precision medicine that takes minimally invasive robotic surgery forward into the molecular realm.
Robotic Liver Resection (RLR) represents a major advance in minimally invasive hepatobiliary surgery, offering enhanced 3D visualization, wristed instruments, and ergonomic benefits. These advantages can reduce blood loss, conversion rates.
Amidst the current wave of artificial intelligence and the accelerated implementation of the smart water conservancy strategy, water conservancy projects face challenges such as massive data volumes, complex operating environments, and diversified scheduling demands.
Quantum computing may be a force multiplier for medical robotics by speeding motion planning, kinematics, and multi-robot coordination; advancing perception using quantum-enabled imaging; and delivering personalized, data-intensive control policies.
To address the accuracy and efficiency challenges of traditional lens placement equipment, a high-precision, lightweight method for scattered lens recognition is proposed. Based on the Yolov5s model, this method first embeds the Depthwise-Conv-BN-ReLU (DCBR).
