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).
The stethoscope, a symbol of clinical medicine for two centuries, is undergoing a technological transformation. Modern digital stethoscopes convert acoustic signals to high-resolution digital waveforms and, when combined with signal processing and Machine Learning.
Digital technology plays a pivotal role in transforming services management and analytics by enabling innovative approaches that surpass traditional requirements. Service managers striving for competitive advantage increasingly rely on advanced technologies.
This study aims to predict divorce risk using machine learning. Questionnaires were employed to identify key factors influencing divorce decisions, categorized into health history, childhood history, marriage history, child information, and child well-being.
In this paper we develop physics informed neural network model to obtain parameters of the multimeter design. We design the voltage and current of the multimeter. We consider fixed geometry of length 0.5 m. The area is 0.025 m2. The model uses generalized
Ensuring quality and relevance of knowledge in clinical decision support systems is an important research direction in decision science. Knowledge useful to specialists for establishing a diagnosis takes into account the forms of the disease.
This article presents a comprehensive architecture for a mobile application, STUR (Student Time Use and Regulation), designed to enhance students' time management. Merging learning analytics, high-dimensional statistics, and mobile computing, our framework supports students through data-driven recommendations and personalized feedback.
As artificial intelligence advances, Large Language Models (LLMs) have shown tremendous potential in medical diagnosis and treatment, yet existing research has not extensively explored their application in kidney stones.
