Earthquakes pose significant risks to infrastructure and supply chains, making the timely and fair distribution of emergency relief materials crucial for reducing casualties and economic losses. This study addresses the challenge of optimizing emergency material dispatch under scenarios where distribution centers fail due to earthquake damage, with the aim of improving both the timeliness and fairness of resource allocation during post-disaster recovery. A multi-objective optimization model is developed, which integrates distribution center failures and the activation of backup centers. The model minimizes total dispatch time, maximizes fairness in supply distribution, and reduces unmet dema... More >

To address key challenges in post-harvest fruit grading—namely the difficulty of distinguishing visually similar varieties, the invisibility of internal quality, and mechanical damage during grasping—this study develops an intelligent robotic grading system that integrates advanced computer vision, Near-Infrared (NIR) spectroscopy, and flexible force-controlled grasping. First, an improved object detection algorithm, YOLOv11-TFE, is proposed to mitigate visual confusion between Qixia Fuji apples and Beijing Pinggu peaches and to handle the irregular geometry of Nanshui pears. By embedding the parameter-free SimAM attention mechanism into the backbone to explicitly enhance and decouple su... More >

Accurate traffic flow prediction is a crucial step in building an intelligent transportation system, and it is of great significance for alleviating urban traffic congestion and optimizing travel routes. Due to the complex spatial topology of the transportation network and the highly nonlinear temporal dynamic characteristics of the flow data, traditional prediction methods are difficult to fully capture the inherent spatio-temporal dependencies. Therefore, this paper proposes a traffic flow prediction model based on variant hybrid multi-hop graph convolution. Firstly, by introducing a multi-hop graph convolution operator, the model explicitly aggregates the spatial information of multiple-o... More >

This study proposes a novel hybrid deep learning framework that integrates a Large Language Model (LLM) with a Transformer architecture for stock price forecasting. The research addresses a critical theoretical gap in existing approaches that empirically combine textual and numerical data without a formal understanding of their interaction mechanisms. We conceptualize a prompt-based LLM as a mathematically defined signal generator, capable of extracting directional market sentiment and an associated confidence score from financial news. These signals are then dynamically fused with structured historical price features through a noise-robust gating mechanism, enabling the Transformer to adapt... More >