The text classification task is the most basic and essential in natural language processing. It can find the rules about documents and distinguish the categories of different papers. Nowadays, graph convolutional neural network is widely used in text classification task because of its scalability and high efficiency. However, the GCN model has the best effect at layer 2. As the number of network layers increases, problems such as over smoothing will occur. This paper proposes a semi-supervised text classification model based on a graph attention neural network, which makes full use of the characteristics of information transmission along with the graph by adding residual connections to deepen the number of network layers. Simultaneously, the attention mechanism can be used to give different weights according to the importance of neighboring nodes to target nodes. A dual-layer attention mechanism at the topic level and node level is proposed to learn the importance of different neighboring nodes and extra neighboring node information to target nodes. Experimental results show that this model is superior to other methods on four benchmark datasets.

The second-order interference of two independent photons with different spectra in a Shih–Alley/Hong–Ou–Mandel interferometer is studied in Feynman’s path integral theory. There is a second-order interference pattern for photons with different spectra if the photons are indistinguishable for the employed detection system. The conditions to observe the second-order temporal beating with photons of different spectra are analyzed. The influence of the response time of the detection system on the observed second-order interference pattern is also discussed. It is a direct result of that measurement in quantum mechanics is dependent on the employed measuring apparatus. The results are helpful to understand the physics of two-photon interference in different schemes.

Introduction: In China’s rapidly expanding organic food market, organic food remains a credence-based food category in which consumers rely on information and certification cues to evaluate product authenticity, safety, and sustainability. This study examines how information seeking and organic food credibility cues shape Chinese consumers’ organic food purchase intention by integrating the Theory of Planned Behavior (TPB) with selected components of Alphabet Theory. Methods: Data were collected from 412 screened consumers in mainland China and analyzed using partial least squares structural equation modeling. Results: Information seeking was positively associated with organic food knowledge, which in turn was positively associated with attitude toward organic food. Attitude, subjective norm, perceived behavioral control, social media, eco-labels, and perceived government support all positively influenced purchase intention. Bootstrapped indirect-effect analysis further confirmed a sequential pathway from information seeking to purchase intention through knowledge and attitude. Discussion: The findings indicate that organic food choice in China is formed through both internal learning-evaluation processes and external credibility-related cues operating at digital, product-certification, and institutional levels. The study contributes to food consumer science by explaining how consumers interpret organic food claims under information asymmetry and offers implications for food retailers, certification bodies, and policymakers.