Search Results (4)

This study introduces an explainable neuro-symbolic and large language model (LLM)-driven framework for intelligent interpretation of corneal topography and precision surgical decision support. In a prospective cohort of 20 eyes, comprehensive IOLMaster 700 reports were analyzed through a four-stage pipeline: (1) automated extraction of key parameters—including corneal curvature, pachymetry, and axial biometry; (2) mapping of these quantitative features onto a curated corneal disease and refractive-surgery knowledge graph; (3) Bayesian probabilistic inference to evaluate early keratoconus and surgical eligibility; and (4) explainable multi-model LLM reporting, employing DeepSeek and GPT-4.0, to generate bilingual physician- and patient-facing narratives. By transforming complex imaging data into transparent reasoning chains, the pipeline delivered case-level outputs within ~95 ± 12 s. When benchmarked against independent evaluations by two senior corneal specialists, the framework achieved 92 ± 4% sensitivity, 94 ± 5% specificity, 93 ± 4% accuracy, and an AUC of 0.95 ± 0.03 for early keratoconus detection, alongside an F1 score of 0.90 ± 0.04 for refractive surgery eligibility. The generated bilingual reports were rated ≥4.8/5 for logical clarity, clinical usefulness, and comprehensibility, with representative cases fully concordant with expert judgment. Comparative benchmarking against baseline CNN and ViT models demonstrated superior diagnostic accuracy (AUC = 0.95 ± 0.03 vs. 0.88 and 0.90, p < 0.05), confirming the added value of the neuro-symbolic reasoning layer. All analyses were executed on a workstation equipped with an NVIDIA RTX 4090 GPU and implemented in Python 3.10/PyTorch 2.2.1 for full reproducibility. By explicitly coupling symbolic medical knowledge with advanced language models and embedding explainable artificial intelligence (XAI) principles throughout data processing, reasoning, and reporting, this framework provides a transparent, rapid, and clinically actionable AI solution. The approach holds significant promise for improving early ectatic disease detection and supporting individualized refractive surgery planning in routine ophthalmic practice. Full article

This paper presents strategies for effectively integrating AI tools into programming education and provides recommendations for enhancing student learning outcomes through intelligent educational systems. Learning computer programming is a cognitively demanding task that requires dedication, logical reasoning, and persistence. Many beginners struggle with debugging and often lack effective problem-solving strategies. To address these issues, this study investigates PyChatAI—a bilingual, AI-powered chatbot designed to support novice Python programmers by providing real-time feedback, answering coding-related questions, and fostering independent problem-solving skills. PyChatAI offers continuous, personalised assistance and is particularly beneficial for students who prefer remote or low-pressure learning environments. An empirical evaluation employing a Solomon Four-Group design revealed significant improvements across all programming skill areas, with especially strong gains in theoretical understanding, code writing, and debugging proficiency. Full article

The advent of the digital era has put forward an urgent demand for the digitization of units of measurement, and the construction of unit ontology is an important method to realize the digitization of units of measurement. However, the existing unit ontology is at the preliminary research stage, especially the bilingual unit of measurement suitable for the construction of Digital China. Based on the Web Ontology Language (OWL), a bilingual unit of measurement ontology, vim, is designed and constructed using the Seven Steps to Ontology Development approach. vim provides a standardized, interoperable, and unified architecture to realize the bilingual digital representation of units in the International Vocabulary of Metrology—Basic and general concepts (VIM) and from the Chinese metrological technical specification JJF 1001-2011 General Terms in Metrology and Their Definitions. The ontology was verified for machine readability, knowledge reasoning capability, and semantic retrieval and applied. The experimental results show that the vim ontology can achieve machine readability with correct syntax, logical consistency, and validity, and can facilitate data communication and sharing. Furthermore, a comparison between vim, OM, and QUDT was conducted. OM and QUDT serve as representative instances in the field of ontology for units. The construction of this ontology lays the foundation for realizing the digitization and standardization of China’s unit of measurement, as well as the machine-readability, interoperability, and sharing of domestic and foreign metrology test data and metrology certificates. Full article

Its purpose is to prove information law by logic, mathematics and translation. The method involves: the generalized bilingual logic established on both Aristotle’s formal logic and Frege’s mathematical logic, the linkage function established on both Turing’ strong artificial intelligence using numbers and Searle’s weak artificial intelligence using characters, the ontological knowledge established on both Saussure’s general linguistics and Chomsky’s formal linguistics. The result is that the basic law can be proved by digital and textual twin matrices. Its significance lies in that the global positioning system should be regarded as a special case of the generalized bilingual system. Full article