Despite extensive special education investments, their causal impact on elementary mathematics remains debated due to confounding factors in observational data.​​ This study leverages causal machine learning models, including Bayesian Additive Regression Trees (BART) and Causal Forests, to estimate heterogeneous effects of special education on elementary mathematics achievement. Using longitudinal data from 7,362 U.S. students (ECLS-K:2011), we implement a four-stage pipeline: (1) ​​LASSO-PLS preprocessing​​ for covariate selection and dimension reduction; (2) ​​Propensity Score Matching (PSM)​​ to address selection bias; (3) ​​BART​​ for Bayesian treatment effect estimation; and (4) ​​Causal Forests​​ for subgroup analysis. Results show no significant average treatment effect (ATE = -0.69, p=0.707) after matching, but reveal critical heterogeneity: students with mid-range kindergarten math ability (MIRT 50-70) gain 6-8 points, while public schools’ buffer negative effects for low-ability learners. Family background factors show no moderation effect. These findings demonstrate that ​​special education's efficacy depends fundamentally on academic readiness​​, supporting precision resource allocation in educational policy.

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With the increasing public awareness of health and safety, there is a growing demand for intelligent systems capable of providing precise answers to medical and health-related questions. However, Large Language Models (LLMs) are prone to "hallucination" phenomena in medical domain responses, generating seemingly plausible but actually inaccurate content, which could lead to serious consequences in medical scenarios. To address this challenge, this study proposes a knowledge graph-based retrieval-augmented question answering framework, with a specific focus on the dermatology domain. First, this study constructed a medical knowledge graph ontology for dermatology, encompassing key dimensions such as disease definitions, symptoms, diagnoses, and treatments. Second, a method was designed utilizing the large language model GLM-4 (General Language Model-4) for automated knowledge extraction from medical guidelines to construct a domain-specific knowledge graph. Third, this study introduced fuzzy entity recognition and knowledge graph enhancement mechanisms, which can identify key entities in questions and retrieve relevant knowledge from the graph to augment the original queries. Furthermore, experiments demonstrate that our approach effectively reduces hallucinations in LLM-generated medical responses.On the test set, a comparison with the baseline method reveals that our proposed framework achieves superior performance, with the average BLEU score increasing from 0.0102 to 0.0157, and the average BERT_SCORE_P, R, and F1 scores improving from 0.5037, 0.7120, and 0.5897 to 0.5273, 0.7346, and 0.6135, respectively.These results indicate significant accuracy improvements, particularly in diagnostic recommendations and treatment plans.This methodology provides a new paradigm for building safer and more reliable medical intelligent systems and can be extended to other specialized medical domains.

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With the growing influence of artificial intelligence, the Internet of Things, and cloud computing, data analysis has moved from a supportive tool to playing a central role in business innovation. Python, thanks to its open-source framework and wide range of libraries, has become a favorite tool among data analysts. This paper focuses on how Python can be applied to real-world financial data, taking Xiaomi Corporation’s stock history as an example. Using libraries such as Pandas, Matplotlib, and mplfinance, we walk through the steps of data cleaning, calculating technical indicators like moving averages and the Relative Strength Index, and producing clear visualizations to observe market trends. Beyond the technical implementation, this study shows how Python can help users uncover meaningful patterns in stock data, improving the basis for investment decisions. The case study illustrates that even complex datasets can be handled smoothly with the right tools, and Python’s simplicity, flexibility, and community support make it especially suited for this kind of task.

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With the global population aging and the increasing demand for health, fueled by the development and application of the Internet of Things, smart healthcare has become a trend in the medical industry, further promoting medical intelligence and data integration. This paper focuses on the application of embedded systems in the smart healthcare Internet of Things (IoT), exploring the current state, key technologies, challenges, and development directions through literature review and case analysis. Research has demonstrated that embedded systems significantly enhance healthcare efficiency and quality, optimise resource allocation, and foster healthcare innovation. Additionally, this paper suggests that in the future of smart healthcare, there is a need to strengthen technology research and development and explore new application models to accelerate smart healthcare growth. It also advocates promoting the unification of standards in the medical system nationwide. Furthermore, the paper emphasizes deepening the integration of AI with medical systems to enhance the security of patient medical data.

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