This study develops a novel deep learning framework for bank distress prediction, designed to overcome the limitations of static network analysis and to enhance model interpretability. We propose a Spatio-Temporal Attention Network that uniquely captures the time-varying nature of systemic risk. Methodologically, it introduces two key innovations: (1) a dynamic interbank network whose connection weights are adjusted by the volatility of the Shanghai Interbank Offered Rate (SHIBOR), reflecting real-time market liquidity changes; and (2) a dual spatio-temporal attention mechanism that identifies critical time steps and pivotal contagion pathways leading to a distress event. Empirical results demonstrate that the model significantly outperforms traditional benchmarks across key metrics including accuracy and F1-score. Most critically, the architecture proves exceptionally effective at reducing Type II errors, substantially minimizing the failure to identify at-risk banks. The model also offers high interpretability, with attention weights visualizing intuitive risk evolution patterns. We conclude that incorporating dynamic, liquidity-adjusted networks is crucial for superior predictive performance in systemic risk modeling.

In the realm of financial stability, accurate credit default discrimination models are crucial for policy-making and risk management. This paper introduces a robust model that enhances credit default discrimination through a sophisticated integration of a filter-wrapper feature selection strategy, instance selection, and an updated version of majority voting. We present a novel approach that combines individual and ensemble classifiers, rigorously tested on datasets from Chinese listed companies and the German credit market. The results highlight significant improvements over traditional models, offering policymakers and financial institutions a more reliable tool for assessing credit risks. The paper not only demonstrates the effectiveness of our model through extensive comparisons but also discusses its implications for regulatory practices and the potential for adoption in broader financial applications.

Traditional decision tree models, which rely exclusively on numerical variables, often encounter difficulties in handling high-dimensional data and fail to effectively incorporate textual information. To address these limitations, we propose the Integrated Multivariate Segmentation Tree (IMST), a comprehensive framework designed to enhance credit evaluation for small and medium-sized enterprises (SMEs) by integrating financial data with textual sources. The methodology comprises three core stages: (1) transforming textual data into numerical matrices through matrix factorization; (2) selecting salient financial features using Lasso regression; and (3) constructing a multivariate segmentation tree based on the Gini index or Entropy, with weakest-link pruning applied to regulate model complexity. Experimental results derived from a dataset of 1,428 Chinese SMEs demonstrate that IMST achieves an accuracy of 88.9%, surpassing baseline decision trees (87.4%) as well as conventional models such as logistic regression and support vector machines (SVM). Furthermore, the proposed model exhibits superior interpretability and computational efficiency, featuring a more streamlined architecture and enhanced risk detection capabilities.

Assessment in vocational education institutions is frequently organized around performance metrics—graduation rates, employment outcomes, and satisfaction scores—gathered too tardily to avert institutional dysfunction. In increasingly unstable policy situations, these models have become precarious: they quantify collapse more frequently than they avert it. This paper presents fuse logic as an innovative mechanism for risk-responsive governance in technical and vocational education and training (TVET). Utilizing systems control theory and the analogy of circuit breakers, fuse logic is a threshold-sensitive, dynamically activated assessment paradigm designed to disconnect institutional activities prior to complete failure. The research formulates a four-stage model—situational sensing, threshold definition, fuse activation, and adaptive reconfiguration—and implements it in a simulated scenario reflecting Chinese TVET trends. When critical metrics surpass risk thresholds (e.g., dropout rate, employment mismatch), fuse logic triggers systematic program shutdowns, stakeholder consultations, and conditional reintegration procedures.This study's contribution is in redefining evaluation from measurement to protection. It advocates a governance framework that permits temporary disconnection to maintain system integrity. Fuse logic enhances conventional quality assurance frameworks by providing an integrated, failure-tolerant layer of organizational resilience. The report concludes with a discussion on transferability, ethical considerations, and prospective avenues for implementation across varied educational systems.

本研究聚焦中国契约型公募 REITs 底层资产所有权保留程度对收益分配公平性与效率的影响机制，基于2021年至2025年6月中国68只公募REITs面板数据及美国15只基础设施 REITs 案例，采用文献分析案例深描实证建模与跨市场对比方法，探讨资产所有权与经营权在不同分离程度下对项目收益激励设计及风险的影响。 研究构建了“所有权保留程度 - 治理机制 - 收益分配”分析框架，得出以下发现：所有权保留程度低的REITs 收益效率显著高于保留程度高的类型，所有权保留比例的降低对营运现金流FFO分配率具有显著正向影响。中国契约型架构下原始权益人通过隐性所有权干预运营，导致中小投资者保护不足，而美国公司型 REITs 中独立董事占比的制衡机制具有借鉴价值。政府参与型 REITs 存在短期收益波动与长期战略增值的平衡难题，产权类 REITs 中所有权保留对收益分配的抑制效应弱于特许经营类。 研究提出创新建议推动基础设施 REITs 所有权保留分层调整机制，完善商业地产REITs 试点的“所有权 - 运营权”分离框架，构建公司型 REITs 与契约型并行制度等。 本研究揭示了契约型架构下所有权保留对收益的作用机理，为中国 REITs 治理优化存量资产盘活及监管设计提供理论依据与实践参考。

进入新时代以来，中国老龄化程度不断加深。为积极应对人口老龄化，近年来国家先后发布《智慧健康养老产业发展行动计划（2021-2025年）》等政策文件，要求不断丰富智慧健康服务，拓展智慧养老场景。由此可见，智慧养老作为新兴业态成为有效满足老年人多元化需求，并适应老龄化发展趋势的有效养老模式。2018年，被确定为全国第三批居家和社区养老服务改革试点地区后，宝鸡市把加快发展养老服务业作为保障和改善民生事业发展的重要内容，逐渐形成了以居家为基础、以社区为依托、以机构为补充、医养结合、智慧融入的具有宝鸡特色的养老服务体系。基于此，本文以宝鸡市为例，在行动者网络理论理论视角下，梳理出宝鸡市智慧养老实践中的重要要素、工作重点和发展动力，归纳总结出宝鸡市智慧养老模式的重要人类行动者和非人类行动者，按照行动者主体构建、问题呈现与强制通行点利益赋予、征召分析和动员分析的思路，构建出金台区智慧养老模式的整体行动网络，同时，研究发现，为构建完善智慧养老模式行动者网络，需要从加快服务设施智能化、提供多元精细化服务、完善兜底性政策保障以及构建智慧养老共同体等方面出发，以助力实现智慧养老模式良性发展。

We propose a novel dual-screening classification framework for fund selection designed to align statistical objectives with investor goals. Testing on the Chinese mutual funds market, a Gradient Boosting model implementing our framework generates a statistically and economically significant 14.65% annual risk-adjusted alpha, substantially outperforming identical models trained under a standard regression framework. Feature importance analysis confirms that fund-level momentum and flows are the most significant predictors of performance in this market. Our findings provide a robust and practical framework for active management, demonstrating that modelling both upside potential and downside risk is critical for superior performance.

在保险决策的全过程中，语言扮演着不可或缺的角色。从保险产品的条款说明、销售人员的口头推介，到广告宣传、客服沟通，语言不仅是信息传递的载体，更是塑造消费者认知、触发情感反应、引导决策行为的关键媒介。然而，传统保险研究对语言因素的关注明显不足，往往将语言视为中性的信息通道，忽视了语言本身的特性及其对心理偏差的调节作用。本章将从语言学和口语传播学的视角出发，系统探讨保险沟通中的语言机制及其对保险决策的影响，揭示语言如何放大或缓解消费者的心理偏差，并为后续行为干预提供新的思路。

传统保险经济学基于理性人假设，认为保险消费者能够做出符合自身利益最大化的理性决策。然而，大量现实观察与实证研究表明，保险决策中存在诸多系统性偏差，这些偏差无法用传统理论解释。本研究从行为保险学视角出发，系统探讨保险决策中的主要心理偏差及其形成机制，并在此基础上构建行为干预的理论框架与实施路径。研究发现，保险决策中的心理偏差可分为认知偏差（如可得性启发、代表性启发、锚定效应）、情感偏差（如损失厌恶、模糊厌恶、后悔厌恶）和社会偏差（如从众行为、羊群效应）三类，这些偏差通过影响风险感知、概率判断和效用评估等环节，导致投保不足、过度投保、产品选择错误、续保率低等市场失灵现象。研究进一步提出，行为干预应从供给端（产品设计优化、信息披露改革、默认选项设置）、需求端（保险素养教育、决策辅助工具、反馈机制设计）和监管端（行为洞察应用、消费者保护强化、干预效果评估）三个维度系统推进。本研究为优化保险市场运行、提升消费者福祉、完善保险监管提供了行为科学视角的理论支撑与实践参考。

人口老龄化引发的健康保障需求升级，推动保险行业进入供给侧创新深水区。本文以癌症复发险为研究载体，系统分析老龄化社会下保险产品创新的逻辑起点、实践路径与风险管控机制。通过对主流癌症复发险产品进行深度案例分析，研究发现：当前市场已形成覆盖30种常见恶性肿瘤的产品体系，核保实现数据风控与基于分期、分型、治疗预后的精细化风险分层；癌症复发险通过病种扩容、保障责任模块化与“保险+健康”服务模式创新，有效填补了带病体保障缺口，在基本医保基础上织密了多层次医疗保障网络。然而，产品发展仍面临逆选择风险、医疗数据壁垒、盈利平衡难题以及复发、特药目录等核心条款定义缺乏行业统一规范等核心挑战。本文构建“技术赋能风控+政策协同监管+跨行业生态共建”的三维风险管理体系，结合日本、美国等国际经验的本土化适配，提出推动两核风控动态迭代、建立行业标准、深化医疗机构服务直连等优化路径。研究为养老金融创新与风险治理提供了微观产品层面的实证参考，对完善多层次社会保障体系具有重要实践意义。