Data assetization is essential for enabling market-oriented allocation of data elements and supporting digital-economy development, yet policy design quality varies across regions. This paper proposes a reproducible policy-text evaluation pipeline that integrates ROSTCM6-based text mining with the Policy Modeling Consistency (PMC) index model. A corpus of 178 provincial-level data-asset policies issued by 31 provincial-level administrative regions in China during 2019-2025 is collected from PKULAW via structured retrieval and screening. High-frequency word statistics and semantic network analysis are used to support indicator construction, resulting in a 9primary/50-secondary evaluation system. Policy quality is quantified through a multi-input-output table, PMC index scoring, grade classification, and PMC surface/radar visualization. Results show an overall mean PMC score of 5.98 ("good" level): policies perform strongly in operability and goal/assessment clarity, while weaknesses concentrate in effectiveness hierarchy, medium- to long-term planning, and policy area coverage. Excellent-grade policies exhibit stronger systematization and broader safeguards, whereas pass-grade policies show narrow coverage and weaker guarantee mechanisms. The proposed framework provides an engineering-oriented method for policy benchmarking and supports targeted optimization of data-asset policy supply.
One of the important input factors used in FEM analysis, or analytical calculations, is the material model data. Over the years, various models of the material flow curves have been proposed. Those models, ranging from ideal plastic material data modelling (simple, but useful for hot plastic forming) to elastic-ideal plastic models, to elastic-strain hardening plastic models, and to definition of plethora of data points for piecewise-linear methods, are commonly used in research papers to describe material behaviour till today. The mathematical models made by approximation of measured results from the tensile tests, have been proven to be the most elegant ones. In this paper, a general data obtained from series of uniaxial tensile tests of HC180Y interstitial free steel metal sheets is given. Coefficients of determination R2 of the mathematical models are also given. This research has been carried as preparation work for subsequent research related to modelling of joining process by mechanical clinching of dissimilar sheet metal plates.
This study examines academic planning in Chinese international education from two complementary angles. At the disciplinary level, an LDA topic model and a research-influence index (zsi) are used to identify research topics across 20 discipline points and to classify their relative strengths, providing references for disciplinary positioning and layout. At the individual level, a dynamic-coupling model is built to explain how students' cross-cultural psychological adaptation and academic planning interact. Objectives: (1) to map consensus and distinctive research directions among discipline points, and (2) to reveal the bidirectional, stage-wise coupling between adaptation and planning. Main conclusions: psychological adaptation directly shapes goal setting and execution and indirectly affects performance through self-efficacy and motivation, while academic progress feeds back to adaptation, mediated chiefly by language ability, cultural intelligence, and social support. On this basis, concrete measures are proposed for students, institutions, and policymakers.
To achieve personalized recommendation for vocational education courses, this study designs an adaptive learning recommendation algorithm. It aims to meet the learning needs of learners at different stages. The study starts from the basic concept of big data-based adaptive learning. It reviews relevant models and work processes. Then it elaborates on the implementation process of this learning model. It also compares and analyzes its differences from traditional teaching. Applying big data adaptive learning helps students better master knowledge points. It improves academic performance and enhances learning enthusiasm. The system consists of three core components. They are a learning analysis module, an intelligent tutoring module, and a personalized recommendation engine. On this basis, a personalized teaching framework is constructed. The framework integrates learner profiling, precise content delivery, personalized learning path planning, and intelligent assessment with feedback.
static firewalls struggle to manage complex, dynamically evolving network traffic, often leading to rule redundancy, conflicts, and latency degradation. To address these challenges, this study proposes an intelligent firewall rule optimization framework based on Proximal Policy Optimization (PPO). The framework models the rule scheduling task as a multi-objective reinforcement learning problem, integrating system latency, throughput, and false positive rate into a composite reward function. A custom simulation environment compatible with OpenAI Gym is developed to represent firewall states and actions, while visualization tools such as reward curves, clip fraction, and action heatmaps enhance interpretability. Experimental evaluations demonstrate that the proposed PPO-based method outperforms baseline algorithms (Q-learning, DQN, A2C) in convergence speed, false positive reduction, and throughput improvement, achieving up to 60% faster stabilization and a 15% lower error rate. The approach offers a scalable and adaptive framework for real-time firewall policy management, contributing to the development of intelligent, self-optimizing network defense systems.
