reinforcement learning

RAVEN Abstraction Engine: Proof-Carrying Cognitive Order, Autonomous Abstraction-Level Genesis, and Certified Effective Depth

RAVEN Abstraction Engine v4.0.0 develops a formal theory of autonomous cognitive-order genesis: the process by which an intelligent system can determine that its current representational and computational hierarchy is insufficient, construct a genuinely new higher-order level, ground that level in prior executable cogn...

Graph-guided MADQN based handover strategy for LEO satellite networks

In low earth orbit satellite networks, the high orbital velocity of satellites causes frequent communication link handovers, which adversely affects user service quality and the efficiency of network resource management. To maintain communication continuity, an effective handover strategy is required. Traditional hando...

Three-dimensional woven fabric composites under ballistic impact: A systematic review of multiscale modeling strategies from fiber to structural scale

Three-dimensional woven fabric composites (3DWFCs) provide superior through-thickness reinforcement and delamination resistance under ballistic impact, yet no multiscale modeling framework has achieved broad, independently validated predictive capability across architectures and impact regimes. This systematic review...

The Role of AI in Digital Circuit Design

Artificial Intelligence (AI) is increasingly being applied to digital circuit design to assist with circuit analysis, optimization, and automation. This study examines the role of AI in digital circuit design, focusing on Graph Neural Networks (GNNs), Reinforcement Learning (RL), and Generative AI. These approaches can...

化事与信任的自我强化——对"大事化小"机制的批判性分析(Defusing Disputes and the Self-Reinforcement of Trust — A Critical Analysis of the "Making Big Things Small" Mechanism)

摘要:"大事化小、小事化了"常被视为一种低成本、低冲突的纠纷解决方式,甚至被赋予"和谐""务实""顾全大局"的正面评价。本文区分"自愿调解"与"压制性化事",批判后者对社会信任的腐蚀机制。借助《列子·杨朱》中"一毛不拔"的典故,本文提出:化事的腐蚀性不在于让渡利益的大小,也不仅在于是否突破某种规则底线,而在于它确立了一种默认预期——权利、真相...

Frequency-enhanced dual-graph modeling with reinforcement learning for wind power forecasting

Exploring the potential of large language models for public participation in urban redevelopment planning

Public participation is essential to human-centered urban planning, yet existing approaches often struggle to translate dispersed public opinions into clear and discussable preference expressions. This limits substantive deliberation between public preferences and expert knowledge, making it difficult to effectively in...

DA-MORL: Structural-Analysis-Coupled Reinforcement Learning for Building Demolition Sequencing (code and data)

Code, trained models, and evaluation data accompanying the manuscript Building demolition sequence optimization using structural-analysis-coupled deep reinforcement learning and a CAD-to-graph representation (submitted to the Journal of Computational Design and Engineering; an earlier version was submitted under the ti...

Penerapan Praktikum Simulasi Gunung Meletus untuk Meningkatkan Keterlibatan dan Pemahaman IPA Siswa Kelas V Sekolah Dasar

Science learning in elementary schools requires concrete learning experiences to help students relate scientific concepts to observable phenomena. This study aimed to describe the implementation of a volcanic eruption simulation practicum and analyze students’ engagement and conceptual understanding of volcanic eruptio...

Swarm and Multi-Agent Robotics: Graph Laplacian Consensus, Formation Control, and Decentralized Learning

This educational module presents a comprehensive academic introduction to Swarm and Multi-Agent Robotics within the Prep4Uni Robotics and AI curriculum. Core Theoretical and Engineering Foundations: Network Representation & Algebraic Graph Theory: Modeling multi-agent systems via time-varying graphs G = (V, E), topolog...

A Reinforcement Learning-Based Framework for Personalized Career Recommendations Among College Students

HRA-TS provides an effective and practical solution for personalized career recommendation in dynamic and privacy-sensitive environments and employs a hybrid long short-term memory–graph attention network encoder to jointly capture users’ evolving behavioral evolution and stable personal attributes.

Sleep as an Offline Consolidation and Recalibration Operator for RS4 Prefiltering

Abstract Sleep is strongly implicated in biological memory consolidation, replay, systems-level redistribution of memory, and synaptic renormalization. The present paper does not claim that the RS4 architecture reproduces those biological mechanisms. Instead, it asks a narrower computational question: if sleep is repre...