为了解决理论计算机科学教育中形式证明和渐近推理的教学难题，本文提出了AlgoRAG系统，该系统结合大型语言模型与专业知识库，通过优化检索和生成过程，有效提高了教学质量和响应效率。
本文提出了一种通过角度扇区划分确定K-Means初始中心点的方法，解决了初始化选择对算法收敛速度和聚类精度的影响问题。
研究非拉丁手写字符识别，通过引入GraphemeNet架构，利用结构先验效率原则，明确编码笔画几何规律，提高准确性和减少参数。
This study addresses high system latency and unclear mechanism synergies under memory pressure by conducting ablation experiments based on Linux PSI, zram, and cgroup v2 to evaluate bounded reclamation strategies. The findings reveal that memory optimization benefits are not universally applicable but are instead highly contingent upon foreground workload characteristics and memory access patterns. Specifically, experiments demonstrate that bounded reclamation reduces p99 tail latency for compute-intensive tasks by 6%, yet yields no significant improvement for I/O-bound workloads such as SQLite. By elucidating the differential efficacy boundaries of memory management mechanisms across heterogeneous workloads, this work provides empirical evidence to support fine-grained latency governance in resource-constrained environments.
This study addresses the lack of personalized agricultural guidance in rural areas by proposing an end-to-end precision agriculture support platform. The system pioneers the application of spatio-temporal graph convolutional networks (STGCNs) for 30-day regional weather forecasting, achieving a mean squared error of 0.011—outperforming baseline models—and integrates soil properties to generate localized crop recommendations. Furthermore, it incorporates domain knowledge into a retrieval-augmented generation (RAG)-based agricultural question-answering module. Deployed via a mobile application, the platform delivers real-time, interactive farming advice across 1,359 locations in Nepal. User feedback confirms its effectiveness, demonstrating significant improvements in farmers’ decision-making capabilities and climate resilience.
为了解决理论计算机科学教育中形式证明和渐近推理的教学难题，本文提出了AlgoRAG系统，该系统结合大型语言模型与专业知识库，通过优化检索和生成过程，有效提高了教学质量和响应效率。
本文提出了一种通过角度扇区划分确定K-Means初始中心点的方法，解决了初始化选择对算法收敛速度和聚类精度的影响问题。
研究非拉丁手写字符识别，通过引入GraphemeNet架构，利用结构先验效率原则，明确编码笔画几何规律，提高准确性和减少参数。
This study addresses high system latency and unclear mechanism synergies under memory pressure by conducting ablation experiments based on Linux PSI, zram, and cgroup v2 to evaluate bounded reclamation strategies. The findings reveal that memory optimization benefits are not universally applicable but are instead highly contingent upon foreground workload characteristics and memory access patterns. Specifically, experiments demonstrate that bounded reclamation reduces p99 tail latency for compute-intensive tasks by 6%, yet yields no significant improvement for I/O-bound workloads such as SQLite. By elucidating the differential efficacy boundaries of memory management mechanisms across heterogeneous workloads, this work provides empirical evidence to support fine-grained latency governance in resource-constrained environments.
This study addresses the lack of personalized agricultural guidance in rural areas by proposing an end-to-end precision agriculture support platform. The system pioneers the application of spatio-temporal graph convolutional networks (STGCNs) for 30-day regional weather forecasting, achieving a mean squared error of 0.011—outperforming baseline models—and integrates soil properties to generate localized crop recommendations. Furthermore, it incorporates domain knowledge into a retrieval-augmented generation (RAG)-based agricultural question-answering module. Deployed via a mobile application, the platform delivers real-time, interactive farming advice across 1,359 locations in Nepal. User feedback confirms its effectiveness, demonstrating significant improvements in farmers’ decision-making capabilities and climate resilience.