Creating a well-structured lesson plan is essential for improving classroom efficiency, yet it is often a labor-intensive process. Recently, many studies have leveraged large language models (LLMs) to generate lesson plans automatically. However, existing methods heavily rely on LLMs that are pre-trained on large-scale universal corpora, which often lack critical educational theory and textbook-specific information. This can lead to inconsistencies and misalignments with textbook content. To address these challenges, we propose CE-LessonPlan, a novel compress-expand framework to generate lesson plans by effectively combining external lesson plan references and textbook information. The framework consists of two key components: a compressor, which synthesizes multiple retrieved references into a cohesive document, and an expander, which integrates textbook-specific information with the parametric knowledge of LLMs to produce another enriched lesson plan. The outputs of the compressor and expander are then seamlessly integrated to create a comprehensive golden context, further enhancing the lesson plan generation process with LLMs. We conduct extensive experiments to demonstrate that CE-LessonPlan outperforms existing methods for generating lesson plans.

In smart grids, smart meters are the metering devices that are responsible for collecting users' information about their electricity consumption. Smart meters provide an effective means for control center (CC) to monitor the power supply and consumption in a timely and accurate fashion. However, the collection of users' metering reports through an untrusted public network raises a serious privacy concern of users. Even though a lot of privacy-preserving approaches that have been proposed in the literature for secure data transmission, they are not directly applicable to the scenario of collecting metering reports in smart grids due to the limited computational resources of smart meters and the networking environment of smart grids. In this paper, we propose a lightweight privacy-preserving scheme for metering data collection in smart grids. There are three advantages of our proposed scheme: 1) Lightweight. There is no need for users' metering devices to carry out intensive encryption and decryption operations. 2) No need of a trusted third party for key generation and key management. 3) Scalability. Our scheme can cater for large scale of users connected via public networks. Experiment results show that the proposed scheme is efficiently and lightweight and is suitable for privacy-preserving data collection in smart grids.

An asymmetric zero‐correlation zone (A‐ZCZ) sequence pair set is a ZCZ sequence pair set consisting of multiple ZCZ sequence pair subsets. Two arbitrary sequence pairs which belong to different subsets should have a large zero cross‐correlation zone (ZCCZ) in the A‐ZCZ sequence pair set. Additionally, each subset is a typical ZCZ sequence pair set. The proposed A‐ZCZ sequence pair sets can be generated based on interleaved technique from perfect sequence or perfect sequence pair of length P = Nmk with N ≥ 1, m ≥ 1, k > 1. The new A‐ZCZ sequence pair sets are applied to quasi‐synchronous code‐division multiple‐access (QS‐CDMA) systems and it can hypothetically achieve larger ZCCZ than typical ZCZ sequence pair sets, as well as eliminating both multiple access and multipath interference in the QS‐CDMA system.