The Dynamic Mathematics Software for Teaching Variance

Abstract. In traditional teaching, many teachers often ignore the process of students' knowledge exploration, devote themselves to instilling knowledge to students in a short time, and then adopt the sea of questions tactics. Such teaching is only limited to the stage of telling students what "variance" is and how to use it, and ignores the birth process and meaning of "variance". The purpose of this research is to see the differences in classes that use dynamic mathematics software and traditional classes. The method in this research is descriptive qualitative. The sample in this study was seventh-grade students at one of the junior high schools in China. The content of this research design is the use of dynamic mathematics technology, focusing on the understanding of the concept of efficiency-improving variance, and the form innovation of the course design is design and record before and after optimization-evaluation and analysis before and after optimization. This article mainly takes the concept of teaching variance topic as an example to try to explore the teaching design of optimizing the important and difficult points of mathematics. This study shows that Hawgent dynamic mathematics software makes students get deep learning and makes teaching and learning activities more active. Then the teacher can continue to use Hawgent dynamic mathematics software to make students more active.   Abstrak. Dalam pengajaran tradisional, banyak guru sering mengabaikan proses eksplorasi pengetahuan siswa, fokus menanamkan pengetahuan kepada siswa dalam waktu singkat, dan kemudian memberi banyak soal untuk diselesaikan. Pengajaran semacam itu hanya sebatas tahap memberi tahu siswa apa itu "varians" dan bagaimana menggunakannya, serta mengabaikan proses penemuan konsep dan makna "varians". Tujuan dari penelitian ini adalah untuk melihat perbedaan kelas yang menggunakan dynamic mathematics software dan kelas tradisional. Metode dalam penelitian ini adalah deskriptif kualitatif. Sampel dalam penelitian ini adalah siswa kelas tujuh di salah satu sekolah menengah pertama di China. Desain penelitian ini adalah pemanfaatan teknologi dynamic mathematics software, menitikberatkan pada pemahaman konsep varians, dan inovasi bentuk rancangan penelitian rekam sebelum dan sesudah optimalisasi-evaluasi dan analisis sebelum dan sesudah optimasi. Artikel ini terutama mengambil konsep pengajaran pada bahasan varians sebagai contoh untuk mencoba mengeksplorasi desain pengajaran dalam mengoptimalkan poin-poin matematika yang penting dan sulit. Penelitian ini menunjukkan bahwa dynamic mathematics software Hawgent membuat siswa mendapatkan pembelajaran yang mendalam dan membuat kegiatan belajar mengajar menjadi lebih aktif. Oleh karena itu, guru dapat menggunakan dynamic mathematics software Hawgent untuk membuat siswa lebih aktif

TNFRSF17 (tumor necrosis factor receptor superfamily, member 17)

B Cell Maturation Antigen (BCMA) is a transmembrane signaling protein preferentially expressed on plasma cells. Its ligands include B-Cell Activating Factor (BAFF) and A Proliferation Inducing Ligand (APRIL). BCMA is involved in JNK and NF-kB activation pathways that induce B-cell development and autoimmune responses. BCMA has been implicated in autoimmune disorders as well as B-lymphocyte malignancies

In situ Observation of Sodium Dendrite Growth and Concurrent Mechanical Property Measurements Using an Environmental Transmission Electron Microscopy–Atomic Force Microscopy (ETEM-AFM) Platform

Akin to Li, Na deposits in a dendritic form to cause a short circuit in Na metal batteries. However, the growth mechanisms and related mechanical properties of Na dendrites remain largely unknown. Here we report real-time characterizations of Na dendrite growth with concurrent mechanical property measurements using an environmental transmission electron microscopy–atomic force microscopy (ETEM-AFM) platform. In situ electrochemical plating produces Na deposits stabilized with a thin Na2CO3 surface layer (referred to as Na dendrites). These Na dendrites have characteristic dimensions of a few hundred nanometers and exhibit different morphologies, including nanorods, polyhedral nanocrystals, and nanospheres. In situ mechanical measurements show that the compressive and tensile strengths of Na dendrites with a Na2CO3 surface layer vary from 36 to >203 MPa, which are much larger than those of bulk Na. In situ growth of Na dendrites under the combined overpotential and mechanical confinement can generate high stress in these Na deposits. These results provide new baseline data on the electrochemical and mechanical behavior of Na dendrites, which have implications for the development of Na metal batteries toward practical energy-storage applications

Cloning and Expression of the Neuropeptide F and Neuropeptide F Receptor Genes and Their Regulation of Food Intake in the Chinese White Pine Beetle Dendroctonus armandi

Neuropeptide F (NPF) is an important signaling molecule that acts as a neuromodulator to regulate a diversity of physiological and behavioral processes from vertebrates to invertebrates by interaction with NPF receptors, which are G protein-coupled receptors (GPCR). However, nothing is known about NPF in Chinese white pine beetle, Dendroctonus armandi, a destructive pest of natural and coniferous forests in the middle Qinling Mountains of China. We have cloned and characterized cDNAs encoding one NPF precursor and two NPF receptors in D. armandi and made bioinformatics predictions according to the deduced amino acid sequences. They were highly similar to that of Dendroctonus ponderosa. The transcription levels of these genes were different between larvae and adults of sexes, and there were significant differences among the different developmental stages and tissues and between beetles under starvation and following re-feeding states. Additionally, downregulation of NPF and NPFR by injecting dsRNA into beetles reduced their food intake, caused increases of mortality and decreases of body weight, and also resulted in a decrease of glycogen and free fatty acid and an increase of trehalose. These results indicate that the NPF signaling pathway plays a significant positive role in the regulation of food intake and provides a potential target for the sustainable management of this pest

DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

Recent advances in natural language processing, primarily propelled by Large Language Models (LLMs), have showcased their remarkable capabilities grounded in in-context learning. A promising avenue for guiding LLMs in intricate reasoning tasks involves the utilization of intermediate reasoning steps within the Chain-of-Thought (CoT) paradigm. Nevertheless, the central challenge lies in the effective selection of exemplars for facilitating in-context learning. In this study, we introduce a framework that leverages Dual Queries and Low-rank approximation Re-ranking (DQ-LoRe) to automatically select exemplars for in-context learning. Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge. Moreover, for the second query, LoRe employs dimensionality reduction techniques to refine exemplar selection, ensuring close alignment with the input question's knowledge. Through extensive experiments, we demonstrate that DQ-LoRe significantly outperforms prior state-of-the-art methods in the automatic selection of exemplars for GPT-4, enhancing performance from 92.5% to 94.2%. Our comprehensive analysis further reveals that DQ-LoRe consistently outperforms retrieval-based approaches in terms of both performance and adaptability, especially in scenarios characterized by distribution shifts. DQ-LoRe pushes the boundary of in-context learning and opens up new avenues for addressing complex reasoning challenges. Our code is released at https://github.com/AI4fun/DQ-LoRe}{https://github.com/AI4fun/DQ-LoRe.Comment: Accepted in ICLR 202