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The purpose of this article was to develop an elementary school robot STEAM program and explore the possibility of field applications. To this end, the authors extracted the contents related to school achievement standards for 5th and 6th grade curricula around the topic of robot soccer, incorporating a relevant curriculum based on the extracted information. The program was composed of a sequence of situations, creative design, and emotional experience. Each step was prepared in the order of understanding the robot, making the soccer robot, and the robot soccer game. The program developed in this article is significant in that it provided an opportunity to enhance the relationship between content knowledge and real life, collaborative and social learning, metacognition education in new learning processes, new technological environments, and encourage interest in the fusion subject.

Voltage-gated sodium (Nav) channels initiate action potentials in brain neurons and are primary therapeutic targets for anti-epileptic drugs controlling neuronal hyperexcitability in epilepsy. The molecular mechanisms underlying abnormal Nav channel expression, localization, and function during development of epilepsy are poorly understood but can potentially result from altered posttranslational modifications (PTMs). For example, phosphorylation regulates Nav channel gating, and has been proposed to contribute to acquired insensitivity to anti-epileptic drugs exhibited by Nav channels in epileptic neurons. However, whether changes in specific brain Nav channel PTMs occur acutely in response to seizures has not been established. Here, we show changes in PTMs of the major brain Nav channel, Nav1.2, after acute kainate-induced seizures. Mass spectrometry-based proteomic analyses of Nav1.2 purified from the brains of control and seizure animals revealed a significant down-regulation of phosphorylation at nine sites, primarily located in the interdomain I-II linker, the region of Nav1.2 crucial for phosphorylation-dependent regulation of activity. Interestingly, Nav1.2 in the seizure samples contained methylated arginine (MeArg) at three sites. These MeArgs were adjacent to down-regulated sites of phosphorylation, and Nav1.2 methylation increased after seizure. Phosphorylation and MeArg were not found together on the same tryptic peptide, suggesting reciprocal regulation of these two PTMs. Coexpression of Nav1.2 with the primary brain arginine methyltransferase PRMT8 led to a surprising 3-fold increase in Nav1.2 current. Reciprocal regulation of phosphorylation and MeArg of Nav1.2 may underlie changes in neuronal Nav channel function in response to seizures and also contribute to physiological modulation of neuronal excitability.

Voltage-gated ion channels underlie electrical activity of neurons and are dynamically regulated by diverse cell signaling pathways that alter their phosphorylation state. Recent global mass spectrometric-based analyses of the mouse brain phosphoproteome have yielded a treasure trove of new data as to the extent and nature of phosphorylation of numerous ion channel principal or α subunits in mammalian brain. Here we compile and review data on 347 phosphorylation sites (261 unique) on 42 different voltage-gated ion channel α subunits that were identified in these recent studies. Researchers in the ion channel field can now begin to explore the role of these novel in vivo phosphorylation sites in the dynamic regulation of the localization, activity, and expression of brain ion channels through multisite phosphorylation of their principal subunits.

In today’s world, the development of social network services (SNS) like Facebook and Instagram has enabled consumers to acquire information about products through various channels. The acquisition of diverse information has led to a diversification in consumer preferences and requirements. As consumer preferences diversify and online channels expand, there is an increasing need for companies to provide personalized marketing. Among the means of personalized marketing, personalized marketing messages are a key tool that can enhance customer engagement. However, a limitation of personalized marketing message services is the cost issue associated with manually writing individual marketing messages for personalization. To solve this problem, when developing automated technology for personalized marketing messages, there were concerns about the complexity of model development and the quality of messages generated automatically. In this study, we propose the Persuasive Message Intelligence (PMI) service, which utilizes the recently prominent Large Language Model for automated individual personalized marketing messages. PMI generates marketing messages through prompt engineering based on the theory of persuasion in marketing and prior research on AI-generated messages, and validates the elements of prompts through surveys. The trial and error of researchers presented in this study, along with the know-how and rules of prompt engineering, will serve as guidelines for those who wish to develop services through prompts in the future.

In this study, we examine the potential of language models for natural language processing (NLP)-based recommendations, with a distinct focus on predicting users’ next product purchases based on their prior purchasing patterns. Our model specifically harnesses tokenized rather than complete product names for learning. This granularity allows for a refined understanding of the interrelations among different products. For instance, items like ‘Chocolate Milk’ and ‘Coffee Milk’ find linkage through the shared token ‘Milk.’ Additionally, we explored the impact of various n-grams (unigrams, bigrams, and trigrams) in tokenization to further refine our understanding of product relationships and recommendation efficacy. This nuanced method paves the way for generating product names that might not exist in current retail settings, exemplified by concoctions like ‘Coffee Chocolate Milk.’ Such potential offerings can provide retailers with fresh product brainstorming opportunities. Furthermore, scrutiny of the frequency of these generated product name tokens can reveal prospective trends in purchasing keywords. This facilitates enterprises in creative brainstorming of novel products and swiftly responding to the dynamic demands and trends of consumers. The datasets used in this study come from UK e-Commerce and Instacart Data, comprising 71,205 and 166,440 rows, respectively. This investigation juxtaposes the NLP-based recommendation model, which employs tokenization, with its non-tokenized counterpart, leveraging Hit-Rate and mean reciprocal rank (MRR) as evaluative benchmarks. The outcomes distinctly favor the tokenized NLP-based recommendation model across all evaluated metrics.