This paper presents an implementation of simple and low-cost medication support system for outpatient of cerebral infarction who has to keep medication every day for long term. The system consists of the outpatient and the pharmacist terminals at the dispensing-pharmacy. The outpatient terminal features the displays of compliance status and 3D simulation of cerebral infarction, contact functions to dispensing-pharmacy (e.g. call, text and voice messages). The pharmacist terminal features response functions to contacts from the outpatients, displays of the compliance status and the 3D simulation of cerebral infarction same as the outpatient terminal. The compliance status of the outpatients is automatically transmitted to the pharmacist terminal through e-mail connections each day. We designed different symptoms of the blood flow simulation according to the blood-stuck level which are estimated by weighted moving average of the daily compliance status ("took medicine" or "did not take medicine"). The different symptoms of 3D simulation are also registered in the database on the outpatient and the pharmacist terminals, respectively, and are switched according to the shared compliance status. Conventional medication support system used MySQL database engine on web server for those features and thus needed to introduce web server in dispensing-pharmacy. The proposed system realizes simple and low-cost by making the database of those compliance status data using the database engine on the terminals.

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本研究では，従来データベースや統計解析実行環境であるR の知識などがある程度必要であったトピックモデルによるテキスト分析に関して，そうした知識を持たなくても分析を可能にする分析支援ソフトウエアを開発した．このソフトウエアは主に，１）形態素解析機能，２）トピックモデル分析の対象とする単語の絞り込み機能，３）トピックモデル分析を実行し，その結果や結果の解釈に必要な情報をExcel ファイルに出力する機能の３つの機能からなる．これを利用すれば，分析そのものではなく分析結果の活用に注力できるようになるため，教育分野の専門家による，IR やFD などの領域におけるトピックモデル分析活用の活性化が期待される．

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Traditionally, in computer systems, file I/O has been a big performance bottleneck for I/O intensive applications. The recent advent of non-volatile byte-addressable memory (NVM) technologies such as STT-MRAM and PCM, provides a chance to store persistent data with a high performance close to DRAM's. However, as the location of the persistent storage device gets closer to the CPU, the system software layers overheads for accessing the data such as file system layer including virtual file system layer and device driver are no longer negligible. In this paper, we propose a light-weight user-level persistent storage, called UStore, which is physically allocated on the NVM and is mapped directly into the virtual address space of an application. UStore makes it possible for the application to fast access the persistent data without the system software overheads and extra data copy between the user space and kernel space. We show how UStore is easily applied to existing applications with little elaboration and evaluate its performance enhancement through several benchmark tests.

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生成AIの急速な進化により、システム開発の環境に大きな変化が起きており、コーディングの主役はAIに移りつつあるという印象がある。 特に2025年11月にGoogleが発表した「Antigravity」は、従来の開発支援ツール（Copilot 等）の域を脱し、AIエージェントが開発プロセスの全工程を主導する「エージェントファーストIDE」という新たなカテゴリーを確立した。 このような環境下で、大学で経営学部生向けに情報処理の講義を受け持つことになり、経営学部の学生に求められる情報リテラシーは、構文の暗記や手動でのコーディングから、AIエージェントを指揮・統制する「オーケストレーター」としての能力へと移行させるべきと考えた。経営学部の学生は、将来的にエンジニアとして直接コードを書くことよりも、ITを活用してビジネス上の課題を解決し、プロジェクトを管理する立場に就く可能性が高い。 この背景から、Google Antigravityを用いた実習講座を構築することとした。単なるプログラミング演習ではなく、AIを「有能な部下」として扱い、システムの要件定義、設計、実装、テストのサイクルを高速に回す「プロジェクトマネジメントの模擬体験」として設計しようとしたのであるが、筆者は上流工程が専門で、開発経験は少ないことから、生成AIを活用して設計した。 本報告では、生成AIを駆使して、Windowsネイティブ環境におけるAntigravity の導入、プロジェクトの「憲法」となる設定ファイルの設計、 を用いたデータベース連携におけるModel Context Protocol (MCP) の技術的課題とその解決策、そして実際にCRUD（Create, Read, Update, Delete）機能のうち「C（登録）」および「R（参照）」を実装した。その過程と実施で得られた考察について報告する。

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The data generated at a very high rate by sensors and RFIDs are required to be handled by continuous queries keeping real time response. Because of its purpose, DSMSs are used in several cases of these large scale systems. On the other hand, sensor terminal systems include light RDBMSs generally in many cases. So if light RDBMSs can handle the high rate data directly, it is convenient for several applications. This paper proposes a speed-up method of stream processing by using a light RDBMS without any special modifications. If DSMSs are categorized by performance such as large, medium and small scale, this method aims at a small or medium scale performance. The database performance mainly depends on storage access time, so this proposed method adopts a memory database, a bulk store records technique and parallel processing while taking advantage of multi-core CPU configurations of terminal systems.

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A digital twin for Aluminium billet DC-casting, a digital replica of the physical casting process, creates a living simulation platform that can analyse, update, and change the process to achieve the multi-objectives process optimization. In this work, we assess the possibility of integrating high throughput micro-macro scale computation, SQL database and an artificial neural network (ANN) to establish an analytical twin for the prediction of a typical Direct Chill casting defect of Al alloys, i.e., hot tearing. The high throughput computation consists of solidification path computation at the microscopic scale (dendrite arm scale with software Alstruc) and heat transfer, fluid flow and stress/strain computation at the macroscopic scale (the scale of billet/ingot dimensions with software Alsim). The two-scale computations are coupled via sharing with Alsim the compositional dependent solidification path (Solid fraction-Temperature curve), thermo-physical properties such as densities, thermal conductivities calculated by Alstruc. Then Alsim calculates all the field variables including thermal stress, volumetric strain, and predicts the locations of the most vulnerable position and its hot tearing susceptibility. We demonstrate that the proposed framework can efficiently predict sump depth and hot-tearing tendency in the center of billets for a range of industrial AA6xxx alloy composition, casting parameters including casting speed and casting temperature. The data generated by the multi-scale computation are used to build a SQL database for training and testing the neural network. The utilities of the trained neural network and established SQL database are discussed for their application to optimize DC casting recipes of 6xxx extrusion billets. Our conclusion is that the proposed high throughput multi-scale simulation,

database and ANN parameterization are three essential pillars supporting the establishment of a digital casting twin, and such a twin can provide a quick screening and selection/adjustment of process parameters before casting or during casting to avoid hot-tears.

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The data generated at a very high rate by sensors and RFIDs are required to be handled by continuous queries keeping real time response. DSMSs (Data Stream Management systems) are used in several cases of these large scale systems for its purpose. This paper describes the results of experimental evaluation of distributed data stream processing using which is a lightweight RDBMS as a stream processing engine node. In particular, it is necessary to correspond also to the increasing data rate flexibly in data stream processing. This paper proved that it could respond to the increase in a data rate by adding a processing node dynamically. In that case, it is necessary to perform synchronous processing in two or more processing nodes. Therefore, within the limits of this experiment, it turned out that it is realizable by performing synchronous processing by the side which divides and passes data.

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　データの格納および管理の手段として，RDBに代わり，高速に大量のデータを処理でき，分散化の容易なNoSQLデータベースが用いられてきている．しかしデータ構造や操作言語の違いから，システム移行は容易ではない．そこで本研究では，SQL文を入力にとり，等価なNoSQLの問い合わせ文を出力する変換プログラムを作成した．対応するNoSQLはMongoDBとHBaseとし，実装においてSQL文の解析および言語間のコード断片の対応付けを行った．評価実験の結果，32例のSQL文のうち，13例でMongoDB，9例でHBaseの問い合わせ文を生成できた．

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佐賀大学では就職活動において，会社説明会の受付と企業情報の管理を会社説明会受付管理システムによって行ってきた。このシステムは，データベースを利用したWeb上での情報の閲覧や編集が可能であり，合同会社説明会の登録と予約に利用されてきた。しかし，使用されているソフトウェアが古く，アクセスが集中すると動作が止まってしまうなどの不具合があった。そこで，本研究では開発環境を刷新し，データベース には動作が軽快かつ高速なを，Webページの作成にはPHPを用い，さらに佐賀大学卒業生の採用実績の情報を追加，その実績に基づいて優先的に表示するなどの改良を加えてゆき，従前の合同会社説明会企業受付システムを大幅に改編した。

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The ability to rapidly identify the correct method for dismantling the components of the interior of a building is important to ensuring the building structure can be easily reused. An information management system has been created that provides facilities for identifying building components and accessing such information as the correct disassembly and disposal procedures for them. RT-Middleware provides the framework for the system. It allows individual components, such as the type of database storing the information, to be swapped with different implementations without adversely affecting the rest of the system.

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大学キャンパスにおいて，Wi-Fi アクセスポイントは学習や研究に不可欠なインフラとなっている．多くの大学が多数のWi-Fiアクセスポイントを学内のさまざまな場所に設置しているが，その利用状況データはセキュリティ確保とプライバシー保護のため，一部の限られたスタッフしか参照することができない．このため，混雑状況の可視化やデータサイエンス教育など，さまざまな応用が期待されるにも関わらず，データを十分に活用することができていない．そこでわれわれは，オープンデータとして公開が可能な，セキュリティとプライバシーを確保したWi-Fi利用状況データセットを構築することにした．これにより，学内外の研究者による研究やデータサイエンス教育，大学DX推進に寄与することができる．

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A journaling file system is a file system that records information about pending updates to the file system before committing the updates. This mechanism raises the reliability of the system because it enables any inconsistencies to be repaired with minimal loss of data. Since there is a tradeoff between the overhead and reliability, ext3, a journaling file system commonly used by the Linux kernel, offers three journaling modes: speed-prioritized mode, reliability-prioritized mode, and intermediate mode. Unfortunately, in ext3, the journaling mode has to be set individually for each file system. Thus, the granularity of the journaling mode setting is very coarse. In addition, the journaling mode must be determined at the time of mounting the file system and cannot be changed without unmounting it. To resolve this problem, this paper proposes a new journaling file system named dajFS (per-directory adaptive journaling file system) that is able to set an appropriate journaling mode for each directory and to switch the journaling mode of a directory to another on the fly without unmounting the file system. Essentially, the journaling mode that is specified for a directory applies to all files that reside directly under that directory. By using dajFS, the user can determine and set a journaling mode for each directory on the basis of the importance of files under that directory. As a result, the user can enjoy moderate granularity with the journaling mode setting.

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The performance of a mobile database management system (DBMS) in which most queries are made up of random data accesses if the NAND flash memory is used as storage media of the DBMS is degraded. The reason for this is that the performance of NAND flash memory is good for writing sequentially but poor when writing randomly. Thus, a new storage structure and querying policies are needed in mobile DBMS when flash memory is used as the storage media. In this letter, we propose a new policy of database page management to enhance the frequent random update performance, and then evaluate the performance experimentally.

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On university campuses, Wi-Fi access points are indispensable infrastructure for learning and research. Although many universities deploy numerous access points across diverse locations, their usage logs are typically restricted to a small group of authorized staff to ensure security and privacy. Consequently, although this usage log data has potential for applications such as crowding visualization and data-science education, it remains underutilized. To address this gap, we construct a Wi-Fi usage dataset that preserves security and privacy while being suitable for release as open data. The resulting resource enables reproducible studies by internal and external researchers and supports hands-on curricula in data science.

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To better support data-intensive workflows which are typically built out of various independently developed executables, this paper proposes extensions to parallel database systems called User-Defined eXecutables (UDX) and collective queries. UDX facilitates the description of workflows by enabling seamless integrations of external executables into SQL statements without any efforts to write programs confirming to strict specifications of databases. A collective query is an SQL query whose results are distributed to multiple clients and then processed by them in parallel, using arbitrary UDX. It provides efficient parallelization of executables through the data transfer optimization algorithms that distribute query results to multiple clients, taking both communication cost and computational loads into account. We implement this concept in a system called ParaLite, a parallel database system based on a popular lightweight database . Our experiments show that ParaLite has several times higher performance over Hive for typical SQL tasks and has 10x speedup compared to a commercial DBMS for executables. In addition, this paper studies a real-world text processing workflow and builds it on top of ParaLite, Hadoop, Hive and general files. Our experiences indicate that ParaLite outperforms other systems in both productivity and performance for the workflow.

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In the Machine-to-Machine (M2M) system, which connects devices to the Internet and provides various remote services, there are many applications that need to carry the stock-type data analysis and the flow-type data analysis at the same time. However, because the stock-type data analysis and the flow-type data analysis are provided as independent systems, it is difficult to combine them. In this paper, we propose a method to process stock-type data analysis and flow-data analysis simultaneously by linking the M2M gateways to the M2M server. In this study, we implemented a prototype system using open source software, such as and R, to evaluate performance and functions of the proposed method.

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