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Geodetic Observation around Mt. Usu by Leveling Survey
No full textIt is widely known that Usu volcano experiences large-scale ground deformation with each eruption. The Geospatial Information Authority of Japan and Hokkaido University have conducted repeated leveling surveys around Usu volcano, capturing ground deformation during both eruptive and quiet periods. We analyzed past survey results to understand the spatial pattern of vertical deformation around Usu volcano and detect variations due to accumulation and intrusion of magma underground. During quiet periods between eruptive activities, signals of uplift patterns increasing westward along the coast of Uchiura Bay and subsidence over the entire mountain after the 2000 eruption were observed. In the early stage of the 2000 eruption, leveling surveys confirmed an overall uplift of the volcano. Using a tensile crack model and Markov Chain Monte Carlo (MCMC) analysis implemented in the pydeform software, we estimated an inflation source located approximately 2 km underground at the center of the mountain, tilted slightly southward in a sill- like shape. The location and inclination of this inflation source align well with the hypocenter distribution of pre-eruption seismic activity, suggesting that the overall uplift at the initial stage of the 2000 eruption was caused by this inflation source. To analyze deformation during quiet periods caused by magma accumulation to deep magma reservoirs, we estimated parameters for vertical displacement observed along the route passing through the southern foot of Usu volcano and along the Uchiura Bay coast between 1985-1986 and 1992, as well as between 2004 and 2010, assuming a spherical pressure source. When no constraints were applied to the parameter search, the optimal solution estimated the source to be located 20 km underground in the landward area north of the survey route, with a radius of 400 m. However, when constrained the depth of the source based on prior petrological studies, the estimated inflation source was at a depth of 10 km with a radius of 3-4km
Report on the Implementation of the 2024 Seminar on Research in Educational Practice
No full textThis report is a record of the 2024 Educational Practice Research Seminar planned and conducted by the editorial boards of the 2024 Seminar on Research in Educational Practice for faculty staff and graduate students. This report first explains the process of writing a research paper on educational practice, which is divided into three phases: formulating the research questions, collecting data during and after the educational practice, and analyzing the data. It also introduces representative research papers on educational practice. Subsequently, this report describes the considerations that must be made when submitting a paper and the essential elements of communication with editorial boards during the period preceding its acceptance. In addition, it responds to pivotal questions that emerged in group discussions during the seminar. Finally, this report highlights the significance and challenges of the seminar based on participants' level of satisfaction and impressions of it
A Consideration for Deployment of abnormality detection System by DNS traffic analysis using RPZ
No full text昨今、不正なDNS通信を行うマルウェアが急激に増えている。そのようなマルウェアに感染したPCは組織のDNSフルリゾルバを経由せずに直接外部にDNSクエリを送る。本研究ではその直接外部のDNSクエリを検知することを目的として、DNSトラフィックを既存研究よりも高速に分析できる方法を実装し、DNSのRPZ(Response Policy Zone)を利用した検知システムを構築した。更には実際の組織ネットワーク環境において利用できる方法を検討する。In this paper, we focus on shortening the processing time of registration in the RPZ. We implement the method to analyze the DNS traffic faster than the previous system, and construct the detection and blocking system using RPZ. Moreover, we consider the deployment of our system in real network environment
長期主義と動物擁護 : 長期主義的観点からは畜産動物の福祉向上は優先されないのか？
No full text本稿では、効果的利他主義（EA）コミュニティ内で近年支持を集めている「長期主義」から見た畜産動物の福祉向上の優先順位について検討する。EA は、科学的証拠に基づき単位資源あたりで為せる善を最大化することを目指す実践・研究プロジェクトであり、この運動内ではこれまで畜産動物の福祉向上は最重要課題の一つとされてきた。しかし、遠い未来の存在の生の改善を重要視する「長期主義」に基づいて畜産動物の問題の優先順位を低く見積もることを正当化する議論が存在し、長期主義の考え方に基づいて資源を配分する団体の意思決定に影響を及ぼしている。本稿は、長期主義的観点に立っても畜産動物の福祉向上を軽視する十分な理由はないと主張する。特に、工場畜産が将来的にも存続する可能性が少しでもあれば、その規模は期待値として非常に大きいというYip Fai Tse の指摘を踏まえると、畜産動物の福祉向上を軽視するどのタイプの反論も成り立たないと指摘し、長期主義的な資源配分の是正を求める。This paper examines the prioritization of farmed animal welfare from the perspective of “longtermism,” which has gained increasing support within the effective altruism (EA) community. Effective altruism is a research and practical project aimed at maximizing good per unit of resource based on scientific evidence. In this context, improving farmed animal welfare has traditionally been considered one of the highest priorities. However, arguments have emerged that justify deprioritizing animal welfare based on longtermism, which emphasizes the improvement of future beings’ lives. These arguments have influenced the decision-making of organizations that allocate resources according to longtermist principles. This paper argues that even from a longtermist viewpoint, there is insufficient justification for deprioritizing farmed animal welfare. Specifically, drawing on Yip Fai Tse’s argument that, if industrial farming persists into the future, its scale in terms of expected value remains vast, it demonstrates that none of the arguments for deprioritizing animal welfare can hold. Thus, it calls for a correction in resource allocation by longtermist organizations in favor of farmed animal welfare
Machine learning based characterization of high risk carriers of HTLV-1-associated myelopathy (HAM)
No full textHTLV-1-associated myelopathy (HAM) develops in a part of HTLV-1-infected individuals while most of the individuals remain asymptomatic. This complicates the identification of HTLV-1 carriers at elevated risk. In this study, we integrated HTLV-1 proviral load and antibody titers against Tax, Env, Gag p15, p19, and p24 proteins in a machine learning (ML) framework to identify and characterize high-risk individuals likely to develop HAM. We stratified asymptomatic carrier samples employing an anomaly detection model. We further developed and validated classifier models capable of distinguishing three clinical subgroups, carrier, ATL, and HAM for assessing the anomaly carrier samples as unseen test data. With most anomaly carrier samples (~ 76.47%) predicted as HAM, further statistical and interpretative analysis revealed the ‘HAM-like’characteristics of the anomaly carrier samples indicating elevated risk. Additionally, significant heterogeneity in immune response was observed among other asymptomatic carriers. As an exploratory, hypothesis-generating study, our findings are preliminary and aim to propose potential biomarkers and computational strategies that warrant validation in future longitudinal investigations. Our machine learning-based approach offers a novel and insightful tool for identifying and evaluating high-risk characteristics for HAM, providing a holistic view of the complex immune dynamics of asymptomatic carriers of HTLV-1
