| 間野 純平 | D, 中間発表 | 計算システムズ生物学 | 金谷 重彦, | 佐藤 嘉伸, | MD.Altaf-Ul-Amin, | 小野 直亮, | 黄 銘 |
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title: Developing blood pressure variability index focusing on blood pressure regulation
abstract: While blood pressure (BP) in Japanese people has been decreasing, the mortality from cardiovascular events, which is thought to be due to hypertension, is still high. Previous studies reported that BP variability (BPV) is an independent predictor of cardiovascular mortality. Since there is no simple and highly reproducible index for evaluating BPV, we aim to develop a new BPV index by focusing on BP regulation. language of the presentation: Japanese 発表題目: 血圧調整能力に着目した血圧変動指標の開発 発表概要: 日本人の血圧は減少傾向にある一方で、高血圧に起因すると考えられる心血管イベントでの死者数は依然として多い。過去の研究より血圧変動は血圧レベルとは独立した心血管イベントリスクであることが知られており注目されている。血圧変動を評価するための簡便で再現性の高い指標は無いため、血圧調整機能に着目することで新規血圧変動指標の開発を目指す。 | |||||||
| 木下 広幸 | D, 中間発表 | 計算システムズ生物学 | 金谷 重彦, | 佐藤 嘉伸, | 小野 直亮, | MD.ALTAF-UL-AMIN, | 黄 銘 |
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title: Association between very short-term and circadian blood pressure variability
abstract: Blood pressure variability (BPV) has been a risk index of a cardiovascular event and mortality. Historically, circadian BPV assessment by intermittent measurement of blood pressure (BP) in the daily living condition with ABPM (ambulatory BP monitor) has been utilized in clinical settings. However, the lack of practicality in terms of inadequate robustness against physical activities and cumbersomeness of repeated compression on the upper arm hinders its day-to-day assessment. We addressed the practical assessment of BPV, focusing on very short-term BPV evaluated from continuous beat-by-beat BP recording in a 30-minute resting condition. From the aspect of correlation to BPV during the daytime, nighttime, and 24-hour, very short-term BPV showed the highest correlation with nighttime BPV compared to daytime and 24-hour BPV. The present result suggested that very short-term BPV measured at resting conditions could reasonably estimate nighttime BPV reflecting a low level of physical activities. language of the presentation: Japanese 発表題目: 発表概要: | |||||||
| HOSSAIN MD. DELWAR | D, 中間発表 | サイバーレジリエンス構成学 | 門林 雄基, | 安本 慶一, | 林 優一, | 宮本 大輔（客員）, | 妙中 雄三 |
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title: *** On Using Deep Learning to Mitigate Cyber Attacks in Smart Cities***
abstract: ***Smart cities are connected with several entities such as Smart Building, Smart Car, Smart Grid, etc. The rapid growth of connected technologies, such as the Internet of Things (IoT), and the ubiquitous nature of the Internet, have made life more convenient for human beings. The rise of that social convenience is accompanied by incessant efforts of miscreants to create new tools, techniques, and tactics to destabilize the comfort of the dwellers and exacerbate the situation by increasing the attack surface of the smart cities cyber-physical systems/connected world. Indeed, we are experiencing rapid growth of technological weapons of mass destruction that can be used by anyone with elementary computer literacy. Among all of its many components, network traffic attack detection is known to be the most critical as it allows us to prevent and/or detect potentially destructive attacks. To secure the cyber-physical systems (CPSs) of Smart Cities, we may deploy an Effective Intrusion Detection System (IDS) to mitigate security breaches. Several Challenges are still available to develop an Effective IDS such as availability of the real-systems datasets, effectiveness & industry acceptance, high false-positive, and false-negative rate. Concerning all those limitations, we are focusing on developing the real-world attack datasets and an effective IDS by using Deep Learning approaches to secure the Smart Building, Smart Car, Smart Grid’s cyber-physical systems (CPSs). *** language of the presentation: *** English*** 発表題目: *** この部分を発表題目に *** 発表概要: *** この部分を発表概要に *** | |||||||