17：30
19：30

張 博

Investigation of DNA Damage-Related Biomarkers in Cancer through Machine Learning and Bioinformatics Approaches

This study delves into the identification and analysis of biomarkers associated with DNA damage in cancer. The primary objective is to elucidate the molecular mechanisms driving DNA damage responses and their implications in cancer progression, utilizing advanced computational techniques.
First, I investigated the roles of DNA-PK and ATM in gene expression responses to radiation. WGCNA identified significant co-expression modules related to radiation exposure, revealing specific genes and pathways involved in the DNA repair process.
Second, I explored the role of ZNF384, a zinc finger protein, in colon adenocarcinoma. Differential gene expression analysis using RNA-Seq data from TCGA-COAD was conducted to identify genes correlated with ZNF384. Initially, correlation analysis and differential gene expression analysis using RNA-Seq data from TCGA-COAD was conducted to identify genes correlated with ZNF384. WGCNA and advanced machine learning algorithms, such as Lasso, random forests, and support vector machines, were employed to refine and predict potential biomarkers with high precision. These methods allowed for the discovery of novel biomarkers by analyzing their expression patterns and interactions within the cellular context. The research also focused on constructing a competitive endogenous RNA (ceRNA) network, PPI, and transcriptional factor analysis to understand the post-transcriptional regulation of crucial genes. This network analysis identified key microRNAs (miRNAs) and their target interactions, which play significant roles in cancer development and progression. To ensure the robustness of our findings, we validated the identified biomarkers using external datasets from the GEO database. Functional assays were conducted to confirm the roles of these biomarkers in DNA damage repair mechanisms.

10：00
12：00

Jang Sejung

Machine Learning Techniques to Solve Vertical Instability in Tokamak

Estimating plasma vertical position using operational data is crucial for safely controlling elongated plasma and mitigating disruptions linked to vertical displacement events (VDEs), which lead to the influx of impurities and wall damage due to plasma interactions with the wall. In order to solve this problem, we utilized machine learning techniques to develop models which can estimate and predict plasma vertical position. So, we create a data-driven model for multivariable regression of VDEs by utilizing the neural network. Time evolution of plasma vertical position is estimated by using long-short term memory networks (LSTM) with Time2Vec technique which incorporates temporal information into a neural network. The model achieved high performance by combining Time2Vec with LSTM. We can also interpret the weights extracted from a trained, data-driven model by comparing the model’　predictions. The vertical position of tokamak plasma is predicted and the physical quantities associated with the instability are quantified using Transformer-based language model. Regarding the plasma vertical position related to plasma disruptions, we adopt a method based on Natural Language Processing (NLP) models in order to investigate associations of observed values and controlled variables, and predict the plasma instabilities.　We can also interpret the precursors and associations of the parameters using Bidirectional Encoder Representations from Transformers (BERT).　

16：00
18：00

田中 敦子

Al₄SiC₄基セラミックスの熱的・機械的特性および高温酸化・腐食挙動に関する研究

セラミックスは軽量で、耐熱性、耐食性に優れることから、原子力・核融合分野や航空宇宙分野での耐熱構造材料として期待されている。航空分野において、セラミックスは航空機用ジェットエンジン高温部材およびその耐熱・耐環境コーティングとしての適用が期待されているが、高温水蒸気や火山灰や砂による腐食が課題となっており、優れた耐食性を有する材料の開発が強く求められている。本研究では耐食性や耐酸化性等に優れるAl₄SiC₄に着目し、耐食性および高温特性の向上を図るためにSiCおよびY₃Al₅O₁₂（YAG）をそれぞれ添加したAl4SiC4基セラミックスを作製し、熱的・機械的特性を評価した。また、高温下での酸化挙動および火山灰を模擬したCMAS（CaO-MgO-Al₂O₃-SiO₂）に対する腐食挙動を評価した。

15：00
17：00

Zhang Weichen

Fukushima Revitalizics: Performance Evaluation of High-powered Battery for Nuclear Decommissioning and Heat Storage

In order to create a sustainable reconstruction of Fukushima, decommissioning and renewable energy self-reliance are very necessary. For addressing these issues in Fukushima Revitalizics of an emerging academic discipline in Tokyo Institute of Technology, this study used pulsed ultrasound to evaluate the radiation-resistant, high-powered batteries for installation in robots used for decommissioning. Hysteresis was found in the reflected waveform shift that occurred throughout the charging and discharging processes, as was discovered by the assessment. In addition, the research evaluated the battery's performance under high radiation conditions, which allowed the researchers to demonstrate the battery's resistance in such environments. In this study, not only were batteries reviewed throughout this research, but also a cost-effective thermal storage technique was investigated. With the use of this technology, heat from the sun and wind is converted and then stored in gravel. This plan aims to facilitate the cost-effective usage of renewable energy sources during the evening hours. Experiments were conducted in order to calculate the parameter coefficients that were necessary for the simulation design of the gravel heat storage furnace. This study emphasizes how important it is to use innovative battery technology and efficient energy storage options to restore Fukushima in a way that is reliable and sustainable.

10：30
12：30

原 大輔

2S by designを取り入れた浮体式洋上原子力発電所の堅牢性の強化

原子力安全の向上や原子力平和利用の推進に向けた浮体式洋上原子力発電所(OFNP)の研究が世界的に進められているが、プラント概念の技術的成立性に焦点が当てられており、洋上といったこれまでにない設置環境における社会実装に必須となる新たな核セキュリティ・核不拡散面での検討は十分ではない。本研究では、洋上や船舶特有の核セキュリティおよび核不拡散上の新たな脅威に対する応答性を定量評価し、核セキュリティ・保障措置/核不拡散性(2S)by designを取り入れた堅牢なOFNP概念を提示した。