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From theory to hydrological practice: Leveraging CYGNSS data over seven years for advanced soil moisture monitoring
No full textSoil moisture (SM) is a key variable in hydrometeorology and climate systems. With the growing interest in capturing fine-scale SM variability for effective hydroclimate applications, spaceborne L-band bistatic radar systems using Global Navigation Satellite System-Reflectometry (GNSS-R) technology hold great potential to meet the demand for high spatiotemporal resolution SM data. Although primarily designed for tropical cyclone monitoring purposes, the first GNSS-R satellite constellation – Cyclone Global Navigation Satellite System (CYGNSS) mission, has demonstrated the benefits of reliably monitoring diurnal SM dynamics through its initial stage of seven-year data record, thanks to its high revisit frequency at sub-daily intervals. Nevertheless, knowledge of SM retrieval from CYGNSS, particularly linked with its distinctive features, remains poorly understood, while numerous existing uncertainties and open issues can restrict its effective SM retrieval and practical applications in the next operating stages. Unlike other review papers, this work aims to bridge this knowledge gap in CYGNSS SM retrieval by highlighting noteworthy design properties based on analyses of its real-world data, while providing a synthesis of recent advances in eliminating external uncertainty factors and improving SM inversion methods. Despite its potential, CYGNSS SM retrieval faces both general and particular challenges arising from common issues in retrieval algorithms for conventional GNSS-R satellites and unique data limitations tied to its technical design. Scientific debates over the contributions of coherent and incoherent components in total CYGNSS signals and accurate partitioning of these two parts are defined as the key algorithm-related challenges to resolve, along with correcting attenuation effects of vegetation and surface roughness. The data-related challenges involve variations in CYGNSS's spatial footprint, temporal frequency, and signal penetration depth across different land surface conditions, inadequate consideration of CYGNSS incidence angle change, excessive dependence on a reference SM dataset for inversion model calibration/training or validation, and computational demands for processing rapid multi-sampling CYGNSS data retrieval. Future research pathways highlight leveraging cutting-edge machine learning/deep learning algorithms to enhance CYGNSS SM data quantity and quality and better interpret its complex interactions with other hydroclimate variables. Assimilating CYGNSS SM data streams into physical models to improve the prediction of related variables and climate extremes also presents a promising prospect. © 2024 Elsevier Inc.FALSEsciescopu
Restoration of NREM Sleep in an Alzheimer's Disease Mouse Model Through 40-Hz Auditory Stimulation by Reducing Tonic GABA Currents in nNOS Neurons
No full textMVPrompt: Building Music-Visual Prompts for AI Artists to Craft Music Video Mise-en-scène
No full textMusic videos have traditionally been the domain of experts, but with text-to-video generative AI models, AI artists can now create them more easily. However, accurately reflecting the desired music-visual mise-en-scène remains challenging without specialized knowledge, highlighting the need for supportive tools. To address this, we conducted a design workshop with seven music video experts, identified design goals, and developed MVPrompt - a tool for generating music-visual mise-en-scène prompts. In a user study with 24 AI artists, MVPrompt outperformed the Baseline, effectively supporting the collaborative creative process. Specifically, the Visual Theme stage facilitated the exploration of tone and manner, while the Visual Scene & Grammar stage refined prompts with detailed mise-en-scène elements. By enabling AI artists to specify mise-en-scène creatively, MVPrompt enhances the experience of making music video scenes with text-to-video generative AI. © 2025 Copyright held by the owner/author(s)
Corrosion analysis of vehicle materials and feasibility assessment of magnesium alloys under deicing agent exposure
No full textThe widespread use of sodium chloride and calcium chloride as deicing agents has raised concerns about the accelerated corrosion of automotive components. This study systematically investigates the corrosion behaviour of commonly used automotive steel sheets-cold-rolled and galvannealed (GA)-under varying temperature conditions and brine environments. Results indicate that while calcium chloride initially induces faster corrosion due to its exothermic dissolution, sodium chloride leads to greater cumulative corrosion over time. Tafel analysis and activation energy calculations (0.23 eV for GA steel and 0.13 eV for magnesium (Mg) alloy) reveal temperature-dependent acceleration mechanisms. Notably, Mg alloy sheets exhibited significantly lower mass loss and slower corrosion rates compared to GA steel, due to their lower activation energy and the self-limiting nature of their protective oxide film. These findings highlight the potential of Mg alloys as a viable lightweight alternative for vehicle chassis and powertrain components in electrified vehicles, where mechanical demands are reduced. The results also provide evidence-based metrics useful for long-term corrosion modelling and material selection in automotive applications.FALSEsciescopu
Development brain aware graph neural network model for cognitive resilience in early Alzheimer’s Disease
No full text알츠하이머병은 인지 기능의 점진적인 손상을 특징으로 하지만, 병리학적 변화에 대한 개인의 질병 양상에는 상당한 차이가 있습니다. 본 연구는 인지적으로 회복력이 있는 개인과 취약한 개인을 구별하는 구조적 연결성 바이오마커를 식별하여 병리학적 증상에도 불구하고 인지 기능을 유지하는 메커니즘에 대한 통찰을 제공합니다. 우리는 인지능력 회복력 있는 그룹과 취약한 그룹 간의 구조적 연결성 차이를 분석하기 위해 베이지안 가설 검정을 사용하여, 인지 회복력과 관련된 중요한 특징들을 밝혀냈습니다. 또한,뇌연결성에대한도메인지식을통합하여임상점수예측정확도를향상시키는그 래프 신경망을 위한 새로운 뇌 특화 리드아웃 레이어를 제안합니다. 실험 결과는 제안된 그래프 신경망 모델이 신경영상 데이터에서 효과적이고 더 높은 해석을 보여주었으며, 신경퇴행성질환의조기발견및표적개입의가능성을강조합니다.우리의연구결과는 인지 회복력에 대한 더 깊은 이해를 제공하며, 알츠하이머병의 진단 및 치료를 향상시키 기 위한 새로운 가능성을 제시합니다.|Alzheimer’s disease (AD) is characterized by progressive impairment of cognitive functions, but significant variability exists in individual susceptibility to neuropathological changes. This study aims to identify structural connectivity biomarkers that distinguish cognitively resilient individuals from those who are vulnerable, providing insights into mechanisms sustaining cognitive performance despite AD pathology. We use Bayesian hypothesis testing to analyze differences in structural connectivity between resilient and vulnerable groups, revealing significant features associated with cognitive resilience. Furthermore, we propose a novel brain-aware readout layer for Graph Neural Networks (GNNs), incorporating domain-specific knowledge of brain connectivity to improve prediction accuracy for clinical scores. Experimental results demonstrate the effectiveness and interpretability of the proposed GNN model in neuroimaging data, highlighting its potential for early detection and targeted intervention in neurodegenerative conditions. Our findings contribute to a deeper understanding of cognitive resilience and present new avenues for enhancing diagnosis and treatment of Alzheimer’s disease.MasterAbstract (English) i
Abstract (Korean) iv
List of Contents v
List of Tables vii
List of Figures viii
1 Introduction 1
1.1 Introduction of Alzheimer’s disease 1
1.2 Resilience in cognitive functions 2
1.3 Research objectives 3
2 Related works 5
2.1 Hypothesis Testing 5
2.1.1 Frequentist Hypothesis Testing 5
2.1.2 Bayesian Hypothesis Testing 6
2.2 Graph Neural Networks 7
2.2.1 Aggregation Layer 7
2.2.2 Readout Layer 10
3 Biomarkers for cognitive resilience 12
3.1 Background 12
3.2 Material and Methods 13
3.2.1 Participants 13
3.2.2 Neuropsychological testing 14
3.2.3 MRI image acquisition and processing 14
3.2.4 [18F]-flutemetamol PET image acquisition and processing 15
3.2.5 Between group difference based on Bayesian Statistics 16
3.2.6 Assessing the Predictive Power of Identified SC 17
3.3 Results 17
3.3.1 Baseline demographic and clinical data 17
3.3.2 Between-group Differences in SC 18
3.3.3 Prediction performance of identified connectomics biomarker 20
3.4 Conclusion 21
4 Brain-Aware Readout Layers in GNNs 22
4.1 Background 22
4.2 Material and Methods 23
4.2.1 Participants 23
4.2.2 Data Preprocessing 23
4.2.3 Brain-aware Graph Neural Network 24
4.3 Experiments and results 28
4.3.1 Experimental Setups 28
4.3.2 Performance Comparison Across Readout Layer on Various GNN
Model 29
4.3.3 Effectiveness of Prior Knowledge in BA Readout Layer 30
4.3.4 Interpretability of the GNN Model with BA Readout Layer 30
4.3.5 Conclusion 33
5 Discussion 36
Summary 37
Acknowledgements 4
Multi-modal study of phase transition in LaCoO3 using a synchrotron-based X-ray analysis techniques
No full text페로브스카이트물질은안정적인구조,광범위한화학적변이,높은이온전도성으로 인해 널리 연구되고 있다. 특히, LaCoO3는 스핀 상태 전이, 금속-절연체 상전이, 구조적 상전이, 산화-환원 반응 등 특징적인 물리적 및 화학적 성질과 활용가능성으로 인하여 많은 과학자들의 관심을 끌어왔다. 최근에 들어서는 LaCoO3가 주변 환경에 따른 구조 적 전이를 갖는 특성에 대한 연구들이 여러 그룹에서 집중적으로 탐구되고있다. 하지만 LaCoO3의상전이는넓은온도범위와압력에걸쳐이뤄지기때문에, in situ 실험을통한 상전이 전 과정에 대한 이해가 아직까지는 미지의 영역으로 남아있었다. 본 연구에서는 in situ X-선회절(XRD),전기적 I–V측정,상압하 X-선광전자분광기 (AP-HAXPES) 을 활용하여 LaCoO3 박막의 토포택틱 상전이 특성을 살펴보았다. in situ XRD 측정을 통해 고진공 조건( 10−5 mbar)에서 350,◦C의 온도에서 LaCoO3의 페로브스카이트 상이 La3Co3O8의 중간 상을 거쳐 La2Co2O5의 브라운밀러라이트 상으로 구조적 상전이를 보이는 것을 명확히 확인 되었다. 또한 100,◦C 근처에서 대기압 조건(1 atm) 하에서 BM상에서 PV상으로의가역적구조상전이가관찰되었다. XRD에서감지된상전이는 전기적 I–V 측정을 통해서도 확인되었으며, 구조적인 상전이와 함께 전기적인 저항의 변화도 함께 나타나는 것을 확인할 수 있었다. 한편, AP-HAXPES를 통한 LCO 박막의 – iii – 전자구조의 변화를 추적하였는데, Co 2p 스펙트럼에서 관찰된 산화 상태는 LCO에서 산소 공공이 SPT와 연관되어 있음을 나타냈다. 또한 가전자대 XPS 스펙트럼 분석을 통하여 PV상에서 BM상으로의 SPT동안밴드갭의증가를관찰하였다.두스펙트럼의 비교를 통하여 LCO의 밴드갭의 증가는 산소 공공의 증가에 따라 나타나는 것을 확인할 수 있었다. ©2025 신 현 석 ALL RIGHTS RESERVED|Perovskite materials have been widely studied because of their stable structure, wide range of chemical variations, and high ionic conductivity. Among these mate- rials, LaCoO3 (LCO) has attracted many researchers due to its interesting physical and chemical characteristics and practical applications, such as spin-state transition, insulator-metal transition, structural phase transition (SPT), magnetic transition, and oxygen reduction/evolution reaction. Of all these features, the topotactic phase tran- sition of LCO has been extensively investigated by many research groups because of its practical application. As yet, comprehensive in situ study of physical/chemical properties across the phase transition have not been performed due to experimental limitations. In this study, by utilizing textitin situ X-ray diffraction (XRD), electri- cal I–V measurements, and ambient pressure hard X-ray photoelectron spectroscopy (AP-HAXPES), we investigated the characteristics of the topotactic phase transition in LCO thin films. The XRD measurements provided clear evidence of a SPT in the LCO thin films from the perovskite (PV) phase of LaCoO3 to the brownmillerite (BM) phase of La2Co2O5 via the intermediate phase of La3Co3O8 at a temperature of 350 ◦C under high vacuum conditions ( 10−5 mbar). The reverse SPT from the BM phase back to the PV phase was also observed under ambient air pressure (1 atm) near 100 ◦C. SPT detected in XRD was corroborated by electrical I–V measurements, indicating phase transitions between the metallic PV phase and the insulating BM phase and vice versa. During the onset of structural phase transition, the bulk chemical and elec- tronic states of the LCO thin films were monitored using AP-HAXPES. The oxidation states observed in the Co 2p spectra suggested that oxygen vacancies are correlated with the SPT in LCO. Furthermore, an increase in the band gap was observed during the SPT from the PV to the BM phase. The shift of the valence band maximum shows the formation of oxygen vacancies causing the modification of the electronic structure of LCO. The analysis of valence band structures was further compared with the I–V measurements. ©2025 HyunSuk Shin ALL RIGHTS RESERVEDDoctorAbstract (English) i
Abstract (Korean) iii
List of Contents v
List of Tables vii
List of Figures viii
1 General Introduction 1
1.1 Basic principles of Photoemission Process 1
1.1.1 The nature of Photoelectron effect 1
1.1.2 Hamiltonian equation 5
1.1.3 Hartree-Fock Approximation and Koopman’s Theorem 7
1.1.4 Quantitative analysis of XPS 12
1.2 Ambient Pressure X-ray Photoelectron Spectroscopy 17
1.2.1 Instrumentation for XPS 17
1.2.2 Ambient Pressure X-ray Photoelectron Spectroscopy 26
2 Investigation on the origin of topotactic phase transition of LaCoO 3
thin film with in situ XRD and AP-HAXPES 31
2.1 Introduction 31
2.2 Experimental methods 36
2.2.1 Sample preparation 36
2.2.2 in situ Ambient Pressure X-ray photoelectron Spectroscopy 38
2.2.3 In situ X-ray probe experiments 38
2.2.4 Ambient Pressure Hard X-ray Photo Emission Spectroscopy 40
2.2.5 X-ray Absorption Spectroscopy 42
2.3 Results and Discussion 44
2.3.1 In-house APXPS measurement 44
2.3.2 X-ray Probe measurement 48
2.3.3 Electrical transport measurement 55
2.3.4 in situ AP-HAXPES measurements 59
– v –
2.3.5 in situ X-ray Absorption Spectroscopy 66
2.4 Conclusions 72
Summary 73
References 76
A Spin-state transition in LCO 90
Acknowledgements 94
– vi
Controllable Subspaces in Structured Networks of Hierarchical Directed Acyclic Graphs: Controllability of Individual Nodes
No full textThis paper introduces the concept of the Fixed Strongly Structurally Controllable Subspace (FSSCS) within the context of structured networks, enabling a comprehensive characterization of controllable subspaces. From a graph-theoretical perspective, we define Fixed Strongly Structurally Controllable (FSSC) nodes, which are nodes (or states) that remain controllable for all network parameters within a structured network. Furthermore, we establish the necessary and sufficient conditions for identifying FSSC nodes in general graphs. This paper also proposes a method to exactly determine the dimension of the Strongly Structurally Controllable Subspace (SSCS) in hierarchical directed acyclic graphs, using a combination of graph-theoretical approaches and controllability matrix analyses. This method facilitates the identification of FSSC nodes and enhances our understanding of the robustness of node controllability against parameter variations in structured networks. © 2025 Elsevier B.V., All rights reserved.FALSEsciescopu
Crystallinity-dependent surface oxidation in Cu Films revealed by a visualization of surface plasmon
No full textWe visualized surface plasmon in poly- and single-crystalline Cu films by exploiting nano-infrared imaging. We clearly observed oscillating patterns in both films which are attributed to the surface plasmon launched from the film edge and the atomic force microscope tip. The surface plasmons observed for poly- and single-crystalline Cu films have different oscillating periods for the given wavelength of incident beam, and different slopes of the surface plasmon dispersion. These behaviors could be understood by a corresponding difference in dielectric constants of the dielectric layer on top of the Cu films; a relatively smaller dielectric constant is required to fit the surface plasmon's dispersion relation of the single-crystalline Cu film implying that the oxidized layer formed on the Cu film surface is thinner than for the poly-crystalline film. This result is in good agreement with the previous observation about the robustness of the single-crystalline Cu film against the surface oxidation. © 2024 Korean Physical SocietyFALSEsciescopu
Improving Gait Balance of the Blind During Robot-Assisted Guidance by Providing Static External Reference
No full textThe gait of blind individuals is characterized by reduced gait stability and postural balance due to their limited sensory information. However, existing systems focus on guidance capability rather than improving gait stability and postural balance. Since static external references (SER), such as fixed handrails, can improve the gait balance of the blind, their functionality should be implemented during robot-assisted guidance. In this study, we focused on the proof-of-concept of whether the provision of SER during robot-assisted guidance based on a 3-degrees-of-freedom mobile manipulator, called the robotic haptic cane (RHC), enhances gait stability and postural balance in blind individuals. Accordingly, we conducted gait experiments with 20 blindfolded sighted participants and one blind participant. The experimental results from the blindfolded sighted participants indicate that gait stability (step width variability) and postural balance (mediolateral trunk tilt) are further decreased by walking faster with a blindfold. However, both gait stability and postural balance improved, especially at a faster speed, by using RHC. According to the experimental results of the blind participant, the preferred speed increased with the use of the RHC. In addition, gait stability and postural balance improved with RHC use regardless of speed. Thus, the RHC can enable blind individuals to walk more safely with enhanced gait stability and postural balance by providing SER during robot-assisted guidance. © 2025 Elsevier B.V., All rights reserved.TRUEsciescopu
XGBoost algorithm to predict heat transfer coefficient for saturated flow boiling in mini/micro-channels
No full textThe heat transfer coefficient in saturated flow boiling in mini/micro-channels is a critical factor in the cooling design of high-heat-flux devices. This study proposes a method to accurately predict the heat transfer coefficient in saturated flow boiling using the XGBoost(eXtreme Gradient Boosting) machine learning algorithm. The database used in this study consists of 11,096 pre- dryout data points obtained by removing 1,878 post-dryout data points from a total of 12,974 data collected from 37 sources, employing an XGBoost incipience dryout prediction model. The dataset encompasses 18 working fluids, hydraulic diameters raning from 0.19 mm to 0.65 mm, mass flow rates from 19.45 kg/m2s to 1,608 kg/m2s, and saturation temperatures from -40℃ to 201.37 ℃. When implementing the XGBoost prediction model, training features were selected based on Permutation Feature importance(PFI) and SHapley Additive exPlanations(SHAP) values, and the optimal combination of hyper-parameters was determined using Optuna. The XGBoost model developed in this study, using 8 training features(Pr_f, x_di, Bo, P_red, Pr_g, rho_r, Fr_fo, h_r), achieved a Mean Absolute Percentage Error(MAE) of 7.05%, demonstrating superior predictive performance compared to existing empirical correlations and other machine learning algorithms, including AdaBoost, Gradient Boosting, and ANN. This research confirms that the XGBoost algorithm is an effective and reliable tool for predicting the heat transfer coefficient, overcoming the limitations of existing correlations and providing performance under various operating conditions.MasterAbstract ⅰ
List of contents ⅱ
List of tables ⅲ
List of figures ⅳ
Nomenclature 1
Ⅰ. Introduction 4
1.1 Saturated flow boiling in mini/micro-channels 4
1.2 Theoretical background of flow boiling heat transfer in mini/micro-channels 4
1.3 Research trends on the prediction of heat transfer coefficients 6
1.4 Objectives 7
Ⅱ. Modeling method 7
2.1 Consolidated universal database of heat transfer coefficient for saturated flow boiling in
mini/micro-channels 7
2.2 Machine learning algorithm: XGBoost(eXtreme Gradient Boosting) 12
2.3 Assessment of predictive accuracy of candidate machine learning algorithm 13
2.4 Input feature selection 15
2.4.1 Error estimation 15
2.4.2 Permutation feature importance(PFI) 16
2.4.3 SHAP value 19
2.4.4 Assessment of predictive performance according to the type of input feature selection 21
2.5 Hyper-parameter optimization: Optuna 24
Ⅲ. Results and discussion 27
3.1 Assessment of previous empirical correlations for the universal consolidated database 27
3.2 Comprehensive assessment of XGBoost model for the consolidated database 32
3.3 Validation of an newly developed XGBoost prediction model for unseen database 34
Ⅳ. Conclusions. 36
Ⅴ. References 3