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Exploring Fingertip Slip Feedback for Haptic Augmentation and Referral Using Thermal Feedback
No full textThermal feedback offers new possibilities for enhancing user experience by combining with vibrations. Yet, little research has explored haptic augmentation and referral through thermal feedback with texture slip feedback on the fingertip. To address this gap, we examined variations in tactile perception and the potential for the thermal referral phenomenon on the fingertip by integrating thermal and texture slip feedback. Using a custom haptic device, we delivered thermal stimuli ranging from 24 to 40°C alongside slip feedback related to three distinct textures (silk, felt, sandpaper) to the middle and distal phalanx of the fingertip, respectively. Participants assessed the perceived intensity, warmness, roughness, and stickiness of the integrated tactile feedback and identified the perceived location of the thermal sensation. Our results showed that thermal feedback can affect the roughness of intermediate texture (felt), increasing the median roughness rating from 43.5 to 60 on a 0-100 scale under cold and hot conditions, respectively. Furthermore, the occurrence rate of thermal referral increased as temperature increased (79.8% at a hot condition). We discuss the efficacy of thermal feedback in augmenting texture slip feedback and haptic referral and highlight implications for future research. © 2025 Elsevier B.V., All rights reserved
Vehicle-to-Everything-Car Edge Cloud Management with Development, Security, and Operations Automation Framework
No full textModern autonomous driving and intelligent transportation systems face critical challenges in managing real-time data processing, network latency, and security threats across distributed vehicular environments. Conventional cloud-centric architectures typically struggle to meet the low-latency and high-reliability requirements of vehicle-to-everything (V2X) applications, particularly in dynamic and resource-constrained edge environments. To address these challenges, this study introduces the V2X-Car Edge Cloud system, which is a cloud-native architecture driven by DevSecOps principles to ensure secure deployment, dynamic resource orchestration, and real-time monitoring across distributed edge nodes. The proposed system integrates multicluster orchestration with Kubernetes, hybrid communication protocols (C-V2X, 5G, and WAVE), and data-fusion pipelines to enhance transparency in artificial intelligence (AI)-driven decision making. A software-in-the-loop simulation environment was implemented to validate AI models, and the SmartX MultiSec framework was integrated into the proposed system to dynamically monitor network traffic flow and security. Experimental evaluations in a virtual driving environment demonstrate the ability of the proposed system to perform automated security updates, continuous performance monitoring, and dynamic resource allocation without manual intervention. © 2025 by the authors.TRUEsciescopu
Programming silk: Two-step crystallization and directional growth in nanofibrillar assembly
No full textReal-time visualization reveals a controllable two-step silk nanofibrillar assembly mechanism where energy landscape manipulation directs conformational transformation from amorphous clusters to beta-sheet crystals, enabling the rational design of enhanced silk materials.FALSEsciescopu
A bleomycin-mimicking manganese-porphyrin-conjugated mitochondria-targeting peptoid for cancer therapy
No full textBleomycin (BLM) is a natural product with established anticancer activity, attributed to its ability to cleave intracellular DNA. BLM complexes with iron (BLM-Fe3+) exhibit peroxidase-like activity, generate reactive oxygen species (ROS), and cause DNA cleavage. Inspired by the mechanism of BLM, we synthesized a novel conjugate of manganese tetraphenylporphyrin (MnTPP) with a biomimetic peptoid (i.e., oligo-N-substituted glycines); this conjugate harnesses the oxidative capabilities of manganese porphyrins combined with the cell-penetrating ability of a previously reported mitochondria-targeting peptoid (MTP). UV–vis spectroscopy showed the formation of Mn(V)-oxo porphyrin, a potent oxidative species, in the presence of hydrogen peroxide, simulating metallobleomycin reactivity. Biological assays demonstrated that MnTPP-MTP significantly boosted ROS production and induced cytotoxicity toward cancer cells, while sparing normal fibroblasts. Tetramethylrhodamine ethyl ester (TMRE) assay revealed reversible, dose-dependent impairment of the mitochondrial membrane potential by MnTPP-MTP treatment. DNA cleavage assays showed that MnTPP-MTP, specifically in the presence of hydrogen peroxide, could elicit substantial DNA damage, in a similar way to BLM. In vivo studies using liposome-encapsulated MnTPP-MTP (lipo-peptoid) indicated superior tumor suppression, without systemic toxicity, when administered locally. Immunofluorescence staining for Ki67 and TUNEL confirmed reduced cell proliferation and increased apoptosis, respectively, validating the anticancer efficacy of lipo-peptoid. These results suggest that MnTPP-MTP, particularly in a liposomal formulation, is a promising new chemotherapeutic agent with robust oxidative mechanisms, poised for further development and application against diverse cancers. © 2024 Elsevier LtdFALSEsciescopu
Evaluation of accuracy on bottom-up NH3 emissions and sensitivity of PM2.5 to emission control in South Korea
No full textWhile winter has traditionally been the focus of air quality research in South Korea, early summer also experiences high levels of fine particulate matter (PM2.5), largely driven by elevated ammonia (NH3) emissions from agricultural activities. The South Korean government decided to reduce agricultural-based NH3 emissions by 30 % in the Special Act on Fine Aerosol to reduce the levels of PM2.5 due to the significant contributions of NH3 to the formation of secondary inorganic aerosols. In this study, we utilized the CMAQ v5.2.1 model to simulate air quality over East Asia during the KORUS-AQ campaign (1 May - June 12, 2016) and evaluate the accuracy of the NH3 emission, their impacts on PM2.5, and the effectiveness of various emission control strategies, particularly in South Korea. The accuracy of NH3 emissions was evaluated by comparing model-predicted NH3 concentrations with the EANET and AMoN-China networks and with the MARGA equipment in Seoul. The comparison at 60 monitoring stations revealed negative mean biases in China (often exceeding 10 mu g m- 3), indicating a possible underestimation of NH3 emissions in the KORUSv5.0 inventory in most parts of China except for some overestimations in Henan and Sichuan provinces. On the other hand, biases were smaller in South Korea and Japan, ranging from -3.80 to 2.99 ppb (or -2.68 to 2.11 mu g m- 3 at the standard ambient temperature and pressure). We found from the sensitivity simulations by incrementally reducing NH3 emissions in South Korea from 0 % to 100 % that NH3 reductions lead to lower aerosol pH and decreased nitrate partitioning (e.g., from 1.31 to -0.54 and from 0.40 to 0.05 for SK, respectively). In addition, the South Korean government's reduction policy of NH3 emissions can improve the mean concentration of PM2.5 between 0.59 and 1.31 mu g m-3 in South Korea. Chemical regime analysis concludes that South Korea exhibits NH3-rich conditions, where NOx plays a more dominant role in nitrate formation. Furthermore, the sensitivity simulations of the multi-pollutant reductions indicate that i) a 40 % of NH3 and SO2 & NOx, ii) a 60 % of NH3, or iii) a 60 % reduction of SO2 & NOx emissions are required to achieve a 10 % decrease in PM2.5 in South Korea.FALSEsciescopu
A Study on Overcoming Informational Incompleteness in Distribution System Operator: Ensuring Robustness in State Estimation and Optimal Operation
No full text분산자원의 배전망 침투율이 증가함에 따라, 배전계통 운영의 불확실성이 심화되고 있으며 이에 따른 안정적 운영의 필요성이 더욱 강조되고 있다. 그러나 배전계통은 측정 인프라의 한계, 측정오차, 긴 관제 주기 등으로 인해 실시간 감시와 제어가 어렵다. 본 학위논문은 이러한 정보의 비완전성을 극복하고, 안정적인 계통 운영을 실현하기 위해 두 가지 핵심 과제를 다룬다. 첫째, 정확한 상태추정을 위해 물리기반 순환 신경망과 그래프 신경망을 통합한 물리정보 인식 기반 상태추정 모델을 제안한다. 제안된 모델은 전역적 토폴로지 정보와 지역적 특징들의 상관관계를 통합적으로 학습함으로써, 미학습된 실제 계통 구조 하에서도 높은 정확도와 강건성, 확장성을 입증하였다. 둘째, 불평형 3상 배전계통의 실시간 최적 운영을 위해, 다중 자원 와서스테인 거리 기반 분포 강건 결합 기회 제약 최적화 알고리즘을 제안한다. 이 모델은 분산자원 및 부하의 데이터 신뢰도를 개별적으로 고려하여, 관제 주기 사이 발생하는 불확실성을 반영한 최악 시나리오에서도 계통 안전성과 운용 효율을 동시에 만족시키는 강건한 해를 도출하였다.|As the penetration of distributed energy resources(DERs) in distribution networks increases, the uncertainty in distribution system operations is intensifying, highlighting the growing need for stable and reliable operation. However, due to limitations in measurement infrastructure, inherent sensor errors, and long supervisory control intervals, real-time monitoring and control remain challenging. This dissertation addresses these issues of information incompleteness and proposes two core solutions for enhancing the robustness of distribution system operations. First, to enable accurate state estimation, a physics-informed state estimation model is developed by integrating recurrent neural networks with graph neural networks. The proposed model jointly learns global topological structure and local spatial correlations, demonstrating high accuracy, robustness, and generalizability even under unseen real-world network topologies. Second, to achieve real-time optimal operation of unbalanced three-phase distribution systems, a multi-source distributionally robust joint chance-constrained optimization algorithm based on the Wasserstein metric is proposed. This approach considers the data reliability of DERs and loads individually, and ensures secure and efficient operation by providing robust decisions against worst-case scenarios that may arise during control intervals.Doctor1 Introduction 1
1.1 Introduction 1
1.2 Scope 4
2 Physics-Aware Recurrent and Graphical Learning-based Robust Distribution System State Estimation 6
2.1 Introduction of distribution system state estimation 6
2.2 Methodological approaches and limitations 11
2.2.1 Line Measurements of Real Distribution Systems 11
2.2.2 Limitations of previous optimization-based DSSE methods 13
2.2.3 Limitations of previous learning-based DSSE methods 14
2.3 Proposed SARNN based ARMA-GNN model for DSSE 18
2.3.1 Line Graph Representation 19
2.3.2 Structure-Aware Recurrent Neural Network(SARNN) 20
2.3.3 ARMA-Based GNN Module 22
2.4 Numerical results 24
2.4.1 Simulation Setup 26
2.4.2 Performance Assessment with Identical Training and Testing DNs 28
2.4.3 Performance Assessment with Unseen Networks 29
2.4.4 Probabilistic Estimation Performance Assessment with pinball Score 32
2.4.5 Scalability Verification Using IEEE Test Feeders 34
2.5 Summary and discussions 36
3 Multi-Source Distributionally Robust Optimal Operation for Unbalanced Distribution Systems under Uncertainty across Real-Time Dispatch Intervals 37
3.1 Introduction of distribution system optimal operation 37
3.2 Data-driven AC-OPF formulation for distribution system operation 41
3.2.1 Linear AC-OPF model formulation 41
3.2.2 Original optimal operation formulation 47
3.2.3 Final General Formulation 54
3.3 Multi-source distributionally robust chance constrained formulation 54
3.3.1 Multi-source distributional ambiguity set 54
3.3.2 Objective function reformulation 56
3.3.3 Chance-constraint reformulation based on conditional value-at-risk approach 57
3.3.4 Complete formulation 63
3.4 Numerical Result 63
3.4.1 Simulation Environment 64
3.4.2 Result Analysis 67
3.5 Summary and Discussions 77
4 Conclusion 79
References 81
A Abbreviations 91
Acknowledgements 93
Curriculum Vitae 9
CFD analysis and design of bypass dual throat nozzle for high-performance fluidic thrust vectoring
No full textThe purpose of the study is to investigate detailed flow properties of the bypass dual throat nozzle (BDTN) for fluidic thrust vectoring, and to find an optimal geometry to maximize its performance. The performance metrics of the BDTN are defined as the thrust efficiency and flow deflection angle at the nozzle exit. Given the nozzle pressure ratio (NPR), secondary flows injected from the bypass duct of the nozzle create circulatory flows in the nozzle cavity, produce complex interactions of shock and expansion waves, and deflect the directions of the exit flows. To identify key parameters for the BDTN performance, a sensitivity study is carried out using the traditional finite difference method as well as the AI-assisted Shapley additive explanation methods with respect to geometric variables of the BDTN. For the design optimization, a total of eight geometric parameters were chosen including an upstream convergent angle (θ1), a bypass injection angle (θ2), cavity divergence and convergence angles (θ3 and θ4), upstream and downstream throat diameters (d2 and d3), bypass channel diameter (d4), and cavity divergence length (l1). Those parameters were varied by 10∼20 % of the baseline values to create more than 100 random BDTN geometries which were solved by the full CFD analysis. The multi-variate Gaussian process regression (GPR) model was developed by training the data as a surrogate model to the CFD analysis of arbitrary BDTN shape during the design iteration. Multi-objective optimization was conducted to generate the Pareto optimal front of multiple design candidates for maximum deflection angle and thrust values. The optimum BDTN geometry produced a deflection angle increased up to 13 %, while thrust value was slightly increased from that of the baseline by less than 1%. The approach provides a foundation for future research into adaptive nozzle designs responsive to real-time flow conditions, potentially expanding the applications of fluidic thrust vectoring. © 2024 Elsevier LtdFALSEsciescopu
Dual effects of estuarine dams on summertime phytoplankton productivity in temperate river-estuary transition zones
No full textThe river transports dissolved and particulate matter to the estuary, where physical and biogeochemical transitions occur. In the river-estuary transition zone (RETZ), phytoplankton response is an effective indicator of environmental variability with their immediate responses. Here, we examine summer-monsoonal variability in productivity and nitrogen uptake of phytoplankton in three major Korean rivers and their estuaries: the Han, Geum, and Yeongsan. Results showed that summer-monsoonal variability of primary productivity in dam-constructed RETZ divided them into riverine zone governed by nutrient-phytoplankton interactions and estuarine zone associated with nitrogenous substrate dynamics. In contrast, free-flowing RETZ exhibited lower variability in phytoplankton productivity due to simultaneously enhanced nitrogenous nutrients and turbidity. Our generalized additive model analysis indicated that composition of particulate organic matter is primarily correlated with phytoplankton biomass associated with light availability. Our findings emphasize the complex interplay of physical and biogeochemical processes in RETZ, mainly influenced by monsoonal variability and river-estuary connectivity. © 2025 Elsevier LtdFALSEsciescopu
Design of Low-Power and High Slew-Rate Class-AB Amplifier
No full textThis paper presents a low-power and high slew-rate Class-AB amplifier. The proposed amplifier is suitable for applications such as professional audio equipment, portable electronic devices, and biomedical systems. The power efficiency and high driving capability of Class-AB amplifiers contribute to extended battery life, support for high-speed operation, and reduction of crossover distortion. However, achieving both high power efficiency and high driving capability is challenging in conventional operational transconductance amplifiers (OTAs) due to the inherent trade-off between power consumption and driving capability. The slew rate is proportional to the current mirror ratio and the quiescent bias current. Enhancing the driving capability by increasing either of these factors often results in a larger parasitic capacitance at the output stage, which degrades the frequency response and increases power dissipation. To address this issue, the proposed Class-AB amplifier incorporates two additional current source circuits for adaptive biasing, configured in a parallel and symmetrical structure with respect to the tail current source of a conventional OTA. Plus, two compensation capacitors are connected between the output terminal and the current source circuit for Miller compensation to enhance the stability of the circuit’s operation. This architecture enables the amplifier to achieve improved performance compared to previous designs. The circuit was designed using the TSMC 0.18 μm CMOS process, and its implementation and simulation were carried out in Cadence Virtuoso using the Spectre simulator. Under a 1.8 V supply voltage and a 1 pF load capacitance, the amplifier achieved slew rates of +117 V/μs and –88 V/μs for the rising and falling edges, respectively, while consuming 3.5 mW of power.MasterI. INTRODUCTION 1
1. 1 Motivation 1
1. 2 Thesis Organization 2
II. CONCEPT OF CLASS-AB AMPLIFIER 4
2. 1 Basic Class-AB Amplifier 4
III. PREVIOUS RESEARCH 6
3. 1 Conventional OTA 6
3. 2 Current Subtractor Circuit 7
IV. PROPOSED IDEA FOR ADAPTIVE BIASING 10
4. 1 Conventional OTA 10
4. 2 Current Subtractor Circuit 11
4. 3 Mismatch Effects 12
4. 4 Verification of Adaptive Bias Cell’s Operation 13
V. IMPLEMENTATION AND SIMULATION 16
5. 1 Implementation The Circuit In Cadence 16
5. 2 AC Simulation 17
5. 3 Transient Simulation For Calculate in Slew-Rate 18
5. 4 Performance 19
VI. SUMMARY 21
6. 1 Conclusion 21
6. 2 Future Work 21
Reference 23
Curriculum Vitae 2
Precisely controlled bimetallic nanocatalysts on mesoporous silica nanoparticle supports for highly efficient and selective nitrate reduction
No full textMonodisperse, bimetallic nanocatalysts, homogeneously supported on mesoporous silica nanoparticles, are demonstrated as highly active, reducing catalysts for rapid and selective nitrate transformation. For this, we finely controlled catalyst size and composition based on new synthesis strategies. Specifically, atomic clusters of Mreducing/Cu nanocatalysts (Mreducing = Pd, Pt) were optimized for nitrate reduction via subsequent Mreducing impregnation (Mreducing/Cu/Mreducing combinations). Reaction kinetics were observed to be affected by the ratio of Mreducing to Cu, for which Pd−rich Pd/Cu/Pd nanocatalysts showed better performance than Cu−rich analogs. Product selectivity (ammonium vs. nitrogen) was highly correlated with nanocatalyst morphology, with nitrogen gas (dominant) selectivity observed for relatively larger Pd particles. Lastly, MSN−Pd/Cu/Pd catalyst are highly stable in water, with no catalyst deterioration over multiple reactions (10 cycles), while maintaining consistently high reaction rates and N2 selectivity. Taken together, this study demonstrates the critical importnace of precise catalyst control and stability towards optimizing nitrate reduction processes. © 2024FALSEsciescopu