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Effects of suspended particles on the toxicity of AgNO3 and silver nanoparticles to Daphnia magna: Importance of adsorption of dissolved Ag and dietary pathway
No full text[No abstract available]TRUEscopu
Formation and Decay of Aqueous Fe(IV) during Fe(II) Ozonation under Acidic Conditions: Kinetics and Mechanistic Insights into pH-Dependent Behavior
No full textAqueous Fe(IV) is a crucial oxidant in iron-mediated oxidation processes relevant to water purification and atmospheric aqueous systems; yet, its chemical behavior under environmentally relevant pH conditions remains poorly understood. This study elucidated the pH-dependent kinetics and mechanisms of Fe(IV) formation, self-decay, and secondary reactions during Fe(II) ozonation at pH 1.0-5.0. The Fe(II)-O-3 reaction involved Fe-II(H2O)(6)(2+) and (H2O)(5)Fe-II(OH)(+), generating Fe(IV) and HO center dot, respectively; thus, HO center dot formation increased with increasing pH at pH > 4.0. The determined pK(a) of Fe(IV) ((FeO2+)-O-IV/(OH)(FeO+)-O-IV) was 3.4, primarily modulating its pH-dependent reactivity. Fe(IV) underwent unimolecular self-decay via (FeO2+)-O-IV (k = 0.07 s(-1)) and (OH)(FeO+)-O-IV (k = 9.7 s(-1)), yielding Fe(III) and O-2, while its bimolecular decay via (OH)(FeO+)-O-IV (k = 8.1 x 10(4) M-1 s(-1)) produced Fe(III) and H2O2. The Fe(IV)-Fe(II) reaction exhibited marked sensitivity to pH and ionic strength, driven by electrostatic interactions. Fe(IV) reacted with H2O2 via (FeO2+)-O-IV (k = 2.4 x 10(4) M-1 s(-1)) and (OH)(FeO+)-O-IV (k = 6.4 x 10(4) M-1 s(-1)), forming Fe(III) and Fe(II), respectively. A kinetic model incorporating these reactions accurately predicted Fe(IV) pH-dependent behaviors and determined the Fe(IV) reaction kinetics with methyl phenyl sulfoxide. These findings significantly advance our understanding of the pH-dependent fate of Fe(IV) in iron-based oxidation processes.FALSEsciescopu
Isolated attosecond pulses generated from a relativistic plasma mirror via noncollinear gating
No full textA train of intense attosecond pulses can be obtained through relativistic high harmonic generation when an intense laser field is reflected on a plasma surface. Separating a single isolated attosecond pulse from the train is critical not only for applications such as time-resolved pump-probe experiments but also for studying laser-plasma interactions with attosecond temporal resolution. Various methods have been developed for an isolated attosecond pulse generation in gas. However, they require ultrashort laser pulses, which are difficult to apply with high-power lasers typically employed in relativistic high harmonic generation. Here, we demonstrate that an isolated attosecond pulse can be obtained through relativistic high harmonic generation using noncollinear temporal gating. Our approach also provides direct access to each attosecond pulse in the train, allowing us to diagnose the laser-plasma interaction, such as plasma denting and reflection positions, in a time-resolved manner. Thus, it offers breakthroughs in attosecond pulse generation at relativistic laser intensities. © 2025 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.TRUEscopu
Deep Learning-Based Phase Unwrapping and Image Denoising for 3D Reconstruction in Digital Holography and Photoacoustic Imaging
No full textFull-field optical and photoacoustic imaging systems—such as Digital Holography and Full-Field Photoacoustic Tomography (FF-PAT)—have become essential tools for non-invasive, high-resolution imaging in biomedical diagnostics and industrial inspection. These systems capture critical phase and displacement information necessary for 3D reconstruction and quantitative analysis. However, phase and displacement maps are often severely corrupted by speckle noise, Gaussian noise, and ambient interference, posing significant challenges for accurate signal recovery. Traditional denoising and unwrapping algorithms often fail under these harsh conditions due to their sensitivity to noise, assumptions about phase smoothness, and high computational costs. This thesis proposes three deep learning-based frameworks to address these challenges, offering robust, accurate, and efficient solutions for denoising and phase recovery in full-field imaging. Each framework is specifically tailored to overcome the limitations of conventional methods and to operate reliably under low- SNR conditions. First, we propose WPD-Net (Wrapped Phase Denoising Network), a lightweight neural network designed for denoising wrapped-phase images affected by complex noise mixtures. WPD-Net integrates Residual Dense Attention Blocks (RDABs) to selectively enhance important features while suppressing irrelevant noise. A growth-rate-based multi-scale feature expansion and dense feature fusion strategy are incorporated to preserve fine structural details and ensure phase continuity. Evaluations on both synthetic and experimental datasets demonstrate that WPD-Net outperforms traditional and deep learning- based denoising approaches in terms of PSNR, SSIM, and visual quality. Its compact design also enables real- time inference, making it highly suitable for biomedical and industrial optical systems where fast, accurate processing is critical. Next, to unify denoising and phase unwrapping into a single step, we introduce DenSFA- PU (Densely Connected Spatial Feature Aggregator for Phase Unwrapping). DenSFA-PU is an end-to-end regression model that directly maps noisy wrapped-phase inputs to unwrapped continuous outputs. The network combines dense connectivity with a Spatial Feature Aggregator (SFA) module, integrating Bi- directional LSTM layers and Bottleneck Attention Modules (BAMs) to capture long-range dependencies and focus on structurally important regions. Extensive experiments show that DenSFA-PU achieves superior results across standard evaluation metrics (PSNR, SSIM, NRMSE) and maintains fast inference times (~29.31 ms per image). This efficiency, combined with high robustness against severe noise, positions DenSFA-PU as a powerful tool for real-time and high-throughput phase imaging applications. Finally, for FF-PAT, we address displacement map denoising—a particularly challenging problem due to the extremely low SNR of photoacoustic signals. We propose an attention-guided, multi-scale feature fusion network that combines RDABs with dual channel and spatial attention modules. Trained exclusively on experimentally acquired FF- PAT data, the model demonstrates strong generalization to real-world noise conditions. It achieves a PSNR of 34.25 dB, significantly outperforming coherent averaging (13.88 dB) and U-Net-based approaches (26.87 dB). With a processing speed of 0.53 seconds per displacement map, the model enables real-time volumetric photoacoustic imaging while preserving critical structural information. ⓒ2025 Muhammad Awais ALL RIGHTS RESERVEDDoctorAbstract i
Contentsiii
List of Figures v
List of Tables viii
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Problem Statement 2
1.3 Contributions 3
1.4 Thesis structure 4
Chapter 2. Background and Related Work 5
2.1 Optical and Photoacoustic Imaging: Principles and Applications 5
2.2 Wrapped Phase and Unwrapping Problem 9
2.2.1 One-dimensional Phase Unwrapping 9
2.2.2 Two-dimensional Spatial Phase Unwrapping 11
2.3 Traditional Unwrapping Techniques 13
2.4 Denoising Wrapped Phase Images 16
2.5 Deep Learning in Phase Unwrapping- 18
2.6 Denoising Displacement Maps in FF-PAT 21
Chapter 3. Wrapped Phase Denoising Network- 24
3.1 Overview 24
3.2 Architecture of WPD-Net 25
3.2.1 Shallow Feature Extraction (SFE) Module- 25
3.2.2 Residual Dense Attention Blocks (RDABs) 25
3.2.3 Dense Feature Fusion (DFF) Module 28
3.3 Advantages of WPD-Net- 29
3.4 Loss Function 31
3.5 Training and Evaluation 32
3.5.1 Dataset Preparation 32
3.5.2 Training Parameters and Evaluation Metrics- 34
3.6 Experimental Results 38
3.6.1 Experiments with Synthetic Data 38
3.6.2 Experiments with Real Data- 44
3.7 Ablation Study- 49
3.8 Conclusions and Discussion 51
Chapter 4. DenSFA-PU Phase Unwrapping Network- 53
4.1 Overview 53
4.2 Architecture of DenSFA-PU 54
4.3 Loss Function 59
4.4 Training and Evaluation 60
4.4.1 Dataset Preparation 60
4.4.2 Training Parameters and Evaluation Metrics- 62
4.5 Experimental Results 66
4.5.1 Experiments with Synthetic Data 66
4.5.2 Experiments with Real Data- 80
4.6 Ablation Study- 83
4.7 Conclusions and Discussion 84
Chapter 5. Denoising in Full-Field Photoacoustic Tomography 87
5.1 Overview 87
5.2 Network Architecture for FF-PAT Displacement Map Denoising- 88
5.3 Loss Function 92
5.4 Experimental Results 92
5.4.1 Data Generation- 92
5.4.2 Results 94
5.5 Ablation Study- 98
5.6 Conclusions and Discussion 98
Chapter 6. Conclusions and Future Work 100
6.1 Conclusions 100
6.2 Future Work 102
References 105
Acknowledgement 116
Curriculum Vitae 11
Integrating AQUATOX, Ecological Big Data, and Machine Learning for Short-Term Chemical impact Assessment on River Ecosystems
No full textThis thesis develops a methodology for assessing the impact of chemical accidents on aquatic ecosystems using ecological modeling. The study consists of three main parts: First, it generates a quantifiable species-specific ecological parameter database for domestic aquatic ecosystems. This involves estimating representative biomass for 687invertebrate species and 157 fish species in Korean rivers using length-weight relationships and literature data. The study proposes and evaluates a method for applying these parameters to ecological models of domestic river ecosystems. The most accurate method (R2 = 0.5529 for invertebrates, R2 = 0.9349 for fish) used genus-level averages of family-level averages of length- weight constants. Second, the research parameterizes food web interactions between aquatic species in Korean rivers. It employs a Deep Neural Network model, constrained by ecological domain knowledge, to estimate food preferences among species groups. The model is trained on big data from species presence surveys across various regions. The optimal model (R2 = 0.8953, RMSE = 0.2369) was achieved when the maximum trophic level for species consuming producers was set at 3.2, and predation was allowed only when the predator's trophic level exceeded that of the prey. Third, the study applies the AQUATOX model to assess the impact of a phenol spill on the Iksan Stream ecosystem. The model is constructed using water quality, flow, and ecological data from various monitoring points along the stream. A control simulation is established to represent the stream's ecosystem without the phenol spill, followed by a perturbed simulation incorporating observed phenol concentrations (maximum 2.9 mg/L). The model's performance is evaluated using Chlorophyll a concentrations, with a no improvement in the perturbed scenario (zero increase in Nash-Sutcliffe Efficiency). The results demonstrate complex ecosystem responses to the phenol spill, highlighting the importance of considering food web interactions and long-term effects in chemical spill impact assessments. This research contributes to the development of more comprehensive environmental risk assessments and management strategies for aquatic ecosystems in Korea.
Yeom, Jaehoon (염재훈). Integrating AQUATOX, Ecological Big Data, and Machine Learning for Short-Term Chemical impact Assessment on River Ecosystems (AQUATOX 모델, 생태 빅데이터, 머신러닝을 활용한 하천 생태계의 단기 화학물질 노출 영 향 평가). School of Environment and Energy Engineering. 2025. 235p. Prof. Sang Don KimDoctorAbstract i
Contents ii
Figure Contents iv
Table Contents viii
Chapter 1 Preface 9
1.1 Overview 9
1.2 Background 12
1.2.1 Chemical accident trends and damage on aquatic environments in the world 12
1.2.2 Definition of Ecological risk assessment (ERA) and community level ERA 14
1.2.3 Modelling approach in high tier ERA 17
1.3 Purpose of this study 18
1.4 Thesis organization 20
Chapter 2 Generating and validating ecological biomass database by using Literature based estimation for
aquatic species 21
2.1 Estimating biomass of aquatic invertebrates in Korean rivers 21
2.1.1 Abstract 21
2.1.2 Introduction 22
2.1.3 Materials and methods 24
2.1.4 Result and Discussions 29
2.1.5 Conclusions 43
2.2 Estimating biomass of fishes in Korean Rivers 45
2.2.1 Abstract 45
2.2.2 Introduction 45
2.2.3 Materials and Methods 47
2.2.4 Result and Discussion 51
2.2.5 Conclusions 69
Chapter 3 Parametrization of food web interaction between species by using machine learning technique 71
3.1 Ecological clustering of aquatic species in Korean rivers 71
3.1.1 Abstract 71
3.1.2 Introduction 71
3.1.3 Materials and Methods 73
3.1.4 Result and Discussions 81
iii
3.1.5 Conclusion 87
3.2 Parametrization of foodweb interaction between aquatic species in Korean rivers 89
3.2.1 Abstract 89
3.2.2 Introduction 89
3.2.3 Materials and Methods 90
3.2.4 Result and Discussions 99
3.2.5 Conclusions 106
Chapter 4 Assessment of AQUATOX model in phenol exposure accdent on Iksan river 108
4.1 Ecological models and AQUATOX model for ecological risk assessments 108
4.1.1 Comparison of ecological models 108
4.1.2 Concept of AQUATOX model 109
113
4.1.3 Methodology to assess chemical impact with AQUATOX model 115
4.2 AQUATOX model for phenol exposure in Iksan River 116
4.2.1 Abstract 116
4.2.2 Introduction 117
4.2.3 Materials and Methods 119
4.2.4 Result and Discussions 128
4.2.5 Conclusions 156
Chapter 5 Conclusions 158
References 161
Appendix 171
Curriculum Vitae 227
Acknowledgement 232
iv
Figure Contents
Figure 1. Pros and cons of various tiers in ERA (Ecological Risk Assessment) [5] 15
Figure 2. Goals of the study 19
Figure 3. Chapter organization 20
Figure 4. Scheme of methodology to interpolate LWRs coefficients 25
Figure 5. Scheme of methodology to evaluate LWRs coefficient 28
Figure 6. Mean length distribution of invertebrate species linving in Korean rivers 29
Figure 7. Predicted dry weight versus Measured dry weight of invertebrate species with no exception
on outliers (Genus level) 33
Figure 8. Predicted dry weight versus Measured dry weight of invertebrate species with no exception
on outliers (Family level) 34
Figure 9. Predicted dry weight versus Measured dry weight of invertebrates species with no exception
on outliers (Order level) 35
Figure 10. Predicted dry weight versus Measured dry weight of invertebrate species with exception on
outliers (Genus level) 36
Figure 11. Predicted dry weight versus Measured dry weight of invertebrate species with exception on
outliers (Family level) 37
Figure 12. Predicted dry weight versus Measured dry weight of invertebrate species with exception on
outliers (Order level) 38
Figure 13. LWRs coefficient matching process for mollusk species 40
Figure 14. Scheme of methodology to evaluate Length-Weight coefficient for fish 48
Figure 15. Number of LWRs Coefficients by Order level 51
Figure 16. Length distribution of target fish species living in Korean rivers. 52
Figure 17. Predicted wet weight versus Measured wet weight of fish species with no exception on
outliers (Genus level, CG ~ G4G Groups) 55
Figure 18. Predicted wet weight versus Measured wet weight of fish species with no exception on
outliers (Genus level, G5G ~ G9G Groups). 56
Figure 19. Predicted wet weight versus Measured wet weight of fish species with no exception on
outliers (Family level, CF ~ G4F Groups) 57
Figure 20. Predicted wet weight versus Measured wet weight of fish species with no exception on
outliers (Family level, G5F ~ G9F Groups) 58
Figure 21. Predicted wet weight versus Measured wet weight of fish species with no exception on
outliers (Order level, CO ~ G4O Groups) 59
Figure 22. Predicted wet weight versus Measured wet weight of fish species with no exception on
outliers (Order level, G5O ~ G9O Groups) 60
Figure 23. Predicted wet weight versus Measured wet weight of fish species with exception on outliers
(Genus level, CG ~ G4G) 63
Figure 24. Predicted wet weight versus Measured wet weight of fish species with exception on outliers
(Genus level, G5G ~ G9G) 64
v
Figure 25. Predicted wet weight versus Measured wet weight of fish species with exception on outliers
(Family level, CF ~ G4F Groups) 65
Figure 26. Predicted wet weight versus Measured wet weight of fish species with exception on outliers
(Family level, G5F ~ G9F Groups) 66
Figure 27. Predicted wet weight versus Measured wet weight of fish species with exception on outliers
(Order level, CO ~ G4O Groups) 67
Figure 28. Predicted wet weight versus Measured wet weight of fish species with exception on outliers
(Order level, C5O ~ G9O Groups) 68
Figure 29. Ecological clustering process of invertebrate species groups 79
Figure 30. Ecological clustering process of fish species groups 80
Figure 31. Result of correlation analysis of invertebrates group with two parameters (Locomotion and
Biomass) 81
Figure 32. Result of correlation analysis of fish group with two parameters (Feeding habit and
Biomass) 82
Figure 33. Result of K-means clustering for invertebrate groups 82
Figure 35. Mean trophic level result of K-means clustering for invertebrate groups (I 42 ~ I 82) 83
Figure 34. Mean trophic level result of K-means clustering for invertebrate groups (I 1 ~ I 41) 83
Figure 36. Outlier result of K-means clustering for invertebrate groups (I 1 ~ I 41) 84
Figure 37. Outlier result of K-means clustering for invertebrate groups (I 42 ~ I 82) 84
Figure 38. Result of K-means clustering for fish groups 85
Figure 39. Mean trophic level result of K-means clustering for fish groups 86
Figure 40. Outlier result of K-means clustering for fish groups 87
Figure 41. Scheme of food preference table for 113 species groups from clustering 93
Figure 42. Scheme of Existence database for aquatic species monitored in Korean river during 2010 to
2020 94
Figure 43. Scheme of Mean trophic level database and distribution assumption 94
Figure 44. X input database for DDN (Deep Neural Network) 95
Figure 45. Y input database for DDN (Deep Neural Network) 95
Figure 46. Scheme for food preference model training methodology by using DNN 96
Figure 47. Model efficiency of expecting food preference table 101
Figure 48. Food preference of each fish group on whole fish group 102
Figure 49. Food preference of each fish group on whole Invertebrates group 103
Figure 50. Food preference of each fish group on whole producer group 103
Figure 51. Food preference of each invertebrates group (I1 ~ I41) on whole invertebrate group 104
Figure 52. Food preference of each invertebrates group (I42 ~ I82) on whole invertebrate group 104
Figure 53. Food preference of each invertebrates group (I1 ~ I41) on whole producer group 105
Figure 54. Food preference of each invertebrates group (I42 ~ I82) on whole producer group 105
vi
Figure 55. Schematic figure of sub-model interaction in the AQUATOX model [33] 110
Figure 56. Window for the parameter of food web interaction in AQUATOX 113
Figure 57. Weibull distribution for the time varying affected fraction of organism from exposure [33]
. 114
Figure 58. Process of simulating AQUATOX model for ecological assessment [146] 115
Figure 59. Site explanation of Iksancheon 119
Figure 60. Monitoring data of pheonol concentration (mg/L) for Iksancheon (Iksancheon3 monitoring
site) 120
Figure 61. Monitoring data of water quality for Iksancheon (Iksancheon3 monitoring site) 121
Figure 62. Monitoring data of flow rate (m3/day) for Iksancheon (Insu monitoring site) 121
Figure 63. Partial correlation analysis of waterquality parameters in Iksancheon 122
Figure 64. Interpolated result for water quality parameters of Iksancheon2 by using LSTM model 129
Figure 65. Interpolated result of flow rate (m3/day) of Iksancheon (Insu monitoring site) by using
LSTM and GRU models 129
Figure 66. Scheme of food web in Iksancheon (Using boundary condition TL 1 predation TL limit -
3.2/ Predator TL 0.3 > Prey TL) 132
Figure 67. Scheme of food web in Iksancheon (Using boundary condition TL 1 predation TL limit -
3.5/ Predator TL 0.3 > Prey TL) 133
Figure 68. Scheme of food web in Iksancheon (Using boundary condition TL 1 predation TL limit -
3.8/ Predator TL 0.3 > Prey TL) 133
Figure 69. Scheme of food web in Iksancheon (Using boundary condition TL 1 predation TL limit –
4.1/ Predator TL 0.3 > Prey TL) 134
Figure 70. SSD curve for Cypriniformes (LC50 for 2 days exposure of phenol) 135
Figure 71. SSD curve for Cypriniformes (LC50 for 1 day exposure of phenol) 136
Figure 72. SSD curve for Cypriniformes (LC50 for 4 days exposure of phenol) 136
Figure 73. SSD curve for Diptera (LC50 for 2 days exposure of phenol) 137
Figure 74. SSD curve for Coleoptera (LC50 for 2 days exposure of phenol) 137
Figure 75. SSD curve for Hemiptera (LC50 for 2 days exposure of phenol) 138
Figure 76. SSD curve for Snail (LC50 for 2 days exposure of phenol) 139
Figure 77. SSD curve for species living in Iksancheon (NOEC values for exposure of phenol) 140
Figure 78. Comparison of monitored and modeled inflow flow rate (m3/day) for target segment during
control simulation of AQUATOX model 141
Figure 79. Comparison of monitored and modeled water quality of target segment during control
simulation of AQUATOX model (Calibration of water quality in control simualtion) 142
Figure 80. Result of modeled biomass density (g/ m2 dry) for each species group of target segment
during control simulation of AQUATOX model 144
Figure 81. result of modeled phenol concentration (ug/L) of target segment during perturbed
simulation of AQUATOX model 146
Figure 82. Comparison of Chlorophyll a concentration (ug/L) of target segment during control and
vii
perturbed simulation of AQUATOX model 146
Figure 83. Difference in Chlorophyll a concentration (ug/L) of target segment between control and
perturbed simulation of AQUATOX model (%) 147
Figure 84. Difference in Biomass denstiy (g/m2 dry) of each species group of target segment between
control and perturbed simulation of AQUATOX model (%) 148
Figure 85. Percent perturbation (%) in Biomass denstiy (g/m2 dry) of each species group of target
segment between control and perturbed simulation of AQUATOX model 150
Figure 86. MAD analysis result of phytoplankton groups in Iksancheon 151
Figure 89. MAD analysis result of Invertebrates groups in Iksancheon 152
Figure 88. MAD analysis result of fish group in Iksancheon 153
Figure 89. Chlorophyll a concentration (ug/L) from control, perturbed simualtion and monitored data
. 155
viii
Table Contents
Table 1. Data sources of parameters used for LWRs 27
Table 2. Availability of LWRs coefficients for generating group coefficients 30
Table 3. Data point and outlier information for invertebrate species (C means Control, G1~ G4 mean
Group 1 ~ Group 4, G, F, and O mean Genus, Family, and Order level each – CG group means
Control group and Genus level approximation) 31
Table 4. Target group of mollusk species for generating mean weight of species 39
Table 5. Generated mean weight for mollusk species 41
Table 6. Analysis on database for monitored weight of fish species (C means Control, G1~ G9 mean
Group 1 ~ Group 9, G, F, and O mean Genus, Family, and Order level each – CG group means
Control group and Genus level approximation) 53
Table 7. Result of data analysis for each group and ourlier ratio (Purple color means R2 >0.5, red color
means R2 > 0.7, bold red color means R2 >0.9) 62
Table 8. Data sources for investigation of ecological indicators 74
Table 9. Characteristics of each ecological group of fish species 75
Table 10. Characteristics of each feeding habit group of fish species 75
Table 11. Characteristics of each ecological group of invertebrate species 76
Table 12. Characteristics of each locomotion group of invertebrate species 76
Table 13. Locomotion score 78
Table 14. Feeding habit score 78
Table 15. Hyperparameters for DNN learning of food preference 97
Table 16. Boundary condition with Domain knowledge for generating Food preference table
(Maximum trophic level to eat P group and Trophic Level Condition between Predator and Prey
groups) 98
Table 17. Model performance of generated food preference table for each Domain knowledge
condition 100
Table 18. Comparison of ecological models for aquatic environments 108
Table 19. Model process of plant compartment in AQUATOX [33] 111
Table 20. Model process of the animal compartment in AQUATOX 112
Table 21. Hyperparameter condition of LSTM model for training water quatlity regression between
upstream and downstream of Iksancheon 123
Table 22. Hyperparameter condition of LSTM and GRU models for interpolation of flow rate in
Iksancheon (Insu monitoring site) 124
Table 23. Model efficiency of spatial interpolation using LSTM on water quality parameters of
Iksancheon2 128
Table 24. Occurence of species groups in Iksancheon 130
Table 25. Food preference table for Iksancheon environemnt - relative preference (%, Using boundary
condition TL 1 predation TL limit 3.5/ Predator TL 0.3 > Prey TL) 13
Nir2 crystal structures reveal a phosphatidic acid–sensing mechanism at ER–PM contact sites
No full textAgonist-induced activation of phosphoinositide-specific phospholipase C (PLC) converts phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] to diacylglycerol (DAG) at the inner leaflet of the plasma membrane (PM). DAG can be enzymatically transformed into phosphatidic acid (PA) and accumulated at the PM. PYK2 N-terminal domain-interacting receptor 2 (Nir2) mediates the formation of ER–PM membrane contact sites (MCSs) by specifically recognizing PA at the PM and directly interacting with ER membrane protein vesicle-associated membrane protein-associated proteins (VAPs). The N-terminal phosphatidylinositol transfer protein domain of Nir2 facilitates PI/PA exchange at ER–PM MCSs to maintain PI and PA levels. Here, we reveal the mechanisms by which Nir2 senses phosphatidic acid (PA) and associates with membranes, based on three crystal structures of its C-terminal Lipin/Ned1/Smp2 (LNS2) domain bound to PA, the diphenylalanine [FF]–containing acidic tract (FFAT) motif complexed with vesicle-associated membrane protein–associated protein B/C (VAPB), and the Asp-Asp-His-Asp (DDHD) domain. The C-terminal LNS2 domain of Nir2 directly interacts with the phosphate in the headgroup of PA via hydrogen bonds involving S1025, T1065, K1103, and K1126. Formation of a salt bridge between E355 in Nir2 and R55 in VAPB is essential for Nir2 FFAT–VAPB interaction. The central DDHD domain of Nir2 forms a twofold symmetric dimer, and this self-association contributes to stable and tight membrane association. These findings reveal how Nir2-mediated ER–PM MCS formation maintains continued PI(4,5)P2-dependent PLC signaling. © 2025 Elsevier B.V., All rights reserved.TRUEsciescopu
Analysis of the Impact of Imbalance Penalties on Virtual Power Plant Bidding Strategies and System Operational Costs in the Korean Wholesale Electricity Market: Findings and Policy Recommendations
No full textThis paper examines how imbalance penalties influence the virtual power plant (VPP) bidding strategies and system operational costs in new Korea’s wholesale electricity market, motivated by the Jeju pilot project for the improvement of the electricity market system. Two novel models are proposed: bidding model of VPP and unit commitment (UC) models. A two-stage stochastic programming approach is developed for the VPP model incorporating hourly day-ahead bidding and 15-minute real-time operations for dispatchable distributed energy resources (DERs). To address imbalance penalties, this paper proposes convex relaxation-based reformulation of the market’s penalty formula. Subsequently, the UC framework, day-ahead unit commitment (DAUC) and real-time unit commitment (RTUC), is employed to assess how VPP bidding strategies affect system operations, quantifying and analyzing system costs and performance. The case studies explore the effects of different imbalance penalty thresholds on bidding strategies and operational outcomes, anticipating a nationwide rollout of the revised market. The findings offer insights and policy recommendations for refining renewable energy bidding system, fostering broader VPP participation, minimizing market imbalances, and effectively integrating renewable resources into Korea’s evolving wholesale electricity market.MasterAbstract ⅰ
List of contents ⅱ
List of tables ⅲ
List of figures ⅴ
Nomenclature ⅶ
I. INTRODUCTION 1
II. MATHEMATICAL MODELING 6
2. 1. Bidding Strategy of Virtual Power Plant 6
2. 2. Unit Commitment 11
III. CASE STUDIES 17
3. 1. Simulation Environment 17
3. 2. Numerical Results 22
IV. CONCLUSION 41
References 4
Development of new NIR exciplex systems based on a PDI derivative in solution
No full textNear-infrared (NIR) exciplexes in solution are developed using the highly soluble, thermally and photochemically robust perylene diimide derivative N,Nʹ-bis(ethylpropyl)perylene-3,4,9,10-tetracarboxylic diimide (EP-PDI) at concentrations where aggregation is negligible as an electron acceptor paired with pyrene (Py) or anthracene (Ant) donors. This approach yields broad NIR exciplex emission extending to approximately 1000 nm. Steady-state spectroscopy in non-polar toluene shows efficient quenching of EP-PDI’s locally excited emission (540 nm) and confirms exciplex formation, with emission wavelength dependent on the donor’s HOMO energy level (Ant > Py). Time-resolved spectroscopy reveals the coexistence of locally excited and exciplex states, with the exciplex lifetime decreasing as donor concentration increases, attributed to thermally driven non-radiative dissipation in fine aggregates. Density functional theory (DFT) calculations indicate favorable HOMO/LUMO alignment for charge transfer, while TD-DFT simulations support exciplex geometries with face-to-face distances of 3.3–3.5 Å and NIR emission profiles. The branched-chain structure and high solubility of EP-PDI, optimized at 4 μM concentration, effectively suppress aggregation and interfacial defects that are common in solid-state systems. This solution-phase design provides a promising foundation for NIR exciplexes in optoelectronic applications such as OLEDs and OPVs as well as phototherapy. Future directions to extend exciplex lifetimes include optimizing solvent viscosity, employing peptoid-based donor-acceptor linkages, and using alkyl chains as linkers between donor and acceptor molecules, which may enhance magnetic field effects and device stability. The integration of experimental and computational insights underscores the potential of stable PDI derivatives for advancing solution-phase exciplex systems.MasterChapter I. Photochemical and Structural Evaluation of PDI Derivatives for Exciplex Formation 1
1.1. Photochemistry 1
1.1.1. Timescales of photochemical processes 2
1.1.2. The Jablonski diagram 3
1.1.3. Steady-State Spectroscopy: Absorption and Photoluminescence 5
1.1.4. Time-Resolved Spectroscopy 7
1.1.5. Photoinduced electron transfer (PET) 9
1.1.6. Exciplex 11
1.2. Simulating Exciplex Emission with DFT and TD-DFT 13
1.3. Perylene Diimide 16
1.3.1. Molecular structure and Aggregation behavior 17
1.3.2. Aggregation morphologies 18
1.3.3. Types of PDI aggregates 20
1.3.4. Environmental factors affecting PDI aggregation 22
1.3.5. Spectroscopic signatures of PDI aggregation 23
1.4. Evaluation of solubility and aggregation of PDI derivatives
1.4.1. Impact of Imide Substituents on PDI Derivative Solubility and Aggregation 25
1.4.2. Solvent screening via steady-state spectroscopy 26
1.4.3. Determination of monomeric states 27
1.5. Exciplex formation with various donors
1.5.1. Selection of donor candidates and HOMO–LUMO energy level analysis 34
1.5.2. Exciplex formation and fluorescence spectrum analysis 35
1.5.3. Cases of exciplex formation absence 36
1.6. Magnetic field effect on exciplex systems
1.6.1. Optimization of solvent environment for magnetic field effect observation 43
1.6.2. Steady-state & Time-resolved spectroscopy under magnetic field 44
Chapter II. Spectroscopic and Theoretical Analysis of NIR Exciplex Formation between EP-PDI and Pyrene
or Anthracene in Solution
2.1. Introduction 51
2.2. Experimental section 55
2.3. Results and Discussion 57
2.4. Conclusions 74
Bibliograph
Appendix
Curriculum Vitae
Acknowledgement
Bile proteomics for discovery of biomarkers to differentiate between gallbladder polyp and gallstone
No full textGallbladder polyps and gallstones, despite having distinct pathological mechanisms, present similar clinical features that challenge accurate diagnosis and timely intervention. This study aimed to identify potential diagnostic biomarkers for distinguishing between these conditions through a comprehensive proteomic analysis of bile juice. Bile samples from patients with gallbladder polyps, gallstones, and healthy controls were analyzed using nano-liquid chromatography-tandem mass spectrometry. A total of 167 proteins were identified in polyp samples and 118 in gallstone samples, with 99 proteins shared between the two groups. Comparative analysis revealed 14 proteins significantly upregulated and 15 downregulated in gallbladder polyp samples. Gene Ontology and KEGG pathway analyses revealed that upregulated proteins in polyps were primarily involved in oxidative stress and metabolic processes, while downregulated proteins were associated with extracellular matrix organization. Most notably, hemoglobin subunits HBA1 and HBB showed significant elevation in polyps, indicating enhanced oxygen transport and inflammation, while extracellular matrix proteins LUM and EZR showed reduced expression, suggesting compromised structural integrity. These findings underscore the potential of differentially expressed proteins in bile as diagnostic biomarkers, offering a promising avenue for early and accurate differentiation between gallbladder polyps and gallstones. © The Author(s) 2025.TRUEsciescopu