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Transferrin-Mediated Nanophotosensitizer to Enhance Phototherapy with Photoacoustic Imaging Under Hypoxia

CCS Chemistry 2025年第4期7卷 1127-1141页

作者： Mengyao Yang Yunyoung Nah Donghyeon Oh Xingshu Li Heejeong Kim Sinyoung Park Junha Lim Aibing Chen Chulhong Kim Won Jong Kim Juyoung Yoon College of Chemical and Pharmaceutical Engineering Hebei University of Science and TechnologyShijiazhuang 050018 Department of Chemistry and Nanoscience Ewha Womans UniversitySeoul 03760 Department of Chemistry POSTECH-Catholic Biomedical Engineering InstitutePOSTECHPohang 37673 Department of Electrical Engineering Convergence IT EngineeringMechanical Engineeringand Medical Science and EngineeringMedical Device Innovation CenterPOSTECHPohang 37673 Fujian Provincial Key Laboratory of Cancer Metastasis Chemoprevention and Chemotherapy College of ChemistryFuzhou UniversityFuzhou 350108 School of Interdisciplinary Bioscience and Bioengineering POSTECHPohang 37673 Graduate Program in Innovative Biomaterials Convergence Ewha Womans UniversitySeoul 03760

Various obstacles[poor tissue penetration,hypoxic tumor microenvironment,and reactive oxygen species(ROS)production quenching due to aggregation]obstruct imaging-guided high-efficiency phototherapy targeting ***,nanophthalocyanine(ZnPcN4-TF)is elaborately designed based on a synthetic phthalocyanine derivative(ZnPcN4)and nonimmunogenic transferrin(TF)through multiple noncovalent interactions for photoacoustic(PA)imaging-guided *** conjugating electron-rich amino groups effectively suppressed fluorescence and ROS generation due to the strong photoinduced electron transfer effect,ZnPcN4 became an ideal photothermal and PA imaging ***4-TF not only effectively targeted tumor sites and accumulated there,but also,surprisingly,facilitated the enhancement of ROS production via a type I process in an aggregate compared to ZnPcN4 itself,potentially due to accelerated electron ***,ZnPcN4-TF had a substantially more powerful photothermal effect than ZnPcN4 ***,this design effectively overcomes the obstacles to photodynamic therapy(aggregation-related ROS generation quenching in a hypoxic environment).Furthermore,PA imaging solves the tissue penetration challenge in optical *** study provides a broader base for designing novel photosensitizers to improve phototherapy under hypoxia.

关键词： photoacoustic imaging type I reactive oxygen species photothermal effect tumor accumulation self-assembly

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Efficient Hyperparameter Optimization Using Deep Q-Network and BRKGA 13

Efficient Hyperparameter Optimization Using Deep Q-Network a...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kobayashi, Kosei Aritsugi, Masayoshi González, Pedro Henrique Mendonça, Israel Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results). © 2024 IEEE.

关键词： Genetic algorithms

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Modeling and Control of Interleaved Boost Converter for Photovoltaic Applications 16

Modeling and Control of Interleaved Boost Converter for Phot...

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16th Brazilian Power Electronics Conference, COBEP 2021

作者： Vieira Bezerra, Marcelo Lopes Rocha Marias, Rafael Federal University of Piauí Graduate Program in Electrical Engineering Teresina Brazil

ISBN: (纸本)9781665421294

This article deals with the modeling and design of the control system of an interleaved DC-DC converter for photovoltaic applications using the maximum power tracking algorithm disturbs and observes. The converter used has advantages over the conventional boost converter, presenting lower current efforts in semiconductor devices. © 2021 IEEE.

关键词： Boost converter

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Deep Convolutional Neural Networks for Accurate Solar Module Classification in Thermal Images

Deep Convolutional Neural Networks for Accurate Solar Module...

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International Electrical engineering Congress (iEECON)

作者： Chutinun Phungket Prasit Nangthin The Graduate Program Faculty of Engineering Faculty of Engineering Pathumwan Institute of Technology Bangkok Thailand Department of Electrical Engineering Faculty of Engineering Pathumwan Institute of Technology Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

The rapid and continuous technological advancements in computer and internet technologies, combined with data management techniques, find applications in various domains. For example, they aid in predictive maintenance systems, evaluate the performance of solar modules, and differentiate efficiently performing solar modules from less efficient ones using thermal imagery. The accuracy of solar module classification significantly impacts the subsequent assessment. In this study, we employ advanced techniques, particularly deep learning through Convolutional Neural Networks (CNN), to classify thermal images of solar modules. The practical aspects of applying CNN to the dataset of thermal images are explored, including transfer learning from pretrained CNN architectures and custom CNN architecture training. Experiments are conducted with three prominent types of solar module conditions: normal, cracked, and dusty. The results reveal that the most effective approach involves learning and fine-tuning pre-trained architectures on detailed studies, achieving a classification accuracy of up to 94.5 % .

关键词： Training Photovoltaic cells Transfer learning Neural networks Computer architecture Thermal conductivity Thermal analysis

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Selective Power Control in Grid-Connected AC Microgrids

IEEE Open Journal of the Industrial Electronics Society

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IEEE Open Journal of the Industrial Electronics Society 2025年 6卷 938-961页

作者： João Marcus S. Callegari Lucas S. Araujo Danilo I. Brandao Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Department of Energy Systems of SINTEF Energy Research Trondheim Norway

The increasing penetration of nonlinear loads (NLLs) and distributed energy resources (DERs) in low-voltage grids poses challenges to power quality and grid hosting capacity (GHC). This article proposes a centralized multimode selective power control strategy for grid-connected ac microgrids (MGs) that does not require prior knowledge of MG parameters. The strategy enhances GHC and power quality across multiple MG nodes through coordinated control in two nonsimultaneous modes. In the centralized mode, a generalized power-based control algorithm enables selective harmonic/distortion power dispatch. This formulation improves disturbance rejection and accuracy in point of common coupling (PCC) power tracking. For the first time, feedback, feedforward, and disturbance decoupling actions are applied to distortion/harmonic power in MGs. In the decentralized mode, harmonic current compensation (HCC) is achieved without communication links, reducing data traffic via a selective voltage-detection-based approach. The proposed strategy enables (i) resistive load synthesis at the PCC to damp upstream grid resonances, (ii) sinusoidal current synthesis (SCS) for current quality enhancement and compliance with standards, and (iii) HCC based on voltage measurements to improve voltage quality at internal nodes. Comprehensive simulations evaluate reference tracking, disturbance rejection, grid short-circuit level effects, and mode transitions. Results show that in decentralized mode, PCC voltage THD improved from 10.65% to 1.09% under weak grids. In centralized mode, with SCS up to the 11th harmonic, PCC current THD was reduced from 61.18% to 3.42% under stiff grids. Experimental results confirm the feasibility of implementation in real MGs.

关键词： Harmonic analysis Voltage control Power harmonic filters Microgrids Impedance Power control Power quality Industrial electronics Proposals Distributed power generation

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Pose Control With an Input-to-state Safe Control Barrier Function for Rigid Body Motion

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2023年第1期143卷 67-75页

作者： Murao, Toshiyuki Matsumoto, Kosuke Kawai, Hiroyuki Ibuki, Tatsuya Fujita, Masayuki College of Engineering Kanazawa Institute of Technology 7-1 Ohgigaoka Nonoichi Ishikawa921-8501 Japan Graduate Program in Mechanical Engineering Kanazawa Institute of Technology 7-1 Ohgigaoka Nonoichi Ishikawa921-8501 Japan School of Science and Technology Meiji University 1-1-1 Higashimita Tama-ku Kawasaki Kanagawa214-8571 Japan Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

In this paper, we present a pose control method for rigid body motion via an input-to-state safe control barrier function (ISSf-CBF). The control objective is that a mobile robot converges to the desired pose with avoiding any obstacles under input disturbances. First, we derive a control error system to discuss stability via the Lyapunov method. Through the kinematic model for a rigid body, we design an ISSf-CBF for obstacle avoidance under input disturbances. Next, we present a control Lyapunov function (CLF) for the control error system. By using the ISSf-CBF and the CLF, we discuss a unified quadratic program formulation that ensures both safety and stability for pose control of rigid body motion under input disturbances. Our proposed approach, which deals with both the position and the orientation of rigid body motion, connects the safety and the stability via the ISSf-CBF and the CLF. Finally, simulation and experimental results confirm the validity of the proposed ISSf-CBF control method compared to the stabilizing one. ©c 2023 The Institute of Electrical Engineers of Japan.

关键词： Lyapunov methods

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Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting 13

Optimization of Node Reduction Using BRKGA for GNN-Based Tra...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Inokuchi, Yuto González, Pedro Henrique Mendonça, Israel Aritsugi, Masayoshi Graduate School of Science and Technology Kumamoto University Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350308365

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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Quantifying intragroup and intergroup connections in non-disjoint groups in social networks: A comprehensive analysis incorporating edges' and nodes' weights

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Information Sciences 2024年 670卷

作者： de Andrade, Ricardo Lopes Rêgo, Leandro Chaves Management Engineering Graduate Program Federal University of Pernambuco Brazil Statistics and Applied Math Department Universidade Federal do Ceará Brazil

In this paper, we propose generalizations of the EI index to quantify the distinction between intragroup and intergroup connections in social networks. These generalizations enable the analysis of interactions between non-disjoint groups, as large-scale disjoint groups are rarely found in many empirical networks. By expanding social network analysis, these measures facilitate the identification of actors with greater similarities or differences, generating previously untapped knowledge. Furthermore, we propose incorporating both edges' and nodes' weights in assessing the EI index. Intuitively, our approach classifies each network edge as internal or external, depending on the attribute groups that each node linked by the given edge belongs to. Since nodes can belong to more than one attribute group, three methods of classifying a network edge are proposed which depend on the number of shared groups by the pair of nodes. We evaluate the new measures in two distinct network contexts. The first context involves a co-authorship network, where researchers, acting as network actors, are categorized based on their respective areas of expertise in management engineering. The second network consists of trade relations among countries in the Americas, with countries grouped according to their participation in trade agreements. The analysis showed which area of management engineering is more independent by having more internal connections and made it possible to investigate the role of trade agreements in the actual trades made by countries. These proposals provide a more comprehensive and nuanced understanding of network dynamics, allowing for a more precise comprehension of interactions and patterns present in social systems. © 2024 Elsevier Inc.

关键词： Commerce

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Cancer Prediction using Clinical and Genomic Data Fusion: A Systematic Review 6

Cancer Prediction using Clinical and Genomic Data Fusion: A ...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Julian, Kelvin Nugroho, Kuncahyo Setyo Nirwantono, Rudi Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies. © 2024 IEEE.

关键词： Lung cancer

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QoE Assessment of Audiovisual Streaming over a Full-Duplex Wireless LAN with Interference Traffic

QoE Assessment of Audiovisual Streaming over a Full-Duplex W...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Toshiro Nunome Daiki Deguchi Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

This paper evaluates the QoE of video and audio transmission over a full-duplex wireless LAN with interference traffic through a computer simulation and a subjective experiment. We employ a simulation environment with a pair of audiovisual transmission and reception terminals and a pair of interference traffic transmission and reception ones. We investigate the effect of the transmission rate of interference traffic and communication distance in a wireless channel on the output quality of the video and audio stream at the reception terminal. We perform a subjective experiment with the output timing of video and audio obtained by the simulation.

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