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SSRN 2024年

作者： Yu, Sung-Bo Susanto, Dieky Chang, Eun-Seo Shin, Hyeung-Keun Jeon, Young-Gyu Hwang, Sang-Yeop Kim, Hyun-Kyung Department of Battery Convergence Engineering Kangwon National University Chuncheon24341 Korea Republic of Interdisciplinary Program in Advanced Functional Materials and Devices Development Kangwon National University Chuncheon24341 Korea Republic of

Black phosphorus (BP) is a potential anode material for sodium-ion batteries due to its high theoretical capacity. However, large volume changes during electrochemical cycling prevent its practical application. Targeted covalent (P–O–C) bonding in a BP/reduced graphene oxide (rGO) composite was increased via high-energy mechanical milling. By attaching oxygen functional groups at the basal and edge planes, a BP/rGO composite with abundant P–O–C bonds was obtained, suppressing volume expansion during cycling. The total chemical bonding in the composite increased by 164%, enhancing the cycle retention from 6.3 to 60.9% over 100 cycles at 1 A g−1. © 2024, The Authors. All rights reserved.

关键词： Sodium-ion batteries

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Segmented OctaSiPM for Dual-ended Readout of BGO to Improve Timing Resolution in Time-of-Flight PET

Segmented OctaSiPM for Dual-ended Readout of BGO to Improve ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： M. Yi D. Lee A. Gola S. Merzi M. Penna J.S. Lee S. R. Cherry S.I. Kwon Department of Biomedical Engineering University of California Davis Davis California United States of America Interdisciplinary Program in Bioengineering College of Engineering Seoul National University Seoul Republic of Korea Fondazione Bruno Kessler Trento Italy Brightonix Imaging Inc Seoul Republic of Korea

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

BGO has been highlighted as a cost-effective option for hybrid scintillator/Cherenkov radiator. However, effectively utilizing its improved timing resolution is hindered by challenges in managing the high detection fluctuation of Cherenkov photons and their spectral overlap with scintillation photons. Our previous study showed that the dual-ended readout and an adaptive timestamp method could enhance the detection of quasi-isotropically and simultaneously emitted Cherenkov photons in BGO. The OctaSiPM, developed by FBK, features a single pixel active area of 2.5 × 1.4 mm 2 and a 2 × 4 SiPM arrangement, offering a total active area of 5.3 × 5.8 mm 2 . In our recent investigation, when one end of a BGO crystal was coupled to two segmented pixels of the OctaSiPM, additional information about the initial photon density of the corresponding event could be provided. Thus, we hypothesize that the combination of the segmentation scheme and the dual-ended readout can provide multidimensional information on gamma events in BGO TOF-PET detectors. In the experimental setup, a Teflon-wrapped polished BGO crystal was read out from both ends using OctaSiPMs, followed by low-noise high-frequency amplification. Coincidence detection of gamma rays was conducted between a BGO detector and an LYSO:Ce:Mg reference detector. Relationship between segmented pixel time differences and top-bottom time differences revealed a unique pattern correlating with Cherenkov photon events. By selectively adjusting time windows, time difference distribution converged towards a single Gaussian distribution, enabling the selection of pure Cherenkov-triggered events. This marks the first instance of achieving a single Gaussian fitting from BGO data, underscoring the novelty and importance of our findings. Moreover, the analysis of initial photon counts provided insights into the likelihood of prompt Cherenkov photon detection and its temporal density.

关键词： Microwave integrated circuits Image segmentation Image resolution Fluctuations Semiconductor detectors Gamma-rays Crystals

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Integration Impacts of EVs on Power systems, Economy, Environment, and Society: A Bibliometricassisted Mini Review

Integration Impacts of EVs on Power Systems, Economy, Enviro...

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Annual Conference of Industrial Electronics Society

作者： Mohamed R. Elkadeem Kotb M. Kotb Zia Ullah Atif S. Alzahrani Mohammad A. Abido Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) KFUPM Saudi Arabia Interdisciplinary Research Center for Hydrogen Technologies and Carbon Management (IRC-HTCM) KFUPM Saudi Arabia School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Materials Science and Engineering Department KFUPM Saudi Arabia Electrical Engineering Department KFUPM Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Saudi Arabia

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Electric vehicles (EVs) offer a sustainable choice to traditional vehicles by reducing greenhouse gas emissions and improving air quality but also contribute to grid flexibility and stability through smart charging and vehicle-to-grid technologies. This paper conducts a mini review on integrating EVs with renewable energy sources and their impacts on power systems, environment, health, economy, and social welfare. For an informed understanding of the structure and growth of the literature along with recent research topics, the review is enriched with a data-driven and visualized bibliometric analysis of publications retrieved from Scopus over the past five years using VOSviewer software. The study delves into the economic implications of EVs, revealing both consumer cost savings and broader economic benefits, including reduced oil demand and associated environmental impacts. Also, the review discusses the significant health benefits of transitioning to EVs, such as the reduction in transportation-related emissions and their contribution to public health improvement. The review's findings indicate a significant interest within the scientific community regarding the study and resolution of various issues surrounding the integration of EVs into contemporary power grids alongside renewable sources, which aligns with the recent surge in scholarly publications. Also, by analyzing the current trends, challenges, and future directions, the study sheds light on the need for continued interdisciplinary research collaboration and policy support for a more sustainable and resilient energy future.

关键词： Renewable energy sources Electric potential Costs Reviews Oils Bibliometrics Collaboration Power system stability Air quality Stability analysis

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Explainable Rip Current Detection and Visualization with XAI EigenCAM

Explainable Rip Current Detection and Visualization with XAI...

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International Conference on Advanced Communication Technology (ICACT)

作者： Juno Choi Muralidharan Rajendran Yong Cheol Suh UR Interdisciplinary Program of Hydrography Pukyong National University Busan Korea Research and Development Team GreenBlue Inc. Busan Korea Department of Civil Engineering Pukyong National University Busan Korea

Rip currents have long posed a serious threat to beachgoers and swimmers. Despite numerous preventive measures throughout the period, the fatality rate [1] underscores the need for a robust rip current detection system. Recently deep learning models have shown promising results in rip current detection, outperforming traditional methods. However, these models still exhibit some accuracy limitations due to insufficient data distribution. To address this challenge, we incorporate a novel largest dataset [2] comprising over 110,215 Korean coastline images. Through the comparative study of the state-of-the-art models, we aim to analyze the detection accuracy of each model and gain a deeper understanding of their intensity over rip current detection. In comparison to the other rip current datasets, the evaluation results on our proposed dataset demonstrate a remarkable elevation in accuracy, reaching 79.4 mAP. Further, we employ the EigenCAM (Eigen Class Activation Maps) to interpret the intense regions of the rip currents and to gain a deeper comprehension of rip current explainability. This comprehensive analysis marks a significant step toward improving rip current safety and understanding.

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MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model

arXiv

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arXiv 2025年

作者： Ha, Sumin Kim, Jun Hyeong Piao, Yinhua Kim, Sun Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Bio-MAX/N-Bio Seoul National University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Korea Republic of AIGENDRUG Co. Ltd. Korea Republic of

Human expertise in chemistry and biomedicine relies on contextual molecular understanding, a capability that large language models (LLMs) can extend through fine-grained alignment between molecular structures and text. Recent multimodal learning advances focus on cross-modal alignment, but existing molecule-text models ignore complementary information in different molecular views and rely on single-view representations, limiting molecular understanding. Moreover, naïve multi-view alignment strategies face two challenges: (1) separate aligned spaces with inconsistent mappings between molecule and text embeddings, and that (2) existing loss objectives fail to preserve complementary information for fine-grained alignment. This can limit the LLM’s ability to fully understand the molecular properties. To address these issues, we propose MV-CLAM, a novel framework that aligns multi-view molecular representations into a unified textual space using a multi-query transformer (MQ-Former). Our approach ensures cross-view consistency while a token-level contrastive loss preserves diverse molecular features across textual queries. MV-CLAM enhances molecular reasoning, improving retrieval and captioning accuracy. The source code of MV-CLAM is available in https://***/sumin124/***. © 2025, CC BY-NC-SA.

关键词： Molecular docking

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Disentangled Motion Modeling for Video Frame Interpolation

arXiv

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arXiv 2024年

作者： Lew, Jaihyun Choi, Jooyoung Shin, Chaehun Jung, Dahuin Yoon, Sungroh Interdisciplinary Program in AI Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of AIIS ASRI and INMC Seoul National University Korea Republic of

Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on the reconstruction loss, recent works have employed generative models for improved perceptual quality. However, they require complex training and large computational costs for pixel space modeling. In this paper, we introduce disentangled Motion Modeling (MoMo), a diffusion-based approach for VFI that enhances visual quality by focusing on intermediate motion modeling. We propose a disentangled two-stage training process. In the initial stage, frame synthesis and flow models are trained to generate accurate frames and flows optimal for synthesis. In the subsequent stage, we introduce a motion diffusion model, which incorporates our novel U-Net architecture specifically designed for optical flow, to generate bi-directional flows between frames. By learning the simpler low-frequency representation of motions, MoMo achieves superior perceptual quality with reduced computational demands compared to the generative modeling methods on the pixel space. MoMo surpasses state-of-the-art methods in perceptual metrics across various benchmarks, demonstrating its efficacy and efficiency in VFI. © 2024, CC BY-NC-SA.

关键词： Pixels

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Numerical investigation of non-premixed hydrogen flame dynamics using RANS models

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Transportation Research Procedia 2025年 84卷 657-664页

作者： Hafidz Azhar Jakaria Naef A.A. Qasem Binash A. Imteyaz Department of Aerospace Engineering King Fahd University of Petroleum & Minerals (KFUPM) Dhahran 31261 Saudi Arabia Interdisciplinary Center for Aviation and Space Exploration King Fahd University of Petroleum & Minerals (KFUPM) Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum & Minerals (KFUPM) Dhahran 31261 Saudi Arabia

Hydrogen has sparked major stakeholders as an alternative to fossil fuel in industrial gas turbines or aerospace applications, considering its clean energy to reduce greenhouse gas emissions. Furthermore, hydrogen is known to have higher gravimetric energy density than conventional fuel. However, understanding hydrogen flames is particularly challenging due to the intricate nature of the turbulence characteristics. Hence, non-premixed turbulent hydrogen flames are numerically investigated in this work using ANSYS Fluent with and without a user-defined function. This work aims to assess the predictability of various RANS turbulence models for an axisymmetric case, including standard, modified, realizable, and Pope correction models. Simulation of test cases has shown that the realizable model exhibits the best accuracy in the centerline region further downstream with high accuracy for the centerline mean temperature. The modified model demonstrates superior predictability near the injector exit plane in the radial direction. Overall, the findings suggest that RANS models are effective for initial data collection in the early stages of the design process, providing valuable insights into the behavior of hydrogen flames.

关键词： Hydrogen flames Numerical RANS Non-premixed turbulent flame User-defined function

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Hybrid IT Project Management Methodology Based on Best Practices of Project Management and Critical Success Factors Influencing IT Project Management 8th

Hybrid IT Project Management Methodology Based on Best Pract...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Hayashida-Marchinares, Augusto Herrera, Jose Rivera, Luis Rodriguez, Ciro Rodriguez Krupa, Miroslav Ph. D. Program of the Faculty of Systems Engineering and Informatics Universidad Nacional Mayor de San Marcos Lima Peru Laboratory of Mathematical Sciences Center of Sciences and Technologies Universidade Estadual Do Norte Fluminense-UENF Rio de Janeiro Brazil Department of Management Faculty of Business Administration Prague University of Economics and Business Prague Czech Republic

ISBN: (纸本)9789819738588

Information Technology (IT) project management is important for the growth of organizations. It involves planning and executing projects to achieve strategic objectives. This paper reviews IT project management methodologies and critical success factors to identify best practices for project success. This paper uses a literature review and analysis of the current state of the art of IT project management methodologies. A total of 39 articles were selected and reviewed. The results of the review include a comparison of the main IT project management methodologies and the critical success factors for project success. The conclusion of the paper is that Hybrid Project Management Models are a promising approach for IT project management. This approach combines the foundations and best practices of traditional and agile project management methodologies, making it well-suited for the complex and dynamic nature of IT projects. Additionally, the paper identifies the following critical success factors for IT project success: Clear and well-defined project objectives, Effective communication and collaboration between stakeholders, Appropriate risk management and contingency planning, Strong leadership and project management skills and Adequate resources and support. By understanding and addressing these critical success factors, organizations can increase their chances of success in IT projects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Project management

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Wireless,multimodal sensors for continuous measurement of pressure,temperature,and hydration of patients in wheelchair

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npj Flexible Electronics 2023年第1期7卷 479-493页

作者： Seokjoo Cho Hyeonseok Han Hyunwoo Park Sung-Uk Lee Jae-Hwan Kim Sung Woo Jeon Mengqiu Wang Raudel Avila Zhaoqian Xi Kabseok Ko Minsu Park Jungyup Lee Myungwoo Choi Je-Sang Lee Weon Gi Min Byeong-Ju Lee Soyeong Lee Jungrak Choi Jimin Gu Jaeho Park Min Seong Kim Junseong Ahn Osman Gul Chankyu Han Gihun Lee Seunghwan Kim Kyuyoung Kim Jeonghyun Kim Chang-Mo Kang Jahyun Koo Sung Soo Kwak Sungbong Kim Dong Yun Choi Seokwoo Jeon Hyung Jin Sung Yong Bae Park Minkyu Je Young Tae Cho Yong Suk Oh Inkyu Park Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Advanced 3D Printing Technology Development Division Korea Atomic Energy Research InstituteDaejeon 34057Republic of Korea Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian 116023P.R.China Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA DUT-BSU Joint Institute Dalian University of TechnologyDalian 116023China Ningbo Institute of Dalian University of Technology Ningbo 315016China Department of Electronics Engineering Kangwon National UniversityChuncheon 24341Republic of Korea Querrey-Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Sibel Health Inc NilesIL 60714USA Department of Materials Science and Engineering KAIST Institute for The Nanocentury(KINC)Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of planning and development Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of rehabilitation medicine Pusan national university hospitalBusan 49241Republic of Korea Department of AI Convergence Network Ajou UniversitySuwon 16499Republic of Korea Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897Republic of Korea Korea Photonics Technology Institute Gwangju 61007Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02841Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02841Republic of Korea Center

Individuals who are unable to walk independently spend most of the day in a *** population is at high risk for developing pressure injuries caused by ***,early diagnosis and prevention of these injuries still remain ***,we introduce battery-free,wireless,multimodal sensors and a movable system for continuous measurement of pressure,temperature,and hydration at skin *** device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity,a temperature sensor for measuring skin temperature,and a galvanic skin response sensor for measuring skin hydration *** movable system enables power harvesting,and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body *** evaluations and numerical simulations of the devices,together with clinical trials for wheelchair patients,demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

关键词： injuries modal mounted

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Enhanced Prognostics and Health Management in Automated Driving systems: Using Graph Neural Networks to Recognize Operational Contexts

Enhanced Prognostics and Health Management in Automated Driv...

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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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