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Three-dimensional computation of light scattering from cells

IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 1996年第4期2卷 898-905页

作者： Dunn, A RichardsKortum, R Biomedical Engineering Program Department of Electrical and Computer Engineering University of Technology Austin TX USA

Using the finite-difference time-domain method, three-dimensional scattering patterns are computed for cells containing multiple organelles. The scattering cross section and average cosine of the scattering angle are computed for cells as a function of volume fraction of melanin granules and mitochondria, Results show that small organelles play a significant role in light scattering from cells, and the volume fraction of organelles affects both the total amount of scattered light and the angular distribution of scattered light.

关键词： Light scattering Optical scattering Mie scattering Electromagnetic scattering Cells (biology) Optical refraction Finite difference methods Time domain analysis Biomedical optical imaging High-resolution imaging

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Unbiased Parameter Estimation for Bayesian Inverse Problems

arXiv

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arXiv 2025年

作者： Chada, Neil K. Jasra, Ajay Maama, Mohamed Tempone, Raul Department of Mathematics City University of Hong Kong China School of Data Science The Chinese University of Hong Kong Shenzhen CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a solution of a differential equation and, even if exact solutions are available, an analytical intractability of the marginal likelihood and its associated gradient, which is used for parameter estimation. The focus of this article is to deliver unbiased estimates of the unknown parameters, that is, stochastic estimators that, in expectation, are equal to the maximizer of the marginal likelihood, and possess no numerical approximation error. Based upon the ideas of [4] we develop a new approach for unbiased parameter estimation for Bayesian inverse problems. We prove unbiasedness and establish numerically that the associated estimation procedure is faster than the current state-of-the-art methodology for this problem. We demonstrate the performance of our methodology on a range of problems which include a PDE and ODE. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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650 nm emitting InGaN red micro-LEDs with ITO n-electrodes

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Applied Physics Express 2025年第6期18卷 061001-061001页

作者： Cesur Altinkaya Rawan S. Jalmood Mohammed A. Najmi Daisuke Iida Kazuhiro Ohkawa Material Science and Engineering Program Physical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

InGaN red micro-LEDs were fabricated with indium tin oxide (ITO) and metal n-electrode designs. Micro-LEDs with ITO electrodes achieved a peak on-wafer external quantum efficiency of 2.1% (at 1.25 A cm⁻²) and wall-plug efficiency of 1.7% (at 0.64 A cm⁻²), representing 1.6 times and 1.5 times improvements compared to metal-based electrodes. Improved performance was attributed to the transparency of ITO, enabling light extraction, while metal electrodes block emission. Both configurations achieved a low leakage current density (≤ 10^–7 A cm⁻²) and a high peak emission wavelength around 650 nm. These results represent a strong potential for low-power consumption required/area-limited AR/VR applications.

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Innovative Integration of Machine Learning Predictive Models Within Blockchain Frameworks for Supply Chain Fault Tolerance 1st

Innovative Integration of Machine Learning Predictive Models...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Kaur, Jatinder Rusho, Maher Ali Yogi, Kottala Sri Soni, Mukesh Raparthi, Mohan Garg, Yakshit Department of Computer Science and Engineering Lovely Professional University Jalandhar Phagwara India Degree Program University of Colorado Boulder Boulder United States Telangana Hyderabad India Dr. D. Y. Patil Vidyapeeth Pune India Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Alphabet Life Science Texas Dallas75063 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031860683

The creative incorporation of machine learning predictive models into blockchain frameworks appears as a game-changing approach for augmenting fault tolerance and increasing operational resilience in the dynamic environment of supply chain management. The potential, results, and implications of the integration are examined in this paper, providing a holistic perspective on its effect on contemporary supply networks. The essay starts by discussing the ever-changing difficulties encountered by supply chain networks operating in the modern global economy. Fault tolerance in the supply chain is becoming a pressing issue, calling for creative solutions to minimize interruptions and maximize efficiency. The importance of using machine learning predictive models into blockchain frameworks is outlined in the introduction, which also provides context for the rest of the paper. Methods used to test the integration's usefulness are described in the research paper. Logical regression, random forests, CNNs, SVMs, and LSTM models are all part of this category of cutting-edge machine learning methods. Machine learning algorithms in blockchain frameworks may protect supply networks from catastrophic breakdowns. The suggested strategy beats conventional models in accuracy (92%), precision (88%), recall (93%), F1-Score (90%), MAE (0.12), and MSE (0.15). The recommended method has a higher MSE than traditional models. The findings suggest that supply chain networks anticipate future occurrences and identify issues *** a result, businesses will be able to prevent and correct problems like shipment delays and stock-outs. The danger of data tampering and fraudulent activities has also been reduced thanks to the use of blockchain technology, which has improved data integrity and trust among supply chain players. This impact is shown by a 30% decrease in data tampering events inside the supply chain. The revolutionary potential of the novel integration of machine learning prediction

关键词： Supply chain management

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GRAPHICS OF TRUNCATED SINUSOIDS

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computerS & GRAPHICS 1989年第4期13卷 545-547页

作者： RAMESH, SR Computer Science and Technology Program University Visvesvaraya College of Engineering Bangalore-1 India

A variety of beautiful images generated by simple non-iterative real functions are discussed. Explanations for their complicated, chaotic behaviour are given.

关键词： computer Graphics

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Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

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IEEE engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

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Optimizing solar-powered multi-generation systems for sustainable energy management in university buildings using Neural Networks and Genetic Algorithms

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Journal of Building engineering 2025年 108卷

作者： Assareh, Ehsanolah Izadyar, Nima Jamei, Elmira Monzavian, Mohammad amin Agarwal, Saurabh Agarwal, Neha Strategic Research Institute (SRI) Asia Pacific University of Technology and Innovation (APU) Technology Park Malaysia Kuala Lumpur Built Environment and Engineering Program College of Sport Health and Engineering (CoSHE) Victoria University Melbourne Australia Department of Renewable Energy Technology Materials and Energy Research Center Dezful Branch Islamic Azad University Dezful Iran Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne 3011 VIC Australia School of Computer Science and Engineering Yeungnam University Gyeongsan 38541 South Korea School of Chemical Engineering Yeungnam University Gyeongsan 38541 South Korea

University campuses operate like small cities, with complex energy demands driven by diverse activities, making energy management particularly challenging in extreme climates. Traditional energy systems often rely on fossil fuels, leading to inefficiencies, high costs, and increased carbon footprints. This study optimizes a solar-powered multi-generation system for a university building in the severe hot climate of Dezful, Iran, using a two-step approach. First, the Building Energy Optimization Tool (BEopt) was used to select optimal building materials, leading to a reduction in total energy demand. Then, Artificial Intelligence algorithms (Neural Networks and Genetic Algorithms) were applied to enhance system performance and cost-effectiveness. The engineering Equation Solver (EES) validated the thermodynamic performance of the optimized system. This optimized system generates 49.55 GW-hours (GWh) of electricity annually, along with 19.47 GWh of heating and 13.93 GWh of cooling, producing a surplus of 34.28 GWh (69 % of total generation). Compared to the base case, electricity generation increased by over 224 %, cooling by 120 %, and heating by more than 3500 %, while CO₂ emissions decreased from 37.18 to 35.34 metric tons/year. The optimized system operates with an exergy efficiency of 23.44 % and a cost rate of 14.93 $/h. What sets this study novel is the integration of demand-side improvements with AI-enhanced multi-generation systems and thermodynamic validation, creating a practical model for achieving net-zero energy on university campuses. This methodology is scalable to more complex energy networks, including district energy systems, with future work focusing on real-time weather integration and advanced forecasting to improve adaptability and resilience. © 2025 The Authors

关键词： Artificial intelligence optimization Energy efficiency Renewable energy integration Solar-powered multi-generation systems Sustainable university campuses

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Medical Image Segmentation by Improved Nested Unet 2nd

Medical Image Segmentation by Improved Nested Unet

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2nd International Conference on Intelligent Systems and Data Science, ISDS 2024

作者： Tran, Song-Toan Le, Minh-Hai Nguyen, Thai-Son Nghi, Vinh-Khanh Nguyen, Thanh-Nguyen School of Engineering and Technology Tra Vinh University Tra Vinh87000 Viet Nam Resource Development Institute Tra Vinh University Tra Vinh87000 Viet Nam PhD program in Computer Science Tra Vinh University Tra Vinh87000 Viet Nam Facilities Management Department Tien Giang University Tien Giang84000 Viet Nam

ISBN: (纸本)9789819796120

Deep learning is revolutionizing the field of medical image segmentation. The U-shaped (Unet) model, with its encoder-decoder architecture and skip connections, has become the dominant architecture for this task. However, variations like Nested-Unet are necessary to address the challenge of adapting network size for datasets of varying scales. This study proposes a novel model based on a multilayer Unet architecture that leverages all output features within nodes of a Nested Unet network. We evaluate the proposed model on four key medical image datasets: electron microscopy (EM), nuclei, polyps (CVC-ClinicDB), and skin lesions. The model achieves impressive Dice scores: 94.08% for EM, 92.68% for nuclei, 87.20% for skin lesions, and 93.02% for the CVC-ClinicDB dataset. These outstanding results demonstrate the superior performance of our proposed model compared to recent advancements in the field. Remarkably, the model achieves this performance while maintaining a size that is half that of a traditional Unet model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Imaging the evolution of lithium-solid electrolyte interface using operando scanning electron microscopy

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Nature Communications 2025年第1期16卷 1-11页

作者： Zhao, Lihong Feng, Min Wu, Chaoshan Guo, Liqun Chen, Zhaoyang Risal, Samprash Ai, Qing Lou, Jun Fan, Zheng Qi, Yue Yao, Yan Department of Electrical and Computer Engineering and Texas Center for Superconductivity at the University of Houston University of Houston Houston TX United States School of Engineering Brown University Providence RI United States Materials Science and Engineering Program University of Houston Houston TX United States Department of Materials Science and Nanoengineering and the Rice Advanced Materials Institute Rice University Houston TX United States Department of Engineering Technology University of Houston Houston TX United States

The quality of Li–solid electrolyte interface is crucial for the performance of solid-state lithium metal batteries, particularly at low stack pressure, but its dynamics during cell operation remain poorly understood due to a lack of reliable operando characterization techniques. Here, we report the evolution of Li–electrolyte interface with high spatial resolution using operando scanning electron microscopy under realistic operating conditions. By tracking the stripping process of both Li and Li-rich Li-Mg alloy anodes, we show that multiple voids coalesce into a single gap and eventually delaminate the interface in Li, whereas the voids split and collapse to partially recover interfacial contact in Li-Mg. Density functional theory calculations show that the stronger Mg-S interaction at the metal–electrolyte interface attracts Mg toward the interface and repels Li-vacancies into the bulk, resulting in a reduced number of voids. The pressure-dependent voltage profiles of Li and Li-Mg stripping suggest that loss of contact due to void formation, rather than Mg accumulation at the interface, is the origin of high overpotential that limits the utilization of metal anodes. Improved interfacial contact enables stable cycling of all-solid-state lithium full cell at low stack pressure (1 MPa) and moderate rate (2 mA cm⁻²) simultaneously. The real-time visualization of Li–electrolyte interface dynamics provides critical insights into the rational design of solid-state battery interfaces. © The Author(s) 2025.

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

引用

MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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