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SSRN 2022年

作者： Zhang, Dian Kanezashi, Masakoto Tsuru, Toshinori Yamamoto, Kazuki Gunji, Takahiro Adachi, Yohei Ohshita, Joji Smart Innovation Program Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-8527 Japan Chemical Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-8527 Japan Department of Pure and Applied Chemistry Faculty of Science and Technology Tokyo University of Science Chiba278-8510 Japan Education and Research Center Hiroshima University Higashi-Hiroshima739-0046 Japan

Reverse osmosis (RO) membranes with polysilsesquioxane (PSQ) framework having high NaCl rejection and favorable resistance to heat and chlorine were prepared by the copolymerization of 3-glycidyloxypropyl-POSS (GPOSS) and bis[3-(triethoxysilyl)propyl]amine (BTESPA). It was found that NaCl rejection and liquid permeability were significantly improved by the introduction of GPOSS-derived units to the BTESPA-based membranes, yielding NaCl rejection of approximately 99% and liquid permeability of 5.5 × 10 -13 m/s·Pa. These PSQ RO membranes with GPOSS-derived units also demonstrated excellent resistance to heat treatment of up to 100 °C and favorable resistance to chlorine in that no obvious RO performance change was observed after exposure to NaOCl aqueous solution (1000 ppm) for 10 h. © 2022, The Authors. All rights reserved.

关键词： Desalination

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Inverse designing metamaterials with programmable nonlinear functional responses in graph space

arXiv

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arXiv 2024年

作者： Maurizi, Marco Xu, Derek Wang, Yu-Tong Yao, Desheng Hahn, David Oudich, Mourad Satpati, Anish Bauchy, Mathieu Wang, Wei Sun, Yizhou Jing, Yun Zheng, Xiaoyu Rayne Department of Materials Science and Engineering University of California Berkeley Berkeley94720 United States Department of Computer Science University of California Los Angeles Los Angeles90095 United States Graduate Program in Acoustics Penn State University PA16802 United States Universite’ de Lorraine CNRS Institut Jean Lamour NancyF-54000 France Department of Civil and Environmental Engineering University of California Los Angeles Los Angeles90095 United States

Material responses to static and dynamic stimuli, represented as nonlinear curves, are design targets for engineering functionalities like structural support, impact protection, and acoustic and photonic bandgaps. Three-dimensional metamaterials offer significant tunability due to their internal structure, yet existing methods struggle to capture their complex behavior-to-structure relationships. We present GraphMetaMat, a graph-based framework capable of designing three-dimensional metamaterials with programmable responses and arbitrary manufacturing constraints. Integrating graph networks, physics biases, reinforcement learning, and tree search, GraphMetaMat can target stress-strain curves spanning four orders of magnitude and complex behaviors, as well as viscoelastic transmission responses with varying attenuation gaps. GraphMetaMat can create cushioning materials for protective equipment and vibration-damping panels for electric vehicles, outperforming commercial materials, and enabling the automatic design of materials with on-demand functionalities. Copyright © 2024, The Authors. All rights reserved.

关键词： Metamaterials

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Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel

arXiv

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arXiv 2022年

作者： Jiang, Ziyang Zheng, Tongshu Liu, Yiling Carlson, David Department of Civil and Environmental Engineering Duke University United States Division of Natural and Applied Science Duke Kunshan University China Program in Computational Biology and Bioinformatics Duke University School of Medicine United States Department of Civil and Environmental Engineering Department of Biostatistics and Bioinformatics Department of Computer Science Duke University United States

It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kernel in a Gaussian process (GP). Many deep learning applications could be enhanced by modeling such known properties. For example, convolutional neural networks (CNNs) are frequently used in remote sensing, which is subject to strong seasonal effects. We propose to blend the strengths of NNs and the clear modeling capabilities of GPs by using a composite kernel that combines a kernel implicitly defined by a neural network with a second kernel function chosen to model known properties (e.g., seasonality). We implement this idea by combining a deep network and an efficient mapping function based on either Nyström approximation or random Fourier features, which we call Implicit Composite Kernel (ICK). We then adopt a sample-then-optimize approach to approximate the full GP posterior distribution. We demonstrate that ICK has superior performance and flexibility on both synthetic and real-world datasets including a remote sensing dataset. The ICK framework can be used to include prior information into neural networks in many applications. © 2022, CC BY.

关键词： Remote sensing

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CIWARS: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

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Journal of Molecular Biology 2025年第15期437卷

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech Blacksburg 24060 VA United States Fralin Life Science Institute Virginia Tech Blacksburg 24060 VA United States Department of Civil and Environmental Engineering Virginia Tech Blacksburg 24060 VA United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24060 VA United States

The rise of antibiotic resistance (AR) poses a substantial threat to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate analysis of longitudinal metagenomic data are lacking. To address this, we introduce CyberInfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive profiling of ARGs and taxonomic profiling of pathogen-associated bacterial taxonomic groups, identifies potential associations of ARGs with mobile genetic elements (MGEs) and pathogen-containing taxa, and assesses resistome risk based on the co-occurrence of ARGs, MGEs, and pathogen-like sequences. Additionally, it detects anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and can serve as a tool to inform effective and timely interventions to mitigate the spread and transmission of AR. Here, CIWARS is demonstrated using longitudinal metagenomic data from a wastewater treatment plant (WWTP) influent and effluent, but it can be extended to any environment. CIWARS provides a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use. The web server is freely available at https://***/. © 2025 Elsevier Ltd

关键词： anomaly detection antibiotic resistance (AR) AR indicator profiling longitudinal metagenomics wastewater-based surveillance

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Reducing Effective System Dimensionality with Long-Range Collective Dipole-Dipole Interactions

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Physical Review Letters 2024年第17期132卷 173803-173803页

作者： Ashwin K. Boddeti Yi Wang Xitlali G. Juarez Alexandra Boltasseva Teri W. Odom Vladimir Shalaev Hadiseh Alaeian Zubin Jacob Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Birck Nanotechnology Center Purdue University West Lafayette Indiana 47907 USA Graduate Program in Applied Physics Northwestern University Evanston Illinois 60208 USA Department of Materials Science and Engineering Northwestern University Evanston Illinois 60208 USA Department of Chemistry Northwestern University Evanston Illinois 60208 USA School of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA

Dimensionality plays a crucial role in long-range dipole-dipole interactions (DDIs). We demonstrate that a resonant nanophotonic structure modifies the apparent dimensionality in an interacting ensemble of emitters, as revealed by population decay dynamics. Our measurements on a dense ensemble of interacting quantum emitters in a resonant nanophotonic structure with long-range DDIs reveal an effective dimensionality reduction to d¯=2.20(12), despite the emitters being distributed in 3D. This contrasts with the homogeneous environment, where the apparent dimension is d¯=3.00. Our work presents a promising avenue to manipulate dimensionality in an ensemble of interacting emitters.

关键词： Light-matter interaction Long-range interactions Metamaterials Optics & lasers Photonics Plasmonics Quantum optics

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在天文学中基于机器学习的流体力学问题加速求解

在天文学中基于机器学习的流体力学问题加速求解

引用

第十三届全国流体力学学术会议

作者：毛顺元董若冰王炜琦 Kwang Moo Yi 陆路王思凡 Paris Perdikaris Department of Physics and Astronomy University of Victoria Department of Computer Science University of British Columbia Department of Statistics and Data Science Yale University Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania Department ofMechanical Engineering and Applied Mechanics University of Pennsylvania

探索行星形成的过程不仅揭示了地球及其生命的起源,还为寻找地外生命提供了关键线索。行星在原行星盘中形成。原行星盘是由气体和尘埃构成的扁平盘状系统,围绕在新生恒星的周围,其跨度可达数千亿公里。近年来,通过分析行星对原行星盘的... 详细信息

探索行星形成的过程不仅揭示了地球及其生命的起源,还为寻找地外生命提供了关键线索。行星在原行星盘中形成。原行星盘是由气体和尘埃构成的扁平盘状系统,围绕在新生恒星的周围,其跨度可达数千亿公里。近年来,通过分析行星对原行星盘的引力扰动,并比较观测和数值模拟,天文学家得以推断正在盘中形成的行星的质量和轨道。然而,这种方法依赖于复杂的流体力学数值模拟,因计算成本高昂而难以普及。为了克服这一挑战,我们开发了一款基于机器学习的工具。该工具应用数据驱动的深度算子网络(DeepONet),将系统的参数映射到流体力学偏微分方程的解,从而无需昂贵的数值模拟即可实现两项功能:1)快速求解正问题,即从行星参数预测盘中物质分布和速度场;2)高效求解反问题,即从观测到的扰动图像中推断行星参数。我们展示了该工具在求解速度、精确性和鲁棒性方面的显著优势,并讨论了其在理论研究和观测中的潜在应用。我们还探索了物理信息神经网络(PINN)在这一问题的应用,特别是针对原初行星盘中旋转吸积流的时间依赖性二维可压缩Navier-Stokes方程。行星引力扰动引入显著的时间依赖性空间特征,使问题复杂化。通过对物理定律而非标注数据的训练,PINN在捕捉系统演化的空间高频特征方面展现潜力。标准PINN在此任务中存在收敛困难、预测误差随时间累积、长时间解偏离物理定律以及捕捉空间高频特征能力不足的问题。为此,我们的研究引入了自适应激活函数、时间推进策略和网络权重继承方法,以提升PINN性能。改进后的PINN模型在与标准PINN的比较中表现出显著更低的预测误差,成功再现了大部分空间高频特征,无需标注数据。这项研究为将PINN应用于类似的时间依赖性流体力学问题提供了重要参考。

关键词：天文行星形成深度学习深度算子网络物理信息神经网络

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Lower lumbar vertebrae replacement by FEA based assessment of suitable ceramic polymer composites

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Materials Today: Proceedings 2023年 90卷 226-233页

作者： Prakriti Prasad Shatakshi Roy Kathan Bhavsar Utkarsh Chadha Tarun Kataray Vezhavendhan R Tolera Kuma Eticha School of Mechanical Engineering (SMEC) Vellore Institute Technology (VIT) Vellore Tamilnadu 632014 India School of Bio Sciences and Technology (SBST) Vellore Institute of Technology (VIT) Vellore Tamilnadu 632014 India Department of Materials Science and Engineering School of Graduate Studies University of Toronto Toronto Ontario M5S 1A1 Canada Department of Biology College of Natural and Computational Sciences Ambo University Ambo Ethiopia

Using bio-materials to develop a safe, secure, and prime substitute as an external entity to interact inside a human environment is a demanding area of research and expertise. The lumbar spine is the most common site for injury in the spinal column as it bears the majority of the human body’s weight while also undergoing significant prolonged loading and stress. In the same direction, a cost-effective Artificial Lumbar vertebra is designed and recognized to be implanted in the spinal region of a body to replace injured or disfigured counterparts. By harnessing the power of 3D bioprinting , biomaterials, and FEA , the paper proves that a simple bio-printed flexible lumbar vertebrae can be integrated into the body to work in conjunction with the rest of the spinal cord just as any other of the vertebrae do, thereby working in coordination with humans. The material thus obtained will be capable of replacing the injuries in the vertebrae caused by mechanical failures like compressive loading, vertebral slip, or vertebral joint fusion. An overall lightweight structure consisting of a firm material is looked forward to for the paper’s fusion. This insight, research, and design have led to the development of the research paper.

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MultiCalib4DEB: A toolbox exploiting multimodal optimisation in Dynamic Energy Budget parameters calibration

arXiv

引用

arXiv 2023年

作者： Robles, Juan Francisco Chica, Manuel Filgueira, Ramón Agüera, Antonio Damas, Sergio Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Benthic resources Norwegian Institute of Marine Research BergenNO-5817 Norway Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Marine Affairs Program Life Sciences Centre Dalhousie University 1459 Oxford Street HalifaxNSB3H 4R2 Canada

1. Calibration is a crucial step for the validation of computational models and a challenging task to accomplish. 2. Dynamic Energy Budget (DEB) theory has experienced an exponential rise in the number of published papers, which in large part has been made possible by the DEBtool toolbox. Multimodal evolutionary optimisation could provide DEBtool with new capabilities, particularly relevant on the provisioning of equally optimal and diverse solutions. 3. In this paper we present MultiCalib4DEB, a MATLAB toolbox directly integrated into the existing DEBtool toolbox, which uses multimodal evolutionary optimisation algorithms to find multiple global and local optimal and diverse calibration solutions for DEB models. 4. MultiCalib4DEB adds powerful calibration mechanisms, statistical analysis, and visualisation methods to the DEBtool toolbox and provides a wide range of outputs, different calibration alternatives, and specific tools to strengthen the DEBtool calibration module and to aid DEBtool users to evaluate the performance of the calibration results. © 2023, CC BY-NC-ND.

关键词： Calibration

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Advances in transcription factor delivery: Target selection, engineering strategies, and delivery platforms

引用

Journal of Controlled Release 2025年 384卷 113885页

作者： Lee, Yeji Yoo, Seongkyeong Cho, Seongeon Kim, Iljin Kim, In-San KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Chemical and Biological Integrative Research Center Biomedical Research Institute Korea Institute Science and Technology Seoul02792 Korea Republic of Department of Pharmacology and Program in Biomedical Science and Engineering Inha University College of Medicine Incheon22212 Korea Republic of Research Center for Controlling Intercellular Communication Inha University College of Medicine Incheon22212 Korea Republic of

Recent advances in delivery systems for transcription factors (TFs) have opened new therapeutic opportunities in regenerative medicine, cancer therapy, and genetic disorders. However, effective TF delivery still faces substantial obstacles, including limited cellular uptake, inefficient nuclear translocation, low cargo stability, and insufficient target specificity. Furthermore, artificial TFs have enabled targeted modulation of gene expression, further expanding their therapeutic potential. This review comprehensively discusses current progress in TF delivery methodologies, including direct TF protein delivery using cell-penetrating peptides, and extracellular vesicles, as well as TF gene delivery approaches utilizing both lipid-based nanoparticles and viral strategies. Notably, engineered nanoparticles have emerged as promising platforms due to their precise control over TF delivery, improved specificity, and minimized off-target effects. Despite these significant advancements, major hurdles in delivery efficiency, cargo stability, and overall safety persist. Overcoming these obstacles will be essential to accelerate the clinical translation of TF-based therapeutics for a broad spectrum of diseases. © 2024

关键词： Transcription factors

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Giant long-range dipole-dipole interactions in a plasmonic lattice

Giant long-range dipole-dipole interactions in a plasmonic l...

引用

Frontiers in Optics + Laser science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Boddeti, Ashwin K. Guan, Jun Sentz, Tyler Juarez, Xitali Newman, Ward Cortes, Cristian Odom, Teri W. Jacob, Zubin School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Intel Corporation BeavertonOR97007 United States Center for Nanoscale Materials Argonne National Laboratory LemontIL60439 United States Graduate Program in Applied Physics Department of Materials Science and Engineering Department of Chemistry Northwestern University EvanstonIL60208 United States

We observe long-range dipole-dipole interactions in a plasmonic lattice. Fluorescence lifetime measurements show density-dependent non-exponential decay dynamics over 800nm mean nearest-neighbour separation distances ... 详细信息

ISBN: (纸本)9781557528209

关键词： Plasmonics

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