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Combining flow virometry with tree-based machine learning models for rapid virus particle estimation in different wastewater matrices

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Water Research 2025年 284卷 123905页

作者： Myshkevych, Yevhen N'Doye, Ibrahima Medina, Julie Sanchez Aljehani, Fahad K. Xiong, Yanghui Laleg-Kirati, Taous-Meriem Hong, Pei-Ying Environmental Science and Engineering Program Division of Biological and Environmental Science and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia KAUST Center of Excellence on Smart Health King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Electrical and Computer Engineering Program Division of Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Paris-Saclay France

Enumerating virus particles (VPs) at different stages of the wastewater treatment process or along the distribution network is essential for ensuring high performance and reducing public health risks. Herein, we aimed to (i) optimize the flow virometry (FVM) protocol for use in wastewater matrices, (ii) correlate FVM data with specific virus genera of interest, and (iii) develop machine learning (ML) models for determining total VP concentration. We identified and tested a comprehensive set of parameters to determine the optimal conditions for wastewater FVM. Specifically, we tested various sample preprocessing steps to enhance FVM detection sensitivity, including the use of different nucleic acid staining dyes, surfactant addition and concentration optimization, glutaraldehyde fixation, and the effect of sample freezing before FVM analysis. Spearman's rank correlation of FVM data with virus genera concentration using a conventional qPCR-based method in 206 samples showed a positive correlation for all five virus genera, ranging from 0.21 to 0.44 (p © 2025 The Author(s)

关键词： Water recycling

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Weight vector tuning and asymptotic analysis of binary linear classifiers

arXiv

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arXiv 2021年

作者： Niyazi, Lama B. Kammoun, Abla Dahrouj, Hayssam Alouini, Mohamed-Slim Al-Naffouri, Tareq Y. Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia Center of Excellence for NEOM Research at KAUST King Abdullah University of Science and Technology Thuwal Saudi Arabia

Unlike its intercept, a linear classifier’s weight vector cannot be tuned by a simple grid search. Hence, this paper proposes weight vector tuning of a generic binary linear classifier through the parameterization of a decomposition of the discriminant by a scalar which controls the trade-off between conflicting informative and noisy terms. By varying this parameter, the original weight vector is modified in a meaningful way. Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning. This yields computational savings as the proposed tuning method reduces to tuning a scalar compared to tuning the native hyperparameter, which may involve repeated weight vector generation along with its burden of optimization, dimensionality reduction, etc., depending on the classifier. It is also found that weight vector tuning significantly improves the performance of Linear Discriminant Analysis (LDA) under high estimation noise. Proceeding from this second finding, an asymptotic study of the misclassification probability of the parameterized LDA classifier in the growth regime where the data dimensionality and sample size are comparable is conducted. Using random matrix theory, the misclassification probability is shown to converge to a quantity that is a function of the true statistics of the data. Additionally, an estimator of the misclassification probability is derived. Finally, computationally efficient tuning of the parameter using this estimator is demonstrated on real data. Copyright © 2021, The Authors. All rights reserved.

关键词： Random variables

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A Machine Learning-Based Approach to Model Highly-Thermally Robust Metasurface Absorber

A Machine Learning-Based Approach to Model Highly-Thermally ...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Sumbel Ijaz Sadia Noureen Bacha Rehman Muhammad Zubair Muhammad Qasim Mehmood Yehia Massoud Electrical Engineering Department MicroNano Lab Information Technology University of the Punjab Lahore Pakistan Department of Computer Science Namal University Mianwali Punjab Pakistan Innovative Technologies Laboratories (ITL) Electrical and Computer Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

For accelerating design procedure of compact and efficient on-chip nano-photonics and to aid computationally expensive, time-exhaustive state-of-the-art iterative simulation schemes, regression-based machine-learning models are demonstrated that predict the optical response and structural parameters of the meta-atoms.

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Achieving maximum scattering circular dichroism through the excitation of anapole states within chiral Mie nanospheres

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Physical Review B 2022年第24期105卷 245412-245412页

作者： Haifeng Hu Qiaoqiang Gan Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China Zhangjiang Laboratory 100 Haike Road Shanghai 201204 China Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai 200093 China Material Science Engineering King Abdullah University of Science and Technology Thuwal 23955-6900 Kingdom of Saudi Arabia

The chirality-dependent asymmetric light-matter interaction can be enhanced using various artificial photonic structures as well as engineered incident field. Compared with chiral photonic structures, the engineered optical field is less explored and recently recognized as a new regime in tailoring light-matter interaction. In this work, we demonstrate that weakly chiral spheres can exhibit the maximum scattering circular dichroism (CD) by tailoring the incident field to construct chirality-sensitive anapole states. By considering the chiral terms radiated from Mie nanospheres as perturbation, a multiscattering model is established to predict the chiroptical Mie scattering response of these nanospheres. This model provides a specific guideline to tailor the incident field to realize the upper limit of the achievable scattering CD.

关键词： Chirality Mie scattering Photonics

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Enhanced In-Flight Connectivity for Urban Air Mobility via LEO Satellite Networks

Enhanced In-Flight Connectivity for Urban Air Mobility via L...

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International Conference on Telecommunications (ICT)

作者： Karnika Biswas Hakim Ghazzai abdullah Khanfor Lokman Sboui CEMSE Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Computer Science Department College of Computer Science & Information Systems Najran University Najran Saudi Arabia Systems Engineering Department École de Technologie Supérieure (ÉTS) University of Québec Montréal Canada

ISBN: (数字)9798350356694

ISBN: (纸本)9798350356700

Urban Air Mobility (UAM) is the envisioned future of inter-city aerial transportation. This paper presents a novel, in-flight connectivity link allocation method for UAM, which dynamically switches between terrestrial cellular and Low Earth Orbit (LEO) satellite networks based on real-time conditions. Our approach prefers cellular networks for cost efficiency, switching to LEO satellites under poor cellular conditions to ensure continuous UAM connectivity. By integrating real-time metrics like signal strength, network congestion, and flight trajectory into the selection process, our algorithm effectively balances cost, minimum data rate requirements, and continuity of communication. Numerical results validate minimization of data-loss while ensuring an optimal selection from the set of available above-threshold data rates at every time sample. Furthermore, insights derived from our study emphasize the importance of hybrid connectivity solutions in ensuring seamless, uninterrupted communication for future urban aerial vehicles.

关键词： Satellites Costs Low earth orbit satellites Transportation Switches Real-time systems Trajectory

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Classification of Pancreatic Cystic Lesions Using ResNet Deep Learning Network in Confocal Laser Endomicroscopy Videos 7

Classification of Pancreatic Cystic Lesions Using ResNet Dee...

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7th Information Systems International Conference, ISICO 2023

作者： Angelina, Clara Lavita Pan, Chien-Ming Lee, Tsung-Chun Han, Ming-Lun Kongkam, Pradermchai Wang, Hsiu-Po Chang, Chuan-Yu Chang, Hsuan-Ting Department of Electrical Engineering National Yunlin University of Science and Technology Yunlin Taiwan Department of Internal Medicine Taipei Medical University Shuang Ho Hospital New Taipei City Taiwan Department of Integrated Diagnostics and Therapeutics National Taiwan University Hospital Taipei Taiwan Gastrointestinal Endoscopy Excellence Center Division of Gastroenterology Chulalongkorn University King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Department of Internal Medicine National Taiwan University Hospital College of Medicine National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Accurate classification of pancreatic cystic lesions is crucial to differentiate mucinous lesions of malignant potential. We utilized the ResNet-50 and ResNet-101 network to develop a model for classification of the pancreatic cystic lesions. A total of 50 videos, 13,425 images, from five types of pancreatic cystic lesions and utilize the image rotation and contrast reversal scheme for the training. We adopt a contrast limited adaptive histogram equalization method onto the test video. Our method can automatically classify the feature type and record the prediction results frame by frame. The method has been evaluated on 18 test videos and achieves an accuracy 94% overall. © 2023 The Authors. Published by Elsevier B.V.

关键词： Deep learning

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∂H: Differentiable Holography

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Laser and Photonics Reviews 2023年第9期17卷

作者： Chen, Ni Wang, Congli Heidrich, Wolfgang Wyant College of Optical Sciences University of Arizona TucsonAZ85721 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Visual Computing Center King Abdullah University of Science and Technology Jeddah Thuwal23955 Saudi Arabia

Over the past decade, the field of holography has gained significant ground due to advances in computational imaging. However, the utilization of computational tools is hampered by the mismatch between experimental setups and the conceptual model. Differentiable holography ((Formula presented.)), a novel framework for automatically self-calibrating experimental imperfections in inverse holographic imaging, is presented here. The technique is demonstrated on auto-focused complex field imaging from a single intensity-only inline hologram. © 2023 Wiley-VCH GmbH.

关键词： Inverse problems

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Band alignment of grafted monocrystalline Si (001)/β-Ga2O3 (010) p-n heterojunction determined by X-ray photoelectron spectroscopy

arXiv

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arXiv 2023年

作者： Gong, Jiarui Zhou, Jie Dheenan, Ashok Sheikhi, Moheb Alema, Fikadu Ng, Tien Khee Pasayat, Shubhra S. Gan, Qiaoqiang Osinsky, Andrei Gambin, Vincent Gupta, Chirag Rajan, Siddharth Ooi, Boon S. Ma, Zhenqiang Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI53706 United States Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States Agnitron Technology Incorporated ChanhassenMN55317 United States Department of Electrical and Computer Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Northrop Grumman Corporation Redondo BeachCA90278 United States

Beta-phase gallium oxide (β-Ga2O3) research has gained accelerated pace due to its superiorly large bandgap and commercial availability of large-diameter native substrates. However, the high acceptor activation energy obstructs the development of homojunction bipolar devices employing β-Ga2O3. The recently demonstrated semiconductor grafting technique provides an alternative and viable approach towards lattice-mismatched β-Ga2O3-based p-n heterojunctions with high quality interfaces. Understanding and quantitatively characterizing the band alignment of the grafted heterojunctions is crucial for future bipolar device development employing the grafting method. In this work, we present a systematic study of the band alignment in the grafted monocrystalline Si/β-Ga2O3 heterostructure by employing X-ray photoelectron spectroscopy (XPS). The core level peaks and valence band spectra of the Si, β-Ga2O3, and the grafted heterojunction were carefully obtained and analyzed. The band diagrams of the Si/β-Ga2O3 heterostructure were constructed using two individual methods, the core level peak method and the valence band spectrum method, by utilizing the different portions of the measured data. The reconstructed band alignments of the Si/β-Ga2O3 heterostructure using the two different methods are identical within the error range. The band alignment is also consistent with the prediction from the electron affinity values of Si and β-Ga2O3. The study suggests that the interface defect density in grafted Si/β-Ga2O3 heterostructure is at a sufficiently low level such that Fermi level pinning at the interface has been completely avoided and the universal electron affinity rule can be safely employed to construct the band diagrams of grafted monocrystalline Si/β-Ga2O3 heterostructures. Copyright © 2023, The Authors. All rights reserved.

关键词： Heterojunctions

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Choosing a Suitable Requirement Prioritization Method: A Survey

SSRN

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SSRN 2023年

作者： Mahmoud Alhenawi, Esra'a Awawdeh, Shatha Khurma, Ruba Abu Castillo, Pedro A. Department of Computer Science King Abdullah II School of Information Technology The University of Jordan Amman Jordan Applied Science Private University Jordan Faculty of Information Technology Computer Science department Al-Ahliyya Amman University Amman Jordan Department of Computer Engineering Automation and Robotics University of Granada Granada Spain

Software requirements prioritization plays a crucial role in software development. It can be viewed as the process of ordering requirements by determining which requirements must be done first and which can be done later. Powerful requirements prioritization techniques are of paramount importance to finish the implementation on time and budget. Many factors affect requirement prioritization such as stakeholder expectations, complexity, dependency, scalability, risk and cost. Therefore, finding the proper order of requirements is a challenging process. Hence, different types of requirements prioritization techniques have been developed to support this task. In this survey we propose a novel classification that can classify the prioritization techniques under two major classes: relative and exact prioritization techniques class where each class is divided into two subclasses. We also provide an overview about fifteen different requirements prioritization techniques that are classified under our proposed classification. Moreover, we make a comparison between methods that are related to the same subclass to analyze their strengths and weakness. Based on the comparison results, the properties for each proposed subclass of techniques are identified. Depending on these properties, we present some recommendations to help project managers in the process of selection the most suitable technique to prioritize requirements based on their projects characteristics. © 2023, The Authors. All rights reserved.

关键词： Requirements engineering

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Binary Grating and Deep Learning for Phase Reconstruction: Enhancing Normal Camera Imaging

Binary Grating and Deep Learning for Phase Reconstruction: E...

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IEEE Photonics Conference (IPC)

作者： Redha H. Al Ibrahim Shuiqin Zheng Tien Khee Ng Boon S. Ooi Photonics Laboratory Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia Full-Stack Photonics Laboratory School of Physical Sciences Great Bay University Dongguan China

This work investigates the use of a binary grating integrated on a camera assisted by deep learning for phase imaging. Holographic images generated by overlapping 4 diffraction orders from the gratings were employed w...

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