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arXiv 2020年

作者： Vybornyi, Ivan Trichili, Abderrahmen Alouini, Mohamed-Slim Computer Electrical and Mathematical Sciences & Engineering King Abdullah University of Science and Technology Thuwal Makkah Province

Based on the fundamental rules of quantum mechanics, two communicating parties can generate and share a secret random key that can be used to encrypt and decrypt messages sent over an insecure channel. This process is known as quantum key distribution (QKD). Contrary to classical encryption schemes, the security of a QKD system does not depend on the computational complexity of specific mathematical problems. However, QKD systems can be subject to different kinds of attacks, exploiting engineering and technical imperfections of the components forming the systems. Here, we review the security vulnerabilities of QKD. We mainly focus on a particular effect known as backflash light, which can be a source of eavesdropping attacks. We equally highlight the methods for quantifying backflash emission and the different ways to mitigate this effect. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum cryptography

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Enhancing PV performance of Al/ZnO/CdS/GaAs/NiO/Au solar cells through diverse layer combinations by SCAPS-1D

Next Research

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Next Research 2025年第1期2卷

作者： Md. Salman Shah Md. Kamrul Hasan Sharat Chandra Barman Jubaer Ahamed Bhuiyan Hayati Mamur Mohammad Ruhul Amin Bhuiyan Department of Electrical and Electronic Engineering Islamic University Kushtia 7003 Bangladesh Department of Electrical and Electronic Engineering Teesta University Rangpur Bangladesh Postdoctoral Associate King Abdullah University of Science and Technology (KAUST) Saudi Arabia Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka 1230 Bangladesh Department of Electrical and Electronics Engineering Manisa Celal Bayar University Manisa 45140 Türkiye

Gallium arsenide (GaAs)-based photovoltaic (PV) cells are widely recognized for their outstanding efficiency and excellent light absorption capabilities, making them a preferred option in advanced solar energy technologies. Despite these advantages, their widespread application is limited by high production costs, thermal management issues, and inefficient charge carrier transport. This study aims to overcome these challenges by developing and optimizing a multilayer solar cell with the configuration Al/ZnO/CdS/GaAs/NiO/Au using SCAPS-1D simulation software. Key parameters, such as layer thickness, donor and acceptor densities, series and shunt resistances, and operating temperature, were systematically optimized to boost the device's performance. Under standard AM1.5 G illumination, the optimized solar cell achieved an open-circuit voltage (V OC ) of 1.0825 V, a short-circuit current density (J SC ) of 31.42 mA/cm², a fill factor (FF) of 87.43 %, and a power conversion efficiency (PCE) of 29.74 %, with quantum efficiency (QE) nearing 100 % within the visible spectrum. These enhancements demonstrate not only the potential for achieving high efficiency but also practical strategies for addressing key limitations in GaAs PV technology. The study highlights the promise of these optimized cells for high-performance applications, including space PVs, concentrated solar power systems, and other scenarios requiring compact and efficient energy solutions.

关键词： Gallium arsenide (GaAs) solar cells SCAPS-1D simulation High-efficiency photovoltaic cells (PV cells) Power conversion efficiency (PCE) Quantum efficiency (QE)

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Schrödinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure

Schrödinger Spectrum Based PPG Features for the Estimation ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Peihao Li Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) KSA

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

In this paper, photoplethysmogram (PPG) features are combined with supervised machine learning algorithms to estimate arterial blood pressure (ABP). Three algorithms for the estimation of cuffless ABP using PPG signals are compared. Since PPG signals are measured non-invasively, this method guarantees an individuals comfort while not omitting important ABP information. The proposed framework predicts the ABP values by processing PPG signals with semi-classical signal analysis (SCSA) method, extracting several categories of features, which reflect the PPG signal morphology variations. Then, regression algorithms are selected for the ABP estimation. The proposed method is evaluated based on a virtual dataset with more than four thousand subjects and MIMIC II database with over eight thousand subjects for model training and testing. Mean average error (MAE) and standard deviation (STD) are evaluated for different machine learning algorithms during the prediction and estimation process. Multiple linear regression (MLR) meets the AAMI standard in terms of estimation accuracy, which proves that the ABP can be accurately estimated in a nonintrusive fashion. Given the easy implementation of the ABP estimation method, we regard that the proposed features and machine learning algorithms for the cuffless estimation of the ABP can potentially provide the means for mobile healthcare equipment to monitor the ABP continuously.

关键词： Feature extraction Estimation Standards Support vector machines Databases Eigenvalues and eigenfunctions Machine learning algorithms

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Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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CAUCHY MARKOV RANDOM FIELD PRIORS FOR BAYESIAN INVERSION

arXiv

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arXiv 2021年

作者： Suuronen, Jarkko Chada, Neil K. Roininen, Lassi School of Engineering Science Lappeenranta-Lahti University of Technology PO Box 20 LappeenrantaFI-53851 Finland Applied Mathematics and Computational Science Program King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

The use of Cauchy Markov random field priors in statistical inverse problems can potentially lead to posterior distributions which are non-Gaussian, high-dimensional, multimodal and heavy-tailed In order to use such priors successfully, sophisticated optimization and Markov chain Monte Carlo (MCMC) methods are usually required In this paper, our focus is largely on reviewing recently developed Cauchy difference priors, while introducing interesting new variants, whilst providing a comparison We firstly propose a one-dimensional second order Cauchy difference prior, and construct new first and second order two-dimensional isotropic Cauchy difference priors Another new Cauchy prior is based on the stochastic partial differential equation approach, derived from Matérn type Gaussian presentation The comparison also includes Cauchy sheets Our numerical computations are based on both maximum a posteriori and conditional mean estimation We exploit state-of-the-art MCMC methodologies such as Metropolis-within-Gibbs, Repelling-Attracting Metropolis, and No-U-Turn sampler variant of Hamiltonian Monte Carlo We demonstrate the models and methods constructed for one-dimensional and two-dimensional deconvolution problems Thorough MCMC statistics are provided for all test cases, including potential scale reduction factors. Copyright © 2021, The Authors. All rights reserved.

关键词： Bayesian networks

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Exploring the Non - Hermitian Topology of Exceptional Points in Lasers and Fiber Loops

Exploring the Non - Hermitian Topology of Exceptional Points...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Alexander Schumer Hadiseh Nasari Yuzhou G. N. Liu Gisela Lopez-Galmiche Jason Leshin Helena E. Lopez-Aviles Lei Ding Yousef Alahmadi Absar U. Hassan Qi Zhong Stefan Rotter Patrick LiKamWa Demetrios N. Christodoulides Mercedeh Khajavlkhan Institutefor Theoretical Physics Vienna University of Technology (TU Wien) Vienna Austria Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles USA CREOL The College of Optics & Photonics University of Central Florida Orlando FL USA Center of Excellence for Telecomm Applications Joint Centers of Excellence Program King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia Department of Physics & Astronomy University of Southern California Los Angeles CA USA

ISBN: (纸本)9798350345995

When non-Hermitian eigenvalue surfaces form intertwined Riemann surfaces, the corresponding non-Hermitian singularities, also know as exceptional points (EPs), are located at the center of this specific topology. Various experimental platforms have recently been presented in which interesting new physics associated with these branch point singularities can be explored. In particular, it has been shown that dynamically encircling EPs may lead to a so-called chiral state transfer. The chirality in this context refers to the fact that in such an encircling protocol, the final state at the end of an EP -enclosing loop solely depends on the encircling direction (clockwise or counterclockwise), but not on the initial state. Such a chiral behavior is intrinsically linked to the state vector deviating from adiabatically following the system's eigenstates associated with the eigenvalue surfaces; instead, due to the non-Hermitian violation of the adiabatic theorem, non-adiabatic jumps let the system undergo transitions from lossy eigenstates to eigenstates with gain. Here, we present two experiments that implement concepts that go beyond this established paradigm.

关键词： Attenuation Chirality Fiber lasers Lasers Surfaces Waveguides

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Application of syntrophic co-culture Chlorella sorokiniana and Streptomyces thermocarboxydus in semi-continuous system for cassava wastewater treatment and biomass production

Green Technologies and Sustainability

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Green Technologies and Sustainability 2025年第3期3卷

作者： Padri, Mohamad Boontian, Nittaya Latief, Maisarah Munirah Amdah, Misdar Tamzil, Mohamed Sahrul School of Environmental Engineering Institute of Engineering Suranaree University of Technology 111 Maha Witthayalai Rd Suranari Mueang Nakhon Ratchasima District Nakhon Ratchasima 30000 Thailand King Abdullah University of Science and Technology Beacon Development Department (KAUST-KBD) Thuwal Makkah 23955-6900 Saudi Arabia Geography Department Faculty of Mathematic and Natural Sciences Makassar State University Parantambung Campus Daeng Tata Street South Sulawesi Makassar 90224 Indonesia Biotechnology Program Graduated School Faculty of Science Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok 10330 Thailand

Application of microalgae and mutualistic symbionts have been widely developed for wastewater treatment and algal biomass production. This study aimed to examine and compare the application of single and co-culture of alga Chlorella sorokiniana P21 with Streptomyces thermocarboxydus BMI in semi-continuous system to treat biogas effluent wastewater and generate algal biomass. A sequence of batch and semi-continuous system was applied to assess the single and co-culture nutrients removal (N, P, and COD) along with the biomass productivity for sterilized and raw (unsterilized) wastewater. Different Hydraulic Retention Times (HRTs) were applied to the system, and it was found that 5 days HRT resulted the highest nutrients removals in single and co-culture conditions. Nevertheless, this HRT was also in line with the predicted optimum HRT based on the growth rate in the batch conditions. However, low HRTs were found not to allow the algae to increase the pH and DO in the culture. This result exhibits the massive potencies of microalgae-bacterial co-culture to apply on wastewater treatment in larger scale with reliable removal and biomass production. © 2025 The Authors

关键词： Actinomycetes Cassava Wastewater Microalgae Co-culture Nutrient removal Semi-continuous system

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Nonlinear Model Predictive Control Design for BSM-MBR: Benchmark of Membrane Bioreactor ⁎

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IFAC-PapersOnLine 2020年第2期53卷 16524-16530页

作者： Xingang Guo Peiying Hong Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Sciences and Engineering Biological and environmental Science and engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia

The optimal control and operation of a Membrane Bioreactor (MBR) process by Nonlinear Model Predictive Control (NMPC) is investigated in this work. First, the Benchmark Simulation Model for MBR (BSM-MBR) provided by Maere et al. (2011) is introduced with a detailed mathematical model. Then, an NMPC is designed by incorporating the nonlinear process model of BSM-MBR to control the dissolved oxygen concentration at a certain level while meeting input and other process constraints. The performance of the NMPC is evaluated under both constant influent scenario and dynamic dry weather influent scenario. The simulation results demonstrate that NMPC works better in the constant influent case compared to the dynamic influent scenario.

关键词： Nonlinear Model Predictive Control (NMPC) Membrane Bioreactor (MBR) Benchmark Simulation Model for MBR (BSM-MBR)

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On differentially private stochastic convex optimization with heavy-Tailed data 37

On differentially private stochastic convex optimization wit...

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37th International Conference on Machine Learning, ICML 2020

作者： Wang, Di Xiao, Hanshen Devadas, Srini Xu, Jinhui Department of Computer Science and Engineering State University of New York at Buffalo BuffaloNY United States King Abdullah University of Science and Technology Thuwal Saudi Arabia CSAIL Mit CambridgeMA United States

ISBN: (纸本)9781713821120

In this paper, we consider the problem of de-signing Differentially Private (DP) algorithms for Stochastic Convex Optimization (SCO) on heavy-Tailed data. The irregularity of such data violates some key assumptions used in almost all existing DP-SCO and DP-ERM methods, re-sulting in failure to provide the DP guarantees. To better understand this type of challenges, we provide in this paper a comprehensive study of DP-SCO under various settings. First, we con-sider the case where the loss function is strongly convex and smooth. For this case, we propose a method based on the sample-And-Aggregate framework, which has an excess population risk of O( d3 n4 ) (after omitting other factors), where n is the sample size and d is the dimensional-ity of the data. Then, we show that with some additional assumptions on the loss functions, it is possible to reduce the expected excess popula-tion risk to O ( d2 n2 ). To lift these additional condi-tions, we also provide a gradient smoothing and trimming based scheme to achieve excess popula-tion risks of O ( d2 n2 ) and O( d 2 3 (n2) 13 ) for strongly convex and general convex loss functions, respec-tively, with high probability. Experiments sug-gest that our algorithms can effectively deal with the challenges caused by data irregularity. © 2020 by the Authors.

关键词： Convex optimization

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Comparison of the Effectiveness of Machine Learning Techniques for the Number of Road Traffic Injuries

Comparison of the Effectiveness of Machine Learning Techniqu...

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International ECTI Northern Section Conference on electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Wirawan Puttamat Wikanda Phaphan Anuchit Jitpattanakul Chainarong Pianpailoon Sakorn Mekruksavanich Department of Mathematics Faculty of Education Chaiyaphum Rajabhat University Chaiyaphum Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Research Group in Statistical Learning and Inference Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Curriculum and Teaching Program (Mathematics) Faculty of Education Sakon Nakhon Rajabhat University Sakon Nakhon Thailand Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand

This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE.

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