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Molecular architecture of mammalian pyruvate dehydrogenase complex

Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the university of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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In-Solution Germanium Selenide Nanosheets as Charge Trapping Layer in Flash Memories

In-Solution Germanium Selenide Nanosheets as Charge Trapping...

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IEEE Silicon Nanoelectronics Workshop (SNW)

作者： Nazek El-Atab Bashayr Alqahtani Program of Electrical and Computer Engineering Computer Electrical Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia

Germanium selenide (GeSe) is a highly promising material with several attractive characteristics, particularly in the field of ferroelectric and phase-change memories due to its outstanding electronic behavior. However, the potential of GeSe as a charge-trapping layer in flash memory has received less attention. Herein, the fabrication of a nonvolatile MOS memory device using GeSe nanosheets as a charge-trapping layer was demonstrated and the materials flakes were examined extensively. The electrical performance of the memory device was investigated. Intriguingly, it exhibited an extraordinarily wide memory window of 9 V under ±10 V electrical biasing. Additionally, the devices presented high endurance of $10^{4}$ programming and erasing cycles, and reliable charge storage of only 56% loss after 10 years.

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DELE: Deductive EL++ Embeddings for Knowledge Base Completion

arXiv

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arXiv 2024年

作者： Mashkova, Olga Zhapa-Camacho, Fernando Hoehndorf, Robert Computer Science Program Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Center of Excellence for Smart Health King Abdullah University of Science and Technology Thuwal Saudi Arabia Center of Excellence for Generative AI King Abdullah University of Science and Technology Thuwal Saudi Arabia

Ontology embeddings map classes, relations, and individuals in ontologies into Rn, and within Rn similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic EL++, several embedding methods have been developed that explicitly generate models of an ontology. However, these methods suffer from some limitations;they do not distinguish between statements that are unprovable and provably false, and therefore they may use entailed statements as negatives. Furthermore, they do not utilize the deductive closure of an ontology to identify statements that are inferred but not asserted. We evaluated a set of embedding methods for EL++ ontologies, incorporating several modifications that aim to make use of the ontology deductive closure. In particular, we designed novel negative losses that account both for the deductive closure and different types of negatives and formulated evaluation methods for knowledge base completion. We demonstrate that our embedding methods improve over the baseline ontology embedding in the task of knowledge base or ontology completion. © 2024, CC BY.

关键词： Ontology

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Multilayer Magnetic Domain Wall MTJ-based Spiking Neural Network

Multilayer Magnetic Domain Wall MTJ-based Spiking Neural Net...

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IEEE Conference on Nanotechnology

作者： Aijaz H. Lone Daniel N. Rahimi Hossein Fariborzi Gianluca Setti Department of Electrical and Computer Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (数字)9798350386240

ISBN: (纸本)9798350386257

Spintronic devices, especially the magnetic tunnel junction and magnetic domain wall-based devices, hold significant promise for applications in energy-efficient data storage and Unconventional computing architectures. We present a novel multilayer spintronic neuromorphic device based on spin-orbit torque-driven domain wall dynamics. The typical leaky integrate and fire LIF neuron-like characteristics are realized using the combination of SOT and demagnetization energy effects. The device characteristics are modelled as the modified LIF model. We test the spiking neuron model for the classification of the MNIST dataset by emulating a 3-layer spiking neural network SNN -based on the DW-MTJ LIF neuron model. The network achieves classification accuracy above 96% thus the proposed device can be integrated with the CMOS for energy efficient neuromorphic computing.

关键词： Semiconductor device modeling Neuromorphic engineering Computational modeling Neurons Spiking neural networks Nonhomogeneous media Energy efficiency

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Design and Characterization of 28 THz Rectenna for Infrared Energy Harvesting

Design and Characterization of 28 THz Rectenna for Infrared ...

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Radio and Antenna Days of the Indian Ocean (RADIO)

作者： Atif Shamim Electrical and Computer Engineering King Abdullah University of Science and Technology (KAUST)

The increasing energy demands of the world’s population and the quickly diminishing fossil fuel reserves together suggest the urgent need to secure long-lasting alternative and renewable energy resources. Infrared (IR) energy harvesting from waste heat can be a promising contribution for sustainable energy. In this talk, we will present a THz antenna integrated with rectifier (rectenna) for harvesting IR energy. The implementation of rectennas for energy harvesting at IR frequencies has remained an elusive research area due to the limitations of nano-scale fabrication and the inability to implement rectifiers that could handle electromagnetic (EM) radiation oscillating a trillion times per second. We will present the design of a resonant bowtie nanoantenna that has been optimized to produce highly enhanced localized fields at the bow tip. The phenomena of plasmon oscillation and hot spot creation at the feed point of the nano-antenna as a result of incident IR energy have been studied through EM simulations and Electron Energy Loss Spectroscopy (EELS). For the rectifier to function at THz frequencies, Metal-Insulator-Metal (MIM) diode has been realized because of its fast response time. To benefit from the field enhancement in the nano-gap, the diode is realized between the overlapped antenna’s arms using a 0.7 nm copper oxide. The thin film diode offers low zero bias resistance of 500 $\Omega$, thus improving the impedance matching with the antenna. In addition, the rectenna prototype demonstrates high zero bias responsivity (4 A/W), which is critical in producing DC current directly from THz signals without the application of an external electric source, particularly for energy harvesting applications. Finally, a new concept of geometric diode will be presented which can also work for THz frequencies.

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ML-Based Edge Application for Detection of Forced Oscillations in Power Grids

ML-Based Edge Application for Detection of Forced Oscillatio...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Dorado-Rojas, Sergio A. Xu, Shunyao Vanfretti, Luigi Ayachi, M. Ilies I. Ahmed, Shehab Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States King Abdullah University of Science and Technology Electrical and Computer Engineering Thuwal Saudi Arabia

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

This paper presents a Machine Learning (ML) solution deployed in an Internet-of-Things (IoT) edge device for detecting forced oscillations in power grids. We base our proposal on a one-dimensional (1D) and two-dimensional (2D) Convolutional Neural Network (CNN) architecture, trained offline and deployed on an Nvidia Jetson TX2. Our work also shows the advantages of optimizing the CNNs models, after training, using TensorRT, a library for accelerating deep learning inference in real-time. Both real-world and synthetic measurement signals are employed to validate the applicability of the proposed approach. © 2022 IEEE.

关键词： Convolutional neural networks

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Assessing aerosol effects on solar irradiance in the NEOM region with the WRF-solar model

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Bulletin of Atmospheric science and technology 2025年第1期6卷 1-24页

作者： Choudhury, Devanil Computer Electrical Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Mecca Province Thuwal 23955 - 6900 Saudi Arabia National Centre for Medium Range Weather Forecasting A-50 Institutional Area Sector-62 Uttar Pradesh Noida 201309 India

This study demonstrates the usability of the Weather Research and Forecasting (WRF)-Solar model, in predicting solar irradiance in the NEOM region of Saudi Arabia across different sky conditions. This research aims to enhance the accuracy of solar radiation forecasting by systematically evaluating the model's responses to external time-varying two-dimensional (2-D) aerosol optical depth (AOD) data at 550 nm under scenarios with varying cloud cover and aerosol levels. Through experiments representing distinct sky conditions, this study demonstrates consistent trends, with the control experiment (CTRL) consistently producing the highest Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) due to lower representation of AOD in the model. Notably, experiments incorporating time-varying 2-D AOD from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis outperform other experiments with NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA- 2) analysis, and climatological AOD datasets, particularly on overcast days. Model validation against observations from the Tabuk university station further substantiates these findings. Furthermore, experimental results highlight the importance of dynamic aerosol data over the climatological or no-aerosol data in improving the solar energy predictions, emphasizing the need for accurate representation of aerosol-cloud-radiation interactions in numerical models for precise solar irradiance forecasting. © The Author(s) 2025.

关键词： Aerosol-cloud-radiation interaction AOD CAMS MERRA- 2 Solar irradiance WRF-Solar

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Blockchain-Driven Framework for Fake Product Detection 2nd

Blockchain-Driven Framework for Fake Product Detection

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Shah, Richa Shah, Het Obaidat, Mohammad S. Jadav, Nilesh Kumar Gupta, Rajesh Tanwar, Sudeep Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat382481 India King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The Amity University UP Noida201301 India

ISBN: (纸本)9783031689048

Blockchain has a vast range of applications and has been acknowledged in this current era. It has solved various issues related to security, tracking, immutability, etc., and also verifies transactional information without relying on centralized third-party systems. One of the most significant drawbacks of the online retail industry is that they showcase fake products in the market. It seems that the product is authentic, but in reality, it is a counterfeit retail product. To overcome this situation, in this paper, we propose a decentralized blockchain-based fake product identification framework for manufacturing industries. We designed a blockchain smart contract for the proposed framework to identify whether the product is authentic or not. The proposed framework uses both the QR code and the serial number for the identification of fake products along with supply chain tracing. The performance of the proposed framework is evaluated using the transaction and execution cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Smart contract

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Sintering-based lithography metal additive manufacturing of chiral mechanical metamaterials with a twist

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Procedia CIRP 2025年 132卷 104-109页

作者： Ruslan Melentiev Ahmed Wagih Bram van der Heijden Gilles Lubineau Carlos A. Grande Advanced Membranes & Porous Materials Center. King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia KAUST Catalysis Center. King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Mechanical Engineering Program Physical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Saudi Arabia Mechanics of Composites for Energy and Mobility Lab King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Chemical Engineering Program. Physical Science and Engineering Division King Abdullah University of Science and Technology

Metamaterials are a class of materials with extraordinary capabilities derived from their engineered structure. In 2017, Frenzel et al. (science 358, 2017) conceptualized “three-dimensional mechanical metamaterials with a twist,” which convert linear deformation into rotational motion—a mechanical response that surpasses the bounds of Cauchy continuum mechanics. However, the lack of suitable manufacturing technologies for producing metallic twist metamaterials has precluded their experimental validation and potential applications to date. Herein, 316L-stainless-steel twist metamaterials are fabricated at various scales using an emerging sintering-based technology—lithography metal manufacturing (LMM) —and their mechanical responses are tested using stereo digital image correlation. Results provide the first empirical evidence of compression-induced rotation in steel twist metamaterials and highlight their sensitivity to manufacturing defects. Download: Download video (753KB) Download: Download video (2MB) Download: Download video (4MB)

关键词： Additive manufacturing sintering stainless steel metamaterials mechanical properties

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