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Ameliorated Golden Jackal Optimization (Agjo) with Enhanced Movement and Multi-Angle Position Updating Strategy

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SSRN

SSRN 2023年

作者： Wahab, Magd Abdel Bai, Jianfu Khatir, Samir Abualigah, Laith Soete Laboratory Department of Electrical Energy Metals Mechanical Constructions and Systems Faculty of Engineering and Architecture Ghent University Belgium Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taiwan

Golden jackal optimization (GJO), a lately published meta-heuristic optimization algorithm, is inspired by the foraging behavior of pairs of golden jackals and shows an acceptable optimization performance. However, GJO exists a shortage in balancing exploration and exploitation, as it completely focused on exploitation in the later iterations. This study proposes a new variant of GJO, named Ameliorated Golden jackal optimization (AGJO). AGJO uses three strategies to alleviate the imbalance between the exploration and exploitation of GJO: the enhanced movement strategy, the global search strategy, and the multi-angle position update strategy for prey. An environmental disturbance factor is added to the third strategy to strengthen GJO's ability to evade the local optimal solution. The performance of AGJO is tested and compared with GJO and seven well-known meta-heuristic algorithms for 23 classical benchmark functions, CEC 2017 and CEC 2019. These results determined that AGJO performs better over 90% of these functions compared to GJO. AGJO is also highly competitive in terms of optimization capability and convergence speed compared with other algorithms. Finally, AGJO is applied to optimize five engineering design problems with constraints. Results show that AGJO is a more potential algorithm for solving real problems with constraints than GJO and other algorithms. © 2023, The Authors. All rights reserved.

关键词： Optimization

IoT Security Frameworks: A Comparative Review with a Focus on Privacy

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IoT Security Frameworks: A Comparative Review with a Focus o...

Cyber Resilience (ICCR), International Conference on

作者： Abduljaleel Al-Hasnawi Yitong Niu Jamal F. Tawfeq Manas Ranjan Pradhan Mohammed Salahat Taher M. Ghazal Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Communication Technology Engineering College of Information Technology Imam Ja'afar Al-Sadiq University Baghdad Iraq School of Aeronautical Engineering AnYang University Anyang China Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq School of Computing Skyline University College University City Sharjah Sharjah UAE College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Computer Science Department Centre for Cyber Physical Systems Khalifa University Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The privacy of sensitive data within the Internet of Things (IoT) is at risk due to potential privacy violations. These violations can occur through illegal access by external entities, as well as inadvertent disclosures or intentional attacks by those within the system who exceed their authorized access privileges. Furthermore, there needs to be more control experienced by data owners regarding their sensitive data as it transitions from their domain to other entities within the Internet of Things (IoT) ecosystem. Numerous methods have been presented in the academic literature over the past few years to effectively tackle the privacy concerns associated with the Internet of Things (IoT). This paper aims to classify existing literature by organizing it according to the specific technology or technique employed to safeguard privacy. The classification encompasses four primary areas of privacy solutions, specifically, solutions based on encryption, solutions based on identity management, solutions based on policy enforcement, and solutions focused on self-protection. The paper examines various state-of-the-art solutions within each category. It begins by providing a broad comparison between these solutions and our work. Subsequently, it focuses on a specific comparison of the privacy objectives of the offered PEFM solutions.

关键词： Privacy Data privacy Reviews Ecosystems Encryption Internet of Things Resilience

Breakdown of the static dielectric screening approximation of Coulomb interactions in atomically thin semiconductors

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arXiv 2024年

作者： Mhenni, Amine Ben Van Tuan, Dinh Geilen, Leonard Petrić, Marko M. Erdi, Melike Watanabe, Kenji Taniguchi, Takashi Tongay, Sefaattin Müller, Kai Wilson, Nathan P. Finley, Jonathan J. Dery, Hanan Barbone, Matteo Walter Schottky Institute TUM School of Natural Sciences MCQST Technical University of Munich Munich Germany Department of Electrical and Computer Engineering University of Rochester RochesterNY United States Walter Schottky Institute TUM School of Computation Information and Technology MCQST Technical University of Munich Munich Germany School for Engineering of Matter Transport and Energy Arizona State University TempeAZ United States Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Physics and Astronomy University of Rochester RochesterNY United States

Coulomb interactions in atomically thin materials are uniquely sensitive to variations in the dielectric screening of the environment, which can be used to control exotic quantum many-body phases and engineer exciton potential landscapes. A static approximation of the dielectric response, where increased dielectric screening is predicted to cause an energy redshift of the exciton resonance, has been until now sufficient. Here, we use charge-tunable exciton resonances to study screening effects in transition metal dichalcogenide monolayers embedded in materials with dielectric constants ranging from 4 to more than 1000, a range two orders of magnitude larger than previous studies. In contrast to the redshift predicted by static models employed until now, we observe a blueshift of the exciton resonance exceeding 30 meV for larger dielectric constant environments. By introducing a dynamical screening model based on a new solution to the Bethe-Salpeter equation, we find that while the exciton binding energy remains mostly controlled by the static dielectric response, the exciton self-energy is dominated by the high-frequency one. Our results supplant the understanding of screening in layered materials and their heterostructures, introduce a knob to tune selected many-body effects, and open new routes to detect and control correlated quantum many-body states and to design optoelectronic and quantum devices. © 2024, CC BY-NC-ND.

关键词： Nanocrystals

A data-driven rutting depth short-time prediction model with metaheuristic optimization for asphalt pavements based on RIOHTrack

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arXiv 2023年

作者： Li, Zhuoxuan Korovin, Iakov Shi, Xinli Gorbachev, Sergey Gorbacheva, Nadezhda Huang, Wei Cao, Jinde The School of Mathematics Southeast University Nanjing210096 China The Scientific Research Institute of Multiprocessor Computer Systems Southern Federal University Taganrog347928 Russia The School of Cyber Science & Engineering Southeast University Nanjing210096 China The Russian Academy of Engineering Moscow125009 Russia The Intelligent Transportation System Research Center Southeast University Nanjing210096 China The School of Mathematics Southeast University Nanjing210096 China Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (RIOHTrack, Road Track Institute) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of Average Root Mean Squared Error, Average Mean Absolute Error, and Average Mean Absolute Percentage Error for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry. © 2023, CC BY.

关键词： Complex networks

Comparative study of quantum emitter fabrication in wide bandgap materials using localized electron irradiation

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arXiv 2023年

作者： Kumar, Anand Cholsuk, Chanaprom Mishuk, Mohammad N. Hazra, Mouli Pillot, Clotilde Matthes, Tjorben Shaik, Tanveer A. Çakan, Aslı Deckert, Volker Suwanna, Sujin Vogl, Tobias Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Munich80333 Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena Jena07745 Germany Abbe Center of Photonics Friedrich Schiller University Jena Jena07743 Germany Jena07745 Germany Optical and Quantum Physics Laboratory Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand

Quantum light sources are crucial foundational components for various quantum technology applications. With the rapid development of quantum technology, there has been a growing demand for materials with the capability of hosting quantum emitters. One such material platform uses fluorescent defects in hexagonal boron nitride (hBN) that can host deep sub-levels within the band gap. The localized electron irradiation has shown its effectiveness in generating deep sub-levels to induce single emitters in hBN. The question is whether localized (electron beam) irradiation is a reliable tool for creating emitters in other wide bandgap materials and its uniqueness to hBN. Here, we investigate and compare the fabrication of quantum emitters in hBN and exfoliated muscovite mica flakes along with other 3D crystals, such as silicon carbide and gallium nitride, which are known to host quantum emitters. We use our primary fabrication technique of localized electron irradiation using a standard scanning electron microscope. To complement our experimental work, we employ density functional theory simulations to study the atomic structures of defects in mica. While our fabrication technique allows to create hBN quantum emitters with a high yield and high single photon purity, it is unable to fabricate single emitters in the other solid-state crystals under investigation. This allows us to draw conclusions on the emitter fabrication mechanism in hBN, which could rely on activating pre-existing defects by charge state manipulation. Therefore, we provide an essential step towards the identification of hBN emitters and their formation process. © 2023, CC BY.

关键词： Silicon carbide

Nonlinear Feedforward enabling Nonlinear Quadrature Measurement toward Fault-tolerant Universal Quantum computation

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Nonlinear Feedforward enabling Nonlinear Quadrature Measurem...

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

作者： Sakguchi, Atsushi Konno, Shunya Hanamura, Fumiya Ogawa, Hisashi Takase, Kan Asavanant, Warit Marek, Petr Filip, Radim Yoshikawa, Jun-Ichi Yonezawa, Hidehiro Huntington, Elanor Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Department of Optics Palacký University 17. listopadu 1192/12 Olomouc77146 Czech Republic Centre for Quantum Computation and Communication Technology School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Centre for Quantum Computation and Communication Technology Research School of Electrical Energy and Materials Engineering College of Engineering and Computer Science Australian National University CanberraACT2600 Australia

We demonstrate nonlinear feedforward combined with non-Gaussian ancillae localized in time, which realizes nonlinear quadrature measurement. Our technology in principle enables fault-tolerant universal quantum computa... 详细信息

ISBN: (纸本)9781557528209

关键词： Quantum computers

Overcoming entropic limitations on asymptotic state transformations through probabilistic protocols

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Physical Review A 2023年第4期107卷 042401-042401页

作者： Bartosz Regula Ludovico Lami Mark M. Wilde Department of Physics Graduate School of Science The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Institut für Theoretische Physik und IQST Universität Ulm Albert-Einstein-Allee 11 D-89069 Ulm Germany QuSoft Science Park 123 1098 XG Amsterdam The Netherlands Korteweg-de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 1098 XG Amsterdam The Netherlands Institute for Theoretical Physics University of Amsterdam Science Park 904 1098 XH Amsterdam The Netherlands School of Electrical and Computer Engineering Cornell University Ithaca New York 14850 USA Hearne Institute for Theoretical Physics Department of Physics and Astronomy and Center for Computation and Technology Louisiana State University Baton Rouge Louisiana 70803 USA

The quantum relative entropy is known to play a key role in determining the asymptotic convertibility of quantum states in general resource-theoretic settings, often constituting the unique monotone that is relevant in the asymptotic regime. We show that this is no longer the case when one allows stochastic protocols that may only succeed with some probability, in which case the quantum relative entropy is insufficient to characterize the rates of asymptotic state transformations, and a new entropic quantity based on a regularization of the Hilbert projective metric comes into play. Such a scenario is motivated by a setting where the cost associated with transformations of quantum states, typically taken to be the number of copies of a given state, is instead identified with the size of the quantum memory needed to realize the protocol. Our approach allows for constructing transformation protocols that achieve strictly higher rates than those imposed by the relative entropy. Focusing on the task of resource distillation, we give broadly applicable strong converse bounds on the asymptotic rates of probabilistic distillation protocols, and show them to be tight in relevant settings such as entanglement distillation with nonentangling operations. This generalizes and extends previously known limitations that are only applicable to deterministic protocols. Our methods are based on recent results for probabilistic one-shot transformations as well as a new asymptotic equipartition property for the projective relative entropy.

关键词： Entanglement manipulation Quantum information processing Quantum information theory Resource theories

Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

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arXiv 2022年

作者： Wu, Fang Jin, Shuting Jiang, Yinghui Jin, Xurui Tang, Bowen Niu, Zhangming Liu, Xiangrong Zhang, Qiang Zeng, Xiangxiang Li, Stan Z. School of Engineering Westlake University Hangzhou310024 China MindRank AI Ltd Hangzhou310000 China School of Informatics Xiamen University Xiamen361005 China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou311200 China College of Computer Science and Technology Zhejiang University Hangzhou310013 China School of Information Science and Engineering Hunan University Hunan410082 China

The latest biological findings observe that the motionless "lock-and-key" theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory-related studies, thus hindering the possibility of supervised learning. A spatial-temporal pre-training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self-supervised learning tasks: atom-level prompt-based denoising generative task and conformation-level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. A huge improvement from current state-of-the-art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision-recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation’s motion in the 3D space and the strength with which the ligand binds with its receptor. Copyright © 2022, The Authors. All rights reserved.

关键词： Molecular dynamics

Short-term train arrival delay prediction:a data-driven approach

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Railway sciences 2024年第4期3卷 514-529页

作者： Qingyun Fu Shuxin Ding Tao Zhang Rongsheng Wang Ping Hu Cunlai Pu Postgraduate Department China Academy of Railway SciencesBeijingChina Signal and Communication Research Institute China Academy of Railway Sciences Corporation LimitedBeijingChina Traffic Management Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail TransitBeijingChina Scientific and Technological Information Research Institute China Academy of Railway Sciences Corporation LimitedBeijingChina Yibin Track Signal and Communication DepotChina Railway Chengdu Group Co.LtdChengduChina School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina

Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays ***,delay predictions often involve imprecise shifts based on known delay ***-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment *** current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen ***/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival *** combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information *** on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding ***-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late *** show precise and rapid *** with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late *** enhanced adaptability to real-world train operations,training with full data is ***/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay *** innovatively compares and analyzes the model’s performance using both full data and delay data ***,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstra

关键词： Train delay prediction Intelligent dispatching command Deep learning Convolutional neural network Long short-term memory Attention mechanism