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2D logistic map with unit transfer function and modulus operation based pseudorandom number generation for image encryption

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Scientific Reports 2025年第1期15卷 1-14页

作者： Ahmad, Raiz Said, Ghawar Flah, Aymen Kraiem, Habib El bayeh, Claude Ziad Khattak, Yousaf Hameed Baig, Faisal Federal Urdu University of Arts Science & Technology Islamabad Islamabad Pakistan Computer Science Department Iqra University Islamabad Pakistan Processes Energy Environment and Electrical Systems National Engineering School of Gabès University of Gabès Gabès Tunisia Applied Science Research Center Applied Science Private University Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India ENET Centre VSB-Technical University of Ostrava Ostrava Czech Republic Jadara University Research Center Jadara University Irbid Jordan Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia College of Engineering and Technology University of Doha for Science and Technology Doha Qatar School of Design Engineering Universitat Politecnicia de Valencia Valencia Spain

This study presents a novel approach to generating high-quality random numbers using a two-dimensional logistic map with a unit transfer function (2DLMUTF). The method is built upon the chaotic dynamics of the logistic map, where the parameter governs the system’s behavior, exhibiting chaotic nature in the range of 3.57 to 4. By applying a unit transfer function and modulus operation, the system’s output is constrained within the [0, 1] range, altering the phase space dynamics compared to traditional 2D logistic maps. Numerical simulations in MATLAB, with parameters =4, =3.8, and initial seed values =0.2350 and =0.3500, were run for iterations. Statistical testing using the NIST SP 800 − 22 test suite showed superior randomness, with the method passing all 15 tests. Additionally, uniformity, autocorrelation, cross-correlation, and entropy analyses confirmed the method’s suitability for cryptographic applications. The generated random numbers were used to create substitution boxes (S-boxes) for image encryption, demonstrating strong encryption performance. Overall, 2DLMUTF offers a computationally efficient and secure solution for random number generation which is suitable for cryptographic and image encryption applications. © The Author(s) 2025.

关键词： Chaotic map Encryption Image cryptography Security analysis Two-dimensional logistic map

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FAIR Convergence Matrix:Optimizing the Reuse of Existing FAIR-Related Resources

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Data Intelligence 2020年第1期2卷 158-170,313页

作者： Hana Pergl Sustkova Kristina Maria Hettne Peter Wittenburg Annika Jacobsen Tobias Kuhn Robert Pergl Jan Slifka Peter McQuilton Barbara Magagna Susanna-Assunta Sansone Markus Stocker Melanie Imming Larry Lannom Mark Musen Erik Schultes GO FAIR International Support and Coordination Office LeidenThe Netherlands Centre for Digital Scholarship Leiden University LibrariesLeidenThe Netherlands Max Planck Computing and Data Facility Gießenbachstraße 285748 GarchingGermany Leiden University Medical Center Leiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK Environment Agency Austria A-1090 ViennaAustria TIB Leibniz Information Centre for Science and Technology HannoverGermany SURF Utrecht 3511 EPThe Netherlands Corporation for National Research Initiatives(CNRI) RestonVirginia 20191USA Stanford Center for Biomedical Informatics Research StanfordCA 94305USA

The FAIR principles articulate the behaviors expected from digital artifacts that are Findable,Accessible,Interoperable and Reusable by machines and by *** by now widely accepted,the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR *** different communities have their own,often well-established implementation preferences and priorities for data reuse,coordinating a broadly accepted,widely used FAIR implementation approach remains a global *** an effort to accelerate broad community convergence on FAIR implementation options,the GO FAIR community has launched the development of the FAIR Convergence *** Matrix is a platform that compiles for any community of practice,an inventory of their self-declared FAIR implementation choices and *** Convergence Matrix is itself a FAIR resource,openly available,and encourages voluntary participation by any self-identified community of practice(not only the GO FAIR Implementation Networks).Based on patterns of use and reuse of existing resources,the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.

关键词： FAIR Implementation Choices and Challenges Convergence FAIR Communities

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RETRACTED ARTICLE: Mitigating the Coexistence Technique in Wireless Body Area Networks By Using Superframe Interleaving

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IETE Journal of research 2023年第11期69卷 CXXXIII-CXLVII页

作者： Samira Kanwal Junaid Rashid Jungeun Kim Sapna Juneja Gaurav Dhiman Amir Hussain 1 Department of Computer Science University of Engineering and Technology Taxila Pakistan 2 Department of Computer Science and Engineering Kongju National University Cheonan31080 Korea 3 KIET Group of Institutions Delhi NCR Ghaziabad India 4 Department of Computer Science Government Bikram College of Commerce Patiala India5 University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan Mohali India6 Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India 7 Centre of AI and Data Science Edinburgh Napier University Edinburgh UK

We, the Editor, Institution and Publisher of the journal IETE Journal of research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:Samira Kanwal, Junaid Rashid, Jungeun Kim, Sapna Juneja, Gaurav Dhiman & Amir Hussain (2022) Mitigating the Coexistence Technique in Wireless Body Area Networks By Using Superframe Interleaving, IETE Journal of research, doi:10.1080/03772063.2022.2043788Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

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Improved PV soiling extraction through the detection of cleanings and change points

TechRxiv

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TechRxiv 2021年

作者： Micheli, Leonardo Theristis, Marios Livera, Andreas Stein, Joshua S. Georghiou, George E. Muller, Matthew Almonacid, Florencia Fernández, Eduardo F. University of Jaén Jaén23071 Spain Sandia National Laboratories AlbuquerqueNM87185 United States Photovoltaic Technology Laboratory FOSS Research Centre for Sustainable Energy Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus National Renewable Energy Laboratory GoldenCO80401 United States CEACTEMA University of Jaén Jaén23071 Spain

Photovoltaic (PV) soiling profiles exhibit a sawtooth shape, where cleaning events and soiling deposition periods alternate. Generally, the rate at which soiling accumulates is assumed to be constant within each deposition period. In reality, changes in rates can occur due to sudden variations in climatic conditions;e.g. dust storms or prolonged periods of rain. The existing models used to extract the soiling profile from PV performance data might fail to capture the change points and occasionally estimate incorrect soiling profiles. This work analyzes how the introduction of change points can be beneficial for soiling extraction. Data from nine soiling stations and a 1 MW site were analyzed by using piecewise regression and three change point detection algorithms. The results showed that accounting for change points can provide significant benefits to the modelling of soiling even if not all the change point algorithms return the same improvements. Considering change points in historical trends is found to be particularly important for studies aiming to optimize cleaning schedules. © 2021, CC BY-NC-SA.

关键词： Deposition

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Optical Thouless pumping transport and nonlinear switching in a topological low-dimensional discrete nematic liquid crystal array

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Physical Review A 2022年第1期105卷 013513-013513页

作者： Pawel S. Jung Midya Parto Georgios G. Pyrialakos Hadiseh Nasari Katarzyna Rutkowska Marek Trippenbach Mercedeh Khajavikhan Wieslaw Krolikowski Demetrios N. Christodoulides CREOL The College of Optics and Photonics University of Central Florida Orlando Florida 32816 USA Faculty of Physics Warsaw University of Technology Warsaw Poland Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA Faculty of Physics University of Warsaw Warsaw Poland Department of Physics & Astronomy Dornsife College of Letters Arts & Sciences University of Southern California Los Angeles California 90089 USA Laser Physics Centre Research School of Physics Australian National University Canberra ACT 0200 Australia Science Program Texas A&M University at Qatar Doha Qatar

We theoretically investigate a Thouless pumping scheme in the one-dimensional topological Su-Schrieffer-Heeger (SSH) model for single and multiple band-gap systems when implemented in a discrete nematic liquid crystal arrangement. For an electrically controlled SSH waveguide array, we numerically demonstrate edge-to-edge light transport at low power levels. On the other hand, at higher powers, the transport is frustrated by light-induced nonlinear defect states, giving rise to robust all-optical switching.

关键词： Optical solitons Topological insulators Liquid crystals Topological materials

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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction

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Scientific Reports 2025年第1期15卷 1-34页

作者： Lee, Sangkeum Almomani, Mohammad H. Alomari, Saleh Ali Saleem, Kashif Smerat, Aseel Snasel, Vaclav Gandomi, Amir H. Abualigah, Laith Department of Computer Engineering Hanbat National University 125 Dongseo-daero Yuseong-gu Daejeon South Korea Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa 13133 Jordan Faculty of Science and Information Technology Jadara University Irbid 21110 Jordan Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh 11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Faculty of Engineering and Information Technology University of Technology Sydney Ultimo 2007 NSW Australia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq University Research and Innovation Center (EKIK) Óbuda University Budapest 1034 Hungary Department of Computer Science Khazar University Baku Azerbaijan

Accurate very short-term wind power forecasting is critical for the reliable integration of renewable energy into modern power systems. However, the inherent variability and non-linearity of wind power data pose significant challenges. To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. The model is optimized using an enhanced Artificial Protozoa Optimizer (IAPO) augmented with an Adaptive Environmental Response Mechanism (AERM), which dynamically adjusts exploration and exploitation strategies based on the problem landscape to improve convergence and hyperparameter tuning efficiency. The proposed IAPO-LSTM model was evaluated on four real-world datasets—NREL WIND, EMD WIND, WWSIS, and ERCOT GRID—and benchmarked against six state-of-the-art forecasting models. Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil’s Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. Additionally, the model demonstrated faster inference times and better statistical significance (p < 0.005) compared to baseline methods. These outcomes confirm that IAPO-LSTM is not only highly accurate but also efficient and robust for real-time wind power forecasting applications. © The Author(s) 2025.

关键词： Adaptive environmental response mechanism Artificial Protozoa optimizer Hybrid deep learning Renewable energy integration Time series prediction Wind power forecasting

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Erratum to: Several Articles in JETP Letters

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JETP Letters 2022年第8期116卷 589-591页

作者： Godunov, S. I. Karkaryan, E. K. Novikov, V. A. Rozanov, A. N. Vysotsky, M. I. Zhemchugov, E. V. Inogamov, N. A. Perov, E. A. Zhakhovsky, V. V. Shepelev, V. V. Petrov, Yu. V. Fortova, S. V. Tarasenko, S. V. Shavrov, V. G. Valeev, B. Yu. Toksumakov, A. N. Kvashnin, D. G. Chernozatonskii, L. A. Makarov, A. S. Kretova, M. A. Afonin, G. V. Qiao, J. C. Glezer, A. M. Kobelev, N. P. Khonik, V. A. Klumov, B. A. Magadeev, E. B. Vakhitov, R. M. Tamm Department of Theoretical Physics Lebedev Physical Institute Russian Academy of Sciences Moscow Russia Alikhanov Institute for Theoretical and Experimental Physics National Research Center Kurchatov Institute Moscow Russia Centre de Physique des Particules de Marseille CPPM Aix-Marseille Universite CNRS/IN2P3 Marseille France Landau Institute for Theoretical Physics Russian Academy of Sciences Chernogolovka Russia Joint Institute for High Temperatures Russian Academy of Sciences Moscow Russia All-Russia Research Institute of Automatics Moscow Russia Institute for Computer Aided Design Russian Academy of Sciences Moscow Russia Moscow Institute of Physics and Technology (National Research University) Dolgoprudnyi Russia Dоnetsk Institute for Physics and Engineering Donetsk Ukraine Kotelnikov Institute of Radio Engineering and Electronics Russian Academy of Sciences Moscow Russia Emanuel Institute of Biochemical Physics Russian Academy of Sciences Moscow Russia Scientific School on Chemistry and Technology of Polymer Materials Plekhanov Russian University of Economics Moscow Russia Voronezh State Pedagogical University Voronezh Russia School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an People’s Republic of China National University of Science and Technology MISiS Moscow Russia Institute of Solid State Physics Russian Academy of Sciences Chernogolovka Russia Bashkir State University Ufa Russia

An Erratum to this paper has been published: https://***/10.1134/S0021364022340021

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SoUthern Cluster sCale Extended Source Survey (SUCCESS): A GMRT and MeerKAT study of nine massive galaxy clusters

arXiv

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arXiv 2022年

作者： Kale, R. Parekh, V. Rahaman, M. Joshi, D.C. Venturi, T. Kolokythas, K. Chibueze, J.O. Sikhosana, S. Pillay, D. Knowles, K. National Centre for Radio Astrophysics Tata Institute of Fundamental Research S. P. Pune University Campus Ganeshkhind Pune411007 India Centre for Radio Astronomy Techniques and Technologies Department of Physics and Electronics Rhodes University PO Box 94 Makhanda6140 South Africa 2 Fir Street Black River Park Cape Town Observatory7925 South Africa Department of Astronomy Astrophysics and Space Engineering Indian Institute of Technology Indore Indore453552 India Institute of Astronomy National Tsing Hua University Hsinchu Taiwan INAF Istituto di Radioastronomia Via Gobetti 101 Bologna40129 Italy Centre for Space Research North-West University Potchefstroom2520 South Africa Department of Physics and Astronomy Faculty of Physical Sciences University of Nigeria Carver Building 1 University Road Nsukka Nigeria Astrophysics Research Centre University of KwaZulu-Natal Durban4041 South Africa School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Westville 3696 South Africa

We aim to carry out a radio study of the SoUthern Cluster sCale Extended Source Survey (SUCCESS) sample consisting of twenty massive (M500> 5 × 1014Mo), nearby (redshift Copyright © 2022, The Authors. All... 详细信息

关键词： Surveys

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A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Khoshvaght, Parisa Haider, Amir Rahmani, Amir Masoud Gharehchopogh, Farhad Soleimanian Anka, Ferzat Lansky, Jan Hosseinzadeh, Mehdi Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of AI and Robotics Sejong University Seoul05006 Korea Republic of Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Fatih Sultan Mehmet Vakif University Istanbul Turkey Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Korea Republic of

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms. © 2025 Elsevier B.V.

关键词： Resource allocation

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Faithful Transfer of 3D Propagation Characteristics of Deterministic and Random Optical Fields to Coded Aperture Imaging Systems Using Lucy-Richardson-Rosen Algorithm

Faithful Transfer of 3D Propagation Characteristics of Deter...

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Next Generation Electronics (NEleX), International Conference on

作者： Agnes Pristy Ignatius Xavier Francis Gracy Arockiaraj Shivasubramanian Gopinath Aravind Simon John Francis Rajeswary Andra Naresh Kumar Reddy Rashid A. Ganeev M. Scott Arockia Singh S.D. Milling Tania Vijayakumar Anand Institute of Physics University of Tartu Estonia School of Electrical and Computer Engineering Ben Gurion University of the Negev Israel Laboratory of Nonlinear Optics University of Latvia Riga Latvia Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Department of Physics and Astronomy Chirchik State Pedagogical University Chirchik Uzbekistan Dr. Jeyasekharan Hospital Nagercoil Tamil Nadu India Dr. Scott's Laser and Piles Fistula Center Nagercoil Tamil Nadu India Department of Orthodontics Rajas Dental College and Hospital Tirunelveli Tamil Nadu India Darshan Dental and Orthodontic Clinic Kanyakumari Tamil Nadu India Optical Sciences Center and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM) School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia

engineering the complex amplitude and polarization of light is essential for various applications. In this direction, many deterministic and random optical beams such as Airy Bessel, and self-rotating beams were developed. While the above beams satisfied the requirements for the targeted applications, they are not suitable for imaging applications in spite of the valuable axial characteristics they possess, as they are not effective object-image mapping elements. Consequently, when exotic beams were implemented for direct imaging, only a distorted image was obtained. However, the scenario is different in coded aperture imaging (CAI) methods, where the imaging mode is indirect, consisting of optical recording and computational image recovery. Therefore, the point spread function (PSF) in CAI is not the recorded intensity distribution but the reconstructed intensity distribution. By employing a suitable computational reconstruction method, it is possible to convert the recorded intensity distribution into a Delta-like function. In this study, Lucy-Richardson-Rosen algorithm has been implemented as a generalized image recovery method for a wide range of optical beams, and the performance is validated in both simulation and optical experiments.

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