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Structure,magnetic and adsorption properties of novel FePt/h-BN heteromaterials

Nano Research 2023年第1期16卷 1473-1481页

作者： Anton S.Konopatsky Vladislava V.Kalinina Alena S.Savchenko Denis.V.Leybo Ekaterina V.Sukhanova Viktor S.Baidyshev Zakhar I.Popov Andrey V.Bondarev Josef Polčák Dmitry V.Shtansky National University of Science and Technology(MISIS) Leninskiy prospect 4Moscow119049Russia Emanuel Institute of Biochemical Physics RAS Kosygina 4Moscow119334Russia Katanov Khakas State University 90 Lenin pr.655017 AbakanRussia Plekhanov Russian University of Economics 36 Stremyanny per.Moscow 117997Russia Department of Control Engineering Faculty of Electrical EngineeringCzech Technical University in PragueTechnicka 2Prague 16627Czech Republic CEITEC-Central European Institute of Technology Brno University of TechnologyPurkyňova 123Brno 61200 BrnoCzech Republic

Nanomaterials with high specific surface area and high absorption capacity are attracting increased interest aimed at imparting the desired magnetic *** work is devoted to the study of the effect of heat treatment in a hydrogen atmosphere on the microstructure,adsorption and magnetic properties of heterogeneous FePt/h-BN *** via the polyol process,FePt nanoparticles(NPs)had a size<2 nm and were uniformly distributed over the surface of hexagonal boron nitride(h-BN)*** temperature-activated fcc→fct phase transformation in ultrafine FePt NPs has been well *** NPs act as active centers dissociating H2 molecules and transfer adsorbed hydrogen atoms to the *** functional theory(DFT)calculations also indicate that the h-BN substrate can absorb hydrogen adsorbed on the FePt *** hydrogen circulation in the FePt/h-BN system promoted the fcc→fct phase transformation and allowed to control the magnetic ***/h-BN nanomaterials also exhibited a high adsorption capacity with respect to various organic dyes.

关键词： FePt/h-BN nanomaterials polyol synthesis phase transformations magnetic properties adsorption

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Optimization of Microwave-Assisted extraction for enhanced bioactive efficiency of Tirmania spp. And Terfezia claveryi: Their response surface methodology and Principal component analysis approach

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Microchemical Journal 2025年 214卷

作者： Hussein, Hawsar Syamand Nedjip, Gjulten Ugurtay, Abdulhalik Ahmed, Akhter Ahmed Karaogul, Eyyup Department of Food Security and Quality Control Khabat Technical Institute Erbil Polytechnic University Erbil Iraq Harran University Faculty of Science and Letters Department of Chemistry Şanlıurfa Turkey Harran University Engineering Faculty Food Engineering Şanlıurfa Turkey Department of Biology College of Science Salahddin University Erbil Iraq

This study explored two truffle species, Tirmania spp. and Terfezia claveryi, collected from five newly identified locations in Iraq, focusing on their nutritional and medicinal value. Microwave-assisted extraction (MAE), optimized via response surface methodology (RSM), was employed to enhance the recovery of antioxidant compounds using solvents with varying polarity indices (PI: 0.10–10.2) and microwave power settings. Optimal conditions for Tirmania spp. involved an ethanol–water mixture (PI 8.4) at 150 W, yielding 47.3 % extract, 121.3 mg gallic acid equivalents per gram dry weight (mg GAE/g) of total phenolics, 79.2 mg quercetin equivalents per gram dry weight (mg Qrc/g) of flavonoids, and 24.2 mg Trolox equivalents per liter (mg Trolox/L) of antioxidant capacity measured by cupric ion reducing antioxidant capacity (CUPRAC) assay. Free radical scavenging activity was assessed using 2,2-diphenyl-1-picrylhydrazyl (DPPH;IC₅₀: 78.2 µg/mL) and 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS;IC₅₀: 23.3 µg/mL) assays. Activation energy analysis highlighted the solvent's efficiency in balancing diffusion and energy transfer. Furthermore, LC-MS/MS analysis revealed that fumaric acid and vanillic acid were the major compounds, with Tirmania spp. generally exhibiting higher concentrations than Terfezia claveryi. Principal component analysis and hierarchical clustering revealed distinct bioactive profiles among samples. Antibacterial activity, assessed via the agar well diffusion method, demonstrated notable inhibition against Escherichia coli and Staphylococcus aureus. These results emphasize the critical role of extraction parameters in enhancing the antioxidant and antibacterial potential of truffles for food and pharmaceutical applications. © 2025

关键词： Antibacterial activity Antioxidant activity LC-MS/MS Microwave-assisted extraction Polarity index Principal component analysis Response surface methodology Terfezia claveryi Tirmania spp.

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Experimental investigation of the impact ethanol-biodiesel-diesel blended fuels on combustion, emission, and performance of compression ignition diesel engine

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Materials Today: Proceedings 2022年 66卷 2830-2835页

作者： Wai, P. Karin, P. Phairote, W. Chollacoop, N. Kosaka, H. Po-ngen, W. Department of Mechanical Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok 10520 Thailand National Metal and Materials Technology Center National Science and Technology Development Agency Pathum Thani 12120 Thailand Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo 179-0085 Japan Department of Teacher Training in Mechanical Engineering Faculty of Technical education King Mongkut's University of Technology North Bangkok Bangsue Bangkok 10800 Thailand

This research was directed to reduce the global diesel engine emissions and dependency on finite fossil fuel reserves. The ethanol was blended by weight ratio with commercial "B20" fuel (20% palm oil's biodiesel and 80% diesel) as B20E5 (95% B20 with 5% ethanol), B20E10 (90% B20 with 10% ethanol) and B20E20 (80% B20 with 20% ethanol). The results of the engine's performance, combustion, emission, and agglomerate particles size using blended fuels were compared with the results of based commercial B20 fuel. All fuel samples were tested on a four-cylinder direct injection diesel engine at a constant load of 140Nm with engine speeds of 1000RPM, 1500RPM and 2000RPM. When the engine speed increased, the brake-specific fuel consumption decreased, and the brake thermal efficiency increased. The B20E20 shows the highest brake-specific fuel consumption because of the low energy content of the fuel blend and the highest thermal efficiency because of a better combustion process. The ethanol-blended fuels show higher peaks of in-cylinder pressure and heat release rate than the base B20 fuel, with B20E20 as the highest. Ethanol blended fuels have significant advantages in particulate matters reduction, especially in idle engine speed. The blended fuels decreased soot and CO2 emissions and increased NOx emission. The agglomerate particles size distribution was analysed with 100 samples for each fuel by using Scanning Electron Microscopy (SEM) and Image J tools. The average agglomerate particle size of B20, B20E5, B20E10 and B20E20 are 0.253 µm, 0.245 µm, 0.225 µm and 0.187 µm, respectively. As conclusion, adding ethanol to diesel fuel provide strong advantages on soot reduction and higher engine efficiency due to the enrich of fuel oxygen. © 2022

关键词： Ethanol

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Role of J/ψ production in electron-ion collisions

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Physical Review D 2025年第1期111卷 L011501-L011501页

作者： Zexuan Chu Jinhui Chen Xiang-Peng Wang Hongxi Xing Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) and Institute of Modern Physics Fudan University Shanghai 200433 China Shanghai Research Center for Theoretical Nuclear Physics NSFC and Fudan University Shanghai 200438 China Technical University of Munich TUM School of Natural Sciences Physics Department James-Franck-Strasse 1 85748 Garching Germany Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 China

Within the framework of nonrelativistic QCD (NRQCD) effective field theory, we study the leptoproduction of J/ψ at next-to-leading order in perturbative QCD for both unpolarized and polarized electron-ion collisions. We demonstrate that the J/ψ-tagged deep inelastic scattering in the future Electron-Ion Collider can be served as a golden channel for reasons including constraining NRQCD long-distance matrix elements, probing the nuclear gluon distribution functions, as well as investigating the gluon helicity distribution inside a longitudinal polarized proton.

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Collaborative learning technologies for project work 1

Collaborative learning technologies for project work

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1st International Conference on Technology Enhanced Learning in Higher Education, TELE 2021

作者： Galkin, Alexander Blyumin, Semen Saraev, Pavel Pimenov, Vladimir Lipetsk State Technical University Faculty of Automation and Computer Science Lipetsk Russia Lipetsk State Technical University Department of Applied Mathematics Lipetsk Russia Lipetsk State Technical University Department of Automated Control Systems Lipetsk Russia Novolipetsk Steel Corporation Lipetsk Russia

ISBN: (纸本)9781665443968

The paper presents an approach to learning based on the implementation of projects under the joint supervision and control of the educational organization and industrial partner. When students carry out projects, collaborative learning technologies are implemented. Periodic joint consultations of students by specialists of different profiles of educational organization and industrial partner help to bring the training closer to the real production process, and upon the completion of the project to get a ready-to-use product. Working in this mode allows students using modern technologies to work on the project, in particular, flexible methodologies of agile development. It helps not to formulate a completely ready technical task for the project, but to adjust it in the process of implementation. Tasks are formulated for a limited time interval of 1-2 weeks, after which the results are evaluated and tasks for the next stage are formed. The paper provides examples of works by students of Lipetsk State technical University, performed in this format. © 2021 IEEE.

关键词： Students

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Estimating infinitesimal generators of stochastic systems with formal error bounds: A data-driven approach 24

Estimating infinitesimal generators of stochastic systems wi...

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24th ACM International Conference on Hybrid Systems Computation and control, HSCC 2021, held as part of the 14th Cyber Physical Systems and Internet-of-Things Week, CPS-IoT Week 2021

作者： Lavaei, Abolfazl Nejati, Ameneh Soudjani, Sadegh Zamani, Majid Institute for Dynamic Systems and Control Eth Zurich Switzerland Department of Electrical Engineering Technical University of Munich Germany School of Computing Newcastle University United Kingdom Department of Computer Science University of Colorado Boulder United States

ISBN: (纸本)9781450383394

In this work, we propose a data-driven technique for a formal estimation of infinitesimal generators of continuous-time stochastic systems with unknown dynamics. In the proposed framework, we first approximate the infinitesimal generator of the solution process via a set of data collected from solution processes of unknown systems. We then put some proper assumptions on dynamics of systems and quantify the closeness between the infinitesimal generator and its approximation while providing a priori guaranteed confidence bound. We show that both the time discretization and the number of data play significant roles in providing a reasonable closeness precision. Motivations and Related Works. Infinitesimal generator of a stochastic process (i.e., a partial differential operator that encodes a great deal of information about the process) plays a principal role in many analysis frameworks including: (i) stability analysis of continuous-time stochastic systems via Lyapunov functions [1], (ii) establishing similarity relations between two continuous-time stochastic systems via stochastic simulation functions [2], (iii) constructing finite abstractions of continuous-time stochastic control systems [3], and (iv) safety analysis of continuous-time stochastic systems via barrier certificates [4], to name a few. In the relevant literature, only [5] studies the estimation of infinitesimal generators, whose approach is based on the assumption of knowing the precise model and discretizing both time and state. To the best of our knowledge, our work is the first to propose a method for estimating the infinitesimal generator of stochastic systems with unknown dynamics while providing formal guarantees. The complete version of this work can be found in [6]. © 2021 Owner/Author.

关键词： Stochastic systems

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An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering

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Array 2025年 26卷

作者： Abualigah, Laith Alomari, Saleh Ali Almomani, Mohammad H. Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Snasel, Vaclav Ezugwu, Absalom E. Computer Science Department Al al-Bayt University Mafraq25113 Jordan Faculty of Science and Information Technology Jadara University Irbid21110 Jordan Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka320 Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom2520 South Africa

Data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges, this paper proposes an Enhanced Walrus Optimizer (IWO) tailored for clustering tasks. The proposed IWO integrates two powerful strategies–Opposition-Based Learning (OBL) and Mutation Search Strategy (MSS)–to improve population diversity and prevent premature convergence, thereby enhancing both exploration and exploitation capabilities. These enhancements enable more accurate and stable identification of cluster centers. The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. Statistical validation using p-values and ranking scores further confirms the superiority of the proposed method. These findings suggest that IWO offers a robust and flexible framework for solving complex clustering problems. Future work will explore hybrid deep learning-integrated models and parallel implementations to enhance scalability. © 2025

关键词： Swarm intelligence

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Applying a Machine Learning Framework to Improve the Efficacy of Network Intrusion Detection Systems 4

Applying a Machine Learning Framework to Improve the Efficac...

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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Abdullah Kumar, Anil Jabbar, Kadim A. Oudah, Mohammed Yousif Khan, Abdul Azeem Verma, Rajan University Institute of Computing Chandigarh University Punjab India SRM University Department of CS & Engineering Lucknow India College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Scientific Research Center Al-Ayen University Thi-Qar Iraq Faculty of Islamic Technology Brunei Lovely Professional University Division of R&D Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Intrusion Detection Systems (IDS) play a vital role in spotting aberrant traffic and analyzing large amounts of data, especially during Distributed Denial of Service (DDoS) assaults. To ensure an IDS operates at peak performance, a reliable traffic classification approach is required. The objective of this research is to identify the most effective machine learning technique for enhancing system accuracy from the three available options: Support Vector Machine (SVM), Decision Jungle (DJ), and Random Forest (RF). Reducing the number of false alarms while increasing the number of correct ones is the goal. The primary objectives of this research are to create a machine learning model for a network intrusion detection system and to evaluate the performance of three algorithms in detecting suspicious network activities. As a standard for assessing IDSs, the study uses the Intrusion Detection Evaluation Dataset in conjunction with the KDD approach. The SVM outperforms DJ (96.50%) and RF (96.76%) with an average accuracy of 98.18%. Similarly, SVM surpasses RF with a precision of 97.96% and DJ with a precision of 97.82%, achieving an average precision of 98.74%. RF outperforms SVM (95.63%) and DJ (95.77%) in terms of average recall, with the highest value of 97.62%. Ultimately, the SVM algorithm has the highest level of performance in the system for detecting intrusions. © 2024 IEEE.

关键词： Network intrusion

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET 18

Lightweight Trust Model with Machine Learning scheme for sec...

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18th International Learning and Technology Conference, L and T 2021

作者： Junejo, Muhammad Haleem Ab Rahman, Ab Al-Hadi Shaikh, Riaz Ahmed Yusof, Kamaludin Mohamad Kumar, Dileep Memon, Imran Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computer Science Bahria University Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET. © 2021 The Authors. Published by Elsevier B.V.

关键词： Machine learning

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Deep Learning-Powered Edge Analytics for IoT-based Sensor Networks

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： M. Saravana Karthikeyan Ponnusamy Subramani M. Panneer Selvam M. L. Sworna Kokila R. Vijayalakshmi P. Sundaravadivel Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Physics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks.

关键词： Deep learning Adaptation models Accuracy Computational modeling Predictive models Prediction algorithms Energy efficiency Real-time systems Internet of Things Computational complexity

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