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10th International Workshop on Chiral Dynamics, CD 2021

作者： del Rio, Elena Perez Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University S. Lojasiewicza 11 Kraków30-348 Poland

KLOE and KLOE-2 data (almost 8 fb−1) constitute the largest sample ever collected at an electron-positron collider operating at the φ peak resonance. In total it corresponds to the production of about 24 billion of φ mesons whose decays include about 8 billion pairs of neutral K mesons and 300 million η mesons. A wide hadron physics program, investigating rare meson decays, γ − γ interaction, dark forces and hadronic cross section, is thus carried out by the KLOE-2 Collaboration. The latest results and present analysis of the KLOE-2 collaboration are presented. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Hadrons

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

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IEIE Transactions on Smart Processing and Computing 2024年第2期13卷 105-112页

作者： Ather, Danish Mary, J. Prisca Singh, Pooja Garg, Kanika Priyadharshini, T.R. Princy, B. Anni Tiwari, Mohit Department of IT and Engineering Amity University in Tashkent Uzbekistan Department of Computer Science and Engineering NPR College of Engineering and Technology Tamil Nadu Dindigul India Department of Physics Faculty of Science SGT University Haryana Gurugram India Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Ghaziabad Modinagar India Department of Information Technology Hindusthan College of Engineering and Technology Pollachi main road Coimbatore India Department of Computer and Communication Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

Wireless sensor network (WSN) applications are added day by day owing to numerous global uses (by the military, for monitoring the atmosphere, in disaster relief, and so on). Here, trust management is a main challenge. Sensor nodes are important in wireless sensor networks, but they are easily depleted because of their short lifespan from continuous sensing activity and low battery capacity. So efficient energy utilization is a challenging task in a WSN. To minimize energy loss, clustering with an optimum path selection process is needed to retain energy in sensor nodes. This manuscript proposes multi-objective Pelican Optimization Algorithm (POA) routing to maintain energy efficiency and minimize transmission distances in wireless sensor networks. A cluster head (CH) is selected by using a Separable Convolution Neural Network (SCNN). Simulation outcomes prove the proposed technique attains 22.3% and 25.04% improvements in energy consumption when compared to the Multi-Objective CH Energy-aware Optimized Routing Approach at WSN (MOCH-EORA-WSN) and Multiple Optimum Cluster Head Multi-Objective Grasshopper Optimization with Harmony-search at WSN (MOCH-MOGOH-WSN), respectively. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Energy utilization

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Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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LSTM Deep Learning Method for Radiation Short and Long-Term Prediction 7th

LSTM Deep Learning Method for Radiation Short and Long-Term ...

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7th International Conference on Smart City Applications, SCA 2022

作者： Khala, Mohamed Abouzid, Houda Teidj, Sara Eloutassi, Omar Messaoudi, Choukri Optoelectronics and Applied Energy Techniques Faculty of Science and Technology Moulay Ismail University of Meknes Meknes Morocco Computer Science and New Technology Trends Abdelmalek Essaadi University Tetouan Morocco Department of Physics Faculty of Science and Technology Moulay Ismail University of Meknes Meknes Morocco

ISBN: (纸本)9783031268519

The renewable energy market, like conventional energy, is often unstable. Solar energy in particular presents disturbances linked to the spatiotemporal fluctuations that solar radiation undergoes. To remedy this problem, it is essential to predict its fluctuations that have exhausting consequences for integration into the electricity network and reduce the dependence on fossil fuels essential for socio-economic development. The main objective of this study is to propose a prognostication model based on artificial intelligence to predict with great accuracy the fluctuations of global solar radiation (GSR). Indeed, we adopted a deep dynamic recurrent memory artificial neural network (ANN) model which can manipulate the dynamics of atmospheric factors and is capable to save the history of these characteristics. For this purpose, we developed two long-short term memory (LSTM) models for short and long term forecasting of global solar radiation time series. The first model is a monovariate LSTM, the second one is a multivariate LSTM, after selecting the input variables (predictors) by correlation study, the models are trained by dividing the data set into two subsections, the first subsection is used for model training and the second subsection is reserved for testing. The indices and results established in this work demonstrate the robustness attributed to our method which can provide power grid managers at Er-Rachidia, MOROCCO (Latitude: 31°55′53″N;Longitude: 4°25 ′35″ W;Altitude: 1039 m) credible predictions to ensure better supervision of the energy system and to adapt solar energy to the electricity production system, especially multivariate LSTM model which proved more accuracy compared to monovariate LSTM. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Multiscale characteristics of sentence length variability in Hopscotch by Julio Cortázar 38

Multiscale characteristics of sentence length variability in...

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38th ECMS International Conference on Modelling and Simulation, ECMS 2024

作者： Dec, Jakub Dolina, Michal Drozdz, Stanislaw Kwapien, Jaroslaw Stanisz, Tomasz Faculty of Computer Science and Telecommunications Cracow University of Technology Poland Institute of Nuclear Physics Polish Academy of Sciences Poland

Sentence length variability (SLV) is a crucial aspect of writing that can significantly impact the overall effectiveness, expressiveness and readability of the written work. In the communication presented here, the dynamics of SLV are subjected to quantitative analysis with an attempt to identify potential multiscale patterns and their multifractal characteristics using the Multifractal Detrended Fluctuation Analysis (MFDFA). This study examines the outstanding and unique work of world literature, such as Hopscotch by Julio Cortázar. The present analysis includes the original version of this work as well as translations into English and Polish. Clear evidence of rich multifractality in the dynamics of SLV in the original arrangement of chapters as well as in numerous permutations of them is obtained. This result quantitatively confirms the widespread belief that Hopscotch is a work with a non-linear narrative. ©ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

关键词： Complexity Hopscotch Large language models Multifractality Natural language

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Dataset of Large Gathering Images for Person Identification and Tracking

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computers, Materials & Continua 2023年第3期74卷 6065-6080页

作者： Adnan Nadeem Amir Mehmood Kashif Rizwan Muhammad Ashraf Nauman Qadeer Ali Alzahrani Qammer H.Abbasi Fazal Noor Majed Alhaisoni Nadeem Mahmood Faculty of Computer and Information System Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science and Information Technology Sir Syed University of Engineering and TechnologyKarachi75300Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Physics Federal Urdu University of ArtsScience&TechnologyKarachi75300Pakistan James Watt School of Engineering University of GlasgowGlasgowG128QQUK Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science University of KarachiKarachi75270Pakistan

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi *** dataset consists of raw and processed images reflecting a highly challenging and unconstraint *** methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face *** raw images in the dataset consist of a total of 4613 frames obtained fromvideo *** processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented *** dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 *** portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research *** have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal *** can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

关键词： Large crowd gatherings a dataset of large crowd images highly uncontrolled environment tracking missing persons face recognition activity monitoring

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Findings of the Third Shared Task on Multilingual Coreference Resolution 7

Findings of the Third Shared Task on Multilingual Coreferenc...

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7th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2024

作者： Novák, Michal Dohnalová, Barbora Konopík, Miloslav Nedoluzhko, Anna Popel, Martin Pražák, Ondřej Sido, Jakub Straka, Milan Žabokrtský, Zdeněk Zeman, Daniel Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Prague Czech Republic University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Pilsen Czech Republic

ISBN: (纸本)9798891761711

The paper presents an overview of the third edition of the shared task on multilingual coreference resolution, held as part of the CRAC 2024 workshop. Similarly to the previous two editions, the participants were challenged to develop systems capable of identifying mentions and clustering them based on identity coreference. This year’s edition took another step towards real-world application by not providing participants with gold slots for zero anaphora, increasing the task’s complexity and realism. In addition, the shared task was expanded to include a more diverse set of languages, with a particular focus on historical languages. The training and evaluation data were drawn from version 1.2 of the multilingual collection of harmonized coreference resources CorefUD, encompassing 21 datasets across 15 languages. 6 systems competed in this shared task. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Strong-coupling superconductivity of the Heusler-type compound ScAu2Al: Ab initio studies

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Physical Review B 2023年第22期108卷 224501-224501页

作者： Gabriel Kuderowicz Bartlomiej Wiendlocha AGH University of Krakow Faculty of Physics and Applied Computer Science Aleja Mickiewicza 30 30-059 Krakow Poland

The ScAu2Al superconducting Heusler-type compound was recently characterized to have the highest critical temperature of Tc=5.12 K and the strongest electron-phonon coupling among the Heusler family. In this work, the electronic structure, phonons, electron-phonon coupling, and superconductivity of ScAu2Al are studied using ab initio calculations. The spin-orbit coupling significantly changes the electronic structure removing the van Hove singularity from the vicinity of the Fermi level. In the phonon spectrum, low-frequency acoustic modes, additionally softened by the spin-orbit interaction, strongly couple with electrons, leading to the electron-phonon coupling constant λ=1.25, a record high among Heuslers. The density functional theory for superconductors is then used to analyze superconducting state in this two-band superconductor. The effect of spin fluctuations (SF) on superconductivity is also analyzed. The calculated critical temperatures of Tc=5.16 K (4.79 K with SF) agree very well with the experiment, confirming the electron-phonon mechanism of superconductivity and showing a weak spin-fluctuation effect. The superconducting gaps formed on two Fermi surface sheets exhibit moderate anisotropy. Their magnitudes confirm the strong coupling regime, as the reduced average values are 2Δb1/kBTc≃4.1 and 2Δb2/kBTc≃4.3. Anisotropy of the gaps and large spread in their values significantly affect the calculated quasiparticle density of states.

关键词： Electron-phonon coupling Multiband superconductivity Spin-orbit coupling Superconducting gap Heusler alloy Ab initio calculations

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Cumulants from fluctuating width of rapidity distribution

arXiv

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arXiv 2023年

作者： Barej, Michal Bzdak, Adam AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków30-059 Poland

In relativistic heavy-ion collisions, the longitudinal fluctuations of the fireball density caused, e.g., by baryon stopping fluctuations result in event-by-event modifications of the shape of the proton rapidity density distribution. The multiparticle rapidity correlation functions due to the varying distribution width of the proton rapidity density in central Au+Au collisions at low energies are derived. The cumulant ratios are calculated and discussed in the context of the recent STAR Collaboration results. We find that the cumulant ratios for small width fluctuations seem to be universal. © 2023, CC BY.

关键词： Heavy ions

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