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2024 International Conference on Physics and Technology of Advanced Materials, ICPTAM 2024

作者： Agusu, La Arsyad, W.O.S. Sudiana, I.N. Alimin, A. Firihu, M.Z. Ahmad, L.O. Darusman, L.M. Muhammad, A.J. Mitsudo, S. Asano, T. Nakagawa, K. Nishiumi, T. Ishikawa, Y. Fujii, Y. Tatematsu, Y. Department of Physics Faculty of Mathematics and Natural Sciences Halu Oleo University Kendari Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Halu Oleo University Kendari Indonesia Department of Applied Physics Faculty of Engineering University of Fukui Fukui Japan Research Center for Development of Far Infrared Region University of Fukui Fukui Japan

In many cases, low-grade nickel laterite ores with nickel (Ni) content of 1.2% or less have been left as land disposal at the mining areas. We are looking for an alternative, more environmentally friendly method to process this waste in a hydrometallurgical scheme. This study investigates the effectiveness of microwave-assisted recovery of transition metals-specifically nickel (Ni), iron (Fe), and cobalt (Co)-from low-grade nickel laterite (Ni © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Cobalt

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Optimal Trace Codes and Their Self-orthogonality

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IEEE Transactions on Information Theory 2025年第6期71卷 4267-4283页

作者： Hyun, Jong Yoon Hu, Zhao Cheon, Eun Ju Wu, Yansheng Konkuk University Glocal Campus 268 Chungwon-daero Chungcheongbuk-do Chungju-si27478 Korea Republic of Hubei University School of Cyber Science and Technology Wuhan430062 China Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security Wuhan430062 China Gyeongsang National University Department of Mathematics RINS Jinju52828 Korea Republic of Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jiangsu Cryptographic Technology Engineering Research Center Nanjing210023 China

The primary objective of this paper is the construction of optimal codes with self-orthogonality that can be used to construct quantum codes. Recently, Ding and Heng explored subfield codes, which can be viewed as trace codes. In this paper, we focus on investigating self-orthogonal optimal trace codes. First, we provide a novel description of trace codes by choosing suitable defining sets. Second, we determine the parameters of the codes and their trace codes whose defining sets are disjoint union of some affine subspaces in both non-projective cases and projective-cases. This result extends the main findings in (Hu, Li, Zeng, Wang, Tang, IEEE Trans. Inform. Theory, 68(7): 4408-4421, 2022). Third, we compute the parameters of trace codes for MacDonald codes, including the first order Reed-Muller codes and simplex codes as special cases. Finally, we examine their self-orthogonality and distance-optimality to find several classes of self-orthogonal Griesmer codes. Additionally, we resolve a problem proposed by Ding and Heng as a byproduct. © 1963-2012 IEEE.

关键词： Turbo codes

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Reduced Order Modeling with Shallow Recurrent Decoder Networks

arXiv

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arXiv 2025年

作者： Tomasetto, Matteo Williams, Jan P. Braghin, Francesco Manzoni, Andrea Kutz, J. Nathan Department of Mechanical Engineering Politecnico di Milano Milano Italy Department of Mechanical Engineering University of Washington SeattleWA United States MOX Department of Mathematics Politecnico di Milano Milano Italy Department of Applied Mathematics and Electrical and Computer Engineering University of Washington SeattleWA United States

Reduced Order Modeling is of paramount importance for efficiently inferring high-dimensional spatio-temporal fields in parametric contexts, enabling computationally tractable parametric analyses, uncertainty quantification and control. However, conventional dimensionality reduction techniques are typically limited to known and constant parameters, inefficient for nonlinear and chaotic dynamics, and uninformed to the actual system behavior. In this work, we propose sensor-driven SHallow REcurrent Decoder networks for Reduced Order Modeling (SHRED-ROM). Specifically, we consider the composition of a long short-term memory network, which encodes the temporal dynamics of limited sensor data in multiple scenarios, and a shallow decoder, which reconstructs the corresponding high-dimensional states. SHRED-ROM is a robust decoding-only strategy that circumvents the numerically unstable approximation of an inverse which is required by encoding-decoding schemes. To enhance computational efficiency and memory usage, the full-order state snapshots are reduced by, e.g., proper orthogonal decomposition, allowing for compressive training of the networks with minimal hyperparameter tuning. Through applications on chaotic and nonlinear fluid dynamics, we show that SHRED-ROM (i) accurately reconstructs the state dynamics for new parameter values starting from limited fixed or mobile sensors, independently on sensor placement, (ii) can cope with both physical, geometrical and time-dependent parametric dependencies, while being agnostic to their actual values, (iii) can accurately estimate unknown parameters, and (iv) can deal with different data sources, such as high-fidelity simulations, coupled fields and videos. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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Waste Material Management with a Coxian Arrival and First Exceptional Service Queueing Model

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Macromolecular Symposia 2025年第1期414卷

作者： Khurana, Rachna Sharma, Manju Jadon, Vinay Kumar Sharma, Amit Alam, Khursheed Sharda School of Basic Sciences and Research Sharda University Agra282007 India Department of Mathematics R.B.S.College Agra282007 India Department of Applied Science Anand Engineering College Agra282007 India The A.H. Siddiqi Centre for Advanced Research in Applied Mathematics and Physics Sharda University Greater Noida India

This paper studies a variant of the C2/G/1 queue, where the service time distribution for a customer depends on whether the server is busy or idle when the customer arrives. This class of models is relevant in scenarios where the server may require varying amounts of time when it resumes service after being idle. Customer arrives in the system according to 2-stage Coxian distribution. By using supplementary variable techniques and recursive method that utilizes generating function, we derived steady-state probabilities to analyze the distribution of queue length and sojourn time. Queueing models of this type have wide application in streamline operations like waste material management, and ensure effectiveness. © 2025 Wiley-VCH GmbH.

关键词： Sales

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TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization

arXiv

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arXiv 2025年

作者： Zhang, Jiayi Zhang, Yiming Zheng, Yuan Wang, Yuchen You, Jinjiang Xu, Yuchen Jiang, Wenxing Dev, Soumyabrata School of Mathematics and Applied Mathematics University of Nottingham Ningbo China School of Computer Science University of Nottingham Ningbo China Department of Electrical and Computer Engineering University of Washington United States School of Computer Science Carnegie Mellon University United States School of Engineering University of Nottingham Ningbo China School of Computer Science University College Dublin Ireland The ADAPT SFI Research Centre Ireland

Urban traffic optimization is crucial for improving transportation efficiency and alleviating congestion. Traditional algorithms [1] like Dijkstra’s [2] and Floyd’s [3], along with Genetic Algorithms (GA) [4], are widely used for route planning but struggle with large-scale, dynamic traffic networks due to computational complexity and limited ability to capture nonlinear spatial-temporal dependencies [5]. To address these challenges, we propose a hybrid framework combining Kolmogorov-Arnold Networks (KAN) with Graph Convolutional Networks (GCN) [6, 7, 8] for urban traffic flow optimization. KAN’s ability to approximate nonlinear functions, along with GCN’s capacity to model spatial dependencies in graph-structured data, provides a more effective representation of traffic dynamics. We evaluate KAN-GCN on real-world datasets and compare it to traditional models like Multi-Layer Perceptrons (MLP) [9] and decision trees. While MLP-GCN shows higher accuracy, KAN-GCN excels in handling noisy and complex traffic patterns [7]. Results highlight challenges in applying KAN to large-scale networks and suggest potential improvements. Additionally, we explore extending the framework to Transformer models for scalable and dynamic traffic forecasting. Our findings indicate that combining KAN with GCN offers promising potential for intelligent traffic management, with further optimization possible for large-scale *** the spirit of reproducible research, the model code, dataset, and results of the experiments in this paper are available at: https://***/ZhangJiayi24/KAN_GCN_*** Codes 90B20, 68T07, 05C85, 90C90 © 2025, CC BY.

关键词： Decision trees

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Correction to: Qualitative Properties and Optimal Control Strategy on a Novel Fractional Three-Species Food Chain Model

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Qualitative Theory of Dynamical Systems 2025年第2期24卷 1-2页

作者： Premakumari, R. N. Baishya, Chandrali Rezapour, Shahram Naik, Manisha Krishna Yaseen, Zaher Mundher Etemad, Sina Department of Studies and Research in Mathematics Tumkur University Tumkur India Department of Mathematics Azarbaijan Shahid Madani University Tabriz Iran Insurance Research Center (IRC) Tehran Iran Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Civil and Environmental Engineering Department King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Mathematics in Applied Sciences and Engineering Research Group Scientific Research Center Al-Ayen University Nasiriyah Iraq

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Optimization of Crop Yields in Sustainable Agriculture: Application of Big Data Analytics and Artificial Intelligence

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 375-386页

作者： Nimma, Divya Dhanke, Jyoti A. Murthy, G.S.N. Khandekar, Sachin Dadu Hymavathi, J. Jangir, Pradeep Singh, Mahesh Department of Computational Science University of Southern Mississippi Hattiesburg 39406 MS United States Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Maharashtra Pune India Department of CSE Aditya University Surampalem India Department of Civil Engineering Sinhgad College of Engineering Maharashtra Pune India Department of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Department of Robotics and Automation Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India

Making informed economic decisions based on agricultural data is challenging without proper crop management. Data has become the single most important part of modern farming, and its rapid evolution is a major contributor to the rise of “smart farming.” Using sensors to gather objective data with the end aim of increasing productivity and sustainability has several advantages. Information that might boost productivity while decreasing resource waste and pollution levels is essential for managed farms. The cornerstone of future sustainable farming will be data-driven agriculture using robotic solutions based on AI-based techniques. This paper examines the current state of advanced farm management systems, discussing topics like data acquisition in crop fields and variable rate applications, to assist farmers in saving money, protecting the environment, and transforming food production so that it can sustainably keep up with the expected increase in population. The advent of machine learning, big data technologies, and high-performance computers has opened up new avenues for data-intensive research in the interdisciplinary subject of agro technology. These avenues of exploration have been aided by the development of more capable computers. A comprehensive literature review on the issue of the applications of machine learning in agricultural manufacturing is provided in this research. Machine learning has shown its utility in agriculture via two applications: filtering and categorizing products. The use of machine learning on sensor data is transforming conventional farm management systems into AI-enabled programs. These services provide farmers instantaneous, complete access to ideas and insights that may guide their decisions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Big data analytics Optimize crop yields Sustainable agriculture

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HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss 15th

HyperCMR: Enhanced Multi-contrast CMR Reconstruction with E...

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15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Xu, Ruru Özer, Caner Oksuz, Ilkay Computer Engineering Department Istanbul Technical University Istanbul Turkey Department of Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul Turkey Department of Applied Mathematics University of Twente Enschede7522 NB Netherlands

ISBN: (纸本)9783031877551

Accelerating image acquisition for cardiac magnetic resonance imaging (CMRI) is a critical task. CMRxRecon2024 challenge aims to set the state of the art for multi-contrast CMR reconstruction. This paper presents HyperCMR, a novel framework designed to accelerate the reconstruction of multi-contrast cardiac magnetic resonance (CMR) images. HyperCMR enhances the existing PromptMR model by incorporating advanced loss functions, notably the innovative Eagle Loss, which is specifically designed to recover missing high-frequency information in undersampled k-space. Extensive experiments conducted on the CMRxRecon2024 challenge dataset demonstrate that HyperCMR consistently outperforms the baseline across multiple evaluation metrics, achieving superior SSIM and PSNR scores. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Some fundamental issues in buoyancy-driven flows with implications for geophysical and astrophysical systems

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Acta Mechanica Sinica 2025年第1期41卷 1-21页

作者： Ke-Qing Xia Kai Leong Chong Guang-Yu Ding Lu Zhang Center for Complex Flows and Soft Matter Research Southern University of Science and TechnologyShenzhen 518055China Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen 518055China Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Institute of Applied Mathematics and MechanicsSchool of Mechanics and Engineering ScienceShanghai UniversityShanghai 200072China

Buoyancy-driven flows are prevalent in a wide range of geophysical and astrophysical systems. In this review, we focus on threepivotal effects that significantly influence the dynamics and transport properties of buoyancy-driven flows and may have impli-cations for natural systems. These effects pertain to the role of boundary conditions, the impact of rotation, and the effect offinite size. Boundary conditions represent how the fluid flow interacts with different kinds of surfaces. Rotation, as the Earth’srotation in geophysical systems or the whirling of astrophysical systems, introduces Coriolis and centrifugal forces, leading tothe profound vortical structure and distinct transport property. Finite size, representing geometrical constraints, influences thebehavior of buoyancy-driven flows across varying geometrical settings. This review aims to provide a holistic understanding ofthe intricate interplay of these factors, offering insights into the complex natural phenomena from the perspectives of the threeeffects.

关键词： Buoyancy-driven flows Geophysical turbulence Rayleigh-B´enard convection

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Gasoline and Crude Oil Price Prediction using Multi-headed Variational Neighbour Search-tuned Recurrent Neural Networks

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Computational Economics 2025年 1-36页

作者： Kljajic, Maja Mizdrakovic, Vule Jovanovic, Luka Bacanin, Nebojsa Simic, Vladimir Pamucar, Dragan Zivkovic, Miodrag Business Economics Singidunum University Danijelova 32 Belgrade 11000 Serbia Department of Mathematics Saveetha School of Engineering SIMATS Thandalam Tamilnadu Chennai 602105 India Informatics and Computing Singidunum University Danijelova 32 Belgrade 11000 Serbia Sinergija University Raje Banjičića Bijeljina 76300 Bosnia and Herzegovina University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 Belgrade 11010 Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City 320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department İstanbul Turkey

The rapidly growing worldwide demand for energy and finite reservoirs of fossil fuels have intensified interest in energy projection research. Artificial intelligence, particularly in time series forecasting, holds significant promise for enhancing predictions of both cost and demand, offering numerous prospective applications over various domains. Numerous variables, ranging from socio-economic conditions to distribution, supply, and international policies, exert influence on global price fluctuations. Thus, crafting precise forecasts necessitates consideration of these multifaceted factors. Through an examination of existing literature, a discernible gap emerges in the quest for advancement in this domain. Consequently, this study proposes to delve into the perspective of employing multi-headed long short-term memory (LSTM) models for gasoline and crude oil price prediction, an area largely untouched by multi-headed approaches. Moreover, recognizing the computational demands of such models, this research emphasizes the development of lightweight methodologies, characterized by a modest neuron within each layer and trained over a limited epoch count. Given the pivotal role of hyper-parameter selection in algorithm performance, an adapted version of the variable neighbour search algorithm is introduced to aid in tuning the model’s architecture and training parameters. A comprehensive side-by-side comparison is undertaken utilizing gasoline and oil data sourced from diverse public repositories, employing a variety of contemporary optimizers. The resultant outcomes are subjected to strict statistical scrutiny to ascertain the robustness and significance of the findings. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Crude oil prices Gasoline prices Modified VNS Multi-headed long short-term memory models Time series prediction

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