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作者： Lloret-Climent, Miguel Nescolarde-Selva, Josué Antonio Mora-Mora, Higinio Signes-Pont, María Teresa Department of Applied Mathematics University of Alicante Alicante Spain Specialized Processors Architecture Laboratory Department of Computer Technology and Computation University of Alicante Alicante Spain

Graph theory is a fundamental tool in the study of economic issues, and input–output tables are one of the main examples. We use the interpretation of the labour market through networks to obtain a better understanding on its overall functioning. One benefit of the network perspective is that a large body of mathematics exists to help analyze many forms of networks models. If an economic system has obtained a suitable model, then it becomes possible to utilize relevant mathematical tools, such as graph theory, to better understand the way the labour market works. This interpretation allows us to employ the concepts of coverage, invariance, orbit and the structural functions supply–demand and competition and interpret them from the point of view of circular flow. In this paper, we aim to interpret the labour market through networks that are represented by graphs and where characteristic concepts of chaos theory such as cover, invariance and orbits interact with the concept circular flow. Finally, an example of this approach to labour markets is described, and some conclusions are drawn. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Chaos theory

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Dual-Band FSS Director using Inverted Interdigital Structure for LTE and WLAN

Dual-Band FSS Director using Inverted Interdigital Structure...

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International Symposium on Antennas and Propagation (ISAP)

作者： P. Chomtong T. Archevapanich P. Akkaraekthalin Department of Teacher Training in Electrical Engineering Rajamangala University of Technology Nonthaburi Thailand Faculty of Engineering and Architecture Rajamangala University of Technology Nonthaburi Thailand Department of Electrical and Computer Engineering King Mongkut’s University of Technology North Bangkok Thailand

This paper proposes a design of dual band FSS director using inverted interdigital technique on a microwave printed circuit board to achieve resonance frequencies at 1.8 GHz for LTE and 5.2 GHz for WLAN. Form the design method, the unit cell size can be reduced from λ/2 to λ/8 compared with the conventional structure caused by the slow wave effect in its structure. The FSS layer was formed by connecting 8×8 designed unit cells to be an array. The FSS layer acts as a director for dipole antennas at the frequencies of 1.8 GHz and 5.2 GHz. The simulation results show that the dipole antenna with the proposed FSS director has higher performances. The antenna gains were obviously improved with the gains of 2.61 dB at 1.8 GHz and 3.97 dB at 5.2 GHz higher than the conventional dipole antenna, which normally has a gain of about 2 dB. This proposed FSS structure can also be applied for other multiband antenna systems.

关键词： Frequency selective surfaces Dipole antennas Resonant frequency Gain Band-pass filters Directive antennas Wireless LAN

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Comprehensive review of optimization strategies for phase change materials: Techniques, applications, and challenges in thermal storage systems

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International Communications in Heat and Mass Transfer 2025年 166卷

作者： Izadi, Mohsen Pop, Ioan Shehzad, Sabir Ali Alqurashi, Faris Mohamed, Mohamed H. Hajjar, Ahmad Mahariq, Ibrahim Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam Mechanical Engineering Department Faculty of Engineering Lorestan University Khorramabad Iran Department of Applied Mathematics Babes-Bolyai University Cluj-Napoca Romania Department of Mathematics COMSATS University Islamabad Sahiwal57000 Pakistan Department of Mechanical Engineering College of Engineering University of Bisha P.O. Box 551 Bisha61922 Saudi Arabia Mechanical Engineering Dept. College of Engineering and Architecture Umm Al-Qura University P.O. 5555 Makkah Saudi Arabia Center for Environmental Intelligence and College of Engineering and Computer Science VinUniversity Hanoi Viet Nam College of Engineering and Architecture Gulf University for Science and Technology Mishref India Najjad Zeenni Faculty of Engineering Al Quds University Jerusalem Korea Republic of University College Korea University Seoul02481 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Jordan Applied Science Research Center Applied Science Private University Amman Jordan

Phase Change Materials (PCMs) are widely recognized for their potential in thermal energy storage systems due to their high latent heat capacity. However, their practical application is significantly hindered by low thermal conductivity, which limits the charging and discharging rates of energy. This review provides a comprehensive and critical synthesis of recent passive techniques developed to enhance the thermal performance of PCMs. These include finned structures, porous media, nanoparticle dispersion, geometrical modifications, and multi-PCM strategies. Quantitative comparisons show that the use of fins can reduce melting time by up to 65 %, while metallic porous matrices can improve thermal conductivity by over 500 %. Incorporation of nanoparticles has demonstrated up to 25 % enhancement in heat transfer rates, albeit with increased viscosity. Geometrical innovations and multi-PCM layering have enabled tailored thermal responses across temperature ranges, suitable for applications such as solar thermal systems and building-integrated energy storage. Furthermore, this review identifies trade-offs associated with each method, including design complexity, cost, and material compatibility, and presents a comparative performance table to guide selection based on system requirements. Hybrid enhancement strategies, such as nanoparticle-doped PCMs embedded in metal foams, are also proposed as a promising direction for future research. The review concludes with specific, forward-looking insights, highlighting opportunities for PCM-based solutions in solar desalination, cold-chain transport, and passive building cooling under real-world constraints. © 2025 Elsevier Ltd

关键词： Metal foams

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Using Convolutional Neural Networks for Muon detection in WCD tank

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Journal of Physics: Conference Series 2020年第1期1603卷

作者： B.S. González R. Conceição B. Tomé M. Pimenta L.J. Herrera A. Guillen Computer Architecture and Technology Department University of Granada Granada Spain. LIP/IST Lisbon Portugal.

The aim of this paper is to study the possibility of improving the gamma/hadron discrimination in extensive air showers. For this purpose, the identification of hadronic extensive air showers is carried out by means of the detection of muons in water Cherenkov detectors (WCDs). Machine learning algorithms have proven to be useful in a wide variety of fields, and due to their outstanding performance in problems involving complex data, Convolutional Neural Networks (CNNs) have been used in the analysis of the signals measured by the WCDs. Taking simulated events, different approaches were proposed attending to the balance of the classes in the training stage. The results obtained are promising and show that machine learning algorithms provide a powerful tool for muon detection and gamma/hadron discrimination to be considered in future gamma-rays detectors like The Southern Wide-field Gamma-ray Observatory (SWGO) to be built in South America.

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Learned belief-propagation decoding with simple scaling and SNR adaptation

arXiv

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arXiv 2019年

作者： Lian, Mengke Carpi, Fabrizio Häger, Christian Pfister, Henry D. Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Engineering and Architecture University of Parma Parma Italy Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden

We consider the weighted belief-propagation (WBP) decoder recently proposed by Nachmani et al. where different weights are introduced for each Tanner graph edge and optimized using machine learning techniques. Our focus is on simple-scaling models that use the same weights across certain edges to reduce the storage and computational burden. The main contribution is to show that simple scaling with few parameters often achieves the same gain as the full parameterization. Moreover, several training improvements for WBP are proposed. For example, it is shown that minimizing average binary cross-entropy is suboptimal in general in terms of bit error rate (BER) and a new "soft-BER" loss is proposed which can lead to better performance. We also investigate parameter adapter networks (PANs) that learn the relation between the signal-to-noise ratio and the WBP parameters. As an example, for the (32, 16) Reed-Muller code with a highly redundant parity-check matrix, training a PAN with soft-BER loss gives near-maximum-likelihood performance assuming simple scaling with only three parameters. Copyright © 2019, The Authors. All rights reserved.

关键词： Belief propagation

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Corrigendum to “Renewables but unjust? Critical restoration geography as a framework for addressing global renewable energy injustice” [Energy Res. Soc. Sci. 114 (2024) 103609]

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Energy Research & Social Science 2024年 115卷

作者： Chinedu C. Nsude Rebecca Loraamm Joshua J. Wimhurst God'sgift N.. Chukwuonye Ramit Debnath Department of Geography and Environmental Sustainability University of Oklahoma Norman OK 73019 USA Center for Peace and Development University of Oklahoma Norman OK 73019 USA South Central Climate Adaptation Science Center University of Oklahoma Norman OK 73019 USA Department of Environmental Science University of Arizona Tucson AZ 85721 USA Cambridge Zero and Computer Laboratory University of Cambridge Cambridge CB3 0HE UK Cambridge Collective Intelligence and Design Group Department of Architecture University of Cambridge CB2 1PX UK Division of Humanities and Social Sciences California Institute of Technology Pasadena CA 92115 USA

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Efficiently Inferring Top-k Elephant Flows based on Discrete Tensor Completion

Efficiently Inferring Top-k Elephant Flows based on Discrete...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Kun Xie Jiazheng Tian Xin Wang Gaogang Xie Jigang Wen Dafang Zhang College of Computer Science and Electronics Engineering Hunan University Changsha China Department of Electrical and Computer Engineering State University of New York at Stony Brook USA State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Finding top- k elephant flows is a critical task in network measurement, with applications such as congestion control, anomaly detection, and traffic engineering. Traditional top- k flow detection problem focuses on using a small amount of memory to measure the total number of packets or bytes of each flow. Instead, we study a challenging problem of inferring the top- k elephant flows in a practical system with incomplete measurement data as a result of sub-sampling for scalability or data missing. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor compared to that based on a 2-D matrix. Taking full advantage of the multilinear structures, we apply tensor completion to first recover the missing data and then find the top- k elephant flows. To reduce the computational overhead, we propose a novel discrete tensor completion model which uses binary codes to represent the factor matrices. Based on the model, we further propose three novel techniques to speed up the whole top- k flow inference process: a discrete optimization algorithm to train the binary factor matrices, bit operations to facilitate quick missing data inference, and simplifying the finding of top- k elephant flows with binary code partition. In our discrete tensor completion model, only one bit is needed to represent the entry in the factor matrices instead of a real value (32 bits) needed in traditional tensor completion model, thus the storage cost is reduced significantly. Extensive experiments using two real traces demonstrate that compared with the state of art tensor completion algorithms, our discrete tensor completion algorithm can achieve similar data inference accuracy using significantly smaller time and storage space.

关键词： Inference algorithms Binary codes Data models Partitioning algorithms Indexes Computational modeling

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Online Internet Anomaly Detection With High Accuracy: A Fast Tensor Factorization Solution

Online Internet Anomaly Detection With High Accuracy: A Fast...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiaocan Li Kun Xie Xin Wang Gaogang Xie Jigang Wen Guangxing Zhang Zheng Qin Department of Electrical and Computer Engineering State University of New York at Stony Brook USA College of Computer Science and Electronics Engineering Hunan University Changsha China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Traffic anomaly detection is critical for advanced Internet management. Existing detection algorithms usually work off-line and cannot timely detect anomalies. They also suffer from high cost for storage and computation. Although online and accurate traffic anomaly detection is very important, it very difficult to achieve. We propose to utilize tensor model to well exploit the multi-dimensional information hidden in the traffic data for more accurate online Internet anomaly detection. We decouple the tensor recovery problem to iteratively solve two sub problems, a tensor factorization sub-problem and an anomaly detection sub-problem. To reduce the high cost for computation and storage involved in tensor factorization, we propose two lightweight techniques to effectively derive factor matrices of tensor in the current window and iteration, taking advantage of tensor decomposition results of the previous window and iteration. We have done extensive experiments using two real traffic traces to compare with three tensor based algorithms and three matrix based algorithms. The experiment results demonstrate that our online anomaly detection algorithm can achieve the same anomaly detection accuracy as that of the best offline tensor based algorithm, but at 6100 times faster speed and with very low storage cost.

关键词： Anomaly detection Monitoring Matrix decomposition Microsoft Windows Principal component analysis Data models

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Forecasting business failure in highly imbalanced distribution based on delay line reservoir 26

Forecasting business failure in highly imbalanced distributi...

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26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018

作者： Rodan, A. Castillo, P.A. Faris, H. Al-Zoubi, Ala M. Mora, A.M. Jawazneh, H. Higher Colleges of Technology United Arab Emirates King Abdullah II School for Information Technology University of Jordan Jordan Department of Computer Architecture and Technology ETSIIT CITIC University of Granada Spain Spain

ISBN: (纸本)9782875870476

Bankruptcy is a critical financial problem that affects a high number of companies around the world. Thus, in recent years an increasing number of researchers have tried to solve it by applying different machine-learning models as powerful tools for the different economical agents related to the company. In this work, we propose the use of a simple deterministic delay line reservoir (DLR) state space by combining it with three popular classification algorithms (J48, k-NN, and MLP) as an efficient and accurate solution to the bankruptcy prediction problem. The proposed approach is evaluated on a real world dataset collected from Spanish companies. Obtained results show that the proposed models have a higher predictive ability than traditional classification approaches (without DLR reservoir state), resulting in a suitable and efficient alternative approach to solve this complex problem. © ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Neural networks

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The scalability for parallel machine learning training algorithm: Dataset matters

arXiv

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arXiv 2019年

作者： Cheng, Daning Zhang, Hanping Xia, Fen Li, Shigang Zhang, Yunquan University of Chinese Academy of Sciences Beijing China SKL of Computer Architecture Institute of Computing Technology CAS China Department of Computer Science ETH Zurich Switzerland University at Buffalo State University of New York Wisdom Uranium Technology Co.Ltd Beijing

To gain a better performance, many researchers put more computing resource into an application. However, in the AI area, there is still a lack of a successful large-scale machine learning training application: The scalability and performance reproducibility of parallel machine learning training algorithm are limited and there are a few pieces of research focusing on why these indexes are limited but there are very few research efforts explaining the reasons in essence. In this paper, we propose that the sample difference in dataset plays a more prominent role in parallel machine learning algorithm scalability. Dataset characters can measure sample difference. These characters include the variance of the sample in a dataset, sparsity, sample diversity and similarity in sampling sequence. To match our proposal, we choose four kinds of parallel machine learning training algorithms as our research objects: (1) Asynchronous parallel SGD algorithm (Hogwild! algorithm) (2) Parallel model average SGD algorithm (Mini-batch SGD algorithm) (3) Decenterilization optimization algorithm, (4) Dual Coordinate Optimization (DADM algorithm). These algorithms cover different types of machine learning optimization algorithms. We present the analysis of their convergence proof and design experiments. Our results show that the characters datasets decide the scalability of the machine learning algorithm. What is more, there is an upper bound of parallel scalability for machine learning algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Scalability

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