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Some -cubic quasi-rung orthopair fuzzy operators for multi-attribute decision-making

Complex & Intelligent Systems 2023年第1期10卷 87-110页

作者： Chu, Yu-Ming Garg, Harish Rahim, Muhammad Amin, Fazli Asiri, Asim Ameer, Eskandar School of Mathematics Hangzhou Normal University Hangzhou People’s Republic of China Department of Mathematics Huzhou University Huzhou People’s Republic of China School of Mathematics Thapar Institute of Engineering and Technology (Deemed University) Patiala India Department of Mathematics Graphic Era Deemed to be University Dehradun India Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics and Statistics Hazara University Mansehra Mansehra Pakistan Department of Mathematics King Abdulaziz University Jeddah Saudi Arabia Department of Mathematics Taiz University Taiz Yemen

This paper aims to support decision-makers improve their ability to accurately capture and represent their judgment in a wide range of situations. To do this, we propose a new type of fuzzy set called a $$p,q$$ -cubic quasi-rung orthopair fuzzy set ( $$p,q$$ -CQOFS). The $$p,q$$ -CQOFS allows for a more flexible and detailed expression of incomplete information through the use of an additional parameter. The paper describes the concept of $$p,q$$ -CQOFS and its relationship to other types of fuzzy sets, introduces score and accuracy functions for $$p,q$$ -CQOFS and analyzes some of its mathematical properties, defines the Hamming distance measure between two $$p,q$$ -CQOFSs and examines some of its important properties, investigates the basic operations of $$p,q$$ -CQOFSs and extends these laws to aggregation operators, and introduces weighted averaging and geometric aggregation operators for combining $$p,q$$ -cubic quasi-rung orthopair fuzzy data.

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Techno-Economic Analysis of Electric Vehicles Charging Hubs in a Renewable Energy Community

Techno-Economic Analysis of Electric Vehicles Charging Hubs ...

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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Blasuttigh, Nicola Negri, Simone Danielis, Romeo Pavan, Alessandro Massi University of Trieste Dep. of Engineering and Architecture Center for Energy Environment and Transport Giacomo Ciamician Trieste Italy Politecnico di Milano Department of Electronics Information and Bioengineering Milano Italy University of Trieste Department of Economics Business Mathematics and Statistics Center for Energy Environment and Transport Giacomo Ciamician Trieste Italy

ISBN: (纸本)9798350389838

This study conducts a technical and economic assessment of a renewable energy community (REC) including multiple electric vehicle charging hubs powered by photovoltaic canopies. The analysis covers the criteria for the sizing of the photovoltaic systems, the evaluation of the energy production profiles, and the estimation of the aggregated consumption profile of the vehicle charging hubs. It provides a discussion of the technical feasibility of the proposed solution and a preliminary economic analysis, considering net present value and investment payback periods. Additionally, on the basis of a recent and comprehensive database of electric vehicles in the Italian fleet, energy flows are accurately determined considering each vehicle characteristics, and their arrival and departure patterns. As a case study, the performance of a renewable energy community comprising eleven parking lots in the city of Milan, Italy, is analysed. The presented results offer an overview of the value of RECs based on charging hubs and highlight potential challenges in their technical and economic management. © 2024 IEEE.

关键词： Charging stations

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Three-way data clustering based on the mean-mixture of matrix-variate normal distributions

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Computational statistics and Data Analysis 2024年 199卷

作者： Naderi, Mehrdad Tamandi, Mostafa Mirfarah, Elham Wang, Wan-Lun Lin, Tsung-I Department of Mathematics Physics and Electrical Engineering Northumbria University United Kingdom Department of Statistics Vali-e-Asr University of Rafsanjan Rafsanjan Iran School of Mathematics and Statistics University of St Andrews St Andrews United Kingdom Department of Statistics Institute of Data Science National Cheng Kung University Tainan701 Taiwan Institute of Statistics National Chung Hsing University Taichung402 Taiwan Department of Public Health China Medical University Taichung404 Taiwan

With the steady growth of computer technologies, the application of statistical techniques to analyze extensive datasets has garnered substantial attention. The analysis of three-way (matrix-variate) data has emerged as a burgeoning field that has inspired statisticians in recent years to develop novel analytical methods. This paper introduces a unified finite mixture model that relies on the mean-mixture of matrix-variate normal distributions. The strength of our proposed model lies in its capability to capture and cluster a wide range of three-way data that exhibit heterogeneous, asymmetric and leptokurtic features. A computationally feasible ECME algorithm is developed to compute the maximum likelihood (ML) estimates. Numerous simulation studies are conducted to investigate the asymptotic properties of the ML estimators, validate the effectiveness of the Bayesian information criterion in selecting the appropriate model, and assess the classification ability in presence of contaminated noise. The utility of the proposed methodology is demonstrated by analyzing a real-life data example. © 2024 Elsevier B.V.

关键词： Normal distribution

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Multi-scale wavelet coherence with its applications

arXiv

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arXiv 2023年

作者： Wu, Haibo Knight, Marina I. Ombao, Hernando Statistics Program King Abdullah University of Science and Technology Saudi Arabia Department of Mathematics University of York United Kingdom

The goal in this paper is to develop a novel statistical approach to characterize functional interactions between channels in a brain network. Wavelets are effective for capturing transient properties of non-stationary signals because they have compact support that can be compressed or stretched according to the dynamic properties of the signal. Wavelets give a multi-scale decomposition of signals and thus can be few for studying potential cross-scale interactions between signals. To achieve this, we develop the scale-specific sub-processes of a multivariate locally stationary wavelet stochastic process. Under this proposed framework, a novel cross-scale dependence measure is developed. This provides a measure for dependence structure of components at different scales of multivariate time series. Extensive simulation studies are conducted to demonstrate that the theoretical properties hold in practice. The proposed cross-scale analysis is applied to the electroencephalogram (EEG) data to study alterations in the functional connectivity structure in children diagnosed with attention deficit hyperactivity disorder (ADHD). Our approach identified novel interesting cross-scale interactions between channels in the brain network. The proposed framework can be applied to other signals, which can also capture the statistical association between the stocks at different time scales. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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The Port - City Relations in the Era of Hybridization 23rd

The Port - City Relations in the Era of Hybridization

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23rd International Conference on Computational Science and Its Applications, ICCSA 2023

作者： Borruso, Giuseppe Balletto, Ginevra DEAMS - Department of Economics Business Mathematics and Statistics "Bruno de Finetti" University of Trieste via A. Valerio 4/1 Trieste34127 Italy DICAAR - Department of Civil Environmental Engineering and Architecture University of Cagliari via Marengo 2 Cagliari Italy

ISBN: (纸本)9783031371226

Ports play an important role as interfaces among sea and land, elements of organization of the coastal space, with important influences on the cities and regions they are inserted in. A wide attention and consideration has been put in time by scholars from different disciplines on the relationships between ports and cities on the waterfront, following the different stages of maritimization and the evolution of the roles of ports and cities. In particular, during this evolution, ports become more and more hybrid in their roles and characters, changing continuously their shapes and functions, and, consequently, the relationship they have with their surrounding areas, cities in particular, being the most proximal elements to ports. In research and planning, however, little attention has been put towards dry ports and inner harbors, as the new areas important for ports, hosting vital operations for linking ports and their hinterlands in order to keep quays free for freight handling on the seaside. In this paper we put the attention on the process of hybridization of ports in time, focusing in particular on the new challenges coming out from the evolution of port activities into inner areas and, therefore, observing the interaction these have with peri-urban and peripheral areas. Analyzing some theoretical and real world cases, a matrix of interactions is proposed to evaluate the positive/negative interactions and the correspondence of planning instruments in tackling the issues of the port and city development. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Urban growth

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Comparative Analysis of Silver and Titanium Oxide Hybrid Nanofluid in MHD Unsteady Flow Past an Accelerated Vertical Plate with Radiation Effects

Hybrid Advances

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Hybrid Advances 2025年 10卷

作者： Lutera, Joseph Nicholaus Goud, B. Shankar Shekar, M.N. Raja Department of Mathematics JNTUH University College of Engineering Science and Technology Hyderabad Kukatpally Hyderabad Telangana 500085 India Department of Mathematics and Statistics (CoSTE) Mbeya University of Science and Technology Mbeya Tanzania

The problem of MHD unsteady movement of the nanofluid past an accelerated perpendicular surface is investigated. Thermal radiation and electromagnetic field properties are taken into explanation of the hybrid nanofluid composed of Silver and Titanium dioxide nanoparticles. The nanoparticles offer an enhanced thermal conductivity property, a factor important in technological advancement including cooling, separation, nuclear reactors, solar energy and electronic devices. The study aims to analyze the characteristics of different nanofluids on the influence of dimensionless parameters for mass and heat transmission. The resultant nondimensionalized boundary problem is solved numerically by the finite difference technique employed to obtain the mathematical results of the partial differential equations. The results demonstrate a comparison of velocity and thermal energy profiles of Silver-water and Titanium dioxide-water nanofluids graphically. Silver particles portray greater impacts on the nanofluid flow rate than the Titanium dioxide particles. The temperature of silver water is greater than the temperature of Titanium dioxide water. The improvement of thermal radiation strength was observed to enhance the temperature and velocity profiles of the nanofluid. The study is crucial in decision-making, especially for the choice of efficient nanofluid in mass and heat transmission applications. © 2025 The Authors

关键词： FDM Hybrid nanofluid MHD Thermal radiation Vertical plate

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Assessing the Prosperity of European Nations: A Cluster Analysis Based Approach

Assessing the Prosperity of European Nations: A Cluster Anal...

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IEEE International Technology Management Conference

作者： Junu Jahana C Tania M Joseph Ayman Alzaatreh Department of Industrial Engineering College of Engineering American University of Sharjah Sharjah UAE Department of Mathematics and Statistics College of Arts and Sciences American University of Sharjah Sharjah UAE

ISBN: (数字)9798350367355

ISBN: (纸本)9798350367362

This study employs a multifaceted analytical approach to evaluate prosperity indicators across European countries, utilizing data from EUROSTAT spanning 2017 to 2022. Applying variable clustering and hierarchical clustering techniques, the research identifies three distinct clusters based on social, economic, and environmental indicators. The results are justifiable, with slight variations from real-case scenarios attributed to factors such as differences in considered factors across studies and variations in the study periods. The comprehensive methodology provides a step-by-step account of the hierarchical clustering and heatmap generation process, ensuring transparency and clarity in the analysis of Social, Environmental, and Economic indicators among different European countries. The study's insights provide valuable implications for policymakers, guiding targeted interventions to address unique challenges and opportunities within each cluster, thereby contributing to a more equitable and sustainable future across European countries.

关键词： Waste management Technological innovation Renewable energy sources Technology management Stability criteria Europe Market research Socioeconomics Standards Investment

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A likelihood approach to nonparametric estimation of a singular distribution using deep generative models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 3414-3455页

作者： Minwoo Chae Dongha Kim Yongdai Kim Lizhen Lin Department of Industrial and Management Engineering Pohang University of Science and Technology Pohang Gyeongbuk South Korea School of Mathematics Statistics and Data Science Data Science Center Sungshin Women's University Seoul South Korea Department of Statistics Seoul National University Seoul South Korea Department of Applied and Computational Mathematics and Statistics University of Notre Dame South Bend IN

We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More specifically, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure. Estimating the distribution supported on this low-dimensional structure, such as a low-dimensional manifold, is challenging due to its singularity with respect to the Lebesgue measure in the ambient space. In the considered model, a usual likelihood approach can fail to estimate the target distribution consistently due to the singularity. We prove that a novel and effective solution exists by perturbing the data with an instance noise, which leads to consistent estimation of the underlying distribution with desirable convergence rates. We also characterize the class of distributions that can be efficiently estimated via deep generative models. This class is sufficiently general to contain various structured distributions such as product distributions, classically smooth distributions and distributions supported on a low-dimensional manifold. Our analysis provides some insights on how deep generative models can avoid the curse of dimensionality for nonparametric distribution estimation. We conduct a thorough simulation study and real data analysis to empirically demonstrate that the proposed data perturbation technique improves the estimation performance significantly.

关键词： data perturbation deep generative model distribution on a lower-dimensional manifold maximum likelihood singular distribution estimation

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical Sciences. All rights reserved.

关键词： Electroencephalography

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Event-Triggered Distributed State Estimation for Nonlinear Reference Systems Under Markovian Switching Topologies 23

Event-Triggered Distributed State Estimation for Nonlinear R...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Chen, Zitao Yan, Jiapei Chen, Kairui Lan, Xuejing Wang, Junwei School of Mechanical and Electrical Engineering Guangzhou University Guangzhou510006 China Seoul National University Department of Mathematical Sciences Seoul08826 Korea Republic of School of Mathematics and Statistics Guangdong University of Foreign Studies Guangzhou510006 China

ISBN: (纸本)9788993215274

This paper proposes a novel distributed observer for state estimation of a class of nonlinear systems under Markovian switching topologies. The communication constraint is that each observer can not observe the reference state individually, but the network is jointly observable. Since communication energy and bandwidth are limited in engineering practice, an event-triggered communication scheme is developed to decrease the transmission frequency. A sufficient condition is then given to guarantee that the estimation error is exponentially bounded in mean square under the Markovian switching topologies and event-triggered mechanism, which is rigorously analyzed. The effectiveness of the proposed method is verified through the Chua's circuit system. © 2023 ICROS.

关键词： State estimation

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