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Reputation and Pricing Dynamics in Online Markets

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arXiv 2021年

作者： Ma, Qian Huang, Jianwei Bąsar, Tamer Liu, Ji Chen, Xudong The School of Intelligent Systems Engineering Sun Yat-sen University China The School of Science and Engineering Chinese University of Hong Kong Shenzhen Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China The Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States The Department of Electrical and Computer Engineering Stony Brook University United States The Department of Electrical University of Colorado Boulder Computer and Energy Engineering United States

We study the economic interactions among sellers and buyers in online markets. In such markets, buyers have limited information about the product quality, but can observe the sellers' reputations which depend on their past transaction histories and ratings from past buyers. Sellers compete in the same market through pricing, while considering the impact of their heterogeneous reputations. We consider sellers with limited as well as unlimited capacities, which correspond to different practical market scenarios. In the unlimited seller capacity scenario, buyers prefer the seller with the highest reputation-price ratio. If the gap between the highest and second highest seller reputation levels is large enough, then the highest reputation seller dominates the market as a monopoly. If sellers' reputation levels are relatively close to each other, then those sellers with relatively high reputations will survive at the equilibrium, while the remaining relatively low reputation sellers will get zero market share. In the limited seller capacity scenario, we further consider two different cases. If each seller can only serve one buyer, then it is possible for sellers to set their monopoly prices at the equilibrium while all sellers gain positive market shares;if each seller can serve multiple buyers, then it is possible for sellers to set maximum prices at the equilibrium. Simulation results show that the dynamics of reputations and prices in the longerterm interactions will converge to stable states, and the initial buyer ratings of the sellers play the critical role in determining sellers' reputations and prices at the stable state. Copyright © 2021, The Authors. All rights reserved.

关键词： Competition

Accurate calculation of the power transfer and efficiency in resonator arrays for inductive power transfer

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Progress In Electromagnetics Research B 2019年 83卷 61-76页

作者： Alberto, José Reggiani, Ugo Sandrolini, Leonardo Albuquerque, Helena Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II Coimbra3030-290 Portugal University of Bologna Department of Electrical Electronic and Information Engineering Viale del Risorgimento 2 BolognaI-40136 Italy Department of Mathematics University of Coimbra Coimbra Portugal

This paper studies the power transfer characteristics of a resonator array for inductive power transfer by means of the accurate analytical solution of its circuit model. Through the mathematical inversion of a tridiagonal matrix, it is possible to obtain closed-form expressions for the current in each resonator and consequently expressions for the power transfer and efficiency of the system. The method can be applied to a resonator array powering a load at the end of the array or a receiver facing the array at any position. With the expressions obtained, it is possible not only to achieve a better understanding of the power transfer characteristics in resonator arrays but also to obtain the conditions for maximum power transfer or maximum efficiency, for several conditions and parameters of the system. A prototype of a stranded-wire resonator array powered by a resonant inverter, capable of delivering power to a load from 65W to 90W with efficiency values between 63% and 88%, was built in order not only to validate the expressions obtained but also to show their practical applicability and demonstrate that these arrays can be used for higher power transfer applications. © 2019, Electromagnetics Academy.

关键词： Resonators

Modeling of adsorptive removal of azithromycin from aquatic media by CoFe₂O₄_/NiO anchored microalgae-derived nitrogen-doped porous activated carbon adsorbent and colorimetric quantifying of azithromycin in pharmaceutical products

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Chemosphere 2023年第1期329卷 138635页

作者： Ameen, Fuad Mostafazadeh, Reza Hamidian, Yasamin Erk, Nevin Sanati, Afsaneh L. Karaman, Ceren Ayati, Ali Department of Botany and Microbiology College of Science King Saud University Riyadh 11451 Saudi Arabia Ankara University Faculty of Pharmacy Department of Analytical Chemistry Ankara 06560 Turkey Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II Coimbra 3030-290 Portugal Akdeniz University Vocational School of Technical Sciences Department of Electricity and Energy Antalya 07070 Turkey ChemBio Cluster ITMO University Lomonosova Street 9 Saint Petersburg 191002 Russian Federation

Herein, it was aimed to optimize the removal process of Azithromycin (Azi) from the aquatic environment via CoFe2O4/NiO nanoparticles anchored onto the microalgae-derived nitrogen-doped porous activated carbon (N-PAC)... 详细信息

Herein, it was aimed to optimize the removal process of Azithromycin (Azi) from the aquatic environment via CoFe₂O₄_/NiO nanoparticles anchored onto the microalgae-derived nitrogen-doped porous activated carbon (N-PAC), besides developing a colorimetric method for the swift monitoring of Azi in pharmaceutical products. In this study, the Spirulina platensis (Sp) was used as a biomass resource for fabricating CoFe₂O₄/NiO@N-PAC adsorbent. The pores of N-PAC mainly entail mesoporous structures with a mean pore diameter of 21.546 nm and total cavity volume (V_total) of 0.033578 cm³. g⁻¹. The adsorption studies offered that 98.5% of Azi in aqueous media could remove by CoFe₂O₄/NiO@N-PAC. For the cyclic stability analysis, the adsorbent was separated magnetically and assessed at the end of five adsorption-desorption cycles with a negligible decrease in adsorption. The kinetic modeling revealed that the adsorption of Azi onto the CoFe₂O₄/NiO@N-PAC was well-fitted to the second-order reaction kinetics, and the highest adsorption capacity was found as 2000 mg. g⁻¹ at 25 °C based on the Langmuir adsorption isotherm model at 0.8 g. L⁻¹ adsorbent concentration. The Freundlich isotherm model had the best agreement with the experimental data. Thermodynamic modeling indicated the spontaneous and exothermic nature of the adsorption process. Moreover, the effects of pH, temperature, and operating time were also optimized in the colorimetric Azi detection. The blue ion-pair complexes between Azi and Coomassie Brilliant Blue G-250 (CBBG-250) reagent followed Beer's law at wavelengths of 640 nm in the concentration range of 1.0 μM to 1.0 mM with a 0.94 μM limit of detection (LOD). In addition, the selectivity of Azi determination was verified in presence of various species. Furthermore, the applicability of CBBG-250 dye for quantifying

关键词： Adsorption Adsorption modeling Azithromycin Biomass-derived activated carbon Spectrophotometry Spirulina platensis

Estimation of Arterial Blood Pressure Waveform from Photoplethysmogram Signal using Linear Transfer Function Approach

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Estimation of Arterial Blood Pressure Waveform from Photople...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ashutosh Dash Nirmalya Ghosh Amit Patra Anirban Dutta Choudhury Department of Electrical Engineering Indian Institute of Technology Kharagpur West Bengal Embedded Systems and Robotics TCS Research and Innovation India

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The primary risk factor of hypertension, is the lack of awareness caused by the unavailability of ubiquitous blood pressure (BP) measurement. In this study, we have investigated the BP estimation using the photoplethysmogram (PPG) signal and a suitable subject-specific mathematical model. The linear transfer function (LTF) technique was used to identify the subject-specific model. Firstly, we tried to identify the model considering arterial blood pressure (ABP) as input and PPG as output, and we achieved an average estimation accuracy (normalized root mean square, NRMSE) of 84.4%. Next, we fitted an inverse model, where ABP is the output, and PPG is the input, and we achieved an average estimation accuracy (NRMSE) of 84.7%. Finallly, We verified that the two identified models mentioned above are inverse of each other. In this study, we have used ABP and PPG signals of 10 (male = 7, female = 3) subjets from the MIMIC II database. The results are quite promising for the use of the PPG in the detection and diagnosis of cardiovascular diseases.

关键词： Mathematical model Estimation Biomedical monitoring Transfer functions Standards Delay effects Arteries

Teaching Autonomous systems Hands-On: Leveraging Modular Small-Scale Hardware in the robotics Classroom

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arXiv 2022年

作者： Betz, Johannes Zheng, Hongrui Zang, Zirui Sauerbeck, Florian Zheng, Y. Rosa Biswas, Joydeep Walas, Krzysztof Dimitrov, Velin Behl, Madhur Krovi, Venkat Mangharam, Rahul The Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States The Institute of Automotive Technology Technical University of Munich Germany The College of Engineering and Applied Science Lehigh University BethlehemPA18015 United States The Department of Computer Science University of Texas Austin AustinTX United States The Institute of Robotics and Machine Intelligence Poznan University of Technology Poland The Department of Computer Science University of Virginia CharlottesvilleVA United States College of Engineering Computing and Applied Sciences Clemson University ClemsonSC29634 United States The Toyota Research Institute CambridgeMA02139 United States

Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in developing, deploying, and applying software to real hardware. Additionally, most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. To address this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This article describes the teaching modules and software stack for teaching at various educational levels with the theme of "racing" and competitions that replace exams. The F1TENTH vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students’ computational thinking through autonomous driving with the F1TENTH vehicle. The F1TENTH car fills the gap between research platforms and low-end toy cars and offers hands-on experience in learning the topics in autonomous systems. Four universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the F1TENTH platform. More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course. © 2022, CC BY.

关键词： Students

MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets

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arXiv 2025年

作者： Xu, Jinfeng Chen, Zheyu Li, Jinze Yang, Shuo Wang, Hewei Li, Yijie Li, Mengran Wu, Puzhen Ngai, Edith C.H. Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong Robotics Institute Carnegie Mellon University PittsburghPA United States School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Population Health Sciences Weill Cornell Medicine New YorkNY United States

The data sparsity problem significantly hinders the performance of recommender systems, as traditional models rely on limited historical interactions to learn user preferences and item properties. While incorporating multimodal information can explicitly represent these preferences and properties, existing works often use it only as side information, failing to fully leverage its potential. In this paper, we propose MDVT, a model-agnostic approach that constructs multimodal-driven virtual triplets to provide valuable supervision signals, effectively mitigating the data sparsity problem in multimodal recommendation systems. To ensure high-quality virtual triplets, we introduce three tailored warm-up threshold strategies: static, dynamic, and hybrid. The static warm-up threshold strategy exhaustively searches for the optimal number of warm-up epochs but is time-consuming and computationally intensive. The dynamic warm-up threshold strategy adjusts the warm-up period based on loss trends, improving efficiency but potentially missing optimal performance. The hybrid strategy combines both, using the dynamic strategy to find the approximate optimal number of warmup epochs and then refining it with the static strategy in a narrow hyper-parameter space. Once the warm-up threshold is satisfied, the virtual triplets are used for joint model optimization by our enhanced pair-wise loss function without causing significant gradient skew. Extensive experiments on multiple real-world datasets demonstrate that integrating MDVT into advanced multimodal recommendation models effectively alleviates the data sparsity problem and improves recommendation performance, particularly in sparse data scenarios. © 2025, CC BY.

关键词： Interactive computer systems

Creating Own AI Datasets from Different Language Sources Efficiently: Advantages, Disadvantages and Best Pracitces to Make Own Datasets

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Creating Own AI Datasets from Different Language Sources Eff...

International IEEE Conference and Workshop: Óbuda on electrical and Power engineering (CANDO-EPE)

作者： György Molnár József Cserkó Enikő Nagy Zoltán Balogh Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Széchenyi University Doctoral School of Multidisciplinary Engineering Sciences (MMTDI) Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Institute of Cyberphysical Systems Óbuda University Budapest Hungary Department of Informatics Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia

Artificial Intelligence (AI) has become a ubiquitous technology that has the potential to revolutionize many industries. However, AI requires access to vast amounts of data to achieve its full potential. By understanding these data sources and the best practices for working with them, we can unlock the full potential of AI and create groundbreaking solutions that benefit society as a whole. Data sources can be utilized to train AI algorithms, including structured and unstructured data, but finding relevant datasets is challenging. The main problem is the international sources. Most of the datasets are available only in English or other common languages. However these are good sources, but translating them is expensive and time-consuming. This paper shows how to generate own datasets from open-source data with translation.

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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Multispectral Vineyard Segmentation: A Deep Learning Comparison Study

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arXiv 2021年

作者： Barros, Tiago Conde, P. Gonçalves, G. Premebida, C. Monteiro, M. Ferreira, C.S.S. Nunes, U.J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra Portugal University of Coimbra Institute for Systems Engineering and Computers at Coimbra Department of Mathematics Coimbra Portugal Research Centre for Natural Resources Environment and Society Polytechnic Institute of Coimbra Coimbra Agrarian Technical School Coimbra Portugal Bolin Centre for Climate Research Department of Physical Geography Stockholm University Stockholm Sweden Navarino Environmental Observatory Navarino Dunes Messinia Greece

Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available on https://***/Cybonic/***. © 2021, CC BY.

关键词： Precision agriculture