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Comparing Competitive Priorities of Slow Fashion and Fast Fashion Operations of Large Retailers in an Emerging Economy

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Global Journal of Flexible systems Management 2022年第1期23卷 1-19页

作者： Sellitto, Miguel Afonso Valladares, Domingos Rafael Ferla Pastore, Erica Alfieri, Arianna Production and Systems Engineering Graduate Program Universidade do Vale do Rio dos Sinos - UNISINOS Av. Unisinos 950 RS São Leopoldo 93022.750 Brazil Politecnico di Torino - POLITO Corso Duca degli Abruzzi 24 Turin Italy

The purpose of this study was to compare the competitive priorities of the slow and fast fashion retail operations of large retailers in an emerging economy. The study embraces the six largest Brazilian companies operating both fast and slow fashion businesses. The methodological path comprises a qualitative phase, in which four experts review the competitive priorities in fashion supply chains. Then follows a quantitative phase, in which the practitioners, supported by AHP, the analytical hierarchy process, weight the fast fashion and slow fashion supply chain priorities. Finally, the study compares the two sets of priorities by considering the uncertainty in the judgment. The most relevant priorities in slow fashion are price and quality, whereas those in fast fashion are customer relationships and flexibility. This study proposes guidelines for simultaneously managing these two strategies. In slow fashion, companies should reduce costs and improve quality by implementing online process controls. In fast fashion, companies should decrease lot sizes, increase the variety of mixes and assortments, and strengthen ties with customers. The main novelty of this study is the evidence that retailers operating fast and slow fashion must simultaneously manage two different strategies to achieve different priorities. In addition, some novel procedures are adopted to mitigate the uncertainty in the judgments. © 2021, Global Institute of Flexible systems Management.

关键词： Competitive priorities Customer relationships Fashion supply chain Fast fashion Flexibility Slow fashion

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Artificial Intelligence-Based Recommendation System for Detecting and Diagnosing Broken Bars in Induction Motors Under Transient Operation

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IFAC-PapersOnLine 2024年第19期58卷 1156-1161页

作者： Narco A.R. Maciejewski Roberto Z. Freire Anderson L. Szejka Thiago P.M. Bazzo Sofia M.A. Lopes Rogério A. Flauzino Department of Electrical Engineering - DAELT-CT Universidade Tecnológica Federal do Paraná - UTFPR Curitiba PR 80230-90 Brazil Production and Systems Engineering - PPGEPS-PB Energy Systems - PPGSE-CT Industrial and Systems Engineering Graduate Program - PPGEPS Pontifical Catholic University of Parana - PUCPR Curitiba PR 80215-901 Brazil Electrical Engineering - PPGEE-SC São Carlos School of Engineering - EESC Universidade de São Paulo - USP São Carlos SP 13566-590 Brazil

Three-phase induction motors are the main elements for converting electrical energy into mechanical energy and are extensively used in industry. Reducing maintenance costs becomes an incentive for developing systems capable of identifying defects. This research proposes a framework for recommending machine learning algorithms that diagnose and detect broken bar defects in three-phase induction motors under transient operation based on artificial intelligence. Employing experimental data, features were extracted and selected based on current, voltage, and vibration. A protocol of insertion of white noise showed that the proposed framework admitted 80% of noise without losing the predictive capacity based on a multicriteria performance measure.

关键词： condition monitoring fault detection fault classification machine learning maintenance engineering smart systems three-phase induction motor

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Stochastic multi-objective optimization of hybrid distributed power generation with battery storage systems

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Energy Sources, Part B: Economics, Planning and Policy 2025年第1期20卷

作者： Barbosa, Aglaucibelly Maciel Rotella Junior, Paulo Peruchi, Rogério Santana Rocha, Luiz Célio Souza Pires, Arthur Leandro Guerra Janda, Karel Post-Graduate Program in Production Engineering and Systems Federal University of Paraiba João Pessoa Brazil Department of Production Engineering Federal University of Paraiba João Pessoa Brazil Faculty of Finance and Accounting Prague University of Economics and Business Prague Czech Republic Institute of Economic Studies Faculty of Social Sciences Charles University Prague Czech Republic Management Department - Federal Institute of Education Science and Technology - North of Minas Gerais Almenara Brazil Institute of Production Engineering and Management Federal University of Itajubá Brazil

Hybrid energy projects are gaining global interest due to their potential to promote sustainable development. This study addresses a gap in the literature by proposing a stochastic model that assesses the economic feasibility of a hybrid energy system combining wind, photovoltaic generation, and battery storage. Unlike traditional deterministic approaches, the model incorporates uncertainties in key financial and operational variables, improving decision-making in real-world conditions. The optimization framework integrates Design of Experiments (DoE), Response Surface Methodology (RSM), and the Desirability function to balance economic return and financial risk. By maximizing the mean and minimizing the variance of the Net Present Value (NPV), the model identifies an optimal system configuration with 92% wind energy, a demand level of 230 kWh/month, and lithium-ion battery storage. This approach reduces computational complexity and enhances hybrid system viability in regions with strong wind resources, providing a valuable tool for investors and policymakers in sustainable energy planning. © 2025 Taylor & Francis Group, LLC.

关键词： Multiobjective optimization

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Logistics for Disaster Waste Management: A Case Study of the 2019 Oil Spill in Brazil 1

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International Conference on production and Operations Management, POMS 2021

作者： Netto, Luiz Fernando Sanghikian, Alex Igor Yoshizaki, Hugo Tsugunobu Yoshida de Brito Junior, Irineu Chico Mendes Institute for Biodiversity Conservation Pirassununga Brazil Department of Production Engineering Sao Paulo State University Sao Paulo Brazil Graduate Program in Logistics Systems Engineering Sao Paulo University Sao Paulo Brazil Environmental Engineering São Paulo State University Sao Jose dos Campos Brazil

ISBN: (数字)9783031068621

ISBN: (纸本)9783031068614

During the response and recovery phases of a disaster, an important issue is waste management, which includes generation, transportation, transfer, processing, recycling, and reuse of debris. In this paper, we analyzed the logistical process related to the management of 5400 metric tons of waste from the 2019 oil spill in Brazil, considering environmental impacts and cleaning efforts. The analyses show the quantities handled by the state governments responsible for the operation and the main destination of the residues, which was the cement industry. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Waste management

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Reviewing Photovoltaics and Electric Vehicles: Synergy and End of Life Management 26th

Reviewing Photovoltaics and Electric Vehicles: Synergy and E...

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26th International Joint Conference on Industrial engineering and Operations Management, IJCIEOM 2021

作者： Marcuzzo, Rafael Campos, Lucila M. S. Maldonado, Mauricio Uriona Vaz, Caroline Rodrigues Graduate Program in Production Engineering Universidade Federal de Santa Catarina Florianópolis Brazil Department of Production Engineering and Systems Universidade Federal de Santa Catarina Florianópolis Brazil Department of Textile Engineering Universidade Federal de Santa Catarina Blumenau Brazil

ISBN: (纸本)9783030785697

Energy supply and mobility are necessary functions of economic development and have become global latent themes concerning sustainability. In the electricity sector renewable energy technologies, such as solar photovoltaic (PV), have grown significantly over its competitors in terms of investment in the past 20 years. Still, adoption of the technology has not grown quickly enough to decarbonize the sector. In the mobility sector, however, combustion engines have been replaced with electric vehicles (EV), illustrating a greater potential to mitigate emission of polluting gases. Still, the production of EV technology can yield high emissions. Instead of either technology working separately, both PV and EV may be able to cooperate in a manner where PV systems power clean electricity to produce and charge EV batteries until they return to a stationary storing system (i.e., second life) for PV systems. The following study investigates the use of EV batteries for PV systems, assessing the possible toxic effects on the environment from manufacturing, using, and disposing the technology. A review of peer-reviewed articles shed light on possible hazards of clean technologies in the energy and mobility sectors. Findings suggest the diffusion of clean technologies must be accompanied by policies that guarantee responsible disposal of them. The study concludes that advanced technological mechanisms for renewable energy may have significantly negative and sometimes obscured environmental impacts, which must be considered in future policies advocating for those technologies. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Environmental technology

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Localization of a Crack in Moving Cylindrical Ferromagnetic Rods by Measuring the Fourier Coefficients of the Leakage Magnetic Flux

Localization of a Crack in Moving Cylindrical Ferromagnetic ...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Kanta Shiku Masafumi Kuromizu Yuji Gotoh Takaaki Nara Graduate School of Information Science and Technology The University of Tokyo bunkyo-ku Japan Division of Materials Science and Production Engineering Graduate School of Engineering Oita University Oita Japan Intelligent Mechanical Systems Program Faculty of Science and Technology Oita University Oita Japan

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Electromagnetic nondestructive inspection for high-speed moving ferromagnetic materials is necessary for manufacturing efficiency. In this paper, we present a novel leakage magnetic flux (LMF) method to estimate the crack position in a moving cylindrical ferromagnetic rod. The method directly measures the first-order Fourier coefficients of the LMF moving with the crack by using only two coils with DC excitation. The location of a crack is estimated by the proposed method, as demonstrated for an S45C cylindrical rod.

关键词： Location awareness Coils Magnetic flux Inspection Ferromagnetic materials Manufacturing Electromagnetics

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Analysis of the Brazilian Climate Data Influence on Soybean Pricing by Using Long Short-Term Memory Models

SSRN

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SSRN 2024年

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Dal Molin, Viviane Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical and Computer Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Department of Academic Informatics Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Graduate Program in Production and Systems Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil

This work uses machine learning to verify whether the Brazilian climate influences international soybean price prediction. For this, climatic data, dollar quotes, inflation, and annual soybean production were collected from different data sources. Then, the collected data were pre-processed and grouped by Brazilian state. Afterward, data from the five most productive Brazilian states are chosen, such as Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS). Afterward, predictive models are built using the long short-term memory (LSTM) method, which is applied in each chosen state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest values of root mean squared error (RMSE) and mean absolute error (MAE). Regarding predictors trained without climate data, the RS and PR obtained the lowest values of RMSE and MAE, respectively. After conducting statistical hypothesis tests, no significant difference was found among models, stating, with 95% certainty, that, in this study, the Brazilian climatic data did not influence the model performance for predicting soybean prices. On the other hand, one of the main contributions of work is conducting an unprecedented study about soybean price prediction using Brazilian climatic data. © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Soybean Price Prediction by Using Lstm Models Based on Brazilian Climatic Data

SSRN

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SSRN 2024年

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Molin, Viviane Dal Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Department of Academic Informatics Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Graduate Program in Production and Systems Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil

关键词： Long short-term memory

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Georeferencing System for Maneuvering of Autonomous Truck in Mining Environment

Georeferencing System for Maneuvering of Autonomous Truck in...

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International Carpathian Control Conference (ICCC)

作者： Emanuel Francisco Vicentini Carvalho Lucas Silva De Oliveira Murillo Ferreira Dos Santos Paolo Mercorelli Matheus Carvalho Gouveia Master Degree Program in Automation and Systems CEFET-MG Leopoldina Brazil Department of Mechatronics Engineering CEFET-MG Divinópolis MG Brazil Department of Electroelectronics CEFET-MG Leopoldina Brazil Institute for Production Technology and Systems (IPTS) Leuphana University of Lüneburg Lüneburg Germany Electrical Engineering B. Sc UFJF-MG Juiz de Fora MG Brazil

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Mining automation began in the 1960s to increase safety in underground mines. Today, robotic and autonomous systems perform specific mining activities, creating new oppor-tunities and a more efficient and safe industry. This paper proposes a novel solution for instrumentation for autonomous trucks working in open-pit mines, specifically during the parking maneuver to load ore. In this case, new hardware and firmware are developed to compute the future coordinates of the excavator bucket using two Global Positioning systems (GPS) receivers. In this approach, employing the concept of geometry and information from the GPS receivers, the future coordinates of the excavator bucket are calculated and made available to control the system, parking the autonomous truck in the desired position. The results demonstrate the accurate positioning of the off-highway truck during the ore-loading process, allowing for the correct execution of the truck's autonomous positioning maneuver.

关键词： Automation Receivers Excavation Hardware Real-time systems Safety Vehicle dynamics

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Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

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Joule 2024年第7期8卷 1958-1981页

作者： Maradesa, Adeleke Py, Baptiste Huang, Jake Lu, Yang Iurilli, Pietro Mrozinski, Aleksander Law, Ho Mei Wang, Yuhao Wang, Zilong Li, Jingwei Xu, Shengjun Meyer, Quentin Liu, Jiapeng Brivio, Claudio Gavrilyuk, Alexander Kobayashi, Kiyoshi Bertei, Antonio Williams, Nicholas J. Zhao, Chuan Danzer, Michael Zic, Mark Wu, Phillip Yrjänä, Ville Pereverzyev, Sergei Chen, Yuhui Weber, André Kalinin, Sergei V. Schmidt, Jan Philipp Tsur, Yoed Boukamp, Bernard A. Zhang, Qiang Gaberšček, Miran O'Hayre, Ryan Ciucci, Francesco Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong Metallurgical and Materials Engineering Colorado School of Mines Golden80401 United States Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing China Green Energy Storage Trento38123 Italy Department of Manufacturing and Production Engineering Faculty of Mechanical Engineering and Ship Technology Institute of Machine and Materials Technology Gdańsk University of Technology Gdańsk Poland Electrode Design for Electrochemical Energy Systems University of Bayreuth Bayreuth Germany School of Chemistry University of New South Wales Sydney Australia School of Advanced Energy Sun Yat-Sen University Shenzhen China Sustainable Energy Center CSEM Neuchâtel2002 Switzerland Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Centre for Electronic and Optical Materials National Institute for Materials Science Tsukuba Japan Department of Civil and Industrial Engineering University of Pisa Pisa Italy Department of Materials Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Department of Chemical Engineering Massachusetts Institute of Technology Cambridge02139 United States Electrical Energy Systems University of Bayreuth Bayreuth Germany Ruder Boskovic Institute Zagreb Croatia Department of Materials and Minerals Resources Engineering National Taipei University Taipei Taiwan Finland Johann Radon Institute for Computational and Applied Mathematics Linz Austria School of Science and Engineering Nanjing Tech. University Nanjing China Karlsruhe Germany Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Physical Science Division Pacific Northwest National Laboratory RichlandWA99354 United States Systems Engineering for Electrical Energy Storage University of B

Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines non-parametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data. © 2024 Elsevier Inc.

关键词： Electrochemical impedance spectroscopy

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