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Urbanization, Sustainability and Society 2025年第1期2卷 282-307页

作者： Deividson Sá Fernandes de Souza Simone Sehnem Patricia Guarnieri Thyago Celso Cavalcante Nepomuceno Fernando Henrique Taques Lucio Camara e Silva Post-Graduate Program in Production Engineering – CAA Federal University of Pernambuco Recife Brazil Department of Management Unoesc Chapecó Brazil and Department of Management Unisul Florianópolis Brazil Faculty of Economics Business Accounting and Public Policies Universidade de Brasília Brasília Brazil Technology Center Universidade Federal de Pernambuco Recife Brazil and Department of Computer Control and Management Engineering Antonio Ruberti University of Rome La Sapienza Rome Italy Post-Graduate Program in Production Engineering – CAA Federal University of Pernambuco Recife Brazil and Department of Applied Economics – ECONRES Research Group Universidad Autónoma de Madrid Madrid Spain

Purpose This paper aims to provide a comprehensive literature review on the practices and challenges in managing waste electrical and electronic equipment (WEEE) in smart cities. Design/methodology/approach A systematic literature review was conducted using the Methodi Ordinatio. Articles published between 2012 and 2022 were analyzed, totaling 149 references, of which 30 were included in the final review. Findings Emerging technologies such as the Internet of Things (IoT), big data and artificial intelligence (AI) are frequently highlighted as promising solutions for efficient e-waste management. Governance models and public policies are widely recognized as crucial for the successful implementation of WEEE management practices in smart cities. Originality/value This study underscores the role of advanced technologies, such as IoT and AI, in enhancing urban mobility and WEEE management. Key challenges include information security, privacy, interoperability, costs and sustainability. The findings reveal a convergence between smart cities and WEEE management, fostering the circular economy and the recovery of valuable materials.

关键词： Smart cities WEEE Systematic literature review Methodi Ordinatio

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Training Swinging Door Trending Compression Algorithm for IoT devices

Training Swinging Door Trending Compression Algorithm for Io...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Juan David Arias Correa Alex Sandro Roschildt Pinto Carlos Montez Post-graduate Program of Automation Systems Engineering Federal University of Santa Catarina (UFSC) Florianópolis Brazil Dept. of Informatics and Statistic Dept. of Automation and Systems Engineering Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The communication system is essential for the devices used in IoT. Nevertheless, the consumption caused by transmitting and receiving data is a concern in many scenarios. That is due to these devices commonly having limited energy and computational capacities. Transmission of redundant or irrelevant samples frequently wastes the device’s resources. Furthermore, Storing a large amount of redundant data could consume storage space and not offer any benefit. Data Compression (DC) methods are a potential solution. DC could reduce communication usage and storage demand. This research proposes the Training Swing Door Trending (TSDT) for being implemented in IoT Devices. TSDT is a new algorithm that improves the classic Swing Door Trending (STD). They represent the data by trend lines and have a constant computational complexity. TSDT has a training step for the automatic configuration of its parameters. This article additionally presents the Compression Factor (C-Score), a new quality metric to analyze the compression results in lossy DC methods. C-Score takes as a basis the F-Score, a measure of predictive performance. The C-Score uses the Compression and Error metrics to evaluate the compression performance in Lossy Algorithms.

关键词： Training Performance evaluation Communication systems Data compression Prediction algorithms Market research Internet of Things Computational complexity Compression algorithms

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GIS-Based Methodology for Crash Prediction on Single-Lane Rural Highways

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Journal of Geographic Information System 2021年第2期13卷 98-121页

作者： Márcia Macedo Emilia Kohlman Rabbani Maria Maia Marlos Macedo Bianca Ferreira Post-Graduate Program in Civil Engineering UPE Recife Brazil Post-Graduate Program in Systems Engineering UPE Recife Brazil Graduate Program in Architecture and Urban Planning UNICAP Recife Brazil

Due to the need to update the current guidelines for highway design to focus on safety, this study sought to build an accident prediction model using a Geographic Information System (GIS) for single-lane rural highways, with a minimum of statistically significant variables, adequate to the Brazilian reality, and improve accident prediction for places with similar characteristics. A database was created to associate the accident records with the geometric parameters of the highway and to fill in the gaps left by the absence of geometric highway plans through geometric reconstitution or semi-automatic extraction of highways using satellite images. The Generalized Estimating Equation (GEE) method was applied to estimate the coefficients of the model, assuming negative distribution of the binomial error for the count of observed accidents. The accident frequency and annual average daily traffic (AADT) were analyzed, along with the spatial and geometric characteristics of 215 km of federal single-lane rural highways between 2007 and 2016. The GEE procedure was applied to two models having three variations of distinct homogeneous segmentation, two based on segments and one based on the kernel density estimator. To assess the effect of constant traffic, two more variations of the models using AADT as an offset variable were considered. The predominant correlation structure in the models was the exchangeable. The principal contributing factors for the occurrence of collisions were the radius of the horizontal curve, the grade, segment length, and the AADT. The study produced clear indicators for the design parameters of roadways that influence the safety performance of rural highways.

关键词： Roads GEE Single-Lane Rural Highways GIS Crash Prediction

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Creating Mathematical Models to Help Formulate Company Strategies for Knowledge Management System Implementation

Creating Mathematical Models to Help Formulate Company Strat...

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International Conference on Information Management and Technology (ICIMTech)

作者： Wahyu Sardjono Widhilaga Gia Perdana Dewi Sagita Pranata Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Post Graduate Program of Management Science State University of Jakarta Jakarta Indonesia Department of Informatics Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences King's College London Strands West Minster UK

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

This research aims to build a mathematical model to formulate the problems of implementing knowledge management systems in companies that often face obstacles in achieving the desired objectives and goals. With increasing competition in the business sector, organizations or organizations realize the importance of utilizing the knowledge assets that reside in each individual and organization. If this knowledge can be managed optimally, it can become a competitive advantage for the company due to the emergence of innovative ideas by the concepts of knowledge management theory. By implementing optimal knowledge management (KM), companies can design innovative solutions to improve business operations and increase overall revenue. The factor analysis method will be used to find the determining factors for the success of the implementation of knowledge management systems (KMS) and the regression analysis method will also be used to form a mathematical model of several new factors which are formed as independent variables with the current level of community understanding of KMS as the dependent variable. The research results provide insight into strategies to improve success factors to successfully facilitate KMS implementation. This study contributes to existing knowledge management by providing insight into dynamics that go beyond the technical aspects of KMS, ultimately developing strategies for more effective and efficient utilization of knowledge and organizational growth and supporting competitive advantage for companies.

关键词： Performance evaluation Knowledge acquisition Collaboration Companies Knowledge management Mathematical models Regression analysis Information management Problem-solving System implementation

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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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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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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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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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Combining Robust Control and Machine Learning for Uncertain Nonlinear systems Subject to Persistent Disturbances

Combining Robust Control and Machine Learning for Uncertain ...

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IEEE Conference on Decision and Control

作者： A. Banderchuk D. Coutinho E. Camponogara Post-graduate Program in Automation and Systems Engineering Universidade Federal de Santa Catarina Florianópolis SC Brazil Department of Automation and Systems Universidade Federal de Santa Catarina Florianópolis SC Brazil

This paper proposes a control strategy consisting of a robust controller and an Echo State Network (ESN) based control law for stabilizing a class of uncertain nonlinear discrete-time systems subject to persistent disturbances. Firstly, the robust controller is designed to ensure that the closed-loop system is Input-to-State Stable (ISS) with a guaranteed stability region regardless of the ESN control action and exogenous disturbances. Then, the ESN-based controller is trained in order to mitigate the effects of disturbances on the system output. A numerical example demonstrates the potential of the proposed control design method.

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