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1,441 results on '"Ferreira, Luis"'

1. 5G Network Slicing as a Service Enabler for theAutomotive Sector

Author

Candal-Ventureira, David, Rúa-Estévez, José Manuel, Fondo-Ferreiro, Pablo, Gil-Castiñeira, Felipe, Fernández-Barciela, Antonio, González-Castaño, Francisco Javier, Diéguez-Pazo, Emilio, and Fernández-Ferreira, Luis

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Network slicing, a key technology introduced in 5G standards, enables mobile networks to simultaneously support a wide range ofheterogeneous use cases with diverse quality of service (QoS) requirements. This work discusses the potential benefits of networkslicing for the automotive sector, encompassing manufacturing processes and vehicular communications. The review of the stateof the art reveals a clear gap regarding the application of network slicing from the perspective of industrial verticals such asautomotive use cases and their specific requirements. Departing from this observation, we first identify limitations of previouscellular technologies and open challenges for supporting the data services required. Then we describe network slicing as an enablerto face these challenges. We present an analysis of the cost equilibrium for network slicing to be effective for car manufacturers,and tests in real 5G networks that demonstrate the performance improvement in OTA updates coexisting with other services., Comment: Article published in Engineering Reports journal

Published
2025
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2. FlexiGen: Stochastic Dataset Generator for Electric Vehicle Charging Energy Flexibility

Author

Cabral, Bernardo, Fonseca, Tiago, Sousa, Clarisse, and Ferreira, Luis Lino

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Electric vehicles (EVs) and renewable energy sources (RES) are vital components of sustainable energy systems, yet their uncoordinated integration can pose substantial challenges to grid stability, such as unmanaged peak loads and energy balance issues. Vehicle-to-Grid (V2G), offer a promising solution to address these challenges by enabling bidirectional energy flow between EVs and the grid. As such, EVs can be used in advances Demand Response (DR) strategies to optimize energy use and mitigate the intermittency of renewable generation. To reach such advantages, optimization algorithms need data on EV energy flexibility, such as charging patterns and usage preferences. However, data collection remains constrained by challenges such as high costs, user engagement, data privacy concerns, and limited access to open-source datasets on EV energy flexibility. This paper presents FlexiGen an open-source stochastic dataset generator tool designed to overcome the data limitations in EV flexibility for V2G and V1G DR applications. FlexiGen generates synthetic datasets encompassing realist EV usage patterns, behaviours and flexibility scenarios for household and office routines. To generate these datasets, FlexiGen uses a series of configurable probabilistic variables, such as stochastic user routines, traffic conditions, charger types, car average electricity consumption and State of Charge (SoC). The generated datasets include an hourly routine with the EV State of connection, Destination Charger, Estimated Departure Time, Required SOC at Departure, Estimated Arrival Time, and Estimated SOC at Arrival. Accompanying this publication an example dataset is generated for 3 households with 1 EV each, and 1 office building with 3 EVs. The generated dataset is analyzed and discussed on the paper and published alongside the open-source code for FlexiGen tool., Comment: 9 pages, 5 figures, 9 tables

Published
2024

3. Co-inoculation with rhizobia and mycorrhizal fungi increases yield and crude protein content of cowpea (Vigna unguiculata (L.) Walp.) under drought stress

Author

Pereira, Sandra, Singh, Shweta, Oliveira, Rui S, Ferreira, Luis, Rosa, Eduardo, and Marques, Guilhermina

Subjects
amf, drought, rhizobia, tripartite symbiosis, vigna unguiculata (l.) walp., Agriculture (General), S1-972
Abstract

Recent trends in sustainable agricultural production seek improved bioinoculants that can improve crop adaptation and production and reduce external inputs of pesticides and synthetic fertilisers, particularly under abiotic and biotic stress conditions. Drought is one of the critical and more frequent conditions that can drastically reduce plant bio-mass and yield. In this sense, the use of bioinoculants is a biological strategy to mitigate climate change and reduce the water needs of plants. Leguminous plants are very important in improving sustainable cropping systems because they can form effective symbiotic associations with both nitrogen-fixing bacteria and arbuscular mycorrhizal fungi. These microorganisms can act as an alternative source of nitrogen and can increase phosphorus utilisation from soils and fertilisers. Cowpea is a multipurpose crop that has caused a great interest due to its resistance to abiotic stress. This pot experiment in a greenhouse with non-sterilised soil aimed to test the effect of three previously selected rhizobial bacteria (Rhizobium sp. (B1), Bradyrhizobium elkanii (B2) and Bradyrhizo bium sp. (B3)) and arbuscular mycorrhizal fungi (Claroideoglomus claroideum BEG210) on the yield and crude protein content of cowpea under drought conditions and also to compare the competitiveness of the inoculated bacteria with native rhizobial bacteria naturally present in the soil. The combined inoculation with each bacteria and arbuscular mycorrhizal fungi Claroideoglomus claroideumBEG210 was shown to increase the crude protein content of cowpea seeds in plants under drought stress (25 % of field capacity) by 13 %, 17 %, and 30 %, respectively. This study shows that these microorganisms are potentially resistant to drought and can be used as a biotechnological tool for sustainable agriculture under drought conditions.

Published
2020
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4. CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

Author

Nweye, Kingsley, Kaspar, Kathryn, Buscemi, Giacomo, Fonseca, Tiago, Pinto, Giuseppe, Ghose, Dipanjan, Duddukuru, Satvik, Pratapa, Pavani, Li, Han, Mohammadi, Javad, Ferreira, Luis Lino, Hong, Tianzhen, Ouf, Mohamed, Capozzoli, Alfonso, and Nagy, Zoltan

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society, Electrical Engineering and Systems Science - Systems and Control
Abstract

As more distributed energy resources become part of the demand-side infrastructure, it is important to quantify the energy flexibility they provide on a community scale, particularly to understand the impact of geographic, climatic, and occupant behavioral differences on their effectiveness, as well as identify the best control strategies to accelerate their real-world adoption. CityLearn provides an environment for benchmarking simple and advanced distributed energy resource control algorithms including rule-based, model-predictive, and reinforcement learning control. CityLearn v2 presented here extends CityLearn v1 by providing a simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create virtual grid-interactive communities for resilient, multi-agent distributed energy resources and objective control with dynamic occupant feedback. This work details the v2 environment design and provides application examples that utilize reinforcement learning to manage battery energy storage system charging/discharging cycles, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Published
2024
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5. EVLearn: Extending the CityLearn Framework with Electric Vehicle Simulation

Author

Fonseca, Tiago, Ferreira, Luis, Cabral, Bernardo, Severino, Ricardo, Nweye, Kingsley, Ghose, Dipanjan, and Nagy, Zoltan

Subjects
Computer Science - Multiagent Systems, Electrical Engineering and Systems Science - Systems and Control
Abstract

Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) emerge as a potential solution to the Electric Vehicles' (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced prospective, the adoption of these strategies is still hindered by an array of operational problems. Key among these is the lack of a simulation platform that allows to validate and refine V2G and G2V strategies. Including the development, training, and testing in the context of Energy Communities (ECs) incorporating multiple flexible energy assets. Addressing this gap, first we introduce the EVLearn, a simulation module for researching in both V2G and G2V energy management strategies, that models EVs, their charging infrastructure and associated energy flexibility dynamics; second, this paper integrates EVLearn with the existing CityLearn framework, providing V2G and G2V simulation capabilities into the study of broader energy management strategies. Results validated EVLearn and its integration into CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario., Comment: 10 pages, 7 figures, 3 tables, 11 equations

Published
2024

6. EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management

Author

Fonseca, Tiago, Ferreira, Luis, Cabral, Bernardo, Severino, Ricardo, and Praca, Isabel

Subjects
Computer Science - Multiagent Systems, Computer Science - Artificial Intelligence
Abstract

This paper investigates the increasing roles of Renewable Energy Sources (RES) and Electric Vehicles (EVs). While indicating a new era of sustainable energy, these also introduce complex challenges, including the need to balance supply and demand and smooth peak consumptions amidst rising EV adoption rates. Addressing these challenges requires innovative solutions such as Demand Response (DR), energy flexibility management, Renewable Energy Communities (RECs), and more specifically for EVs, Vehicle-to-Grid (V2G). However, existing V2G approaches often fall short in real-world adaptability, global REC optimization with other flexible assets, scalability, and user engagement. To bridge this gap, this paper introduces EnergAIze, a Multi-Agent Reinforcement Learning (MARL) energy management framework, leveraging the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. EnergAIze enables user-centric and multi-objective energy management by allowing each prosumer to select from a range of personal management objectives, thus encouraging engagement. Additionally, it architects' data protection and ownership through decentralized computing, where each prosumer can situate an energy management optimization node directly at their own dwelling. The local node not only manages local energy assets but also fosters REC wide optimization. The efficacy of EnergAIze was evaluated through case studies employing the CityLearn simulation framework. These simulations were instrumental in demonstrating EnergAIze's adeptness at implementing V2G technology within a REC and other energy assets. The results show reduction in peak loads, ramping, carbon emissions, and electricity costs at the REC level while optimizing for individual prosumers objectives., Comment: 6 pages, 6 figures, 2 tables

Published
2024

7. RateRL: A Framework for Developing RL-Based Rate Adaptation Algorithms in ns-3

Author

Queiros, Ruben, Ferreira, Luis, Fontes, Helder, and Campos, Rui

Subjects
Computer Science - Networking and Internet Architecture
Abstract

The increasing complexity of recent Wi-Fi amendments is making the use of traditional algorithms and heuristics unfeasible to address the Rate Adaptation (RA) problem. This is due to the large combination of configuration parameters along with the high variability of the wireless channel. Recently, several works have proposed the usage of Reinforcement Learning (RL) techniques to address the problem. However, the proposed solutions lack sufficient technical explanation. Also, the lack of standard frameworks enabling the reproducibility of results and the limited availability of source code, makes the fair comparison with state of the art approaches a challenge. This paper proposes a framework, named RateRL, that integrates state of the art libraries with the well-known Network Simulator 3 (ns-3) to enable the implementation and evaluation of RL-based RA algorithms. To the best of our knowledge, RateRL is the first tool available to assist researchers during the implementation, validation and evaluation phases of RL-based RA algorithms and enable the fair comparison between competing algorithms.

Published
2023

8. An IoT Cloud and Big Data Architecture for the Maintenance of Home Appliances

Author

Chaves, Pedro, Fonseca, Tiago, Ferreira, Luis Lino, Cabral, Bernardo, Sousa, Orlando, Oliveira, Andre, and Landeck, Jorge

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Billions of interconnected Internet of Things (IoT) sensors and devices collect tremendous amounts of data from real-world scenarios. Big data is generating increasing interest in a wide range of industries. Once data is analyzed through compute-intensive Machine Learning (ML) methods, it can derive critical business value for organizations. Powerfulplatforms are essential to handle and process such massive collections of information cost-effectively and conveniently. This work introduces a distributed and scalable platform architecture that can be deployed for efficient real-world big data collection and analytics. The proposed system was tested with a case study for Predictive Maintenance of Home Appliances, where current and vibration sensors with high acquisition frequency were connected to washing machines and refrigerators. The introduced platform was used to collect, store, and analyze the data. The experimental results demonstrated that the presented system could be advantageous for tackling real-world IoT scenarios in a cost-effective and local approach., Comment: 6 pages, 6 figures, IECON 2022

Published
2022

9. Value of Bidirectional V2G Smart Charging Responsive Services: Insights from a Simple CA Model

Author

Carvalho, Pedro M. S. and Ferreira, Luis A. F. M.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In this paper, particle-hopping cellular automaton (CA) models of elastic demand are used to investigate the value added to plug-in electric vehicles (PEV) aggregators by adopting vehicle-to-grid (V2G) responsive services. CA models used earlier to study load-sifting responses are modified to capture discharge/ recharge capabilities of V2G. Results on ramping responses from CA are then analysed to discuss the small contribution to system controllability added by V2G responsive services., Comment: 3 pages, 4 figures

Published
2022

10. A Low-Cost Multi-Agent System for Physical Security in Smart Buildings

Author

Fonseca, Tiago, Dias, Tiago, Vitorino, João, Ferreira, Luís Lino, and Praça, Isabel

Subjects
Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Systems and Control
Abstract

Modern organizations face numerous physical security threats, from fire hazards to more intricate concerns regarding surveillance and unauthorized personnel. Conventional standalone fire and intrusion detection solutions must be installed and maintained independently, which leads to high capital and operational costs. Nonetheless, due to recent developments in smart sensors, computer vision techniques, and wireless communication technologies, these solutions can be integrated in a modular and low-cost manner. This work introduces Integrated Physical Security System (IP2S), a multi-agent system capable of coordinating diverse Internet of Things (IoT) sensors and actuators for an efficient mitigation of multiple physical security events. The proposed system was tested in a live case study that combined fire and intrusion detection in an industrial shop floor environment with four different sectors, two surveillance cameras, and a firefighting robot. The experimental results demonstrate that the integration of several events in a single automated system can be advantageous for the security of smart buildings, reducing false alarms and delays., Comment: 10 pages, 2 tables, 3 figures, ICCCN 2022 conference

Published
2022

12. A QUBO Formulation for Minimum Loss Spanning Tree Reconfiguration Problems in Electric Power Networks

Author

Silva, Filipe F. C., Carvalho, Pedro M. S., Ferreira, Luis A. F. M., and Omar, Yasser

Subjects
Quantum Physics, Computer Science - Emerging Technologies, Mathematics - Optimization and Control
Abstract

We introduce a novel quadratic unconstrained binary optimization (QUBO) formulation for a classical problem in electrical engineering -- the optimal reconfiguration of distribution grids. For a given graph representing the grid infrastructure and known nodal loads, the problem consists in finding the spanning tree that minimizes the total link ohmic losses. A set of constraints is initially defined to impose topologically valid solutions. These constraints are then converted to a QUBO model as penalty terms. The electrical losses terms are finally added to the model as the objective function to minimize. In order to maximize the performance of solution searching with classical solvers, with hybrid quantum-classical solvers and with quantum annealers, our QUBO formulation has the goal of being very efficient in terms of variables usage. A standard 33-node test network is used as an illustrative example of our general formulation. Model metrics for this example are presented and discussed. Finally, the optimal solution for this example was obtained and validated through comparison with the optimal solution from an independent method., Comment: 21 pages, 9 figures. v2: Added new sections on model scaling and validation. Minor editorial changes

Published
2021
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15. A lower bound for the spectral gap of the conjugate Kac process with 3 interacting particles

Author

Ferreira, Luís Simão

Subjects
Mathematical Physics
Abstract

In this paper, we proceed as suggested in the final section of arXiv:1812.03874v2 and prove a lower bound for the spectral gap of the conjugate Kac process with 3 interacting particles. This bound turns out to be around $0.02$, which is already physically meaningful, and we perform Monte Carlo simulations to provide a better empirical estimate for this value via entropy production inequalities. This finishes a complete quantitative estimate of the spectral gap of the Kac process.

Published
2021

30. Patterns of oral anticoagulant use and outcomes in Asian patients with atrial fibrillation: a post-hoc analysis from the GLORIA-AF Registry

Author

Abban, Dzifa Wosornu, Abdul, Nasser, Abud, Atilio Marcelo, Adams, Fran, Addala, Srinivas, Adragão, Pedro, Ageno, Walter, Aggarwal, Rajesh, Agosti, Sergio, Agostoni, Piergiuseppe, Aguilar, Francisco, Linares, Julio Aguilar, Aguinaga, Luis, Ahmed, Jameel, Aiello, Allessandro, Ainsworth, Paul, Aiub, Jorge Roberto, Al-Dallow, Raed, Alderson, Lisa, Aldrete Velasco, Jorge Antonio, Alexopoulos, Dimitrios, Manterola, Fernando Alfonso, Aliyar, Pareed, Alonso, David, Alves da Costa, Fernando Augusto, Amado, José, Amara, Walid, Amelot, Mathieu, Amjadi, Nima, Ammirati, Fabrizio, Andrade, Marianna, Andrawis, Nabil, Annoni, Giorgio, Ansalone, Gerardo, Ariani, M.Kevin, Arias, Juan Carlos, Armero, Sébastien, Arora, Chander, Aslam, Muhammad Shakil, Asselman, M., Audouin, Philippe, Augenbraun, Charles, Aydin, S., Ayryanova, Ivaneta, Aziz, Emad, Backes, Luciano Marcelo, Badings, E., Bagni, Ermentina, Baker, Seth H., Bala, Richard, Baldi, Antonio, Bando, Shigenobu, Banerjee, Subhash, Bank, Alan, Esquivias, Gonzalo Barón, Barr, Craig, Bartlett, Maria, Kes, Vanja Basic, Baula, Giovanni, Behrens, Steffen, Bell, Alan, Benedetti, Raffaella, Mazuecos, Juan Benezet, Benhalima, Bouziane, Bergler-Klein, Jutta, Berneau, Jean-Baptiste, Bernstein, Richard A., Berrospi, Percy, Berti, Sergio, Berz, Andrea, Best, Elizabeth, Bettencourt, Paulo, Betzu, Robert, Bhagwat, Ravi, Bhatta, Luna, Biscione, Francesco, Bisignani, Giovanni, Black, Toby, Bloch, Michael J., Bloom, Stephen, Blumberg, Edwin, Bo, Mario, Bøhmer, Ellen, Bollmann, Andreas, Bongiorni, Maria Grazia, Boriani, Giuseppe, Boswijk, D.J., Bott, Jochen, Bottacchi, Edo, Kalan, Marica Bracic, Bradman, Drew, Brautigam, Donald, Breton, Nicolas, Brouwers, P.J.A.M., Browne, Kevin, Cortada, Jordi Bruguera, Bruni, A., Brunschwig, Claude, Buathier, Hervé, Buhl, Aurélie, Bullinga, John, Cabrera, Jose Walter, Caccavo, Alberto, Cai, Shanglang, Caine, Sarah, Calò, Leonardo, Calvi, Valeria, Sánchez, Mauricio Camarillo, Candeias, Rui, Capuano, Vincenzo, Capucci, Alessandro, Caputo, Ronald, Rizo, Tatiana Cárdenas, Cardona, Francisco, Carlos da Costa Darrieux, Francisco, Duarte Vera, Yan Carlos, Carolei, Antonio, Carreño, Susana, Carvalho, Paula, Cary, Susanna, Casu, Gavino, Cavallini, Claudio, Cayla, Guillaume, Celentano, Aldo, Cha, Tae-Joon, Cha, Kwang Soo, Chae, Jei Keon, Chalamidas, Kathrine, Challappa, Krishnan, Chand, Sunil Prakash, Chandrashekar, Harinath, Chartier, Ludovic, Chatterjee, Kausik, Chavez Ayala, Carlos Antero, Cheema, Aamir, Cheema, Amjad, Chen, Lin, Chen, Shih-Ann, Chen, Jyh Hong, Chiang, Fu-Tien, Chiarella, Francesco, Chih-Chan, Lin, Cho, Yong Keun, Choi, Jong-Il, Choi, Dong Ju, Chouinard, Guy, Hoi-Fan Chow, Danny, Chrysos, Dimitrios, Chumakova, Galina, José Roberto, Eduardo Julián, Valenzuela, Chuquiure, Nica, Nicoleta Cindea, Cislowski, David J., Clay, Anthony, Clifford, Piers, Cohen, Andrew, Cohen, Michael, Cohen, Serge, Colivicchi, Furio, Collins, Ronan, Colonna, Paolo, Compton, Steve, Connolly, Derek, Conti, Alberto, Buenostro, Gabriel Contreras, Coodley, Gregg, Cooper, Martin, Coronel, Julian, Corso, Giovanni, Sales, Juan Cosín, Cottin, Yves, Covalesky, John, Cracan, Aurel, Crea, Filippo, Crean, Peter, Crenshaw, James, Cullen, Tina, Darius, Harald, Dary, Patrick, Dascotte, Olivier, Dauber, Ira, Davalos, Vicente, Davies, Ruth, Davis, Gershan, Davy, Jean-Marc, Dayer, Mark, De Biasio, Marzia, De Bonis, Silvana, De Caterina, Raffaele, De Franceschi, Teresiano, de Groot, J.R., De Horta, José, De La Briolle, Axel, Topete, Gilberto de la Pena, Vicenzo de Paola, Angelo Amato, de Souza, Weimar, de Veer, A., De Wolf, Luc, Decoulx, Eric, Deepak, Sasalu, Defaye, Pascal, Del-Carpio Munoz, Freddy, Brkljacic, Diana Delic, Deumite, N. Joseph, Di Legge, Silvia, Diemberger, Igor, Dietz, Denise, Dionísio, Pedro, Dong, Qiang, Rossi dos Santos, Fabio, Dotcheva, Elena, Doukky, Rami, D'Souza, Anthony, Dubrey, Simon, Ducrocq, Xavier, Dupljakov, Dmitry, Duque, Mauricio, Dutta, Dipankar, Duvilla, Nathalie, Duygun, A., Dziewas, Rainer, Eaton, Charles B., Eaves, William, Ebels-Tuinbeek; Clifford Ehrlich, L.A., Eichinger-Hasenauer, Sabine, Eisenberg, Steven J., El Jabali, Adnan, El Shahawy, Mahfouz, Hernandes, Mauro Esteves, Izal, Ana Etxeberria, Evonich, Rudolph, III, Evseeva, Oksana, Ezhov, Andrey, Fahmy, Raed, Fang, Quan, Farsad, Ramin, Fauchier, Laurent, Favale, Stefano, Fayard, Maxime, Fedele, Jose Luis, Fedele, Francesco, Fedorishina, Olga, Fera, Steven R., Gomes Ferreira, Luis Gustavo, Ferreira, Jorge, Ferri, Claudio, Ferrier, Anna, Ferro, Hugo, Finsen, Alexandra, First, Brian, Fischer, Stuart, Fonseca, Catarina, Almeida, Luísa Fonseca, Forman, Steven, Frandsen, Brad, French, William, Friedman, Keith, Friese, Athena, Fruntelata, Ana Gabriela, Fujii, Shigeru, Fumagalli, Stefano, Fundamenski, Marta, Furukawa, Yutaka, Gabelmann, Matthias, Gabra, Nashwa, Gadsbøll, Niels, Galinier, Michel, Gammelgaard, Anders, Ganeshkumar, Priya, Gans, Christopher, Quintana, Antonio Garcia, Gartenlaub, Olivier, Gaspardone, Achille, Genz, Conrad, Georger, Frédéric, Georges, Jean-Louis, Georgeson, Steven, Giedrimas, Evaldas, Gierba, Mariusz, Ortega, Ignacio Gil, Gillespie, Eve, Giniger, Alberto, Giudici, Michael C., Gkotsis, Alexandros, Glotzer, Taya V., Gmehling, Joachim, Gniot, Jacek, Goethals, Peter, Goldbarg, Seth, Goldberg, Ronald, Goldmann, Britta, Golitsyn, Sergey, Gómez, Silvia, Mesa, Juan Gomez, Gonzalez, Vicente Bertomeu, Gonzalez Hermosillo, Jesus Antonio, González López, Víctor Manuel, Gorka, Hervé, Gornick, Charles, Gorog, Diana, Gottipaty, Venkat, Goube, Pascal, Goudevenos, Ioannis, Graham, Brett, Greer, G. Stephen, Gremmler, Uwe, Grena, Paul G., Grond, Martin, Gronda, Edoardo, Grönefeld, Gerian, Gu, Xiang, Torres Torres, Ivett Guadalupe, Guardigli, Gabriele, Guevara, Carolina, Guignier, Alexandre, Gulizia, Michele, Gumbley, Michael, Günther, Albrecht, Ha, Andrew, Hahalis, Georgios, Hakas, Joseph, Hall, Christian, Han, Bing, Han, Seongwook, Hargrove, Joe, Hargroves, David, Harris, Kenneth B., Haruna, Tetsuya, Hayek, Emil, Healey, Jeff, Hearne, Steven, Heffernan, Michael, Heggelund, Geir, Heijmeriks, J.A., Hemels, Maarten, Hendriks, I., Henein, Sam, Her, Sung-Ho, Hermany, Paul, Hernández Del Río, Jorge Eduardo, Higashino, Yorihiko, Hill, Michael, Hisadome, Tetsuo, Hishida, Eiji, Hoffer, Etienne, Hoghton, Matthew, Hong, Kui, Hong, Suk keun, Horbach, Stevie, Horiuchi, Masataka, Hou, Yinglong, Hsing, Jeff, Huang, Chi-Hung, Huckins, David, Hughes, kathy, Huizinga, A., Hulsman, E.L., Hung, Kuo-Chun, Hwang, Gyo-Seung, Ikpoh, Margaret, Imberti, Davide, Ince, Hüseyin, Indolfi, Ciro, Inoue, Shujiro, Irles, Didier, Iseki, Harukazu, Israel, C. Noah, Iteld, Bruce, Iyer, Venkat, Jackson-Voyzey, Ewart, Jaffrani, Naseem, Jäger, Frank, James, Martin, Jang, Sung-Won, Jaramillo, Nicolas, Jarmukli, Nabil, Jeanfreau, Robert J., Jenkins, Ronald D., Sánchez, Carlos Jerjes, Jimenez, Javier, Jobe, Robert, Joen-Jakobsen, Tomas, Jones, Nicholas, Moura Jorge, Jose Carlos, Jouve, Bernard, Jung, Byung Chun, Jung, Kyung Tae, Jung, Werner, Kachkovskiy, Mikhail, Kafkala, Krystallenia, Kalinina, Larisa, Kallmünzer, Bernd, Kamali, Farzan, Kamo, Takehiro, Kampus, Priit, Kashou, Hisham, Kastrup, Andreas, Katsivas, Apostolos, Kaufman, Elizabeth, Kawai, Kazuya, Kawajiri, Kenji, Kazmierski, John F., Keeling, P., Kerr Saraiva, José Francisco, Ketova, Galina, Khaira, Ajit Singh, Khripun, Aleksey, Kim, Doo-Il, Kim, Young Hoon, Kim, Nam Ho, Kim, Dae Kyeong, Kim, Jeong Su, Kim, June Soo, Kim, Ki Seok, Kim, Jin bae, Kinova, Elena, Klein, Alexander, Kmetzo, James J., Kneller, G. Larsen, Knezevic, Aleksandar, Angela Koh, Su Mei, Koide, Shunichi, Kollias, Anastasios, Kooistra, J.A., Koons, Jay, Koschutnik, Martin, Kostis, William J., Kovacic, Dragan, Kowalczyk, Jacek, Koziolova, Natalya, Kraft, Peter, Kragten, Johannes A., Krantz, Mori, Krause, Lars, Krenning, B.J., Krikke, F., Kromhout, Z., Krysiak, Waldemar, Kumar, Priya, Kümler, Thomas, Kuniss, Malte, Kuo, Jen-Yuan, Küppers, Achim, Karla Kurrelmeyer, Kwak, Choong Hwan, Laboulle, Bénédicte, Labovitz, Arthur, Lai, Wen Ter, Lam, Andy, Lam, Yat Yin, Zanetti, Fernando Lanas, Landau, Charles, Landini, Giancarlo, Figueiredo, Estêvão Lanna, Larsen, Torben, Lavandier, Karine, LeBlanc, Jessica, Lee, Moon Hyoung, Lee, Chang-Hoon, Lehman, John, Leitão, Ana, Lellouche, Nicolas, Lelonek, Malgorzata, Lenarczyk, Radoslaw, Lenderink, T., González, Salvador León, Leong-Sit, Peter, Leschke, Matthias, Ley, Nicolas, Li, Zhanquan, Li, Xiaodong, Li, Weihua, Li, Xiaoming, Lichy, Christhoh, Lieber, Ira, Limon Rodriguez, Ramon Horacio, Lin, Hailong, Lip, Gregory Y.H., Liu, Feng, Liu, Hengliang, Esperon, Guillermo Llamas, Navarro, Nassip Llerena, Lo, Eric, Lokshyn, Sergiy, López, Amador, López-Sendón, José Luís, Lorga Filho, Adalberto Menezes, Lorraine, Richard S., Luengas, Carlos Alberto, Luke, Robert, Luo, Ming, Lupovitch, Steven, Lyrer, Philippe, Ma, Changsheng, Ma, Genshan, Madariaga, Irene, Maeno, Koji, Magnin, Dominique, Maid, Gustavo, Mainigi, Sumeet K., Makaritsis, Konstantinos, Malhotra, Rohit, Manning, Rickey, Manolis, Athanasios, Manrique Hurtado, Helard Andres, Mantas, Ioannis, Jattin, Fernando Manzur, Maqueda, Vicky, Marchionni, Niccolo, Ortuno, Francisco Marin, Santana, Antonio Martín, Martinez, Jorge, Maskova, Petra, Hernandez, Norberto Matadamas, Matsuda, Katsuhiro, Maurer, Tillmann, Mauro, Ciro, May, Erik, Mayer, Nolan, McClure, John, McCormack, Terry, McGarity, William, McIntyre, Hugh, McLaurin, Brent, Medina Palomino, Feliz Alvaro, Melandri, Francesco, Meno, Hiroshi, Menzies, Dhananjai, Mercader, Marco, Meyer, Christian, Meyer, Beat J., Miarka, Jacek, Mibach, Frank, Michalski, Dominik, Michel, Patrik, Chreih, Rami Mihail, Mikdadi, Ghiath, Mikus, Milan, Milicic, Davor, Militaru, Constantin, Minaie, Sedi, Minescu, Bogdan, Mintale, Iveta, Mirault, Tristan, Mirro, Michael J., Mistry, Dinesh, Miu, Nicoleta Violeta, Miyamoto, Naomasa, Moccetti, Tiziano, Mohammed, Akber, Nor, Azlisham Mohd, Mollerus, Michael, Molon, Giulio, Mondillo, Sergio, Moniz, Patrícia, Mont, Lluis, Montagud, Vicente, Montaña, Oscar, Monti, Cristina, Moretti, Luciano, Mori, Kiyoo, Moriarty, Andrew, Morka, Jacek, Moschini, Luigi, Moschos, Nikitas, Mügge, Andreas, Mulhearn, Thomas J., Muresan, Carmen, Muriago, Michela, Musial, Wlodzimierz, Musser, Carl W., Musumeci, Francesco, Nageh, Thuraia, Nakagawa, Hidemitsu, Nakamura, Yuichiro, Nakayama, Toru, Nam, Gi-Byoung, Nanna, Michele, Natarajan, Indira, Nayak, Hemal M., Naydenov, Stefan, Nazlić, Jurica, Cristian Nechita, Alexandru, Nechvatal, Libor, Negron, Sandra Adela, Neiman, James, Neuenschwander, Fernando Carvalho, Neves, David, Neykova, Anna, Miguel, Ricardo Nicolás, Nijmeh, George, Nizov, Alexey, Campos, Rodrigo Noronha, Nossan, Janko, Novikova, Tatiana, Nowalany-Kozielska, Ewa, Nsah, Emmanuel, Nunez Fragoso, Juan Carlos, Nurgalieva, Svetlana, Nuyens, Dieter, Nyvad, Ole, Odin de Los Rios Ibarra, Manuel, O'Donnell, Philip, O'Donnell, Martin, Oh, Seil, Oh, Yong Seog, Oh, Dongjin, O'Hara, Gilles, Oikonomou, Kostas, Olivares, Claudia, Oliver, Richard, Ruiz, Rafael Olvera, Olympios, Christoforos, omaszuk-Kazberuk, Anna, Asensi, Joaquín Osca, Jose, eena Padayattil, Padilla Padilla, Francisco Gerardo, Rios, Victoria Padilla, Pajes, Giuseppe, Pandey, Shekhar, Paparella, Gaetano, Paris, F., Park, Hyung Wook, Park, Jong Sung, Parthenakis, Fragkiskos, Passamonti, Enrico, Patel, Rajesh J., Patel, Jaydutt, Patel, Mehool, Patrick, Janice, Jimenez, Ricardo Pavón, Paz, Analía, Pengo, Vittorio, Pentz, William, Pérez, Beatriz, Pérez Ríos, Alma Minerva, Pérez-Cabezas, Alejandro, Perlman, Richard, Persic, Viktor, Perticone, Francesco, Peters, Terri K., Petkar, Sanjiv, Pezo, Luis Felipe, Pflücke, Christian, Pham, David N., Phillips, Roland T., Phlaum, Stephen, Pieters, Denis, Pineau, Julien, Pinter, Arnold, Pinto, Fausto, Pisters, R., Pivac, Nediljko, Pocanic, Darko, Podoleanu, Cristian, Politano, Alessandro, Poljakovic, Zdravka, Pollock, Stewart, Garcéa, Jose Polo, Poppert, Holger, Porcu, Maurizio, Reino, Antonio Pose, Prasad, Neeraj, Précoma, Dalton Bertolim, Prelle, Alessandro, Prodafikas, John, Protasov, Konstantin, Pye, Maurice, Qiu, Zhaohui, Quedillac, Jean-Michel, Raev, Dimitar, Raffo Grado, Carlos Antonio, Rahimi, Sidiqullah, Raisaro, Arturo, Rama, Bhola, Ramos, Ricardo, Ranieri, Maria, Raposo, Nuno, Rashba, Eric, Rauch-Kroehnert, Ursula, Reddy, Ramakota, Renda, Giulia, Reza, Shabbir, Ria, Luigi, Richter, Dimitrios, Rickli, Hans, Rieker, Werner, Vera, Tomas Ripolil, Ritt, Luiz Eduardo, Roberts, Douglas, Briones, Ignacio Rodriguez, Rodriguez Escudero, Aldo Edwin, Pascual, Carlos Rodríguez, Roman, Mark, Romeo, Francesco, Ronner, E., Roux, Jean-Francois, Rozkova, Nadezda, Rubacek, Miroslav, Rubalcava, Frank, Russo, Andrea M., Rutgers, Matthieu Pierre, Rybak, Karin, Said, Samir, Sakamoto, Tamotsu, Salacata, Abraham, Salem, Adrien, Bodes, Rafael Salguero, Saltzman, Marco A., Salvioni, Alessandro, Vallejo, Gregorio Sanchez, Fernández, Marcelo Sanmartín, Saporito, Wladmir Faustino, Sarikonda, Kesari, Sasaoka, Taishi, Sati, Hamdi, Savelieva, Irina, Scala, Pierre-Jean, Schellinger, Peter, Scherr, Carlos, Schmitz, Lisa, Schmitz, Karl-Heinz, Schmitz, Bettina, Schnabel, Teresa, Schnupp, Steffen, Schoeniger, Peter, Schön, Norbert, Schwimmbeck, Peter, Seamark, Clare, Searles, Greg, Seidl, Karl-Heinz, Seidman, Barry, Sek, Jaroslaw, Sekaran, Lakshmanan, Serrati, Carlo, Shah, Neerav, Shah, Vinay, Shah, Anil, Shah, Shujahat, Sharma, Vijay Kumar, Shaw, Louise, Sheikh, Khalid H., Shimizu, Naruhito, Shimomura, Hideki, 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Emmanuel, Trendafilova, Elina, Tsai, W. Kevin, Tse, Hung Fat, Tsutsui, Hiroshi, Tu, Tian Ming, Tuininga, Ype, Turakhia, Minang, Turk, Samir, Turner, Wayne, Tveit, Arnljot, Tytus, Richard, Valadão, C., van Bergen, P.F.M.M., van de Borne, Philippe, van den Berg, B.J., van der Zwaan, C., Van Eck, M., Vanacker, Peter, Vasilev, Dimo, Vasilikos, Vasileios, Vasilyev, Maxim, Veerareddy, Srikar, Miño; Asok Venkataraman, Mario Vega, Verdecchia, Paolo, Versaci, Francesco, Vester, Ernst Günter, Vial, Hubert, Victory, Jason, Villamil, Alejandro, Vincent, Marc, Vlastaris, Anthony, Dahl, Jürgen vom, Vora, Kishor, Vranian, Robert B., Wakefield, Paul, Wang, Ningfu, Wang, Mingsheng, Wang, Xinhua, Wang, Feng, Wang, Tian, Warner, Alberta L., Watanabe, Kouki, Wei, Jeanne, Weimar, Christian, Weiner, Stanislav, Weinrich, Renate, Wen, Ming-Shien, Wiemer, Marcus, Wiggers, Preben, Wilke, Andreas, Williams, David, Williams, Marcus L., Witzenbichler, Bernhard, Wong, Brian, Lawrence Wong, Ka Sing, Wozakowska-Kaplon, Beata, Wu, Shulin, Wu, Richard C., Wunderlich, Silke, Wyatt, Nell, Wylie, John (Jack), Xu, Yong, Xu, Xiangdong, Yamanoue, Hiroki, Yamashita, Takeshi, Bryan Yan, Ping Yen, Yang, Tianlun, Yao, Jing, Yeh, Kuo-Ho, Yin, Wei Hsian, Yotov, Yoto, Zahn, Ralf, Zarich, Stuart, Zenin, Sergei, Zeuthen, Elisabeth Louise, Zhang, Huanyi, Zhang, Donghui, Zhang, Xingwei, Zhang, Ping, Zhang, Jun, Zhao, Shui Ping, Zhao, Yujie, Zhao, Zhichen, Zheng, Yang, Zhou, Jing, Zimmermann, Sergio, Zini, Andrea, Zizzo, Steven, Zong, Wenxia, Zukerman, L. Steven, Romiti, Giulio Francesco, Corica, Bernadette, Proietti, Marco, Mei, Davide Antonio, Frydenlund, Juliane, Bisson, Arnaud, Olshansky, Brian, Chan, Yi-Hsin, Huisman, Menno V., and Chao, Tze-Fan

Published
2023
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32. Optimising maintenance: What are the expectations for Cyber Physical Systems

Author

Jantunen, Erkki, Zurutuza, Urko, Ferreira, Luis Lino, and Varga, Pal

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Systems and Control
Abstract

The need for maintenance is based on the wear of components of machinery. If this need can be defined reliably beforehand so that no unpredicted failures take place then the maintenance actions can be carried out economically with mini-mum disturbances to production. There are two basic challenges in solving the above. First understanding the development of wear and failures, and second managing the measurement and diagnosis of such parameters that can reveal the development of wear. In principle the development of wear and failures can be predicted through monitoring time, load or wear as such. Moni-toring time is not very efficient, as there are only limited numbers of components that suffer from aging which as such is the result of chemical wear i.e. changes in the material. In most cases the loading of components influences their wear. In principle the loading can be stable or varying in nature. Of these two cases the varying load case is much more challenging than the stable one. The monitoring of wear can be done either directly e.g. optical methods or indirectly e.g. vibration. Monitoring actual wear is naturally the most reliable approach, but it often means that additional investments are needed. The paper discusses how the monitoring of wear and need for maintenance can be done based on the use of Cyber Physical Systems., Comment: 2016 3rd International Workshop on Emerging Ideas and Trends in Engineering of Cyber-Physical Systems (EITEC)

Published
2019
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39. Development of a Residual Biomass Supply Chain Simulation Model Using AnyLogistix: A Methodical Approach.

Author

Chidozie, Bernardine, Ramos, Ana, Vasconcelos, José, and Ferreira, Luis Pinto

Subjects
RENEWABLE energy sources, RENEWABLE natural resources, GEOGRAPHIC information systems, ENERGY consumption, BIOMASS energy, SUPPLY chain management
Abstract

Background: In the pursuit of sustainable energy sources, residual biomass has emerged as a promising renewable resource. However, efficiently managing residual biomass poses significant challenges, particularly in optimizing supply chain operations. Advanced modeling approaches are necessary to address these complexities. This study aims to develop a comprehensive methodological framework for creating simulation models tailored to agroforestry residual biomass supply chains. Methods: The study employs a hybrid simulation approach, integrating geographic information system mapping with a case study analysis. The simulation was conducted over a 365-day period, using the anyLogistix software (version 2.15.3.202209061204) to model various supply chain dynamics. The framework also accounts for financial, operational, customer satisfaction, and environmental metrics. Results: The simulation results showed a total expenditure of EUR 5,219,411.3, with transportation being the primary cost driver, involving 5678 trips and a peak capacity of 67.16 m3. CO2 emissions were measured at 487.7 kg/m3. The model performed as expected, highlighting the need for sustainable logistics strategies to reduce costs, lower losses, and improve productivity. Conclusions: This study presents one of the first detailed methodological frameworks for simulating agroforestry residual biomass supply chains. It provides valuable managerial insights into the financial, operational, and environmental aspects of supply chain management. The findings may stakeholders make informed decisions to enhance the sustainability of biomass utilization in energy production. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Highlighting Sustainability Criteria in Residual Biomass Supply Chains: A Dynamic Simulation Approach.

Author

Chidozie, Bernardine, Ramos, Ana, Vasconcelos, José, Ferreira, Luis Pinto, and Gomes, Reinaldo

Abstract

As environmental sustainability gains importance, enhancing supply chains to minimize environmental hazards is essential, particularly in industries using residual biomass. This study tackles this by investigating the integration of sustainability criteria into supply chain optimization for a biomass energy company in Portugal, using a combination of simulation modeling through anyLogistix software (version: 2.15.3.202209061204) and multi-criteria decision-making. Four supply chain scenarios were designed and simulated, differing in their number of distribution centers, the adoption of green logistics, and split-by-ratio distribution strategies over a 305-day period. Through the weighted sum model, Scenario C emerged as the optimal configuration, achieving a balance between operational efficiency and sustainability by reducing CO2 emissions by up to 90% and lowering transportation costs without compromising revenue. Sensitivity analysis further highlighted the trade-offs between cost efficiency, lead times, and environmental impact, showing that the strategic placement of distribution centers and the use of eco-friendly vehicles significantly improve the sustainability of the biomass supply chain. These findings provide practical insights for decision-makers, demonstrating how digital modeling tools can enhance supply chain management by optimizing environmental and operational goals simultaneously. This research contributes to the fields of sustainable logistics and supply chain management by validating the effectiveness of green logistics strategies and multi-criteria decision-making approaches in reducing environmental impact while maintaining economic viability. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Treatment of Mid-Face Aging with Calcium Hydroxylapatite: Focus on Retaining Ligament Support.

Author

Sato, Mauricio, Muniz, Mariana, and Ferreira, Luis Ricardo Coelho

Subjects
FACIAL bones, DERMAL fillers, HYALURONIC acid, HYDROXYAPATITE, LIGAMENTS
Abstract

The mid-face has been rendered as the cornerstone of facial aesthetic improvement, since treating the mid-face has also beneficial effects on neighboring esthetic units and therefore should be one of the first areas to be assessed. Retaining ligaments (RL) bind soft tissue layers of the face to the underlying facial skeleton. It remains controversial whether RL suffers laxity with aging or if changes in 42 bone and other structures where ligaments are inserted lead to altered mechanical function of the latter. Enhancement of the supportive effect of the ligaments could help restore the soft tissues to their original anatomical positions and achieve a lifting effect. While injectable hyaluronic acid implants have been used to improve ligament support, calcium hydroxylapatite has one of the highest viscoelasticities when compared with other dermal fillers and can induce collagen synthesis, which could provide long-term laxity correction. In this preliminary report of a case series, our goal was to describe the use of calcium hydroxylapatite in different dilutions and combinations for different aging profiles to treat the midface as an alternative for hyaluronic acid injections and to describe the ultrasonographic behavior in the long run. [ABSTRACT FROM AUTHOR]

Published
2024
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