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Results From a Large-Scale IoT-based Intervention for Energy Saving and Sustainability Awareness via Behavior Change in 25 K-12 Schools in Europe

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IEEE Internet of Things Journal 2025年第12期12卷 19308-19325页

作者： Mylonas, Georgios Amaxilatis, Dimitrios Paganelli, Federica Chatzigiannakis, Ioannis Koulouris, Pavlos Falkouskaya, Yelizaveta Markopoulos, Panos Athena Research Center Industrial Systems Institute Patras26504 Greece Comp. Tech. Institute and Press "Diophantus" 26504 Greece Spark Works Ltd GalwayH91 AHR1 Ireland Univ. of Pisa Dpt. of Computer Science Pisa56127 Italy CNIT Florence50134 Italy Sapienza University of Rome Dpt. of Computer Control and Management Engineering 00185 Italy Ellinogermaniki Agogi Athens Greece Eindhoven University of Technology AI Systems Institute Industrial Design Noord-Brabant Eindhoven5612 AE Netherlands

The unfolding climate crisis has resulted in a rising interest for increasing sustainability awareness and achieving energy savings worldwide. Several interventions within educational environments have been aimed at mobilizing younger audiences towards such goals. However, most of the interventions carried out so far are based on a subset of possible tools (e.g., IoT monitoring, gamification) and offer "all or nothing"approaches, which do not cope well with the uniqueness of schools and the need to adapt interventions to specific contexts (e.g. school size, building, curriculum, teacher involvement). We present a large-scale intervention in 25 school buildings in Europe over the course of two school years. Our intervention was based on the installation of a relatively low-cost IoT infrastructure in schools, that produced real-time energy-related data from the school buildings involved, and the application of a strategy combining, in a flexible and customizable way, educational activities, monitoring tools, gamification, and competition to motivate behavior change and achieve energy savings. Overall, 2983 students and 204 educators were directly involved to a variable degree of participation. Our results indicate a positive overall result in terms of short-term energy savings in most schools involved, with an average reduction of 20-25% in the energy that could be affected by end-users, as well as a 32.9% increase after the intervention in students self-reporting good or very good sustainability awareness. These findings suggest that such interventions can be a valuable step towards educating young students regarding sustainability and towards sustainable schools, if designed and implemented properly. © 2014 IEEE.

关键词： Sustainable development Buildings Energy conservation Europe Meteorology Internet of Things Energy consumption Real-time systems Energy efficiency Psychology

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When Circular Economy Meets the Smart City Ecosystem: Defining the Smart and Circular City

When Circular Economy Meets the Smart City Ecosystem: Defini...

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IEEE International Smart Cities Conference (ISC2)

作者： Georgios Mylonas Athanasios Kalogeras Sobah Abbas Petersen Luis Muñoz Ioannis Chatzigiannakis Industrial Systems Institute Athena Research Center Patras Greece Dpt. of Computer Science Norwegian University of Science and Technology Trondheim Norway Dpt. of Communications Engineering Universidad de Cantabria Santander Spain Dpt. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

Smart cities have been a very active research area in the past 20 years, while continuously adapting to new technological advancements and keeping up with the times regarding sustainability and climate change. In this context, there have been numerous proposals to expand the scope of smart cities, focusing on resilience and sustainability, among other aspects, resulting in terms like smart sustainable cities. At the same time, there is an ongoing discussion regarding the degree in which smart cities put people at their centre. In this work, we argue toward expanding the current smart city definition by integrating the circular economy as one of its central pillars and adopting the term smart (and) circular city. We discuss the ways a smart and circular city encompasses both sustainability and smartness in an integral manner, while also being well-positioned to foster novel business activity and models and helping to place citizens at the heart of the smart city. In this sense, we also argue that previous research in smart cities and technologies, such as those related to Industry 4.0, can serve as a cornerstone to implement circular economy activities within cities, at a scale that exceeds current activities that are based on more conventional approaches. We also outline current open challenges in this domain and research questions that still need to be addressed.

关键词： Heart Climate change Smart cities Ecosystems Focusing Fourth Industrial Revolution Proposals Sustainable development Resilience Business

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When Circular Economy Meets the Smart City Ecosystem: Defining the Smart and Circular City

arXiv

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arXiv 2024年

作者： Mylonas, Georgios Kalogeras, Athanasios Petersen, Sobah Abbas Muñoz, Luis Chatzigiannakis, Ioannis Industrial Systems Institute Athena Research Center Patras Greece Dpt. of Computer Science Norwegian University of Science and Technology Trondheim Norway Dpt. of Communications Engineering Universidad de Cantabria Santander Spain Dpt. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

关键词： Circular economy

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DPPL Hallway Tracker: Hospital Contact Tracing During the COVID-19 Pandemic 9

DPPL Hallway Tracker: Hospital Contact Tracing During the CO...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Marinoni, Christian Ponzi, Valerio Comminiello, Danilo Dpt. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 Roma00184 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

During the COVID-19 pandemic, the use of a people tracking system could have been crucial, particularly in sensitive environments, such as hospitals. DPPL Hallway Tracker is a framework that uses security camera footage to determine which rooms in a corridor a person has entered. It generates a database containing all the people identified and allows quick identification of potential cases of infection based on the time spent in a room and its maximum capacity. DPPL Hallway Tracker is structured in two phases: detection and re-identification. In the first phase, it exploits Mask RCNN to identify people and room doors. In the second one, it uses the deep association metric model from DeepSORT to re-identify a person as he leaves a room. © 2023 Copyright for this paper by its authors.

关键词： COVID-19

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Analysis and Detection of Melanoma through Collective Intelligence with AI

Analysis and Detection of Melanoma through Collective Intell...

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Annual IEEE Symposium on computer-Based Medical systems

作者： Enrique Fernández-Morales Carlos L. Sanchez-Bocanegra Rafael Pastor-Vargas José-Juan Pereyra-Rodriguez Juan M. Haut José Alberto Benítez-Andrades Communications and Control Systems Dpt. Computer Science Faculty UNED Madrid Spain Communications and Control Systems Dpt. Computer Science Faculty UNED Malaga Spain Virgen Del Rocio Hospital Universitario Seville Spain School of Tecnology University of Extremadura Cáceres Spain Dept. of Electric Systems and Automatics Engineering Universidad de León León Spain

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.

关键词： Accuracy Melanoma Collective intelligence Image capture Transformers Solids Artificial intelligence

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Ageing does not affect the dynamic balance margins of gait

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Gait & Posture 2021年 90卷 248-249页

作者： Sloot, L. Millard, M. Mombaur, K. Hallemans, A. van Criekinge, T. Heidelberg University Institute of Computer Engineering ZITI Heidelberg Germany University of Waterloo Dpt of Systems Design Engineering & Dpt of Mechanical and Mechatronics Engineering Waterloo Canada University of Antwerp Dpt of Rehabilitation Sciences and Physiotherapy- Faculty of Medicine and Health Science Antwerp Belgium KU Leuven Dpt of Rehabilitation Sciences and Physiotherapy Brugge Belgium

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Recognition of food-texture attributes using an in-ear microphone

arXiv

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arXiv 2021年

作者： Mars, Monica Diou, Christos Papapanagiotou, Vasileios van den Boer, Janet Delopoulos, Anastasios Multimedia Understanding Group Dpt. Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Greece Department of Informatics and Telematics Harokopio University of Athens Greece Dpt. of Biomedical Signals and Systems Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Division of Human Nutrition and Health Wageningen University Netherlands

Food texture is a complex property;various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness. Copyright © 2021, The Authors. All rights reserved.

关键词： Wearable technology

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Robust Voltage Control in DC Networks with Uncertain Nonlinear Load Dynamics

Robust Voltage Control in DC Networks with Uncertain Nonline...

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IEEE Conference on Decision and Control

作者： Amirreza Silani Giacomo Casadei Michele Cucuzzella Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen AG Groningen The Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Laboratoire Ampere Dpt. EEA of the Ecole Centrale de Lyon Université de Lyon Ecully France Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this letter, we propose a control scheme for regulating the voltage in Direct Current (DC) power networks. In contrast with other works in the literature where the loads are assumed to be constant, we consider uncertain time-varying loads described as the outputs of nonlinear dynamical exosystems with uncertain parameters. Based on the output regulation methodology, the proposed control scheme ensures the robust stability of the overall network and achieves voltage regulation in presence of impedance (Z), current (I), and power (P) loads. The simulation results illustrate excellent performance of the proposed control scheme.

关键词： Robust stability Simulation Regulation Impedance Voltage control

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Fake news detection in Spanish using deep learning techniques

arXiv

引用

arXiv 2021年

作者： Martínez-Gallego, Kevin Álvarez-Ortiz, Andrés M. Arias-Londoño, Julián D. Intelligent Information Systems Lab Universidad de Antioquia Calle 67 No. 53 - 108 Medellín050010 Colombia Intelligent Information Systems Lab Dpt. of Systems Engineering and Computer Science Universidad de Antioquia Calle 67 No. 53 - 108 Medellín050010 Colombia

This paper addresses the problem of fake news detection in Spanish using Machine Learning techniques. It is fundamentally the same problem tackled for the English language;however, there is not a significant amount of publicly available and adequately labeled fake news in Spanish to effectively train a Machine Learning model, similarly to those proposed for the English language. Therefore, this work explores different training strategies and architectures to establish a baseline for further research in this area. Four datasets were used, two in English and two in Spanish, and four experimental schemes were tested, including a baseline with classical Machine Learning models, trained and validated using a small dataset in Spanish. The remaining schemes include state-of-the-art Deep Learning models trained (or fine-tuned) and validated in English, trained and validated in Spanish, and fitted in English and validated with automatic translated Spanish sentences. The Deep Learning architectures were built on top of different pre-trained Word Embedding representations, including GloVe, ELMo, BERT, and BETO (a BERT version trained on a large corpus in Spanish). According to the results, the best strategy was a combination of a pre-trained BETO model and a Recurrent Neural Network based on LSTM layers, yielding an accuracy of up to 80%;nonetheless, a baseline model using a Random Forest estimator obtained similar outcomes. Additionally, the translation strategy did not yield acceptable results because of the propagation error;there was also observed a significant difference in models performance when trained in English or Spanish, mainly attributable to the number of samples available for each language. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Miniaturized multi-band stopband filter using circular split ring resonator and null gap separations between all parallel lines

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Progress In Electromagnetics Research C 2020年 106卷 137-150页

作者： Ahmed, Badiaa Ait Aghzout, Otman Naghar, Azzeddin Alejos, Ana V. Dpt. of Computer Science Engineering SIGL-Lab. ENSA University of Abdelmalek Essaadi Tétouan Morocco BSA-Innovation LAB Radio Frequency Systems RFS Lannion France Dpt. of Signal Theory and communications University of Vigo Pontevedra Spain

This paper presents a new multi-band stopband filter loaded by a shorted metamaterial circuit. Firstly, two filters loaded by stubs and open ring resonators (ORRs) are studied and compared. The ORRs allow more effects in terms of miniaturization by a shifting toward low frequencies and rejection bandwidth (57.34%). To improve the filter efficiency, coupled split ring resonators (SRRs) are used. The final filter is characterized by a miniaturized size of 18.840 mm2, wide rejection bandwidth, high selectivity level, and multiple resonances over S, C, X, and Ku bands. L-C equivalent circuit model filter and other characteristics are investigated. A prototype of the filter with coupled SRRs has been fabricated and measured. Good matching among EM-simulation, equivalent circuit modelling, and measured results are achieved. © 2020, Electromagnetics Academy. All rights reserved.

关键词： Circuit simulation

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