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Advances in 3D fusion of multimodal medical images: 3D reconstruction of bone, muscle, and ligament structures under load from radiographs and magnetic resonance imaging

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Research on Biomedical engineering 2025年第1期41卷 1-35页

作者： da Silva, Daniel S. Astolfi, Rodrigo S. Jagatheesaperumal, Senthil K. Selvaraj, Priyadharsini Russel, Newlin Shebiah de Albuquerque, Victor Hugo C. Post-Graduation Program of Medical Surgical Sciences Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Teleinformatics Engineering Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Electronics and Communication Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India Department of Computer Science and Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India

Purpose: Foot and ankle pathologies are among the most prevalent conditions within the human locomotor system. Imaging examinations are crucial for diagnosing, treating, and achieving satisfactory functional outcomes for these conditions. However, conventional imaging exams often fail to capture all the necessary details for precise analysis, necessitating the use of multiple imaging modalities to evaluate bones, ligaments, cartilage, and other structures. Objective: This research aims to explore computational technologies and algorithms for 3D reconstruction and fusion of foot and ankle images, alongside the analysis, segmentation, classification, and evaluation techniques applied to these images. Methods: The study investigates different computational approaches for 3D reconstruction and multimodal image fusion, integrating X-ray and MRI data. Advanced segmentation and classification techniques are applied to enhance the precision of structural assessment. Additionally, we assess the availability and quality of databases for training and validating these models. Results: Our findings highlight a significant gap in research concerning these specific applications, as well as a shortage of suitable databases for 3D reconstruction and fusion of images from X-rays and MRI. Conclusion: The study emphasizes the need for dedicated datasets and further research to develop effective computational solutions for multimodal foot and ankle imaging. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Magnetic resonance imaging

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COLLABORATIVE LEARNING IN STEAM TEACHING FOR STUDENTS IN THE CENTRAL AMAZON 21

COLLABORATIVE LEARNING IN STEAM TEACHING FOR STUDENTS IN THE...

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21st International Conference on e-Society, ES 2023 and 19th International Conference on Mobile Learning, ML 2023

作者： Rodrigues, Juliana Gisele Corrêa de Medeiros Silva, Adan Sady da Silva Junior, Jucimar Maia Post-graduation program in Biodiversity and Biotechnology from BIONORTE Amazonas State University Brazil Dept. of Meteorology Amazonas State University Brazil Dept. of Computer Engineering Amazonas State University Brazil

ISBN: (纸本)9789898704474

The State of Amazonas has one of the most modern industrial and technological centers in Latin America. As a result, the search for skilled professionals for this job market is constantly growing. It is believed that STEAM teaching is an important tool to improve engagement, creativity, innovation, problem-solving skills, and other cognitive benefits generating more skilled professionals in the technology market. One approach that can be applied to STEAM teaching in schools is collaborative learning through maker culture. From mobile labs the STEM Academy Project takes STEAM teaching to high school students in public schools in the state of Amazonas. In the courses taught, collaborative learning is also applied in order for students to understand topics such as basic electronics, robotics, and programming, thus stimulating students' social skills and shared mutual learning, and attracting them to degree courses in STEAM fields. © 2023 Proceedings of the International Conferences on e-Society 2023, ES 2023 and Mobile Learning 2023, ML 2023. All rights reserved.

关键词： Students

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Mitigation of Hallucinations in Language Models in Education: A New Approach of Comparative and Cross-Verification

Mitigation of Hallucinations in Language Models in Education...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Wildemarkes de Almeida da Silva Luis Carlos Costa Fonseca Sofiane Labidi José Chrystian Lima Pacheco Post-Graduation Program in Computer and Systems Engineering State University of Maranhão - UEMA São Luís Brazil Bachelor of Science and Technology Program -BICT Federal University of Maranhão - UFMA São Luís Brazil Graduation in Computer and Systems Engineering State University of Maranhão - UEMA São Luís Brazil

ISBN: (数字)9798350362053

ISBN: (纸本)9798350362060

The rapid growing application of language models (LLMs) in education offers exciting prospects for personalized learning and interactive experiences. However, a critical challenge emerges - the risk of "hallucinations," where LLMs generate factually incorrect or misleading information. This paper proposes Comparative and Cross-Verification Prompting (CCVP), a novel technique specifically designed to mitigate hallucinations in educational LLMs. CCVP leverages the strengths of multiple LLMs, a Principal Language Model (PLM) and Auxiliary Language Models (ALMs), to verify the accuracy and educational relevance of the PLM's response to a prompt. Through a series of prompts and assessments, CCVP harnesses the diverse perspectives of various LLMs and incorporates human expertise for intricate cases. This method addresses the limitations of relying on a single model and fosters critical thinking skills in learners within the educational context. We detail the CCVP approach with examples specifically applicable to educational settings, such as geography. We also discuss its strengths and limitations, including computational cost, data reliance, and ethical considerations. We highlight its potential applications in educational disciplines, including fact-checking content, detecting bias, and promoting responsible LLM use. CCVP presents a promising avenue for ensuring the accuracy and trustworthiness of LLM-generated educational content. Further research and development will refine its scalability, address potential biases, and solidify its position as a vital tool for harnessing the power of LLMs while fostering responsible knowledge dissemination in education.

关键词： Geography Solid modeling Accuracy Scalability Prevention and mitigation Computational modeling Education

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Analyses of Photovoltaic systems Performance with Fuzzy Inference systems and Bayesian Networks

Analyses of Photovoltaic Systems Performance with Fuzzy Infe...

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Power Electronics and Control (SEPOC), Seminar on

作者： Nicole Polityto Cremasco Myriam Regattieri De Biase da Silva Delgado Jair Urbanetz Junior Luiz Ledo Mota Melo Jorge Assade Leludak Keiko Verônica Ono Fonseca Program of Post-Graduation of Electrical Engineering and Industrial Informatics(CPGEI) of Federal University of Technology of Paraná(UTFPR) Program of Post-Graduation of Electrical Engineering and Industrial Informatics(CPGEI) of the Federal University of Technology of Paraná(UTFPR) Program of Post-Graduation in Energy Systems of Federal University of Technology of Paraná(UTFPR Department of Electrical Engineering (DAELT)) Federal University of Technology of Paraná(UTFPR) Curitiba Brazil

Renewable energy sources have been increasingly targeted as an alternative for reducing fossil fuel emissions. This scenario requires a better understanding of energy systems' performance and limitations, notably using photovoltaic setups under diverse meteorological conditions and determining device measurement uncertainties. Accordingly, this study conducts a performance evaluation of a photovoltaic system of copper, indium, and gallium (di)selenide technology. It compares the results provided by a reference normalized equation with two interpretable artificial intelligence techniques, Fuzzy Inference System and Bayesian Networks, when temperature is also considered. In the experiments, the study found significant percentage differences between the normalization procedure and the fuzzy system on days with lower irradiation values. On the other hand, the results provided by Bayesian networks presented lower difference percentages in relation to the normalization equation values.

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CVO: Curriculum Vitae Optimization by Recommending Keywords to Undergraduate Students 11th

CVO: Curriculum Vitae Optimization by Recommending Keywords...

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11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022

作者： Santos, Cibele Góes, Fabrício Martins, Carlos da Cunha, Felipe Post-Graduation Program in Electrical Engineering Pontifícia Universidade Católica de Minas Gerais MG Belo Horizonte30535-901 Brazil Informatics Department University of Leicester Leicester United Kingdom Post-Graduation Program in Informatics Pontifícia Universidade Católica de Minas Gerais MG Belo Horizonte30535-901 Brazil Department of Computer Science Pontifícia Universidade Católica de Minas Gerais MG Belo Horizonte30535-901 Brazil

ISBN: (纸本)9783031336133

Candidate selection platforms have been widely used in companies that seek agility in the process of hiring. Candidates who do not meet the requirements of a job vacancy are disqualified in the first step, called screening. This stage has been automated due to the large volume of curriculum vitae (CV) of candidates per vacancy, particularly for internship vacancies. As a consequence, candidates receive little to none feedback and do not know how to improve/optimize their CVs for new applications. The goal of this paper is to realize the curriculum vitae optimization (CVO) process for internship vacancies by implementing a recommendation system that given an undergraduate student CV, it suggests the addition of relevant keywords, taking into account the student’s undergraduate course. This system is implemented based on the clustering of CVs keywords, from an internship recruitment private company database, into profile groups which are linked to internship vacancies. The experimental results showed that recommendations improved students CVs similarity (competitiveness within a specific field) from 18.83%, with 3 keywords recommendation, up to 50.67%, with 10 words. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Students

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The role of online platforms to enable the process of industrial symbiosis: An analysis of tools available in the market

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Cleaner Production Letters 2022年 3卷

作者： Silva, Maicon Gonçalves Carvalho, Tais Soares de Castagna, Annemarlen Gehrke Strauhs, Faimara do Rocio Piekarski, Cassiano Moro Federal Technology University of Paraná Post-Graduation Program in Urban Environmental Sustainability (PPGSAU) Laboratory of Studies in Sustainable Productive Systems (LESP) Brazil Federal Technology University of Paraná Post-Graduation Program in Production Engineering (PPGEP) Laboratory of Studies in Sustainable Productive Systems (LESP) Brazil Federal Technology University of Paraná Post-Graduation Program in Urban Environmental Sustainability (PPGSAU) Brazil

The Circular Economy is driven by sustainable strategies of management and the reuse of materials and power to try to decrease the demand of resources and minimize the environmental impacts. Aligned to said perspective, the Industrial Symbiosis (IS) is opposite to the linear production's logic, enabling the sharing of resources, waste, and by-products, that is, the circular flow of power, water, materials, and information. In the last few years, to improve these IS strategies, platforms and tools were created to intermediate this process and facilitate the match between those who demand and offer waste and resources, such as the online waste markets or e-marketplaces. From this perspective, the objective of this study is to evaluate how these tools contributed to the IS process, going beyond the problem of the local factor. For this purpose, ten online platforms, found in the literature, were selected. The analysis was made based on the identification of key elements, which enabled the creation of a list with different types of traded materials, sectors and areas served, how wide was the service, the year when the activities started and, ultimately, the business model adopted. As a result, the evaluation shows that these tools enhance the symbiosis process because they approach the parties and facilitate the identification of synergies. Generally, the use of online platforms widens the geographic scope of the IS processes, enabling the interaction of parties from different places and regions. Was observed that these tools are characterized by the use of intelligent systems, based on algorithms that identify and recommend relations, providing greater assertiveness and speed to the decision-making process. Among the research's findings, we must stress that the IS potential extends much beyond the reuse of waste. There are opportunities for other synergies, such as infrastructure and service sharing. Finally, it was highlighted that, although they present many benefits

关键词： Circular economy Industrial symbiosis Online platforms Reuse of resources Sustainable strategies

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The Arquive of Tatuoca Magnetic Observatory Brazil: from paper to intelligent bytes

The Arquive of Tatuoca Magnetic Observatory Brazil: from pap...

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IEEE International Conference on Big Data

作者： Cristian Berrío-Zapata Ester Ferreira da Silva Mayara Costa Pinheiro Vinicius Augusto Carvalho de Abreu Cristiano Mendel Martins Mario Augusto Góngora Kelso Dunman Faculty of Archival Science Information Science Post Graduation Program (PPGCI) Universidade Federal do Pará (UFPA) Belém Brazil Archives and Libraries in the Digital Environment Universidad Carlos III (UC3M) Madrid Spain Post Graduation Program (PPGCI) Universidade Federal do Pará (UFPA) Belém Brazil Faculty of Computer Science Information Science Post graduation Program (PPGCI) Universidade Federal do Pará (UFPA) Belém Brazil Faculty of Geology Geoscience Post graduation Program (PPGGF) Universidade Federal do Pará (UFPA) Belém Brazil Faculty of Computing Engineering and Media De Montfort University Leicester UK School of Information MAS/LIS program University of British Columbia Canada

ISBN: (纸本)9781665480468

The Magnetic Observatory of Tatuoca (TTB) was installed by Observatório Nacional (ON) in 1957, near Belém city in the state of Pará, Brazilian Amazon. Its history goes back to 1933, when a Danish mission used this location to collect data, due to its privileged position near the terrestrial equator. Between 1957 and 2007, TTB produced 18,000 magnetograms on paper using photographic variometers, and other associated documents like absolute value forms and yearbooks. Data was obtained manually from these graphs with rulers and grids, taking 24 average readings per day, that is, one per hour. In 2017, the Federal University of Pará (UFPA in the Portuguese acronym) and ON collaborated to rescue this physical archive. In 2022 UFPA took a step forward and proposed not only digitizing the documents but also developing an intelligent agent capable of reading and extracting the information of the curves with a resolution better than an hour, being this the central goal of the project. If the project succeeds, it will rescue 50 years of data imprisoned in paper, increasing measurement sensitivity far beyond what these sources used to give. This will also open the possibility of applying the same AI to similar documents in other observatories or disciplines like seismography. This article recaps the project, and the complex challenges faced in articulating Archival Science principles with AI and Geoscience.

关键词： Deep learning Observatories Magnetic resonance imaging Urban areas Collaboration Big Data Data mining

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Anomaly detection in the registry of the secondary energy distribution network 31

Anomaly detection in the registry of the secondary energy di...

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31st International Conference on Software engineering and Knowledge engineering, SEKE 2019

作者： Fonsêca, Carlos Maciel, Alexandre Post-Graduation Program in Computer Engineering University of Pernambuco Recife Brazil

ISBN: (纸本)1891706489

The paper aims to create an intelligent model of data analysis in the registry of the secondary energy distribution network. With emphasis on the search for possible inconsistencies that can be only cadastral or really physical. For this, it uses some techniques of data mining giving focus for the detection of anomalies. This study used a private database containing information about the assets that make up the secondary energy distribution network, such as: poles, transformers, disconnectors, among others. The research was developed following all steps presented in the CRISP-DM methodology. To detect the anomalies, it was used algorithms Isolation Forest, DBSCAN and BIRCH. As a result, the three algorithms pointed to a set of specialty anomalies, validated by a specialist, however, Isolation forest was more accurate in the inference of the anomalies. From this study, distribution companies will be able to identify risky or financially problematic situations in advance. © 2019 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Data mining

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Big data in healthcare:Conceptual network structure,key challenges and opportunities

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Digital Communications and Networks 2023年第4期9卷 856-868页

作者： Leonardo B.Furstenaua Pedro Leivas Michele Kremer Sott Michael S.Dohan Jose Ricardo Lopez-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Post-Graduate Program in Industrial Engineering Federal University of Rio Grande Do SulPorto Alegre90035-190Brazil University of Santa Cruz Do Sul Av.Independencia 2293Santa Cruz Do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Faculty of Business Administration Lakehead UniversityThunder BayOntarioCanada Autonomous University of Zacatecas Av.Ramon Lopez Velarde Col.CentroZacatecas98000Mexico Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain York University ON M3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.

关键词： Big data Healthcare digitalization Bibliometric Strategic intelligence Co-word analysis SciMAT

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Influence of Phase Transformation Coefficient on Thermomechanical Modeling of Laser Powder Bed Fusion for Maraging 300 Steel

SSRN

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SSRN 2024年

作者： dos Santos Silva, Bruno Caetano de Figueiredo Soares, Lucas Coelho, Rodrigo Santiago Sitek, Wojciech Król, Mariusz Batalha, Gilmar Ferreira University Center SENAI CIMATEC SENAI Innovation Institute for Forming and Joining of Materials Av. Orlando Gomes 1845 Piata BA Salvador41650-010 Brazil Department of Mechatronics and Mechanical Systems Engineering Polytechnic School of Engineering University of Sao Paulo Av. Prof. Mello Moraes 2231 Butantã SP Sao Paulo05508-030 Brazil University Center SENAI CIMATEC Post-Graduation Program MPDS/GETEC/MCTI Av. Orlando Gomes 1845 Piata Salvador-BA41650-010 Brazil Department of Engineering Materials and Biomaterials Faculty of Mechanical Engineering Silesian University of Technology 18A Konarskiego St. Gliwice44-100 Poland

Manufacturing maraging steel components using laser-based powder bed fusion (LPBF) presents an attractive proposition for industries due to the material's combination of mechanical properties such as hardness, wear resistance, and toughness, and the capability to produce parts with complex geometries and high precision. Despite these advantages, the LPBF process induces defects such as distortion and residual stress associated with the complex thermal cycles, compromising final part quality. Numerical simulations have been developed to predict these defects. However, LPBF simulations remain challenging due to the complexity of the process and the substantial computational resources required. For maraging steel, for example, the occurrence of phase transformation promotes compressive stress that interferes in results and makes simulations inaccurate. This research aims to simulate a geometry with a circular inner channel and investigate distortion, volume fraction of martensite and equivalent stress results. Modeling was performed by varying phase transformation rate parameters to assess the impact of transformations on simulation outcomes. Results showed minimal impact of these parameters on distortions and equivalent stress. Equivalent stress results were compared with literature findings, while distortion results were validated against experimental data to validate the accuracy of the model. © 2024, The Authors. All rights reserved.

关键词： Phase transitions

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