Implantação de nova tecnologia para o armazenamento de materiais em indústria automobilística

Tesis Materias > Ingeniería Universidad Internacional Iberoamericana México > Docencia > Trabajos finales de Máster Cerrado Portugués As indústrias automobilísticas brasileiras, para que se mantenham competitivas no mercado nacional e internacional, precisam lançar todos os anos novos produtos, personalizados as necessidades de seus clientes. Este cenário de mudanças contínuas nas características de seus produtos, influencia diretamente na configuração de seus almoxarifados, pois é necessária a alteração de insumos e peças, alterando características das embalagens armazenadas. Além disso, é comum que se tenham alterações mensais, ou mesmo quinzenais, em volumes de produção planejados, resultando em grande variação nos volumes necessários de estoques. De forma a suprir a necessidade dessas áreas de armazenamento, as indústrias tendem a realizar a locação de galpões externos, gerando aumento significativo de custo indireto de produção e impactando em seus resultados, como custos com aluguel, transporte, tempo e dificuldade no gerenciamento do abastecimento das linhas de produção (perda produtiva). A justificativa de preferir ter essa despesa de locação ao invés do investimento na implantação de um novo almoxarifado, acontece principalmente devido aos altos investimentos necessários e a consequente insatisfatória relação custo-benefício. Ao mesmo tempo, reduzir esse investimento é complexo, pois a Construção Civil é um setor que não conta com profissionais especialistas em inovação ou com motivação em fazê-la. A metodologia utilizada para a pesquisa, com enfoque quase experimental, será a análise de caso real de implantação de almoxarifado para indústria automobilística localizada na cidade de Sete Lagoas, Minas Gerais - Brasil, evidenciando os ganhos alcançados devido a eficaz direção do projeto. O resultado da pesquisa apresentará, após uma série de comparações entre tecnologias, inclusive seus custos de implantação, proposta de implantação de almoxarifado com menor custo de execução por metro quadrado, se comparada com as tecnologias tradicionalmente usadas, gerando melhor custo-benefício e demonstrará como uma eficiente gestão do projeto contribuiu para alcançar os objetivos e metas dessa empresa, proporcionando a ela menores investimentos, reduzindo seu custo operacional e melhorando sua produtividade e competitividade no mercado. metadata de Oliveira Junior, Paulo Roberto mail ENGENHARIA.PAULOJR@GMAIL.COM (2022) Implantação de nova tecnologia para o armazenamento de materiais em indústria automobilística. Masters thesis, SIN ESPECIFICAR.

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Resumen

As indústrias automobilísticas brasileiras, para que se mantenham competitivas no mercado nacional e internacional, precisam lançar todos os anos novos produtos, personalizados as necessidades de seus clientes. Este cenário de mudanças contínuas nas características de seus produtos, influencia diretamente na configuração de seus almoxarifados, pois é necessária a alteração de insumos e peças, alterando características das embalagens armazenadas. Além disso, é comum que se tenham alterações mensais, ou mesmo quinzenais, em volumes de produção planejados, resultando em grande variação nos volumes necessários de estoques. De forma a suprir a necessidade dessas áreas de armazenamento, as indústrias tendem a realizar a locação de galpões externos, gerando aumento significativo de custo indireto de produção e impactando em seus resultados, como custos com aluguel, transporte, tempo e dificuldade no gerenciamento do abastecimento das linhas de produção (perda produtiva). A justificativa de preferir ter essa despesa de locação ao invés do investimento na implantação de um novo almoxarifado, acontece principalmente devido aos altos investimentos necessários e a consequente insatisfatória relação custo-benefício. Ao mesmo tempo, reduzir esse investimento é complexo, pois a Construção Civil é um setor que não conta com profissionais especialistas em inovação ou com motivação em fazê-la. A metodologia utilizada para a pesquisa, com enfoque quase experimental, será a análise de caso real de implantação de almoxarifado para indústria automobilística localizada na cidade de Sete Lagoas, Minas Gerais - Brasil, evidenciando os ganhos alcançados devido a eficaz direção do projeto. O resultado da pesquisa apresentará, após uma série de comparações entre tecnologias, inclusive seus custos de implantação, proposta de implantação de almoxarifado com menor custo de execução por metro quadrado, se comparada com as tecnologias tradicionalmente usadas, gerando melhor custo-benefício e demonstrará como uma eficiente gestão do projeto contribuiu para alcançar os objetivos e metas dessa empresa, proporcionando a ela menores investimentos, reduzindo seu custo operacional e melhorando sua produtividade e competitividade no mercado.

Tipo de Documento: Tesis (Masters)
Palabras Clave: Gestão de Projetos, Construção Civil, Gestão do conhecimento, Logística, Almoxarifado.
Clasificación temática: Materias > Ingeniería
Divisiones: Universidad Internacional Iberoamericana México > Docencia > Trabajos finales de Máster
Depositado: 04 Dic 2023 23:30
Ultima Modificación: 04 Dic 2023 23:30
URI: https://repositorio.unini.edu.mx/id/eprint/1106

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Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.

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Naveed Anwer Butt mail , Dilawaiz Sarwat mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Nagwan Abdel Samee mail , Imran Ashraf mail ,

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This systematic literature review (SLR) investigates the integration of deep learning (DL), vision-language models(VLMs), and multi-agent systems in the analysis of pathology images and automated report generation. The rapidadvancement of whole-slide imaging (WSI) technologies has posed new challenges in pathology, especially due to thescale and complexity of the data. DL techniques in general and convolutional neural networks (CNNs) and transform-ers in particular have significantly enhanced image analysis tasks including segmentation, classification, and detection.However, these models often lack generalizability to generate coherent, clinically relevant text, thus necessitating theintegration of VLMs and large language models (LLMs). This review examines the effectiveness of VLMs and LLMsin bridging the gap between visual data and clinical text, focusing on their potential for automating the generationof pathology reports. Additionally, multi-agent systems, which leverage specialized artificial intelligence (AI) agentsto collaboratively perform diagnostic tasks, are explored for their contributions to improving diagnostic accuracy andscalability. Through a synthesis of recent studies, this review highlights the successes, challenges, and future direc-tions of these AI technologies in pathology diagnostics, offering a comprehensive foundation for the development ofintegrated, AI-driven diagnostic workflows.

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A scalable and secure federated learning authentication scheme for IoT

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Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models

Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture. The results on the hold-out test set show that the CNN–LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN–LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems.

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Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were updated, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations.

Producción Científica

Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Imanol Eguren García mail imanol.eguren@uneatlantico.es, Álvaro Lasarte García mail , Arturo Ortega-Mansilla mail arturo.ortega@uneatlantico.es, Thomas Prola mail thomas.prola@uneatlantico.es, Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es,

Elío Pascual