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  1. Book ; Online: Evolutionary Algorithms in Engineering Design Optimization

    Greiner, David / Gaspar‐Cunha, António / Hernández-Sosa, Daniel / Minisci, Edmondo / Zamuda, Aleš / Gaspar‐Cunha, António

    2022  

    Keywords Technology: general issues ; History of engineering & technology ; Automatic Voltage Regulation system ; Chaotic optimization ; Fractional Order Proportional-Integral-Derivative controller ; Yellow Saddle Goatfish Algorithm ; two-stage method ; mono and multi-objective optimization ; multi-objective optimization ; optimal design ; Gough-Stewart ; parallel manipulator ; performance metrics ; diversity control ; genetic algorithm ; bankruptcy problem ; classification ; T-junctions ; neural networks ; finite elements analysis ; surrogate ; beam improvements ; beam T-junctions models ; artificial neural networks (ANN) limited training data ; multi-objective decision-making ; Pareto front ; preference in multi-objective optimization ; aeroacoustics ; trailing-edge noise ; global optimization ; evolutionary algorithms ; nearly optimal solutions ; archiving strategy ; evolutionary algorithm ; non-linear parametric identification ; multi-objective evolutionary algorithms ; availability ; design ; preventive maintenance scheduling ; encoding ; accuracy levels ; plastics thermoforming ; sheet thickness distribution ; evolutionary optimization ; genetic programming ; control ; differential evolution ; reusable launch vehicle ; quality control ; roughness measurement ; machine vision ; machine learning ; parameter optimization ; distance-based ; mutation-selection ; real application ; experimental study ; global optimisation ; worst-case scenario ; robust ; min-max optimization ; optimal control ; multi-objective optimisation ; robust design ; trajectory optimisation ; uncertainty quantification ; unscented transformation ; spaceplanes ; space systems ; launchers
    Language 0|e
    Size 1 electronic resource (314 pages)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Publishing place Basel
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021609168
    ISBN 9783036527154 ; 303652715X
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening.

    Camara, José / Cunha, Antonio

    Medicina (Kaunas, Lithuania)

    2024  Volume 60, Issue 3

    Abstract: Glaucoma is one of the leading causes of irreversible blindness in the world. Early diagnosis and treatment increase the chances of preserving vision. However, despite advances in techniques for the functional and structural assessment of the retina, ... ...

    Abstract Glaucoma is one of the leading causes of irreversible blindness in the world. Early diagnosis and treatment increase the chances of preserving vision. However, despite advances in techniques for the functional and structural assessment of the retina, specialists still encounter many challenges, in part due to the different presentations of the standard optic nerve head (ONH) in the population, the lack of explicit references that define the limits of glaucomatous optic neuropathy (GON), specialist experience, and the quality of patients' responses to some ancillary exams. Computer vision uses deep learning (DL) methodologies, successfully applied to assist in the diagnosis and progression of GON, with the potential to provide objective references for classification, avoiding possible biases in experts' decisions. To this end, studies have used color fundus photographs (CFPs), functional exams such as visual field (VF), and structural exams such as optical coherence tomography (OCT). However, it is still necessary to know the minimum limits of detection of GON characteristics performed through these methodologies. This study analyzes the use of deep learning (DL) methodologies in the various stages of glaucoma screening compared to the clinic to reduce the costs of GON assessment and the work carried out by specialists, to improve the speed of diagnosis, and to homogenize opinions. It concludes that the DL methodologies used in automated glaucoma screening can bring more robust results closer to reality.
    MeSH term(s) Humans ; Optic Disk/diagnostic imaging ; Glaucoma/diagnosis ; Optic Nerve Diseases/diagnosis ; Optic Nerve ; Mass Screening ; Tomography, Optical Coherence
    Language English
    Publishing date 2024-03-02
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2188113-3
    ISSN 1648-9144 ; 1010-660X
    ISSN (online) 1648-9144
    ISSN 1010-660X
    DOI 10.3390/medicina60030428
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: New insights into the interaction of emodin with lipid membranes.

    da Cunha, Antonio R / Duarte, Evandro L / Vignoli Muniz, Gabriel S / Coutinho, Kaline / Lamy, M Teresa

    Biophysical chemistry

    2024  Volume 309, Page(s) 107233

    Abstract: Emodin is a natural anthraquinone derivative found in nature, widely known as an herbal medicine. Here, the partition, location, and interaction of emodin with lipid membranes of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) are experimentally ... ...

    Abstract Emodin is a natural anthraquinone derivative found in nature, widely known as an herbal medicine. Here, the partition, location, and interaction of emodin with lipid membranes of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) are experimentally investigated with different techniques. Our studies have considered the neutral form of emodin (EMH) and its anionic/deprotonated form (EM
    MeSH term(s) Emodin ; Dimyristoylphosphatidylcholine/chemistry ; Lipid Bilayers/chemistry ; Spin Labels
    Chemical Substances Emodin (KA46RNI6HN) ; Dimyristoylphosphatidylcholine (U86ZGC74V5) ; Lipid Bilayers ; Spin Labels
    Language English
    Publishing date 2024-04-02
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 185052-0
    ISSN 1873-4200 ; 0301-4622
    ISSN (online) 1873-4200
    ISSN 0301-4622
    DOI 10.1016/j.bpc.2024.107233
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Optimization of Polymer Processing: A Review (Part I-Extrusion).

    Gaspar-Cunha, António / Covas, José A / Sikora, Janusz

    Materials (Basel, Switzerland)

    2022  Volume 15, Issue 1

    Abstract: Given the global economic and societal importance of the polymer industry, the continuous search for improvements in the various processing techniques is of practical primordial importance. This review evaluates the application of optimization ... ...

    Abstract Given the global economic and societal importance of the polymer industry, the continuous search for improvements in the various processing techniques is of practical primordial importance. This review evaluates the application of optimization methodologies to the main polymer processing operations. The most important characteristics related to the usage of optimization techniques, such as the nature of the objective function, the type of optimization algorithm, the modelling approach used to evaluate the solutions, and the parameters to optimize, are discussed. The aim is to identify the most important features of an optimization system for polymer processing problems and define the best procedure for each particular practical situation. For this purpose, the state of the art of the optimization methodologies usually employed is first presented, followed by an extensive review of the literature dealing with the major processing techniques, the discussion being completed by considering both the characteristics identified and the available optimization methodologies. This first part of the review focuses on extrusion, namely single and twin-screw extruders, extrusion dies, and calibrators. It is concluded that there is a set of methodologies that can be confidently applied in polymer processing with a very good performance and without the need of demanding computation requirements.
    Language English
    Publishing date 2022-01-05
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2487261-1
    ISSN 1996-1944
    ISSN 1996-1944
    DOI 10.3390/ma15010384
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Optimization of Polymer Processing: A Review (Part II-Molding Technologies).

    Gaspar-Cunha, António / Covas, José A / Sikora, Janusz

    Materials (Basel, Switzerland)

    2022  Volume 15, Issue 3

    Abstract: The application of optimization techniques to improve the performance of polymer processing technologies is of great practical consequence, since it may result in significant savings of materials and energy resources, assist recycling schemes and ... ...

    Abstract The application of optimization techniques to improve the performance of polymer processing technologies is of great practical consequence, since it may result in significant savings of materials and energy resources, assist recycling schemes and generate products with better properties. The present review aims at identifying and discussing the most important characteristics of polymer processing optimization problems in terms of the nature of the objective function, optimization algorithm, and process modelling approach that is used to evaluate the solutions and the parameters to optimize. Taking into account the research efforts developed so far, it is shown that several optimization methodologies can be applied to polymer processing with good results, without demanding important computational requirements. Furthermore, within the field of artificial intelligence, several approaches can reach significant success. The first part of this review demonstrated the advantages of the optimization approach in polymer processing, discussed some concepts on multi-objective optimization and reported the application of optimization methodologies to single and twin screw extruders, extrusion dies and calibrators. This second part focuses on injection molding, blow molding and thermoforming technologies.
    Language English
    Publishing date 2022-02-01
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2487261-1
    ISSN 1996-1944
    ISSN 1996-1944
    DOI 10.3390/ma15031138
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device.

    Neto, Alexandre / Camara, José / Cunha, António

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 4

    Abstract: Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can ... ...

    Abstract Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can increase the frequency of screening and alert patients earlier for a more thorough evaluation. This work explored and compared the performance of classification and segmentation methods for glaucoma screening with retinal images acquired by both retinography and mobile devices. The goal was to verify the results of these methods and see if similar results could be achieved using images captured by mobile devices. The used classification methods were the Xception, ResNet152 V2 and the Inception ResNet V2 models. The models' activation maps were produced and analysed to support glaucoma classifier predictions. In clinical practice, glaucoma assessment is commonly based on the cup-to-disc ratio (CDR) criterion, a frequent indicator used by specialists. For this reason, additionally, the U-Net architecture was used with the Inception ResNet V2 and Inception V3 models as the backbone to segment and estimate CDR. For both tasks, the performance of the models reached close to that of state-of-the-art methods, and the classification method applied to a low-quality private dataset illustrates the advantage of using cheaper lenses.
    MeSH term(s) Computers, Handheld ; Deep Learning ; Diagnostic Techniques, Ophthalmological ; Glaucoma/diagnostic imaging ; Humans ; Optic Disk
    Language English
    Publishing date 2022-02-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22041449
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: A Systematic Review on Automatic Insect Detection Using Deep Learning

    Teixeira, Ana Cláudia / Ribeiro, José / Morais, Raul / Sousa, Joaquim J. / Cunha, António

    Agriculture. 2023 Mar. 19, v. 13, no. 3

    2023  

    Abstract: Globally, insect pests are the primary reason for reduced crop yield and quality. Although pesticides are commonly used to control and eliminate these pests, they can have adverse effects on the environment, human health, and natural resources. As an ... ...

    Abstract Globally, insect pests are the primary reason for reduced crop yield and quality. Although pesticides are commonly used to control and eliminate these pests, they can have adverse effects on the environment, human health, and natural resources. As an alternative, integrated pest management has been devised to enhance insect pest control, decrease the excessive use of pesticides, and enhance the output and quality of crops. With the improvements in artificial intelligence technologies, several applications have emerged in the agricultural context, including automatic detection, monitoring, and identification of insects. The purpose of this article is to outline the leading techniques for the automated detection of insects, highlighting the most successful approaches and methodologies while also drawing attention to the remaining challenges and gaps in this area. The aim is to furnish the reader with an overview of the major developments in this field. This study analysed 92 studies published between 2016 and 2022 on the automatic detection of insects in traps using deep learning techniques. The search was conducted on six electronic databases, and 36 articles met the inclusion criteria. The inclusion criteria were studies that applied deep learning techniques for insect classification, counting, and detection, written in English. The selection process involved analysing the title, keywords, and abstract of each study, resulting in the exclusion of 33 articles. The remaining 36 articles included 12 for the classification task and 24 for the detection task. Two main approaches—standard and adaptable—for insect detection were identified, with various architectures and detectors. The accuracy of the classification was found to be most influenced by dataset size, while detection was significantly affected by the number of classes and dataset size. The study also highlights two challenges and recommendations, namely, dataset characteristics (such as unbalanced classes and incomplete annotation) and methodologies (such as the limitations of algorithms for small objects and the lack of information about small insects). To overcome these challenges, further research is recommended to improve insect pest management practices. This research should focus on addressing the limitations and challenges identified in this article to ensure more effective insect pest management.
    Keywords agriculture ; artificial intelligence ; automatic detection ; crop yield ; data collection ; human health ; insect control ; insect pests ; systematic review
    Language English
    Dates of publication 2023-0319
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article ; Online
    ZDB-ID 2651678-0
    ISSN 2077-0472
    ISSN 2077-0472
    DOI 10.3390/agriculture13030713
    Database NAL-Catalogue (AGRICOLA)

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  8. Article: Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?

    Camara, José / Rezende, Roberto / Pires, Ivan Miguel / Cunha, António

    Journal of clinical medicine

    2022  Volume 11, Issue 13

    Abstract: Public databases for glaucoma studies contain color images of the retina, emphasizing the optic papilla. These databases are intended for research and standardized automated methodologies such as those using deep learning techniques. These techniques are ...

    Abstract Public databases for glaucoma studies contain color images of the retina, emphasizing the optic papilla. These databases are intended for research and standardized automated methodologies such as those using deep learning techniques. These techniques are used to solve complex problems in medical imaging, particularly in the automated screening of glaucomatous disease. The development of deep learning techniques has demonstrated potential for implementing protocols for large-scale glaucoma screening in the population, eliminating possible diagnostic doubts among specialists, and benefiting early treatment to delay the onset of blindness. However, the images are obtained by different cameras, in distinct locations, and from various population groups and are centered on multiple parts of the retina. We can also cite the small number of data, the lack of segmentation of the optic papillae, and the excavation. This work is intended to offer contributions to the structure and presentation of public databases used in the automated screening of glaucomatous papillae, adding relevant information from a medical point of view. The gold standard public databases present images with segmentations of the disc and cupping made by experts and division between training and test groups, serving as a reference for use in deep learning architectures. However, the data offered are not interchangeable. The quality and presentation of images are heterogeneous. Moreover, the databases use different criteria for binary classification with and without glaucoma, do not offer simultaneous pictures of the two eyes, and do not contain elements for early diagnosis.
    Language English
    Publishing date 2022-07-02
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662592-1
    ISSN 2077-0383
    ISSN 2077-0383
    DOI 10.3390/jcm11133850
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: "Epidemic" of violence in Brazilian schools and its impact on the health of survivors: a perspective based on adverse childhood experiences.

    Jural, Lucas Alves / Risso, Patricia de Andrade / Cunha, Antônio José Ledo Alves da / Fagundes, Fábio Anevan / Fonseca-Gonçalves, Andréa / Paiva, Saul Martins / Maia, Lucianne Cople

    Cadernos de saude publica

    2024  Volume 40, Issue 3, Page(s) e00169723

    Title translation “Epidemia” de violência nas escolas brasileiras e os efeitos na saúde dos sobreviventes: uma perspectiva a partir das experiências adversas na infância.
    MeSH term(s) Humans ; Adverse Childhood Experiences ; Brazil ; Violence ; Epidemics ; Survivors
    Language English
    Publishing date 2024-03-11
    Publishing country Brazil
    Document type Journal Article
    ZDB-ID 1115730-6
    ISSN 1678-4464 ; 0102-311X
    ISSN (online) 1678-4464
    ISSN 0102-311X
    DOI 10.1590/0102-311XPT169723
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4981: Show issues Location:
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  10. Article ; Online: Environment and child health.

    da Silva, Giselia Alves Pontes / da Cunha, Antonio José Ledo Alves

    Jornal de pediatria

    2022  Volume 98 Suppl 1, Page(s) S1–S3

    MeSH term(s) Child ; Child Health ; Environment ; Humans
    Language English
    Publishing date 2022-01-10
    Publishing country Brazil
    Document type Journal Article
    ZDB-ID 731324-x
    ISSN 1678-4782 ; 0021-7557
    ISSN (online) 1678-4782
    ISSN 0021-7557
    DOI 10.1016/j.jped.2021.12.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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