5,410 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    An advanced deep learning models-based plant disease detection: A review of recent research

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    Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    New Building Blocks for Cancer Phototherapeutics: 5d Metallocorroles

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    Corroles are ring-contracted, triprotic analogues of porphyrins. This PhD study expands earlier knowledge in particular on ReO corroles. Early on, it became apparent that ReO corroles exhibit the highest phosphorescence quantum yields among all metallocorroles. They also sensitize singlet oxygen formation and serve as oxygen sensors and as triplet-triplet annihilation upconverters. I accordingly wanted to synthesize new classes of functionalized 5d corroles as well as to examine ReO corroles as photosensitizers in in vitro photodynamic therapy experiments. I found that amphiphilic meta/para-carboxyl-appended ReO triphenylcorroles exhibit high photocytotoxicity against multiple cancer cell lines. In the synthetic realm, one study examined electrophilic chlorination and bromination of ReO corroles. X-ray structures of ReO octachloro- and octabromocorroles yielded a host of insights into the conformational preferences of sterically hindered corrole derivatives. Another synthetic study afforded an innovative approach to water-soluble iridium corroles, involving the use of water-soluble axial ligands. I also undertook extensive studies of formylation of ReO and Au triarylcorroles, arriving at the rather elegant conclusion that whereas the former largely afford 3-monoformyl products, the latter preferentially yield 3,17-diformylproducts, presumably reflecting the higher nucleophilicity of the Au complexes. The formylcorrole products could be readily postfunctionalized, such as via the Knoevenagel reaction. The 5d formylcorroles should serve as valuable starting materials for bio- and nanoconjugated 5d metallocorroles for advanced, targeted cancer therapies. I feel privileged to have developed a new class of triplet photosensitizers – the ReO corroles – that to this day remain unique to our Tromsø laboratory. I am confident, however, that we shall soon see exciting applications of these compounds as advanced photodynamic, photothermal and multimodal cancer therapeutics

    Study of the behavior of a thermoplastic injection mold and prediction of fatigue failure with numerical simulation

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    Tese de doutoramento em Engenharia MecânicaO objetivo deste trabalho é a criação de uma metodologia de análise da resistência à fadiga de moldes de injeção de termoplásticos. Uma metodologia capaz de satisfazer o mercado atual que exige a diminuição do tempo de entrega e custos de moldes de injeção, sem comprometer a sua fiabilidade. Para o desenvolvimento desta metodologia, foram utilizados modelos digitais. Com estes modelos é possível executar-se várias iterações sem os custos de um modelo físico. Além do menor custo dos modelos digitais, também é possível compreender o comportamento de cada molde no decorrer da fase de projeto. Com o aumento da complexidade dos componentes injetados, o estudo da resistência à fadiga tende a ser cada vez mais importante. Neste trabalho serão apresentados cuidados a ter na preparação dos modelos digitais, de forma a obter-se resultados fiáveis. No desenvolvimento desta metodologia, usaram-se dois softwares de simulação numérica para gerar os modelos digitais. Um deles dedica-se ao estudo reológico de peças termoplásticas e outro ao comportamento estrutural dos moldes de injeção. A execução de simulações numéricas requer uma boa caracterização dos materiais usados. No caso dos termoplásticos, os fabricantes têm uma grande base de dados com a informação necessária para as simulações numéricas. No entanto, para as simulações estruturais, os fabricantes tendem apenas a fornecer os dados das curvas monotónicas, os quais não fornecem qualquer informação sobre o comportamento à fadiga. Portanto, neste trabalho foram estudados modelos empíricos que se adaptam aos aços usados em moldes de injeção, a partir dos quais é possível gerar as curvas S-N e e-N. De modo a avaliar qual o modelo empírico que se adaptaria melhor a esta área, foram realizados ensaios experimentais com provetes feitos em EN 1.2311. A partir destes ensaios, escolheu-se o modelo empírico mais conservador. Com base no modelo empírico escolhido, foi desenvolvida uma aplicação capaz de gerar as curvas S-N e e-N, a partir das informações fornecidas pela aciaria. Além da caracterização dos materiais, também é importante que as condições de carregamento do modelo numérico estrutural sejam o mais aproximadas possível do que irá ocorrer no modelo físico. Como as cargas deste modelo numérico podem ser previstas a partir do modelo numérico reológico, a criação de uma ponte entre estes dois modelos numéricos é imprescindível. Logo, neste trabalho foi construída uma aplicação capaz de converter os dados gerados pelo software comercial Moldflow em ficheiros capazes de serem lidos por softwares comerciais de simulação numérica estrutural. Usando esta aplicação para a conversão dos dados, foram realizadas simulações e comparadas com os respetivos modelos físicos. Verificou-se que é possível replicar o comportamento do molde em modelos digitais. No entanto, os modelos digitais dos moldes de injeção estudados tenderam a apresentar resultados conservadores quando comparados com os modelos físicos. Por fim, foi desenvolvida uma aplicação capaz de usar dados calculados a partir de softwares comerciais de cálculo numérico estrutural para a determinação da resistência dos moldes à fadiga. Aqui foi tido em conta o modelo para geração das curvas de fadiga dos materiais validado. Os modelos de cálculo à fadiga na aplicação baseiam-se na regra de Palmgren – Miner para a determinação dos ciclos até à nucleação da fissura. O cálculo das tensões alternadas foi realizado a partir de dois métodos, o critério da tensão de corte octaédrica e o método de Sines. Para testar a aplicação foram escolhidos cinco moldes que apresentaram falhas por fadiga. Em seguida, foi aplicada a metodologia proposta neste trabalho para a determinação da resistência dos mesmos à fadiga. A partir da aplicação desta metodologia e das ferramentas desenvolvidas para o seu emprego, foi possível verificar que esta é capaz de prever as zonas onde ocorreram as falhas, bem como outras com probabilidade de nucleação de fissuras. Portanto, no decorrer deste trabalho foi possível criar uma metodologia e ferramentas de apoio para o cálculo de moldes à fadiga. Assim, projetistas de moldes podem ter uma boa perspetiva da resistência à fadiga de moldes de injeção ainda em projeto, tendo por base métodos científicos.The objective of this work is to create a methodology to analyze the fatigue resistance of thermoplastic injection molds. A methodology capable of satisfying the current market that demands a decrease in the delivery time and costs of injection molds, without compromising their reliability. To develop this methodology, digital models were used. With these models it is possible to execute several iterations without the costs of a physical model. Besides the lower cost of digital models, it is also possible to understand the behavior of each mold during the design phase. With the increasing complexity of injected components, the study of fatigue resistance tends to be more and more important. In this work, care will be presented in the preparation of the digital models, in order to obtain reliable results. In the development of this methodology, two numerical simulation software’s were used to generate the digital models. One of them is dedicated to the rheological study of thermoplastic parts and the other to the structural behavior of injection molds. The execution of numerical simulations requires a good characterization of the materials used. In the case of thermoplastics, manufacturers have a large database with the information needed for numerical simulations. However, for structural simulations, manufacturers tend to provide only monotonic curve data, which do not provide any information about fatigue behavior. Therefore, in this work, empirical models that fit the steels used in injection molds were studied, from which it is possible to generate the S-N and e-N curves. In order to evaluate which empirical model would best fit this area, experimental tests were performed with specimens made in EN 1.2311. From these tests, the most conservative empirical model was chosen. Based on the chosen empirical model, an application capable of generating the S-N and e-N curves from the information provided by the steel mill was developed. Besides the characterization of the materials, it is also important that the loading conditions of the numerical structural model are as close as possible to what will occur in the physical model. Since the loads of this numerical model can be predicted from the rheological numerical model, the creation of a bridge between these two numerical models is essential. Therefore, in this work was built an application capable of converting the data generated by the commercial software Moldflow into files capable of being read by commercial structural numerical simulation software. Using this application for data conversion, simulations were performed and compared with the respective physical models. It was found that it is possible to replicate the mold behavior in digital models. However, the digital models of the injection molds studied tended to present conservative results when compared to the physical models. Finally, an application capable of using data calculated from commercial numerical structural calculation software was developed for determining the fatigue resistance of molds. Here the validated model for generating the fatigue curves of the materials was taken into account. The fatigue calculation models in the application are based on the Palmgren - Miner rule for the determination of the cycles until crack nucleation. The alternating stresses calculation was performed from two methods, the octahedral shear stress criterion and the Sines method. To test the application, five molds that presented fatigue failures were chosen. Then, the methodology proposed in this work was applied to determine their fatigue resistance. From the application of this methodology and the tools developed for its use, it was possible to verify that it is able to predict the areas where the failures occurred, as well as others with a probability of crack nucleation. Therefore, during this work it was possible to create a methodology and support tools for the calculation of fatigue molds. Thus, mold designers can have a good perspective of the fatigue resistance of injection molds still in project, based on scientific methods

    Colloquium: Quantum and Classical Discrete Time Crystals

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    The spontaneous breaking of time translation symmetry has led to the discovery of a new phase of matter - the discrete time crystal. Discrete time crystals exhibit rigid subharmonic oscillations, which result from a combination of many-body interactions, collective synchronization, and ergodicity breaking. This Colloquium reviews recent theoretical and experimental advances in the study of quantum and classical discrete time crystals. We focus on the breaking of ergodicity as the key to discrete time crystals and the delaying of ergodicity as the source of numerous phenomena that share many of the properties of discrete time crystals, including the AC Josephson effect, coupled map lattices, and Faraday waves. Theoretically, there exists a diverse array of strategies to stabilize time crystalline order in both closed and open systems, ranging from localization and prethermalization to dissipation and error correction. Experimentally, many-body quantum simulators provide a natural platform for investigating signatures of time crystalline order; recent work utilizing trapped ions, solid-state spin systems, and superconducting qubits will be reviewed. Finally, this Colloquium concludes by describing outstanding challenges in the field and a vision for new directions on both the experimental and theoretical fronts.Comment: 29 pages, 13 figures; commissioned review for Reviews of Modern Physic

    Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

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    Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively

    Land Use and Land Cover Mapping in a Changing World

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    It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classification systems
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