12,501 research outputs found
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text
With the surging inclination towards carrying out tasks on computational
devices and digital mediums, any method that converts a task that was
previously carried out manually, to a digitized version, is always welcome.
Irrespective of the various documentation tasks that can be done online today,
there are still many applications and domains where handwritten text is
inevitable, which makes the digitization of handwritten documents a very
essential task. Over the past decades, there has been extensive research on
offline handwritten text recognition. In the recent past, most of these
attempts have shifted to Machine learning and Deep learning based approaches.
In order to design more complex and deeper networks, and ensure stellar
performances, it is essential to have larger quantities of annotated data. Most
of the databases present for offline handwritten text recognition today, have
either been manually annotated or semi automatically annotated with a lot of
manual involvement. These processes are very time consuming and prone to human
errors. To tackle this problem, we present an innovative, complete end-to-end
pipeline, that annotates offline handwritten manuscripts written in both print
and cursive English, using Deep Learning and User Interaction techniques. This
novel method, which involves an architectural combination of a detection system
built upon a state-of-the-art text detection model, and a custom made Deep
Learning model for the recognition system, is combined with an easy-to-use
interactive interface, aiming to improve the accuracy of the detection,
segmentation, serialization and recognition phases, in order to ensure high
quality annotated data with minimal human interaction.Comment: 17 pages, 8 figures, 2 table
Applying Robotic Process Automation to improve Sales Operations at EDP Comercial
Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research and CRMThis study aims to address the world of Robotic Process Automation (RPA), unraveling some myths and truths about it and, as it is a project work report, we have the opportunity to see the real impact that this technology can have in a company. This project was carried out in the Business Growth Area (Área de Dinamização de Negócio - ADN) team, who gives support to the whole Direction of Face-to-Face Channels (Direção de Canais Presenciais - DCP), which contains 4 channels: official Stores, Agents, which are partner but not official EDP stores, Door to Door (D2D) agents, which sell products door-to-door, and Distribution Agents Network (Rede de Agentes de Distribuição – RAD), which are distribution agents. These channels are responsible for all physical sales of EDP Comercial, which is responsible for all the sales of the Group EDP. When this project began, some problems were immediately detected, as various processes were being carried out manually that did not make any sense, both in monetary terms and, most importantly for EDP, in terms of time wasting, which means that the work activity was not being done efficiently enough and they saw this as an opportunity to explore the RPA world. So, the proposed work was to identify which processes could be improved and then build robots that could assume those activities. The most important result of this project was, as initially expected, the increase in efficiency in the work of the people who no longer have to perform routine tasks and can focus their energy on more important projects
Analysis of intracellular tyrosine phosphorylation in circulating neutrophils as a rapid assay for the in vivo effect of oral tyrosine kinase inhibitors
Tyrosine kinases are crucial signaling components of diverse biological processes and are major therapeutic targets in various malignancies and immune-mediated disorders. A critical step of development of novel tyrosine kinase inhibitors is the transition from the confirmation of the in vitro effects of drug candidates to the analysis of their in vivo efficacy. To facilitate this transition, we have developed a rapid in vivo assay for the analysis of the effect of oral tyrosine kinase inhibitors on basal tyrosine phosphorylation of circulating mouse neutrophils. The assay uses a single drop of peripheral blood without sacrificing the mice. Flow cytometry using intracellular staining by fluorescently labeled anti-phosphotyrosine antibodies revealed robust basal tyrosine phosphorylation in resting circulating neutrophils. This signal was abrogated by the use of isotype control antibodies or by pre-saturation of the anti-phosphotyrosine antibodies with soluble phosphotyrosine amino acids or tyrosine-phosphorylated peptides. Basal tyrosine phosphorylation was dramatically reduced in neutrophils of triple knockout mice lacking the Src-family tyrosine kinases Hck, Fgr, and Lyn. Neutrophil tyrosine phosphorylation was also abrogated by oral administration of the Abl/Src-family inhibitor dasatinib, a clinically used anti-leukemic agent. Detailed dose-response and kinetic studies revealed half-maximal reduction of neutrophil tyrosine phosphorylation by 2.9 mg/kg dasatinib, with maximal reduction observed 2 h after inhibitor administration. Taken together, our assay allows highly efficient analysis of the in vivo effect of orally administered tyrosine kinase inhibitors, and may be used as a suitable alternative to other existing approaches
Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021
É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
Effective Numerical Simulations of Synchronous Generator System
Synchronous generator system is a complicated dynamical system for energy
transmission, which plays an important role in modern industrial production. In
this article, we propose some predictor-corrector methods and
structure-preserving methods for a generator system based on the first
benchmark model of subsynchronous resonance, among which the
structure-preserving methods preserve a Dirac structure associated with the
so-called port-Hamiltonian descriptor systems. To illustrate this, the
simplified generator system in the form of index-1 differential-algebraic
equations has been derived. Our analyses provide the global error estimates for
a special class of structure-preserving methods called Gauss methods, which
guarantee their superior performance over the PSCAD/EMTDC and the
predictor-corrector methods in terms of computational stability. Numerical
simulations are implemented to verify the effectiveness and advantages of our
methods
Performance Investigation of the Solar Membrane Distillation Process Using TRNSYS Software
Membrane distillation (MD) is a separation process used for water desalination, which operates at low pressures and feeds temperatures. Air gap membrane distillation (AGMD) is the new MD configuration for desalination where both the hot feed side and the cold permeate side are in indirect contact with the two membrane surfaces. The chapter presents a new approach for the numerical study to investigate various solar thermal systems of the MD process. The various MD solar systems are studied numerically using and including both flat plate collectors (the useful thermal energy reaches 3750 kJ/hr with a total area of 4 m2) and photovoltaic panels, each one has an area of 1.6 m2 by using an energy storage battery (12 V, 200 Ah). Therefore, the power load of solar AGMD systems is calculated and compared for the production of 100 L/day of distillate water. It was found that the developed system consumes less energy (1.2 kW) than other systems by percentage reaches 52.64% and with an average distillate water flow reaches 10 kg/h at the feed inlet temperature of AGMD module 52°C. Then, the developed system has been studied using TRNSYS and PVGIS programs on different days during the year in Ain Temouchent weather, Algeria
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