9,694 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
Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images
Currently, analysis of microscopic In Situ Hybridization images is done
manually by experts. Precise evaluation and classification of such microscopic
images can ease experts' work and reveal further insights about the data. In
this work, we propose a deep-learning framework to detect and classify areas of
microscopic images with similar levels of gene expression. The data we analyze
requires an unsupervised learning model for which we employ a type of
Artificial Neural Network - Deep Learning Autoencoders. The model's performance
is optimized by balancing the latent layers' length and complexity and
fine-tuning hyperparameters. The results are validated by adapting the
mean-squared error (MSE) metric, and comparison to expert's evaluation.Comment: 9 pages; 9 figure
Intrathecal versus peripheral inflammatory protein profile in MS patients at diagnosis: a comprehensive investigation on serum and CSF
Intrathecal inflammation plays a key role in the pathogenesis of multiple sclerosis (MS). To better elucidate its relationship with peripheral inflammation, we investigated the correlation between cerebrospinal fluid (CSF) and serum levels of 61 inflammatory proteins. Paired CSF and serum samples were collected from 143 treatment-naïve MS patients at diagnosis. A customized panel of 61 inflammatory molecules was analyzed by a multiplex immunoassay. Correlations between serum and CSF expression levels for each molecule were performed by Spearman's method. The expression of sixteen CSF proteins correlated with their serum expression (p-value < 0.001): only five molecules (CXCL9, sTNFR2, IFNα2, Pentraxin-3, and TSLP) showed a Rho value >0.40, suggesting moderate CSF/serum correlation. No correlation between inflammatory serum patterns and Qalb was observed. Correlation analysis of serum expression levels of these sixteen proteins with clinical and MRI parameters pinpointed a subset of five molecules (CXCL9, sTNFR2, IFNα2, IFNβ, and TSLP) negatively correlating with spinal cord lesion volume. However, following FDR correction, only the correlation of CXCL9 remained significant. Our data support the hypothesis that the intrathecal inflammation in MS only partially associates with the peripheral one, except for the expression of some immunomodulators that might have a key role in the initial MS immune response
Short exposure to photo-oxidative damage triggers molecular signals indicative of early retinal degeneration
IntroductionAge-related macular degeneration (AMD) is the leading cause of blindness in the developed world, currently affecting over 350 billion people globally. For the most prevalent late-stage form of this disease, atrophic AMD, there are no available prevention strategies or treatments, in part due to inherent difficulties in early-stage diagnosis. Photo-oxidative damage is a well-established model for studying inflammatory and cell death features that occur in late-stage atrophic AMD, however to date has not been investigated as a potential model for studying early features of disease onset. Therefore, in this study we aimed to determine if short exposure to photo-oxidative damage could be used to induce early retinal molecular changes and advance this as a potential model for studying early-stage AMD.MethodsC57BL/6J mice were exposed to 1, 3, 6, 12, or 24h photo-oxidative damage (PD) using 100k lux bright white light. Mice were compared to dim-reared (DR) healthy controls as well as mice which had undergone long periods of photo-oxidative damage (3d and 5d-PD) as known timepoints for inducing late-stage retinal degeneration pathologies. Cell death and retinal inflammation were measured using immunohistochemistry and qRT-PCR. To identify retinal molecular changes, retinal lysates were sent for RNA sequencing, following which bioinformatics analyses including differential expression and pathway analyses were performed. Finally, to investigate modulations in gene regulation as a consequence of degeneration, microRNA (miRNA) expression patterns were quantified using qRT-PCR and visualized using in situ hybridization.ResultsShort exposure to photo-oxidative damage (1-24h-PD) induced early molecular changes in the retina, with progressive downregulation of homeostatic pathways including metabolism, transport and phototransduction observed across this time-course. Inflammatory pathway upregulation was observed from 3h-PD, preceding observable levels of microglia/macrophage activation which was noted from 6h-PD, as well as significant photoreceptor row loss from 24h-PD. Further rapid and dynamic movement of inflammatory regulator miRNA, miR-124-3p and miR-155-5p, was visualized in the retina in response to degeneration.ConclusionThese results support the use of short exposure to photo-oxidative damage as a model of early AMD and suggest that early inflammatory changes in the retina may contribute to pathological features of AMD progression including immune cell activation and photoreceptor cell death. We suggest that early intervention of these inflammatory pathways by targeting miRNA such as miR-124-3p and miR-155-5p or their target genes may prevent progression into late-stage pathology
Penerapan Metode Breadth First Search (BFS) Pada Rekomendasi Jurusan Berdasarkan Minat Berbasis Sistem Informasi Cerdas
Remaja yang telah lulus dari jenjang pendidikan tinggi, Sekolah Menengah Atas (SMA) akan dihadapkan dengan pilihan yaitu melanjutkan ke jenjang pendidikan selanjutnya. Hal tersebut menyebabkan kesulitan bagi remaja dalam mengambil keputusan untuk memilih jurusan di perguruan tinggi yang sesuai dan tidak tepat sasaran. Untuk membantu mengatasi permasalah tersebut dibangunlah sistem informasi cerdas dengan menggunakan metode Breadth First Search (BFS) yang terdiri dari 8 kategori peminatan dan 54 data minat.Sistem informasi cerdas ini dibangun menggunakan bahasa pemograman PHP dan database MySql, Sistem informasi cerdas ini menghasil kan output jurusan beserta rekomendasi unversitas-nya, berdasarkan kategori yang didapatkan oleh calon mahasiswa. Berdasarkan pengujian UAT, mendapatkan hasil 87,6% dengan kategori sangat baik, dan hasil dari pengujian black box diketahui sistem yang telah dibangun berjalan dengan baik dan sesaui dengan yang diharapkan.
Kata kunci: Breadth First Search, Rekomendasi jurusan, Rothwell Miller Interest Blank, Sistem Informasi Cerda
Qluster: An easy-to-implement generic workflow for robust clustering of health data
The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors
An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images
In recent years, deep learning's identification of cancer, lung disease and heart disease, among others, has contributed to its rising popularity. Deep learning has also contributed to the examination of COVID-19, which is a subject that is currently the focus of considerable scientific debate. COVID-19 detection based on chest X-ray (CXR) images primarily depends on convolutional neural network transfer learning techniques. Moreover, the majority of these methods are evaluated by using CXR data from a single source, which makes them prohibitively expensive. On a variety of datasets, current methods for COVID-19 detection may not perform as well. Moreover, most current approaches focus on COVID-19 detection. This study introduces a rapid and lightweight MobileNetV2-based model for accurate recognition of COVID-19 based on CXR images; this is done by using machine vision algorithms that focused largely on robust and potent feature-learning capabilities. The proposed model is assessed by using a dataset obtained from various sources. In addition to COVID-19, the dataset includes bacterial and viral pneumonia. This model is capable of identifying COVID-19, as well as other lung disorders, including bacterial and viral pneumonia, among others. Experiments with each model were thoroughly analyzed. According to the findings of this investigation, MobileNetv2, with its 92% and 93% training validity and 88% precision, was the most applicable and reliable model for this diagnosis. As a result, one may infer that this study has practical value in terms of giving a reliable reference to the radiologist and theoretical significance in terms of establishing strategies for developing robust features with great presentation ability
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A novel approach for communicating with patients suffering from completely locked-in-syndrome (CLIS) via thoughts: brain computer interface system using EEG signals and artificial intelligence
This paper investigates the development of an intelligent system method to address completely locked-in-syndrome (CLIS) that is caused by some illnesses such as Amyotrophic Lateral Sclerosis (ALS) as the most predominant type of Motor Neuron Disease (MND). In the last stages of ALS and despite the limitations in body movements, patients however will have a fully functional brain and cognitive capabilities and able to feel pain but fail to communicate. This paper aims to address the CLIS problem by utilizing EEG signals that human brain generates when thinking about a specific feeling or imagination as a way to communicate. The aim is to develop a low-cost and affordable system for patients to use to communicate with carers and family members. In this paper, the novel implementation of the ASPS (Automated Sensor and Signal Processing Selection) approach for feature extraction of EEG is presented to select the most suitable Sensory Characteristic Features (SCFs) to detect human thoughts and imaginations. Artificial Neural Networks (ANN) are used to verify the results. The findings show that EEG signals are able to capture imagination information that can be used as a means of communication; and the ASPS approach allows the selection of the most important features for reliable communication. This paper explains the implementation and validation of ASPS approach in brain signal classification for bespoke arrangement. Hence, future work will present the results of relatively high number of volunteers, sensors and signal processing methods
Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images
Detecting liver abnormalities is a difficult task in radiation planning and treatment. The modern development integrates medical imaging into computer techniques. This advancement has monumental effect on how medical images are interpreted and analyzed. In many circumstances, manual segmentation of liver from computerized tomography (CT) imaging is imperative, and cannot provide satisfactory results. However, there are some difficulties in segmenting the liver due to its uneven shape, fuzzy boundary and complicated structure. This leads to necessity of enabling optimization in interactive segmentation approach. The main objective of reinforcing optimization is to search the optimal threshold and reduce the chance of falling into local optimum with survival of the fittest (SOF) technique. The proposed methodology makes use of pre-processing stage and reinforcing meta heuristics optimization based fuzzy c-means (FCM) for obtaining detailed information about the image. This information gives the optimal threshold value that is used for segmenting the region of interest with minimum user input. Suspicious areas are recognized from the segmented output. Both public and simulated dataset have been taken for experimental purposes. To validate the effectiveness of the proposed strategy, performance criteria such as dice coefficient, mode and user interaction level are taken and compared with state-of-the-art algorithms
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
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