Design of a post-processor for fused deposition modeling systems using a Mitsubishi robotic arm

[EN] This work presents a post-processor development for the use of a robot, Mitsubishi RV-2AJ, in fused deposition modeling processes, FDM. For this purpose, a material feeding and dragging system is designed and adapted to a Mitsubishi robotic arm. An interface has also been developed to facilitate communication between the PC and the Robot. For the Robot to perform its new function, it is necessary to start from a digital model, compatible with the 3D printing technique, generating the paths, instructions, and work tasks. This information is post-processed to be interpreted in the language of the Robot. To communicate the different elements with the Robot controller, the control and the programming are made through a minimum system of electronics. Finally, an application is developed, and the procedure and sequence necessary to carry out the printing process is structured.[ES] Este trabajo presenta el desarrollo de un post-procesador para el uso de un robot, Mitsubishi RV-2AJ, en procesos de modelado por deposición fundida (MDF). Para este propósito se diseña y se adapta un sistema de alimentación y arrastre de material a un brazo robótico Mitsubishi. También se ha desarrollado una interfaz para facilitar la comunicación entre el computador y el robot. Para que el robot realice su nueva función, es necesario partir de un modelo digital, compatible con la técnica de impresión 3D, generándose las trayectorias, instrucciones y tareas de trabajo. La información es post-procesada para que sea interpretada en el lenguaje del robot. Para comunicar los diferentes elementos con la controladora del robot se realiza el control y la programación mediante un sistema mínimo de electrónica. Finalmente se desarrolla una aplicación, se estructura el procedimiento y la secuencia necesaria para realizar el proceso de impresión.Torres, S.; Gutiérrez, SC. (2020). Diseño de un post-procesador para sistemas de modelado por deposición fundida utilizando un brazo robótico Mitsubishi. NOVASINERGIA. 3(2):57-66. https://doi.org/10.37135/ns.01.06.05S57663

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein

Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning

Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled training data, Noise2Recon outperforms compressed sensing and deep learning baselines, including supervised networks, augmentation-based training, fine-tuned denoisers, and self-supervised methods, and matches performance of supervised models, which were trained with 14x more fully-sampled scans. Noise2Recon also outperforms all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to other OOD factors, such as changes in acceleration factors and different datasets. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. Our code is available at https://github.com/ad12/meddlr

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein

Infectivity of a rickettsia isolated from coho salmon Oncorhynchus kisutch

Two species of salmonids were tested for their susceptibility to infection by a rickettsia isolated in cell culture from diseased coho salmon Oncorhynchus kisutch in Chile. Mortality approached 100% in coho and Atlantic salmon Salmo salar injected with 10-fold dilutions of cell culture medium containing the rickettsia. Typical disease signs were present in the coho salmon but were not observed in inoculated Atlantic salmon. However, the rickettsia was recovered in pure culture from moribund fish in each of the injected groups of either species. The rickettsia was thereby demonstrated to be pathogenic and the cause of the ongoing epizootic affecting salmonids cultured in Chile. Horizontal transmission was not demonstrated in a group of uninoculated coho salmon held in the same tank with experimentally infected fish.Keywords: Salmo salar, Rickettsia, Oncorhynchus kisutch, pathogensKeywords: Salmo salar, Rickettsia, Oncorhynchus kisutch, pathogen

Micromechanical study of the load transfer in a polycaprolactone-collagen hybrid scaffold when subjected to unconfined and confined compression

Scaffolds are used in diverse tissue engineering applications as hosts for cell proliferation and extracellular matrix formation. One of the most used tissue engineering materials is collagen, which is well known to be a natural biomaterial, also frequently used as cell substrate, given its natural abundance and intrinsic biocompatibility. This study aims to evaluate how the macroscopic biomechanical stimuli applied on a construct made of polycaprolactone scaffold embedded in a collagen substrate translate into microscopic stimuli at the cell level. Eight poro-hyperelastic finite element models of 3D printed hybrid scaffolds from the same batch were created, along with an equivalent model of the idealized geometry of that scaffold. When applying an 8% confined compression at the macroscopic level, local fluid flow of up to 20 [Formula: see text]m/s and octahedral strain levels mostly under 20% were calculated in the collagen substrate. Conversely unconfined compression induced fluid flow of up to 10 [Formula: see text]m/s and octahedral strain from 10 to 35%. No relevant differences were found amongst the scaffold-specific models. Following the mechanoregulation theory based on Prendergast et al. (J Biomech 30:539-548, 1997. https://doi.org/10.1016/S0021-9290(96)00140-6 ), those results suggest that mainly cartilage or fibrous tissue formation would be expected to occur under unconfined or confined compression, respectively. This in silico study helps to quantify the microscopic stimuli that are present within the collagen substrate and that will affect cell response under in vitro bioreactor mechanical stimulation or even after implantation

The Enthusiast’s Eye: The Value of Unsanctioned Knowledge in Design Historical Scholarship

If design history research relies solely on institutionalized documentation and academic scholarship – that is, sanctioned knowledge – not only will its purview be limited to a very narrow segment of design culture, it will also lose out on a vast array of sources to valuable knowledge about our material environment produced by amateurs, collectors, and enthusiasts – what we in this article define as “unsanctioned knowledge.” Because of its dissociation with professional institutions and academic protocols and their – albeit admittedly utopian, but nonetheless upheld – ideals of objectivity, this type of knowledge is typically considered fundamentally subjective in nature and therefore of little or no relevance and value to academic scholarship. In this article, we argue that, to the contrary, design historical scholarship has much to gain from engaging more seriously with the unsanctioned knowledge represented by the enthusiast's eye