Experimental Study on Spectral Characteristics of Kerosene Swirl Combustion

AbstractAn experimentalstudy has been conducted to investigate characteristics of emission spectra from combustion of kerosene liquid fuel, i.e., jet A-1. Radicals of interest in hydrocarbon combustion are OH*, CH*, and C2*. An experimental study about chemiluminescence characteristics of liquid fuel combustion has been devised to investigate emission characteristics depending on various operating parameters. A swirl combustor is designed for providing similar environments to those of actual liquid rocket engines. The model combustor has a central fuel injector making a hollow cone spray, which is surrounded by swirling flow. Kerosene flame exhibited highly luminous characteristics being attributed to CO2* chemiluminescence.OH* and CH* chemiluminescence intensities show a very similar trend as a function of equivalence ratio. And their intensities decrease along with an increase in equivalence ratio. The chemiluminescence intensity ratios between these two radicals show very close values to one regardless of equivalence ratio.C2* chemiluminescence intensity reveals relatively strong relations with equivalence ratio compared to CH* and OH*. Its intensity values increase as mixture becomes rich and also an increase in inlet air temperature enhances its intensities. The ratios between C2* and CH* manifest a linear relation as a function of equivalence ratio

Learning Topology-Specific Experts for Molecular Property Prediction

Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structural patterns limits its prediction performance. In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics. That is, each expert learns topology-specific discriminative features while being trained with its corresponding topological group. To tackle the key challenge of grouping molecules by their topological patterns, we introduce a clustering-based gating module that assigns an input molecule into one of the clusters and further optimizes the gating module with two different types of self-supervision: topological semantics induced by GNNs and molecular scaffolds, respectively. Extensive experiments demonstrate that TopExpert has boosted the performance for molecular property prediction and also achieved better generalization for new molecules with unseen scaffolds than baselines. The code is available at https://github.com/kimsu55/ToxExpert.Comment: 11 pages with 8 figure

On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention

Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. While there have been great advances in STR methods, current methods still fail to recognize texts in arbitrary shapes, such as heavily curved or rotated texts, which are abundant in daily life (e.g. restaurant signs, product labels, company logos, etc). This paper introduces a novel architecture to recognizing texts of arbitrary shapes, named Self-Attention Text Recognition Network (SATRN), which is inspired by the Transformer. SATRN utilizes the self-attention mechanism to describe two-dimensional (2D) spatial dependencies of characters in a scene text image. Exploiting the full-graph propagation of self-attention, SATRN can recognize texts with arbitrary arrangements and large inter-character spacing. As a result, SATRN outperforms existing STR models by a large margin of 5.7 pp on average in "irregular text" benchmarks. We provide empirical analyses that illustrate the inner mechanisms and the extent to which the model is applicable (e.g. rotated and multi-line text). We will open-source the code

Inspector Gadget: A Data Programming-based Labeling System for Industrial Images

As machine learning for images becomes democratized in the Software 2.0 era, one of the serious bottlenecks is securing enough labeled data for training. This problem is especially critical in a manufacturing setting where smart factories rely on machine learning for product quality control by analyzing industrial images. Such images are typically large and may only need to be partially analyzed where only a small portion is problematic (e.g., identifying defects on a surface). Since manual labeling these images is expensive, weak supervision is an attractive alternative where the idea is to generate weak labels that are not perfect, but can be produced at scale. Data programming is a recent paradigm in this category where it uses human knowledge in the form of labeling functions and combines them into a generative model. Data programming has been successful in applications based on text or structured data and can also be applied to images usually if one can find a way to convert them into structured data. In this work, we expand the horizon of data programming by directly applying it to images without this conversion, which is a common scenario for industrial applications. We propose Inspector Gadget, an image labeling system that combines crowdsourcing, data augmentation, and data programming to produce weak labels at scale for image classification. We perform experiments on real industrial image datasets and show that Inspector Gadget obtains better performance than other weak-labeling techniques: Snuba, GOGGLES, and self-learning baselines using convolutional neural networks (CNNs) without pre-training.Comment: 10 pages, 11 figure

Small-molecule inhibitors targeting proteasome-associated deubiquitinases

The 26S proteasome is the principal protease for regulated intracellular proteolysis. This multi-subunit complex is also pivotal for clearance of harmful proteins that are produced through-out the lifetime of eukaryotes. Recent structural and kinetic studies have revealed a multitude of conformational states of the proteasome in substrate-free and substrate-engaged forms. These confor-mational transitions demonstrate that proteasome is a highly dynamic machinery during substrate processing that can be also controlled by a number of proteasome-associated factors. Essentially, three distinct family of deubiquitinases–USP14, RPN11, and UCH37–are associated with the 19S regulatory particle of human proteasome. USP14 and UCH37 are capable of editing ubiquitin conjugates during the process of their dynamic engagement into the proteasome prior to the catalytic commitment. In contrast, RPN11-mediated deubiquitination is directly coupled to substrate degradation by sensing the proteasome’s conformational switch into the commitment steps. Therefore, proteasome-bound deubiquitinases are likely to tailor the degradation events in accordance with substrate processing steps and for dynamic proteolysis outcomes. Recent chemical screening efforts have yielded highly selective small-molecule inhibitors for targeting proteasomal deubiquitinases, such as USP14 and RPN11. USP14 inhibitors, IU1 and its progeny, were found to promote the degradation of a subset of substrates probably by overriding USP14-imposed checkpoint on the proteasome. On the other hand, capzimin, a RPN11 inhibitor, stabilized the proteasome substrates and showed the anti-proliferative effects on cancer cells. It is highly conceivable that these specific inhibitors will aid to dissect the role of each deubiquitinase on the proteasome. Moreover, customized targeting of proteasome-associated deubiquitinases may also provide versatile therapeutic strategies for induced or repressed protein degradation depending on proteolytic demand and cellular context. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1

Simultaneous fabrication of line and dot dual nanopatterns using miktoarm block copolymer with photocleavable linker

Block copolymers with various nanodomains, such as spheres, cylinders, and lamellae, have received attention for their applicability to nanolithography. However, those microdomains are determined by the volume fraction of one block. Meanwhile, nanopatterns with multiple shapes are required for the next-generation nanolithography. Although various methods have been reported to achieve dual nanopatterns, all the methods need sophisticated processes using E-beam. Here, we synthesized a miktoarm block copolymer capable of cleavage of one block by ultraviolet. Original cylindrical nanodomains of synthesized block copolymer were successfully transformed to lamellar nanodomains due to the change of molecular architecture by ultraviolet. We fabricated dual nanopatterns consisting of dots and lines at desired regions on a single substrate. We also prepared dual nanopatterns utilizing another phase transformation from spheres to cylinders in a block copolymer with higher interaction parameter. Since our concept has versatility to any block copolymer, it could be employed as next-generation nanolithography.112Ysciescopu

Evaluation of Skeletal and Dental Asymmetries in Patients with Angle Class II Subdivision Malocclusion with 3-Dimensional Analysis of Cone-Beam Computed Tomography

• Dentofacial asymmetries can present substantial challenges to orthodontic treatment.1 They, which can be congenital, developmental, and acquired, are based on discrepancies in the two halves of the face with reference to size, form, and arrangement of facial landmarks. • Class II subdivision malocclusions show more than half-step Class II occlusion on one side of the dental arch and Class I molar occlusion on the other side of the dental arch. They attribute to 50% of all Class II malocclusions and are one of the most frequent dental asymmetries in the orthodontic population.2 • Cone-beam computed tomography (CBCT) can be used to examine skeletal and dental asymmetries in Class II subdivision malocclusions and other morphological features of the craniofacial structures of facial asymmetry.3 • Mandibular asymmetry (skeletal) was the primary factor that contributed to Angle Class II subdivision malocclusions. Class II side had shorter total mandibular length and ramus height and deviated mandibular dental midline landmarks (pogonion and menton). Mandibular dental landmarks were positioned more latero-posterio-superiorly.

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp