Chemical Property-Guided Neural Networks for Naphtha Composition Prediction

The naphtha cracking process heavily relies on the composition of naphtha, which is a complex blend of different hydrocarbons. Predicting the naphtha composition accurately is crucial for efficiently controlling the cracking process and achieving maximum performance. Traditional methods, such as gas chromatography and true boiling curve, are not feasible due to the need for pilot-plant-scale experiments or cost constraints. In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction. Our proposed framework comprises two parts: a Watson K factor estimation network and a naphtha composition prediction network. Both networks share a feature extraction network based on Convolutional Neural Network (CNN) architecture, while the output layers use Multi-Layer Perceptron (MLP) based networks to generate two different outputs - Watson K factor and naphtha composition. The naphtha composition is expressed in percentages, and its sum should be 100%. To enhance the naphtha composition prediction, we utilize a distillation simulator to obtain the distillation curve from the naphtha composition, which is dependent on its chemical properties. By designing a loss function between the estimated and simulated Watson K factors, we improve the performance of both Watson K estimation and naphtha composition prediction. The experimental results show that our proposed framework can predict the naphtha composition accurately while reflecting real naphtha chemical properties.Comment: Accepted at IEEE International Conference on Industrial Informatics 2023(INDIN 2023

Interpretable pap smear cell representation for cervical cancer screening

Screening is critical for prevention and early detection of cervical cancer but it is time-consuming and laborious. Supervised deep convolutional neural networks have been developed to automate pap smear screening and the results are promising. However, the interest in using only normal samples to train deep neural networks has increased owing to class imbalance problems and high-labeling costs that are both prevalent in healthcare. In this study, we introduce a method to learn explainable deep cervical cell representations for pap smear cytology images based on one class classification using variational autoencoders. Findings demonstrate that a score can be calculated for cell abnormality without training models with abnormal samples and localize abnormality to interpret our results with a novel metric based on absolute difference in cross entropy in agglomerative clustering. The best model that discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003 area under operating characteristic curve (AUC) and one that discriminates high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other clustering methods, our method enhances the V-measure and yields higher homogeneity scores, which more effectively isolate different abnormality regions, aiding in the interpretation of our results. Evaluation using in-house and additional open dataset show that our model can discriminate abnormality without the need of additional training of deep models.Comment: 20 pages, 6 figure

Predictors of the Change in the Expression of Emotional Support within an Online Breast Cancer Support Group: A Longitudinal Study

OBJECTIVES: To explore how the expression of emotional support in an online breast cancer support group changes over time, and what factors predict this pattern of change. METHODS: We conducted growth curve modeling with data collected from 192 participants in an online breast cancer support group within the Comprehensive Health Enhancement Support System (CHESS) during a 24-week intervention period. RESULTS: Individual expression of emotional support tends to increase over time for the first 12 weeks of the intervention, but then decrease slightly with time after that. In addition, we found that age, living situation, comfort level with computer and the Internet, coping strategies were important factors in predicting the changing pattern of expressing emotional support. CONCLUSIONS: Expressing emotional support changed in a quadratic trajectory, with a range of factors predicting the changing pattern of expression. PRACTICAL IMPLICATIONS: These results can provide important information for e-health researchers and physicians in determining the benefits individuals can gain from participation in should CMSS groups as the purpose of cancer treatment

The mediating effects of parenting style on the relationship between parental stress and behavioral problems in girls with precocious puberty in Korea: a cross-sectional study

Background This study aimed to examine the mediating effects of parenting style on the relationship between parental stress and behavioral problems of girls with precocious puberty. Methods This cross-sectional study analyzed a convenience sample of 200 mothers of girls with precocious puberty at a university hospital located in a metropolitan area. The Parental Stress measurement, Parents as Social Context Questionnaire, and Korean version Child Behavior Checklist (K-CBCL) 6–18 were measured via self-report questionnaires. Descriptive, t-test, Pearson correlation, and bootstrapping analyses were used to analyze the data. Results Negative parenting styles had a full mediating effect on the relationship between parental stress and internalizing and externalizing behavioral problems. Conclusions Care plans for parents of girls with precocious puberty should be designed and applied in health care settings to reduce internalizing and externalizing behavioral problems by decreasing negative parenting styles.This work was supported by the Sungshin Womens University Research Grant of 2020

Pilot KaVA monitoring on the M87 jet: confirming the inner jet structure and superluminal motions at sub-pc scales

We report the initial results of our high-cadence monitoring program on the radio jet in the active galaxy M87, obtained by the KVN and VERA Array (KaVA) at 22 GHz. This is a pilot study that preceded a larger KaVA-M87 monitoring program, which is currently ongoing. The pilot monitoring was mostly performed every two to three weeks from December 2013 to June 2014, at a recording rate of 1 Gbps, obtaining the data for a total of 10 epochs. We successfully obtained a sequence of good quality radio maps that revealed the rich structure of this jet from <~1 mas to 20 mas, corresponding to physical scales (projected) of ~0.1-2 pc (or ~140-2800 Schwarzschild radii). We detected superluminal motions at these scales, together with a trend of gradual acceleration. The first evidence for such fast motions and acceleration near the jet base were obtained from recent VLBA studies at 43 GHz, and the fact that very similar kinematics are seen at a different frequency and time with a different instrument suggests these properties are fundamental characteristics of this jet. This pilot program demonstrates that KaVA is a powerful VLBI array for studying the detailed structural evolution of the M87 jet and also other relativistic jets.Comment: 10 pages, 9 figures, accepted for publication in PAS

Robust singlet dimers with fragile ordering in two-dimensional honeycomb lattice of Li2_2RuO3_3

When an electronic system has strong correlations and a large spin-orbit interaction, it often exhibits a plethora of mutually competing quantum phases. How a particular quantum ground state is selected out of several possibilities is a very interesting question. However, equally fascinating is how such a quantum entangled state breaks up due to perturbation. This important question has relevance in very diverse fields of science from strongly correlated electron physics to quantum information. Here we report that a quantum entangled dimerized state or valence bond crystal (VBC) phase of Li2RuO3 shows nontrivial doping dependence as we perturb the Ru honeycomb lattice by replacing Ru with Li. Through extensive experimental studies, we demonstrate that the VBC phase melts into a valence bond liquid phase of the RVB (resonating valence bond) type. This system offers an interesting playground where one can test and refine our current understanding of the quantum competing phases in a single compound.Comment: Scientific Reports (in press