1,135 research outputs found
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 LiRuO
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
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