In this Letter, we apply deep-learning methods to the image-to-image translation from solar magnetograms to solar ultraviolet (UV) and extreme UV (EUV) images. For this, We consider two convolutional ...neural network models with different loss functions, one (Model A) is with L1 loss (L1), and the other (Model B) is with L1 and cGAN loss (LcGAN). We train the models using pairs of Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) nine-passband (94, 131, 171, 193, 211, 304, 335, 1600, and 1700 ) UV/EUV images and their corresponding SDO/Helioseismic and Magnetic Imager (HMI) line-of-sight (LOS) magnetograms from 2011 to 2016. We evaluate the models by comparing pairs of SDO/AIA images and the corresponding ones generated in 2017. Our main results from this study are as follows. First, the models successfully generate SDO/AIA-like solar UV and EUV images from SDO/HMI LOS magnetograms. Second, in view of three metrics (pixel-to-pixel correlation coefficient, relative error, and the percentage of pixels having errors less than 10%), the results from Model A are mostly comparable or slightly better than those from Model B. Third, in view of the rms contrast measure, the generated images by Model A are much more blurred than those by Model B because of LcGAN specialized for generating realistic images.
Abstract
In this study, we suggest a pixel-to-pixel image translation method among similar types of filtergrams such as solar extreme-ultraviolet (EUV) images. For this, we consider a deep-learning ...model based on a fully connected network in which all pixels of solar EUV images are independent of one another. We use six-EUV-channel data from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), of which three channels (17.1, 19.3, and 21.1 nm) are used as the input data and the remaining three channels (9.4, 13.1, and 33.5 nm) as the target data. We apply our model to representative solar structures (coronal loops inside of the solar disk and above the limb, coronal bright point, and coronal hole) in SDO/AIA data and then determine differential emission measures (DEMs). Our results from this study are as follows. First, our model generates three EUV channels (9.4, 13.1, and 33.5 nm) with average correlation coefficient values of 0.78, 0.89, and 0.85, respectively. Second, our model generates the solar EUV data with no boundary effects and clearer identification of small structures when compared to a convolutional neural network–based deep-learning model. Third, the estimated DEMs from AI-generated data by our model are consistent with those using only SDO/AIA channel data. Fourth, for a region in the coronal hole, the estimated DEMs from AI-generated data by our model are more consistent with those from the 50 frames stacked SDO/AIA data than those from the single-frame SDO/AIA data.
Purpose The purpose of this paper is to explore the moderating role of Lean Six Sigma (LSS) practices in explaining the relationship between quality management practices (QMPs) and quality ...performance. Design/methodology/approach Partial least square-based structural equation modeling (PLS-SEM) was used to empirically examine the moderating effect of LSS practices on QMPs and quality performance in Malaysian medical device manufacturing companies. Findings Findings revealed that both QMPs and LSS practices have a significant and positive effect on quality performance. Furthermore, LSS practices served as a substitute for moderating the positive relationship between QMPs and quality performance in such a way that the relationship becomes weaker as LSS practices increase. Originality/value LSS is acknowledged as the most well-known hybrid methodology; however, due to its relative newness, it has not been studied in great detail. Unlike previous studies, this paper argued that Lean and Six Sigma practices are distinct from its predecessor TQM practices; moreover, both Lean and Six Sigma practices do not need to substitute QM/TQM practices instead of complimenting the QMPs. In addition, this study adds to the growing body of QM literature by empirically examine the effect of LSS practices in moderating the relationship between QMPs and quality performance.
Abstract
We address the question of which combination of channels can best translate other channels in ultraviolet (UV) and extreme UV (EUV) observations. For this, we compare the image translations ...among the nine channels of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO) using a deep-learning (DL) model based on conditional generative adversarial networks. In this study, we develop 170 DL models: 72 models for single-channel input, 56 models for double-channel input, and 42 models for triple-channel input. All models have a single-channel output. Then we evaluate the model results by pixel-to-pixel correlation coefficients (CCs) within the solar disk. Major results from this study are as follows. First, the model with 131 Å shows the best performance (average CC = 0.84) among single-channel models. Second, the model with 131 and 1600 Å shows the best translation (average CC = 0.95) among double-channel models. Third, among the triple-channel models with the highest average CC (0.97), the model with 131, 1600, and 304 Å is suggested in that the minimum CC (0.96) is the highest. Interestingly, they represent coronal, upper photospheric, and chromospheric channels, respectively. Our results may be used as a secondary perspective in addition to primary scientific purposes in selecting a few channels of an UV/EUV imaging instrument for future solar satellite missions.
We apply an ensemble technique for major flare prediction by considering short-, mid-, and long-term active region (AR) properties and their relative contributions. For this, we consider magnetic ...parameters from Solar Dynamics Observatory/Helioseismic and Magnetic Imager and flare lists from Geostationary Operational Environmental Satellites. In this study, we simultaneously consider flaring rates during short- (1 day), mid- (several days), and long-term (several years) timeframes. In our model, the predicted rate is given by a weighted combination of the three rates such that the sum of their weights is 1. We calculate the Brier skill scores (BSSs) for investigating prediction performance and weights of these three terms to provide optimal results. The BSS (0.22) of the model with only long-term properties is higher than that with only short-term (0.07) or mid-term (0.08) properties. When short-/mid-term properties are additionally considered, the BSS is improved to 0.28/0.24. Our model has the best performance (BSS = 0.29) when all terms are considered, and their relative contributions to the short-, mid-, and long-term rates are 20%, 20%, and 60%, respectively. In addition, the model with three terms is more effective at predicting major flares in strong ARs. In view of the energy storage and release process, long-term magnetic properties may indicate the storage of magnetic free energy, while short- and mid-term flare history may reflect a recent trend of energy release process. Our results suggest that the performances of other existing flare models based on long-term properties should be improved by considering short- and/or mid-term flare history.
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Thyroid cancer is the most common endocrine malignancy, the global incidence rate of which is rapidly rising. Surgery and radioiodine therapies are common and effective treatments ...only for nonmetastasized primary tumors. Therefore, effective treatment modalities are imperative for patients with radioiodine-resistant thyroid cancer. Honokiol, a biophenolic compound derived from Magnolia spp., has been shown have diverse biological and pharmacological activities, including anti-inflammatory, antioxidative, antiangiogenic, and anticancer properties. In the present study, three human thyroid cancer cell lines, namely anaplastic, follicular, and poorly differentiated thyroid cancer cells, were used to evaluate the chemotherapeutic activity of honokiol. Cell viability, cell cycle, apoptosis, and autophagy induction were determined through flow cytometry and western blot analysis. We found that honokiol treatment can suppress cell growth, induce cell cycle arrest, and enhance the induction of caspase-dependent apoptosis and autophagy in cancer cells. Moreover, honokiol treatment modulated signaling pathways including Akt/mTOR, ERK, JNK, and p38 in the studied cells. In addition, the antitumorigenic activity of honokiol was also confirmed in vitro and in vivo. Our data provide evidence that honokiol has a unique application in chemotherapy for human thyroid cancers.
Sex determination is essential for identifying unidentified individuals, particularly in forensic contexts. Traditional methods for sex determination involve manual measurements of skeletal features ...on CBCT scans. However, these manual measurements are labor-intensive, time-consuming, and error-prone. The purpose of this study was to automatically and accurately determine sex on a CBCT scan using a two-stage anatomy-guided attention network (SDetNet). SDetNet consisted of a 2D frontal sinus segmentation network (FSNet) and a 3D anatomy-guided attention network (SDNet). FSNet segmented frontal sinus regions in the CBCT images and extracted regions of interest (ROIs) near them. Then, the ROIs were fed into SDNet to predict sex accurately. To improve sex determination performance, we proposed multi-channel inputs (MSIs) and an anatomy-guided attention module (AGAM), which encouraged SDetNet to learn differences in the anatomical context of the frontal sinus between males and females. SDetNet showed superior sex determination performance in the area under the receiver operating characteristic curve, accuracy, Brier score, and specificity compared with the other 3D CNNs. Moreover, the results of ablation studies showed a notable improvement in sex determination with the embedding of both MSI and AGAM. Consequently, SDetNet demonstrated automatic and accurate sex determination by learning the anatomical context information of the frontal sinus on CBCT scans.
Purpose
With the growing pressure to gain optimum level of quality and speed, Lean Six Sigma (LSS) practices have drawn considerable attention as a viable alternative for process improvement. ...However, previous studies revealed that there is very little systematic and rigorous research to validate the claims. In this regard, this paper aims to empirically examine the effect of LSS practices on quality performance in the medical device manufacturing industry.
Design/methodology/approach
For this study, partial least square–based structural equation modeling (PLS-SEM) was used to empirically examine the effect of LSS practices on quality performance in Malaysian medical device manufacturing industry.
Findings
The findings of this paper revealed that LSS practices have a significant and positive effect on quality performance in the medical device manufacturing industry.
Practical implications
This paper will serve as a valuable implication for industry practitioners in providing them with a clearer managerial direction to exploit the strength of LSS practices to achieve company’s quality goals. Moreover, this study will serve as a basis for future LSS scholars, providing them with valuable insights and directions for future research.
Originality/value
This paper develops a conceptual LSS framework that captures the integrated nature of two methodologies and provides empirical evidence that supports the positive influence of LSS practices on quality performance; hence, it contributes to the growing body of LSS literature in both theoretical and empirical sense.
•Dental pattern diversity increases when dental treatment timeline was considered.•Personal identification becomes more simple with increased dental pattern diversity.•Panoramic radiography is ...powerful tool for building up antemortem database.•Deep learning would greatly contribute on rapid dental pattern database construct.
The present study was conducted to improve human identification based on dental pattern with adopting chronology of dental treatment within the system. Five hundred adult patients were randomly selected, and their initial and recent panoramic radiography images were assumed as antemortem (AM) and postmortem (PM) images, respectively. For each radiographic image, the dental pattern was analyzed. The analysis system was newly developed considering sequence of dental treatment in time order. AM and PM databases were constructed with information of dental patterns, patient age, and gender. For the PM database, age information was stored as the actual age ± 10 years, which was defined as the estimated age. According to dental pattern of PM record, the possible AM records were selected as candidates. Then candidates were sorted in order of dental pattern similarity to the PM record, and the rank of the true AM record was identified. The total 500 AM records were reduced to 14.5 ± 13.4 candidates in average when the dental pattern, gender, and estimated age were considered. When the candidates were sorted in order of similarity, the true AM record received an average ranking of 2.0 ± 2.6. When dental pattern and gender were considered, 46.7 ± 42.3 candidates were selected among 500 records and the true AM record was ranked at 3.0 ± 5.0, in average. The dental pattern analysis adopting dental treatment chronology was contributed to reduce the sample population. This method would become more efficient and comprehensive if the dental pattern analysis process is automatized in the near future.
In this study, the effects of bisphenol A (BPA) and diphenyl carbonate (DPC) molar ratio, on the properties of melt-polymerized polycarbonate (PC) were investigated. The molecular size distribution ...theory proposed by Flory was applied, to melt polymerization of PC to predict physical properties, affected by the molar ratio of BPA and DPC. A terminal OH group affected the viscosity of PC at high temperatures, leading to instability during processing. With increase in the DPC/BPA molar ratio, terminal OH content decreased, albeit different from the theoretical predicted value, because of the volatilization of DPC. Additionally, BPA residual amount was affected by BPA and DPC molar ratio. BPA is regulated in countries because of its similarity to estrogen, and BPA residues can be predicted and managed by using the Flory equation.