Search Results (3)

In this paper, we used data from publicly traded restaurant firms between 2000 and 2019 to test the effectiveness of multiple discriminant analysis (MDA) and logistic regression (logit) in predicting the probability of bankruptcy in the restaurant industry. We constructed various financial ratios extracted from the financial information and analyzed them to determine the optimal models. Our results show that liquid ratios (particularly the quick ratio), operating cash flow, and working capital emerge as the most crucial indicators of potential bankruptcy filings for restaurant firms. The results also show that the logit model performs better within the sample. However, both models exhibit similar predictive capacities with out-of-sample data. Full article

Purposes: To provide abdominal contrast-enhanced MR image synthesis, we developed an gradient regularized multi-modal multi-discrimination sparse attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring. Methods: With IRB approval, 165 abdominal MR studies from 61 liver cancer patients were retrospectively solicited from our institutional database. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast-enhanced (T1ce) images. The GRMM-GAN synthesis pipeline consists of a sparse attention fusion network, an image gradient regularizer (GR), and a generative adversarial network with multi-discrimination. The studies were randomly divided into 115 for training, 20 for validation, and 30 for testing. The two pre-contrast MR modalities, T2 and T1pre images, were adopted as inputs in the training phase. The T1ce image at the portal venous phase was used as an output. The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). A Turing test and experts’ contours evaluated the image synthesis quality. Results: The proposed GRMM-GAN model achieved a PSNR of 28.56, an SSIM of 0.869, and an MSE of 83.27. The proposed model showed statistically significant improvements in all metrics tested with p-values < 0.05 over the state-of-the-art model comparisons. The average Turing test score was 52.33%, which is close to random guessing, supporting the model’s effectiveness for clinical application. In the tumor-specific region analysis, the average tumor contrast-to-noise ratio (CNR) of the synthesized MR images was not statistically significant from the real MR images. The average DICE from real vs. synthetic images was 0.90 compared to the inter-operator DICE of 0.91. Conclusion: We demonstrated the function of a novel multi-modal MR image synthesis neural network GRMM-GAN for T1ce MR synthesis based on pre-contrast T1 and T2 MR images. GRMM-GAN shows promise for avoiding repeated contrast injections during radiation therapy treatment. Full article

The higher demand for honey from consumers, combined with its limited availability, has led to different types of honey adulteration, causing substantial economic as well as negative impacts on consumers’ nutrition and health. Therefore, a need has emerged for reliable and cost-effective quality control methods to detect honey adulteration to ensure both the safety and quality of honey. To simulate the process with those applied by beekeepers in real-time, bee colonies were fed with different types of bee feeding (sugar syrup, candy paste and commercial syrup). The produced samples were analyzed for their carbohydrate profile and their invertase activity with the aim to find the effects of bee feeding on the quality of the final product. Honey samples produced after feeding with commercial syrup presented low fructose (22.9 %) and glucose (31.7 %) concentrations and high content of maltose (20.1%), while the samples that came from bee feeding with sugar syrup and candy paste had high concentrations of sucrose (6.2 % and 3.2 %, respectively), exceeding in some cases the legislative limits. Moreover, the samples coming from sugar feeding had lower values of invertase activity, while the group with inverted syrup was clearly discriminated through multi-discriminant analysis. The invertase activity of control samples was found at 153.7 U/kg, which was significantly higher compared to the other groups. The results showed that bee feeding during honey production might lead to adulteration, which can be detected through routine analyses, including the carbohydrate profile and the invertase activity. Full article