Outcome & Complications of Decompressive Craniectomy with Expansion Duroplasty in Severe Head Injury

Objective:  A descriptive case series was conducted to find the frequency of complications and complications of decompressive craniectomy with expansion duraplasty in severe head injury. Material and Methods:  189 patients fulfilling the selection criteria were included. All patients had TBI which was confirmed by CT scan. Surgery was performed on the day of admission under general anesthesia and a large trauma flap. Patients were monitored daily by evaluators from the date of surgery until hospital discharge or death. Patients were followed up for 3 months and the outcome was assessed using the Glasgow outcome scale (GOS). Results:  Mean age of the patients was 36.57 years. There were 61.4% (116) males and 38.6% (73) females. 3.7% had CSF leakage. 1.6% had meningitis. Wound infection was seen in 7.4% of patients. Forty percent had a favorable outcome and 60% had a poor outcome. Fifty patients out of 111 patients between 18 – 40 years showed good outcomes. Twenty-six out of 78 from the 41 – 60 years age group showed good outcomes. Out of 189 total, 76 patients had a good outcome. The outcome was good in 63 patients out of 148 patients with GCS 5 – 8, whereas 13 (out of 41) patients had a good outcome with GCS below 5. Conclusion:  We discovered that the result was good in 40% of patients, with 11 percent of complications recorded. Therefore, we concluded that decompressive craniectomy with expansion duraplasty is an effective procedure for the treatment of the severe head injury

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

Financial threats are displaying a trend about the credit risk of commercial banks as the incredible improvement in the financial industry has arisen. In this way, one of the biggest threats faces by commercial banks is the risk prediction of credit clients. Recent studies mostly focus on enhancing the classifier performance for credit card default prediction rather than an interpretable model. In classification problems, an imbalanced dataset is also crucial to improve the performance of the model because most of the cases lied in one class, and only a few examples are in other categories. Traditional statistical approaches are not suitable to deal with imbalanced data. In this study, a model is developed for credit default prediction by employing various credit-related datasets. There is often a significant difference between the minimum and maximum values in different features, so Min-Max normalization is used to scale the features within one range. Data level resampling techniques are employed to overcome the problem of the data imbalance. Various undersampling and oversampling methods are used to resolve the issue of class imbalance. Different machine learning models are also employed to obtain efficient results. We developed the hypothesis of whether developed models using different machine learning techniques are significantly the same or different and whether resampling techniques significantly improves the performance of the proposed models. One-way Analysis of Variance is a hypothesis-testing technique, used to test the significance of the results. The split method is utilized to validate the results in which data has split into training and test sets. The results on imbalanced datasets show the accuracy of 66.9% on Taiwan clients credit dataset, 70.7% on South German clients credit dataset, and 65% on Belgium clients credit dataset. Conversely, the results using our proposed methods significantly improve the accuracy of 89% on Taiwan clients credit datase..

Performance evaluation of ruthenium complexes and organic sensitizers in ZnO-based dye-sensitized solar cells

Ruthenium (Ru) dyes are a well-known player in the field of dye-sensitized solar cells (DSSCs) due to their high efficiency and excellent stability. Their properties and complexes have been studied for almost three decades. Although these sensitizers show better performances, their high cost makes these third-generation solar devices less economical. Organic dyes have recently been explored as an alternative to Ru-based dyes due to their easy and low-cost synthesis. A comparative performance evaluation of Ru complexes and dicyanoisophorone and rhodanine organic dyes in ZnO-based DSSCs is here reported. All the Ru complexes showed better performance in comparison to organic dyes except R-4. Among the Ru sensitizers, R-3 exhibited the highest efficiency of 1.21% followed by R-2, which is attributed to the presence of several anchoring groups such as carboxyl, nitro and amine. However, the presence of more nitrogen-based groups has drastically reduced the performance as observed for R-4, which is the least performing dye among the Ru-based ones. On the contrary, organic sensitizers S-06 and P-4 revealed to be less efficient with respect to R-3 owing to the presence of only one anchoring group and weak photoanode/dye interaction