Optical, microstructural and electrical studies on sol gel derived TiO2 thin films

TiO2 thin films have many interesting optical, physical, electrical and chemical properties that offer many applications in different fields of science and technology. The sol-gel spin coating technique has immense advantageous; such as low cost, usage of very simple equipment and relatively easy process control method. The optical, structural, microstructural and electrical properties have been analyzed through four point probe, XRD, SEM, high resolution electron microscopy, AFM and UV-VIS-NIR spectrophotometer. This paper is a research article about the sol-gel spin coated TiO2 thin film. The results will focus on the preparation and coating of TiO2 thin films on glass substrate at different annealing temperatures

Hydrogen Production Using TiO2-Based Photocatalysts: A Comprehensive Review

Titanium dioxide (TiO2) is one of the most widely used photocatalysts due to its physical and chemical properties. In this study, hydrogen energy production using TiO2- and titanate-based photocatalysts is discussed along with the pros and cons. The mechanism of the photocatalysis has been elaborated to pinpoint the photocatalyst for better performance. The chief characteristics and limitations of the TiO2 photocatalysts have been assessed. Further, TiO2-based photocatalysts modified with a transition metal, transition metal oxide, noble metal, graphitic carbon nitride, graphene, etc. have been reviewed. This study will provide a basic understanding to beginners and detailed knowledge to experts in the field to optimize the TiO2-based photocatalysts for hydrogen production

Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network

Background: The nextwave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a novel machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy.Methods: We collected clinical and laboratory data prospectively on all adult patients (≥18 years of age) that were admitted in the inpatient setting at Aga Khan University Hospital between February 2020 and September 2020 with a clinical diagnosis of COVID-19 infection. Only patients with a RT-PCR (reverse polymerase chain reaction) proven COVID-19 infection and complete medical records were included in this study. A Novel 3-phase machine learning framework was developed to predict mortality in the inpatients setting. Phase 1 included variable selection that was done using univariate and multivariate Cox-regression analysis; all variables that failed the regression analysis were excluded from the machine learning phase of the study. Phase 2 involved new-variables creation and selection. Phase 3 and final phase applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models, etc. The accuracy of these models were evaluated using test-set accuracy, sensitivity, specificity, positive predictive values, negative predictive values and area under the receiver-operating curves.Results: After application of inclusion and exclusion criteria (n=)1214 patients were selected from a total of 1228 admitted patients. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our deep neural network (DNN) (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the receiver-operator curve of 88.5.Conclusion: Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy

Combined oral contraceptives and their impact on lipids, blood pressure, and body mass index in pregnant women

Objective: To determine how combination oral contraceptive pills (COCPs) affect women of reproductive age's lipid profiles, blood pressure, and body mass index. Methodology: This cross-sectional study looked at the family planning programmes at the tertiary referral hospitals in Peshawar. We looked at married, childbearing women (aged 14 to 49). Group 1 (those who had used COCPs for at least six months) and Group 2 (controls of a comparable age who had not used COCPs) were created. Fasting blood TC, TG, HDL-C, LDL-C, and VLDL-C levels were assessed using a chemical analyser. Hb and platelet levels were assessed by a haematology analyst. Everyone had their BMI and blood pressure measured. The parameters of the oral and control groups were compared using SPSS. Results: The average BMI of Group 1 (Oral COCP) was 28.12 kg/m2 (+/- 0.50 SEM), while the average BMI of Group 2 (Control) was 26.25 kg/m2 (+/- 0.43 SEM). The mean BMIs of the two groups were very different (p-value: 0.0003). Women in Group 1 who took combined oral contraceptives had a much higher BMI than women in Group 2 who did not. BMI is used to measure health. It is based on height and weight.&nbsp