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The present research aims to design the new dyes with a D-p-A structure to apply Dye-sensitized solar cells. The D-p-A dyes constitute S1 to S9, using 9-vinyl-9H-carbazole (D) as a donor (D); different thiophene derivatives are p-spacers and cynoacrylic acid is an acceptor. For the electronic and optical properties calculation of the studied dyes, use DFT/B3LYP/6-311G++ levelof theory by using Gaussian 09W software. Using optimized geometrical structures to calculate the UV absorption spectra, utilize the TD-DFT method in the gas and acetonitrile solvent. The HOMO and LUMO energy gap (Eg) values of S5 and S9 dyes show a narrow band gap with 1.8 eV and 1.7 eV, respectively. So all studied dyes have LUMO values higher than the TiO2 conduction band and HOMO values lower than the electrolyte redox potential. The UV absorption spectral data shows S5 and S9 have maximum absorption at 759 nm and 803 nm, respectively. So all the studied dyes are suitable for DSSC application. [ABSTRACT FROM AUTHOR]

The article discusses research which described the development and evaluated cross breed hybrids for Bombyx mori bidensovirus (BmBDV) resistance through molecular marker assisted selection. The study details the screening and selection of silkworm breeds for BmBDV resistance, isolation of genomic DNA, BmBDV resistant marker analysis and BmBDV inoculum preparation. It identifies the economic traits of BmBDV resistant cross breeds, mortality rate and survival rate for BmBDV infection.

Now a days microgrid is one of the most widely used method in power network to reduce system losses as well as improve the reliability in the field of electrical systems. Integration of power projects typically involves adding new distributed energy sources with and without compensating devices to an existing power system network. It is essential to design new protection scheme due to changes in the topology and dynamic behavior of the system. Now fast fault detection algorithmic approaches are necessary to integrate different types of generating sources and loads under smart environment. The protection scheme must provide physical monitoring as well as parametrical with the help of new technologies. Internet-of-things(IoT) is one of the source to monitor electrical systems under various environmental conditions of the system. Wavelet (WT) basically investigates the fault transient signals of different frequency and divides the waveform into different approximate and detailed coefficient values, which provides the important knowledge about the classification and location of fault. The detection of faulty-line and the location of fault by implementation wavelet detailed coefficients of Bior1.5 mother wavelet. This proposed method provides fault analysis of IoT based protection of microgrid with Grid-connected and Islanded Mode Using Wavelet Approach under various types of faults. [ABSTRACT FROM AUTHOR]

Background: Diabetes, is known to have a bilateral relationship with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Precise mechanism of diabetes onset in COVID-19 patients remains unclear. Aim: To analyse the incidence of new onset diabetes (NODM) among COVID-19 patients, as well as the effect of body mass index (BMI), family history, and steroid use on the incidence of the disease. Methods: Adult, not known diabetic patients, tested positive with Rapid Antigen Test or RT-PCR admitted to a tertiary care hospital and research institute were included in the present prospective observational study. The patients who developed NODM and NOPD (New Onset Pre-diabetes) during the three months follow-up and the risk factors associated were assessed. Patients with HbA1c >6.4% were diagnosed with NODM. An HbA1c of 5.7% to 6.4% was used to characterize NOPD. Results: Out of 273 previously not known diabetic COVID-19 infected individuals, a total of 100 were studied for three months after consent. Mean age of the patients 48.31 ± 19.07 years with male predominance (67%). Among these, 58% were non-diabetics and 42% were pre-diabetics. 6 (10.3%) of the 58 non-diabetics developed NOPD, and 8 (13.8%) developed NODM. 6 (14.2%) of the 42 pre-diabetics became non-diabetic, and 16.6% (7) developed NODM. Family history of DM (P < 0.001), severity at admission (P < 0.006), diabetic ketoacidosis (P < 0.0275), and persistent symptoms were associated significantly with NODM. Those with NODM had significantly greater BMI, O2 duration, steroid duration, FBS, and PPBS (P < 0.001 for all). Nearly 67% of the patients who developed NOPD had shortness of breath as the common symptom at time of admission (P = 0.0165). Conclusion: The incidence of NODM was strongly influenced by positive family history of DM, higher BMI, steroid dosage, and its duration. Hence, patients with COVID-19 need to be under surveillance for blood glucose screening. [ABSTRACT FROM AUTHOR]