Objective: The primary objective of this study was to compare the efficacy of 3 different anesthesia induction approach (Inj. Propofol, Inj. Etomidate and Inj. propofol plus Inj Etomidate) in maintaining hemodynamic stability during induction and following endotracheal intubation in elective surgery. Material and method:Ethical committee clearance taken, 90 patients aged 15 to 60 years of either sex and ASA physical status I or II scheduled for elective surgery under general anesthesia were taken for study. Written and informed consent was taken. The patients were randomly placed into three groups. Group I induced with Inj. Propofol (2.5 mg/kg) intravenous, Group II with Inj. Etomidate (0.3 mg/kg) intravenous and Group III with Inj. Propofol (1 mg/kg) plus Inj. Etomidate (0.2 mg/kg) intravenous. Heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial blood pressure (MAP) and oxygen saturation (SPO 2 ) were noted at different time interval.Results: Heart rate in all study groups decreases after induction and it was more in group I compared to group II and III (p<0.000) and after intubation HR increases in all three groups but this increase is greater in group II than other two groups. MAP among all three groups decreases after induction and it was more in group I than group II and III. Significant increase in MAP was seen at 1 min after intubation in all three groups but this increase was not sustained and returned to baseline in group II and III. Conclusion:The combination of etomidate plus propofol has better hemodynamic stability than etomidate alone at 1 min after intubation, though etomidate was equally stable at other points of time. The combination proved to be significantly better than either propofol or etomidate alone.

In the present study, needle-punch nonwoven jute fiber reinforced epoxy composites are fabricated by compression molding techniques by varying alumina ceramic particulates (0-15 wt%) in the composites. The physical tests are studied such as void content, hardness, and water absorption analysis and mechanical tests are performed such as: tensile strength, impact strength, and fracture toughness, respectively. The results indicated that with the addition of 5 wt% of alumina particulate to the unfilled needle-punch nonwoven jute epoxy composite is increased the hardness by 13.15%, tensile strength by 30%, flexural strength by 20%, and impact strength by 9.01%, respectively. Finally, thermo-mechanical test such as dynamic mechanical analysis, thermo-gravimetric analysis, and thermal conductivity analysis of the unfilled and particulate filled polymer composites are characterized. The thermal conductivity of the unfilled composite is decreased by 3.01% by the addition of 5 wt% of alumina particulate. Further, it is also observed that jute reinforced epoxy composites filled with 15 wt% alumina particulate presented highest storage modulus, loss modulus, and thermal stability as compared with 0 wt%, 5 wt%, and 10 wt% alumina particulate filled jute epoxy composite. POLYM. COMPOS., 39:1553-1561, 2018.

The Covid-19 pandemic is one of the most significant global health concerns that have emerged in this decade. Intelligent healthcare technology and techniques based on speech signal and artificial intelligence make it feasible to provide a faster and more efficient timely detection of Covid-19. The main objective of our study is to design speech signal-based noninvasive, low-cost, remote diagnosis of Covid-19. In this study, we have developed system to detect Covid-19 from speech signal using Mel frequency magnitude coefficients (MFMC) and machine learning techniques. In order to capture higher-order spectral features, the spectrum is divided into a larger number of subbands with narrower bandwidths as MFMC, which leads to better frequency resolution and less overall noise. As a consequence of an improvement in frequency resolution as well as a decrease in the quantity of noise that is included with the extraction of MFMC, the higher-order MFMCs are able to identify Covid-19 from speech signals with an increased level of accuracy. The procedures for machine learning are often less complicated than those for deep learning, and they may commonly be carried out on regular computers. However, deep learning systems need extensive computing power and data storage. Twelve, twenty-four, thirty, and forty spectral coefficients are obtained using MFMC in our study, and from these coefficients, performance is accessed using machine learning classifiers, such as random forests and K -nearest neighbor (KNN); however, KNN has performed better than the other model with having AUC score of 0.80.

One of the most important properties of Discrete Cosine Transform (DCT) is high power compaction, due to this property DCT is used for coding, image processing, image compression etc. The DCT application can be speed up by Signed Discrete Cosine Transforms (SDCT). The SDCT is the modification approximates of the DCT. In this paper by proposing signum function we proposed a flow diagram of algorithm and its architecture by considering 8 point transforms.

Venous air embolism (VAE), though, clinically benign in majority of cases, the significant ones can lead to life-threatening cardiopulmonary and neurological consequences. Though studies mention the success rate of only 6 to 16% in aspirating air from the central venous catheter (CVC) during VAE, the technique is very specific for diagnosing VAE and has high therapeutic significance. We report a case in which delayed aspiration of air emboli from the CVC in suspected massive VAE during decompressive craniectomy resulted in rapid resolution of hemodynamic instability. If not inserted previously, CVC may be considered in a hemodynamically unstable patient with suspected VAE.