Computed tomographic evaluation of inflammatory sinonasal diseases

Background: Computed Tomography plays a major diagnostic role in patients with inflammatory sinonasal diseases and determines the mode of management by displaying the complex osteomeatal anatomy, determining anatomical variations, extent of disease and characterizing various inflammatory sinonasal diseases. Purpose of the study was to assess the role of CT in evaluation of inflammatory sinonasal diseases by evaluating the sensitivity and specificity of CT in diagnosis of various inflammatory Sinonasal diseases.Methods: In this hospital based prospective study 122 patients with symptomatic inflammatory sinonasal diseases were evaluated by 16 slice MDCT. CT diagnosis is correlated with final diagnosis obtained from findings of nasal endoscopy/Functional Endoscopic Sinus Surgery, histopathological examination and fungal culture. Statistical analysis was done by descriptive and inferential statistics using Test statistics (sensitivity, specificity, PPV, NPV and accuracy) and Z test for single proportions (Z value >1.96 is considered significant). Software used in the analysis was SPSS 17.0 version and graph pad prism 6.0 version and p <0.05 is considered as level of significance.Results: On correlating CT diagnosis with final diagnosis, Chronic Sinusitis had 98.41% sensitivity and 96.61% specificity, fungal sinusitis had 66.67% sensitivity and specificity 99.14%, polyps had sensitivity of 94.59% and specificity of 97.6%, the rest of the inflammatory conditions had sensitivity 93.7 % and 99% specificity. P value in all instances was <0.05, i.e. <0.0001, indicating the significance of the findings.Conclusions: CT is the diagnostic modality of choice in evaluation of various inflammatory pathologies and associated complications thereby planning the further management of the patient

NONLINEAR CONTROL DESIGN FOR SOLID OXIDE FUEL CELLS

Ever increasing energy consumption, rising public awareness for environmental protection and higher prices of fossil fuels have motivated many to look for renewable energy sources. SOFC is one of the best alternative energy source but it is a highly nonlinear system. One of the main goals of this research is designing a nonlinear control. In this thesis, non-isothermal model of solid oxide fuel cell has been developed under the general condition of unchoked outlet flow. For isothermal dynamic model of SOFC, a controller based on on full state feedback linearization is designed and compared with controller based on linearization and gain scheduling. Analysis of controllability and observability is also given for the nonlinear isothermal model

PHYTOCHEMICAL ANALYSIS, ANTIOXIDANT AND HEPATOPROTECTIVE ACTIVITY OF ACTINIOPTERIS RADIATA

Objective: The medicinal plants have been using to treat ailments since ancient times. The recent advances in science and technology impel humans to evaluate medicinal plants therapeutic efficiency and isolation of bioactive compounds in pure forms before their use in development of new drugs and their derivatives. But even now, abundant medicinal plants unevaluated scientifically. The current study was aimed to explore phytochemical constituents, antioxidant and hepatoprotective activities of Actiniopteris radiata root parts. Methods: Standard procedures have been used to perform phytochemical analysis. Antioxidant activity was carried using In vitro methods on superoxide, hydroxyl, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radicals. Hepatoprotective activity was studied by paracetamol-induced liver toxicity on WISTAR albino rats. The parameters assessed were Aspartate aminotransferase (SGOT/AST), Alanine aminotransferase (SGPT/ALT), alkaline phosphatase (ALP) and total bilirubin levels. Results: The tested extracts (hexane, ethyl acetate, and hydro-alcoholic) possess biologically active compounds such as sterols, terpenoids, glycosides, phenolics, alkaloids, flavonoids. The hydro-alcoholic extract has more phenolic contents (24.28±0.3) and flavonoid contents (22.68±0.6). The extracts showed dose dependent activity on tested free radicals and extracts showed more percentage inhibition at 320µg. The hydro-alcoholic extract showed more percentage inhibition i.e. 71.00±2.08 on DPPH free radical, 79.67±1.20 on hydroxyl free radical and 80.33±1.20 on superoxide free radical. As antioxidant activity of hexane and ethyl acetate extracts was less and they also showed less percentage protection on liver toxicity, hydro-alcoholic extract showed more percentage protection on biomedical enzyme levels of liver toxicity at high concentration i.e., 400 mg/kg b.w. The percentage protection on the enhancement of AST (SGOT), ALT (SGPT), ALP, and total bilirubin levels were 82.24%, 82.14%, 84.18%, and 82.85% are significant (P&lt;0.01) as Liv52 shown percentage protection on the enhancement of Aspartate aminotransferase (SGOT), alanine aminotransferase (SGPT), Alkaline phosphatase (ALP) and total bilirubin levels were 93.58%, 92.83%, 94.67% and 93.57%. Conclusion: The current study was aimed to explore phytochemical constituents, antioxidant and hepatoprotective activities of Actiniopteris radiata root parts extracts. The outcome of the current research results provides scientific evidence of the traditional usage of Actiniopteris radiata

Nexus between Returns in Commodity Market and Equity Market: A Case of Indian Steel Industry

There has been a rapid expansion in the commodities market in the recent past due to the sharp rise in appetite of institutional investors or financial institutions for commodities for asset management purpose. Commodities markets are demonstrating an ever-increasing volatility due to structural shifts in supply and demand sides leading to wide price swings. This volatility is puzzling researchers, academicians, and portfolio Managers who try to understand the volatility to design transmission mechanisms. The article attempts to examine the relationship between the steel price in commodity market and equity share price of steel industries. The article also attempts to examine association between CNX metal index with spot market steel price, spot market commodity metal index and Nifty index. The method used is primarily regression analysis. It has been found that the variables individually are having comparatively lesser impact on CNXMIR but jointly they are explaining 73% of total variation

Identification of causative pathogen and its antibiotic sensitivity in cases of preterm premature rupture of membranes

Background: Pre-labor rupture of membranes is defined as amniotic membrane rupture before the onset of labor contractions, and if it happens before 37 weeks, it is called preterm premature rupture of membranes (PPROM). Several organisms commonly present in the vaginal tract are E.coli, Group-B streptococci, staphylococcus aureus, chlamydia trachomatis, Gardnerella vaginalis and Enterococcus faecalis which secrete proteases that degrade collagen thereby weakening  the fetal membranes leading to PPROM. Appropriate antibiotic therapy has a significant role in the prevention and treatment of maternal and neonatal complications.Methods: This was a prospective observational study done in the department of obstetrics and gynaecology, Narayana medical college, Nellore. Selectively 100 patients with complaint of PPROM admitted to labor room were included in the study. Diagnosis of membrane rupture was established by speculum examination, and high vaginal swabs are taken and sent to laboratory for identifying bacteria using gram staining and cultured in aerobic and anaerobic methods. Antimicrobial susceptibility testing of the organisms was performed by disk diffusion method by Kirby and Bauer.Results: Out of 100, high vaginal swabs had growth in 82 patients, and 18 were sterile. The repeatedly isolated organism in patients with PPROM is E.coli amounting 32%, followed by candidal species 20%. Staphylococci are scoring 11% and enterococci 8%. However, organisms like gardenella vaginalis and Group B streptococcus are least common with a score of 6% and 5% respectively. In this study, E.coli is highly sensitive to tigecycline, colistin 100% each and highly resistant to gentamycin and amikacin.Conclusions: In this study, E.coli is related to the maximum number of cases with preterm premature rupture of membranes. Appropriate use of antibiotics significantly lowers maternal morbidity and neonatal mortality

TRIZ inspired design guidelines for remanufacturing using additive manufacturing

Remanufacturing is the process of turning used products into new ones in terms of quality, functionality, and aesthetics with relatively lower product price. To make the process of remanufacturing easier, certain changes may be required in the design of a remanufactured product. The advent of additive manufacturing has opened new opportunities for the design for remanufacturing with an increase in the feasibility of various product design options for remanufacturing. Although there are design guidelines scattered across literature, a set of design rules or guidelines that facilitate remanufacturing using additive manufacturing is not clearly provided in the extant literature. To fill this void, this research focuses on building a set of design guidelines based on a TRIZ matrix to solve conflicting problems or issues on design for remanufacturing. The proposed TRIZ matrix facilitates necessary design guidelines for remanufacturing using additive manufacturing. A case study is provided to elaborate on the application of the design guidelines derived from the proposed methodology. This study develops a user-friendly TRIZ tool which will help identify all the parameters that need to be considered for remanufacturing and provides feasible solutions to successfully remanufacture a product. This tool will be of great use to industries and designers trying to design parts for remanufacturing

A Quantum Convolutional Neural Network Approach for Object Detection and Classification

This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasing amount of data, utilizing computing methods like CNN in real-time has become challenging. QCNNs overcome this challenge by utilizing qubits to represent data in a quantum environment and applying CNN structures to quantum computers. The time and accuracy of QCNNs are compared with classical CNNs and ANN models under different conditions such as batch size and input size. The maximum complexity level that QCNNs can handle in terms of these parameters is also investigated. The analysis shows that QCNNs have the potential to outperform both classical CNNs and ANN models in terms of accuracy and efficiency for certain applications, demonstrating their promise as a powerful tool in the field of machine learning