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A novel g-C3N4/ZnO-TiO2 nanocomposite was prepared by facile calcination and hydrothermal assisted route to boost the photocatalytic efficiency of g-C3N4. The structural, morphology and optical properties of as-obtained nanocomposites (NCs) were examined by various physicochemical methods such as XRD, FT-IR, FESEM-EDX, HR-TEM, UV-DRS and PL spectra relatively. The as-obtained g-C3N4/ZnO-TiO2 heterojunction NCs displays an optimum efficiency (93.6%) and high photo-degradation rate constant towards the photo-degradation of RhB dye in 75 min under visible-light exposure. Likewise, the synergistic effects on heterostructure strong-coupling interfaces assembly of g-C3N4 and TiO2/ZnO NPs, which facilitates the excellent photo-excited charge separation efficiency, suppressing the recombination rate and also widening the visible-light fascination ability. Hence, the resulting in a prolonged lifetime leading to continuous generation of h+ and •OH− species which were eventually improved the photo-degradation process. Also, the as-obtained hybrid g-C3N4/ZnO-TiO2 photocatalysts (PCs) revealed superior recycling stability. Moreover, a probable photocatalytic reaction mechanism of the novel PCs was also suggested. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between impact of electronic resources and its usage in selected management institution libraries in Coimbatore. This is a quantitative approach-based study done by using questionnaire to gather data and information. A valid response rate of 76% has been obtained. Survey method is used for this study, primary data is collected via questionnaire. Five-point Likert scale is used, SPSS software helped to analyse the data in respect of descriptive elements. The study is relevant as it will help to build up awareness about e-resources and view them as valuable scholarly tools. Another reason for the importance of the study is that it enquires about the purposes for which the academia in higher education uses e-resources, thus contributing to knowledge in those areas. The study recommends library professional to take initiative to make awareness about the e-resources and intensive marketing strategies should be adopted to increase usage of electronic resources in management institution libraries in Coimbatore. [ABSTRACT FROM AUTHOR]

In this work, strategies for the formation of duplex coatings with enhanced bioactivity, mechanical properties and corrosion resistance have been focused. TiO 2 arrays were fabricated on Ti alloy were carried out in a single step using suitable electrolyte by anodization method. Here, we have synthesized a novel bioactive material, minerals incorporated Hydroxyapatite (La/Tb-HAP)-chitosan-casein duplex coatings on anodized Ti via electrodeposition method. The fabricated novel composite coatings were characterized by FT-IR, XRD, FESEM and EDAX analyses. Also, the mechanical properties of duplex coatings were scrutinized by Dermitron thickness and microhardness tester. The corrosion resistance of the as-developed duplex coatings was studied by electrochemical techniques using Ringers solution as the electrolyte. In addition, the antibacterial activity, cell viability, live and dead staining were executed to substantiate the biocompatibility of TNT/CS-CA@M-HAP duplex coatings. From the overall summary of this work, it is proved that the resultant CS-CA@M-HAP coatings on TNT exhibit excellent bioactivity and improved corrosion resistance over pure Ti and serve as a potential candidate for orthopedic applications. [ABSTRACT FROM AUTHOR]

Image data points mining is concerned with the extraction of knowledge relationship among image data and other patterns inherent in the images. Taxonomy-Aware Catalog Integration (TCI) processing step ensures that the master taxonomy Rule-based Multivariate Text Feature Selection (RMTFS) method takes into consideration both the semantic information and the syntactic relationships between n-gram features. In realizing the relationship between the attributes, the Decision Tree-based Label Feature Classifier (DTLFC) mechanism is proposed. In the initial step in the DTLFC mechanism, pixel-wise image feature points are extracted and the same is converted into a table of a database. The feature descriptor thus formed by combining a features set and a specific pixel's label is represented by a tuple of the transformed database table and helps in the generation of the decision tree using a given image points data set in constructing a pixel-wise image processing model. Both experimental and theoretical analysis demonstrate that the DTLFC mechanism can attain a very efficient and effective level of classification on image data points. Compared with the TCI and RMTFS methods, the DTLFC mechanism creates an effective feature classifier in terms of filtering efficiency, classification accuracy, processing time, computational cost, scalability, and the intensity rate of dominant attributes. [ABSTRACT FROM AUTHOR]

• The input image is given to Grey Code Approximation Pre-processing (GCAP) algorithm to enhance the quality of image. • The preprocessed image is given to Adaptive Fuzzy C-Means (AFCM) algorithm and is applied in segmenting dominant regions in an input image. • A technique for classifying images called DCNN analyses the input image and categorizes it as benign, malignant and normal. • The method extracts the features of the image and train with VGG-16 Net classifier. • Accordingly, the mammogram class is identified towards detecting brain tumor. Breast cancer mortality can be prevented by only early, accurate mammography screening and diagnosis. Although CNN-based computer-aided diagnosis (CAD) systems for breast cancer have made tremendous progress recently, accurate identification of mammography lesions is still difficult because of poor signal-to-noise ratio (SNR) and physiological features. In this manuscript, an Adaptive Fuzzy C-Means Segmentation and Deep Learning Model for Efficient Mammogram Classification Using VGG-Net (AFCM-DCNN) is proposed. The input image is given to Grey Code Approximation Pre-processing (GCAP) algorithm to enhance the quality of image by adjusting the pixel contrast. The preprocessed image is given to Adaptive Fuzzy C-Means (AFCM) algorithm and is applied in segmenting dominant regions in an input image. But in conventional AFCM technique, the centroid values get generated randomly, which consumes more computational time. Hence to enhance the performance of traditional AFCM, centroid value is optimally chosen by means of optimization algorithm. A technique for classifying images called DCNN analyses the input image and categorizes it as either benign, malignant and normal. The method extracts the features of the image and train with VGG-16 Net classifier. The neurons at the output layer have been designed to compute Class Centric Disease Support (CCDS) towards various classes. Accordingly, the mammogram class is identified towards detecting the brain tumor. The performance of the proposed method AFCM-DCNN exhibits higher accuracy of 29.3%, 25.6% and 24.6%, higher sensitivity of 15.4%, 16.6% compared with the existing methods. Therefore, in future work, hope to enhance the performance depending on transfer learning with similar data. [ABSTRACT FROM AUTHOR]

Abstract The extracts of Moringa oleifera , a traditional medicinal and culinary plant containing phytochemicals with diverse therapeutic properties were investigated. Five parts of the plant were extracted with 5 different solvents to analyze the total phenolics content (TPC) and total flavonoids content (TFC) which were then tested for their antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging, reducing potential, and total antioxidant capacity assays. The TPC-TFC were highest in leaves. TPC in ethyl acetate extract (200 ± 10 mg/g) and TFC in aqueous extract (69 ± 3 mg/g) were the highest. The antioxidant activity was highest in the leaves when compared with other parts like roots, barks, and flowers. Many phenolic compounds were quantified using UHPLC, the flavonols and hydroxybenzoic acids were dominant in the leaves, roots, barks, flowers and seeds. Myricetin (1530 ± 10 µg/g) and quercetin (985 ± 4 µg/g) in leaves along with gentisic acid (85 ± 2 µg/g) and biochanin A (45 ± 1 µg/g) in the roots were considerably higher than other phenolic components found in other parts of the plant. The antibacterial activity of methanol, ethanol, and ethyl acetate leaves extracts showed larger zones of inhibition against Pseudomonas aeruginosa than Erwinia carotovora. Using minimum inhibitory concentration (MIC), the growth inhibition of P. aeruginosa and E. carotovora were found to be 86 ± 1% (ethyl acetate extracts of roots) and 79 ± 0.3% (ethanolic extracts of leaves), respectively. These results suggested that ethyl acetate, ethanol, and methanol extracts might provide the highest amount of potentially beneficial compounds that may be helpful in treating some diseases. [ABSTRACT FROM AUTHOR]

Reservoir operation plays an important role in economic development of a region. Hedging operations were used for municipal, industrial, and irrigation water supplies from reservoirs in the past. However, hedging operation for hydropower reservoir operation is very rare. A practically simple and useful new form of Standard Operation Policy and a new form of hedging rules for hydropower production are introduced in this paper and demonstrated with a case study for hydropower reservoir operation of Indirasagar reservoir system in India. The performance of optimal hedging rules is compared with that of a new standard operation policies and the superiority (reliability increases by about 10%) of the hedging rules is demonstrated. When the number of decision variables is increased from 5 to 15, energy production increases by 0.7%, the spill is reduced by 16.8%, and reliability slightly decreases by 2.1%. A bi-level simulation-optimization algorithm is used for optimizing the hedging rules. For optimization, Genetic Algorithm, artificial bee colony algorithm, and imperialistic competitive algorithms are utilized. The results indicate that all the three algorithms are competitive and artificial bee colony algorithm is marginally better than the other two. [ABSTRACT FROM AUTHOR]

Titanium dioxide (TiO 2 ) arrays were fabricated on Ti alloy by anodization method. Synthesis of CaSiO 3 (CS) and various concentrations (1X-5X) of Sr 2+ substitutions in CS coatings on TiO 2 substrate was achieved through an electrophoretic deposition technique. Fast release of mineral ions from implant surface produce over dosage effect and it is a potential hazardous factor for osteoblasts. So, in order to prevent the fast release of minerals, biopolymer coating was applied above the composite coatings. The coatings were characterized by FTIR, XRD, FE-SEM and EDX techniques. The mechanical, anticorrosion, antimicrobial properties and biocompatibility of the coatings were evaluated. Studies on the mechanical properties indicate that the addition of Sr 2+ and biopolymer increase the hardness strength of the coatings. The metal ion release from the coatings was studied by ICP-AES. The electrochemical properties of the coatings were studied in Ringer’s solution, in which CS-3X/Chi-PVP coating on TiO 2 exhibits good anticorrosion property and high resistivity against Escherichia coli and Staphylococcus aureus compared to CS-3X coating on TiO 2 . In vitro cell experiments indicate that osteoblasts show good adhesion and high growth rates for CS-3X/Chi-PVP coated TiO 2 substrate, indicating that the surface cytocompatibility of CS-3X/Chi-PVP coated TiO 2 substrate is significantly improved by the controlled release of mineral ions. In conclusion, the surface modification of TiO 2 /CS-3X/Chi-PVP coated titanium is a potential candidate for implant coating. [ABSTRACT FROM AUTHOR]