Melanoma is a specific type of skin cancer that can be lethal if not diagnosed and treated early. This paper presents a deep-learning approach for the automatic identification of melanoma on dermoscopic images from the ISIC Archive dataset and non-dermoscopic images from the MED-NODE dataset. The method involves the development of Convolutional Neural Network (CNN) and ResNet50 models, along with various pre-processing techniques. The CNN and ResNet50 models detect melanoma from dermoscopic images with 98.07% and 99.83% accuracy respectively, using hair removal and augmentation techniques. For non-dermoscopic images, the CNN and ResNet50 models achieve an accuracy of 97.06% and 100% respectively, using the hair removal technique. Furthermore, combining age and gender as additional factors in identifying melanoma in dermoscopic images, leads to an accuracy of 96.40% using CNN. The results of this research suggest that the developed models when combined with various pre-processing techniques and the integration of age and gender as additional factors, can be an efficient tool in the early detection of melanoma.

A proper procedure must be created to make sure that capsaicin, whether in dosage form or in bulk, can be identified. The method development makes sure that the amount of a capsaicin can be simply determined. To determine validation parameters like accuracy, precision, LOD, LOQ, recovery study and range for capsaicin. In the present study a proper UV Spectroscopic method was developed and validated for determination of capsaicin. UV Spectrophotometric method was done at 280nm and samples were prepared with Methanol. The linearity demonstrated a correlation coefficient of 0.9922 various validation parameters like accuracy, precision, LOD, LOQ, recovery study and range were found to be within the specified range. The proposed method was simple, rapid, precise, accurate and sensitive and can be used for routine analysis of capsaicin.

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The present study focused on the responses of six freshwater algal species (Anabaena ambigua, Anabaena subcylindrica, Nostoc commune, Nostoc muscorum, Spirogyra sp., and Spirulina sp.) to manganese and zinc. Laboratory experiments were conducted for the assessment of biochemical responses to manganese and zinc at various concentrations (0.1, 0.5, 1.0, 2.0, 3.0, 4.0, and 5.0 mg/L) for 15 days of exposure. After the incubation period, 10 ml of sample was centrifuged at 6000 rpm for 15 min and the pellets were used for measurement of the various experimental parameters. The toxicological study of manganese on algae showed that Anabaena ambigua was most sensitive algae. Regarding effects of manganese concentrations, chlorophyll, protein, carbohydrate, starch, and amino acid were inhibited 50% (IC 50 ) at 3 mg/L, whereas the toxicological study of zinc on algae Anabaena subcylindrica showed most adverse effects. Regarding effects of zinc concentrations, chlorophyll, protein, carbohydrate, starch, and amino acid were inhibited 50% (IC 50 ) at 1 mg/L. The inhibitory and stimulatory effects of either of the used heavy metals depend on concentration. Different organisms, however, have different sensitivities to the same metal, and the same organism may be more or less damaged by different metals.

Ninety six (96) ground water samples collected from Bhander and Seondha block (Datia District)were analyzed for Iron. ,Copper, Manganese, Zinc, and Lithium. Average was found for Iron. ,Copper, Manganese, Zinc, and Lithium were 0.257,0.019,0.037,0.014,0.011 ppm respectively.