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A new approach to identify the texture based on image processing of thin sections of different basalt rock samples is proposed here. This methodology uses RGB or grayscale image of thin section of rock sample as an input and extracts 27 numerical parameters. A multilayer perceptron neural network takes as input these parameters and provides, as output, the estimated class of texture of rock. For this purpose, we have use 300 different thin sections and extract 27 parameters from each one to train the neural network, which identifies the texture of input image according to previously defined classification. To test the methodology, 90 images (30 in each section) from different thin sections of different areas are used. This methodology has shown 92.22% accuracy to automatically identify the textures of basaltic rock using digitized image of thin sections of 140 rock samples. Therefore, present technique is further promising in geosciences and can be used to identify the texture of rock fast and accurate.

Treatment performance of a field-scale horizontal subsurface (SF) constructed wetland (CW) was evaluated for removal efficiency of BOD, TSS, NH4-N, NO3-N, TKN and P from municipal wastewater emanating from a small community of residential areas in Ujjain, Central India. The SF wetland had a rectangular size and covered an effective surface area of 41.82 m2 with a water retention capacity of 18 m3. The SF medium was composed of a gravel bed supported below on a layer of puddled local clay and overlaid by a thin synthetic liner. CW was planted initially with locally grown grass, Phragmites karka. Plants placed in this rectangular design at the rate of 3 to 4 plants per m2 increased to 6157 plants within ten months producing a biomass of 121 tonnes ha−1. The influent was pretreated before entering the SF system, through two baffles; a grass covered small ditch, followed by a narrow tunnel of packed biofilmed boulders. Removal rates of TSS (48%), TKN (36%), NH4-N (22%) and NO3-N as zero percent were realised. An earthen channel provided initial pretreatment by a land treatment system. Average treatment performance after five months from this SF system recorded removal efficiencies of 78% for NH4-N, TSS; 58-65% for P, BOD and TKN. Effluent dissolved oxygen levels increased to 34% indicating existence of aerobic conditions in the rooted-gravel bed. The SF system overall results established: (a) very cost-effective treatment technology, (b) SF removal efficiency above 50% for BOD, NH4-N, TKN, and P. This SF system presents a unique design consideration compared with the land-intensive Kickuth standard system design.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is common among non-smokers exposed to solid fuel combustion at home. Different clinical characteristics in these patients may have significant therapeutic and prognostic implications.METHODS: We used medical record review and a questionnaire among COPD patients at 15 centres across India to capture data on demographic details, different types of exposures and clinical characteristics. Chest radiography and pulmonary function testing were performed in all 1984 cases; C-reactive protein and exhaled breath nitric oxide were measured wherever available.RESULTS: There were 1388 current or ex-smokers and 596 (30.0%) non-smokers who included 259 (43.5%) male and 337 (56.5%) female patients. Sputum production was significantly more common in smokers with COPD (P < 0.05). The frequency of acute symptomatic worsening, emergency visits and hospitalisation were significantly higher (P < 0.05) in non-smokers with COPD; however, intensive care unit admissions were similar in the two groups. There was no significant difference with respect to the use of bronchodilators, inhalational steroids or home nebulisation among smoker and non-smoker patients. The mean predicted forced expiratory volume in 1 sec in smokers (43.1%) was significantly lower than in non-smokers (46.5%).CONCLUSION: Non-smoker COPD, more commonly observed in women exposed to biomass fuels, was characterised by higher rate of exacerbations and higher healthcare resource utilisation.

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