Solar absorption behaviour of multilayer stacks

In this paper, the absorption spectra of multilayer structures have been obtained and analysed in the wavelength range 0.5–10 μm. The multilayer system is an SMS type in which M is a thin metallic layer sandwiched between two semiconducting layers. The systems (i) B.S./Cu/B.S. and (ii) Cu₂S/Cu/Cu₂S (where B.S. = black sulphide) are developed by the methods of electrodeposition/chemical dipping, whereas the ZnS/Cu/ZnS system is fabricated in vacuo. The /ε ratios are also calculated using the experimentally observed absorption spectra, and found to be higher in the case of the ZnS/Cu/ZnS system, in comparison to the other two systems. The absorption spectra have also been calculated using our theoretical model of the above-mentioned systems.     

Thermal performance of a non-air-conditioned building with PCCM thermal storage wall

This paper presents a time-dependent periodic heat transfer analysis of a non-air-conditioned building having a south-facing wall of phase-changing component material (PCCM). A rectangular room (6 × 5 × 4 m) based on the ground is considered. The effects of heat transfer through walls and roof, heat conduction to the basement ground and furnishings, heat gain through window and heat loss due to air ventilation have been incorporated in the periodic time-dependent heat transfer analysis. The time-dependent heat flux through the PCCM south-facing wall has been obtained by defining the effective thermal properties of the PCCM for a conduction process with no phase change. Numerical calculations are made for a typical mild winter day (7 March 1979) at New Delhi for heat flux entering through the wall and inside air temperature. Further, a PCCM wall of smaller thickness is more desirable, in comparison to an ordinary masonry concrete wall, for providing efficient thermal energy storage as well as excellent thermal comfort in buildings.     

Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.

     

Origin and Regionality of Wines—the Role of Molecular Spectroscopy

The origin of wine has an important relevance not only in relation to the sustainability of the production systems but also in relation to wine quality, having great impact in terms of the economy of small producer. Terms such as protected geographical status (PGS), protected designation of origin (PDO), protected geographical indication (PGI) and traditional speciality guaranteed (TSG) are been used to characterise wines from different regions and countries. Despite the number of papers and reports published in the literature in relation to issues such as authenticity and discrimination in wine, few reports can be found that explore the use of molecular spectroscopy to specifically target denomination of origin. This report reviews the use of molecular spectroscopy to address such issues within the wine industry.     

Assessing Flood Induced Land-Cover Changes Using Remote Sensing and Fuzzy Approach in Eastern Gujarat (India)

Natural hazards such as flooding can cause changes in land-cover. The present study deals with the changes in land-cover in three worst affected districts (Anand, Vadodara and Kheda) of Gujarat state in India due to severe flood during 2005. The Indian Remote Sensing (IRS) P6 Linear Imaging Self Scanning (LISS) III satellite imageries of pre- and post-flooding periods were used as sources of information for the study area. Three classification approaches (unsupervised ISODATA, supervised Maximum Likelihood Classifier, and fuzzy rule based) were used to extract flood induced land-cover information. Results obtained from the above classification approaches were compared. Soft computing technique such as fuzzy based image classification gave better separability amongst classes as compared to hard classification techniques. The accuracy assessment showed that the fuzzy approach can predict land-cover more accurately than traditional approach and also showed great potential for dealing with mapping of flood induced land-cover. Unsupervised classification results for the period October 2004 to October 2005 revealed decrease in inland water bodies (14.49%) and agricultural area (6.42%) while increase in remaining land-cover. During February 2005 to February 2006, all land-cover classes decreased except agricultural fallow and sparse vegetation. In case of supervised classification, decreasing trend was observed only in case of agricultural area (6.78%) during October 2004 to October 2005. Similarly, during February 2005 to February 2006, increase in coastal water bodies (0.73%) and sparse vegetation (1.7%) was observed where as decreasing trend was noticed in the remaining land-cover classes. In fuzzy based classification, only decrease in agricultural area (7.09%) was observed from October 2004 to October 2005, whereas during February 2005 to February 2006, decrease in area was exhibited in all land-cover classes except coastal water bodies and sparse vegetation. Change detection indicated interchange of areas between inland and coastal water bodies and decrease in agricultural area leading to increase in area of agricultural fallow and sparse vegetation.