Enhancing Art Gallery Visitors’ Learning Experience using Wearable Augmented Reality: Generic Learning Outcomes Perspective

The potential of ICT-enhanced visitor learning experience is increasing with the advancement of new and emerging technologies in art gallery settings. However, studies on the visitor learning experience using wearable devices, and in particular those investigating the effects of wearable augmented reality on the learning experience within cultural heritage tourism attractions are limited. Using the Generic Learning Outcomes framework, this study aims to assess how the wearable augmented reality application enhances visitor’s learning experiences. Forty-four volunteers who were visiting an art gallery were divided into two groups, an experimental group and a control group. Following their visit to the gallery, the volunteers, who had and had not used wearable computing equipment, were interviewed, and the data were analysed using thematic analysis. Findings revealed that the wearable augmented reality application helps visitors to see connections between paintings and personalise their learning experience. However, there are some drawbacks such as lack of visitor-visitor engagement and the social acceptability

Comparison of Simpler Radiation-Based ET Models with Penman Monteith model for Humid Region

The three relatively simpler radiation-based models: Ture, Jensen-Haise and Priestley-Taylor were used to estimate reference evapotranspiration (ET0> at different humid locations of Assam, India. The Penman-Monteith model was selected as the standard for evaluating the performance of the other three radiation-based models. The averaged monthly meteorological data were used for estimating the ETo\u27 Good correlation was found between ETo values estimated by each of the three radiation-based models. The least and the highest values of RMSE varied between 0.16 (Gohpur) and 0.82 (Dhubri) for Priestley-Taylor model; 0.95 (Dhubri) and 1.68 (Silicorie) for Ture model; and 1.89 (Dhubri) and 2.97 (Silicorie) for lensen-Haise model. The study revealed that among all the three radiation-based models, the Priestley-Taylor model yielded average estimate of ET0 closest to the Penman-Monteith model

Influence of Meteorological Parameters on Pan Evaporation at Agartala

Evaporation is one of the vital processes in the water resources planning and management. The influence of meteorological parameters such as air temperature, relative humidity, sunshine duration, vapour pressure. wet bulb temperature, number of rainy days and wind speed on pan evaporation at Agartala was examined. The linear and exponential methods were used to check the correlation between various individual meteorological parameters and pan evaporation. The multiple regression analysis was also carried out to observe the combined effect of all the meteorological parameters on pan evaporation over Agartala. The wind speed and the mean temperature were found to have positive and significant influence on pan evaporation. In case of combined effect of various meteorological parameters on pan evaporation at Agartala. the pan evaporation was affected by mean temperature, wind speed, sunshine hours and mean relative humidity

Modelling of Reference Evapotranspiration using Neural Network and Regression Approaches for Semi-humid Region of Sikkim

This study compared the applicability of multiple linear regression (MLR) and artificial neural network (ANN) approaches for estimating the weekly reference evapotranspiration (ET0) in a semi-humid area of Tadong in Gangtok district of Sikkim state in India. Daily meteorological data (1991 - 2020) were used for developing MLR and ANN model using various combinations of meteorological data. Predicted ET0 values were compared with the FAO-56 Penman Monteith (PM) equation estimated ET0. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and Nash Sutcliffe efficiency (NSE) were used to assess the performance of MLR and ANN models. For ANN model, one hidden layer and four neurons was found as the best ANN architecture. ANN-4 model with inputs parameters of maximum air temperature (Tmax), minimum air temperature (Tmin), maximum relative humidity (RHmax), minimum relative humidity (RHmin), and sunshine hours (SSH) had the smallest RMSE (0.609 mm.day-1 and 0.880 mm.day-1), MAE (0.473 mm.day-1 and 0.551mm.day-1) and the highest R2 (0.752 and 0.657) and NSE (0.782 and 0.696) during training and testing phases, respectively. The MLR-4 model with same combination of input parameters (Tmax, Tmin, RHmax, RHmin, SSH) resulted in smallest RMSE (0.704 mm.day-1 and 0.714 mm.day-1), MAE (0.562 mm.day-1and 0.583 mm.day-1); and the highest R2 (0.679 and 0.602) and NSE (0.708 and 0.597) during training and testing phase, respectively. Study also showed increase in model performance of ANN and MLR models with increase in number of meteorological parameters. All the ANN models gave comparable results, but ANN 4 model resulted in higher prediction accuracy for the sub-humid region of Tadong in Gangtok district of North-east India. The developed ANN-4 model could be helpful for preparing irrigation schedules and planning, and management of water resources in the data scarce northeast region of India and other identical climatic regions

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Not AvailableTrends in rainfall, rainy days and 24 h maximum rainfall are investigated using the Mann- Kendall non-parametric test at twenty-four sites of subtropical Assam located in the northeastern region of India. The trends are statistically confirmed by both the parametric and non-parametric methods and the magnitudes of significant trends are obtained through the linear regression test. In Assam, the average monsoon rainfall (rainy days) during the monsoon months of June to September is about 1606 mm (70), which accounts for about 70% (64%) of the annual rainfall (rainy days). On monthly time scales, sixteen and seventeen sites (twenty-one sites each) witnessed decreasing trends in the total rainfall (rainy days), out of which one and three trends (seven trends each) were found to be statistically significant in June and July, respectively. On the other hand, seventeen sites witnessed increasing trends in rainfall in the month of September, but none were statistically significant. In December (February), eighteen (twenty-two) sites witnessed decreasing (increasing) trends in total rainfall, out of which five (three) trends were statistically significant. For the rainy days during the months of November to January, twenty-two or more sites witnessed decreasing trends in Assam, but for nine (November), twelve (January) and eighteen (December) sites, these trends were statistically significant. These observed changes in rainfall, although most time series are not convincing as they show predominantly no significance, along with the well-reported climatic warming in monsoon and post-monsoon seasons may have implications for human health and water resources management over bio-diversity rich Northeast India.Not Availabl