Visualizing museums through the visitors’ eye: An n-gram model-based text analysis approach

All over the world Museums are developing a Customer Service mindset characteristic of large businesses. Part of this shift can be seen in the rapid adoption of museum visitor surveys that gather feedback about a visitor’s experience in relation to their demographic characteristics — such as likes, dislikes, etc. as per their age, gender and income. The concept of listening to the visitor through such census data collection is not new, it was there from the very beginning, but in certain aspects, it appears to be deficient. For example, it becomes arduous and difficult to identify if the visitors find the place “memorable”. Did they feel “welcome” from the beginning? Do they think “science” is given a due share in that science museum? Can we build an at-a-glance graphical representation of the museum’s image in the visitor’s mental map? Census type data collection is very manpower intensive and is often biased as the visitors feel righteous to answer the questionnaire more “correctly” than “frankly”. They are not generally considered “easy in-person survey tools”. In this paper, we will discuss a very new way of listening to the visitors and reconstructing their free and fair post-visit overall mental map of the museum. In this new effort, we have tried to construct an automated AI-based word-cloud image from visitors’ feedback offered voluntarily in social networks like, say, in Google review. This image is helpful in summarizing and analysing a large amount of feedback data on a single page by focusing on the keywords and phrases adopted by the visitor in describing her experience. This approach is especially useful for centralized analysis of feedback data on museums having a large number of branches. Hence, we have applied this method to process feedback on the National Council of Science Museums, the largest body of science museums in India managing twenty-five centres all over the country. For the very first time applying this method, we have been able to visualize our museums through the visitor’s eye rendering the analytic result in the form of a word-cloud that reflects what the visitors fancy most when they recall the museum experience after the visit

Visualizing museums through the visitors’ eye: An n-gram model-based text analysis approach

21-32    All over the world Museums are developing a Customer Service mindset characteristic of large businesses. Part of this shift can be seen in the rapid adoption of museum visitor surveys that gather feedback about a visitor’s experience in relation to their demographic characteristics — such as likes, dislikes, etc. as per their age, gender and income. The concept of listening to the visitor through such census data collection is not new, it was there from the very beginning, but in certain aspects, it appears to be deficient. For example, it becomes arduous and difficult to identify if the visitors find the place “memorable”. Did they feel “welcome” from the beginning? Do they think “science” is given a due share in that science museum? Can we build an at-a-glance graphical representation of the museum’s image in the visitor’s mental map?     Census type data collection is very manpower intensive and is often biased as the visitors feel righteous to answer the questionnaire more “correctly” than “frankly”. They are not generally considered “easy in-person survey tools”. In this paper, we will discuss a very new way of listening to the visitors and reconstructing their free and fair post-visit overall mental map of the museum. In this new effort, we have tried to construct an automated AI-based word-cloud image from visitors’ feedback offered voluntarily in social networks like, say, in Google review. This image is helpful in summarizing and analysing a large amount of feedback data on a single page by focusing on the keywords and phrases adopted by the visitor in describing her experience. This approach is especially useful for centralized analysis of feedback data on museums having a large number of branches.     Hence, we have applied this method to process feedback on the National Council of Science Museums, the largest body of science museums in India managing twenty-five centres all over the country. For the very first time applying this method, we have been able to visualize our museums through the visitor’s eye rendering the analytic result in the form of a word-cloud that reflects what the visitors fancy most when they recall the museum experience after the visit

Using nighttime lights data to assess the resumption of religious and socioeconomic activities post-COVID-19

The COVID-19 pandemic greatly impacted socioeconomic life globally. Nighttime-lights (NTLs) data are mainly related to anthropogenic phenomena and thus have the ability to monitor changes in socioeconomic activity. However, the overglow effect is a source of uncertainty and affects the applicability of NTL data for accurately monitoring socioeconomic changes. This research integrates the NTL and fine bare-land-cover data to construct a novel index named the Bare Adjusted NTL Index (BANTLI) to lessen the overglow uncertainty. BANTLI was used to measure the post-pandemic resumption of religious rituals and socioeconomic activity in Makkah and Madinah at different spatial levels. The results demonstrate that BANTLI significantly eliminates the overglow effect. In addition, BANTLI brightness recovered during the post-pandemic periods, but it has remained below the level of the pre-pandemic period. Moreover, not all wards and rings are affected equally: wards and rings that are near the city center experienced the most explicit reduction of BANTLI brightness compared with the suburbs. The Hajj pilgrimage period witnessed a larger decrease in BANTLI brightness than the pandemic period in Makkah. The findings indicate that (i) BANTLI successfully mitigates the overglow effect in the NTL data, and (ii) the cultural context is important to understand the impact of COVID-19

Optimization of Oxalic Acid Pre-Treatment and Enzymatic Saccharification in Typha latifolia for Production of Reducing Sugar

Background Plants with high biomass can be manipulated for their reducing sugar content which ultimately upon fermentation produces ethanol. This concept was used to enhance the production of reducing sugar from cattail (Typha latifolia) by oxalic acid (OAA) pre-treatment followed by enzymatic saccharification. Result The optimum condition of total reducing sugar released from OAA pre-treatment was found to be 22.32 mg/ml (OAA—1.2%; substrate concentration (SC)—6%; reaction time (RT)—20 min) using one variable at a time (OVAT). Enzymatic saccharification yielded 45.21 mg/ml of reducing sugar (substrate concentration (SC)—2.4%; enzymatic dosage—50 IU/g; pH 7.0; temp—50 °C) using response surface methodology (RSM). Conclusion We conclude that Typha can be used as a potential substrate for large-scale biofuel production, employing economical bioprocessing strategies