Understanding Stage Management in the 21st century in Australia: A Preliminary Survey

As a vocation that has been around for at least 150 years, stage management has gone through years of evolution in its scope of practice. From existing as a purely mechanical part of the theatre process to becoming vital co-creators in collaboration with Directors, Designers, and Playwrights, the roles and skills of a Stage Manager has expanded beyond the theatre into the events and entertainment industry that includes large sporting events, rock concerts, and corporate productions. Academic research into Stage Management is presently in its infancy, with a dearth of published literature. This research contributes a timely and critical reflection of what it is to be a Stage Manager in Australia in the 21st century through presenting the findings of an online survey conducted from March to May 2020 by industry professionals. The survey was conducted before the COVID-19 pandemic developed, and therefore the impact of this event is not reflected in the working lives of the participants. However, the research does include a presentation of several approaches to blended learning in Stage Management in response to how the pandemic has affected the teaching of Stage Management during COVID-19. This research showed that although the industry is dynamic and offers secure and consistent employment, there are areas of possible development in education and the management of work-life balance. The survey revealed that industry professionals on reflection would have liked more industry connections and opportunities for internships at an undergraduate level; whilst for mid-career workers, the development of a professional master\u27s degree would be appropriate to cover areas of business management, new technologies, and intensive courses in a second language to further career progression and to open opportunities for the industry to internationalise within the region.

Water level reconstruction and prediction based on space-borne sensors: A case study in the Mekong and Yangtze river basins

Water level (WL) measurements denote surface conditions that are useful for monitoring hydrological extremes, such as droughts and floods, which both affect agricultural productivity and regional development. Due to spatially sparse in situ hydrological stations, remote sensing measurements that capture localized instantaneous responses have recently been demonstrated to be a viable alternative to WL monitoring. Despite a relatively good correlation with WL, a traditional passive remote sensing derived WL is reconstructed from nearby remotely sensed surface conditions that do not consider the remotely sensed hydrological variables of a whole river basin. This method’s accuracy is also limited. Therefore, a method based on basin-averaged, remotely sensed precipitation from the Tropical Rainfall Measuring Mission (TRMM) and gravimetrically derived terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) is proposed for WL reconstruction in the Yangtze and Mekong River basins in this study. This study examines the WL reconstruction performance from these two remotely sensed hydrological variables and their corresponding drought indices (i.e., TRMM Standardized Precipitation Index (TRMM-SPI) and GRACE Drought Severity Index (GRACE-DSI)) on a monthly temporal scale. A weighting procedure is also developed to explore a further potential improvement in the WL reconstruction. We found that the reconstructed WL derived from the hydrological variables compares well to the observed WL. The derived drought indices perform even better than those of their corresponding hydrological variables. The indices’ performance rate is owed to their ability to bypass the influence of El Niño Southern Oscillation (ENSO) events in a standardized form and their basin-wide integrated information. In general, all performance indicators (i.e., the Pearson Correlation Coefficient (PCC), Root-mean-squares error (RMSE), and Nash–Sutcliffe model efficiency coefficient (NSE)) reveal that the remotely sensed hydrological variables (and their corresponding drought indices) are better alternatives compared with traditional remote sensing indices (e.g., Normalized Difference Vegetation Index (NDVI)), despite different geographical regions. In addition, almost all results are substantially improved by the weighted averaging procedure. The most accurate WL reconstruction is derived from a weighted TRMM-SPI for the Mekong (and Yangtze River basins) and displays a PCC of 0.98 (and 0.95), a RMSE of 0.19 m (and 0.85 m), and a NSE of 0.95 (and 0.89); by comparison, the remote sensing variables showed less accurate results (PCC of 0.88 (and 0.82), RMSE of 0.41 m (and 1.48 m), and NSE of 0.78 (and 0.67)) for its inferred WL. Additionally, regardless of weighting, GRACE-DSI displays a comparable performance. An external assessment also shows similar results. This finding indicates that the combined usage of remotely sensed hydrological variables in a standardized form and the weighted averaging procedure could lead to an improvement in WL reconstructions for river basins affected by ENSO events and hydrological extremes

Attitude, acceptability and knowledge of HPV vaccination among local university students in Hong Kong

© 2016 by the authors; licensee MDPI, Basel, Switzerland. The Human Papillomavirus (HPV) vaccine has the great potential to prevent HPV-related infections for millions of women and men worldwide. However, the success of the vaccine is highly dependent on the vaccination rate. Factors influencing the attitudes of undergraduate students towards HPV vaccination should be studied. This is a cross-sectional survey that was conducted to estimate the HPV vaccination rate among undergraduate students in Hong Kong, and to identify the predictors of their attitude towards HPV vaccination. The results showed that the HPV vaccination rate was 13.3%. Factors related to knowledge of vaccination were the main predictors of the studentsâ attitude towards vaccination (there were seven predictors, with B = 1.36 to 2.30; p < 0.05), followed by gender (B =-1.40; p < 0.05), acceptable maximum price (B = 0.35; p < 0.05), and willingness to receive the HPV vaccine if it can protect against cervical/anal cancer and genital warts (B =-1.90; p < 0.001).Theregressionmodelthatwasdevelopedbasedonthepredictorshadamoderateeffect size (adj-R 2 = 0.33). To conclude, the HPV vaccination rate among undergraduate students in Hong Kong was low. They should be provided with more active education and activities to promote HPV vaccination to improve their knowledge on the subject.Link_to_subscribed_fulltex

Water Balance Standardization Approach for Reconstructing Runoff Using GPS at the Basin Upstream

While in-situ estuarine discharge has been correlated and reconstructed well with localized remotely-sensed data and hydraulic variables since the 1990s, its correlation and reconstruction using averaged GPS-inferred water storage from satellite gravimetry (i.e., GRACE) at the basin upstream based on the water balance standardization (WBS) approach remains unexplored. This study aims to illustrate the WBS approach for reconstructing monthly estuarine discharge (in the form of runoff (R)) at Mekong River Delta, by correlating the averaged GPS-inferred water storage from GRACE of the upstream Mekong Basin with the in-situ R at the Mekong River Delta estuary. The resulting R based on GPS-inferred water storage is comparable to that inferred from GRACE, regardless of in-situ stations within Mekong River Delta being used for the R reconstruction. The resulting R from the WBS approach with GPS water storage converted by GRACE mascon solution attains the lowest normalized root-mean-square error of 0.066, and the highest Pearson correlation coefficient of 0.974 and Nash-Sutcliffe efficiency of 0.950. Regardless of using either GPS-inferred or GRACE-inferred water storage, the WBS approach shows an increase of 1&ndash;4% in accuracy when compared to those reconstructed from remotely-sensed water balance variables. An external assessment also exhibits similar accuracies when examining the R estimated at another station location. By comparing the reconstructed and estimated Rs between the entrance and the estuary mouth, a relative error of 1&ndash;4% is found, which accounts for the remaining effect of tidal backwater on the estimated R. Additional errors might be caused by the accumulated errors from the proposed approach, the unknown signals in the remotely-sensed water balance variables, and the variable time shift across different years between the Mekong Basin at the upstream and the estuary at the downstream

Mekong Delta Runoff Prediction Using Standardized Remotely-Sensed Water Balance Variables

A suitable routing model for predicting future monthly water discharge (WD) is essential for operational hydrology, including water supply, and hydrological extreme management, to mention but a few. This is particularly important for a remote area without a sufficient number of in-situ data, promoting the usage of remotely sensed surface variables. Direct correlation analysis between ground-observed WD and localized passive remotely-sensed surface variables (e.g., indices and geometric variables) has been studied extensively over the past two decades. Most of these related studies focused on the usage of constructed correlative relationships for estimating WD at ungauged locations. Nevertheless, temporal prediction performance of monthly runoff (R) (being an average representation of WD of a catchment) at the river delta reconstructed from the basin&rsquo;s upstream remotely-sensed water balance variables via a standardization approach has not been explored. This study examined the standardization approach via linear regression using the remotely-sensed water balance variables from upstream of the Mekong Basin to reconstruct and predict monthly R time series at the Mekong Delta. This was subsequently compared to that based on artificial intelligence (AI) models. Accounting for less than 1% improvement via the AI-based models over that of a direct linear regression, our results showed that both the reconstructed and predicted Rs based on the proposed approach yielded a 2&ndash;6% further improvement, in particular the reduction of discrepancy in the peak and trough of WD, over those reconstructed and predicted from the remotely-sensed water balance variables without standardization. This further indicated the advantage of the proposed standardization approach to mitigate potential environmental influences. The best R, predicted from standardized water storage over the whole upstream area, attained the highest Pearson correlation coefficient of 0.978 and Nash&ndash;Sutcliffe efficiency of 0.947, and the lowest normalized root-mean-square error of 0.072

Water Level Reconstruction Based on Satellite Gravimetry in the Yangtze River Basin

The monitoring of hydrological extremes requires water level measurement. Owing to the decreasing number of continuous operating hydrological stations globally, remote sensing indices have been advocated for water level reconstruction recently. Nevertheless, the feasibility of gravimetrically derived terrestrial water storage (TWS) and its corresponding index for water level reconstruction have not been investigated. This paper aims to construct a correlative relationship between observed water level and basin-averaged Gravity Recovery and Climate Experiment (GRACE) TWS and its Drought Severity Index (GRACE-DSI), for the Yangtze river basin on a monthly temporal scale. The results are subsequently compared against traditional remote sensing, Palmer&rsquo;s Drought Severity Index (PDSI), and El Ni&ntilde;o Southern Oscillation (ENSO) indices. Comparison of the water level reconstructed from GRACE TWS and its index, and that of remote sensing against observed water level reveals a Pearson Correlation Coefficient (PCC) above 0.90 and below 0.84, with a Root-Mean-Squares Error (RMSE) of 0.88&ndash;1.46 m, and 1.41&ndash;1.88 m and a Nash-Sutcliffe model efficiency coefficient (NSE) above 0.81 and below 0.70, respectively. The ENSO-reconstructed water levels are comparable to those based on remote sensing, whereas the PDSI-reconstructed water level shows a similar performance to that of GRACE TWS. The water level predicted at the location of another station also exhibits a similar performance. It is anticipated that the basin-averaged, remotely-sensed hydrological variables and their standardized forms (e.g., GRACE TWS and GRACE-DSI) are viable alternatives for reconstructing water levels for large river basins affected by the hydrological extremes under ENSO influence

Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables

Basin water supply, planning, and its allocation requires runoff measurements near an estuary mouth. However, insufficient financial budget results in no further runoff measurements at critical in situ stations. This has recently promoted the runoff reconstruction via regression between the runoff and nearby remotely-sensed variables on a monthly scale. Nonetheless, reconstructing daily runoff from individual basin-upstream remotely-sensed climatic variables is yet to be explored. This study investigates standardized data regression approach to reconstruct daily runoff from the individual remotely-sensed climatic variables at the Mekong Basin&rsquo;s upstream. Compared to simple linear regression, the daily runoff reconstructed and forecasted from the presented approach were improved by at most 5% and 10%, respectively. Reconstructed runoffs using neural network models yielded ~0.5% further improvement. The improvement was largely a function of the reduced discrepancy during dry and wet seasons. The best forecasted runoff obtained from the basin-upstream standardized precipitation index, yielded the lowest normalized root-mean-square error of 0.093