How academic sabbaticals used and how they contribute to research – a small-scale study of the University of Cambridge using interviews and analysis of administrative data

Background: Academic sabbaticals are seen as an important aspect of academic life and require considerable resources, however, little research has been done into how they are used and whether their effects can be measured. We explored these issues at the University of Cambridge. Methods: A mixed method approach including 24 interviews with academics, eight interviews with administrators; alongside analysis of administrative and publication data between 2010 and 2019. Results: Academics underline the importance of sabbaticals in providing uninterrupted time for research that is used to think, explore new ideas, master new techniques, develop new collaborations, draw together previous work, set work in a wider context, and provide personal discretion in research direction. They also highlight sabbaticals’ contributions in allowing the beneficial effects of combining teaching and research, while mitigating some of the disadvantages. However, it is difficult to detect the effect of sabbaticals on publications using a time series approach. Conclusions: Sabbaticals provide manifold contributions to academic research at the University of Cambridge; however, detecting and quantifying this contribution, and extending these findings requires wider and more detailed investigation.

Time-series analysis and statistical forecasting of daily rainfall in Kupang, East Nusa Tenggara, Indonesia: a pilot study

This pilot study presents a novel statistical time-series approach for analyzing daily rainfall data in Kupang, East Nusa Tenggara, Indonesia. By using the piecewise cubic hermite interpolation algorithm, we succeeded in filling in the null values in the daily rainfall time series. We then analyzed the monthly average and its pattern using the continuous wavelet transform (CWT) algorithm, which shows the strong annual pattern of rainfall in this region. In addition, we use the rainfall anomaly index (RAI) function to standardize daily rainfall as an indicator of dry/wet conditions in this region. Then we also use the daily RAI time-series objects from 1978 to 2020 for modeling and predicting daily RAI over the next year. The result is the root mean squared error (RMSE) of 0.8424041040593219. This Prophet model is also able to capture the linear trend of increasing drought throughout the study time period and the annual pattern of wet/dry conditions which is in accordance with previous study by Aldrian and Susanto (2003).

Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach

Chocolate is the raw material for making cakes, so consumption of chocolate also increases on Eid al-Fitr. However, this is different in the United States where the tradition of sharing chocolate cake is carried out on Christmas. To monitor the existence of this chocolate can be through the movement of data on Google Trends. This study aims to predict the existence of chocolate from the Google trend where the use of chocolate by the community fluctuates according to the calendar variance and seasonal rhythm. The method used is classic time series, namely nave, double exponential smoothing, multiplicative decomposition, addictive decomposition, holt winter multiplicative, holt winter addictive, time series regression, hybrid time series, ARIMA, and ARIMAX. Based on MAPE in sample, the best time series model to model the existence of chocolate in Indonesia is ARIMAX (1,0,0) while for the United States it is Hybrid Time Series Regression-ARIMA(2,1,[10]). For forecasting the existence of chocolate in Indonesia, the best models in forecasting are ARIMA (([11],[12]),1,1) and Naïve Seasonal. In contrast to the best forecasting model for the existence of chocolate in the United States, namely Hybrid Naïve Seasonal-SARIMA (2,1,0)(0,0,1)12 Hybrid Time Series Regression- ARIMA(2,1,[10]), Time Series Regression, Winter Multiplicative, ARIMAX([3],0,0).

Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature

Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.

Price Analysis and Forecasting for Bitcoin Using Auto Regressive Integrated Moving Average Model

This paper investigated Bitcoin daily closing price using time series approach to predict future values for financial managers and investors. Daily data were sourced from CoinDesk, with Bitcoin Price Index (BPI) for 5 years (January 1, 2016 to May 31, 2021) extracted. Data analysis and modelling of price trend using Autoregressive Integrated Moving Average (ARIMA) model was carried out, and a suitable model for forecasting was proposed. Results showed that ARIMA(6,1,12) model was the most suitable based on a combination of number of significant coefficients and values of volatility, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A two-month test window was used for forecasting and prediction. Results showed a decline in prediction accuracy as number of days of the test period increased; from 99.94% for the first 7 days, to 99.59 % for 14 days and 95.84% for 30 days. For the two-month test period, percentage accuracy was 84.75%. The study confirms that the ARIMA model is a veritable planning tool for financial managers, investors and other stakeholders; especially for short-term forecasting. It is however imperative that the influence of external factors, such as investors’/influencers’ comments and government intervention, that may affect forecasting be taken into consideration.

Abstract 11233: A Trigger and Response System for Preventing Cardiac Arrest in the Intensive Care Unit

Background: Although patients in the ICU are closely monitored, some ICU cardiac arrest events may be preventable. In this study we sought to reduce the rate of ICU cardiac arrests. Methods: This was a prospective study of a novel clinical trigger and response tool deployed in the ICUs of a single, tertiary academic medical center. An interrupted time series approach was used to assess the impact of the tool on ICU cardiac arrests. Results: Forty-three patients experienced an ICU cardiac arrest in the pre-intervention epoch (6.79 arrests per 1000 discharges) and 59 patients experienced an ICU cardiac arrest in the intervention epoch (7.91 arrests per 1000 discharges). In the intervention epoch, the clinical trigger and response tool was activated 106 times over a 1-year period, most commonly due to unexpected new or worsening hypotension. There was no step change in arrest-rate (2.24 arrests/1000 patients, 95%CI -1.82, 6.28, p=0.28) or slope change (-0.02 slope of arrest rate, 95%CI -0.14, 0.11, p=0.79) comparing the pre-intervention and intervention time epochs (see Figure). Cardiac arrests occurring in the pre-intervention epoch were more likely to be deemed ‘potentially preventable’ than those in the intervention epoch (25.6% prior to the intervention vs. 12.3% during the intervention, OR 0.58, 95%CI 0.20, 0.88, p<0.01). Conclusions: A trigger-and-response tool did not reduce the incidence of ICU cardiac arrest. Arrests occurring after introduction of the tool were less likely to be rated as ‘potentially preventable.’