Past Observations Research Articles

Multi-Colour Photometric Observations of Transiting Exoplanets to Verify Binary Star Systems

Within this paper, we report the observation of transiting exoplanets, K2-29b, TrES-3b, WASP-36b, and HAT-P54b, with a focused comparison of the well defined binary system K2-29b to the proposed binary WASP-36b. Performing multicolour photometry, debayering images to three distinct colour channels, a comparison of the detrended light curves of each target is used to define parameters to identify the presence of any discrepancy between the observation, defined system models or past observations. The exoplanet system K2-29b, observed in separate red and blue filters, produced minimal deviation from expected RP /R∗ values with RP /R∗ = 0.1404 +0.0040/-0.0042 and RP /R∗ = 0.1468 +0.0082/-0.0088 respectively. In contrast to this, we observe a significant deviation in the blue debayered value of RP /R∗ for WASP-36b, with the expected radius ratio defined to be ±RP /R∗ = 0.1368±0.0006. Initial airmass detrending model fitting for the blue debayered channel produced a value of RP /R∗ = 0.1162+0.0098/ -0.0102, with RP /R∗ Drift = 3.01σ, this later being reduced through a quadratic detrending scheme to RP /R∗ Drift = 2.11σ. Despite this model being an improved representation, this measure of RP /R∗ drift is significantly beyond the tolerance of ±1σ and as such motivates further study as this effect has been denoted in concordant research to suggest presence of a secondary star in this system.

‘구경하는 아이’ 다르게 보기

This study attempts at understanding young children’s onlooking behaviors from different perspectives by making connections to and diffracting through theories and concepts, while moving away from stage theories of children’s social play development which are often narrowing and blinding our vision. An entry to this study emerged as we unexpectedly encountered with pink teddy-bear socks which were appearing in the edges of several photos in the process of re-viewing play observation records. We generated data from the past play observation records (journals, photos, video clips), new participant observation records, dialogues with the teacher, researchers’ thinking-with-data~theory and joint affective writings. Through the data of onlooking children who freely chose to enjoy watching over a play for an extended period of time, move around co-existing plays, or jump into an affect-laden play, we learned about their agency and the value of onlooking behaviors as a joyful play itself. Moreover, their onlooking was one of competent play participation strategies to understand ongoing play frameworks and find the right moment to get involved, and also functioned well in those moments to help peers in conflicts or in need. Entanglements of children, teacher, materials, and space-times were witnessed in our data, as the waves of onlooking~playing~learning constantly emerge, overlap, diffract, intra-act in their material-discursive arrangements. We hope that re-configurations of onlooking children may disturb and unsettle adults’ perspectives on young children as not-yet, immature, lacking subjects based on the standardized, universalized developmental stages and, furthermore, help us to reconsider young children’s agency to affect and be affected in the entangled relationality, and generate differences in the world they live.

Crucial Role of Bacterial Processes in the Net Community Production of the Amundsen Sea Polynya Disclosed by a Modeling Study

AbstractWe investigated seasonal net community production (NCP) variations in the productive Amundsen Sea Polynya, integrating observational data and ecosystem modeling. NCP estimates (NCPO2/Ar) from in situ O2/Ar data during the austral summer (January‐March) from 2011 to 2018 were compared with those from a one‐dimensional ecosystem model. Early January saw the highest NCPO2/Ar values ranging from 115 to 139 mmol O2 m−2 d−1 among observations. Over the summer, NCPO2/Ar gradually decreased, reaching 40 mmol O2 m−2 d−1 by late February. Late summer values, though one‐third of early January, remained notably positive, indicating net autotrophy. This persisted despite sea surface temperature dropping from >−0.4°C in January to −1.33°C in late February. Refining NCPO2/Ar, we modified bacterial dynamics in our ecosystem model. Significantly improved model performance resulted from two key modifications. First, we introduced bacterial uptake dependency on Phaeocystis primary production. Second, we heightened temperature‐dependent bacterial respiration and production approximately fifteenfold. These changes revealed NCP's remarkable sensitivity to minor temperature fluctuations (<1°C). Furthermore, modified bacterial dynamics delayed the net primary production peak by 2 weeks, underlining the importance of phytoplankton‐bacteria interaction in the ocean carbon cycle. Model results estimated annual NCP in the Amundsen Sea Polynya at 4.04 mol C m−2, aligning with summer NCP estimates (0.2–5.9 mol C m−2) in observational study. Our study advances NCP understanding in polar regions, emphasizing comprehensive observations, including bacterial processes, for understanding intricate biotic interactions. These findings align with past observations on bacterial metabolism and Phaeocystis ecological properties in the Antarctic oceans.

Operational low-flow forecasting using LSTMs

This study focuses on exploring the potential of using Long Short-Term Memory networks (LSTMs) for low-flow forecasting for the Rhine River at Lobith on a daily scale with lead times up to 46 days ahead. A novel LSTM-based model architecture is designed to leverage both historical observation and forecasted meteorological data to carry out multi-step discharge time series forecasting. The feature and target selection for this deep learning (DL) model involves evaluating the use of different spatial resolutions for meteorological forcing (basin-averaged or subbasin-averaged), the impact of incorporating past discharge observations, and the use of different target variables (discharge Q or time-differenced discharge dQ). Then, the model is trained using the ERA5 dataset as meteorological forcing, and employed for operational forecast with ECMWF seasonal forecast (SEAS5) data. The forecast results are compared to a benchmark process-based model, wflow_sbm. This study also explores the flexibility of the DL model by fine-tuning the pretrained model with limited SEAS5 dataset. Key findings from feature and target selection include: (1) opting for subbasin-averaged meteorological variables significantly improves model performance compared to a basin-averaged approach. (2) Utilizing dQ as the target variable greatly boosts short-term forecast accuracy compared to using Q, with a mean absolute error (MAE) of 25 m3 s−1 and mean absolute percentage error (MAPE) of 0.02 for the first lead time, ensuring reliability and accuracy at the onset of the forecast horizon. (3) While incorporating historical discharge improves the forecasting of Q, its impact on predicting dQ is less pronounced for short lead times. In the operational forecast with SEAS5, compared to the wflow_sbm model, the DL model exhibits skill in forecasting low flows as evidenced by Continuous Ranked Probability Skill Score (CRPSS) median values of all lead times above zero, and better accuracy in forecasting drought events within short lead times. The wflow_sbm model shows higher accuracy for longer lead times. In the exploration of fine-tuning approach, the fine-tuned model generates marginal short-term enhancements in forecasting low-flow events over a non-fine-tuned model. Overall, this study contributes to advancing the field of low-flow forecasting using deep learning approach.

ARIMA and TFARIMA Analysis of the Main Water Quality Parameters in the Initial Components of a Megacity’s Drinking Water Supply System

The objective of this paper is to use autoregressive, integrated, and moving average (ARIMA) and transfer function ARIMA (TFARIMA) models to analyze the behavior of the main water quality parameters in the initial components of a drinking water supply system (DWSS) of a megacity (Bogota, Colombia). The DWSS considered in this study consisted of the following components: a river, a reservoir, and a drinking water treatment plant (WTP). Water quality information was collected daily and over a period of 8 years. A comparative analysis was made between the components of the DWSS based on the structure of the ARIMA and TFARIMA models developed. The results show that the best water quality indicators are the following: turbidity > color > total iron. Increasing the time window of the ARIMA analysis (daily/weekly/monthly) suggests an increase in the magnitude of the AR term for each DWSS component (WTP > river > reservoir). This trend suggests that the turbidity behavior in the WTP is more influenced by past observations compared to the turbidity behavior in the river and reservoir, respectively. Smoothing of the data series (moving average) as the time window of the ARIMA analysis increases leads to a greater sensitivity of the model for outlier detection. TFARIMA models suggest that there is no significant influence of past river turbidity events on turbidity in the reservoir, and of reservoir turbidity on turbidity at the WTP outlet. Turbidity outlier events between the river and reservoir occur mainly in a single observation (additive outliers), and between the reservoir and WTP also have a permanent effect over time (level shift outliers). The AR term of the models is useful for studying the transfer of effects between DWSS components, and the MA term is useful for studying the influence of external factors on water quality in each DWSS component.

On the optimal forecast with the fractional Brownian motion

This paper investigates the performance of different forecasting formulas with fractional Brownian motion based on discrete and finite samples. Existing literature presents two formulas for generating optimal forecasts when continuous records are available. One formula relies on a history over an infinite past, while the other is designed for a record limited to a finite past. In reality, only observations at discrete time points over a finite past are available. In this case, the forecasting formula, which has been widely used in the literature, is the one obtained by Gatheral et al. (Volatility is rough. Quant. Finance, 2018, 18(6), 933–949) that truncates and discretizes the formula based on continuous records over an infinite past. The present paper advocates an alternative forecasting formula, which is the conditional expectation based on finite past discrete-time observations. The findings suggest that the conditional expectation approach produces more accurate forecasts than the existing method, as demonstrated by both simulated data and actual daily realized volatility (RV) observations. Moreover, we also provide empirical evidence showing that the conditional expectation approach can lead to larger economic values than the existing method.

Resource-rational account of sequential effects in human prediction.

An abundant literature reports on 'sequential effects' observed when humans make predictions on the basis of stochastic sequences of stimuli. Such sequential effects represent departures from an optimal, Bayesian process. A prominent explanation posits that humans are adapted to changing environments, and erroneously assume non-stationarity of the environment, even if the latter is static. As a result, their predictions fluctuate over time. We propose a different explanation in which sub-optimal and fluctuating predictions result from cognitive constraints (or costs), under which humans however behave rationally. We devise a framework of costly inference, in which we develop two classes of models that differ by the nature of the constraints at play: in one case the precision of beliefs comes at a cost, resulting in an exponential forgetting of past observations, while in the other beliefs with high predictive power are favored. To compare model predictions to human behavior, we carry out a prediction task that uses binary random stimuli, with probabilities ranging from 0.05 to 0.95. Although in this task the environment is static and the Bayesian belief converges, subjects' predictions fluctuate and are biased toward the recent stimulus history. Both classes of models capture this 'attractive effect', but they depart in their characterization of higher-order effects. Only the precision-cost model reproduces a 'repulsive effect', observed in the data, in which predictions are biased away from stimuli presented in more distant trials. Our experimental results reveal systematic modulations in sequential effects, which our theoretical approach accounts for in terms of rationality under cognitive constraints.

A Complete Unveiling of the Mechanism and Chirality in Photoisomerization of Arylazopyrazole 3pzH: Combined Electronic Structure Calculations and AIMS Dynamic Simulations.

The arylazopyrazole 3pzH as a novel photoswitch exhibits quantitative switching and high thermal stability. In this work, combined electronic structure calculations and ab initio multiple spawning (AIMS) dynamic simulations were performed to systemically investigate the cis ↔ trans photoisomerization mechanism and the chiral preference after photoexcitation of 3pzH to the first excited singlet state (S1). Unlike most of the azoheteroarene photoswitches reported previously, many twisted and T-shaped cis isomers were found to be stable for 3pzH in the S0 state, owing to the moderate interaction between the hydrogen atom and π electrons of the aromatic ring. Two twisted cis isomers with different chirality ((M)-Z1 and (P)-Z1), the most stable T-shaped cis isomer ((T)-Z2), and the most stable planar trans isomer (E2) were selected as the initial structures to carry out the AIMS nonadiabatic dynamic simulations. Following excitation to the S1 state, all of the cis isomers decayed to conical intersection (CI) regions via the same bicycle pedal mechanism, while the evolution of the trans isomers to their CI regions was achieved via rotation around the N═N bond. More importantly, chiral preferences were found for the twisted cis isomers in the S1 state through the AIMS dynamic simulations due to the steric effect and static electronic repulsion. Notably, chirality was also observed in S1 isomerization starting from the planar E2 isomer because of the dynamic effect. After the nonadiabatic transition to the S0 state, the bicycle pedal mechanism was found to play a crucial role in cis ↔ trans photoisomerization. The simulated photoisomerization productivities were generally consistent with past experimental observations. Our calculations not only uncover the underlying reason for the excellent photoswitching properties of 3pzH but also enrich the knowledge of photoisomerization for azoheteroarene photoswitches, which will surely benefit their rational design.

Updating catastrophe models to today’s climate – An application of a large ensemble approach to extreme rainfall

As climate change unfolds, extreme weather events and natural hazards relevant for the insurance industry may change in frequency and intensity. In order to guarantee risk-adequate pricing, risk modelers have to ask themselves the question whether a certain ‘hazard’ parameter based on past observations and calibrated on past data is still valid or needs to be updated. Using the example of heavy rainfall between 3 h and 72 h as a proxy for flash flooding, we apply a unique high-resolution single model initial-condition large ensemble. We outline a methodology by which risk modelers can assess whether, where and by how much updating the hazard component of their risk model is needed. Therefore, we compare two time periods: 1980–1999 (as an example for a typical baseline period) and 2015–2034 (representing a 20-year period centered around today). We argue that assessing changes over the full ensemble space is vital for (i) the identification of homogeneous regions where a certain signal emerges, and (ii) the quantification of risk changes in the tail of the extreme value distribution that may still be hidden in the mean response to climate change. In the example case of 3-hourly 50-year rainfall return levels, we find a significant increase between 1980 and 1999 and 2015–2034 over 44 % of European land area. We also identify specific risk regions such as northwestern Spain where changes in the very tail (100-year rainfall return level) already emerge before more common extremes (1-year rainfall return level). Failing to detect and consider these tail changes or assuming equal changes for common and rare extremes may therefore lead to an under- or overestimation of the true level of risk today.

X-ray Observations of the Intermediate Polar TX Col

We present the timing analysis of the intermediate polar TX Col in the X-ray band using the observations made by Chandra, Swift, and Suzaku during the years 2000, 2007, and 2009, respectively. The spin, orbital, and beat periods derived from these data are consistent with the earlier findings. We found that the spin modulation was dominant during the Chandra observation, whereas both orbital and beat modulations were dominant during the Swift and Suzaku observations. These findings and past X-ray observations indicate that TX Col is changing its accretion geometry from disc dominance to stream dominance and vice versa.

S 308 and other X-ray emitting bubbles around Wolf–Rayet stars

Context. S 308 is an X-ray emitting bubble that surrounds the Wolf–Rayet star WR6. The structure shines in the optical as well and is thus known as the Dolphin Nebula. Due to its large angular extent, it has been covered at only 90% with past XMM-Newton observations. Aims. Thanks to the unique dataset provided by the all-sky survey performed in X-rays by SRG/eROSITA, we can show for the first time the image of the bubble in its entire extent in this band, together with its spectral characterization. Moreover, we have tried to apply the same procedure for other wind-blown bubbles detected in the optical/IR and we searched for X-ray extended emission around them. Methods. We first analyzed the diffuse emission of S308, providing a detailed spectral analysis. We then considered a sample of 22 optical/IR selected wind-blown bubbles from a previous study based on WISE data, providing an estimate of the X-ray flux for the first time. Results. We obtained the best fit for S308 with a two-temperature non-equilibrium plasma model (kT1 = 0.8-0.3+0.8 keV and kT2 = 2-1+3 keV) showing super-solar N abundance and low absorption. We did not detect any of the 22 optical/IR emitting bubbles in X-rays, but using our best fit model, we estimated the 3σ flux upper limits for each bubble. Conclusions. We demonstrate the new possibility offered by SRG/eROSITA to study known wind-blown bubbles and look for other ones. A two-temperature plasma description seems to fit the data quite well for S308. Since all of the 22 bubbles studied still remain undetected by SRG/eROSITA, it is very likely that absorption effects and spatial compactness are responsible for the challenges standing in the way of detecting these bubbles in soft X-rays.

Estuarine Exchange Flow in the Salish Sea

AbstractThe Salish Sea is a large, fjordal estuarine system opening onto the northeast Pacific Ocean. It develops a strong estuarine exchange flow that draws in nutrients from the ocean and flushes the system on timescales of several months. It is difficult to apply existing dynamical theories of estuarine circulation there because of the extreme bathymetric complexity. A realistic numerical model of the system was manipulated to have stronger and weaker tides to explore the sensitivity of the exchange flow to tides. This sensitivity was explored over two timescales: annual means and the spring‐neap. Two theories for the estuarine exchange flow are: (a) “gravitational circulation” where exchange is driven by the baroclinic pressure gradient due to along‐channel salinity variation, and (b) “tidal pumping” where tidal advection combined with flow separation forces the exchange. Past observations suggested gravitational circulation was of leading importance in the Salish Sea. We find here that the exchange flow increases with stronger tides, particularly in annual averages, suggesting it is controlled by tidal pumping. However, the landward salt transport due to the exchange flow decreases with stronger tides because greater mixing decreases the salinity difference between incoming and outgoing water. These results may be characteristic of estuarine systems that have rough topography and strong tides.

Higher-Order INAR Model Based on a Flexible Innovation and Application to COVID-19 and Gold Particles Data

INAR models have the great advantage of being able to capture the conditional distribution of a count time series based on their past observations, thus allowing it to be tailored to meet the unique characteristics of count data. This paper reviews the two-parameter Poisson extended exponential (PEE) distribution and its corresponding INAR(1) process. Then the INAR of order p (INAR(p)) model that incorporates PEE innovations is proposed, its statistical properties are presented, and its parameters are estimated using conditional least squares and conditional maximum likelihood estimation methods. Two practical data sets are analyzed and compared with competing INAR models in an effort to gauge the performance of the proposed model. It is found that the proposed model performs better than the competitors.

The 2023 US 50-State National Seismic Hazard Model: Overview and implications

The US National Seismic Hazard Model (NSHM) was updated in 2023 for all 50 states using new science on seismicity, fault ruptures, ground motions, and probabilistic techniques to produce a standard of practice for public policy and other engineering applications (defined for return periods greater than ∼475 or less than ∼10,000 years). Changes in 2023 time-independent seismic hazard (both increases and decreases compared to previous NSHMs) are substantial because the new model considers more data and updated earthquake rupture forecasts and ground-motion components. In developing the 2023 model, we tried to apply best available or applicable science based on advice of co-authors, more than 50 reviewers, and hundreds of hazard scientists and end-users, who attended public workshops and provided technical inputs. The hazard assessment incorporates new catalogs, declustering algorithms, gridded seismicity models, magnitude-scaling equations, fault-based structural and deformation models, multi-fault earthquake rupture forecast models, semi-empirical and simulation-based ground-motion models, and site amplification models conditioned on shear-wave velocities of the upper 30 m of soil and deeper sedimentary basin structures. Seismic hazard calculations yield hazard curves at hundreds of thousands of sites, ground-motion maps, uniform-hazard response spectra, and disaggregations developed for pseudo-spectral accelerations at 21 oscillator periods and two peak parameters, Modified Mercalli Intensity, and 8 site classes required by building codes and other public policy applications. Tests show the new model is consistent with past ShakeMap intensity observations. Sensitivity and uncertainty assessments ensure resulting ground motions are compatible with known hazard information and highlight the range and causes of variability in ground motions. We produce several impact products including building seismic design criteria, intensity maps, planning scenarios, and engineering risk assessments showing the potential physical and social impacts. These applications provide a basis for assessing, planning, and mitigating the effects of future earthquakes.

The antidiabetic SGLT2 inhibitor canagliflozin reduces mitochondrial metabolism in a model of skeletal muscle insulin resistance.

Sodium-glucose cotransporter 2 (SGLT2) inhibitors such as canagliflozin (CANA) have emerged as an effective adjuvant therapy in the management of diabetes, however, past observations suggest CANA may alter skeletal muscle mass and function. The purpose of this work was to investigate the effects of CANA on skeletal muscle metabolism both with and without insulin resistance. C2C12 myotubes were treated with CANA with or without insulin resistance. Western blot and qRT-PCR were used to assess protein and gene expression, respectively. Cell metabolism was assessed via oxygen consumption and extracellular acidification rate. Mitochondrial, nuclei and lipid content were measured using fluorescent staining and microscopy. CANA decreased mitochondrial function and glycolytic metabolism as did insulin resistance, however, these changes occurred without significant alterations in gene expression associated with each pathway. Additionally, while insulin resistance reduced insulin-stimulated pAkt expression, CANA had no significant effect on insulin sensitivity. CANA appears to reduce mitochondrial and glycolytic metabolism without altering gene expression governing these pathways, suggesting a reduction in substrate may be responsible for lower metabolism.

Developing Pesantren Educator Resources through Optimizing the Learning Organization

This article aims to describe the application of the Learning Organization in developing the competence of teacher educator resources in pesantren. This research has been conducted at Amanatul Ummah Islamic Boarding School (Madrasah Bertaraf Internasional). The research that has been used is qualitative with a case study approach. The data collection techniques use interviews with informants, namely, the head of the MBI coordinator, teachers, and some students. Observation involves past participant observation, and documentation includes reviewing data related to professional development carried out by teachers. The findings of this study are as follows: strict recruitment of education personnel who are under the pesantren's national insight and affiliated with Aswaja NU, strengthening the pesantren organizational culture through leadership training via organizational activities, and teachers conducting scientific integration, combining the Egyptian Al Azhar curriculum with the national curriculum. Teachers' professional development cannot be separated from the support of pesantren, which provides more compensation and discipline in carrying out teachers' duties.

İnşaat Maliyet Endeksi Tahmininde Holt-Winters Üstel Düzeltme Parametrelerinin PSO ve İleri Walk-Forward Cross-Validation ile Optimizasyonu

This research aims to enhance the accuracy of Construction Cost Index (CCI) forecasting using Holt-Winters exponential smoothing (ES) by optimizing its parameters, focusing on minimizing the Mean Absolute Percentage Error (MAPE) for precise CCI forecasts. To reach this aim, The Holt-Winters model parameters are optimized through Particle Swarm Optimization (PSO) and Walk-Forward Cross-Validation (WFCV). PSO, a metaheuristic optimization algorithm, is being applied to search for optimal values of the smoothing parameters (alpha, beta, and gamma) that determine the weightage of past observations, trends, and seasonality, respectively. WFCV is assessed the model's performance and ensures robustness. Reduced MAPEs of 22 for CCI forecasts and 2 for training data are the findings of the optimized Holt-Winters model. The obtained alpha, beta, and gamma values are 0.99, 0.77, and 0, respectively, highlighting the importance of while neglecting seasonality. Convergence graphs demonstrate the superiority of the optimization approach over conventional parameter values or random selections. By employing PSO and WFCV, the study efficiently fine-tunes the Holt-Winters model for precise CCI forecasting. Optimized parameter values enable data driven decision-making in construction project cost estimation and budget management. This research contributes a reliable and robust optimization methodology for CCI forecasting, supporting advancements in the field.

Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models

The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.

Artificial Neural Networks and Time Series of Counts: A Class of Nonlinear INGARCH Models

Abstract Time series of counts are frequently analyzed using generalized integer-valued autoregressive models with conditional heteroskedasticity (INGARCH). These models employ response functions to map a vector of past observations and past conditional expectations to the conditional expectation of the present observation. In this paper, it is shown how INGARCH models can be combined with artificial neural network (ANN) response functions to obtain a class of nonlinear INGARCH models. The ANN framework allows for the interpretation of many existing INGARCH models as a degenerate version of a corresponding neural model. Details on maximum likelihood estimation, marginal effects and confidence intervals are given. The empirical analysis of time series of bounded and unbounded counts reveals that the neural INGARCH models are able to outperform reasonable degenerate competitor models in terms of the information loss.

Digital Transformation, Governance, and Coordination in Times of Crisis: An Analysis of Australia, Denmark, and the Republic of Korea

Australia, Denmark, and the Republic of Korea are front-runners in the public sector use of Information Communication Technology (ICT) globally, resulting in a high degree of digitisation of public services production and delivery. While a multitude of factors may account for their successes, what is the role played by their strategic approach to governance and inter-governmental cooperation? How have their approaches to governance and multi-stakeholder cooperation influenced the success of their digital transformation, boosted innovation, and enabled them to rapidly respond to the pandemic crisis? The article's initial findings support past academic observations emphasising that the successful digital transformation of the public sector largely depends on strategic focus, a strong governance model, and a high level of intergovernmental cooperation. The analysis finds strong evidence that their existing governance and intergovernmental cooperation frameworks, in combination with their established service production and delivery ecosystems, have allowed the three countries to move towards real user-centric, integrated service production and delivery prior to the COVID-19 pandemic. The analysis also finds evidence of a relatively high level of public sector business continuity for service production and delivery in light of the COVID-19 pandemic and associated restrictions on mobility.