A Taxonomy of Software Delivery Performance Profiles: Investigating the Effects of DevOps Practices

This research develops a taxonomy of Software Delivery Performance Profiles for DevOps development settings. We base the underlying Software Delivery Performance measure on the application of the Economic Order Quantity (EOQ) model to software development. Consistent with the objectives of both, development and operations departments, the measure includes attributes for throughput (release frequency and lead-time to delivery) and for stability (mean time to restore). Using a sample of 7,522 DevOps professionals globally, we conduct a hierarchical cluster analysis and find that the throughput and stability measures move in tandem and form three distinct Software Delivery Performance Profiles. Further analysis will show how the use of individual DevOps practices impacts Performance Profiles of development settings. When completed, the study will support the utility of DevOps and the effectiveness of individual DevOps practices

Machine Learning based Parameter Sensitivity of Regional Climate Models -- A Case Study of the WRF Model for Heat Extremes over Southeast Australia

Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model's performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global sensitivity analysis method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study's results will help in further optimising WRF parameters to improve model simulation

Stability of string defects in models of non-Abelian symmetry breaking

In this paper we describe a new type of topological defect, called a homilia string, which is stabilized via interactions with the string network. Using analytical and numerical techniques, we investigate the stability and dynamics of homilia strings, and show that they can form stable electroweak strings. In SU(2)xU(1) models of symmetry breaking the intersection of two homilia strings is identified with a sphaleron. Due to repulsive forces, the homilia strings seperate, resulting in sphaleron annihilation. It is shown that electroweak homilia string loops cannot stabilize as vortons, which circumvents the adverse cosmological problems associated with stable loops. The consequences for GUT scale homilia strings are also discussed.Comment: 15 pages, revtex, with 8 figures. Submitted to PR

Assessing Climate Change Impacts on the Stability of Small Tidal Inlets: Part 2- Data Rich Environments

Climate change (CC) is likely to affect the thousands of bar-built or barrier estuaries (here referred to as Small tidal inlets - STIs) around the world. Any such CC impacts on the stability of STIs, which governs the dynamics of STIs as well as that of the inlet-adjacent coastline, can result in significant socio-economic consequences due to the heavy human utilisation of these systems and their surrounds. This article demonstrates the application of a process based snap-shot modelling approach, using the coastal morphodynamic model Delft3D, to 3 case study sites representing the 3 main STI types; Permanently open, locationally stable inlets (Type 1), Permanently open, alongshore migrating inlets (Type 2) and Seasonally/Intermittently open, locationally stable inlets (Type 3). The 3 case study sites (Negombo lagoon - Type 1, Kalutara lagoon - Type 2, and Maha Oya river - Type 3) are all located along the southwest coast of Sri Lanka. After successful hydrodynamic and morphodynamic model validation at the 3 case study sites, CC impact assessment are undertaken for a high end greenhouse gas emission scenario. Future CC modified wave and riverflow conditions are derived from a regional scale application of spectral wave models (WaveWatch III and SWAN) and catchment scale applications of a hydrologic model (CLSM) respectively, both of which are forced with IPCC Global Climate Model output dynamically downscaled to approximately 50 km resolution over the study area with the stretched grid Conformal Cubic Atmospheric Model CCAM. Results show that while all 3 case study STIs will experience significant CC driven variations in their level of stability, none of them will change Type by the year 2100. Specifically, the level of stability of the Type 1 inlet will decrease from 'Good' to 'Fair to poor' by 2100, while the level of (locational) stability of the Type 2 inlet will also decrease with a doubling of the annual migration distance. Conversely, the stability of the Type 3 inlet will increase, with the time till inlet closure increasing by approximately 75%. The main contributor to the overall CC effect on the stability of all 3 STIs is CC driven variations in wave conditions and resulting changes in longshore sediment transport, not Sea level rise as commonly believed

Examining the impact of multiple climate forcings on simulated Southern Hemisphere climate variability

The study examines the influence of external climate forcings, and atmosphere–ocean–sea–ice coupled interaction on the Southern Hemisphere (SH) atmospheric circulation variability. We analysed observed and simulated changes in view of Antarctic sea–ice and Southern Ocean surface temperature trends over recent decades. The experiment embraces both idealised and comprehensive methods that involves an Earth System Model (ESM) prototype. The sensitivity experiment is conducted in a manner that decomposes the signatures of sea–ice, sea surface temperature and feedback mechanisms. The results reveal that the Southern Annular Mode (SAM) multidecadal variability is found to be modulated by coupled interactions whereas its sub-seasonal to interannual vacillation seems to follow a random trajectory. The latter may strengthen the notion that its predictability is limited even with the use of ESMs. Most of the atmospheric circulation variability and recent changes may be explained by the ocean thermal forcing and coupled interactions. However, the influence of sea–ice forcing alone is largely indistinguishable and predominantly localised in nature. The result also confirms that the Antarctic dipole-like sea–ice pattern, a leading climate mode in the SH, has intensified in the last three decades irrespective of season. The probable indication is that processes within the Southern Ocean may play a key role, which deserves further investigation.The National Research foundation through the Alliance for Collaboration on Climate & Earth Systems Science (ACCESS). The iDEWS project, which supported the study under the auspices of the Japan Science and Technology Agency/Japan Agency for Medical Research and Development through the Science and Technology Research Partnership for Sustainable Development (SATREPS), and the ACCESS in South Africa.http://link.springer.com/journal/3822021-04-27hj2020Geography, Geoinformatics and Meteorolog

Rainfall simulations of high-impact weather in South Africa with the conformal cubic atmospheric model (CCAM)

Warnings of severe weather with a lead time longer that two hours require the use of skillful numerical weather prediction (NWP) models. In this study, we test the performance of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Conformal Cubic Atmospheric Model (CCAM) in simulating six high-impact weather events, with a focus on rainfall predictions in South Africa. The selected events are tropical cyclone Dineo (16 February 2017), the Cape storm (7 June 2017), the 2017 Kwa-Zulu Natal (KZN) floods (10 October 2017), the 2019 KZN floods (22 April 2019), the 2019 KZN tornadoes (12 November 2019) and the 2020 Johannesburg floods (5 October 2020). Three configurations of CCAM were compared: a 9 km grid length (MN9km) over southern Africa nudged within the Global Forecast System (GFS) simulations, and a 3 km grid length over South Africa (MN3km) nudged within the 9 km CCAM simulations. The last configuration is CCAM running with a grid length of 3 km over South Africa, which is nudged within the GFS (SN3km). The GFS is available with a grid length of 0.25 , and therefore, the configurations allow us to test if there is benefit in the intermediate nudging at 9 km as well as the effects of resolution on rainfall simulations. The South AfricanWeather Service (SAWS) station rainfall dataset is used for verification purposes. All three configurations of CCAM are generally able to capture the spatial pattern of rainfall associated with each of the events. However, the maximum rainfall associated with two of the heaviest rainfall events is underestimated by CCAM with more than 100 mm. CCAM simulations also have some shortcomings with capturing the location of heavy rainfall inland and along the northeast coast of the country. Similar shortcomings were found with other NWP models used in southern Africa for operational forecasting purposes by previous studies. CCAM generally simulates a larger rainfall area than observed, resulting in more stations reporting rainfall. Regarding the different configurations, they are more similar to one another than observations, however, with some suggestion that MN3km outperforms other configurations, in particular with capturing the most extreme events. The performance of CCAM in the convective scales is encouraging, and further studies will be conducted to identify areas of possible improvement.The AIMS NEI Women in Climate Change Science (WiCCS) fellowship and the Water Research Commission.https://www.mdpi.com/journal/atmospheream2023Geography, Geoinformatics and Meteorolog

The Role of Task and Process Conflict in Strategizing

The implementation of strategic initiatives is central to organizational success because it involves not just the execution of strategy, but also the formulation of strategy content. Yet, strategy implementation is complex, partially because it is critically affected by human dynamics. These dynamics are an integral but poorly understood aspect of how organizations negotiate multiple goals. Conflict is one dynamic that has received little attention in the context of strategy implementation. The authors address this gap by studying task and process conflict as a firm implements a strategy in real time. The study demonstrates that process conflict directs attention to problems with how to implement a strategy, while task conflict directs attention to problems with the content of the strategy. Critically, however, managers can only harness generative effects of conflict if they correctly diagnose process and task conflict, and respond to both forms of conflict. This requires an understanding of the entwined nature of task and process conflict, and highlights the necessity of aligning responses to these forms of conflict. Thus, this study offers conflict as one explanatory mechanism of how actors execute strategy and clarify strategy content

Facing differences with an open mind: Openness to Experience, salience of intra-group differences, and performance of diverse groups.

This study examined how the performance of diverse teams is affected by member openness to experience and the extent to which team reward structure emphasizes intragroup differences. Fifty-eight heterogeneous four-person teams engaged in an interactive task. Teams in which reward structure converged with diversity (i.e., "faultline" teams) performed more poorly than teams in which reward structure cut across differences between group members or pointed to a "superordinate identity." High openness to experience positively influenced teams in which differences were salient (i.e., faultline and "cross-categorized" teams) but not teams with a superordinate identity. This effect was mediated by information elaboration