Academic literature on the topic 'Reproducibility of scenario'

This paper investigates statistical reproducibility of the [Formula: see text]-test. We formulate reproducibility as a predictive inference problem and apply the nonparametric predictive inference method. Within our research framework, statistical reproducibility provides inference on the probability that the same test outcome would be reached, if the test were repeated under identical conditions. We present an nonparametric predictive inference algorithm to calculate the reproducibility of the [Formula: see text]-test and then use simulations to explore the reproducibility both under the null and alternative hypotheses. We then apply nonparametric predictive inference reproducibility to a real-life scenario of a preclinical experiment, which involves multiple pairwise comparisons of test groups, where different groups are given a different concentration of a drug. The aim of the experiment is to decide the concentration of the drug which is most effective. In both simulations and the application scenario, we study the relationship between reproducibility and two test statistics, the Cohen’s [Formula: see text] and the [Formula: see text]-value. We also compare the reproducibility of the [Formula: see text]-test with the reproducibility of the Wilcoxon Mann–Whitney test. Finally, we examine reproducibility for the final decision of choosing a particular dose in the multiple pairwise comparisons scenario. This paper presents advances on the topic of test reproducibility with relevance for tests used in pharmaceutical research.

Appropriate soft tissue tension in total knee replacement (TKR) is an important factor for a successful outcome. The purpose of our study was to assess both the reproducibility of a modern intraoperative pressure sensor (IOP) and if a surgeon could unconsciously influence measurement. A consecutive series of 80 TKRs were assessed with an IOP between January 2018 and December 2020. In the first scenario, two blinded sequential measurements in 48 patients were taken; in a second scenario, an initial blinded measurement and a subsequent unblinded measurement in 32 patients were taken while looking at the sensor monitor screen. Reproducibility was assessed by intraclass correlation coefficients (ICCs). In the first scenario, the ICC ranged from 0.83 to 0.90, and in the second scenario it ranged from 0.80 to 0.90. All ICCs were 0.80 or higher, indicating reproducibility using a IOP and that a surgeon may not unconsciously influence the measurement. The use of a modern IOP to measure soft tissue tension in TKRs is a reproducible technique. A surgeon observing the measurements while performing IOP may not significantly influence the result. An IOP gives additional information that the surgeon can use to optimize outcomes in TKR.

The article opens by briefly reviewing studies of sexuality in and around organizations from the 1970s. These studies showed considerable theoretical, empirical and conceptual development, as in the concept of organization sexuality. Building on this, the article’s first task is to analyse alternative future scenarios for organization sexualities, by way of changing intersections of gender, sexuality and organizational forms. Possible gendered future scenarios are outlined based on, first, gender equality/inequality and, second, gender similarity/difference between women, men and further genders: hyper-patriarchy scenario—men and women becoming more divergent; with greater inequality; late capitalist gender scenario—genders becoming more convergent, with greater inequality; bi-polar scenario—men and women becoming more divergent, with greater equality; postgender scenario—genders becoming more convergent, with greater equality. Somewhat similar scenarios for organization sexualities are elaborated in terms of gender/sexual equality and inequality and sexual/gender similarity and difference: heteropatriarchies scenario—greater sexual/gender difference and greater sexual or sexual/gender inequality; late capitalist sexual scenario—greater sexual/gender similarity and greater sexual or gender/sexual inequality; sexual differentiation scenario—greater sexual/gender difference and greater sexual or sexual/gender equality; sexual blurring scenario—greater sexual/gender similarity and greater sexual or sexual/gender equality. The article’s second task is to addresses the impact of globalizations and transnationalizations, specifically information and communication technologies and other socio-technologies, for future scenarios of organization sexualities. The characteristic affordances of ICTs—technological control, virtual reproducibility, conditional communality, unfinished undecidability—are mapped onto the four scenarios above and the implications outlined.

Abstract. Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface–subsurface hydrological model that employs a finite-volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://ghdc.ac.cn, last access: 1 September 2023), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis.

Precipitation and near-surface air temperatures are significant meteorological forcing for streamflow prediction where most basins are partially or fully data-scarce in many parts of the world. This study aims to evaluate the consistency of MSWXv100-based precipitation, temperatures, and estimated potential evapotranspiration (PET) by direct comparison with observed measurements and by utilizing an independent combination of MSWXv100 dataset and observed data for streamflow prediction under four distinct scenarios considering model parameter and output uncertainties. Initially, the model is calibrated/validated entirely based on observed data (Scenario 1), where for the second calibration/validation, the observed precipitation is replaced by MSWXv100 precipitation and the daily observed temperature and PET remained unchanged (Scenario 2). Furthermore, the model calibration/validation is done by considering observed precipitation and MSWXv100-based temperature and PET (Scenario 3), and finally, the model is calibrated/validated entirely based on the MSWXv100 dataset (Scenario 4). The Kling–Gupta Efficiency (KGE) and its components (correlation, ratio of bias, and variability ratio) are utilized for direct comparison, and the Hanssen–Kuiper (HK) skill score is employed to evaluate the detectability strength of MSWXv100 precipitation for different precipitation intensities. Moreover, the hydrologic utility of MSWXv100 dataset under four distinct scenarios is tested by exploiting a conceptual rainfall-runoff model under KGE and Nash–Sutcliffe Efficiency (NSE) metrics. The results indicate that each scenario depicts high streamflow reproducibility where, regardless of other meteorological forcing, utilizing observed precipitation (Scenario 1 and 3) as one of the model inputs, shows better model performance (KGE = 0.85) than MSWXv100-based precipitation, such as Scenario 2 and 4 (KGE = 0.78–0.80).

Abstract. During the entire procedure of risk assessment for hydrologic hazards, the selection of consistent and reliable scenarios, constructed in a strictly systematic way, is fundamental for the quality and reproducibility of the results. However, subjective assumptions on relevant impact variables such as sediment transport intensity on the system loading side and weak point response mechanisms repeatedly cause biases in the results, and consequently affect transparency and required quality standards. Furthermore, the system response of mitigation measures to extreme event loadings represents another key variable in hazard assessment, as well as the integral risk management including intervention planning. Formative Scenario Analysis, as a supplement to conventional risk assessment methods, is a technique to construct well-defined sets of assumptions to gain insight into a specific case and the potential system behaviour. By two case studies, carried out (1) to analyse sediment transport dynamics in a torrent section equipped with control measures, and (2) to identify hazards induced by woody debris transport at hydraulic weak points, the applicability of the Formative Scenario Analysis technique is presented. It is argued that during scenario planning in general and with respect to integral risk management in particular, Formative Scenario Analysis allows for the development of reliable and reproducible scenarios in order to design more specifically an application framework for the sustainable assessment of natural hazards impact. The overall aim is to optimise the hazard mapping and zoning procedure by methodologically integrating quantitative and qualitative knowledge.

Handcrafted radiomics features (HRFs) are quantitative features extracted from medical images to decode biological information to improve clinical decision making. Despite the potential of the field, limitations have been identified. The most important identified limitation, currently, is the sensitivity of HRF to variations in image acquisition and reconstruction parameters. In this study, we investigated the use of Reconstruction Kernel Normalization (RKN) and ComBat harmonization to improve the reproducibility of HRFs across scans acquired with different reconstruction kernels. A set of phantom scans (n = 28) acquired on five different scanner models was analyzed. HRFs were extracted from the original scans, and scans were harmonized using the RKN method. ComBat harmonization was applied on both sets of HRFs. The reproducibility of HRFs was assessed using the concordance correlation coefficient. The difference in the number of reproducible HRFs in each scenario was assessed using McNemar’s test. The majority of HRFs were found to be sensitive to variations in the reconstruction kernels, and only six HRFs were found to be robust with respect to variations in reconstruction kernels. The use of RKN resulted in a significant increment in the number of reproducible HRFs in 19 out of the 67 investigated scenarios (28.4%), while the ComBat technique resulted in a significant increment in 36 (53.7%) scenarios. The combination of methods resulted in a significant increment in 53 (79.1%) scenarios compared to the HRFs extracted from original images. Since the benefit of applying the harmonization methods depended on the data being harmonized, reproducibility analysis is recommended before performing radiomics analysis. For future radiomics studies incorporating images acquired with similar image acquisition and reconstruction parameters, except for the reconstruction kernels, we recommend the systematic use of the pre- and post-processing approaches (respectively, RKN and ComBat).

Accurate navigation is a crucial asset for safe aviation operation. The GNSS (Global Navigation Satellite System) is set to play an always more important role in aviation but needs to cope with the risk of interference, possibly causing signal disruption and loss of navigation capability. It is crucial, therefore, to evaluate the impact of interference events on the GNSS system on board an aircraft, in order to plan countermeasures. This is currently obtained through expensive and time-consuming flight measurement campaigns. This paper shows on the other hand, a method developed to create a virtual digital twin, capable of reconstructing the entire flight scenario (including flight dynamics, actual antenna, and impact of installation on aircraft) and predicting the signal and interference reception at airborne level, with clear benefits in terms of reproducibility and easiness. Through simulations that incorporate jamming scenarios or any other interference scenarios, the effectiveness of the aircraft’s satellite navigation capability in the real environment can be evaluated, providing valuable insights for informed decision-making and system enhancement. By extension, the method shown can provide the ability to predict real-life outcomes even without the need for actual flight, enabling the analysis of different antenna-aircraft configurations in a specific interference scenario.

It is commonly agreed that in silico scientific experiments should be executable and repeatable processes. Most of the current approaches for computational experiment conservation and reproducibility have focused so far on two of the main components of the experiment, namely, data and method. In this paper, we propose a new approach that addresses the third cornerstone of experimental reproducibility: the equipment. This work focuses on the equipment of a computational experiment, that is, the set of software and hardware components that are involved in the execution of a scientific workflow. In order to demonstrate the feasibility of our proposal, we describe a use case scenario on the Text Analytics domain and the application of our approach to it. From the original workflow, we document its execution environment, by means of a set of semantic models and a catalogue of resources, and generate an equivalent infrastructure for reexecuting it.

In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available.