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The Kieker observability framework version 2
No full textObservability of a software system aims at allowing its engineers and operators to keep the system robust and highly available. With this paper, we present the Kieker Observability Framework Version 2, the successor of the Kieker Monitoring Framework. In this tool artifact paper, we do not just present the Kieker framework, but also a demonstration of its application to the TeaStore benchmark, integrated with the visual analytics tool ExplorViz. This demo is provided both as an online service and as an artifact to deploy it yourself.</p
Introducing a nonlinear macroeconomic model based on TE, SINDYC, and phase plane analysis
No full textAn important macroeconomics task is identifying variable change patterns through mathematical modeling. To delve into the interconnections among macroeconomic variables, this study employs two data science methods: Transfer Entropy (TE) and Sparse Identification Nonlinear Dynamics Control (SINDyC). TE is utilized to select variables and explore their relationships, while SINDyC extracts a multi-dimensional nonlinear dynamics model and analyzes the stability of the model using phase plane analysis. This paper offers a perspective on the relationship between macroeconomic variables by identifying stable areas within the system. The results could help policymakers gain valuable insights and a deeper understanding of the interactions among nonlinear dynamics in system variables. The results emphasize various interest rate selection scenarios to demonstrate stability regions of the inflation and unemployment rate, as influenced by different entries of GDP per capita. Phase plane to begin with result recognizes stable areas within the unemployment-inflation relationship, unveiling nuanced dynamics within distinctive GDP per capita and interest rate scenarios. In contrast, a steady GDP per capita zone is built up over shifted scenarios of the inflation rate and interest rate in the phase plane second simulation results. The third result in the phase plane claimed a stable pattern of the inflation rate versus GDP per capita, considering various constants of the interest rate. Furthermore, this new perspective offers a valuable approach to quantitative macroeconomic analysis, providing policymakers with comprehensive data that can enhance their understanding and decision-making processes
Patient perceptions of primary care rapid respiratory microbiological point-of-care testing: a qualitative study
No full textObjectives: rapid microbiological point-of-care tests (RM-POCTs) have the potential to reduce antimicrobial overuse for respiratory tract infections (RTIs). However, patient perspectives regarding RM-POCTs remain unclear. Therefore, this study aimed to explore patients’ and parents’ experiences using RM-POCTs for RTIs and their views on how RM-POCTs influence treatment decisions, symptom management and future consulting.Design: a qualitative study using in-depth, semistructured interviews. Data were analysed thematically, informed by a realist approach.Setting: interviewees were recruited from a multicentre, individually randomised controlled efficacy trial evaluating the use of a multiplex RM-POCT for suspected RTIs in primary care.Participants: purposive sample of primary care patients (n=21 adults, 9 parents) participating in the trial.Results: in general, participants viewed RM-POCTs favourably. Patients believed RM-POCTs reduced diagnostic uncertainty but emphasised that RM-POCTs should be used alongside clinical judgement. For some, additional information from RM-POCTs created positive outcome expectancies and reduced the perception that antibiotics were necessary. Others felt invalidated by RM-POCTs’ results or believed further support was necessary to understand when antibiotics were needed and how they could manage symptoms. While RM-POCTs may reduce reconsulting for the same illness, participants indicated future consulting behaviours would persist for self-limiting symptoms or health anxiety. Increased consulting may occur if patients perceive RM-POCTs to reduce pressure on primary care.Conclusion: RM-POCT offers the potential to improve self-efficacy beliefs and reduce reconsulting for the same illness. Effective clinician communication and patient education may be beneficial alongside RM-POCTs to minimise unintended outcomes and enhance patients’ ability to determine when primary care attendance is necessary in the future
Assessing the extent to which ‘warming stripes’ are an effective format for communicating environmental risks
No full textIn 2018, Professor Ed Hawkins published an image consisting of blue and red vertical stripes in various colour saturations that represented the change of the average global annual temperature between 1850 and 2018. Subsequently, the image (a.k.a., warming stripes or climate stripes) ‘went viral’ on social media (#showyourstripes) and became an iconic symbol of the threat posed by climate change. Furthermore, these stripe graph formats are now increasingly being adapted by the scientific community (e.g., the IPCC) and used to communicate other environmental risks (e.g., biodiversity loss, sea-level change). However, no studies have empirically assessed the extent to which stripe graphs influence knowledge, perceptions, and behaviours concerning environmental issues. To address this knowledge gap, we conducted a study in which participants were divided into three groups. Group 1 saw Hawkins’ original blue-red stripe graph. Group 2 saw the same graph, but the blue-red hues had been changed to yellow-purple hues respectively. Group 3 (control condition) did not see a graph. Participants then completed a series of measures, including climate change knowledge, perceived risk, behavioural intentions, and subjective graph evaluations. Our analysis identified no between-group differences for knowledge, risk perceptions, and behavioural intentions. However, we found participants evaluated the blue-red graph significantly (ps < .0001) more likeable, trustworthy, helpful, and accurate than the yellow-purple graph, even though the two graphs depicted exactly the same data. We also found that stripe graphs influenced participants to make inaccurately high estimates of future global temperatures. Hence, our results suggest that, while the blue-red stripe graph is extremely popular, it may not be effective at enhancing knowledge or motivating mitigation behaviours. Considering the popularity of stripe graphs among laypeople and, increasingly, the scientific community, it appears further research is needed to identify how the format can be enhanced to better achieve important environmental risk communication goals
Associations of ADHD traits, sleep/circadian factors, depression and quality of life
No full textBACKGROUND: Individuals with attention deficit hyperactivity disorder (ADHD) are at a higher risk of depression and lower quality of life (QoL); however, it is unclear whether disrupted sleep and circadian rhythms mediate this increased risk.OBJECTIVES: We investigated whether disruption of self-reported sleep and circadian factors mediate the associations of ADHD traits with depression symptom severity and QoL.METHODS: 1364 participants (mean: 51.86 (SD=0.37) years, 75% women) from a large-scale cross-sectional online survey (Netherlands Sleep Registry) completed a sociodemographic questionnaire, the Adult ADHD Rating Scale, Hospital Anxiety and Depression Scale, Satisfaction With Life Scale (SLS) and Cantril Ladder (CL) (QoL measures), Insomnia Severity Index, Pittsburgh Sleep Quality Index and Munich Chronotype Questionnaire.FINDINGS: Higher ADHD traits were significantly associated with depression symptom severity (p=0.03), lower QoL (p<0.001), insomnia severity (p<0.001), lower sleep quality (p<0.001) and later chronotype (p=0.01). No sleep or circadian factor significantly mediated the association of the severity of symptoms of ADHD and depression (all p>0.1). Conversely, only insomnia severity significantly mediated the association of ADHD traits and QoL (SLS: standardised β=-0.10, 95% CI (-0.12 to -0.04); CL: standardised β=0.103, 95% CI (0.04 to 0.16)).CONCLUSION: ADHD traits were associated with lower QoL and it was partially mediated by insomnia severity. Future studies targeting insomnia complaints in this population may help mitigate their depression complaints and improve their QoL.CLINICAL IMPLICATIONS: Our results may help current clinical guidelines that do not typically link sleep/circadian complaints to QoL in ADHD assessment.</p
Digital innovation to improve quality of care in the emergency department: a multi-method study
No full textEmergency departments (EDs) are vital parts of healthcare systems. However, they often encounter operational challenges, such as overcrowding, long wait times, and inefficient resource utilisation. In the United Kingdom, the National Health Service (NHS) has prioritised reducing patient wait times and improving care quality by setting a goal to treat 76% of ED patients within four hours. This research addresses these challenges by developing and evaluating simulation-based strategies to optimise ED operations performance and enhance care quality.The thesis employed a sequential mixed-methods approach. It began with a systematic literature review to identify gaps in applying mathematical and simulation models in ED settings. This was followed by cross-sectional data analysis and direct non-participant observations at University Hospital Southampton (UHS), aimed at understanding patient pathways, operational bottlenecks, and key performance indicators. Discrete Event Simulation (DES) was then used to replicate the department’s operational dynamics and evaluate the impact of potential interventions.The simulation results indicated that a combination of resource and process improvements increased the percentage of patients treated within four hours from 43.83% to 76.05%. The most effective interventions included adding a second triage room and nurse to reduce front-end delays, reducing laboratory turnaround time by 20%, and increasing clinical staff from five to six, nurses from 15 to 19, and healthcare assistants from 16 to 19, all of which collectively enhanced patient flow and care delivery across the ED.This thesis contributes to academic and practical understanding by presenting a validated simulation framework that supports evidence-based decision-making. The findings offer healthcare managers and policymakers a robust, low-risk method for evaluating and implementing targeted interventions without disrupting real-world operations. By addressing systemic inefficiencies and promoting patient-centred strategies, the study supports broader NHS objectives to improve the quality and responsiveness of emergency care
“Happy Farmers” in Volta Delta, Ghana? Exploring the relationship between environmental conditions and happiness
No full textCommunities’ wellbeing in rural lower-middle-income countries is interlinked with climate and landscape characteristics. Rural inhabitants are often assumed to be “happy farmers”, content with their livelihoods and social connections, despite the financial and material insecurities associated with their fragile environments. However, is this assumption an accurate reflection of reality? This study explores relationships between environmental conditions and subjective wellbeing in Volta Delta, Ghana. Subjective wellbeing is captured through a life domains happiness measure, calculated using the “Deltas, Vulnerability and Climate Change: Migration & Adaptation” survey dataset. A binary logistic model evaluates associations between low happiness, and environmental and control characteristics constructed from survey and remote sensing datasets. The quantitative approach supports the “happy farmer” identity, with lower probabilities of low happiness amongst rural households with a strong attachment to agricultural landscapes. However, the limited availability of permanent employment could offset these subjective benefits. Nevertheless, happiness is not a substitute for objective wellbeing, often defined through monetary wealth; therefore, sustainability policy should not be discouraged from providing tangible support to vulnerable communities. Volta Delta consists of varying landscapes, with model results also illustrating lower happiness within coastal locations, potentially linked to fears of hazards, restricted natural resource governance, and threats to intergenerational land and livelihoods. This study highlights the key role of environmental conditions in potentially influencing subjective wellbeing. Exploring relationships with subjective outcomes ensures sustainability policy captures non-tangible outcomes and feedback effects, which, if incorporated alongside objective targets, can ensure all costs, benefits and challenges are accounted for
Predicting transonic flowfields in non–homogeneous unstructured grids using autoencoder graph convolutional networks
No full textThis paper addresses the challenges posed by non-homogeneous unstructured grids, which are commonly used in computational fluid dynamics. The prevalence of these grids in fluid dynamics scenarios has driven the exploration of innovative approaches for generating reduced-order models. Our approach leverages geometric deep learning, specifically through the use of an autoencoder architecture built on graph convolutional networks. This architecture enhances prediction accuracy by propagating information to distant nodes and emphasizing influential points. Key innovations include a dimensionality reduction module based on pressure-gradient values, fast connectivity reconstruction using Mahalanobis distance, optimization of the network architecture, and a physics-informed loss function based on aerodynamic coefficient. These advancements result in a more robust and accurate predictive model, achieving systematically lower errors compared to previous graph-based methods. The proposed methodology is validated through two distinct test cases—wing-only and wing-body configurations—demonstrating precise reconstruction of steady-state distributed quantities within a two-dimensional parametric space
Towards an artificial intelligence-driven framework for project risk management
No full textProject risk management is a process of identifying, assessing, and responding to potential adverse events throughout the life cycle of a project to minimize their impacts and capitalize on opportunities. Traditional project risk management approaches often rely on subjective expert judgment, static risk models, siloed data, and reactive strategies. However, with the increasing complexity and dynamism of projects, new sophisticated project risk management approaches are necessary to address the limitations of traditional methods. Artificial intelligence (AI), particularly through machine learning, can reimagine how risks are managed in projects, offering enhanced predictive capabilities, real-time insights, and adaptive strategies. While there is ongoing research, existing studies have only provided partial insights, leading to several debates on the suitable model for project risk management. Moreover, existing research has largely focused on generic risk prediction, while other important aspects of project risk management, such as risk prediction explainability, scoring, prioritization, and mitigation, remain largely unexplored. To address these knowledge gaps, we develop an intelligent AI-driven project risk management framework that combines AI models, tools, and techniques with risk identification, evaluation, and mitigation capabilities
Remote microphone virtual sensing with nested microphone sub-arrays
No full textThis study uses the Remote Microphone Technique to investigate the use of nested sub-arrays that incorporate either pressure, or both pressure and pressure gradient information to estimate the pressure at remote locations in a random sound field. The sub-arrays of either pressure sensors or closely spaced microphone pairs are nested to form both uniform linear and circular arrays. The performance of the different configurations is evaluated through both experiments and simulations in terms of the level of estimation error and the spatial extent over which a low estimation error can be achieved. The presented results show that the use of nested arrays of closely spaced microphone pairs outperforms conventional arrays that use pressure alone, both in terms of the estimation error and the size of the estimation zone. Overall, the circular configurations are shown to outperform the equivalent linear configurations. The gains in nominal performance, however, are paid for by an increase in the condition number, which influences the robustness of these arrays to uncertainties. The paper highlights the importance of array topology and the advantages provided by the inclusion of pressure gradient information into the estimation of the pressure at remote locations
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