Uncertainty and interconnectedness in complex engineering and engineered systems such as power-grids and telecommunication networks are sources of vulnerability compromising the resilience of these systems. Conditions of uncertainty and interconnectedness change over time and depend on emerging socio-technical contexts, thus conventional methods which can conduct normative, descriptive and prescriptive assessment of complex engineering and engineered systems resilience are limited. This paper brings together contributions of experts in complex engineering and engineered systems who have identified six methods, three each for uncertainty and interconnectedness, which form the foundational methods for knowing complex engineering and engineered systems resilience. The paper has reviewed how these methods contribute to overcoming uncertainty or interconnectedness and how they are implemented using case studies in order to illustrate essential approaches to enhancing resilience. It is hoped that this approach will allow the subject to be quantified and best practice standards to develop.

Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.

The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.

Obesity is a pressing public health risk issue worldwide. Women, in particular, face a higher risk of obesity. Recent research has highlighted the association between obesity and female sexual dysfunction. Therefore, the objective of this study is to investigate the global prevalence of sexual dysfunction in obese and overweight women through a systematic review and meta-analysis. In this study, a systematic search was conducted across electronic databases, including PubMed, Scopus, Web of Science, Embase, ScienceDirect, and Google Scholar. The search aimed to identify studies published between December 2000 and August 2022 that reported metabolic syndrome's impact on female sexual dysfunction. The review included nine studies with a sample size of 1508 obese women. The I2 heterogeneity index indicated high heterogeneity (I2: 97.5). As a result, the random effects method was used to analyze the data. Based on this meta-analysis, the prevalence of sexual dysfunction in women with obesity was reported as 49.7% (95%CI: 35.8–63.5). Furthermore, the review comprised five studies involving 1411 overweight women. The I2 heterogeneity test demonstrated high heterogeneity (I2: 96.6). Consequently, the random effects model was used to analyze the results. According to the meta-analysis, the prevalence of sexual dysfunction in overweight women was 26.9% (95% CI: 13.5–46.5). Based on the results of this study, it has been reported that being overweight and particularly obese is an important factor affecting women's sexual dysfunction. Therefore, health policymakers must acknowledge the significance of this issue in order to raise awareness in society about its detrimental effect on the female population.

Development of reliable fault diagnostics for intermittent failure modes are an important tool to adequately deal with realistic failure behavior within complex systems. A large proportion of previous work utilizing model-based fault diagnostics has focused on persistent faults and often neglects the case where the system intermittently switches between a faulty and non-faulty behavior at discrete random intervals. Such intermittent behavior complicates the diagnostics task, with difficulties in detecting and isolating intermittent faults, which occur with low frequency but yet at high enough frequency to be unacceptable. Accurate assessment of intermittent failure probabilities is critical to diagnosing and repairing equipment and requires the development of models to describe the dynamics of the intermittent failure. This paper presents an overall framework for detecting sensor faults, through the use of nonlinear unknown input observers which are applicable to both persistent and intermittent faults. The work presented demonstrates the detection capabilities of the approach through the use of robust residuals insensitive to system uncertainties and the application of adaptive thresholds

This paper presents a novel mathematical framework for assessing and predicting the resilience of critical coastal infrastructures against wave overtopping hazards and extreme climatic events. A probabilistic sensitivity analysis model is developed to evaluate the relative influence of hydrodynamic, geomorphological, and structural factors contributing to wave overtopping dynamics. Additionally, a stochastic Gaussian process (GP) model is introduced to predict the mean overtopping discharge from coastal defences. Both the sensitivity analysis and the predictive models are validated using a large homogeneous dataset comprising 163 laboratory and field-scale tests. Statistical evaluations demonstrate the superior performance of the GPs in identifying key parameters driving wave overtopping and predicting mean discharge rates, outperforming existing regression-based formulae. The proposed model offers a robust predictive tool for assessing the performance of critical coastal protection infrastructures under various climate scenarios.