The aim of this study was to investigate the association between serum albumin to serum creatinine ratio (sACR) and the prognosis of heart failure (HF). In this single‐center prospective cohort study, a total of 2625 patients with HF were enrolled between March 2012 and June 2017. All patients were divided into three groups according to the tertiles of sACR. Of 2625 patients, the mean age was 57.0 ± 14.3 years. During a median follow‐up time of 23 months, 666 end point events occurred. Prognosis analysis indicated that the lowest sACR was significantly associated with higher mortality risk of HF (hazard ratio [HR] = 1.920, 95% confidence interval [CI] = 1.585–2.326, p < 0.001) when compared with the highest tertile. After adjusting for covariates including age, gender, diabetes, systolic blood pressure (SBP), diastolic blood pressure, heart rate, total cholesterol, triglycerides, HDL‐C, LDL‐C, white blood cell count, hemoglobin, glycosylated hemoglobin, and β‐blocker use, the HRs for mortality risk of HF was 1.513 (95% CI = 1.070–2.139, p = 0.019). Subgroup analysis indicated that the mortality risk of HF statistically significantly reduced with the rise in sACR in patients with no β‐blocker use, patients with serum creatine less than 97 μmol/L. However, stratification by age, sex, history of hypertension, diabetes, and smoking, level of glycosylated hemoglobin, and albumin have no obvious effect on the association between sACR and the prognosis of HF. Additionally, patients with lower sACR displayed reduced left ventricular ejection fraction and increased left ventricular end‐diastolic diameter. The discriminant power of sACR alone and in combination with age, gender, SBP, heart rate, and glycosylated hemoglobin were excellent with C statistic of 0.655 and 0.889, respectively. Lower sACR was an independent risk factor for mortality risk of HF.

To optimise the configuration and reconfiguration strategies in integrated modular avionics (IMA), modelling and evaluating the reliability of the reconfigurable IMA system have drawn great attention from system engineers and designers. Currently most researches regard each configuration as a single system state, but the detailed system architecture under each configuration or the events that trigger the reconfiguration behaviour are not involved. In this study, a modelling method is proposed for these complex processes of the IMA reconfiguration. It employs architecture analysis and design language (AADL) along with its Error Model Annex, ARINC653 Annex, and mode transition mechanisms to model the correlated component error state transitions, system configuration architectures, and reconfiguration behaviours. Based on the proposed AADL modelling approach, these model information is extracted purposefully to establish computable models. Then a reliability analysis approach is proposed which is able to result in various reliability attributions, such as component reliability, configuration reliability under the software architecture, single task reliability, and multi‐tasking reliability. These evaluation processes take into account both of the fault isolation reconfiguration and task mode reconfiguration for the first time. Finally, the case study is involved, which demonstrates the feasibility of the analysis approach for IMA based on an AADL model.

With the rapid development of high integrations in large complex systems, such as aircraft, satellite, and railway systems, due to the increasingly complex coupling relationship between components within the system, local disturbances or faults may cause global effects on the system by fault propagation. Therefore, there are new challenges in safety analysis and risk assessment for complex systems. Aiming at analyzing and evaluating the inherent risks of the complex system with coupling correlation characteristics objectively, this paper proposes a novel risk assessment and analysis method for correlation in complex system based on multi-dimensional theory. Firstly, the formal description and coupling degree analysis method of the hierarchical structure of complex systems is established. Moreover, considering the three safety risk factors of fault propagation probability, potential severity, and fault propagation time, a multi-dimensional safety risk theory is proposed, in order to evaluate the risk of each element within the system effecting on the overall system. Furthermore, critical safety elements are identified based on Pareto rules, As Low As Reasonably Practicable (ALARP) principles, and safety risk entropy to support the preventive measures. Finally, an application of an avionics system is provided to demonstrate the effectiveness of the proposed method.

Imbalanced data are a major factor for degrading the performance of software defect models. Software defect dataset is imbalanced in nature, i.e., the number of non-defect-prone modules is far more than that of defect-prone ones, which results in the bias of classifiers on the majority class samples. In this paper, we propose a novel credibility-based imbalance boosting (CIB) method in order to address the class-imbalance problem in software defect proneness prediction. The method measures the credibility of synthetic samples based on their distribution by introducing a credit factor to every synthetic sample, and proposes a weight updating scheme to make the base classifiers focus on synthetic samples with high credibility and real samples. Experiments are performed on 11 NASA datasets and nine PROMISE datasets by comparing CIB with MAHAKIL, AdaC2, AdaBoost, SMOTE, RUS, No sampling method in terms of four performance measures, i.e., area under the curve (AUC), F₁, AGF, and Matthews correlation coefficient (MCC). Wilcoxon sign-ranked test and Cliff’s $\delta$ are separately used to perform statistical test and calculate effect size. The experimental results show that CIB is a more promising alternative for addressing the class-imbalance problem in software defect-prone prediction as compared with previous methods.