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The HL-RF iterative algorithm of the first order reliability method (FORM) is popularly applied to evaluate reliability index in structural reliability analysis and reliability-based design optimization. However, it sometimes suffers from non-convergence problems, such as bifurcation, periodic oscillation, and chaos for nonlinear limit state functions. This paper derives the formulation of the Lyapunov exponents for the HL-RF iterative algorithm in order to identify these complicated numerical instability phenomena of discrete chaotic dynamic systems. Moreover, the essential cause of low efficiency for the stability transform method (STM) of convergence control of FORM is revealed. Then, a novel method, directional stability transformation method (DSTM), is proposed to reduce the number of function evaluations of original STM as a chaos feedback control approach. The efficiency and convergence of different reliability evaluation methods, including the HL-RF algorithm, STM and DSTM, are analyzed and compared by several numerical examples. It is indicated that the proposed DSTM method is versatile, efficient and robust, and the bifurcation, periodic oscillation, and chaos of FORM is controlled effectively.

Reliability-based design optimization (RBDO) offers a powerful tool to handle optimization problems with inherently unavoidable uncertainty factors. However, solving the engineering systems with high fidelity remains a great challenge. In this study, a novel fidelity transformation framework is proposed to address this issue, where an arbitrary high-fidelity RBDO method can be converted into an arbitrary low-fidelity RBDO method without sacrificing the accuracy. The fidelity transformation factor plays the central role. Furthermore, two fidelity transformation strategies are developed to solve the RBDO problem efficiently and accurately. In addition, the well-known performance measure approach and sequential optimization and reliability assessment method are employed as the low-fidelity RBDO methods. In this way, six new methods are developed based on three high-fidelity RBDO methods and two low-fidelity RBDO methods. One highly mathematical example, two numerical examples, and a stiffened panel with cutouts are used to demonstrate the generality, fidelity, and superiority of the proposed methods.

In current years, the metamodel-based reliability analysis method has been developed to assess the failure probability for engineering problems involving time-consuming computational model. Despite the fact that some sequential metamodel-based reliability analysis methods have improved the computational efficiency, there still exists a certain possibility to further reduce the computational effort without loss of accuracy. In this study, an active weight learning method based upon the Kriging model is well proposed for reliability analysis. An active weight learning function based on the optimization theory is built to replace the traditional learning function, in which the important degrees of sampling points on the limit state function are assigned as different weight indices. The Kriging surrogate model is updated according to the proposed active weight learning function. In addition, the proposed strategy is extended to solve the system reliability problem, which can effectively avoid the nonlinearity of composite function in the traditional approach. A novel stopping criterion is also exploited to guarantee the convergence of the proposed method. Five numerical examples are provided to verify the effectiveness of the proposed method and convergence strategy. Results indicate that the proposed method can significantly improve the computational efficiency of reliability analysis without sacrificing computational accuracy.

Objectives To develop radiomics-based nomograms for preoperative microvascular invasion (MVI) and recurrence-free survival (RFS) prediction in patients with solitary hepatocellular carcinoma (HCC) ≤ 5 cm. Methods Between March 2012 and September 2019, 356 patients with pathologically confirmed solitary HCC ≤ 5 cm who underwent preoperative gadoxetate disodium–enhanced MRI were retrospectively enrolled. MVI was graded as M0, M1, or M2 according to the number and distribution of invaded vessels. Radiomics features were extracted from DWI, arterial, portal venous, and hepatobiliary phase images in regions of the entire tumor, peritumoral area ≤ 10 mm, and randomly selected liver tissue. Multivariate analysis identified the independent predictors for MVI and RFS, with nomogram visualized the ultimately predictive models. Results Elevated alpha-fetoprotein, total bilirubin and radiomics values, peritumoral enhancement, and incomplete or absent capsule enhancement were independent risk factors for MVI. The AUCs of MVI nomogram reached 0.920 (95% CI: 0.861–0.979) using random forest and 0.879 (95% CI: 0.820–0.938) using logistic regression analysis in validation cohort ( n = 106). With the 5-year RFS rate of 68.4%, the median RFS of MVI-positive (M2 and M1) and MVI-negative (M0) patients were 30.5 (11.9 and 40.9) and > 96.9 months ( p < 0.001), respectively. Age, histologic MVI, alkaline phosphatase, and alanine aminotransferase independently predicted recurrence, yielding AUC of 0.654 (95% CI: 0.538–0.769, n = 99) in RFS validation cohort. Instead of histologic MVI, the preoperatively predicted MVI by MVI nomogram using random forest achieved comparable accuracy in MVI stratification and RFS prediction. Conclusions Preoperative radiomics-based nomogram using random forest is a potential biomarker of MVI and RFS prediction for solitary HCC ≤ 5 cm. Key Points • The radiomics score was the predominant independent predictor of MVI which was the primary independent risk factor for postoperative recurrence. • The radiomics-based nomogram using either random forest or logistic regression analysis has obtained the best preoperative prediction of MVI in HCC patients so far. • As an excellent substitute for the invasive histologic MVI, the preoperatively predicted MVI by MVI nomogram using random forest (MVI-RF) achieved comparable accuracy in MVI stratification and outcome, reinforcing the radiologic understanding of HCC angioinvasion and progression.

Arachidonic acid (AA), an important polyunsaturated fatty acid in the brain, is hydrolyzed by a direct action of phospholipase A 2  (PLA 2 ) or through the combined action of phospholipase C and diacylglycerol lipase, and released into the cytoplasm. Various derivatives of AA can be synthesized mainly through the cyclooxygenase (COX), lipoxygenase (LOX) and cytochrome P450 (P450) enzyme pathways. AA and its metabolic enzymes and metabolites play important roles in a variety of neurophysiological activities. The abnormal metabolites and their catalytic enzymes in the AA cascade are related to the pathogenesis of various central nervous system (CNS) diseases, including epilepsy. Here, we systematically reviewed literatures in PubMed about the latest randomized controlled trials, animal studies and clinical studies concerning the known features of AA, its metabolic enzymes and metabolites, and their roles in epilepsy. The exclusion criteria include non-original studies and articles not in English.

Time-variant reliability analysis plays a vital role in improving the validity and practicability of product reliability evaluation over a specific time interval. Sampling-based extreme value method is the most direct way to implement accurate reliability assessment. Its adoption for time-variant reliability analysis, however, is limited due to the computational burden caused by repeatedly evaluating performance function. This paper proposes a semi-analytical extreme value method to improve the computational efficiency of extreme value method. The time-variant performance function is transformed into dependent instantaneous performance functions in which the stochastic processes are discretized by the expansion optimal linear estimation method to simulate the dependence among different time instants. Each instantaneous function is separately approximated by Taylor series expansion at the most probable point through instantaneous reliability analysis. Based on the approximated performance functions, the computational cost of sampling-based extreme value method is significantly reduced. Results of three numerical examples demonstrate the efficacy of the proposed method.

Background Pericoronary adipose tissue (PCAT) density is a biomarker of vessel inflammation, which is supposed to be increased in patients with type 2 diabetes mellitus (T2DM). However, whether the coronary inflammation revealed by this novel index could be alleviated after evolocumab treatment in T2DM remains unknown. Methods From January 2020 to December 2022, consecutive T2DM patients with low-density lipoprotein cholesterol ≥ 70 mg/dL on maximally tolerated statin and taking evolocumab were prospectively included. In addition, patients with T2DM who were taking statin alone were recruited as control group. The eligible patients underwent baseline and follow-up coronary CT angiography with an interval of 48-week. To render patients with evolocumab as comparable to those controls, a propensity-score matching design was used to select the matched pairs with a 1:1 ratio. Obstructive lesion was defined as the extent of coronary artery stenosis ≥ 50%; the numbers inside the brackets were interquartile ranges. Results A total of 170 T2DM patients with stable chest pain were included [(mean age 64 ± 10.6 [range 40–85] years; 131 men). Among those patients, 85 were in evolocumab group and 85 were in control group. During follow-up, low-density lipoprotein cholesterol (LDL-C) level (2.02 [1.26, 2.78] vs. 3.34 [2.53, 4.14], p  < 0.001), and lipoprotein(a) (12.1 [5.6, 21.8] vs. 18.9 [13.2, 27.2], p  = 0.002) were reduced after evolocumab treatment. The prevalence of obstructive lesions and high-risk plaque features were significantly decreased ( p  < 0.05 for all). Furthermore, the calcified plaque volume were significantly increased (188.3 [115.7, 361.0] vs. 129.3 [59.5, 238.3], p  = 0.015), while the noncalcified plaque volume and necrotic volume were diminished (107.5 [40.6, 180.6] vs. 125.0 [65.3, 269.7], p  = 0.038; 0 [0, 4.7] vs. 0 [0, 13.4], p  < 0.001, respectively). In addition, PCAT density of right coronary artery was significantly attenuated in evolocumab group (− 85.0 [− 89.0, − 82.0] vs. − 79.0 [− 83.5, − 74.0], p  < 0.001). The change in the calcified plaque volume inversely correlated with achieved LDL-C level (r =  − 0.31, p  < 0.001) and lipoprotein(a) level (r =  − 0.33, p  < 0.001). Both the changes of noncalcified plaque volume and necrotic volume were positively correlated with achieved LDL-C level and Lp(a) ( p  < 0.001 for all). However, the change of PCAT RCA density only positively correlated with achieved lipoprotein(a) level (r = 0.51, p  < 0.001). Causal mediation analysis revealed Lp(a) level mediated 69.8% ( p  < 0.001) for the relationship between evolocumab and changes of PCAT RCA . Conclusions In patients with T2DM, evolocumab is an effective therapy to decrease noncalcified plaque volume necrotic volume, and increase calcified plaque volume. Furthermore, evolocumab could attenuate PCAT density, at least in part, via the reduction of lipoprotein(a).

Multiobjective reliability-based design optimization (RBDO) is a research area, which has not been investigated in the literatures comparing with single-objective RBDO. This work conducts an exhaustive study of fifteen new and popular metaheuristic multiobjective RBDO algorithms, including non-dominated sorting genetic algorithm II, differential evolution for multiobjective optimization, multiobjective evolutionary algorithm based on decomposition, multiobjective particle swarm optimization, multiobjective flower pollination algorithm, multiobjective bat algorithm, multiobjective gray wolf optimizer, multiobjective multiverse optimization, multiobjective water cycle optimizer, success history-based adaptive multiobjective differential evolution, success history-based adaptive multiobjective differential evolution with whale optimization, multiobjective salp swarm algorithm, real-code population-based incremental learning and differential evolution, unrestricted population size evolutionary multiobjective optimization algorithm, and multiobjective jellyfish search optimizer. In addition, the adaptive chaos control method is employed for the above-mentioned algorithms to estimate the probabilistic constraints effectively. This comparative analysis reveals the critical technologies and enormous challenges in the RBDO field. It also offers new insight into simultaneously dealing with the multiple conflicting design objectives and probabilistic constraints. Also, this study presents the advantage and future development trends or incurs the increased challenge of researchers to put forward an effective multiobjective RBDO algorithm that assists the complex engineering system design.

Reliability-based design optimization (RBDO) is a powerful tool for design optimization with consideration of uncertainty. It can be solved by double loop approaches or single loop approaches, while double loop approaches are robust but their implementation is computationally costly. On the other hand, single loop approaches are highly efficient but may have convergence problem for highly nonlinear performance measure functions. To mend their respective drawbacks, we resort to a transition between them and propose the so-called adaptive hybrid approach (AHA) to take advantage of these two approaches. Based on a function type criterion, AHA adaptively selects the single loop or double loop approaches during the iteration. When single loop strategy is selected, the advanced mean value (AMV) method is used. When double loop strategy is selected, an improved adaptive chaos control (ACC) method is proposed to searches for the most probable target point (MPTP) of black-box function robustly and efficiently. Four illustrative examples, including two nonlinear analytical problems and two engineering applications, demonstrate the superior efficiency and robustness of the AHA over other prevalent approaches.