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Continuous glucose monitoring (CGM) has the potential to improve glucose control in the intensive care unit (ICU) setting. We sought to evaluate the accuracy of the intermittently scanned CGM (isCGM) system in critically ill patients.Adult patients were consecutively enrolled from three ICUs from August 2020 to January 2021. The performance of FreeStyle Libre Pro was evaluated against the venous blood glucose samples as a reference. Numerical accuracy was examined by the mean absolute relative difference (MARD), the Bland-Altman analysis, and the International Organization for Standardization criteria. Clinical accuracy was assessed by performing the Clarke and consensus error grid analysis.A total of 122 patients were included and 3416 matched glucose pairs were analyzed. The overall MARD was 18.0%, and the highest MARD (33.1%) was observed in the hypoglycemic range (lt;70 mg/dL). The Bland-Altman analysis revealed a mean bias of -11.7 mg/dL, with the 95% limits of agreement of -73.0 to 49.5 mg/dL. The percentages of isCGM glucose values within ±15%/15, ±20%/20, and ±30%/30 mg/dL were 49.8%, 64.7%, and 84.5%, respectively. The Clarke and consensus error grid analysis showed acceptable clinical accuracy with 98.5% and 98.8% of glucose values falling into zones A and B.Our study demonstrated suboptimal overall accuracy of isCGM for critically ill patients. Whether the adjunctive use of isCGM could improve glucose management and health outcomes in the critically ill needs further investigation.ChiCTR2100042036, Chinese Clinical Trial Registry.

PurposeThis study aims to propose an efficient method for solving reliability-based design optimization (RBDO) problems.Design/methodology/approachIn the proposed algorithm, genetic algorithm (GA) is employed to search the global optimal solution of design parameters satisfying the reliability and deterministic constraints. The Kriging model based on U learning function is used as a classification tool to accurately and efficiently judge whether an individual solution in GA belongs to feasible region.FindingsCompared with existing methods, the proposed method has two major advantages. The first one is that the GA is employed to construct the optimization framework, which is helpful to search the global optimum solutions of the RBDO problems. The other one is that the use of Kriging model is helpful to improve the computational efficiency in solving the RBDO problems.Originality/valueSince the boundaries are concerned in two Kriging models, the size of the training set for constructing the convergent Kriging model is small, and the corresponding efficiency is high.

By drawing inspiration from photoredox catalysis, the field of radical-mediated alkane functionalization has made remarkable advancements recently to address the challenges of today and future.

Adaptive Kriging combined with Monte Carlo simulation (AK-MCS) is popularly used to estimate the failure probability, but its candidate sample pool (CSP) size should be large enough to guarantee the accuracy of failure probability estimation, especially for small failure probability. To select training point for updating the Kriging model, AK-MCS needs to traverse the CSP to calculate the U-learning function of every point, which is time-demanding. Thus, an enhanced AK-MCS (shorten as EAK-MCS Ⅰ) is proposed by use of an efficient CSP reduction strategy. In EAK-MCS Ⅰ, the candidate samples are classified into two types, i.e., the samples with accurately judged states and the other samples. Obviously, removing the first type samples from CSP does not need extra computational cost, which has no effect on the precision of estimating failure probability and can improve the efficiency of training Kriging model. Hence, EAK-MCS Ⅰ is more efficient than AK-MCS in estimating failure probability. Additionally, for the risk of misjudging the states of the samples by the Kriging model trained in the reduced CSP for complicated problem, another version of EAK-MCS Ⅰ (shorten as EAK-MCS Ⅱ) is proposed by nesting an adaptive strategy to calibrate the Kriging model. The proposed methods improve the efficiency of AK-MCS and inherit all advantages of AK-MCS.

To improve the computational efficiency of the fuzzy simulation in estimating failure possibility of the structure containing fuzzy uncertainty, a fuzzy importance sampling method is proposed in this paper. In the proposed method, the optimal importance sampling density for estimating failure possibility is deduced as the product of the indicator function related to failure domain and the joint membership function of fuzzy model inputs at first. Subsequently, the Markov chain simulation and an adaptive kernel sampling method are employed to generate a group of samples asymptotically following the optimal importance sampling density. Based on this group of samples, an approximate expression of the optimal importance sampling density expression is constructed. Finally, the failure possibility can be efficiently estimated by using a small number of samples generated by the approximate optimal importance sampling density. Results of three examples demonstrate that the proposed method is a more efficient and robust method for estimating the failure possibility estimation compared with the original fuzzy simulation method.

Alkoxy radicals are highly reactive species that have long been recognized as versatile intermediates in organic synthesis. However, their development has long been impeded due to a lack of convenient methods for their generation. Thanks to advances in photoredox catalysis, enabling facile access to alkoxy radicals from bench-stable precursors and free alcohols under mild conditions, research interest in this field has been renewed. This review comprehensively summarizes the recent progress in alkoxy radical-mediated transformations under visible light irradiation. Elementary steps for alkoxy radical generation from either radical precursors or free alcohols are central to reaction development; thus, each section is categorized and discussed accordingly. Throughout this review, we have focused on the different mechanisms of alkoxy radical generation as well as their impact on synthetic utilizations. Notably, the catalytic generation of alkoxy radicals from abundant alcohols is still in the early stage, providing intriguing opportunities to exploit alkoxy radicals for diverse synthetic paradigms.

For improving the efficiency of the time-dependent reliability analysis by the double-loop Kriging model combined with Monte Carlo simulation (DLK-MCS) and the single-loop one combined with MCS (SLK-MCS), this paper proposes a candidate sample pool (CSP) reduction strategy, and this strategy is nested to the DLK-MCS and SLK-MCS to respectively establish an advanced DLK-MCS (A-DLK-MCS) and an advanced SLK-MCS (A-SLK-MCS). In the proposed CSP reduction strategy, the samples with correctly recognized states by the current Kriging model are removed from the CSP gradually. Then in the process of updating the Kriging model, the samples with correctly recognized states can be avoided to be identified repeatedly, which results in the efficiency of the A-DLK-MCS and A-SLK-MCS which is higher than that of the DLK-MCS and SLK-MCS, respectively. Furthermore, the calibration operation is carried out for ensuring the accuracy of the convergent Kriging model obtained by the CSP reduction strategy. Three examples are used to verify the rationality of the proposed CSP reduction strategy. The results show that the proposed strategy can reduce the time of training Kriging model in both DLK-MCS and SLK-MCS while ensuring the accuracy.

Patients with sepsis-induced acute respiratory distress syndrome (ARDS) have higher mortality and poor prognosis than pneumonia-induced ARDS. Pulmonary fibrosis is an irreversible accumulation of connective tissue in the interstitium of the lung and closely associated with the epithelial–mesenchymal transition (EMT) of type II alveolar epithelial cells (AECIIs). Therefore, it is undoubtedly worth studying whether the EMT of AECIIs in sepsis-induced ARDS patients is different from that in patients with pneumonia-induced ARDS in the regulatory mechanism. Here, we will report for the first time that an lncRNA-ASLNC12002 is highly expressed in AECIIs of patients with sepsis-induced pneumonia and promotes EMT in AECIIs. The research results showed that the expression of ASLNC12002 in AECIIs derived from patients with sepsis-induced ARDS is significantly higher than that in normal people and pneumonia-induced ARDS patients. Mechanism research showed that ASLNC12002 can cause the inactivation of the anti-EMT pathway NR2F2/miR128-3p/Snail1 by acting as the sponge of miR128-3p. Functional experiments showed that targeted silencing of ASLNC12002 could effectively inhibit EMT progression in AECIIs of patients with sepsis-induced pneumonia by restoring NR2F2/miR128-3p/Snail1 pathway. In a word, our study shows for the first time that the inactivation of NR2F2/miR128-3p/Snail1 pathway caused by the enhanced expression of ASLNC12002 is the direct reason why AECIIs in sepsis-induced ARDS patients are prone to get EMT progress. ASLNC12002 has the potential to become a biological target for the prevention and treatment of pulmonary fibrosis in patients with sepsis-induced ARDS. At the same time, the expectation that ASLNC12002 and its related products may be used as clinical markers for the evaluation of early pulmonary fibrosis in ARDS patients should not be ignored.

The moment-independent global sensitivity (MIGS) analysis can help decision makers to efficiently reduce the uncertainty of model output. This article proposes a novel single-loop simulation (NSLS)...

Failure credibility is popular in measuring safety degree of structure under fuzzy uncertainty, but the heavy computational cost is still a challenge in estimating the failure credibility. To alleviate this issue, an iterative method combining adaptive Kriging and fuzzy simulation (AK-FS) has been developed by Ling et al. However, for the problem with complex performance function, a large candidate sampling pool is needed in the AK-FS, which makes the training process of the Kriging model fairly time consuming. In order to improve the estimation efficiency of failure credibility through reducing the size of candidate sampling pool in AK-FS, an efficient sample reduction strategy based on adaptive Kriging (SR-AK) is proposed in this paper. In the SR-AK, the estimation of failure credibility is transformed into searching two active points in candidate sampling pool. After updating the Kriging model in each circle, current active points can be easily identified. Then, according to the properties of the active points and the prediction characteristics of Kriging model, the samples in current candidate sampling pool can be divided into two sets, i.e., the samples affect the estimation of active points and the samples have no effect on it. Obviously, the samples in the latter set can be deleted from current candidate sampling pool to reduce its size. By using this sample reduction strategy, the process for training Kriging model is accelerated circle by circle, which is very helpful to save the analysis time and improve the computational efficiency in estimating failure credibility. Four examples are employed to demonstrate the performance of the proposed SR-AK in fuzzy safety degree analysis.

Previous experimental studies have found that biochar after KOH activation can significantly improve the efficiency of NO removal, but its mechanism is still unclear. To investigate the reaction mechanism of this denitration reaction, in this study, the aromatic benzene ring structure was used to simulate the surface of biochar, and the reaction process was calculated by density functional theory (DFT). The reaction process on the pristine biochar was simulated for comparison. The results indicated that there were two potential mechanisms for NO removal and had the identical rate-determining step, with an activation energy of 161.5 kJ/mol. Second, the influence by K coadsorbates on the NO reduction mechanism was studied. The adsorption by K atoms does not alter the last reaction step, but it was found to reduce the activation energy of this rate-determining step (to 129.3 kJ/mol). A third type of reaction mechanism was theoretically studied for the situation with both K and OH coadsorbates on the biochar surface. The reaction mechanism changed with an increase in the overall reaction rate by increasing the pre-exponential factor. In summary, the rate-determining activation energy for the heterogeneous NO reduction was found to decrease in the following order: 161.5 kJ/mol (pristine biochar) → 129.3 kJ/mol (activation by K adsorbates) → 125.8 kJ/mol (activation by both K and OH adsorbates); the pre-exponential factor was found to change in the following order: 6.23 × 10

The elementary effects method is a screening global sensitivity measure to identify the few important inputs in a model which contains many inputs. In this work, a new advanced trajectory design me...

Most of the existing methods for estimating time-dependent failure credibility (TDFC) are based on optimization algorithms, which may result in a heavy burden on the computational cost and accuracy issue related to the local optimization. In order to achieve the best compromise between computational accuracy and cost, an efficient method is proposed in this work by embedding a single-loop adaptive Kriging model (S-AK) into the dichotomy searching algorithm (S-AK-DSA). The proposed S-AK-DSA can be regarded as a double-loop procedure. In the inner loop, the Kriging model of the real time-dependent performance function (TD-PF) is updated iteratively to accurately predict the signs of the upper/lower boundary of the TD-PF minimum with respect to the time variable at the given membership level. Based on the inner loop, the outer loop searches TDFC by continuously dichotomizing the searching interval of the TDFC. The advantages of S-AK-DSA are mainly manifested in two aspects. Firstly, S-AK-DSA converts the problem of optimizing the exact value of the upper/lower boundary into the problem of accurately identifying their signs, which can avoid the use of optimization algorithms. Secondly, at different membership levels, the S-AK-DSA method chooses the candidate sample pool and continuously updates the current Kriging model of TD-PF, and the adaptive learning function as well as appropriate stopping criterion can effectively reduce the cost of predicting the signs of the upper/lower boundary and improve the computational accuracy. Four case studies are introduced to demonstrate the feasibility and superiority of the proposed S-AK-DSA approach.

Random uncertainty and evidence uncertainty usually coexist in actual structures. For efficient reliability analysis of structure in presence of hybrid random and evidence uncertainties (RA-HRE), a...

Failure credibility is popular in measuring safety degree of structure under fuzzy uncertainty due to its excellent property of self-duality. Existing methods for estimating failure credibility can be mainly divided into two categories, i.e., the simulation-based methods and the optimization-based methods. The simulation-based methods are universal and robust, but time-consuming in dealing with high-dimensional problems. The optimization-based methods are efficient and flexible, but the precision of the failure credibility estimate will be impacted by the local optimum solution resulted from some optimization techniques. Thus, in order to overcome the disadvantages of existing methods, an efficient method by combining genetic algorithm and active learning Kriging (AK-GA) is proposed to estimate failure credibility in this paper. Firstly, by use of the fuzzy simulation, the estimation of failure credibility is transformed into finding for the failure/safety sample that processes the maximum joint membership function (MF) from the fuzzy simulation pool, which makes the credibility estimation less difficult to be computed. Secondly, the genetic algorithm is used to find the failure/safety sample with maximum joint MF. Thirdly, to drastically improve the computational efficiency of the proposed method, active learning Kriging is embedded to accurately and efficiently distinguish the failure and safety sample in computing the fitness function of genetic algorithm. Four mathematical and engineering test examples are used to demonstrate the accuracy and efficiency of the proposed AK-GA for estimating failure credibility.

The global reliability sensitivity index (GRSI) can measure the effect of model input variable on the failure probability of the structure and provide guidance for the reliability-based design optimization. In this paper, to efficiently estimate the GRSI, an equivalent form of the GRSI is derived by elaborative combination of Bayes’ theorem and the law of total expectation in the successive intervals without overlapping, and it not only makes the total computational cost independent of the dimensionality of model inputs, but also avoids approximating the probability density function approximation used in the existing Bayes’ theorem based global reliability sensitivity analysis. For further improving the efficiency of estimating the GRSI by the equivalent form, two algorithms are presented by nesting the adaptive Kriging (AK) into Monte Carlo simulation (MCS) and importance sampling (IS), respectively, which are abbreviated as AK-MCS and AK-IS. Results of one numerical example and four engineering applications show that the number of model evaluations by the AK-IS is less than $\text{2}\% $ of that by direct IS, and the model evaluation number by AK-MCS is less than $\text{4}\% $ of that by direct MCS under the convergent condition. The results illustrate that the proposed methods for estimating the GRSI are practical for engineering applications.

Reliability measures the ability that the structure finishes its intended function without failures by taking uncertainties into account. Reliability sensitivity commonly is defined as the partial derivative of the failure probability with respect to the distribution parameter, which is often of great importance for the reliability-based design optimization. In this paper, two improvements and one extension of the subdomain sampling (SS) method are researched. The first improvement is the criterion for adaptively determining the number of subdomains. The second improvement is that based on the first improvement, adaptive Kriging (AK) model is embedded into the modified SS (MSS) method to substitute the actual limit state function to identify the limit states of the samples generated in the MSS method. Through adaptively partitioning the distribution region, the size of candidate sampling pool in each circle of updating process of Kriging model is decreased compared with that in the method with the candidate samples being directly sampled in the whole uncertain distribution region, which improves the efficiency of each circle’s updating process. Then, the MSS-based adaptive Kriging (AK-MSS) method is extended to the reliability sensitivity analysis where no extra model evaluations are needed after the failure probability is assessed by the AK-MSS method. That is to say, the reliability and the reliability sensitivity can be simultaneously estimated by the AK-MSS method. Results of case studies in this paper demonstrate the effectiveness of the AK-MSS method.

With the aim of efficiently estimating the upper bound of time-dependent failure probability for the time-dependent reliability problem involving random and interval inputs (RI-TDFP), an extended c...

By the average absolute difference between the unconditional failure probability and the conditional one on fixing an input at its realization, the failure-probability-based-sensitivity (FP-S) is defined to quantify the effect of the fixed input on the failure probability, which provides important information for reliability-based design optimization of the structure. Among the estimation methods for FP-S, the Bayes theorem-based methods are competitive, but the conditional probability density function (PDF) should be estimated in this type method. To alleviate the computational complexity of estimating conditional PDF, a novel FP-S estimation method is proposed by use of the conditional probability theorem. In the proposed method, the conditional failure probability on fixing the input at its realization is approximated by the conditional failure probability on fixing the input in a small interval, in which the conditional probability theorem of the random event can be used to transform FP-S as estimations of a series of probabilities, and they can be simultaneously completed by a numerical simulation for estimating the unconditional failure probability. For ensuring the precision of the approximation introduced by replacing the realization with the small interval, a selection strategy for the small interval is proposed. Comparing with the competitive Bayes theorem-based estimation for FP-S, the proposed method replaces the conditional PDF estimation with the conditional probability estimation, which greatly reduces the computational complexity and improves the accuracy of the FP-S estimation. By combining with the adaptive kriging surrogate model, the efficiency of the proposed method can be drastically improved, and the presented examples demonstrate the efficiency and accuracy of the proposed method.