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Abstract As the core foundation of reliability engineering and one of the most crucial aspects of general quality, reliability has been attracting increasing attention from industry and academia. Numerous publications on reliabili...
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Abstract As the core foundation of reliability engineering and one of the most crucial aspects of general quality, reliability has been attracting increasing attention from industry and academia. Numerous publications on reliability analysis (RA) have appeared in academic journals, conference proceedings, and technical reports. However, there has not been a systematic summary that covers RA data, models, methods, indicators, and software comprehensively. To fill this gap, this paper analyzed the challenges and the state of the art, as well as determined the future prospects for research on RA. This paper introduces the features of modern complex electromechanical systems (CES), then summarizes the current challenges to the RA of CES. Next, it reviews the latest developments, such as reliability theories, models, methods, and software, then summarizes and compares their advantages and limitations. Finally, it discusses future prospects from the perspectives of reliability data, modeling, decoupling, evaluation indicators, and timely prediction. It is expected that this paper would serve as an introduction to RA for researchers new to this field and as a summary of the current frontiers of RA for experienced researchers.
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Abstract Coupled faults are formed by the nonlinear coupling of multiple lower‐level faults in complex electromechanical systems (CES). Although fault decoupling plays a crucial role in locating fault cause and isolating fault co...
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Abstract Coupled faults are formed by the nonlinear coupling of multiple lower‐level faults in complex electromechanical systems (CES). Although fault decoupling plays a crucial role in locating fault cause and isolating fault components, it still faces challenges due to the harsh reality of common mode failure, networked propagation, and a lack of accurate fault mechanism knowledge in the fault coupling process. A novel physics‐data‐fusion‐based decoupling model for coupled faults of CES was proposed using standard meta components, rigorous formulation, and intuitive representation. First, a hierarchical graph representing the static complex decoupling model was defined by composing proposed meta models. Second, the dynamic model parameters inspired by the time‐varying fault characteristics were determined using real‐time operation data analysis. Then, based on a proposed numerical reasoning formula, the most likely fault cause was determined, which can also identify fault level by level. Finally, the decoupling model was proved to be reasonable and effective with an offshore wind turbine case. As a graphical modelling method, it handles the decoupling process by fusing static physics and dynamic data of coupled faults. While inheriting the benefits of conventional models, it overcomes the limitations of these existing methods for real‐time results. Moreover, the proposed method provided a foundation for tracing the root cause of performance fluctuations, fault detection, and isolation of CES.
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As one of the most important approaches for analyzing the mechanism of fault pervasion, fault root cause tracing is a powerful and useful tool for detecting the fundamental causes of faults so as to prevent any further propagation...
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As one of the most important approaches for analyzing the mechanism of fault pervasion, fault root cause tracing is a powerful and useful tool for detecting the fundamental causes of faults so as to prevent any further propagation and amplification. Focused on the problems arising from the lack of systematic and comprehensive integration, an information transfer-based novel data-driven framework for fault root cause tracing of complex electromechanical systems in the processing industry was proposed, taking into consideration the experience and qualitative analysis of conventional fault root cause tracing methods. Firstly, an improved symbolic transfer entropy method was presented to construct a directed-weighted information model for a specific complex electromechanical system based on the information flow. Secondly, considering the feedback mechanisms in the complex electromechanical systems, a method for determining the threshold values of weights was developed to explore the disciplines of fault propagation. Lastly, an iterative method was introduced to identify the fault development process. The fault root cause was traced by analyzing the changes in information transfer between the nodes along with the fault propagation pathway. An actual fault root cause tracing application of a complex electromechanical system is used to verify the effectiveness of the proposed framework. A unique fault root cause is obtained regardless of the choice of the initial variable. Thus, the proposed framework can be flexibly and effectively used in fault root cause tracing for complex electromechanical systems in the processing industry, and formulate the foundation of system vulnerability analysis and condition prediction, as well as other engineering applications.
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Background The prevalence of depression in sexually transmitted infections (STIs) patients is much higher than general public. However, studies focusing on comprehensive psychosocial effects on depression among STIs patients are l...
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Background The prevalence of depression in sexually transmitted infections (STIs) patients is much higher than general public. However, studies focusing on comprehensive psychosocial effects on depression among STIs patients are limited. This study aimed to examine association of multiple psychosocial syndemic conditions with depression among STIs patients in Shanghai, China. Methods We conducted a cross-sectional study and recruited 910 STIs patients from Shanghai Skin Disease Hospital. Participants self-reported their demographics and themselves completed the scales of depression, self-esteem, loneliness, social support, entrapment, defeat and interpersonal needs. Logistic regressions were performed to detect the possible contributing psychosocial factors for depression and to verify the syndemic conditions of psychosocial problems. Results Of the STIs patient sample, the prevalence of depression was 17.9%. Multivariable analysis showed low-level self-esteem (odds ratio [ORm]: 2.18, 95% CI [1.19–4.00]) and social support (ORm: 2.18, 95% CI [1.37–3.46]), high-level entrapment (ORm: 6.31, 95% CI [3.75–10.62]) and defeat (ORm: 2.60, 95% CI [1.51–4.48]) increased the risk of depression. Psychosocial syndemic conditions magnified effect in fusing depression (adjusted odds ratio [AOR]: 11.94, 95% CI [7.70–18.53]). Participants with more than 4 psychosocial problems were about 22 times more likely to have depression (AOR: 22.12, 95% CI [13.19–37.09]). Conclusions The psychosocial problems syndemic magnifying the risk of depression was confirmed and psychosocial interventions to prevent depression is needed among STIs patients.
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The drive train is one of the core structures of a wind turbine, and its working condition seriously affects the performance quality. It is important to identify the fault pattern of the drive bearings in time to ensure the safety...
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The drive train is one of the core structures of a wind turbine, and its working condition seriously affects the performance quality. It is important to identify the fault pattern of the drive bearings in time to ensure the safety and reliability of the wind turbine. However, in traditional methods, offline modeling and online identification are often fragmented, and such a mechanism limits the adaptive updating of the model. To realize real-time updating and self-learning of the identification model, we proposed a novel self-learning framework for the intelligent fault identification of wind turbine drive bearings. First, a complete ensemble empirical mode decomposition with adaptive noise analysis-based quantification scheme for intrinsic mode function values is proposed. Then, based on the intrinsic mode function values, we offer an attention mechanism for fault feature identification and construct an initial fault pattern database using unsupervised clustering techniques. Second, abnormal data are identified by the proposed artificial immunity-based outlier detection algorithm to determine the type of immune response. Third, we design an automatic update strategy based on incremental learning to realize adaptive creation, deletion, and modification of fault patterns. The proposed intelligent framework is applied to the fault diagnosis of a real offshore wind turbine drive train, showing its advantages in intelligent fault identification and model updating.
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Many conventional reliability analysis methods for multi-state systems assume independence among the various subsystems. To avoid this, we propose a probabilistic model checking-based approach for failure correlation analysis of m...
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Many conventional reliability analysis methods for multi-state systems assume independence among the various subsystems. To avoid this, we propose a probabilistic model checking-based approach for failure correlation analysis of multi-state systems. First, an improved Apriori algorithm is used to determine the effect of a subsystem failure on the associated subsystems. Next, a copula function is applied to establish the relationship between failures of the associated subsystems. Finally, probabilistic model-checking is used to analyze the reliability of the entire system and all its subsystems. The effectiveness of the proposed method is verified using an application involving offshore wind turbines. The results show that the proposed method can be used flexibly and effectively for multi-state system reliability analysis. Moreover, the proposed method lays the foundation for system maintenance strategy formulation and other engineering applications.
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Introduction Internal migrant Men who have sex with men (IMMSM), which has the dual identity of MSM and floating population, should be more concerned among the vulnerable groups for HIV in society. Establishing appropriate predict...
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Introduction Internal migrant Men who have sex with men (IMMSM), which has the dual identity of MSM and floating population, should be more concerned among the vulnerable groups for HIV in society. Establishing appropriate prediction models to assess the risk of HIV infection among IMMSM is of great significance to against HIV infection and transmission. Methods HIV and syphilis infection were detected using rapid test kits, and other 30 variables were collected among IMMSM through questionnaire. Taking HIV infection status as the dependent variable, three methods were used to screen predictors and three prediction models were developed respectively. The Hosmer-Lemeshow test was performed to verify the fit of the models, and the net classification improvement and integrated discrimination improvement were used to compare these models to determine the optimal model. Based on the optimal model, a prediction nomogram was developed as an instrument to assess the risk of HIV infection among IMMSM. To quantify the predictive ability of the nomogram, the C-index measurement was performed, and internal validation was performed using bootstrap method. The receiver operating characteristic (ROC) curve, calibration plot and dynamic component analysis (DCA) were respectively performed to assess the efficacy, accuracy and clinical utility of the prediction nomogram. Results In this study, 12.52% IMMSMs were tested HIV-positive and 8.0% IMMSMs were tested syphilis-positive. Model A, model B, and model C fitted well, and model B was the optimal model. A nomogram was developed based on the model B. The C-index of the nomogram was 0.757 (95% CI: 0.701–0.812), and the C-index of internal verification was 0.705. Conclusions The model established by stepwise selection methods incorporating 11 risk factors (age, education, marriage, monthly income, verbal violence, syphilis, score of CUSS, score of RSES, score of ULS, score of ES and score of DS) was the optimal model that achieved the best predictive power. The risk nomogram based on the optimal model had relatively good efficacy, accuracy and clinical utility in identifying internal migrant MSM at high-risk for HIV infection, which is helpful for developing targeted intervention for them.
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Introduction: Internal migrant workers have a great chance to experience defeat due to their low social status and economic situation. It has been reported that defeat might play a prospective role in predicting depression and anx...
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Introduction: Internal migrant workers have a great chance to experience defeat due to their low social status and economic situation. It has been reported that defeat might play a prospective role in predicting depression and anxiety; however, defeat is rarely explored among internal migrant workers due to the lack of appropriate measurement scales. The defeat scale (DS) can measure the feeling of defeat, social hierarchy reduction, and loss in social struggle. But its reliability and validity among internal migrant workers have not been reported. This study aimed to verify the content validity and structural validity of the DS among internal migrant workers in China and to explore its correlations with anxiety and depression. Methods: 1805 internal migrant workers (IMWs) were recruited by stratified multistage sampling from 16 factories in Shenzhen, China. The content validity index (CVI) was used to assess content validity. Cronbach's coefficient alpha of each factor and the total scale were calculated to assess the reliability of DS. The scree test was used to determine the number of factors. Convergent validity and discriminant validity were estimated by calculating the average variance extracted and composite reliability. Logistic regression was performed to explore the effects of DS scores on anxiety and depression. Results: Mean score of DS among IMWs was 18.42 ± 9.40. There were 606 (33.6%) IMWs who were considered to have depression symptoms, and 524 (29.0%) IMWs were considered to have anxiety symptoms. A two-factor model was obtained and fitted well (CFI = 0.956, GFI = 0.932, IFI = 0.956, RMSEA = 0.068, SRMR = 0.052). Cronbach's alpha reliability coefficient for the DS was 0.92. Logistic regression showed that DS scores were positively associated with anxiety and depression among IMWs. Conclusions: DS performed well among IMWs on content validity and structural validity, and it was suitable as a measurement instrument to assess defeat among this population. Defeat was positively associated with anxiety and depression and might play an important role in the mental health of IMWs.
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Rationale: Early discovery, accurate diagnosis, and staging of lung cancer is essential for patients to receive appropriate treatment. PET/CT has become increasingly recognized as a valuable imaging modality for these patients, bu...
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Rationale: Early discovery, accurate diagnosis, and staging of lung cancer is essential for patients to receive appropriate treatment. PET/CT has become increasingly recognized as a valuable imaging modality for these patients, but there remains room for improvement in PET tracers. We aimed to evaluate the feasibility of using [ 68 Ga]Ga-FAPI-RGD, a dual-targeting heterodimeric PET tracer that recognizes both fibroblast activation protein (FAP) and integrin α v β 3 for detecting lung neoplasms, by comparing it with [ 18 F]FDG and single-targeting tracers [ 68 Ga]Ga-RGD and [ 68 Ga]Ga-FAPI. Methods: This was a pilot exploratory study of patients with suspected lung malignancies. All 51 participants underwent [ 68 Ga]Ga-FAPI-RGD PET/CT, of which: 9 participants received dynamic scans, 44 participants also underwent [ 18 F]FDG PET/CT scan within two weeks, 9 participants underwent [ 68 Ga]Ga-FAPI PET/CT scan and 10 participants underwent [ 68 Ga]Ga-RGD PET/CT scan. The final diagnosis was made based on histopathological analyses and clinical follow-up reports. Results: Among those who underwent dynamic scans, the uptake of pulmonary lesions increased over time. The optimal timepoint for a PET/CT scan was identified to be 2 h post-injection. [ 68 Ga]Ga-FAPI-RGD had a higher detection rate of primary lesions than [ 18 F]FDG (91.4% vs. 77.1%, p < 0.05), higher tumor uptake (SUVmax, 6.9 ± 5.3 vs. 5.3 ± 5.4, p < 0.001) and higher tumor-to-background ratio (10.0 ± 8.4 vs. 9.0 ± 9.1, p < 0.05), demonstrated better accuracy in mediastinal lymph node evaluation (99.7% vs. 90.9%, p < 0.001), and identified more metastases (254 vs. 220). There was also a significant difference between the uptake of [ 68 Ga]Ga-FAPI-RGD and [ 68 Ga]Ga-RGD of primary lesions (SUVmax, 5.8 ± 4.4 vs. 2.3 ± 1.3, p < 0.001). Conclusion: In our small scale cohort study, [ 68 Ga]Ga-FAPI-RGD PET/CT gave a higher primary tumor detection rate, higher tracer uptake, and improved detection of metastases compared with [ 18 F]FDG PET/CT, and [ 68 Ga]Ga-FAPI-RGD also had advantages over [ 68 Ga]Ga-RGD and was non-inferior to [ 68 Ga]Ga-FAPI. We thus provide proof-of-concept for using [ 68 Ga]Ga-FAPI-RGD PET/CT for diagnosing lung cancer. With the stated advantages, the dual-targeting FAPI-RGD should also be explored for therapeutic use in future studies.
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Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients...
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Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients. Methods: This retrospective cohort study is based on the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese multicenter lung cancer database. We selected 22,368 eligible LUAD patients diagnosed between 2010 and 2015 from the SEER database and screened them based on the inclusion and exclusion criteria. Subsequently, the patients were randomly divided into the training cohort (n?=?15,657) and the testing cohort (n?=?6711), with a ratio of 7:3. Meanwhile, 736 eligible LUAD patients from the Chinese multicenter lung cancer database diagnosed between 2011 and 2021 were considered as the validation cohort. Results: We established a nomogram based on each independent prognostic factor analysis for 1-, 3-, and 5-year overall survival (OS) . For the training cohort, the area under the curves (AUCs) for predicting the 1-, 3-, and 5-year OS were 0.806, 0.856, and 0.886. For the testing cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.804, 0.849, and 0.873. For the validation cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.86, 0.874, and 0.861. The calibration curves were observed to be closer to the ideal 45° dotted line with regard to 1-, 3-, and 5-year OS in the training cohort, the testing cohort, and the validation cohort. The decision curve analysis (DCA) plots indicated that the established nomogram had greater net benefits in comparison with the Tumor-Node-Metastasis (TNM) staging system for predicting 1-, 3-, and 5-year OS of lung adenocarcinoma patients. The Kaplan–Meier curves indicated that patients’ survival in the low-risk group was better than that in the high-risk group ( P?<?.001). Conclusion: The nomogram performed very well with excellent predictive ability in both the US population and the Chinese population.
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