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This article focuses on a specific type of coherent system in which, when the coherent system fails, either no active component remains or the remaining active components are connected in parallel. Roy and Gupta (2023) also considered the same type of coherent systems. We observe the restart of such a coherent system using two cold standby components. In our study, standbys are not placed into action simultaneously. They are put into action one by one. We compute the reliability function of the considered coherent system, which is equipped with two cold standby components. In addition, we show that when we use a stronger standby component (in the sense of failure rate order) at first, we get a longer system lifetime. Also, we compute three different mean residual life functions of the system. We have presented a few numerical results to illustrate the importance of our analysis. [ABSTRACT FROM AUTHOR]

Abbasabad is one of the largest symbolic political urban projects in contemporary Iran, covering over 5.5 million square metres. Despite its constant political significance since the 1960s and envisioning six exclusive master plans during two regimes, the project faced considerable delays and devastating fragmentation. While all the narratives attribute the unsuccessful history in Abbasabad to macropolitical drawbacks, this study traces the failure by focusing on the emergence of an unseen, non-discursive, and non-human actor. This actor is the highway, which is present throughout Abbasabad and divides the announced official project into several regions, each with its distinct functions and visions. Because the project is ultimately a loss, its history of failure matters more than the official narratives represented in the master plans. Using the Foucauldian genealogical approach and shifting from the notion of politics to relational politics given in ANT, highways emerge as actors with different roles and significant impacts, evolving and interacting unstoppably with Abbasabad. This narrative takes a more grounded perspective, indicating that highways have influenced the master plans, not vice versa. Moreover, politicians had the opportunity to consider such factors; however, macropolitical forces belittled them by pursuing mega-scaled integrated concepts. [ABSTRACT FROM AUTHOR]

Abstract“Applications” of knowledge symbolically and structurally “codify” thinking, often displacing the human who is relegated to passive, routine reproduction of operations and left with no space or time to understand or question the relations underlying the processes. This is both mirrored and augmented by the schematic narrowing of computational, calculative reason and nebulous or hidden code that is often read-only if human-readable at all. According to French philosopher Bernard Stiegler, this has toxic effects on learning, systemically and progressively embedding failures to think, hindering the potential for the collective and individual human adoption and adaptation of the knowledge of life. This article presents a process-based model to care for this problem through Stieglerian critical pedagogy, phenomenography, and process philosophy. The model evolved out of a course with the aim to achieve collective cultural literacy in that which “goes without saying” in the increasingly ubiquitous digital design of everyday life. [ABSTRACT FROM AUTHOR]

Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis. Kernels are used to handle even more complicated and enormous quantities of medical data by injecting non-linearity into linear models. In this study, a Multiple Kernel Learning (MKL) method has been proposed to optimize survival outcomes under the Accelerated Failure Time (AFT) model, a useful alternative to the Proportional Hazards (PH) frailty model. In other words, a survival parametric regression framework has been presented for clinical data to effectively integrate kernel learning with AFT model using a gradient descent optimization strategy. This methodology involves applying four different parametric models, evaluated using 19 distinct kernels to extract the best fitting scenario. This culminated in a sophisticated strategy that combined these kernels through MKL. We conducted a comparison between the Frailty model and MKL due to their shared fundamental properties. The models were assessed using the Concordance index (C-index) and Brier score (B-score). Each model was tested on both a case study and a replicated/independent dataset. The outcomes showed that kernelization enhances the performance of the model, especially by combining selected kernels for MKL. [ABSTRACT FROM AUTHOR]

This study investigates the impact of active learning instruction on the motivational orientation of pre-service language teachers. The data were collected by using the AGQ-R and the StRIP questionnaire, and analyzed through repeated measures of MANOVAs and correlation coefficient. Pre-service language teachers reported a higher approach goal orientation emphasizing the desire to succeed rather than avoidance goal orientation, i.e., the fear of failure. In terms of classroom practices, the participants report having experienced all types of instruction (passive, active, interactive and constructive) in the language education program. Although the pre-service teachers' perception of instruction as passive is preponderant in this study, it does not necessarily point to a higher use of traditional passive lecture and note taking practices. Regardless of the perceived instruction type, the participants' motivation and engagement remain high, as they report high cognitive and emotional engagement, as well as very high participation and low distraction levels. Regarding correlations, motivation, engagement and active instruction are all highly correlated with each other, as highly motivated and engaged individuals tend to benefit more from active instruction than less motivated students, who chiefly rely on passive instruction for academic success. The results of the study may indicate an emergent need for a careful balance between various teaching strategies and approaches in language education programs in particular, and possibly at tertiary level pedagogy in general. [ABSTRACT FROM AUTHOR]

Purpose: Fierce competition in the crowdfunding market has resulted in high failure rates. Owing to their dedication and efforts, many founders have relaunched failed campaigns as a second attempt. Despite the need for a better understanding, the success of campaign relaunches has not been well-researched. To fill this research gap, this study first theorizes how founders' learning may enhance their competencies and influence investors' attribution of entrepreneurial failure. The study then empirically documents the extent and conditions under which such learning efforts impact campaign relaunch performance. Design/methodology/approach: This study examines 5,798 Kickstarter-relaunched campaigns. The founders' learning efforts are empirically captured by key changes in campaign design that deviate from past business practices. Word movers' distances and perceptual hashing algorithms (pHash) are used separately to measure differences in campaign textual descriptions and pictorial designs. Findings: Differences in textual descriptions and pictorial designs during campaign failure–relaunch are positively associated with campaign relaunch success. The impacts are further amplified when the previous failures are more severe. Originality/value: This study is one of the first to examine the success of a campaign relaunch after an initial failure. This study contributes to a better understanding of founders' learning in crowdfunding contexts and provides insights into the strategies founders can adopt to reap performance benefits. [ABSTRACT FROM AUTHOR]

Approximately 9% of people think about suicide during their lifetime. Suicidal thoughts are consistently associated with perceived failures in emotion regulation. However, factors contributing to these perceptions remain insufficiently clear. New evidence suggests that when people know little about the cause of their emotions (i.e., low source attribution of emotion), they perceive themselves as less successful in regulating them. Therefore, emotion regulation deficits in people with suicidal thoughts might be related to lower knowledge about sources of emotions. We examined this question in two ecological momentary assessment studies (N1 = 396, N2 = 195). We found that participants with current suicidal thoughts knew less about the sources of their emotions compared to participants with no suicidal thoughts history (Studies 1 and 2), and even when compared to controls with similar levels of psychiatric symptoms but no history of suicidal thoughts (Study 2). Using language processing, we found that written descriptions of the source of participants' emotions were less concrete among those with suicidal thoughts compared to participants with no suicidal thoughts history. Among suicidal participants, suicidal thoughts were more likely to be present in moments when participants knew less than usual about the source of their negative emotions (Study 2), and low knowledge of the source was associated with more frequent and prolonged suicidal thoughts (Studies 1 and 2). Finally, lower perceived success in emotion regulation mediated the association between source attribution of emotion and the occurrence of suicidal thoughts. Findings suggest that reduced knowledge about the source of negative emotions might increase the risk for suicidal thinking. General Scientific Summary: This study suggests that people with current suicidal thoughts know less about the source of their negative emotions compared to people without suicidal thoughts. It also suggests that in moments when people with suicidal thoughts know less than usual about the source of their negative emotions, they are more likely to think about suicide. [ABSTRACT FROM AUTHOR]

We show that ordinary appointments can act as effective substitutes for hard commitment devices and increase demand for a critical healthcare service, particularly among those with self-control problems. We show this using an experiment that randomly offered HIV testing appointments and hard commitment devices to high-risk men in Malawi. Appointments more than double testing rates, with effects concentrated among those who demand commitment. In contrast, most men who take up hard commitments lose their investments. Appointments overcome commitment problems without the potential drawback of commitment failure, and have the potential to increase demand for healthcare in the developing world. [ABSTRACT FROM AUTHOR]

In this research, the mystical experiences of Bayazid Bastami in the narrative of Attar's Tazkira Al -Awaliya were analyzed with a semiotic -semantic approach in order to explain the mechanism and how the meaning is formed in the text in question. In this discourse, Bayezid is cared for and chosen by God before birth. Then, in the process of development, there is an activism that does not reach the goal during austerities, worships, worldly travels and all the actions that are considered valuable actions in the ontology of Islamic mystics, including Attar as the narrator of the text. The outcome of these actions is being; Unsuccessful outcomes and shortcomings, failures and emotional stress, intense desire versus lack of necessity, and complete denial of what is right eventually cause him to dissolve his sense of self, transcend his limits, and unite with existence and God. The stronger and more intense the desire, the more severe the failure resulting from not being able to be and the more meaningful the emotional tension. A meaning that is full of emotion, perception and presence. The feeling and perception of the narrator, sometimes with an external phenomenon and sometimes by confronting the inner world, leads to the emergence of meaning. Some external experiences are the result of adaptation, reflection and some internal experiences are the result of attraction, co - presence and unity. The mention of Bayezid and his ascension is a mirror of the whole view of what mystics call mystical experience. An unexpected event, a passivity that brings with it an outburst of meaning and enthusiasm of the language. The research method is descriptive -analytical. The theoretical approach of the research is based on the theoretical foundations of semiotic -semantic knowledge in the analysis of mystical experiences. The scope of the research includes re -reading and reflecting on the mystical experiences of Bayazid Bastami in the narrative of Attar's Tazkireh Awaliya, which tries to analyze the formation of meaning in an emotional discourse, between the system of action, tension and excitement. [ABSTRACT FROM AUTHOR]

Objectives: Tracheoesophageal prosthesis (TEP) is a common method for post‐laryngectomy speech rehabilitation. Despite its common use, some patients ultimately fail TEP rehabilitation. TEP dysfunction negatively affects quality of life due to poor voice quality and need for repeated interventions to restore TEP function. Occasionally, voice rehabilitation with TEP is completely unsuccessful. We performed a scoping review to characterize the main reasons for total TEP failure, in hopes of guiding selection of optimal TEP candidates. Study design: Scoping review using PubMed of all English language articles from 1990 to 2020 addressing causes of TEP failure. Methods: This scoping review followed the population, intervention, comparison, outcome and study (PICOS) guidelines. Total TEP failure was defined as complete loss or abandonment of TEP voice or tract. A comprehensive search strategy using PubMed's MeSH subject headings and keywords was created. Causes and rates of failure were reviewed. Results: Among 544 peer‐reviewed journal articles reviewed for inclusion. Seventy articles met inclusion criteria, resulting in a total of 4928 TEP voice restoration patients for analysis. 15.2% of these patients had total TEP failure. The most common reasons for failure were dissatisfaction with voice (26.3%), leakage (17.9%), inadequate patient motivation (14.7%), comorbidities (14.2%), stoma problems (11.6%), and abandonment of TEP after dislodgement (10.6%). Conclusion: Common reasons for TEP failure included voice dissatisfaction, leakage, lack of patient motivation, patient comorbidities, and stoma problems. These factors should be considered when selecting candidates for TEP voice restoration. [ABSTRACT FROM AUTHOR]

Product development in innovation-driven industries often fails. Although such failures cause new ventures to struggle to raise follow-on investments, some overcome this challenge. Why? Synthesizing insights from the research on signaling and social evaluation theory, we identify how certain resources can mitigate the risk of funding termination for new ventures despite product development failure. Through a longitudinal study of 254 venture capital-funded biotechnology ventures, we show that internal quality signals decrease in their effectiveness while industry endorsement signals increase in their effectiveness as quality signals over time. Our findings contribute to the literatures on entrepreneurial failure, entrepreneurial resource acquisition and signaling. [ABSTRACT FROM AUTHOR]

Collaborative spaces such as fab labs, incubators, and coworking spaces have become a worldwide phenomenon based on their promise of fostering creativity and entrepreneurship. Mature, large organizations have also tried to benefit from this by creating such spaces in-house. This paper studies a failed attempt to create one such space in a large bureaucratic organization. Our study shows that succeeding in actualizing a space for play, creativity, and entrepreneurship in the place of the office requires attention to organizational 'common sense'. With our process perspective and its upgrading of the role of affect, our study's contributions highlight (1) The potential for research in organizational entrepreneurship of an affect-based conceptualization of common sense, which also emphasizes the political, in understanding the failure of entrepreneuring, and (2) The importance of 'spacing' and the space-place dynamics for entrepreneuring in organizations. [ABSTRACT FROM AUTHOR]

Unexpected bearing failures always cause severe production disruptions. Such faults often arise under specific operating conditions correlated with the on-site environment. Therefore, it is highly desired to fine-tune the preventive monitoring models with prior fault data on site. However, bearings in industrial equipment typically exhibit low failure rates, and thus prior fault data cannot be collected in time before faults occur. To tackle this issue, we propose a novel preventive monitoring technology that utilizes a contrastive fine-tuning method for a pre-trained model, only leveraging on-site normal data from bearings. This approach aligns the features of the pre-trained model with practical bearing characteristics, enhancing failure prediction capabilities. Experiments demonstrate its efficiency and effectiveness in the prediction of practical bearing failures. Compared to other popular methods, it can identify an incipient fault at least 63 min earlier, i.e. , 312 min before the bearing fault occurs. • The first solution for preventive monitoring of bearing failures without on-site fault data. • A pre-trained feature learning model of bearing failure. • A fine-tuned feature alignment only with normal data in operating conditions. • Experiments on a real-world testbed demonstrate the proposed solution's performance. • The potential to predict failures of new equipment and guide production optimization. [ABSTRACT FROM AUTHOR]

• A novel robustness index for turbidity (TRI 95B) was proposed in this study. • A comprehensive comparison and validation against already existing TRIs was assessed. • TRI 95B showed better correspondence to the plant performances than others TRIs. • TRI 95B showed better compliance with the new regulation while considering the lower T goal. • TRI 95B was able to identify failures at a lower discrepancy respect to the T goal. Drinking water treatment plants (DWTPs) are facing emerging challenges affecting raw water quality. In addition, the new regulatory framework (EU 2184/2020) sets stricter limits for turbidity and percentile statistics for continuous compliance, demanding greater robustness of the treatment processes. To achieve this aim, this study proposes a turbidity robustness index (TRI), named TRI 95B , to be used as a warning tool for detecting deviations from water quality standards. TRI 95B has been compared with the TRIs existing in the literature. Furthermore, the TRI 95B validation has been performed by a three-year monitoring dataset of a full-scale DWTP. The proposed TRI 95B index has two key novelties compared to the existing indices required for adapting to the new drinking water regulation: i. introduces the 95th percentile as a statistical indicator; ii. considers an additional term that sets an alert when a threshold value is exceeded. The comparison results suggest a better correspondence to the real plant performances of TRI 95B than the other TRIs. Indeed, both the sensitivity and specificity of TRI 95B were significantly higher than the other TRIs, indicating a better capacity to correctly classify both positive and negative cases. Moreover, while the previous TRIs identify a critical operating condition when the turbidity goal was significantly exceeded, TRI 95B highlights a failure condition at a lower discrepancy. Therefore, TRI 95B is also able to identify short-duration and low magnitude failures, thus coping with the purpose of the new regulation for drinking water. [Display omitted] [ABSTRACT FROM AUTHOR]

This article studies composite neural learning-based adaptive failure compensation control issues for the autonomous surface vehicle with full-state constraints. Initially, the control strategy solve the problems of computational complexity and state constraints and eliminate the negative effect of filter error on tracking performance by integrating with the command-filtered backstepping technique and barrier Lyapunov functions. Then, a composite neural learning framework is established, where the effect caused by approximation error on tracking accuracy can be efficiently reduced by constructing the serial-parallel estimation model to obtain the estimations of the system states. Furthermore, an adaptive resilient trajectory tracking controller is designed, which can ensure that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded satisfying the preset constraints even if the expected actuator faults occur suddenly. Finally, the feasibility and superiority of the designed control strategy are clarified by simulation results. [ABSTRACT FROM AUTHOR]

According to the behavioral tagging theory, various stages of fear memory, such as contextual fear conditioning, memory retrieval, and fear extinction, can be facilitated by the exploration of a novel open field (OF). A critical time window of efficacy exists for this process. Novel exploration closely adjacent to weak learning may interfere with the setting of the learning tag, leading to a negative effect. In this mouse study, we consistently showed that exposure to a novel or familiar OF immediately prior to the retention test impaired the retrieval of long-term contextual fear memory. However, OF exposure had no effect on the retrieval of recent or remote cued fear memory or short-term contextual fear memory or the reconsolidation of contextual fear memory. In addition, OF exposure impaired spaced but not massed extinction of contextual fear memory. These results suggest that interfering stimulus may result in the transient forgetting of fear memory; however, temporary loss of fear may lead to retention failure of fear extinction. The results of this study are an important complement to the behavioral tagging theory and may provide new guidance for the treatment of post-traumatic stress disorder. • OF exposure had no effect on the retrieval of recent or remote cued fear memory. • OF exposure impaired the retrieval of LTM but not STM of contextual fear. • OF exposure had no effect on the reconsolidation of contextual fear memory. • OF exposure impaired spaced, not massed, extinction of contextual fear memory. [ABSTRACT FROM AUTHOR]

• Introduces a novel TBM decision-making method based on failure probability, combining stress release coefficients and Monte Carlo simulations. • Demonstrates the relationship between tunneling speed and cumulative failure probability, offering a quantitative basis for adjusting TBM parameters in real-time. • Utilizes Bayesian updating to dynamically improve model accuracy, reducing uncertainty with minimal data inputs. • Proposes an optimal tunneling speed range that minimizes cumulative failure risks in large deformation soft rock zones. • Provides a clear decision-making framework for TBM operators, significantly improving operational safety and efficiency. This study addresses the over-reliance on operator experience in Tunnel Boring Machine (TBM) operations by proposing a failure probability-based decision-making method to enhance the scientific accuracy and reliability of construction decisions. In large deformation soft rock zones, TBMs are significantly affected by surrounding rock stress and deformation, which can lead to reduced tunneling speed or even shield jamming, severely impacting construction progress and safety. To reduce subjective biases in operator decision-making, this study combines the stress release coefficient with a failure probability model to establish a limit equilibrium equation. Using Monte Carlo simulations, failure probabilities under varying geological conditions are evaluated, and the optimal tunneling parameters are selected by analyzing the cumulative effect over time. Additionally, the study incorporates a Bayesian updating method, dynamically adjusting model parameters based on periodic monitoring data, further reducing uncertainty and improving the accuracy of the decision support system. The results show that while higher tunneling speeds increase the instantaneous failure probability, considering the cumulative effect over time, the overall failure index decreases with increased speed. Conversely, lower speeds result in a lower instantaneous failure probability but prolonged exposure to high-risk conditions increases the overall failure index. With this decision-making method, operators can quantitatively and in real-time adjust tunneling parameters under complex geological conditions, minimizing failure risks and improving construction efficiency. [ABSTRACT FROM AUTHOR]

Due to the variable-thickness parallelogram plates in ocean structure are characterized by the presence of strong moment singularity at supported obtuse corners, the wide application has turned the nonlinear mechanical analysis into one of the most important engineering concerns. In the paper, a refined geometrically nonlinear bending model of variable-thickness orthotropic parallelogram plates in curved hull under thermo-mechanical loads is proposed, while influences of linearly or quadratically thickened thickness in symmetrical or unsymmetrical profile on mechanical properties are thoroughly investigated. Three kinds of spatially thermal field in parallelogram domain are formulated on account of the coupled interaction of effects between in-plane distribution and thickness variation corresponding to the Dirichlet, Neumann and Robin thermal boundaries. A novel thickness-dependent Airy stress function is introduced overcoming the failure of traditional Airy stress function in equilibrium of in-plane forces, while the highly coupled and variable-coefficient nonlinear governing partial differential equations are firstly derived. The homotopy-based wavelet method is adopted to investigate the nonlinear thermo-elastic bending behaviors, while convergent process is verified and precision of obtained series solutions has been validated in excellent agreement with published results. The significant conclusion can be made that large-deflection nonlinear bending of such plates can be simplified with little discrepancy by omitting terms involving the derivatives of thickness variation in compatibility equation of deformation, which is generalized to the thermo-mechanical bending and greatly simplifies the analyzing procedures. • Nonlinear thermomechanical model of variable-thickness parallelogram plate is refined. • An Airy function is introduced overcoming the failure in equilibrium of in-plane forces. • Nonlinear thermomechanical bending is simplified by omitting derivatives of thickness. • Accurate bending solutions are obtained in excellent agreement with published results. [ABSTRACT FROM AUTHOR]

The problem of autonomous navigation has attracted significant attention from robotics research community in the last few decades. In this paper, we address the problem of low data utilization due to the large amount of episode experience value data. A maximum entropy algorithm based on prioritized experience replay (Learning Good Experience based on Soft Actor-Criti, LGE-SAC) is proposed to quickly reproduce past good experience episodes. As the deep reinforcement learning method is susceptible to failure to plan ahead and explore the target position in a long sequence environment, a deep Residual Soft Actor-Critic (RSAC) is proposed to alleviate this problem. The reinforcement learning policy is fused with the Artificial Potential Field method to improve the generalization ability of the proposed algorithm, thus improving robot adaptation in new test environments. In order to validate the effectiveness of the proposed algorithm, we conducted simulation experiments in Gazebo simulator environment and real experiments on a Turtlebot3 robot equipped with LiDAR sensor. Simulation and experiment results show that the proposed algorithm effectively avoids obstacles and succeeds in reaching the goal compared to other obstacle avoidance algorithms. In comparison with the Artificial Potential Field method, the planning success rate of the proposed RSAC algorithm in the test environment is increased by 30%, and at the same time, the number of planning steps is reduced by half, and the generalization ability is improved. [ABSTRACT FROM AUTHOR]

Accurate and efficient estimation of small failure probability subjected to high-dimensional and multiple failure domains is still a challenging task in structural reliability engineering. In this paper, we propose a stratified beta-spheres sampling method (SBSS) to tackle this task. Initially, the whole support space of random input variables is divided into a series of subdomains by using multiple specified beta-spheres, which is a hypersphere centered in the origin in standard normal space, then, the corresponding samples truncated by beta-spheres are generated explicitly and efficiently. Based on the truncated samples, the real failure probability can be estimated by the sum of failure probabilities of these subdomains. Next, we discuss and demonstrate the unbiasedness of the estimation of failure probability. The proposed method stands out for inheriting the advantages of Monte Carlo simulation (MCS) for highly nonlinear, high-dimensional problems, and problems with multiple failure domains, while overcoming the disadvantages of MCS for rare event. Furthermore, the SBSS method equipped with importance sampling technique (SBSS-IS) is also proposed to improve the robustness of estimation. Additionally, we combine the proposed SBSS and SBSS-IS methods with GPR model and active learning strategy so as to further substantially reduce the computational cost under the desired requirement of estimated accuracy. Finally, the superiorities of the proposed methods are demonstrated by six examples with different problem settings. • A stratified beta-sphere sampling method (SBSS) is first proposed for reliability analysis. • SBSS works well for rare event analysis with high nonlinearity and high dimension. • An important sampling technique is developed to improve the estimated robustness. • Active learning strategy is introduced to further reduce the computational cost. • The superiorities of the developed methods are proved by extensive examples. [ABSTRACT FROM AUTHOR]

The well-established Bayesian failure probability inference (BFPI) framework offers a solid foundation for developing new Bayesian active learning reliability analysis methods. However, there remains an open question regarding how to effectively leverage the posterior statistics of the failure probability to design the two key components for Bayesian active learning: the stopping criterion and learning function. In this study, we present another innovative Bayesian active learning reliability analysis method, called 'Weakly Bayesian Active Learning Quadrature' (WBALQ), which builds upon the BFPI framework to evaluate extremely small failure probabilities. Instead of relying on the posterior variance, we propose a more computationally feasible measure of the epistemic uncertainty in the failure probability by examining its posterior first absolute central moment. Based on this measure and the posterior mean of the failure probability, a new stopping criterion is devised. A recently developed numerical integrator is then employed to approximate the two analytically intractable terms inherent in the stopping criterion. Furthermore, a new learning function is proposed, which is partly derived from the epistemic uncertainty measure. The performance of the proposed method is demonstrated by five numerical examples. It is found that our method is able to assess extremely small failure probabilities with satisfactory accuracy and efficiency. • Another novel Bayesian active learning method is proposed for structural reliability analysis. • An alternative measure for capturing the epistemic uncertainty in the failure probability is derived. • A new stopping criterion is developed based on the measure and the posterior mean of the failure probability. • A new learning function is proposed based partly on the measure. • Our method can evaluate extremely small failure probabilities on the order of 1 0 − 9 – 1 0 − 7 . [ABSTRACT FROM AUTHOR]

• Creates a comprehensive framework to introduce efficient equipment inspection programs. • Minimizes risk and cost simultaneously using a two-objective genetic algorithm. • Enhances risk management in energy, oil, and gas sectors, preventing catastrophic events. • Empowers decision-making with cost-effective inspection plans and comparative evaluation. • Practical and effective approach demonstrated in a Petrochemical Plant case study. Efficient equipment maintenance is paramount across various industries to mitigate energy wastage and avert potential disasters such as hazardous emissions, fires, and explosions. Within this context, the adoption of risk-based inspection strategies has emerged as a crucial method for assessing equipment integrity. This study integrates the principles of Risk-Based Inspection (RBI) with a novel two-objective mathematical model, resulting in a comprehensive framework for equipment inspection programs. The primary aim of this framework is to reduce overall risk exposure while optimizing inspection expenditures. Unlike conventional approaches, this methodology eliminates the necessity to define threshold risk levels. By integrating inspection costs into the model, the assessment of Failure Consequences, and thereby, the decision-making process has been streamlined. This innovative algorithm effectively balances the reduction of failure likelihood with the minimization of inspection costs, enhancing decision-making capabilities. Importantly, this approach offers significant protection against energy wastage and the occurrence of leaks through robust risk management strategies. The algorithm employs specialized operators to expedite the discovery of optimal solutions. Empirical validation through a case study conducted at a Petrochemical Plant highlights the practicality and effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Cumulative time-dependent failure probability function (C-T-FPF) can reflect the effects of distribution parameters of random inputs and the upper bound of service time interval, which vary in their design domains, on time-dependent failure probability. However, solving C-T-FPF involves a time-consuming triple-layer framework. Thus, we propose an efficient method by combining cross-entropy-based importance sampling (IS) with adaptive Kriging model (CE-IS-AK). The innovations of CE-IS-AK include two aspects. Firstly, we construct a space augmented by both distribution parameters and the upper bound of service time interval, on which the triple-layer framework is decoupled to a single-layer one. And in the augmented space, an optimal IS density is proposed to reduce the required candidate sample size for estimating C-T-FPF. Secondly, we employ Gaussian mixture model (GMM) to approximate the optimal IS density, and the parameters in GMM are determined by minimizing the cross entropy of GMM and the optimal IS density. Moreover, to reduce the model evaluations in the proposed method, Kriging model is adaptively embedded to replace the actual model, and a first failure instant based learning function is proposed to train Kriging model adaptively. Due to the proposed single-layer framework and IS strategy assisted by the Kriging model, the efficiency is greatly improved for estimating C-T-FPF, which is validated by several examples. [ABSTRACT FROM AUTHOR]

• A novel 2D reinforcement learning (RL) based fault tolerant tracking control strategy is proposed. • Model-based 2D policy iteration algorithm is given. • A 2D data-driven Q-learning algorithm is proposed. Aiming at the characteristics of batch process changing along with time and batch directions, the existence of unmodeled dynamics, and the partial failure of actuators or/and sensors, we propose a novel 2D reinforcement learning (RL) fault tolerant control strategy without considering model parameters. Firstly, a two-Dimensional (2D) augmented state space model and 2D Q function-based fault tolerant control (FTC) framework is established. The 2D Bellman equation can be acquired by analyzing the relationship between the 2D value function and the 2D Q function. Based on the extended model and Q-learning concept of RL, a data-driven FTTC independent of model parameters is designed, and a 2D data-driven Q-learning algorithm is proposed. Finally, taking the pressure holding phase in the injection process as the experimental object, the control effect is compared with that of the traditional model-based FTC, and better tracking performance and unbiasedness to the probing noise can be proved. [ABSTRACT FROM AUTHOR]

• A rate-dependent similarity theory is proposed for scaling tensile damage and failure in buried silo structures against soil explosions. • The theory addresses the challenge of non-scalability of material strain-rate effects between scaled models and prototypes. • Using the theory, tensile damage and failure in concrete silos at different scales with β = 1, 1/2, 1/5, 1/10, 1/20, 1/50, and 1/100 are identical. • The theory provides a solid theoretical foundation for the design of scaled model tests, ensuring the scaled model truly represents the prototype. • A VBA-based program in Microsoft Excel is provided for easy application of the theory. The conventional similarity theory derived from dimensional analysis struggles with the well-known issue of non-scalability of material strain-rate effects between scaled models and prototypes. This limitation has significantly hindered the application of scaled model tests, particularly small-scale centrifugal model tests, in the study of structures against blast loading. To overcome this challenge, this study proposes a rate-dependent similarity theory for scaling the dynamic tensile responses and failure of large-scale underground concrete silos (46 m in height) subjected to large-yield soil explosions. The proposed theory includes a correction method derived from a verified dimensionless number, D cs , which accurately reflects the overall bending-induced tensile response and failure mechanism of concrete silos. The correction strategy involves maintaining an equal D cs between the scaled model and the prototype by adjusting the explosive weight and the concrete's static tensile strength in the scaled model to account for differences in strain-rate effects. To verify the theory, a series of geometrically similar silo models with scaling factors β = 1, 1/2, 1/5, 1/10, 1/20, 1/50, and 1/100 were designed. High-fidelity numerical simulations were performed using a fully coupled numerical model encompassing the explosive-soil-silo system. The results demonstrate that, with the conventional dimensional analysis-based similarity theory, the tensile damage and failure of the scaled silo models differ significantly from those of the prototype. However, with the proposed rate-dependent similarity theory, the failure patterns of the silo models with β = 1 ∼ 1/100 are almost identical, indicating that the proposed theory can effectively address the troublesome issue of dissimilar material strain-rate effects between scaled models and prototypes. This similarity theory offers a solid theoretical foundation for designing scaled models that accurately reflect prototype behavior, thereby advancing the application of scaled model tests in the study of structures against blast loading. [ABSTRACT FROM AUTHOR]

• Novel adaptive virtual model aided multi-physical dynamic and reliability analyses. • Stochastic multi-physical nonlinear biaxial buckling of smart SGPLRP-MEE composites. • Considered high-dimensional uncertainties, magnetic and electrical fields. • Sequential design of experiment enhanced adaptive Kriging is newly developed. • Verified advantages, accuracy, efficacy against existing machine learning baselines. This paper proposes the novel multi-physical nonlinear dynamic biaxial buckling and reliability analysis for sandwich graphene platelets reinforced porous plates with magneto-electro-elastic sheets (SGPLRP-MEE) advanced by the adaptive virtual model, accounting for physically inspired multi-dimensional uncertainties. To circumvent cumbersome multi-physical simulations while minimizing computational costs, an adaptive virtual modelling technique, namely the sequential design of experiment enhanced adaptive Kriging (SDOE-AKriging), is newly developed. The adaptive virtual model can accurately depict the sophisticated relationship between intrinsic uncertainties and concerned multi-physical responses using the fewest possible training samples, significantly reducing the computational expenditure in a dual fashion. Consequently, comprehensive quantified statistical information, structural reliability, and parameter sensitivity regarding multi-physical buckling failure behaviour are obtained, which are essential for smart composite safety assessment and serviceability limit state design. The superior performance, desirable accuracy, and advantageous efficiency of the SDOE-AKriging are validated through benchmark tests and extensive numerical experiments against existing machine learning algorithms. Additionally, the model's flexible modularity and rapid information update merits facilitate the establishment of a continuous failure diagnosis-prognosis loop, enabling swift decision-making and timely failure prevention in rapidly changing conditions. Parametric studies further reveal that focusing solely on deterministic responses can yield misleading conclusions in practical design scenarios. [Display omitted] [ABSTRACT FROM AUTHOR]

• Fully characterize the statistical properties of posterior failure probability. • Enable the reuse of evaluation results stemming from previous analyses. • Give the quantile and distribution of posterior failure probability in closed form. • Verify the accuracy and efficiency of the proposed method. Bayesian updating plays an important role in reducing epistemic uncertainty and making more reliable predictions of the structural failure probability. In this context, it should be noted that the posterior failure probability conditional on the updated uncertain parameters becomes a random variable itself. Hence, characterizing the statistical properties of the posterior failure probability is important, yet challenging task for risk-based decision-making. In this study, an efficient framework is proposed to fully characterize the statistical properties of the posterior failure probability. The framework is based on the concept of Bayesian updating and keeps the effect of aleatory and epistemic uncertainty separated. To improve the efficiency of the proposed framework, a weighted sparse grid numerical integration is suggested to evaluate the first three raw moments of the corresponding posterior reliability index. This enables the reuse of evaluation results stemming from previous analyses. In addition, the proposed framework employs the shifted lognormal distribution to approximate the probability distribution of the posterior reliability index, from which the mean, quantile, and even the distribution of the posterior failure probability can be easily obtained in closed form. Four examples illustrate the efficiency and accuracy of the proposed method, and results generated with Markov Chain Monte Carlo combined with plain Monte Carlo simulation are employed as a reference. [ABSTRACT FROM AUTHOR]