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This paper proposes a new approach to the interface between linguistic form and speech acts. The basic idea is to rethink prevailing canonicity assumptions about the inventory of syntactic forms used to perform speech acts. I argue for a new concept of canonicity in that domain, which is based on the following claim: pragmatically unmarked versions of the major speech acts requests, questions, and assertions comply with the socio-pragmatic principle of 'maximize politeness'. According to this principle, speakers try to minimize the risk of failure in achieving the relevant illocutionary goals of individual speech acts, and they can minimize that risk by using unambiguous linguistic forms that express politeness. I illustrate this account for unmarked forms of requests, questions, and assertions in German because in this language, the pragmatically unmarked versions of each of those speech acts can be signaled by dedicated particle elements (bitte 'please' in requests; denn 'then' in questions; and ja 'yes' in assertions). I claim that these particles are an overt realization of a syntactic head of a functional projection that encodes socio-pragmatic meaning in the left periphery of the clause. The paper sketches a unified syntactic analysis that holds across speech acts and that can potentially be extended to further phenomena of politeness marking in natural language. • Presents a new approach to the interface between syntax and social meaning. • Introduces the socio-pragmatic principle of 'maximize politeness'. • Demonstrates that particle elements are used in unmarked versions of major speech acts. • Provides a starting point for further studies of politeness marking in language. [ABSTRACT FROM AUTHOR]

The advent of CubeSats, compact satellites with a standardized form factor, has disrupted traditional space research paradigms, fostering innovation and collaboration across academic institutions and industries. Following deployment, CubeSats typically employ magnetorquer rods to initiate the detumbling sequence, gradually reducing the angular velocity before transferring control to reaction wheels for complete spin neutralization. However, this conventional approach entails a substantial spacecraft space requirement, necessitating an alternative and disruptive methodology. Furthermore, considering that approximately 50% of Attitude Determination and Control Subsystems (ADCS) failures are attributed to faults related to the moving parts in reaction wheels, strategies are taken into consideration to address a worst-case scenario of reaction wheels failure. This paper introduces a disruptive approach that uses diverse geometries and a non-unity track width ratio in PCB-integrated magnetorquers with reaction wheels for comprehensive control. We demonstrate the effectiveness of various coil configurations through a series of extensive simulations and establish a systematic framework to select optimal hybrid designs tailored to specific mission requirements. [ABSTRACT FROM AUTHOR]

Currently, the rapid development of the industrial Internet has led to the creation of a massive number of intelligent agents that are widely and distributively applied in various edge scenarios. The work conditions in these edge scenarios are complex, uncertain, and random. Traditional manual updates or human judgments are used for task decision-making in large-scale distributive intelligent agent edge work scenarios, which lack dynamic perception and autonomous recognition capabilities for edge work conditions. This inevitably leads to low decision-making accuracy, poor reliability, and ultimately, task failure. To address this issue, this study proposes an adaptive task identification strategy based on cloud-edge collaboration. This method utilizes a cloud-edge collaborative industrial intelligent application architecture to achieve cloud-based training and encapsulation of the task model, with online calling at the edge-end. Then, edge-end intelligent agents identify edge work conditions through multi-source data fusion, enabling accurate task decision-making. Finally, the edge-end requests the cloud for task model matching. The effectiveness of the proposed method is validated in an industrial safety situation virtual detection system. [ABSTRACT FROM AUTHOR]

• A failed innovation may signal that line of research must be abandoned. • But on the other hand this line of research could be fruitful if further pursued. • In this case a failure is not a reliable signal that this line should be abandoned. • We show that the latter case is especially relevant in complex technologies. This paper explores whether failures in innovation projects at the firm level contribute to strengthening firms' innovative activities and the odds of future innovation. Building on the literature on NK fitness landscapes, our paper focuses on technological complexity as a key factor that influences the response to innovation failures, guiding whether to discontinue the current path or, alternatively, to leverage the failure as a foundation for future innovation success. We then test the hypothesis derived from our model using a panel data set constituted by ten waves of the Community Innovation Survey held in the Netherlands from 1996 to 2014. Our findings show the fundamental relevance of the different forms of learning that are available to the firm. In particular, we highlight the positive role of learning after a previous innovation project has been abandoned. Previous failure in innovating produces some forms of learning inside the firms, and this learning positively contributes to subsequent successful innovation. Moreover, by differentiating between radical and incremental innovation, and between complex and less-complex innovation landscapes, we highlight the role of incremental innovation over the whole spectrum of landscape complexity, while the role of radical innovation appears to be more limited to less turbulent technological landscapes. [ABSTRACT FROM AUTHOR]

• Satellite model based on attitude error in MRP and angular velocity error. • EMPC applied to pointing an underactuated satellite for large attitude maneuvers. • Satisfactory final pointing error of the algorithm, even in a critical actuator failure scenario. The space sector has become increasingly relevant, especially satellites, due to their ability to access different parts of the planet in a short space of time. The challenge of controlling the satellite's attitude is crucial to the success of a mission. However, the actuator failure condition makes this task even more challenging since the system becomes underactuated, and the control strategies that deal with this scenario and present a satisfactory computational cost are limited unless a simplification of the system dynamics is adopted, such as considering the total angular momentum equal to zero or the diagonal moment of inertia matrix. In this work, extended model predictive control (EMPC) was used to point a satellite in the nadir direction in the presence of actuator failure. This controller uses a model of the system to optimize the control variable over a finite prediction horizon, obtaining a near-optimal solution. The control strategy was tested through a Monte Carlo simulation in two failure scenarios, the first with a failure in the reaction wheel and then also with a failure in the magnetic-torque rod. The results showed that for the first failure scenario, the time required to perform pointing and the final error were lower than for the second failure scenario. The computational load was evaluated through a closed-loop simulation using an embedded processor based on the ARM platform. The simulation shows that the model requires less memory and is more efficient in terms of execution time. The robustness analysis shows that the controller is robust to external disturbance torques and errors of up to 50% in the inertia of the model, with no significant loss in performance. We suggest that the proposed technique is capable of performing pointing with satisfactory accuracy, even with two actuator failures. In addition, this approach can yield substantial cost savings, which is more pronounced for small satellite platforms such as CubeSats. [ABSTRACT FROM AUTHOR]

The MRCS is a key gatekeeping assessment in the UK, completion of which is a prerequisite for progression into higher specialist surgical training (HST) programmes. As a result, examination success or failure can have a significant and lasting impact on career progression. Yet despite such high stakes, little was known about factors that may influence examination performance. To address this important gap in the literature, a series of large longitudinal cohort studies were undertaken. The work used data crossmatched from several national medical education databases to create the most extensive investigation of training outcomes for UK surgical trainees to date. MRCS data were matched to sociodemographic factors, training history and measures of prior academic attainment, and multivariate analyses identified independent predictors of MRCS success. Three key quantifiable factors were identified that predict success at MRCS: institutional differences in teaching and training, academic ability and individual differences in personal and social circumstances. This invited report for the Syme Medal discusses the key findings from this body of research and the implications for policy and practice. The research studies discussed in this report are driving evidence-based changes at the national level. The findings contribute to the optimisation of surgical training and the recognition of candidates at increased risk of failure. This results in the appropriate reallocation of resources and support, enabling greater fairness, equity, diversity and inclusivity in surgical career progression. [ABSTRACT FROM AUTHOR]

For plain woven SiC/SiC composites used in aerospace thermal-structural applications, not only the pristine material properties but the open-hole performance are of critical concern for the components in service. This study investigated the influence of intrinsic pores induced during SiC matrix preparation and manufactured holes on the macro mechanical properties of plain woven SiC/SiC through experimental and numerical methods. Tensile tests at room temperature were conducted on SiC/SiC specimens with different hole apertures and configurations, and the digital image correlation (DIC) method was employed for full-field strain measurement. Moreover, micro-computed tomography (μ-CT) was utilized to characterize the internal microstructure, original defects, and failure fractures. Based on the reconstructed mesoscale model, the tensile strength and failure mechanisms of pristine and open-hole specimens were predicted and analyzed, with the models validated against the experimental results. The results showed that the inter-tow defects play an important role on the deformation and failure of pristine SiC/SiC specimens during tensile loading. The notch strength results indicated that SiC/SiC exhibits weak notch sensitivity, and the local mesoscopic deformation of the notch is affected by the combination of macroscopic stress gradients and mesoscopic structures. The mesoscale simulation results showed that the position of the hole has a significant impact on the strength of the single layer SiC/SiC composite. The more the notch invades the warp, the greater the decrease in load-bearing capacity, and the relative position of the weft mainly determines the crack propagation path. The failure mode in the simulation results is consistent with the experimental findings, indicating that the simulation model, which considers the weave architecture, can better explain the notch failure behavior of Ceramic Matrix Composites (CMCs). [ABSTRACT FROM AUTHOR]

A set of n drones with limited communication range is deployed to monitor a terrain partitioned into n pairwise disjoint and closed convex trajectories, one per drone. There is exactly one communication link between two trajectories if they are close enough, and drones can communicate provided they visit the link at the same time. If each robot flies around an assigned area and shares information with the neighbors periodically the system is said to be synchronized. Over time, one or more drones may fail and the ability to survey, communicate, and stay connected decreases, thus the robustness against drone failure becomes crucial. In this paper we study various problems related to the proper functioning of a synchronized system under drone failure. First, we provide efficient algorithms, both centralized and decentralized, for determining the connected components induced by the set of surviving drones. Second, we study coverage, isolation, and connectivity under a probabilistic failure model and show that, in the case of grids, the system is quite robust in the sense that it can tolerate a large probability of failure before drones fail to completely cover the terrain, become isolated, or the system loses full connectivity. [ABSTRACT FROM AUTHOR]

Procrastination is an irrational delay of task completion. Previous studies have demonstrated that individuals who often procrastinate present deficits in attentional control and performance monitoring and that these dysfunctions might be differentially manifested depending on the motivational context. Building upon these results, the present event-related potential (ERP) study aimed to investigate the impact of norm-referenced feedback on executive functions among students with high (HP; N = 75) or low (LP; N = 77) procrastination levels. Participants completed the parametric Go/No-Go task, while receiving either positive or negative false feedback indicating how well they performed in comparison to others. The results indicated that positive (as opposed to negative) feedback led to higher self-reported arousal and increased post-error slowing in HP (vs. LP) participants. Moreover, neurophysiological measures indicated lower neural activation linked to attentional control (P300) and performance monitoring (ERN, CRN and Pe) in HP than LP participants, while the groups did not differ in these indices during the positive feedback condition. Obtained findings indicate that HP might be more sensitive to the motivating effects of success and more vulnerable to the detrimental influence of failure. • High (vs. low) procrastination was linked to higher arousal after positive evaluation. • High procrastination was linked to lower brain activity after negative evaluation. • Norm-referenced feedback might impact procrastination-related cognitive dysfunctions. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effect of three different osteosynthesis plate systems on failure rates and complications after continuity-interrupting mandibular resections with alloplastic reconstructions. Records of respective patients from 2010 to 2020 were analyzed retrospectively. The analyses included the osteosynthesis plate type (2.4 MANDIBULAR (RP1: MANDIBULAR [Medicon®, Tuttlingen, Germany]; RP2: Modus® Reco 2.5 [Medartis®, Basel, Switzerland]; and RP3: Modus 2 Mandible [Medartis®, Basel, Switzerland]), extent & location of the defect, age, sex, radiotherapy, and nicotine abuse. In case of failure, timepoint, and the problem, namely oral/extraoral dehiscence, screw loosening, and plate fractures that led to removal, were analyzed. Complications were classified according to Clavien−Dindo system. A total of 136 patients were included. The mean follow-up time was 18 ± 26 months. Survival rates after 1, 2, and 5 years were 69.9%, 66.9%, and 64.7%, respectively. Although survival was not significantly associated with the reconstruction system, the most frequent complications were seen in cases of RP1 & RP2 when compared to RP3 (p = 0.033). In brief, dehiscences were seen significantly less often in cases of RP3 (12.5%) when compared to RP1 (44.7%) and RP2 (26.9%; p = 0.024). Fractures of the osteosynthesis systems occurred in 3 of 4 cases (75%) with RP1, in 1 of 4 cases (25%) using RP2, and in no single case using the RP3 system (p = 0.03). Most of the observed complications occurred up to 12 months postoperatively. A total plate survival rate of 64.7% and a total plate complication rate of 47.8% were seen. In conclusion, it seems that RP3 should be preferred over RP1 and RP2 regarding failure rates and complications. [ABSTRACT FROM AUTHOR]

Artificial Intelligence and Machine Learning is a highly active area of research across numerous subgenres. One such example is Reinforcement Learning, which relies on trial and error based sampling of the environment to train an agent at completing a given task, commonly applied to Atari 2600 games within research applications. Many variants of Reinforcement Learning algorithms exist, all of which apply numerous hyperparameters to control the learning process in some way, from strength of backpropagation updates through to rates of exploration. The breadth of choice across these hyperparameters makes optimal training a challenging task, with no feedback given until significant time has lapsed in training. What is of interest here is the relative importance across these hyperparameters as well as any relationship amongst them at deriving high performance agents. Through this research we apply the common statistical approach of Design of Experiments to the task of understanding the state space of the numerous hyperparameters present in Reinforcement Learning algorithms such as the Double Deep Q-Network and Prioritized Experience Replay methods. We identify the learning rate as the only primary contributor of success or failure of value based Reinforcement Learning approaches to achieve optimal reward gain. This finding suggests the possibility of significantly reduced effort and time for considering the effects of non-dependent hyperparameters. [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]

• 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]

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]

• 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]

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]

• 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]

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]

• 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]

The critical current density (CCD) test protocols are widely adopted to examine the quality of solid electrolyte (SE), the stability of lithium (Li)/SE interfaces, and solid-solid interfacial kinetics in all solid-state lithium batteries (SSLBs). This work systematically investigated the impact of major experimental parameters on CCD values of Li symmetric cells using argyrodite Li 6 PS 5 Cl SE. Four categories of experimental parameters were investigated: (i) SE parameters, (ii) Li metal parameters, (iii) cell die parameters, and (iv) CCD testing protocols. Each parameter uniquely influences the density of the SE or the SE/Li interface, thereby affecting the resulting CCD values. Notably, CCD values are highly dependent on the cell operating histories, particularly the operating period (h) and the applied current density (mA/cm2). Consequently, without standardizing and unifying CCD testing protocols, obtaining reliable and repeatable CCD values remain challenging. To overcome this limitation, we propose the cumulative areal capacity (CAC) test protocol – the total capacity (mAh/cm2) delivered by a cell before its failure – as a more accurate measure of cell performance. CAC analysis of over 30 Li symmetric cells demonstrated statistical convergence in CAC values, highlighting its potential as a supplementary or even alternative testing protocol to conventional CCD methods. [Display omitted] • Impact of experimental parameters on CCD of Li symmetric cells with sulfide SE. • CCD values are highly dependent on the cell's operating histories. • Propose and statistically validated CAC as an alternative testing protocols. • Cell with higher-density SE demonstrated a wider CAC convergence window. • CAC window as useful metric for designing and estimating rate capability of SSLBs. [ABSTRACT FROM AUTHOR]

Attempts have been made to predict academic performance (AP) from different factors, but in most cases it has been done with one measure or by combining two. We propose a study in which as predictors of AP we use: intelligence, estimated based on six factors (Gf , Gc , Gv , Gs , Gsm and Gy), general factor of personality, motivation (intrinsic, achievement and fear of failure), emotional intelligence, study skills, and psychopathology (anxiety, depression and stress). We evaluated 603 primary and secondary school students aged 12.88 (SD = 1.47), 47.6 % female. Results coming from structural equation modeling indicate that AP in primary school is explained by g (β = 0.40), Gf (β = 0.24), psychopathology (β = −0.17) and personality (β = 0.86) (Goodness of fit: GFI = 0,983, NFI = 0,916, SRMR = 0,089); and in secondary school by g (β = 0.64), Gs (β = 0.29) and personality (β = 0.68) (Goodness of fit: GFI = 0.990, NFI = 0.884, SRMR = 0.098). The results coincide with previous studies, with small discrepancies that may be due to the use of multiple predictors of AP. In conclusion, cognitive factors appear to be the best predictor of AP in primary and secondary school. • Cognitive factors in the prediction of academic performance. • No cognitive factors in the prediction of academic performance. • Intelligence and personality in the prediction of academic performance. • Motivation, emotional intelligence and psychopathology in the prediction of academic performance. [ABSTRACT FROM AUTHOR]

This article investigates the challenge of potential optical guidance failure at the end of the underwater docking process due to relative attitude deviation between the Autonomous Underwater Vehicle (AUV) and the recovery platform, especially in the context of a moving base. A novel anti-disturbance control strategy is presented, accounting for environmental dynamics and optical guidance limitations. Firstly, we establish constraint models such as the effective field of view and the guidance beam associated with the optical guidance method. We then divide the recovery control strategy area based on the constraint model and propose the Line-of-sight Considering Visual Constraints (LOSCVC) docking control strategy. This strategy utilizes the line-of-sight method and the virtual leader method to make motion decisions based on the relative attitude offset and completes the docking operation by tracking the motion of the virtual leader. Furthermore, we consider the effect of ocean currents on the docking process of the moving base and design a disturbance compensation control law based on the Lyapunov redesign method. This strategy ensures the AUV maintains effective and continuous optical guidance during the capture phase, thereby achieving precise, safe, and rapid docking control. Simulation results validate the effectiveness and robustness of the proposed docking control strategy. • A new control methodology, LOSCVC, addresses AUV tracking complexities in underwater recovery, factoring in visual guidance restrictions and current disruptions. • A model is established for line-of-sight constraining in the optical guidance context, leading to the segmentation of the recovery control strategy area based on this theoretical foundation. • A virtual leader strategy, leveraging line-of-sight principles, optimizes trajectories through a dipole vector field for azimuth adjustments, ensuring uninterrupted optical guidance. • Back-stepping-derived control laws, complemented by Lyapunov-based compensation,facilitate precise AUV maneuvering and enhance disturbance resistance. [ABSTRACT FROM AUTHOR]

With the global rise of TikTok popularity, the research on how TikTok use may affect its users is still limited. We aimed to contribute to this research by taking a novel approach: distinguishing between general TikTok use (duration and frequency) and TikTok self-control failure. We expected that using TikTok when it conflicts with other important goals or tasks (TikTok self-control failure) would be more strongly and negatively linked to well-being, academic performance, bedtime procrastination, and sleep quality as compared to general TikTok use. We tested our expectations in two samples: adolescents (N 1 = 249) and university students (N 2 = 222) during the COVID-19 pandemic lockdown. We found interesting relationships between TikTok use, TikTok self-control failure, and the outcome variables. However, our hypothesis was confirmed in both studies only for bedtime procrastination. We argue that this might be because bedtime procrastination could be a more 'proximal' outcome variable compared to the other tested variables. The present research offers empirical data useful for discussions regarding the potential negative effects of TikTok use on young people. It also shows that differentiating between general measures of social media use (i.e., duration and frequency) and a measure of social media self-control failure might provide more detailed insights into the relationships between variables of interest for media researchers. [ABSTRACT FROM AUTHOR]

In industrial contexts, the stratification of materials by size is a common requirement, particularly in sectors such as food production, construction, and mineral extraction. Vibrating screens, which have been in use for over a century, play a crucial role in this process by applying forced vibration to classify materials according to particle size. In mineral processing facilities, these screens are essential, operating under demanding conditions and heavy loads. This paper focuses on vibrating screens used for the particle size separation of iron ore and quarry rocks. Given these operational challenges, this equipment is prone to various structural and mechanical failures, including cracks, bearing malfunctions, and universal joint issues. This study, a collaborative effort between a machinery manufacturer and mining companies, identifies the main causes of failures in vibrating screens, highlighting deficiencies in design and manufacturing processes as well as lapses in operational and maintenance practices. Recognizing the significant impact that a vibrating screen's maintenance shutdown can have on an entire mineral processing facility, the paper emphasizes failure prevention and aims to contribute to improved maintenance practices in industries, with the goal of enhancing operational efficiency and reducing losses. [ABSTRACT FROM AUTHOR]

To tackle the challenges of gathering and labeling data in practical engineering applications, a multimode collaborative transfer learning method is proposed to bridge the reality gap between labeled twin fault data and unlabeled real-world data. A bearing fault digital twin model is built to produce failure twin data of the test bearing under different health conditions. Three simulation source domains are constructed in accordance with the twin data's transferable modes. The iterative joint geometric-statistical alignment is used to perform the collaborative transfer of twin data's modes, which suppresses the negative transfer brought on by insufficient transferable information and the large inter-domain discrepancy. The fuzzy integral decision fusion is optimized and used to automatically label the real-world samples, which increases the cross-domain category alignment feasibility. The experimental results of extensive diagnosis tasks verified that the proposed method significantly outperforms the state-of-the-art domain adaption methods and can achieve an average classification accuracy of more than 91% in the bearing fault diagnosis with the support of fault twin data alone, without the supervision and guidance of historical labeling data. [ABSTRACT FROM AUTHOR]

We show that the semi-strictly generated internal homs of Gray -categories [ A , B ] ssg defined in [19] underlie a closed structure on the category Gray - Cat of Gray -categories and Gray -functors. The morphisms of [ A , B ] ssg are composites of those trinatural transformations which satisfy the unit and composition conditions for pseudonatural transformations on the nose rather than up to an invertible 3-cell. Such trinatural transformations leverage three-dimensional strictification [19] while overcoming the challenges posed by failure of middle four interchange to hold in Gray -categories [3]. As a result we obtain a closed structure that is only partially monoidal with respect to [8]. As a corollary we obtain a slight strengthening of strictification results for braided monoidal bicategories [13] , which will be improved further in a forthcoming paper [21]. [ABSTRACT FROM AUTHOR]

We investigated the frequency of sensory monofixation in patients with adult-onset divergence insufficiency esotropia and whether preoperative sensory monofixation was associated with surgical failure. Twenty-five patients with esotropia greater at distance than near who underwent bilateral medial rectus recessions were included. Near stereoacuity was measured preoperatively and 8 weeks postoperatively using the Randot Preschool test. Patients with best-corrected visual acuity worse than 0.3 logMAR in either eye or preoperative diplopia "rarely" or "never" in straight-ahead gaze at distance were excluded to minimize the inclusion of decompensated childhood strabismus. Sensory monofixation was defined as stereoacuity of 200 arcsec or worse and bifixation as 40 or 60 arcsec. Surgical failure was defined as esodeviation of >4Δ or exodeviation of >10Δ at distance or near, assessed 8 weeks (range, 6-17 weeks) postoperatively. We calculated the frequency of monofixation and surgical failure rates among patients with preoperative monofixation and those with preoperative bifixation. Preoperatively, sensory monofixation was common in divergence insufficiency–type esotropia (16 of 25 [64%]; 95% CI, 45%-83%). None of those with preoperative sensory monofixation had surgical failure, which does not support an association of surgical failure with preoperative monofixation. [ABSTRACT FROM AUTHOR]

Dissociative detachment experiences (e.g., derealization, absorption) underpin much psychopathology and are often researched in relation to co-occurring affective states such as shame, the latter being known to occur in situations involving actual or perceived negative social evaluation. The association between shame and dissociative detachment may also be moderated by individual differences in people's need to belong. The goal of the present study was to experimentally investigate the effects of social exclusion on shame and dissociative detachment depending on perceivers' need to belong. After measuring individual differences in need to belong and shame proneness, the Cyberball paradigm was utilized to communicate either social inclusion or exclusion. Following Cyberball, a sample of 281 participants completed measures of state shame and state dissociation. Social exclusion increased dissociative detachment, and these effects were mediated by increases in state shame. These effects were particularly evident among people with a high need to belong. Other individual differences such as rejection sensitivity may also moderate the relationship between exclusion and shame. While Cyberball can be used to investigate shame, it can only induce shame via social exclusion, whereas shame can also be elicited in other ways such as performance failure. Social exclusion can elicit shame, which is most acutely felt by those with a strong need to belong, and this aversive experience appears to be managed by dissociative detachment. • Social exclusion increases shame and dissociative detachment. • Shame mediates effect of social exclusion on dissociative detachment. • Effects of social exclusion on shame and detachment moderated by need to belong. • Mediating role of shame on detachment only among those with high in need to belong. [ABSTRACT FROM AUTHOR]

Offshore wind turbines face substantial challenges in operation and maintenance due to the harsh marine environment and remote locations. Predictive maintenance, encompassing fault diagnostics and failure prognostics, is a promising maintenance strategy to address these challenges. To contribute to this strategy, an integrated data-driven model is developed for probabilistic failure prognostics at the component level. The remaining useful life of a gearbox pump in an offshore wind turbine is predicted accurately based on supervisory control and data acquisition data. In this approach, light gradient boosting machines are tuned to model normal temperatures. The gated recurrent unit outperforms other neural networks and is selected to process temperature residuals with a Bayesian neural network. Results show that the prediction at the 50% percentile precedes the true failure time by 3.83 h. Moreover, there is 97.5% confidence that the true failure time falls within around ± 5.3 h of the predicted time. Furthermore, the earliest alarm is issued at the 2.5% percentile, precisely 9.17 h prior to the true failure time. This study demonstrates the effectiveness of supervised learning and normal behavior modeling for probabilistic failure prognostics of offshore wind turbine components. • Integrates LightGBM for normal behavior modeling and GRU with BNN for RUL prediction. • Provides a metric for LightGBM to minimize features while maintaining performance. • Rolling window and PCA boost prediction accuracy by reducing noise and seasonality. • BNN works with GRU to realize probabilistic RUL prediction and improve accuracy. [ABSTRACT FROM AUTHOR]

Mobile robotic platforms that are expected to be engaged in applications domains characterized by unstructured terrains and environment settings will unavoidably face mobility constraints that may not be overcome by classical navigation planning and obstacle avoidance/negotiation tools. Endowing these robots with additional skills, which enable them to interact and manipulate obstacles blocking their pathway, will significantly enhance their ability to deal with such conditions, permitting them to perform their mission more robustly when encountering such unstructured and cluttered scenes. This paper proposes a novel hierarchical simulation-based push planner framework that searches for a sequence of pushing actions to move obstacles toward a planned goal position. This aims at overcoming obstacle challenges that block the navigation of the robot toward a target location and, therefore, can lead to the failure of the navigation plan and the overall mission of the robot. The planned pushing actions enable the robot to relocate objects in the scene avoiding obstacles and considering environmental constraints identified by an elevation or an occupancy map. The online simulations of the pushing actions are carried out by exploiting the Mujoco physics engine. The framework was validated in the Gazebo simulation environment and in real platforms such as the hybrid wheeled-legged robot CENTAURO and the mobile cobot RELAX. • Constrained two-level push planner framework based on parallelized simulation steps. • Evaluate environmental data for collision-free planning and robot pose adaptation. • Autonomous selection of the object to push and its target location to solve the task. • Extensive validation on the real robots RELAX and CENTAURO, in blocked scenarios. [ABSTRACT FROM AUTHOR]

This study reports a pilot phase 2 trial evaluating de-escalation of whole brain radiation therapy (WBRT) dose with escalation to gross disease (BMET) via simultaneous integrated boosts (SIB) in patients with metastatic brain cancer. SIB to BMETs has shown to have improved local control (LC), and similar trials of WBRT with SIB studies have shown comparative LC. There have been concerns that WBRT can lead to early cognitive decline, which becomes more important as these patients are living longer. We report the results of in-brain distant failure (IBDF) and LC, as well as cognitive outcomes. Adult patients with pathologically confirmed primary solid tumor malignancy and imaging evidence of ≤ 30 cc of BMETs (individual lesions ≤ 5 cc) who have dsGPA estimated survival (OS) of at least 6 months were enrolled and treated with WBRT to 25 Gy with SIBs of 42 Gy, all in 10 daily fractions. The primary objective was IBDF in the non-SIB volumes at 6 months. Secondary objectives included LC of the SIB BMETs at 6 months and evaluation of neurocognitive function utilizing the Hopkins Verbal Learning Test-Revised (HVLT-R) at 6 months. Between 9/2017 and 5/2022, 20 patients were enrolled. Although 20 patients were enrolled, 9 patients had evaluable data with a median follow up of 8.8 months. The median BMETs present for evaluable patients at the time of treatment was 8 (inner quartiles 6, 11), with up to 25 lesions boosted. Four patients (44.4%; 95% CI: 0.14-0.79) had an IBDF by 6 months, exceeding our historical estimate of 20%. At 6 months, 100% of patients had LC of their SIB BMET. The 6-month OS was 100% in evaluable patients and 70% in all enrolled patients. The HVLT-R tests of available patient data showed raw score mean changes in baseline to 6 month total recall, delayed recall, recognition discrimination, and retention (%) scores of -0.75, -1.38, -2.12, and -13.50, respectively. No patients had G3-5 treatment-related toxicities and 9 out of 20 patients (45%) had a total of 14 G3-5 overall adverse events. IBDF was higher than expected in this patient cohort, while SIB of BMETs resulted in excellent LC and was well tolerated. The results of this study are difficult to interpret given only 45% of patients were deemed evaluable; despite use of dsGPA for eligibility criteria, many patients did not survive to 6 months. The high 6-month IBDF proportion could be explained by the overall small sample size in this pilot trial. WBRT with SIB to multiple brain mets was feasible, though additional study of dose de-escalated WBRT is warranted. [ABSTRACT FROM AUTHOR]

This paper presents a novel, cost-effective method for a rapid and quantitative characterization of discontinuities in fractured rock cliffs using Unmanned Aerial Vehicle-derived Digital Outcrop Models (DOMs). The proposed workflow combines manual extraction of discontinuity data from the DOM with a kinematic analysis using an automated algorithm (ROKA), which upgrades and renews the traditional Markland's test. The case study, a designed railway tunnel portal in NW Italy (Finale Ligure) demonstrates the advantages and limitations of this approach compared to current methods. Manual mapping is compared with (semi-)automatic extraction of discontinuity data from the DOM and validated with field-based scan lines. The results show that in complex geological settings, manual mapping provides more statistically robust and geologically reliable measurements of discontinuity orientation, validated by the user's expertise. Traditional stereographic approaches in kinematic analysis are limited in characterizing spatially variable slopes and discontinuity networks, providing only generic estimations of unstable slope surfaces and discontinuity intersections. This limitation is overcome by new algorithms that incorporate 3D spatial relationships of both the discontinuity network and the rock slope, resulting in a more accurate and site-specific kinematic characterization of the rock cliff, emphasized by 3D visualizations of the critical planes and potential rock failure volumes. The new approach significantly impacts the time, effort, and cost of the engineering projects, allowing to quantitatively define the potential unstable areas with a fast analysis. The Authors stress the importance of integrating digital workflows with comprehensive field-based characterizations of the local litho-stratigraphic and structural setting to effectively utilize large datasets and partially automated procedures. [Display omitted] • A strong field understanding improves the kinematic analysis based on a 3D model. • Manual fractures mapping is more effective in complex geological settings. • Automatic fractures mapping could be effective for low deformed and clean rock slopes. • Traditional kinematic analyses cannot depict the complex 3D geometry of rock slopes. • ROKA algorithm localizes in 3D the critical slope areas and discontinuities. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases (NDDs) are an increasing group of chronic and progressive neurological disorders that ultimately lead to neuronal cell failure and death. Despite all efforts throughout decades, their burden on individuals and society still casts one of the most massive socioeconomic problems worldwide. The neuronal failure observed in NDDs results from an intricacy of events, mirroring disease complexity, ranging from protein aggregation, oxidative stress, (neuro)inflammation, and even blood-brain barrier (BBB) dysfunction, ultimately leading to cognitive and motor symptoms in patients. As a result of such complex pathobiology, to date, there are still no effective treatments to treat/halt NDDs progression. Fortunately, interest in the bioavailable low molecular weight (LMW) phenolic metabolites derived from the metabolism of dietary (poly)phenols has been rising due to their multitargeted potential in attenuating multiple NDDs hallmarks. Even if not highly BBB permeant, their relatively high concentrations in the bloodstream arising from the intake of (poly)phenol-rich diets make them ideal candidates to act within the vasculature and particularly at the level of BBB. In this review, we highlight the most recent - though still scarce - studies demonstrating LMW phenolic metabolites' ability to modulate BBB homeostasis, including the improvement of tight and adherens junctional proteins, as well as their power to decrease pro-inflammatory cytokine secretion and oxidative stress levels in vitro and in vivo. Specific BBB-permeant LMW phenolic metabolites, such as simple phenolic sulfates, have been emerging as strong BBB properties boosters, pleiotropic compounds capable of improving cell fitness under oxidative and pro-inflammatory conditions. Nevertheless, further studies should be pursued to obtain a holistic overview of the promising role of LMW phenolic metabolites in NDDs prevention and management to fully harness their true therapeutic potential. • The BBB safeguards the brain, being not only key in homeostasis but also along neurodegenerative disorders progression. • Phenolic metabolites are abundant in circulation in contrast to their parent counterparts present in the food. • Phenolic metabolites' potential to act within vascular and at BBB level in the scope of neurodegeneration is still amost neglected. • Here we review the still scarce but promissing data on phenolic metabolites BBB benefits, even if not highly BBB permeable. [ABSTRACT FROM AUTHOR]

Periprosthetic joint infections (PJI) pose a significant challenge in orthopaedic surgery, often requiring extensive surgical debridement and prolonged antibiotic treatment to eliminate the causative pathogens. Rifampin, known for its potent activity against biofilms, has been crucial in managing PJI by penetrating and disrupting these formations, thereby improving treatment efficacy. In this sense, antibiotic protocols lacking rifampin have shown increased failure rates. Consequently, the development of rifampin resistance could severely influence the prognosis of PJI. The aim of this clinical study was to assess how rifampin resistance affects the functional outcome in patients with PJI. In this single-centre comparative cohort study, we systematically documented all patients who presented with a PJI during the period spanning from 2018 to 2020. Two distinct groups were established for the study: Group 1 comprised 35 patients with a PJI caused by rifampin-susceptible pathogens and group 2 consisted of 28 patients with PJI caused by rifampin-resistant pathogens. A total of 63 patients (34 females) with a mean age of 68 years and a mean follow up of 37 months were included. The examination of patient-specific parameters did not reveal any identified risk factors as influential. Patients with a rifampin-resistant pathogen underwent a greater number of surgical revisions (6.9 ± 5.1 compared to 3.59 ± 3.39, p = 0.0011) and had extended durations of antibiotic treatment (p = 0.0052). The results of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score revealed significant differences in clinical outcome between both groups in every domain, even when stratified by acute and chronic entities. In total the WOMAC increased significantly from 21.57 ± 14.9 points in group 1 to 71.47 ± 62.7 points in group 2 (p < 0.001). The higher failure rates observed in group 2 were not statistically significant (p = 0.44). The current study demonstrates that PJI caused by rifampin-resistant bacteria are associated with a significantly worse functional outcome in both acute and chronic infection types without significantly affecting total failure rates. [ABSTRACT FROM AUTHOR]

Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method for efficient and accurate network reliability estimation under node failure and edge failure scenarios. The proposed sampling method adopts Monte Carlo technique to sample component lifetimes and the K-terminal spanning tree algorithm to accelerate structure function computation. Unlike existing methods that compute only one structure function value per sample, our method generates multiple component state vectors and corresponding structure function values from each sample. Network reliability is estimated based on survival signatures derived from these values. A transformation technique extends this method to handle both node failure and edge failure. To enhance efficiency of proposed sampling method and achieve adaptability to network topology changes, we introduce an active learning method utilizing a random forest (RF) classifier. This classifier directly predicts structure function values, integrates network behaviors across diverse topologies, and undergoes iterative refinement to enhance predictive accuracy. Importantly, the trained RF classifier can directly predict reliability for variant networks, a capability beyond the sampling method alone. Through investigating several network examples and two practical applications, the effectiveness of both proposed methods is demonstrated. • Two novel methods are proposed for efficient network reliability estimation. • K-terminal spanning tree scheme is used to speed up structure function computation. • Both proposed methods can handle node failure and edge failure scenarios. • Proposed active learning method can be directly applied to variant networks. • Proposed methods are validated by six network examples and two real-world applications. [ABSTRACT FROM AUTHOR]

In engineering asset management, accurate failure risk estimation is essential for averting equipment breakdowns and optimizing risk-based maintenance strategies. Data quality and model fitting are two critical sources of prediction uncertainty in risk estimation. While much attention has been devoted to model fitting for risk estimation, the critical role of data quality has often been overshadowed. To bridge this research gap, this paper presents a novel data quality management framework tailored for industrial equipment failure risk estimation. The framework covers the steps from data to model. It consists of the following main phases: data development, data quality assessment, data quality requirement decision-making, data quality improvement, and risk estimation model development. The framework provides detailed guidelines that can facilitate data practitioners to build individualized data quality requirement decision-making models for failure risk estimation of their equipment. The decision-making model can measure the adequacy of existing data to build a risk estimation model that meets the specified requirements and further determines the best risk estimation model given the available data. A case study using actual data collected during oil well drilling operations from multiple oil fields demonstrates the practicality and effectiveness of the framework. In this case study, four risk estimation models are compared, including two baseline models (mean time to failure and median time to failure) and two machine-learning models (quantile regression and hidden Markov model). In addition, a decision tree-based decision model is developed to determine whether the data quality meets the requirements and the best risk estimation model in case the data quality meets the requirements. • Describe data quality metrics for industrial equipment failure risk estimation. • Introduce a novel equipment failure risk estimation model performance indictor. • Propose a data quality management framework for failure risk estimation. • The framework is validated on electronic board field failure data. [ABSTRACT FROM AUTHOR]

FRP bar has emerged as a strong contender for replacing conventional steel reinforcement to address costly corrosion in reinforced concrete structures. The bond between FRP bar and concrete plays a paramount role in transferring loads and sustaining structural integrity. In this study, the effect of exposure conditions on the FRP rebar-to-concrete bond strength (BS) was explored. Traditional design guidelines and analytical models for estimating such BS are often labor-intensive and may encounter significant complexity and limitations when applied to diverse exposure scenarios. To overcome this limitation, this study leverages machine learning (ML) techniques to predict the FRP bar-to-concrete BS and failure pattern under various exposure conditions. A pertinent comprehensive database on BS and failure type under diverse exposure conditions was compiled. The performance of eight ML algorithms in predicting the failure mode and BS was evaluated. Current design guidelines and existing models were used for benchmark comparison. The results show that the XGB and GBT models outperformed the other ML algorithms in failure mode identification and BS prediction. The FRP rebar bonded length was identified as the most significant feature influencing BS, followed by the concrete compressive strength, rebar diameter, FRP surface condition, and exposure duration. Finally, this study presents an automated toolbox that can be used through a free access user-friendly graphical interface for estimating the BS and failure patterns under diverse exposure conditions. [ABSTRACT FROM AUTHOR]

The neural underpinning of cooperative and competitive constructive activity has been investigated using mass-univariate approaches. In this study, we sought to compare the results of these approaches with the results of multivariate pattern analysis (MVPA). In particular, we wanted to test whether MVPA supports the claim made in previous studies that cooperation is associated with the activity of reward-related brain circuits. Participants were required to construct a pattern on the screen either individually or in cooperation or competition with another person during an fMRI scan. Both the MVPA classification methods and the representational similarity analysis indicated the involvement of orbitofrontal and ventromedial prefrontal areas in processes that distinguish between cooperation and competition, and activation analysis showed that these areas are more active during cooperation than during competition. However, a single trial analysis showed that the effect was reversed when only winning trials were considered. In these trials, activation of reward-related areas was higher during competition than during cooperation. Moreover, the contrast between won and lost trials in terms of reward circuits involvement was sharper under competition than under cooperation. Thus, although cooperation can be generally more rewarding than competition, it is associated with smaller difference between trials lost and trials won in terms of reward circuits activation. One may speculate that in cooperation, victory and defeat are shared with the partner and, contrary to competition, are not experienced as personal achievement or failure. • Highest classification accuracy of cooperation and competition is found in vmPFC. • Activation analysis shows that this area is more active during cooperation. • This effect is reversed when only winning trials are considered. • In competition, contrast between won and lost trials is sharper than in cooperation. [ABSTRACT FROM AUTHOR]

Few theoretical and empirical works have described impostor phenomenon (IP) and no study explored impostorism from a dark perspective. We adopted a person-centered approach to identify clusters of impostors based on scores on the IP, perfectionistic self-presentation and the Dark Triad using a sample of 306 university students (M age = 22.82). The latent profile analysis suggested a 6-cluster solution, which was further compared in terms of psychopathology symptoms (i.e., burnout, generalized anxiety, and fear of failure) by means of (M)ANOVAs. Our results indicated that impostorism relies on a self-presentation strategy. Moreover, individuals scoring high on IP experienced enhanced levels of psychopathology symptoms. Looking at IP from a continuum perspective might improve our understanding about what makes someone suffer from impostorism. • Impostor phenomenon (IP) is more common than previously thought. • Dark personality traits lead to individual differences in IP. • Manipulation and self-sabotage as strategies to obtain social integration. • Higher levels of IP determine elevated levels of emotional, physical and mental exhaustion, anxiety, and fear of failing. [ABSTRACT FROM AUTHOR]

This paper presents a framework for evaluating the wind-induced resistance of overhead transmission lines (OTLs), aiming to overcome the limitations associated with disregarding temperature effects and relying on a single metric for failure assessment. The proposed framework factors in temperature influence, carefully considers applied loads and structural characteristics, and accurately calculates failure probability based on a specific case study. Meteorological data is initially collected to create a probabilistic model of wind speed and temperature distribution, accounting for correlation. Components and systems are graded based on structural characteristics and damage modes. The neural network surrogate model is trained with finite element computations for load-structural response sample sets. The surrogate model, in conjunction with logistic regression, generates samples to determine fragility. Finally, the total structural failure probability and monthly failure likelihood are computed by integrating the joint probability model and fragility. Analyses highlight temperature's significant impact on conductor-ground line safety, validating the proposed framework's rationale. [Display omitted] • A joint probability distribution model for wind speed and temperature based on measured data is developed. • An OTL performance classification criteria is proposed. • An RBFNN surrogate model is modeled. • Fragility and failure probability of OTL is studied in detail. [ABSTRACT FROM AUTHOR]

When stress invariants up to the second order are employed to construct failure criterion for brittle materials, it involves three independent terms and therefore there are three coefficients to be determined. However, there are only two conditions available associated with the strengths under uniaxial tension and compression. Systematic examinations have given to the invariants of the failure envelope as a quadric surface according to analytic geometry. For the failure envelope to meet the basic assumptions, in particular, infinite strength under and only under hydrostatic compression, one of the coefficients can be eliminated based on rigorous mathematical inferences. As a result, it reproduces the Raghava-Caddell-Yeh criterion, which has never been rationally established before but is now in this paper. The failure envelope takes the form of circular paraboloid for brittle materials in general. The criterion degenerates to the von Mises criterion, giving a circular cylindrical failure envelope for ductile materials as a special case. It is as rational as the von Mises criterion in the sense that the assumptions made and the conditions available are logically sufficient for the complete establishment of the failure criterion without any ambiguity. • The rationality of the Raghava-Caddell-Yeh (RCY) criterion has been established for the first time, which is applicable to brittle materials with ductile materials as a special case, where 'rationality' means that the assumptions made and the conditions available (all conventional as detailed below) are logically sufficient for the final outcome of the criterion. • The failure envelope is examined as a quadric surface and characterised through its invariants, through which one of the constants can be eliminated based on rigorous logic and mathematics. • The RCY criterion is the most general form that can be delivered by a single polynomial failure function of stresses up to the second order. • The RCY criterion has reached its full maturity and is as rational as the von Mises criterion, in fact, the latter being a special case of the former. [ABSTRACT FROM AUTHOR]

Control room operators on offshore platforms play an important role in realizing the safety instrumentation functions of the high integrity pressure protection system (HIPPS) during overpressure emergencies. Human reliability analysis is critical to the overpressure operation of offshore HIPPS, yet there are few studies. To remedy this gap, the paper proposes an integrated human reliability analysis method to analyse the impact of human intervention on offshore HIPPS safety. Considering the effects of context and cognitive performance on human reliability comprehensively, the improved cognitive reliability and error analysis model (CREAM) is established; The correction effect of external environmental factors on response failure probability is quantified, and the improved human cognitive reliability (HCR) model is developed; The two improved models are organically combined to form an integrated methodology. Based on the behaviour characteristics of personnel emergency response, human error probability is calculated in stages to analyse human reliability. The findings indicate that the overall human error probability for HIPPS overpressure operations is 0.124 and particular attention needs to be paid to observation and decision-making operations. Besides the theoretical background, the paper provides practical contributions to personnel training, mission planning, and risk management of HIPPS to minimize the possibility of human error. • An integrated human reliability analysis method for offshore HIPPS is proposed. • The cognitive performance correction coefficient is introduced to improve CREAM. • The modification effect of external environmental factors is quantified to improve the HCR model. • Human error probabilities are calculated in stages based on personnel task characteristics. [ABSTRACT FROM AUTHOR]