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This paper focuses on the multi-choice tasks, which aim to select the correct choice for a given query by reasoning over texts, such as sentences and passages. Benefiting from the provided knowledge in these tasks, the reasoning of multi-choice models becomes well-founded. However, besides the evidence of the correct choice, the text usually contains some content related to other choices as well, which causes multi-choice models vulnerable to being confused by candidate choices, and we denote it as choice confusion problem. To alleviate this challenge, we propose two auxiliary mechanisms to distinguish the confusing choices. Specifically, a query-guided attention (QGA) mechanism is designed to automatically filter the text contents causing confusion by measuring the syntactic relevance between different contents and the query. Meanwhile, a confusion-aware training (CAT) mechanism is designed to learn the correct and easily confused choices asynchronously and perform a pushing-away operation between their selection processes. We conduct experiments on multi-choice tasks based on sentences and passages. The results show that our framework improves the choice selection accuracy compared to strong baselines. Furthermore, the ablation test and case study verify the effectiveness of our proposed QGA and CAT, especially in addressing the choice confusion problem. [ABSTRACT FROM AUTHOR]

Purpose: Triple-negative breast cancer (TNBC) represents the worst prognostic subtype of breast cancer and lacks targeted therapeutic drugs. Signal transducer and activator of transcription 3 (STAT3) is overexpressed and constitutively activated in TNBCs and associated with poor patient outcomes. However, no agents targeting STAT3 have been successfully developed and marketed. Selective Estrogen Receptor Modulators (SERMs) have been reported as potential inhibitors of the IL-6/STAT3 signaling pathway. Naphthalene compounds have good pharmacological activity and significant anti-cancer activity. In this study, we synthesized a new series of naphthalene derivatives with the general structure of SERM and evaluated their effects on TNBC and STAT3 signals. Methods: A new series of compounds based on the scaffold of SERMs and an amino group were designed and screened based on the structure–activity relationship by MTT assay. The binding activity of SMY002 to STAT3 was predicted and validated by docking and SPR. The STAT3 signaling target and anti-cancer effects of SMY002 were evaluated with three TNBC cell lines and the mice transplanted tumor model. Results: Among the compounds, SMY002 displayed the most potent activity, which could directly interact with STAT3 SH2-domain, and strongly inhibit the phosphorylation, dimerization, nuclear distribution, transcriptional activity, and target genes expression of STAT3. Furthermore, SMY002 markedly suppressed migration, invasion, survival, growth, and metastasis of TNBC cells in vitro and in vivo via down-regulating the expression of Cyclin D1 and MMP9. Conclusions: SMY002 can significantly inhibit the growth and metastasis of TNBC cells by targeting the STAT3 signal. [ABSTRACT FROM AUTHOR]

A general methodology for the dynamic modeling and analysis of planar flexible deployable structures consisting of scissor-like elements (SLEs) is presented. This modeling method is based on a comprehensive consideration of the symmetry and array characteristics of deployable structure and on an improved absolute node coordinate formulation (ANCF), which can model the warping of beam section using the locking-free shear deformable beam element. An effective node separation method is proposed to reduce the number of degrees of freedom of the dynamic equation in the ANCF framework, eliminate the constraint equations within and between SLEs and obtain a compact matrix. This reduction method has good adaptability and can be extended to all kinds of scissor deployable structures with array characteristics, whether they are planar or spatial structures. In addition, the modified generalized α method is utilized to solve the motion equations of deployable structure and eliminate the false high-frequency response generated in the calculation process. Finally, the methodology is validated using a cantilever beam case, and the parametric dynamic response of 2 × 2 and 1 × 2 deployable structures is implemented in this paper. The obtained results show that flexibility has an important impact on the dynamic characteristics of large deployable structures, and the deployable structure is unstable near 0 ∘ and 90 ∘ , and its safe working angle is 17 ∘ – 75 ∘ . It is necessary to carry out parametric research on this structures in the design stage because the prediction of these parameters such as initial configuration and component materials can improve the stability and deployment accuracy of deployable structures in orbit. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel multi-phase sequential preventive maintenance (PM) model with multiple maintenance objectives for repairable mechanical systems under deterioration. Although sequential PM scheduling has appeared in literature, conventionally the length of time between maintenance events in the later of replacement process changes very frequently, which has led to poor operability in practice, and has consequently brought a great challenge for maintenance managers. To solve this problem, the concept of multiple phases sequential PM, in which the replacement cycle is divided into several phases, each with equal PM intervals, is proposed to make the policy more suitable in practical applications. A new relationship between cost and maintenance quality, which considers the age reduction factor as a function of maintenance cost and system age, is established to describe the effect of maintenance. The maintenance modeling and optimizing problems are formulated using a multi-attribute value model (MAVM) and a computation algorithm is presented to find the optimum solution. A case study of examining equipment is presented to illustrate the performance of the proposed policy. A comparison with other methods is given to illustrate the effectiveness of our approach. Finally, a sensitivity analysis is performed on the optimality of the maintenance schedule based on related parameters. [ABSTRACT FROM AUTHOR]

The computer numerical control (CNC) equipment is playing essential role in production and manufacturing, thus, its reliability estimation is crucial for industrial systems. Existing research studies mainly focused on univariate degradation data or multiple correlated data, which might not be accurate in equipment reliability assessment. In real CNC equipment, the degradation data consists of correlated and non-correlated degradation data and the data sample might be scarce, which bring great challenges for maintenance managers to assess risk and prevent failure ahead of fault occurrence. This paper proposes a reliability assessment approach based on multiple non-correlated and correlated degradation data with focusing on small size of data sample. Using the multiple degradation data of CNC equipment, a covariance matrix is built to determine the correlation between the performance signals. If the performance signals are not correlated, the degradation trajectory of the performance signals is curve fitted by least squares support vector machines (LS-SVM). The reliability which indicates the degradation curve not reaching its specified threshold is calculated. If the performance signals are correlated, according to the degradation data, the degradation curve of the performance signal is fitted by multivariate regression using LS-SVM, and the joint probability density function is derived. According to both the reliabilities of non-correlated degradation data and correlated degradation data, the joint reliability of the system is calculated. Finally, using a specific type of CNC machine tool as an example, the performance reliability assessment method is verified. [ABSTRACT FROM AUTHOR]

The study aims to work on multi -level condition-based maintenance (CBM) policy for multi-unit systems under economic dependence. Failure modes of units are classified as hard failure or soft failure and taken into account in this paper. Unlike previous works which only considered setup cost dependence with a single failure mode, two types of economic dependence are considered, which are setup cost-saving, and additional cost and time that can be saved when identical maintenance tasks and expertise are needed. In this study, hard failure provides an opportunity for additional inspection and repair of soft-type units, and soft failure creates an opportunity for replacement of the hard-type unit as its age exceeds opportunistic threshold. They both undergo preventive maintenance (PM) when the level/age exceeds preventive threshold at inspection epochs. The objective of this study is to determine the optimal opportunistic control limit and preventive control limit for each unit such that the system's average long-run maintenance cost per unit time is minimized. The optimization problem is formulated in a semi-Markov decision process (SMDP) framework. The explicit formula for the mean residual life is derived for the proposed model which enables the estimation of the remaining useful life and allows maintenance engineers to plan maintenance early based on the observed information. Finally, the method is illustrated by an industrial case study of a feed system of a machine tool. A comparison with other methods is given, which illustrates the effectiveness of our approach. [ABSTRACT FROM AUTHOR]