Vibration fatigue reliability analysis of aircraft landing gear based on fuzzy theory under random vibration

The failure of aircraft landing gear (ALG) is major caused by vibration fatigue. And its main failure mode is fatigue fracture. Currently the reliability of ALG is usually calculated by the stress strength interference (SSI) model, which is based on the binary state assumption. While in reality, the strength is degraded with time and the boundary of the failure and success is blur, so the binary state assumption is deviated from the fact. To overcome this problem, this paper uses the membership function (MF) to represent fuzzy safe state which caused by the strength degradation under the failure mode of vibration fatigue. Moreover, a fuzzy reliability model (FRM) of ALG is proposed based on fuzzy failure domain (FFD). Finally, the feasibility of method is tested through a simulation example. By comparing the simulation results (SRs) of the FRM with SRs of the static SSI model and the dynamic SSI model, the rationality of the method is verified. The FRM can calculate the reliability without the gradual degradation processes, thus it is used more widely

Saturation mutagenesis reveals manifold determinants of exon definition.

To illuminate the extent and roles of exonic sequences in the splicing of human RNA transcripts, we conducted saturation mutagenesis of a 51-nt internal exon in a three-exon minigene. All possible single and tandem dinucleotide substitutions were surveyed. Using high-throughput genetics, 5560 minigene molecules were assayed for splicing in human HEK293 cells. Up to 70% of mutations produced substantial (greater than twofold) phenotypes of either increased or decreased splicing. Of all predicted secondary structural elements, only a single 15-nt stem-loop showed a strong correlation with splicing, acting negatively. The in vitro formation of exon-protein complexes between the mutant molecules and proteins associated with spliceosome formation (U2AF35, U2AF65, U1A, and U1-70K) correlated with splicing efficiencies, suggesting exon definition as the step affected by most mutations. The measured relative binding affinities of dozens of human RNA binding protein domains as reported in the CISBP-RNA database were found to correlate either positively or negatively with splicing efficiency, more than could fit on the 51-nt test exon simultaneously. The large number of these functional protein binding correlations point to a dynamic and heterogeneous population of pre-mRNA molecules, each responding to a particular collection of binding proteins

Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao

Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user feedback. Since ad impressions are purposely selected by the model itself, their distribution differs from the inference distribution and thus exhibits sample selection bias (SSB) that affects model performance. Existing studies on SSB mainly employ sample re-weighting techniques which suffer from high variance and poor model calibration. Another line of work relies on costly uniform data that is inadequate to train industrial models. Thus mitigating SSB in industrial models with a uniform-data-free framework is worth exploring. Fortunately, many platforms display mixed results of organic items (i.e., recommendations) and sponsored items (i.e., ads) to users, where impressions of ads and recommendations are selected by different systems but share the same user decision rationales. Based on the above characteristics, we propose to leverage recommendations samples as a free lunch to mitigate SSB for ads CTR model (Rec4Ad). After elaborating data augmentation, Rec4Ad learns disentangled representations with alignment and decorrelation modules for enhancement. When deployed in Taobao display advertising system, Rec4Ad achieves substantial gains in key business metrics, with a lift of up to +6.6\% CTR and +2.9\% RPM

Association Between Regulatory T Cells and Ischemic Heart Disease: a Mendelian Randomization Study

BACKGROUND: An imbalance of innate and acquired immune responses is significantly involved in the pathophysiology of coronary atherosclerosis and the occurrence of ischemic heart disease (IHD). Regulatory T cells (Tregs) play an essential regulatory role in atherosclerotic plaque formation and maintenance; therefore, dysfunction of Tregs triggers the formation of atherosclerotic plaques and accelerates their progression. However, due to the inherent limitations of observational research, clinical evidence is limited concerning the relationship between the variation in peripheral Tregs and the risk of IHD, and the cause-and-effect relationship between these factors is unclear. Mendelian randomization (MR) uses genetic variation as a proxy for exposure and can be used to inferentially determine the causal effect of exposure on outcomes. We thus used MR analysis to investigate whether there is a causal relationship between the biomarkers of Tregs and IHD. METHODS: Selected genetic variants (P RESULTS: We identified a set of 197 single-nucleotide polymorphisms (SNPs) that served as instrumental variables (IVs) for evaluating 51 Treg subtypes. Thirteen significant variables were found to be potentially associated with IHD. After false-discovery rate (FDR) adjustment, we identified four Treg subtypes to be causally protective for IHD risk: CD28 on activated & secreting CD4 Tregs [odds ratio (OR) =0.89; 95% confidence interval (CI): 0.82-0.96; P=3.10E-03; adjusted P=0.04], CD28 on activated CD4 Tregs (OR =0.87; 95% CI: 0.80-0.95; P=3.10E-03; adjusted P=0.04), CD28 on CD4 Tregs (OR =0.87; 95% CI: 0.80-0.96; P=3.41E-03; adjusted P=0.04), and CD28 on resting CD4 Treg cell (OR =0.91; 95% CI: 0.85-0.97; P=3.48E-03; adjusted P=0.04). Reverse MR analysis found eight potential causal variables, but these associations were nonsignificant after FDR correction (all adjusted P values \u3e0.05). CONCLUSIONS: This study identified the significance of elevated CD28 expression on CD4 Tregs as a novel molecular modifier that may influence IHD occurrence, suggesting that targeting CD28 expression on CD4 Tregs could offer a promising therapeutic approach for IHD

CAMP:Co-Attention Memory Networks for Diagnosis Prediction in Healthcare

Diagnosis prediction, which aims to predict future health information of patients from historical electronic health records (EHRs), is a core research task in personalized healthcare. Although some RNN-based methods have been proposed to model sequential EHR data, these methods have two major issues. First, they cannot capture fine-grained progression patterns of patient health conditions. Second, they do not consider the mutual effect between important context (e.g., patient demographics) and historical diagnosis. To tackle these challenges, we propose a model called Co-Attention Memory networks for diagnosis Prediction (CAMP), which tightly integrates historical records, fine-grained patient conditions, and demographics with a three-way interaction architecture built on co-attention. Our model augments RNNs with a memory network to enrich the representation capacity. The memory network enables analysis of fine-grained patient conditions by explicitly incorporating a taxonomy of diseases into an array of memory slots. We instantiate the READ/WRITE operations of the memory network so that the memory cooperates effectively with the patient demographics through co-attention mechanism. Experiments on real-world datasets demonstrate that CAMP consistently performs better than state-of-the-art methods