Analysis of Reliability Correlation Degree of Rolling Bearings Based on Zero-Failure Data

Modern equipment has higher requirements for the reliability of rolling bearings. The time and economic cost of obtaining bearing failure data through test methods are getting higher. Usually, truncation time tests of small sample are used to obtain zero-failure data of bearings. Based on the zero-failure data model and multi-layer Bayesian theory, this paper improves the reliability evaluation method of rolling bearings by changing the values of hyperparameters, and calculates the estimated value of failure probability at each truncation time to obtain the reliability of the bearing. This paper adopts the theory of grey relational degree to analyze the relationship and change law of bearing reliability at each truncation time, to understand the reliability change trend of rolling bearing more comprehensively. Experiments show that the method is reasonable

A Method for Analyzing Correlation Grades of Factors Influencing the Behavior of Bolted Connections Based on the Grey Correlation Degree

Failure caused by loosening of bolted connections is a common problem in practice. To reasonably optimize the factors influencing the behavior of bolted connections, this paper proposed a method for analyzing the correlation grades of factors influencing the behavior of bolted connection based on the grey correlation degree. First, Deng\u27s correlation degree is optimized from three different perspectives: dimensionless factors, resolution coefficient, and weight assignment. Then, a model of the bolted connection is established based on the degrees of correlation of influencing factors. To avoid inverse correlation of influencing factors caused by fluctuation of the residual preload, a difference classification method based on qualitative analysis is proposed. By grading the correlation degree of each influencing factor, optimal selection of parameters in bolted connections can be achieved. Finally, the bolted connection on a third-rail current collector slide was taken as an example and residual preload analysis was performed. It can be concluded that reasonably selecting the bolt material and size and optimizing the friction condition of various contact surfaces is necessary to effectively improve the reliability of the bolted connection. The method was verified by simulations

Investigation of Multi-Stage Evaporation and Wave Multiplicity of Two-Phase Rotating Detonation Waves Fueled by Ethanol

In this study, a numerical investigation based on the Eulerian-Lagrangian model is conducted to explore a rotating detonation engine (RDE) fueled by liquid ethanol. The focus is on examining the characteristic phenomena of the two-phase rotating detonation wave (RDW) caused by droplet evaporation and varying inlet conditions. To enhance the evaporation of liquid fuel, pre-heated air is used, and both liquid and pre-vaporized ethanol are simultaneously injected. The distribution of ethanol droplets reveals an initial concentration near the injection surface and accumulation in the fuel-refill zone. Here, liquid droplets gradually evaporate after absorbing latent heat from the surrounding gas. The subsequent interactions between the evaporating droplets and the RDW vary with the droplet size. For droplets with diameters of $d_0$ = 5-15 $\mu$m, after being swept by the RDW, a secondary evaporation process occurs, leading to an enlargement of the width of the reaction zone. However, the chemical reactions still predominantly take place in close proximity to the detonation front. As further increases, droplet evaporation persists in the post-detonation expansion zone over a long distance until the remaining droplets are fully evaporated and eventually burned by the hot products. The study also analyzes the extinction of rotating detonations and the emergence of new detonation waves resulting from local explosions and consequent shock collisions. It is demonstrated that variations in the diameter of injected droplets and inlet temperature can lead to different operating modes with varying numbers of RDWs

Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce

In the dynamic field of eCommerce, the quality and comprehensiveness of product descriptions are pivotal for enhancing search visibility and customer engagement. Effective product descriptions can address the 'cold start' problem, align with market trends, and ultimately lead to increased click-through rates. Traditional methods for crafting these descriptions often involve significant human effort and may lack both consistency and scalability. This paper introduces a novel methodology for automating product description generation using the LLAMA 2.0 7B language model. We train the model on a dataset of authentic product descriptions from Walmart, one of the largest eCommerce platforms. The model is then fine-tuned for domain-specific language features and eCommerce nuances to enhance its utility in sales and user engagement. We employ multiple evaluation metrics, including NDCG, customer click-through rates, and human assessments, to validate the effectiveness of our approach. Our findings reveal that the system is not only scalable but also significantly reduces the human workload involved in creating product descriptions. This study underscores the considerable potential of large language models like LLAMA 2.0 7B in automating and optimizing various facets of eCommerce platforms, offering significant business impact, including improved search functionality and increased sales.Comment: 9 pages, 4 figures, EMNLP2023 workshop, The 2023 Conference on Empirical Methods in Natural Language Processin

Fabrication and characterisation of periodically poled lithium niobate waveguide using femtosecond laser pulses

We present in this letter the fabrication and characterization of thermally stable type II waveguides in Z-cut periodically poled lithium niobate crystals. The waveguides were fabricated by using a femtosecond laser and were utilized for second harmonic generation. Our experiments have shown that a quasiphase matching wavelength of 1548.2nm, a tuning bandwidth of 2nm, and a tuning temperature range of 150.4±1.6°C can be achieved

Machine Learning for Prediction of Sudden Cardiac Death in Heart Failure Patients With Low Left Ventricular Ejection Fraction: Study Protocol for a Retrospective Multicentre Registry in China

Introduction: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF. Methods and analysis: We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study. Ethics and dissemination: The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences

Imaging Trans-Cellular Neurexin-Neuroligin Interactions by Enzymatic Probe Ligation

Neurexin and neuroligin are transmembrane adhesion proteins that play an important role in organizing the neuronal synaptic cleft. Our lab previously reported a method for imaging the trans-synaptic binding of neurexin and neuroligin called BLINC (Biotin Labeling of INtercellular Contacts). In BLINC, biotin ligase (BirA) is fused to one protein while its 15-amino acid acceptor peptide substrate (AP) is fused to the binding partner. When the two fusion proteins interact across cellular junctions, BirA catalyzes the site-specific biotinylation of AP, which can be read out by staining with streptavidin-fluorophore conjugates. Here, we report that BLINC in neurons cannot be reproduced using the reporter constructs and labeling protocol previously described. We uncover the technical reasons for the lack of reproducibilty and then re-design the BLINC reporters and labeling protocol to achieve neurexin-neuroligin BLINC imaging in neuron cultures. In addition, we introduce a new method, based on lipoic acid ligase instead of biotin ligase, to image trans-cellular neurexin-neuroligin interactions in human embryonic kidney cells and in neuron cultures. This method, called ID-PRIME for Interaction-Dependent PRobe Incorporation Mediated by Enzymes, is more robust than BLINC due to higher surface expression of lipoic acid ligase fusion constructs, gives stronger and more localized labeling, and is more versatile than BLINC in terms of signal readout. ID-PRIME expands the toolkit of methods available to study trans-cellular protein-protein interactions in living systems.National Institutes of Health (U.S.) (DP1 OD003961

A Sigmoidal and Distance Combined Transformation Method for Nearly Singular Integral on Asymmetric Patch

This paper is devoted to developing a new computational method for nearly singular integral computation in the application of the boundary element method for the analysis of thin-shell-like structures in mechanical engineering. Based on the traditional distance transformation method, a sigmoidal transformation method is introduced to further cluster the integral points around the source point with respect to the circumferential direction. The combined method provides accurate results without employing a large quantity of integral points. Numerical examples demonstrate that the computational accuracy and efficiency of the proposed method is significantly higher than that of the traditional single distance transformation method, especially in the case of the asymmetric integral patch

CONTACT ANALYSIS OF ELASTIC COMPOSITE CYLINDRICAL ROLLER BEARING

In order to research into the contact between the rolling element and the rollaway nest of the elastic composite cylindrical roller bearing,the finite element method and the classical Hertz contact theory are separately used to calculate the contact stress and being out of shape of the cylindrical roller bearing. The two results have the error of ten percent. Thus the finite element method is verified to be accurate in roller bearing. Since the contact pairs of the inner and outer confine of the two types of roller bearings are similar,the finite element method could be used in the research of the contact stress of the elastic composite cylindrical roller bearing. Calculation and analysis are made on the contact stress,contact half width,contact displacement and the distribution along the axial direction under different burdening. The results show that under a certain burdening,elastic composite cylindrical roller contact displacement and contact half width increase with the expansion of filling degree; Under different burdening,elastic composite cylindrical roller contact stress and equivalent stress both have the minimum value along with the expansion of filling degree,and with the increase of burdening,the change of the minimum value is organized. In terms of contact stress,the elastic composite cylindrical roller bearing is superior to the solid cylindrical roller bearing. Reasonable filling degree can reduce the contact stress of elastic composite cylindrical roller bearings and improve the"edge effect"