Saddle point of attachment in horseshoe vortex system

Laminar juncture flow has been well studied experimentally and numerically in recent years. New topology upstream in terms of saddle point of attachment has been investigated by both approaches. In this work, the obstacle standing on the flat plate was replaced with a jet flow. Numerical simulation and theoretical analysis were performed to investigate the upstream topology. The numerical results were validated with the mathematical theory and topology rules. The upstream critical point satisfies the condition of occurrence for saddle point of attachment in the horseshoe vortex system. In addition to the classical topology led by the saddle point of separation, the new topology led by saddle point of attachment was discussed for the first time in a crossflow with jet. The transition of critical point from separation to attachment is determined by the jet to crossflow velocity ratio, boundary-layer thickness of flat plate, and the oscillation of the jet. With decreasing the velocity ratio, the flow topology changes from new topology to the classic topology when the boundary layer thickness at the upstream edge of the jet is about 0.2 diameter. But if boundary layer thickness is close to one diameter the variation of the velocity ratio has no effect on the topology while changing the location of the saddle point. The transition of the critical point from separation to attachment was also observed with the increasing boundary layer thickness. Under the influence of jet oscillation, the characteristics of the critical point could change between separation and attachment in a higher frequency

A Comprehensive Optimum Design Method of Monitorability-based Design for Mechanical System Using Collaborative Theory

Abstract: The study aims to investigate the mechanical system optimum design based on collaborative theory. Due to the complexity of the modern machinery, mechanical systems are readily to damage when unexpected failures occur on important components. It is therefore, critical to monitor the machine state for preventing the impending faults. The key issues to realize the feasible and reliable mechanical condition monitoring is information acquisition, which relies on the available design of the detection devices. Literature review indicates that an extensive attention has been put on the so called Monitorability in the systematic design of mechanical systems. Monitorability is emphasized that in the original design of mechanical systems one should consider available information acquisition property. Moreover, monitorability-based design is known as a design attribute of mechanical system worldwide. However, less work has been done in this field. In this study, a novel method based on collaborative theory is proposed for the monitorability design. The connotation and application of collaborative theory for monitorability design are discussed in details. The information synergy model and organization framework of monitorability-based design are established by using computer technology and network technology. The experiments demonstrate the effectiveness of the proposed monitorability design system for a more powerful optimum design of mechanical systems and show a promising future for the industrial applications

Recent Progress on Mechanical Condition Monitoring and Fault Diagnosis

AbstractMechanical equipments are widely used in various industrial applications. Generally working in severe conditions, mechanical equipments are subjected to progressive deterioration of their state. The mechanical failures account for more than 60% of breakdowns of the system. Therefore, the identification of impending mechanical fault is crucial to prevent the system from malfunction. This paper discusses the most recent progress in the mechanical condition monitoring and fault diagnosis. Excellent work is introduced from the aspects of the fault mechanism research, signal processing and feature extraction, fault reasoning research and equipment development. An overview of some of the existing methods for signal processing and feature extraction is presented. The advantages and disadvantages of these techniques are discussed. The review result suggests that the intelligent information fusion based mechanical fault diagnosis expert system with self-learning and self-updating abilities is the future research trend for the condition monitoring fault diagnosis of mechanical equipments

Circular RNA Hsa_Circ_0091579 Serves as a Diagnostic and Prognostic Marker for Hepatocellular Carcinoma

Background/Aims: An increasing number of studies have suggested that circular RNAs (circRNAs) have vital roles in carcinogenesis and tumor progression. However, the function of circRNAs in hepatocellular carcinoma (HCC) remains poorly characterized. Methods: We investigated the levels of circRNAs in patients with HCC to identify potential diagnostic biomarkers. We examined circRNA expression profiles in liver tumors and paired non-cancerous liver tissues from three HCC patients with cancer thrombus using a circRNA microarray. Bioinformatics analysis was performed to find circRNAs with significantly altered expression levels between tumors and their paired non-tumor tissues. We confirmed our initial findings by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Receiver operating characteristic (ROC) curves were also applied to identify a candidate circRNA with the optimal specificity and sensitivity. Finally, X-tile software was adopted to calculate the most efficient cut-off value for hsa_circ_0091579 expression. Results: Microarray analysis identified 20 unique circRNAs that were differentially expressed between tumor and non-tumor tissues (P < 0.05). The expression of these 20 circRNAs was verified by qRT-PCR. The expression of hsa_circ_16245-1 and hsa_circ_0091579 mRNA was consistent with their levels as tested by the microarray. The ROC curves showed that both hsa_circ_16245-1 and hsa_circ_0091579 had favorable specificity and sensitivity. We further confirmed that hsa_circ_0091579 was significantly upregulated in HCC and its high expression was intimately associated with a worse overall survival in patients with HCC. Conclusion: Hsa_circ_0091579 may play a critical role in HCC progression and serve as a potential biomarker for the prognosis of patients with HCC

H2-Mapping: Real-time Dense Mapping Using Hierarchical Hybrid Representation

Constructing a high-quality dense map in real-time is essential for robotics, AR/VR, and digital twins applications. As Neural Radiance Field (NeRF) greatly improves the mapping performance, in this paper, we propose a NeRF-based mapping method that enables higher-quality reconstruction and real-time capability even on edge computers. Specifically, we propose a novel hierarchical hybrid representation that leverages implicit multiresolution hash encoding aided by explicit octree SDF priors, describing the scene at different levels of detail. This representation allows for fast scene geometry initialization and makes scene geometry easier to learn. Besides, we present a coverage-maximizing keyframe selection strategy to address the forgetting issue and enhance mapping quality, particularly in marginal areas. To the best of our knowledge, our method is the first to achieve high-quality NeRF-based mapping on edge computers of handheld devices and quadrotors in real-time. Experiments demonstrate that our method outperforms existing NeRF-based mapping methods in geometry accuracy, texture realism, and time consumption. The code will be released at: https://github.com/SYSU-STAR/H2-MappingComment: Accepted by IEEE Robotics and Automation Letter

RobustCCC: a robustness evaluation tool for cell-cell communication methods

Cell-cell communication (CCC) inference has become a routine task in single-cell data analysis. Many computational tools are developed for this purpose. However, the robustness of existing CCC methods remains underexplored. We develop a user-friendly tool, RobustCCC, to facilitate the robustness evaluation of CCC methods with respect to three perspectives, including replicated data, transcriptomic data noise and prior knowledge noise. RobustCCC currently integrates 14 state-of-the-art CCC methods and 6 simulated single-cell transcriptomics datasets to generate robustness evaluation reports in tabular form for easy interpretation. We find that these methods exhibit substantially different robustness performances using different simulation datasets, implying a strong impact of the input data on resulting CCC patterns. In summary, RobustCCC represents a scalable tool that can easily integrate more CCC methods, more single-cell datasets from different species (e.g., mouse and human) to provide guidance in selecting methods for identification of consistent and stable CCC patterns in tissue microenvironments. RobustCCC is freely available at https://github.com/GaoLabXDU/RobustCCC