Use of vibration and acoustic emissions to determine roughness of surfaces in sliding contact

Vibration and acoustic emission (AE) techniques have been used widely for machine condition monitoring, especially for fault detection and diagnosis, yet little work has been done on the development of vibration and AE methods for studying wear, even though wear is one of the main causes of faults. As widely reported, surface roughness and its changes have a close relationship with the wear status of bodies in sliding contact. In a wear process, the surface of a moving component evolves continuously, with the change starting at a micro-level and gradually progressing to a macro-level. Therefore, it is important to monitor and quantify the change in the surface roughness, which often has to be carried out after stopping the machine and examining the surface using offline analysis techniques. This thesis aims to (a) study the correlation between vibration and AE features and surface roughness for surfaces in sliding contact, and (b) estimate the surface roughness using vibration and/or AE indicator(s). Tests were conducted on a tribometer and spur gearbox rig. The surface roughness of tested discs and gear pairs were measured, along with vibration and AE data, which was analysed using a number of advanced signal processing techniques to establish a relationship between the two types of signals and surface roughness. One new roughness indicator has been proposed and one potential indicator is also outlined. Both indicators are for AE signal. The new indicator focuses on the non-Gaussian property of the signal. The potential indicator relates to the energy of AE signal. As the test went on, the potential indicator showed that the energy of AE signal became concentrate. This phenomenon may be related to the development process of pitting. Based on the key discoveries of this work, future works are suggested at the end of the thesis

A Comprehensive Empirical Investigation on Failure Clustering in Parallel Debugging

The clustering technique has attracted a lot of attention as a promising strategy for parallel debugging in multi-fault scenarios, this heuristic approach (i.e., failure indexing or fault isolation) enables developers to perform multiple debugging tasks simultaneously through dividing failed test cases into several disjoint groups. When using statement ranking representation to model failures for better clustering, several factors influence clustering effectiveness, including the risk evaluation formula (REF), the number of faults (NOF), the fault type (FT), and the number of successful test cases paired with one individual failed test case (NSP1F). In this paper, we present the first comprehensive empirical study of how these four factors influence clustering effectiveness. We conduct extensive controlled experiments on 1060 faulty versions of 228 simulated faults and 141 real faults, and the results reveal that: 1) GP19 is highly competitive across all REFs, 2) clustering effectiveness decreases as NOF increases, 3) higher clustering effectiveness is easier to achieve when a program contains only predicate faults, and 4) clustering effectiveness remains when the scale of NSP1F is reduced to 20%

SURE: A Visualized Failure Indexing Approach using Program Memory Spectrum

Failure indexing is a longstanding crux in software testing and debugging, the goal of which is to automatically divide failures (e.g., failed test cases) into distinct groups according to the culprit root causes, as such multiple faults in a faulty program can be handled independently and simultaneously. This community has long been plagued by two challenges: 1) The effectiveness of division is still far from promising. Existing techniques only employ a limited source of run-time data (e.g., code coverage) to be failure proximity, which typically delivers unsatisfactory results. 2) The outcome can be hardly comprehensible. A developer who receives the failure indexing result does not know why all failures should be divided the way they are. This leads to difficulties for developers to be convinced by the result, which in turn affects the adoption of the results. To tackle these challenges, in this paper, we propose SURE, a viSUalized failuRe indExing approach using the program memory spectrum. We first collect the run-time memory information at preset breakpoints during the execution of failed test cases, and transform it into human-friendly images (called program memory spectrum, PMS). Then, any pair of PMS images that serve as proxies for two failures is fed to a trained Siamese convolutional neural network, to predict the likelihood of them being triggered by the same fault. Results demonstrate the effectiveness of SURE: It achieves 101.20% and 41.38% improvements in faults number estimation, as well as 105.20% and 35.53% improvements in clustering, compared with the state-of-the-art technique in this field, in simulated and real-world environments, respectively. Moreover, we carry out a human study to quantitatively evaluate the comprehensibility of PMS, revealing that this novel type of representation can help developers better comprehend failure indexing results.Comment: Due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract here is shorter than that in the PDF fil

Whole genome sequence analysis of blood lipid levels in \u3e66,000 individuals

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids

MetaMHC: a meta approach to predict peptides binding to MHC molecules

As antigenic peptides binding to major histocompatibility complex (MHC) molecules is the prerequisite of cellular immune responses, an accurate computational predictor will be of great benefit to biologists and immunologists for understanding the underlying mechanism of immune recognition as well as facilitating the process of epitope mapping and vaccine design. Although various computational approaches have been developed, recent experimental results on benchmark data sets show that the development of improved predictors is needed, especially for MHC Class II peptide binding. To make the most of current methods and achieve a higher predictive performance, we developed a new web server, MetaMHC, to integrate the outputs of leading predictors by several popular ensemble strategies. MetaMHC consists of two components: MetaMHCI and MetaMHCII for MHC Class I peptide and MHC Class II peptide binding predictions, respectively. Experimental results by both cross-validation and using an independent data set show that the ensemble approaches outperform individual predictors, being statistically significant. MetaMHC is freely available at http://www.biokdd.fudan.edu.cn/Service/MetaMHC.html

Predicting essential proteins by integrating orthology, gene expressions, and PPI networks

<div><p>Identifying essential proteins is very important for understanding the minimal requirements of cellular life and finding human disease genes as well as potential drug targets. Experimental methods for identifying essential proteins are often costly, time-consuming, and laborious. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction networks (PINs). However, most of these methods have limited prediction accuracy due to the noisy and incomplete natures of PINs and the fact that protein essentiality may relate to multiple biological factors. In this work, we proposed a new centrality measure, OGN, by integrating orthologous information, gene expressions, and PINs together. OGN determines a protein’s essentiality by capturing its co-clustering and co-expression properties, as well as its conservation in the evolution process. The performance of OGN was tested on the species of <i>Saccharomyces cerevisiae</i>. Compared with several published centrality measures, OGN achieves higher prediction accuracy in both working alone and ensemble.</p></div

Tailor-Made Boronic Acid Functionalized Magnetic Nanoparticles with a Tunable Polymer Shell-Assisted for the Selective Enrichment of Glycoproteins/Glycopeptides

Biomedical sciences, and in particular biomarker research, demand efficient glycoproteins enrichment platforms. In this work, we present a facile and time-saving method to synthesize phenylboronic acid and copolymer multifunctionalized magnetic nanoparticles (NPs) using a distillation precipitation polymerization (DPP) technique. The polymer shell is obtained through copolymerization of two monomers-affinity ligand 3-acrylaminophenylboronic acid (AAPBA) and a hydrophilic functional monomer. The resulting hydrophilic Fe3O4@P(AAPBA-co-monomer) NPs exhibit an enhanced binding capacity toward glycoproteins by an additional functional monomer complementary to the surface presentation of the target protein. The effects of monomer ratio of AAPBA to hydrophilic comonomers on the binding of glycoproteins are systematically investigated. The morphology, structure, and composition of all the synthesized microspheres are characterized by transmission electron microscopy (TEM), X-ray powder diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and vibrating sample magnetometer (VSM). The hydrophilic Fe3O4@P(AAPBA-co-monomer) microspheres show an excellent performance in the separation of glycoproteins with high binding capacity; And strong magnetic response allows them to be easily separated from solution in the presence of an external magnetic field. Moreover, both synthetic Fe(3)O(4)gP(AAPBA) and copolymeric NPs show good adsorption to glycoproteins in physiological conditions (pH 7.4). The Fe3O4@P(AAPBA-co-monomer) NPs are successfully utilized to selectively capture and identify the low-abundance glycopeptides from the tryptic digest of horseradish peroxidase (HRP). In addition, the selective isolation and enrichment of glycoproteins from the egg white samples at physiological condition is obtained by Fe3O4@P(AAPBA-co-monomer) NPs as adsorbents

The number of essential proteins predicted by OGN, BC, CC, DC, EC, SC, CoEWC, SON, and LBCC.

<p>(a)-(f) show the results of these methods when select top 100 to 600 ranked proteins as candidate essential proteins.</p