A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction

While linear mixed model (LMM) has shown a competitive performance in correcting spurious associations raised by population stratification, family structures, and cryptic relatedness, more challenges are still to be addressed regarding the complex structure of genotypic and phenotypic data. For example, geneticists have discovered that some clusters of phenotypes are more co-expressed than others. Hence, a joint analysis that can utilize such relatedness information in a heterogeneous data set is crucial for genetic modeling. We proposed the sparse graph-structured linear mixed model (sGLMM) that can incorporate the relatedness information from traits in a dataset with confounding correction. Our method is capable of uncovering the genetic associations of a large number of phenotypes together while considering the relatedness of these phenotypes. Through extensive simulation experiments, we show that the proposed model outperforms other existing approaches and can model correlation from both population structure and shared signals. Further, we validate the effectiveness of sGLMM in the real-world genomic dataset on two different species from plants and humans. In Arabidopsis thaliana data, sGLMM behaves better than all other baseline models for 63.4% traits. We also discuss the potential causal genetic variation of Human Alzheimer's disease discovered by our model and justify some of the most important genetic loci.Comment: Code available at https://github.com/YeWenting/sGLM

Implementation and Validation of non-semantic Out-of-Distribution Detection on Image Data in Manufacturing

Small changes in the production environment can have a negative impact on the performance of machine learning models. This study investigates the feasibility of various methods for detecting non-semantic Out-of-Distribution (OOD) cases in input images, which could be caused by hardware-side malfunctions, such as a defective camera flash. For this purpose, we design four experiments based on a real-world computer vision use case to simulate hardware problems that may occur in manufacturing and verify the performance of the various methods for detecting OOD cases. Furthermore, we explore the optimal sample size of input data to ensure that OOD cases can be found efficiently and successfully. The experimental results show that the tested methods can effectively and correctly detect the presence of non-semantic OOD data. The next step is to focus on securing ML models to identify malignant OOD cases, which negatively affects the performance of deep learning models

Elastic loading enhanced NH3 sensing for surface acoustic wave sensor with highly porous nitrogen doped diamond like carbon film

We proposed a surface acoustic wave (SAW) NH3 gas sensor based on nitrogen doped diamond like carbon (N-DLC) film. The N-DLC film, prepared using a microwave electron cyclotron resonance plasma chemical vapor deposition (ECR-PECVD) method, is highly porous and physically and chemically stable, and have active polar groups on its surface, which can selectively absorb polar NH3 gas molecules. These features of the film lead to the high sensitivity, low noise and excellent stability of the sensor. The sensor can achieve capabilities of in-situ monitoring NH3 in a concentration range from 100 ppb to 100 ppm with fast response (∼5 s) and recovery (∼29 s) at room temperature. The NH3 sensing mechanism is attributed to the decreased porosity of the N-DLC film caused by adsorbed NH3 molecules on its polar groups, which leads an increase of the elastic modulus of the film

Preferred nanocrystalline configurations in ternary and multicomponent alloys

In nanocrystalline alloys, a range of configurations c an have low energies when solute atoms have favorable interactions with interfaces. Whereas binary nanostructured alloys have been well studied, here we lay groundwork for the computational thermodynamic exploration of alloy configurations in multicomponen t nanocrystalline alloys. Multicomponent nanostructured systems are shown to occupy a vast space, with many topological possibilities not accessible in binary systems, and where the large majority of interesting configurations will be missed by a regular solution approximation. We explore one interesting ternary case in which the first alloying element stabilizes grain boundaries, and the second forms nano-sized precipitates.United States. Army Research Office (grant W911NF-14-1-0539)Massachusetts Institute of Technology. Institute for Soldier NanotechnologiesNational Defense Science and Engineering Graduate (NDSEG) Fellowshi

Research on dynamic characteristics and identification method of local defect on the roll surface

Local defects are generally produced on the surfaces of roll during long-term work, which not only causes abnormal vibration of the roll mill but also affects the quality of the produced steel strips. In particular, identifying the defects on a well-lubricated roll surface is a challenge. Therefore, a time-varying oil film stiffness model is proposed based on the elastohydrodynamic lubrication theory. A Sendzimir twenty-high roll mill model was developed and combined with the time-varying oil film stiffness model to analyse the vibration characteristics of the roll mill. Simultaneously, a new method for real-time identification of the defect sizes during the rolling process was proposed. Agreement between the simulated and experimental results was used to validate the effectiveness of the proposed model. The changes to the oil film stiffness and roll mill vibration characteristics for different defect sizes on the roll surface are thus analyzed to provide theoretical support for the identification of the local defects

Properties Analysis of Oil Shale Waste as Partial Aggregate Replacement in Open Grade Friction Course

Open graded friction course (OGFC) is a high permeable mixture used to reduce noise, improve friction. However, limitations with the use of OGFC are due to the relatively low strength and stiffness. Therefore, investigating environmental and economic benefits, as well as service life of OGFC technology is the future of the pavement. In this study, a new modified OGFC (SM-OGFC) was prepared by replacing the fine aggregate below 4.75 mm in OGFC with the oil shale waste (OSW), and the silane coupling agent modifier was used to assist modification. The preparation process of SM-OGFC was optimized by central composite design, to obtain an SM-OGFC with the best mechanical properties. The Marshall test, rutting test, −15 °C splitting test, −10 °C beam bending test, immersion Marshall test, spring-thawing stability test, Cantabro test and permeability test were conducted to evaluate the properties of SM-OGFC. The results prove that SM-OGFC has excellent overall performance in comparison with OGFC and styrene-butadiene-styrene (SBS) modified OGFC. Furthermore, Scanning Electron Microscopy (SEM) observation illustrates that the unique laminar columnar connected structure and cell-like structure antennae of OSW could be the main reasons why SM-OGFC obtained excellent performance. Furthermore, economic analysis indicated that the SM-OGFC mixture had higher cost effectiveness