Machine Learning Methods in Real-World Studies of Cardiovascular Disease

Objective: Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application. Methods: This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD. Conclusion: ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field

Schallamach waves and friction-induced self-oscillations in a prototypical belt drive

The high adhesive friction between an elastomer and its counterpart generally impedes sliding in the accepted sense. Instead, displacement is accommodated by Schallamach waves of detachment, which are surface wrinkles that move across the contact zone. However, this has received little research attention in belt drive systems; sliding-based friction models are invariably employed for belt drive mechanics studies. In light of this discrepancy, the rolling contact mechanics in a simple flat belt drive will be explored by considering Schallamach waves of detachment, with particular focus as follows. 1) A thorough understanding of the mechanism of detachment events and friction generated at the belt-pulley interface will be developed. 2) The characteristics of detachment wave-induced oscillations, including the belt and pulley oscillations, will be studied, focusing on their dependence on driving speed, loading conditions and the system inertia. Also, it is of interest to examine these waves and the global system oscillations in a fully-coupled manner such that 3) downstream effects of detachment events couple to the dynamic response of the belt drive system, and 4) the system dynamics couple to the generation of detachment waves. Further, the research intends 5) to propose a rolling friction model for elastomers, capable of computing mechanical energy losses associated with the contact instabilities. 6) A novel surface design will be examined to check the ability to influence, control and tailor the presence of detachment wave-induced oscillations in belt drives. 7) Belts incorporating tensile cords, which are more comparable to belts used in industry, will be examined to find whether detachment waves are universal in belt drive systems. (Co-advisors: Dr. Michael Varenberg and Dr. Michael J. Leamy)Ph.D

Remote Sensing Imagery Super Resolution Based on Adaptive Multi-Scale Feature Fusion Network

Due to increasingly complex factors of image degradation, inferring high-frequency details of remote sensing imagery is more difficult compared to ordinary digital photos. This paper proposes an adaptive multi-scale feature fusion network (AMFFN) for remote sensing image super-resolution. Firstly, the features are extracted from the original low-resolution image. Then several adaptive multi-scale feature extraction (AMFE) modules, the squeeze-and-excited and adaptive gating mechanisms are adopted for feature extraction and fusion. Finally, the sub-pixel convolution method is used to reconstruct the high-resolution image. Experiments are performed on three datasets, the key characteristics, such as the number of AMFEs and the gating connection way are studied, and super-resolution of remote sensing imagery of different scale factors are qualitatively and quantitatively analyzed. The results show that our method outperforms the classic methods, such as Super-Resolution Convolutional Neural Network(SRCNN), Efficient Sub-Pixel Convolutional Network (ESPCN), and multi-scale residual CNN(MSRN)

High-Resolution Optical Remote Sensing Image Registration via Reweighted Random Walk Based Hyper-Graph Matching

High-resolution optical remote sensing image registration is still a challenging task due to non-linearity in the intensity differences and geometric distortion. In this paper, an efficient method utilizing a hyper-graph matching algorithm is proposed, which can simultaneously use the high-order structure information and radiometric information, to obtain thousands of feature point pairs for accurate image registration. The method mainly consists of the following steps: firstly, initial matching by Uniform Robust Scale-Invariant Feature Transform (UR-SIFT) is carried out in the highest pyramid image level to derive the approximate geometric relationship between the images; secondly, two-stage point matching is performed to find the matches, that is, a rotation and scale invariant area-based matching method is used to derive matching candidates for each feature point and an efficient hyper-graph matching algorithm is applied to find the best match for each feature point; thirdly, a local quadratic polynomial constraint framework is used to eliminate match outliers; finally, the above process is iterated until finishing the matching in the original image. Then, the obtained correspondences are used to perform the image registration. The effectiveness of the proposed method is tested with six pairs of high-resolution optical images, covering different landscape types—such as mountain area, urban, suburb, and flat land—and registration accuracy of sub-pixel level is obtained. The experiments show that the proposed method outperforms the conventional matching algorithms such as SURF, AKAZE, ORB, BRISK, and FAST in terms of total number of correct matches and matching precision

Energy Revolution and Security Guarantee of China’s Energy Economy

Energy is the blood for economic operation. Energy structure adjustment may significantly impact the macroeconomic operation of a country. This study quantitatively estimates the effect of energy structure adjustment on energy prices and further evaluates the impact of the resulted price changes on macroeconomic operations using an econometric model and statistical methods. The results indicate that 1% increase of the proportion of natural gas consumption in the total energy consumption raises energy prices by 0.8% while 1% increase of the coal consumption ratio lowers energy prices by 0.2%. Moreover, technological progress, energy investment growth, increased marketization, and energy efficiency improvement are conducive to reducing energy prices. We conclude that China’s energy revolution is less likely to affect the security of the energy economy and the energy price rise remains within the acceptable range of national economy and energy consumption. To better ensure the security of energy economy in the process of China’s energy revolution, we propose that China should focus on technology innovation and deepen the reforms in the energy market to improve the competitiveness of the energy market. These measures are beneficial for mitigating the energy price increase pressure from energy revolution

In situ sensing physiological properties of biological tissues using wireless miniature soft robots

Implanted electronic sensors, compared with conventional medical imaging, allow monitoring of advanced physiological properties of soft biological tissues continuously, such as adhesion, pH, viscoelasticity, and biomarkers for disease diagnosis. However, they are typically invasive, requiring being deployed by surgery, and frequently cause inflammation. Here we propose a minimally invasive method of using wireless miniature soft robots to in situ sense the physiological properties of tissues. By controlling robot-tissue interaction using external magnetic fields, visualized by medical imaging, we can recover tissue properties precisely from the robot shape and magnetic fields. We demonstrate that the robot can traverse tissues with multimodal locomotion and sense the adhesion, pH, and viscoelasticity on porcine and mice gastrointestinal tissues ex vivo, tracked by x-ray or ultrasound imaging. With the unprecedented capability of sensing tissue physiological properties with minimal invasion and high resolution deep inside our body, this technology can potentially enable critical applications in both basic research and clinical practice.ISSN:2375-254

Wireless soft millirobots for climbing three-dimensional surfaces in confined spaces

Wireless soft-bodied robots at the millimeter scale allow traversing very confined unstructured terrains with minimal invasion and safely interacting with the surrounding environment. However, existing untethered soft millirobots still lack the ability of climbing, reversible controlled surface adhesion, and long-term retention on unstructured three-dimensional (3D) surfaces, limiting their use in biomedical and environmental applications. Here, we report a fundamental peeling-and-loading mechanism to allow untethered soft-bodied robots to climb 3D surfaces by using both the soft-body deformation and whole-body motion of the robot under external magnetic fields. This generic mechanism is implemented with different adhesive robot footpad designs, allowing vertical and inverted surface climbing on diverse 3D surfaces with complex geometries and different surface properties. With the unique robot footpad designs that integrate microstructured adhesives and tough bioadhesives, the soft climbing robot could achieve controllable adhesion and friction to climb 3D soft and wet surfaces including porcine tissues, which paves the way for future environmental inspection and minimally invasive medicine applications.ISSN:2375-254