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"Li, Junheng"
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Tiger woman on Wall Street : winning business strategies from Shanghai to New York and back
\"Powerful methods for global investing from a dual expert on Chinese and U.S. markets Tiger Woman on Wall Street is the remarkable story of how one woman has risen to the top of the traditionally male-dominated world of finance. Raised by \"tiger parents\" in China in the 1980s, when the Chinese economy was just starting to boom, Junh Li came to the United States to attend college and climbed her way up to the top with a relentless personal drive and a remarkable talent for investing and finance.Tiger Woman on Wall Street is both an autobiographical tell-all and a critical review of Chinese and American comparative cultures and economies. It gives international investors both the insight and the hard advice they need to navigate the increasing complexities of the global economy.Junheng Li runs the independent equity research firm, JL Warren Capital LLC, to advise institutional asset managers on investing in small- to mid-capitalization companies both in the U.S. and China. She was previously senior equity analyst at Aurarian Capital Management\"-- Provided by publisher.
Book
Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence
Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be transiently suppressed via transcranial electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained shortly after the end of the stimulation and can be phenomenologically predicted. Finally, we use feature-based statistical-learning and neurophysiological-modelling to show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the aberrant oscillations in the olivocerebellar loop, thus establishing its causal role. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may in the future represent a powerful neuromodulatory strategy to treat brain disorders.
Aberrant synchronous oscillations have been associated with numerous brain disorders, including essential tremor. The authors show that synchronous cerebellar activity can casually affect essential tremor and that its underlying mechanism may be related to the temporal coherence of the tremulous movement.
Journal Article
Biodegradable and flexible arterial-pulse sensor for the wireless monitoring of blood flow
The ability to monitor blood flow is critical to patient recovery and patient outcomes after complex reconstructive surgeries. Clinically available wired implantable monitoring technology requires careful fixation for accurate detection and needs to be removed after use. Here, we report the design of a pressure sensor, made entirely of biodegradable materials and based on fringe-field capacitor technology, for measuring arterial blood flow in both contact and non-contact modes. The sensor is operated wirelessly through inductive coupling, has minimal hysteresis, fast response times, excellent cycling stability, is highly robust, allows for easy mounting and eliminates the need for removal, thus reducing the risk of vessel trauma. We demonstrate the operation of the sensor with a custom-made artificial artery model and in vivo in rats. This technology may be advantageous in real-time post-operative monitoring of blood flow after reconstructive surgery.
A battery-free implantable pressure sensor made entirely of biodegradable materials and based on fringe-field capacitor technology can wirelessly measure arterial blood flow in live rats.
Journal Article
Coal and Gangue Detection Networks with Compact and High-Performance Design
The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant challenges in dense scenes, where label rewriting problems severely affect model performance, particularly when coal and gangue are closely distributed in conveyor belt images. This paper introduces CGDet (Coal and Gangue Detection), a novel compact convolutional neural network that addresses these challenges through two key innovations. First, we proposed an Object Distribution Density Measurement (ODDM) method to quantitatively analyze the distribution density of coal and gangue, enabling optimal selection of input and feature map resolutions to mitigate label rewriting issues. Second, we developed a Relative Resolution Object Scale Measurement (RROSM) method to assess object scales, guiding the design of a streamlined feature fusion structure that eliminates redundant components while maintaining detection accuracy. Experimental results demonstrate the effectiveness of our approach; CGDet achieved superior performance with AP50 and AR50 scores of 96.7% and 99.2% respectively, while reducing model parameters by 46.76%, computational cost by 47.94%, and inference time by 31.50% compared to traditional models. These improvements make CGDet particularly suitable for real-time coal and gangue sorting in underground mining environments, where computational resources are limited but high accuracy is essential. Our work provides a new perspective on designing compact yet high-performance object detection networks for dense scene applications.
Journal Article
A Preliminary Study on the “Hitchhiking” of Radionuclides on Microplastics: A New Threat to the Marine Environment from Compound Pollution
With the widespread use of plastic products globally, the issue of microplastics as environmental pollutants has become increasingly severe. Due to their small size, large surface area, and hydrophobic properties, microplastics are capable of adsorbing various pollutants, particularly radionuclides, which, in turn, can impact the stability of ecosystems. This laboratory study investigates the adsorption capacity of microplastics (PVC) for radionuclides (Ra-226, Cs-137, and K-40) under controlled conditions, examining the effects of spatial distribution and particle size. The laboratory experiment results indicate that the adsorption of Ra-226 by microplastics was significantly higher in the bottom water compared to the surface layer, with concentrations of 13.29 mBq/kg on microplastics mixed with the bottom water and 1.65 mBq/kg in the surface layer. The concentration of Cs-137 on microplastics mixed with the bottom water was 6.99 mBq/kg, while on microplastics mixed with the surface water, the concentration was 1.31 mBq/kg. In contrast, the adsorption of K-40 was lower, with concentrations of 2.1 mBq/kg and 0.35 mBq/kg on microplastics mixed with the bottom and surface water, respectively. Furthermore, microplastics with smaller particle sizes exhibited stronger adsorption capacities. The adsorption concentrations of Ra-226 and Cs-137 by 50 µm microplastics were 13.29 mBq/kg and 6.99 mBq/kg, respectively, while the concentrations for 100 µm and 150 µm particles decreased to 3.14 mBq/kg and 1.39 mBq/kg, and 2.2 mBq/kg and 0.35 mBq/kg, respectively. These findings suggest that the adsorption capacity of microplastics is significantly influenced by particle size and sediment depth, highlighting the potential risk of exacerbating the spread of radioactive pollutants in marine ecosystems.
Journal Article
Determination of the Accuracy of the Straight Bevel Gear Profiles by a Novel Optical Coaxial Multi-Ring Measurement Method
Straight bevel gears are widely used in mining equipment, ships, heavy industrial equipment, and other fields due to their high capacity and robust transmission. Accurate measurements are essential in order to determine the quality of bevel gears. We propose a method for measuring the accuracy of the top surface profile of the straight bevel gear teeth based on binocular visual technology, computer graphics, error theory, and statistical calculations. In our method, multiple measurement circles are established at equal intervals from the small end of the top surface of the gear tooth to the large end, and the coordinates of the intersection points of these circles with the tooth top edge lines of the gear teeth are extracted. The coordinates of these intersections are fitted to the top surface of the tooth based on NURBS surface theory. The surface profile error between the fitted top surface of the tooth and the designed surface is measured and determined based on the product use requirements, and if this is less than a given threshold, the product is acceptable. With a module of 5 and an eight-level precision, such as the straight bevel gear, the minimum surface profile error measured was −0.0026 mm. These results demonstrate that our method can be used to measure surface profile errors in the straight bevel gears, which will broaden the field of in-depth measurements for the straight bevel gears.
Journal Article
A coaxial multi-ring detection method for measuring the pitch and thickness accuracy of cylindrical gears
Performance of gear transmissions affects the performance of mechanical equipment. It is necessary to develop more reliable gear pitch and tooth thickness accuracy detection methods in order to evaluate gear transmission performance and detect gear pitch and tooth thickness accuracy more accurately. Based on the basic theory of gears, binocular vision technology, and statistical principles, a new method that measures gear pitch and tooth thickness using machine vision is proposed: coaxial multi-ring detection (MCD). There is no contact, no damage, and a high degree of efficiency with this method. Using this method, we are able to detect the pitch and thickness accuracy of each gear tooth multiple times within the tooth width range and in multiple directions perpendicular to the gear axis. We statistically analyze the measurement results to determine the gear’s most accurate detection results. The measurement method for gear machining accuracy is investigated using the coaxial multi-ring detection method. The statistical analysis of multiple measurement results is carried out, and the measurement results obtained are highly consistent with those of the gear detection center. In conclusion, the measurement results of this method are highly reliable, and they can be used as a reliable basis for evaluating gear transmission performance.
Journal Article
A Comparison of Dynamic SPECT Coronary Flow Reserve with TIMI Frame Count in the Treatment of Non-Obstructive Epicardial Coronary Patients
Background: Microvascular dysfunction in patients with non-obstructive epicardial coronary may aggravate patient's symptoms or lead to various clinical events. Objective: To investigate the correlation between dynamic single photon emission computed tomography (D-SPECT) derived coronary flow reserve (CFR) and TIMI frame count (TFC) in patients with non-obstructive epicardial coronary patients. Methods: Patients with suspected or known stable CAD who were recommended to undergo invasive coronary angiography were prospectively enrolled in this study. Those who had non-obstructive coronary received TIMI frame count (TFC) and D-SPECT. A cutoff value of >40 was defined as slow flow referred to TFC. Results: A total of 47 patients diagnosed with non-obstructive coronary were enrolled. The mean age of patients was 66.09 [+ or -] 8.36 years, and 46.8% were male. Dynamic SPECT derived coronary flow reserve (CFR) was significantly correlated with TIMI frame count in 3 epicardial coronary (LAD: r = -0.506, P = 0.0003; LCX: r= -0.532, P = 0.0001; RCA: r = -0.657, P < 0.0001). The sensitivity and specificity of CFR in identifying abnormal TIMI frame count < 40 was 100.0% and 57.6% in LAD, 62.5% and 87.0% in LCX, 83.9% and 75.0% in RCA, respectively. The optimal CFR cut-off values were 2.02, 2.47, and 1.96 among the three vessels. Conclusion: In patients with non-obstructive coronary, CFR derived from D-SPECT was strongly correlated with TFC. This study demonstrates that that CFR may be an alternative non-invasive method for identifying slow flow in non-obstructive coronary. Keywords: dynamic single photon emission computed tomography, thrombolysis in myocardial infarction frame count, slow flow, non-obstructive coronary, coronary flow reserve
Journal Article
Hierarchical Optimization-Based Control for Dynamic Loco-Manipulation on Humanoid Robots
Recent advancements in robotics have spurred vast interest in the development and investigation of practical applications of humanoid robots. To harness the full potential of these robots, particularly in terms of dynamic and robust motions, reliable motion control schemes are essential. This thesis presents the design, development, and implementation of hierarchical Model-Predictive Control (MPC)-based control strategies on humanoid robot Locomotion and Manipulation (Loco-manipulation), demonstrated on our in-house designed mini-humanoid robot series, Humanoids for Enhanced ConTrol and Open-source Research (HECTOR). The investigated control approaches delve into two major directions. First, we explore hierarchical optimization-based control frameworks for dynamic motion generation and control on bipedal humanoid robots, with a focus on adaptive-frequency strategies at both the MPC and periodic gait levels with careful model design, to achieve adaptive discrete terrain locomotion and robust push-recovery. Next, we dive into the hierarchical optimization-based control frameworks with Reduced-order Modeling (ROM) for humanoid loco-manipulation. We examine the progression from Single Rigid-body Models (SRBM) to kino-dynamic representations that account for both the robot and the manipulated objects, to push the limit of efficient whole-body loco-manipulation. Lastly, combining the lessons learned from the two directions of investigation, we extend the adaptive frequency feature in hierarchical optimization-based control through data-driven augmentations to model the complex gait frequency decision-making process for humanoid loco-manipulation. We provide thorough numerical and hardware validations and examinations of the proposed hierarchical optimization-based control frameworks.
Dissertation