BIOMECHANICS ANALYSIS OF "BRUSH KNEE AND TWIST STEPS" MOVEMENT IN TAI CHI

The purpose of this study was to analyze the biomechanical characteristics of a typical Tai Chi (TC) movement - "brush knee and twist steps". A 3-Dimensional fixed video filming method was used for data collection. Three elite professional athletes of TC performed this movement three times and the best one was selected for analysis. The kinematics data included the distance of hands and feet, the angle between the feet, the joint angles of wrist, elbow and knee, the 3-dimensional displacement, velocity and acceleration of CG. The analysis showed that TC exercise could enhance the lower extremity muscular strength movement coordination, and the neuromuscular control for posture and balance

Compressed glassy carbon: An ultrastrong and elastic interpenetrating graphene network

Carbon’s unique ability to have both sp2 and sp3 bonding states gives rise to a range of physical attributes, including excellent mechanical and electrical properties. We show that a series of lightweight, ultrastrong, hard, elastic, and conductive carbons are recovered after compressing sp2-hybridized glassy carbon at various temperatures. Compression induces the local buckling of graphene sheets through sp3 nodes to form interpenetrating graphene networks with long-range disorder and short-range order on the nanometer scale. The compressed glassy carbons have extraordinary specific compressive strengths—more than two times that of commonly used ceramics—and simultaneously exhibit robust elastic recovery in response to local deformations. This type of carbon is an optimal ultralight, ultrastrong material for a wide range of multifunctional applications, and the synthesis methodology demonstrates potential to access entirely new metastable materials with exceptional properties

Animal Migration Patterns Extraction Based on Atrous-Gated CNN Deep Learning Model

Weather radar data can capture large-scale bird migration information, helping solve a series of migratory ecological problems. However, extracting and identifying bird information from weather radar data remains one of the challenges of radar aeroecology. In recent years, deep learning was applied to the field of radar data processing and proved to be an effective strategy. This paper describes a deep learning method for extracting biological target echoes from weather radar images. This model uses a two-stream CNN (Atrous-Gated CNN) architecture to generate fine-scale predictions by combining the key modules such as squeeze-and-excitation (SE), and atrous spatial pyramid pooling (ASPP). The SE block can enhance the attention on the feature map, while ASPP block can expand the receptive field, helping the network understand the global shape information. The experiments show that in the typical historical data of China next generation weather radar (CINRAD), the precision of the network in identifying biological targets reaches up to 99.6%. Our network can cope with complex weather conditions, realizing long-term and automated monitoring of weather radar data to extract biological target information and provide feasible technical support for bird migration research

Suppression of experimental abdominal aortic aneurysms in mice by treatment with pyrrolidine dithiocarbamate, an antioxidant inhibitor of nuclear factor-κB

ObjectiveProinflammatory cytokines and matrix metalloproteinases (MMPs) are prominent mediators of the connective tissue destruction that characterizes abdominal aortic aneurysms (AAAs), and nuclear factor (NF)-κB is a cytokine-responsive transcription factor that promotes macrophage MMP expression. The purpose of this study was to determine whether aneurysmal degeneration is influenced by pyrrolidine dithiocarbamate (PDTC), a pharmacologic inhibitor of NF-κB.MethodsAdult male C57BL/6 mice underwent transient elastase perfusion of the abdominal aorta to induce the development of AAAs. Animals were treated every 48 hours by intraperitoneal injection with either saline (n = 34) or PDTC 20 mg/kg (n = 49). Aortic diameter (AD) measurements were used to determine the extent of aortic dilatation before and immediately after elastase perfusion and again at day 14.ResultsAll saline-treated mice developed AAAs associated with mononuclear inflammation and destruction of medial elastin (overall increase in AD, mean ± SEM, 169.1% ± 7.5%). In contrast, the incidence of AAAs was only 63% in PDTC-treated mice, with a reduction in the overall increase in AD to 109.8% ± 4.2% (P < .0001 vs saline), decreased inflammation, and structural preservation of aortic wall connective tissue. Although aneurysm development in saline-treated mice was associated with a marked increase in aortic tissue NF-κB and activator protein 1 DNA-binding activities, both activities were substantially reduced in PDTC-treated animals. PDTC-treated mice also exhibited significantly lower serum and aortic wall concentrations of interleukin 1β and interleukin 6, as well as lower amounts of aortic wall MMP-9, as compared with saline-treated controls.ConclusionsTreatment with PDTC inhibits elastase-induced experimental AAAs in the mouse, along with suppression of aortic wall NF-κB and activator protein 1 transcription factor activities, reduced expression of proinflammatory cytokines, and suppression of MMP-9. NF-κB is therefore a potentially important therapeutic target for the suppression of aneurysmal degeneration.Clinical relevanceDevelopment and progression of human AAAs is associated with inflammation and enzymatic degradation of connective tissue proteins. MMP-9 is one of the enzymes involved in aneurysm disease, and its production may be induced in part by activation of the transcription factor NF-κB. In this mouse model, treatment with pyrrolidine dithiocarbamate (a pharmacologic inhibitor of NF-κB) acted to suppress MMP-9 and aneurysm development. It is hoped that treatment strategies that target NF-κB may eventually be shown to suppress the growth of small aortic aneurysms in patients

Sorting Data via a Look-Up-Table Neural Network and Self-Regulating Index

The so-called learned sorting, which was first proposed by Google, achieves data sorting by predicting the placement positions of unsorted data elements in a sorted sequence based on machine learning models. Learned sorting pioneers a new generation of sorting algorithms and shows a great potential because of a theoretical time complexity ON and easy access to hardware-driven accelerating approaches. However, learned sorting has two problems: controlling the monotonicity and boundedness of the predicted placement positions and dealing with placement conflicts of repetitive elements. In this paper, a new learned sorting algorithm named LS is proposed. We integrate a back propagation neural network with the technique of look-up-table in LS to guarantee the monotonicity and boundedness of the predicted placement positions. We design a data structure called the self-regulating index in LS to tentatively store and duly update placement positions for eliminating potential placement conflicts. Results of three controlled experiments demonstrate that LS can effectively control the monotonicity and boundedness, achieve a better time consumption than quick sort and Google’s learned sorting, and present an excellent stability when the data size or the number of repetitive elements increases

Evaluation of short-term streamflow prediction methods in Urban river basins

Efficient and accurate streamflow predictions are important for urban water management. Data-driven models, especially neural network (NN) models can predict streamflow fast, while the results are uncertain in some complex river systems. Physically based models can reveal the underlying physics, but it is relatively slow and computationally costly. This work focuses on evaluating the reliability of three NN models (artificial neural networks (ANN), long short-term memory networks (LSTM), adaptive neuro-fuzzy inference system (ANFIS)) and one physically based model (SOBEK) in terms of efficiency and accuracy for average and peak streamflow simulation. All the models are applied for a tidal river and a mountainous river in Shenzhen. The results show that, the ANN model calculates fastest since the hidden layer's structure is simple. The LSTM model is reliable in average streamflow simulation in tidal river with the lowest bias while the ANFIS model has the best accuracy for peak streamflow simulation. Furthermore, the SOBEK model shows reliability in simulating average and peak streamflow in mountainous river due to its ability to capture uneven spatial rainfall in the area. Overall, the results indicate that the LSTM model can be a helpful supplementary to physically based models in streamflow simulation of complex urban river systems, by giving fast streamflow predictions with usually acceptable accuracy. Our results can provide helpful information for hydrological engineers in the application of flooding early warning and emergency preparedness in the context of flooding risk management.</p