Mountainous city

My thesis studies how glaciation and vegetation systems define the landscapes of Mount Gongga, and asks how these and their modes of relation and zonation might provide a model for more sustainable and fully adaptable structures to support urbanism. Using mountain systems as the metaphor, I proposed a mountainous city model focusing on efficient urban ecosystems and spontaneous human activities. By the methods of deconstructing and mimetic modeling, they triggered a series of rethinking about the formation and relation of urban spaces. The mountainous city has four distinctive vertical layered zones, which integrated with hydrological and ecological systems and vertical ductile matrix

High resolution object detection algorithm based on parallel

With the continuous development and improvement of the aviation information warfare system, target detection technology has also become a key part of the airborne system to perceive the environment. Traditional target detection technology is now difficult to meet the requirements of high precision and high real-time performance in airborne scenarios. With the continuous development of deep learning technology, neural network has become the latest method to deal with object detection task, which greatly improves its accuracy and processing efficiency. However, due to the different targets detected in airborne scenes, the scale of data needed to be processed by using neural networks to process target detection tasks also expands dramatically, and the computing resources provided by single chip are already difficult to meet the needs of target detection algorithm execution in airborne environment. This paper proposes a set of high-precision target detection algorithms based on parallelism, which greatly improves the precision and processing efficiency of the target detection algorithm in airborne scenarios

BIOMECHANICS TRENDS IN GRIP AND PINCH STRENGTH IN TWO AGE GROUPS OF CHINESE

Hands are one of the most complex and useful systems of the human body with grip and pinch strength being the most important biomechanics factor to assess the hand functions. Many diseases including malfunction of nervous systems and osteoarthritis may lead to weakness or abnormality of hand grip and pinch strength. The measurement of grip and pinch strength could be used to assess the degree of injury degree, treatment effect and recovery, thus making it necessary to build a biomechanics normative database of grip and pinch strength for use by researchers (such as doctors and sport researchers) from different fields (i.e. professional injury assessment, ergonomics and product design). The database is expected to provide detailed features of grip and pinch strength of the Chinese people

Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network

It is a challenging problem to predict trends of futures prices with traditional econometric models as one needs to consider not only futures' historical data but also correlations among different futures. Spatial-temporal graph neural networks (STGNNs) have great advantages in dealing with such kind of spatial-temporal data. However, we cannot directly apply STGNNs to high-frequency future data because future investors have to consider both the long-term and short-term characteristics when doing decision-making. To capture both the long-term and short-term features, we exploit more label information by designing four heterogeneous tasks: price regression, price moving average regression, price gap regression (within a short interval), and change-point detection, which involve both long-term and short-term scenes. To make full use of these labels, we train our model in a continual manner. Traditional continual GNNs define the gradient of prices as the parameter important to overcome catastrophic forgetting (CF). Unfortunately, the losses of the four heterogeneous tasks lie in different spaces. Hence it is improper to calculate the parameter importance with their losses. We propose to calculate parameter importance with mutual information between original observations and the extracted features. The empirical results based on 49 commodity futures demonstrate that our model has higher prediction performance on capturing long-term or short-term dynamic change

Techno-economic and environmental evaluation of the production of biodiesel from rice-straw in China

Rice straw (RS) is the residue obtained during the rice processing process, and is recognized as one of the most abundant biomass resources in the world. Approximately 800 million to 1 billion tons of rice straw are produced globally every year, and most of them are considered general waste and typically end up in landfills or incineration. This approach wastes resources and can also lead to environmental pollution. In the current study, the RS was used as the source of biodiesel production and a comprehensive process model of the RS valorization process was developed to evaluate the energy flow, production efficiency, production costs, and greenhouse gas emissions in Hunan Province, China. The evaluation results showed that the energy efficiency of biodiesel production from rice straw and the overall energy efficiency of the rice straw valorization process are reported as 52.1% and 56.1%, respectively. The minimum selling price of biodiesel, which is CNY 3.03/kg, is considerably lower than the current market prices for similar products in China. The largest proportion of the production cost of biodiesel is the cost of natural gas, followed by utilities, capital, transportation, plant maintenance and overheads, consumables, labor, and waste disposal. For the current RS valorization plant with a 5000 kg/h RS feed rate, the investment payback times are 8.9 yr and 7.1 yr when the biodiesel is sold at the lowest (CNY 4/kg) and highest (CNY 4.6/kg) market price, respectively. Environmental analysis shows that the greenhouse gas emissions intensity of biodiesel production is 75.8 g CO2eq/MJ, which is only about 52% of traditional fossil diesel and indicating that biodiesel is an environmentally friendly energy source

Potential bioactive compounds and mechanisms of Fibraurea recisa Pierre for the treatment of Alzheimer’s disease analyzed by network pharmacology and molecular docking prediction

IntroductionHeat-clearing and detoxifying Chinese medicines have been documented to have anti-Alzheimer’s disease (AD) activities according to the accumulated clinical experience and pharmacological research results in recent decades. In this study, Fibraurea recisa Pierre (FRP), the classic type of Heat-clearing and detoxifying Chinese medicine, was selected as the object of research.Methods12 components with anti-AD activities were identified in FRP by a variety of methods, including silica gel column chromatography, multiple databases, and literature searches. Then, network pharmacology and molecular docking were adopted to systematically study the potential anti-AD mechanism of these compounds. Consequently, it was found that these 12 compounds could act on 235 anti-AD targets, of which AKT and other targets were the core targets. Meanwhile, among these 235 targets, 71 targets were identified to be significantly correlated with the pathology of amyloid beta (Aβ) and Tau.Results and discussionIn view of the analysis results of the network of active ingredients and targets, it was observed that palmatine, berberine, and other alkaloids in FRP were the key active ingredients for the treatment of AD. Further, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis revealed that the neuroactive ligand-receptor interaction pathway and PI3K-Akt signaling pathway were the most significant signaling pathways for FRP to play an anti-AD role. Findings in our study suggest that multiple primary active ingredients in FRP can play a multitarget anti-AD effect by regulating key physiological processes such as neurotransmitter transmission and anti-inflammation. Besides, key ingredients such as palmatine and berberine in FRP are expected to be excellent leading compounds of multitarget anti-AD drugs

Designing Low-PAPR Waveform for OFDM-Based RadCom Systems

This paper is focused on the fusion of radar and wireless communication, called RadCom, which has been extensively studied in recent years for future intelligent transportation systems. We propose a new waveform design algorithm for reducing peak-to-average power ratio (PAPR) in OFDM-based RadCom systems. We consider a flexible and generic RadCom structure in which a number of non-contiguous sub-bands for data transmission are located within a large contiguous spectrum band for radar detection/sensing. New RadCom waveforms with low PAPR are obtained by carrying out optimization over those subcarriers which are complementary to the communication bands. As an application of the majorization-minimization (MM) optimization method, our major contribution is an l -norm cyclic algorithm which is capable of efficiently reducing the maximum PAPR of RadCom waveforms. We show by numerical simulation results that significant performance enhancements can be achieved compared to OFDM RadCom waveforms from legacy approaches