The National Health and Nutrition Examination Survey (NHANES) studies the nutritional and health status over the whole U.S. population with comprehensive physical examinations and questionnaires. However, survey data ...
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BACKGROUND:Ageing is a complex and multi-dimensional process that manifests heterogeneities across different organs/systems, individuals and countries. We aimed to delineate the life-course percentile curves and estab...
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BACKGROUND:Ageing is a complex and multi-dimensional process that manifests heterogeneities across different organs/systems, individuals and countries. We aimed to delineate the life-course percentile curves and establish the normative values of multi-systemic (e.g., muscle-skeletal, brain, cardiovascular and pulmonary) ageing metrics for people under distinct sociodemographic contexts (i.e., sex, income and education).
METHODS:Three national datasets, the UKB (the United Kingdom), the NHANES (the United States) and the CHARLS (China) were utilized for the analyses. We selected 14 ageing metrics (e.g., body mass index, grip strength, fat-free mass index, bone mineral content [BMC], bone mineral density [BMD], diastolic blood pressure, cognitive function and frailty index_Lab) that represent the functions of different organs/systems and plotted their sex-, educational- and income-specific percentile curves utilizing the GMALSS model. We also estimated the age-specific normative values for each ageing metric in distinct sociodemographic contexts.
RESULTS:The functions of all metrics, except for cognitive function, manifested a progressive decline or maintained stability after adulthood (20s), especially after middle age (40s-50s). The cognitive function showed an evident decline in old age (70s-75s) (e.g., in the CHARLS: the median [IQR] cognitive function scores were 11.6 [9.1, 13.8], 10.3 [7.5, 12.9], 8.3 [5.5, 11.0] at the ages of 60, 70 and 80 for males, respectively). In the stratified analyses, males and females manifested disparities in percentile curves of ageing metrics involving the muscle-skeletal and cardiovascular systems. For instance, BMC and BMD manifested an evident decline after middle age in females, whereas they showed a slow decline after adulthood in males. Notably, we observed substantial income and educational disparities in percentile curves of several ageing metrics within Chinese participants: the 'low-income' and 'low-education' subgroups m
The stock market volatility will be affected by many economic factors. On the contrary, the stock market will also influence the formulation of the economy and policy. It will be very useful if a qualitative structure...
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The stock market volatility will be affected by many economic factors. On the contrary, the stock market will also influence the formulation of the economy and policy. It will be very useful if a qualitative structure can be computed to figure out the corresponding level between the stock market with these factors. The dynamical Bayesian factor graph is a suitable method to solve this problem. We choose a series of economic factors to represent the possible influence from macroeconomy, and macro-policy. We have found that the changes in the stock market during the bull market will affect the macroeconomy factors, and this effect will continue to weaken as the stock market moves to the bear market.
The performance of three typical big data processing platform: Hadoop, Spark and dataMPI are compared based on different parallel clustering algorithms: parallel K-means, parallel fuzzy K-means and parallel Canopy. Ex...
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The performance of three typical big data processing platform: Hadoop, Spark and dataMPI are compared based on different parallel clustering algorithms: parallel K-means, parallel fuzzy K-means and parallel Canopy. Experiments are performed on different text as well as numeric dataset and clusters of different scale. The results show that: (1) for the same data set, when the memory of each node is 4GB, dataMPI can achieve about 60% performance improvement compared with Hadoop, and can achieve about 32% performance improvement compared with Spark; (2) in order to obtain a high clustering performance, a cluster with 6 nodes and 6GB memory of each node should be selected.
We consider high-dimensional measurement errors with high-frequency data. Our objective is on recovering the high-dimensional cross-sectional covariance matrix of the random errors with optimality. In this problem, no...
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We compare the performance of three parallel clustering algorithms:Canopy,K-means and fuzzy K-means in real cluster *** constructing cluster platform of different scale,we compare these algorithms from three metrics:r...
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ISBN:
(纸本)9781509036202
We compare the performance of three parallel clustering algorithms:Canopy,K-means and fuzzy K-means in real cluster *** constructing cluster platform of different scale,we compare these algorithms from three metrics:run time,speedup and *** results show that:(1) if both the data set and the number of nodes in the cluster are the same,both the runtime and the sizeup of Canopy is the smallest,and the speedup of Canopy is the largest among these three algorithms;(2) when the number of nodes is larger than 2,for each algorithm,the difference between the sizeup on different data set becomes more and more smaller as the number of nodes increases;at the same time,when the number of nodes is larger than 2 and the data set is the same,the sizeup of Canopy has the maximum decrease with the increase of the number of nodes;(3) when the number of nodes keeps the same,the difference between the sizeup of these three algorithms becomes more and more apparent as the size of the data set increases.
The environmental security of agriculture is closely related to human beings. Analytical training of agricultural environmental data, forecasting its development trend, has positive significance for the protection of ...
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The environmental security of agriculture is closely related to human beings. Analytical training of agricultural environmental data, forecasting its development trend, has positive significance for the protection of the safety of agricultural products. This paper proposes an agricultural environment prediction model based on deep learning LSTM (Long Short-Term Memory). By analyzing the agricultural environment parameters of the current period, the environmental parameters of the next moment can be predicted to achieve the purpose of early warning. The experimental results show that the model’s prediction results have little deviation from the actual values; on this basis, the LSTM model is optimized to replace LSTM with GRU(Gated Recurrent Unit), and the model is more effective.
UAV is an unmanned aerial vehicle controlled by a remote radio signal or a trajectory planning software carried itself. It is widely used in military, civil and scientific research fields. However, due to the lack of ...
UAV is an unmanned aerial vehicle controlled by a remote radio signal or a trajectory planning software carried itself. It is widely used in military, civil and scientific research fields. However, due to the lack of real-time decision-making ability, the UAV has high fault rate. The flight quality assessment of UAV and the construction of fault prediction model can be used for debugging and fault-removing to customer's aircraft, and also to increase the added value of the civilian UAV products. Before building a fault prediction model, a very important step is to identify the pattern of sampled data. For each group of flight data, the efficiency and accuracy rate of manual quality evaluation and fault identification are not acceptable. Based on the UAV flight data accumulated in the big data platform of an UAV production company in Shenyang, Liaoning Province of China, this paper proposes a semi-supervised clustering technique to do automatic pattern recognition for the sampling points. According to the characteristics of UAV flight data, two different methods are designed to choose initial centroids. Meanwhile, we use the existing normal flight data to train distance thresholds to combine some clusters to eliminate the resulting error clustering. Real flight data or flight test data with manually added labels are used to run the proposed algorithms to verify the recognition results. The experimental results show that the proposed methods greatly improve the efficiency and accuracy of adding precise labels to the historical flight data and play a role in assisting the manual recognition of sampling points while strengthening the management and statistics.
In high dimensional analysis, effects of explanatory variables on responses sometimes rely on certain exposure variables, such as time or environmental factors. In this paper, to characterize the importance of each pr...
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Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (...
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