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Effect of Sensorimotor Training on Balance and Activity of Daily Living of Home-based Rehabilitation in Patients with Stroke
Published 2016-08-01“…Objective:To obtain the popularization of this appropriate training program from hospital to patients'family, we would evaluate the changes of the balance and activity of daily living (ADL) of home-based rehabilitation in patients with stroke through sensorimotor training program performed by either the therapists or patients'carers.Methods:According to the random number table 40 discharged patients with stroke were randomly assigned to experimental group (the carers group) and control group (the therapists group), and 20 patients in each group. …”
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RESEARCH ON VARIABLE GRADIENT THIN-WALLED ENERGY ABSORBING COMPONENT OF SCOUR-PROOF HYDRAULIC SUPPORT
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Multi-query based key node mining algorithm for social networks
Published 2024-02-01“…Mining key nodes in complex networks has been a hotly debated topic as it played an important role in solving real-world problems.However, the existing key node mining algorithms focused on finding key nodes from a global perspective.This approach became problematic for large-scale social networks due to the unacceptable storage and computing resource overhead and the inability to utilize known query node information.A key node mining algorithm based on multiple query nodes was proposed to address the issue of key suspect mining.In this method, the known suspects were treated as query nodes, and the local topology was extracted.By calculating the critical degree of non-query nodes in the local topology, nodes with higher critical degrees were selected for recommendation.Aiming to overcome the high computational complexity of key node mining and the difficulty of effectively utilizing known query node information in existing methods, a two-stage key node mining algorithm based on multi-query was proposed to integrate the local topology information and the global node aggregation feature information of multiple query nodes.It reduced the calculation range from global to local and quantified the criticality of related nodes.Specifically, the local topology of multiple query nodes was obtained using the random walk algorithm with restart strategy.An unsupervised graph neural network model was constructed based on the graphsage model to obtain the embedding vector of nodes.The model combined the unique characteristics of nodes with the aggregation characteristics of neighbors to generate the embedding vector, providing input for similarity calculations in the algorithm framework.Finally, the criticality of nodes in the local topology was measured based on their similarity to the features of the query nodes.Experimental results demonstrated that the proposed algorithm outperformed traditional key node mining algorithms in terms of time efficiency and result effectiveness.…”
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Analysis and Research of Asymmetric Helical Gear Oil Injection Lubrication Considering Convective Heat Transfer Coefficient
Published 2022-07-01“…The computational fluid dynamic （CFD） method is used to study the two injection modes of the non-symmetrical helical gear injection，namely，the injection mode of the entry side and the injection mode of the exit side. …”
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Unblocking stuffy orifice and scraping technique for a child with allergic rhinitis of Qi deficiency of lung and spleen and related nursing measures (局部通窍刮痧技术治疗1例肺脾气虚型变应性鼻炎患儿的护理体会)...
Published 2022-06-01“…This paper observed the effect of unblocking stuffy orifice and scraping technique in a child with allergic rhinitis of Qi deficiency of lung and spleen Key issues of nursing assessment, routine nursing, acupoint selection, operation method and precautions of scraping technique were summarized. …”
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Control Index for Water Regulation in Fish Breeding Season for Dongta Spawning Ground after Operation of Datengxia Gorge Water Conservancy Project
Published 2022-01-01“…After the operation of the Datengxia Gorge water conservancy project,the change in the hydrological rhythm of the downstream river channel may have a certain impact on the function of the Dongta spawning ground about 10 km downstream.To maintain the normal function of the Dongta spawning ground,we should analyze the hydrological rhythm process required for fish spawning and use it as a control index to implement ecological regulation of reservoirs and thus restore the ecological environment of river channels.According to the observation data of early fish resources and hydrology over the past dozen years,the correlation between early fish resources and hydrological characteristic values in the Dongta spawning ground is analyzed by different statistical methods such as grey relational analysis and linear regression.In combination with the practical experience of ecological regulation in the Xijiang River Basin,the control index requirements for water regulation during the fish breeding period by the Datengxia Gorge water conservancy project are put forward.Specifically,by the regulation of the Datengxia Gorge water conservancy project from May to July every year,when the section flow rate of Dahuangjiangkou exceeds 3 500 m<sup>3</sup>/s,the water rise duration shall be no less than four days,with the daily rise rate in the water rise stage of more than 1 000 m<sup>3</sup>/s.The time for water to recede shall be no less than three days,and the flow rate in the water recession stage shall be above 3 500 m<sup>3</sup>/s.The regulation shall be performed at least once each year.The research results can provide a technical reference for maximizing the function of the Dongta spawning ground in the fish breeding period by the regulation of the Datengxia Gorge water conservancy project.…”
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Research on credit card transaction security supervision based on PU learning
Published 2023-06-01“…The complex and ever-evolving nature of credit card cash out methods and the emergence of various forms of fake transactions present challenges in obtaining accurate transaction information during customer interactions.In order to develop an accurate supervision method for detecting fake credit card transactions, a PU (positive-unlabeled learning) based security identification model for single credit card transactions was established.It was based on long-term transaction label data from cashed-up accounts in commercial banks’ credit card systems.A Spy mechanism was introduced into sample data annotation by selecting million positive samples of highly reliable cash-out transactions and 1.3 million samples of transactions to be labeled, and using a learner to predict the result distribution and label negative samples of non-cash-out transactions that were difficult to identify, resulting in 1.2 million relatively reliable negative sample labels.Based on these samples, 120 candidate variables were constructed, including credit card customer attributes, quota usage, and transaction preference characteristics.After importance screening of variables, nearly 50 candidate variables were selected.The XGBoost binary classification algorithm was used for model development and prediction.The results show that the proposed model achieve an identification accuracy of 94.20%, with a group stability index (PSI) of 0.10%, indicating that the single credit card transaction security identification model based on PU learning can effectively monitor fake transactions.This study improves the model discrimination performance of machine learning binary classification algorithm in scenarios where high-precision sample label data is difficult to obtain, providing a new method for transaction security monitoring in commercial bank credit card systems.…”
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SSA-ELM Hydrological Time Series Forecast Model Based on Wavelet Packet Decomposition and Phase Space Reconstruction
Published 2022-01-01“…Considering the nonlinear and multi-scale characteristics of hydrological time series,this paper proposes a squirrel search algorithm (SSA)-extreme learning machine (ELM) forecasting model based on wavelet packet decomposition (WPD) and phase space reconstruction.It is then applied to the Shangguo Hydrological Station in Yunnan Province for monthly runoff and precipitation forecasting.Specifically,WPD is performed to decompose the runoff and precipitation time series data,and the Cao method is applied to reconstruct the phase space of each subseries component.Then,the principle of SSA is outlined,and objective functions are constructed through the training samples of each component.The objective functions are optimized by SSA,and the results are compared with the optimization results of the whale optimization algorithm (WOA),the gray wolf optimization (GWO) algorithm,and the particle swarm optimization (PSO) algorithm.Finally,the weight of the ELM input layer and the hidden layer bias obtained by optimization based on SSA,WOA,GWO algorithm,and PSO algorithm,respectively,are utilized to build SSA-ELM,WOA-ELM,GWO-ELM,and PSO-ELM models,which,in addition to the unoptimized ELM models,are applied to forecast each subseries component,and the forecast results are summed and reconstructed to obtain the final forecasting results.The results show that SSA outperforms WOA,GWO algorithm,and PSO algorithm in optimizing the objective functions of each component and that it offers better optimization accuracy.The mean relative error,mean absolute error,mean square root error,and forecast pass rate of the proposed SSA-ELM model for monthly runoff and monthly precipitation forecast are 5.32% and 3.84%,0.078 m<sup>3</sup>/s and 0.169 mm,0.103 m<sup>3</sup>/s and 0.209 mm,97.5% and 95.8%,respectively,indicating that its forecasting accuracy is higher than that of other models such as the WOA-ELM model.…”
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