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In view of problems of randomness and subjectivity in determining weight of existing rockburst prediction methods，a discrete Hopfield neural network (DHNN) model for prediction of classification of rockburst intensity was proposed。The model selects stress coefficient, rockbrittleness coefficient and elastic energy index as evaluation index, divides rockburst grade into 4 stages, such as strong rockburst, medium rockburst, weak rockburst and no rockburst, then encodes them. The model need't normalize sample data with simpler encoding ,lesser iterations of network and better associative memory ability, only be converted to '1' and '-1' of the two value model, therefore, the classification prediction of rockburst intensity is more scientific and reasonable. The model can provide a new way for classification prediction of rockburst intensity in deep underground engineering. The prediction results of typical rockburst engineering examples prove the correctness of the model.

Abstract Background Family members of patients with end stage renal disease were reported to have an increased prevalence of chronic kidney disease (CKD). However, studies differentiated genetic and non-genetic family members are limited. We sought to investigate the prevalence of CKD among fist-degree relatives and spouses of dialysis patients in China. Methods Seventeen dialysis facilities from 4 cities of China including 1062 first-degree relatives and 450 spouses of dialysis patients were enrolled. Sex- and age- matched controls were randomly selected from a representative sample of general population in Beijing. CKD was defined as decreased estimated glomerular (eGFR 2) or albuminuria. Results The prevalence of eGFR less than 60 mL/min/1.73 m2, albuminuria and the overall prevalence of CKD in dialysis spouses were compared with their counterpart controls, which was 3.8% vs. 7.8% (P 2, albuminuria and the overall prevalence of CKD in dialysis relatives were also compared with their counterpart controls, which was 1.5% vs. 2.4% (P = 0.12), 14.4% vs. 8.4% (P Conclusions The association between being family members of dialysis patients and presence of CKD is different between first-degree relatives and spouses. The underlying mechanisms deserve further investigation.

Abstract Background Bioimpedance analysis (BIA) has been reported as helpful in identifying hypervolemia. Observation data showed that hypervolemic maintenance hemodialysis (MHD) patients identified using BIA methods have higher mortality risk. However, it is not known if BIA-guided fluid management can improve MHD patients’ survival. The objectives of the BOCOMO study are to evaluate the outcome of BIA guided fluid management compared with standard care. Methods This is a multicenter, prospective, randomized, controlled trial. More than 1300 participants from 16 clinical sites will be included in the study. The enrolment period will last 6 months, and minimum length of follow-up will be 36 months. MHD patients aged between 18 years and 80 years who have been on MHD for at least 3 months and meet eligibility criteria will be invited to participate in the study. Participants will be randomized to BIA arm or control arm in a 1:1 ratio. A portable whole body bioimpedance spectroscopy device (BCM—Fresenius Medical Care D GmbH) will be used for BIA measurement at baseline for both arms of the study. In the BIA arm, additional BCM measurements will be performed every 2 months. The primary intent-to-treat analysis will compare outcomes for a composite endpoint of death, acute myocardial infarction, stroke or incident peripheral arterial occlusive disease between groups. Secondary endpoints will include left ventricular wall thickness, blood pressure, medications, and incidence and length of hospitalization. Discussions Previous results regarding the benefit of strict fluid control are conflicting due to small sample sizes and unstable dry weight estimating methods. To our knowledge this is the first large-scale, multicentre, prospective, randomized controlled trial to assess whether BIS-guided volume management improves outcomes of MHD patients. The endpoints of the BOCOMO study are of utmost importance to health care providers. In order to obtain that aim, the study was designed with very careful important considerations related to the endpoints, sample size, inclusion criteria, exclusion criteria and so on. For example, annual mortality of Beijing MHD patients was around 10%. To reach statistical significance, the sample size will be very large. By using composite endpoint, the sample size becomes reasonable and feasible. Limiting inclusion to patients with urine volume less than 800 ml/day the day before dialysis session will limit confounding due to residual renal function effects on the measured parameters. Patients who had received BIS measurement within 3 months prior to enrolment are excluded as data from such measurements might lead to protocol violation. Although not all patients enrolled will be incident patients, we will record the vintage of dialysis in the multivariable analysis. Trial registration Current Controlled Trials NCT01509937

The deterioration of cable joints will lead to the increase of heat loss,and then lead to the rise of surface temperature of the joints.At the same time,the surface temperature is affected by many factors such as operating load,environmental temperature.In general,the relationship between deterioration degree and temperature data shows a non-linear distribution.For this reason,a prediction method based on improved sparrow search algorithm(ISSA) optimization for kernel extreme learning machine(KELM) is proposed to predict the insulation deterioration degree of cable joints.Firstly,based on the experimental validation of the multi-physical coupling model of cable joints,the surface temperature distribution data of cable joints at different deterioration levels,loads and ambient temperatures are obtained for building the training set,validation set and test set.Secondly,the sparrow search algorithm is optimized based on the idea of flight behavior in the bird swarm algorithm(BSA),which ensures global convergence without losing population diversity and effectively jumps out of local optimum.Then,ISSA algorithm is used to optimize the penalty coefficient C and the kernel function σ of KELM and the prediction model of insulation deterioration state is obtained.Research results show that the predictive effect of ISSA-KELM is much better than that of other models.