Aiming at the surrounding rock control problem of mining and preparation entries in Xingdong mine with large mining depth, and the comprehensive control countermeasures including high pre-stress cable truss system, th...Aiming at the surrounding rock control problem of mining and preparation entries in Xingdong mine with large mining depth, and the comprehensive control countermeasures including high pre-stress cable truss system, this study put forward powerful anchor support system and anchor cable adaption technology to surrounding rock deformation. Furthermore, the control measures possess the supporting performance with ‘‘primary rigid-following flexible-new rigid, and primary resistance-following yield-new resistance'', which suits deep roadway surrounding rock control. The mechanical model of truss anchor supporting roof beams was established, and the inverted arch deflection produced by the cable pre-stress with stress increment effect and roof beam deflection were obtained. And then the system working mechanism was illustrated. Finally, the surrounding rock support parameters were determined by means of comprehensive methods, and put into practice. The results show that surrounding rock deformation realized secondary stability after three months. The roadway sides convergence value was less than 245mm, and roof subsidence was less than 124mm. In addition, there was no expansion and renovation during service period.展开更多
目的探讨保留十二指肠的胰头切除术(duodenum-preserving pancreatic head resection,DPPHR)与胰管切开取石胰腺管空肠吻合术(Partington手术)对于胰腺头部结石手术治疗的疗效差别,评估二者手术的优缺点,并对其进行一定的优化和改进。...目的探讨保留十二指肠的胰头切除术(duodenum-preserving pancreatic head resection,DPPHR)与胰管切开取石胰腺管空肠吻合术(Partington手术)对于胰腺头部结石手术治疗的疗效差别,评估二者手术的优缺点,并对其进行一定的优化和改进。方法通过回顾性分析2017年1月1日至2022年10月1日以来,昆明医科大学第二附属医院肝胆胰外科一至四病区收治的139例需行手术治疗的胰腺头部结石患者的临床、影像和病理资料;根据对患者所施行的手术方式不同,将其分为DPPHR组和Partington组,分析2组患者临床特征以及外科治疗的近期和远期疗效。结果术前一般资料分析差异均无统计学意义(P>0.05);DPPHR组以Ⅰ型结石为主(80.2%),而Partington组以Ⅰ、Ⅲ型结石为主(45.3%、47.2%),差异有统计学意义(P<0.001);近期疗效中,DPPHR组在手术时间、术中出血量、术后住院天数和术后并发症发生率方面较Partington组具优势(P<0.05);远期疗效中,DPPHR组在结石复发再手术、术后胰腺功能等方面均优于Partington组(P<0.05);Logistic多因素分析显示CP≥5 a(95%CI 1.057~3.884,P=0.012)、术后长期有饮酒史(95%CI 0.987~3.128,P=0.025),CP≥5a和术后长期有饮酒史均是影响胰腺头部结石术后无疼痛复发生存期的独立危险因素。结论胰腺头部结石患者近期疗效(手术时间、术中出血量、术后住院天数和术后并发症发生率)和远期疗效(结石复发再手术、术后胰腺功能)分析,DPPHR手术均优于Partington术,可认为DPPHR手术目前成为一种有效的治疗PDS的方法;但还需为患者制定个体化手术策略,并重视术后护理和随访,以便为患者提供最佳预后。展开更多
Remote sensing image analysis is a basic and practical research hotspot in remote sensing science.Remote sensing images contain abundant ground object information and it can be used in urban planning,agricultural moni...Remote sensing image analysis is a basic and practical research hotspot in remote sensing science.Remote sensing images contain abundant ground object information and it can be used in urban planning,agricultural monitoring,ecological services,geological exploration and other aspects.In this paper,we propose a lightweight model combining vgg-16 and u-net network.By combining two convolutional neural networks,we classify scenes of remote sensing images.While ensuring the accuracy of the model,try to reduce the memory of themodel.According to the experimental results of this paper,we have improved the accuracy of the model to 98%.The memory size of the model is 3.4 MB.At the same time,The classification and convergence speed of the model are greatly improved.We simultaneously take the remote sensing scene image of 64×64 as input into the designed model.As the accuracy of the model is 97%,it is proved that the model designed in this paper is also suitable for remote sensing images with few target feature points and low accuracy.Therefore,the model has a good application prospect in the classification of remote sensing images with few target feature points and low pixels.展开更多
背景重复经颅磁刺激(repetitive transcranial magnetic stimulation,rTMS)是一项脑卒中后失语治疗的新技术,但存在定位不准确的问题。神经影像导航下重复经颅磁刺激已应用于临床,但对脑卒中后失语的临床疗效尚未见研究报道。目的分析...背景重复经颅磁刺激(repetitive transcranial magnetic stimulation,rTMS)是一项脑卒中后失语治疗的新技术,但存在定位不准确的问题。神经影像导航下重复经颅磁刺激已应用于临床,但对脑卒中后失语的临床疗效尚未见研究报道。目的分析评价神经影像导航定位引导重复经颅磁刺激治疗亚急性脑卒中非流利性失语患者的安全性和有效性。方法本研究为随机、双盲、前瞻性临床试验。选择2020年10月-2022年4月于解放军总医院第一医学中心康复医学科住院的脑卒中后非流利性失语患者,随机数字法分为导航rTMS组(neuronavigational rTMS,nTMS)和传统的国际10-20脑电系统定位组(conventional TMS,cTMS),两组均行右半球额下回三角部(pars triangularis,PTr)低频rTMS治疗。在治疗前、治疗10 d后,通过不良事件评分量表评价治疗的安全性,以中文语失语症评分(aphasia battery of Chinese,ABC)评价和比较组内和组间的言语功能。结果nTMS组15例,rTMS组14例,两组性别、年龄、病程等一般资料差异无统计学意义(P>0.05)。治疗过程中,无不良事件发生。治疗前,两组复述、命名、听理解、阅读等言语评分无统计学差异(P>0.05);治疗后,nTMS组自发言语、听理解、复述、命名、阅读、空间与结构、运用均较治疗前显著改善,差异有统计学意义(P<0.05),cTMS组听理解、命名、阅读均较治疗前显著改善,差异有统计学意义(P<0.05);组间比较,导航rTMS组复述、命名功能较传统定位组改善更显著,差异有统计学意义(P<0.05)。结论导航下右侧额下回三角部低频rTMS治疗相比传统定位组可更显著改善亚急性脑卒中后非流利性失语患者的命名、复述功能。展开更多
基金provided by the National Natural Science Foundation of China(No.51234005)National Basic Research Program of China under Grant(No.2010CB226802)Fundamental Research Funds for the Central Universities(No.2010QZ06)
文摘Aiming at the surrounding rock control problem of mining and preparation entries in Xingdong mine with large mining depth, and the comprehensive control countermeasures including high pre-stress cable truss system, this study put forward powerful anchor support system and anchor cable adaption technology to surrounding rock deformation. Furthermore, the control measures possess the supporting performance with ‘‘primary rigid-following flexible-new rigid, and primary resistance-following yield-new resistance'', which suits deep roadway surrounding rock control. The mechanical model of truss anchor supporting roof beams was established, and the inverted arch deflection produced by the cable pre-stress with stress increment effect and roof beam deflection were obtained. And then the system working mechanism was illustrated. Finally, the surrounding rock support parameters were determined by means of comprehensive methods, and put into practice. The results show that surrounding rock deformation realized secondary stability after three months. The roadway sides convergence value was less than 245mm, and roof subsidence was less than 124mm. In addition, there was no expansion and renovation during service period.
基金This researchwas supported byNationalKeyResearch andDevelopment Program sub-topics[2018YFF0213606-03(Mu Y.,Hu T.L.,Gong H.,Li S.J.and Sun Y.H.)http://www.most.gov.cn]Jilin Province Science and Technology Development Plan(focuses on research and development projects)[20200402006NC(Mu Y.,Hu T.L.,Gong H.and Li S.J.)http://kjt.jl.gov.cn]+1 种基金Science and Technology Support Project for Key Industries in Southern Xinjiang[2018DB001(Gong H.,and Li S.J.)http://kjj.xjbt.gov.cn]Key technology R&D project of Changchun Science and Technology Bureau of Jilin Province[21ZGN29(Mu Y.,Bao H.P.,Wang X.B.)http://kjj.changchun.gov.cn].
文摘Remote sensing image analysis is a basic and practical research hotspot in remote sensing science.Remote sensing images contain abundant ground object information and it can be used in urban planning,agricultural monitoring,ecological services,geological exploration and other aspects.In this paper,we propose a lightweight model combining vgg-16 and u-net network.By combining two convolutional neural networks,we classify scenes of remote sensing images.While ensuring the accuracy of the model,try to reduce the memory of themodel.According to the experimental results of this paper,we have improved the accuracy of the model to 98%.The memory size of the model is 3.4 MB.At the same time,The classification and convergence speed of the model are greatly improved.We simultaneously take the remote sensing scene image of 64×64 as input into the designed model.As the accuracy of the model is 97%,it is proved that the model designed in this paper is also suitable for remote sensing images with few target feature points and low accuracy.Therefore,the model has a good application prospect in the classification of remote sensing images with few target feature points and low pixels.
