360度评估在某三甲妇幼保健院妇产科专业住培管理中的应用

目的 为了解住院医师和带教老师在培训中存在的问题,加强住培过程管理,提升培训质量。方法 在某省妇幼保健院妇产科住培专业基地组织360度评估,共有住院医师636人次、带教老师602人次和护理人员361人次完成评估。结果 评估问卷信度效度评价指标均在0.95以上,具有较高的可靠性。带教老师对住院医师的评估结果提示,被评估住院医师在职业精神[(4.7±0.5)分]、患者照护[(4.6±0.6)分]和沟通合作[(4.6±0.7)分]维度获得较高得分,在知识技能[(4.2±0.8)分]和教学能力[(4.1±0.8)分]维度得分较低。对评估分数进行单因素分析,带教老师对住院医师的评估中,高年级得分高于低年级(F=4.29,P<0.05),但不同年级住院医师对带教老师的评估差异无统计学意义(F=0.098,P> 0.05),不同护理人员对住院医师的评估差异无统计学意义(F=0.151,P>0.05)。在三类评估中,对带教老师评估的平均得分虽然最高[(96.5±8.3)分],但主观反馈中,住院医师给带教老师提出教学时间不够,教学意识不强等具体建议。结论 应用360度评估工具,有利于师生发现问题、解决问题,也为专业基地制定培养计划和规范过程管理提供参考依据。

基于患者360视图的护理不良事件管理系统构建与应用

目的构建基于患者360视图的护理不良事件管理系统,优化护理不良事件管理流程。方法护理不良事件管理系统包含可视化安全预警、向导式上报及反馈追踪、分层系统培训3个功能模块,于2021年1月正式应用。结果系统应用后,压力性损伤、给药错误以及不良事件整体发生率均较应用前下降,其中给药错误发生率差异具有统计学意义(P<0.05),而管路滑脱、跌倒/坠床的发生率较应用前略有增加;护理不良事件整改达标率由92.8%提高至95.7%(P<0.05);组织科护士长培训12次,组织全院护士长专项培训20余次,开展护理质量安全管理标准化操作规程工作坊12场次。结论基于患者360视图的护理不良事件管理系统可以提高护理风险识别率,降低护理不良事件发生率。下一步需在充分考虑患者特点与需求的基础上进一步优化系统功能。

放射科住院医师规范化培训师资队伍现状及360度评估应用的探索

住院医师规范化培训(简称住培)师资队伍的质量及水平是影响培训效果的关键因素,放射科作为住培专业基地及临床辅助轮转学科,其师资队伍管理及带教规范执行情况对提高临床各专业住培质量具有重要影响。该文基于上海交通大学医学院附属新华医院放射科专业基地目前师资队伍现状,结合国家住培制度,应用其自主设计的线上360度师资评估法,进行同行医师,上、下级医师,医技护理,住院医师互评及自评等多维度师资评估的实践探索,旨在规范放射科师资队伍管理,提升师资队伍带教水平与能力,保证住培教学质量。

面向360°全景视频的帧内预测编码的快速算法

为了节省360°全景视频的编码时间,对通用视频编码标准中的编码单元划分决策过程进行了研究,提出了一种面向360°全景视频的帧内预测编码的快速算法。通过优化编码树单元(Coding Tree Unit,CTU)的编码深度范围和编码单元的划分模式的选择过程,减少编码时间。实验结果表明,在全帧内模式下,所提算法比原始算法平均可以节省34.33%的时间复杂度,同时带来的BDBR平均增量仅为1.665%,BDPSNR的平均降低量仅为0.076 dB。

条形缺陷对L360管线钢环焊缝硫化物应力腐蚀开裂敏感性的影响

通过抗硫化物应力腐蚀试验、慢应变速率拉伸试验、氢含量测试等方法研究条形缺陷对H_(2)S环境下L360管线钢环焊缝硫化物应力腐蚀开裂敏感性的影响。结果表明,环焊缝在72%、85%和110%SMYS(最小屈服强度)应力水平下,热影响区出现裂纹且裂纹长度随加载应力增大而增大;条形缺陷环焊缝试样在屈服点后快速发生断裂,其断裂应变(0.024)仅为非缺陷环焊缝试样的21.05%;条形缺陷存在促进氢离子在裂纹尖端聚集从而加剧发生开裂倾向,消氢处理技术可以有效降低氢含量,降低环焊缝抗硫化物应力腐蚀开裂敏感性降低。

无锡“锡绣”艺术与360°数码印花服饰图案设计融合创新的可行性分析

探索了无锡“锡绣”与360°数码印花服饰图案设计的融合创新问题。通过分析锡绣艺术和数码印花技术的特点,提出融合策略,并探讨市场需求、消费者心理及面临的挑战。案例分析与实证研究验证了融合创新的可行性和市场潜力。该研究为传统文化与现代时尚的结合提供了新视角,有助于锡绣艺术的传承和时尚产业的发展。

选择性360°和180°微脉冲激光小梁成形术治疗原发性开角型青光眼的效果差异研究

对比选择性360°和180°微脉冲小梁成形术(MLT)对原发性开角型青光眼患者治疗的效果差异。方法 将2020年2月到2023年2月到我院治疗的80例原发性开角型青光眼患者根据入院单双日分为观察组(单日组41例)和对照组(双日组39例)。分别行360°微脉冲小梁成形术和180°微脉冲小梁成形术进行治疗,对比疗效差异。结果 ①两组患者术前、术后1h、术后1d、6个月眼压无差异(P>0.05);术后1周、术后1个月、3个月有差异(P<0.05);②两组患者术前、术后1h、术后1d、术后1周、术后1个月、3个月、6个月患眼视力水平无明显差异(P>0.05)。结论 选择性360°和选择性180°MLT都能提升原发性开角型青光眼患者的视力水平,降低眼压,可根据患者的需求辩证的选择。

JCZ-360超宽频带地震计研究与实验

介绍了JCZ系列超宽频带地震计JCZ-360的研究进展、定型测试、台站实验观测等情况。结果显示,JCZ-360超宽频带地震计能有效抑制外界大气压力、外部磁场干扰及温度变化对仪器的影响,性能更稳定,在超低频端噪声水平更低。JCZ-360超宽带地震计不仅可以观测到全频段地震波,还可以记录到固体潮汐、地球自由振荡等信息,具备传统地震和地球物理场的综合观测能力。

非洲猪瘟病毒MGF360-13L蛋白的原核表达及多克隆抗体制备

本研究旨在通过构建非洲猪瘟病毒(ASFV)MGF360-13L基因原核表达系统表达13L蛋白,并制备其鼠源多克隆抗体。利用生物信息学方法,对ASFV MGF360-13L基因进行序列比对,分析其同源性、构建遗传进化树;将非洲猪瘟病毒MGF360-13L基因密码子优化后进行合成,连接至PET32a载体构建重组质粒pET32a-13L,转化至E.coliBL21(DE3)感受态细胞,经IPTG诱导表达获得目的蛋白,采用镍柱纯化法进行蛋白纯化。将纯化后的蛋白乳化后免疫8周龄BALB/c雌鼠制备多克隆抗体,间接ELISA和Western blot检测抗体特异性。SDS-PAGE结果显示,重组蛋白分子量大小为58.2 kDa,主要以包涵体形式存在;Western blot结果显示免疫猪阳性血清可特异性识别该蛋白,具有良好的反应性,表明该重组蛋白获得正确表达。利用该蛋白免疫小鼠制备多克隆抗体进行Western blot,结果显示其能与MGF360-13L重组蛋白发生特异性反应。间接ELISA测定抗体效价高达1∶256000。本研究成功制备非洲猪瘟病毒MGF360-13L重组蛋白,以其为免疫原制备的多克隆抗体具备较高的特异性和反应性,为进一步阐述MGF360-13L蛋白的生物学功能和研制非洲猪瘟新型疫苗奠定了基础。

360度视频与视口预测方法综述

360度视频是获取沉浸式虚拟现实体验的便捷媒介之一,近年来受到了广泛关注。视口预测技术是缓解360度视频高网络带宽要求的重要手段。聚焦视口预测技术,首先介绍360度视频的基本概念、背景和360度视频流式框架,对比常用的球面到平面投影方法、视频编解码标准;分析360度视频高网络资源消耗的原因,体现视口预测技术对360度视频流式的重要作用;介绍360度注意力数据集,总结主流公开数据集;将现有的视口预测方法,分为基于用户历史轨迹的方法和基于视频内容的方法,进行系统性的综述,梳理视口预测方法的发展脉络,介绍视口预测方法的最新工作,比较不同方法的特点、优势和不足,并简略介绍了360度显著性检测,360度显著性检测是基于视频内容的视口预测方法中的重点。最后进行总结,分析了现阶段视口预测方法面临的问题,展望了包括视口预测方法在内的360度视频相关技术的未来发展趋势。

360°咬合金属板屋面风致易损性分析

360°咬合金属板屋面作为众多形式金属屋面中的一种,具有良好的抗压能力,然而在真实风环境下,该类屋面的抗风揭能力尚有待研究。针对360°咬合金属板屋面系统的风灾易损程度开展了量化分析研究,以判定其真实抗风能力。为此,研究结合了力学试验与数值仿真等技术手段,对某TB开关站厂房的360°咬合金属板屋面系统的承载力以及风荷载效应进行分析。基于概率学知识,提出连接件失效概率的高效计算方法,进而快速分析金属板单元失效概率以及屋面整体的易损性,并提供损失的平均水准及变异程度量化结果。研究发现,荷载最不利工况通常对应来流垂直于屋脊线时,此时该类屋面在风力达到9级时出现破坏的迹象,当风力达到12级时,整个屋面极有可能被整体掀飞。

KPI+360度评估在高校行政管理人员考核中的应用研究——以北京某大学为例

文章以高校行政管理人员考核特点为切入点,探索分析了行政管理岗位考核中存在的问题,提出了采用关键绩效指标(KPI)与360度评估相结合进行考核的意见,并构建了行政管理人员绩效考核的指标体系,其中岗位职责履行、工作业绩考核以KPI为主,工作态度考核评价等则以360度评估为主。

Feedback linearization and equivalent-disturbance compensation control strategy for piezoelectric stage

Piezoelectric stages use piezoelectric actuators and flexure hinges as driving and amplifying mechanisms,respectively.These systems have high positioning accuracy and high-frequency responses,and they are widely used in various precision/ultra-precision positioning fields.However,the main challenge with these devices is the inherent hysteresis nonlinearity of piezoelectric actuators,which seriously affects the tracking accuracy of a piezoelectric stage.Inspired by this challenge,in this work,we developed a Hammerstein model to describe the hysteresis nonlinearity of a piezoelectric stage.In particular,in our proposed scheme,a feedback-linearization algorithm is used to eliminate the static hysteresis nonlinearity.In addition,a composite controller based on equivalent-disturbance compensation was designed to counteract model uncertainties and external disturbances.An analysis of the stability of a closed-loop system based on this feedback-linearization algorithm and composite controller was performed,and this was followed by extensive comparative experiments using a piezoelectric stage developed in the laboratory.The experimental results confirmed that the feedback-linearization algorithm and the composite controller offer improved linearization and trajectory-tracking performance.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

Process metallurgy and data-driven prediction and feedback of blast furnace heat indicators

The prediction and control of furnace heat indicators are of great importance for improving the heat levels and conditions of the complex and difficult-to-operate hour-class delay blast furnace(BF)system.In this work,a prediction and feedback model of furnace heat indicators based on the fusion of data-driven and BF ironmaking processes was proposed.The data on raw and fuel materials,process op-eration,smelting state,and slag and iron discharge during the whole BF process comprised 171 variables with 9223 groups of data and were comprehensively analyzed.A novel method for the delay analysis of furnace heat indicators was established.The extracted delay variables were found to play an important role in modeling.The method that combined the genetic algorithm and stacking efficiently im-proved performance compared with the traditional machine learning algorithm in improving the hit ratio of the furnace heat prediction model.The hit ratio for predicting the temperature of hot metal in the error range of±10℃ was 92.4%,and that for the chemical heat of hot metal in the error range of±0.1wt%was 93.3%.On the basis of the furnace heat prediction model and expert experience,a feedback model of furnace heat operation was established to obtain quantitative operation suggestions for stabilizing BF heat levels.These sugges-tions were highly accepted by BF operators.Finally,the comprehensive and dynamic model proposed in this work was successfully ap-plied in a practical BF system.It improved the BF temperature level remarkably,increasing the furnace temperature stability rate from 54.9%to 84.9%.This improvement achieved considerable economic benefits.

Performance enhancement of a viscoelastic bistable energy harvester using time-delayed feedback control

This paper focuses on the stochastic analysis of a viscoelastic bistable energy harvesting system under colored noise and harmonic excitation, and adopts the time-delayed feedback control to improve its harvesting efficiency. Firstly, to obtain the dimensionless governing equation of the system, the original bistable system is approximated as a system without viscoelastic term by using the stochastic averaging method of energy envelope, and then is further decoupled to derive an equivalent system. The credibility of the proposed method is validated by contrasting the consistency between the numerical and the analytical results of the equivalent system under different noise conditions. The influence of system parameters on average output power is analyzed, and the control effect of the time-delayed feedback control on system performance is compared. The output performance of the system is improved with the occurrence of stochastic resonance(SR). Therefore, the signal-to-noise ratio expression for measuring SR is derived, and the dependence of its SR behavior on different parameters is explored.

A feedback control method for phase signal demodulation in fber-optic hydrophones

In the realm of acoustic signal detection,the identification of weak signals,particularly in the presence of negative signal-to-noise ratios,poses a significant challenge.This challenge is further heightened when signals are acquired through fiber-optic hydrophones,as these signals often lack physical significance and resist clear systematic modeling.Conventional processing methods,e.g.,low-pass filter(LPF),require a thorough understanding of the effective signal bandwidth for noise reduction,and may introduce undesirable time lags.This paper introduces an innovative feedback control method with dual Kalman filters for the demodulation of phase signals with noises in fiber-optic hydrophones.A mathematical model of the closed-loop system is established to guide the design of the feedback control,aiming to achieve a balance with the input phase signal.The dual Kalman filters are instrumental in mitigating the effects of signal noise,observation noise,and control execution noise,thereby enabling precise estimation for the input phase signals.The effectiveness of this feedback control method is demonstrated through examples,showcasing the restoration of low-noise signals,negative signal-to-noise ratio signals,and multi-frequency signals.This research contributes to the technical advancement of high-performance devices,including fiber-optic hydrophones and phase-locked amplifiers.

Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

A Tutorial on Quantized Feedback Control

In this tutorial paper, we explore the field of quantized feedback control, which has gained significant attention due to the growing prevalence of networked control systems. These systems require the transmission of feedback information, such as measurements and control signals, over digital networks, presenting novel challenges in estimation and control design. Our examination encompasses various topics, including the minimal information needed for effective feedback control, the design of quantizers, strategies for quantized control design and estimation,achieving consensus control with quantized data, and the pursuit of high-precision tracking using quantized measurements.

Towards engineering a portable platform for laparoscopic pre-training in virtual reality with haptic feedback

Background Laparoscopic surgery is a surgical technique in which special instruments are inserted through small incision holes inside the body.For some time,efforts have been made to improve surgical pre training through practical exercises on abstracted and reduced models.Methods The authors strive for a portable,easy to use and cost-effective Virtual Reality-based(VR)laparoscopic pre-training platform and therefore address the question of how such a system has to be designed to achieve the quality of today's gold standard using real tissue specimens.Current VR controllers are limited regarding haptic feedback.Since haptic feedback is necessary or at least beneficial for laparoscopic surgery training,the platform to be developed consists of a newly designed prototype laparoscopic VR controller with haptic feedback,a commercially available head-mounted display,a VR environment for simulating a laparoscopic surgery,and a training concept.Results To take full advantage of benefits such as repeatability and cost-effectiveness of VR-based training,the system shall not require a tissue sample for haptic feedback.It is currently calculated and visually displayed to the user in the VR environment.On the prototype controller,a first axis was provided with perceptible feedback for test purposes.Two of the prototype VR controllers can be combined to simulate a typical both-handed use case,e.g.,laparoscopic suturing.A Unity based VR prototype allows the execution of simple standard pre-trainings.Conclusions The first prototype enables full operation of a virtual laparoscopic instrument in VR.In addition,the simulation can compute simple interaction forces.Major challenges lie in a realistic real-time tissue simulation and calculation of forces for the haptic feedback.Mechanical weaknesses were identified in the first hardware prototype,which will be improved in subsequent versions.All degrees of freedom of the controller are to be provided with haptic feedback.To make forces tangible in the simulation,characteristic values need to be determined using real tissue samples.The system has yet to be validated by cross-comparing real and VR haptics with surgeons.