Effect of Emergency Care Process Optimization on Rescue Efficiency of Emergency Patients

Objective: This paper discusses the impact of emergency care process optimization on the rescue efficiency of emergency patients. Methods: 102 cases of emergency patients received from January 2017 to February 2018 in our hospital were selected as research objects. According to the order of treatment, they were divided into control group and observation group. The routine nursing process was given to the control group, and the observation group was given an optimized nursing process to compare the rescue efficiency and nursing satisfaction of the two groups. Results: According to the results of the study, the nursing satisfaction of the two groups was compared. Among them, the total satisfaction of the observation group was 49, accounting for 96.07%;the control group was very satisfied with the nursing work, accounting for 82.35%. There was a significant difference in nursing satisfaction between the two groups, which was statistically significant (P<0.05). Comparing the rescue efficiency of the two groups of patients, the observation time, rescue time, infusion time and disease remission time were significantly lower than the control group, the rescue success rate was 94.11%, and the rescue success rate of the control group was 78.43%. The results have statistical significance (P < 0.05). Conclusion: The optimization of emergency nursing process can greatly improve the rescue efficiency of emergency patients, reduce the disability rate and mortality, improve the quality of nursing, and enhance the satisfaction of nursing. It is worthy of clinical promotion practice.

From statistical analysis to process optimization during cladding using a Nd:YAG laser

Application of statistical methods to optimize the process parameters was achieved by employing full factorial design of experiments,which was accomplished by cladding using stepwise ramped laser power.The correlations between clad geometry and dilution(clad characteristics)and the main process parameters laser power(P_(l)),cladding speed(v_(c)),the powder feed rate(m)were obtained through application of variance analysis technique(ANOVA).The obtained correlations between the main processing parameters and the clad characteristics are discussed and a statistical model was developed.The desirability investigations using the developed statistical model were performed by considering the clad geometry,aspect ratio,dilution and hardness.Optimal parameters for cladding Stellite 6 on AISI 420 steel substrate and for cladding Nucalloy 488V on S355 J2 steel substrate were obtained.The optimal processing parameters can be applied to clad other materials with similar chemical compositions.

Optimal Size for Maximal Energy Efficiency in Information Processing of Biological Systems Due to Bistability

Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculate the mutual information, energy cost, and energy efficiency of an array of these bistable units. We find that the optimal number of units could maximize this array＇s energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.

Optimizing Healthcare Big Data Processing with Containerized PySpark and Parallel Computing: A Study on ETL Pipeline Efficiency

In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request.

Speed profile optimization of catenary-free electric trains with lithium-ion batteries

Catenary-free operated electric trains, as one of the recent technologies in railway transportation, has opened a new field of research: speed profile optimization and energy optimal operation of catenary-free operated electric trains. A well-formulated solution for this problem should consider the characteristics of the energy storage device using validated models and methods. This paper discusses the consideration of the lithium-ion battery behavior in the problem of speed profile optimization of catenary-free operated electric trains. We combine the single mass point train model with an electrical battery model and apply a dynamic programming approach to minimize the charge taken from the battery during the catenary-free operation. The models and the method are validated and evaluated against experimental data gathered from the test runs of an actual battery-driven train tested in Essex, UK. The results show a significant potential in energy saving. Moreover, we show that the optimum speed profiles generated using our approach consume less charge from the battery compared to the previous approaches.

A Discrete Multi-Objective Squirrel Search Algorithm for Energy-Efficient Distributed Heterogeneous Permutation Flowshop with Variable Processing Speed

In the manufacturing industry,reasonable scheduling can greatly improve production efficiency,while excessive resource consumption highlights the growing significance of energy conservation in production.This paper studies the problem of energy-efficient distributed heterogeneous permutation flowshop problem with variable processing speed(DHPFSP-VPS),considering both the minimum makespan and total energy consumption(TEC)as objectives.A discrete multi-objective squirrel search algorithm(DMSSA)is proposed to solve the DHPFSPVPS.DMSSA makes four improvements based on the squirrel search algorithm.Firstly,in terms of the population initialization strategy,four hybrid initialization methods targeting different objectives are proposed to enhance the quality of initial solutions.Secondly,enhancements are made to the population hierarchy system and position updating methods of the squirrel search algorithm,making it more suitable for discrete scheduling problems.Additionally,regarding the search strategy,six local searches are designed based on problem characteristics to enhance search capability.Moreover,a dynamic predator strategy based on Q-learning is devised to effectively balance DMSSA’s capability for global exploration and local exploitation.Finally,two speed control energy-efficient strategies are designed to reduce TEC.Extensive comparative experiments are conducted in this paper to validate the effectiveness of the proposed strategies.The results of comparing DMSSA with other algorithms demonstrate its superior performance and its potential for efficient solving of the DHPFSP-VPS problem.

盖板件高效铣削表面粗糙度预测与工艺参数优化

为了保证7075铝合金盖板件铣削表面质量和提高铣削加工效率,进行工艺参数优化,通过响应曲面法(RSM)构建铣削加工表面粗糙度预测模型,分析铣削工艺参数对表面粗糙度的影响规律。基于表面粗糙度预测模型建立高效铣削工艺参数优化目标方程,采用改进的粒子群算法(PSO)对目标方程进行优化,运用优化后的工艺参数对某盖板件进行铣削加工。试验结果表明:采用优化后的工艺参数进行盖板件铣削加工可以满足表面粗糙度要求,验证了预测模型的准确性和改进PSO方法的可行性。

基于能效的机械零件激光熔覆工艺参数优化

为降低激光熔覆工艺的能量消耗、减少加工时间,本文提出一种基于能效的激光熔覆工艺参数优化方法。以比能耗作为激光熔覆能效的衡量标准,通过分析激光熔覆各子系统的能耗特性,建立激光熔覆能耗模型。在此基础上,计算系统总能耗与成型体积间的比能耗,构建以比能耗和加工时间为优化目标的激光熔覆工艺参数多目标优化模型并采用NSGA-Ⅱ算法进行求解。通过对环形薄壁件进行激光熔覆试验,验证了比能耗模型的准确性,并证明优化后的工艺参数能有效提升激光熔覆的能效,减少加工时间。

铝型材冷却初拉伸控制

针对铝型材加工过程中的拉伸夹具及冷却时间度量问题,设计适用于不同型号的拉伸夹具,确定了不同加工参数下的最佳冷却时间。通过优化拉伸夹具的设计和冷却时间的控制,能够提高铝型材的加工质量和生产效率。实验结果表明,该方法能够有效地改善铝型材的性能和形状,具有较高的应用价值。

基于误差最小化的铸造不锈钢零件高速切削工艺方法研究

铸造不锈钢零件在切削过程中容易产生变形和振动,导致表面粗糙、切削尺寸偏差大。为了解决这一问题,提出基于误差最小化的铸造不锈钢零件高速切削工艺方法研究。在数控机床的支持下,采用在线测量的方式测量铸造不锈钢零件数据,将数控机床测量的坐标值转换为零件表面实测点,获取合适的测量点完成对加工路径的规划,在此基础上通过粗加工和精加工结合的形式对铸造不锈钢零件进行高速切削处理,并以误差最小为目的对加工误差进行补偿处理。实验结果表明:提出的基于误差最小化的高速切削工艺方法加工误差小、精度高,零件表面质量高,该工艺方法的实用性得到了增强。

高精度线轨底座机加工工艺优化研究

充分利用现有低负荷或闲置的设备解决新的产能需求,是每个企业都有可能面临的问题。文章介绍某企业通过优化产品机械加工工艺流程,开发新工装夹具等方式,将部分产能转移到闲置设备上,腾出产能用于投产新产品,从而提高整体设备利用率,满足新产品的量产需求。

高含气井低速叶轮动态气液分离设备参数优化

针对目前高含气井电潜螺杆泵举升效率低的问题,提出一种低速叶轮动态气液分离方法。综合高气油比、低转速、低液量等因素的影响,建立低速叶轮气液分离场气泡运移数学模型和动态气液分离RNG k-ε模型,依据SIMPLE压力-速度耦合算法求解结果,揭示叶片数目、叶片螺距和叶片长度等参数对动态气液分离效率的影响规律,并依据叶轮结构优化正交试验结果,探究转速和含气量等操作参数的敏感性及其对分离效率的影响。研究结果表明:增加叶片数目、叶片螺距和叶片长度时,低速叶轮动态分离效率先增大后减小;叶片数目为15片,叶片螺距为250 mm,叶片长度为420 mm时,分离效率达到最大值81%;增大转速可有效提升低速叶轮动态分离效率,转速由150 r/min提至300 r/min,分离效率由38%显著升至81%;同时低速叶轮动态分离效率随含气量的增加而不断减小,入口含气量由35%增至50%,分离效率则由87%降至63%。所得结论可为适用于电潜螺杆泵的低速叶轮动态气液分离设备的参数优化提供指导。

考虑交互作用的A100钢高速铣削参数区间优选

为获得理想的高速铣削A100钢的铣削力和铣削温度,提出了一种考虑交互作用的铣削工艺参数取值区间的优选方法。以铣削力和铣削温度为响应变量并作为优化目标,设计了四因素四水平的正交仿真试验,得到了各工艺参数对响应变量的影响规律。利用熵权法得到了各响应变量的权重系数,结合灰色关联度分析理论将多个优化目标转化为一个综合优化目标。通过综合优化目标极差分析的主效应图得到了A100钢高速铣削工艺参数的优选区间,并利用枚举法得到了优化目标值的变化范围,经过与正交表中的响应值进行对比,结果表明所得优化目标的最大值均小于正交试验结果的平均值,满足优化预期。

基于T-PLS-GRA的造纸干燥过程能耗非优原因追溯模型

本研究建立了一种结合T-PLS(全潜结构投影)和GRA(灰色关联度分析)的造纸干燥过程能耗非优原因追溯模型。该模型首先基于机理知识和方差特性,去除造纸干燥过程生产数据中的非核心特征变量,并通过3σ原则和箱形图剔除异常值;然后使用施胶前定量与卷取车速数据,结合K-Means聚类算法,实现不同生产状态的分类;最后针对不同的生产状态,对比T-PLS和PLS建立的经济指标计算模型,选用基于T-PLS-GRA算法,构建造纸干燥过程能效非优原因追溯模型。结合国内某造纸厂实时生产数据对该模型进行了验证。结果表明,该模型基于经济指标判断工业生产状态过程,对非优过程预测精准率为77.7%,可较好地跟踪造纸过程设备运行状态的变化过程;并且能追溯非优状态原因及整个工况下,非优生产状态中最大贡献变量出现频次,为企业改进工艺流程及节能优化提供了参考依据。

高效多丝熔化极气体保护焊(GMAW)工艺的研究现状及进展

随着全球制造业竞争的日益激烈,我国提出了“中国制造2025”制造强国战略,其重点发展的十大领域中,与焊接技术密切相关的就高达八个,不仅极大地推动了焊接技术的革新发展,而且对焊接效率和质量均提出了更高的要求。由于熔化极气体保护焊(Gas metal arc welding,GMAW)易于实现自动化焊接,具有生产效率高、焊接质量好及位置适应性好等优点,所以广泛应用于机械制造业中。实现高效GMAW的主要途径有提高焊接速度以及焊接熔敷率。针对以上两种途径,国内外焊接工作者在双丝GMAW的基础上,引入了第三根甚至多根焊丝,研发了各种多丝GMAW工艺。本文针对国内外研发的各类多丝GMAW工艺进行了分析,重点介绍了多丝GMAW工艺的焊接原理、工艺特点及其应用,通过上述分析对各类多丝GMAW工艺进行归纳总结,并进一步展望了多丝焊接的发展方向,即多丝GMAW工艺亟需在电弧物理理论、设备开发和新焊材研发等方面展开深入的研究工作。

机器学习辅助钙钛矿薄膜制备工艺优化及特征重要性评估

钙钛矿太阳能电池仅用十年左右的时间将效率提升至认证的26.1%,非常接近晶硅太阳能电池26.81%的认证效率,展现出巨大的产业化潜力。当前,钙钛矿太阳能电池器件效率还在提升,然而在器件制备过程中,钙钛矿太阳能电池的性能受到许多不可分割的因素影响,传统方法往往采用试错的方式来优化钙钛矿太阳能电池的制备工艺,花费了大量的时间。贝叶斯优化是一种全局优化算法,在解决人工智能的黑盒问题方面取得了很大的成功。本文利用贝叶斯优化算法对钙钛矿层涉及到的碘化铅(PbI2)过量百分比、退火温度、退火时间、真空萃取时间四个工艺参数进行优化选择,显著降低了研发成本,缩短了研发时间。通过五轮实验迭代,累计34组工艺条件,制备出了器件效率为23.56%的反型钙钛矿太阳能电池。

激光熔覆Fe/Ti_(3)SiC_(2)复合涂层工艺优化及其组织结构与性能

通常在曲轴与轴瓦等摩擦副表面制备传统硬质涂层提高其耐磨性,但这对对偶件没有减磨效果,整体减磨效果不佳。为了提高摩擦副的使用寿命,采用高速激光熔覆技术在45钢表面制备Fe/Ti_(3)SiC_(2)耐磨减摩复合涂层,优化激光熔覆工艺参数,并研究工艺参数对涂层组织结构与性能的影响。研究表明,涂层工艺参数对涂层的摩擦性能影响程度大小依次是:激光功率、送粉量、扫描速率,最佳工艺参数为激光功率2.5 kW、送粉量为15 g/min、扫描速率为14 mm/s。涂层显微硬度达到591.7 HV0.2,涂层与基体结合处主要由柱状晶、树枝晶和平面晶组成,激光功率增加导致晶粒粗化,适当增大扫描速率和送粉量可使晶粒得到细化。摩擦磨损结果显示,在室温、载荷30 N和时间30 min的摩擦磨损试验中,该复合涂层表现出最佳摩擦性能,涂层磨损量0.4 mg,对偶件的磨损量0.7 mg。与未熔覆复合涂层的基体相比,复合涂层的磨损量降低了94%,同时其对偶件的磨损量降低了65%,表明Fe/Ti_(3)SiC_(2)复合涂层在大幅提升工件表面耐磨性能的同时,还能减少其对偶部件的磨损,使整个摩擦系统性能得到系统提升,是一种高性能的耐磨减摩复合涂层。该研究解决了传统硬质涂层提升工件自身耐磨性能,但会增加其对偶部件磨损的技术难题。

基于粉碎齿刀具的吸波蜂窝高速铣削加工试验研究

针对某一典型吸波蜂窝材料,采用粉碎齿刀具在不同切削参数下进行高速铣削试验,从切削力、加工表面质量、切削功率、切削温度等角度分析铣削参数对加工过程参量的影响规律,进而确定吸波蜂窝高速铣削的优化工艺参数。研究表明,提高切削速度有利于降低切削力和改善表面加工质量,当进给速度、切削深度或切削宽度增大时,因单位时间内材料切除体积增大,导致切削力和切削温度升高,造成表面缺陷加剧。以抑制毛刺缺陷形成为主要目标时,综合考虑工艺参数对切削力、切削功率和切削温度的影响,推荐优化工艺参数为主轴转速16000 r·min^(-1)、进给速度4000 mm·min^(-1)、切削深度10 mm和切削宽度15 mm。研究结果可为粉碎齿刀具高速铣削吸波蜂窝材料的工艺优化提供数据参考。

新型高强度钛合金切削刀具技术研究

针对TB18航空结构件在实际加工过程中加工效率低、刀具寿命短等问题,开展TB18材料与通用钛合金材料的切削过程对比试验,分析影响刀具切削性能的关键因素,并结合具体的加工需求,通过典型刀具选型改进、新型刀具研制和切削参数优化,提高TB18材料加工效率,延长刀具使用寿命。

基于遗传算法的飞轮储能电机多工况效率优化

由于热泵系统具有电热转换效率高、电机转速控制便捷、系统灵活调节能力强的特点,其运用于电热耦合系统可实现热力系统与电力系统的协同运行支撑,而配备高速飞轮电机的电热耦合热泵系统具备大惯量、长延时特征,在电热协同运行领域具备更强的支撑能力。但现有飞轮储能电机多基于额定工作点进行效率优化,难以实现全工作周期内综合效率最优。为此,本工作基于电热协同实际工作场景得到单个完整工作周期下飞轮电机的运行工况变化,提出基于遗传算法的飞轮电机多工况效率优化方法。首先,建立适用于高速电机的效率计算模型,并根据热泵运行场景计算出单次工作下飞轮电机转速曲线;其次,确定待优化变量,通过参数灵敏度分析去除关联度较小参数以减少计算量,并根据实际工况提出电机综合效率指标以量化电机在全工作周期内的效率表现;最后,基于遗传算法寻优得到最优工作点,优化后综合效率增加0.34%,全速范围内电机效率均得到提升,单个工作周期能量损耗下降38.7 kJ,较优化前能量损耗降低14.8%。优化结果证明此方法可提高飞轮电机全工作周期内的运行效率,降低热泵运行过程中的能耗损失,提升飞轮储能系统的经济性。