Quick start-up of EGSB reactor treating fresh leachate of municipal solid waste

An expanded granular sludge bed （EGSB） reactor inoculated with anaerobic granular sludge was started up with its COD removal performance, self-balancing of pH, biogas production rate and characteristics of the granular sludge during the start-up period being investigated. The results indicated that the EGSB reactor can be started up successfully in 27 d by increasing the organic loading rate rapidly. The removal efficiency of COD was maintained above 93% with influent COD concentration of 25 000 mg/L and OLR of 37.94 kgCOD/（m 3 ·d）. The EGSB reactor with good pH self-balancing could be fed with fresh leachate of low pH value （4 5）. The biogas production rate was closely related with OLR and COD reduction. Every gram of COD fed （consumed） to the reactor produced 0.34 L STP （0.36 L STP ） biogas with 0.21 L STP （0.23 L STP ） methane. Thus, 65% 70% of the produced biogas was methane. Sixty one percentage of COD fed to the reactor was converted to methane, another 33% was converted to biomass by metabolism, and the other 6% was left in the liquid phase. The specific methanogenic activity （SMA） of the granular sludge had increased by 92% after 27 d dynamic incubation. The granular sludge in the reactor had good settlement performance with majority diameter of 1 2 mm.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Numerical study of plasmas start-up by electron cyclotron waves in NCST spherical tokamak and CN-H1 stellarator

According to the physics of tokamak start-up,this study constructs a zero-dimensional(0D)model applicable to electron cyclotron(EC)wave assisted start-up in NCST spherical torus(spherical tokamak)and CN-H1 stellarators.Using the constructed 0D model,the results obtained in this study under the same conditions are compared and validated against reference results for pure hydrogen plasma start-up in tokamak.The results are in good agreement,especially regarding electron temperature,ion temperature and plasma current.In the presence of finite Ohmic electric field in the spherical tokamak,a study on the EC wave assisted start-up of the NCST plasma at frequency of 28 GHz is conducted.The impact of the vertical magnetic field B_(v)on EC wave assisted start-up,the relationship between EC wave injection power P_(inj),Ohmic electric field E,and initial hydrogen atom density n_(H0)are explored separately.It is found that under conditions of Ohmic electric field lower than ITER(~0.3 V m^(-1)),EC wave can expand the operational space to achieve better plasma parameters.Simulating the process of28 GHz EC wave start-up in the CN-H1 stellarator plasma,the plasma current in the zerodimensional model is replaced with the current in the poloidal coil of the stellarator.Plasma startup can be successfully achieved at injection powers in the hundreds of kilowatts range,resulting in electron densities on the order of 10^(17)-10^(18)m^(-3).

基于Quick BI融合看板的数字化供电所管理系统

针对客户功率因数异常偏低、客户停电次数无法感知等疑难问题量身打造了Quick BI融合看板。看板依托Quick BI平台灵活接入多源数据,基于数据中台完成大数据分析,打通营销、配电等专业间数据壁垒,具备功率因数调整电费过高提醒、光伏发电量为0监测、客户实时掉电监测次数等7大功能,以解决过去供电所存在的客户信息掌握不及时不全面、台区隐患排查成本高、隐蔽问题发现难度大等工作痛点、难点。有效推动了基层供电所赋能减负,助力供电所实现了客户零感知服务。

Numerical Simulation of a Two-Phase Flow with Low Permeability anda Start-Up Pressure Gradient

A new numerical model for low-permeability reservoirs is developed.The model incorporates the nonlinear characteristics of oil-water two-phase flows while taking into account the initiation pressure gradient.Related numerical solutions are obtained using a finite difference method.The correctness of the method is demonstrated using a two-dimensional inhomogeneous low permeability example.Then,the differences in the cumulative oil and water production are investigated for different starting water saturations.It is shown that when the initial water saturation grows,the water content of the block continues to rise and the cumulative oil production gradually decreases.

Influence of quick release valve on braking performance and coupler force of heavy haul train

We establish a simulation model based on the theory of air flow to analyze the accelerated release effect of the quick release valve inside the air brake control valve.In addition, the combined simulation system of train air brake system and longitudinal train dynamics is used to analyze how the parameters of the quick release valve in the 120/120–1 brake control valve affect the propagation characteristics of the train brake pipe pressure wave, the release action range of the accelerated brake, and the longitudinal coupler force for a 20,000-ton heavy haul train on the section of the Datong–Qinhuangdao Railway. The results show that the quick release valve can effectively accelerate the rising speed of the train brake pipe pressure during the initial release, as the accelerated release effect is evident before the train brake pipe pressure reaches582 k Pa. The quick release valve can effectively accelerate the release of the rear cars, reducing the longitudinal coupler force impact due to time delay of the release process. The quick release valve can effectively reduce the tensile coupler force in the train by as much as 20% in certain cases.

After Artificial Intelligence Breaks Longstanding Matrix Multiplication Records,Humans Quickly Do Better

In October 2022,machine learning experts at Deep Mind(London,UK),a subsidiary of Google(Mountain View,CA,USA),reported a“breakthrough”on an extremely common mathematical algorithm called matrix multiplication[1].In previous years,DeepMind has made headlines with its successes in using deep learning to master various games,such as Go[2],chess,and even the strategic board game Diplomacy,and,in a more recent and clearly practical application with its Alpha Fold program[3].

Quality Control of YFOSC Data I. The On-site Quick Analysis System

The Lijiang 2.4 m Telescope(LJT)is one of the most important telescopes for general astronomical observations in China.Yunnan Faint Object Spectrograph and Camera(YFOSC)is a widely used instrument mounted on the LJT,which occupies for~80%of the observing time of the LJT,and thus instrument health and data quality of the YFOSC are very important for both the telescope maintenance team and users of the LJT.So we develop an automated data analysis system for the quality control(QC)of YFOSC data.This system is also a new function of the observing support of the YFOSC.Based on the system,YFOSC data can be reduced quickly as they are acquired and QC parameters are extracted.Observers can assess the quality of their data and make a possible revision of their observing plan in time.These parameters can also be used to check the health of the YFOSC,which is helpful for the telescope maintenance team to find potential problems.All of these aim at improving the productivity of the LJT.

Dynamic evaluation of a scaled-down heat pipe-cooled system during start-up/shut-down processes using a hardware-in-the-loop test approach

Micro-mobile heat pipe-cooled nuclear power plants are promising candidates for distributed energy resource power genera-tors and can be flexibly deployed in remote places to meet increasing electric power demands.However,previous steady-state simulations and experiments have deviated significantly from actual micronuclear system operations.Hence,a transient analysis is required for performance optimization and safety assessment.In this study,a hardware-in-the-loop(HIL)approach was used to investigate the dynamic behavior of scaled-down heat pipe-cooled systems.The real-time features of the HIL architecture were interpreted and validated,and an optimal time step of 500 ms was selected for the thermal transient.The power transient was modeled using point kinetic equations,and a scaled-down thermal prototype was set up to avoid mod-eling unpredictable heat transfer behaviors and feeding temperature samples into the main program running on a desktop PC.A series of dynamic test results showed significant power and temperature oscillations during the transient process,owing to the inconsistency of the rapid nuclear reaction rate and large thermal inertia.The proposed HIL approach is stable and effective for further studying of the dynamic characteristics and control optimization of solid-state small nuclear-powered systems at an early prototyping stage.

The role of quick Sepsis-related Organ Failure Assessment score as simple scoring system to predict Fournier gangrene mortality and the correlation with Fournier’s Gangrene Severity Index: Analysis of 69 patients

Objective:Fournier’s gangrene is a rare but life-threatening infection disease with high mortality rate.The quick Sepsis-related Organ Failure Assessment(qSOFA)is a new and simpler scoring system that may identify patients with suspected infection who are at greater risk for a poor outcome.The purpose of this study was to find out role of qSOFA in determining prognosis of Fournier’s gangrene patients.Methods:This study is a case control with retrospective review of Fournier’s gangrene patients treated at Hasan Sadikin Hospital from January 2013 to December 2017 who met inclusion criteria.Participants were divided into two groups according to qSOFA score as high qSOFA(2-3)and low qSOFA(0-1).Results:From 69 patients,the mortality rate was 24.6%.The sensitivity of qSOFA score to predict mortality was 88.2%;the specificity was 94.2%;positive predictive value was 83.3%;negative predictive value was 96.1%;positive likelihood ratio was 15.2;negative likelihood ratio was 0.12;and the area under the receiver operating characteristic curve of qSOFA was 94.2%.There was significant association between qSOFA scale and mortality with p-value of 0.0001.The qSOFA score has strong positive correlation with Fournier’s Gangrene Severity Index(p<0.0001,r=0.704).Conclusion:qSOFA scoring system has a high prognostic value and can be used to determine prognosis of Fournier’s gangrene patients.

Robot Positioning Based on Multiple Quick Response Code Landmarks

The scheme of robot positioning based on multiple quick response(QR)code landmarks is proposed.Firstly,the pose of the robot relative to the QR code landmarks is obtained by extracting the feature points of the QR code,combined with the visual computing technology.Then,the conversion of the absolute pose of the robot is completed based on the absolute position of the QR code landmark,and thus the real-time position of the robot is obtained.The proposed scheme makes full use of the QR code fault tolerance and multiple QR code landmarks to improve the calculation accuracy.It has good robustness and versatility,and it is easy to implement.It can help the robot to complete the positioning in the actual work,making robot navigation more accurate.

吡虫啉和啶虫脒胶体金免疫层析法试纸条性能评估及应用效果探究

目的研究吡虫啉和啶虫脒胶体金免疫层析法试纸条在不同基质中残留量检测的应用效果。方法实验考察了市场上主要流通的5种不同品牌的胶体金免疫层析法试纸条在蔬菜、水果和茶鲜叶基质中的检出限、灵敏度、交叉反应率、特异性、假阳性和假阴性率。结果各品牌胶体金试纸条在3种不同类型基质(白菜、苹果和茶鲜叶)中,检出限浓度水平有假阴性出现,其余浓度水平灵敏度较好;特异性和交叉反应性,结果均为阴性;假阳性率和假阴性率考察中,假阳性率为0%,但有假阴性情况出现。结论胶体金免疫层析法在检测吡虫啉、啶虫脒农药残留时具有简便快捷的特点,在蔬菜、水果基质的检测中,判定结果较为准确可靠;在茶叶基质中,由于基质效应的影响,出现假阴性的机率较高。

改良QuEChERS技术结合超高效液相色谱-串联质谱法测定水产品中扑草净及其代谢物残留

采用改良QuEChERS技术作为前处理方法,建立了水产品中扑草净及其代谢物残留的超高效液相色谱-串联质谱分析方法。样品用乙腈提取,经增强型脂质去除吸附剂与Cleanert LipoNo组合净化,采用选择反应监测正离子模式测定,内标法定量。结果表明:扑草净及其代谢物在1.0~500 ng/mL质量浓度范围内线性关系良好,确定系数R2大于0.992,方法检出限和定量限分别低至0.20μg/kg和0.50μg/kg,在草鱼、克氏原螯虾和中华绒螯蟹等基质的加标实验中呈现良好的准确度与精密度,平均加标回收率和相对标准偏差分别为74.4%~113.7%和3.17%~11.47%。经内标法校正,5种扑草净及其代谢物在不同水产品中的基质效应不明显。方法实现了水产品中扑草净及其代谢物的识别与定量分析,并通过检测药浴扑草净的克氏原螯虾阳性样品验证了方法的适用性。

空间机器人预定义任意时间最优轨迹规划

针对双臂自由漂浮空间机器人(FFSR)在太空抓取任务中的轨迹规划问题,设计了一种FFSR系统的可实现误差预定义时间收敛的新型轨迹规划算法,用布谷鸟搜索算法进行参数优化,以实现末端执行器误差的快速收敛并获得平滑轨迹。首先,导出FFSR的基于姿态误差的运动学模型;然后,将累积危险场避碰算法应用于预定义任意时间轨迹规划,实现高跟踪精度的快速避碰轨迹;最后,利用布谷鸟搜索算法对预定义任意时间参数进行优化,得到机械臂低关节角速度的运动轨迹。仿真结果表明所提算法能够在预定义的时间内实现末端执行器误差快速收敛,而且能够得到更平滑的运动轨迹。

地浸砂岩型铀矿松散岩层单动双管取心钻具的研制与试验

为解决单管钻具取心工艺方法在深部目的层/矿层等松散岩层取心中存在的取心质量差、取心效率低和操作难度大的问题,开展了地浸砂岩型铀矿松散岩层单动双管钻具的研制和试验。通过设计和改进单动机构、断心结构、液力退心机构和快拆机构,研制了两类分别具有可直接液力退心和快速拆卸功能的SD型单动双管钻具。试验结果表明:SD110型双管钻具可在免拆内管的情况下实现地表快速液力退心,提高双管钻具退心效率;SDKC110型钻具可快速拆装更换内管,实现退心和钻进平行作业。与单管钻具相比,SD系列单动双管钻具在松散岩层的平均回次长度提高28.62%,平均机械钻速提高11.61%;与常规单动双管钻具相比,退心操作时间降低了32.69%。SD系列单动双管钻具适用于在松散破碎地层进行取心钻进应用,可有效提高岩心采取效率和岩心质量。

快速序贯器官功能衰竭评估评分联合氧合指数与急诊危重症患者危重程度的关系研究

目的探讨快速序贯器官功能衰竭评估(qSOFA)评分联合氧合指数(OI)对急诊危重症患者危重程度的预测价值。方法回顾性选取2020年1月至2021年10月河北省第七人民医院收治的176例急诊危重症患者作为研究对象,包括脓毒症患者92例,社区获得性肺炎患者84例。根据入院24 h内的急性生理学和慢性健康状况评价Ⅱ(APACHEⅡ)评分结果,将所选患者分为轻度组(15分≤APACHEⅡ评分<20分,n=58)、中度组(20分≤APACHEⅡ评分<30分,n=70)和重度组(APACHEⅡ评分≥30分,n=48)。比较3组患者的血气指标[二氧化碳分压(PaCO_(2))、动脉氧分压(PaO_(2))、OI]、qSOFA评分;采用Pearson相关分析血气指标与qSOFA评分的相关性;采用多分类有序Logistic回归分析急诊危重症患者危重程度的影响因素;并采用受试者工作特征(ROC)曲线分析qSOFA评分联合OI预测急诊危重症患者危重程度的价值。结果重度组的PaCO_(2)和qSOFA评分均高于中度组和轻度组,且中度组的PaCO_(2)和qSOFA评分显著高于轻度组,差异均有统计学意义(P<0.05);3组的PaO_(2)和OI呈现下降趋势,重度组低于中度组和轻度组,中度组低于轻度组,差异均有统计学意义(P<0.05)。Pearson相关分析结果显示,PaCO_(2)与qSOFA评分呈正相关(r=0.238,P<0.05);PaO_(2)、OI与qSOFA评分呈负相关(r=-0.197、-0.328,P<0.05)。多分类有序Logistic回归分析结果显示,PaCO_(2)和qSOFA评分越高,急诊危重症患者危重程度越严重;但PaO_(2)和OI越低急诊危重症患者危重程度越严重。ROC曲线分析结果示,qSOFA评分联合OI预测急诊危重症患者危重程度的曲线下面积(AUC)最大为0.895,大于各单独指标预测和其他联合指标的预测(P<0.05)。结论qSOFA评分、OI与急诊危重症患者危重程度有关,二者联合预测急诊危重症患者危重程度的价值更高,可作为临床诊断和评估急诊危重症的辅助指标。

速泡鲜腐竹加工工艺研究

在传统腐竹加工工艺的基础上,采用冷冻工艺制备速泡鲜腐竹。研究鼓风干燥预处理方式下冷冻时间、冷冻温度对速泡鲜腐竹复水率、泡发时间、感官品质的影响。分析不同加工工艺下腐竹的质构、色泽、泡发时间和复水率,以及炒制、油炸和煮制食用方式下产品的品质特点。结果表明,速泡鲜腐竹的最佳工艺为鼓风干燥温度40℃、干燥时间20 min、冷冻时间8 h、冷冻温度-20℃,该条件下制备的速泡鲜腐竹的水分含量为42.55%,泡发时间为3 min,复水率为58.87%。与市售干腐竹相比,速泡鲜腐竹的泡发时间缩短了90倍,油炸时的吸油率降低48.51%,速泡鲜腐竹无需浸泡,可以直接炒制、油炸和煮制,极大提高了食用的便捷性。

基于食品冻结曲线的小龙虾液氮速冻装备设计与试验

针对液氮速冻产品冻裂率高、液氮损耗量大的问题,基于食品冻结曲线,设计三腔体液氮速冻设备,包括预冷腔(4.2 m,-70℃)、结晶腔(5.8 m,-100℃)和深冷腔(5.0 m,-120℃)。相较于单腔体液氮速冻设备(15.0 m,-100℃),三腔体液氮速冻设备使预包装调理小龙虾的冻结时间缩短10.0%,冻裂率降低40.4%,液氮节约15.0%;质构(硬性、弹性、内聚性、回复性、咀嚼性)和色泽(L*,b*)显著改善,挥发性盐基氮显著降低,水分、盐溶性蛋白和感官(色泽和质地)品质显著提高。研究为液氮速冻技术和装备的改进提供参考和借鉴。

基于QAR数据的民机高高原进近着陆风险评估方法

民机高高原进近着陆是高原飞行高风险阶段。为有效实施高高原进近着陆风险识别和等级判据,提出基于熵权可变模糊识别的长短时记忆网络与深度神经网络(LSTM-DNN)相融合的深度学习风险评估方法。基于快速存取记录器(QAR)记录的高高原飞行数据,借鉴民机飞行品质监控(FOQA)咨询通告和行业QAR监控标准,结合指标重要度分析与Delphi专家调查,提取着陆时航向变化大、航迹低、610~305 m进近时下降率大、接地时垂直加速度及153~15 m进近时下降率大5个关键监控项目作为民机高高原进近着陆风险评估指标。为克服评估指标权重主观性偏差,应用熵权法确定评估指标权重,基于可变模糊识别方法构建风险等级隶属函数,建立基于LSTM-DNN的民机高高原进近着陆风险评估模型。以成都—拉萨进近着陆航段为例,提取QAR数据,对该风险评估模型进行训练与测试,并与Logistic多元回归、支持向量机(SVM)等评估方法进行比较,结果表明:所提方法平均准确率达到94.18%,最高可达94.79%,验证了方法的客观有效性。

基于自适应深度集成网络的概念漂移收敛方法

概念漂移是流数据挖掘领域中的一个重要且具有挑战性的难题.然而,目前的方法大多仅能够处理线性或简单的非线性映射,深度神经网络虽然有较强的非线性拟合能力,但在流数据挖掘任务中,每次只能在新得到的1个或一批样本上进行训练,学习模型难以实时调整以适应动态变化的数据流.为解决上述问题,将梯度提升算法的纠错思想引入含概念漂移的流数据挖掘任务之中,提出了一种基于自适应深度集成网络的概念漂移收敛方法(concept drift convergence method based on adaptive deep ensemble networks,CD_ADEN).该模型集成多个浅层神经网络作为基学习器,后序基学习器在前序基学习器输出的基础上不断纠错,具有较高的实时泛化性能.此外,由于浅层神经网络有较快的收敛速度,因此所提出的模型能够较快地从概念漂移造成的精度下降中恢复.多个数据集上的实验结果表明,所提出的CD_ADEN方法平均实时精度有明显提高,相较于对比方法,平均实时精度有1%~5%的提升,且平均序值在7种典型的对比算法中排名第一.说明所提出的方法能够对前序输出进行纠错,且学习模型能够快速地从概念漂移造成的精度下降中恢复,提升了在线学习模型的实时泛化性能.