预测儿童Ⅱ期人工晶状体植入术后青光眼相关不良事件的风险:一项为期3年的研究

目的:建立并评估儿童Ⅱ期人工晶状体(intraocular lens,IOL)植入术后青光眼相关不良事件(glaucomarelated adverse events,GRAEs)的预测模型。方法:选取于中山大学中山眼科中心行Ⅱ期IOL植入术的无晶状体眼患儿205例(356眼),并在术后对其随访3年。采用Cox比例风险模型确定GRAEs的预测因子,并建立列线图预测模型。采用随时间变化的受试者工作特征(receiver operating characteristic,ROC)曲线、决策曲线分析、Kaplan-Meier曲线评估模型性能,并通过Bootstrapping的C指数和校准图进行内部验证。结果:行Ⅱ期IOL植入术时年龄较大(HR=1.50,95%CI:1.03~2.19)、术后一过性高眼压(HR=9.06,95%CI:2.97~27.67)和IOL睫状沟植入术(HR=14.55,95%CI:2.11~100.57)是GRAEs的危险因素(均P<0.05),并据此建立了两个列线图预测模型。在术后1、2、3年,模型1的ROC曲线下面积(area under curve,AUC)分别为0.747(95%CI:0.776~0.935)、0.765(95%CI:0.804~0.936)和0.748(95%CI:0.736~0.918),模型2的AUC分别为0.881(95%CI:0.836~0.926)、0.895(95%CI:0.852~0.938)和0.848(95%CI:0.752~0.945)。在内部验证和评价中,两种模型均表现出良好的性能和临床净效益。Kaplan-Meier曲线显示两个不同的风险组在两个模型中都能被显著且稳健地区分。此外,本研究也构建了在线风险计算器。结论:两种列线图均能灵敏、准确地识别Ⅱ期IOL植入术后GRAEs的高危患儿,有助对其进行早期识别和及时干预。

In situ high-quality LiF/Li_(3)N inorganic and phenyl-based organic solid electrolyte interphases for advanced lithium–oxygen batteries

Lithium metal shows a great advantage as the most promising anode for its unparalleled theoretical specific capacity and extremely low electrochemical potential.However,uncontrolled lithium dendrite growth and severe side reactions of the reactive intermediates and organic electrolytes still limit the broad application of lithium metal batteries.Herein,we propose 4-nitrobenzenesulfonyl fluoride(NBSF)as an electrolyte additive for forming a stable organic-inorganic hybrid solid electrolyte interphase(SEI)layer on the lithium surface.The abundance of lithium fluoride and lithium nitride can guarantee the SEI layer's toughness and high ionic conductivity,achieving dendrite-free lithium deposition.Meanwhile,the phenyl group of NBSF significantly contributes to both the chemical stability of the SEI layer and the good adaptation to volume changes of the lithium anode.The lithium-oxygen batteries with NBSF exhibit prolonged cycle lives and excellent cycling stability.This simple approach is hoped to improve the development of the organic-inorganic SEI layer to stabilize the lithium anodes for lithium-oxygen batteries.

Research on Interpolation Method for Missing Electricity Consumption Data

Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-quality data consistently.In the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate.For the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data.First,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data information.All missing data are randomly interpolated within the upper and lower limits.Then,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption data.At last,this process is implemented iteratively until the missing values do not change.Under a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete data.Also,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

Seeker optimization algorithm:a novel stochastic search algorithm for global numerical optimization

A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.

Significance of Axial Length Monitoring in Children with Congenital Cataract and Update of Measurement Methods

Congenital cataract is the main cause of blindness in children, with significantly varying treatment effects. The development of axial length is an important factor that affects the prognosis of these children.However,when compared with the eyes of normal children,the mechanism of growth of the axial length is so complicated that the reported findings differ significantly in terms of the measuring apparatus,assessment methods,and statistical outcome,making the rule of axial length development still unclear. In this paper, we first review the process of axial length development in normal healthy children and compare different hypotheses about certain factors that could affect the development of axial length. The results of some current research about the characteristics of axial length development in congenital cataract children are then reviewed. Lastly, the advantages and disadvantages of current axial length measurements methods are compared and analyzed.The purpose of this review is to improve our understanding of the complexity and importance of axial length development and to suggest better use of axial length monitoring measurements in congenital cataract children for pediatric ophthalmologists,with the hope of offering assistance that will enhance long-term therapeutic effects for these children.

System integration of China's first PEMFC locomotive

In the face of growing environmental pollution, developing a fuel-cell-driven shunting locomotive is a great challenge in China for environmental protection and energy saving, which combines the environmental advantages of an electric locomotive with the lower infrastructure costs of a diesel-electric locomotive. In this paper, the investigation status and the development trend of the fuel-cell-driven shunting locomotive were introduced. Through innovation of the power system using fuel cells, an experiment prototype of a fuel-cell shunting locomotive was developed, which would reduce the effects on the environment of the existing locomotives. This was the first locomotive to use a proton exchange membrane fuel-cell （PEMFC） power plant in China. From October 2012, we started to test the fuel-cell power plant and further test runs on the test rail-line in Chengdu, Sichuan. The achieved encouraging results can provide fundamental data for the modification of the current individual fuel cell locomotives or further development of the fuel-cell hybrid ones in China.

Exploration of Management Workflow of Cataract Surgery in an Impoverished Population in Urban China

Purpose:.To explore and establish a rational management workflow for a free cataract surgery program for the poor population in urban China, aiming to improve surgical efficiency.Methods:.Establishment of a management workflow mainly includes system design and an auxiliary facility. System design procedures consist of outpatient screening, outpatient physical examination,.surgical procedures,.and postoperative clinic visits. After establishing the management workflow of cataract surgery, a free cataract surgery program was conducted for 15 months.Results: Based upon the established management mode, 9003 patients received preoperative screening and 2358 underwent cataract surgery..During the 15-month investigation,.each procedure was successfully conducted,.the efficiency of screening and operation attained the highest standards in China,.and no surgical malpractice occurred intraoperatively.Conclusion:.In this study,.a management workflow for cataract surgery was designed for a poverty relief project in urban China. During the 15-month project, the degree of patient satisfaction was enhanced without disrupting the normal practice and safety of the sponsor hospital.

Air flow control based on optimal oxygen excess ratio in fuel cells for vehicles

Air flow control is one of the most important control methods for maintaining the stability and reliability of a fuel cell system, which can avoid oxygen starvation or oxygen saturation. The oxygen excess ratio （OER） is often used to indicate the air flow condition. Based on a fuel cell system model for vehicles, OER performance was analyzed for different stack currents and temperatures in this paper, and the results show that the optimal OER was affected weakly by the stack temperature. In order to ensure the system working in optimal OER, a control scheme that includes an optimal OER regulator and a fuzzy control was proposed. According to the stack current, a reference value of air flow rate was obtained with the optimal OER regulator and then the air compressor motor voltage was controlled with the fuzzy controller to adjust the air flow rate provided by the air compressor. Simulation results show that the control method has good dynamic and static characteristics.

Energy management strategy based on dynamic programming with durability extension for fuel cell hybrid tramway

This paper proposes an energy management strategy for a fuel cell(FC)hybrid power system based on dynamic programming and state machine strategy,which takes into account the durability of the FC and the hydrogen consumption of the system.The strategy first uses the principle of dynamic programming to solve the optimal power distribution between the FC and supercapacitor(SC),and then uses the optimization results of dynamic programming to update the threshold values in each state of the finite state machine to realize real-time management of the output power of the FC and SC.An FC/SC hybrid tramway simulation platform is established based on RTLAB real-time simulator.The compared results verify that the proposed EMS can improve the durability of the FC,increase its working time in the high-efficiency range,effectively reduce the hydrogen consumption,and keep the state of charge in an ideal range.

Application of visual electrophysiology for the diagnosis and treatment of cataracts

Visual electrophysiology is widely used in clinical ophthalmology. It is also of significant value in the objective assessment of visual function in adult and pediatric cataract patients and for the diagnosis of and research on retinal and visual pathway diseases. This article systematically reviews visual electrophysiology techniques, their applications in the diagnosis and treatment of adult and pediatric cataracts, and factors influencing the application of visual electrophysiology during surgical treatment for cataracts.

Real-time Energy Management Method for Electric-hydrogen Hybrid Energy Storage Microgrids Based on DP-MPC

With the increasing presence of intermittent energy resources in microgrids,it is difficult to precisely predict the output of renewable resources and their load demand.In order to realize the economical operations of the system,an energy management method based on a model predictive control(MPC)and dynamic programming(DP)algorithm is proposed.This method can reasonably distribute the energy of the battery,fuel cell,electrolyzer and external grid,and maximize the output of the distributed power supply while ensuring the power balance and cost optimization of the system.Based on an ultra-shortterm forecast,the output power of the photovoltaic array and the demand power of the system load are predicted.The offline global optimization of traditional dynamic programming is replaced by the repeated rolling optimization in a limited period of time to obtain power values of each unit in the energy storage system.Compared with the traditional DP,MILP-MPC and the logic based real-time management method,the proposed energy management method is proved to be feasible and effective.

Two-stage Scheduling for Island CPHH IES Considering Plateau Climate

In this paper,an island integrated energy system(IES)combining power,heat and hydrogen(CPHH)is built,and optimal power-heat-hydrogen scheduling is studied under actual plateau meteorological data.Considering the comprehensive demands of residents,an IES including renewable energy sources(RESs),fuel cell,electrolyzer,electric boiler,thermal energy storage device and new energy vehicles is established and operated.In the first stage of operation,according to the prediction of RESs and demands,day-ahead optimization is carried out which aims to minimize operation cost,while electric vehicles(EVs)are considered as special demand response(DR)loads.For the second stage of operation,ultra-short-term prediction is implemented to provide prediction data for model predictive control(MPC),realizing real-time operation.Functioning as a CPHH system,the fuel cell and electrolyzer collaborate to meet various needs of the plateau residential area with high efficiency.In addition,the method and IES structure proposed in this paper is compared with other options.

Two-stage distributionally robust optimization-based coordinated scheduling of integrated energy system with electricity-hydrogen hybrid energy storage

A coordinated scheduling model based on two-stage distributionally robust optimization(TSDRO)is proposed for integrated energy systems(IESs)with electricity-hydrogen hybrid energy storage.The scheduling problem of the IES is divided into two stages in the TSDRO-based coordinated scheduling model.The first stage addresses the day-ahead optimal scheduling problem of the IES under deterministic forecasting information,while the sec-ond stage uses a distributionally robust optimization method to determine the intraday rescheduling problem under high-order uncertainties,building upon the results of the first stage.The scheduling model also considers col-laboration among the electricity,thermal,and gas networks,focusing on economic operation and carbon emissions.The flexibility of these networks and the energy gradient utilization of hydrogen units during operation are also incor-porated into the model.To improve computational efficiency,the nonlinear formulations in the TSDRO-based coordinated scheduling model are properly linearized to obtain a Mixed-Integer Linear Programming model.The Column-Constraint Generation(C&CG)algorithm is then employed to decompose the scheduling model into a mas-ter problem and subproblems.Through the iterative solution of the master problem and subproblems,an efficient analysis of the coordinated scheduling model is achieved.Finally,the effectiveness of the proposed TSDRO-based coordinated scheduling model is verified through case studies.The simulation results demonstrate that the proposed TSDRO-based coordinated scheduling model can effectively accomplish the optimal scheduling task while consider-ing the uncertainty and flexibility of the system.Compared with traditional methods,the proposed TSDRO-based coordinated scheduling model can better balance conservativeness and robustness.

Stability-constrained Two-stage Robust Optimization for Integrated Hydrogen Hybrid Energy System

In order to cope with the challenges brought by multiple uncertainties to integrated hydrogen hybrid energy systems,a stability-constrained two-stage robust optimization method considering small disturbance stability and characteristics of dynamic response is proposed in this paper.In the first operating stage,the charging/discharging state of the battery and the startstop state of the electrolyzer and fuel cell are determined.Then,power constraints for stabilizing ESSs power output is considered between the first and second stage optimization to improve the small disturbance stability of the robust operation plan.Then,the goal of minimizing the operation cost and determine a robust operation operating plan under the worst case is conducting in the second stage optimization.Through the small-signal model with time-delay effect,the eigenvalue analysis method is used to find the ESSs power range,and the small-signal stability of obtained robust operation plan can be enhanced.Finally,the effectiveness and superiority of the proposed method are proved by comparing with the traditional static robust optimization method.Impacts of uncertain parameters on economy and stability are also investigated in a typical example.

Hierarchical distributed control for decentralized battery energy storage system based on consensus algorithm with pinning node

A decentralized battery energy storage system(DBESS)is used for stabilizing power fluctuation in DC microgrids.Different state of charge(SoC)among various battery energy storage units(BESU)during operation will reduce batteries’service life.A hierarchical distributed control method is proposed in this paper for SoC balancing and power control according to dispatching center requirement in DBESS.A consensus algorithm with pinning node is employed to allocate power among BESUs in the secondary control whereas in the primary control,the local controller of BESU adjusts output power according to the reference power from secondary control.Part of BESUs are selected to be pinning node for accepting command from dispatching center while other BESUs as following nodes which exchange output power and SoC information with the adjacent nodes through communication network.After calculating reference power of each BESU by adopting consensus algorithm,the power sharing in DBESS is achieved according to their respective SoC of BESUs.Meanwhile,the total output power of DBESS follows the varying requirements of dispatching center.The stability of DBESS is also improved because of having no center controller.The feasibility of the proposed control strategy is validated by simulation results.