Evaluation of the Impact of Refined Quality Control Management Model on the Qualified Rate of Disinfection and Sterilization of Surgical Instruments in the Sterilization Supply Center

Objective:To evaluate the application value of a refined quality control management model for a sterilization supply center.Methods:A retrospective analysis was conducted on the work situation of the sterilization supply center from January 2021 to January 2023.The work situation before January 31,2022,was classified as the control group;a routine quality control management model was implemented,and the work situation after January 31,2022,was classified as the observation group.The quality of medical device management and department satisfaction between the two groups were compared.Results:The timely recovery and supply rate,classification and cleaning pass rate,disinfection pass rate,packaging pass rate,sterilization pass rate,and department satisfaction score in the observation group were all higher than those of the control group(P<0.05).Conclusion:Implementing a refined quality control management model in the sterilization supply center can improve the quality management level of medical devices and department satisfaction and is worthy of promotion.

Whole-Process Project Cost Management Based on Building Information Modeling (BIM)

The whole-process project cost management based on building information modeling(BIM)is a new management method,aiming to realize the comprehensive optimization and improvement of project cost management through the application of BIM technology.This paper summarizes and analyzes the whole-process project cost management based on BIM,aiming to explore its application and development prospects in the construction industry.Firstly,this paper introduces the role and advantages of BIM technology in engineering cost management,including information integration,data sharing,and collaborative work.Secondly,the paper analyzes the key technologies and methods of the whole-process project cost management based on BIM,including model construction,data management,and cost control.In addition,the paper also discusses the challenges and limitations of the whole-process BIM project cost management,such as the inconsistency of technical standards,personnel training,and consciousness change.Finally,the paper summarizes the advantages and development prospects of the whole-process project cost management based on BIM and puts forward the direction and suggestions for future research.Through the research of this paper,it can provide a reference for construction cost management and promote innovation and development in the construction industry.

A Proposed Method for Evaluating Management Feasibility When Determining Weed Control Priorities after Major Fires and Floods

Major fires and floods have enormous impacts on natural ecosystems and are predicted to increase in frequency with global warming.Land managers need to make decisions on the prioritisation of weeds for control in post-disturbance landscapes,but little is available in the way of guidance to support timely decision making.Semi-quantitative models(e.g.,scoring systems)have been employed routinely in weed risk assessment,which considers the potential impacts posed by weeds,as well as the likelihood of these impacts being realised.Some progress has been made in the development of similar models addressing the topic of weed risk management.Under conditions prevailing after major disturbances,changes(both positive and negative)can be expected in the multiple factors that determine weed management feasibility,relative to pre-disturbance conditions.A semi-quantitative model is proposed that is based on the key factors that contribute to weed management feasibility in post-disturbance environments,along with annotated modules that could be used by land managers in both post-fire and post-flood situations.The fundamental challenge for weed management in these scenarios lies in the identification of differences between weeds and native species in relation to(1)patterns of seedling emergence;and(2)detectability relative to the growth stage.These two factors will determine the timing of control actions that are designed to address the trade-off between weed control and off-target damage during the period when both types of plants are recovering from a major disturbance event.The model is intuitively sound,but field testing is required to determine both its practical value and any necessary improvement.

Three-Dimensional Water Quality Model Based on FVCOM for Total Load Control Management in Guan River Estuary,Northern Jiangsu Province

Guan River Estuary and adjacent coastal area(GREC) suffer from serious pollution and eutrophicational problems over the recent years.Thus,reducing the land-based load through the national pollutant total load control program and developing hydrodynamic and water quality models that can simulate the complex circulation and water quality kinetics within the system,including longitudinal and lateral variations in nutrient and COD concentrations,is a matter of urgency.In this study,a three-dimensional,hydrodynamic,water quality model was developed in GREC,Northern Jiangsu Province.The complex three-dimensional hydrodynamics of GREC were modeled using the unstructured-grid,finite-volume,free-surface,primitive equation coastal ocean circulation model(FVCOM).The water quality model was adapted from the mesocosm nutrients dynamic model in the south Yellow Sea and considers eight compartments:dissolved inorganic nitrogen,soluble reactive phosphorus(SRP),phytoplankton,zooplankton,detritus,dissolved organic nitrogen(DON),dissolved organic phosphorus(DOP),and chemical oxygen demand.The hydrodynamic and water quality models were calibrated and confirmed for 2012 and 2013.A comparison of the model simulations with extensive dataset shows that the models accurately simulate the longitudinal distribution of the hydrodynamics and water quality.The model can be used for total load control management to improve water quality in this area.

Model predictive control for water management and energy security in arid/semiarid regions

This paper aims to develop a realistic operational optimal management of a water supply system in an arid/semiarid region under climate change conditions.The developed model considers the dynamic variation of water demand,rainfall,weather,and seasonal change in electricity price.It is mathematically developed as a multi-constraint non-linear programming model based on model predictive control principles.The model optimises the quantities of water supplied by each source every month and improves the energy efficiency in a water supply system with multiple types of sources.The effectiveness of the developed MPC model is verified by applying it to a case study and comparing the results with those obtained with an open loop model.Results showed that using the MPC model leads to a 4.16%increase in the water supply cost compared to the open loop model.However,when considering uncertainties in predicting water demands,aquifer recharges,rainfall,and evaporation rate,the MPC model was better than the open loop model.Indeed,the MPC model could meet the water demand at any period due to its predictability of variations,which was not the case with the open loop model.Moreover,a sensitivity analysis is conducted to verify the capacity of the developed model to deal with some phenomena due to climatic changes,such as in rainfall.

Control and Management Measures of Highway Project Cost under the PPP Model

In recent years,China’s society and economy has been developing very rapidly,and various industries are showing a trend of vigorous development.The importance of control and management of project cost in highway project management is becoming more and more prominent.PPP model has been widely used in the control and management of highway project cost with its own unique advantages.The author analyzes the important advantages of the PPP model and the existing problems in the cost control and management of highway projects,and puts forward effective measures for the cost control and management of highway projects using the PPP model,in hope of helping with the smooth implementation of the work.

Effective Energy Management Scheme by IMPC

The primary purpose of the Energy Management Scheme(EMS)is to monitor the energy fluctuations present in the load profile.In this paper,the improved model predictive controller is adopted for the EMS in the power system.Emperor Penguin Optimization(EPO)algorithm optimized Artificial Neural Network(ANN)with Model Predictive Control(MPC)scheme for accurate prediction of load and power forecasting at the time of preoptimizing EMS is presented.For the power generation,Renewable Energy Sources(RES)such as photo voltaic(PV)and wind turbine(WT)are utilized along with that the fuel cell is also presented in case of failure by the RES.Such a setup is connected with the grid and applies to the household appliances.In improved model predictive control(IMPC),the set of constraints for the powerflow in the system is optimized by the ANN,which is trained by EPO.Such a tuning based prediction model is presented in the IMPC technique.The proposed work is implemented in the MATLAB/Simulink platform.The energy management capability of the proposed system is analyzed for different atmospheric conditions.The total system cost,life cycle cost and annualized cost for IMPC are 48%,45%and 15%,respectively.From the performance analysis,the cost obtained by the proposed method is very low compared to that obtained by the existing techniques.

Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Epidemic Regularity and Control Effect of Huanglongbing (HLB) in Different Management Approaches

In order to reveal the epidemic regularity of Huanglongbing （HLB） in different management approaches, different citrus production areas were selected between 2002 and 2012 to compare epidemic regularity of different types and control effects of different management approaches with plant incidence rate. All survey data in 11 years were used to build a mathematical model, and epidemic evolution and control effects were quantitatively analyzed. The results indicated that diffusion and prevalence of HLB generally increased linearly. In naturally growing citrus orchards without artificial control, the annual diseased plant rate was 11.11%, and the epidemic diffusion model was y1 = 12. 24x - 1.382 8 （ n =9, r =0. 976 9 ＊ ＊ ）. Under general prevention and control conditions, the annual diseased plant rate was 4.69%, the epidemic diffusion model was Y2 = 5. 449 8x - 1.603 5 （ n = 11, r =0. 974 9 ＊ ＊ ）, and the control effect was 43.93% （22.93% - 55.04% ）. In citrus orchards with integrated prevention and control, the epidemic diffusion model was Y3 = 0. 366 3x - 0. 342 2 （ n = 11, r = 0. 989 8 ＊ ＊ ）, the control effect was 96.15% （94.95% -97.40% ）, and the annual diseased plant rate was 0.31%. Thus, HLB is preventable and controllable as long as integrated prevention and control work is implemented well.

Robust Resource Management Control for CO2 Emission and Reduction of Greenhouse Effect: Stochastic Game Approach

With the increasingly severe global warming, investments in clean technology, reforestation and political action have been studied to reduce CO2 emission. In this study, a nonlinear stochastic model is proposed to describe the dynamics of CO2 emission with control inputs: clean technology, reforestation and carbon tax, under stochastic uncertainties. For the efficient resources management, a robust tracking control is designed to force resources tracking a desired reference output. The worst-case effect of stochastic parametric fluctuations, external disturbances and uncertain initial conditions on the tracking performance is considered and minimized from the dynamic game theory perspective. This stochastic game problem, in which one player (stochastic uncertainty) maximizes the tracking error and another player (control input) minimizes the tracking error, could be equivalent to a robust minimax tracking problem. To avoid solving the HJI, a fuzzy model is proposed to approximate the nonlinear CO2 emission model. Then the nonlinear stochastic game problem could be easily solved by fuzzy stochastic game approach via LMI technique.

Performance Study of the MPC based on BPNN Prediction Model in Thermal Management System of Battery Electric Vehicles

In this paper,a model predictive control(MPC)based on back propagation neural network(BPNN)prediction model was proposed for compressor speed control of air conditioning system(ACS)and battery thermal management system(BTMS)coupling system of battery electric vehicle(BEV).In order to solve the problem of high cooling energy consumption and inferior thermal comfort in the cabin of the battery electric vehicle thermal management system(BEVTMS)during summer time,this paper combines the respective superiorities of artificial neural network(ANN)predictive modeling and MPC,and creatively combines the two methods and uses them in the control of BEVTMS.Firstly,based on ANN and heat transfer theory,BPNN prediction model,ACS and BTMS coupling system were established and verified.Secondly,a mathematical method of MPC was established to control the speed of the compressor.Then,the state parameters of the coupled system were predicted using a BPNN prediction model,and the predicted values were passed to the MPC,thus achieving accurate control of the compressor speed using the MPC.Finally,the effects of PID control and MPC based on BPNN prediction model on thermal comfort of cabin and compressor energy consumption at different ambient temperatures were compared in simulation under New European Driving Cycle(NEDC)conditions.The results showed for the constructed BPNN prediction model predicted and tested values of the selected parameters the mean squared error(MSE)ranged from 2.498%to 8.969%,mean absolute percentage error(MAPE)ranged from 4.197%to 8.986%,and mean absolute error(MAE)ranged from 3.202%to 8.476%.At ambient temperatures of 25℃,35℃ and 45℃,the MPC based on the BPNN prediction model reduced the cumulative discomfort time in the cabin by 100 s,39 s and 19 s,respectively,compared with the PID control.Under three NEDC conditions,the energy consumption is reduced by 1.82%,2.35%and 3.48%,respectively.When the ambient temperature was 35℃,the MPC based on BPNN prediction model can make the ACS and BTMS coupling system have better thermal comfort,and the energy saving effect of the compressor was more obvious with the temperature.

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control LFC is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-Area interconnected power system with wind turbines, this paper presents a distributed model predictive control DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints GRCs, load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints. © 2014 Chinese Association of Automation.

Application of Digital Twin in Smart Battery Management Systems

Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of the research and preliminary application of the digital twin in complex systems such as aerospace,we will have the opportunity to use the digital twin to solve the bottleneck of current battery research.Firstly,this paper arranges the development history,basic concepts and key technologies of the digital twin,and summarizes current research methods and challenges in battery modeling,state estimation,remaining useful life prediction,battery safety and control.Furthermore,based on digital twin we describe the solutions for battery digital modeling,real-time state estimation,dynamic charging control,dynamic thermal management,and dynamic equalization control in the intelligent battery management system.We also give development opportunities for digital twin in the battery field.Finally we summarize the development trends and challenges of smart battery management.

Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

The paper proposes an adoption of slope,elevation,speed and route distance preview to achieve optimal energymanagement of plug-in hybrid electric vehicles(PHEVs).Theapproach is to identify route features from historical and real-time traffic data,in which information fusion model and trafficprediction model are used to improve the information accuracy.Then,dynamic programming combined with equivalent con-sumption minimization strategy is used to compute an optimalsolution for real-time energy management.The solution is thereference for PHEV energy management control along the route.To improve the system's ability of handling changing situation,the study further explores predictive control model in the real-time control of the energy.A simulation is performed to modelPHEV under above energy control strategy with route preview.The results show that the average fuel consumption of PHEValong the previewed route with model predictive control(MPC)strategy can be reduced compared with optimal strategy andbase control strategy.

Hypertext Technique and its Application to Engineering Data Management

Since the late of previous decade, hypertext technique has been applied in many areas. A hypertext data model with version control which is applied to a digital delivery for engineering documents named Optical Disk based Electronic Archives Management System(ODEAMS) is presented first and it has successfully solved some problems in engineering data management. Then, this paper describes some details to implement the hypertext network in ODEAMS after introducing the requirements and characters of engineering data management.

Model of Healthcare-Associated Infection Control in Primary Health Care Institutions:A Structural Equation Modeling

The purpose of this study was to construct the model of organization system,managemcnt,training and surveillance in healthcare-associated infection prevention and control(IC)of primary health care institutions and identify its efleet on patient safety and decreasing economic burden by standardizing IC.A cross-sectional survey was conducted with questionnaires.Data were collected from 268 primary health care institutions in Hubei province,China.Hypotheses on the model of IC were analyzed by means of confirmatory factor analysis and structural equation modeling.The results showed that the fit indices of the hypothesized model of IC satisfied recommended levels:root mean square error of approximation(RMSEA)=0.071;comparative fit index(CFI)=0.965;tucker lewis index(TLI)=0.956:weighted root mean square residual(WRMR)=1.014.The model showed that organization system had a direct effect on management(β=0.311.P<0.01),and training(β=0.365,P<0.01).Management and training played an intermediary role that partially promoted organization system impact on surveillance.Results also showed that institutional factors such as the number of physicians、the ninnber of nurses,the designated capacity of beds,the actual number of open beds and surgery trips had positive impacts on management(β=0.050,P<0.01;β=0.181,P<0.01;β-0.111.P<0.01;β=0.064,P<0.01;β=0.084,P=0.04);nd training(β=0.21,P=0.03;β=0.050,P=0.02;β=0.586.P=0.01;0=0.995,P=0.02;β=0.223.P=0.03).In conclusion.the model of organization system,managemcnt,training and surveillancc in IC of primary health care institutions is valuable tor guiding IC practice.

Impact of Capacity Parameters on Flexible Inventory Control Decision Model

From the mathematical point of view,the flexible inventory control model is proved in the practical problem application and the rationality of the capacity parameter selection and calculation.The purpose is to actively respond to demand fluctuations when there is a demand forecast error or a missing part of the demand information,and to avoid the risk of passive variable demand forecasting to set the immutable inventory capacity.At the same time,the game is controlled by the flexible and variable inventory control strategy and the customer’s willingness to demand.The paper mainly studies the influence of the setting of capacity parameters on the booking-limit decision and its benefits under the control of flexible space with variable total capacity.Through the two trends of capacity increase flexibility and capacity reduction flexibility in the flexible inventory control model,the mathematical performance and marginal utility methods are introduced to change the performance of the booking-limit control decision model under different scenarios.The correlation analysis between the capacity limit level and the return under the optimal Bookinglimit decision,and the above two flexibility parameters are obtained.

Economic Model Predictive Control for Hot Water Based Heating Systems in Smart Buildings

This paper presents a study to optimize the heating energy costs in a residential building with varying electricity price signals based on an Economic Model Predictive Controller (EMPC). The investigated heating system consists of an air source heat pump (ASHP) incorporated with a hot water tank as active Thermal Energy Storage (TES), where two optimization problems are integrated together to optimize both the ASHP electricity consumption and the building heating consumption utilizing a heat dynamic model of the building. The results show that the proposed EMPC can save the energy cost by load shifting compared with some reference cases.

Cost Management Research Based on E-commerce Logistics Mode

This paper describes the real-time and the importance of the study about e-commerce logistics cost management ,analyzes the development status of today＇ s e-commerce logistics and compares it with the traditional logistics, defines the e-commerce logistics cost management .Based on this, it summarizes the cost structure of e-commerce logistics, proposes factors affecting the cost of logistics, builds the basic ideas of logistics cost management, and thus introducts the accounting methods about logistics cost .Finally, puts forward to the content and methods of logistics costs budget, controling the cost effectively.

Real-time Energy Management of Low-carbon Ship Microgrid Based on Data-driven Stochastic Model Predictive Control

With increasing restrictions on ship carbon emis-sions,it has become a trend for ships to use zero-carbon energy such as solar to replace traditional fossil energy.However,uncer-tainties of solar energy and load affect safe and stable operation of the ship microgrid.In order to deal with uncertainties and real-time requirements and promote application of ship zero-carbon energy,we propose a real-time energy management strategy based on data-driven stochastic model predictive control.First,we establish a ship photovoltaic and load scenario set consid-ering time-sequential correlation of prediction error through three steps.Three steps include probability prediction,equal probability inverse transformation scenario set generation,and simultaneous backward method scenario set reduction.Second,combined with scenario prediction information and rolling op-timization feedback correction,we propose a stochastic model predictive control energy management strategy.In each scenario,the proposed strategy has the lowest expected operational cost of control output.Then,we train the random forest machine learn-ing regression algorithm to carry out multivariable regression on samples generated by running the stochastic model predictive control.Finally,a low-carbon ship microgrid with photovoltaic is simulated.Simulation results demonstrate the proposed strategy can achieve both real-time application of the strategy,as well as operational cost and carbon emission optimization performance close to stochastic model predictive control.Index Terms-Data-driven stochastic model predictive control,low-carbon ship microgrid,machine learning,real-time energy management,time-sequential correlation.