Preparedness training programs improved the emergency response and crucial outcomes among nursing students:a scoping review

Objective:The study aims to review and discuss the outcomes of emergency preparedness training programs among nursing students.Methods:A scoping review was conducted based on the guidelines of Arskey and O’Malley.Databases through PubMed,Science Direct,and Google Scholar were used to obtain the relevant articles within the scoping review.Results:The researchers found 1175 articles.After manual review,28 articles were identified and assessed.The review reported that the emergency preparedness training program significantly affects preparedness,knowledge,performance,self-efficacy,learning motivation,self-confidence,satisfaction,critical decision-making,and problem-solving.Conclusions:Emergency training effectively increases knowledge,skills,confidence,satisfaction,and team performance.

Compliance to Medication Management and Mental Health Status of Elderly Hypertensive Patients: Basis of Educational Health Program

Objective:The study aimed to assess medication management compliance and mental health in elderly patients with hypertension.Method:The study evaluated medication compliance and mental health status of elderly hypertensive patients in China using simple random sampling.Data was collected using the Morisky Medication Compliance Questionnaire,Hospital Anxiety and Depression Scale,and a checklist.Ethical practices were strictly observed.Results:A study of 100 elderly hypertensive patients found poor drug management compliance,with female patients showing worse compliance.Female patients were more vulnerable to anxiety and depression.The study also found no significant association between gender,age,education level,marital status,living standards,and medication compliance.Barriers to medication management included food and daily necessities,lack of awareness about the importance of drug treatment,and basic family needs.The lowest-ranked barriers were lack of support from government health clinics,low income,and lack of family support.Conclusion:Based on the results,the study proposes an educational plan for elderly hypertensive patients and their families,to be evaluated and implemented by the hospital and township community service center.The plan aims to improve medication management and lifestyle modification compliance,encourage active participation,and provide access to medical and mental health clinics,support groups,and counseling services.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

基于Stackelberg博弈的燃料电池混合动力汽车跟车能量管理

跟车场景下燃料电池混合动力汽车(FCHEV)的速度与能量管理协同优化是实现车辆节能的重要有效手段,针对现有策略中双能量源退化与能耗耦合关系不明,且难以兼顾全局优化与实时性能的问题,提出一种基于Stackelberg博弈的FCHEV跟车能量管理策略。首先,建立了燃料电池/锂电池的能耗与性能退化模型,并纳入到统一量纲的整车综合使用成本函数中;其次,提出了基于分层解耦的跟车能量管理策略,实现跟车速度与功率分配的解耦控制;最后,综合考虑跟车安全性、舒适性、燃料经济性和能源耐久性,建立跟车控制层与能量管理层对应的双层规划模型,并基于Stackelberg博弈思想设计了双层差分遗传算法对策略核心参数进行离线优化。仿真和实验结果表明:相较于模型预测控制方法,该方法可降低平均车间距误差37.7%、平均冲击度2.4%、等效氢气消耗9.3%和能源退化成本13.9%,实现了优化性能与实时性的兼顾。

Implementation and achievements of enhanced recovery after surgery program in perioperative management of gastric cancer patients

Objective:The enhanced recovery after surgery(ERAS)program is less implemented in gastric cancer patients.The purpose of this survey is to investigate the implementation status of ERAS in perioperative period in gastric cancer.Methods:This clinical observational study enrolled 329 patients between January 2020 and August 2020 in a single gastric cancer center.The questionnaire consisted of 4 par ts:basic information,preoperative status,intraoperative status,and postoperative status of ERAS implementation in gastric cancer surgery.Results:In the preoperative period,patients'education and counseling(100%)were well adopted.Smoking cessation(34.6%),drinking cessation(36.9%),avoidance of preoperative mechanical bowel preparation(24.3%),respiratory function training(11.2%),and administration of carbohydrate-rich drink before surgery(0.6%)were relatively not well adopted.During the operation,maintenance of intraoperative normothermia and fluid management(100%),as well as epidural analgesia(81.5%),were well adopted.Thromboprophylaxis was performed in 133(40.4%)patients.In the postoperative period,early active mobilization was implemented about 9.5 h,and early ambulation was implemented about 39.5 h,after surgery.A total of 140(42.5%)patients received prolonged prophylactic antibiotics;268(81.5%)patients were provided diet upon gas passage;and 320(97.3%)patients received intravenous fluid administration more than 5 d after surgery.The practice rate of early removal of urinary catheter(0%)and nasogastric tube(15.5%)was relatively low.A total of 11(3.3%)patients experienced postoperative complication,and 1(0.3%)patient received unplanned reoperation.The average costs were￥59,500,and the average hospital stay was 12(5,36)d.Conclusions:Standard perioperative management of ERAS program in gastric cancer surgery in China still requires improvement.

Multistage Stochastic Programming Model for the Portfolio Problem of a Property-Liability Insurance Company

The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod model for its portfolio problem. The model is a multistage stochastic programming which considers transaction costs, cash flow between time periods, and the matching of asset and liability; it does not depend on the assumption for normality of return distribution. Additionally, an investment constraint is added. The numerical example manifests that the multiperiod model can more effectively assist the property-liability insurer to determine the optimal composition of insurance and investment portfolio and outperforms the single period one.

Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive Dynamic Programming

The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming(ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First,the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy transmissions.Second, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.

A simulation-based two-stage interval-stochastic programming model for water resources management in Kaidu-Konqi watershed,China

This study presented a simulation-based two-stage interval-stochastic programming （STIP） model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing （ITSP）. The distributed hydrological model was used for establishing a rainfall-runoff forecast system, while random parameters were pro- vided by the statistical analysis of simulation outcomes water resources management planning in Kaidu-Konqi The developed STIP model was applied to a real case of watershed, where three scenarios with different water re- sources management policies were analyzed. The results indicated that water shortage mainly occurred in agri- culture, ecology and forestry sectors. In comparison, the water demand from municipality, industry and stock- breeding sectors can be satisfied due to their lower consumptions and higher economic values. Different policies for ecological water allocation can result in varied system benefits, and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.

Goal programming approach for sustainable forest management(case study in Iranian Caspian forests)

We used a goal programming technique to determine the optimal harvest volume for the Iranian Caspian forest. We collected data including volume, growth, wood price at forest roadside, and variable harvesting costs. The allometric method was used to quantify seques- trated carbon. Regression analysis was used to derive growth models. Expected mean price was estimated using wood price and variable harvesting costs. Questionnaire was used to determine the constraints and the equation coefficients of the goal programming model. The optimal volume was determined using the goal programming method according to multipurpose forest management. LINGO software was used for analysis. Results indicated that the optimum volumes of species were 250.25 m3.ha-1 for beech, 59 m3.ha-1 for hornbeam, 73 m3.ha-1 for oak, 41 m3.ha-1 for alder, and 32 m3.ha-1 for other species. The total optimum volume is 455.25 m3.ha-1.

SMART目标管理方案结合程序化管理在骨科器械管理中的效果研究

目的:探究SMART目标管理方案结合程序化管理在骨科器械管理中的临床应用效果。方法:从目的明确性、目的可评价性、目的可达成性和目标相关性4个方面实施SMART方案结合程序化管理的骨科器械管理模式。选取医院收治的80例骨科手术患者,按照随机数表法将其分为对照组和观察组,每组40例,对照组使用的手术器械实施常规模式器械管理,观察组使用的手术器械实施SMART方案结合程序化管理,采用自制调查问卷调研手术主刀医生、责任护士和患者对器械的临床服务质量满意度,比较两组器械处置时间、器械不良事件发生率和临床服务质量。结果:观察组使用的手术器械从消毒供应室接收到消毒程序结束时间、器械冷却至手术室接收时间和处置总时间分别为(10.37±2.39)min、(16.45±3.02)min和(46.28±4.28)min,且均少于对照组手术器械的处置时间,差异有统计学意义(t=6.382,t=7.294,t=6.045;P<0.05);观察组使用的手术器械数量错误、器械不洁、器械湿包以及器械不良事件总发生率均为0,显著低于对照组,差异有统计学意义(x^(2)=6.382,x^(2)=7.294,x^(2)=6.045,x^(2)=9.758;P<0.05);患者、主刀医生和责任护士对观察组使用的手术器械临床服务质量总满意度均为100%,显著高于对照组,差异有统计学意义(x^(2)=5.403,x^(2)=7.009,x^(2)=6.297;P<0.05)。结论:SMART方案结合程序化管理应用于骨科器械管理可有效缩短器械处置时间,降低器械不良事件发生率,提高器械临床服务质量。

An integrated pest management program for managing fusarium head blight disease in cereals

Fusarium head blight(FHB)is a worldwide devastating disease of small grain cereals and Fusarium graminearum species complex(FGSC)is the major pathogen causing the disease.The epidemics of FHB lead to the reduction of grain yield and economic losses.Additionally,mycotoxins produced by the FHB pathogens are hazardous to the health of human and livestock.In this review,we summarize the epidemiology of FHB,and introduce effects of this disease on economy,environment and food safety.We focus on the integrated management approaches for controlling FHB including agronomic practices,resistant cultivars,chemical control,and biocontrol.In addition,we also discuss the potential novel management strategies against FHB and mycotoxin.

基于非线性规划与XGBoost的燃料电池汽车多目标能量管理策略

为了解决燃料电池汽车功率分配中的实时性与准确性问题,提出使用离线非线性规划+在线XGBoost算法对燃料电池汽车功率进行预测.首先搭建燃料电池混合动力汽车的动力系统模型,并且通过聚类分析获取车辆行驶的典型混合工况;其次使用非线性规划算法离线计算在该工况下燃料电池与锂电池的最优分配比例;最后XGBoost算法以非线性规划计算结果为训练数据进行模型训练验证.结果表明:所提出的算法强化了目前离线计算中对于燃料电池混合动力系统动态性能多目标优化的考虑,增强了在线机器学习训练数据的准确性,同时所提出的XGBoost算法可以加快计算速度以及避免数据的过拟合,实现对燃料电池混合动力汽车功率的精确估计.

Penalty model for delay of bidding section construction period in South-to-North Water Diversion Eastern Route Project from perspective of programs

According to the multi-project and program management theory, this paper analyzes the program generation principle and establishes a program based on progress goals. On the basis of the present situation of calculation of penalty for delay of the bidding section construction period with the critical path method, we studied the effects of contractor-induced delay of the bidding section construction period in detail, including the effects on the construction period of the bidding section itself, the earliest start times of the next bidding section and other subsequent bidding sections, and the construction period of the program, and then constructed a penalty model for delay of the bidding section construction period from the perspective of programs. Using the penalty model, we conducted a practical analysis of penalty for delay of the construction period of the Baoying station program in the South-to-North Water Diversion Project. The model can help determine the amount of penalty for delay of the construction period in bidding sections scientifically and reasonably,

Bilevel Programming Model for Joint Scheduling of Arrival and Departure Flights Based on Traffic Scenario

In order to meet the needs of collaborative decision making,considering the different demands of air traffic control units,airlines,airports and passengers in various traffic scenarios,the joint scheduling problem of arrival and departure flights is studied systematically.According to the matching degree of capacity and flow,it is determined that the traffic state of arrival/departure operation in a certain period is peak or off-peak.The demands of all parties in each traffic state are analyzed,and the mathematical models of arrival/departure flight scheduling in each traffic state are established.Aiming at the four kinds of joint operation traffic scenarios of arrival and departure,the corresponding bi-level programming models for joint scheduling of arrival and departure flights are established,respectively,and the elitism genetic algorithm is designed to solve the models.The results show that:Compared with the first-come-firstserved method,in the scenarios of arrival peak&departure off-peak and arrival peak&departure peak,the departure flight equilibrium satisfaction is improved,and the runway occupation time of departure flight flow is reduced by 38.8%.In the scenarios of arrival off-peak&departure off-peak and departure peak&arrival off-peak,the arrival flight equilibrium delay time is significantly reduced,the departure flight equilibrium satisfaction is improved by 77.6%,and the runway occupation time of departure flight flow is reduced by 46.6%.Compared with other four kinds of strategies,the optimal scheduling method can better balance fairness and efficiency,so the scheduling results are more reasonable.

Evaluating the Performance of Iranian Football Teams Utilizing Linear Programming

In this paper, we utilize Data Envelopment Analysis (DEA), which is a linear programming-based technique, for evaluating the performance of the teams which operate in the Iranian primer football league. We use Analytical Hierarchy Process (AHP) technique for aggregating the sub-factors which involve in input-output factors, and then DEA is used for calculating the efficiency measures. Also, AHP is used to construct some weight restrictions for increasing the discrimination power of the used DEA model. For calculating the efficiency measures, input-oriented weight-restricted BCC model is utilized.

Evaluation of the effects of a leader-training program on public health nurse leadership for staff in Japan

This paper is a report of a study of the effects of a leader-training program, focusing on how public health nurse (PHN) leaders manage staff. In Japan, a system of training after graduation or training after acquisition of the PHN license has not been established. At present, PHN leaders are unable to devote as much time as they previously could to in-service education, and this has made it difficult to establish a suitable full-fledged in-service education system. Therefore, it is paramount for PHN leaders to acquire the necessary competence to train staff efficiently and, ultimately, play a more prominent role in the in-service education of novice PHNs. The effecttiveness of this program was evaluated before and after conducting the program over a 1-year period. A total of 145 leaders working for local government agencies in Japan participated in the program, and data from 87 participants were analyzed. Before the training program, Plan-Do-Check-Act cycle activities were less frequent than after the program. Intervention using a leader-training program that combines group and on-the-job training is effective for PHN leaders.

Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming U+0028 ADP U+0029 technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method. © 2017 Chinese Association of Automation.

Energy Management of Networked Smart Railway Stations Considering Regenerative Braking, Energy Storage System, and Photovoltaic Units

The networking of microgrids has received significant attention in the form of a smart grid.In this paper,a set of smart railway stations,which is assumed as microgrids,is connected together.It has been tried to manage the energy exchanged between the networked microgrids to reduce received energy from the utility grid.Also,the operational costs of stations under various conditions decrease by applying the proposed method.The smart railway stations are studied in the presence of photovoltaic(PV)units,energy storage systems(ESSs),and regenerative braking strategies.Studying regenerative braking is one of the essential contributions.Moreover,the stochastic behaviors of the ESS’s initial state of energy and the uncertainty of PV power generation are taken into account through a scenario-based method.The networked microgrid scheme of railway stations(based on coordinated operation and scheduling)and independent operation of railway stations are studied.The proposed method is applied to realistic case studies,including three stations of Line 3 of Tehran Urban and Suburban Railway Operation Company(TUSROC).The rolling stock is simulated in the MATLAB environment.Thus,the coordinated operation of networked microgrids and independent operation of railway stations are optimized in the GAMS environment utilizing mixed-integer linear programming(MILP).

Impact of chronic disease self-management programs on type 2 diabetes management in primary care

AIM: To assess the effectiveness of the Chronic Disease Self-Management Program(CDSMP) on glycated hemoglobin A1c(HbA1c) and selected self-reported measures.METHODS: We compared patients who received a diabetes self-care behavioral intervention, the CDSMP developed at the Stanford University, with controls whoreceived usual care on their HbA1c and selected self-reported measures, including diabetes self-care activities, health-related quality of life(HRQOL), pain and fatigue. The subjects were a subset of participants enrolled in a randomized controlled trial that took place at seven regional clinics of a university-affiliated integrated healthcare system of a multi-specialty group practice between January 2009 and June 2011. The primary outcome was change in HbA1c from randomization to 12 mo. Data were analyzed using multilevel statistical models and linear mixed models to provide unbiased estimates of intervention effects.RESULTS: Demographic and baseline clinical characteristics were generally comparable between the two groups. The average baseline HbA1c values in the CDSMP and control groups were 9.4% and 9.2%, respectively. Significant reductions in HbA1c were seen at 12 mo for the two groups, with adjusted changes around 0.6%(P < 0.0001), but the reductions did not differ significantly between the two groups(P = 0.885). Few significant differences were observed in participants' diabetes self-care activities. No significant differences were observed in the participants' HRQOL, pain, or fatigue measures.CONCLUSION: The CDSMP intervention may not lower HbA1c any better than good routine care in an integrated healthcare system. More research is needed to understand the benefits of self-management programs in primary care in different settings and populations.