A COMBINED MODEL OF WIND, WAVE, TIDE AND STORM SURGES

A combined numerical model of wind, wave, tide, and storm surges was built on the basis of the “wind field model in limited sea surface areas”. When used to forecast the sea surface wind, wave height and water level, it can describe them very well.

Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation

This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

Study of establishing disease-syndrome combined with animal model for immune thrombocytopenic purpura without additional conditions

Objective:To explore the feasibility of establishing the disease-syndrome combined animal model for immune thrombocytopenic purpura(ITP)without additional conditions.Methods:Three batches of data related to the ITP model mice obtained by replication at different time were analyzed,and whether the APS-injected model mice replicated through the passive immune modeling method could simulate the pathogenesis and clinical characteristics of human ITP was evaluated according to the differentiation criteria for diseasesyndrome combined model.Results:The APS-injected replicated ITP model mice possessed the following traits:(1)Compared with the normal group,the platelet count was significantly decreased,and coagulation time was significantly increased in the model group(P<.01).(2)Compared with the normal group,the medullary thrombocytogenous megakaryocytes were significantly decreased(P<.05,.01,.001).(3)The APS-injected sites and other parts of the model mice had spontaneous hemorrhage.(4)Behavioral changing signs were observed 1 week after the modeling(i.e.low activity,delayed activity,poor appetite,skin petechia/hemorrhage and spontaneous hemorrhage at the injected sites or other parts),and were getting more and more severe.Conclusion:According to the syndrome differentiation criteria for disease-syndrome combined model of ITP,the APS-injected animal model of ITP replicated through the passive immune modeling method without additional conditions possesses the characteristics of disease-syndrome combined model.It provides an ideal tool for the development of traditional Chinese medicine pharmacology experiment.

Effects of ginsenoside of stem and leaf combined with choline on learning and memory ability of rat models with Alzheimer diseases

BACKGROUND: Central adrenergic nerve and 5-serotonergic nerve can influence central cholinergic nerve on learning and memory and make easy for study; however, ginsenoside of stem and leaf (GSL) can improve functions of central adrenergic nerve; moreover, 5-serotonergic nerve and the combination with choline can produce synergistic effect and enhance learning and memory ability so as to improve learning and memory disorder of patients with Alzheimer disease (AD). OBJECTIVE: To observe the effects of GSL combining with choline on learning and memory of AD model rats.DESIGN: Randomized grouping design and controlled animal study.SETTING: Department of Pharmacology, Taishan Medical College. MATERIALS: The experiment was carried out in the Pharmacological Department of Medical College of Jilin University from October 1996 to January 1997. Forty healthy male Wistar rats of clean grade were randomly divided into 5 groups, including sham-injury group, model group, GSL group, choline group and combination group, with 8 rats in each group. Main medications: GSL with the volume more than 92.8% was provided by Department of Chemistry, Norman Bethune Medical College of Jilin University. Panaxatriol, the main component, was detected with thin layer scanning technique and regarded as the index of GSL quality [(55±1)%, CV = 2%, n = 5]. Choline was provided by the Third Shanghai Laboratory Factory. METHODS: 150 nmol quinolinic acid was used to damage bilateral Meynert basal nuclei of adult rats so as to establish AD models. Rats in GSL, choline and combination groups were intragastric administrated with 400 mg/kg GSL, 200 mg/kg choline (20 mL/kg), and both respectively last for 17 days starting from two days before operation. Rats in sham-injury group and model group were perfused with the same volume of distilled water once in each morning for the same days. ① Passive avoidance step-down test: Five minutes later, rats jumped up safe platform when they were shocked with 36 V alternating current. If rats jumped down from the platform and the feet touched railings, the response was wrong. Numbers of wrong response were recorded within 3 minutes, and then the test was redone after 24 hours. ② Morris water-maze spatial localization task: Swimming from jumping-off to platform directly was regarded as right response. Additionally, 4 successively right responses were regarded as the standard. Each rat was trained 10 times a day with 120 s per time for 3 successive days. The interval was 30 s. Three days later, numbers of right response were recorded. The training times were increased to 30 for unlearned rats. ③ Measurement of activity of choilne acetylase in cerebral cortex: Rats were sacrificed at 17 days after operation to obtain cerebral cortex to measure activity of choilne acetylase with radiochemistry technique. ④ Synergistic effect: It was expressed as Q value: Q value = factual incorporative effect/anticipant incorporative effect; Q ≥ 1 was regarded as synergistic effect. Anticipant incorporative effect = (EA+EB-EA·EB), EA and EB were single timing effect, respectively in GSL group and choline group. E (step-down test and Morris water maze test) = (x in model group - factual value in medicine groups)/x in model group; E (activity of choilne acetylase) = (factual value in medicine groups -x in model group)/x in model group. MAIN OUTCOME MEASURES: ① Passive avoidance step-down test and Morris water-maze spatial localization task in the study of learning and memory; ② activity of choilne acetylase. RESULTS: All 40 rats were involved in the final analysis. ① Passive avoidance response: At learning phase on first day and retesting phase on the next day, numbers of wrong responses within 3 minutes were more in model group than sham operation group, and there was significant difference [(5.88±1.46), (2.25±0.87) times; (2.63±1.06), (0.50±0.53) times; P < 0.01]; numbers of wrong responses within 3 minutes were less in combination group than model group, and there was significant difference [learning phase: (1.12±0.83), (5.88±1.46) times; retesting phase: (0.38±0.74), (2.63±1.06) times, P < 0.01]; moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.07 and 1.59, respectively and it showed synergistic effect. ② Spatial localization task: Training times were more in model group than sham operation group, and there was significant difference [(2.9±2.5), (12.6±3.5) times; P < 0.01]. Training times were less in combination group than model group, and there was significant difference [(11.8±2.4), (27.9±2.5) times, P < 0.01]; moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.07 and it showed synergistic effect. ③ Activity of choilne acetylase: Activity was lower in model group than sham operation group, and there was significant difference [(30.56±8.33), (61.11±8.33) nkat/g; P < 0.01]. Activity was higher in combination group than model group and there was significant difference [(50.00±8.33), (30.56±8.33) nkat/g, P < 0.01]; moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.5 and it showed synergistic effect. CONCLUSION: GSL in combination with choline can synergically improve the disorder of learning and memory of AD model rats. Its mechanism may be involved in enhancing the function of central cholinergic system.

Evaluation of the Application Effect of Short Video Combined with BOPPPS Teaching Mode in Clinical Anesthesia Teaching

Objective:To study the application effect of short video combined with BOPPPS teaching mode in clinical anesthesia practice.Method:48 students assigned to clinical anesthesia in digestive endoscopy of Shanxi Bethune Hospital from July 1,2022 to April 1,2023 were selected as research objects.They were randomly divided into the control group(PowerPoint presentation teaching group)and the observation group(short video combined with BOPPPS teaching group),with 24 students in each group.After the internship,the students’theoretical and technical scores were tested,the effects of the two teaching modes were compared,and the students’satisfaction was investigated.Results:The test scores of students in the observation group were significantly better than those in the control group(P<0.05).The short video combined with BOPPPS teaching mode can significantly improve students’learning interest,operation skills,and memory(P<0.05).The students’satisfaction in the observation group was higher than that in the control group(P<0.05).Conclusion:In clinical practice,the application of short video combined with BOPPPS teaching mode has achieved great effect,which is worth further promotion and research.

Application of BIM Technology in Rapid Modeling of Residential Building Design

Combining with engineering examples,this paper introduces BIM into architectural design model and adopts Revit rapid modeling.It focuses on realizing rapid combination modeling of similar components through secondary development,improving information conversion efficiency between engineering CAD and Revit building structure modeling,and quickly generating BIM structure construction drawings.

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.

Combined Prediction for Vehicle Speed with Fixed Route

Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning,but the prediction accuracy still needs to be improved.The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy;problems,such as over fitting,occur in the process of improving prediction accuracy.The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction.By combining the two prediction algorithms,the fusion of prediction performance is achieved,the limit of the single prediction performance is crossed,and the goal of improving vehicle speed prediction performance is achieved.In this paper,an extraction method suitable for fixed route vehicle speed is designed.The application of Markov and back propagation(BP)neural network in predictions is introduced.Three new combined prediction methods,all named Markov and BP Neural Network(MBNN)combined prediction algorithm,are proposed,which make full use of the advantages of Markov and BP neural network algorithms.Finally,the comparison among the prediction methods has been carried out.The results show that the three MBNN models have improved by about 19%,28%,and 29%compared with the Markov prediction model,which has better performance in the single prediction models.Overall,the MBNN combined prediction models can improve the prediction accuracy by 25.3%on average,which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

Settlement Prediction of Dredger Fill with the Optimal Combination Model

Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model( i. e., cannot reflect various regulations of settlement at some stages or the entire process). In this study,the correlation coefficient,maximum error values,and other values were obtained according to the fitting and predicted results of a single model. The coefficient of variation was then introduced to determine the weight of each model forming the combination. The proposed model was used to fit and predict for settlement and overcome the issue of utilizing a single model while determining the weight. The fitting predictive effect was also analyzed using the settlement fitting precision results. The fitting precision of optimizing the combination model is high. The predicted data of the post-construction settlement are closer to the calculated value of the settlement monitoring data. Moreover,the proposed model has good practicability,does not require the interval data of settlement,and restricts the model number. Thus,this model can be applied in the engineering field.

山东省中医类医院卫生人力资源需求预测

目的了解山东省中医类医院卫生人力资源的现状,预测卫生人力资源未来的需求量并提出合理建议,以期为相关部门制定中医药人力资源规划提供依据和数据支持。方法运用差分自回归移动平均(auto-regressive moving average,ARIMA)模型、灰色系统预测模型(grey system forecasting model,GM)中的GM(1,1)模型以及两者的线性组合模型预测2021—2025年山东省中医类医院卫生人力资源需求量,比较不同模型预测的精准度。结果组合模型的系统误差小,预测效果最好;卫生技术人员、执业(助理)医师、中医类别执业(助理)医师、注册护士、药师(士)及中药师(士)2025年对应的人力资源预测值分别是107457人、43304人、22807人、51372人、5718人、3242人。结论山东省中医类别执业(助理)医师数量储备充足,但中药师(士)相对短缺,人才结构不合理,医护比有待优化。建议政府适当地增加中药师(士)的编制,促进执业(助理)医师与中药师(士)平衡发展;增加对中医类医院的财政拨款,加强人才引进力度,创新人才培养机制,优化山东省中医药人才结构;制定科学合理的排班制度,提高护士的社会地位,进一步优化医护比。

A new hybrid method with data‑characteristic‑driven analysis for artificial intelligence and robotics index return forecasting

Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.

University's Dynamic Performance Appraisal System Based on Data

Teachers are key participants in universities,and the performance appraisal of teacher is an important part of college work.By analyzing the data of behavior generated by different departments in university,analytic hierarchy process(AHP) is used to establish the preliminary library of performance indicators for teachers,and the correlation among all the performance indicators is inspected by using data mining method at this time.On this basis,a more objective,comprehensive and scientific performance appraisal system is constructed through principal components analysis(PCA),which is more suitable for university itself.Finally,in order to solve the problems existed in current performance appraisal system,a dynamic evaluation model is put forward by regulating the weight of indicator according to the historical data,highlighting the continuity of the system.

Estimating PM_(2.5) concentrations in a central region of China usinga three-stage model

Owing to uneven environmental monitoring site distribution,there are significant spatial data gaps for concentrations of ambientfine particles with diameters≤2.5µm(PM_(2.5))obtained using traditional monitoring methods.Satellite products are an alternative data source for locations where monitoring sites are unavailable.The Moderate Resolution Imaging Spectroradiometer(MODIS)aerosol optical depth(AOD)product has been widely used in PM_(2.5) assessment for years;however,it has obvious data gaps in winter.Here,the Visible Infrared Imaging Radiometer Suite(VIIRS)AOD was applied to supplement MODIS AOD data to obtain a fused AOD dataset.A three-stage model consisting of a corrected AOD model,mixed effects model,and geographically weighted regression model was developed and used with meteorological and vegetation factors to estimate PM_(2.5).Results showed overall modelfitting by cross-validation(CV)with an R^(2) of 0.92,mean absolute error of 5.72μg/m^(3),and root mean square error of 7.15μg/m^(3).The combination of MODIS AOD and VIIRS AOD was a suitable method for enhancing AOD coverage.The CV R^(2) value of the three-stage model(0.92)was higher than that of the two-stage model(0.9).Hence,the three-stage model could achieve a betterfit in estimating PM_(2.5) on a regional scale.

医院季度收入预测的比较研究

目的 探讨具有季度波动和长期趋势特征事物预测问题的方法论,为卫生领域同类预测问题提供定量决策方法论依据。方法 以某所医院季度收入预测作为实证算例载体,分别用乘积季节SARIMA法、X11-ARIMA法、X11-曲线函数外推法建立简单组合模型以及二次加权组合模型。结果 乘积季节SARIMA模型、X11-ARIMA模型、X11-二次多项式的平均相对误差分别为1.28%、0.96%、1.11%。对以上简单组合模型继续进行深度加权组合,预测误差为0.70%,模型预测性能更优。结论 SARIMA模型、X11-ARIMA模型、X11-二次多项式以及二次组合模型对于局部周期性波动、全局稳定性趋势的时序数据资料拟合效果不错;对于卫生管理领域预测问题有适用性,鉴于多种匹配方法原理不同、信息利用差异大,基于多种方法的二次组合模型取得更优性能。

Predicting Surface Urban Heat Island in Meihekou City, China: A Combination Method of Monte Carlo and Random Forest

Given the rapid urbanization worldwide, Urban Heat Island(UHI) effect has been a severe issue limiting urban sustainability in both large and small cities. In order to study the spatial pattern of Surface urban heat island(SUHI) in China’s Meihekou City, a combination method of Monte Carlo and Random Forest Regression(MC-RFR) is developed to construct the relationship between landscape pattern indices and Land Surface Temperature(LST). In this method, Monte Carlo acceptance-rejection sampling was added to the bootstrap layer of RFR to ensure the sensitivity of RFR to outliners of SUHI effect. The SHUI in 2030 was predicted by using this MC-RFR and the modeled future landscape pattern by Cellular Automata and Markov combination model(CA-Markov). Results reveal that forestland can greatly alleviate the impact of SUHI effect, while reasonable construction of urban land can also slow down the rising trend of SUHI. MC-RFR performs better for characterizing the relationship between landscape pattern and LST than single RFR or Linear Regression model. By 2030, the overall SUHI effect of Meihekou will be greatly enhanced, and the center of urban development will gradually shift to the central and western regions of the city. We suggest that urban designer and managers should concentrate vegetation and disperse built-up land to weaken the SUHI in the construction of new urban areas for its sustainability.

A Hierarchical Forecasting Model for China's Foreign Trade

Analyzing the underlying characteristics of trade values movements has attracted much attention in the domestic research.However,the proposed understanding of these characteristics is limited by the intrinsic complexity of the imports/exports.Since economic systems are naturally organized by hierarchies,a novel hierarchical model is proposed in this paper to forecast China's foreign trade.First,the foreign trade data are disaggregated from perspectives of trading partners and trading products,forming two independent hierarchical models with total exports and imports as target variables.Second,a bottom-up strategy is applied.All bottom time series are modelled by corresponding control variables according to trading theories.Forecasts for bottom time series are then combined to generate initial forecasts for total exports and imports.Finally,forecasts for total imports and exports from the two hierarchical models,plus a single VECM model are combined to generate final forecasts.Empirical experiments show that this proposed forecasting model approach significantly outperforms benchmark models and produces consistent forecasts for both total imports and exports or detailed items,which helps a lot for analyzing future trading structure evolution and making foreign trade policies.

Toward equation structural modeling:an integration of interpretive structural modeling and structural equation modeling

Interpretive structural modeling(ISM)is an interactive process in which a malformed(bad structured)problem is structured into a comprehensive systematic model.Yet,despite many advantages that ISM provides,this method has some shortcomings,the most important one of which is its reliance on participants’intuition and judgment.This problem undermines the validity of ISM.To solve this problem and further enhance the ISM method,the present study proposes a method called equation structural modeling(ESM),which draws on the capacities of structural equation modeling(SEM).As such,ESM provides a statistically verifiable framework and provides a graphical,hierarchical and intuitive model.

Statistical method in quark combination model

We present a new method for solving the probability distribution for baryons,antibaryons,and mesons at the hadronization of the constituent quark and antiquark system.The hadronization is governed by the quark combination rule in the quark combination model developed by the Shandong Group.We employ the method of the generating function to derive the outcome of the quark combination rule,which is significantly simpler and easier to generalize than the original method.Furthermore,we use the formula of the quark combination rule and its generalization to study the property of the multiplicity distribution of net-protons.Taking a naive case of quark number fluctuations and correlations at hadronization,we calculate ratios of multiplicity cumulants of final-state net-protons and discuss the potential applicability of the quark combination model by studying hadronic multiplicity fluctuations and the underlying phase transition property in relativistic heavy-ion collisions.

Toxic effects of three perfluorinated or polyfluorinated compounds(PFCs)on two strains of freshwater algae:Implications for ecological risk assessments

Perfluorinated or polyfluorinated compounds(PFCs)continue entering to the environmental as individuals or mixtures,but their toxicological information remains largely unknown.Here,we investigated the toxic effects and ecological risks of Perfluorooctane sulfonic acid(PFOS)and its substitutes on prokaryotes(Chlorella vulgaris)and eukaryotes(Microcystis aeruginosa).Based on the calculated EC50 values,the results showed that PFOS was significantly more toxic to both algae than its alternatives including Perfluorobutane sulfonic acid(PFBS)and 6:2 Fluoromodulated sulfonates(6:2 FTS),and the PFOS-PFBS mixture was more toxic to both algae than the other two PFC mixtures.The action mode of binary PFC mixtures on Chlorella vulgaris was mainly shown as antagonistic and on Microcystis aeruginosa as synergistic,by using Combination index(CI)model coupled with Monte Carlo simulation.The mean risk quotient(RQ)value of three individual PFCs and their mixtures were all below the threshold of 10^(−1),but the risk of those binary mixtures were higher than that of PFCs individually because of their synergistic effect.Our findings contribute to enhance the understanding of the toxicological information and ecological risks of emerging PFCs and provide a scientific basis for their pollution control.

A template-based knowledge reuse method for generating multitype 3D railway scenes

Constructing 3D railway scenes helps improve the railway information construction ability and the comprehensive railway management level.However,the existing modeling methods have disadvantages such as low flexibility in scene configuration,weak extensibility of scene objects and poor reusability of modeling knowledge.To enable multitype 3D railway scenes to be automatically constructed,a template-based knowledge reuse method was proposed in this paper.First,based on parsing of modeling operation characteristics and modeling knowledge expression,a modeling knowledge template was designed to store modeling knowledge and modeling operations in a parameterized way.The contents of this template were described in terms of semantic information,geometric information,topologic information and reference system information,and the method for instantiating the modeling knowledge template was proposed.Second,a scene configuration template was designed to organize the instantiated modeling knowledge files and scene objects.By the use of a flexible tree structure,the scene configuration template enables extensibility of scene objects.Finally,the two types of templates were parsed in terms of scene organization,linear referencing information,geometric and topologic relationships,and semantic descriptions.In this way,different types of 3D railway scenes were generated automatically.After a prototype system was developed,experiments on automatic modeling of two 3D railway scenes were carried out to verify the template-based knowledge reuse method.The experimental results show that the proposed method can be used to generate multitype 3D railway scenes automatically.With this method,model combination can be configured,model types can be extended and modeling knowledge can be reused.