Auto-parametric resonance of a continuous-beam-bridge model under two-point periodic excitation:an experimental investigation and stability analysis

The auto-parametric resonance of a continuous-beam bridge model subjected to a two-point periodic excitation is experimentally and numerically investigated in this study.An auto-parametric resonance experiment of the test model is conducted to observe and measure the auto-parametric resonance of a continuous beam under a two-point excitation on columns.The parametric vibration equation is established for the test model using the finite-element method.The auto-parametric resonance stability of the structure is analyzed by using Newmark's method and the energy-growth exponent method.The effects of the phase difference of the two-point excitation on the stability boundaries of auto-parametric resonance are studied for the test model.Compared with the experiment,the numerical instability predictions of auto-parametric resonance are consistent with the test phenomena,and the numerical stability boundaries of auto-parametric resonance agree with the experimental ones.For a continuous beam bridge,when the ratio of multipoint excitation frequency(applied to the columns)to natural frequency of the continuous girder is approximately equal to 2,the continuous beam may undergo a strong auto-parametric resonance.Combined with the present experiment and analysis,a hypothesis of Volgograd Bridge's serpentine vibration is discussed.

Effects of Continuous Precision Nursing Model on Knowledge, Attitudes, and Practices (KAP) Behavior and Cardiac Function in Patients after Percutaneous Coronary Angiography and Stent Implantation

Objective:To explore the impact of a continuous precision nursing model on patients’Knowledge,Attitudes,and Practices(KAP)and cardiac function during the nursing process of patients undergoing percutaneous coronary angiography and stent implantation.Methods:Ninety patients who underwent percutaneous coronary angiography and stent implantation in our hospital from April 2022 to April 2023 were selected and randomly divided into the control group(45 cases),in which routine nursing support was carried out during the treatment process,and the observation group(45 cases),in which continuous precision nursing model was carried out during the treatment process.Comparisons were made between the two groups of patients on their KAP,cardiac function,and quality of life during recovery.Results:There was no difference in the left ventricular ejection fraction(LVEF),cardiac output(CO),and cardiac index(CI)levels before intervention.After the intervention,the levels of cardiac function in the observation group were higher than those of the control group(P<0.05).There was no difference in the Exercise of Self-Care Agency(ESCA)self-care ability scale scores before the intervention.After the intervention,the observation group had higher ESCA scores than the control group(P<0.05).Conclusion:Implementation of a continuous precision nursing model in the care of patients undergoing percutaneous coronary angiography and stent implantation improved the patient’s cardiac function,and KAP,and promoted recovery.

Optimal state and branch sequence based parameter estimation of continuous hidden Markov model

A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.

Effect of Continuous Nursing Model on Nursing Effect of Continuous Peritoneal Dialysis

Objective:To analyze and study the effect of continuous nursing mode for continuous peritoneal dialysis nursing.Methods:40 patients with continuous peritoneal dialysis received in our hospital were randomly selected as the research object.The research time was from June 2018 to June 2020.The patients were divided into two groups by random number table method.The patients with routine nursing mode were named as the control group and the patients with continuous nursing mode were named as the observation group(20 cases in each group).The clinical nursing effects of different nursing modes are compared.Results:After nursing,the nursing compliance of the observation group was 95%,which was higher than 70% of the control group.There was significant difference between the two groups(P<0.05).Comparing the blood routine related indexes of the two groups,the blood potassium,hemoglobin,serum creatinine and carbon dioxide binding force of the observation group were better than those of the control group(P<0.05).The incidence of peritonitis and rehospitalization rate in half a year in the observation group were lower than those in the control group(P<0.05).Conclusion:The continuous nursing model for patients undergoing continuous peritoneal dialysis can improve the treatment effect of patients,significantly improve the compliance of patients,significantly improve the serological indexes,promote the health of patients,reduce the incidence of peritonitis,and significantly reduce the rehospitalization rate in half a year.It has a broad prospect of clinical promotion.

MODELING, VALIDATION AND OPTIMAL DESIGN OF THE CLAMPING FORCE CONTROL VALVE USED IN CONTINUOUSLY VARIABLE TRANSMISSION

Associated dynamic performance of the clamping force control valve used in continuously variable transmission （CVT） is optimized. Firstly, the structure and working principle of the valve are analyzed, and then a dynamic model is set up by means of mechanism analysis. For the purpose of checking the validity of the modeling method, a prototype workpiece of the valve is manufactured for comparison test, and its simulation result follows the experimental result quite well. An associated performance index is founded considering the response time, overshoot and saving energy, and five structural parameters are selected to adjust for deriving the optimal associated performance index. The optimization problem is solved by the genetic algorithm （GA） with necessary constraints. Finally, the properties of the optimized valve are compared with those of the prototype workpiece, and the results prove that the dynamic performance indexes of the optimized valve are much better than those of the prototype workpiece.

A continuous time delay-difference type model(CTDDM) applied to stock assessment of the southern Atlantic albacore Thunnus alalunga

A continuous time delay-difference model(CTDDM) has been established that considers continuous time delays of biological processes.The southern Atlantic albacore(Thunnus alalunga) stock is the one of the commercially important tuna population in the marine world.The age structured production model(ASPM) and the surplus production model(SPM) have already been used to assess the albacore stock.However,the ASPM requires detailed biological information and the SPM lacks the biological realism.In this study,we focus on applying a CTDDM to the southern Atlantic albacore(T.alalunga) species,which provides an alternative method to assess this fishery.It is the first time that CTDDM has been provided for assessing the Atlantic albacore(T.alalunga) fishery.CTDDM obtained the 80%confidence interval of MSY(maximum sustainable yield) of(21 510 t,23 118 t).The catch in 2011(24 100 t) is higher than the MSY values and the relative fishing mortality ratio(F_(2011)/F_(MSY)) is higher than 1.0.The results of CTDDM were analyzed to verify the proposed methodology and provide reference information for the sustainable management of the southern Atlantic albacore stock.The CTDDM treats the recruitment,the growth,and the mortality rates as all varying continuously over time and fills gaps between ASPM and SPM in this stock assessment.

Water Model Study of Level Flunction in Continuous Casting Mold

Using 0.6-scale warer modelling based on Fr-We number similitude criteria, the influences of the submerged entry nozzle configuration and operating practices on the level fluctuation in the mold which caused surface defects and mold power catching, were studied. It was found that the level flunction was resulted from gas injection, impacting of the stream and standing wave. The level turblence raises with the incresing of the gas injection, however the casting rate, immersion depth and jet angel of SEN have a dual influenc on the level fluctuation.

Parameterized continuous models of fuzzy reasoning

In this paper,the ideas of universal logic is introduced into fuzzy systems.After giving the definitions of the softened fuzzy reasoning models based on Schweizer-Sklar t-norms and Schweizer-Sklar implications,i.e.,α-models andβ-models,we give the sufficient and necessary conditions for these models to be continuous,and discuss the continuity of some commonly used models.We also prove that when anα-model or aβ-model is used as a fuzzy controller,it has universal property with respect to function approximation.The results we obtained show thatα-models andβ-models are more flexible than the existing models in applications.

Continuous Variable Quantum Teleportation in Beam-Wandering Modeled Atmosphere Channel

We investigate the continuous variable quomtum teleportation in atmosphere channels. The beam-wandering mode/is employed to analyze the teleportation of the unknown single-mode coherent state. Two methods, one is deterministic by increasing the aperture size of the detecting device and one is probabilistic by entanglement distillation, are proposed to improve the teleportation fidelity in the presence of atmosphere noises.

Modeling the dendritic evolution and micro-segregation of cast alloy with cellular automaton

In order to precisely describe the dendritic morphology and micro-segregationduring solidification process, a novel continuous model concerning the different physicalproperties in the solid phase, liquid phase and interface is developed. Coupling the heat and solutediffusion with the transition rales, the dendrite evolution is simulated by cellular automatonmethod. Then, the solidification microstructure evolution of a small ingot is simulated by usingthis method. The simulated results indicate that this model can simulate the dendrite growth, showthe second dendrite arm and tertiary dendrite arm, and reveal the micro-segregation in theinter-dendritic zones. Furthermore, the columnar-to-equiaxed transition (CET) is predicted.

A time fractional model to represent rainfall process

This paper deals with a stochastic representation of the rainfall process. The analysis of a rainfall time series shows that cumulative representation of a rainfall time series can be modeled as a non-Gaussian random walk with a log-normal jump distribution and a time-waiting distribution following a tempered a-stable probability law. Based on the random walk model, a fractional Fokker-Planck equation （FFPE） with tempered a-stable waiting times was obtained. Through the comparison of observed data and simulated results from the random walk model and FFPE model with tempered a-stable waiting times, it can be concluded that the behavior of the rainfall process is globally reproduced, and the FFPE model with tempered a-stable waiting times is more efficient in reproducing the observed behavior.

Demand Model for Investment-oriented Life Insurance under uncertain lifetime

In this paper, we set up continuous time model with Poisson Process to analyze demand of investment-oriented life insurance. Individual life time is assumed random, and he is received fixed income, investment-oriented life insurance is an important financial asset under this model. Dynamic programming is applied to analyze this problem. The optimal explicit solutions are obtained in the case of CRRA utilities, and draw its demand curve with numerical simulation.

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

Research on modeling method of continuous spectrum water quality online detection based on random forest

It's common to use the method of continuous spectroscopy in water quality testing. But there're some problems with it. For example, the scanning results have a large number of nonlinear signals, and the covariance between variables is serious, which can lead to a decrease in the model prediction accuracy. In this paper, the standard solutions of nitrate nitrogen(NO_(3)-N) and nitrite nitrogen(NO_(2)-N) were used as the subject to be tested, and the data of the scanned waves and absorbance were obtained by use of spectral detector. The data were processed by noise reduction first and then the random forest(RF) algorithm was adopted to establish the regression relationship between concentration and absorbance. For comparison, partial least squares(PLS) and support vector machine(SVM) algorithm models were also established. For the same given data, the three reverse models can make the projection of the concentration respectively. The experimental results show that the RF algorithm predicts NO_(2)-N concentrations significantly better than the SVM algorithm and PLS algorithm. This proves that the RF algorithm has good prediction ability in spectral water quality detection because of its high model accuracy and better adaptability, which could be a reference for similar research on continuous spectral water quality online detection.

Tomographic determination of 3－D crustal structure──Joint inversion of explosion and earthquake data

In this paper, the theory and method, obtaining the tomographic determination of three-dimensional velocity structure of the crust by use of the joint inversion of explosion and earthquake data, are given. The velocity distribution of the crust is regarded as a continuous function of the spatial coordinates without parametrization of the velocity model ahead, so that the inversion solution would not be influenced by different parametrization procedures.The expressions of integration kernels, which relates the two kinds of data sets, are also given. The authors have processed the observed data in Tangshan earthquake region by the method proposed in this paper, and obtained the tomographic results of the middle and upper crust structures in this region. The comparison of these results with the result obtained only by the explosion data, has also been made.

Dynamic Responses Analysis of a Building Structure Subjected to Ground Shock from a Tunnel Explosion

Dynamic responses of a multi-storey building without or with a sliding base-isolation device for ground shock induced by an in-tunnel explosion are numerically analyzed. The effect of an adjacent tunnel in between the building and the explosion tunnel, which affects ground shock propagation , is considered in the analysis. Different modeling methods, such as the eight-node equal-parametric finite element and mass-lumped system, are used to establish the coupling model consisting of the two adjacent tunnels, the surrounding soil medium with the Lysmer viscous boundary condition, and the multi-storey building with or without the sliding base-isolation device. In numerical calculations , a continuous friction model, which is different from the traditional Coulomb friction model, is adopted to improve the computational efficiency and reduce the accumulated errors. Some example analyses are subsequently performed to study the response characteristics of the building and the sliding base-isolation device to ground shock. The effect of the adjacent tunnel in between the building and the explosion tunnel on the ground shock wave propagation is also investigated. The final conclusions based on the numerical results will provide some guidance in engineering practice.

Intelligent Optimization Methods for the Design of an Overhead Travelling Crane

In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design（CMSOD） belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search（ACAM） is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm（GA） and particle swarm optimization（PSO） are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.

Construction of a 3D meso-structure and analysis of mechanical properties for deposit body medium

For deposit body medium, the internal structural properties may be the controlling factors for the strength of the material and the mechanical response. Based on the results of soil-rock meso-statistics using digital imaging, a simulated annealing algorithm is adopted to expand the meso-structural features of deposit bodies in 3D. The construction of the 3D meso-structure of a deposit body is achieved, and then the particle flow analysis program PFC3 D is used to simulate the mechanical properties of the deposit body. It is shown that with a combination of the simulated annealing algorithm and the statistical feature functions, the randomness and heterogeneity of the rock distribution in the 3D inner structure of deposit body medium can be realized, and the reconstructed structural features of the deposit medium can match the features of the digital images well. The spatial utilizations and the compacting effects of the body-centered cubic, hexagonal close and face-centered packing models are high, so these structures can be applied in the simulations of the deposit structures. However, the shear features of the deposit medium vary depending on the different model constructive modes. Rocks, which are the backbone of the deposit, are the factors that determine the shear strength and deformation modulus of the deposit body. The modeling method proposed is useful for the construction of 3D meso-scope models from 2D meso-scope statistics and can be used for studying the mechanical properties of mixed media, such as deposit bodies.

A Study on consumers＇ continuing to use online group-buying platforms： The impact of price performance expectations

In this study, we aim to understand the characteristics of online group-buying consumers and to investigate salient factors which influence the continuance intention of online group-buying platforms （OGBP） to bridge this knowledge gap. An expectation-confirmation model of information systems （IS） continuance is adapted to construct a research model in online group-buying contexts. A total of 289 complete and valid responses were collected. Our findings contribute to academics and practitioners in two ways： Firstly, our respondents show that they are young （93% of the respondents＇ ages range between 19 and 28 years old）, female （88% of the respondents）, and thrifty （82% of the respondents＇ transaction amounts are below US$16）. Secondly, based on our results, price performance expectations have a direct impact on confirmation. In addition, in contrast to the IS continuance model （Bhattacherjee, 2001）, the effect of perceived usefulness on satisfaction is not supported. Thus, in online group-buying settings, confirmation is the key antecedent of satisfaction. Satisfaction and perceived usefulness are significantly associated with OGBP continuance intention. Consequently, in addition to offering a wide assortment of merchandise and a convenient online shopping experience to enhance customers＇ perceived usefulness of OGBP, OGBP managers should aim low-price marketing strategies at this female, young, thrifty and price-sensitive segment to transcend consumers＇ price expectations and attract consumers＇ continued intention to visit OGBP.

Two-Phase Rate Adaptation Strategy for Improving Real-Time Video QoE in Mobile Networks

With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.