Modeling Analysis of Factors Influencing Wind-Borne Seed Dispersal: A Case Study on Dandelion

A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.

Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

Transient pressure analysis of polymer flooding fractured wells with oil-water two-phase flow

The oil-water two-phase flow pressure-transient analysis model for polymer flooding fractured well is established by considering the comprehensive effects of polymer shear thinning,shear thickening,convection,diffusion,adsorption retention,inaccessible pore volume and effective permeability reduction.The finite volume difference and Newton iteration methods are applied to solve the model,and the effects of fracture conductivity coefficient,injected polymer mass concentration,initial polymer mass concentration and water saturation on the well-test type curves of polymer flooding fractured wells are discussed.The results show that with the increase of fracture conductivity coefficient,the pressure conduction becomes faster and the pressure drop becomes smaller,so the pressure curve of transitional flow goes downward,the duration of bilinear flow becomes shorter,and the linear flow appears earlier and lasts longer.As the injected polymer mass concentration increases,the effective water phase viscosity increases,and the pressure loss increases,so the pressure and pressure derivative curves go upward,and the bilinear flow segment becomes shorter.As the initial polymer mass concentration increases,the effective water phase viscosity increases,so the pressure curve after the wellbore storage segment moves upward as a whole.As the water saturation increases,the relative permeability of water increases,the relative permeability of oil decreases,the total oil-water two-phase mobility becomes larger,and the pressure loss is reduced,so the pressure curve after the wellbore storage segment moves downward as a whole.The reliability and practicability of this new model are verified by the comparison of the results from simplified model and commercial well test software,and the actual well test data.

An Application of Machine Learning to Thalassemia Diagnosis

Mediterranean anemia is a genetic disease that currently relies heavily on expert clinical experience to determine whether patients are affected. This method is overly reliant on expert experience and is not precise enough. This paper proposes two modeling methods to predict whether patients have Mediterranean anemia. The first method involves using Principal Component Analysis (PCA) to reduce the dimensionality of the data, followed by logistic regression modeling (PCA-LR) on the reduced dataset. The second method involves building a Partial Least Squares Regression (PLS) model. Experimental results show that the prediction accuracy of the PCA-LR model is 87.5% (degree = 2, λ=4), and the prediction accuracy of the PLS model is 92.5% (ncomp = 4), indicating good predictive performance of the models.

Modeling of Economic Cost Distribution in Screw Thread

The screw threading produce is a important process course.The economics modeling is established in this paper so this process will be investigated.In this paper modeling of process is analyzed.The Q and cost decreases with the increasing quantity while the same turn as AC>AFC>MC is found.AC and AFC is main factors to affect quantity then MC do.It fits to concept of them well.The cost AC and AFC is higher upon L=4 than L=6 while MC is higher upon L=6 than L=4.Meantime the TC and VC is higher upon L=6 than that upon L=4,is the near parallel line.The TC>VC is turn to affect cost.There is a difference for them.TC and VC is main effective costs to compare with AC,AFC&MC.The TC and VC become bigger upon K=5 than K=7.

Experimental Study and Model Analysis of the Performance of IPMC Membranes with Various Thickness

Ionic Polymer-Metal Composite （IPMC） is a new electro-active polymer, which has the advantages of light weight, flexibility, and large stroke with low driving voltage. Because of these features, IPMC can be applied to bionic robotic actuators, artificial muscles, as well as dynamic sensors. However, IPMC has the major drawback of low generative blocking force. In this paper, in order to enhance the blocking force, the Nation membranes with thickness of 0.22 mm, 0.32 mm, 0.42 mm, 0.64 mm and 0.8 mm were prepared by casting from liquid solution. By employing these Nation membranes, IPMCs with varying thickness were fabricated by electroless plating. The elastic modulus of the casted Nation membranes were obtained by a nano-indenter, and the current, the displacement and the blocking force were respectively measured by the apparatus for actuation test. Finally, the effects of the thickness on the performance of IPMC were analyzed with an electromechanical model. Experimental study and theory analysis indicate that as the thickness increases, the elastic modulus of Nation membrane and the blocking force of IPMC increase, however, the current and the displacement decrease.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Accuracy Analysis and Design of A3 Parallel Spindle Head

As functional components of machine tools, parallel mechanisms are widely used in high efficiency machining of aviation components, and accuracy is one of the critical technical indexes. Lots of researchers have focused on the accuracy problem of parallel mechanisms, but in terms of controlling the errors and improving the accuracy in the stage of design and manufacturing, further efforts are required. Aiming at the accuracy design of a 3-DOF parallel spindle head（A3 head）, its error model, sensitivity analysis and tolerance allocation are investigated. Based on the inverse kinematic analysis, the error model of A3 head is established by using the first-order perturbation theory and vector chain method. According to the mapping property of motion and constraint Jacobian matrix, the compensatable and uncompensatable error sources which affect the accuracy in the end-effector are separated. Furthermore, sensitivity analysis is performed on the uncompensatable error sources. The sensitivity probabilistic model is established and the global sensitivity index is proposed to analyze the influence of the uncompensatable error sources on the accuracy in the end-effector of the mechanism. The results show that orientation error sources have bigger effect on the accuracy in the end-effector. Based upon the sensitivity analysis results, the tolerance design is converted into the issue of nonlinearly constrained optimization with the manufacturing cost minimum being the optimization objective. By utilizing the genetic algorithm, the allocation of the tolerances on each component is finally determined. According to the tolerance allocation results, the tolerance ranges of ten kinds of geometric error sources are obtained. These research achievements can provide fundamental guidelines for component manufacturing and assembly of this kind of parallel mechanisms.

Nonlinear Dynamic Analysis of Flexible Marine-Risers

A nonlinear dynamic analysis model is estabilished on the basis of 'lumped mass' approach, which takes the influence of the fluid flow within the pipe into consideration. Numerical results are compared with the published works, and the effects of internal fluid flow, internal pressure, dyanmics as well as the nonlinear characteristics on the behavior of flexible risers are discussed. From this work, some useful conclusions are drawn.

Effects of modeling means on properties of monitoring models of spot welding quality

Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.

Unsteady aerodynamics modeling for flight dynamics application

In view of engineering application, it is practicable to decompose the aerodynamics into three components： the static aerodynamics, the aerodynamic increment due to steady rotations, and the aerodynamic increment due to unsteady separated and vortical flow. The first and the second components can be presented in conventional forms, while the third is described using a one-order differential equation and a radial-basis-function （RBF） network. For an aircraft configuration, the mathematical models of 6- component aerodynamic coefficients are set up from the wind tunnel test data of pitch, yaw, roll, and coupled yawroll large-amplitude oscillations. The flight dynamics of an aircraft is studied by the bifurcation analysis technique in the case of quasi-steady aerodynamics and unsteady aerodynam- ics, respectively. The results show that： （1） unsteady aerodynamics has no effect upon the existence of trim points, but affects their stability; （2） unsteady aerodynamics has great effects upon the existence, stability, and amplitudes of periodic solutions; and （3） unsteady aerodynamics changes the stable regions of trim points obviously. Furthermore, the dynamic responses of the aircraft to elevator deflections are inspected. It is shown that the unsteady aerodynamics is beneficial to dynamic stability for the present aircraft. Finally, the effects of unsteady aerodynamics on the post-stall maneuverability

Analysis of Frequency Characteristics and Sensitivity of Compliant Mechanisms

Based on a modified pseudo-rigid-body model,the frequency characteristics and sensitivity of the large-deformation compliant mechanism are studied.Firstly,the pseudo-rigid-body model under the static and kinetic conditions is modified to enable the modified pseudo-rigid-body model to be more suitable for the dynamic analysis of the compliant mechanism.Subsequently,based on the modified pseudo-rigid-body model,the dynamic equations of the ordinary compliant four-bar mechanism are established using the analytical mechanics.Finally,in combination with the finite element analysis software ANSYS,the frequency characteristics and sensitivity of the compliant mechanism are analyzed by taking the compliant parallel-guiding mechanism and the compliant bistable mechanism as examples.From the simulation results,the dynamic characteristics of compliant mechanism are relatively sensitive to the structure size,section parameter,and characteristic parameter of material on mechanisms.The results could provide great theoretical significance and application values for the structural optimization of compliant mechanisms,the improvement of their dynamic properties and the expansion of their application range.

An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks

As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.

Safety analysis of wheel brake system based on STAMP/STPA and Monte Carlo simulation

The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with traditional probability techniques based on the linear chain of events. However, it could not comprehensively analyze system safety problems, especially in operating environment, interaction of subsystems, and human factors. Thus,we consider system safety as a control problem based on the system-theoretic accident model, the processes(STAMP) model and the system theoretic process analysis(STPA) technique to compensate the deficiency of traditional techniques. Meanwhile,system safety simulation is considered as system control simulation, and Monte Carlo methods are used which consider the range of uncertain parameters and operation deviation to quantitatively study system safety influence factors in control simulation. Firstly,we construct the STAMP model and STPA feedback control loop of the wheel brake system based on the system functional requirement. Then four unsafe control actions are identified, and causes of them are analyzed. Finally, we construct the Monte Carlo simulation model to analyze different scenarios under disturbance. The results provide a basis for choosing corresponding process model variables in constructing the context table and show that appropriate brake strategies could prevent hazards in aircraft landing taxiing.

Population Viability Analysis of Gloydius shedaoensis from Northeastern China:A Contribution to the Assessment of the Conservation and Management Status of an Endangered Species

Shedao pit-vipers(Gloydius shedaoensis) on Snake Island in the Liaoning Province, China, are among the most imperiled species in China. The isolated and unique populations are crucial in the recovery of this endangered species by providing a way for conservation and management. Research based on the ecological simulation tools can evaluate alternative mitigation strategies in terms of their benefits to the populations, which are vital for informed decision-making. In this paper, using the program VORTEX 9.42, we developed a population viability analysis(PVA) for the Shedao pit-viper to:(1) address the extinction likelihood of the population;(2) simulate population dynamics under various environment events, and(3) evaluate the efficacy of current protection and management strategies. Overall, we found the population to be susceptible to the factors of catastrophic events, mortality and environment capacity. The population is recovering slowly at present on account of improvement of habitat and greater food availability. Under the current conditions, the probability of extinction in 100 years is approximately zero. These data coincide with the evidence that the wild population may be arriving at K. Our results strengthen the view that protection and management can create a pronounced effect on populations of this endangered species.

Assessment of pipeline stability in the Gulf of Mexico during hurricanes using dynamic analysis

Pipelines are the critical link between major offshore oil and gas developments and the mainland. Any inadequate on-bottom stability design could result in disruption and failure, having a devastating impact on the economy and environment. Predicting the stability behavior of offshore pipelines in hurricanes is therefore vital to the assessment of both new design and existing assets. The Gulf of Mexico has a very dense network of pipeline systems constructed on the seabed. During the last two decades, the Gulf of Mexico has experienced a series of strong hurricanes, which have destroyed, disrupted and destabilized many pipelines. This paper first reviews some of these engineering cases. Following that, three case studies are retrospectively simulated using an in-house developed program. The study utilizes the offshore pipeline and hurricane details to conduct a Dynamic Lateral Stability analysis, with the results providing evidence as to the accuracy of the modeling techniques developed.

Mesh Smoothing Method Based on Local Wave Analysis

As the mesh models usually contain noise data,it is necessary to eliminate the noises and smooth the mesh.But existed methods always lose geometric features during the smoothing process.Hence,the noise is considered as a kind of random signal with high frequency,and then the mesh model smoothing is operated with signal processing theory.Local wave analysis is used to deal with geometric signal,and then a novel mesh smoothing method based on the local wave is proposed.The proposed method includes following steps：Firstly,analyze the principle of local wave decomposition for 1D signal,and expand it to 2D signal and 3D spherical surface signal processing;Secondly,map the mesh to the spherical surface with parameterization,resample the spherical mesh and decompose the spherical signals by local wave analysis;Thirdly,propose the coordinate smoothing and radical radius smoothing methods,the former filters the mesh points＇ coordinates by local wave,and the latter filters the radical radius from their geometric center to mesh points by local wave;Finally,remove the high-frequency component of spherical signal,and obtain the smooth mesh model with inversely mapping from the spherical signal.Several mesh models with Gaussian noise are processed by local wave based method and other compared methods.The results show that local wave based method can obtain better smoothing performance,and reserve more original geometric features at the same time.

Analysis of sp Pillar Stability Experiment: Continuous thermo-mechanical model development and calibration

The paper describes an analysis of thermo-mechanical （TM） processes appearing during the Aspo Pillar Stability Experiment （APSE）. This analysis is based on finite elements with elasticity, plasticity and dam- age mechanics models of rock behaviour and some least squares calibration techniques. The main aim is to examine the capability of continuous mechanics models to predict brittle damage behaviour of gran- ite rocks. The performed simulations use an in-house finite element software GEM and self-developed experimental continuum damage MATLAB code. The main contributions are twofold. First, it is an inverse analysis, which is used for （1） verification of an initial stress measurement by back analysis of conver- gence measurement during construction of the access tunnel and （2） identification of heat transfer rock mass properties by an inverse method based on the known heat sources and temperature measurements. Second, three different hierarchically built models are used to estimate the pillar damage zones, i.e. elas- tic model with Drucker-Prager strength criterion, elasto-plastic model with the same yield limit and a combination of elasto-plasticity with continuum damage mechanics. The damage mechanics model is also used to simulate uniaxial and triaxial compressive strength tests on the ,Aspo granite.

Association between Macroscopic-factors and Identified HIV/AIDS Cases among Injecting Drug Users: An Analysis Using Geographically Weighted Regression Model

Drug use （DU）, particularly injecting drug use （IDU） has been the main route of transmission and spread of Human Immunodeficiency Virus （HIV）/Acquired Immune Deficiency Syndrome （AIDSJ among injecting drug users （IDUs）[1]. Previous studies have proven that needles or cottons sharing during drug injection were major risk factors for HIV/AIDS transmission at the personal level[z4]. Being a social behavioral issue, HIV/AIDS related risk factors should be far beyond the personal level. Therefore, studies on HIV/AIDS related risk factors should focus not only on the individual factors, but also on the association between HIV/AIDS cases and macroscopic-factors, such as economic status, transportation, health care services, etc[1]. The impact of the macroscopic-factors on HIV/AIDS status might be either positive or negative, which are potentially reflected in promoting, delaying or detecting HIV/AIDS epidemics.

Parameters Sensitivity Analysis and Correction for Concrete Damage Plastic Model

In order to understand the effect of hardening ductility parameters and softening ductility parameters of the concrete damage plastic model in LS-DYNA,a sensitivity and reliability analysis of these parameters through a convenient cube unit test was conducted. The results showed that the peak strength strain was independent of the hardening ductility parameter DH,but affected by AH,BH,and CH. The softening ductility was mainly related to the softening ductility parameter AS,but not affected by the damage ductility exponent BS. In case that the model with default parameters failed to match the AS-controlled damage softening phase,an optimized model with an AS correction was developed. The corrected model with the AS value of 2 matched well with the code model,and exhibited good feasibility in predicting the stress-strain curve of different grades of concrete. Moreover,the practicability of the corrected model was further validated by the conventional triaxial test. The simulated curve exhibited favorable consistence with the trial curve. Therefore,the model with parameter correction could provide a prospective reference for predicting the mechanical properties of concrete.