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.

Refined model analysis of basement rock degradation mechanism of heavy-haul railway tunnel

This study investigated the degradation mechanism of the surrounding rock of a heavy-haul railway under a water-rich condition,based on the construction of the Taihangshan tunnel for the Wari Railway,a heavy-haul railway that used standard construction practices for axle loads of 30 t.Remote monitoring demonstrated that the coupling effect between the dynamic load of a heavy-haul train and the groundwater leads to the deterioration and hollowing of the surrounding rock.This study clarified the void evolution process and deterioration mechanism of the basement rock under the comprehensive influence of the groundwater–train dynamic load using a refined discrete element numerical simulation.The results revealed that the groundwater was the primary influencing factor in the deterioration of the lower part of the heavy-haul railway tunnel.Rock particles were gradually lost under the effects of long-term erosion due to groundwater and heavy-haul trains,which inevitably damaged the basement rock after the construction was completed.Based on this observation,the critical conditions for the deterioration and attenuation law of the physical parameters of the basement rock were obtained.The results of this study can provide ideas and serve as a reference for the forecasting and disaster treatment of basement rock damage in heavy-haul railway tunnels.

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.

Impairment of Continuous Insulin Delivery Therapy and Analysis from Graeco-Latin Square Design Model

The desire to deliver measured amount of insulin continuously to patients with type I diabetes, for glycemic control, has attracted a lot of attention. Continuous subcutaneous insulin infusion has seen some success in recent years. However, occlusion of insulin delivery may prevent the patient from receiving the prescribed dosage, with adverse consequence. An in vitro study of insulin delivery is performed, using different insulin pumps, insulin analogs and operating conditions. The aim is to identify incidences of occlusion due to bubble formation in the infusion line. A detailed statistical analysis was performed on the data collected to determine any significant differences and deviations in insulin delivery rates that might be due to factors such as: pump type, the set basal flow rate, insulin type, vibration, and possible insulin occlusion due to air bubble formation within the infusion line. Our findings from the Graeco-Latin Square design model show that there are statistical differences due to the devices, and statistical identifiable clusters are used to distinguish the devices. Such hierarchical models used to describe the analyses, include the flow rate, the pump types, and the activity level.

Research on the Land Consolidation Strategy of Dalian City Based on PESTEL Analysis Model

Standing at the starting point of the"13 th Five-Year Plan",the general situation of Dalian City,the current status and characteristics of land use,and the strategy of land consolidation planning during the"12 th Five-Year Plan"period are analyzed,and then the political,economic,social,technological,environmental and legal factors that influence the land consolidation of Dalian City are analyzed based on the PESTEL model.Afterwards,measures of land consolidation in Dalian City during the"13 th Five-Year Plan"period are proposed,such as transforming the concept of land consolidation,coordinating opinions of agricultural departments and making scientific decisions,reasonably arranging agricultural space,optimizing the layout of rural residential areas and encouraging moderate scale management of land,broadening the content of land consolidation,and implementing soil testing fertilization.

Fifth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (5th-CASAM-N): II. Paradigm Application to a Bernoulli Model Comprising Uncertain Parameters

This work presents the application of the recently developed “Fifth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (5th-CASAM-N)” to a simplified Bernoulli model. The 5th-CASAM-N builds upon and incorporates all of the lower-order (i.e., the first-, second-, third-, and fourth-order) adjoint sensitivities analysis methodologies. The Bernoulli model comprises a nonlinear model response, uncertain model parameters, uncertain model domain boundaries and uncertain model boundary conditions, admitting closed-form explicit expressions for the response sensitivities of all orders. Illustrating the specific mechanisms and advantages of applying the 5th-CASAM-N for the computation of the response sensitivities with respect to the uncertain parameters and boundaries reveals that the 5th-CASAM-N provides a fundamental step towards overcoming the curse of dimensionality in sensitivity and uncertainty analysis.

Security Risk Analysis Model for Identification and Resolution System of Industrial Internet

Identification and resolution system of the industrial Internet is the“neural hub”of the industrial Internet for coordination.Catastrophic damage to the whole industrial Internet industry ecology may be caused if the identification and resolution system is attacked.Moreover,it may become a threat to national security.Therefore,security plays an important role in identification and resolution system of the industrial Internet.In this paper,an innovative security risk analysis model is proposed for the first time,which can help control risks from the root at the initial stage of industrial Internet construction,provide guidance for related enterprises in the early design stage of identification and resolution system of the industrial Internet,and promote the healthy and sustainable development of the industrial identification and resolution system.

Spatial Analysis and Modelling of Wind Farm Site Suitability in Nasarawa State, North-Central Nigeria

There has been an increasing global and local interest in developing renewable, clean, and cheap energy towards achieving Goal number 7 of the Sustainable Development Goals (SDG). However, decisions involving suitable and sustainable locations for renewable energy projects remain an important task. This study employed Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) to spatially analyze and model wind farm site suitability in Nasarawa State. The aim is to integrate the environmental, social, and economic aspects of decision-making for identifying sustainable wind farm sites. The study distinguished between two sets of decision criteria: decision constraints and decision factors. The former defined the exclusion zones while the latter were standardized based on fuzzy logic to depict varying degrees of suitability across the State. The MCDA applied the weighted linear combination method, with relative weights generated through pairwise comparisons of the analytic hierarchy process to analyze three policy scenarios: equal weights, environmental/social priority, and economic priority scenario. A combination of resulting composite maps from the constraints and the factors gave the final suitability maps. The resulting suitability index (SI) for the respective policy scenario describes the degrees of suitability: Ideal locations were denoted by one (1) and the not suitable locations by zero (0), with values in-between depicting varying degrees of wind farm site suitability. Based on the SI, priority locations indicating areas with good prospects, in addition to the most suitable parcels of land, were identified and delineated. The composite decision constraint revealed that wind farm projects would not be viable in more than half (57.58%) of the State. Wind speed was the major constraint and accounted for the exclusion of 46.25%, with a mean fuzzy membership value of 0.2008 indicating low suitability across the State. Also, the average acceptable wind farm location for the three-policy scenario was 33.33% of the entire study area. Lafia, Obi, Keana, Awe, Nasarawa-Eggon, Wamba and Kokona LGAs were the identified priority Local Government Areas (LGAs). However, only Lafia, Obi, and Nasarawa-Eggon were consistent with changes in the policy objectives. All the priority LGAs have one or more of the most suitable parcels within their administrative boundaries except for Wamba. Despite the severe limitations of wind speed, substantial parts of Nasarawa State still provide great development potentials for wind energy. The “most suitable” locations in Lafia, Nasarawa-Eggon, and Obi LGAs should have first consideration for the development of wind energy in the State.

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.

The finite element analysis of articular cartilage fiber-reinforced composite model under rolling load

Articular cartilage is a layer of low-friction,load-bearing soft hydrated tissue covering bone-ends in diarthrosis,which plays an important role in spreading the load,reducing the joint contact stress,joint friction and wear during exercise.The vital mechanical function

Suggestion of advanced regression model on friction angle of fault gouge in South Korea

Although friction characteristics of fault gouge are important to understand reactivation of fault,behavior of earthquake,and mechanism of slope failure,analysis results of fault gouge have low accuracy mostly than those of soils or rocks due to its high heterogeneity and low strength.Therefore,to improve the accuracy of analysis results,we conducted simple regression analysis and structural equation model analysis and selected major influential factors of friction characteristics among many factors,and then we deduced advanced regression model with the highest coefficient of determination(R^(2)) via multiple regression analysis.Whereas most coefficients of determination in simple regression analysis are below0.3-0.4,coefficient of determination in multiple regression analysis is remarkably large as 0.657.

Principal Model Analysis Based on Bagging PLS and PCA and Its Application in Financial Statement Fraud

Motivated by the Bagging Partial Least Squares(Bagging PLS)and Principal Component Analysis(PCA)algorithms,a novel approach known as Principal Model Analysis(PMA)method is introduced in this paper.In the proposed PMA algorithm,the PCA and the Bagging PLS are combined.In this method,multiple PLS models are trained on sub-training sets,derived from the training set using the random sampling with replacement approach.The regression coefficients of all the sub-PLS models are fused in a joint regression coefficient matrix.The final projection direction is then estimated by performing the PCA on the joint regression coefficient matrix.Subsequently,the proposed PMA method is compared with other traditional dimension reduction methods,such as PLS,Bagging PLS,Linear discriminant analysis(LDA)and PLS-LDA.Experimental results on six public datasets demonstrate that our proposed method consistently outperforms other approaches in terms of classification performance and exhibits greater stability.Additionally,it is employed in the application of financial statement fraud identification.PMA and other five algorithms are utilized to financial statement fraud which concerned by the academic community,and the results indicate that the classification of PMA surpassed that of the other methods.

Analysis of static structural mechanics of vertical axis wind turbine with lift-drag combined starting structures

The finite element analysis was carried out for a composite vertical axis wind turbine with lift-drag combined starting structures to ensure the structure safety of a vertical axis wind turbine(VAWT).The static and modal analysis of rotor of a composite vertical axis wind turbine was conducted by using ANSYS software.The relevant contour sketch of stress and deformation was obtained.The analysis was made for static structural mechanics,modal analysis of rotor and the total deformation and vibration profile to evaluate the influence on the working capability of the rotor.The analysis results show that the various structure parameters lie in the safety range of structural mechanics in the relative standards.The analysis showing the design safe to operate the rotor of a vertical axis wind turbine.The methods used in this study can be used as a good reference for the structural mechanics′analysis of VAWTs.

2D Finite Element Analysis of Asynchronous Machine Influenced Under Power Quality Perturbations

Asynchronous machines are predominantly preferred in industrial sectors for its reliability.Power quality perturbations have a greater impact on industries;among the different power quality events,voltage fluctuations are the most common and that may cause adverse effect on machine’s operation since they are longer enduring.The article discusses a numerical technique for evaluating asynchronous motors while taking into account magnetic saturation,losses,leakage flux,and voltage drop.A 2D linear analysis involving a multi-slice time stepping finite element model is used to predict the end effects.As an outcome,the magnetic saturation and losses are estimated using amodified 2D nonlinear time-stepping finite element formulation.The method takes the electromagnetic fields at the ends of the motor into account using limited computer resources.The proposed method will greatly reduce computation timewith limited computer resources for analyzing themachine’s performance with high precision.The analyzed findings assist in preventing voltage variance issues in the power network system and provide suggestions for developing a robust system.

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 and Simulation of ΔH in Solid State Synthesis of Nanocomposites Al-Cu-Zr

Amorphous structure generated by high-energy ball miller(BM)is often used as a precursor for generating nanocomposites through controlled devitrification.The amorphous forming composition range of ternary Al-Cu-Zr system was calculated using the extended Miedema’s semiempirical model.Eleven compositions of Al-Cu-Zr system showed a wide range of negative enthalpy of mixing(-ΔH^(mix))and amorphization(-ΔH^(amor))among the constituent elements was selected for synthesis by BM.They yielded either nanocomposites of partial amorphous and crystalline structure or no amorphous phase at all in the as-milled condition.The Al_(88)Cu_(6)Zr_(6) alloy with relatively small negativeΔH^(mix)(-0.4 kJ/mol)andΔH^(amor)(-14.8 kJ/mol)became completely amorphous after 120 h of milling.

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.