Health diagnosis of ultrahigh arch dam performance using heterogeneous spatial panel vector model

Currently,more than ten ultrahigh arch dams have been constructed or are being constructed in China.Safety control is essential to long-term operation of these dams.This study employed the flexibility coefficient and plastic complementary energy norm to assess the structural safety of arch dams.A comprehensive analysis was conducted,focusing on differences among conventional methods in characterizing the structural behavior of the Xiaowan arch dam in China.Subsequently,the spatiotemporal characteristics of the measured performance of the Xiaowan dam were explored,including periodicity,convergence,and time-effect characteristics.These findings revealed the governing mechanism of main factors.Furthermore,a heterogeneous spatial panel vector model was developed,considering both common factors and specific factors affecting the safety and performance of arch dams.This model aims to comprehensively illustrate spatial heterogeneity between the entire structure and local regions,introducing a specific effect quantity to characterize local deformation differences.Ultimately,the proposed model was applied to the Xiaowan arch dam,accurately quantifying the spatiotemporal heterogeneity of dam performance.Additionally,the spatiotemporal distri-bution characteristics of environmental load effects on different parts of the dam were reasonably interpreted.Validation of the model prediction enhances its credibility,leading to the formulation of health diagnosis criteria for future long-term operation of the Xiaowan dam.The findings not only enhance the predictive ability and timely control of ultrahigh arch dams'performance but also provide a crucial basis for assessing the effectiveness of engineering treatment measures.

Perforation studies of concrete panel under high velocity projectile impact based on an improved dynamic constitutive model

The finite-depth concrete panels have been widely applied in the protective structures,and its impact resistance and dynamic fracture failures,especially the scabbing/perforation limits,under high velocity projectile impact,are mainly concerned by protective engineers,which are numerically studied based on an improved dynamic concrete model in this study.Firstly,based on the framework of the KCC(Karagozian&Case concrete)model,a dynamic concrete model is proposed which considers an independent tensile damage model and a continued transition between dynamic tensile and compressive properties.Secondly,the strength surface,equation of state and damage parameters of the proposed model are comprehensively calibrated by a triaxial compressive test with high confinement pressure,the rationality of which is further verified based on the single element tests,e.g.,uniaxial and triaxial compression as well as uniaxial,biaxial and triaxial tension.Thirdly,a series of projectile high velocity impact tests on thin and thick concrete panels are simulated,which indicates that the projectile residual velocity and dynamic fracture failures are reproduced satisfactorily,while the KCC model underestimates both the spalling and scabbing dimensions severely.Finally,based on the validated concrete model and finite element analyses approach,the validations of the existing five empirical formulae are evaluated,in terms of the depth of penetration(DOP)and scabbing/perforation limits of concrete panel.Both the Army corps of engineers(ACE)and modified National Defense Research Committee(NDRC)formulae are recommended in the design of the protective structure to avoid scabbing failure.

Empirical Research on the Relationship between Education Poverty Alleviation and Rural Revitalization—Spatial Panel Simultaneous Equation Model Based on Provincial Panel Data

On the basis of using entropy weight method to measure China’s education poverty alleviation and rural revitalization evaluation indicators, using the panel data of 30 provinces in China (excluding Xizang, Hong Kong, Macao and Taiwan) from 2012 to 2021, a spatial panel simultaneous equation model is constructed based on adjacency matrix, geographical distance matrix and economic geographical distance matrix deeply study the interaction mechanism and spatial spillover effects between education poverty alleviation and rural revitalization through the generalized spatial three-stage least squares method (GS3SLS). The results indicate that there is a significant spatial spillover effect and a positive spatial correlation between education poverty alleviation and rural revitalization, and there is a significant interactive effect between the two variables, while promoting each other positively. Therefore, the government should clarify the deep relationship between education poverty alleviation and rural revitalization based on the current background, and better consolidate and expand the effective connection between the achievements of education poverty alleviation and rural revitalization.

How R&D investment promotes green technology innovation in the context of digitalization?-An empirical analysis based on provincial panel data

Green technology innovation is an important driving force and source to promote my country’s high-quality development,and it is the core path to achieve sustainable development.This paper uses my country’s provincial panel data from 2016 to 2019 to study the impact mechanism of R&D investment on green technology innovation,and introduces the level of digitization,using the panel threshold model to discuss its role in the impact mechanism of R&D investment on green technology innovation.The study found that when the level of digitalization in a region is low,increasing R&D investment does not necessarily improve the ability of green technology innovation;when the level of digitalization is relatively high,R&D investment has a positive role in promoting green technology innovation.Therefore,it is necessary to improve policies to encourage enterprises to increase investment in research and development;at the same time,it is necessary to promote the coordinated development of digital foundation,digital investment,digital literacy,digital economy and digital application,and promote the deep integration of digitalization and green technology innovation.

Numerical and Experimental Study on Ventilation Panel Models in a Subway Passenger Compartment

The internal flow field study of car compartments is an important step in railroad vehicle design and optimization. The flow field profile has a significant impact on the temperature distribution and passenger comfort level. Experimental studies on flow field can yield accurate results but carry a high time and computational cost. In contrast, the numerical simulation method can yield an internal flow field profile in less time than an experimental study. This study aims to improve the computational efficiency of numerical simulation by adapting two simplified models—the porous media model and the porous jump face model—to study the internal flow field of a railroad car compartment. The results provided by both simplified models are compared with the original numerical simulation model and with experimental data. Based on the results, the porous media model has a better agreement with the original model and with the experimental results. The flow field parameters (temperature and velocity) of the porous media model have relatively small numerical errors, with a maximum numerical error of 4.7%. The difference between the numerical results of the original model and those of the porous media model is less than 1%. By replacing the original numerical simulation model with the porous media model, the flow field of subway car compartments can be calculated with a reduction of about 25% in computing resources, while maintaining good accuracy.

Panel data models with cross-sectional dependence: a selective review

In this review, we highlight some recent methodological and theoretical develop- ments in estimation and testing of large panel data models with cross-sectional dependence. The paper begins with a discussion of issues of cross-sectional dependence, and introduces the concepts of weak and strong cross-sectional dependence. Then, the main attention is primarily paid to spatial and factor approaches for modeling cross-sectional dependence for both linear and nonlinear （nonparametric and semiparametric） panel data models. Finally, we conclude with some speculations on future research directions.

Small Sample Estimation in Dynamic Panel Data Models: A Simulation Study

We used simulated data to investigate both the small and large sample properties of the within-groups (WG) estimator and the first difference generalized method of moments (FD-GMM) estimator of a dynamic panel data (DPD) model. The magnitude of WG and FD-GMM estimates are almost the same for square panels. WG estimator performs best for long panels such as those with time dimension as large as 50. The advantage of FD-GMM estimator however, is observed on panels that are long and wide, say with time dimension at least 25 and cross-section dimension size of at least 30. For small-sized panels, the two methods failed since their optimality was established in the context of asymptotic theory. We developed parametric bootstrap versions of WG and FD-GMM estimators. Simulation study indicates the advantages of the bootstrap methods under small sample cases on the assumption that variances of the individual effects and the disturbances are of similar magnitude. The boostrapped WG and FD-GMM estimators are optimal for small samples.

A review of mid-frequency vibro-acoustic modelling for highspeed train extruded aluminium panels as well as the most recent developments in hybrid modelling techniques

The present paper reviews the vibro-acoustic modelling of extruded aluminium train floor structures including the state-of-the-art of its industrial applications, as well as the most recent developments on mid-frequency mod- elling techniques in general. With the common purpose to predict mid-frequency vibro-acoustic responses of stiffened panel structures to an acceptable accuracy at a reasonable computational cost, relevant techniques are mainly based on one of the following three types of mid-frequency vibro- acoustic modelling principles： （1） enhanced deterministic methods, （2） enhanced statistical methods, and （3） hybrid deterministic/statistical methods. It is shown that, although recent developments have led to a significant step forward in industrial applicability, mature and adequate prediction tech- niques, however, are still very much required for solving sound transmission through, and radiation from, extruded aluminium panels used on high-speed trains. Due to their great potentials for predicting mid-frequency vibro-acoustics of stiffened panel structures, two of recently developed mid-frequency modelling approaches, i.e. the so-called hybrid finite element-statistical energy analysis （FE-SEA） and hybrid wave-based method- statistical energy analysis （WBM-SEA）, are then recapitulated.

FEM equivalent model for press bend forming of aircraft integral panel

An original plastic equivalent model was proposed to solve the problem of excessive FEM simulation time when designing the press bend forming path and optimizing the process parameters of press bend forming of the integrally stiffened aircraft panels. Based on the in-depth analysis of the mechanics of the bending and springback of the detailed model and the equivalent model of the integral panels,the plastic equivalent model of the virtual material with special initial yield stress and hardening coefficients was constructed. FEM results indicate that the objective of getting the similar contour with the same press bend forming path is achieved with the error less than 6%,and the efficiency of FEM simulation is improved by more than 80%. The plastic equivalent model is valuable and essential for the further research on the press bend forming process of large scale complicated integral panels.

Dynamic modeling of spacecraft solar panels deployment with Lie group variational integrator

The spacecraft with multistage solar panels have nonlinear coupling between attitudes of central body and solar panels, especially the rotation of central body is considered in space. The dynamics model is based for dynamics analysis and control, and the multistage solar panels means the dynamics modeling will be very complex. In this research, the Lie group variational integrator method is introduced, and the dynamics model of spacecraft with solar panels that connects together by flexible joints is built. The most obvious character of this method is that the attitudes of central body and solar panels are all described by three-dimensional attitude matrix. The dynamics models of spacecraft with one and three solar panels are established and simulated. The study shows Lie group variational integrator method avoids parameters coupling and effectively reduces difficulty of modeling. The obtained continuous dynamics model based on Lie group is a set of ordinary differential equations and equivalent with traditional dynamics model that offers a basis for the geometry control.

Systematic studies on scan and driving model for flat panel display

Aiming at the time redundancy in the fiat panel display （FPD） imaging process, the paper studied some problems for FPD gray scale controlling based on the fraetal theory, dissertates the construction of the space-time mapping topology architecture, the proposition of optimal scanning structure for FPD＇s gray imaging, and the creation of the fractal theoretic model. Then the logic implementation and system application are presented based on the fraetal model of the optimal scan architecture, and the application results achieved target of eliminating time redundancy and increasing the scanning availability. The novel control mode that the fractal scanning IP core described with Verilog language embedded in the FPGA hardware frame can efficiently increase the imaging gray scales and quality in the FPDs scanning controller and speed up the frame frequency of display system.

Comparison of Uniform and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model

Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.

Some Additional Moment Conditions for a Dynamic Count Panel Data Model with Predetermined Explanatory Variables

This paper proposes some additional moment conditions for the linear feedback model with explanatory variables being predetermined, which is proposed by [1] for the purpose of dealing with count panel data. The newly proposed moment conditions include those associated with the equidispersion, the Negbin I-type model and the stationarity. The GMM estimators are constructed incorporating the additional moment conditions. Some Monte Carlo experiments indicate that the GMM estimators incorporating the additional moment conditions perform well, compared to that using only the conventional moment conditions proposed by [2,3].

The influence of heterogeneous environmental regulation on the green development of the mining industry: empirical analysis based on the system GMM and dynamic panel data model

The intensity of environmental regulation (ERI) affects the short-term effect of the level of green mining (GML),and which structure determines the long-term mechanism.Based on the panel data from 2001 to 2015,with the dynamic panel model and system GMM estimation method were employed to test the influence of heterogeneous environmental regulation on green mining and its transmission mechanism.The results show that,there is a 'U' type nonlinear relationship between the ERI and GML.The direct effect of command-control-based (CAC) and the market incentive-based (MBI) environmental regulation on green development of mining shows the characteristics of inhibition and promotion.There is a 'U' type of indirectly moderating effect between technological innovation and the energy consumption structure on the GML.The technological innovation promotes the green development of the mining industry only after pass the inflection point of MBI,while the CAC plays a significant guiding role in upgrading of the energy consumption structure.There is an inhibition and promotion effect of MBI on the GML in the southeast coastal area,and the CAC is not significantly.Meanwhile,both of the ERI shows no positive effects in the central and western inland region.

Comparison of Different Models for the Simulation of Photovoltaic Panels

This article contains the description of a circuital model, which was developed to represent the energy production of a photovoltaic panel in a more accurate way, taking into consideration the decrease of its operational time. Furthermore, a comparison among the experimental, the posed simulated model in PSIM and the results obtained by a piece of software developed by some students of the Universidad Distrital is performed in order to verify the values provided by the software and demonstrate the optimal operation of the developed model.

The Disagreement between Anisotropic-Isotropic Diffuse Solar Radiation Models as a Function of Solar Declination: Computing the Optimum Tilt Angle of Solar Panels in the Area of Southern-Italy

In this paper a simulation to maximize the global solar radiation on a sloped collecting surface was applied to typical latitudes in the area of southern Italy, to calculate the optimum tilt angle of solar panels on building structures or large photovoltaic power plants located in that geographical area. Indeed, the area of southern Italy and in particular Sicily and Calabria are the top of European locations for acquiring solar energy. Some models of diffuse solar irradiance were taken into account to determine panels inclinations that maximized the impinging solar radiation by means of global horizontal solar radiation data provided from the Italian Institute of ENEA (Italy). An algorithm was used for the simulation providing a set of tilt angles for each latitude. The optimum tilt angle values obtained from the simulation resulted to be strictly related to the model of diffuse solar radiation that was used. Indeed, the disagreement between the values obtained using anisotropic models of diffuse solar radiation and those obtained from the isotropic model resulted to decrease significantly with increasing solar declination, showing that the isotropic model can be reliable only in summer months.

Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

Recently,the demand for renewable energy has increased due to its environmental and economic needs.Solar panels are the mainstay for dealing with solar energy and converting it into another form of usable energy.Solar panels work under suitable climatic conditions that allow the light photons to access the solar cells,as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these photons.Thus,the panels are unable to work under these conditions.A layer of snow forms on the solar panels due to snowfall in areas with low temperatures.Therefore,it causes an insulating layer on solar panels and the inability to produce electrical energy.The detection of snow-covered solar panels is crucial,as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper operation.This paper presents five deep learning models,■-16,■-19,ESNET-18,ESNET-50,and ESNET-101,which are used for the recognition and classification of solar panel images.In this paper,two different cases were applied;the first case is performed on the original dataset without trying any kind of preprocessing,and the second case is extreme climate conditions and simulated by generating motion noise.Furthermore,the dataset was replicated using the upsampling technique in order to handle the unbalancing issue.The conducted dataset is divided into three different categories,namely;all_snow,no_snow,and partial snow.The fivemodels are trained,validated,and tested on this dataset under the same conditions 60%training,20%validation,and testing 20%for both cases.The accuracy of the models has been compared and verified to distinguish and classify the processed dataset.The accuracy results in the first case showthat the comparedmodels■-16,■-19,ESNET-18,and ESNET-50 give 0.9592,while ESNET-101 gives 0.9694.In the second case,the models outperformed their counterparts in the first case by evaluating performance,where the accuracy results reached 1.00,0.9545,0.9888,1.00.and 1.00 for■-16,■-19,ESNET-18 and ESNET-50,respectively.Consequently,we conclude that the second case models outperformed their peers.

Toward a sustainable growth path in Arab economies:an extension of classical growth model

Background/Objectives:Many economies are on the trajectory of alternative growth drivers other than conventional capital and labor.Access to credit facilities is a pertinent indicator of economic growth.In line with the United Nations Sustainable Development Goals(UNSDGs-8)agenda,the national goal for sustainable development for most economies and Arab economies is no exception.Therefore,the current study adopts a traditional growth model by exploring the relationship between gross domestic product(GDP)per capita,credit for private sectors,ratio of exports,real GDP,and per labor force participants for selected Arab economies annually from 2001 to 2020.Research design:This study leverages the Fourier Kwiatkowski–Phillips–Schmidt–Shin(KPSS)unit root test and second-generation panel econometrics as estimation techniques,such as Westerlund and Edgerton panel cointegration test,and the use of two estimators,namely the augmented mean group(AMG)and common correlated error mean group(CCEMG),to obtain robust results.Findings:Empirical findings from Westerlund and Edgerton panel cointegration tests validate the long-run equilibrium relationship among the outlined variables.Further empirical results indicate that the share of exports is negatively significant with economic growth in countries such as Kuwait,Lebanon,Tunisia,and Jordan.Additionally,savings and labor force participation have a positive relationship with economic growth in individual countries such as Algeria and Bahrain.As per the panel,there is no significant relationship between labor force participation and economic growth.This indicates that the skilled labor force enhanced economic growth.Conclusions:These findings come with inherent far-reaching policy suggestions for economies and panels.Further details on country-specific policy actions are presented in the concluding section.

Quasi Maximum Likelihood for MESS Varying Coefficient Panel Data Models with Fixed Effects

The study of spatial econometrics has developed rapidly and has found wide applications in many different scientific fields,such as demog­raphy,epidemiology,regional economics,and psychology.With the deepening of research,some scholars find that there are some model specifications in spatial econometrics,such as spatial autoregressive(SAR)model and matrix exponential spatial specification(MESS),which cannot be nested within each other.Compared with the common SAR models,the MESS models have computational advantages because it eliminates the need for logarithmic determinant calculation in maxi­mum likelihood estimation and Bayesian estimation.Meanwhile,MESS models have theoretical advantages.However,the theoretical research and application of MESS models have not been promoted vigorously.Therefore,the study of MESS model theory has practical significance.This paper studies the quasi maximum likelihood estimation for ma­trix exponential spatial specification(MESS)varying coefficient panel data models with fixed effects.It is shown that the estimators of model parameters and function coefficients satisfy the consistency and asymp­totic normality to make a further supplement for the theoretical study of MESS model.

Benefit Analysis of Chemical Fertilizer Input for Staple Grain Production Based on Threshold Panel Data Model

Based on the threshold panel data model,this paper analyzed the nonlinear relationship between chemical fertilizer input and grain output. At the provincial level,from the perspective of average and marginal net benefits,it compared the effects of chemical fertilizer input on farmers' income. It reached the conclusion that the chemical fertilizer input efficiency has deviated from the optimal level. Thus,only by increasing the marginal productivity of chemical fertilizers,may it be able to increase farmers' income.