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The purpose of this paper is: to track the methods by which farmers access groundwater for irrigation in the North China Plain (NCP); to explore whether climate factors influence farmers’ decisions on the methods of groundwater access for irrigation; and to examine whether the amount of groundwater use for irrigation and crop yield systematically differ across groups of farmers using various methods of groundwater access, and how climate factors affect them.

Descriptive statistical analysis and econometric models are used on household survey data collected over several years and county-level climate data.

Over the past few decades, a significant share of farmers have switched the methods of groundwater access from collective tubewells to own tubewells or groundwater markets. Farmers who bought water from groundwater markets applied less water to wheat plots than those who had their own tubewells. However, wheat yield was not negatively affected. Both average climate conditions and long-term variations were found to be related to farmers’ choice of methods of groundwater access for irrigation. More frequent droughts and increasingly volatile temperatures both increased the likelihood of farmers gaining groundwater irrigation from markets.

The analysis results suggest farmers are using groundwater markets to help them adapt to climate change. Applying empirical analysis to identify the impact of the methods by which farmers access groundwater for irrigation on the amount of groundwater use and crop yield will help policy makers design reasonable adaptation policies for the NCP.

This paper aims at fabricating large metallic components with high deposition rates, low equipment costs through wire and wire and arc additive manufacturing (WAAM) method, in order to achieve the morphology and mechanical properties of manufacturing process, a bead morphology prediction model with high precision for ideal deposition of every pass was established.

The dynamic response of the process parameters on the bead width and bead height of cold metal transfer (CMT)-based AM was analyzed. A laser profile scanner was used to continuously capture the morphology variation. A prediction model of the deposition bead morphology was established using response surface optimization. Moreover, the validity of the model was examined using 15 groups of quadratic regression analyzes.

The relative errors of the predicted bead width and height were all less than 5% compared with the experimental measurements. The model was then preliminarily used with necessary modifications, such as further considering the interlayer process parameters, to guide the fabrication of complex three-dimensional components.

The morphology prediction of WAAMed bead is a critical issue. Most research has focused on the formability and defects in CMT-based WAAM and little research on the effect of process parameters on the morphology of the deposited layer in CMT-based WAAM has been conducted. To test the sensitivities of the processing parameters to bead size, the dynamic response of key parameters was investigated. A regression model was established to guide the process parameter optimization for subsequent multi-layer or component deposition.

The purpose of this paper is to establish the optimization model of designs for sparse distributed pile foundation based on multi‐goals fuzzy optimization theory, to promote the application of the optimization model into project.

In the designing of sparse distributed pile foundation, there are many feasible design schemes, the selection of designs is a decision making of multi‐goals and factors. Owing to uncertain and imprecise environment in which the designing of sparse distributed pile foundation exists, the theories of fuzzy optimization are chosen as mathematical framework to optimize the design schemes.

Since relative optimal degree is used to judge, the optional result of using fuzzy optimization theories in sparse pile foundation design selection is more rational according to a site project.

The availability of data and precision of index weight selection are the main limitations as to which model will be applied.

A very useful optimal method for the sparse pile foundation design selection.

The new approach of optimal selection for sparse pile foundation design due to fuzzy optimization theories.

The purpose of this paper is to present a two-dimensional (2D) model of the base force element method (BFEM) based on the complementary energy principle. The study proposes a model of the BFEM for arbitrary mesh problems.

The BFEM uses the base forces given by Gao (2003) as fundamental variables to describe the stress state of an elastic system. An explicit expression of element compliance matrix is derived using the concept of base forces. The detailed formulations of governing equations for the BFEM are given using the Lagrange multiplier method. The explicit displacement expression of nodes is given. To verify the 2D model, a program on the BFEM using MATLAB language is made and a number of examples on arbitrary polygonal meshes and aberrant meshes are provided to illustrate the BFEM.

A good agreement is obtained between the numerical and theoretical results. Based on the studies, it is found that the 2D formulation of BFEM with complementary energy principle provides reliable predictions for arbitrary mesh problems.

Due to the use of Lagrange multiplier method, there are more basic unknowns in the control equation. The proposed method will be improved in the future.

This paper presents a new idea and a new numerical method, and to explore new ways to solve the problem of arbitrary meshes.

The paper presents a 2D model of the BFEM using the complementary energy principle for arbitrary mesh problems.

The purpose of this paper is to explore the impacts of climate change on crop net revenue by region. Particularly, the authors focus on the impact differences between north and south regions.

The authors applied the Ricardian approach which assumes that each farmer wishes to maximize revenue subject to the exogenous conditions of their farm. The climate data are based on actual measurements in 753 national meteorological stations and the socio-economic data covers 8,405 farms across 28 provinces in China.

On average, the rise of annual temperature will hurt farms both in the north or south. The impacts of climate change on both precipitation and temperatures have different seasonal impacts on producers in the north and the south of China. As a consequence, the impact on net farm revenues varies with farms in the north and the south being adversely affected (to different degrees) by a rise in the temperature, but both benefiting from an anticipated increase in rainfall. The results also reveal that irrigation is one key adaption measure to dealing with climate change. Whether in the north or south of China, increasing temperature is beneficial to irrigated farms, while for rainfed farms, higher temperature will result in a reduction in net revenues. The results also reveal that farms in the north are more vulnerable to temperature and precipitation variation than that in the south. Irrigated farms in the south are more vulnerable to precipitation variation than that in the north; but rainfed farms in the north are more vulnerable to precipitation variation than that in the south.

Applying empirical analysis to identify the differences of climate change impacts between north and south regions will help policy makers to design reasonable adaptation policies for various regions.

This paper studies the pricing of collateralized debt obligation (CDO) using Monte Carlo and analytic methods. Both methods are developed within the framework of the reduced form model. One-factor Gaussian Copula is used for treating default correlations amongst the collateral portfolio. Based on the two methods, the portfolio loss, the expected loss in each CDO tranche, tranche spread, and the default delta sensitivity are analyzed with respect to different parameters such as maturity, default correlation, default intensity or hazard rate, and recovery rate. We provide a careful study of the effects of different parametric impact. Our results show that Monte Carlo method is slow and not robust in the calculation of default delta sensitivity. The analytic approach has comparative advantages for pricing CDO. We also employ empirical data to investigate the implied default correlation and base correlation of the CDO. The implication of extending the analytical approach to incorporating Levy processes is also discussed.

The main theme of this volume is credit risk and credit derivatives. Recent developments in financial markets show that appropriate modeling and quantification of credit risk is fundamental in the context of modern complex structured financial products. Moreover, there is a need for further developments in our understanding of this important area. In particular modeling defaults and their correlation has been a real challenge in recent years, and still is. This problem is even more relevant after the so-called subprime crisis that hit in the summer of 2007. This makes the volume very timely and hopefully useful for researchers in the area of credit risk and credit derivatives.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

The study shows that AI resources help e-commerce platforms reduce seller opportunism by enhancing digital empowerment and value cocreation.

The briefing saves busy executives, strategists, and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Drawing on the wisdom of ancient Chinese philosopher Xunzi, this paper aims to present a novel mechanism for governing opportunism, referred to as “cultivational governance.” By examining the role of artificial intelligence (AI) resources possessed by e-commerce platforms, the authors explore how these resources contribute to mitigating seller opportunism. The central hypothesis of this study posits that two distinct types of AI resources, namely, AI technology resources and AI human resources, serve as crucial factors in curbing seller opportunism. Furthermore, the authors propose that platform digital empowerment and value cocreation act as mediating variables linking AI resources to opportunism.

Based on the resource-based view and resource orchestration theory, the authors developed a framework and tested it using survey data from sellers. This framework encompasses five key variables: e-commerce platform’s AI technology resources, AI human resources, platform digital empowerment, value cocreation and seller opportunism. Regression analysis was used for data analysis.

The empirical results validate the effectiveness of cultivational governance mechanisms, as both AI resources effectively suppress seller opportunism through digital empowerment and value cocreation. Specifically, e-commerce platforms’ AI technology resources significantly promote value cocreation and platform digital empowerment, while AI human resources primarily contribute to platform digital empowerment. Although platform digital empowerment encourages value cocreation, its direct impact on reducing seller opportunism was not supported. Notably, value cocreation negatively affects seller opportunism.

The present research mainly contributes to the marketing channel governance literature by introducing a new approach to inhibit opportunism, namely, the cultivational governance mechanism.

The purpose of this paper is to study the ablative behavior of the silicone resin-coated carbon fabric (coated fabric) that will swell significantly during ablation.

The ablation experiments of three coated fabrics were conducted by quartz lamp radiant. Based on the experimental analysis, a numerical model was proposed for the coated fabrics to study the ablative process in term of the energy balance, mass conservation and thermal decomposition equations.

Results showed that the average relative errors between the simulated temperatures and experimental values of back surfaces of coated fabric 1, 2 and 3 were 10.01, 7.53 and 7.32%, respectively. The average density of silicone resin of coated fabric 1 was reduced by 47.96%, and the closer the distance from the heated surface was, the more the density decreased. The thermal conductivity and specific heat capacity of silicone resin of coated fabric 1 increased with time. Before 50 s, each decomposition rate curve showed an inflection point, at which the silicone resin decomposed most intensely.

Based on experimental observations, the ablative behavior of the material with fixed expansion layer was simulated. In the further research, the moving expansion layer could be considered.

This paper provides the theoretical basis to evaluate the effectiveness of thermal protection materials that will swell during ablation.