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This study attempts to uncover the nonlinear relationship between public procurement and corporate total factor productivity (CTFP), and investigates the mediating roles of R&D investment and rent-seeking cost. Additionally, it conducts a heterogeneity analysis for firms with varying levels of political connections and corporate social responsibility (CSR).

Employing Ordinary Least Squares (OLS) and Olley-Pakes (OP) methods, the authors gauge CTFP and manually identify government customers to quantify public procurement. Leveraging panel data from Chinese listed companies, this study explores the relationship between public procurement and CTFP.

This study unveils a U-shaped relationship between public procurement and CTFP, highlighting R&D investment and rent-seeking costs as potential mechanisms. Furthermore, it identifies heterogeneous effects among companies with varying levels of political connections and CSR on the relationship between public procurement and CTFP, including their mediating effects.

This research enhances understanding of demand-side policies and provides crucial insights for the government to further improve public procurement policies.

By offering empirical evidence of how public procurement impacts CTFP, this paper enriches the literature on the behavioral repercussions of public procurement and the determinants of CTFP. It also overcomes the “black box” of the mechanism between public procurement and CTFP, based on the government’s dual role as a pathfinder and customer of enterprises. It broadens the application scenarios of institutional theory and principal-agent theory. Additionally, the heterogeneity analysis of firms with varying political connections and CSR extends the frontiers of related research.

This paper aims to propose and establish a set of new benchmark models to investigate and confidently validate the modeling and prediction of total stray-field loss inside magnetic and non-magnetic components under harmonics-direct current (HDC) hybrid excitations. As a new member-set (P21^e) of the testing electromagnetic analysis methods Problem 21 Family, the focus is on efficient analysis methods and accurate material property modeling under complex excitations.

This P21^e-based benchmarking covers the design of new benchmark models with magnetic flux compensation, the establishment of a new benchmark measurement system with HDC hybrid excitation, the formulation of the testing program (such as defined Cases I–V) and the measurement and prediction of material properties under HDC hybrid excitations, to test electromagnetic analysis methods and finite element (FE) computation models and investigate the electromagnetic behavior of typical magnetic and electromagnetic shields in electrical equipment.

The updated Problem 21 Family (V.2021) can now be used to investigate and validate the total power loss and the different shielding performance of magnetic and electromagnetic shields under various HDC hybrid excitations, including the different spatial distributions of the same excitation parameters. The new member-set (P21^e) with magnetic flux compensation can experimentally determine the total power loss inside the load-component, which helps to validate the numerical modeling and simulation with confidence. The additional iron loss inside the laminated sheets caused by the magnetic flux normal to the laminations must be correctly modeled and predicted during the design and analysis. It is also observed that the magnetic properties (B27R090) measured in the rolling and transverse directions with different direct current (DC) biasing magnetic field are quite different from each other.

The future benchmarking target is to study the effects of stronger HDC hybrid excitations on the internal loss behavior and the microstructure of magnetic load components.

This paper proposes a new extension of Problem 21 Family (1993–2021) with the upgraded excitation, involving multi-harmonics and DC bias. The alternating current (AC) and DC excitation can be applied at the two sides of the model’s load-component to avoid the adverse impact on the AC and DC power supply and investigate the effect of different AC and DC hybrid patterns on the total loss inside the load-component. The overall effectiveness of numerical modeling and simulation is highlighted and achieved via combining the efficient electromagnetic analysis methods and solvers, the reliable material property modeling and prediction under complex excitations and the precise FE computation model using partition processing. The outcome of this project will be beneficial to large-scale and high-performance numerical modeling.

The purpose of this paper is to show how intelligence techniques have been used in information management systems.

The results of a literature review on intelligence decision systems used in enterprise information management are analyzed. The intelligence techniques used in enterprise information management are briefly summarized.

Intelligence techniques are rapidly emerging as new tools in information management systems. Especially, intelligence techniques can be used to utilize the decision process of enterprises information management. These techniques can increase sensitiveness, flexibility and accuracy of information management systems. The hybrid systems that contain two or more intelligence techniques will be more used in the future.

The intelligence decision systems are briefly introduced and then a literature review is given to show how intelligence techniques have been used in information management systems.

This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

The purpose of this paper is to deal with the performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm. It provides the optimum unit availability level for different combinations of failure and repair rates of the subsystems of the stock preparation unit of the paper plant concerned.

In this paper, efforts have been made to develop performance models based on real situations for the stock preparation unit. The performance in terms of availability has been evaluated on the basis of Markov birth‐death process. After that, the performance optimization using genetic algorithm is done, which gives the optimum unit availability levels for different combinations of failure and repair rates of the subsystems of stock preparation units for enhancing the overall performance of the paper plant.

The effect of genetic algorithm parameters, namely number of generations, population size and crossover probability on the unit performance i.e. availability has been analyzed and discussed with the concerned paper plant management. It is found that these results are highly beneficial to the maintenance engineers for the purpose of effective maintenance planning to enhance the overall performance (availability) of the stock preparation unit of the paper plant.

Most of the researchers have confined their work to the development and analysis of theoretical models which has little practical significance. To fulfill this deficiency, efforts have been made in the present work to develop a model based on real situations for the stock preparation unit.

This paper aims to deal with the performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm. It provides the optimum unit availability level for different combinations of failure and repair rates of the subsystems of the stock preparation unit of the paper plant concerned.

Efforts have been made to develop the performance model based on a real situation for the stock preparation unit. The performance in terms of availability has been evaluated on the basis of Markov birth‐death process. After that, the performance optimization using genetic algorithm is performed, which gives the optimum unit availability levels for different combinations of failure and repair rates of the subsystems of stock preparation unit for enhancing the overall performance of the paper plant.

The effect of genetic algorithm parameters such as number of generations, population size and crossover probability on the unit performance, i.e. availability, has been analyzed and discussed with the concerned paper plant management. It is found that these results are highly beneficial to the maintenance engineers for the purpose of the effective maintenance planning to enhance the overall performance (availability) of stock preparation unit of the paper plant.

Most other researchers have confined their work to the development and analysis of theoretical models which has little practical significance. To fulfill this deficiency, efforts have been made in the present work to develop a model based on real situation for stock preparation unit.

The USA is one of the largest oil producers in the world. For this purpose, the authors model and predict the US conventional and unconventional oil production during the period 2000–2030.

In this research, the system dynamics (SD) model has been used. In this model, economic, technical, geopolitical, learning-by-doing and environmental (social costs of carbon) issues are considered.

The results of the simulation, after successfully passing the validation test, show that the US unconventional oil production rate under the optimistic scenario (high oil prices) in 2030 is about 12.62 million barrels/day (mb/day), under the medium oil price scenario is about 11.4 mb/day and under the pessimistic scenario (low oil price) is about 10.18 mb/day. The results of US conventional oil production forecasting under these three scenarios (high, medium and low oil prices) show oil production of 4.62, 4.26 and 3.91 mb/day, respectively.

The contribution of this study is important in several respects: First, by modeling SD that technical, economic, proven reserves and technology factors are considered, this paper models US conventional and unconventional oil production separately. In this modeling, nonlinear relationships and feedback loops are presented to better understand the relationships between variables. Second, given the importance of environmental issues, the modeling of social costs of CO₂ emissions per barrel of oil is also presented and considered as a part of oil production costs. Third, conventional and unconventional US oil production by 2030 is forecast separately, the results of this study could help policymakers to develop unconventional oil and plan for energy self-sufficiency.