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In the literature, while designing broadband matching networks, transducer power gain (TPG) is used to measure the transferred power. Generally, in TPG expressions, load and back-end impedances of the matching network are used. This study aims to derive a new quality factor-based TPG expression.

In deriving the new expression, narrowband L type-matching network design approach is used and the new expression in terms of back-end quality factor, load quality factor and output port quality factor is obtained. Then, a broadband-matching network design approach using the derived TPG expression is proposed.

Two broadband double-matching networks are designed by using the proposed design approach using the derived TPG expression. Performances of the designed-matching networks are compared with the performances of the matching networks designed by means of simplified real frequency technique which is a well-known technique in the literature, and it is shown that they are nearly the same.

In broadband-matching problems, generally an impedance-based TPG expression is used, and it must be satisfied by the designed broadband-matching networks. But, in the literature, there is no quality factor-based TPG expression that can be used in broadband-matching problems. So, this gap in the literature has been filled by this paper.

This study examined the reciprocal influence of demand learning and preference matching in the context of store brand customization. The demand-learning effect refers to the collection of market demand information through production, based on pre-order demands, enabling retailers to accurately predict and allocate product quantities, thus improving inventory management. The preference-matching effect involves engaging consumers in the production and design processes of store brands to align fully with their preferences, thereby increasing the purchase impact of store brand products and promoting consumption.

We employ game-theoretic models to analyze a two-echelon supply chain consisting of a manufacturer and a retailer. The retailer offers both national brands, manufactured by the supplier and in-house store brands. To enhance their competitive edge, the retailer can adopt a customized strategy targeting the store brand to attract a wider consumer base.

The analysis reveals that, under low commission fees, the manufacturer consistently opts for high production quantities, irrespective of the level of demand uncertainty. However, when the perceived value of a store brand is low and demand uncertainty is either low or high, the retailer should choose a minimal or zero production quantity. The decision-making process is influenced by the customization process, wherein the effects of demand learning and preference matching occasionally mutually reinforce each other. Specifically, when the perceived value of a store brand is low, or the product cost is high, along with high customization costs, the interplay between demand learning and preference matching becomes mutually inhibiting. Consequently, the significance of store brand customization diminishes.

This study enhances the current body of knowledge by providing a deeper understanding of the theoretical value of store brand customization. In addition, it offers valuable decision-making support to enterprises by assisting them in selecting appropriate inventory and customization strategies.

The purpose of this paper is to explore the matching relationship between factor endowment and industrial structure, and its impact on economic growth.

The assortative matching method is developed to quantitatively measure the matching between factor endowment and industrial structure. A series of empirical tests are then carried out to evaluate the impact on the economic development of the matching.

1) The matching between factor endowment and industrial structure has a significantly positive impact on economic growth. (2) Economic growth reaches its maximum when the gap between the two sectors narrows to zero. (3) This effect is particularly significant for countries with higher GDP per capita and GNI per capita. (4) The results remain robust after employing a series of tests.

Aggressive industrial policies are not desirable. The optimal industrial structure is the one that complied with the comparative advantage of the given factor endowment in the economy.

So far, there has been a significant lack of an applicable quantitative indicator for measuring the matching between factor endowment and industrial structure, which is essential for conducting empirical tests and providing evidence for related economic theories.

In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

This paper aims to investigate the law and logic mechanism of management control matching pattern and company strategy aggressiveness under different strategies by textual analysis, based on the empirical data of Chinese A-share listed companies during the period from 2010 to 2018. Additional analyses further investigate the moderating effect of environmental uncertainty and R&D intensity on the relationship between management control matching type and strategy aggressiveness. The conclusion can help relevant departments to develop management control theory and method system with Chinese characteristics and provide theoretical reference for the matching mode of dual control.

This paper uses the text analysis method. The main explanatory variables are analyzed using the computer SQL Server database software through the relevant text of the board of directors report in the company annual report. Other financial data came from the CSMAR database, excluding ST and PT and companies with missing data, and 16,902 samples were finally obtained. This paper conducted statistical analysis through Stata12.

This paper shows that the matching pattern between formal and informal control is divided into three types. They have different impacts on strategy aggressiveness. Specifically, consistent matching type II significantly positively influences the aggressiveness of offensive strategy. Consistent matching type I significantly positively influences the aggressiveness of defensive strategy. Complementary matching type I significantly positively influences the aggressiveness of analytical strategy. Additional analyses find that compared with non-high-tech companies, high-tech companies have more significant influence on the relationship between management control matching pattern and company strategic aggressiveness. And compared with other two “strategy-control” matching patterns, both environmental uncertainty and product innovation have more significant influence on the relationship between consistent matching II and offensive strategy aggressiveness.

To the best of the authors’ knowledge, this paper divides the formal and informal control matching patterns of management control into three categories for the first time. It examines the relationship between the formal and informal control matching of management accounting and the degree of strategy aggressiveness. The conclusion provides new empirical evidence to promote the effective implementation of development strategies for companies. It can help relevant departments to develop management control theory and method systems with Chinese characteristics and provide theoretical references for the matching mode of dual control.

In this paper, the authors explore the consequences of showrooming and price matching strategy to combat showrooming under the consideration that brick-and-mortar (BM) stores and online retailers hold different costs.

The authors use a duopoly model to analyze the impact of showrooming behavior on competition between a BM store and an online retailer with different types of customers and different costs. Then, they consider the price matching strategy that a BM store could employ to combat showrooming and explore the effect of such a strategy.

Showrooming behavior is detrimental to the profit of the BM store, and the online retailer suffers a loss of their profit unless the relative cost of the BM store is high and only part of the customers exhibit showrooming behavior. As the fraction of customers who seek price matching increases, profits of both the BM store and the online retailer initially decrease and then may increase, even if there is no showrooming.

Unlike existing studies that ignore different costs between online and offline retailers, the authors set different costs between the BM store and the online retailer to consider the effects of showrooming and price matching strategy.

This paper mainly focuses on how to induce all members to represent members' true preferences for supply and demand matching of E-commerce platform in order to generate stable matching schemes with more social welfare of Multi-agent Matching Platform (MMP) and individually stable advantages than traditional methods.

An MMP is designed. Meanwhile, a true preference inducing method, Lower-Bid Ranking (LBR), is proposed to reduce the number of false preferences, which is helpful to solve the problem that too much false preferences leads to low efficiency of platform operation and supply and demand matching. Then, a systematic model of LBR-based Stable Matching (SM-LBR) is proposed.

To obtain an ideal transaction partner, the adequate preference ordering and modifying according to market environment is needed for everyone, and the platform should give full play to the platforms' information advantages and process historical transaction and cooperation data. Meanwhile, the appropriate supply and demand matching is beneficial to improve the efficiency and quality of platform operation, and the design of matching guidance mechanism is essential.

The numerical experiments show that, the proposed model (SM-LBR) can induce members to represent the model's true preferences for stable matching and generate effective matchings with more social welfare of MMP and individually stable advantages than traditional methods, which may provide necessary method and model reference for the research of stable matching and E-commerce platform operation.

Template matching is one of the most suitable choices for full six degrees of freedom pose estimation in many practical industrial applications. However, the increasing number of templates while dealing with a wide range of viewpoint changes results in a long runtime, which may not meet the real-time requirements. This paper aims to improve matching efficiency while maintaining sample resolution and matching accuracy.

A multi-pyramid-based hierarchical template matching strategy is proposed. Three pyramids are established at the sphere subdivision, radius and in-plane rotation levels during the offline template render stage. Then, a hierarchical template matching is performed from the highest to the lowest level in each pyramid, narrowing the global search space and expanding the local search space. The initial search parameters at the top level can be determined by the preprocessing of the YOLOv3 object detection network to further improve real-time performance.

Experimental results show that this matching strategy takes only 100 ms under 100k templates without loss of accuracy, promising for real industrial applications. The authors further validated the approach by applying it to a real robot grasping task.

The matching framework in this paper improves the template matching efficiency by two orders of magnitude and is validated using a common template definition and viewpoint sampling methods. In addition, it can be easily adapted to other template definitions and viewpoint sampling methods.

This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

The uncertainty of consumers' perceived value makes online education enterprises face great challenge in developing the pricing strategy. So the purpose of this paper is to research the pricing strategies of online education products by considering knowledge consumers' characteristics.

Considering consumer matching degree and price comparison, this study establishes the utility functions of consumers in normal sales period and discount selling period. On this basis, the research builds pricing models of the online education enterprise under the strategy of price undertaking and intertemporal pricing strategy. It further discusses the impact of consumer matching degree, consumer price sensitivity and different types of consumers on the product price and profit of online education enterprises, and reveals the optimal pricing strategy of the enterprise.

Consumer matching degree and price sensitivity coefficient have positive effects on product price and enterprise profit, but they have different effects on product demand; there are differences in the perceived value of the three types of consumers, and matching consumers are the optimal consumer group; the intertemporal pricing strategy is better than the strategy of price undertaking only when the price sensitivity coefficient is greater than a critical value.

This study enriches the literature on the pricing model of online education products and owns a practical significance to guide the online enterprise to make marketing strategies to increase profit.