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The purpose of this work is that of providing the guidelines of an efficient implementation of power flow computations using the MATLAB computation environment.

The goal of obtaining high efficiency from MATLAB programs often proves elusive unless special care is taken in exploiting the vectorising capability of MATLAB programming. In the present paper the implementation of Newton‐Raphson power flow in MATLAB is examined with particular emphasis on the way of obtaining a vectorisable code capable of achieving effective numerical performance by exploiting its formulation in terms of complex variables.

Tests on actual networks with up to 1,300 buses are presented. They show that the complex power flow is as efficient as the best implementations of the Newton Raphson power flow using real variables, as long as the operations involved are reordered with the aim of exploiting the vectorisation capabilities of the MATLAB environment.

It is shown that improved numerical efficiency in the MATLAB can be obtained through its formulation in terms of complex variables. The complex Newton‐Raphson load flow, not very common in practical uses, is shown to have many desirable qualities from the point of view of MATLAB programming and is presented in detail.

In this paper we talk about complex matrix quaternions (biquaternions) and we deal with some abstract methods in mathematical complex matrix analysis.

We introduce and investigate the complex space HC consisting of all 2 × 2 complex matrices of the form ξ=z1+iw1z2+iw2−z‾2−iw‾2z‾1+iw‾1, (z1,w1,z2,w2)∈C4.

We develop on HC a new matrix holomorphic structure for which we provide the fundamental operational calculus properties.

We give sufficient and necessary conditions in terms of Cauchy–Riemann type quaternionic differential equations for holomorphicity of a function of one complex matrix variable ξ∈HC. In particular, we show that we have a lot of holomorphic functions of one matrix quaternion variable.

The purpose of this study is to simultaneously determine the constitutive parameters and boundary conditions by solving inverse mechanical problems of power hardening elastoplastic materials in three-dimensional geometries.

The power hardening elastoplastic problem is solved by the complex variable finite element method in software ABAQUS, based on a three-dimensional complex stress element using user-defined element subroutine. The complex-variable-differentiation method is introduced and used to accurately calculate the sensitivity coefficients in the multiple parameters identification method, and the Levenberg–Marquardt algorithm is applied to carry out the inversion.

Numerical results indicate that the complex variable finite element method has good performance for solving elastoplastic problems of three-dimensional geometries. The inversion method is effective and accurate for simultaneously identifying multi-parameters of power hardening elastoplastic problems in three-dimensional geometries, which could be employed for solving inverse elastoplastic problems in engineering applications.

The constitutive parameters and boundary conditions are simultaneously identified for power hardening elastoplastic problems in three-dimensional geometries, which is much challenging in practical applications. The numerical results show that the inversion method has high accuracy, good stability, and fast convergence speed.

Many firms engage in co-opetitive projects during which they have simultaneously competitive and collaborative relationships with many rivals in a complex network. A co-opetitive network offers them access to a large volume of resources and knowledge, for example, to support new markets and/or territories. So, does the network grow with the scope of the co-opetition project? The paper aims to discuss this issue.

An empirical study of 106 French boating intermediate-sized enterprises (ETIs) and small and medium enterprises provides a descriptive and explanatory analysis of co-opetitive networks.

The results support this definition of a complex co-opetitive network only when the objectives of a firm are part of the geographical expansion of its activities. In contrast, these relations remain simple (dyadic) when a firm favours a strategy of diversifying its activities while maintaining its unique local geographical market.

First, the work is based on a quantitative methodology, so is static. It would be interesting to analyze the process of the building of co-opetitive relationships and opportunism between rival firms, for example, through a qualitative study. Second, this work focusses on boating companies in France. It may be appropriate to consider the sanctions placed on the opportunism of foreign firms in co-opetition. Third, future work could increase understanding, not only of the nature of reprisals inflicted on individualistic co-opetitors, but also on the structure, objectives and results of these reprisals.

The study deepens our knowledge of the definition, composition and determinants of co-opetitive networks.

The purpose of this paper is to examine the timing and the determinants of electing Statement of Financial Accounting Standard (SFAS) No. 159 in the banking industry.

The authors hypothesize certain factors that will potentially affect banks' election decisions and separate banks into three groups: early electors, late electors and non‐electors by hand‐collecting the election decisions and the timing of the election decisions. Univariate and logit rank regressions are used to identify the determining factors between electors (vs non‐electors) and between early electors (vs late electors).

The authors find that compared to banks not electing SFAS No. 159 (non‐electors), banks electing SFAS No. 159 (early electors as well as late electors) face greater earnings pressures, have less volatile earnings and larger size, and are active in hedging activities. In addition, compared to banks electing SFAS No. 159 at required election date (late electors), banks electing SFAS No. 159 early (early electors) have weak financial strength, less volatile earnings, and are more likely to be audited by non‐Big‐4 auditors.

The study only focuses on the banking industry, so the results from may not be generalized to other industries. Future studies could explore how SFAS No. 159 impacts firms in different industries.

The authors' overall results suggest that the banks might have many considerations in mind when they elect to use SFAS No. 159. The results provide useful information for regulatory bodies to evaluate the efficacy of issuing the standard. Early electors could have exploited the opportunities provided by the transition provisions of this standard to boost their regulatory capital ratios.

In the complex ecosystem of mobile applications multiple factors have been used to explain users’ behavior, without though focusing on how different combinations of variables may affect user behavior. The purpose of this paper is to show how price value, game content quality, positive and negative emotions, gender and gameplay time interact with each other to predict high intention to download mobile games.

Building on complexity theory, the authors present a conceptual model followed by research propositions. The propositions are empirically validated through configurational analysis, employing fuzzy-set qualitative comparative analysis (fsQCA) on 531 active users of mobile games.

Findings identify ten solutions that explain high intention to download mobile games. Alternative paths are identified depending on the gender and the time users spend playing mobiles games. The authors highlight the role of price value and game content quality, as well as that of positive emotions, which are always core factors when present.

To identify complex interactions among the variables of interest, fsQCA is employed, differentiating from traditional studies using variance-based methods, leading to multiple solutions explaining the same outcome. None of the variables explains the intention to download on its own, but only when they combine with each other. The authors extend existing knowledge on how price value, game content quality, emotions, gender and gameplay time combine to lead to high intention to download mobile games; and present a methodology for how to bridge complexity theory with fsQCA, improving our understanding of intention to adopt mobile applications.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

The purpose of this paper is to explore how the number and quality of games that publishers have released, popularity of game genre, age rating and package size are configured to determine the mobile game takeoff in a short time.

Based on the signaling theory, the authors present a conceptual model. Using actual data about 170 mobile games at their initial stage in the Apple App store, the authors test the conceptual model by applying fuzzy qualitative comparative analysis (fsQCA).

The findings identify four solutions that explain Mobile game takeoff in a short time. The authors highlight the role of the number and quality of games released by publishers, as well as that of popular game genres, which are always core factors when present.

This paper complements the previous research on the diffusion of mobile games by exploring which information combinations can lead to mobile games takeoff in a short time from the perspective of configuration. FsQCA serves as a better tool for explaining the complex relationships among variables than a regression analysis approach does. The authors extend existing knowledge on how the number and quality of games that publishers have released, popularity of game genre, age rating and package size combine to lead to takeoff of mobile games in a short time.