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This paper aims to describe a CAD tool of permanent magnet electric machines. The software tool is operational from the first design phases and allows to study coupled phenomena.

The described design methodology is based on the association of analytical and numerical approaches. A coupled electromagnetic‐thermal model is used for the analytical study. The numerical one uses the finite element method.

The sequential implementation of the two approaches highlights their complementarities and improves the time consuming and the reliability of the design process. The thermal coupling allows to anticipate the overheating of the machine and to take it into account in the design process.

The simplifying assumptions made in the analytical model degrade the accuracy of temperature calculation.

This is a useful tool for electric machines manufacturer planning to obtain a fast and reliable solution in response to datasheet specifications.

This paper describes a multidisciplinary design methodology which is performed by coupling methods and software tools at the aim to take advantage of each one.

Reducing eddy current losses in magnets of electrical machines can be obtained by means of several techniques. The magnet segmentation is the most popular one. It imposes the least restrictions on machine performances. This paper investigates the effectiveness of the magnet circumferential segmentation technique to reduce these undesirable losses. The full and partial magnet segmentation are both studied for a frequency range from few Hz to a dozen of kHz. To increase the efficiency of these techniques to reduce losses for any working frequency, an optimization strategy based on coupling of finite elements analysis and genetic algorithm is applied. The purpose of this paper is to define the parameters of the total and partial segmentation that can ensure the best reduction of eddy current losses.

First, a model to analyze eddy current losses is presented. Second, the effectiveness of full and partial magnet circumferential segmentation to reduce eddy loss is studied for a range of frequencies from few Hz to a dozen of kHz. To achieve these purposes a 2-D finite element model is developed under MATLAB environment. In a third step of the work, an optimization process is applied to adjust the segmentation design parameters for best reduction of eddy current losses in case of surface mounted permanent magnets synchronous machine.

In case of the skin effect operating, both full and partial magnet segmentations can lead to eddy current losses increases. Such deviations of magnet segmentation techniques can be avoided by an appropriate choice of their design parameters.

Few works are dedicated to investigate partial magnet segmentation for eddy current losses reduction. This paper studied the effectiveness and behaviour of partial segmentation for different frequency ranges. To avoid eventual anomalies related to the skin effect an optimization process based on the association of the finite elements analysis to genetic algorithm method is adopted.

This paper aims to examine the impact of CEO (chief executive officer) compensation and CEO attributes on the level of tax aggressiveness of French companies.

The sample comprises 180 firm-year observations of 40 companies listed on the CAC 40 during the period ranging from 2008 to 2018. For the purpose of overcoming the problems of heteroscedasticity and autocorrelation, the authors apply the generalized least square panel regression.

This study’s results corroborate the importance of CEO compensation and CEO attributes as determinants of tax aggressiveness. In addition, the authors come up with the fact that CEO compensation has a negative effect on tax aggressiveness, and that older CEOs and CEOs with accounting expertise are negatively linked with tax aggressiveness. The authors also find out that there is a positive relationship between the CEO tenure and tax aggressiveness. Moreover, the authors report that foreign CEOs are more likely to engage in tax aggressiveness practices than local CEOs.

The unavailability of all annual reports and the use of only one proxy to measure tax aggressiveness present limitations. This study shows significant implications for shareholders, regulators and researchers. As a matter of fact, shareholders will observe the effect of appointing a foreign CEO on the tax aggressiveness level. This study may also provide regulators with new ideas regarding the role of the CEO and its impact on aggressive decision-making. And it brings forth new insight for researchers through adding a foreign CEO as a new determinant of tax aggressiveness.

According to the authors’ knowledge, this study is the first to provide empirical evidence regarding the effect of both CEO compensation and CEO attributes on tax aggressiveness. It also looks into the impact of a foreign CEO on tax aggressiveness.

This study aims to examine the impact of culture on earnings management after changing to International Financial Reporting Standards (IFRS).

The study’s sample selection comprises all publicly listed firms in 25 countries between 2000 and 2017 from DataStream database with cultural dimensions ratings from Hofstede et al. (2010). The initial sample contained 2,451 firms.

This study provides evidence that the interaction between national culture and IFRS adoption remains influential in explaining differences in the magnitude of earnings management behavior across countries.

This study higlights how IFRS and the cultural values interact with each other and affect earnings quality. In particular, the authors provide evidence on the relationship between individualism, uncertainty avoidance, power distance and masculinity of national culture and earnings management and, primarily, find that national culture significantly influences the decisions of managers after adopting IFRS.

Patterns have proven to be useful for documenting general reusable solutions to a commonly occurring problem. In recent years, several different business process management (BPM)-related patterns have been published. Despite the large number of publications on this subject, there is no work that provides a comprehensive overview and categorization of the published business process model patterns. The purpose of this paper is to close this gap by providing a taxonomy of patterns as well as a classification of 89 research works.

The authors analyzed 280 research articles following a structured iterative procedure inspired by the method for taxonomy development from Nickerson et al. (2013). Using deductive and inductive reasoning processes embedded in concurrent as well as joint research activities, the authors created a taxonomy of patterns as well as a classification of 89 research works.

In general, the findings extend the current understanding of BPM patterns. The authors identify pattern categories that are highly populated with research works as well as categories that have received far less attention such as risk and security, the ecological perspective and process architecture. Further, the analysis shows that there is not yet an overarching pattern language for business process model patterns. The insights can be used as starting point for developing such a pattern language.

Up to now, no comprehensive pattern taxonomy and research classification exists. The taxonomy and classification are useful for searching pattern works which is also supported by an accompanying website complementing the work. In regard to future research and publications on patterns, the authors derive recommendations regarding the content and structure of pattern publications.

The purpose of this study is to examine the interplay between integrated reporting quality (IRQ) and capital markets. More specifically, the authors test the impact of IRQ on stock liquidity, cost of capital and analyst forecast accuracy.

The sample consists of listed firms on the Johannesburg Stock Exchange in South Africa, covering the period from 2012 to 2020. The IRQ measure used in this study is based on data from Ernst and Young. To test the proposed hypotheses, the authors conducted a generalized least squares regression analysis.

The empirical results evince a positive relationship between IRQ and stock liquidity. However, the authors did not find a significant effect of IRQ on the cost of capital and financial analysts’ forecast accuracy. In robustness tests, it was shown that firms with a higher IRQ score exhibit higher liquidity and improved analyst forecast accuracy. Additional analysis indicates a negative association between IRQ and the cost of capital, as well as a positive association between IRQ and financial analyst forecast accuracy for firms with higher IRQ scores (TOP ten, Excellent, Good).

The study stands as one of the initial endeavors to investigate the impact of IRQ on the capital market. It provides valuable insights for managers and policymakers who are interested in enhancing disclosure practices within the financial market. Furthermore, these findings are significant for investors as they make informed investment decisions.

This paper aims to investigate not only the performance of Portuguese mutual funds investing in domestic and international equities but also which fund characteristics, such as age, size, family size, expense ratios and flows, influence future performance.

Fund performance is evaluated over the 2005–2022 period by a robust six-factor model, while the impact of fund characteristics on performance is assessed by a set of fixed-effects panel data regressions with two-way cluster-robust standard errors.

The results show that, while funds investing in domestic equities predominantly exhibit neutral performance, most international equity funds have significantly negative alphas. The authors document a negative and statistically significant relationship between fund age and performance for all fund categories. Total expense ratios have an inverse relationship with domestic equity fund performance but do not impact the performance of international equity funds significantly. Though fund flows have a neutral effect on performance across the overall period, they are important determinants of both domestic and international funds’ performance in more recent years.

The authors contribute to the literature by carrying out a comprehensive analysis, based on recent and robust methodologies, of the impact of mutual fund characteristics on the future performance of Portuguese equity funds. The research findings serve as a premise for advising investors on how to choose the top-performing funds.

The extant literature provides much-needed support to understand marketing accountability and how marketing actions are related to financial performance (FP). However, we have limited understanding of the relationships between marketing actions and firms' social performance (SP) and environmental performance (EP). Understanding these links is critical to enhancing sustainable FP, SP, and EP. Moreover, the literature provides limited understanding of the measures by which SP and EP may be operationalized, or the data necessary to reach a conclusion. This study bridges these gaps by extensively reviewing the extant literature to offer a set of measures and data sources to operationalize SP and EP, and empirically show their relationships with marketing actions. We find that greenhouse gas (GHG) emission, environmental disclosure score, waste reduction, energy consumption, and recycling are prominent measures of EP, and that social disclosure score, philanthropy or community spending, and diversity of gender and race are prominent measures of SP. The KLD, ASSET4, and Bloomberg are prominent sources of data that can be used to operationalize SP, to which CDP may be added for EP. We also show that marketing actions positively affect EP and SP. This study contributes to the extant literature on SP and EP by identifying measures and data sources and linking marketing actions to both performance types. It contributes to policy development by identifying the importance of EP and SP and how marketing actions can help achieve such performance.

The purpose of this paper is to investigate the influence of different dietary fibers on color, nutritional, physico-chemical, sensory and textural properties of fresh sausage.

This study was undertaken to determine the effects of partial beef meat substitution (15, 10.5 and 6 percent) at different combinations: barley beta-glucan concentrate (BBC at 1 percent) alone, carrot fiber (ID809 at 1 percent) alone and a mixture of both fibers (BBC and ID809 at 0.5 percent of each), on the quality of fresh sausage.

Results revealed that the supplementation of dietary fibers generally decreased protein contents and increased moisture. Color parameters were significantly affected by the addition of dietary fibers. Meat substitution with dietary fibers tended toward red coloration, which enhanced sensory properties of fresh sausages, especially general acceptability. Fresh sausages formulated with a mixture of dietary fibers recorded good firmness. Indeed, it increased extrusion force values and provided a greater hardness at sensory level. This manuscript reports that beef sausage formulated with a mixture of BBC and ID809 and reduced beef meat rate improved acceptability to consumer.

The paper approaches a new subject that may be relevant to functional food. There are few research works to evaluate consumer preferences of new product developments and to achieve healthier and more economic fresh sausages formulated with a mixture of two fibers after reducing beef meat content.

The purpose of this paper is to focus on compressive strength modelling of cementitious mixtures like mortar and Roller-compacted concrete (RCC) containing rubber aggregates from shredded worn tires and filler using adaptive neuro fuzzy inference systems (ANFIS).

The volume substitution contains a ratio of rubber aggregates vs sand in mortar and with crushed sand in RCC and ranges from 0 to 50 per cent. As for the filler, they are substituted with sand by 5 per cent in mortar mixture. The methodology consists of optimizing the percentage of substitution in cementitious mixtures to ensure better mechanical properties of materials like compressive strength. The prediction of compressive strength and the optimization of cementitious mixtures encourage their uses in such construction pavements, in area games or in other special constructions. These cementitious materials are considered as friendly to the environment by focussing on their improved deformability.

The results of this paper show that the performance of the constructed fuzzy method was measured by correlation of experimental and model results of mortar and RCC mixtures containing both rubber aggregates and filler. The comparison between elaborated models through the error and the accuracy calculations confirms the reliability of the ANFIS method.

The purpose of this paper is to assess the performance of the constructed fuzzy model by the ANFIS method for two types of cementitious materials like mortar and RCC containing rubber aggregates and filler. The fuzzy method could predict the compressive strength based on the limited measurement values in the mechanical experiment. Furthermore, the comparison between the elaborated models confirms the reliability of the ANFIS method through the error and the accuracy calculations for the best cementitious material mixtures.