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The purpose of this paper is to present a methodology for calculating eddy current losses in the core of a single-phase power voltage transformer, which, unlike a standard power transformer, has an open-type core (I-type core). In those apparatus, reduction of core losses is achieved by using a multipart open-type core that is created by merging a larger number of leaner cores.

3D FEM approach for calculation of eddy current losses in open-type cores based on a weak AλA formulation is presented. Method in which redundant degrees of freedom are eliminated is shown. This enables faster convergence of the simulation. The results are benchmarked using simulations with standard AVA formulation.

Results using weak AλA formulation with elimination of redundant degrees of freedom are in agreement with both simulation using only weak AλA formulation and with simulation based on AVA formulation.

The presented methodology is valid in linear cases, whereas the nonlinear case will be part of future work.

Presented procedure can be used for the optimization when designing the open-type core of apparatus like power voltage transformers.

The presented method is specifically adapted for calculating eddy currents in the open-type core. The method is based on a weak formulation for the magnetic vector potential A and the current vector potential λ, incorporating numerical homogenization and a straightforward elimination of redundant degrees of freedom, resulting in faster convergence of the simulation.

The goal of this research is to investigate the convergence behavior of the Newton iteration, when solving the nonlinear problem with consideration of hysteresis effects. Incorporating the vector hysteresis model in the magnetic vector potential formulation has encountered difficulties. One of the reasons is that the Newton method is very sensitive regarding the starting point and states distinct requirements for the nonlinear function in terms of monotony and smoothness. The other reason is that the differential reluctivity tensor of the material model is discontinuous due to the properties of the stop operators. In this work, line search methods to overcome these difficulties are discussed.

To stabilize the Newton iteration, line search methods are studied. The first method computes an error-oriented search direction. The second method is based on the Wolfe-Powell rule using the Armijo condition and curvature condition.

In this paper, the differentiation of the vector stop model, used to evaluate the Jacobian matrix, is studied. Different methods are applied for this nonlinear problem to ensure reliable and stable finite element simulations with consideration of vector hysteresis effects.

In this paper, two different line search Newton methods are applied to solve the magnetic field problems with consideration of vector hysteresis effects and ensure a stable convergence successfully. A comparison of these two methods in terms of robustness and efficiency is presented.

This paper aims to overcome the lack of methodologies for optimizing the volume of bulky low-frequency inductors that the authors came across with when working on the design of hybrid active power filters. Sound work was published concerning this well-known technology, but it became evident that the mentioned optimization topic was left unaddressed.

Using the Lagrange multipliers optimization method combined with the electromagnetic laws of inductor design, it was possible to establish a new design method to determine the optimal solutions that fulfil any given scenario of specifications. In other words, it is now possible to obtain the inductor’s geometric and electric parameters that not only satisfy the system’s electromagnetic requirements but also lead to smaller, lighter or economical solutions.

A generalized set of equations was obtained to facilitate the calculations of all the inductor-building parameters. As expected, these equations take as inputs the inductor’s required inductance, its maximum current and the desired resistance, but also a customizable cost function. The later cost function will optimize the inductor’s volumes of copper and iron and can be settled, among other purposes, for minimizing the total weight, volume or cost.

All the mathematical expressions to obtain the general optimal solutions are given as well as practical graphics for the three above-mentioned optimization criteria. Using these charts, the reader will be able to obtain by simple inspection the optimal solutions for a large, generalized universe of intended specifications.

Implementing the sustainable development goals (SDGs) has proven a significant challenge for companies. While multinational enterprises (MNEs) have shown a real intention to contribute to these goals, they face major barriers in implementing the SDGs in their core business strategies. Extant academic studies on this phenomenon have primarily explored why companies “should” address the SDG agenda but have not (yet) explored what “works,” what does not “work,” and why. Therefore, evidence of a sizable gap between intention and realization is growing. Besides, there is a limited explanation for the existence of this gap and no validated implementation models that could help overcome it. Additionally, management research remains relatively fragmented. The diversity of existing theoretical and empirical frameworks makes it difficult to consolidate scientific and practical insights on “how” to guide companies to accelerate the global goals through their core operations.

This study is one of the first attempts to draw lessons from extant research on effective SDGs’ implementation strategies. For that, we upgrade the “SDG Compass,” which has been recognized as a leading framework for SDGs implementation in companies’ core activities. A critical assessment of the literature on the SDGs implementation has been conducted through a systematic literature review (SLR) and bibliometric analysis. This has helped us identify gaps in the SDG implementation practice and accumulate relevant insights supporting a more integrated and upgraded implementation framework: the SDG Compass+. This framework can advance coordinated theoretical and practical research by identifying the antecedents and critical factors of impactful SDG implementation strategies.

The ongoing business dynamics show two aspects for generating innovation: first, high-impact innovations are developed jointly by several actors, such as universities, enterprises, and governments. Second, startups are better suited to develop innovation during crises or periods of low growth as experienced at the moment. Based on these aspects and drawing on the constructs of the triple helix, this study analyzes the influence between the characteristics of the actors on the quality of the startup ecosystem from a global view.

The study examines the cross-section data of 35 countries between 2017 and 2018 and applies the partial least squares structural equation modeling (PLS-SEM) for assessing the relationships between the triple helix on the quality of the startup ecosystem on a country-level.

The findings suggest that each actor of the triple helix individually does not positively affect the quality of the startup ecosystem. Yet, when analyzing the actors jointly by creating a second-order latent variable (i.e. triple helix), the study found out that in this way, the triple helix construct has a positive effect on the quality of the startup ecosystem.

Although a large body of prior literature indicates the importance of generating interrelationships among the different entities involved in ecosystems, few studies provide empirical evidence from a global perspective of the need for these entities to act in an overlapping manner. The present study supports previous research and reinforces the importance of the triple helix for a more innovative environment.

Through a theoretical lens of psychological ownership, this study aims to investigate how technology mindfulness may stimulate metaverse tourism users’ feelings of individual psychological ownership, aesthetic value and conversational value, which in turn fosters intention to engage in prosocial behaviors.

The study used a scenario-based survey that allowed U.S.-based participants to create their own avatars and imagine using their avatars to explore heritage sites in the metaverse. Structural equality modeling was applied for data analysis.

The results from 357 valid responses indicate that technology mindfulness arouses tourists’ individual psychological ownership, aesthetic value, conversational value and prosocial behavioral intentions. The moderating role of biospheric value orientation on willingness to donate and intention to volunteer is investigated.

The research sheds light on the significance of technology mindfulness, conversational value and psychological ownership perspectives in the metaverse, which have been previously overlooked. The authors used a scenario-based survey for mental stimulation due to current metaverse technology limitations.

The study is one of the first to explore the possibility of encouraging prosocial behaviors using metaverse-facilitated technology. The research offers guidelines to engage hospitality and tourism customers in the metaverse that can blend their virtual experiences into the real world.

This study represents one of the pioneering efforts to gain an in-depth understanding of the application of metaverse in triggering prosocial behavior toward heritage sites, explained via a technology mindfulness-driven model with a psychological ownership perspective.

This work aims to develop a web platform for inspecting roof structures for technical assistance supported by drones and artificial intelligence. The tools used were HTML, CSS and JavaScript languages; Firebase software for infrastructure; and Custom Vision for image processing.

This study adopted the design science research approach, and the main stages for the development of the web platform include (1) creation and validation of the roof inspection checklist, (2) validation of the use of Custom Vision as an image recognition tool, and (3) development of the web platform.

The results of automatic recognition showed a percentage of 77.08% accuracy in identifying pathologies in roof images obtained by drones for technical assistance.

This study contributed to developing a drone-integrated roof platform for visual data collection and artificial intelligence for automatic recognition of pathologies, enabling greater efficiency and agility in the collection, processing and analysis of results to guarantee the durability of the building.

Indigenous health care models have continued to play significant roles in the development and sustenance of livelihoods in African communities. Such models are used not only to provide affordable community health care services but also as an employment resource for traditional practitioners within African communities. Yet, for lack of documentation and lack of discussion in the management and Indigenous Knowledge System literature, the models are hardly incorporated into the official health care services of African countries. This chapter addresses this problem by documenting the benefits and merits of the entrepreneurial principles embedded in the Tsonga traditional health care practice. First, it highlights how the unique indigenous traditional model is used for administering health care in a selected Tsonga-speaking community in South Africa. Second, it analyses the entrepreneurial principles embedded in the model. Finally, it discusses how such principles have been used as tools for entrepreneurship and enhancing the economic upkeep of the traditional health care practitioners (THPs). The chapter's main argument is that even though the Tsonga traditional health care practice model is complex, mystical, and in most cases challenging to explain to a non-native, it presents a great entrepreneurial opportunity for South Africa.

The objective of this study is to investigate the relationship between trade credit supply and financial distress outcomes, considering the role that trade credit plays as a substantial source of liquidity for distressed companies. Specifically, it examines whether there is an association between trade credit supply and the outcomes experienced by companies that undergo the voluntary administration (VA) insolvency procedure under Australian corporate law.

The study examines a sample of companies that were listed on the Australian Securities Exchange and entered VA between 2002 and 2019. Ordered logistic regression is used to determine the relation between trade credit and VA outcomes. The VA outcomes considered are as follows: (1) company liquidation, (2) orderly dissolution through an agreement with creditors, or (3) an agreement with creditors for reorganization of all or part of the company's business.

The findings show that trade creditors' willingness to supply credit is influenced by their rational expectations about the future prospects of financially distressed customers. Higher levels of trade credit and an increase in trade credit supply prior to VA are associated with a greater probability of achieving a reorganization versus a liquidation or dissolution outcome.

There is no apparent prior study investigating the connection between trade credit supply and outcomes for distressed companies entering insolvency administration. Therefore, this study provides novel evidence on the role of trade credit in the context of financial distress. Understanding the relationship between trade credit supply and outcomes is particularly significant considering that many jurisdictions offer distressed companies the opportunity to pursue reorganization under their insolvency laws. Examining financial distress and trade credit in the Australian creditor-friendly context expands on existing research. Prior research has predominantly relied on data from the United States, which has debtor-friendly bankruptcy law. Consequently, these studies may lack generalizability to jurisdictions with creditor-friendly law such as Australia.