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The purpose of this paper aims to design a robust wind turbine emulator (WTE) based on a three-phase induction motor (3PIM).

The 3PIM is driven by a soft voltage source inverter (VSI) controlled by a specific space vector modulation. By adjusting the appropriate vector sequence selection, the desired VSI output voltage allows a real wind turbine speed emulation in the laboratory, taking into account the wind profile, static and dynamic behaviors and parametric variations for theoretical and then experimental analysis. A Mexican hat profile and a sinusoidal profile are therefore used as the wind speed system input to highlight the electrical, mechanical and electromagnetic system response.

The simulation results, based on relative error data, show that the proposed reactive power control method effectively estimates the flux and the rotor time constant, thus ensuring an accurate trajectory tracking of the wind speed for the wind emulation application.

The proposed architecture achieves its results through the use of mathematical theory and WTE topology combine with an online adaptive estimator and Lyapunov stability adaptation control methods. These approaches are particularly relevant for low-cost or low-power alternative current (AC) motor drives in the field of renewable energy emulation. It has the advantage of eliminating the need for expensive and unreliable position transducers, thereby increasing the emulator drive life. A comparative analysis was also carried out to highlight the online adaptive estimator fast response time and accuracy.

The purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor (IGBT) faults has become an essential topic of study. Demand for sustainable energy sources has been prompted by rising environmental pollution and energy requirements. Renewable energy has been identified as a viable substitute for conventional fossil fuel energy generation. Because of its rapid installation time and adaptable expenditure for construction scale, wind energy has emerged as a great energy resource. Power converter failure is particularly significant for the reliable operation of wind power conversion systems because it not only has a high yearly fault rate but also a prolonged downtime. The power converters will continue to operate even after the failure, especially the open-circuit fault, endangering their other parts and impairing their functionality.

The most widely used signal processing methods for locating open-switch faults in power devices are the short-time Fourier transform and wavelet transform (WT) – based on time–frequency analysis. To increase their effectiveness, these methods necessitate the intensive use of computational resources. This study suggests a fault detection technique using empirical mode decomposition (EMD) that examines the phase currents from a power inverter. Furthermore, the intrinsic mode function’s relative energy entropy (REE) and simple logical operations are used to locate IGBT open switch failures.

The presented scheme successfully locates and detects 21 various classes of IGBT faults that could arise in a two-level three-phase voltage source inverter (VSI). To verify the efficacy of the proposed fault diagnosis (FD) scheme, the test is performed under various operating conditions of the power converter and induction motor load. The proposed method outperforms existing FD schemes in the literature in terms of fault coverage and robustness.

This study introduces an EMD–IMF–REE-based FD method for VSIs in wind turbine systems, which enhances the effectiveness and robustness of the FD method.

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.

An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.

The results indicate that the proposed service resource management strategy, considering user co-creation in the service delivery, process improved both the service provider’s business revenue and users' individual benefits.

The findings may facilitate the design and implementation of health information services that can achieve a high user service experience with low service operation costs.

This study is amongst the first to propose a service resource management model in I-HISS, considering the value co-creation of the user in the service-dominant logic. The novel artificial intelligence algorithm is developed using the deep reinforcement learning method to learn the adaptive service resource management strategy. The results emphasise user engagement in the health information service process.

This study aims to highlight that security and flexibilities remain the main points of contention in the cordiality business. This research points to planning a framework that empowers hotel users to get to the room using a mobile access key. Advancing secured facilities, mobile phone “Near Field Communication” (NFC) innovation as the entrance device by carrying out an application containing an imitated mobile key for explicit verification access is used.

The proposed system is evaluated by triangulation of experimental, numerical and rational evaluation using partial least square structural equation modeling (PLS-SEM) with Malaysian hotel guests and employees.

The discoveries with the hypothesis supported validated that the suggested solution can eliminate physical cards, boost protection and encourage a contactless ecosystem. Theoretical, management and societal contributions are discussed here.

This experiment comes with the constraints that it was conducted in only two hotels and does not fully reflect the choices of a wider range of travellers. Secondly, the cost of existing NFC smart locks is still relatively high, and along with the development of technology, the price will decrease when supply exceeds demand.

To promote high-security attributes, NFC technology as the access system by implementing an application containing an emulated smart key for specific authentication access is used. The host-card emulation enables cost-effectiveness profit and initiating a defence system in the pandemic era.

To promote high-security attributes, NFC technology is used as the access system by implementing an application containing an emulated smart key for specific authentication access. The host-card emulation enables cost-effectiveness profit and initiating a defence system in the pandemic era.

The novelty of this study comes from the use of commonly available smartphone NFC features that are yet to be applied in the tourism ecosystem. The research provokes the applied concept of mobile smartkeys.

Consumer product firms renowned for marketing appear to be complementing brand creation, extension and acquisition with minority equity investments in entrepreneurial brand ventures (EBVs) for strategic purposes. Similarly, EBVs are looking for growth and resources that can be accessed via inter-organizational partnerships. This flourishing industry practice and the paucity of empirical research indicates the potential for new studies. The research objective was to examine why and how large incumbents were implementing strategic brand venturing (SBV), and with this understanding to develop a framework useful for descriptive and normative purposes.

This qualitative research study comprised in-depth interviews and multiple data sources across seven case studies drawn from US subsidiaries of global firms within the consumer products industry. Grounded in resource theory, the dimensions of strategic brand equity investments are abductively derived.

The findings delineate 16 process capabilities within four aggregate clusters entailing, the designing of the SBV program, opportunity identification, brand entrepreneur partnerships and venture portfolio management. Prefaced by endogenous and exogenous antecedents, these process capabilities help to contribute strategic and financial value when implemented.

This qualitative research study yielded analytical rather than statistical generalizations. A range of market and economic factors exist in the United States contributing towards a favorable entrepreneurial and brand incubation climate. This may render the SBV concept as contingent and contextual. Furthermore, the view of brand entrepreneurs' regarding the design of the process model were not explicitly sought but inferred from the discourses of the venturing units interviewed.

The article outlines several important implementation imperatives for corporations endeavoring to competitively advantage their brand portfolios via adoption of a minority equity investing strategy in EBVs. Practitioners are cautioned against myopically adopting this process alone as a success heuristic given other factors may impact success such as changes in corporate strategy or upper echelon sponsorship.

Mission preservation for social brand ventures being tethered to a large incumbent may need to be taken into account prior to and during SBV relationships.

The research contributes to the call for greater insights into the investment processes used in venturing relationships as well as coverage of new industry sectors beyond technology industries that often characterize corporate venture capital studies. Several novel findings emerged related to the importance of—the industry ecosystem; symbiosis between the founding brand entrepreneur and brand culture; synchronization of investment strategies with an emerging brand life-cycle model and serendipitous corporate entrepreneurial opportunities.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

This paper reports the results of research that examines the interrelationships between efficacy of sustainability values (SV) and pro-sustainable behaviors of potential tourists. A partially mediated model is postulated and tested to help explain additional error variance in predicting consumers’ destination choice decisions in tourism, hence voiding a critical research gap. Coined as the “environmentally intellectualist behavior,” a new mediator variable is tested to explain additional error variance in human-value models.

The study is based on data collected from two representative samples of potential tourists from the USA and Canada. Data analyses include exploratory and confirmatory factor analyses that were used to examine the underlying domain structures of SV, followed by a predictive model using structural equation modeling.

The study findings suggest that values are salient factors that underlie pro-sustainable tourism and travel behavior. Moreover, the results confirm the existence of a higher-order sustainability construct. The study contributes original insights to the field by demonstrating that there are direct and indirect positive relationships between SV, environmental behaviors and decisions of consumers who take a pro-sustainable stance when traveling.

By modeling values as antecedents to attitudes and testing interrelationships between SV and the mediator variables coined as the environmentally intellectual behavior, the authors developed and tested a predictive model to explain destination- and product choice decisions. The model tested herein advances the value theory in two fundamental ways: first, this study demonstrates that SV can be modeled as higher-order factors. Second, values are antecedents to attitude and other variables, therefore must be included in consumer behavior models. Finally, the culture or origin of tourists matters when examining the impact of values on tourists’ choice decisions. Political actions and environmental attitudes can be modeled as mediators to explain additional error variance.

With hybrid simulation techniques getting popular for systems improvement in multiple fields, this study aims to provide insight on the use of hybrid simulation to assess the effect of lean manufacturing (LM) techniques on manufacturing facilities and the transition of a mass production (MP) facility to incorporating LM techniques.

In this paper, the authors apply a hybrid simulation approach to improve an educational automotive assembly line and provide guidelines for implementing different LM techniques. Specifically, the authors describe the design, development, verification and validation of a hybrid discrete-event and agent-based simulation model of a LEGO® car assembly line to analyze, improve and assess the system’s performance. The simulation approach examines the base model (MP) and an alternative scenario (just-in-time [JIT] with Heijunka).

The hybrid simulation approach effectively models the facility. The alternative simulation scenario (implementing JIT and Heijunka LM techniques) improved all examined performance metrics. In more detail, the system’s lead time was reduced by 47.37%, the throughput increased by 5.99% and the work-in-progress for workstations decreased by up to 56.73%.

This novel hybrid simulation approach provides insight and can be potentially extrapolated to model other manufacturing facilities and evaluate transition scenarios from MP to LM.