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Detecting the orientation of cracks is a major challenge in the development of eddy current nondestructive testing probes. Eddy current-based techniques are limited in their ability to detect cracks that are not perpendicular to induced current flows. This study aims to investigate the application of the rotating electromagnetic field method to detect arbitrary orientation defects in conductive nonferrous parts. This method significantly improves the detection of cracks of any orientation.

A new rotating uniform eddy current (RUEC) probe is presented. Two exciting pairs consisting of similar square-shaped coils are arranged orthogonally at the same lifting point, thus avoiding further adjustment of the excitation system to generate a rotating electromagnetic field, eliminating any need for mechanical rotation and focusing this field with high density. A circular detection coil serving as a receiver is mounted in the middle of the excitation system.

A simulation model of the rotating electromagnetic field system is performed to determine the rules and characteristics of the electromagnetic signal distribution in the defect area. Referring to the experimental results aimed to detect artificial cracks at arbitrary angles in underwater structures using the rotating alternating current field measurement (RACFM) system in Li et al. (2016), the model proposed in this paper is validated.

CEDRAT FLUX 3D simulation results showed that the proposed probe can detect cracks with any orientation, maintaining the same sensitivity, which demonstrates its effectiveness. Furthermore, the proposed RUEC probe, associated with the exploitation procedure, allows us to provide a full characterization of the crack, namely, its length, depth and orientation in a one-pass scan, by analyzing the magnetic induction signal.

The purpose of this paper is to investigate the applicability of a direct current (DC) hysteresis measurement on power transformer terminals for the subsequent hysteresis model parametrization in transformer grey box topology models.

Two transformer topology models with two different hysteresis models are used together with a DC hysteresis measurement via the power transformer terminals to parameterize the hysteresis models by means of an optimization. The calculated current waveform with the derived model in the transformer no-load condition is compared to the measured no-load current waveforms to validate the model.

The proposed DC hysteresis measurement via the power transformer terminals is suitable to parametrize two hysteresis models implemented in transformer topology models to calculate the no-load current waveforms.

Different approaches for the measurement and utilization of transformer terminal measurements for the hysteresis model parametrization are discussed in literature. The transformer topology models, derived with the presented approach, are able to reproduce the transformer no-load current waveform with acceptable accuracy.

Despite being a seminal explanation of the workforce emotional experiences, capable of mapping the path from the antecedents to consequences, affective events theory (AET) only offers a “macrostructure” of a working environment. To date, little is known about the universal features of the work environment that may guide the understanding of imperative work aspects triggering employees’ emotions at work. Hence, the study proposes and validates that Stafford Beer’s viable system model (VSM) can provide a holistic view of the organizational work environment, enabling a comprehensive understanding of work events or factors triggering workforce emotions.

First, the VSM structural layout is used to fill in the “macrostructure” of the “working environment” in AET to diagnose the functional and relational aspects of the work and the related work events occurring within. Using a deductive approach, 31 work events were adopted to determine the impact of VSM-based work environment events on the employees’ emotional experiences and subsequent work attitudes (job satisfaction) and behaviors (citizenship behavior). To field test the proposed nexus of VSM and AET, the survey was conducted on two hundred and fifteen employees from 39 different organizations. PLS-SEM tested the explanatory power of the suggested VSM’s systemic approach for understanding the affective work environment in totality.

The findings confirmed that the VSM metalanguage provides a holistic view of the organizational functioning and social connectivity disposing of affective work events, helpful in assessing their aggregate influence on employees’ emotions and work-related outcomes.

The findings identify how employees' emotions can be triggered by everyday work operations and social relations at work, which can affect their extra-role behaviors and necessary work-related attitudes.

The study utilized Beer’s VSM framework based on the systemic principle of “holistic view” for ascertaining the affective work environment and its related features holistically, which filled in well the macrostructure of “work environment features” with micro-structures of organizational inter-related aspects which are yet to be known in AET – a seminal explanation for managing workforce emotions.

The purpose of this study was to develop and test a research model that explored leader psychological capital (LPsyCap) as a predictor of follower creative performance (FCRP). The model also investigated follower job crafting (FJC) and follower knowledge collecting (FKC) and follower knowledge donating (FKD) behaviors as the multiple mediators of the trickle-down effect of LPsyCap on FCRP in a sequential manner.

Data were obtained from hotel employees in Korea. The hypotheses were tested using structural equation modeling with the user-defined estimands function.

LPsyCap boosted employees’ FCRP. FJC and FKC behaviors mediated the trickle-down effect of LPsyCap on FCRP in a sequential manner. As predicted, FKC behavior parallelly mediated the effect of FJC on FCRP.

Hotel firms should boost a workplace that would encourage employees to redefine and redesign their jobs. To achieve this, the presence of structural (e.g. learning new things in the workplace) and social (e.g. supervisor coaching) job resources and challenging job demands (e.g. willingly taking on additional tasks) would pay dividends.

This paper is one of its kind focusing on unexplored parallel and sequential multiple mediation effects that highlight FJC and FKC as the two potential mediators in the association between LPsyCap and FCRP.

This study aims to study the factors that drive non-users of digital banking services rendered by Islamic banks in Malaysia towards their adoption of digital services in the banking 4.0 era using the innovation diffusion theory (IDT), also known as the diffusion theory of innovation (DOI).

IDT theory and literature on intention to adopt digital bank services were reviewed in a bid to contribute to the factors that drive non-users to adopt digital banking.

The review suggests that the adoption of digital banking is determined not only by perceived relative advantage, and perceived compatibility but also by additional factors in IDT theory, which are technology self-efficacy and perceived expected benefits. On the contrary, perceived complexity does not turn out to be a factor of digital banking adoption.

Considering this paper in terms of the limited scope of the theory rendered and the context, it should be given proper attention when interpreting future outcomes when further investigations are brought into play in terms of population and sampling method.

This paper serves as a guide to ensure the better planning of non-users’ adoption factors related to Islamic bank customers in both theory and practice.

DOI is extended in the context of digital banking, as evidenced by empirical results, and literature shows that IDT integrated with the technology self-efficacy model is yet to be proposed in the digital banking adoption by Islamic bank customers. Additionally, variables, namely, perceived expected benefits and technology self-efficacy, are proposed in IDT’s existing model. Current findings will therefore serve as a relevant reference for digital technology specialists, policymakers, Islamic banks’ IT managers, academicians and future researchers.

Drawing upon social exchange theory, this research investigates the interplay of corporate philanthropy, environmental marketing strategy, relationship quality, greenwashing, and customer citizenship behavior in the family-owned hotels of an emerging market.

A field survey questionnaire was used to gather the data from 394 hotel customers by randomly selecting three premium family-owned hotels in Lahore: Faletti’s, Avari, and Holiday Inn. The data was analyzed using the structural regression modeling (SRM) technique with the assistance of AMOS version 24.

The results show that corporate philanthropy and environmental marketing strategy positively influence relationship quality, and relationship quality positively influences customer citizenship behavior. Relationship quality partially mediates the association between corporate philanthropy and customer citizenship behavior, but we found that greenwashing does not have a moderating role.

This research has theoretical implications for marketing scholars and practical implications of family-owned hotels in emerging markets.

The study has contributed contextually by collecting a unique dataset from family-owned hotels in an emerging market. Theoretically, we have conceptualized a model through the Social Exchange Theory by recommending relationship quality as a mediator and greenwashing as a moderator.

The purpose of this paper is to reveal the dynamic contact characteristics of the slip ring. Dynamic contact resistance models considering wear and self-excited were established based on fractal theory.

The effects of tangential velocity, stiffness and damping coefficient on dynamic contact resistance are studied. The relationships between fractal parameters, wear time and contact parameters are revealed.

The results show that the total contact area decreases with the friction coefficient and fractal roughness under the same load. Self-excited vibration occurs at a low speed (less than 0.6 m/s). It transforms from stick-slip motion at 0.4 m/s to pure sliding at 0.5 m/s. A high stiffness makes contact resistance fluctuate violently, while increasing the damping coefficient can suppress the self-excited vibration and reduce the dynamic contact resistance. The fractal contact resistance model considering wear is established based on the fractal parameters models. The validity of the model is verified by the wear tests.

The results have a great significance to study the electrical contact behavior of conductive slip ring.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2023-0300/

Research indicates that inconsistent gender norm presentations are met with backlash, which is particularly damaging to women. With social media use in selection rising, it is important to understand if this remains consistent for job applicants on social media.

In two experiments, this study investigates hiring managers' reactions to job applicant (in)consistent gender norm-based communication on Facebook (n = 197) and YouTube (n = 203). Participants located in the United States were asked to review social media materials, reported perceptions of task and social attraction, and make hiring recommendations.

Inconsistent with work on backlash in face-to-face settings, results demonstrated that masculine communication styles on social media may be detrimental to job seekers, and this was more pronounced for male job seekers. Feminine presentation styles had more favorable results.

The findings challenge the long-held understanding that men have more leeway to behave in agentic ways in job seeking contexts. While this may remain true in face-to-face settings, these findings suggest that social media, lacking media richness, may be a context in which males experience backlash for agentic behavior.

The research offers a novel perspective investigating traditional gender expectations in the digital realm, paving the way for a more comprehensive understanding of gender in employment contexts. This study contributes to the growing body of research on online behavior and expands understanding of how hiring managers react to gender norms in the era of social media.

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints.

The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data.

Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%.

The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.