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Numerical magnetic field analysis is used for predicting the performance of an induction motor and a slip‐ring generator having different faults implemented in their structure. Virtual measurement data provided by the numerical magnetic field analysis are analysed using modern signal processing techniques to get a reliable indication of the fault. Support vector machine based classification is applied to fault diagnostics. The stator line current, circulating currents between parallel stator branches and forces between the stator and rotor are compared as media of fault detection.

The purpose of this paper is to investigate the mechanisms of low alloyed medium-carbon steel (LAMCS) corrosion in 0.5 M H₂SO₄ inhibited by seeds oils of rubber (SOR), Neem (SON) and Jatropha (SOJ) containing varying degree of free fatty acid (FFA).

Specific gravity, acid values and FFA compositions of oils were determined. Potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM) and X-ray diffraction (XRD) are techniques used to investigate the corrosion inhibition mechanisms with evaluated Gibbs free energy of adsorption.

Corrosion inhibition efficiencies of oils reached values >99% as obtained from PDP and EIS. Protective oxide layer was formed on LAMCS consequent on containment of carbonyl and hydroxyl groups in the FFA of SOR, SON and SOJ, respectively. The SOR and SOJ are found to be mixed inhibitors, whereas SON behaved as anodic inhibitor. Mechanism of adsorption of SOR was synergistic between physisorption and chemisorption, while SON and SOJ exhibited physisorption. SEM micrographs images showed that uninhibited sample exhibited thicker mass of corrosion products. Formation of protective oxide layer was confirmed by XRD diffractograms.

This study has shown that the need for modification of vegetable seed oils containing FFA is unnecessary as the hydroxyl and carbonyl groups of the FFA contained in the respective oil were found to be the center of adsorption of the oils on the steel surface. Hence, cost and by-products associated with modification of oils used as corrosion inhibitors are eliminated.

SOR, which has the highest percentage FFA, was found to be the most influential on the corrosion inhibition mechanism of LAMCS, specifically within 0.01–0.02 g/mL concentration. FFA contained in the respective seed oil aided formation of protective oxide layer at interface between H₂SO₄ and LAMCS, relative to amount composed.

This was an explorative study, the purpose of which was to understand how Chinese flexitarians were motivated to reduce their animal-based food consumption and specifically to explore why and what was behind these changes. Further, this study provides insights into how a transition towards a more plant-based diet might occur.

This study conducted 30 in-depth online interviews with Chinese flexitarians from two major Chinese cities. Interview transcripts were analysed through the lens of the Food Choice Process Model.

Participants adopted flexitarian diets due to health (n = 22), sustainability (n = 5) or ethical (n = 3) concerns. Many participants stated that they still enjoyed animal-sourced foods, were uncomfortable talking about their dietary transition and needed more social support. Consequently, most participants were unlikely to progress to vegetarian or vegan diets. While there are some commonalities between these findings and the results of similar studies with Western consumers, the many culturally and context-specific results revealed herein make an important contribution to the sustainability literature.

This is the first study exploring Chinese consumers’ flexitarianism experiences. These findings can be used by the food industry and interested business actors to improve communication and promotional strategies that encourage consumers to make healthier and more sustainable food choices.

As an emerging technology marked by rapid advancements, artificial intelligence (AI) has the potential to profoundly transform higher education, particularly after the emergence of ChatGPT at the end of November 2022. Because of its immense power, it poses greater expectations and challenges than any technological advancement in the past. Thus, this paper aims to identify and discuss the need for AI-driven transformation, its challenges as well as policies and expectations of AI's successful integration into teaching and learning. This study examined recent literature and policy documents mostly published after ChatGPT's launch. This study may assist students and instructors in promoting awareness of the importance of ethical, positive and productive AI applications. Besides, the findings of this paper may aid in developing and adopting appropriate educational policies, improving curricula and training pre-service and in-service instructors in adopting knowledge, competencies and strategies for the efficient integration of AI in the teaching process. Thus, the study may help build a strategy for integrating AI systematically into higher education processes and contributing to the transformation of higher education.

This chapter proposes that artificial general intelligence (AGI), once fully deployed, will pose existential questions for higher education institutions as they currently operate. Drawing on the theoretical perspectives of the theory of technological determinism and disruptive innovation theory, the authors attempt to project how higher education institutions will be impacted by recent and upcoming developments in AI by juxtaposing current operations against expected future transformations. This chapter comes in four parts. In Part 1, the authors briefly review the advancements in AI technology, focusing on the distinction between artificial narrow intelligence (ANI) and AGI. Part 2 introduces the theory of technological determinism and disruptive innovation theory as a framework for conceptualizing the magnitude of the transformations expected to overtake higher education and their inevitability in some fashion. In Part 3, the authors tentatively juxtapose a series of expected AI-driven disruptions against their current forms. These disruptions will affect the academic infrastructure, instructional practice, curriculum design, grading and assessment, graduation pathways and global accessibility. Finally, the authors consider the implications of these transformations, which, taken together, amount to recasting the university system and constitute an invitation for theorizing these disruptions, assessing their impact and mitigating their effects. This chapter offers a dual contribution to the book. The first is conceptual, and it allows policymakers, educators and stakeholders to use existing theoretical frameworks to understand, imagine and possibly steer the direction of academic institutions so our futures are aided by AGI and not determined by it. The second contribution is practical. Identifying how and where disruptions will occur helps the authors oversee the transition to the new academic landscape with as much foresight and forethought as possible.

The purpose of this paper is to present a study and analysis of insulation failure inter‐turn fault in induction machines (IMs).

A time stepping finite element method (FEM) analysis is performed for the study of IM with inter‐turn fault and determining the machine parameters (self and mutual inductances) after occurring fault. A simple dynamic model for IM with inter‐turn fault is presented. The model parameters are obtained by FEM analysis. An experimental test is also carried out to verify the results.

The behavior of IM is studied under various insulation failure inter‐turn fault conditions and severity using FEM. The paper's results help the machine designers to improve the fault tolerance as well the overall design of the machine drive system. It can also be useful for predict and detection of fault in IM.

Predicting and detection of turn faults in IM are in industry very helpful because it avoids the fully damage of IM and it is more easy to repair the machine. Designing a fault tolerant IM is required in some applications for increasing the reliability.

By using FEM for studying the fault, the machine parameters which are calculated with FEM and the study's results are very precise and accurate because the flux fluctuation after occurring fault has been taken into account. On the other hand, the fault model is very fast, global and accurate. It can be used in model‐based health monitoring systems.

Research advocates for the use of good teaching practices and approaches while integrating technology in digitally enhanced learning. This is on the premise that previous studies on mobile learning have neglected this aspect of technology integration resulting in numerous challenges. Moreover, there is evidence in the literature showing a scarcity of studies on the use of mobile learning in teaching productive skills. On the other hand, linguists recommend the use of responsive lesson design frameworks in language teaching, claiming effectiveness in teaching all language skills. However, responsive lesson design frameworks are yet to be implemented in a classroom setting. To bridge these gaps in scientific literature, our study decides to utilize the CAPE framework as a good teaching method for improving the productive skills of students in mobile-based instruction.

This study utilizes a mixed-methods research design with an experimental approach. Post-tests and interviews were employed to elicit information from the student-participants on the objective of the study.

Following the analysis of the collected data, notable findings were obtained. While there was evidence to show that the students perceived m-learning as boring and ineffective when incorporated with a traditional lesson framework, our study unveiled that students showed a different perception when incorporated with the CAPE framework.

Our study unveils that integrating responsive lesson frameworks with m-learning improves the speaking and writing skills of students.

This study provides empirical evidence to show the role of good teaching practices like integrating responsive LDFs and mobile learning in improving the productive skills of students. This study is the first to investigate the integration of CAPE and mobile learning in enhancing expressive skills.

The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition‐monitoring system.

A detailed review of previous work carried out by several researchers and maintenance engineers in the area of machine‐fault signature‐analysis is performed. A hybrid expert system is developed using ANN, Fuzzy Logic and Wavelet Transform. A Knowledge Base (KB) is created with the help of fuzzy membership function. The triangular membership function is used for the generation of the knowledge base. The fuzzy‐BP approach is used successfully by using LR‐type fuzzy numbers of wavelet‐packet decomposition features.

The development of a hybrid system, with the use of LR‐type fuzzy numbers, ANN, Wavelets decomposition, and fuzzy logic is found. Results show that this approach can successfully diagnose the bearing condition and that accuracy is good compared with conventionally EBPNN‐based fault diagnosis.

The work presents a laboratory investigation carried out through an experimental set‐up for the study of mechanical faults, mainly related to the rolling element bearings.

The main contribution of the work has been the development of an expert system, which identifies the fault accurately online. The approaches can now be extended to the development of a fault diagnostics system for other mechanical faults such as gear fault, coupling fault, misalignment, looseness, and unbalance, etc.

As a common transmission line, the microstrip line plays an important role in high-speed circuits. The purpose of this paper was to investigate the effects of the circuit design of microstrip lines on the signal integrity (SI). In addition, the influence of the type and thickness of the solder resist ink on SI was analyzed to provide guidance for the related producing process design of printed circuit boards (PCBs).

Microstrip line properties consisting of shape, line-width/line-space ratio, reference layer design and as-covered solder resist ink were designed to measure the insertion loss (S₂₁) in high-speed PCB.

The study showed that the insertion loss (S₂₁) of straight, meander, snake-shaped and wavy microstrip lines was approximately consistent. A microstrip line with width/space ratio less than 0.96 is necessary, as the differential line closing produces a mutual interference. Reference layer including the discontinuous area should be repaired by adjusting the microstrip line parameters. With regard to the solder resist ink, the insertion loss of novel solder resist ink decreased by 0.163 dB/in at 12.9 GHz and 0.164 dB/in at 14 GHz, compared with traditional solder resist ink. Accordingly, the insertion loss effectively improved at a lower thickness of solder resist.

This paper demonstrated that the common designing factors of line shape, line/space ratio, reference layer and solder resist influence microstrip line SI in the significant reference of designer-making PCB layout.

This study aims to investigate the impact of overnight off-work relaxation on the performance of frontline service employees (FLEs). Specifically, the authors focused on FLEs' customer-directed extra-role service behavior (C-ERSB) and coworker-directed extra-role service behavior (CW-ERSB) as indicators of outstanding service performance. Drawing on the conservation of resources (Hobfoll, 1989) and ego depletion theories (Baumeister, 2002), the authors hypothesized that the positive effect of overnight relaxation on ERSBs will be mediated by the state of recovery. Additionally, the authors examined the boundary conditions of these relationships by testing the moderating effects of work–family conflict (WFC) and family–work conflict (FWC).

The study employed an episodic sampling method. One hundred thirty-five FLEs completed two daily surveys (before- and after-work) over five consecutive workdays, yielding 636 time-lagged day-level observations. Multilevel path modeling was performed to analyze the mediation and second-stage moderated mediation effects.

Results showed that overnight off-work relaxation was positively related to FLEs' next-day C-ERSB and CW-ERSB via next-morning recovery state. The positive relationship between overnight off-work relaxation and the next-morning recovery state was weaker for FLEs who experienced overnight WFC. FWC during work hours weakened the positive relationship between the next-morning recovery state and CW-ERSB, but not the relationship between the next-morning recovery state and C-ERSB.

The study used an episodic sampling method to reveal the significance of off-work relaxation, recovery and family–work interface on FLEs' ERSBs, a critical yet underexplored phenomenon in service literature. This study sheds light on the pathways to achieve exceptional service performance by revealing the importance of overnight off-work relaxation and the conditions that promote ERSBs.