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This paper aims to solve the problems of large hard switching loss and unclear resonant parameter design in the existing inverter power supply topology.

This paper proposes a simple and reliable two-stage isolated inverter composed of series quasi-resonant push-pull and external freewheeling diode full-bridge inverter. The power supply topology is analyzed, the topology mode is analyzed, the mathematical model of the converter is established and the DC gain of the converter is deduced. The relationship between the load and the output gain of the resonant tank is presented, a new resonant parameter design method is proposed, and the parameter design of the resonant element of the converter is clarified.

The resonant components of the converter are designed according to the proposed resonant parameter design method, and the correctness of the method is verified by simulation and the development and testing of a 500 W experimental prototype. After experimental tests, the peak efficiency of the experimental prototype can reach 94%. Because the experimental prototype achieves soft switching, the heat generation of the switch is greatly reduced, so the heavy heat sink is removed, and the volume is reduced by about 30% compared with the traditional power supply, and the total harmonic distortion of the output voltage is about 2%.

The feasibility of the scheme is verified by experiments, which is of great significance for improving the efficiency of the inverter power supply and parameter optimization.

The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra).

The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments.

The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results.

The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity.

No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/

The purpose of this study is to reveal the friction reduction performance and mechanism of granular flow lubrication during the milling of difficult-to-machining materials and provide a high-performance lubrication method for the precision cutting of nickel-based alloys.

The milling tests for Inconel 718 superalloy under dry cutting, flood lubrication and granular flow lubrication were carried out, and the milling force and machined surface quality were used to evaluate their friction reduction effect. Furthermore, based on the energy dispersive spectrometer (EDS) spectrums and the topographical features of machined surface, the lubrication mechanism of different granular mediums was explored during granular flow lubrication.

Compared with flood lubrication, the granular flow lubrication had a significant force reduction effect, and the maximum milling force was reduced by about 30%. At the same time, the granular flow lubrication was more conducive to reducing the tool trace size, repressing surface damage and thus achieving better surface quality. The soft particles had better friction reduction performance than the hard particles with the same particle size, and the friction reduction performance of nanoscale hard particles was superior to that of microscale hard particles. The friction reduction mechanism of MoS₂ and WS₂ soft particles is the mending effect and adsorption film effect, whereas that of SiO₂ and Al₂O₃ hard particles is mainly manifested as the rolling and polishing effect.

Granular flow lubrication was applied in the precision milling of Inconel 718 superalloy, and a comparative study was conducted on the friction reduction performance of soft particles (MoS₂, WS₂) and hard particles (SiO₂, Al₂O₃). Based on the EDS spectrums and topographical features of machined surface, the friction reduction mechanism of soft and hard particles was explored.

Founded in 2004, OPPO has experienced the boom of the Chinese mobile phone market, the trend of mobile Internet and the prosperity of the smartphone market. While adjusting its business structure based on changes in the market environment, it has transitioned itself from an audio device manufacturer to a smart-phone manufacturer that offers hardware, software, and service.

This case study focuses on OPPO's evolution and strategy, and provides an insight into its history, competition, and strategic choices based on whether or not OPPO should release a feature phone with a foldable display at the MWC 2019, and discusses the core competitiveness that helped OPPO succeed against the market downturn. This case study helps students understand the development of corporate strategies and the process of building core competitiveness in the microcompetition in the red ocean market. We also wish to help students understand how to come up with the most appropriate decision-making framework and conduct a critical analysis on the issues based on the internal and external factors of their businesses while they make strategic decisions. When it comes to different dimensions and indicators coming to contradictory conclusions in particular, what should the manager of a business do to make the correct strategic decision?

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The purpose of this paper is to: (1) identify strategies to (re)establish organizational legitimacy which dominates the literature; (2) propose and empirically illustrate an analytical framework that establishes the linkages between the dimensions of purposefulness, transparency and participation identified in this literature review as important resources in the creation of organizational legitimacy.

Based on a review of the academic literature, we propose a three-dimensional conceptual framework for understanding and studying strategic communication in contexts of high sustainability pressure. The empirical material we use for illustration is the letters from the chief executive officer (CEO) and the chairman published in the integrated annual report of a Danish company that is well known for its focus on sustainability.

The analysis shows that all three dimensions, i.e. purposefulness, transparency and participation, are present in this data, which the authors find supportive of the theoretical argument that strategic communication needs to encompass all three concepts in order to appear legitimate in contexts of high sustainability pressure.

In recent years, there has been an increased focus on strategic communication of sustainability. However, there is still a lack of general consensus of what is understood by strategic communication in contexts of high sustainability pressure. Overlapping concepts and dimensions make operationalization difficult. This, for example, is a problem for corporations who are increasingly asked by their stakeholders to account for their sustainability activities and engage in conversations of strategic significance to their sustainability goals.

Proper performance assessment of residential building renovation is crucial to sustainable urban development. However, a comprehensive review of the literature in this research domain is lacking. This study aims to uncover the study trend, research hotspots, prominent contributors, research gaps and directions in this field.

With a hybrid review approach adopted, relevant literature was examined in three stages. In Stage 1, literature retrieved from Scopus was screened for their relevance to the study topic. In Stage 2, bibliographic data of the shortlisted literature underwent scientometric analyses by the VOSviewer software. Finally, an in-depth qualitative review was made on the key literature.

The research hotspots in performance assessment of residential building renovation were found: energy efficiency, sustainability, thermal comfort and life cycle assessment. After the qualitative review, the following research gaps and future directions were unveiled: (1) assessments of retrofits incorporating renewable energy and energy storage systems; (2) evaluation of policy options and financial incentives to overcome financial constraints; (3) establishment of reliable embodied energy and carbon datasets; (4) indoor environment assessment concerning requirements of COVID-19 prevention and involvement of water quality, acoustic insulation and daylighting indicators; and (5) holistic decision-making model concerning residents' intentions and safety, health, well-being and social indicators.

Pioneered in providing the first comprehensive picture of the assessment studies on residential building renovations, this study contributes to offering directions for future studies and insights conducive to making rational decisions for residential building renovations.

The purpose of our study is to study the impact of chief executive officer (CEO) optimism and narcissism on the company's capital structure.

(1) The authors opt for regression, machine learning and text analysis to explore the impact of narcissism and optimism on the capital structure. (2) We analyze CEO interviews and employ three methods to evaluate narcissism: the dictionary proposed by Anglin, which enabled us to assess the following components: authority, superiority, vanity and exhibitionism; count of first-person singular and plural pronouns and count of CEO photos displayed. Following this approach, we were able to make a more thorough assessment of corporate narcissism. (3) Latent Dirichlet allocation (LDA) technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs and to find differences between the topics of interviews and letters provided by narcissistic and non-narcissistic CEOs.

Our research demonstrates that narcissism has a slight and nonlinear impact on capital structure. However, our findings suggest that there is an impact of pessimism and uncertainty under pandemic conditions when managers predicted doom and completely changed their strategies. We applied various approaches to estimate the gender distribution of CEOs and found that the median values of optimism and narcissism do not depend on sex. Using LDA, we examined the content and key topics of CEO interviews, defined as positive and negative. There are some differences in the topics: narcissistic CEOs are more likely to speak about long-term goals, projects and problems; they often talk about their brand and business processes.

First, we examine the COVID-19 pandemic period and evaluate how CEO optimism and pessimism affect their financial decisions under specific external conditions. The pandemic forced companies to shift the way they worked: either to switch to the remote work model or to interrupt operations; to lose or, on the contrary, attract clients. In addition, during this period, corporate management can have a different outlook on their company’s financial performance and goals. The LDA technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs. Second, we use three methods to evaluate narcissism. Third, the research is based on a set of advanced methods: machine learning techniques (random forest to reveal a nonlinear impact of CEO optimism and narcissism on capital structure).

While worker well-being is increasingly recognised as a performance driver and marker of socially responsible organisations, workaholism is ubiquitous and remains poorly understood. This study aims to uncover workaholism precursors, dynamics and trajectories, and explains how organisations can manage its emergence and impact.

Fifteen semi-structured interviews were conducted with a diverse sample of self-identified workaholics in New Zealand and analysed through interpretivist phenomenological analysis.

This study contributes to the workaholism literature by elucidating how the work–identity link is formed and maintained, the psychophysiological experiences and worldviews of workaholics and the role families, organisations and culture play in workaholism. The findings also elucidate the relationship between workaholism, work addiction and engagement.

The authors outline how leaders and organisations can detect and manage workaholism risk factors and understand its trajectories to develop healthy workplaces.

The retrospective experiential accounts obtained from a diverse sample of workaholics enabled the identification of workaholism precursors, including some previously undetected in the literature, their complex interrelations with environmental factors and workaholism trajectories.

This study aims to analyze how the interplay between hard and soft elements of total quality management (TQM) produces the conditions for sustaining success in the quest for quality.

A qualitative analysis (Gioia method) was carried out on an original dataset collected through both direct and indirect methods (i.e. archival sources, interviews and observations) to generate a new interpretive framework.

The interpretative framework identifies four categories of elements: trigger elements create the starting conditions for a quality virtuous cycle; benchmarking tools set the standards of performance; improvement tools enable exploration of the space of possible alternative practices and finally, catalytic forces allow the institutionalization of effective techniques discovered in this search process into new standards.

The findings the authors present in this paper are derived by a single case study, limiting the generalizability of our results in other settings.

This study has three implications: first, the design of trigger elements is critical for the success of any TQM initiative; second, the interplay of improvement and benchmarking tools at several levels should be coherent and third, to exploit the potential of TQM, efforts should be devoted to the dissemination of new effective practices by means of catalyzing elements.

The model provides a more specific understanding of the nature and purpose of the hard and soft elements of TQM and the dynamic interaction between the two classes of elements over time.