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This paper aims to propose an effective dynamic calibration and compensation method to solve the problem that the statically calibrated force sensor would produce large dynamic errors when measuring dynamic signals.

The dynamic characteristics of the force sensor are analyzed by modal analysis and negative step dynamic force calibration test, and the dynamic mathematical model of the force sensor is identified based on a generalized least squares method with a special whitening filter. Then, a compensation unit is constructed to compensate the dynamic characteristics of the force measurement system, and the compensation effect is verified based on the step and knock excitation signals.

The dynamic characteristics of the force sensor obtained by modal analysis and dynamic calibration test are consistent, and the time and frequency domain characteristics of the identified dynamic mathematical model agree well with the actual measurement results. After dynamic compensation, the dynamic characteristics of the force sensor in the frequency domain are obviously improved, and the effective operating frequency band is widened from 500 Hz to 1,560 Hz. In addition, in the time domain, the rise time of the step response signal is reduced from 0.29 ms to 0.17 ms, and the overshoot decreases from 26.6% to 9.8%.

An effective dynamic calibration and compensation method is proposed in this paper, which can be used to improve the dynamic performance of the strain-gauge-type force sensor and reduce the dynamic measurement error of the force measurement system.

Using the theoretical framework of the substantive economy, this study aims to point out the main aspects of the substantive mode of operation that help the integration of disadvantaged people while at the same time shedding light on the barriers that hinder economically efficient functioning in a market economy.

Research focuses on Hungarian rural work integration social cooperatives, which are engaged in producing activity by the employment of disadvantaged people. In the research, mixed methods were applied: results of a questionnaire survey covering 102 cooperatives, as well as 20 semi-structured interviews and experiences from the field. A total of 17 indicators were used to explore the substantive operational features, promoting mechanisms and problems in the following areas: organisational goals and outcomes; integrating roles and functions; productive functions; and the embeddedness of cooperatives.

As for results, substantive operational mechanisms and tools that support the integration of disadvantaged people have been identified such as mentoring, social incentives, the ability to create local value or the expansion of local community services. At the same time, several barriers have been detected that make it difficult to operate economically, such as cooperatives being a stepping stone for workers, excessive product heterogeneity or the lack of vertically structured bridging relationships.

The value of the study is to counterpoint the mechanisms promoting social purposes of work-integration social cooperatives and the obstacles to their long-term sustainability within the framework of the substantive economy, to better understand their functioning and the less quantifiable factors of their performance.

This paper analytically models and numerically investigates two operating levers, namely optimization of product price and optimization of product quality in the context of a manufacturer that sells the products directly in the marketplace. The study attempts to identify how optimizing product quality and product price can fulfill a manufacturer's economic aims such as maximization of the manufacturer's profit and market demand. Anchored to the extant literature, the demand is modeled as a parametric joint multiplicative function of price and quality. Further, price is modeled as a function of product quality.

First, the authors evolve the analytical expression for the manufacturer's profit. Thereafter, following the mathematical principles of unconstrained optimization, the authors arrive at the conditions for optimal product quality and product price. The authors further perform numerical experiments to understand the behavior of economic dimensions such as profit and demand with respect to sensitivities associated with cost, quality and price.

The authors find that under product quality optimization, the optimal product quality is a unique solution in that a highest possible theoretical manufacturer's profit is obtained. However, in the case of product price optimization, the optimal product price is non-unique and is a function of product quality. The authors further find that in the context of functional quality-level expectations, product quality optimization as an operating lever gives a better dividend. However, in the case of higher product quality expectations, product price optimization performs better than product quality optimization. Further, several novel findings are also obtained from numerical experimentations.

The findings of the authors' study have implications for types of industries characterized by relatively low as well as relatively high competitive intensity. Further, as opposed to several extant studies that have often carried out joint optimization of quality and price, the authors' study is one of the first to study the impact of product price and product quality on the manufacturer's economic objective in a disparate and focused manner, thus capturing individual effects.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Independent directors are important corporate decision participants and makers. Based on the Chinese cultural background, this paper interprets the listing order of independent directors as independent directors’ status, exploring their influence on the corporate research and development (R&D) behavior.

This paper studies A-share listed firms in China from 2008 to 2018 as the sample. The main method is ordinary least square (OLS) regression. We also use other methods to deal with endogenous problems, such as the firm fixed effect method, change model method, two-stage instrumental variable method, and Heckman two-stage method.

(1) Higher independent directors’ status attribute to more effective exertion of supervision and consultation function, and positively enhance the corporate R&D investment. The increase of the independent director’ status by one standard deviation will increase the R&D investment by 4.6%. (2) The above effect is more influential in firms with stronger traditional culture atmosphere, higher information opacity and higher performance volatility. (3) High-status independent directors promote R&D investment by improving the scientificity of R&D evaluation and reducing information asymmetry. (4) The enhancing effect of independent director’ status on R&D investment is positively associated with the firm’s patent output and market value.

This paper contributes to understanding the relationship between the independent directors’ status and their duty execution from an embedded cultural background perspective. The findings of the study enlighten the improvement of corporate governance efficiency and the healthy development of the capital market.

The present paper aims to analyse the synergistic effect of pocket orientation and piezo-viscous-polar (PVP) lubrication on the performance of multi-recessed hybrid journal bearing (MHJB) system.

To simulate the behaviour of PVP lubricant in clearance space of the MHJB system, the modified form of Reynolds equation is numerically solved by using finite element method. Galerkin’s method is used to obtain the weak form of the governing equation. The system equation is solved by Gauss–Seidal iterative method to compute the unknown values of nodal oil film pressure. Subsequently, performance characteristics of bearing system are computed.

The simulated results reveal that the location of pressurised lubricant inlets significantly affects the oil film pressure distribution and may cause a significant effect on the characteristics of bearing system. Further, the use of PVP lubricant may significantly enhances the performance of the bearing system, namely.

The present work examines the influence of pocket orientation with respect to loading direction on the characteristics of PVP fluid lubricated MHJB system and provides vital information regarding the design of journal bearing system.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0241/

Smart farming technologies (SFTs) can increase yields and reduce the environmental impacts of farming by improving the efficient use of inputs. This paper is to estimate farmers' preference and willingness to pay (WTP) for a well-defined SFT, smart drip irrigation (SDI) technology.

This study conducted a discrete choice experiment (DCE) among 1,300 maize farmers in North China to understand their WTP for various functions of SDI using mixed logit (MIXL) models.

The results show that farmers have a strong preference for SDI in general and its specific functions of smart sensing and smart control. However, farmers do not have a preference for the function of region-level agronomic planning. Farmers' preferences for different functions of SDI are heterogeneous. Their preference was significantly associated with their education, experience of being village cadres and using computers, household income and holding of land and machines. Further analysis show that farmers' WTP for functions facilitated by hardware is close to the estimated prices, whereas their WTP for functions wholly or partially facilitated by software is substantially lower than the estimated prices.

Findings from the empirical study lead to policy implications for enhancing the design of SFTs by integrating software and hardware and optimizing agricultural extension strategies for SFTs with digital techniques such as videos.

This study provides initial insights into understanding farmers' preferences and WTP for specific functions of SFTs with a DCE.

“First principles” of international business (IB) thinking should be applied systematically when assessing the functioning of internationally operating firms. The most important first principle is that entrepreneurially oriented firms seek to create, deliver and capture economic value through cross-border linkages. Such linkages invariably require complementary resources from a variety of parties with idiosyncratic vulnerabilities to be meshed. Starting from first principles allows bringing to light evidence-based insight. For instance, most companies are not global and even the world’s largest firms rarely change the location of key strategic functions. International new ventures (INVs), emerging economy multinational enterprises (MNEs) and family firms face unique vulnerabilities but also command resources that can be used to create value across borders. The quest for “optimal” international diversification appears to be a futile academic exercise, and in emerging economies with institutional voids, relational networks – and more broadly, informal institutions – are unlikely to function as scalable substitutes for formal institutions. In global value chains (GVCs), many lead firms and their partners have been able to craft governance mechanisms that reduce bounded rationality and bounded reliability challenges, and it is also critical for them to use governance as a tool to create entrepreneurial space. Finally, many of the world’s largest companies have been on successful trajectories toward reducing their climate change footprint for a few decades. But these firm-specific trajectories are fraught with challenges and cannot just be imposed via unilateral, macro-level targets decided upon by individuals and institutions lacking a clear understanding of innovation and capital expenditure processes in business.

Australian agriculture is facing increasingly uncertain weather patterns which is impacting financial performance, exacerbated by worsening terms of trade and a decline in commodity prices. Increasing the resilience and adaptive capacity of the primary production sector is of key importance. Governments and farmer groups often depict technology adoption as the salvation of farming, frequently ignoring the importance of decision-making processes and soft information skills and needs. The purpose of this study is to explore farmer decision-making and resilience and, in doing so, address ongoing challenges with soft information, including the inaccessibility of accounting data and a lack of awareness of its formal role in strategic decisions.

Drawing on a strategic choice perspective, we explore the links between farmer characteristics, attitudes, technology orientation, decision-making and financial performance to investigate how accounting data and tools could better support growers’ adaptive capacity. Detailed on-farm interviews were conducted with 25 grape growers across the Riverland in South Australia, with information thematically and descriptively analysed.

Results show that farmers with low operating profit margins spend double the time making decisions and struggle with minimising variable costs, especially water costs. Lower profit growers were also less likely to perceive climate change as a threat and demonstrated lower resilience.

The results highlight the potential for accountants to make more use of data-driven technological advances and for this information to be used to enhance on-farm strategic decision-making and support innovative business models. Simply packaged biophysical and financial data could also support strategic decisions and adaptation of farmers struggling to make a profit.

Subcooled flow boiling phenomenon is characterized by coolant phase change in the vicinity of the heated wall. Although coolant phase change from liquid to vapour phase significantly enhances the heat transfer coefficient due to latent heat of vaporization, eventually the formed vapor bubbles may coalesce and deteriorate the heat transfer from the heated wall to the liquid phase. Due to the poor heat transfer characteristics of the vapour phase, the heat transfer rate drastically reduces when it reaches a specific value of wall heat flux. Such a threshold value is identified as critical heat flux (CHF), and the phenomenon is known as departure from nucleate boiling (DNB). An accurate prediction of CHF and its location is critical to the safe operation of nuclear reactors. Therefore, the present study aims at the prediction of DNB type CHF in a hexagonal sub-assembly.

Computational fluid dynamics (CFD) simulations are performed to predict DNB in a hexagonal sub-assembly. The methodology uses an Eulerian–Eulerian multiphase flow (EEMF) model in conjunction with multiple size group (MuSiG) model. The breakup and coalescence of vapour bubbles are accounted using a population balance approach.

Bubble departure diameter parameters in EEMF framework are recalibrated to simulate the near atmospheric pressure conditions. The predictions from the modified correlation for bubble departure diameter are found to be in good agreement against the experimental data. The simulations are further extended to investigate the influence of blockage (b) on DNB type CHF at low operating pressure conditions. Larger size vapour bubbles are observed to move away from the corner sub-channel region due to the presence of blockage. Corner sub-channels were found to be more prone to experience DNB type CHF compared to the interior and edge sub-channels.

An accurate prediction of CHF and its location is critical to the safe operation of nuclear reactors. Moreover, a wide spectrum of heat transfer equipment of engineering interest will be benefited by an accurate prediction of wall characteristics using breakup and coalescence-based models as described in the present study.

Simulations are performed to predict DNB type CHF. The EEMF and wall heat flux partition model framework coupled with the MuSiG model is novel, and a detailed variation of the coolant velocity, temperature and vapour volume fraction in a hexagonal sub-assembly was obtained. The present CFD model framework was observed to predict the onset of vapour volume fraction and DNB type CHF. Simulations are further extended to predict CHF in a hexagonal sub-assembly under the influence of blockage. For all the values of blockage, the vapour volume fraction is found to be higher in the corner region, and thus the corner sub-channel experiences CHF. Although DNB type CHF is observed in corner sub-channel, it is noticed that the presence of blockage in the interior sub-channel promotes the coolant mixing and results in higher values of CHF in the corner sub-channel.