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This paper aims to investigate the mechanism of spur gears running-in and to solve the lubrication problems of teeth running-in.

The elastohydrodynamic lubrication (EHL) model considering solid particles was established by applying multi-grid and multiple-grid integration methods to the numerical solution.

In the region where debris settle, transient pressure increases sharply, and a noticeable increase in the running-in load causes a remarkable increase in both the centre and maximum pressures and a slight increase in the minimum film thickness. Roughness wavelength makes a considerable difference to the minimum film thickness at double-to-single tooth transient. A considerable increase in rotation velocity can cause a remarkable reduction in both the centre and maximum pressures but an amazing increase in the minimum film thickness. The effects of roughness amplitude on the maximum pressure are considerably distinct.

Research on EHL of spur gears in the running-in process considering solid particles, surface roughness and time-variant effect is meaningful to practical gears running-in. Thermal effect can be included in the next study.

The analysis results can be applied to predict and improve lubrication performance of the meshing teeth.

The aim is to reduce gears’ manufacture and running-in costs and improve economic performance.

The EHL model that considers solid particles was established. The Reynolds equation was deduced taking the effects of solid particles into account. The EHL of spur gears running-in was investigated considering the time-variant effect, surface roughness, running-in load and rotation speed.

This study aims to examine whether audit partners’ gender affects the year-to-year changes (year-to-year additions and drops) of key audit matters (KAMs) identified in the audit report. This study also examines whether female audit partners’ audit experiences, accounting education and narcissism reduce the difference in time variances of KAMs reporting between female and male audit partners. This study defines the year-to-year additions and drops of KAMs as the time variance of KAMs.

Data of this study includes the audit reports of Australian Securities Exchange 300 companies for the period from 2017 to 2021. This study also applies the theory of female auditors’ preference for anchoring and availability heuristics. This study uses multivariate regression with robust standard errors clustered by the firms. This study also uses several robustness tests.

The findings suggest that female audit partners disclose fewer time variant KAMs in that they have a lower tendency both to add new KAMs and to drop old KAMs. Further analysis suggests that the differences between female and male audit partners decrease as the female audit partners’ experience increases or if the female audit partner possesses a bachelor’s degree in accounting. Female audit partners’ narcissism also reduces the gender gap in the time variances of KAMs.

The fact that female audit partners report more stable KAMs implies that there are differences between female and male audit partners in the way audit risk assessments are conducted, audits are planned and professional judgement is applied by female and male audit partners.

The findings imply that female audit partners’ experience, accounting education and narcissistic personality can play a significant role in explaining the differences in audit outcomes produced by male and female audit partners.

This study is novel in showing that female audit partners report more stable and less time-variant KAMs. The findings of this study may inform audit firms and regulators that female audit partners’ experience, tertiary qualifications in accounting and narcissistic personality traits may be effective means of reducing the gender gap in auditing. The findings also imply that auditors’ observable and unobservable personality traits affect audit outcomes.

Human error is among the leading causes of construction-based accidents. Previous studies on the factors affecting human error are rather vague from the perspective of complex and changeable working environments. The purpose of this paper is to develop a dynamic causal model of human errors to improve safety management in the construction industry. A theoretical model is developed and tested through a case study.

First, the authors defined the causal relationship between construction and human errors based on the cognitive reliability and error analysis method (CREAM). A dynamic Bayesian network (DBN) was then developed by connecting time-variant causal relationships of human errors. Next, prediction, sensitivity analysis and diagnostic analysis of DBN were applied to demonstrate the function of this model. Finally, a case study of elevator installation was presented to verify the feasibility and applicability of the proposed approach in a construction work environment.

The results of the proposed model were closer to those of practice than previous static models, and the features of the systematization and dynamics are more efficient in adapting toward increasingly complex and changeable environments.

This research integrated CREAM as the theoretical foundation for a novel time-variant causal model of human errors in construction. Practically, this model highlights the hazards that potentially trigger human error occurrences, facilitating the implementation of proactive safety strategy and safety measures in advance.

The purpose of this paper is to investigate the significant factors determining foreign direct investment (FDI) in Cambodia, Laos, and Vietnam (ASEAN3) and Indonesia, Malaysia, the Philippines, Thailand, and Singapore (ASEAN5).

This paper applies the first differencing technique to estimate the parameters on the constructed panel data starting from 2000 to 2011.

Due to the different stages of economic development between ASEAN3 and ASEAN5, the determinants of FDI are different. We found that there are significantly positive effects of infrastructure facility, level of openness, and negative effect of inflation on FDI inflow in ASEAN3; while real exchange rate, gross domestic product and net official development assistance have no effect on its FDI. The finding in ASEAN5 showed that market size and infrastructure facility are significant factors to attract FDI. Furthermore, even though there are an increase in inflation rate as well as a decrease in level of openness measurement, ASEAN5 are still attractive to foreign investors.

The time variant and invariant unobserved effects that are ignored in the previous studies are considered in this study.

The purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.

In this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.

The proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.

The management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.

The proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

Introduction of law school admission examinations has increased the debate regarding the relevance of prior studies for the enrollees in the program. The key issues of contention are whether prior studies reliably predict academic achievement of enrollees, and demonstrate proficiencies required for admission in the program. The purpose of this paper is to use administrative records of law students at Makerere University – over a four-year stipulated period of bachelor’s studies – to investigate the above-mentioned issues further.

Panel data of 2,485 records enable us to explore time variant and invariant factors in the course of bachelor’s candidature.

About 81 per cent of differentials in academic achievement was found to be due to variation across students. In addition to confirming prior academic attainment as a contributing factor to variations in academic achievement, our findings affirm that competence in literature, history and divinity predicts success on the program.

Competence in these subjects (literature, history and divinity) certainly demonstrates proficiencies required by a student of law. Thus, admission based on competence in these subjects provides insights into proficiencies required by enrollees to excel academically in the program.

Unlike prior studies, this study explores time-variant factors in the course of bachelor’s candidature, and is based on subjects taken during prior studies (secondary education) rather than a pooled index or weighted score adopted on admission to the program.

The main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.

The authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.

Innovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.

This paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.

This article is a study of the distributed lag relationship between completions and starts of private dwellings in the UK and extends some results previously published in this journal. The main finding confirms that a proper study of builders' behaviour, as manifested in the distributed lag relationship between completions and starts, must allow for both disequilibrium effects and time varying lag structures. However, in examining the economic variables that modified builders' behaviour (and hence the lag structure) it was found that both the rate of house price inflation and the level of building society mortgage advances had an important influence. An increase in both caused builders, in the expectation of easier sales, to quicken their rates of completion. Additional plausible influences — like the level of construction activity in the public sector were, however, not supported by the data.

In order to accomplish real‐time alignment of Shipborne strapdown inertial navigation system (SINS) on moving bases, a novel solution method of utilizing neural networks for rapid transfer alignment of Shipborne SINS was investigated.

The system error state equations and measurement equations of the Shipborne transfer alignment were established. Based on the nonlinear and time‐variant SINS model on moving bases, a neural network learning algorithm based on Kalman filtering was presented, and the methods of constructing and training of neural networks input‐output sample pairs suitable for Shipborne SINS were proposed.

Velocity and attitude errors between the master and slave inertial navigation system (INS) are chosen as network's inputs, and the information of sample pairs is affluent, which can advance the stability and generalization of the neural networks. The neural networks algorithms based on Kalman filtering not only have the self‐learning ability, but also remain recursive optimal estimation capability of Kalman filtering. Through the introducing of the local level trajectory frame, the trained neural networks can be independent on a ship heading, and only dependent on the relative position errors between master with slave INS and the inertial sensor errors.

This article presents an innovative solution method of utilizing neural networks for rapid transfer alignment of Shipborne SINS.

The paper introduces a resource-based linear programming model for resource optimization in small innovative enterprises (SIE).

The model is grounded on resource-based view on the firm and dynamic capabilities approach. Linear programming technique is used to provide the actual framework to the resource-based model.

The paper introduces a new resource-based linear programming model for resource optimization in small innovative enterprises. The conceptual model is grounded on resource-based view (RBV) and dynamic capabilities strategy. The RVB of firm and firm strategy is based on the concept of economic rent. Linear programming technique is used to provide the actual framework to the resource-based model. In developing the versatility concept, study suggests a distinct sight regarding resource fungibility. Study classifies resources into multipliable, rentable and expendable resources to increases adequacy of the model. The developed model includes both tangible and intangible assets such as human capital. The survival rate of SIE in the early stages of life cycle is very low due to the competition among SIEs. In this regard, the greatest advancement of the developed resource-based linear programming model is its simplicity and versatility which is much desirable for the SIE especially in their initial stages of the life cycle. Kelliher and Reinl (2009) argued that micro firms have unique advantage over bigger firms in following term: rate of learning or redeployment of strategy in micro firms is faster than the rate of change in their environment. One very significant feature of the developed resource-based linear programming model is that mathematically the proposed model could easily be transformed into mixed integer or stochastic linear programming models to meet the time variant requirement of small firms especially when it expands its operation.

The survival rate of SIE in the early stages of life cycle is very low due to the competition among SIEs. In this regard, the greatest advancement of the developed resource-based linear programming model is its simplicity and versatility which is much desirable for the SIE especially in their initial stages of the life cycle. Kelliher and Reinl (2009) argued that micro firms have unique advantage over bigger firms in following term: rate of learning or redeployment of strategy in micro firms is faster than the rate of change in their environment. One very significant feature of the developed resource-based linear programming model is that mathematically the proposed model could easily be transformed into mixed integer or stochastic linear programming models to meet the time variant requirement of small firms especially when it expands its operation.

One very significant contribution of the present study is that the study develops a new resource-based model for SIE especially for the SIE in the initial stages of the life cycle, to gain competitive advantages. Furthermore, the present study contributes to the existing literature in strategy at least in three senses as mentioned below: 1. further addition of SIE research based on the RBV and dynamic capabilities in the strategy literature 2. in developing the versatility concept, the study suggests a distinct sight regarding resource fungibility and it classifies resources into three categories as follows: multipliable, rentable and expendable resources to increases adequacy of the model. 3. Finally, the study introduces a new resource-based linear programming model for SIE resources allocation. To the best of author’s knowledge, no such similar model is introduced by any previous studies for small firm. The greatest advancement of the developed resource-based linear programming model is its simplicity and versatility.