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Explores the issue of global licensing of technological advancement. Deals particularly with the legal side of things, minimizing risk in particular. Indicates that the fastest and best way of penetrating foreign markets is to use a local branch already established in the foreign market, or, alternatively, establish a subsidiary or joint venture. Focuses then on licensing and some of the problems that can arise from that – piracy, exploitation, competition and financial implications if things go wrong. Suggests ways to circumvent this through licensing agreements, patents, trademarks, copyright, technology transfer agreements, and/or national intellectual property laws. Defines a licensing agreement, covering the subject matter of the license, technical assistance provisions, specification of the scope of the license, royalty compensation, quality standards and warranties, infringement of licensed rights, and duration and termination of the agreement. Mentions, also, antitrust considerations and the tax aspects of licensing. Recommends this approach as it spells out terms and conditions clearly to all parties, thereby, hopefully, reducing misunderstanding and disputes.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

The purpose of this paper is to give a review on the newest developments of high-performance finite element methods (FEMs), and exhibit the recent contributions achieved by the authors’ group, especially showing some breakthroughs against inherent difficulties existing in the traditional FEM for a long time.

Three kinds of new FEMs are emphasized and introduced, including the hybrid stress-function element method, the hybrid displacement-function element method for Mindlin–Reissner plate and the improved unsymmetric FEM. The distinguished feature of these three methods is that they all apply the fundamental analytical solutions of elasticity expressed in different coordinates as their trial functions.

The new FEMs show advantages from both analytical and numerical approaches. All the models exhibit outstanding capacity for resisting various severe mesh distortions, and even perform well when other models cannot work. Some difficulties in the history of FEM are also broken through, such as the limitations defined by MacNeal’s theorem and the edge-effect problems of Mindlin–Reissner plate.

These contributions possess high value for solving the difficulties in engineering computations, and promote the progress of FEM.

Feature fatigue (FF) will lead to negative Word-Of-Mouth (WOM), which damages the brand’s long-term profit and ultimately decreases the manufacturer’s customer equity (CE). It becomes severer in multi-generation products because of the significant impacts of earlier generation products on the CE of later ones. The purpose of this paper is to alleviate FF, it is imperative for designers to decide what features should be integrated to balance initial revenue and long-term profit so as to maximize CE.

In this paper, a novel method based on the Norton-Bass model is proposed to alleviate FF of multi-generation products to help designers find optimal feature combination that maximizes CE. The authors take the effects of adding features on product capability and usability into account, and integrate product capability, usability, WOM and earlier-generation product’s effects into the Norton-Bass model to predict the impacts of FF on CE in current product development. A case study of a virtual product is presented to illustrate and validate the proposed method.

The advantage of the proposed method is highlighted in the cases of large feature number, high-product complexity (low-product usability) and multi-generation products. The experiments show that the earlier generations do affect the later ones from the perspective of maximizing CE. The superiority of the proposed method compared with the traditional way to put all potential features into a product during the product development is demonstrated. And the more features, the larger CE obtained using the proposed model than the one obtained by traditional way.

Although, there are reports attempting to analyze and alleviate FF, most of these studies still suffer the limitations that cannot point out what features should be added to the product with the objective of maximizing CE. In addition, few studies have been carried out to alleviate FF of multi-generation products. A novel method based on the Norton-Bass model and a genetic algorithm is proposed to alleviate FF of multi-generation products to help designers find optimal feature combination that maximizes CE.

This study considered organizational politics as a source of stress and examined its spillover effects on the family domain. By integrating the work–home resource theory and transactional theory of stress, the authors developed a moderated mediation model that examined the moderating role of family support in the relationship between employee's perceptions of organizational politics and their family satisfaction through work-to-family conflict.

The authors examined the moderated mediation model using a sample of 223 full-time employees in Bangladesh. Data were collected using a three-wave survey research design.

The results indicate that organizational politics is negatively related to family satisfaction; work-to-family conflict medicates this relationship. Besides, family support attenuates the mediating effect of work-to-family conflict on the relationship between organizational politics and family satisfaction.

Managers should reduce the extent of organizational politics to avoid its impact on the nonwork domain. Moreover, social support from family members might play a crucial role in reducing the negative consequence of organizational politics in the family domain. By taking human resource practices such as training, increased communication, family-friendly policies, organizations may improve the ability of workers to cope with the negative consequences of organizational politics.

The current study uncovered the spillover effect of organizational politics on the nonwork domain. This research contributed to the burgeoning stream of organizational politics and work–family interface literature by investigating the influence of organizational politics in undermining family satisfaction and exploring the mediating mechanism linking organizational politics and family satisfaction as well as the boundary conditions of family social support.

The coordinated development of the urbanization and construction industry is crucial for the sustainable development of cities. However, the coupling relationship and coordination mechanism between them remain unclear. To bridge this gap, this study attempts to explore the level of coupling coordination between new urbanization and construction industry development and investigate the critical driving factors influencing their coupling coordination degree.

By referring to the existing literature, two index systems were established to evaluate the development level of the new urbanization and construction industry. The spatiotemporal characteristics of the coupled coordinated development of the new urbanization and construction industry in China from 2014 to 2020 were investigated using the coupling coordination model. The Markov chain and geographic detector were adopted to understand the transition probability and driving factors of the coupling coordination degree.

The results indicate that the coupling degree of China's new urbanization and construction industry is high, and the two systems exhibit obvious interaction phenomena. However, the construction industry in most provinces lags behind the new urbanization. A positive interactive relationship and coordination mechanism has not been established between the two systems. Furthermore, the  coupling contribution degree of the driving factors from high to low is as follows: market size > labor resource concentration > government investment ability > economic development level > industrial structure > production efficiency > technology level. Accordingly, a driving mechanism including market, policy, economic, and production technology drivers was developed.

This study contributes to the existing body of knowledge by providing a set of scientific analysis methods to address the deficiency of coordination mechanism research on new urbanization and the construction industry. The results also provide a theoretical basis for decision makers to develop differentiated sustainable development policies.

The purpose of this paper is to provide comprehensive analysis and understanding of lean construction (LC) and to help researchers to find new gaps and research opportunities and develop potential collaboration chances.

The bibliographic data were obtained in peer-reviewed journals. The science mapping analysis and social network analysis (SNA) were used to conduct the analysis and visualization. Science mapping was performed to identify the research topics, evolution and the relationships between these topics. Also, the most influential LC-related articles in each topic were identified based on the concept of H-classics. SNA was also used to explore the collaboration status of authors and to identify the core authors in the LC field.

The results showed that topics in the LC field are consistently and continuously changing, which also reveals the fact that the lean concept system has evolved over time to a certain extent. The topics that could form the knowledge base of the LC research field in the future are related to two thematic areas: supply chain management (SCM) and planning and scheduling. SCM focuses on developing a system where supplier and client work together in coordination to deliver materials, works, equipment and labor and other resources. Moreover, prefabrication is a new hot topic, while BIM is a well-developed and isolated topic in the last five years. This result indicates that more studies need to be conducted in the future, to promote the integration of BIM and lean practices in the construction phase, as well as to explore the value of BIM in the reduction or elimination of waste and lean project delivery. As for global collaborations, LC research has been widely pursued throughout the world. The USA and the UK play the most dominant role in the international collaboration network.

Since the analysis of data takes some time, the specific results of this paper are limited to articles published in peer-reviewed journals, which leads to a certain degree of research lag.

This paper provides insights (such as major journals, institutions, key scholars, the evolution of topics, highly indexed articles and the new LC trends in the last five years) for researchers in the LC field.

This paper gives a general review of the above-mentioned literature, including the number of LC-related articles published in each year, as well as the major journals and main contributors to the field of LC.

Computer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.

The developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.

Three DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.

This study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

The purpose of this paper is to construct an unbiased interval grey number prediction model with new information priority for dealing with the jumping errors from difference equation to the differential equation in the prediction model of interval grey number.

First, this study obtains a set of linear equations about the model parameters by taking the minimum error sum of squares between the accumulative sequence and its simulation values as criterion, and solves them on the basis of the Crammer rule. Then, according to the new information priority principle, it selects the last number of the accumulated generation sequence as the initial value and gives the expression of the time response function by the recursive iteration method to establish the interval grey number prediction model.

This paper provides an unbiased interval grey number prediction model with new information priority, and the example analysis shows that the method proposed in this paper has higher prediction precision and practicality.

If there is a better method to whiten the interval grey number, so as to fully tap the grey information contained in it, the accuracy of the model will be higher.

The model proposed in this paper can avoid the error caused by jumping from difference equation to differential equation and make full use of new information. It can be better used in a problem where new information has a great influence on prediction results.

This paper selects the last number of the accumulated generation sequence as the initial value and gives the expression of the time response function by the recursive iteration method. Then, it constructs an unbiased interval grey number prediction model with new information priority.

The hydro-viscous drive (HVD) has been widely used in fan transmission in vehicles, fans, and scraper conveyors for step-less speed regulating and soft starting. It is an efficient method to save energy and reduce consumption. This study aims to analyze the influencing factors of oil film shear torque accurately.

The shear torque calculation model of double arc oil groove friction pairs was established. The influence of groove structure parameters on shear torque was analyzed. The interaction between viscosity temperature and shear torque was considered. Meanwhile, the equivalent radius was calculated when the rupture of oil film appeared. Finally, the test rig of torque characteristics was set up. The variance of shear torque with the input rotation speed under different oil film thickness, different oil temperature, and different flow rate was seen.

The results show that the shear torque increases with the growth of rotation speed. However, the increase of torque is quite gradual because of the effect of the change of viscosity, which is caused by the rise of temperature. The shear torque increases with the decrease of thickness, the increase of inlet flow rate, and the decrease of inlet oil temperature. Meanwhile, when the feeding flow rate is less than the theoretical, the oil film gets ruptured and the shear torque decreases sharply.

The influence on shear torque during full film shear stage in HVD can be achieved much more accurately through both experimental research and theoretical modeling in which groove parameters, influence of temperature, and oil film rupture are considered. Therefore, the shear torque of HVD can be predicted by theoretical model and experimental research in full film shear stage.