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The purpose of this paper is to establish an accurate model to quantify the effect of conductor roughness on insertion loss (IL) and provide improved measurements and suggestions for manufacturing good conductive copper lines of printed circuit board.

To practically investigates the modified model of conductor roughness, three different kinds of alternate oxidation treatments were used to provide transmission lines with different roughness. The IL results were measured by a vector net analyzer for comparisons with the modified model results.

An accurate model, with only a 1.8% deviation on average from the measured values, is established. Compared with other models, the modified model is more reliable in industrial manufacturing.

This paper introduces the influence of tiny roughness structures on IL. Besides, this paper discusses the effect of current distribution on IL.

This paper aims to report a comprehensive analysis of literature on stakeholder relationships towards identifying patterns of relationships within the off-site construction context.

Key scholarly databases were accessed and after a filtering process, 74 relevant papers were retrieved for analysis. The papers were analysed using qualitative content analysis and scientometric techniques through the application of software Leximancer and VOSviewer.

Research synthesis methods used in the present study generate compatible results. Through text mining analysis, the key themes identified in the off-site construction stakeholder relationships literature included “collaboration”, “building information modelling”, “social network analysis”, supply chain. As a finding by scientometric analysis, collaboration, BIM, supply chain management, housing and social network analysis were the most frequently entered keywords context of off-site construction. Regarding authorship pattern, the whole network of collaboration was fragmented into multiple isolated clusters, implying that the authors had tendency to cooperate in small groups.

The paper can bring together an important area of research not previously studied in detail. It will primarily assist academics in the first instance; however, the research leads to important findings that will ultimately assist policymakers and practitioners better understand factors affecting stakeholder relationships and in particular network thinking and collaborative mind-sets.

The review contributes a needed systematic and theoretical foundation for future stakeholder relationship studies and practices in off-site construction sector. It provides the basis for future studies and is a seminal analysis of stakeholder management and off-site construction. The scientometric methodology offers scholars a different approach to analysing and visualising literature reviews.

It is a very prominent problem that Chinese universities lack school-running characteristics. In the past ten years, because of undergraduate teaching assessment requirements of the Ministry of Education, universities attach great importance to school-running characteristics. What is the reality and how to improve the effectiveness of creating the school-running characteristics of universities? It is a problem that needs to be solved. The purpose of this paper is to discuss these issues.

Using the survey method, literature study, case studies and other methods, this study reviewed ten years of school-running characteristics construction and explored some laws of creating school-running characteristics.

This study found although universities in China are beginning to attach great importance to the school-running characteristics, but they are still staying in the summarization of characteristics. School-running characteristics are very rough. Creating school-running characteristics are mainly efforts responding to the superior government. Creating school-running characteristics should be based on category characteristic. Universities need to change in competition and create characteristics within its history and culture. Universities need to refine the core idea of education, develop a big picture and then renew them in the assessment cycle.

The originality of this study was that it put forward some new laws including changing from summarizing to creating its own school-running characteristics, putting category characteristic as the prerequisite and considering the core idea of education as the focus of school-running characteristics. This research will enrich the theory building of higher education research and has some value in promoting the creation of school-running characteristics.

The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms.

Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods.

The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M.

This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

From the intra- and inter-regional dimensions, this paper investigates the linkage between industrial agglomeration and inventory performance, and further demonstrates the moderating role of firm size and enterprise status in the supply chain on this linkage.

Using a large panel dataset of Chinese manufacturers in the Yangtze River Delta for the period from 2008 to 2013, this study employs the method of spatial econometric analysis via a spatial Durbin model (SDM) to examine the effects of industrial agglomeration on inventory performance. Meanwhile, the moderation model is applied to examine the moderating role of two firm-level heterogeneity factors.

At its core, this research demonstrates that industrial agglomeration is associated with the positive change of inventory performance in the adjacent regions, whereas that in the host region as well as in general does not significantly increase. Additionally, both firm size and enterprise status in the supply chain can positively moderate these effects, except for the moderating role of firm size on the positive spillovers.

In view of firm heterogeneity, managers should take special care when matching their abilities of inventory management with the agglomeration effects. Firms with a high level of inventory management are suited to stay in an industrial cluster, while others would be better in the adjacent regions to enhance inventory performance.

This paper is the first to systematically analyze the effects of industrial agglomeration on inventory performance within and across clusters, and confirm that these effects are contingent upon firm size and enterprise status in the supply chain. It adds to the existing literature by highlighting the spatial spillovers from industrial clusters and enriching the antecedents of inventory leanness.

The purpose of this paper is to investigate the preparation and the flexural property of hollow glass microspheres (HGMs) filled resin-matrix composites, which have been widely applied in deep-sea fields.

The composites with different contents of HGMs from 47 to 57 Wt.% were studied. The voids in syntactic foams and their flexural properties were investigated.

The results showed that the voids quantity increased because of the increment of HGM content, whereas the exural strength and the exural modulus decreased. The fracture mechanism of the composites was also investigated by scanning electron microscope, which indicated that the composites failed by the crack extending through the microspheres.

The advantages of HGMs with similar hollow spheres will be further investigated in a future research.

Results demonstrated that the properties of the composite might be tailored for specific application conditions by changing the HGM volume fraction.

The HGM filled resin-matrix composite materials have their unique properties and significant application potential. In this work, the resin-HGM composites were synthesized by mechanically mixing defined quantities of HGMs into epoxy resin, by which a kind of syntactic foams with good flexural properties could be obtained.

The purpose of this paper is to introduce a robotic boring system for intersection holes in aircraft assembly. The system is designed to improve the boring quality and position accuracy of the intersection holes.

To improve the boring quality of intersection holes, a robot posture optimization model is established. The target of the model is to maximize the robot stiffness and the variate is location of the robot on the guideway. The model is solved by the iterative IKP algorithm based on the Jacobian matrix. To improve the position accuracy of intersection holes, a robot positioning accuracy compensation method is introduced. In the method, a laser tracker is used to measure the actual position and orientation of the boring bar. Combined with the desired position and orientation, the error can be obtained and compensated.

In practical case of the robotic boring system, the robot stiffness is effectively improved and the surface roughness of intersection holes achieves a grade of Ra0.8. Besides, the robot end achieves a position accuracy of 0.05 mm and an orientation accuracy of 0.05°.

The robotic boring system has been applied successfully in one of the aircraft assembly projects in northwest China.

The robotic boring system can be applied for machining intersection holes in an aircraft assembly. With the robot posture optimization method and accuracy compensation method, the boring quality and position accuracy of the intersection holes can be guaranteed.

The purpose of this paper is to empirically investigate the effect of lean manufacturing on productivity changes and to identify the root sources of productivity changes. Furthermore, the authors explore the moderating effects of research and development (R&D) to examine the relationship between lean manufacturing and productivity changes.

This paper employs the propensity score matching (PSM) model combined with the difference-in-difference (DID) estimation to overcome the selectivity bias. The Malmquist productivity index is used to capture productivity changes. By analyzing 671 Chinese manufacturing listed firms from 2009 to 2014, the moderating effects of R&D on the relationship between lean manufacturing and productivity changes are measured.

The results reveal that lean manufacturing implementation has non-significant effects on productivity changes in principle, while a detailed analysis indicates that lean manufacturing could improve scale efficiency significantly. While engaged in R&D could significantly improve the efficiency of technological changes for lean manufacturing implementation firms, there exist negative effects on pure technical efficiency.

This research only covers manufacturing listed firms in China. Further studies should extend the generalizability of the findings.

This study helps managers to identify the important role of R&D on the relationship between lean manufacturing and productivity changes and provides insights into how to improve the lean manufacturing performance.

This paper appears to be one of the earliest studies on the relationship between lean manufacturing and productivity changes by applying the PSM combined with DID estimation in Chinese manufacturing environment.

This paper explores the relationship between inventory productivity and the likelihood of venture survival and then examines how financial constraints moderate the inventory productivity–survival linkage.

Accelerated failure time (AFT) model is employed to study the link between inventory productivity and venture survival by using small- and medium-sized enterprise (SME) data from Chinese Annual Survey of Industrial Firms (CASIF) database over the period 1999–2007.

The paper demonstrates a converse U-curve relation between inventory productivity and venture survival. Additionally, financial constraints as the moderator weaken the marginal effect of inventory productivity on venture survival.

Managers should pay more attention to the important inventory performance indicator: inventory productivity. In the context of prominent financing difficulties, managers should be rapid to adjust the competitive strategy and optimize the internal production process according to the inherent nature of risks in a friction environment, and thus generate resources that enterprises cannot raise in the financial market.

This study may be the first to practically investigate the role of inventory productivity on venture survival and the moderating effect of financing constraints on this relationship. It adds to abundant articles as regards the interface between operation management and venture survival by exploring how financial constraints moderate the inventory productivity–survival linkage.