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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.

The purpose of this paper is to form copper coin-embedded printed circuit board (PCB) for high heat dissipation.

Manufacturing optimization of copper coin-embedded PCB involved in the design and treatment of copper coin, resin flush removal and flatness control. Thermal simulation was used to investigate the effect of copper coin on heat dissipation of PCB products. Lead-free reflow soldering and thrust tests were used to characterize the reliable performance of copper coin-embedded PCB.

The copper coin-embedded PCB had good agreement with resin flush removal and flatness control. Thermal simulation results indicated that copper coin could significantly enhance the heat-dissipation rate by means of a direct contact with the high-power integrated circuit chip. The copper coin-embedded PCB exhibited a reliable structure capable of withstanding high-temperature reflow soldering and high thrust testing.

The use of a copper coin-embedded PCB could lead to higher heat dissipation for the stable performance of high-power electronic components. The copper coin-embedded method could have important potential for improving the design for heat dissipation in the PCB industry.

The purpose of this study is to identify the factors determining collaborative innovation effect of manufacturing firms in emerging economies.

Based on a survey of 1,206 Chinese manufacturing firms and using structural equation modelling, this study explores the factors determining the effect of collaborative innovation among manufacturing firms (namely, internal capabilities, government policies, collaboration mechanisms and social networks) and examines the relationship between collaborative innovation effect and innovation performance.

The study finds that there are significantly positive relationships between firms’ internal capabilities, government policies, collaboration mechanisms and social networks and collaborative innovation effect among firms.

These findings reveal that policymakers should create an effective institutional culture and market environment to facilitate firms’ collaborative innovation.

This paper draws on the resource-based view of firms and contributes to understanding of how the development of factors determining firms’ collaborative innovation effect can improve innovation performance. This study extends established frameworks on collaborative innovation in relation to four dimensions, namely, firms’ internal capabilities, government policies, collaboration mechanisms and social networks, uniquely identifying the limits of specific dimensions. Moreover, this study addresses government policies and “Guanxi culture” specific to China that provide new insights into how firms’ collaborative innovation is improved from the perspectives of business–governmental relations and social networks.

A key finding in the extant literature on adopting information systems has been the importance of management support and a champion. Further research has indicated that business managers need to have appropriate IT knowledge and skills to lead adoption adequately. In the context of small and medium enterprises (SMEs), this role is usually assumed by the owner/manager. This research aims to synthesise these two tenets by identifying and understanding the type of business intelligence and analytics (BI&A) leadership skills that owners/managers need to develop during the adoption of BI&A.

Five BI&A knowledge areas are identified and connected to different types of BI&A leadership skills through qualitative in-depth case studies of fourteen Australian SMEs.

The case studies reveal that several BI&A leadership skills need to be developed to bring SMEs to higher stages of BI&A adoption.

This study proposes a BI&A leadership skills development framework that allows practitioners to develop progressive BI&A skills concerning managing data, analytical skills, business processes, social and cultural change, and investment decisions to achieve sustainable operational, management and strategic goals.

The paper takes a unique approach that links five knowledge areas to BI&A leadership skills that owners/managers need to ensure for effective adoption and orchestration of BI&A in their organisations. The BI&A leadership framework includes a developmental approach that relates to the iterative and complex nature of BI&A adoption.

Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey systems in marine economics is gaining importance. The purpose of this paper is to summarize and review literature on grey models, providing new directions in their application in the marine economy.

This paper organized seminal studies on grey systems published by Chinese core journal database – CNKI, Web of Science and Elsevier from 1982 to 2018. After searching the aforementioned database for the said duration, the authors used the CiteSpace visualization tools to analyze them.

The authors sorted the studies according to their countries/regions, institutions, keywords and categories using the CiteSpace tool; analyzed current research characteristics on grey models; and discussed their possible applications in marine businesses, economy, scientific research and education, marine environment and disasters. Finally, the authors pointed out the development trend of grey models.

Although researches are combining grey theory with fractals, neural networks, fuzzy theory and other methods, the applications, in terms of scope, have still not met the demand. With the increasingly in-depth research in marine economics and management, international marine economic research has entered a new period of development. Grey theory will certainly attract scholars’ attention, and its role in marine economy and management will gain considerable significance.

This paper aims to discuss the low carbon supply chain practices in China’s textile industry. To curb greenhouse gas emissions, the Chinese government has launched restrict regulatory system and imposed the energy consumption constraint in the textile industry to guarantee the achievability of low carbon economy. The authors aim to examine how the energy consumption constraint affects the optimal decisions of the supply chain members and address the supply chain coordination issue.

The authors conduct two case studies from Chinese textile companies and examine the impact of energy consumption constraints on their production and operations management. Based on the real industrial practices, the authors then develop a simple analytical model for a low carbon supply chain in which it consists of one single retailer and one single manufacturer, and the manufacturer determines the choice of clean technology for energy efficiency improvement and emission reduction.

From the case studies, the authors find that the textile companies develop clean technologies to reduce carbon emission in production process under the energy consumption enforcement. In this analytical model, the authors derive the optimal decisions of the supply chain members and reveal that supply chain coordination can be achieved if the manufacturer properly sets the reservation wholesale price (WS) despite the production capacity can fulfill partial market demand under a WS (or cost sharing) contract. The authors also find that the cost-sharing contract may induce the manufacturer to increase the investment of clean technology and reduce the optimal WS.

This paper discusses low carbon supply chain practices in China’s textile industry and contributes toward green supply chain development. Managerial implications are identified, which are beneficial to the entire textile industry in the developing countries.

Considering the small sample size and non-linear characteristics of historical energy consumption data from certain provinces in Southwest China, the authors propose a hybrid accumulation operator and a hybrid accumulation grey univariate model as a more accurate and reliable methodology for forecasting energy consumption. This method can provide valuable decision-making support for policy makers involved in energy management and planning.

The hybrid accumulation operator is proposed by linearly combining the fractional-order accumulation operator and the new information priority accumulation. The new operator is then used to build a new grey system model, named the hybrid accumulation grey model (HAGM). An optimization algorithm based on the JAYA optimizer is then designed to solve the non-linear parameters θ, r, and γ of the proposed model. Four different types of curves are used to verify the prediction performance of the model for data series with completely different trends. Finally, the prediction performance of the model is applied to forecast the total energy consumption of Southwest Provinces in China using the real world data sets from 2010 to 2020.

The proposed HAGM is a general formulation of existing grey system models, including the fractional-order accumulation and new information priority accumulation. Results from the validation cases and real-world cases on forecasting the total energy consumption of Southwest Provinces in China illustrate that the proposed model outperforms the other seven models based on different modelling methods.

The HAGM is used to forecast the total energy consumption of the Southwest Provinces of China from 2010 to 2020. The results indicate that the HAGM with HA has higher prediction accuracy and broader applicability than the seven comparative models, demonstrating its potential for use in the energy field.

The HAGM(1,1) is used to predict energy consumption of Southwest Provinces in China with the raw data from 2010 to 2020. The HAGM(1,1) with HA has higher prediction accuracy and wider applicability compared with some existing models, implying its high potential to be used in energy field.

Theoretically, this paper presents, for the first time, a hybrid accumulation grey univariate model based on a new hybrid accumulation operator. In terms of application, this work provides a new method for accurate forecasting of the total energy consumption for southwest provinces in China.

Since 2000, China, along with the USA, UK, France, Japan and many other developed countries have drawn up new blueprints for the development of a marine economy. At present, international marine economics research has entered into a new period of development, and the research methods of ocean econometrics are becoming more complex and mature. The purpose of this paper is to review the progress of international marine econometrics research and gives the development direction of marine econometrics.

The Web of Science core collection database was utilized, harvesting data from 1996 to May 2018, measuring the marine economy research from 1,489 articles as its sample, using CiteSpace visualization analysis tools.

Mapping the knowledge map from annual international marine economic metrology, literature identification, keywords, involving disciplines and related journals, countries (regions) and research and analyzing the research status of reveals the research frontiers of international marine economy measurement (learning) by using CiteSpace.

The conceptions and characteristics of marine econometrics are defined and analyzed, and the theoretical method of marine econometrics is sorted out. Mapping the knowledge diagram of marine econometrics and discussing the research status of international marine economics, and clarifying the existing problems, future opportunities and challenges of international marine econometrics research.

This paper aims to systematically examine the key notion of integration of non-market and market strategies in the increasingly popular study of corporate non-market strategies.

This paper is based on a brief literature review of the non-market strategy (NMS) research that shows the existing literature does not offer a clear and systematic account of the key notion of integration. It suggests any systematic account of integration should address at least three interrelated questions, i.e. why, what and how to integrate non-market and market strategies?

For the why question, the authors use a formal model to demonstrate that the essence of the most important type of integration synergy lies in the positive spillover or externality from non-market to market strategies. For the what question, the authors identify the contents of integration at three levels, i.e. the level of non-market environment analysis, the level of NMS choice, and the level of non-market dynamic interactions. For the how question, the authors argue that the combination of non-market and market strategies should be seamless in terms of horizontal, vertical and intentional coordination. Overall, the authors argue, only when the right contents are combined and seamlessly coordinated will there be high synergies from integration of non-market and market strategies.

Managers are advised to give non-market strategies full attention. Managers charged with non-market tasks should explore how to seamlessly coordinate non-market and market strategies in order to gain maximal synergies.

This paper is the first to examine the key notion of integration in a systematic manner. It is the first to propose a three-question solution to systematic understanding of the notion and the first to propose the seamless coordination concept and its associated three aspects of seamless coordination.

The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.