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Global production networks (GPN) propel process and product upgrading of the latecomer firm (LCF), promoting its present operating efficiency on one hand, but, on the other hand, probably hindering the LCF's function and chain upgrading, resulting in the undermining of its future adaptive capability. Previous studies have suggested that ambidexterity is influential to the upgrading of the LCF. However, little is known about how the LCF builds ambidexterity to upgrade in GPN. The purpose of this paper is to examine how the LCF constructs ambidexterity resulting from tie diversity to break through the upgrading dilemma.

This paper intends to fill relevant gaps in the literatures on the LCF and explore the emerging fields of ambidexterity. The authors employ a longitudinal case study by examining how a manufacturer – Haitian Group – originating from China's plastic equipment industry has managed its diverse ties to build ambidexterity over the past 20 years.

The research reveals that: the key to successfully transferring from process and product upgrading to function or chain upgrading in GPN for the LCF is to establish its ambidexterity over time; LCF could achieve ambidexterity through creating diverse ties in GPN, namely develop diverse cooperative partners and patterns in different value functions over time; and the process of the LCF building ambidexterity in GPN is incremental, which needs the previous exploitation as a basis.

Previous studies have paid little attention to how the LCF makes use of tie diversity to build ambidexterity to sustainable upgrading in GPN. This paper fills the gaps and contributes to the theory of upgrading in GPN.

In order to be a true global leader, Chinese firms must be able to be trailblazers in both emerging and advanced markets.

Five major Chinese companies are studied: Each of the five leaders has shown interest in building an organization that can nurture innovation that is more than incremental.

Each of the five companies needs a clear long-term vision that will guide innovation and point to where these companies should place their slow-to-pay-off R&D bets.

The agility to take advantage quickly as opportunities emerge–by practicing frequent experimentation, promptly divesting failed projects and rapidly scaling up investments in successful ones–has been an important ability that has contributed to the success of the five companies.

Breakthrough product innovation will be an important competence for the future success of these five Chinese companies. Some of the five companies have developed a growing appetite for external collaboration to drive innovation.

This study aims to focus on the authority formation process of Chinese enterprise leaders, with the purpose of finding out how an ordinary newly established firm leader develops into a real top leader and achieves the status of legitimacy in a well-known enterprise.

Based on constructivist grounded theory, this paper investigates the formation mechanism of entrepreneurial authority in China by using the rich data of Liu Chuan-zhi’s leader activities.

In the “evolution” path of authority formation, leaders continually consolidate and improve their authority through two classes of exceptional management activities: “emergency rescue” and “promotion activities”. The successful realization of exceptional management activities benefits from a leader’s management experience accumulation and relationship maintenance with the government. In the “design” path of authority formation, leaders consolidate and improve their authority by exercising their position of power. Leaders’ legitimacy is reflected by making strategic decision and demonstrating discretion of position power. Additionally, passing on an inspiring leader’s thoughts and ideas to an organization’s members is accomplished through the construction of organization culture, institutionalization and convention.

First, the findings are based on only Liu Chuan-zhi’s case. The authors still need more cases to compare and develop the findings and seek theoretical saturation in a broader sense. Second, the qualitative analysis is based on secondary data and future research could consider the introduction of interviews, video and other types of research data.

Under the parallel paths which are “evolution” and “design”, the dynamic leader authority formation model in China is founded.

Patent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability.

First, we construct a PKG to integrate online company behaviors and patent information using natural language processing techniques. Second, a bidirectional long short-term memory network (BiLSTM) is utilized with an attention mechanism to establish the connecting paths of a company — patent pair in PKG. Finally, the prediction score of a company — patent pair is calculated by assigning different weights to their connecting paths. The semantic relationships in connecting paths help explain why a candidate patent is recommended.

Experiments on a real dataset from a patent trading platform verify that IKPRM significantly outperforms baseline methods in terms of hit ratio and normalized discounted cumulative gain (nDCG). The analysis of an online user study verified the interpretability of our recommendations.

A meta-path-based recommendation can achieve certain explainability but suffers from low flexibility when reasoning on heterogeneous information. To bridge this gap, we propose the IKPRM to explain the full paths in the knowledge graph. IKPRM demonstrates good performance and transparency and is a solid foundation for integrating interpretable artificial intelligence into complex tasks such as intelligent recommendations.

Reverse osmosis (RO) has become an important method of desalination to meet the ever-growing water needs around the world. Its integration with renewable energy source (RES) reduces the environmental impact of gas emissions and cost of conventional fossil energy sources. The optimal sizing of energy sources to power RO desalination system is intended mainly to minimize the annualized cost of the system and by extension minimize freshwater cost while maximizing production.

In this study, a mathematical optimization approach is used to determine the optimal energy mix, which includes grid power, diesel generator and a photovoltaic (PV) module to supply an RO desalination unit. Three cases of optimal sizing approach were compared. Case 1 is a system with only grid power and diesel generator as energy sources; Case 2 has PV incorporated in the energy supply mix while Case 3 has the three energy sources and a Time of Use (TOU) demand response program on the demand side.

The results of implementing the optimization models show that Case 3 turnout the highest freshwater production (1,521 m³/day) at a unit cost of 1.36$/m³ when compared to Case 1 with daily freshwater production of 1,250 m³/day at a unit cost of 1.68$/m³ and Case 2 having a daily freshwater production of 1,501 m³/day at a unit cost of 1.33$/m³.

The integration of RES to power desalination system with application of TOU demand response is the significance of this study.

The purpose of this paper was to obtain the lubrication characteristics of heavy hydrostatic bearing in heavy equipment manufacturing industry through theoretical analysis and numerical simulation.

This paper discusses the influence of oil film thickness variation on velocity field, outlet-L and outlet-R flow velocity under the hydrostatic bearing running in no-load 0 N, load 400 KN, full load 1,500 KN and rotating speeds of 10 r/min, 20 r/min, 30 r/min, 40 r/min, 50 r/min and 60 r/min, by using dynamic mesh technology and FLUENT software.

When the working table rotates clockwise, in the change process of oil film thickness, the fluid flow pattern of the lubricating oil at the edge of the sealing oil is the rule of laminar flow, and the oil cavity has a vortex. The outlet-R flow velocity becomes higher and higher by increasing the bearing load and working table speed, and the flow velocity increases with the decrease in oil film thickness; the outlet-L flow velocity increases with the decrease in oil film thickness under low rotating speed (less than 10 r/min) condition and decreases with the decrease of oil film thickness under high rotating speed (more than 60 r/min) condition.

The influence of the oil film thickness on the flow state distribution of the oil film was analyzed under different working conditions, and the influence rules of oil film thickness on the flow velocity of hydrostatic bearing oil pad was obtained by using dynamic mesh technology.

This paper aims to propose a methodology to remove inherent implicit creep from the Eurocode 3 material model for steel and to present a creep-free analysis on simply supported steel members.

Most of the available material models of steel are based on transient coupon tests, which inherently include creep strain associated with particular heating rates and load ratios.

The creep-free analysis aims to reveal the influence of implicit creep by investigating the behaviour of simply supported steel beams and columns exposed to various heating regimes. The paper further evaluates the implicit consideration of creep in the Eurocode 3 steel material model.

A modified Eurocode 3 carbon steel material model for creep-free analysis is proposed for general structural fire engineering analysis.

Internet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm’s success in complex problems, the authors used this algorithm to allocate resources in IoT.

For the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm.

Simulation results show that the proper performance of the proposed technique. The proposed method, in terms of “energy” and “delay,” is better than other ones (GA, PSO and distance-based algorithm).

The paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practical projects. In the future, the focus will be on resource failure and reducing the service level agreement violation rate concerning the number of resources.

The proposed technique can solve the mentioned problem in the IoT with the best resource utilization, low delay and reduced energy consumption. The proposed forest optimization-based algorithm is a promising technique to help enterprises participate in IoT initiatives and develop their business.

The purpose of this paper is to examine the inter-relations among the US stock indices.

Data of nine US stock indices spanning a period of sixteen years (2000-2015) are employed for this purpose. Asymmetries are examined via an error correction model. Non-linear inter-relations are researched via Breitung’s nonlinear cointegration, a M-G nonlinear causality model, shocks to the forecast error variance, a shock spillover index and an asymmetric VAR-GARCH (VAR-ABEKK) approach.

The inter-relations are significant. The results are robust across all types of inter-relations. They are highest in the Lehman Brothers sub-period. Higher stability after the EU debt crisis, enhances independence and growth for the US stock indices.

To the best of the knowledge, this is the first study to examine the inter-relations of US stock indices. Most studies on inter-relations concentrate on the portfolio analysis to reveal diversification benefits among various asset markets internationally. Hence this study contributes to this literature on the inter-relations of a specific asset market (stock), and in a specific nation (USA). The evident inter-relations support the notion of diversification benefits in the US stock markets.