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This paper aims to propose a method that can directly print low-melting-point alloy In61Bi26Sn9Ga4 into a variety of macroscopic 3D structures at room temperature via adhesion mechanism.

In the first section, the principle of the direct printing system is described. As process parameters and material properties have both geometric and physical significance to printing, the approach the authors take is to study the relationships between key parameters and ultimate printed dimension. The surface tension of the fusible alloy is measured under different temperature ranges.

The interaction between the initial standoff distance and the geometry of the first layer is critically important for the adhesion of the liquid metal to the substrate and metal deposition. The characterization of the layer stacking in the direct printing process, stability ranges of the layer thickness and printing speed are also demonstrated. The direct printing system is suitable for making 3D structures with low-melting-point alloy under the summarized range of printing conditions.

This study may arouse big public attention among society.

This study shows possibilities of manufacturing macroscopic 3D metal objects by continuously depositing molten alloy with low viscosity and high surface tension around room temperature. This study provides a supplement to realize compound printing with metal and nonmetal materials together for building terminal functional devices in a low cost and efficient way.

As the application of crowdsourcing contests grows, leveraging the participation of superstars (i.e. solvers who have outstanding performance records in a crowdsourcing platform) becomes an emergent approach for managers to solve crowdsourced problems. Although much is known about superstars’ performance implications, it remains unclear whether and how their participation affects the size of a contest crowd for a crowdsourcing contest. Based on social contagion theory, this paper aims to examine the impact of superstars’ participation on the crowd size and studies how this impact varies across solvers with different heterogeneity in terms of skills, exposure and cultural proximity with superstars in crowdsourcing contests.

This paper uses secondary data from one crowdsourcing platform that includes 6,587 innovation contests to examine superstars’ main and contextual effects on the crowd size of a contest.

Our results reveal that superstars’ participation positively affects the crowd size of a contest in general. This finding suggests that social contagion is a fundamental mechanism underlying crowd formation in crowdsourcing contests. Our results also indicate that in contests that involve multiple superstars, superstars’ effect on crowd size becomes negative when we simultaneously consider other solvers’ heterogeneity in terms of skills, exposure and cultural background, and this negative effect will be intensified by increases in the skill gap, extent of exposure and cultural proximity between superstars and other solvers in the same contest.

Our research enhances the understanding of the influence of superstars and the mechanism underlying the emergence of contest crowds in crowdsourcing contests and contributes knowledge to better understand social contagion in a competitive setting. The results are meaningful for sourcing managers and platform supervisors to design contests and supervise crowd size in crowdsourcing contests.

The purpose of this paper is to investigate how customer pressure influences green innovation in the context of Chinese third-party logistics (3PL) providers, and especially the role of organizational culture in moderating this relationship.

Based on survey data collected from 165 3PL providers in China, hierarchical moderated regression analysis was conducted to test the hypotheses.

Customer pressure is an important driver of green innovation amongst 3PL providers. Flexibility-oriented organizational culture strengthens the effect of this driving force, while control-oriented organizational culture weakens this force. Green innovation significantly contributes to financial performance and flexibility orientation strengthens this contribution, while control orientation weakens it.

This research examines the contingency effect of organizational culture in helping to resolve inconsistencies in the relationship between customer pressure and green innovation. Although the inconsistencies cannot be resolved completely, the research opens an avenue to explore other contingency factors or the possibility of a non-linear relationship.

3PL firms could undertake green innovation to satisfy customers’ environmental requirements. To develop their green innovation initiatives, managers should allow their employees greater autonomy and design (or re-design) operations procedures and regulations to be more flexible, thus enabling the diffusion of green innovation and avoiding or reducing the potential influence of control-oriented organization culture.

The study considers the conditional effect of organizational culture to reconcile the mixed results in the literature regarding the relationship between customer pressure and green innovation of logistics service providers.

The purpose of this paper is to explore how external pressures, internal capability and transaction attributes of logistics outsourcing synergically influence the extent of asset-based and non-asset-based logistics outsourcing.

Based on the data surveyed from 250 manufacturing companies in China, this study employed fuzzy-set qualitative comparative analysis (fsQCA) to deduce multiple configurations for logistics outsourcing decisions.

The results suggest that asset-based logistics outsourcing is primarily driven by external imitation pressures or internal demands for logistics technologies, while non-asset-based logistics outsourcing is mainly driven by the demands for external management-based logistics services. Asset specificity plays a positive role in promoting both asset-based and non-asset-based logistics outsourcing. The requirement for third-party logistics (3PL) management capability depends on the outsourcing types and outsourcing causes.

This study provides guidance to practitioners for them to make outsourcing decisions. It suggests that asset-based logistics outsourcing is more appropriate when there are high external imitation pressures or more internal logistics demands, while non-asset-based logistics outsourcing should be used only when a firm needs management-based logistics services. Besides, 3PL users are suggested to outsource their logistics when their 3PL providers are required to make specific investments. In addition, managers should carefully evaluate firms’ capabilities in managing outsourcing relationships.

Previous studies largely ignored the interaction effects of a set of factors on logistics outsourcing decisions, and to date, little research empirically examined how outsourcing is driven in terms of different types of outsourcing. Drawing on the institutional theory, dynamic capability view, and transaction cost theory and overarching under the complexity theory, this study examines how institutional, organizational and transactional factors interplay with each other to influence different types of logistics outsourcing (i.e. asset based and non-asset based). Methodologically, the configural analysis (i.e. fsQCA) is applied to explore complex causal configurations that drive logistics outsourcing.

Digitalization brings complexity and challenges to the relationship governance between logistics outsourcers and their providers. Drawn on resource dependence theory (RDT) and resource-based view (RBV), this study aims to examine the role of relationship commitment in simultaneously managing dependence and improving operational performance in logistics outsourcing in the digital economy, as well as the contingent factors (i.e. communication, relationship length, and company size) that affect the effectiveness of relationship commitment.

Based on data collected from 130 third-party logistics (3PL) users in China, our model was tested with the partial least squares (PLS) approach.

First, relationship commitment is necessary for 3PL users to manage dependence on 3PL providers and improve operational performance in the digital economy. Second, communication helps 3PL users to develop higher relationship commitment but weakens the motivating effect of dependence on relationship commitment. Third, a long relationship history develops inertia to diminish the effectiveness of dependence on driving relationship commitment while it boosts the impact of relationship commitment on operational performance. Last, company size is an important signal to amplify the effectiveness of relationship commitment for operational performance enhancement.

This study contributes to the logistics outsourcing literature by integrating RDT and RBV to explain the twofold roles of relationship commitment, simultaneously tackling dependence and enhancing operational performance in the digital economy. Additionally, it expands the understanding of the boundary conditions (e.g. communication, relationship length, and company size) on these twofold roles.

Based on signaling theory, this paper aims to explore the impact of supply chain transparency (SCT) on firms' bank loan (BL) and supply chain financing (SCF) in the context of voluntary disclosure of supplier and customer lists.

Based on panel data collected from Chinese-listed firms between 2012 and 2021, fixed-effect models and a series of robustness checks are used to test the predictions.

First, improving SCT by disclosing major suppliers and customers promotes BL but inhibits SCF. Specifically, customer transparency (CT) is more influential in SCF than supplier transparency (ST). Second, supplier concentration (SC) weakens SCT’s positive impact on BL while reducing its negative impact on SCF. Third, customer concentration (CC) strengthens the positive impact of SCT on BL but intensifies its negative impact on SCF. Last, these findings are basically more pronounced in highly competitive industries.

This study contributes to the SCT literature by investigating the under-explored practice of supply chain list disclosure and revealing its dual impact on firms' access to financing offerings (i.e. BL and SCF) based on signaling theory. Additionally, it expands the understanding of the boundary conditions affecting the relationship between SCT and firm financing, focusing on supply chain concentration. Moreover, it advances signaling theory by exploring how financing providers interpret the SCT signal and enriches the understanding of BL and SCF antecedents from a supply chain perspective.

Tip leakage vortex flow (TLV) is a common flow phenomenon in the axial-flow hydraulic machinery. High-efficiency simulation of TLV is still not an easy task because of the complex turbulent vortex-cavitation interactions. As an important basis of CFD, turbulence model directly affects the efficient computation of TLV. The purpose of this paper is to evaluate the newly developed MST turbulence model in predicting the TLV flows.

By using the MST turbulence model and the ZGB cavitation model, numerical simulations of the TLV generated by a NACA0009 hydrofoil were performed under the cavitation-free and cavitation conditions, and the results were compared with the available experimental data.

The important features of TLV are well captured by the MST-based simulation scheme, and the problem of under-predicting the cavitating TLV tube is well solved. Turbulent viscosity is reasonably adjusted in the TLV core regions, and the LES-like mode is activated, which is beneficial to obtain more turbulent information on the same URANS grids. The requirements of grid size and time step of the MST model are much lower than that of the LES method, thereby weighing a good balance between the simulation accuracy and computation cost.

The MST turbulence model is suitable for the high-efficiency simulation of the TLV flows, which can lay a good foundation for efficient engineering computations of the cavitating TLV in the axial-flow hydraulic machinery.

The purpose of this paper is to present a new approach for selecting the good heavy oil reservoirs to develop preferentially, which can avoid the huge economical loss resulted from wrong decision.

A new method of ranking the development priority of heavy oil reservoir is present, in which the neural network is applied for the first time to acquire reservoir parameters' weights through training samples and the genetic algorithm is used to optimize the joint weighs of neurons in case that neural network falling into local minimum. Additionally, the paper establishes subordinate function of every parameter. Eventually, comprehensive evaluation values of all heavy oil reservoirs are obtained.

The method can ensure the veracity and creditability of the parameters' weights, avoid the randomicity brought by experts.

Accessibility of the data of many heavy oil reservoirs is the main limitation.

A very useful and new method for the decision makers of heavy oil reservoirs development.

The new approach of ranking the development priority of heavy oil reservoir based on the neural network and the genetic algorithm. The paper is aimed at the leaders who manage the development of heavy oil reservoirs.

High speed axial flow pumps are widely used in aircraft fuel systems. Conventional axial flow pumps often generate radial secondary flows at partial-load conditions which influence the flow structure and form a “saddle-shaped” region in the Q-H curve that can destabilize the operation. Thus, the “saddle-shaped” Q-H region must be eliminated. The paper aims to discuss these issues.

The swept stacking method is often used for radial flow control in turbo-machinery impeller blade design. Hence, this study uses the swept stacking method to design a high speed axial flow pump. The detached eddy simulation method and experiments are used to compare the performance of a swept blade impeller in a high speed axial fuel pump with the original straight blade impeller. Both the pump performance and internal flow characteristics are studied.

The results show separation vortices in the impeller with the straight blade design at partial-load conditions that are driven by the rotating centrifugal force to gather near the shroud. The swept geometry provides an extra force which is opposite to the rotating centrifugal force that creates a new radial equilibrium which turns the flow back towards the middle of the blade which eliminates the vortices and the “saddle-shaped” Q-H region. The swept blade impeller also improves the critical cavitation performance. Analysis of the pressure pulsations shows that the swept blade design does not affect the stability.

This study is the initial application of swept blades for axial flow liquid pumps. The results show how the swept stacking changes the radial equilibrium of the high density, high viscosity flow and the effects on the mass transfer and pressure pulsations. The swept blade effectively improves the operating stability of high speed fuel pumps.

The velocity head is usually neglected in the energy equation for a pipeline junction when one-dimensional (1D) hydraulic transient flow is solved by method of characteristics. The purpose of this paper is to investigate the effect of velocity head on filling transients in a branched pipeline by an energy equation considering velocity head.

An interface tracking method is used to locate the air–water interface during pipeline filling. The pressured pipe flow is solved by a method of characteristics. A discrete gas cavity model is included to permit the occurrence of column separation. A universal energy equation is built by considering the velocity head. The numerical method is provisionally verified in a series pipeline and the numerical results and experimental data accord well with each other.

The numerical results show that some differences in filling velocity and piezometric head occur in the branched pipeline. These differences arise because the velocity head in the energy equation can become an important contributor to the hydraulic response of the system. It is also confirmed that a local high point in the profile is apt to experience column separation during rapid filling. Significantly, the magnitude of overpressure and cavity volume induced by filling transients at the local high point is predicted to increase with the velocity in the pipes.

The velocity head in the energy equation for a pipeline junction could play an important role in the prediction of filling velocity, piezometric head and column separation phenomenon, which should be given more attention in 1D hydraulic transient analysis.