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Online collaboration in today's world is a topic of genuine interest to Internet researchers. The purpose of this paper is to explore the role of group knowledge heterogeneity (GKH) in open collaboration performance using the mediating mechanisms of group cognition (GC) and interaction to understand the determinants of the success of online open collaboration platforms.

Study findings are based on partial least squares structural equation modeling (PLS-SEM), the formal mediation test and moderating effect analysis from Wikipedia's 160 online open collaborative groups.

For online knowledge heterogeneous groups, open collaboration performance is mediated by both GC and collaborative interaction (COL). The mediating role of GC is weak, while the mediating role of COL is strengthened when knowledge complexity (KC) is higher. By dividing group interaction into COL and communicative interaction (COM), the authors also observed that COL is effective for online open collaboration, whereas COM is limited.

These findings suggest that for more heterogeneous large groups, group interaction would explain more variance in performance than GC, offering an in-depth understanding of the relationship between group heterogeneity and open collaboration performance, answering what determines the success of online open collaboration platforms as well as explaining the inconsistency in prior findings. In addition, this study expands the application of Interactive Team Cognition (ITC) theory to the online open collaboration context.

Online knowledge integration has been an important concern of the online knowledge community as it can lead to various positive outcomes of online knowledge coproduction. This paper identifies online knowledge integration factors by considering group heterogeneity and group interaction process.

Based on the categorization-elaboration model (CEM) and interactive team cognition (ITC) theory, a research model that reflects the antecedent's factors and mediating factors of online knowledge integration was developed and empirically examined based on data collected from 2,339,836 data extracted from Wikipedia.

Group interaction process plays an essential mediator role in online knowledge integration. Group knowledge heterogeneity negatively influences online knowledge integration and group experience heterogeneity positively, and they both positively promote online knowledge integration through group interaction process with different paths.

Our research concerns the OKC context in one setting (Wikipedia). We expect that the results will generalize to other OKC platforms.

The findings of the study could assist the online knowledge community's organizers to understand the motivational mechanisms of online knowledge integration. Group interaction process could be regarded as the key role to promote group wisdom and maintain group independence.

We advance the understanding of the online knowledge integration and gain a richer understanding of the importance of group interaction independence for online knowledge integration based on the agreement of group wisdom. It suggested keeping group interaction independence is an important aspect for highly online knowledge integration among heterogeneity groups.

This study extends CEM and ITC theory to the domain of knowledge integration context and finds the mechanism between group heterogeneity and online knowledge integration by introducing the group interaction process.

Along with the “reform and open door” policy launched in the late 1970s, China has experienced an annual average GDP growth rate of 9.8% between 1978 and 2002 (Hu, 2003, October 19). China's economy system has also gone through a fundamental transition from a central planning system to a socialist free market economy. To cope with the booming economy and radical social changes, the higher education system of China has been undergoing a process of expansion with marketization (World Bank, 1997).

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

The purpose of this paper is to study the dynamic features of friction coefficient during running-in state based on recurrence analysis, so as to recognize the running-in state of crankshaft journal bearings.

The friction coefficient was measured in the friction experiments and the dynamic features are analyzed by recurrence plots (RPs), unthreshold recurrence plots (URPs) and recurrence quantification analysis.

During the running-in process, RPs have gone through disrupted patterns, drift patterns and homogeneous patterns successively. URP shows that the phase trajectory spirals in the disrupted pattern gradually converge in the drift pattern and remain stable in the homogeneous pattern. Three independent measures, recurrence rate, entropy and laminarity, are chosen to characterize friction coefficient from the perspective of point, diagonal line and vertical line structures of the RPs.

The results provide a feasible way to monitor the running-in process and recognize the running-in state.

Hydrostatic thrust bearing is a key component of the vertical CNC machining equipment, and often results in friction failure under the working condition of high speed and heavy load. The lubricating oil film becomes thin or breaks because of high speed and heavy load and it affects the high precision and stable operation of the vertical CNC machining equipment; hence, it is an effective way of avoiding friction failure for achieving the oil film shape prediction

For the hydrostatic thrust bearing with double rectangular cavities, researchers solve the deformation of the friction pairs in hydrostatic bearing by using the computation of hydrodynamics, elasticity theory, finite element method and fluid-thermal-mechanical coupled method. The deformation includes heat deformation and elasticity deformation, the shape of gap oil film is got according to the deformation of the friction pairs in hydrostatic bearing, and gets the shape of gap oil film, and determines the influencing factors and laws of the oil film shape, and achieves the prediction of oil film shape, and ascertains the mechanism of friction failure. An experimental verification is carried out.

Results show that the deformation of the rotational workbench is upturned along its radial direction under the working condition of high speed and heavy load. However, the deformation of the base is downturned along its radial direction and the deformation law of the gap oil film along the radius direction is estimated; the outer diameter is close but the inner diameter is divergent wedge.

The conclusion can provide a theoretical basis for the oil film control of hydrostatic thrust bearing and improve the stability of vertical CNC machining equipment.

This study aims to evaluate the thermal performance of phase change materials (PCMs) microcapsules (MCs) attached using SiO₂ microspheres and investigate the thermal regulation effect on the coated denim fabric.

The PCM microcapsule was prepared by in situ polymerization using a mixture of solid paraffin and butyl stearate as core material (CM) and methyl methacrylate as a monomer. The SiO₂ microparticles were attached to the outer layer of the membrane to enhance the thermal performance of MCs. The morphology, chemical structure, latent heat storage and thermal resistance of MCs were characterized. PCM MCs were coated on the denim fabric and thermo-gravimetric analysis was conducted; thermal insulation and thermal infrared imaging performance of the coated fabrics were also investigated.

The diameters of SiO₂ particles and PCMs MCs were 300-500 nm and 1 μm, respectively. SiO₂ was wrapped on single-wall PCMs MCs with the mass ratio of 1:5. With the addition of SiO₂, the phase transition temperature range of MCs increased from 34°C to 39°C, and the endothermic and exothermic latent heat decreased by 5.35 J/g and 10.07 J/g, respectively. The degradation rate of MCs was significantly slowed down at high temperature. The denim fabric coated with MCs revealed thermal regulation property. After absorbing heat, the MCs slowed down the rate of heat loss and extended the heat release time.

The phase transition temperature of the composite CM was wide, and the latent heat storage was reduced. The addition of SiO₂ particles can significantly slow down the rate of heat loss, but it further reduces the latent heat storage performance.

The method developed provided a simple and practical solution to improve the thermal regulation performance of fabrics.

The method of adjusting the phase transition temperature range of the composite CM is novel and many applications could be found in preparation of PCMs and thermal management.

The purpose of this paper is to investigate the effect of laser peening (LP) on mechanical and wear properties of 304 stainless steel sheet.

Three-dimensional morphology, micro-hardness and micro-structure of shocked samples were tested. The wear amount, wear track morphology and wear mechanism were also characterized under dry sliding wear using Al₂O₃ ceramics ball.

The LP treatment generates deformation twins that contribute to the grain refinement and hardness increase. The wear test displays that the wear mechanism of samples is mainly abrasive wear and oxidation wear at 10 N load. While at 30 N, the delamination and adhesion areas of treated sample are reduced visibly compared to untreated ones.

This study specifically investigates the mechanical and wear properties of 304 stainless steel after the direct action of LP on its surface, which shows an effective improvement on the wear resistance. For example, the wear loss of processed sample is reduced by 19% at 30 N, the friction coefficient decreases from 0.4714 to 0.4308 and the groove depth is reduced from 78.1 to 74.4 µm under same condition.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0007/

This study aims to expand the reliability and special functions of lightweight materials for high-end equipment and green manufacturing, so that it is the first such research to carry out nano-composite technology of nickel-coated carbon nanotubes (Ni-CNTs)-based titanium-zirconium chemical conversion on aluminum alloy substrate.

Corrosion behavior of various coatings was investigated using dropping corrosion test, linear polarization and electrochemical impedance spectroscopy. The results showed that the corrosion resistance of the nano-composite conversion coatings was significantly improved to compare with the conventional titanium-zirconium conversion coating. The morphology and microdomain characteristics of the nano-composite conversion coatings were characterized by SEM/eds/EPMA, which indicated that the CNT or Ni-CNTs addition promoting the integrity coverage of coatings in a short time.

Surface morphology of titanium-zirconium (Ti-Zr)/Ni-CNT specimens exhibited smooth, compact and little pores. The nano-composite conversion coatings are mainly composed of Al, O, C and Ti elements and contain a small amount of F and Zr elements, which illuminated that CNT or Ni-CNT addition could co-deposit with aluminum and titanium metal oxides.

The study of corrosion resistance of nano-composite conversion coatings and the micro-zone film-formation characteristics would be provided theoretical support for the development of basic research on surface treatment of aluminum alloys.

The purpose of this paper is to prepare Ti(C,N) coatings on TA15 treated and not treated by shot peening using double glow plasma alloying technique. The effect of shot peening on the wear behavior of Ti(C,N) coatings is discussed.

The Ti(C,N) coatings were prepared by double glow plasma alloying technique on two different TA15 substrate; one is shot peened and the other is not.

Ti(C,N) coating on SP-treated TA15 was thicker and denser, and the grain size was smaller compared with that on original TA15. Compared with the Ti(C,N) coating on original TA15, the wear resistance of that on SP-treated TA15 is improved. Ti(C,N) coating on SP-treated TA15 showed higher nanohardness and bearing capacity than that on original TA15.

For double glow plasma alloying technique, surface quality, surface activity and other factors will have influence on the thickness and density of the coating. The wear mechanisms of Ti(C,N) coating on original TA15 are serious abrasive wear and oxidation wear. However, the wear mechanism of Ti(C,N) coating on SP-treated TA15 is slightly oxidation wear.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2020-0283/