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The purpose of this paper is to analyze the mechanism of nanofluid enhanced heat transfer in microchannels and promote the application of nanofluids in industrial processes such as solar collectors, electronic cooling and automotive batteries.

The two-phase lattice Boltzmann method was used to calculate the flow and heat transfer characteristics of Al₂O₃ nanofluids in a microchannel at Re = 50. By comparing the simulation results of pure water, nanofluids without calculated nanoparticle-fluid interaction forces and nanofluids with calculated nanoparticle-fluid interaction forces, the effects of physical properties improvement and interaction forces on flow and heat transfer are quantified.

The findings show that the nanofluid (φ = 3%, R = 10 nm) increases the average Nusselt number by 22.40% at Re = 50. In particular, 16.16% of the improvement relates to nanoparticles optimizing the thermophysical parameters of the base fluid. The remaining 6.24% relates to the disturbance of the thermal boundary layer caused by the interaction between nanoparticles and the base fluid. Moreover, the nanoparticle has a negligible effect on the average Fanning friction factor. Ultimately, we conclude that the nanofluid is an excellent heat transfer working medium based on its performance evaluation criterion, PEC = 1.225.

To the best of the authors' knowledge, this research quantifies for the first time the contribution of nanoparticle-liquid interactions and nanofluids physical properties to enhanced heat transfer, advancing the knowledge of the nanoparticle's behavior in liquid systems.

With the development of e-commerce network platforms, platform enterprises have mostly completed the first stage of user accumulation during the start-up period. How to enhance users’ stickiness and stimulate their continual participation in platform business activities through innovation and platform design has become a decisive factor for platform enterprises. To increase the motivation of e-commerce platform users, this paper explores the positive impact of gamified rewards on platform user stickiness by dividing the gamified rewards design into social and functional rewards, and studies the mediating role of self-identification and the moderating role of perceived goal progress and information disclosure.

This study applies the “S-O-R” (stimulus–organism–response) model as the theoretical basis for constructing a model of user stickiness for e-commerce platforms and subdivides gamified reward design into social rewards and functional rewards to explore how they affect platform user stickiness and the boundaries of the influencing mechanism.

It turns out both types of gamified rewards promote users’ perception of self-identification, which in turn affects the intention to continue using the platform. In addition, platforms with designs about users’ quantified self-behavior – perceived goal progress in the gaming experience can effectively enhance the effectiveness of users’ gamification rewards. Information disclosure moderates the relationship between the two types of gamification design and self-identification. For functional reward designs and social reward designs, information disclosure can improve users’ self-identification and therefore enhance users’ stickiness.

This study verifies the impact of gamification design on platform user stickiness, confirming the mediating role of self-identification and the moderating role of perceived goal progress and information disclosure, which has theoretical and practical implications for how platform enterprise can maintain user activity in the digital context.

This paper aims to propose a novel system design and control algorithm of lower limb exoskeleton, which provides walking assistance and load sharing for the wearer.

In this paper, the valve-controlled asymmetrical hydraulic cylinder is selected for driving the hip and knee joint of exoskeleton. Pressure shoe is developed that purpose on detecting changes in plantar force, and a fuzzy recognition algorithm using plantar pressure is proposed. Dynamic model of the exoskeleton is established, and the sliding mode control is developed to implement the position tracking of exoskeleton. A series of prototype experiments including benchtop test, full assistance, partial assistance and loaded walking experiments are set up to verify the tracking performance and power-assisted effect of the proposed exoskeleton.

The control performance of PID control and sliding mode control are compared. The experimental data shows the tracking trajectories and tracking errors of sliding mode control and demonstrate its good robustness to nonlinearities. sEMG of the gastrocnemius muscle tends to be significantly weakened during assisted walking.

In this paper, a structure that the knee joint and hip joint driven by the valve-controlled asymmetrical cylinder is used to provide walking assistance for the wearer. The sliding mode control is proposed to deal with the nonlinearities during joint rotation and fluids. It shows great robustness and frequency adaptability through experiments under different motion frequencies and assistance modes. The design and control method of exoskeleton is a good attempt, which takes positive impacts on the productivity or quality of the life of wearers.

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.

Online retailers are pioneering the incentivization of customers to generate more product reviews by rewarding them. However, little is known about the impact of reward types on customers' review behavior, including review frequency and sentiment. To address this gap, we investigated the effects of different reward types on customers' review behavior and how these rewards influence customers' review behavior.

We collected secondary data and empirically tested the hypothesis by analyzing the change in reward policy. Regression and two-stage Heckman models were applied to investigate the effects, with the latter used to control potential selection issues.

The results revealed that monetary rewards can stimulate customers to generate more positive product reviews. Furthermore, the reward amount has a negative moderating effect on the aforementioned relationship. Additionally, customer tenure negatively moderates the relationship between monetary rewards and review behavior.

This study contributes to the understanding of user-generated content motivation and provides managerial implications for reward programs.

The piston ring-cylinder liner pair is one of the most important tribological systems of an internal combustion engine. The friction loss of the piston ring-cylinder liner pair accounts for the largest portion of total efficiency losses. Therefore, improving the tribological system design of the piston ring-cylinder liner pair can reduce friction losses and bring tremendous economic benefits to society. This paper aims use surface texturing, which is proving to be an effective method, for improving the tribological performance of sliding surfaces.

In this paper, an experimental study using a pin-on-disk tribometer was carried out to evaluate the effects of surface texturing on friction reduction of piston rings under various loads and sliding velocities. Rectangular- and circular-shaped textures with different depths and area densities were produced by a Femtosecond laser. Comparison experiments were conducted with un-textured rings.

The results indicate that the friction performance of the ring surface was significantly improved by surface texturing, and the running-in stage was also shortened. More specifically, it was found that the rectangular-shaped texture had a better effect on friction reduction than the circular-shaped texture. Results also indicate that an optimum texture density existed for the rectangular-shaped texture. Additionally, it was observed that the average friction coefficient reduction of the textured ring decreased with increasing load and increased with increasing sliding velocity.

Consequently, these findings provide a more in-depth understanding of the relationship between micro-textures and tribological properties of piston rings in lubricating sliding.

This paper aims to present a one-shot gesture recognition approach which can be a high-efficient communication channel in human–robot collaboration systems.

This paper applies dynamic time warping (DTW) to align two gesture sequences in temporal domain with a novel frame-wise distance measure which matches local features in spatial domain. Furthermore, a novel and robust bidirectional attention region extraction method is proposed to retain information in both movement and hold phase of a gesture.

The proposed approach is capable of providing efficient one-shot gesture recognition without elaborately designed features. The experiments on a social robot (JiaJia) demonstrate that the proposed approach can be used in a human–robot collaboration system flexibly.

According to previous literature, there are no similar solutions that can achieve an efficient gesture recognition with simple local feature descriptor and combine the advantages of local features with DTW.

This paper aims to investigate the impact of relationship quality among team members in the project team on knowledge transfer effectiveness and analyze the role of organizational structure in the influencing process.

The hypotheses are verified by the Structural Equation Modeling (SEM) analysis using Smart PLS 3 software with the data collected from 236 questionnaire samples in Chinese construction industry.

The results indicate that relationship quality has a direct impact on knowledge transfer in project teams and centralization has a negative impact on relationship quality. Moreover, relationship quality plays a mediating role between centralization and knowledge transfer effectiveness and formalization plays a negative moderating role in the effect of relationship quality on knowledge transfer effectiveness.

This paper studies intra-project knowledge transfer from the perspective of relationship quality of project teams and explores the antecedent and moderating role of organizational structure in the influence of relationship quality on knowledge transfer.

The purpose of this paper is to establish a new dynamic coupled discrete-element contact model used for investigating fresh concrete with different grades and different motion states, and demonstrate its correctness and reliability according to the rheological property results of flow fresh concrete in different working states through simulating the slump process and mixing process.

To accurately express the motion and force of flowing fresh concrete in different working states from numerical analysis, a dynamic coupled discrete-element contact model is proposed for fresh concrete of varying strength. The fluid-like fresh concrete is modelled as a two-phase fluid consisting of mortar and aggregate. Depending on the contact forms of the aggregate and mortar, the model is of one of the five types, namely, Hertz–Mindlin, pendular LB contact, funicular mucous contact, capillary LB contact or slurry lift/drag contact.

To verify the accuracy of this contact model, concrete slump and cross-vane rheometer tests are simulated using the traditional LB model and dynamic coupled contact model, for five concrete strengths. Finally, by comparing the simulation results from the two different contact models with experimental data, it is found that those from the proposed contact model are closer to the experimental data.

This contact model could be used to address issues such as (a) the mixing, transportation and pumping of fresh concrete, (b) deeper research and discussion on the influence of fresh concrete on the dynamic performance of agitated-transport vehicles, (c) the behaviour of fresh concrete in mixing tanks and (d) the abrasion of concrete pumping pipes.

To accurately express the motion and force of flowing fresh concrete in different working states from numerical analysis, a dynamic coupled discrete-element contact model is proposed for fresh concrete of varying strength.

Consumers and businesses are becoming increasingly conscious of sustainable business practices and are often willing to pay a premium for responsibly sourced and manufactured products. Many countries and organizations have implemented regulations and standards for sustainability and companies face penalties or are barred from exporting for not meeting the requirements. Rooted in the resource-based view theory, this study aims to test a moderated mediation model to improve the sustainability performance of exporting firms.

Textile firms generating more than 25% of export revenues were targeted for this research. The data collected from 245 middle management-level employees were tested for reliability and validity. The structural equation modelling in AMOS 26 was used to test hypotheses.

Organizational readiness for green innovation (ORGI) has a direct positive effect on sustainability performance. The mediation analysis implies that ORGI translates into sustainability performance through improvement in green innovation performance. The moderating effect of knowledge integration highlights the importance of being prepared internally and actively seeking and incorporating external knowledge to improve green innovation performance.

The findings offer a solid foundation for informed decision-making, policy development and strategies to improve sustainability performance while aligning with the global nature of the textile industry and its inherent challenges. The proposed model and practical implications guide policymakers and managers of exporting firms to foster a culture of green innovation to leverage the effect of their readiness for green innovation on sustainability performance.