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This study aims to examine the effects on student motivation and perception of technological interventions within undergraduate mechanical engineering and product design and manufacture programs at a Sino-foreign international university. The authors use an augmented reality game application within a class on Design for Manufacturing and Assembly (DfMA) that was developed using the approaches of microlearning and digital game-based learning (DGBL).

Structured as design-based research, the study reports on developing innovative educational interventions and provides an empirical investigation of their effectiveness. Data were collected using a mixed methods approach, using pre- and post-tests and questionnaires, together with researcher observations and participant interviews.

Through two rounds of playtests, the game positively affected intrinsic motivation and encouraged higher-order cognitive learning, critical thinking, communication and collaboration. Collaborative learning plays a significant role, DGBL is preferred over traditional methods and microlearning reduces information density and cognitive overload.

The study contributes to our understanding of digital game-based interventions on students’ intrinsic motivation and provides insights into effective ways to design instructional materials in similar teaching and learning settings.

The purpose of this paper is to discuss the characteristics of several stochastic simulation methods applied in computation issue of structure health monitoring (SHM).

On the basis of the previous studies, this research focusses on four promising methods: transitional Markov chain Monte Carlo (TMCMC), slice sampling, slice-Metropolis-Hasting (M-H), and TMCMC-slice algorithm. The slice-M-H is the improved slice sampling algorithm, and the TMCMC-slice is the improved TMCMC algorithm. The performances of the parameters samples generated by these four algorithms are evaluated using two examples: one is the numerical example of a cantilever plate; another is the plate experiment simulating one part of the mechanical structure.

Both the numerical example and experiment show that, identification accuracy of slice-M-H is higher than that of slice sampling; and the identification accuracy of TMCMC-slice is higher than that of TMCMC. In general, the identification accuracy of the methods based on slice (slice sampling and slice-M-H) is higher than that of the methods based on TMCMC (TMCMC and TMCMC-slice).

The stochastic simulation methods evaluated in this paper are mainly two categories of representative methods: one introduces the intermediate probability density functions, and another one is the auxiliary variable approach. This paper provides important references about the stochastic simulation methods to solve the ill-conditioned computation issue, which is commonly encountered in SHM.

Despite the growing popularity of internet based social media on e-commerce platforms, systematic examination of the emerging phenomenon is scarce. This paper aims to study whether online retailers ' social activity on e-commerce platforms improves their business performance, and if it does, what are the underlying mechanisms.

The paper proposes a typology of online retailers ' social activities on e-commerce platforms. Then drawing on social capital theory and social network theory, the authors develop hypotheses that relate online retailers ' social activities to their business performance. The hypotheses are tested using a large dataset collected from an e-commerce platform in China.

The paper shows that: online retailers ' social activities for friend-making improve their business performance, regardless of the directional attribute of the activities; social activities for advice-seeking decrease online retailers ' business performance; and social activities for advice-giving increase online retailers ' business performance.

The data in the empirical study are from an e-commerce platform in China, hence the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed hypotheses further.

The paper includes implications for e-commerce market makers and online retailers operating on e-commerce platforms. The authors show that online retailers ' social activities on e-commerce platforms can be an important source of business value. However certain types of social activities may hurt online retailers ' business performance, implying the necessity of a thoughtful social activity strategy in online marketplaces.

This paper represents an early effort to study whether online retailers ' social activities on e-commerce platforms improve their business performance and the underlying mechanisms of the effect.

In order to discover a new adsorbent that can be used to purify dye wastewater in the textile and apparel industry, a novel type of graphene oxide/gluten composite material using an improved acid bath coagulation method was synthesized, which can remove methylene blue in an aqueous environment.

After experimentally compounding different ratios of graphene oxide and gluten, the graphene oxide/gluten composite material with 20% graphene oxide content and superlative adsorption effect was chosen. The synthesized material was characterized by different techniques such as FT-IR and SEM, indicating the microstructure of the material and the success of the composite. Various factors were considered, namely, the influence of temperature, dosage, pH and contact time. The isotherms, kinetics and thermodynamic parameters were successively discussed.

The qmax value of 214.29 mg/g of the material was higher compared to the general sorbent, thus, the graphene oxide/gluten composite material was a suitable sorbent for methylene blue removal. Overall, the graphene oxide/gluten composite material can be considered as an effectual and prospective adsorbent to remove methylene blue in the textile and apparel industrial effluent.

Graphene oxide is a potentially excellent sorbent. However, the high dispersibility of GO is detrimental to adsorption, it disperses rapidly in an aqueous solution making separation and recovery difficult. The high load capacity and recyclability of gluten as a colloid make it a suitable carrier for fixing GO. Studies on the combination of GO and GT into composite adsorption material and for the removal of dyes from dyeing wastewater have not been reported. The composite material research on GO and GT can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.

This study aims to investigate the effects of graphite-MoS₂ composite solid lubricant on the tribological properties of copper-based bearing materials under dry conditions.

The mixture of Graphite-MoS₂ was inlaid in ZQSn6-6–3 tin bronze and ZQAl9-4 aluminum bronze matrix. These copper-embedded self-lubricating bearing materials were considered in friction pairs with 2Cr13 stainless steel, and their tribological properties were studied by using an MM200 wear test machine.

The results show that the friction coefficients and wear rates of copper-embedded self-lubricating bearing materials are lower than those of the ordinary copper-based bearing materials. The wear performance of the tin bronze inlaid self-lubricating bearing material is better than that of the aluminum bronze inlaid self-lubricating bearing material. The wear mechanism of the tin bronze bearing material is mainly adhesive wear, and that of the aluminum bronze bearing material is mainly grinding wear, oxidation wear and adhesive wear. The copper-embedded self-lubricating bearing materials had no obvious abrasion, whereas the aluminum bronze inlaid self-lubricating bearing material exhibited deep furrows and obvious abrasion under high loads.

These results are helpful for the application of copper-embedded self-lubricating bearing materials.

The purpose of this paper is to summarize the current applications of BIM, the integration of related technologies and the tendencies and challenges systematically.

Using quantitative and qualitative bibliometric statistical methods, the current mode of interaction between BIM and other related technologies is summarized.

This paper identified 24 different BIM applications in the life cycle. From two perspectives, the implementation status of BIM applications and integrated technologies are respectively studied. The future industry development framework is drawn comprehensively. We summarized the challenges of BIM applications from the perspectives of management, technology and promotion, and confirmed that most of the challenges come from the two driving factors of promotion and management.

The technical challenges reviewed in this paper are from the collected literature we have extracted, which is only a part of the practical challenges and not comprehensive enough.

We summarized the current mode of interactive use of BIM and sorted out the challenges faced by BIM applications to provide reference for the risks and challenges faced by the future industry.

There is little literature to integrate BIM applications and to establish BIM related challenges and risk frameworks. In this paper, we provide a review of the current implementation level of BIM and the risks and challenges of stakeholders through three aspects of management, technology and promotion.

The purpose of this paper is to study the dynamic characteristics and seismic performance of the composite open-web grid floor structure.

Studied by using mode-superposition response spectrum method and time history analysis method.

The results show that the vertical mode-superposition response spectrum method is close to the time history analysis method. The floor has strong seismic performance, and the deflection and internal force are not large under vertical seism. The vertical seismic action suggested that 10% of the representative value of gravity load should be used to ensure the safety of the structure.

In the design, the mid-span section should be properly strengthened or the variable section design should be adopted.

The purpose of this study is to provide effective and environmental-friendly corrosion inhibitors derived from graphene oxide for Q235 steel.

Nontoxic and environment-friendly 4-aminobenzoic acid was used to functionalize graphene oxide via amidation and diazotization. The obtained amidation 4-aminobenzoic acid functionalized graphene oxide (PAGO) and diazotization 4-aminobenzoic acid functionalized graphene oxide (PDGO) were characterized by FTIR, Raman and TEM, while the inhibition efficiencies were analyzed by electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP). Furthermore, theoretical inhibition efficiencies were investigated by density functional theory (DFT) approach.

At a concentration of 40 ppm, the maximum inhibition efficiency of PAGO and PDGO were 97.90% and 96.72% in EIS measurement, respectively, which were in accordance with PDP data. Moreover, experimental results were supported by DFT-based quantum chemical calculation.

Environmental-friendly PAGO and PDGO were synthesized successfully. The synthetic inhibitors exhibited excellent inhibition efficiencies in EIS and PDP measurements. Furthermore, a computational study using DFT supported the trend that PAGO was better inhibitor than PDGO.

The purpose of this paper is to purify the wastewater in the garment industry.

The preparation of the calcium alginate (CA)/activated carbon (AC) composite membrane was achieved by vacuum freeze-drying and the cross-linking reaction between sodium alginate and CaCl₂. Effective parameters in the methylene blue (MB) adsorption such as temperature, dose, contact time and pH were discussed. The adsorption properties of the composite membrane were investigated by isotherm, kinetics and thermodynamic analysis. The adsorption equilibrium data were described by the adsorption isotherm Langmuir model and the Freundlich model. The pseudo-first-order, pseudo-second-order and intra-particle diffusion equations were selected to evaluate the kinetics. The thermodynamic study described that the adsorption reaction was spontaneous and exothermic.

The AC/CA membrane is an efficient and powerful adsorbent to remove MB in printing and dyeing wastewater, and provides a new idea for the selection of adsorption materials for industrial printing and dyeing wastewater.

The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials.

The paper contributes to its wider and convenientapplication in wastewater treatment.

Studies on the combination of CA and AC into adsorption membranes and for the removal of dyes from printing and dyeing wastewater have not been reported. A novel composite material is provided for treatment dyeing wastewater in garment production. The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.

This study aims to tackle the critical issue of detecting stock market manipulation, which undermines the integrity and stability of financial markets globally. Even enhanced with machine learning, traditional statistical methods often struggle to analyze high-frequency trading data effectively due to inherent noise and the limited availability of publicly known manipulation cases. This leads to poor model generalization and a tendency toward over-fitting. Focusing on China's securities market, our study introduces an innovative approach that employs deep learning-based high-frequency jump tests to overcome these challenges and to develop a more effective method for identifying manipulative activities.

We employed the “Jump Variation – Time-of-Day” (JV-TOD) non-parametric technique for jump tests on high-frequency data, coupled with the synthetic minority over-sampling technique (SMOTE) algorithm for re-balancing sample data. Our approach trains a deep neural network (DNN) on refined data to enhance its ability to identify manipulation patterns accurately.

Our results show that the deep neural network model, calibrated with high-frequency price jump data, identifies manipulation behavior more specifically and accurately than traditional models. The model achieved an accuracy rate of 94.64%, an F1-score of 95.26% and a recall rate of 95.88%, significantly outperforming traditional models. These results demonstrate the effectiveness of our approach in mitigating over-fitting and improving the robustness of market manipulation detection.

The proposed model provides regulatory entities and financial institutions with a more efficient tool to monitor and counteract market manipulation, thereby improving market fairness and investor protection.

By integrating the JV-TOD jump test with deep learning, this study proposed a new approach to market manipulation detection. The innovation is in its capacity to detect subtle manipulation signals that traditional methods typically overlook. Our model, which is trained on jump test data enhanced by the SMOTE algorithm, excels at learning complex manipulation patterns. This enhances both detection accuracy and robustness. In contrast to existing methods that are challenged by the noisy and intricate nature of high-frequency data, our approach shows enhanced performance in identifying nuanced market manipulations, offering a more effective and reliable method for detecting market manipulation.