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The purpose of this study is to reveal the effect of nano-Al₂O₃ particle addition on the nucleation/growth kinetics, microhardness, wear resistance and corrosion resistance of Co–P–xAl₂O₃ nanocomposite plating.

The kinetics and properties of Co–P–xAl₂O₃ nanocomposite plating prepared by electroplating were investigated by electrochemical measurements, scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, Vickers microhardness measurement, SRV5 friction and wear tester and atomic force microscopy.

A 12 g/L nano-Al₂O₃ addition in the plating solution can transform the nucleation/growth kinetics of the plating from the 3D progressive model to the 3D instantaneous model. The microhardness of the plating increased with the increase of nano-Al₂O₃ content in plating. The wear resistance of the plating did not adhere strictly to Archard’s law. An even and denser corrosion product film was generated due to the finer grains, with a high corrosion resistance.

The effect of different nano-Al₂O₃ addition on the nucleation/growth kinetics and properties of Co–P–xAl₂O₃ nanocomposite plating was investigated, and an anticorrosion mechanism of Co–P–xAl₂O₃ nanocomposite plating was proposed.

The purpose of this study is to propose a research model based on the stimulus–organism–response (S–O–R) model to examine whether network externality, personalization and sociability as environmental feature antecedents to learners’ learning engagement (LE) can influence their learning persistence (LP) in massive open online courses (MOOCs).

Sample data for this study were collected from learners who had experience in taking MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 371 usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that learners’ perceived network externality, personalization and sociability in MOOCs positively affected their cognitive LE, psychological LE and social LE elicited by MOOCs, which jointly led to their LP in MOOCs. The results support all proposed hypotheses, and the research model accounts for 76.2% of the variance in learners’ LP in MOOCs.

This study uses the S–O–R model as a theoretical base to construct learners’ LP in MOOCs as a series of the inner process, which is affected by network externality, personalization and sociability. It is worth noting that three psychological constructs including cognitive LE, psychological LE and social LE are used to represent learners’ organismic states of MOOCs usage. To date, hedonic/utilitarian concepts are more often adopted as organisms in previous studies using the S–O–R model, and psychological constructs have received lesser attention. Hence, this study’ contribution on the application of capturing psychological constructs for completely expounding three types of environmental features as antecedents to learners’ LP in MOOCs is well documented.

This study investigates how firms’ awareness of sustainability affects the revision of their internationalization strategy. Adopting a resource-based view (RBV) approach, the authors argue that sustainable-oriented firms have a higher propensity to de-internationalize (i.e., to go back to their home country) when confronted with the need to relocate foreign manufacturing subsidiaries, as the shortening of value chains would allow the reduction of transportation emissions and enhanced corporate image as green-oriented entities. Furthermore, the authors explore the role exerted by a stringent regulatory setting in the home country on the likelihood of de-internationalization. The empirical test conducted on a sample of relocations performed across European nations in 2002–2014 reveals that multinational enterprises (MNEs) – regardless of their sustainability orientation – have a higher probability to de-internationalize when their home countries have strict institutional contexts in place.

This study aims to investigate the corrosion inhibitor effect of migrating corrosion inhibitor (MCI) on Q235 steel in high alkaline environment under cathodic polarization.

This study investigated the electrochemical characteristics of Q235 steel with and without MCI by polarization curve and electrochemical impedance spectroscopy. Besides, the surface composition of Q235 steel under different environments was analyzed by X-ray photoelectron spectroscopy. In addition, the migration characteristic of MCI and the adsorption behavior of MCI under cathodic polarization were studied using Raman spectroscopy.

Diethanolamine (DEA) and N, N-dimethylethanolamine (DMEA) can inhibit the increase of Fe(II) in the oxide film of Q235 steel under cathodic polarization. The adsorption stability of DMEA film was higher under cathodic polarization potential, showing a higher corrosion inhibition ability. The corrosion inhibition mechanism of DEA and DMEA under cathodic polarization potential was proposed.

The MCI has a broad application prospect in the repair of damaged reinforced concrete due to its unique migratory characteristics. The interaction between MCIs, rebar and concrete with different compositions has been studied, but the passivation behavior of the steel interface in the presence of both the migrating electric field and corrosion inhibitors has been neglected. And it was investigated in this paper.

This study aims to investigate the individual electrochemical transients arising from local anodic events on stainless steel, to uncover the potential mechanisms producing different types of transients and to derive appropriate parameters indicative of the corrosion severity of such transient events.

An equivalent circuit model was used for the transient analysis, which was performed using a local current allocation rule based on the relative instant cathodic resistance of the coupled electrodes, as well as the kinetic parameters derived from the electrochemical polarization measurement.

The shape and size of the electrochemical current transients arising from SS 316 L were influenced by the film stability, local anodic dissolution kinetics and the symmetry of the cathodic kinetics between the coupled electrodes, where the ultralong transient might correspond to the propagation of film damage with a slow anodic dissolution rate. The dynamic cathodic resistance during the final stage of transient current growth can serve as a characteristic parameter that reflects the loss of passive film protection.

Estimation of the local anodic current trace opens a new way for individual electrochemical transient analysis associated with the charges involved, local current densities and changes in film resistance throughout localized corrosion processes.

This study aims to investigate the influence mechanism of brazing and aging on the strengthening and corrosion behavior of novel multilayer sheets (AA4045/AA7072/AA3003M/AA4045).

Polarization curve tests, immersion experiments and transmission electron microscopy analysis were used to study the corrosion behavior and tensile properties of the sheets before and after brazing and aging.

The strength of the sheet is weakened after brazing due to brittle eutectic phases, and recovered after aging due to enhanced precipitation strengthening in the AA7072 interlayer. The core of nonbrazed sheets cannot be protected due to the significant galvanic coupling effect between the intermetallic particles and the substrate. Brazing and aging treatments promote the redissolved of second phased and limit corrosion along the eutectic region in the clad, allowing the core to be protected.

AA7xxx alloy was added to conventional brazed sheets to form a novel Al alloy composite sheet with AA4xxx/AA7xxx/AA3xxx structure. The strengthening and corrosion mechanism of the sheet was proposed. The added interlayer can sacrificially protect the core from corrosion and improves strength after aging treatment.

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

Auxetics metamaterials show high performance in their specific characteristics, while the absolute stiffness and strength are much weaker due to substantial porosity. This paper aims to propose a novel auxetic honeycomb structure manufactured using selective laser melting and study the enhanced mechanical performance when subjected to in-plane compression loading.

A novel composite structure was designed and fabricated on the basis of an arrowhead auxetic honeycomb and filled with polyurethane foam. The deformation mechanism and mechanical responses of the structure with different structural parameters were investigated experimentally and numerically. With the verified simulation models, the effects of parameters on compression strength and energy absorption characteristics were further discussed through parametric analysis.

A good agreement was achieved between the experimental and simulation results, showing an evidently enhanced compression strength and energy absorption capacity. The interaction between the auxetic honeycomb and foam reveals to exploit a reinforcement effect on the compression performance. The parametric analysis indicates that the composite with smaller included angel and higher foam density exhibits higher plateau stress and better specific energy absorption, while increasing strut thickness is undesirable for high energy absorption efficiency.

The results of this study served to demonstrate an enhanced mechanical performance for the foam filled auxetic honeycomb, which is expected to be exploited with applications in aerospace, automobile, civil engineering and protective devices. The findings of this study can provide numerical and experimental references for the design of structural parameters.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

The purpose of this study is to determine if blockchain-supported carbon offset information provision and shipping options with different cost and environmental footprint implications impact consumer perceptions toward retailers and logistics service providers. Blockchain and carbon neutrality, each can be expensive to adopt and complex to manage, thus getting the “truth” on decarbonization may require additional costs for consumers.

Experimental modeling is used to address these critical and emergent issues that influence practices across a set of supply chain actors. Three hypotheses relating to the relationship between blockchain-supported carbon offset information and consumer perceptions and intentions associated with the product and supply chain actors are investigated.

The results show that consumer confidence increases when supply chain carbon offset information has greater reliability, transparency and traceability as supported by blockchain technology. The authors also find that consumers who are provided visibility into various shipping options and the product's journey carbon emissions and offset – from a blockchain-supported system – they are more willing to pay a premium for both the product and shipping options. Blockchain-supported decarbonization information disclosure in the supply chain can lead to organizational legitimacy and financial gains in return.

Understanding consumer action and sustainable consumption is critical for organizations seeking carbon neutrality. Currently, the literature on this understanding from a consumer information provision is not well understood, especially with respect to blockchain-supported information transparency, visibility and reliability. Much of the blockchain literature focuses on the upstream. This study focuses more on consumer-level and downstream supply chain blockchain implications for organizations. The study provides a practical roadmap for considering levels of blockchain information activity and consumer interaction.