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The purpose of this paper is to examine the performance of a fastener composite coating system, sherardized (SD) coating/zinc-aluminum (ZA) coating whether it has good performance in marine environment.

In this paper, SD coating was fabricated on fastener surface by solid-diffusion method. ZA coating was fabricated by thermal sintering method. Corrosion behaviours of the composite coating were investigated with potentiodynamic polarization curves, open circuit potential and electrochemical impedance spectroscopy methods.

Neutral salt spray (NSS) and deep sea exposure tests revealed that the composite coating had excellent corrosion resistance. Polarization curve tests showed that corrosion current density of the sample with composite coating was significantly decreased, indicating an effective corrosion protection of the composite coating. OCP measurement of the sample in NaCl solution demonstrated that the composite coating had the best cathodic protection effect. The good corrosion resistance of the composite coating was obtained by the synergy of SD and ZA coating.

SD/ZA coating can be used in marine environment to prolong the life of carbon steel fastener.

SD/ZA composite coating can reduce the risk and accident caused by failed fastener, avoid huge economic losses.

A new kind of composite coating was explored to protect the carbon steel fastener in marine environment. And the composite coating has the long-term anti-corrosion performance both in simulated and marine environment test.

The purpose of this paper is to improve the bioactivity of variable gradient TC4 porous scaffolds prepared by selective laser melting (SLM) through the micro-arc oxidation (MAO) technique.

Variable gradient TC4 porous scaffolds were prepared by SLM, then treated with MAO at different oxidation voltages. The microstructure, thickness and composition of MAO coatings were characterized by scanning electron microscope (SEM), energy-dispersive spectroscopy (EDS) and X-ray diffraction. The bioactivity of the MAO coatings was tested by simulated body fluid (SBF) immersion test.

SEM and EDS results show that with the increase of oxidation voltage, the content of Ca and P elements and the thickness of the MAO coatings increases. The thickness of the coating inside the scaffold is smaller than that of the outside regions. SBF immersion experiments showed that MAO-treated TC4 porous scaffolds had highest bioactivity at 440 V.

The variable gradient porous scaffolds were treated with MAO in the electrolyte containing Ca and P elements for the first time. The effect of oxidation voltages on the different region of porous scaffolds was studied in detail.

This paper aims to propose a method of the safety boundary protection for unmanned aerial vehicles (UAVs) in the icing conditions.

Forty icing conditions were sampled in the continuous maximum icing conditions in the Appendix C of the Federal Aviation Regulation Part 25. Icing numerical simulations were carried out for the 40 samples and the anti-icing thermal load distribution in full evaporation mode were obtained. Based on the obtained anti-icing thermal load distribution, the surrogated model of the anti-icing thermal load distribution was established with proper orthogonal decomposition and Kriging interpolation. The weather research and forecasting (WRF) model was used for meteorological simulations to obtain the icing meteorological conditions in the target area. With the obtained icing conditions and surrogated model, the anti-icing thermal load distribution in the target area and the variation with time can be determined. According to the energy supply of the UAVs, the graded safety boundaries can be obtained.

The surrogated model can predict the effects of five factors, such as temperature, velocity, pressure, median volume diameter (MVD) and liquid water content (LWC), on the anti-icing thermal load quickly and accurately. The simulated results of the WRF mode agree well with the observed results. The method can obtain the graded safety boundaries.

The method has a reference significant for the safety of the UAVs with the limited energy supply in the icing conditions.

In computational chemistry, the chemical bond energy (pKa) is essential, but most pKa-related data are submerged in scientific papers, with only a few data that have been extracted by domain experts manually. The loss of scientific data does not contribute to in-depth and innovative scientific data analysis. To address this problem, this study aims to utilize natural language processing methods to extract pKa-related scientific data in chemical papers.

Based on the previous Bert-CRF model combined with dictionaries and rules to resolve the problem of a large number of unknown words of professional vocabulary, in this paper, the authors proposed an end-to-end Bert-CRF model with inputting constructed domain wordpiece tokens using text mining methods. The authors use standard high-frequency string extraction techniques to construct domain wordpiece tokens for specific domains. And in the subsequent deep learning work, domain features are added to the input.

The experiments show that the end-to-end Bert-CRF model could have a relatively good result and can be easily transferred to other domains because it reduces the requirements for experts by using automatic high-frequency wordpiece tokens extraction techniques to construct the domain wordpiece tokenization rules and then input domain features to the Bert model.

By decomposing lots of unknown words with domain feature-based wordpiece tokens, the authors manage to resolve the problem of a large amount of professional vocabulary and achieve a relatively ideal extraction result compared to the baseline model. The end-to-end model explores low-cost migration for entity and relation extraction in professional fields, reducing the requirements for experts.

The paper aims to expand the scope of application of the lattice Boltzmann method (LBM), especially in the field of aircraft engineering. The traditional LBM is usually applied to incompressible flows at a low Reynolds number, which is not sufficient to satisfy the needs of aircraft engineering. Devoted to tackling the defect, the paper proposes a developed LBM combining the subgrid model and the multiple relaxation time (MRT) approach. A multilayer adaptive Cartesian grid method to improve the computing efficiency of the traditional LBM is also employed.

The subgrid model and the multilayer adaptive Cartesian grid are introduced into MRT-LBM for simulations of incompressible flows at a high Reynolds number. Validated by several typical flow simulations, the numerical methods in this paper can efficiently study the flows under high Reynolds numbers.

Some numerical simulations for the lid-driven flow of cavity, flow around iced GLC305, LB606b and ONERA-M6 are completed. The paper presents the investigation results, indicating that the methods are accurate and effective for the separated flow after icing.

LBM is developed with the addition of the subgrid model and the MRT method. A numerical strategy is proposed using a multilayer adaptive Cartesian grid method and its treatment of boundary conditions. The paper refers to innovative algorithm developments and applications to the aircraft engineering, especially for iced wing simulations with flow separations.

The implementation of interaction design concepts to facilitate communication between users and shopping websites has gained increasing attention in recent years. Mouse hover is a vital interaction method for users to access shopping sites and significantly impacts their decision-making experience. A well-designed mouse hover function can effectively enhance the user's search performance and improve the user experience. The purpose of this study is to investigate whether the dynamic prompt designs at the hover position and the degree of feedback transparency may affect the user's task performance and personal feelings when operating the hover function on the shopping website.

The study employed two independent variables in the experiments: dynamic prompt and background transparency of hover feedback. A between-subject design of 2 (single flicker and continuous flicker) × 3 (transparency at 0%, 25%, and 50%) was adopted in the experiment. A total of 60 participants were invited to participate in the experiment using the purposive sampling method. Participants were asked to complete four operation tasks, and the time of each was recorded. They were then asked to complete the system usability scale (SUS) questionnaire and conduct subjective evaluations before they were briefly interviewed.

The generated results revealed that: (1) In the interaction with a shopping website, the degree of background transparency of hover feedback affected the participants' task performance. (2) The effect of a single flicker had significantly higher subjective evaluation results regarding the degree of rationality compared with a continuous flicker. (3) The participants' perceptions of the information clarity were affected when the background transparency of hover feedback was 50%. (4) The effect of a continuous flicker was better than that of a single flicker when the background transparency of hover feedback was 25%. The participants' attraction to a continuous flicker was significantly higher than that of a single flicker. Nonetheless, when the degree of background transparency was at 0% or 50%, the results were the opposite.

The findings generated from the research can be a reference for the development of hover operation in the user interface design for shopping websites.

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.

For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.

The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).

From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.

The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

The purpose of this study was to fabricate silicone products that had different hardnesses and moduli, thus partially addressing the limitations of homogeneous materials whose deformation depends on altered structure or dimensions, and to provide new dimensions for the design of silicone soft structures.

A soft material three-dimensional printing platform with a dual-channel printing capability was designed and built. Using the material extrusion method, material screening was first performed using single-channel printing, followed by dual-channel-regulated printing experiments on products having different hardness and modulus values.

The proportion of additives has an effect on the accuracy of the printed product. Material screening revealed that Sylgard 527 and SE 1700 could be printed without additives. The hardness and mechanical properties of products are related to the percentage in their composition of hard and soft materials. The hardness of the products could be adjusted from 26A to 42A and the Young’s modulus from 0.875 to 2.378 Mpa.

Existing silicone products molded by casting or printing are mostly composed of a single material, whose uniform hardness and modulus cannot meet the demand for differentiated deformation in the structure. The existing multihardness silicone material printing method has the problems of long material mixing time and slow hardness switching and complicated multi-extrusion head switching. In this study, a simple, low-cost and responsive material extrusion-based hardness programmable preparation method for silicone materials is proposed.

In order to encourage the high-water-consumption (HWC) manufacturers to carry out water-saving transformation relying on self-strength or outsourcing to a water-saving service company (WSSC) during production processes, government subsidies are provided according to water-saving efforts (WSE) or investment cost. In this context, the authors derive the participant's equilibrium decisions and the manufacturer's water-saving strategy. Additionally, the effects of subsidies on WSE and stakeholders' profits are discussed.

Mathematical models including optimization model and Stackelberg game model are constructed under different subsidy schemes.

The study finds that (1) there exists a threshold related to the subsidy coefficient for the HWC manufacturer when choosing between self-saving and outsourcing-saving. (2) When the technological competitive advantage between WSSC and manufacturer is within a certain range, government's subsidy promotes HWC enterprises to choose outsourcing-saving. (3) Given a water-saving mode, subsidy on investment cost is more effective for the government to achieve more environmental performance.

First, subsidy endogeneity can be considered to explore the optimal interval for government subsidies to maximize social welfare. Second, in outsourcing-saving, other types of contract can be discussed. Another extension is about model uncertainties. Finally, other policies on improving water efficiency can be also examined.

The paper includes implication for HWC manufacturers to select the best water-saving mode under subsidy, and it allows policymakers to understand the efficiency of proposed subsidies.

Decisions on water-saving efforts, selection of water-saving modes and operational planning are also regarded as business strategies in the paper. Particularly, the influences of different government subsidies are also considered and compared.

This paper aims to propose two portfolio selection models with hesitant value-at-risk (HVaR) – HVaR fuzzy portfolio selection model (HVaR-FPSM) and HVaR-score fuzzy portfolio selection model (HVaR-S-FPSM) – to help investors solve the problem that how bad a portfolio can be under probabilistic hesitant fuzzy environment.

It is strictly proved that the higher the probability threshold, the higher the HVaR in HVaR-S-FPSM. Numerical examples and a case study are used to illustrate the steps of building the proposed models and the importance of the HVaR and score constraint. In case study, the authors conduct a sensitivity analysis and compare the proposed models with decision-making models and hesitant fuzzy portfolio models.

The score constraint can make sure that the portfolio selected is profitable, but will not cause the HVaR to decrease dramatically. The investment proportions of stocks are mainly affected by their HVaRs, which is consistent with the fact that the stock having good performance is usually desirable in portfolio selection. The HVaR-S-FPSM can find portfolios with higher HVaR than each single stock and has little sacrifice of extreme returns.

This paper fulfills a need to construct portfolio selection models with HVaR under probabilistic hesitant fuzzy environment. As a downside risk, the HVaR is more consistent with investors’ intuitions about risks. Moreover, the score constraint makes sure that undesirable portfolios will not be selected.