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This study aims to identify effective ways to promote plant-based foods in quick-service restaurants by considering customers’ food-related health involvement.

This study conducted a 2 (message format: myth/fact or fact-only) × 2 (message focus: benefit- or attribute-focused) × 2 (health involvement: high or low) quasi-experimental design via a scenario-based online survey. A multivariate analysis of covariance and a bootstrapping approach were used to test the hypotheses (N = 365).

The results indicated that message format and focus jointly influenced customers’ perceived health consequences of plant-based foods and purchase intentions; customers’ health involvement altered the two-way interaction between message format and focus; and perceived health consequences mediated the effects of message format and focus as and customers’ health involvement on purchase intentions.

This study identifies the effectiveness of message format and focus in promoting plant-based foods and extends the sustainable product promotion literature by using resource matching theory and the elaboration likelihood model. Future studies should use field studies to examine how can message framing influence customers’ actual behaviors when purchasing plant-based foods.

This study can help quick-service restaurants better promote plant-based foods considering message format and focus and customers’ food-related health involvement.

This is one of only a few studies that have tested how messages containing both negative and positive information about a product could help promote plant-based foods.

This study aims to investigate consumers’ intention to use onsite restaurant interactive self-service technology (ORISST) using a modified value attitude-behavior model. To extend the understanding of how consumers’ dining value focus could influence their intention to use ORISST, this study examines the conditional indirect effects of restaurant type (quick-service vs fine-dining) within the proposed model.

An online survey was developed and distributed to randomly selected respondents in the USA. A total of 588 (quick-service: 295; fine-dining: 293) responses were used for the data analysis. Structural equation modeling with a robust maximum likelihood method was used to examine the proposed model. To investigate the moderated effects of restaurant type, a latent moderated mediation model was used.

The results showed that consumers’ value perceptions toward technology use in restaurants influenced their intention to use ORISST via both hedonic and utilitarian expectations. Latent moderated mediation analyzes revealed that the mediation effect of hedonic expectation between perceived value and the intention was stronger in fine-dining than in quick-service restaurants.

This study extends the understanding of consumer intentions to use interactive self-service technology in restaurants by building on a model that is customer-oriented instead of tech-specific. Furthermore, the conditional effects of restaurant type are investigated using the latent moderated structural equation method. The findings of this study provide guidelines for managers of quick-service and fine-dining restaurants to better incorporate ORISST in their restaurants, to boost customer experiences and to increase operational efficiency.

Fluorine materials have received the keen attention of many researchers because of their water repellency and low surface free energy. The purpose of this paper is to prepare self-crosslinking fluorocarbon polyacrylate latexes containing different fluorocarbon chain lengths by semi-continuous seeded emulsion polymerization technology.

Methyl methacrylate (MMA), butyl acrylate (BA), hydroxypropyl methacrylate (HPMA) and fluorine-containing monomers were used as main monomers. The fluorine-containing monomers included hexafluorobutyl methacrylate (HFMA), dodecafluoroheptyl methacrylate (DFMA) and trifluorooctyl methacrylate (TFMA). Potassium persulfate (KPS) was used as thermal decomposition initiator, non-ionic surfactant alkyl alcohol polyoxyethylene (25) ether (DNS-2500) and anionic surfactant sodium dodecylbenzene sulfonate (SDBS) as mixed emulsifier.

Through optimizing the reaction conditions, the uniform and stable latex is gained. The polymer of structure was characterized by Fourier transform infrared spectroscopy (FTIR). Thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and contact angle (CA) were tested on latex films. The particle size and distribution range of emulsion were tested with nano particle size analyzer. After comprehensively comparing the latexes and films prepared by HFMA, DFMA and TFMA, the performance of DFMA monomer modified is better.

The self-crosslinking acrylic emulsion is prepared via semi-continuous seeded emulsion polymerization, which methyl methacrylate (MMA), butyl acrylate (BA), hydroxypropyl methacrylate (HPMA) and fluorine-containing monomers were used as main monomers. The fluorine-containing monomers were composed of hexafluorobutyl methacrylate (HFMA), dodecafluoroheptyl methacrylate (DFMA) and trifluorooctyl methacrylate (TFMA). Potassium persulfate (KPS) was used as thermal decomposition initiator, non-ionic surfactant alkyl alcohol polyoxyethylene (25) ether (DNS-2500) and anionic surfactant sodium dodecylbenzene sulfonate (SDBS) as mixed emulsifier. There are two main innovations. One is that the self-crosslinking acrylic emulsion is prepared successfully. The other is that the effects of monomers containing different fluorocarbon chain lengths on polyacrylate, such as monomer conversion rate, coagulation rate, mechanical stability, chemical stability, emulsion particle size and storage stability, are studied in detail.

Silane cross-linkers have been used to strengthen the mechanical stabilities and friction resistance of plastic products. Therefore, the effect of silane cross-linkers on latex has been studied through preparing modified self-cross-linking long fluorocarbon polyacrylate latex. In this paper, nonionic surfactant alcohol ether glycoside (AEG1000) and anionic polymerizable surfactant 1-allyloxy-3-(4-nonylphenol)-2-propanol polyoxyethylene (10) ether ammonium sulfate (DNS-86) acted as mixed emulsifier and 3-(methacryloyloxy) propyltrimethoxysilane (KH-570) and bis (2-ethylhexyl) maleate (DOM) were used as functional monomers.

The modified acrylate polymer latex was synthesized through the semi-continuous seeded emulsion polymerization with methyl methacrylate (MMA), butyl acrylate (BA), dodecafluoroheptyl methacrylate (DFMA) and hydroxypropyl methacrylate (HPMA) as main monomers. Potassium persulfate (KPS) was applied to initiate polymerization reaction, nonionic surfactant AEG1000 and DNS-86 acted as emulsifier, KH-570 and DOM were used as functional monomers, respectively.

The optimum conditions of synthesizing the modified latex were the following. The mass ratio of monomers containing MMA, BA, DFMA, HPMA, KH-570 and DOM was 13.58:13.58:0.90:1.20:0.15:0.60, the usage of initiator KPS was 0.5% of the total weight of monomers and the amount of emulsifier was 7% of all monomers with AEG1000:DNS-86 = 1:1. The results indicated that the conversion of monomer was 99% and the coagulation was about 2.0%.

The resultant latex was modified silane cross-linker KH-570 and DOM, which positively affected the comprehensive properties of latex and its film. Apart from this, the novel mixed emulsifier was used to improve the size and distribution of latex particles and reduce environmental problems caused by the use of emulsifiers.

The purpose of this study is to establish a modified accident causation model for highway construction accidents (ACM-HC) and describe the establishment process of the ACM-HC.

Based on the 2–4 Model, a framework of the ACM-HC was constructed, and the accident causal factors (CF) were extracted from four aspects of human, material, environment and management. In addition, association rule mining (ARM) was introduced to analyze accident investigation reports to obtain the interrelationships between the factors. Based on the framework, factors and ARM results, the ACM-HC was established. Finally, the ACM-HC was verified with a tunnel collapse accident.

Both the external and internal causes of contractor cause accidents. The flaws of safety management of other stakeholders are external causes. In terms of the internal causes, there are four stages: direct causes, indirect causes, radical causes and root causes. More specifically, the direct causes refer to the unsafe acts and the unsafe conditions; ineffective safety supervision and poor individual factors of frontline workers constitute the indirect causes; the radical causes lie in the flaws of construction procedures and technical schemes; the root causes are related to the poor individual factors of decision makers and managers.

The ACM-HC expresses the causes, sequence and mechanism of highway construction accidents in a visual way. In addition, this study describes a process of using a qualitative–quantitative hybrid approach to establish a modified ACM, which provides a different perspective for the establishment of an ACM.

Snake-inspired robots are of great significance in many fields because of their great adaptability to the environment. This paper aims to systematically illustrate the research progress of snake-inspired robots according to their application environments. It classifies snake-inspired robots according to the numbers of degrees of freedom in each joint and briefly describes the modeling and control of snake-inspired robots. Finally, the application fields and future development trends of snake-inspired robots are analyzed and discussed.

This paper summarizes the research progress of snake-inspired robots and clarifies the requirements of snake-inspired robots for self-adaptive environments and multi-functional tasks. By equipping various sensors and tool modules, snake-inspired robots are developed from fixed-point operation in a single environment to autonomous operation in an amphibious environment. Finally, it is pointed out that snake-inspired robots will be developed in terms of rigid and flexible deformable structure, long endurance and multi-function and intelligent autonomous control.

Inspired by the modular and reconfigurable concepts of biological snakes, snake-inspired robots are well adapted to unknown and changing environments. Therefore, snake-inspired robots will be widely used in industrial, military, medical, post-disaster search and rescue applications. Snake-inspired robots have become a hot research topic in the field of bionic robots.

This paper summarizes the research status of snake-inspired robots, which facilitates the reader to be a comprehensive and systematic understanding of the research progress of snake-inspired robots. This helps the reader to gain inspiration from biological perspectives.

Parameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.

The MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.

The numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.

A novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.

The coupling process between the loading mechanism and the tank car mouth is a crucial step in the tank car loading process. The purpose of this paper is to design a method to accurately measure the pose of the tanker car.

The collected image is first subjected to a gray enhancement operation, and the black parts of the image are extracted using Otsu’s threshold segmentation and morphological processing. The edge pixels are then filtered to remove outliers and noise, and the remaining effective points are used to fit the contour information of the tank car mouth. Using the successfully extracted contour information, the pose information of the tank car mouth in the camera coordinate system is obtained by establishing a binocular projection elliptical cone model, and the pixel position of the real circle center is obtained through the projection section. Finally, the binocular triangulation method is used to determine the position information of the tank car mouth in space.

Experimental results have shown that this method for measuring the position and orientation of the tank car mouth is highly accurate and can meet the requirements for industrial loading accuracy.

A method for extracting the contours of various types of complex tanker mouth is proposed. This method can accurately extract the contour of the tanker mouth when the contour is occluded or disturbed. Based on the binocular elliptic conical model and perspective projection theory, an innovative method for measuring the pose of the tanker mouth is proposed, and according to the space characteristics of the tanker mouth itself, the ambiguity of understanding is removed. This provides a new idea for the automatic loading of ash tank cars.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

The purpose of this paper is to show that the droplet impact phenomenon is important for the advancement of industrial technologies in many fields such as spray cooling and ink jet printing. Droplet bouncing on the nonwetting surfaces is a special phenomenon in the impact process which has attracted lots of attention.

In this work, the authors fabricated two kinds of representative nonwetting surfaces including superhydrophobic surfaces (SHS) and a slippery liquid-infused porous surface (SLIPS) with advanced UV laser processing.

The droplet bouncing behavior on the two kinds of nonwetting surfaces were compared in the experiments. The results indicate that the increasing Weber number enlarges the maximum droplet spreading diameter and raises the droplet bounce height but has no effect on contact time.

In addition, the authors find that the topological SHS and SLIPS with the laser-processed microwedge groove array produce asymmetric droplet bouncing with opposite offset direction. Microdroplets can be continuously transported without any additional driving force on such a topological SLIPS. The promising method for manipulating droplets has potential applications for the droplet-based microfluidic platforms.