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The purpose of this paper was to prepare a ternary hierarchical rough particle to accelerate the anti-corrosive design for coastal concrete infrastructures.

A kind of micro-nano hydrophobic ternary microparticles was fabricated from SiO₂/halloysite nanotubes (HNTs) and recycled concrete powders (RCPs), which was then mixed with sodium silicate and silane to form an inorganic slurry. The slurry was further sprayed on the concrete surface to construct a superhydrophobic coating (SHC). Transmission electron microscopy and energy-dispersive X-ray spectroscopy mappings demonstrate that the nano-sized SiO₂ has been grafted on the sub-micron HNTs and then further adhered to the surface of micro-sized RCP, forming a kind of superhydrophobic particles (SiO₂/HNTs@RCP) featured of abundant micro-nano hierarchical structures.

The SHC surface presents excellent superhydrophobicity with the water contact angle >156°. Electrochemical tests indicate that the corrosion rate of mild steel rebar in coated concrete reduces three-order magnitudes relative to the uncoated one in 3.5% NaCl solution. Water uptake and chloride ion (Cl^-) diffusion tests show that the SHC exhibits high H₂O and Cl^- ions barrier properties thanks to the pore-sealing and water-repellence properties of SiO₂/HNTs@RCP particles. Furthermore, the SHC possesses considerable mechanical durability and outstanding self-cleaning ability.

SHC inhibits water uptake, Cl^- diffusion and rebar corrosion of concrete, which will promote the sustainable application of concrete waste in anti-corrosive concrete projects.

Textile reinforced concrete (TRC) has excellent bearing capacity and anti-crack and corrosion resistance capacity, which are suitable for strengthening concrete structure under harsh environments.

In this thesis, flexural properties of RC beams strengthened with TRC under chloride wet–dry cycles were studied and the effects of the concentration of the salt solution, number of wet–dry cycles, bending stress level and TRC form were considered. Four-point bend loading mode was adopted for the step-loading procedure.

As the number of wet–dry cycles was relatively few, the trend of the yield and ultimate load with the increasing concentration of salt solution and wet–dry cycles were not obvious. However, the beams under high sustained bending stress level (0.5) had a decrease in the bearing capacity and an increase in mid-span deflection because of the larger degree of the corrosion of steel bars and the weaker bond capacity between the steel bar and concrete. Besides, there was little difference between the precast TRC plate and the casting TRC on beams in terms of the capacity of anti-crack, bearing and deflection.

In this paper, preliminary work has been carried out, but some of the factors were not comprehensive considered, which are inevitable. As the time of dry–wet cycles was short and TRC layer had good anti-crack and anti-permeability performance, smaller chloride ions’ penetration resulted in the corrosion ratio of steel bars to be lower.

It should be noted that under high corrosion rates of the reinforcement, the whole TRC strengthening layer might be spalled off if only the strengthening form at the beam bottom is used, and thus the U-type strengthening form could be considered, which means that the beam is strengthened at both the bottom and side surfaces.

This research only considers the flexural performance of the beams strengthened with TRC in conventional environment, and there is little research on the TRC-strengthened beam under corrosion environment. On the basis of previous research, this paper carried out the experimental study on beams strengthened with TRC under chloride wet–dry cycle environment, and the effects of the concentration of the salt solution, number of wet–dry cycles, bending stress level and TRC form were considered.

This chapter explores Prospect Theory — a descriptive model of modelling individual choice making under risk and uncertainty, and its applications to a range of travel behaviour contexts.

The chapter provides background on Prospect Theory, its basic assumptions and formulations, and summarises some of its theoretical developments, applications and evidence in the field of transport research.

A body of empirical evidence has accumulated showing that the principle of maximisation of expected utility provides limited explanation of travel choices under risk and uncertainty. Prospect Theory can be seen as an alternative and promising framework for travel choice modelling (although not without theoretical and practical controversy). These findings are supported by empirical observations reported in the literature reviewed in this chapter.

The chapter provides a detailed account of the design and results of accumulated research in travel behaviour research that is based on Prospect Theory’s observations, insights and formulations. The potential of Prospect Theory for particular decision-making in travel behaviour research is articulated, main findings are presented and discussed, and limitations are identified, leading to further research needs.

Buying online has become a widespread and common activity for consumers, and, for many organizations, e-commerce has become a very profitable alternative to sell their products and services, also allowing them to leverage their strategy in new geographical markets immediately. Although the literature on the subject is comprehensive, there is a gap in identifying the holistic constructs that are the determinants of consumers' choice of an online store. This research resorts to an exploratory study, based on a nonsystematic literature review, seeking to identify these constructs. The results obtained allowed us to identify the following constructs: consumer behavior, customer experience, web content, catalog, terms and conditions, customer support, perceived value, trust, security and privacy, satisfaction, and loyalty. Customer experience, satisfaction, and loyalty constructs stand out from a strategic perspective.

The purpose of this paper is to report a study of how the test instrument of a simulated work task situation is used in empirical evaluations of interactive information retrieval (IIR) and reported in the research literature. In particular, the author is interested to learn whether the requirements of how to employ simulated work task situations are followed, and whether these requirements call for further highlighting and refinement.

In order to study how simulated work task situations are used, the research literature in question is identified. This is done partly via citation analysis by use of Web of Science®, and partly by systematic search of online repositories. On this basis, 67 individual publications were identified and they constitute the sample of analysis.

The analysis reveals a need for clarifications of how to use simulated work task situations in IIR evaluations. In particular, with respect to the design and creation of realistic simulated work task situations. There is a lack of tailoring of the simulated work task situations to the test participants. Likewise, the requirement to include the test participants’ personal information needs is neglected. Further, there is a need to add and emphasise a requirement to depict the used simulated work task situations when reporting the IIR studies.

Insight about the use of simulated work task situations has implications for test design of IIR studies and hence the knowledge base generated on the basis of such studies.

Simulated work task situations are widely used in IIR studies, and the present study is the first comprehensive study of the intended and unintended use of this test instrument since its introduction in the late 1990’s. The paper addresses the need to carefully design and tailor simulated work task situations to suit the test participants in order to obtain the intended authentic and realistic IIR under study.

The purpose of this paper is to investigate consumer preference of and willingness to pay for eco-labelled eggs using cross-sectional data collected from Chongqing Municipality in China.

This study employs both conditional logistic model and mixed logistic model to conduct the empirical analysis.

The empirical results show that individual heterogeneous preference and attributes of eco-labelled eggs significantly influence consumer choices. More specifically, higher per capita income, families with pregnant women or children, higher level of trust in and knowledge of eco-labels contribute positively to choosing eco-labelled eggs, and attributes of eco-labelled eggs such as certification labels, free-range husbandry, and nutrition enrichment significantly increase consumer utility. Generally, the price premium that consumers are willing to pay for eggs containing “organic”, “free-range husbandry”, and “nutrition enrichment” labels is 375.0% more, equivalent to 42.8 Yuan/kg.

This study used first-hand survey data to reveal consumers’ heterogeneous preference of and willingness to pay for eco-labelled eggs based on the random utility theory.

The purpose of this paper is to examine the factors (including conservation payments) that influence household decisions to participate in off-farm work and estimate the impact of participation on household welfare under the auspices of the Grain for Green (GfG) program.

The authors used survey data from 225 farm households on the Loess Plateau and addressed the possible sample selection and endogeneity problems by employing a jointly estimated endogenous switching regression (ESR) model.

The findings of this paper are as follows: off-farm participation is positively related to households’ educational attainment and negatively related to their land resource endowment and the presence of children; participation in off-farm work exerts positive effects on household income and per capita household income, but negative effects on farm productivity; and conservation payments show no significant impact on off-farm participation, no significant impact on any of the three household welfare indicators for off-farm non-participant households, but a significantly negative impact for off-farm participant households.

This paper makes two contributions. First, the authors address the selection bias and endogeneity problem of GfG participating households by employing the ESR method and explicitly estimating the treatment effects of off-farm participation on their household welfare. Neglecting these problems leads to biased estimates and misleading policy implications. Second, this analysis stresses the important role of government in reducing market or institutional failure and other barriers that impede farmers’ efficient allocation choices instead of compensating households for conserving sloping land, shedding new light on the most effective policy options to achieve the program’s goals.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

This paper aims to propose a method to quickly set the heating zone temperatures and conveyor speed of the reflow oven. This novel approach intensely eases the trial and error in reflow profiling and is especially helpful when reflowing thick printed circuit boards (PCBs) with bulky components. Machine learning (ML) models can reduce the time required for profiling from at least half a day of trial and error to just 1 h.

A highly compact computational fluid dynamics (CFD) model was used to simulate the reflow process, exhibiting an error rate of less than 1.5%. Validated models were used to generate data for training regression models. By leveraging a set of experiment results, the unknown input factors (i.e. the heat capacities of the bulkiest component and PCB) can be determined inversely. The trained Gaussian process regression models are then used to perform virtual reflow optimization while allowing a 4°C tolerance for peak temperatures. Upon ensuring that the profiles are inside the safe zone, the corresponding reflow recipes can be implemented to set up the reflow oven.

ML algorithms can be used to interpolate sparse data and provide speedy responses to simulate the reflow profile. This proposed approach can effectively address optimization problems involving multiple factors.

The methodology used in this study can considerably reduce labor costs and time consumption associated with reflow profiling, which presently relies heavily on individual experience and skill. With the user interface and regression models used in this approach, reflow profiles can be swiftly simulated, facilitating iterative experiments and numerical modeling with great effectiveness. Smart reflow profiling has the potential to enhance quality control and increase throughput.

In this study, the employment of the ultimate compact CFD model eliminates the constraint of components’ configuration, as effective heat capacities are able to determine the temperature profiles of the component and PCB. The temperature profiles generated by the regression models are time-sequenced and in the same format as the CFD results. This approach considerably reduces the cost associated with training data, which is often a major challenge in the development of ML models.

Unlike some developed countries, Chinese food safety certification system is multi-level including organic/green/hazard-free certifications. The purpose of this paper is to assess consumers’ preferences for tomatoes carrying these different labels.

Data used in this study came from choice experiments (CEs) conducted in Shandong province, China. Based on experiment data, a random parameter logit model was established to analyze consumers’ willingness-to-pay (WTP).

Consumers’ WTP for organic tomatoes was higher than that for hazard-free and green-certified tomatoes. Furthermore, consumers’ WTP for the European Union (EU) organic label was higher than that for the Chinese organic label, whereas a non-significant difference existed between the levels of consumers’ WTP for hazard-free and green-certified tomatoes. Consumers with different food safety risk perception (FSRP) had large differences in WTP, whereas those with varying environmental awareness (ENAW) had similar levels of WTP.

This contribution is the first research which focuses on consumers’ WTP for EU organic label, Chinese organic label, green label, or hazard-free label on tomato through CEs in China. Furthermore, the influence of consumers’ FSRP and ENAW on their preference was analyzed through a random parameter logit model.