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In this paper, the effects of the adhesive and curing agent contents on the tensile strength, bending strength, gas evolution and gas permeability of three-dimensional printed sand molds are studied. A strength model of the three-dimensional printed sand molds is proposed. The multi-material composite sand mold forming test is carried out. In addition, the mesostructure of the sand mold is studied.

The performances of three-dimensional printed sand mold such as tensile strength, bending strength, gas evolution and gas permeability are studied using the standard test methods. The mesostructure of the sand mold is studied by digital core technology.

A sand mold strength model based on the resin adhesive content, curing agent content and sand mold compactness are obtained. Two types of multi-material composite three-dimensional printed sand molds are proposed. An increase in the curing agent content in the sand mold widens the mesoscopic characteristic size distribution of the sand mold, and large-sized mesostructures appear, resulting in a decrease in the sand mold bearing capacity.

Process parameters that affect the performance of three-dimensional printed sand mold are revealed. The sand mold bearing curve provides a reference for the ultimate design of three-dimensional printed sand mold.

The paper deals with experimental work on the performance and mesostructure of multi-material composite three-dimensional printed sand mold with different contents of adhesive and curing agent. That gives a perspective on future designs of sand mold based on these principles.

This paper aims to determine how the viscosity and curing agent content affect the flowability of moist silica sand granules. In addition, a coating device was designed according to the flow properties of silica sand granules.

The flowability of silica sand granules premixed with two curing agents of different viscosities is studied using a Jenike shear apparatus. An open-ended device was used in discharge testing of sand granules with a design based on the variable dip angle of the two plates and variable outlet size.

The test results show that increasing the curing agent content would significantly decrease the flowability of silica sand granules, and a curing agent of higher viscosity has a greater effect on the flowability of silica sand. The presence of a curing agent strengthens the cohesion among sand granules, lubricates them and restrains their deformation. The shape function of the coating device was obtained by theoretical derivation.

The flow properties provide a valuable theoretical guidance for the design of coating device for sand mold printing.

This paper deals with experimental work on flow properties of silica sand granules with different viscosities and curing agent content. The shape function of a wedge-shaped coating device is obtained based on experimental data.

This paper examines the gender patterns of occupational mobility in post-reform urban China using a national representative dataset. The results show there are marked gender differences in both direction and self-reported cause of occupational mobility. With respect to the direction of mobility, married women are more likely than married men to undergo downward occupational changes, but are less likely to experience upward moves. In terms of the cause of mobility, compared to married men, married women are less likely to change jobs for career development or move to a new job assigned by the employer, but are more likely to change jobs for family reasons or as a result of involuntary separation. The results also show that the public-sector restructuring has increased the incidence of downward occupational mobility, more for women than men. The analysis suggests that women are disadvantaged in the occupational mobility process by a variety of social and institutional factors.

Transport and pandemics are interlinked given the ubiquitous nature of modern transport systems. The COVID-19 pandemic has provided much evidence for both virus contagion but also containment and how transport plays a role in both. As the world and its cities experienced lockdowns, there were travel restrictions, physical social distancing rules, transport systems shut down, changed operations, a re-opening with lower demands in some sectors (e.g., air transport and urban public transport services) and an increased demand in others (e.g., freight and home deliveries). These changes brought about a series of reactions at all levels, from governments and local authorities, operators of all transport modes but also personal and individual behaviour. This volume provides evidence on an array of transport and pandemic experiences through a collection of works from around the world, each chapter discussing a mode, a region and possible future outcomes. This introductory chapter provides the context for this volume with an overview of literature that looks at transport and pandemics, a timeline of events that marked the COVID-19 pandemic developments across different parts of the world, and finally an overview of the chapters in the volume. It concludes with some insights from the editors on the future of transport in a post-COVID world.

This chapter analyzes the behaviors people planned to change to mitigate the adverse effects of the pandemic using the Theory of Planned Behavior (TPB) as a frame. The TPB argues that behaviors are largely determined by an individual’s intention to engage in the behavior, and behavioral intensions are functions of attitudes, perceived behavioral control, and subjective norms. After reviewing the theoretical perspective, data on respondents’ plans to change their leisure activities such as eating in restaurants, buying take-out food or using food delivery services, traveling, gathering in public, and visiting with friends are analyzed. I also look at their planned changes in their use of public transportation, e-commerce, and other online activities. Using factor analysis, behaviors group into two distinct factors: planned changes to interactions (e.g., avoiding public gatherings, visit bars and restaurants less often, and visit friends and relatives less often) and planned changes to consuming behaviors (e.g., purchasing online services more, ordering take-out food more often). Investigating if planned changes vary across country and respondent attitudes, Finnish respondents were significantly more likely to say they planned to change their interactions than were Americans, while Americans were significantly more likely to report they planned to change their consuming behaviors. The chapter concludes by considering if people’s beliefs about what caused the pandemic explain these observed differences. This was the case in terms of changes to interactions, but it was not the case for plans to change consuming behaviors to mitigate the pandemic’s threats.

With the spread of the coronavirus disease across over 100 countries and its status upgraded to that of a pandemic on 11 March 2020 (World Health Organization), increased attention is being placed on the policy measures that may be required to effectively curb the rate of contagion within and across countries. Currently, several governments, such as China, Italy, Spain, Japan and the Republic of Korea, have implemented emergency measures informed by the principle of social distancing to limit the spread of coronavirus (World Health Organization). Ever since the virus was first identified in Wuhan City in December 2019, this succession of uncoordinated policy responses offers a set of natural experiments that should be analysed to understand the successes and failures of containment at the societal level. In this analysis, we focus on the case of Italy, the hardest hit country in Europe (Dong, Du, & Gardner, 2020; World Health Organization). The objective of this short note is to provide an even-handed analysis of the actions taken by the Italian government to cope with the transmission of the virus and to highlight lessons in emergency management that can be learnt for other countries currently facing the onset of the Covid-19 epidemic.

This paper aims to develop a feasible visual weld detection method to solve the problems in multi-layer welding detection (e.g. cover pass welding detection) for seam tracking and non-destructive testing. It seeks for an adaptive and accurate way to determine the edge between the seam and the base metal in the grayscale image of weld automatically. This paper tries to contribute to next-generation real-time robotic welding systems for multi-layer welding.

This paper opted for invariant moments to characterize the seam and the base metal for classification purposes. The properties of invariant moments, such as high degree of self-similarity and separation, affine invariance and repetition invariance, were discussed to verify the adaptability of the invariant moment in weld detection. Then, a weld detection method based on invariant moments was proposed to extract the edge between the seam and the base metal, including image division, invariant moment features extraction, K-Means adaptive thresholding, maximum connected domain detection and edge position extraction.

This paper highlights the significance of high degree of self-similarity and separation, affine invariance and repetition invariance of the invariant moment for weld detection. An adaptive, effective and accurate method is proposed to detect the edge between the seam and the base metal based on invariant moments.

It is necessary to verify the applicability of the proposed method in variable welding conditions further. Further works will focus on the establishment of a real-time seam tracking system during the whole multi-layer/multi-pass welding process based on such adaptive visual features.

This paper includes the implications for development of an adaptive and real-time weld detection method, which is expected to be applied to online seam tracking in multi-layer welding.

This paper presents an accurate weld detection method in multi-layer welding, overcoming difficulties in effectiveness, adaptability and efficiency of existing weld detection methods.

Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase.

This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features.

The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction.

In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.

The purpose of this paper is to present recent work designing a mechanical robotic hand for self‐adaptive grasping, human‐like appearance, which can be used in a humanoid robot. Conventional robotic devices are relatively complex, large, cumbersome and difficult to be installed in a humanoid robot arm. Under‐actuated robot hands use less motors to drive more rotating joints, thus to simplify the mechanical structure, decrease the volume and weight and finally lower the difficulty of control and the cost.

A novel under‐actuated finger mechanism is designed, which is based on a gear‐rack mechanism, spring constraint and an active sleeve middle phalanx. The principle analyses of its self‐adaptive grasp and end power grasping are given. A new multi‐fingered hand named as TH‐3R Hand is designed based on the finger.

The design finger mechanism can be used in a robotic hand to make the hand obtain more degrees of freedom (DOF) with fewer actuators, and good grasping function of shape adaptation, decrease the requirement of control system. TH‐3R Hand has five fingers, 15 DOF. All fingers are similar. TH‐3R Hand has many advantages: it is simple in structure, light in weight, easy to control and low in cost. TH‐3R Hand can passively adapt different shapes and sizes of the grasped object. Experimental studies have demonstrated the self‐adaptation in grasping of the finger.

The implication of this research is that under‐actuated robotic hands are appropriate for the missions of grasping different objects. The limitation of the research to date is that issues of sensors, control, and communication have not yet been addressed.

Key technologies of the under‐actuated finger and TH‐3R Hand, with self‐adaptive grasping, human‐like appearance and low‐cost lightweight, are feasible. These technologies have the potential to make a significant impact.

These results present a self‐adaptive under‐actuated grasp concept and a humanoid robotic hand with under‐actuated gear‐rack mechanism.

This study aims to examine how financial analysts evaluate other comprehensive income (OCI) information with a focus on the information content and economic substance of OCI gain and loss.

This study conducted a 2 × 2 between-subject experiment by manipulating profitability (net profit or net loss) and OCI (OCI gain or loss). A total of 103 equity research analysts participated in the experiment.

The results show that when the company suffers a net loss, the presence of unrealized gain in OCI appears to cause concern for analysts, in that they assigned a lower valuation to the OCI gain company than the OCI loss company. However, in the cases where the company is profitable, analysts appeared to respond to the direction of OCI (i.e. gain or loss) and incorporated the directional information in their valuation judgment.

The experimental results complement prior archival research on OCI valuation. This study extends prior work on OCI’s decision usefulness, improves understanding of the impact of OCI on firm valuation and contributes to the ongoing debate about whether OCI is viewed as a performance measure. The findings indicate that the effect of OCI gains or losses is most pronounced when the company experiences a loss. During such instances, analysts may interpret a combination of net loss and OCI gain as a potential indicator of earnings management opportunities. Consequently, they may perceive it as a signal of deteriorating future financial performance.