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The purpose of this study is to address the welding demands within large steel structures by presenting a global spatial motion planning algorithm for a mobile manipulator. This algorithm is based on an independently developed wall-climbing robot, which comprises a four-wheeled climbing mobile platform and a six-degree-of-freedom robotic manipulator, ensuring high mobility and operational flexibility.

A convex hull feasible domain constraint is developed for motion planning in the mobile manipulator. For extensive spatial movements, connected sequences of convex polyhedra are established between the composite robot’s initial and target states. The composite robot’s path and obstacle avoidance optimization problem are solved by constraining the control points on B-spline curves. A dynamic spatial constraint rapidlye-xploring random trees-connect (RRTC) motion planning algorithm is proposed for the manipulator, which quickly generates reference paths using spherical spatial constraints at the manipulator’s end, eliminating the need for complex nonconvex constraint modeling.

Experimental results show that the proposed motion planning algorithm achieves optimal paths that meet task constraints, significantly reducing computation times in task conditions and shortening operation times in non-task conditions.

The algorithm proposed in this paper holds certain application value for the realization of automated welding operations within large steel structures using mobile manipulator.

In prior literature, online endorsement system allowing the users to “like” or “dislike” shared information is found very useful in information filtering and trust elicitation in most social networks. This paper shows that such systems could fail in the context of investment communities due to several psychological biases.

This study develops a series of regression analyses to model the “like”/“dislike” voting process and whether or not such endorsement distinguishes between valuable information and noise. Trading simulations are also used to validate the practical implications of the findings.

The main findings of this research are twofold: (1) in the context of investment communities, online endorsement system fails to signify value-relevant information and (2) bullish information and “wisdom over the past event” information receive more “likes” and fewer “dislikes” on average, but they underperform in stock market price discovery.

This study demonstrates that biased endorsement may lead to the failure of the online endorsement system as information gatekeeper in investment communities. Two underlying mechanisms are proposed and tested. This study opens up new research opportunities to investigate the causes of biased endorsement in online environment and motivates the development of alternative information filtering systems.

The purpose of this paper is to propose an accurate and practical imitation learning for robotics. The modified dynamic movement primitives (DMPs), global fitting DMPs (GLDMPs), is presented. Framework design, theoretical derivation and stability proof of GLDMPs are discussed in the paper.

Based on the DMPs, the hierarchical iterative parameter adaptive framework is developed as the hierarchical iteration stage of the GLDMPs to tune the designed parameters adaptively to extract richer features. Inspired by spatial transformations, the coupling analytical module which can be regarded as a reversible transformation is proposed to analyze the high-dimensional coupling information and transfer it to trajectory.

With the proposed framework and module, DMPs derive majority features of the demonstration and cope with three-dimensional rotations. Moreover, GLDMPs achieve favorable performance without specialized knowledge. The modified method has been demonstrated to be stable and convergent through inference.

GLDMPs have an advantage in accuracy, adaptability and practicality for it is capable of adaptively computing parameters to extract richer features and handling variations in coupling information. With demonstration and simple parameter settings, GLDMPs can exhibit excellent and stable performance, accomplish learning and generalize in other regions. The proposed framework and module in the paper are useful for imitation learning in robotics and could be intuitive for similar imitation learning methods.

This research aims to examine the relations between Shariah compliance and earnings quality.

The authors study three Shariah features: Shariah compliance status, level of Shariah compliance (H-Score) and Shariah compliance persistence. The sample consists of 463 firms from the Middle East and North Africa from 2011 to 2018. A variable determining the level of Shariah compliance was created in accordance with the methodology of S&P 500 Shariah and its underlying index, S&P 500. Then, a probate relapse study was created to identify the link between Shariah compliance and earnings quality.

Results show that Shariah-compliant firms engage in lower earnings management compared to their Shariah-non-compliant counterparts. This paper reveals that Shariah compliance status and high level of Shariah compliance have significant positive association with earnings quality. The authors also find novel evidence that persistence of the Shariah-compliant status has a significant negative association with earnings quality.

This study only examines firms listed on MENA stock markets. It is recommended to further study different markets in addition to the emerging Arab markets in order to compare and contrast the results. Further, larger sample observations from a greater date range can be used.

Few studies have examined the earnings management behavior of Shariah-compliant firms vs Shariah-non-compliant ones in emerging markets; however, no study has focused on Shariah-compliant firms and their level of Shariah compliance. To the best of our knowledge, this is the first study which uses all four proxies for earnings quality in association with Shariah compliance and used new Shariah variables such as Level of Shariah Compliance and Persistent Shariah Compliance status.

Endurance time is an important factor limiting the progress of flapping-wing aircraft. In this study, this paper developed a prototype of a double-wing flapping-wing micro air vehicle (FMAV) that mimics insect-scale flapping wing for flight. Besides, novel methods for optimal selection of motor, wing length and battery to achieve prolonged endurance are proposed. The purpose of this study is increasing the flight time of double-wing FMAV by optimizing the flapping mechanism, wings, power sources, and energy sources.

The 20.4 g FMAV prototype with wingspan of 21.5 cm used an incomplete gear flapping wing mechanism. The motor parameters related to the lift-to-power ratio of the prototype were first identified and analyzed, then theoretical analysis was conducted to analyze the impact of wing length and flapping frequency on the lift-to-power ratio, followed by practical testing to validate the theoretical findings. After that, analysis and testing examined the impact of battery energy density and efficiency on endurance. Finally, the prototype’s endurance duration was calculated and tested.

The incomplete gear facilitated 180° symmetric flapping. The motor torque constant showed a positive correlation with the prototype’s lift-to-power ratio. It was also found that the prototype achieved the best lift-to-power ratio when using 100 mm wings.

A gear-driven flapping mechanism was designed, capable of smoothly achieving 180° symmetric flapping. Besides, factors affecting long-duration flight – motor, wings and battery – were identified and a theoretical flight duration analysis method was developed. The experimental result proves that the FMAV could achieve the longest hovering time of 705 s, outperforming other existing research on double-wing FMAV for improving endurance.

This paper aims to investigate the differences in hot corrosion behavior of the GTD222 superalloy and TiC/GTD222 composite in a mixed salt of 75% Na₂SO₄ and 25% K₂SO₄ at 900°C.

The GTD222 superalloy and TiC/GTD222 nickel-based composite were prepared using selective laser melting (SLM). Subsequently, the hot corrosion behavior of the two alloys was systematically investigated in a salt mixture consisting of 75% Na₂SO₄ and 25% K₂SO₄ (Wt.%) at 900°C.

The TiC/GTD222 composite exhibited better hot corrosion resistance compared to the GTD222 superalloy. First, the addition of alloying elements led to the formation of a protective oxide film on the TiC/GTD222 composites 20 h before hot corrosion. Second, TiC/GTD222 composite corrosion surface has a higher Ti content, after 100 h of hot corrosion, the composite corrosion surface Ti content of 10.8% is more than two times the GTD222 alloy 4% Ti. The Ti and Cr oxides are tightly bonded, effectively resisting the erosion of corrosive elements.

The hot corrosion behavior of GTD222 superalloy and TiC/GTD222 composites prepared by SLM in a mixed salt of 75% Na₂SO₄ and 25% K₂SO₄ was studied for the first time. This study provides insights into the design of high-temperature alloys resistant to hot corrosion.

Of the studies covering culinary tourism and traditional culture, Generation Z remains an underexplored group despite being the primary consumers of future tourism. Research into young consumers’ motivations is hoped to clarify, protect, and sustain Hong Kong Tong sui among this generation. As such, this study focuses on Generation Z to understand the types of novel experiences Tong sui can create for them, and it is worth exploring in depth the diet that young people are interested in from the perspective of traditional food.

Based on Creswell’s guidelines for quantitative research, we investigated the relationships among multiple elements of Hong Kong Tong sui and Generation Z tourists. A corresponding questionnaire was designed to gather information for statistical analysis and contains five sections. The first part solicited basic respondent information. Sections 2–5 presented several key terms (nostalgic emotion, food authenticity, FCV, and purchase intention) to frame the questionnaire in terms of our hypotheses. Items were scored on 7-point Likert-type scales for ease of processing in SPSS 26.0.

All hypotheses were supported, with nostalgia having the most significant influence on Generation Z’s purchase intentions. Besides, young generations are willing to buy traditional food when exposed to the restaurants or enterprises have nostalgic elements and inspire nostalgia in consumer. Generation Z’s thoughts about food authenticity notably played a more significant role than FCV in terms of the nostalgia of traditional food. In addition, food authenticity and FCV were found to indirectly mediate the relationship between nostalgic emotion and purchase intention.

Examining these types of campaigns geared toward Generation Z tourists to advertise traditional food has enriched this tactic’s applicability. The approach also constructs a foundation to scrutinize the appeal of traditional dishes among young consumer groups to potentially strengthen such food’s influence.

This study highlights the importance of leveraging nostalgia and understanding the emotional connection that Generation Z has with traditional cuisine. It suggests that enterprises can develop new products or revive traditional recipes that cater to the nostalgic preferences of Generation Z. Marketing innovations, such as using social media influencers, can also be employed to create awareness and generate interest in traditional food. Traditional food can contribute to tourist destinations' promotion and differentiate them from competitors, boosting the tourism and catering industries and creating employment opportunities.

The promotion and preservation of traditional food can contribute to the preservation of cultural identity and heritage. By engaging local communities in culinary initiatives and fostering community pride, traditional food tourism can strengthen the connection to cultural heritage and encourage the preservation of traditional culinary practices. This engagement can lead to sustainable development by promoting local traditions and ensuring the participation of the local community in tourism activities.

This study offers novel theoretical insights into traditional food consumption and food marketing, thus narrowing gaps in research on young consumers and traditional food. Results enrich the understanding of Generation Z’s intentions to purchase traditional food by highlighting tourists’ preferences. Guidelines are also provided for the operators on creating nostalgic campaigns that appeal to young generations.

Drawing upon socio-technical system theory, this study intends to investigate the effects of the congruence and incongruence between artificial intelligence (AI) and explorative learning on supply chain resilience as well as the moderating role of organizational inertia.

Using survey data collected from 170 Chinese manufacturing firms, we performed polynomial regression and response surface analyses to test our hypotheses.

We find that the congruence between AI and explorative learning enhances firms’ supply chain resilience, while the incongruence between these two factors impairs their supply chain resilience. In addition, compared with low–low congruence, high–high congruence between AI and explorative learning improves supply chain resilience to a greater extent. Moreover, organizational inertia attenuates the positive influence of the congruence between AI and explorative learning on supply chain resilience, while it aggravates the negative influence of the incongruence between these two factors on supply chain resilience.

Our study expands the literature on supply chain resilience by demonstrating that the congruence between a firm’s AI (i.e. technical aspect) and explorative learning (i.e. social aspect) boosts its supply chain resilience. More importantly, our study sheds new light on the role of organizational inertia in moderating the congruent effect of AI and explorative learning, thereby extending the boundary condition for socio-technical system theory in the supply chain resilience literature.

This paper aims to propose an incremental graph indexing method based on probabilistic inferences in Bayesian network (BN) for approximate nearest neighbor search (ANNS) that adds unindexed queries into the graph index incrementally.

This paper first uses the attention mechanism based graph convolutional network to embed a social network into the low-dimensional vector space, which could improve the efficiency of graph index construction. To add the unindexed queries into the graph index incrementally, this study proposes to learn the rule-based BN from social interactions. Thus, the dependency relations of unindexed queries and their neighbors are represented, and the probabilistic inferences in BN are then performed.

Experimental results demonstrate that the proposed method improves the search precision by at least 5% and search efficiency by 10% compared to the state-of-the-art methods.

This paper proposes a novel method to construct the incremental graph index based on probabilistic inferences in BN, such that both indexed and unindexed queries in ANNS could be addressed efficiently.