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The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

The design of cantilever pile walls (CPWs) presents several common challenges. These challenges include soil variability, groundwater conditions, complex loading conditions, construction considerations, structural integrity, uncertainties in design parameters and construction and monitoring costs. Accordingly, this paper is to provide a detailed literature review on the design criteria of CPWs, specifically in cohesionless soil. This study aims to present a comprehensive overview of the current state of knowledge in this area.

The paper uses a literature review approach to gather information on the design criteria of CPWs in cohesionless soil. It covers various aspects such as excavation support systems (ESSs), deformation behavior, design criteria, lateral earth pressure calculation theories, load distribution methods and conventional design approaches.

The review identifies and discusses common challenges associated with the design of CPWs in cohesionless soil. It highlights the uncertainties in determining load distribution and the potential for excessive wall deformations. The paper presents various approaches and methodologies proposed by researchers to address these challenges.

The paper contributes to the field of geotechnical engineering by providing a valuable resource for geotechnical engineers and researchers involved in the design and analysis of CPWs in cohesionless soil. It offers insights into the design criteria, challenges and potential solutions specific to CPWs in cohesionless soil, filling a gap in the existing knowledge base. The paper draws attention to the limitations of existing analytical methods that neglect the serviceability limit state and assume rigid plastic soil behavior, highlighting the need for improved design approaches in this context.

This study aims to investigate the most effective approach for governments and enterprises to combat desertification by considering the governance cycle. The focus is on understanding how the government can incentivize enterprises to actively engage in desertification combat efforts.

Both the government and the enterprise are treated as rational entities, making strategic choices for joint participation in combating desertification. Recognizing the dynamic nature of the desertification combat area, differential game models are employed to identify the optimal mode for combating desertification.

The findings underscore the significant influence of the governance cycle duration on the selection of desertification combat modes for government and enterprise. A cooperative mode is best suited to a short governance cycle, while an ecological subsidy mode is optimal for a longer cycle. Enhancing governance technology and shortening the governance cycle are conducive to combating desertification. Reducing taxes alone may not be an effective control strategy; rather, the government can better motivate enterprises by adopting tax rate policies aligned with the chosen governance mode.

This research contributes by elucidating the impact mechanism of the government cycle’s length on the desertification combat process. The results may offer valuable insights for governments in formulating strategies to encourage corporate participation in combating desertification and provide theoretical support for selecting optimal desertification combat modes.

This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.

iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.

The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.

iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.

Learning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.

A total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.

The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.

This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

During metro construction using the shield method, the construction process's complexity, the construction environment's variability, and other factors can easily lead to tunnel construction accidents. This paper aims to explore the interconnections between risk factors and related accident types, as well as the risk chain formed between risk factors, and to analyze the key risk factors and vulnerabilities in shield tunnel construction through empirical data.

Based on the social network analysis theory, the connection of various risk factors in subway shield tunnel construction is explored, and the mechanism of multiple risk factors is studied. Through literature analysis, articles on safety risk factors in metro shield tunnel construction are organized and studied, and the identified safety risk factors can comprehensively reflect the significant risks that need to be concerned in metro shield tunnel construction.

The results show that a small world characterizes the SNA network of safety risk factors for metro shield tunnel construction: The frequency of association between the five risk factors “unsafe behavior,” “site management,” “safety supervision and inspection,” “safety education system” and “safety protection” is higher than that of other factors. Only a few risks, such as “site management,” “safety supervision and inspection,” and “rapid response capability,” directly lead to accidents. In addition, risk factors such as the “safety education system” and “safety protection” will indirectly cause unsafe behaviors of construction personnel.

During construction, the probability of occurrence of risk factors may vary with the construction phase and area and is not considered in this paper. In addition, although this paper identifies, determines and analyzes the risk factors affecting the safety of metro shield tunnel construction, including the importance of each risk factor and the connection between them, more detailed information before and after the accident could not be obtained based on the accident investigation report alone. Therefore, future research can collect the same accident case from more sources to obtain more information.

The theory of accident causation has been improved at the theoretical level. The identified safety risk factors can comprehensively reflect the significant risks that need to be paid attention to in metro shield tunnel construction. From a practical point of view, the results of the study provide a basis for the rational control of the risk factors in the construction of subway shield tunnels, which can help guide practitioners to do a good job of risk prevention before the construction of metro shield tunnels and reduce the probability of related accidents.

This study expands the application of social network analysis in the field of subway tunnel construction risk, quantitatively analyzes the key risk factors and vulnerabilities in shield method tunnel construction and proposes policy recommendations for future metro tunnel construction safety management.

The current study immerses itself in the realm of diversification prospects within a select group of preeminent global stock exchanges. Specifically, the study casts its discerning gaze upon the financial hubs of the United States, Hong Kong, Germany, France, Amsterdam and India. In this expansive vista of international financial markets, the present analytical study aims to unravel the multifaceted opportunities that lie therein for astute portfolio management and strategic investment decisions.

The study encompasses daily time series data spanning from 2019 to 2022. To assess the interconnectedness among these stock indices, advanced statistical techniques, including Johansen cointegration methods and vector autoregressive (VAR) models, have been applied.

The research outcomes reveal both unidirectional and bidirectional relationships between the Indian, Hong Kong and US stock exchanges, encompassing both short-term and long-term time frames. Interestingly, the empirical findings indicate the presence of diversification opportunities between the Indian stock exchange and the stock exchanges of Germany, France and Amsterdam.

These insights hold significant value for both Indian and international investors, including foreign institutional investors (FIIs), domestic institutional investors (DIIs) and retail investors, as they can utilize this knowledge to construct more effective and diversified investment portfolios by understanding the intricate interconnections between these prominent global stock exchanges.

This research undertaking aspires to bring coherence to a landscape rife with divergent interpretations and methodological divergences. We are poised to offer a comprehensive analysis, a beacon of clarity amidst the murkiness, to shed light on the intricate web of interconnections that underpin the world's stock exchanges. In so doing, we seek to contribute a seminal piece of scholarship that transcends the existing ambiguities and thus empowers the field with a deeper understanding of the multifaceted dynamics governing international stock markets.

The present study aims to evaluate the effect of climate change on the flood hazard potential in the Kelantan River Basin using current and future scenarios.

The intensity-duration-frequency (IDF) was used to estimate the current 50- and 100-year return period 24-h design rainfall, and the climate change factor (CCF) was used to compute the future design rainfall. The CCF was calculated from the rainfall projections of two global climate models, CGCM1 and CCSM3, with different pre-processing steps applied to each. The IDF data were used in the rainfall-runoff-inundation model to simulate current and future flood inundation scenarios.

The estimated CCF values demonstrate a contrast, whereby each station had a CCF value greater than one for CGCM1, while some stations had a CCF value of less than one for CCSM3. Therefore, CGCM1 projected an aggravation and CCSM3 a reduction of flood hazard for future scenarios. The study reveals that topography plays an essential role in calculating the CCF.

To the best of the author’s knowledge, this is the first study to examine flood projections in the Kelantan River Basin. It is, therefore, hoped that these results could benefit local managers and authorities by enabling them to make informed decisions regarding flood risk mitigation in a climate change scenario.

Anchored in the theories of brand gestalt and stakeholder perspectives, this study aims to undertake a comprehensive examination of the brand gestalt concept, emphasizing its multidimensional nature and the process of co-creation.

Focused within the context of the Wonderful Indonesia brand, the research draws upon a rich qualitative data set derived from in-depth interviews conducted with 18 international tourists, supplemented by netnography (or internet ethnography) of websites, social media and online articles related to Wonderful Indonesia. Using grounded theory methodology, the qualitative data undergo rigorous analysis to identify emergent themes and patterns.

The research elucidates the four dimensions (4S) comprising brand gestalt: storyscapes, sensescapes, servicescapes and stakeholderscapes. Each dimension is further delineated into essential categories, providing a comprehensive understanding of brand gestalt. This study highlights the collaborative nature of brand gestalt, emphasizing the involvement of multiple stakeholders in shaping the brand's identity and perception. Consumer perceptions of co-creation are identified as significant contributors to brand gestalt, enhancing the brand's value proposition.

Destination management and practitioners can use the insights from the research to refine their brand management and marketing strategies by leveraging the dimensions of brand gestalt. Recognizing the collaborative construct of brand gestalt can guide businesses in fostering meaningful relationships with stakeholders and aligning branding efforts with collective visions. Understanding the role of consumer co-creation in brand development can inform strategies aimed at enhancing brand equity and fostering consumer loyalty.

This study extends existing literature on brand gestalt by providing a comprehensive examination of its four dimensions and essential categories. By emphasizing the collaborative nature of brand gestalt, this study contributes to advancing the understanding of brand co-creation paradigms. The identification of consumer perceptions of co-creation as a significant factor in brand gestalt adds novel insights to the literature, offering valuable implications for brand management and marketing strategies.

Robotic restaurants are very novel, and service robots in these restaurants are identified as offering unique advantages in terms of efficiency, tireless service and potentially lower operational costs. However, studying customer engagement with the robots can reveal aspects of robotic service that resonate with diners. Understanding how diners interact with robots can help create a more engaging and enjoyable atmosphere, bringing more business to restaurants. Building on the stimulus-organism-response (SOR) theory and place attachment theory, the purpose of this paper is to study the impact of the robotic service quality (RSQ) on the customer attachment to the robotic restaurant with the mediating role of the different dimensions of the customer engagement, like the Absorptive Attention, Enthusiastic Participation and Social Connection. Subsequently, the impact of the customer attachment to the robotic restaurant on different dimensions of customer engagement behaviors like augmenting, co-developing, influencing and mobilizing behaviors was also studied.

The cross-sectional data from 786 robotic restaurant diners in India who answered the self-administered structured questionnaires is utilized for this descriptive study. The study employed a purposive sampling strategy. The SMART-PLS 4.0 program was used to run structural equation modeling and analyze the data.

The results indicate that customer engagement dimensions like Absorptive Attention, Enthusiastic Participation and Social Connection differentially mediate the relationship between RSQ and customer attachment with the robotic restaurant. Customer attachment to the restaurant and the robotic services subsequently positively impact customer engagement behaviors.

The study relied upon cross-sectional data from the Indian population above 18 years to test the proposed model. Further studies could test the model across different populations to generalize the study results.

This study addresses the need to investigate robotic restaurant diners’ engagement behaviors. By testing place attachment theory and the SOR framework, this study is the first to show that RSQ will impact the customer attachment with the robotic restaurant and that different dimensions of customer engagement mediate the relationship. It also validates the previous research findings that customer engagement is not a single global construct, and different sub-dimensions are to be explored. This study is also the first to show customer attachment to the robotic restaurant will impact customer engagement behaviors differently.