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Automated driving systems (ADSs) are being developed to avoid human error and improve driving safety. However, limited focus has been given to the fallback behavior of automated vehicles, which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure. Therefore, this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction.

Owing to an undetected area resulting from a front sensor malfunction, the proposed ADS first creates virtual vehicles to replace existing vehicles in the undetected area. Afterward, the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle; an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure, avoiding potential collisions with surrounding vehicles. This fallback approach was tested in typical cases related to car-following and lane changes.

It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure, revealing that the proposed method is effective for the test scenarios.

This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure, and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure. This proposal gives a different view on the fallback safety problem from the normal strategy, in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped.

This study aims to research the large cross-section tunnel stability evaluation method corrected after considering the thickness-span ratio.

First, taking the Liuyuan Tunnel of Huanggang-Huangmei High-Speed Railway as an example and taking deflection of the third principal stress of the surrounding rock at a vault after tunnel excavation as the criterion, the critical buried depth of the large section tunnel was determined. Then, the strength reduction method was employed to calculate the tunnel safety factor under different rock classes and thickness-span ratios, and mathematical statistics was conducted to identify the relationships of the tunnel safety factor with the thickness-span ratio and the basic quality (BQ) index of the rock for different rock classes. Finally, the influences of thickness-span ratio, groundwater, initial stress of rock and structural attitude factors were considered to obtain the corrected BQ, based on which the stability of a large cross-section tunnel with a depth of more than 100 m during mechanized operation was analyzed. This evaluation method was then applied to Liuyuan Tunnel and Cimushan No. 2 Tunnel of Chongqing Urban Expressway for verification.

This study shows that under different rock classes, the tunnel safety factor is a strict power function of the thickness-span ratio, while a linear function of the BQ to some extent. It is more suitable to use the corrected BQ as a quantitative index to evaluate tunnel stability according to the actual conditions of the site.

The existing industry standards do not consider the influence of buried depth and span in the evaluation of tunnel stability. The stability evaluation method of large section tunnel considering the correction of overburden span ratio proposed in this paper achieves higher accuracy for the stability evaluation of surrounding rock in a full or large-section mechanized excavation of double line high-speed railway tunnels.

Digitalization accelerates the need of tourism and hospitality ecosystems to reframe business models in line with a data-driven orientation that can foster value creation and innovation. Since the question of data-driven business models (DDBMs) in hospitality remains underexplored, this paper aims at (1) revealing the key dimensions of the data-driven redefinition of business models in smart hospitality ecosystems and (2) conceptualizing the key drivers underlying the emergence of innovation in these ecosystems.

The empirical research is based on semi-structured interviews collected from a sample of hospitality managers, employed in three different accommodation services, i.e. hotels, bed and breakfast (B&Bs) and guesthouses, to explore data-driven strategies and practices employed on site.

The findings allow to devise a conceptual framework that classifies the enabling dimensions of DDBMs in smart hospitality ecosystems. Here, the centrality of strategy conducive to the development of data-driven innovation is stressed.

The study thus developed a conceptual framework that will serve as a tool to examine the impact of digitalization in other service industries. This study will also be useful for small and medium-sized enterprises (SMEs) managers, who seek to understand the possibilities data-driven management strategies offer in view of stimulating innovation in the managers' companies.

The paper reinterprets value creation practices in business models through the lens of data-driven approaches. In this way, this paper offers a new (conceptual and empirical) perspective to investigate how the hospitality sector at large can use the massive amounts of data available to foster innovation in the sector.

This study furthers one’s understanding of the motivations of the crowdfunding crowd by empirically examining critical factors that influence the crowd's decision to support a crowdfunding project.

Backer's comments from a sample of the top 100 most funded technology product projects on KickStarter were collected. A latent Dirichlet allocation (LDA) analysis strategy was adopted to investigate critical motivational factors. Three experts mapped those factors to the known theoretical constructs of social exchange theory (SET).

Although backers are motivated by value, they are also motivated by far less tangible social factors including trust and a feeling of psychological ownership. Findings suggest that the crowd is far more than a passive group of investors or customers and should be viewed as participatory stakeholders. This study serves as guidance for project owners hoping to motivate the crowd and for future investigators examining backer motivations in other types of crowdsourcing projects.

Online chatter in the form of user-generated comments is an excellent data source for researchers to mine for value and meaning.

Given strong feelings of psychological ownership, project owners should actively engage the crowd and solicit the crowd for advice and help in order to motivate them.

The study presents the first empirical exploration of backer motivations using LDA guided by theory and the knowledge of experts. A framework of latent motivational factors is proposed.

Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults.

This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced.

This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods.

This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.

Decision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.

In this research, a probabilistic decision-making method based on the Markov decision process (MDP) is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data. The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states, actions and basic models. Transition and reward models are defined by using a complete prediction model of the surrounding cars. An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.

Results show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy.

This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.

This paper aims to comprehensively clarify the research status of thermal transport of supercritical aviation kerosene, with particular interests in the effect of cracking on heat transfer.

A brief review of current research on supercritical aviation kerosene is presented in views of the surrogate model of hydrocarbon fuels, chemical cracking mechanism of hydrocarbon fuels, thermo-physical properties of hydrocarbon fuels, turbulence models, flow characteristics and thermal performances, which indicates that more efforts need to be directed into these topics. Therefore, supercritical thermal transport of n-decane is then computationally investigated in the condition of thermal pyrolysis, while the ASPEN HYSYS gives the properties of n-decane and pyrolysis products. In addition, the one-step chemical cracking mechanism and SST k-ω turbulence model are applied with relatively high precision.

The existing surrogate models of aviation kerosene are limited to a specific scope of application and their thermo-physical properties deviate from the experimental data. The turbulence models used to implement numerical simulation should be studied to further improve the prediction accuracy. The thermal-induced acceleration is driven by the drastic density change, which is caused by the production of small molecules. The wall temperature of the combustion chamber can be effectively reduced by this behavior, i.e. the phenomenon of heat transfer deterioration can be attenuated or suppressed by thermal pyrolysis.

The issues in numerical studies of supercritical aviation kerosene are clearly revealed, and the conjugation mechanism between thermal pyrolysis and convective heat transfer is initially presented.

The purpose of the paper is to present an empirical study that examines the impact of digitalization on informative, strategic and operational marketing activities in manufacturing companies from the entrepreneurial perspective.

A research project was carried out in 205 Italian manufacturing companies by using the questionnaire method. An exploratory research study was conducted with hierarchical cluster analysis.

The analysis shows the existence of seven clusters of manufacturing companies that differ by the impact of digitalization on marketing activities from the entrepreneurial perspective. Two clusters have a high positive impact of digitalization, primarily on informative and strategic marketing activities. Two clusters are characterized by a low positive impact of digitalization and three clusters perform an intermediate level of digitalization. Furthermore, these groups of clusters differ in terms of the influence of digitalization on customer value.

The small size of the sample and the geographic origin of the companies imply limited generalizability; further research on the topic is thus recommended.

The study suggests that companies should digitalize many key marketing activities to increase marketing effectiveness and customer value. To achieve high levels of digitalization and thus increase their competitiveness, manufacturing companies should consider the importance of relevant technologies and skills.

By focussing on the impact of digitalization on informative, strategic and operational marketing, which has not yet been empirically investigated, the present study reveals many new elements concerning the marketing process in the digital era from the entrepreneur's point of view.

The purpose of this paper is to analyze the role that food and beverage (F&B) sensory stimuli play in building non-food-themed touristic experiences, such as wellness tourism experiences.

This paper adopts an asynchronous netnographic approach supported by software (T-Lab, 2021). The study was conducted on a database consisting of 3,141 reviews in English, left by customers of 38 wellness facilities (Spa Retreats) spread across 5 continents.

The analysis reveals that F&B stimuli contribute significantly to the tourist's perception of the wellness experience in a two-fold manner: on the one hand, they support the wellness experience, and on the other, through specific proposals (e.g. wine, vegan, detox, etc.), they qualify and differentiate the wellness experience in a hedonic rather than eudaimonic way.

The present study contributes to managerial literature on the topic of gastronomic tourism and wellness tourism by providing, on an international scale, empirical evidence of (a) the importance and role of F&B touchpoints in hybrid gastronomic experiences; and (b) the presence of a variety of “wellness experiencescapes”.

This study is the first attempt to measure the role of F&B in tourists' perceptions of non-food-themed experiences. The research not only provides new data on the wellness experience through a cross-continental analysis but also offers useful theoretical and managerial insights for the design of wellness tourism experiences.

To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The purpose of this paper is to provide theoretical guidance and reference standards for the indicator system design process, laying a solid foundation for the application of the indicator system, by systematically exploring the expert evaluation method to optimize the index system to enhance its credibility and reliability, to improve its resolution and accuracy and reduce its objectivity and randomness.

The paper is based on system theory and statistics, and it designs the main line of “relevant theoretical analysis – identification of indicators – expert assignment and quality inspection” to achieve the design and optimization of the indicator system. First, the theoretical basis analysis, relevant factor analysis and physical process description are used to clarify the comprehensive evaluation problem and the correlation mechanism. Second, the system structure analysis, hierarchical decomposition and indicator set identification are used to complete the initial establishment of the indicator system. Third, based on expert assignment method, such as Delphi assignments, statistical analysis, t-test and non-parametric test are used to complete the expert assignment quality diagnosis of a single index, the reliability and validity test is used to perform single-index assignment correction and consistency test is used for KENDALL coordination coefficient and F-test multi-indicator expert assignment quality diagnosis.

Compared with the traditional index system construction method, the optimization process used in the study standardizes the process of index establishment, reduces subjectivity and randomness, and enhances objectivity and scientificity.

The innovation point and value of the paper are embodied in three aspects. First, the system design process of the combined indicator system, the multi-dimensional index screening and system optimization are carried out to ensure that the index system is scientific, reasonable and comprehensive. Second, the experts’ background is comprehensively evaluated. The objectivity and reliability of experts’ assignment are analyzed and improved on the basis of traditional methods. Third, aim at the quality of expert assignment, conduct t-test, non-parametric test of single index, and multi-optimal test of coordination and importance of multiple indicators, enhance experts the practicality of assignment and ensures the quality of expert assignment.