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Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.

This study reports on the effect of demand variation on the optimal location of road-pricing cordons. The optimal road-pricing cordon, in this study, aims to maximise the social welfare function. This optimisation program is categorised as Bi-level optimisation programming which is a NP hard problem. The paper first describes the method developed to solve the optimal toll problem for a given set of chargeable links. The tests were carried out with a small toy network and a larger scale network. For the small network, four single user class demand characteristics were varied individually; these were the elasticity of trip generation with respect to increases in travel cost, value of travel time, volume of traffic, and traffic distribution pattern. For the larger scale network, only elasticity, value of time, and trip volume were tested. The results of the larger scale network are also analysed by including the cost per toll point. The tests with the larger scale network were modified so that the constraint of uniform charge is applied. The results showed that demand variation could influence the best location of toll points. This finding raises the question of whether the implementation of the same cordon all day in an urban traffic network is the optimal approach under the existence of demand variations by time of day, and also whether the evaluation process of the cordon location should consider the effect of different time periods together.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

The purpose of this study is to investigate the effect of chlorination on the structure and properties of reclaimed rubber and to discuss the feasibility of a novel method to chlorinate reclaimed rubber.

A series of chlorinated reclaimed rubber with different chlorination degrees (CD) was prepared by suspension chlorination in aqueous phase (SCAP). Their structure and performance were characterized by Fourier transform infrared spectrometer, energy dispersive spectroscopy, scanning electron microscopy, thermogravimetric and mechanical property test.

The chemistry structure, mechanical performance and heat resistance of CRR is affected greatly by its CD.

Although in the present work only chlorination of reclaimed rubber is researched, but this method can be used to modify other recycled rubber.

SCAP is a useful method to produce CRR, and it is feasible for production of chlorinated recycled rubber in large scale. The present work provides a new strategy to fabricate new materials based on recycled rubber.

Chlorination of reclaimed rubber by SCAP is useful to convert waste rubber into new materials, and it is useful to decrease environment pollution.

SCAP method provides a new technology to chlorinate waste rubber with many merits, such as chlorination rate of RR is accelerated and the reaction can be controlled or adjusted easily. Moreover, conversion of chlorine is increased remarkably.

The application of smart contract can greatly reduce transaction costs and improve transaction efficiency. The existing smart contract are expensive, single application scenario and inefficient. This paper aims to propose a new smart contract model to solve these problems.

By investigating the research history, models and platforms, this paper summarizes the shortcomings of existing smart contracts. Based on the content and architecture of traditional contract, a smart contract model with wider application scope is designed.

In this paper, several models are used to describe the operation mechanism of smart contracts. To facilitate computer execution, a decomposition method is proposed, which divides smart contracts into several sub-contracts. Then, the advantages and deployment methods of smart contract are discussed. On this basis, a specific example is given to illustrate how the application of smart contract will change our life.

Smart contract is gradually applied to more fields. In this paper, the structure and operation mechanism of smart contract system in reality are given, which will be beneficial to the application of smart contract to more complex systems.

The purpose of the current study is to explore the potential possibility of acceleration in recognition, remedial process of heart disease and continuous electrocardiogram (ECG) signal acquisition. The textile-based ECG electrode is prepared by inkjet printing of activator followed by electroless plating of nickel (Ni) particle.

The electrical resistance shows a range of around 0.1 Ω/sq, which sounds quite proper for ECG signal acquisition, as the potential difference according to heart activity on skin surface is in milivolt range. Surface modifications of Ni–phosphorus (P)-plated polyester fiber were studied by scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray diffractionmethods. The quality of the acquired signal from printed square-shaped sensors in two sizes with areas of 9 and 16 cm2 compared with the standard Ag/Agcl electrode using commercial ECG with the patient in the sitting position.

Comparison of these data led to the consideration of small fabric sensor for better performance and the least disturbance regarding homogeneity and attenuation in electric field scattering. Using these types of sensors in textile surface because of flexibility will provide more freedom of action to the user. Wearable ECG can be applied to solve the problems of the aging population, increasing demand for health services and lack of medical expert.

In the present research, a convenient, inexpensive and reproducible method for the patterning of Ni features on commercial polyester fabric was investigated. Printed designs with high electrical conductivity can be used as a cardiac receiving signals’ sensor.

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.