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The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

This paper aims to clarify the resistance degree of group road logistics to flood disaster resilience. The paper measures the resilience of group road logistics by establishing network structure model. The purpose of this study is to improve the resilience of road log.

This paper adopts Delphi method to collect data, interviews mainly flood management experts and supply chain risk management experts, and then analyzes the data through the network structure model combined with interpretative structure model (ISM) and analytical network process (ANP).

The results show that flood frequency and drainage systems are the main factors affecting the resilience of road transport logistics in urban areas. These research results provide useful guidance for the effective planning and design of urban road construction and infrastructure.

However, the main factors affecting the resilience of road transport logistics are likely to change with the development of factors such as climate, economy and environment. Therefore, in future work, the authors' research will focus on the further application of this evaluation method.

The results show that the impact of flooding on the four dimensions of road logistics resilience varies. This shows that in deciding what intervention measures are to be taken to improve the resilience of the road network to flooding, various measures need to be considered.

This paper provides a more scientific analysis of the risk management ability of the road network in the face of floods. In addition, it also provides a useful reference for urban road planners.

This paper addresses a clear need to study how to build models to improve the resilience of road logistics in flood risk.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

Utilizing the theoretical foundation of the stimulus-organism-response framework, the present work developed and investigated a conceptual model. The work explores the effects of perceived risk of COVID-19 on tourists' choice hesitation and choice confidence. Furthermore, it examines the impacts of choice hesitation and choice confidence on psychological distress, which, in turn, influences purchase intentions and risk-protective behavior. Additionally, the study assesses the boundary effects of vulnerability on the association between choice hesitation, choice confidence, and psychological distress.

An online survey was administered in China during COVID-19 to assess the postulated hypotheses. We collected 491 responses using purposive sampling, and covariance-based structural equation modeling (CB-SEM) was performed to investigate the relationships.

Results show that the perceived risk of COVID-19 positively influences the choice hesitation and negatively impact choice confidence. It was also found that choice hesitation and choice confidence positively developed psychological distress, which, in turn, negatively triggered purchase intentions and positively developed risk-protective behavior. Additionally, perceived vulnerability had a significant moderating impact on the proposed relationships, strengthening psychological distress.

In the current context, this study measures bipolar behavioral outcomes using the S-O-R model. Because cognitive processes influence participation in health preventative behavior during the spread of diseases, we highlighted how the perception of risk and vulnerability to a pandemic serves as a reliable indicator of certain behaviors. This study advances understanding of how the psychological mindset of tourists copes with such circumstances. Due to the pandemic, tourists face limitations in their choices and are placing greater emphasis on adopting protective measures to mitigate associated risks.

For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).

Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.

Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.

These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.

Internal knowledge transfer is crucial for firms to improve their employees’ abilities and improve their work performance. However, there is still a gap in the knowledge management field regarding whether internal knowledge transfer can leverage employee personality traits and service performance in service-oriented organizations. To address this gap, this study aims to validate a multilevel model of the mediating (i.e. internal knowledge transfer as a mediator) and moderating (i.e. task interdependence as a moderator) mechanisms underlying personality traits and employee service performance.

Multilevel structural equation modeling was applied for model validation using an original data set from 45 team leaders and 333 employees working in Chinese hotels.

Internal knowledge transfer mediated the link between extraversion and employee service performance and the link between openness to experience and employee service performance. Task interdependence played a moderating role that strengthened both the impacts of extraversion and openness to experience on internal knowledge transfer.

Through the use of an original data set, this study advances the knowledge management discipline by investigating the mediating impact of internal knowledge transfer between personality traits and employee service performance and revealing the moderating impact of task interdependence that underlies the links between personality traits and internal knowledge transfer.

The purpose of this paper is to describe the mediating effects of transformational and transactional leadership styles on the relationship between organizational ethics and workplace incivility.

The study employed cross-sectional survey design. Data were obtained from 209 employees from the telecommunication sector. The structural equation model was employed as the analytical tool to test the hypotheses of the study.

Organizational ethics was negatively related to workplace incivility. Both transformational and transactional leadership styles mediated the relationship between organizational ethics and workplace incivility.

Human resource developers and managers can employ, appraise, train and develop managers who can ethically demonstrate transactional, and transformational leadership behaviors to deal with workplace incivilities.

The paper makes an important contribution to the existing organizational literature by establishing the relevance of transformational and transactional leadership styles as mediators of the nexus between organizational ethics and workplace incivility in the telecommunication sector.

Motivated by the wide application of procurement strategies in retailing, this paper aims to examine the effect of procurement strategies on decisions and profits and strategic inventory (SI) is considered.

The game-theoretic models are developed under a two-period fresh product supply chain (FSC), and consist of the mode of purchasing products only in the first period without SI (Scenario S), the mode of purchasing products in every period without SI (Scenario T) and the mode of purchasing products in every period with SI (Scenario TS).

Conducting the calculating and comparing, some major findings can be concluded. In general, two-period purchasing strategies (Scenarios T and TS) promote a higher freshness-keeping effort than the single buying strategy (Scenario S). Regarding the pricing strategy, SI and Scenario S can both contribute to obtaining a lower wholesale price, the retailer's pricing is relatively complicated and hinges on the consumer's sensitivity to freshness-keeping effort and the holding cost. Besides, comparing the sales quantity and the profit, the authors find that Scenario TS stimulates more demands and brings more profits for the manufacturer. However, Scenario TS is not the optimal selection for the reason that SI sometimes hurts the retailer and even the whole supply chain. Whereas, when the holding cost is in a certain range, Scenario TS will lead to a win-win situation.

The main findings of this study can give the enterprises some advice on the procurement strategies of fresh products and the decisions of pricing and the freshness-keeping effort.

The purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.