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Climate change places significant pressure on the tourism sector by altering environmental and socio-economic conditions that influence tourist behaviour and the attractiveness of destinations. Rising temperatures, changing precipitation patterns and the increasing frequency and severity of extreme weather events affect tourism supply and demand. On the supply side, climate change threatens tourism infrastructure, natural attractions, recreational opportunities and accessibility of destinations. Coastal destinations are particularly vulnerable to sea-level rise and coastal flooding, which can damage tourism assets. On the demand side, changing climatic conditions alter visitor comfort levels, health risks and the seasonality of destinations, influencing tourists' choice of destinations. In addition, small island destinations face unnecessary risks due to their economic dependence on climate-sensitive activities such as beach and nature tourism. Adapting the tourism sector to climate change requires reducing vulnerability through diversification, green infrastructure, ecosystem conservation, community-based adaptation and policy support. Mitigating tourism's contribution to climate change requires minimising energy use, switching to renewable energy, improving efficiency, reducing long-haul flights and promoting sustainable consumption and production. Collective and concerted efforts by all stakeholders are needed to transition to a climate-resilient and low-carbon tourism sector that continues to provide socio-economic benefits while minimising its environmental footprint.

Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

While prior research has established that traumatic brain injury (TBI) is a risk factor for violent offending, there is little understanding of mechanisms that may underpin this relationship. This is problematic, as a better understanding of these mechanisms could facilitate more effective targeting of treatment. This study aims to address these gaps in the extant literature by examining TBI as a predictor of violent offending and test for mediation effects through cognitive constructs of dual systems imbalance and hostility among a sample of justice-involved youth (JIY).

The Pathways to Desistance data were analyzed. The first three waves of this data set comprising the responses of 1,354 JIY were analyzed. Generalized structural equation modeling was used to test for direct and indirect effects of interest. A bootstrap resampling process was used to compute unbiased standard errors for determining the statistical significance of mediation effects.

Lifetime experience of TBI was associated with increased violent offending frequency at follow-up. Hostility significantly mediated this relationship, but dual systems imbalance did not. This indicated that programming focused on reducing hostility among JIY who have experienced TBI could aid in reducing violent recidivism rates.

To the best of the author’s knowledge, this study was the first to identify significant mediation of the relationship between TBI and violent offending through hostility.

The objective of this paper is to define a structural equation model (SEM) that examines how fashion consumers' expectations of consumer brand engagement (CBE) influence purchase intention, both directly and through consumer happiness as a mediator. For this purpose, the authors will develop the components of CBE and consumer happiness variables, whose relevance in the literature has increased considerably.

For this purpose, a cross-sectional descriptive study was carried out by means of a survey developed on the basis of the scales found in the literature, with a sample of 1,296 consumers representative of the Spanish population. The method used to test the hypotheses was partial least squares SEM.

The results of this research indicate that both CBE and consumer happiness positively influence the purchase intention of fashion brands. In turn, the importance of the dimensions of the antecedent variables can be established.

The current research does not allow a longitudinal analysis of the consumption of fashion retailers. The findings of this scientific study provide a wealth of theoretical and practical information for managers to develop management models based on the sensory enjoyment of their consumers.

One of the novelties of this study is that it considers the consumer happiness variable and its dimensions in a consumer behavior model together with CBE.

The purpose of this study is to identify individuals’ preferences for the information attributes of Airbnb, a representative peer-to-peer (P2P) accommodation platform. In the sharing economy, platforms are considered to be the principal intermediaries in supply and demand, and these platforms have distinctly different attributes from traditional accommodation reservation systems.

The present study used a choice experiment, which is a method for systematically identifying attributes’ preferences in the form of marginal willingness-to-pay (MWTP). Amazon Mechanical Turk, a crowdsourcing marketplace, was used for data collection, and 243 respondents ultimately participated in the survey.

Results showed that respondents’ choices were positively affected by the number of pictures of an accommodation, host experience, cancellation policy and local information but negatively affected by cost. Regarding MWTPs, host experience had the highest value (US$84.25), followed by cancellation policy (US$40), photos (US$26.67) and local information (US$10.92).

These study results could provide strategic guidance for guiding the development of P2P accommodation platforms by providing a prioritized list of preferred attributes for Airbnb.

本研究旨在识别个人对Airbnb这一代表性P2P住宿平台信息属性的偏好。在共享经济中, 平台被视为供需的主要中介, 而这些平台与传统住宿预订系统有明显不同的属性。

本研究采用选择实验法（CE）, 这是一种系统地确定属性偏好的方法, 表现为边际支付意愿（MWTP）。数据采集使用了Amazon Mechanical Turk（MTurk）, 最终有243名受访者参与了调查。

结果显示, 受访者的选择受到住宿图片数量、房东经验、取消政策和本地信息的积极影响, 而受到价格的负面影响。关于边际支付意愿, 房东经验具有最高价值（84.25美元）, 其次依次为取消政策（40美元）、照片（26.67美元）和本地信息（10.92美元）

通过提供优先考虑的 Airbnb 偏好属性列表, 本研究结果可以为引导P2P住宿平台的发展提供战略指导.

The impact of COVID-19 on tourism destinations has been severe, but a future crisis is never far away. How communities can better prepare for disasters to come in the near future continues to be researched. This research aims to understand the tourism community’s responses to the COVID-19 pandemic and present the Tourism Community Resilience Model as a useful instrument to help communities better respond to disasters in the future.

This research uses a qualitative research approach which seeks to understand phenomena, events, social activities, attitudes, beliefs, perceptions and individual and group opinions that are dynamic in character in accordance with the situation in the field. Research primary data is in the form of Kuta Traditional Village local community responses in enduring the COVID-19 pandemic conducted between January and May 2022. These data were obtained through in-depth observations and interviews involving informants based on purposive sampling, including traditional community leaders, village officials, tourism actors (i.e. street vendors, tourist local guides, taxi drivers and art workers) and tourism community members. We selected the informants who are not only directly impacted by the pandemic, but also some of them have to survive during the pandemic because they do not have other job options. The results of previous research and government data concerning the pandemic and community resilience were needed as secondary data, which were obtained through a study of the literature. The data which had been obtained were further analysed based on the Interpretative Phenomenological Analysis (IPA) technique, which seeks to make meaning of something from the participants’ perspective and the researchers’ perspective as a result there occurs a cognition of a central position.

Based on findings from Bali, Indonesia, this resilience model for the tourism community was created in response to the difficulties and fortitude shown by the community during the COVID-19 pandemic. It comprises four key elements, namely the Local Wisdom Foundation, Resource Management, Government Contributions and External Community Support. These elements are all rooted in the concepts of niskala (spirituality) and sekala (real response); it is these elements that give the tourism community in the Kuta Traditional Village a unique approach, which can inspire other tourism destinations in other countries around the world.

A tourism community resilience model based on local community responses has implications for the process of enriching academic research and community management practices in facing future crisis, particularly by involving local wisdom foundation.

A tourism community resilience model based on local community responses has implications for the process of enriching academic research and community management practices in facing future crisis, particularly by involving local wisdom foundation.

The existence of the resilience model strengthens local community social cohesion, which has been made stronger by the bonds of culture and shared faith in facing disaster. This social cohesion then stimulates the strength of sustainable and long-term community collaboration in the post-pandemic period. For tourism businesses, having strong connections with the local communities is an important condition to thrive.

The value of this research is the Tourism Resilience Community Model, which is a helpful tool to optimise and improve future strategies for dealing with disasters. Illustrated by this Balinese example, this paper emphasises the importance of adding social factors such as niskala and sekala to existing community resilience models. Addressing these local characteristics is the innovative aspect of this paper and will help inspire communities around the world to prepare for future disasters better and build more sustainable and resilient tourism destinations elsewhere.

Smart farming technologies (SFTs) can increase yields and reduce the environmental impacts of farming by improving the efficient use of inputs. This paper is to estimate farmers' preference and willingness to pay (WTP) for a well-defined SFT, smart drip irrigation (SDI) technology.

This study conducted a discrete choice experiment (DCE) among 1,300 maize farmers in North China to understand their WTP for various functions of SDI using mixed logit (MIXL) models.

The results show that farmers have a strong preference for SDI in general and its specific functions of smart sensing and smart control. However, farmers do not have a preference for the function of region-level agronomic planning. Farmers' preferences for different functions of SDI are heterogeneous. Their preference was significantly associated with their education, experience of being village cadres and using computers, household income and holding of land and machines. Further analysis show that farmers' WTP for functions facilitated by hardware is close to the estimated prices, whereas their WTP for functions wholly or partially facilitated by software is substantially lower than the estimated prices.

Findings from the empirical study lead to policy implications for enhancing the design of SFTs by integrating software and hardware and optimizing agricultural extension strategies for SFTs with digital techniques such as videos.

This study provides initial insights into understanding farmers' preferences and WTP for specific functions of SFTs with a DCE.

This study examines the influencing factors of smart technology use behaviour (STUB), influencing tourist satisfaction and enhancing revisit intention for the Chinese tourism destination. Further, the moderating role of place attachment on the relationship between STUB, tourist satisfaction and revisit intention has been examined.

The study employs quantitative methodology by incorporating the planned behaviour theory to develop the hypotheses. Using an online survey link, 409 responses were collected from the tourists employing a non-probability convenience random sampling technique.

The partial least squire-structural equation modelling (PLS-SEM) results show that social influence significantly affects STUB, tourist satisfaction and revisit intention. Also, the anticipated positive behaviour has positive and significantly affects STUB and revisit intention. Finally, the findings show that tourist satisfaction significantly affects revisit intention in the tourist destinations in China.

A quantitative research design was applied, employing a random sampling technique, and surveys were conducted with tourists only in current research. However, future research can incorporate a wide range of methodology by collecting data from other tourism stakeholders to have an in-depth evaluation of repeat visitation behaviour. Future research can enhance the current conceptual framework by including other relevant variables like negative anticipated emotions at other locations, as the current study was conducted in the Chinese context.

This research adds value to the tourism destination to formulate tourist satisfaction and revisit intention. Implications are provided for a more nuanced understanding and effective planning in tourism destinations while considering smart technology use.

In the past decade, the biopolymeric properties of chitosan (CH) have been largely exploited for various applications. This paper aims to study the use of CH in its nanoform, i.e. as nanofibers blended with polyvinyl alcohol (PVA) for various antimicrobial applications in detail. In particular, their ability toward bacterial growth inhibition, in vitro drug release and their biocompatibility toward tissue growth have been investigated in detail.

Electrospinning technique was adapted for depositing CH/PVA blended nanofilms on the silver foil under optimized conditions of high voltage. Three different concentrations of blended nanofiber samples were prepared and their antimicrobial properties were studied.

The bead diameter and average diameter of blended nanofibers increase with CH concentration. Antibacterial activity increases as CH concentration increases. Increased hydrophilicity in CH-enriched samples contributes to a higher drug release profile.

To the best of the authors’ knowledge, chick chorioallantoic membrane assay analysis has been carried out for the first time for CH/PVA films which shows that CH/PVA blends are biocompatible. CH after being converted as nanoparticles exhibits higher drug release rate by in vitro method.

Developments regarding the use of artificial intelligence (AI) in transportation systems, one of the important stakeholders of tourism, are remarkable. However, no review thus far has provided a comprehensive overview of research on AI in transportation systems.

To fill this gap, this study uses the VOSviewer software to present a bibliometric review of the current scientific literature in the field of AI-related tourism research. The theme of AI in transportation systems was explored in the Web of Science database.

The original search yielded 642 documents, which were then filtered by parameters. For publications related to AI in transportation systems, the most cited documents, leading authors, productive countries, co-occurrence analysis of keywords and bibliographic matching of documents were examined. This report shows that there has been a recent increase in research on AI in transport systems. However, there is only one study on tourism. The country that contributed the most is China with 298 studies. The most used keyword in the documents was intelligent transportation system.

The bibliometric analysis of the existing work provided a valuable and seminal reference for researchers and practitioners in AI-related in transportation system.