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Wine tourism is gaining increasing popularity among Chinese tourists, making it necessary to thoroughly examine tourist behavior. While online reviews posted by wine tourists have been extensively studied from the perspectives of destinations and wineries, the perspective of the tourists themselves has been overlooked. To address this gap, this study aims to identify significant attributes intrinsic to the tourism experiences of Chinese wine tourists by adopting a text-mining approach from a tourist-centric perspective.

The authors use topic modeling to extract these attributes, calculate topic intensity to understand tourists’ attention distribution across these attributes and conduct topical sentiment analysis to evaluate tourists’ satisfaction levels with each attribute. The authors perform importance-performance analyses (IPAs) using topic intensity and sentiment scores. Furthermore, the authors conduct semistructured in-depth interviews with Chinese wine tourists to gain insights into the underlying reasons behind the key findings.

The study identifies eleven attributes for domestic wine tourists and seven attributes for outbound wine tourists. From the reviews of both domestic and outbound tourists, three common attributes have been identified: “scenic view”, “wine tasting and purchase” and “wine knowledge”.

According to the results of the IPAs, there is a pressing need for enhancements in the wine tasting and purchasing experience at domestic wine attractions. Additionally, managers of domestic wine attractions should continue to prioritize the positive aspects of the family trip experience and scenic views. On the other hand, for outbound wine attractions, it is crucial for managers to maintain their efforts in providing opportunities for wine knowledge acquisition, ensuring scenic views and upholding the reputation of wine regions.

First, this study breaks new ground by adopting a tourist-centric perspective to extract significant attributes from real wine tourism reviews. Second, the authors conduct a comparative analysis between Chinese wine tourists who travel domestically and those who travel abroad. The third novel aspect of this study is the application of IPA based on textual review data in the context of wine tourism. Fourth, by integrating topic modeling with qualitative interviews, the authors use a mixed-method approach to gain deeper insights into the experiences of Chinese wine tourists.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

This study aims to investigate the impact of regular Kung Fu and Taekwondo practice on the health and quality of life among elderly individuals in the Maule region, Chile.

The authors designed a 12-week Kung Fu and Taekwondo workshop with activities suitable for their age. Through semistructured interviews (at the beginning and the end of the workshop), along with periodic monitoring of vital signs and cardiovascular components, the authors observed an improvement in participants’ physical (strength, speed of reaction and flexibility) and psychological conditions (self-esteem and resilience), quality of life (relationships with family and friends and ability to deal with stressful events in working life) and health (waist circumference, percentage of oxygen saturation in blood, blood pressure, among other values).

From these results, the authors affirm that this workshop improves health and physical condition and helps the participants develop the coping capacity to deal with stressful situations and complicated interpersonal relationships. In this sense, the authors conclude that Kung Fu and Taekwondo as regular sports activities can benefit senior citizens’ aging process.

This research is based on an original study project.

Vocational education and entrepreneurship are constantly increasing in research fields. However, what is the current state of vocational education and entrepreneurial research? Where will the area go next? These questions are still unanswered; thus, this study tries to map the research landscape of the study area to get insights and provide directions for future research.

This research collected extant literature on vocational education and entrepreneurship using Scopus scientific database. Bibliometric analysis has been performed to extract insights from 175 documents published in the study area. Content analysis on the extant literature has also been committed to getting contextual information and developing an integrated research framework for future researchers.

The bibliometric analysis revealed that training, career choice, curriculum, self-employment, student psychology, better job opportunity, learning environment and innovation are the most discussed in the vocational education and entrepreneurship literature. Developed nation’s strong presence, indicated by the number of publications in the field.

This study significantly contributes to entrepreneurship by disclosing advances in the literature and some of the most active research fronts in this sector, delivering insights that have yet to be wholly appreciated or appraised. The study also developed an integrated framework that could benefit various vocations, education and entrepreneurship stakeholders.

This study aims to evaluate a human rights-informed dementia prevention program promoting better health and social care among older adults. In this study, the authors examined whether a dual-task training would improve cognition in healthy older adults.

Individuals attending the systematic health education program for older adults based in Japan were recruited for study inclusion, and divided into a dual-task training group (TG) and a control group (CG). The TG underwent 90 min of a weekly dual-task training for 12 weeks. Severity of dementia was measured using the Mini-Mental State Examination (MMSE) test. Brain function was assessed using a go/no-go task paradigm, during which cerebral blood flow was additionally measured using functional near-infrared spectroscopy to quantify oxyhemoglobin (oxy-Hb).

MMSE total score, number of errors in the go/no-go tasks and oxy-Hb values showed significant improvements in the TG.

Owing to the small number of participants allocated to the CG, the results must be interpreted with caution. Replication and further validation based on large-scale, randomized-controlled trials is warranted.

This study highlights potential benefits of incorporating an early prevention training for dementia into a human rights-friendly health education program.

This study suggests a potential means to reduce costs of social security and health care by introducing a human rights-informed dementia prevention program.

The results suggest that dual-task training may improve cognitive function in healthy older adults, thereby contributing to better health and improvement of social health care, based on a human rights-informed health education program for the prevention of dementia.

Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.

In this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.

This paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R² score of 0.99, in contrast to Support Vector Regression, which yielded an R² score of 0.74.

The full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

The purpose of this study is to explore the experiences of those volunteering within a restorative justice service thus enabling an insight into their perceptions of the different methods used, their beliefs about restorative justice effectiveness, and its place within the criminal justice system. The study also sought to identify any challenges and positive experiences the participants encountered during their role as volunteers, with volunteering during the COVID-19 pandemic explored specifically.

Data was collected from the participants (n = 5) via semi-structured interviews and analysed using thematic analysis, thus enabling patterns within the experience of the volunteers to be identified.

A prior understanding and interest in restorative justice was evident within the data, with participants demonstrating a preference for direct, face-to-face mediation. The perceived lack of support from external agencies was discussed along with the role of education in their volunteering experience. Finally, it was acknowledged that although face-to-face practice was deemed the most effective overall, certain practices adopted during COVID-19 enabled aspects of the role to be carried out more efficiently and equally as effectively.

The findings from this study draw out real-world implications, producing tangible action points for restorative justice services. Some tentative suggestions for future practice are outlined.

The volunteers’ role within restorative justice is often overlooked within the literature (Paul and Borton, 2013) and time constraints can add additional barriers to a hard-to-reach population. However, volunteers play a vital role in restorative justice. By exploring and listening to the volunteers’ experience, this study expands an additional strand within the literature in terms of what makes restorative justice effective and the challenges that are faced from a volunteer perspective.

This paper aims to focus on the issue of high employee turnover in the Indian tech industry. An integrative review is conducted to analyse the past and current state of literature, as well as prepare a research agenda for future studies.

A pool of 72 articles published between 2010 and 2022 is reviewed with a special focus on Indian tech employees. This study elucidates the extent and impact of employee retention strategies through content analysis.

Two broad perspectives have been established in the literature: the reasons for quitting and the explanations for staying. By means of a comprehensive review, this paper combines these two aspects of literature and suggests factors under organization’s control to retain competent tech employees.

The study is designed to integrate the two theoretical viewpoints of employee turnover literature by consolidating the reasons behind quitting behaviour and staying intention. Codes combining the two aspects are presented as a valuable resource to retain tech talent.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

Construction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.

The study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.

The classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.

The findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.

The classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.