Cultural tourism, an important component of the wider tourism industry, has received significant attention due to the complex interplay between cultural heritage and tourism experiences. This form of tourism invites tourists to discover the arts, traditions, and lifestyles of diverse communities, thereby enriching intercultural encounters. Examining the rapidly evolving field of cultural tourism research, this article looks at its many facets, highlighting its growth, thematic focus, and global importance. In order to better understand the wealth and highlight the body of work, this study undertakes a bibliometric analysis of the concept of cultural tourism. This exploration employs bibliometric searching of journals indexed in the web of science database from 1996 to 2023, using the biblioshiny software in rstudio. This approach provides a global perspective, revealing a prolific and multidisciplinary production of the concept of cultural tourism. The study identifies a total of 369 articles published between 1996 and 2023, involving 781 authors and 244 journals. The results underline the widespread engagement with the subject across diverse scientific communities and geographical regions.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

A gradually detailed geophysical investigation took place on Ancient Marina territory. In that area was extended Ancient Tritaea, according to responsible Archaeological Services. The first approach had been attempted since 1988 by applied electric mapping based on a twin-probe array. Later, the survey extended to the peripheral zone under the relative request from the 6th Archaeological Antiquity. A new approach was implemented by combining three different geophysical techniques, like electrical mapping, total intensity, and vertical gradient. These were applied on discrete geophysical grids. Electric mapping tried to separate the area into low and high-interest subareas according to soil resistance allocation. That technique detected enough geometrical characteristics, which worked as the main lever for the application of two other geophysical techniques. The other two techniques would be to certify the existence of geometrical characteristics, which divorced them from geological findings. Magnetic methods were characterized as a rapid technique with greater sensitivity in relation to electric mapping. Also, vertical gradient focuses on the horizontal extension of buried remains. Processing of magnetic measurements (total and vertical) certified the results from electric mapping. Also, both of the techniques confirmed the existence of human activity results, which were presented as a cross-section of two perpendicular parts. The new survey results showed that the new findings related to results from the previous approach. Geophysical research in that area is continuing.

Currently, coal resource-based cities (CRBCs) are facing challenges such as ecological destruction, resource exhaustion, and disordered urban development. By analyzing the landscape pattern, the understanding of urban land use can be clarified, and optimization strategies can be proposed for urban transformation and sustainable development. In this study, based on the interpretation of remote sensing data for three dates, the landscape pattern changes in the urban area of Huainan City, a typical coal resource-based city in Anhui Province, China were empirically investigated. The results indicate that: (1) There is a significant spatial-temporal transformation of land use, with construction land gradually replacing arable land as the dominant land use type in the region. (2) Landscape indices are helpful to reveal the characteristics of land transfer and distribution of human activities during a process. At the landscape type level, construction land, grassland, and water bodies are increasingly affected by human activities. At the landscape composition level, the number of landscape types increases, and the distribution of different types of patches becomes more balanced. In addition, to address the problems caused by the coal mining subsidence areas in Huainan city, three landscape pattern optimization strategies are proposed at both macro and micro levels. The research findings contribute to a better understanding of land use changes and their driving forces, and offer valuable alternatives for ecological environment optimization.

Using the unified theory of acceptance and use of technology (UTAUT), this study investigated the effect of perceived usefulness, perceived ease of use, social influence, facilitating condition, lifestyle compatibility, and perceived trust on both the intention to use and adoption of an e-wallet among adults. This quantitative study employed a cross-sectional research technique to collect data from 501 respondents via Google Form. The acquired data was assessed using partial least squares structural equation modelling (PLS-SEM). Therefore, perceived usefulness, perceived simplicity of use, social influence, lifestyle compatibility, and perceived trust all had a strong positive impact on both intentions to use and adoption of an e-wallet. This study demonstrated that the intention to use an e-wallet mediated the links between predictors and e-wallet adoption. Respondents’ age and gender moderated the effect of lifestyle compatibility on their intention to use an e-wallet. The study’s findings can assist managers and policymakers establish successful ways that capture customers’ intention to use and experience with employing an e-wallet amid a tumultuous market. Finally, such well-crafted policies may stimulate the digital platform and web-based apps, as well as raise e-wallet acceptance rates in undeveloped countries.