The extent to which businesses incorporate Naga worship into their strategies and operations and its effect on their success remains ignored. This study employed a multidisciplinary approach to examine the diverse practices of Naga worship in business contexts across different regions. This study utilized a mixed-methods research design to provide insights into the strategic integration of Naga worship into business practices and its impact on business performance. It employed a questionnaire to gather insights from respondents about their demographic data, awareness of Naga worship, its integration into business practices, consumer perceptions and behaviors, and overall business performance. Follow-up, in-depth interviews were developed to probe deeper into respondents’ experiences, motivations, and perceptions regarding the integration of Naga worship into their business practices. Most respondents agreed to integrate Naga worship into their company practices or marketing plans by using Naga symbols in branding, doing rituals for success, providing Naga-themed products and services, and scheduling activities on auspicious Naga-related dates. Respondents perceived companies that venerate Naga as culturally genuine and focused on the community. Worshipping the Naga deity improved the brand’s and corporation’s image and reputation. People patronized these enterprises by buying products and services associated with Naga culture. A substantial portion of respondents believe that worshiping Naga enhances commercial prosperity. Yet, a few participants from different regions mentioned difficulties regarding the integration of Naga religious customs.

Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.

The Huaiyang Canal, a significant section of the Grand Canal, boasts representative tourist attractions. This study analysis of online reviews from Ctrip and Mahive using R language, Gephi, ROST CM, and SPSS has provided insights into tourists’ perceptions of the Huaiyang Canal’s image. Key findings include: (1) Dominant landscape images encompass gardens, canals, and buildings, emphasizing the historical and cultural assets. Both cultural and natural landscapes equally captivate tourists. (2) The canal’s tourism image perception follows a “garden-history-canal” hierarchy with the canal as the central space and history expanding its tourism features. (3) The perceptions can be categorized into historical and cultural landscapes, man-made projects, and attraction perception. Despite varying tourist numbers in Huaian and Yangzhou, scenic spot experiences are similar. The overall perception of tourists is largely positive, but some express concerns about service attitudes and travel time planning.

This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.

It increased the demands on ground-water supplies that prolonged drought and improper maintenance of water resources. So it is necessary to evaluate ground-water resources in the hard rock terrain. In recent years, Remote-Sensing methods have been increasingly recognized as a means of obtaining crucial geoscientific data for both regional and site-specific investigations. This work aims to develop and apply integrated methods combining the information obtained by geo-hydrological field mapping and those obtained by analyzing multi-source remotely sensed data in a GIS environment for better understanding the Groundwater condition in hard rock terrain. In this study, digitally enhanced Landsat ETM+ data was used to extract information on geology, geomorphology. The Hill-Shading techniques are applied to SRTM DEM data to enhance terrain perspective views, and extract Geomorphological features and morphologically defined structures through the means of lineament analysis. A combination of Spectral information from Landsat ETM+ data plus spatial information from SRTM-DEM data is used to address the groundwater potential of alluvium, colluvium, and fractured crystalline rocks in the study area. The spatial distribution of groundwater potential zones shows regional patterns related to lithologies, lineaments, drainage systems, and landforms. High-yielding wells and springs are often related to large lineaments and corresponding structural features such as dykes. The results show that the combination of remote sensing, GIS, traditional fieldwork, and models provide a powerful tool for water resources assessment and management, and groundwater exploration planning.