This article explored mineral resources and their relation to structural settings in the Central Eastern Desert (CED) of Egypt. Integration of remote sensing (RS) with aeromagnetic (AMG) data was conducted to generate a mineral predictive map. Several image transformation and enhancement techniques were performed to Landsat Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) data. Using band ratios and oriented principal component analysis (PCA) on OLI data allowed delineating hydrothermal alteration zones (HAZs) and highlighted structural discontinuity. Moreover, processing of the AMG using Standard Euler deconvolution and residual magnetic anomalies successfully revealed the subsurface structural features. Zones of hydrothermal alteration and surface/subsurface geologic structural density maps were combined through GIS technique. The results showed a mineral predictive map that ranked from very low to very high probability. Field validation allowed verifying the prepared map and revealed several mineralized sites including talc, talc-schist, gold mines and quartz veins associated with hematite. Overall, integration of RS and AMG data is a powerful technique in revealing areas of potential mineralization involved with hydrothermal processes.

Sustainable ocean tourism is required to establish a balance between the environmental, economic, social and cultural aspects of ocean tourism development. Sustainable ocean tourism also contributes to local and national economies, enhancing the quality of social life and protecting the ecology. Sustainable ocean tourism expands the positive contribution of tourism to biodiversity conservation and poverty reduction and aims to attain the common goals of sustainable developments for ocean tourism. Sustainable ocean tourism is possible due to the roles of regulators and private and government institutions. Government policies, regulations and guidelines play vital roles towards achieving the sustainability of ocean tourism. However, the role of institutions also cannot be ignored, which provide support in the innovation of technologies and the implementation of policies. The paper targets to investigate the roles of regulations, policies and institutions in the sustainability of ocean tourism. A primary online survey on the perception of tourism experts was conducted for this study using Google Forms. The tourism experts were invited from all over the world to participate in the survey. The study received a total of 33 responses, out of which only 30 valid responses were considered. Using the Tobit regression model, the study found that, while regulations in India relative to foreign countries significantly boost the sustainability of ocean tourism, government policies and public institutions in India relative to foreign countries remain insignificant in predicting the sustainability of ocean tourism. Therefore, government policies and public institutions in India need to be revised and reformulated to make them important drivers of the sustainability of ocean tourism.

Small and medium-sized enterprises as the main body of Chinese enterprises should be an important driving force for China's economic development. However, the problems of salary management faced by small and medium-sized enterprises are increasingly hindering their own survival and development. Whether it is standing on the position of the enterprise or the workers, the pay problem is that they are more concerned about the problem, while the remuneration is also an indispensable modern means of competition and incentives. Salary management is not only indispensable content of enterprise human resources, but also the establishment of modern enterprise system, and optimization of the allocation of social resources requirements. Enterprise salary management operation flexibility or not, directly affect the production and operation management, which will affect the long-term development of enterprises. This paper analyzes the problems of salary management in small and medium-sized enterprises (SMEs), such as unreasonable pay system, lack of forward-looking management system, and so on, and analyzes and discusses their own countermeasures.

Machine analysis of detection of the face is an active research topic in Human-Computer Interaction today. Most of the existing studies show that discovering the portion and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. To overcome these problems, we present a method based on Extended Semi-Local Binary Patterns. For each frame, an aggregation of the pixel values over a neighborhood is considered and a local binary pattern is obtained. From these a binary code is obtained for each pixel and then histogram features is computed. Adaboost algorithm is used to learn and classify these discriminative features with the help of exemplar face and non-face signature of the images for detecting the location of face region in the frame. This Extended Semi Local Binary Pattern is sturdy to variations in illumination and noisy images. The developed methods are deployed on the real time YouTube video face databases and found to exhibit significant performance improvement owing to the novel features when compared to the existing techniques.

The xanthorrhiza species of the genus Arracacia belongs to the Apiaceae family and is known for its ability to generate tuberous reservoir roots that are harvested annually and marketed fresh in South American countries such as Colombia, Brazil, Venezuela, Peru, Bolivia and Ecuador. In Colombia, arracacha is planted mainly in 15 departments and the regional cultivars are differentiated by the color of the leaves, petiole and tuberous root, the best known being amarilla común or paliverde, yema de huevo, and cartagenera. There are studies that have characterized regional materials by applying a limited number of descriptors, but they do not allow knowing the morphology and phenotypic differentiation of each one; therefore, their definition and characterization constitute a support in breeding programs that allow the efficient use of the genetic potential and increase the knowledge about the diversity of cultivars. Phenotypic characterization and description of three cultivars was performed during two production cycles (2016 and 2018) in two phases (vegetative and productive) applying 74 morphological variables (42 qualitative and 32 quantitative) organized in seven groups of variables: plant, leaf, leaflet, petiole, propagule, stock and tuberous root. A factorial analysis for mixed data (FAMD) was performed, which incorporated a multivariate analysis with all variables and identified 11 discriminant variables, 8 qualitative and 3 quantitative, which can be used in processes of characterization of arracacha materials. A morphological description of each cultivar was made, which means that this is the first complete characterization study of regional arracacha materials in Colombia.