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.

Despite Cameroon’s immense sand reserves, several enterprises continue to import standardized sands to investigate the properties of concretes and mortars and to guarantee the durability of built structures. The present work not only falls within the scope of import substitution but also aims to characterize and improve the properties of local sand (Sanaga) and compare them with those of imported standardized sand widely used in laboratories. Sanaga sand was treated with HCl and then characterized in the laboratory. The constituent minerals of Sanaga sand are quartz, albite, biotite, and kaolinite. The silica content (SiO₂) of this untreated sand is 93.48 wt.%. After treatment, it rose 97.5 wt.% for 0.5 M and 97.3 wt.% for 1 M HCl concentration. The sand is clean (ES, 97.67%–98.87%), with fineness moduli of 2.45, 2.48, and 2.63 for untreated sand and sand treated with HCl concentrations of 0.5 and 1 M respectively. The mechanical strengths (39.59–42.4 MPa) obtained on mortars made with untreated Sanaga sand are unsatisfactory compared with those obtained on mortars made with standardized sand and with the expected strengths. The HCl treatment used in this study significantly improved these strengths (41.12–52.36 MPa), resulting in strength deficiencies of less than 10% after 28 curing days compared with expected values. Thus, the treatment of Sanaga sand with a 0.5 M HCl concentration offers better results for use as standardized sand.

This paper describes the significance, content, progress and corresponding basic theory and experimental research methods of micron/nanometer scale thermal science and engineering, which is one of the latest cutting-edge disciplines, and analyzes the effects of micron nanometer devices on the scale effect series of challenging hot issues, discussed the corresponding emergence of some new phenomena and new concepts, pointed out that the micron/nano thermal science aspects of the recent development of several types of theory and experimental technology success and shortcomings, and summed up a number for the exploration of the new ways and new directions, especially on some typical micron/nano-thermal devices and micro-scale biological heat transfer in some important scientific issues and their engineering applications were introduced.

Species of the Moraceae family are of great economic, medicinal and ecological importance in Amazonia. However, there are few studies on their diversity and population dynamics in residual forests. The objective was to determine the composition, structure and ecological importance of Moraceae in a residual forest. The applied method was descriptive and consisted of establishing 16 plots of 20 m × 50 m (0.10 ha), in a residual forest of the Alexánder von Humboldt substation of the National Institute of Agrarian Innovation-INIA, Pucallpa, department of Ucayali, where individuals of arboreal or hemi-epiphytic habit, with DBH ≥ 2.50 cm, were evaluated. The floristic composition was represented by 33 species, distributed in 12 genera; five species not recorded for Ucayali were found. Structurally, the family was represented by 138 individuals/ha with a horizontal distribution similar to an irregular inverted “J”. However, there were different horizontal structures among species. It was determined that 85% of the species were in diameter class I (2.50 to 9.99 cm), being the most abundant Pseudolmedia laevis (Ruiz & Pav.) J.F. Macbr. (41.88 individuals/ha); and the most dominant were Brosimum utile (Kunth) Oken (1.71 m²∕ha) and Brosimum alicastrum subsp. bolivarense (Pittier) C.C.Berg (0.90 m²/ha). Likewise, P. laevis and B. utile were the most ecologically important. The information from the present research will allow the establishment of a baseline, which can be used to propose the management of Moraceae in residual forests in the same study area.

Mapping land use and land cover (LULC) is essential for comprehending changes in the environment and promoting sustainable planning. To achieve accurate and effective LULC mapping, this work investigates the integration of Geographic Information Systems (GIS) with Machine Learning (ML) methodology. Different types of land covers in the Lucknow district were classified using the Random Forest (RF) algorithm and Landsat satellite images. Since the research area consists of a variety of landforms, there are issues with classification accuracy. These challenges are met by combining supplementary data into the GIS framework and adjusting algorithm parameters like selection of cloud free images and homogeneous training samples. The result demonstrates a net increase of 484.59 km² in built-up areas. A net decrement of 75.44 km² was observed in forest areas. A drastic net decrease of 674.52 km² was observed for wetlands. Most of the wastelands have been converted into urban areas and agricultural land based on their suitability with settlements or crops. The classifications achieved an overall accuracy near 90%. This strategy provides a reliable way to track changes in land cover, supporting resource management, urban planning, and environmental preservation. The results highlight how sophisticated computational methods can enhance the accuracy of LULC evaluations.

In our study, we examined 11 designated tourist destinations in Hungary, which can also be interpreted as tourism products including services, infrastructure and attractions. The National Tourism Development Strategy (NTS) also puts a strong emphasis on digitalisation, as it is an unstoppable process with a significant impact on tourism, thanks to globalisation, increasing competition, accelerating information flows and the dominant paradigm shifts on the demand and supply side. We used both qualitative and quantitative methods in our primary research. First, we conducted in-depth interviews with several important tourism service providers in Hungary on the topic of the digitalisation of tourism. A professional questionnaire, addressed to the offices responsible for destination management was distributed in the designated tourist destinations in Hungary in order to get a more comprehensive picture of the attitudes towards digitalisation in the regions under study. In the course of our work, we managed to classify the destinations into three distinctly different clusters. Our hypothesis—that the higher the digitalisation of a tourist destination is, the higher the average length of stay—was partially confirmed by calculating the regional value of the digitalisation, logistic regression analysis, slope and the individual factor categories.