The Guacimal River catchment has an area of 181 km² and is located in the NW of Costa Rica, between the coordinates 84.745° W-10.016° N and 84.909° W-10.325° N. In this territory, as in most of the country, detailed geomorphological studies are scarce; therefore, the objective of this paper is to present the geomorphological mapping at a scale of 1:25,000 of the Guacimal River, which allows us to explain the dynamics of the agents involved in the modeling of the catchment. The work methodology consisted of three stages: pre-mapping, field activity and post-mapping, which resulted in a map in which ten relief forms are represented, ordered according to their morphogenesis in endogenous modeled and exogenous (fluvial, gravitational and littoral). This document will be the base line for land use planning, both continental and coastal, and for local risk management.

The present study assessed the potential of sediment loading in Beteni, Lauruk, Andheri, and Harpan sub-watersheds of Phewa Lake and estimated the sediment yield in the year 2020. Morphometry, land use/land cover, geology, climate, and human and development factors of the sub-watersheds were studied to assess the potential of sediment loading in the sub-watersheds. SRTM DEM was used for the computation of morphometric parameters and land use/land cover maps were prepared by using Landsat imagery. Geology, rainfall data, census data, and road maps were collected from various secondary sources. The sediment yields of the four sub-watersheds in the year 2020 were estimated by measuring the sediment volume deposited in the sediment retention ponds at the outlet of each sub-watershed. Results indicated that Beteni had the highest potential for sediment loading, while Harpan had the lowest. Likewise, the sediment yields for Beteni, Lauruk, Andheri, and Harpan sub-watersheds in 2020 were estimated at 1,420.67 m³/km²/year, 2,280.14 m³/km²/year, 1,666.77 m³/km²/year, and 766.42 m³/km²/year, respectively. To reduce sedimentation in Phewa Lake, it is recommended to regularly maintain siltation dams and construct check dams along the drainage slopes, alongside other soil conservation measures and appropriate land use practices in the upstream areas of the sub-watersheds.

The electron/hole transport layer can promote charge transfer and improve device performance, which is used in perovskite solar cells. The nanoarray structure transport layers can not only further promote carrier transport but also reduce recombination. It also has a great potential in enhancing perovskite light absorption, improving device stability and inhibiting the crack nucleation of different structure layers in perovskite solar cells. This paper reviewed the research progress of perovskite solar cells with different nanoarray structure transport layers. The challenges and development directions of perovskite solar cells based on nanoarray structure transport layers are also summarized and prospected.

Unmanned Aerial Vehicles (UAVs) have gained spotlighted attention in the recent past and has experienced exponential advancements. This research focuses on UAV-based data acquisition and processing to generate highly accurate outputs pertaining to orthomosaic imagery, elevation, surface and terrain models. The study addresses the challenges inherent in the generation and analysis of orthomosaic images, particularly the critical need for correction and enhancement to ensure precise application in fields like detailed mapping and continuous monitoring. To achieve superior image quality and precision, the study applies advanced image processing techniques encompassing Fuzzy Logic and edge-detection techniques. The study emphasizes on the necessity of an approach for countering the loss of information while mapping the UAV deliverables. By offering insights into both the challenges and solutions related to orthomosaic image processing, this research lays the groundwork for future applications that promise to further increase the efficiency and effectiveness of UAV-based methods in geomatics, as well as in broader fields such as engineering and environmental management.

Today urban development lacks ecological foundations in many cities of Turkey. The purpose of this study is to reveal the relationship between urban green spaces and ecological zones in the sample of Aksaray/Turkey. In this study, a study design has been created to improve the urban ecological infrastructure and to associate the green space network with the ecological zones. This design is divided into four parts as data processing, landscape pattern of urban green spaces, analysis of the spatial boundaries of urban natural ecological zones, and determination of the importance of spatial regions by overlaying two different stratified analyses. This study proposes a methodological framework that can be integrated into efforts to identify ecological zones to increase the sustainability of urban ecology and green space quality. One potential limitation of the proposed methodology can be the lack of consensus and enthusiasm among the administrative actors regarding the issue. Therefore, it is recommended that the administrative bodies should be correctly informed by the relevant scholars and practitioners who are working on the subject.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.