Soil salinization is a difficult challenge for agricultural productivity and environmental sustainability, particularly in arid and semi-arid coastal regions. This study investigates the spatial variability of soil electrical conductivity (EC) and its relationship with key cations and anions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, CO₃²⁻, HCO₃⁻, SO₄²⁻) along the southeastern coast of the Caspian Sea in Iran. Using a combination of field-based soil sampling, laboratory analyses, and Landsat 8 spectral data, linear Multiple Linear Regression and Partial Least Squares Regression (MLR, PLSR) and nonlinear Artifician Neural Network and Support Vector Machine (ANN, SVM) modeling approaches were employed to estimate and map soil EC. Results identified Na⁺ and Cl⁻ as the primary contributors to salinity (r = 0.78 and r = 0.88, respectively), with NaCl salts dominating the region’s soil salinity dynamics. Secondary contributions from Potassium Chloride KCl and Magnesium Chloride MgCl₂ were also observed. Coastal landforms such as lagoon relicts and coastal plains exhibited the highest salinity levels, attributed to geomorphic processes and anthropogenic activities. Among the predictive models, the SVM algorithm outperformed others, achieving higher R² values and lower RMSE (RMSE_Test = 27.35 and RMSE_Train = 24.62, respectively), underscoring its effectiveness in capturing complex soil-environment interactions. This study highlights the utility of digital soil mapping (DSM) for assessing soil salinity and provides actionable insights for sustainable land management, particularly in mitigating salinity and enhancing agricultural practices in vulnerable coastal systems.

Research networks organized around a particular topic are built as knowledge is produced and socialized. These are parts of a seminal or initial production, to which new authors and subtopics are added until research and knowledge networks are formed around a particular area. The purpose of the research was to find this type of relationship or network between authors, institutions, and countries that have contributed to the issue of the circular economy and specifically its relationship with sustainability. This allows those interested in the said object of study to know the research advances of the network, enter their research lines, or create new networks according to their interests or needs. The study used a bibliometric-type descriptive quantitative approach using the Scopus scientific database, the R Studio data analytics application, and the Bibliometrix library. The results were found to determine a relationship building from 2006, which makes it an emerging topic. However, the growth it has achieved in recent years of more than 31% shows a strong interest in the subject. Of the subtopics that have been addressed, sustainability, recycling, solid waste, wastewater, and renewable energy. Similarly, sectors such as construction, the automotive industry, tourism, cities, the agricultural sector, the chemical industry, and the implementation of technologies 4.0 and 5.0 in their processes stood out. The most prominent country in the scientific approach to this area is Italy. The most prominent author for his citations is Molina-Moreno, the source of knowledge that stands out for his contributions is the University of Granada and different networks have been built around their knowledge.

Soil erosion is characterized by the wearing away or loss of the uppermost layer of soil, driven by water, wind, and human activities. This process constitutes a significant environmental issue, with adverse effects on water quality, soil health, and the overall stability of ecosystems across the globe. This study focuses on the Anuppur district of Madhya Pradesh, India, employing the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information System (GIS) tools to estimate and spatially analyze soil erosion and fertility risk. The various factors of the model, like rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), conservation practices (P), and cover management factor (C), have been computed to measure annual soil loss in the district. Each factor was derived using geospatial datasets, including rainfall records, soil characteristics, a Digital Elevation Model (DEM), land use/land cover (LULC) data, and information on conservation practices. GIS methods are used to map the geographical variation of soil erosion, providing important information on the area’s most susceptible to erosion. The outcome of the study reveals that 3371.23 km², which constitutes 91% of the district’s total area, is identified as having mild soil erosion; in contrast, 154 km², or 4%, is classified as moderate soil erosion, while 92 km², representing 2.5%, falls under the high soil erosion category. Ad

The Ecuadorian electricity sector encompasses generation, transmission, distribution and sales. Since the change of the Constitution in Ecuador in 2008, the sector has opted to employ a centralized model. The present research aims to measure the efficiency level of the Ecuadorian electricity sector during the period 2012–2021, using a DEA-NETWORK methodology, which allows examining and integrating each of the phases defined above through intermediate inputs, which are inputs in subsequent phases and outputs of some other phases. These intermediate inputs are essential for analyzing efficiency from a global view of the system. For research purposes, the Ecuadorian electricity sector was divided into 9 planning zones. The results revealed that the efficiency of zones 6 and 8 had the greatest impact on the overall efficiency of the Ecuadorian electricity sector during the period 2012–2015. On the other hand, the distribution phase is the most efficient with an index of 0.9605, followed by sales with an index of 0.6251. It is also concluded that the most inefficient phases are generation and transmission, thus verifying the problems caused by the use of a centralized model.