The study examines the relationship between EPS and the gearing ratios and return on equity (ROE) ratio of 9 public listed firms on the Malaysian Stock Exchange from 2014 to 2022 financial years. The firms are selected at random. From this study it was established that there is a negative relation between EPS and gearing and a positive relation between EPS and ROE. Companies that want to attract more investors need to keep their gearing ratio low and increase the return on equity ratio high. To obtain the benefits of gearing or external funding, there need to be a balance between equity and debts. There is no one optimal balance between debt and equity. This balance is difference for each company and the sector they operate in. It is important for managers of companies to find the optimal balance between debt and equity, unique to their company.

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.

This work investigated the photocatalytic properties of polymorphic nanostructures based on silica (SiO₂) and magnetite (Fe₃O₄) for the photodegradation of tartrazine yellow dye. In this sense, a fast, easy, and cheap synthesis route was proposed that used sugarcane bagasse biomass as a precursor material for silica. The Fourier transform infrared (FTIR) spectroscopy results showed a decrease in organic content due to the chemical treatment with NaOH solution. This was confirmed through the changes promoted in the bonds of chromophores belonging to lignin, cellulose, and hemicellulose. This treated biomass was calcined at 800 ℃, and FTIR and X-ray diffraction (XRD) also confirmed the biomass ash profile. The FTIR spectrum showed the formation of silica through stretching of the chemical bonds of the silicate group (Si-O-Si), which was confirmed by DXR with the predominance of peaks associated with the quartz phase. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) confirmed the morphological and chemical changes due to the chemical and thermal treatments applied to this biomass. Using the coprecipitation method, we synthesized Fe₃O₄ nanoparticles (Np) in the presence of SiO₂, generating the material Fe₃O₄/SiO₂-Np. The result was the formation of nanostructures with cubic, spherical, and octahedral geometries with a size of 200 nm. The SEM images showed that the few heterojunctions formed in the mixed material increased the photocatalytic efficiency of the photodegradation of tartrazine yellow dye by more than two times. The degradation percentage reached 45% in 120 min of reaction time. This mixed material can effectively decontaminate effluents composed of organic pollutants containing azo groups.

This study comprehensively evaluates the system performance by considering the thermodynamic and exergy analysis of hydrogen production by the water electrolysis method. Energy inputs, hydrogen and oxygen production capacities, exergy balance, and losses of the electrolyzer system were examined in detail. In the study, most of the energy losses are due to heat losses and electrochemical conversion processes. It has also been observed that increased electrical input increases the production of hydrogen and oxygen, but after a certain point, the rate of efficiency increase slows down. According to the exergy analysis, it was determined that the largest energy input of the system was electricity, hydrogen stood out as the main product, and oxygen and exergy losses were important factors affecting the system performance. The results, in line with other studies in the literature, show that the integration of advanced materials, low-resistance electrodes, heat recovery systems, and renewable energy is critical to increasing the efficiency of electrolyzer systems and minimizing energy losses. The modeling results reveal that machine learning programs have significant potential to achieve high accuracy in electrolysis performance estimation and process view. This study aims to contribute to the production of growth generation technologies and will shed light on global and technological regional decision-making for sustainable energy policies as it expands.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

Water scarcity, particularly in arid and semi-arid regions, is a critical issue affecting forest management. This study investigates the effects of drought stress on the water requirement and morphological characteristics of two important tree species Turkish pine and Chinaberry. Using a factorial design, the study examines the impact of three age stages (one-year-old, three-year-old, and five-year-old plants) and three levels of drought stress on these species. Microlysimeters of varying sizes were employed to simulate different drought conditions. Soil moisture was monitored to show the effect of the various irrigation schedules. The study also calculated reference crop evapotranspiration (ET₀) using the PMF-56 method and developed plant coefficients (Kc) for the species. Results showed that evapotranspiration increased with soil moisture, peaking during summer and decreasing in winter. Turkish pine exhibited higher plant ET than Chinaberry, particularly among one-year-old seedlings. Drought stress significantly reduced evapotranspiration and water uses for both species, highlighting the importance of efficient water management in afforestation projects. The findings underscore the necessity of selecting drought-resistant species and optimizing irrigation practices to enhance the sustainability of green spaces in arid regions. These insights are crucial for improving urban forestry management and mitigating the impacts of water scarcity in Iran and similar climates globally.