Every plant is significantly important in tackling climate change, including Makila (Litsea angulata BI) an endemic wood species found in the forest of Moluccas Provinces. Therefore, this research aimed to examine the role of the Makila plant in tackling climate change by measuring biomass content using constructing an allometric equation. The method used was a destructive sampling, where 40 units of Makila plant at the sampling level were felled, and sorted according to root, stem, branch, rating, and leaf segments. Each segment was weighed both at wet and after drying, followed by a classical assumption test in data processing, and the formulation of an allometric equation. The regression model was examined for normality and suitability in predicting independent variables, ensuring there were no issues with multicollinearity, heteroscedasticity, and autocorrelation. The results yielded a multiple linear regression, namely: Y = −1131.146 + 684.799X₁ + 4.276X₂, where Y is biomass, X₁ is the diameter, and X₂ is the tree height. Based on the results of the t-test: variable X₁ partially affected Y while variable X₂ partially had no effect on Y. The F-test indicated that variables X₁ and X₂ jointly affected Y with R Square: 0.919 or 91.9% and the rest was influenced by other unexplored factors. To simplify biomass prediction and field measurement, a regression equation that used only 1 independent variable, namely tree diameter, was used for the experiment. Allometric equation only used 1 variable, Y = −1,084,626 + 675,090X₁, where X₁ = tree diameter, Y = Total biomass with R = 0.957, and R² = 0.915. Considering the potential for time, cost, and energy savings, as well as ease of measurement in the field, the biomass of young Makila trees was simply predicted by measuring the tree diameter and avoiding the height. This method used the strong relationship between biomass, plant diameter, and height to facilitate the estimation of biomass content accurately by entering the results of field measurements.

Proper understanding of LULC changes is considered an indispensable element for modeling. It is also central for planning and management activities as well as understanding the earth as a system. This study examined LULC changes in the region of the proposed Pwalugu hydropower project using remote sensing (RS) and geographic information systems (GIS) techniques. Data from the United States Geological Survey's Landsat satellite, specifically the Landsat Thematic Mapper (TM), the Enhanced Thematic Mapper (ETM), and the Operational Land Imager (OLI), were used. The Landsat 5 thematic mapper (TM) sensor data was processed for the year 1990; the Landsat 7 SLC data was processed for the year 2000; and the 2020 data was collected from Operation Land Image (OLI). Landsat images were extracted based on the years 1990, 2000, and 2020, which were used to develop three land cover maps. The region of the proposed Pwalugu hydropower project was divided into the following five primary LULC classes: settlements and barren lands; croplands; water bodies; grassland; and other areas. Within the three periods (1990–2000, 2000–2020, and 1990–2020), grassland has increased from 9%, 20%, and 40%, respectively. On the other hand, the change in the remaining four (4) classes varied. The findings suggest that population growth, changes in climate, and deforestation during this thirty-year period have been responsible for the variations in the LULC classes. The variations in the LULC changes could have a significant influence on the hydrological processes in the form of evapotranspiration, interception, and infiltration. This study will therefore assist in establishing patterns and will enable Ghana's resource managers to forecast realistic change scenarios that would be helpful for the management of the proposed Pwalugu hydropower project.

This research study explores the addition of chromium (Cr⁶⁺) ions as a nucleating agent in the alumino-silicate-glass (ASG) system (i.e., Al₂O₃-SiO₂-MgO-B₂O₃-K₂O-F). The important feature of this study is the induction of nucleation/crystallization in the base glass matrix on addition of Cr⁶⁺ content under annealing heat treatment (600 ± 10 °C) only. The melt-quenched glass is found to be amorphous, which in the presence of Cr⁶⁺ ions became crystalline with a predominant crystalline phase, Spinel (MgCr₂O₄). Microstructural experiment revealed the development of 200–500 nm crystallite particles in Cr⁶⁺-doped glass-ceramic matrix, and such type microstructure governed the mechanical properties. The machinability of the Cr-doped glass-ceramic was thereby higher compared to base alumino-silicate glass (ASG). From the nano-indentation experiment, the Young’s modulus was estimated 25(±10) GPa for base glass and increased to 894(±21) GPa for Cr-doped glass ceramics. Similarly, the microhardness for the base glass was 0.6(±0.5) GPa (nano-indentation measurements) and 3.63(±0.18) GPa (micro-indentation measurements). And that found increased to 8.4(±2.3) (nano-indentation measurements) and 3.94(±0.20) GPa (micro-indentation measurements) for Cr-containing glass ceramic.

The article is devoted to the issues of political and legal regulation of climate adaptation in the regions of the Russian Federation. Against the background of the adopted federal national adaptation plan, regions are tasked with identifying key areas of activity taking into account natural-climatic, demographic, environmental and technological specifics. The authors focus on the similarities and differences of the presented adaptation plans, emphasizing that work to improve this system continues within the framework of Russia’s international obligations. The Arctic regions deserve special attention, as they also differ from each other both in the selected climate adaptation activities (from ecology to energy saving) and in their number. This review provides a clear picture of how the federal ecological system can develop.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.