This study employs a transfer matrix, dynamic degree, stability index, and the PLUS model to analyze the spatiotemporal changes in forest land and their driving factors in Yibin City from 2000 to 2022. The results reveal the following: (1) The land use in Yibin City is predominantly characterized by cultivated land and forest land (accounting for over 95% of the total area). The area of cultivated land initially increased and then decreased, while forest land continued to decline and construction land expanded significantly. The rate of forest land loss has slowed (with the dynamic degree decreasing from −0.62% to −0.04%), and ecosystem stability has improved (the F-value increased from 2.27 to 2.9). The conversion of cultivated land to forest land is the primary driver of forest recovery, whereas the conversion of forest land to cultivated land is the main cause of reduction; (2) cultivated land is concentrated in the central and northeastern regions, while forest land is distributed in the western and southern mountainous areas. Construction land is predominantly located in urban areas and along transportation routes. Areas of forest land reduction are mainly found in the central and southern regions with rapid economic development, while areas of forest land increase are concentrated in high-altitude zones or key ecological protection areas. Stable forest land is distributed in the western and southern ecological conservation zones; (3) changes in forest land are primarily influenced by annual precipitation, elevation, and distance to rivers. Road accessibility and GDP have significant impacts, while slope, annual average temperature, and population density exert moderate influences. Distance to railways, aspect, and soil type have relatively minor effects. The findings of this study provide a scientific basis for the sustainable management of forest resources and ecological conservation in Yibin City.

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.

This study explores approaches to optimizing inclusive education through international and local perspectives. It examines the role of educators in inclusive settings, highlights strategies for early detection of children’s developmental needs, and evaluates inclusive school management practices. Using qualitative case study methods, the research includes comprehensive observations and interviews at Fatma Kenanga Islamic Character School. Findings emphasize the importance of individualized learning plans, shadow teacher involvement, and collaborative stakeholder engagement. Integrating global insights, this study contributes to advancing inclusive education practices in Indonesia and beyond.

Molybdenum (Mo) is considered and described as an essential element for living organisms’ development. Until now, no studies have been performed on genes involved in the Mo transporter in ancestral Ipomoea species. This study aimed to identify potential Mo genes in Ipomoea trifida and I. triloba genomes using bioinformatics tools. We identified four Mo transporter genes, two in I. trifida and two in I. triloba. Based on the RNA-seq datasets, we observed that Mo genes are expressed (in silico) and present different mechanisms between the tissues analyzed. The information generated in this study fills missing gaps in the literature on the Mo gene in an important agronomic crop.

This study investigated the students’ perceptions of a self-paced fitness program that is integrated with SitFit, a fitness tracker that measures body inclination during sit-up exercises, and their acceptance of digital innovation in physical education. The data was gathered from a survey of 1001 Thai undergraduates. Results revealed that attitudes toward using the technology and the perceived ease of use were important predictors of behavioral intention to use the sit-up fitness tracker. consistent with previous TAM studies. Subsequently, SitFit was developed based on exercise principles and expert advice to enable users to exercise more effectively while reducing injury risk.