This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.

Taking the west slope of Cangshan Mountain in Yangbi County, Dali as the research site, on the basis of investigating the local natural geographical conditions, topography and biodiversity status of Cangshan Mountain, the CAP protection action planning method was adopted, and the priority protection objects were determined to be native forest vegetation, rare and endangered flora and fauna, alpine vertical ecosystems, hard-leaf evergreen broad-leaved forests and cold-tempered coniferous forests; The main threat factors were commercial collection, tourism development and overgrazing. Biodiversity conservation on the western slope of Cangshan Mountain should take species as “point”, regional boundary as “line”, ecosystem and landscape system as “plane”, so as to realize the overall planning structure system combining “point—line—plane”, which can be divided into conservation core area, buffer zone and experimental area. The results can provide a reference for biodiversity conservation on the western slope of Cangshan Mountain.

Research into electro-conductive textiles based on conductive polymers like polypyrrole has increased in recent years due to their high potential applications in various fields. Conductive polymers behave like insulators in their neutral states, with typical electrical conductivity in the range 10^–10 to 10^–25 Scm^–1. These neutral polymers can be converted into semi-conductive or conductive states with conductivities ranging from 1 Scm^–1 to 10^–4 Scm^–1 through chemical or electro-chemical redox reactions. By applying these polymers to a textile surface, we can obtain novel composites that are strong, flexible, lightweight, and highly electroconductive. These textile composites are suitable for applications such as heating pads, sensors, corrosion-protecting materials, actuators, electrochromic devices, EMI shielding, etc. The methods of application of conductive polymers onto the textile surface, such as in-situ chemical, in-situ electrochemical, in-situ vapor phase, in-situ polymerization in a supercritical fluid, and solution coating processes, are described here briefly. The merits and demerits of these methods are mentioned here. The reaction mechanisms of chemical and electrochemical polymerization proposed by the different researchers are described. Different factors affecting the kinetics of chemical and electrochemical polymerization are accounted for. The influence of textile materials on the kinetics of chemical polymerization is reviewed and reported.

The semiclassical boron–boron interatomic pair potential is constructed in an integral form allowing its converting into the analytical one. It is an ab initio B–B potential free of any semiempirical adjusting parameters, which would serve as an effective tool for the theoretical characterization of all-boron and boron-rich nanomaterials.

New hybrid magnetic materials based on HDPE filled with Со and Ni nanoparticles have been prepared via the metal vapor synthesis. Properties of the metal-polymer composites have been elucidated as a function of MVS parameters and metal nature. The Faraday method has been applied to characterize the magnetic properties of the systems. The microstructure of the samples has been studied with a number of X-ray and synchrotron techniques, including XRD, EXAFS and SAXS. Core-level and valence band spectra were measured by XPS. The peak at binding energy of 282.8 eV characteristic of C-Ni bond was recorded in the C 1s spectrum. It was shown that properties of nanocomposite materials with similar compositions are determined both by the synthesis conditions and post-synthesis factors.