Applied Sciences

Chronic wounds pose a significant therapeutic challenge due to their complex etiology, long patient treatment time and high treatment costs. Wound healing is hindered by the presence of necrotic tissue, elevated pH, and biofilm formation. The key therapeutic approach involves regular wound debridement combined with appropriate antimicrobial agents. Due to increasing bacterial resistance and biofilm forming, there is an urgent need for effective therapies that eliminate biofilm and support the healing process. Here we discuss innovative approaches for treating infected chronic wounds, including the use of hydrogels, photodynamic therapy, probiotics, and phage therapy. Advances in these methods, such as chemical modifications and nanotechnology applications, open up new possibilities for effective wound treatment. The greatest potential for clinical application lies in strategies based on advanced hydrogels and antibacterial dressings, as well as photodynamic therapy.

Accurate prediction of soil shear strength is critical for safe and cost-effective geotechnical design. This study investigates the application of four machine learning (ML) models—Random Forest (RF), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Regression (SVR)—to predict the shear strength of soils from Bahir Dar city using laboratory-obtained geotechnical data. A total of 298 soil samples and 13 geotechnical parameters were collected from depths of 0.13–35 m, encompassing both disturbed and undisturbed conditions. The dataset was divided into training (80%) and testing (20%) sets, and models were trained with optimized hyperparameters. The RF model achieved the highest accuracy (R² = 0.9992, RMSE = 0.0983), followed by DT (R² = 0.9974, RMSE = 0.1812). ANN and SVR showed lower predictive accuracy, with SVR demonstrating the largest maximum errors. Predicted vs. actual plots, kernel density estimates, and absolute error per sample analysis confirmed that tree-based models provide the most reliable predictions, while ANN and SVR exhibited sporadic large deviations. SHAP analysis revealed that Cohesion, Clay content, and Plasticity Index are the most influential factors in predicting shear strength. The results demonstrate that ensemble tree-based ML models offer a robust and accurate tool for geotechnical prediction, capturing complex nonlinear relationships in soil behavior.

Laser scanners based on the Simultaneous Localization and Mapping (SLAM) principle generate extremely dense point clouds burdened with a high level of surface noise arising from random measurement errors and repeated scanning of identical regions. This increases data volume and complicates subsequent processing. The present study introduces four novel noise filtering and subsampling algorithms that selectively preserve the points closest to the true surface. Each algorithm assigns a filtering characteristic to individual points based either on their distance from a locally estimated (planar or quadratic) surface or on the degree of local eccentricity in the spherical neighborhood of the point. The proposed methods were tested on point clouds acquired using three SLAM scanners (Emesent Hovermap ST-X, FARO Orbis, and ZEB Horizon) in three different scenes with reference data acquired by a static terrestrial scanner Leica P40. All four proposed methods effectively reduced surface noise and data volume (improving the RMSDs by 45.4–75.8% compared to the original cloud after thinning to 10% of cloud size). This clearly outperformed the standard subsampling tools, namely random subsampling (RMSD remained constant after subsampling), octree, or spatial subsampling (worsening of RMSDs with increasing subsampling). The most reliable surface noise removal in point clouds dominated by planar surfaces (building interior with planar walls) was achieved using the method based on local plane fitting. In contrast, the use of a quadratic surface proved more effective for uneven or rugged surfaces.