This study focuses on optimizing machining parameters in the micro-milling of AlSi10Mg aluminum alloy produced via the powder bed fusion additive manufacturing process. Although additive manufacturing enables complex geometries and minimizes material waste, challenges remain in reducing surface roughness and cutting forces during post-processing. Micro-milling experiments were conducted using spindle speeds up to 60,000 rpm, with varied feed rates and cutting depths. Cutting forces (Fx, Fy, and Fz) were measured using a Kistler-9119AA1 mini dynamometer, while surface roughness (Ra) was evaluated with a Nanovea-ST400 3D optical profilometer. Five advanced machine learning models, random forest regressor (RFR), gradient boosting regressor (GBR), LightGBM, CatBoost, and k-nearest neighbors (KNN), were employed to predict cutting forces and surface roughness, with CatBoost achieving the highest predictive accuracy (R² > 0.96). Among all models, CatBoost achieved the best predictive performance, with test R² values exceeding 0.96 for both force and Ra estimations. Experimental and ML-based results demonstrated that higher feed rates and depths of cut increased cutting forces, particularly in the Fx direction, while elevated spindle speeds reduced forces due to thermal softening. Surface roughness was minimized at lower feed rates and higher spindle speeds. The optimal machining conditions for achieving Ra < 1 µm were identified as ap = 50 µm, n = 30,000 rpm, and fz = 0.25 µm/tooth. This integrated approach supports precision machining of AM aluminum alloys.

The present work attempts to determine the most suitable surface processing techniques to reduce the slipping risk of pedestrians while walking barefoot and wearing shoes on marble coverings. In this study, slip angles of surface-processed marbles in different plate dimensions have been determined by using DIN EN 51097, DIN EN 51130, and TS EN 14231 Standards “The determination of the characteristics that reduce slipping on wet, barefoot surfaces.” Slip angles and surface slip resistances have been determined by using ramp test equipment and pendulum method on five different types of classified marbles applied polishing and honing surface processing techniques. It was found that the parameters which affected slip resistance and slip angle values of the marbles were plate size and the applied surface processing techniques. The marbles were then grouped according to safe utilization places depending on slip angles and slip resistances, according to the statistical results.

Background: Acute myocardial infarction (AMI) constitutes a major health problem with high mortality rates worldwide. In patients with ST-segment elevation myocardial infarction (STEMI), no-reflow phenomenon is a condition that adversely affects response to therapy. Previous studies have demonstrated that the CALLY index, calculated using C-reactive protein (CRP), albumin, and lymphocytes, is a reliable indicator of mortality in patients with non-cardiac diseases. The objective of this study is to investigate the potential utility of the CALLY index in detecting no-reflow patients and to determine the predictability of this phenomenon using machine learning (ML) methods. Methods: This study included 1785 STEMI patients admitted to the clinic between January 2020 and June 2024 who underwent percutaneous coronary intervention (PCI). Patients were in no-reflow status, and other clinical data were analyzed. The CALLY index was calculated using data on patients’ inflammatory status. The Extreme Gradient Boosting (XGBoost) ML algorithm was used for no-reflow prediction. Results: No-reflow was detected in a proportion of patients participating in this study. The model obtained with the XGBoost algorithm showed high accuracy rates in predicting no-reflow status. The role of the CALLY index in predicting no-reflow status was clearly demonstrated. Conclusions: The CALLY index has emerged as a valuable tool for predicting no-reflow status in STEMI patients. This study demonstrates how machine learning methods can be effective in clinical applications and paves the way for innovative approaches for the management of no-reflow phenomenon. Future research needs to confirm and extend these findings with larger sample sizes.

This study used various test methods to analyze the impact resistance of marble, limestone, and travertine to determine their breaking potential when they are used for interior and exterior floor covering. A total of 17 kinds of natural rocks (7 marbles, 5 travertines, and 5 limestones) were categorized according to their commonly used geological formations and were subjected to various characterization tests (mineralogical and petrographic characteristics, chemical properties, as well as physical and mechanical properties). The rupture energy caused by an impact on natural rocks was determined according to the BS EN 14158 standard, whereas restitution coefficients were determined according to the BS EN ISO 10545-5 standard. The impact resistance, rupture energy, and restitution coefficient of polished natural stone plates that were used in the tests were statistically analyzed. The parameters that affected these variables of natural rocks were plate thickness, mineralogical and petrographic characteristics, and physical and mechanical properties. Finally, the potential fractures of the specimens were classified based on impact resistance, rupture energy, and restitution coefficient.