From statistical inference to machine learning: A paradigm shift in contemporary cardiovascular pharmacotherapy.

AIMS: Heart failure with reduced ejection fraction (HFrEF) poses significant challenges for clinicians and researchers, owing to its multifaceted aetiology and complex treatment regimens. In light of this, artificial intelligence methods offer an innovative approach to identifying relationships within complex clinical datasets. Our study aims to explore the potential for machine learning algorithms to provide deeper insights into datasets of HFrEF patients. METHODS: To this end, we analysed a cohort of 386 HFrEF patients who had been initiated on sodium-glucose co-transporter-2 inhibitor treatment and had completed a minimum of a 6-month follow-up. RESULTS: In traditional frequentist statistical analyses, patients receiving the highest doses of beta-blockers (BBs) (chi-square test, P = .036) and those newly initiated on sacubitril-valsartan (chi-square test, P = .023) showed better outcomes. However, none of these pharmacological features stood out as independent predictors of improved outcomes in the Cox proportional hazards model. In contrast, when employing eXtreme Gradient Boosting (XGBoost) algorithms in conjunction with the data using Shapley additive explanations (SHAP), we identified several models with significant predictive power. The XGBoost algorithm inherently accommodates non-linear distribution, multicollinearity and confounding. Within this framework, pharmacological categories like 'newly initiated treatment with sacubitril/valsartan' and 'BB dose escalation' emerged as strong predictors of long-term outcomes. CONCLUSIONS: In this manuscript, we not only emphasize the strengths of this machine learning approach but also discuss its potential limitations and the risk of identifying statistically significant yet clinically irrelevant predictors.

The Influence of Visibility on the Opportunity to Perform Flight Operations with Various Categories of the Instrument Landing System.

Meteorological conditions significantly affect air traffic safety and can also affect a pre-planned flight plan. Difficult meteorological conditions are particularly hazardous during take-off and landing procedures. Still, they can also cause disruptions to air traffic by causing, for example, delays to air traffic or diversion of aircraft to other airports. From the airlines' point of view, such situations are not beneficial if flights are diverted to other airports due to reduced visibility at the airport caused by fog and haze. For flight operations, a popular navigation system with a precision approach is the ILS, which has several categories enabling an approach even in adverse meteorological conditions. However, not every airport has a high-category ILS, and setting up such navigation equipment is lengthy and costly. The main objective of this article is to analyze the impact of meteorological conditions, particularly visibility, on the possibility of performing flight procedures with different ILS categories. The study was designed to quantify the limitations associated with meteorological conditions with specific ILS equipment at a given airport. The research questions for this study include the following: What were the meteorological conditions in terms of visibility? What impact did the visibility parameter have on the performance of landing operations at the airport under study? Can an indication of the probability of stopping landing operations be important in recommendations for scheduling airline flights to avoid delays? Three airports were selected for the analysis: Warsaw Chopin Airport, Warsaw Modlin Mazowiecki Airport, and Krakow John Paul II Airport. The analysis was based on approximately 52,000 METAR dispatches in 2019 and 2022. The research indicated during which periods landing procedures were most frequently halted and calculated such a change with a different category of ILS. For the Kraków Airport, the probability of stopping landing procedures in any month was calculated, along with recommendations for flight schedule planning for this airport. The research results can be used to better plan airline flight schedules, avoiding hours with a high probability of reduced visibility, which may result in rerouting flights to another airport. Long-term low clouds and reduced visibility affect the safety of operations but also cause delays.

Internal Damage Detection of Composite Structures Using Passive RFID Tag Antenna Deformation Method: Basic Research.

This manuscript deals with the detection of internal cracks and defects in aeronautical fibreglass structures. In technical practice, it is problematic to accurately determine the service life or MTBF (Mean Time Between Failure) of composite materials by the methods used in metallic materials. The problem is mainly the inhomogeneous and anisotropic structure of composites, possibly due to the differences in the macrostructure during production, production processes, etc. Diagnostic methods for detecting internal cracks and damage are slightly different, and in practice, it is more difficult to detect defects using non-destructive testing (NDT). The article deals with the use of Radio frequency identification (RFID) technology integrated in the fibreglass laminates of aircraft structures to detect internal defects based on deformation behaviour of passive RFID tag antenna. The experiments proved the potential of using RFID technology in fibreglass composite laminates when using tensile tests applied on specimens with different structural properties. Therefore, the implementation of passive RFID tags into fibreglass composite structures presents the possibilities of detecting internal cracks and structural health monitoring. The result and conclusion of the basic research is determination of the application conditions for our proposed technology in practice. Moreover, the basic research provides recommendations for the applied research in terms of the use in real composite airframe structures.

On the image inpainting problem from the viewpoint of a nonlocal Cahn-Hilliard type equation.

Motivated by the fact that the fractional Laplacean generates a wider choice of the interpolation curves than the Laplacean or bi-Laplacean, we propose a new non-local partial differential equation inspired by the Cahn-Hilliard model for recovering damaged parts of an image. We also note that our model is linear and that the computational costs are lower than those for the standard Cahn-Hilliard equation, while the inpainting results remain of high quality. We develop a numerical scheme for solving the resulting equations and provide an example of inpainting showing the potential of our method.