Analyzing the Interactions between Environmental Parameters and Cardiovascular Diseases Using Random Forest and SHAP Algorithms.

Background: Cardiovascular diseases (CVD) remain the predominant global cause of mortality, with both low and high temperatures increasing CVD-related mortalities. Climate change impacts human health directly through temperature fluctuations and indirectly via factors like disease vectors. Elevated and reduced temperatures have been linked to increases in CVD-related hospitalizations and mortality, with various studies worldwide confirming the significant health implications of temperature variations and air pollution on cardiovascular outcomes.

Random Forest and Feature Importance Measures for Discriminating the Most Influential Environmental Factors in Predicting Cardiovascular and Respiratory Diseases.

Background: Several studies suggest that environmental and climatic factors are linked to the risk of mortality due to cardiovascular and respiratory diseases; however, it is still unclear which are the most influential ones. This study sheds light on the potentiality of a data-driven statistical approach by providing a case study analysis.

Methods: Daily admissions to the emergency room for cardiovascular and respiratory diseases are jointly analyzed with daily environmental and climatic parameter values (temperature, atmospheric pressure, relative humidity, carbon monoxide, ozone, particulate matter, and nitrogen dioxide).

Effects of Meteo-Climatic Factors on Hospital Admissions for Cardiovascular Diseases in the City of Bari, Southern Italy.

The objective of this study was to determine the relationship between weather conditions and hospital admissions for cardiovascular diseases (CVD). The analysed data of CVD hospital admissions were part of the database of the Policlinico Giovanni XXIII of Bari (southern Italy) within a reference period of 4 years (2013-2016). CVD hospital admissions have been aggregated with daily meteorological recordings for the reference time interval.

Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea.

Although the Mediterranean Sea is a crucial hotspot in marine biodiversity, it has been threatened by numerous anthropogenic pressures. As flagship species, Cetaceans are exposed to those anthropogenic impacts and global changes. Assessing their conservation status becomes strategic to set effective management plans.

Accurate Spectral Estimation Technique Based on Decimated Linear Predictor for Leak Detection in Waterworks.

Rural pipelines dedicated to water distribution, that is, waterworks, are essential for agriculture, notably plantations and greenhouse cultivation. Water is a primary resource for agriculture, and its optimized management is a key aspect. Saving water dispersion is not only an economic problem but also an environmental one.

Analysis of Correlation between Climate Change and Human Health Based on a Machine Learning Approach.

Climate change increasingly affects every aspect of human life. Recent studies report a close correlation with human health and it is estimated that global death rates will increase by 73 per 100,000 by 2100 due to changes in temperature. In this context, the present work aims to study the correlation between climate change and human health, on a global scale, using artificial intelligence techniques.

A Sensing and Monitoring System for Hydrodynamic Flow Based on Imaging and Ultrasound.

A built environment, that also includes infrastructures, needs to be taken under control to prevent unexpected modifications, otherwise it could react as a loose cannon. Sensing techniques and technologies can come to the rescue of built environments thanks to their capabilities to monitor appropriately. This article illustrates findings related to monitoring a channel hydrodynamic behavior by means of sensors based on imaging and ultrasound.

A Comparison between the Decimated Padé Approximant and Decimated Signal Diagonalization Methods for Leak Detection in Pipelines Equipped with Pressure Sensors.

Pipelines conveying fluids are considered strategic infrastructures to be protected and maintained. They generally serve for transportation of important fluids such as drinkable water, waste water, oil, gas, chemicals, etc. Monitoring and continuous testing, especially on-line, are necessary to assess the condition of pipelines.

Effectiveness of KTP laser versus 980 nm diode laser to kill Enterococcus faecalis in biofilms developed in experimentally infected root canals.

This study aimed to evaluate the antibacterial action of KTP (potassium-titanyl-phosphate) laser irradiations (compared with 980 nm diode laser), associated with conventional endodontic procedures, on Enterococcus faecalis biofilms. Fifty-six dental roots with single canals were prepared with Ni-Ti rotary instruments, autoclaved, inoculated with an E. faecalis suspension and incubated for 72 h.