Comparison of conventional and machine learning regression models for accurate prediction of selected optical active components - A case study: The Gulf of Izmit.

This study hypothesizes that advanced machine learning (ML) models can more accurately predict certain critical water quality parameters in marine environments compared to conventional regression techniques. We specifically evaluated the spatio-temporal distribution of Chlorophyll-a (Chl-a) and Secchi Disk Depth (SDD) in the Gulf of Izmit using in-situ measurements and Sentinel-2 satellite imagery from October 2021 and 2022. Among the models tested, the Support Vector Regression (SVR) model showed better predictive performance, achieving the lowest RMSE for SDD (1.

The relationships of inflammatory blood markers with maternal periodontal and dental states and their effects on preterm membrane rupture development.

Background: The influence of maternal oral and dental health on the occurrence of Preterm Premature Rupture of Membranes (P-PROM) and its underlying mechanisms remain uncertain. This research seeks to investigate the impact of maternal oral and dental health on the incidence of P-PROM and its association with inflammatory markers in the blood.

Methods: This study adopts a prospective case-control design methodology.

Prognostic factors, treatment and outcome in a Turkish population with endometrial stromal sarcoma.

Purpose: To analyze treatment modalities and prognostic factors in patients with Stage I-II endometrial stromal sarcoma (ESS).

Materials And Methods: Twenty four patients (nineteen with low-grade ESS [LGESS] and five with high-grade ESS [HGESS]) were assessed retrospectively in terms of general characteristics, prognostic factors, treatment methods and survival.

Results: Twenty patients were at Stage I and three were at Stage II.