Evaluating the effect of histopathological parameters of prostate adenocarcinoma on prognosis in radical prostatectomy specimens.

Background: Over the past decade, significant updates have been made regarding the classification and grading of prostate adenocarcinoma in radical prostatectomy specimens, following decisions reached in international conferences and through impactful publications. These alterations are closely linked to patient prognosis.

Objectives: Observe the incidence of these changes and their impact on patient prognosis.

Deep learning-based burned forest areas mapping via Sentinel-2 imagery: a comparative study.

In order to evaluate the effects of forest fires on the dynamics of the function and structure of ecosystems, it is necessary to determine burned forest areas with high accuracy, effectively, economically, and practically using satellite images. Extraction of burned forest areas utilizing high-resolution satellite images and image classification algorithms and assessing the successfulness of varied classification algorithms has become a prominent research field. This study aims to indicate on the capability of the deep learning-based Stacked Autoencoders method for the burned forest areas mapping from Sentinel-2 satellite images.

Prevalence of anemia among Indigenous children in Latin America: a systematic review.

Objective: To describe the prevalence pattern of anemia among Indigenous children in Latin America.

Methods: PRISMA guidelines were followed. Records were identified from the databases PubMed, Google Scholar, and Lilacs by two independent researchers between May and June 2021.

Geographic information system-assisted site quality assessment for hazelnut cultivation using multi-criteria decision analysis in the Black Sea region, Turkey.

Developing land suitability models for strategically critical agricultural products to expand sustainable agricultural policies and sensitive agriculture management has become a significant trend. This study aims to improve a unique land suitability model for hazelnut cultivation by applying the criteria set (7 main criteria, 35 sub-criteria) including qualitative and quantitative reasons, integrated fuzzy analytic hierarchy process, inverse distance weighting, multi-criteria decision analysis, geographic information system, and weighted linear combination approaches. The model developed in the present study was applied and tested in Ünye District of Ordu Province, where hazelnut production in the Eastern Black Sea region of Turkey is an important economic activity.

Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study.

The surface areas of lakes alter constantly due to many factors such as climate change, land use policies, and human interventions, and their surface areas tend to decrease. It is necessary for obtain baseline datasets such as surface areas and boundaries of water bodies with high accuracy, effectively, economically, and practically by using satellite images in terms of management and planning of lakes. Extracting surface areas of water bodies using image classification algorithms and high-resolution RGB satellite images and evaluating the effectiveness of different image classification algorithms have become an important research domain.