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This study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. Unlike traditional manual inspections, which are time-consuming and error-prone, our approach leverages advanced segmentation techniques to ensure accurate, pixel-level classification of various crack types. The main challenge of this study was determining the damage caused to highways by earthquakes with magnitudes greater than 7.0, which occur approximately once every 200 years. The most crucial step in the automatic detection of these damages is the reliable preparation of a high-accuracy dataset. To achieve this, pixel-based labels were created by experts in the construction field by analyzing each pixel value. Following two major earthquakes, a unique dataset for segmenting roadway deterioration was created through intensive and detailed studies. This study aims to present the performance results of popular deep learning-based segmentation models in an unbiased manner, providing a feasible infrastructure for future real-time applications. The innovative aspect of this research lies in the creation of a unique post-earthquake dataset, collected and labeled from highways affected by the February 6, 2023 earthquakes in Turkey (Mw = 7.7 and Mw = 7.6). Deep learning models, including SegNet, Attention SegNet, U-Net, FCN (8s), and DeepLab, were trained and tested on this dataset. Among these, the SegNet model achieved the best performance with an average accuracy of 86.72%, precision of 92.99%, and sensitivity of 78.45%. By demonstrating superior performance metrics compared to existing methods, this study provides a robust framework for future infrastructure monitoring and maintenance strategies, ensuring safer and more resilient transportation networks in disaster-prone regions.

One of today’s most pressing environmental issues is related to waste disposal, especially landfilling. The quality of groundwater has been seriously compromised due to improper storage methods. Problems related to human health and environmental pollution are more common in open dumps. These irregular disposal sites cause many disadvantages and serious health problems. For this reason, cases that caused slope failures in landfills have been examined. This study investigates stability issues related to solid waste landfills, focusing on the assessment of waste stability. The study includes analyses of slope failure cases in various landfills: Hekimbasi, Dona Juana, Gnojna Grora, Hiriya, Payatas, and Xerolakka. Using Plaxis 2D software, the data obtained from these analyses have been rigorously evaluated to determine the most appropriate landfill management strategies. Slope collapses in landfills are generally caused by unsuitable landfill design, lack of daily cover laying and waste compaction, inadequate waste segregation, insufficient leachate drainage and gas discharge, and excessive slope angle and height. The presence of one or more of these conditions can result in a decline in site stability. Instabilities are experienced in these areas where engineering controls are inadequate.

Revealing long-term trends in hydrometeorological variables plays a critical role in the sustainable management and planning of water resources. These analyses are necessary to understand climate change impacts, taking precautions for natural disasters, plan agricultural activities, and develop water management strategies. The aim of this study is to examine the changes in monthly and annual total precipitation and evapotranspiration values in the Maritsa River Basin, a transboundary water basin between Bulgaria, Greece, and Türkiye. For this, precipitation values for the 1982-2023 water years were taken from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data set, and evapotranspiration values for the 1982-2023 water years were taken from the European Reanalysis 5th Generation-Land (ERA5-Land) data set. The Mann-Kendall, Sen's slope estimator, and Innovative Trend Analysis (ITA) methods were used to determine trends. According to the test results, there is a statistically significant increase in annual total precipitation values within the 95% confidence interval and in annual total evapotranspiration values within the 99% confidence interval. Specifically with all three methods positive and statistically significant trends are observed in precipitation in October, January, May and June. In the monthly evapotranspiration trend analysis, a statistically significant increase is observed except for November, December, June and July. Trend increases were visualized using the graphical method ITA. Significant increasing trends in both monthly and annual precipitation and evapotranspiration reveal changes in the hydrological cycle of the basin. The test results can be used in planning and solving problems related to the basin area.

Introduction: Platelet-rich plasma (PRP) injections may be administered using needles of different thicknesses and lengths, depending on the purpose of use and tissue type. However, needle-related parameters (size/length) should not affect the treatment results. We investigated the effect of needles with different diameters on platelet concentration in vitro. Materials and Methods: Blood samples were obtained from 50 healthy volunteers. PRP was then obtained by single-spin (n = 25) and double-spin centrifugation (n = 25). After the prepared PRP was drawn into an injector, first without needles (needle-free), and then, 26G, 27G, 30G, and 33G needles were attached to the same injector, and 0.4 cc of PRP was injected into counting containers. Following sampling, the platelet concentration in the PRP samples was counted by a hematology analyzer, and the results obtained were compared. A one-way analysis of variance test was applied for the statistical analysis of the results. Differences between the groups were considered statistically significant at a value of P > 0.05. Results: The PRP platelet count mean differences between the needle-free group and the 33G, 30G, 27G, and 26G needle groups in the PRP prepared by single-spin centrifugation were 1.019, 0.994, 1,025, and 1.000, respectively, and in the PRP prepared by double-spin centrifugation, they were 1.004, 1.004, 1.008, and 1.004, respectively. Results reveal no significant difference in the platelet concentration in the different needle groups. Conclusions: The use of fine-caliber needles in PRP prepared by single- and double-spin centrifugation had no impact on the resulting platelet concentration.

Background Heart failure (HF) has become a public healthcare concern with significant costs to countries because of the aging world population. Acute heart failure (AHF) is a common condition faced frequently in emergency departments, and patients often present to hospitals with complaints of breathlessness. The patient must be evaluated with anamnesis, physical examination, blood, and imaging results to diagnose AHF. Brain natriuretic peptide (BNP) is a widely accepted biomarker for the diagnosis of HF. Methods The files of the patients who applied to the emergency department with complaints of breathlessness were scanned, and BNP and urinary density (UD) levels were evaluated for the diagnosis of HF in patients. Results The results support that BNP is an effective biomarker in AHF, as is widely accepted. When the correlation between BNP and UD measurements was examined in the present study, a negative correlation was detected between the parameters. The results also suggested that low UD values may help diagnose AHF. Conclusion If similar results are obtained in prospective multicenter studies with the participation of more patients, UD value can be used as a biomarker for the diagnosis of AHF.

The present study employs machine learning regression analyses to investigate the efficiency of photovoltaic (PV) panels utilizing solar energy under the influence of environmental factors. The experimental study was conducted on two 100-watt monocrystalline and two polycrystalline PV panels, which were divided into clean and dirty groups. The following variables were monitored and recorded for a period of six months: radiation, panel temperature, air temperature, wind speed, humidity, pressure, and ultraviolet (UV) radiation. Additionally, current, voltage, and power were recorded. These measurements were taken during the production of energy by PV panels. Monocrystalline PV panels exhibited an 8.6% increase in energy efficiency, while polycrystalline PV panels demonstrated a 6.2% increase, following the collection and cleaning of data in accordance with the IEC 61724 standard. Six distinct machine learning regression analyses were conducted on the dataset. The results were compared using the Root Mean Square Error (RMSE) and the coefficient of determination (R2). The Random Forest model demonstrated the greatest predictive success, while the Support Vector Regression (SVR) model exhibited the lowest performance.

In a single-switch forward converter, which is a type of single-input multiple-output (SIMO) DC–DC converter, voltage changes are observed at the outputs due to the current passing through the diodes, the difficulty of winding the transformer in full and half windings, differences in resistance in the windings, the presence of leakage inductance and the existence of common inductance between the windings. These voltage changes are undesirable, and cross-regulation techniques have been developed to keep the outputs at the desired value. One of these techniques, the weighted control method, in which voltage information is received from many outputs, is addressed in this article. The reason we apply this technique is to avoid additional components and their control problems. In addition, this technique is simpler and cheaper than other cross-regulation techniques. In this way, we will contribute to the literature on cross-regulation, showing that cross-regulation remains below 3.33% and circuit efficiency reaches 86.83% by using the weighted control method.