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In this study, photon interaction parameters such as radiation protection efficiency ( RPE ), total mass attenuation coefficient ( μ/ρ ), linear attenuation coefficients ( μ ), half value layers ( HVL ), tenth value layers ( TVL ), mean free paths ( MFP ), effective atomic numbers ( Z Eff ) and effective electron densities ( N Eff ) were experimentally investigated for the BCZrB-0, BCZrB-10, BCZrB-20, BCZrB-30, BCZrB-40 and BCZrB-50 in the energy range from 59.5 keV to 1332.5 keV. In the experiment, HPGe detector and 241 Am (59.5 keV), 133 Ba (81 keV, 276.4 keV, 302.9 keV, 356 keV and 383.9 keV), 22 Na (511 keV and 1274.5 keV), 137 Cs (661.7 keV), and 60 Co (1173.2 keV and 1332.5 keV) radioactive point sources were used. Experimental results compared with results of the WinXCOM, GEANT4 simulation code and FLUKA simulation code. Energy absorption build-up factors ( EABF ) and exposure build-up factors ( EBF ) were calculated with G-P fitting method in the energy region 0.015 MeV ≤ E ≤ 15 MeV. Kerma relative to air values were investigated in the energy region 0.001 MeV ≤ E ≤ 20 MeV. Effective removal cross section, total macroscopic cross section and number of transmitted neutron values were defined for composites. The agreement between experimental and WinXCOM program, between experimental and GEANT 4 simulation codes and between experimental and FLUKA simulation codes of gamma-ray interaction parameters are < 5.1%, < 6.5% and < 5%, respectively. All of the results are compatible with each other and BCZrB-50 is a good gamma-ray radiation absorber in the studied composites and BCZrB-0 is the best neutron absorber in the studied composites.

With the repetition of mucilage event, which is triggered by many different anthropogenic, climatic and microbiological factors, in the Marmara Sea in 2021, the importance of water quality in the seas has come to the fore again. To present the spatial distribution of the mucilage, a feasibility study has been carried out with point-based water quality measurements and remote sensing data. In-situ measurements are collected routinely within the scope of the Integrated Marine Pollution Monitoring Program (DEN-IZ) which was conducted in cooperation with the Ministry of Environment, Urbanisation and Climate Change and the Scientific and Technological Research Council of Turkey - Marmara Research Center (TUBITAK-MAM). In this preliminary study, 16 in-situ measurements, 5 of which were taken from water containing mucilage, on 29 April 2021 in the Gulf of Gemlik were used. Then, univariate regression analyzes were performed in two different scenarios (i.e. 5 mucilage points and all in-situ points) with Sentinel-2 satellite imagery and in-situ water quality measurements for 2 different parameters (i.e. chlorophyll-a – Chl-a) and turbidity). According to R2 and accuracy assessment measures (f- and t- statistics etc.), the most suitable models were determined for two scenarios and two parameters. Finally, the performances of the selected models were tested with 2 different in-situ measurements and satellite images (dated 22 and 27 April) taken from dates close to the data set used; and it was concluded that the models created with 16 points were successful for both Chl-a and turbidity estimation for this preliminary study.

As a result of increasing use of wind energy as a sustainable source of electricity, large Wind Parks with numerous Wind Turbines have been constructed. Wind turbines are extremely tall objects consisting of stationary and moving parts. The presence of wind turbines in the vicinity of weather radar systems can significantly impact their performance, leading to false alarms and errors in radar measurements. Accurate weather forecasting is challenging in this circumstance. Large Radar Cross Section (RCS) of wind turbines results in interference, also known asWind Turbine Clutter (WTC) orWind Turbine Interference (WTI), within and beyond the radar main beam, Multipath Interference (MPI), and phenomena referred to as ”shadowing effects” behind the wind turbines. These effects vary significantly in both time and space as a result of various wind turbine operations and meteorological conditions. It can often be difficult to distinguish wind turbine returns from weather-like signals. For the assessment of WTC or WTI, it is essential to understand the scattering properties of these wind turbines. In this paper, the bistatic scattering characteristics of a wind park turbine using a Unmanned Aerial Vehicle (UAV)-mounted receiver recording C-band weather radar signals were investigated by determining the average received power (PRxAvg (θs)) and RCS of wind turbine as a function of the scattering angle. For this purpose, the measurements and data provided by the German Meteorological Service (DWD, DeutscherWetterdienst) were utilised. The average received power as a function of scattering angle (θs) was calculated by using I-Q (In-phase and Quadrature) signals. Forward, back and side scattering of the calculated average received power were analysed separately. Moreover, Front-to-Back ratio, Front-to-Right side ratio and Front-to-Left side ratio were calculated and compared using forward, back and side scatter values. RCS values were also calculated depending on the scattering angle (θs) of the wind turbine.

A forest fire is stated as an ecological disaster whether it is man-made or caused naturally. İzmir is one of the regions where forest fires are most intensified in Turkey. The study area located at Aegean region of Turkey suffered two forest fires in 2017; Menderes and Bayındır areas. This study presents the integration of remote sensing (Sentinel 2 and Landsat 8 satellite images) and GIS data to map and evaluate the forest burned areas due to both forest fires. For this purpose, different indexes such as Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalized Burn Ratio (NBR) and Normalized Burn Ratio Thermal (NBRT) Burn Index are applied besides different classification algorithms. The results showed that different vegetation types/zones are being affected. Sentinel 2 and Landsat 8 data are integrated to the GIS established with fieldwork data to analyse and also validate the results. Digital Elevation Model (DEM) data produced from ASTER satellite is also overlaid to the outcomes to emphasize the destructed forest areas. The efficiency of using two different satellites are outlined by comparing the accuracy of forest fire maps produced.

Summary In this study, ERα gene Pvu II and Xba I polymorphisms and COL1A1 gene Sp1 polymorphisms in postmenopausal women were compared with lumbar vertebra and femoral neck BMD values. In conclusion, it was designated that Pvu II polymorphism was effective on average lumbar vertebra BMD value in postmenopausal women of our study group. Introduction Bone mineral density (BMD), the major determinant of osteoporotic fracture risk, has a strong genetic component. Several candidate gene polymorphisms have been implicated in the regulation of this process. In this study, the relationship among BMD values of lumbar vertebra and femoral neck and ERα gene Pvu II and Xba I polymorphisms and COL1A1 gene Sp1 polymorphism in 126 postmenopausal women (30 normal, 46 osteopenic, and 50 osteoporotic in terms of bone mineral density) was researched. Methods The ERα gene Pvu II and Xba I genotypes were determined by polymerase chain reaction–restriction fragment length polymorphism (PCR–RFLP) whereas the COL1A1 gene Sp1 genotype was determined by real-time PCR. BMDs at the lumbar spine (vertebrae L1–L4) and hip (femur neck) were measured by dual-energy X-ray absorptiometry. Results According to our study results, the significant difference was found in women with normal, osteopenic, and osteoporotic bone mass in terms of ERα gene Pvu II polymorphism “pp” genotype frequency. The “pp” genotype frequency was significantly lower in women with normal bone mass. Average lumbar vertebra BMD value of women with “PP” genotype was significantly higher than that with “pp” genotype. On the other hand, in the evaluations on ERα gene Xba I polymorphism and COL1A1 gene Sp1 polymorphism, it was noted that there was no difference in terms of average BMD values, genotype, and allele frequencies among groups. Conclusion In conclusion, it was designated that ERα gene Pvu II polymorphism was effective on average lumbar vertebra BMD value in postmenopausal women of our study group.

For the management of the forest and the assessment of impacts on ecosystems, post-fire burned area mapping is crucial for sustainable environment and forestry. While optical remote sensing data has been extensively used for monitoring forest fires due to its spatial and temporal resolutions, it is susceptible to limitations imposed by poor weather conditions. To overcome this challenge, the complementary use of optical and Synthetic Aperture Radar (SAR) data is beneficial, as SAR can penetrate clouds and capture images in all-weather conditions. However, SAR lacks the necessary spectral features for comprehensive forest fire monitoring and burned area mapping. To overcome these limitations, this study proposes a Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) based deep feature translation method for burned area mapping by combining optical and SAR data. This approach allows for the retrieval of precise information of interest with a level of precision that cannot be achieved by either optical or SAR data alone. The Cycle-GAN uses a cyclic structure to transfer data from one domain (optical) to another domain (SAR) into the same feature space. As a result, it can maintain its spectral characteristics while providing ongoing and current information for monitoring forest fires. For this purpose, Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalised Burn Ratio (NBR) were determined using optical data and image translation was performed with Cycle-GAN on SAR data. The accuracy of the fake BAI, MIRBI and NBR spectral burn indices determined from the SAR was established by correlating the original spectral burn indices determined from the optical data. The results demonstrate a significant correlation between the real and generated fake burn indices, particularly with a noteworthy correlation coefficient of 0.93 observed for the NBR index. In addition, the findings validate the effectiveness of the generated indices in accurately representing and quantifying the extent of burned areas.

Achieving high efficiency by taking advantage of agricultural land at a high level allows the continued vitality of the soil and also contributes to the country's economy. The land with the most fertilizer from agricultural land is generally the first class agricultural land (I.) followed by second (II.) and third class (III.) agricultural lands. It is accepted that all these lands are considered to be protected and various restrictions have been introduced to these lands. Soil conservation, use and development of balanced is possible to be defined in detail by exploiting the developing science and technology possibilities, determination well-defined properties and the implementation of policies by making the necessary plans. For this reason, Trabzon province is selected as the pilot region land use capability of agricultural land classes (especially urban-rural area and plateau) ongoing changes in the past years until today are examined depending on the land use first, second and third class. In this context, satellite images for 2002, 2005, 2009 and 2017 and land use data including the non-agricultural use of the province of Trabzon has been discussed and the temporal changes of agricultural areas depending on land use capability have been examined using Geographic Information Systems (GIS). In all the productive areas of Trabzon Province, the increase in urban-rural development has been examined in detail because of especially the creation of planned areas and the occurrence of construction needs. This study is a small-scale case study and the results are examined and analyzed using GIS.

Optical and microwave remote sensing technologies have become key tools for local and global change detection applications. Generally, optical data has been the focus of remote sensing for change detection because of the varied spatial and temporal resolutions that allow for reliable information. However, the dependence of optical data on weather conditions prevents continuous and up-to-date information. On the other hand, Synthetic Aperture Radar (SAR) data can record all-weather and all-time polarization information which is critical for change detection in poor weather conditions; nonetheless, SAR is not precise as optical data for forestry change detection applications as it cannot provide the spectral features of interest. The combined processing of optical and SAR images allow for the retrieval of information of interest with a precision that none of them could achieve alone. In this context, Cycle-Consistent Generative Adversarial Networks (CycleGAN) based deep feature translation method was proposed in this study for change detection. The CycleGAN transfers images from one domain (optical) to another domain (SAR) into the same feature space using a cyclic structure. Thus, it can provide continuous and up-to-date information for change detection while keeping its spectral features. The accuracy of the fake images generated from CycleGAN was evaluated by correlating them with spectral indices (e.g., Normalized Difference Vegetation Index (NDVI), Modified Radar Vegetation Index (mRVI), and Modified Radar Forest Degradation Index (mRFDI)) directly obtained from optical and SAR data. As a result, the best correlation coefficients (R) were found between real NDVI (optical data) and fake NDVI (CycleGAN) with 0.98 and 0.97 for two different dated datasets.

According to genotype status of these two polymorphisms, clopidogrel-treated patients can be protected from stent thrombosis.

Diabetes mellitus (DM) is one of the most common diseases worldwide, and is a significant risk factor for healthcare-associated infections (HAIs). Our aim in this study was to compare the distributions of HAIs and the causative pathogens between diabetic and non-diabetic patients. In this study, 716 HAIs in 465 diabetic patients and 761 HAIs in 465 non-diabetic patients were evaluated. HAIs in patients with DM were most frequently urinary tract infections (UTIs) [266 infections (37.2 %)], followed by blood stream infections (BSIs) [161 infections (22.5 %)], surgical site infections (SSIs) [127 infections (17.7 %)], pneumonia [107 infections (14.9 %)] and any other infections [161 infections (22.5 %)]. The rates of UTIs, BSIs, SSIs, pneumonia and any other infections were similar between diabetic and non-diabetic patients. In terms of the causative pathogens, Staphylococcus aureus more frequently caused SSIs and Candida spp more frequently caused UTIs in diabetic patients compared with non-diabetic patients. We found no differences in the distribution of HAIs between patients without and with DM. However, S . aureus and Candida spp were more common causative pathogens of SSIs and URTIs, respectively, in diabetic patients than in non-diabetic patients.