Investigating the effect of infill walls on steel frame structures

Infill walls consisting of materials such as hollow concrete, hollow clay and autoclaved aerated concrete bricks are not only preferred in reinforced concrete buildings but also in steel frame structures. It is a well-known fact that infill walls limit the displacement of frames under horizontal loads. However, they may also bring about certain problems due to being placed randomly in horizontal and discontinuously in vertical directions for some architectural reasons. Moreover, cracks in frame-wall joints are observed in steel frame structures in which ductile behaving steel and brittle behaving infill walls are used together. In this study, the effect of infill walls on steel frames has been investigated. In the steel frame structure chosen for the study, four different situations consisting of different combinations of infill walls have been modeled by using ETABS Software. Later, the pushover analyses have been performed for all the models and their results have been compared. As a result of the analyses done by using the equivalent diagonal strut model, it has been found out that infill walls limit the displacement of steel frames and increase the performance of a structure. However, it has been also determined that in the steel frame structure in which the infill walls have been placed discontinuously in vertical and asymmetrically in horizontal, infill walls may lead to torsional and soft story irregularities. As a result, it is possible to observe cracks in the joints of infill walls and steel frame, the deformation properties of which differ, unless necessary precautions are taken

DEEP LEARNING BASED AERIAL IMAGERY CLASSIFICATION FOR TREE SPECIES IDENTIFICATION

Forest monitoring and tree species categorization has a vital importance in terms of biodiversity conservation, ecosystem health assessment, climate change mitigation, and sustainable resource management. Due to large-scale coverage of forest areas, remote sensing technology plays a crucial role in the monitoring of forest areas by timely and regular data acquisition, multi-spectral and multi-temporal analysis, non-invasive data collection, accessibility and cost-effectiveness. High-resolution satellite and airborne remote sensing technologies have supplied image data with rich spatial, color, and texture information. Nowadays, deep learning models are commonly utilized in image classification, object recognition, and semantic segmentation applications in remote sensing and forest monitoring as well. We, in this study, selected a popular CNN and object detection algorithm YOLOv8 variants for tree species classification from aerial images of TreeSatAI benchmark. Our results showed that YOLOv8-l outperformed benchmark’s initial release results, and other YOLOv8 variants with 71,55% and 72,70% for weighted and micro averaging scores, respectively

Anxiety levels of healthcare personnel in different stages of COVID-19 pandemic: A nationwide study from Turkey

Aim: SARS CoV-2 transmission in healthcare personnel was first reported on January 20, 2020. The aim of this study was to evaluate the anxiety levels experienced by healthcare personnel in Turkey during the COVID-19 pandemic and the factors affecting these levels.Material and Methods: A survey investigating sociodemographic features and examining anxiety levels was conducted among approximately 1000 healthcare personnel who were expected to take active roles in the pandemic across Turkey. The survey was conducted in three stages: before the pandemic spread to Turkey, at the beginning of the pandemic and when the pandemic became prominent. A logistic regression analysis was performed to determine the factors affecting anxiety and predictors of anxiety levels.Results: In the first survey, always (odds ratio, 15.781; p<0.01) and often (odds ratio, 5.365; p<0.05) media use, in the second survey media use (p<0.05) and profession (odds ratio, 0.021; p<0.05) and in the third survey, marital status (odds ratio, 17.716; p<0.01) and gender (odds ratio, 4.431; p<0.05) were determined as the predictors of anxiety related to COVID-19.Discussion: As a result of this study, healthcare personnel groups were defined (women, nurses, married people) who need special intervention and support to provide spiritual comfort when working on the front line in the fight against COVID-19. Further comprehensive studies are needed of the extent of psychological support required by healthcare personnel and to whom and how this support should be provided

ANTINEPHROLITHIATIC ACTIVITY OF PERSEA AMERICANA (AVOCADO) AND VIBURNUM OPULUS (GUELDER ROSE) AGAINST ETHYLENE GLYCOL-INDUCED NEPHROLITHIASIS IN RATS

Background: Nephrolithiasis is a severe health problem causing morbidity. Chemolisis, extracorporeal shock wave lithotripsy (SWL), percutaneous nephrolithotomy (PNL), retrograde ureterorenoscopy (URS), and open and laparoscopic surgery are used for treatment with various success rates. Medical treatments with fewer complications were investigated thoroughly. Materials and Methods: In this study, we evaluated the effects of Persea americana (avocado) leaves and Viburnum opulus (guelder rose) fruits on nephrolithiasis in an animal model and used 42 rats. The groups received both low and high doses of Persea americana leaves and Viburnum opulus fruit ethanol extracts orally for 28 days. These two plants have been used for years in Turkey for their nephrolithiatic effect. Results: Avocado and guelder rose increased the urine volume and urine citrate levels, decreased urine cystine and oxalate levels, and lowered the crystal deposits in kidney tissue. Avocado and guelder rose also prevented oxidant damage and crystal formation in kidney tissue samples. Conclusion: The two plants that have been used for years for nephrolithiasis treatment in Turkey can safely be used for kidney stones

Why do some patients with stage 1A and 1B endometrial endometrioid carcinoma experience recurrence? A retrospective study in search of prognostic factors

Objectives: Endometrial endometrioid carcinoma (EEC) is the most encountered subtype of endometrial cancer (EC). Our study aimed to investigate the factors affecting recurrence in patients with stage 1A and 1B EEC. Material and methods: Our study included 284 patients diagnosed with the International Federation of Gynecology and Obstetrics stage 1A/1B EEC in our center from 2010 to 2018. The clinicopathological characteristics of the patients were obtained retrospectively from their electronic files. Results: The median age of the patients was 60 years (range 31–89). The median follow-up time of the patients was 63.6 months (range 3.3–185.6). Twenty-two (7.74%) patients relapsed during follow-up. Among the relapsed patients, 59.1% were at stage 1A ECC, and 40.9% were at stage 1B. In our study, the one-, three-, and five-year recurrence-free survival (RFS) rates were 98.9%, 95.4%, and 92.9%, respectively. In the multivariate analysis, grade and tumor size were found to be independent parameters of RFS in all stage 1 EEC patients. Furthermore, the Ki-67 index was found to affect RFS in stage 1A EEC patients, and tumor grade affected RFS in stage 1B EEC patients. In the time-dependent receiver operating characteristic curve analysis, the statistically significant cut-off values were determined for tumor size and Ki-67 index in stage 1 EEC patients. Conclusions: Stage 1-EEC patients in the higher risk group in terms of tumor size, Ki-67, and grade should be closely monitored for recurrence. Defining the prognostic factors for recurrence in stage 1 EEC patients may lead to changes in follow-up algorithms

The impact of Ki-67 index, squamous differentiation, and several clinicopathologic parameters on the recurrence of low and intermediate-risk endometrial cancer

Endometrial endometrioid carcinoma (EEC) represents approximately 75-80% of endometrial carcinoma cases. Three hundred and thirty-six patients with EEC followed-up in the authors’ medical center between 2010 and 2018 were included in our study. Two hundred and seventy-two low and intermediate EEC patients were identified using the European Society for Medical Oncology criteria and confirmed by histopathological examination. Recurrence was reported in 17 of these patients. The study group consisted of patients with relapse. A control group of 51 patients was formed at a ratio of 3:1 according to age, stage, and grade, similar to that in the study group. Of the 17 patients with recurrent disease, 13 patients (76.5%) were Stage 1A, and 4 patients (23.5%) were Stage 1B. No significant difference was found in age, stage, and grade between the case and control groups (p > 0.05). Body mass index, parity, tumor size, lower uterine segment involvement, SqD, and Ki-67 index with p<0.25 in the univariate logistic regression analysis were included in the multivariate analysis. Ki-67 was statistically significant in multivariate analysis (p = 0.018); however, there was no statistical significance in SqD and other parameters. Our data suggest that the Ki-67 index rather than SqD needs to be assessed for recurrence in patients with low- and intermediate-risk EEC

Reliability estimation using Markov chain Monte carlo-based tail modeling

Tail modeling is an efficient method used in reliability estimation of highly safe structures. Classical tail modeling is based on performing limit-state function evaluations through a sampling scheme, selecting a threshold value to specify the tail part of the cumulative distribution function, fitting a proper model to the tail part, and estimating the reliability. In this approach, limit-state function calculations that do not belong to the tail part are mostly discarded, and so majority of limit-state evaluations are wasted. In this paper, Markov chain Monte Carlo method with Metropolis–Hastings algorithm is used to draw samples from the tail part only so that a more accurate reliability index prediction is achieved. A commonly used proposal distribution formula is modified by using a scale parameter. The optimal value of this scale parameter is obtained for various numerical example problems with a varying number of random variables, and an approximate relationship is obtained between the optimal value of the scale parameter and the number of random variables. The approximate relationship is tested on the reliability prediction of a horizontal axis wind turbine and observed to work well. It is also found that the proposed approach is more accurate than the classical tail modeling when the number of variables is less than or equal to four. For a larger number of random variables, none of the two approaches are found to be superior to another

Earthquake hazard analysis for East Anatolian Fault Zone, Turkey

© 2015, Springer Science+Business Media Dordrecht.The aim of this study was to investigate the earthquake hazard of the East Anatolian Fault Zone by determining the a and b parameters in a Gutenberg–Richter magnitude–frequency relationship. For this purpose, the East Anatolian Fault Zone is divided into five different source zones based on their tectonic and seismotectonic regimes. We calculated the b value, which is the slope of the frequency–magnitude Gutenberg–Richter relationship, from the maximum likelihood method (ML). Also, we estimated the mean return periods, the most probable maximum magnitude in the time period of t years and the probability for an earthquake occurrence for an earthquake magnitude ≥M during a time span of t years. We then produced a and b value maps using the ML. We obtained the lowest b value in Region 1 covered Karlıova triple junction. This conclusion is strongly supported from the probability value, which shows the largest value (90 %) for an earthquake with magnitude greater than or equal to 6.0. The mean return period for such a magnitude is the lowest in this region (43 years). The most probable magnitude in the next 100 years was calculated, and we determined the highest value around Karlıova triple junction. According to these parameters, Region 1 covered the Karlıova triple junction and is the most dangerous area around the East Anatolian Fault Zone