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Nöbetli Çalışan Erkeklerde Beyin Kökenli Nörotrofik Faktör, Glial Hücre Dizisinden Kaynaklanan Nörotrofik Faktör ve Nörotrofin-3 Düzeylerinin ve Beyin Hacimlerinin İncelenmesi: Klinik Araştırma
No full textObjective: This study aims to examine the levels of brain-derived neurotrophic factor (BDNF), glial cell line-derived neurotrophic factor (GDNF) and neurotrophin-3 (NT-3) and brain volumes in shift-working men. Material and Methods: Forty-nine men who have been working in shifts for at least 1 year were included in the study. Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality, Montreal Cognitive Assessment (MoCA) test and trail making test (TMT) were used to assess cognitive performance, and Beck Anxiety Inven-tory (BAI) was used to assess anxiety level. Afterwards, plasma BDNF, GDNF and NT-3 levels were examined. Brain volume measurement was performed. Re-sults: Since the PSQI results of shift-working men were above 5, it was determined that they had “poor sleep quality”. According to the BAI results, 85% of them had anxiety. Primary-secondary school graduate shift-working men had lower MoCA value and TMT-A and TMT-B test scores were higher (p<0.05). Working in shifts for 6 years or more caused a significant decrease in BDNF and NT-3 levels (p<0.05). According to the correlation analysis results, it was observed that the prefrontal cortex, dorsolateral prefrontal cortex and cerebral cortex in the left lobe of the brain were smaller in elderly shift workers (p[removed
Temporal evolution of entropy and chaos in low amplitude seismic wave prior to an earthquake
No full textThis study investigates the temporal changes of entropy and chaos in low-amplitude continuous seismic wave data prior to two moderate-level earthquakes. Specifically, we examine seismic signals before and during the Istanbul-Turkey earthquake of September 26, 2019 (M = 5.7), and the Duzce-Turkey earthquake of November 17, 2021 (M = 5.2), which occurred near the Marmara Sea region on the north-Anatolian fault line. We aim to identify changes in complexity and chaotic characteristics in the pre-earthquake seismic waves and explore the possibility of earthquake forecasting minutes before an earthquake. To accomplish this, we utilize windowed scalogram entropy and sample entropy methods and compared the results with Lyapunov exponents and windowed scale index. Our findings indicate that measuring the temporal change of entropy using windowed scalogram entropy is sensitive to the change in complexity due to the frequency shifts during the weak ground motion approaching an earthquake. On the other hand, Lyapunov exponents and sample entropy appear more effective in their response to the change in complexity and chaotic characteristics due to the change in the signal amplitude. Additionally, the windowed scale index can detect temporal fluctuations in the aperiodicity of the signal. Overall, our results suggest that all four methods can be valuable in characterizing complexity and chaos in short-time pre-earthquake seismic signals, differentiating earthquakes, and contributing to the development of earthquake forecasting techniques. © 2023 Elsevier Lt
Empirical Model for the Prediction of Ground Motion Duration on Soft Soils
No full textIn the civil engineering field, several attenuation relationships are proposed to estimate the ground motion duration (GMD) of earthquakes for hard and medium soil types, considering various seismic quantities. However, there is a lack of prediction equations that are directly used for soft soil sites. It is a known fact that soft soil has very adverse impacts on the seismic behavior of structures due to the amplification of displacement demands, and hence, GMD is also affected by this situation. Considering the importance of the topic, special attention is paid to the prediction of GMD of earthquakes at soft soil sites. In the study, two important topics are emphasized. First, a technique explicitly evolved to quantify the significant earthquake GMD based on the estimated time interval between 5 and 95% of the Arias intensity is presented. Second, a prediction equation for significant GMD is proposed for soft soils by using several earthquake database collections. The collected database is statistically evaluated for different earthquake parameters such as moment magnitude (Mw), closest distance to the rupture (Rrup) and average shear wave velocity for the top 30 m of soil (Vs,30), and a prediction equation is suggested using several regression coefficients based on these parameters. Consequently, the proposed prediction equation is compared with the existing models used in the literature, and the efficiency of the model is evaluated
A novel hybrid model to predict concomitant diseases for Hashimoto’s thyroiditis
No full textHashimoto’s thyroiditis is an autoimmune disorder characterized by the destruction of thyroid cells through immune-mediated mechanisms involving cells and antibodies. The condition can trigger disturbances in metabolism, leading to the development of other autoimmune diseases, known as concomitant diseases. Multiple concomitant diseases may coexist in a single individual, making it challenging to diagnose and manage them effectively. This study aims to propose a novel hybrid algorithm that classifies concomitant diseases associated with Hashimoto’s thyroiditis based on sequences. The approach involves building distinct prediction models for each class and using the output of one model as input for the subsequent one, resulting in a dynamic decision-making process. Genes associated with concomitant diseases were collected alongside those related to Hashimoto’s thyroiditis, and their sequences were obtained from the NCBI site in fasta format. The hybrid algorithm was evaluated against common machine learning algorithms and their various combinations. The experimental results demonstrate that the proposed hybrid model outperforms existing classification methods in terms of performance metrics. The significance of this study lies in its two distinctive aspects. Firstly, it presents a new benchmarking dataset that has not been previously developed in this field, using diverse methods. Secondly, it proposes a more effective and efficient solution that accounts for the dynamic nature of the dataset. The hybrid approach holds promise in investigating the genetic heterogeneity of complex diseases such as Hashimoto’s thyroiditis and identifying new autoimmune disease genes. Additionally, the results of this study may aid in the development of genetic screening tools and laboratory experiments targeting Hashimoto’s thyroiditis genetic risk factors. New software, models, and techniques for computing, including systems biology, machine learning, and artificial intelligence, are used in our study. © 2023, BioMed Central Ltd., part of Springer Nature
Validity and Reliability of the Turkish Version of the Spirituality Instrument-27 (SpI-27©)
No full textIntroduction: This methodological study aimed to adapt the Spirituality Instrument-27 (SpI-27©) to the Turkish language and culture. Design: The psychometric study was carried out with 267 individuals who were hospitalized in the cardiology clinic and who were diagnosed with a chronic disease. Data collection tools were the demographic questionnaire and the SpI-27©. The Statistical Package for Social Sciences was used for data analysis and confirmatory factor analysis. Results: The Kaiser–Meyer–Olkin index was 0.848 and Bartlett's test of sphericity was statistically significant (p = 0.000). The root mean square error of approximation was 0.05, the standardized root mean squared residual was 0.04, the adjusted goodness-of-fit index was 0.87, the goodness-of-fit index was 0.92, the nonformed fit index was 0.91, and the comparative fit index was 0.90. Connectedness with others accounted for 38.24 of the total variance, self-transcendence accounted for 11.71, self-cognizance accounted for 10.56, and conservationism and belief accounted for 9.82 of the total variance (total variance was 70.34%). The highest item factor loading of the scale was 0.812 and the lowest one was 0.398. Cronbach's alpha coefficient of the scale was 0.927. Conclusion: This measurement tool will enable researchers to plan and implement nursing interventions accurately and effectively by assessing the spiritual needs of individuals with nonmalignant chronic diseases. The use of this tool in different languages can help diagnose the spiritual needs of nurses working with multicultural and multilingual patients. © The Author(s) 2023
AN APPLICATION IN 3PLS AND 4PLS
No full textBackground: The purpose of this study is to investigate how artificial intelligence (AI) and robotic awareness, perceived organizational support, and competitive psychological climate approaches relate to turnover intention. In the literature, studies on robotic awareness and turnover intention have been undertaken in a variety of industries. In this respect, this study aims to address the absence in the literature of research on logistics services providers. This study aims to help businesses understand how to retain employees and foster a more inclusive and supportive workplace. Methods: The study utilizes survey information from 100 senior managers in the operations function of logistics service providers. The outcomes are obtained by modeling structural equations with SmartPLS. Data from the survey were gathered using the snowball sampling technique. Results: The results of the research reveal the effect of artificial intelligence and robotic awareness on competitive psychological and turnover intention. Conclusions: The study aims to explore the role of a competitive psychological climate and organizational support in mediating the relationship between AI and robotics awareness and turnover intention. We identify that awareness of AI and robotics has a considerable, favorable effect on the psychological climate of competition and turnover intention. We also find that the competitive psychological atmosphere has a substantial, favorable effect on turnover intention. In addition, organizational support has been demonstrated to have a substantial, favorable effect on turnover intention. However, it was not possible to identify the mediating role of organizational support and the psychological environment of competition in moderating the association between awareness of AI and robotics and turnover intention. On the basis of the research's findings, suggestions were made. © 2023, Poznan School of Logistics. All rights reserved
Early Software Defects Density Prediction: Training the International Software Benchmarking Cross Projects Data Using Supervised Learning
No full textRecent reviews of the literature indicate the need for empirical studies on cross-project defect prediction (CPDP) that would allow aggregation of the evidence and improve predictive performance. Most empirical studies predict defects at granularity levels of method, class, file, and module/package during the coding phase, and thereby avoid external failure costs. The main goal of this study is to perform an empirical study on early defect prediction at the beginning of a project at the product level of granularity for using it as input in planning quality activities of the project. Hence, both internal and external failure costs could be avoided as much as possible through proper planning of quality. We first made a systematic mapping study (SMS) on secondary studies (literature reviews) on defect prediction to identify the most used datasets, the project attributes and metrics utilized as estimators, and the supervised learning methods employed for training the data. Then, we made an empirical study on defect density prediction using cross-project data. We collected 760 project data from the International Software Benchmarking (ISBSG) dataset version 11, which reported both defects and functional size attributes. We trained the prediction models using: i) the complete set of project attributes, ii) the individual attributes, and iii) multiple subsets of attributes. We employed classification and regression approaches of machine learning. The machine learning models are trained using original values of the dataset, and z-score and logged transformations of original values to explore the effects of data normalization on prediction. Most machine learning models trained on the z-score transformation of the dataset performed best for classifying defects. The Multilayer-Perceptron (Neural Network) model trained on the z-score transformation of complete dataset predicted defects with the highest F1-score of 0.89 using binary classification. The logged transformation and feature selection methods improved the results for multivariable regression. The multivariable regression predicted defects with the highest Root Mean Squared Error (RMSE) and R2 (r-squared) values of 0.4 and 0.9, respectively, with a subset of 11 features using logged transformation. The results of classification and regression approaches indicate that defects can be predicted with reasonable accuracy at the software product level using cross-project data. © 2013 IEEE
A cross-sectional study on the primary complaints of Turkish doctors
No full textIn 2022, Turkey encountered the formidable task of addressing an unprecedented loss of medical doctors and seeking remedies for potential issues within the healthcare system. This study set out to explore the inclination of 402 actively practicing Turkish doctors to depart from Turkey, assess the socio-demographic and socio-economic factors influencing this trend, and establish the hierarchy of raised concerns among doctors. Employing a cross-sectional and analytical approach, the study drew comparisons between doctors' demographic characteristics and the significance of their grievances, while also examining the correlation between the importance of complaints and the desire to remain in Turkey. The doctors' primary complaints encompassed financial challenges, instances of violence in the healthcare sector, and insufficient examination durations. The migration of doctors poses a substantial risk to healthcare accessibility, public health, and the sustainability of Turkey's healthcare delivery capacity. To mitigate this risk and curb doctor migration, corrective measures must be implemented to improve working conditions. Additionally, there is a need for further scientific research focusing on doctors' concerns, particularly in developing countries like Turkey, to expand the current body of literature on this subject. © 2023 The Author
Can serum soluble urokinase plasminogen activator receptor be an effective biomarker in comparing the inflammatory response between laparoscopic and open appendectomy?
No full textIntroduction: The inflammatory response after laparoscopy and laparotomy has been compared in studies in adults, but only a few studies have compared the immune response between laparoscopy and laparotomy in children. Aim: To compare open and laparoscopic appendectomies regarding a new biomarker, suPAR, to evaluate the inflammatory response. Material and methods: Patients between 3 and 17 years of age who were admitted to the pediatric surgery department and scheduled for appendectomy due to appendicitis were enrolled in the investigation. The patients were randomized to receive either laparoscopic (n = 20) or conventional open appendectomy (n = 20). The primary outcome was a change in preoperative and postoperative suPAR levels. The secondary outcomes were the white blood cell count, lymphocytes, neutrophils, platelets, C-reactive protein level, appendix diameter, symptoms, symptom duration, surgical complications, operative time, rescue analgesics, hospital stay, and family satisfaction. Results: The mean age of the patients undergoing laparoscopic appendectomy was 10.55 ±2.743 (3-17) years. The mean age of the patients undergoing open appendectomy was 11.40 ±3.515 (3-17) years. A statistically significant difference was found when the postoperative suPAR values between the two groups were compared (p = 0.048). The operative time and hospital stay in the laparoscopic group were significantly shorter than those in the open group (p = 0.001, p = 0.047). Conclusions: Laparoscopic appendectomy is associated with a shorter operative time, a shorter hospital stay, and a smaller inflammatory response caused by surgical stress than open appendectomy. suPAR is an effective marker for comparing postoperative inflammatory stress between open and closed appendectomies. © 2023 Termedia Publishing House Ltd.. All rights reserved
Smell and taste disorders during pregnancy and the postpartum period
No full textThe senses of smell and taste are two senses that complement each other. The flavor is a term that combines taste and smell. Retronasal olfaction occurs when the odor molecules that emerge during chewing, ingestion, and swallowing of food reach the olfactory epithelium. Therefore, some patients who experience smell problems may also complain about taste problems; however, this usually reflects the loss of smell perception rather than real taste disturbance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022