142 research outputs found
Corrigendum to “Exploring Preschool Teachers' Pedagogical Content Knowledge: The Effect of Professional Experience” [Journal of Science Learning 4(2) (2021) 160-172]
No full textThe authors regret that The co-author Semanur Nacar is not included in the author list. Please add the name as co-author of this paper since this manuscript contained data collected with my graduate student (Semanur Nacar). In the original manuscript, the author name is Ali Yigit Kutluca. In the corrected version, the authors contain Ali Yigit Kutluca, Semanur Nacar. The authors would like to apologize for any inconvenience caused. This manuscript was produced from the second author's master's thesis "Examination of pedagogical content knowledge towards science teaching of preschool teachers continuing master's education"
Are There Any Effects of Respiratory Muscle Strength on Dyspnea, Core Muscle Strength and Functional Capacity in Adolescents with Substance Use Disorder?
No full textAn investigation of localised surface plasmon resonance (LSPR) of Ag nanoparticles produced by pulsed laser deposition (PLD) technique
No full text32nd International Physics Congress of Turkish-Physical-Society (TPS) -- SEP 06-09, 2016 -- Bodrum, TURKEYNoble metal nano-structures such as Ag, Cu, Au are used commonly to increase power conversion efficiency of the solar cell by using their surface plasmons. The plasmonic metal nanoparticles of Ag among others that have strong LSPR in near-UV range. They increase photon absorbance via embedding in the active semiconductor of the solar cell. Thin films of Ag are grown in the desired particle size and interparticle distance easily and at low cost by PLD technique. Ag nanoparticle thin films were grown on micro slide glass at 25-36 mJ laser pulse energies under by PLD using nsNd:YAG laser. The result of this work have been presented by carrying out UV-VIS and AFM analysis. It was concluded that a laser energy increases, the density and size of Ag-NPs arriving on the substrate increases, and the interparticle distance was decreases. Therefore, LSPR wavelength shifts towards to longer wavelength region.Turkish Phys SocScientific and Technical Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [1649B031503748]; Scientific Research Projects Coordination Unit of Selcuk UniversitySelcuk University [15201070, 15301020]This work was supported by the; Scientific and Technical Research Council of Turkey (TUBITAK) under Grant No. 1649B031503748 and Scientific Research Projects Coordination Unit of Selcuk University, Project No. 15201070 and 15301020. The data presented in this work will be as a part of Ph.D. thesis of Serap YIGIT GEZGI
Application of artificial neural networks in dementia and alzheimer's diagnosis
No full textDiagnosis in the early phases of many diseases makes it possible to treat the disease and affects the treatment process positively. This is especially important for diseases like Alzheimer in the field of neurology. The use of a computerized support system, which can autonomously perform the diagnostic process by the expert in this process, saves time and helps to reduce the most human errors. In this study, machine learning models with the ability to diagnose dementia and Alzheimer's disease were developed by predicting the Clinical Dementia Rating (CDR) value. Artificial Neural Networks (ANN), Logistic Regression (LR), k-nearest neighbors (KNN), and Decision Tree (DT) classifiers were applied to compare the classification performances. The Open Access Series of Imaging Studies (OASIS) longitudinal and cross-sectional datasets have been used to train models. As a result of the tests, best performance of the detection and identification of Alzheimer's disease has been shown by LR and YSA models
Applying deep learning models to structural MRI for stage prediction of Alzheimer's disease
No full textAlzheimer's disease is a brain disease that causes impaired cognitive abilities in memory, concentration, planning, and speaking. Alzheimer's disease is defined as the most common cause of dementia and changes different parts of the brain. Neuroimaging, cerebrospinal fluid, and some protein abnormalities are commonly used as clinical diagnostic biomarkers. In this study, neuroimaging biomarkers were applied for the diagnosis of Alzheimer's disease and dementia as a noninvasive method. Structural magnetic resonance (MR) brain images were used as input of the predictive model. T1 weighted volumetric MR images were reduced to two-dimensional space by several preprocessing methods for three different projections. Convolutional neural network (CNN) models took preprocessed brain images, and the training and testing of the CNN models were carried out with two different data sets. The CNN models achieved accuracy values around 0.8 for diagnosis of both Alzheimer's disease and mild cognitive impairment. The experimental results revealed that the diagnosis of patients with mild cognitive impairment was more difficult than that of patients with Alzheimer's disease. The proposed deep learning-based model might serve as an efficient and practical diagnostic tool when MRI data are integrated with other clinical tests
Does weight loss affect the parameters that are metabolically related to cardiovascular diseases?
No full textWOS: 000482650400004PubMed ID: 30957127Objectives: To assess the differences in the parameters that are metabolically related to cardiovascular diseases after weight loss in obese people with coronary artery diseases (CADs). Methods: This study was conducted on 184 patients who were diagnosed with CADs in Istanbul University Cardiology Institute Hospital, Istanbul, Turkey. The levels of leptin, fibrinogen, homocysteine, high-sensitivity C-reactive protein (hs-CRP), triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), fasting blood glucose and insulin, glycated hemoglobin, and uric acid of the obese patients who were put on calorie restricted diet were evaluated retrospectively and compared before and after weight loss. For comparison, non-obese control patients were also studied. Student's t-test and Chi-square test were used for the statistical analysis. Results: Levels of homocysteine, glycated hemoglobin, and leptin were significantly higher in the obese patients than in the non-obese patients. Diabetic obese patients with CADs lost (11.1%) and non-diabetic obese patients with CADs lost (10.5%) of their body weight in 6 months. The levels of cholesterol, LDL-C, and fibrinogen were significantly improved in both groups. Conclusion: The obese patients lost weight after being on calorie-restricted diets and showed significant improvement in the levels of cholesterol, LDL-C, fibrinogen. There was no significant difference in the levels of homocysteine, hs-CRP, and leptin before and after weight loss in both diabetic and non-diabetic obese patients
The role of covariates on inferring the Q-matrix and learning trajectory
No full textSubmission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-12-01The student, Hulya Duygu Yigit, accepted the attached license on 2021-10-06 at 20:00.The student, Hulya Duygu Yigit, submitted this Dissertation for approval on 2021-10-07 at 09:40.This Dissertation was approved for publication on 2021-10-11 at 08:56.DSpace SAF Submission Ingestion Package generated from Vireo submission #17152 on 2022-04-29 at 16:09:05Made available in DSpace on 2022-04-29T21:58:16Z (GMT). No. of bitstreams: 2
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemAuthor requested closed access (OA after 2yrs) in Vireo ETD systemLimited"Chapter 2: In learning environments, understanding the longitudinal path of learning is one of the main goals. Cognitive diagnostic models (CDMs) for measurement combined with a transition model for mastery may be beneficial for providing fine-grained information about students’ knowledge profiles over time. An efficient algorithm to estimate model parameters would augment the practicality of this combination. In this study, the Expectation-Maximization (EM) algorithm is presented for the estimation of student learning trajectories with the GDINA (generalized deterministic inputs, noisy, “and” gate) and some of its submodels for the measurement component, and a first-order Markov model for learning transitions are implemented. A simulation study is conducted to investigate the efficiency of the algorithm in estimation accuracy of student and model parameters under several factors—sample size, number of attributes, number of time points in a test, and complexity of the measurement model. Attribute- and vector-level agreement rates as well as the root mean square error rates of the model parameters are investigated. In addition, the computer run times for converging are recorded. The result shows that for a majority of the conditions, the accuracy rates of the parameters are quite promising in conjunction with relatively short computation times. Only for the conditions with relatively low sample sizes and high numbers of attributes, the computation time increases with a reduction parameter recovery rate. An application using spatial reasoning data is given. Based on the Bayesian information criterion (BIC), the model fit analysis shows that the DINA (deterministic inputs, noisy, “and” gate) model is preferable to the GDINA with these data.
Chapter 3: The rise of online learning platforms requires new approaches for developing formative assessments that provide accurate, fine-grained information on student learning profiles. Restricted latent classification models (RLCMs) serve a central role in the development and implementation of formative assessments. The latent structure for RLCMs is defined by the Q matrix, which is a binary matrix that specifies the relationship between underlying attributes and observed responses. Recent research developed fully exploratory approaches for inferring the RLCM Q matrix. Although exploratory methods exist for uncovering the latent structure educational researchers are also interested in understanding the role of intervention effects and student covariates on item performance and skill mastery. Consequently, the purpose of our project is to extend the exploratory RLCM framework to jointly uncover the latent structure and assess the role of student covariates on item performance and attribute mastery. We consider a general modeling framework for including covariates and consider two special cases which correspond to different research settings. Our models provide researchers with tools for evaluating intervention effects aimed at enhancing learning outcomes and documenting the extent to which the relationship between the latent structure and responses is invariant to student background characteristics. We develop a new Bayesian formulation to estimate model parameters and report Monte Carlo evidence pertaining to accurate recovery of Q and other model parameters. We apply the methods to a dataset including 516 students' performance on a spatial rotation test (Culpepper & Balamuta, 2017). In addition, including covariates also benefits us by providing insights about the relationships between the covariates and the item success and attribute mastery probabilities.
Chapter 4: In educational environments and online learning platforms, formative assessments can yield valuable information about students' knowledge profiles. Knowing which attribute a student has been mastered versus has not been yet will help educators provide well-targeted instructions. In this respect, exploratory restricted latent class models have significantly been used to estimate students learning profiles from their response patterns. Although students' response patterns are the primary source for estimating students' item performance and skill mastery profiles, students' covariates may also provide beneficial information in the process. However, one main challenge is to decide which covariates to include in the model when many covariates are available. Thus, this chapter applies a ""spike-slab"" variable selection algorithm on covariates in an exploratory RCLM, which simultaneously estimates a mapping between items and the attributes. We develop a Bayesian formulation to estimate model parameters while imposing a variable selection algorithm on covariates. We report Monte Carlo evidence pertaining to accurate recovery of Q and other model parameters while correctly identifying the active covariates from inactive ones.
REAL ESTATE DEVELOPMENT PROCESSES AND ITS FINANCIAL REFLECTION ON THE HOUSING MARKET
No full textAbstract: Real estate property is defined as a land and everything which built on land. People began to desire better living, working, resting places, as the result of changes of live standards. These needs had begun with living areas, after that real estate is presented in different types to meet people’s needs.
Real estate development, consist of a lot of activities which are interested in land development and building construction. Real estate development is a multifaceted business. This complicated process involves much input from a wide range of professionals. Real estate development process because of being complicated, demands an extensive investment analysis.
In this study, a research was conducted on how real estate development processes affect the housing market. The research phase started with the determination of existing conditions. At this stage, market conditions, economic conditions, social acceptances, good and constraining aspects of the land, and regulatory factors were investigated. During the analysis phase, the opportunities offered by the land, its constraints and costs were analyzed. In the synthesis phase, all the results were brought together to create a functional plan. The synthesis process ensures that unworkable ideas are eliminated and an original idea is created. All models were created because the real estate development process has a complex structure and carries with it major risk factors. Risks in business, management, financing, politics, inflation, liquidity and interest rates are seen as risks of great importance in the real estate process.
Keywords: Real estate, construction, residential buildings, housing market, finance.
Title: REAL ESTATE DEVELOPMENT PROCESSES AND ITS FINANCIAL REFLECTION ON THE HOUSING MARKET
Author: Dr. Pelin YIGIT
International Journal of Recent Research in Civil and Mechanical Engineering (IJRRCME)
ISSN 2393-8471
Vol. 10, Issue 1, April 2023 - September 2023
Page No: 21-37
Paper Publications
Website: www.paperpublications.org
Published date: 20-September-2023
DOI: https://doi.org/10.5281/zenodo.8362178
Paper Download Link (Source)
https://www.paperpublications.org/upload/book/REAL%20ESTATE%20DEVELOPMENT%20PROCESSES-20092023-1.pdfInternational Journal of Recent Research in Civil and Mechanical Engineering (IJRRCME), ISSN 2393-8471, Paper Publications, Website: www.paperpublications.or
Effect of Sacubitril/Valsartan Combined with Dapagliflozin on Long-Term Cardiac Mortality in Heart Failure with Reduced Ejection Fraction
No full textThe angiotensin receptor-neprilysin inhibitor (ARNI) sacubitril/valsartan and sodium-glucose cotransporter-2 (SGLT-2) inhibitor dapagliflozin have been shown to reduce rehospitalization and cardiac mortality in patients with heart failure (HF) with reduced ejection fraction (HFrEF). We aimed to compare the long-term cardiac and all-cause mortality of ARNI and dapagliflozin combination therapy against ARNI monotherapy in patients with HFrEF. This retrospective study involved 244 patients with HF with New York Heart Association (NYHA) class II-IV symptoms and ejection fraction <= 40%. The patients were divided into 2 groups: ARNI monotherapy and ARNI+dapagliflozin. Median follow-up was 2.5 (.16-3.72) years. One hundred and seventy-five (71.7%) patients were male, and the mean age was 65.9 (SD, 10.2) years. Long-term cardiac mortality rates were significantly lower in the ARNI+dapagliflozin group (7.4%) than in the ARNI monotherapy group (19.5%) (P = .01). Dapagliflozin [Hazard Ratio (HR) [95% Confidence Interval (CI)] = .29 [.10-.77]; P = .014] and left ventricular ejection fraction (LVEF) [HR (95% CI) = .89 (.85-.93); P < .001] were found to be independent predictors of cardiac mortality. Our study showed a significant reduction in cardiac mortality with ARNI and dapagliflozin combination therapy compared with ARNI monotherapy
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