https://hdl.handle.net/20.500.12809/236 2026-04-19T13:42:23Z https://hdl.handle.net/20.500.12809/11032 Testicular involvement of Brucellosis: A 10-year, multicentre study Güler Dinçer, Nevin; Çelik, Mehmet; Akgül, Fethiye; Alkan, Sevil; Altındağ, Sevin Introduction: The genito-urinary system is one of the most common areas of involvement in brucellosis. To present the epidemiological, clinical, and laboratory characteristics of patients with testicular involvement associated with brucellosis, together with the diagnostic and therapeutic approaches. Methodology: Patients followed up for brucellosis-related testicular involvement between January 2012 and November 2022 were included in the study. Brucellosis is defined as the production of Brucella spp. in cultures, or clinical symptoms together with the serum standard tube agglutination test titer of ≥ 1/160. Inflammation in scrotal Doppler ultrasonography was based on testicular involvement. Results: A retrospective evaluation was made of the data of 194 patients with brucellosis-related testicular involvement. The rate of determination of testicular involvement in brucellosis was 2.57%. The most affected patients were determined in the 16-30 years age range. On presentation, brucellosis was in the acute stage in 83.7% of patients. The most common symptoms on presentation were swelling and/or pain in the testes (86.6%). In the patients where a spermiogram could be performed, oligospermia was determined in 41.7%, and aspermia in 8.3%. When the testicular involvement of brucellosis was evaluated, epididymo-orchitis was present at the rate of 55.7%, epididymitis at 27.3%, and testis abscess at 5.1%. Conclusions: Although epididymo-orchitis was the most frequently determined form of involvement in this study, there was also seen to be a significant number of patients presenting with epididymitis. Male patients presented with the clinical status of brucellosis should be questioned about swelling and pain in the testes to avoid overlooking testicular involvement. 2023-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/10997 Modified Local Linear Estimators in Partially Linear Additive Models with Right-Censored Data Based on Different Censorship Solution Techniques Yılmaz, Ersin; Aydın, Dursun; Ahmed, Syed Ejaz This paper introduces a modified local linear estimator (LLR) for partially linear additive models (PLAM) when the response variable is subject to random right-censoring. In the case of modeling right-censored data, PLAM offers a more flexible and realistic approach to the estimation procedure by involving multiple parametric and nonparametric components. This differs from the widely used partially linear models that feature a univariate nonparametric function. The LLR method is employed to estimate unknown smooth functions using a modified backfitting algorithm, delivering a non-iterative solution for the right-censored PLAM. To address the censorship issue, three approaches are employed: synthetic data transformation (ST), Kaplan-Meier weights (KMW), and the kNN imputation technique (kNNI). Asymptotic properties of the modified backfitting estimators are detailed for both ST and KMW solutions. The advantages and disadvantages of these methods are discussed both theoretically and practically. Comprehensive simulation studies and real-world data examples are conducted to assess the performance of the introduced estimators. The results indicate that LLR performs well with both KMW and kNNI in the majority of scenarios, along with a real data example 2023-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/10967 Estimation of Right-censored SETAR-type Nonlinear Time-series Model Ahmed, Syed Ejaz; Aydın, Dursun; Yılmaz, Ersin This paper focuses on estimating the Self-Exciting Threshold Autoregressive (SETAR) type time-series model under right-censored data. As is known, the SETAR model is used when the underlying function of the relation-ship between the time-series itself (Yt), and its p delays $$({Y{t - j}}){j = 1}^p$$ violates the lin-earity assumption and this function is formed by multiple behaviors that called regime. This paper addresses the right-censored dependent time-series problem which has a serious negative effect on the estimation performance. Right-censored time series cause biased coefficient estimates and unqualified predictions. The main contribution of this paper is solving the censorship problem for the SETAR by three different techniques that are kNN imputation which represents the imputation techniques, Kaplan-Meier weights that is applied based on the weighted least squares, synthetic data transformation which adds the effect of censorship to the modeling process by manipulating dataset. Then, these solutions are combined by the SETAR-type model estimation process. To observe the behavior of the nonlinear estimators in practice, a simulation study and a real data example are carried out. The Covid-19 dataset collected in China is used as real data. Results prove that although the three estimators show satisfying performance, the quality of the estimate SETAR model based on the kNN imputation technique dominates the other two estimators. 2023-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/10811 Using machine learning algorithms to identify predictors of social vulnerability in the event of a hazard: Istanbul case study Kalaycıoğlu, Oya; Akhanlı, Serhat Emre What extent an individual or group will be affected by the damage of a hazard depends not just on their exposure to the event but on their social vulnerability - that is, how well they are able to anticipate, cope with, resist, and recover from the impact of a hazard. Therefore, for mitigating disaster risk effectively and building a disaster-resilient society to natural hazards, it is essential that policy makers develop an understanding of social vulnerability. This study aims to propose an optimal predictive model that allows decision makers to identify households with high social vulnerability by using a number of easily accessible household variables. In order to develop such a model, we rely on a large dataset comprising a household survey (n = 41 093) that was conducted to generate a social vulnerability index (SoVI) in Istanbul, Turkiye. In this study, we assessed the predictive ability of socio-economic, socio-demographic, and housing conditions on the household-level social vulnerability through machine learning models. We used classification and regression tree (CART), random forest (RF), support vector machine (SVM), naive Bayes (NB), artificial neural network (ANN), k-nearest neighbours (KNNs), and logistic regression to classify households with respect to their social vulnerability level, which was used as the outcome of these models. Due to the disparity of class size outcome variables, subsampling strategies were applied for dealing with imbalanced data. Among these models, ANN was found to have the optimal predictive performance for discriminating households with low and high social vulnerability when random- majority under sampling was applied (area under the curve (AUC): 0.813). The results from the ANN method indicated that lack of social security, living in a squatter house, and job insecurity were among the most important predictors of social vulnerability to hazards. Additionally, the level of education, the ratio of elderly persons in the household, owning a property, household size, ratio of income earners, and savings of the household were found to be associated with social vulnerability. An open-access R Shiny web application was developed to visually display the performance of machine learning (ML) methods, important variables for the classification of households with high and low social vulnerability, and the spatial distribution of the variables across Istanbul neighbourhoods. The machine learning methodology and the findings that we present in this paper can guide decision makers in identifying social vulnerability effectively and hence let them prioritise actions towards vulnerable groups in terms of needs prior to an event of a hazard. 2023-01-01T00:00:00Z