Forecasting The Exchange Rate Series With Ann: The Case Of Turkey

As it is possible to model both linear and nonlinear structures in time series by using Artificial Neural Network (ANN), it is suitable to apply this method to the chaotic series having nonlinear component. Therefore, in this study, we propose to employ ANN method for high volatility Turkish TL/US dollar exchange rate series and the results show that ANN method has the best forecasting accuracy with respect to time series models, such as seasonal ARIMA and ARCH models. The suggestions about the details of the usage of ANN method are also made for the exchange rate of Turkey.Activation function, ARIMA, ARCH, Artificial neural network, Chaotic series, Exchange rate, Forecasting, Time series

Volume CXIV, Number 4, November 7, 1996

Objective: Turner syndrome (TS) is a chromosomal disorder caused by complete or partial X chromosome monosomy that manifests various clinical features depending on the karyotype and on the genetic background of affected girls. This study aimed to systematically investigate the key clinical features of TS in relationship to karyotype in a large pediatric Turkish patient population.Methods: Our retrospective study included 842 karyotype-proven TS patients aged 0-18 years who were evaluated in 35 different centers in Turkey in the years 2013-2014.Results: The most common karyotype was 45,X (50.7%), followed by 45,X/46,XX (10.8%), 46,X,i(Xq) (10.1%) and 45,X/46,X,i(Xq) (9.5%). Mean age at diagnosis was 10.2±4.4 years. The most common presenting complaints were short stature and delayed puberty. Among patients diagnosed before age one year, the ratio of karyotype 45,X was significantly higher than that of other karyotype groups. Cardiac defects (bicuspid aortic valve, coarctation of the aorta and aortic stenosis) were the most common congenital anomalies, occurring in 25% of the TS cases. This was followed by urinary system anomalies (horseshoe kidney, double collector duct system and renal rotation) detected in 16.3%. Hashimoto's thyroiditis was found in 11.1% of patients, gastrointestinal abnormalities in 8.9%, ear nose and throat problems in 22.6%, dermatologic problems in 21.8% and osteoporosis in 15.3%. Learning difficulties and/or psychosocial problems were encountered in 39.1%. Insulin resistance and impaired fasting glucose were detected in 3.4% and 2.2%, respectively. Dyslipidemia prevalence was 11.4%.Conclusion: This comprehensive study systematically evaluated the largest group of karyotype-proven TS girls to date. The karyotype distribution, congenital anomaly and comorbidity profile closely parallel that from other countries and support the need for close medical surveillance of these complex patients throughout their lifespa

Deep Learning Forecasting Model for Market Demand of Electric Vehicles

The increasing demand for electric vehicles (EVs) requires accurate forecasting to support strategic decisions by manufacturers, policymakers, investors, and infrastructure developers. As EV adoption accelerates due to environmental concerns and technological advances, understanding and predicting this demand becomes critical. In light of these considerations, this study presents an innovative methodology for forecasting EV demand. This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). The model comprises convolutional, activation function, max pooling, LSTM, and dense layers. Experimental research has investigated four different categories of electric vehicles: battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and all electric vehicles (ALL). Performance measures were calculated after conducting experimental studies to assess the model’s ability to predict electric vehicle demand. When the performance measures (mean absolute error, root mean square error, mean squared error, R-Squared) of EVs-PredNet and machine learning regression methods are compared, the proposed model is more effective than the other forecasting methods. The experimental results demonstrate the effectiveness of the proposed approach in forecasting the electric vehicle demand. This model is considered to have significant application potential in assessing the adoption and demand of electric vehicles. This study aims to improve the reliability of forecasting future demand in the electric vehicle market and to develop relevant approaches

Response to growth hormone treatment in very young patients with growth hormone deficiencies and mini-puberty

WOS: 000423677900009PubMed ID: 29353264Background: The aim of the study was to assess the response to growth hormone (GH) treatment in very young patients with GH deficiency (GHD) through a national, multi-center study. Possible factors affecting growth response were assessed (especially mini-puberty). Methods: Medical reports of GHD patients in whom treatment was initiated between 0 and 3 years of age were retrospectively evaluated. Results: The cohort numbered 67. The diagnosis age was 12.4 +/- 8.6 months, peak GH stimulation test response (at diagnosis) as 1.0 +/- 1.4 ng/mL. The first and second years length gain was 15.0 +/- 4.3 and 10.4 +/- 3.4 cm. Weight gain had the largest effect on first year growth response; whereas weight gain and GH dose were both important factors affecting second year growth response. In the multiple pituitary hormone deficiency (MPHD) group (n = 50), first year GH response was significantly greater than in the isolated GH deficiency (IGHD) group (n = 17) (p = 0.030). In addition first year growth response of infants starting GH between 0 and 12 months of age (n = 24) was significantly greater than those who started treatment between 12 and 36 months of age (n = 43) (p < 0.001). These differences were not seen in the second year. Delta Length/height standard deviation score (SDS), Delta body weight SDS, length/height SDS, weight SDS in MPHD without hypogonadism for the first year of the GH treatment were found as significantly better than MPHD with hypogonadism. Conclusions: Early onsets of GH treatment, good weight gain in the first year of the treatment and good weight gain-GH dose in the second year of the treatment are the factors that have the greatest effect on length gain in early onset GHD. The presence of the sex steroid hormones during minipubertal period influence growth pattern positively under GH treatment (closer to the normal percentage according to age and gender).Turkish Pediatric Endocrinology and Diabetes Society [022014]This work was supported by the Turkish Pediatric Endocrinology and Diabetes Society (Grant Number: 022014)

The Growth Characteristics of Patients with Noonan Syndrome: Results of Three Years of Growth Hormone Treatment: A Nationwide Multicenter Study.

Objective: Noonan syndrome (NS) is a multisystem disorder, and short stature is its most striking manifestation. Optimal growth hormone (GH) treatment for NS is still controversial. In this study, using a nationwide registration system, we aimed to evaluate the growth characteristics and the clinical features of NS patients in Turkey and their growth response to GH treatment

Anthropometric findings from birth to adulthood and their relation with karyotpye distribution in Turkish girls with Turner syndrome

WOS: 000373099300016PubMed ID: 26788866To evaluate the anthropometric features of girls with Turner syndrome (TS) at birth and presentation and the effect of karyotype on these parameters. Data were collected from 842 patients with TS from 35 different centers, who were followed-up between 1984 and 2014 and whose diagnosis age ranged from birth to 18 years. Of the 842 patients, 122 girls who received growth hormone, estrogen or oxandrolone were excluded, and 720 girls were included in the study. In this cohort, the frequency of small for gestational age (SGA) birth was 33%. The frequency of SGA birth was 4.2% (2/48) in preterm and 36% (174/483) in term neonates (P<0.001). The mean birth length was 1.3cm shorter and mean birth weight was 0.36kg lower than that of the normal population. The mean age at diagnosis was 10.1 +/- 4.4 years. Mean height, weight and body mass index standard deviation scores at presentation were -3.1 +/- 1.7, -1.4 +/- 1.5, and 0.4 +/- 1.7, respectively. Patients with isochromosome Xq were significantly heavier than those with other karyotype groups (P=0.007). Age at presentation was negatively correlated and mid-parental height was positively correlated with height at presentation. Mid-parental height and age at presentation were the only parameters that were associated with height of children with TS. The frequency of SGA birth was found higher in preterm than term neonates but the mechanism could not be clarified. We found no effect of karyotype on height of girls with TS, whereas weight was greater in 46,X,i(Xq) and 45,X/46,X,i(Xq) karyotype groups. (c) 2016 Wiley Periodicals, Inc