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Article ; Online: A Bell's Palsy Case Probably Related to Sertraline Use.

Ceylan, Mehmet Emin / Ünsalver, Barış Önen / Ceylan, Hatice Zeynep

2024 Volume 54, Issue 2, Page(s) 51–52

MeSH term(s)	Humans ; Bell Palsy/chemically induced ; Sertraline/adverse effects
Chemical Substances	Sertraline (QUC7NX6WMB)
Language	English
Publishing date	2024-03-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	4113-0
ISSN	2472-2448 ; 0048-5764 ; 0376-0162
ISSN (online)	2472-2448
ISSN	0048-5764 ; 0376-0162
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The impact of COVID-19 on the electricity demand: a case study for Turkey.

Ceylan, Zeynep

International journal of energy research

2021 Volume 45, Issue 9, Page(s) 13022–13039

Abstract: Due to the extraordinary impact of the Coronavirus Disease 2019 (COVID-19) and the resulting lockdown measures, the demand for energy in business and industry has dropped significantly. This change in demand makes it difficult to manage energy generation, ...

Abstract	Due to the extraordinary impact of the Coronavirus Disease 2019 (COVID-19) and the resulting lockdown measures, the demand for energy in business and industry has dropped significantly. This change in demand makes it difficult to manage energy generation, especially electricity production and delivery. Thus, reliable models are needed to continue safe, secure, and reliable power. An accurate forecast of electricity demand is essential for making a reliable decision in strategic planning and investments in the future. This study presents the extensive effects of COVID-19 on the electricity sector and aims to predict electricity demand accurately during the lockdown period in Turkey. For this purpose, well-known machine learning algorithms such as Gaussian process regression (GPR), sequential minimal optimization regression (SMOReg), correlated Nyström views (XNV), linear regression (LR), reduced error pruning tree (REPTree), and M5P model tree (M5P) were used. The SMOReg algorithm performed best with the lowest mean absolute percentage error (3.6851%), mean absolute error (21.9590), root mean square error (29.7358), and root relative squared error (36.5556%) values in the test dataset. This study can help policy-makers develop appropriate policies to control the harms of not only the current pandemic crisis but also an unforeseeable crisis.
Language	English
Publishing date	2021-03-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	1480879-1
ISSN	1099-114X ; 0363-907X
ISSN (online)	1099-114X
ISSN	0363-907X
DOI	10.1002/er.6631
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Article: Short-term prediction of COVID-19 spread using grey rolling model optimized by particle swarm optimization.

Ceylan, Zeynep

Applied soft computing

2021 Volume 109, Page(s) 107592

Abstract: The prediction of the spread of coronavirus disease 2019 (COVID-19) is vital in taking preventive and control measures to reduce human health damage. The Grey Modelling (1,1) is a popular approach used to construct a predictive model with a small-sized ... ...

Abstract	The prediction of the spread of coronavirus disease 2019 (COVID-19) is vital in taking preventive and control measures to reduce human health damage. The Grey Modelling (1,1) is a popular approach used to construct a predictive model with a small-sized dataset. In this study, a hybrid model based on grey prediction and rolling mechanism optimized by particle swarm optimization algorithm (PSO) was applied to create short-term estimates of the total number of confirmed COVID-19 cases for three countries, Germany, Turkey, and the USA. A rolling mechanism that updates data in equal dimensions was applied to improve the forecasting accuracy of the models. The PSO algorithm was used to optimize the Grey Modelling parameters (1,1) to provide more robust and efficient solutions with minimum errors. To compare the accuracy of the predictive models, a nonlinear autoregressive neural network (NARNN) was also developed. According to the analysis results, Grey Rolling Modelling (1,1) optimized by PSO algorithm performs better than the classical Grey Modelling (1,1), Grey Rolling Modelling (1,1), and NARNN models for predicting the total number of confirmed COVID-19 cases. The present study can provide an important basis for countries to allocate health resources and formulate epidemic prevention policies effectively.
Language	English
Publishing date	2021-06-09
Publishing country	United States
Document type	Journal Article
ISSN	1568-4946
ISSN	1568-4946
DOI	10.1016/j.asoc.2021.107592
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Article ; Online: Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression.

Ceylan, Zeynep

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA

2020 Volume 38, Issue 8, Page(s) 840–850

Abstract: Accurate estimation of municipal solid waste (MSW) generation has become a crucial task in decision-making processes for the MSW planning and management systems. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization ... ...

Abstract	Accurate estimation of municipal solid waste (MSW) generation has become a crucial task in decision-making processes for the MSW planning and management systems. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization was used to forecast the MSW generation of Turkey. The Bayesian optimization method, which can efficiently optimize the hyperparameters of kernel functions in the machine learning algorithms, was applied to reduce the computation redundancy and enhance the estimation performance of the models. Four socio-economic indicators such as population, gross domestic product per capita, inflation rate, and the unemployment rate were used as input variables. The performance of the Bayesian GPR (BGPR) model was compared with the multiple linear regression (MLR) and Bayesian support vector regression (BSVR) models. Different performance measures such as mean absolute deviation (MAD), root mean square error (RMSE), and coefficient of determination (R
MeSH term(s)	Bayes Theorem ; Models, Theoretical ; Refuse Disposal ; Socioeconomic Factors ; Solid Waste/analysis ; Turkey ; Waste Management
Chemical Substances	Solid Waste
Language	English
Publishing date	2020-03-03
Publishing country	England
Document type	Journal Article
ZDB-ID	1480483-9
ISSN	1096-3669 ; 1399-3070 ; 0734-242X
ISSN (online)	1096-3669 ; 1399-3070
ISSN	0734-242X
DOI	10.1177/0734242X20906877
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Estimation of COVID-19 prevalence in Italy, Spain, and France.

Ceylan, Zeynep

The Science of the total environment

2020 Volume 729, Page(s) 138817

Abstract: At the end of December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan city, China. As of April 15, 2020, >1.9 million COVID-19 cases were confirmed worldwide, including >120,000 deaths. There is an urgent need to monitor and predict COVID-19 ...

Abstract	At the end of December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan city, China. As of April 15, 2020, >1.9 million COVID-19 cases were confirmed worldwide, including >120,000 deaths. There is an urgent need to monitor and predict COVID-19 prevalence to control this spread more effectively. Time series models are significant in predicting the impact of the COVID-19 outbreak and taking the necessary measures to respond to this crisis. In this study, Auto-Regressive Integrated Moving Average (ARIMA) models were developed to predict the epidemiological trend of COVID-19 prevalence of Italy, Spain, and France, the most affected countries of Europe. The prevalence data of COVID-19 from 21 February 2020 to 15 April 2020 were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.
MeSH term(s)	Betacoronavirus ; COVID-19 ; Coronavirus Infections/epidemiology ; France/epidemiology ; Humans ; Pandemics ; Pneumonia, Viral/epidemiology ; Prevalence ; SARS-CoV-2 ; Spain/epidemiology
Keywords	covid19
Language	English
Publishing date	2020-04-22
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2020.138817
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Zs.A 949: Show issues

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Article ; Online: The effect of finger puppet on pain and emotional manifestation for venous blood collection in the pediatric emergency department: A randomized controlled trial.

Ceylan, Murat / Erkut, Zeynep

International emergency nursing

2023 Volume 70, Page(s) 101348

Abstract: Aim: To determine the effect of distraction with a finger puppet for venous blood collection in the pediatric emergency department on children's pain and emotional manifestation.: Methods: Randomized controlled trial with 80 children (aged 3-6 years) ...

Abstract	Aim: To determine the effect of distraction with a finger puppet for venous blood collection in the pediatric emergency department on children's pain and emotional manifestation. Methods: Randomized controlled trial with 80 children (aged 3-6 years) who applied to the pediatric emergency department between October 2021 and March 2022. The attention of child was distracted from the procedure by playing with finger puppets before and during the venous blood collection in the finger puppet group. The children in the control group underwent routine blood collection. The procedural pain was measured with the Face, Legs, Activity, Cry, Consolability Scale (FLACC) and the emotional response was measured with the Children's Emotional Manifestation Scale (CEMS). Results: The mean FLACC pain scores of the children in the finger puppet group were statistically significantly lower than the children in the control group (p < 0.001). It was also found that the finger puppet group's mean scores of CEMS before and during the procedure were statistically lower than those of the control group (p < 0.001). Conclusions: Finger puppets can be used to reduce pain and positively change children's emotional responses during painful procedures such as blood collection.
Language	English
Publishing date	2023-09-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2420747-0
ISSN	1878-013X ; 1755-599X
ISSN (online)	1878-013X
ISSN	1755-599X
DOI	10.1016/j.ienj.2023.101348
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Zs.A 3822: Show issues

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Article ; Online: The relationship between chronotype and food addiction: Serial mediation of social jetlag and psychological pain.

Ceylan, Burcu / Kocoglu-Tanyer, Deniz / Sacikara, Zeynep / Sultan Dengiz, Kubra

Chronobiology international

2024 Volume 41, Issue 4, Page(s) 485–494

Abstract: This study evaluates how food addiction is related to chronotype, social jetlag, and psychological pain. Of the participants ( ...

Abstract	This study evaluates how food addiction is related to chronotype, social jetlag, and psychological pain. Of the participants (
MeSH term(s)	Humans ; Female ; Male ; Circadian Rhythm/physiology ; Young Adult ; Sleep/physiology ; Food Addiction/psychology ; Adult ; Students/psychology ; Adolescent ; Body Mass Index ; Feeding Behavior/physiology ; Jet Lag Syndrome ; Risk Factors ; Time Factors ; Surveys and Questionnaires ; Pain/psychology ; Chronotype
Language	English
Publishing date	2024-02-14
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	998996-1
ISSN	1525-6073 ; 0742-0528
ISSN (online)	1525-6073
ISSN	0742-0528
DOI	10.1080/07420528.2024.2315220
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 2068: Show issues

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Article ; Online: Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan, Zeynep

Science of The Total Environment

2020 Volume 729, Page(s) 138817

Keywords	Environmental Engineering ; Waste Management and Disposal ; Pollution ; Environmental Chemistry ; covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2020.138817
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan, Zeynep

Sci Total Environ

Abstract	At the end of December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan city, China. As of April 15, 2020, >1.9 million COVID-19 cases were confirmed worldwide, including >120,000 deaths. There is an urgent need to monitor and predict COVID-19 prevalence to control this spread more effectively. Time series models are significant in predicting the impact of the COVID-19 outbreak and taking the necessary measures to respond to this crisis. In this study, Auto-Regressive Integrated Moving Average (ARIMA) models were developed to predict the epidemiological trend of COVID-19 prevalence of Italy, Spain, and France, the most affected countries of Europe. The prevalence data of COVID-19 from 21 February 2020 to 15 April 2020 were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #32360907
Database	COVID19

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Article ; Online: Estimation of COVID-19 prevalence in Italy, Spain, and France

Ceylan, Zeynep

reponame:Expeditio Repositorio Institucional UJTL ; instname:Universidad de Bogotá Jorge Tadeo Lozano

2020

Abstract: At the end of December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan city, China. As of April 15, 2020, N1.9 million COVID-19 cases were confirmed worldwide, including N120,000 deaths. There is an urgent need to monitor and predict COVID-19 ...

Abstract	At the end of December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan city, China. As of April 15, 2020, N1.9 million COVID-19 cases were confirmed worldwide, including N120,000 deaths. There is an urgent need to monitor and predict COVID-19 prevalence to control this spread more effectively. Time series models are significant in predicting the impact of the COVID-19 outbreak and taking the necessary measures to respond to this crisis. In this study, Auto-Regressive Integrated Moving Average (ARIMA) models were developed to predict the epidemiological trend of COVID-19 prevalence of Italy, Spain, and France, the most affected countries of Europe. The prevalence data of COVID-19 from 21 February 2020 to 15 April 2020 were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.
Keywords	COVID-19 ; Infection disease ; Pandemic ; Time series ; Forecasting ; Síndrome respiratorio agudo grave ; SARS-CoV-2 ; Coronavirus ; ARIMA ; covid19
Publisher	Science Direct
Publishing country	co
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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