This study aims to develop an optimization model for obtaining the maximum benefit from wind power plants (WPPs) to help with reducing external dependence in terms of energy. In this sense, design of experiment and optimization methods are comprehensively combined in the wind energy field for the first time. Existing data from installed WPPs operating in Turkey for the years of 2017 and 2018 are analyzed. Both the individual and interactive effects of controllable factors, namely turbine power (MW), hub height (m) and rotor diameter (m), and uncontrollable factor as wind speed (m/s) on WPPs are investigated with the help of Box-Behnken design. Nonlinear optimization models are utilized to estimate optimum values for each decision variable in order to maximize the amount of energy to be produced for the future. Based on the developed nonlinear optimization models, the optimum results with high desirability value (0.9587) for the inputs of turbine power, hub height, rotor diameter and wind speed are calculated as 3.0670 MW, 108.8424 m, 106.7597 m, and 6.1684 m/s, respectively. The maximum energy output with these input values is computed as 9.952 million kWh per unit turbine, annually. The results of this study can be used as a guideline in the design of new WPPs to produce the maximum amount of energy contributing to supply escalating energy needs by more sustainable and clean ways for the future.

ÖzAcil servisler sağlık sistemin temel yapı taşını oluşturmaktadır. Bu çalışmada Türkiye'deki acil servislerin çalışma sistemleri incelenerek, acil servislerdeki problemler dikkate alınmıştır. Özellikle metropol şehirlerde acil servislerdeki yoğunluk ölçülemez haldedir. Bunun başlıca nedeni acil servislere gelen çoğu hastanın acil diye nitelendirilen bir sağlık sorununun bulunmamasıdır. Bu durum, hastaların bekleme ve hastanede kalış sürelerinin çok uzun olmasına ve tedavi edilen hasta sayısının azalmasına neden olmaktadır. Bu çalışma ile acil servislerde acil olmayan ya da ayakta tedavi edilebilecek olan hastaların yüksek eğitimli uzman hemşireler istihdamı ile tedavi edilerek hastanedeki bekleme süresinin azaltılması amaçlanmıştır. 1/24 (günlük) ve 7/24 (haftalık) çalışma esasına göre uygulanan kesikli-olay simülasyon örneği ile YUH istihdamı sağlanarak tedavi edilen hasta sayısında, 1/24 esasına göre %26,71 ve 7/24 esasına göre %15,13 oranında artış sağlandığı görülmüştür. Hastaların acil servise kayıt yaptıkları andan itibaren tedavi olmak için bekledikleri süre 1/24 esasına göre %38,67 ve 7/24 esasına göre %53,66 oranlarında iyileşme sağlanarak bekleme süresi kısaltılmıştır. Aynı şekilde bir hastanın tedavi olmak için acil servislerde geçirmesi gereken süre ortalama 82,46 dakikadan 53,97dakikaya düşürülmüştür. Bulgular arasında, acil servislerde istihdam edilen kaynaklardan yeteri kadar verim alınamamasıyla YUH istihdamı sayesinde kaynaklara ait verimlilik oranlarında bir denge sağlandığı görülmüştür. Ek olarak, YUH istihdamı ile doktorların çalışma yoğunluklarının azaldığı tespit edilmiştir. AbstractEmergency departments are the cornerstones of the healthcare systems. However, emergency services are not able to do their primary duty. This is mainly due to the fact that most of the patients who come to the emergency department do not have any serious health problem defined as emergency. This study examined the working system of emergency services in Turkey. Especially, in metropolitan cities the intensity in emergency services is becoming immeasurable. This situation causes the waiting time of the patients to be excessive. This research aimed to decrease the waiting time of the patients by being treated by high-educated specialist nurses (HSN) in the emergency departments that are not urgent or outpatient. HSN, as a different healthcare employee class, has been proposed to treat patients who are engaged in emergency services but in fact whose health status is not urgent.A discrete event simulation approach was applied to obtain tangible results with real numerical data. Strategies or scenarios that are expected to occur in normal life not only require high costs but also need a lot of time. In such studies, simulation applications enable to get results in a faster and shorter time. Otherwise, both higher budget and time are needed in the studies that require application to put it into practice. Therefore, it is indispensable for researchers to make simulation applications which need short time and low cost b...

A healthcare resource allocation generally plays a vital role in the number of patients treated (pnt) and the patient waiting time (wt) in healthcare institutions. This study aimed to estimate pnt and wt as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δi) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δi of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ0.0, δ0.1, δ0.2, and δ0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for pnt; 0.9514, 0.9517, 0.9514, and 0.9514 for wt, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ0.2 (AB had a better accuracy at 0.8709 based on the value of δ0.2 for pnt) in the test stage. According to the AB algorithm based on the δ0.0, δ0.1, δ0.2, and δ0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for pnt; 0.8820, 0.8821, 0.8819, and 0.8818 for wt in the training phase, respectively. All scenarios created by the δi coefficient should be preferred for ED since the income provided by the pnt value to the hospital was more than the cost of healthcare resources. On the contrary, the wt estimation results of ML algorithms based on the δi coefficient differed. Although wt values in all ML algorithms with δ0.0 and δ0.1 coefficients reduced the cost of the hospital, wt values based on δ0.2 and δ0.3 increased the cost of the hospital.

This study aimed to use the design of experiment technique to calculate the optimum values of dependent and independent variables considered in the production of quality sugar. This study consists of two stages. Firstly, four different independent variables and one dependent variable data were used for this study. The levels and limits of independent factors were determined as a result of descriptive statistical analysis. They were considered the experiment as a two-level system; a full-factorial design of experiment, including 2 4 experiments with three replications, was made. Statistical analysis was performed using the data obtained, and it was determined whether the independent variables had an effect on the dependent variable. In the second stage of the study, the optimization model developed in order to obtain optimum results for both dependent and independent variables was run. According to the optimum data obtained in the optimization model, the quality-sugar score was calculated as 13.5821 according to 95% confidence and prediction interval, while the color of sugar was determined to be brown.