Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

Hemşire Çizelgelemesinde Esnek Vardiya Planlaması ve Hastane Uygulaması

Abstract: Oz Musteri tatmininin her gecen gun daha da zorlastigi hizmet sektorlerinde, isverenler sunulan hizmetin kalitesini arttirmak ve hizmetin devamliligini saglamak icin yeni arayislar icine girmekteler. Isverenler, musteriden once hizmeti sunan kisilerin memnuniyetini saglayarak rekabet gucunu arttirma yollarina basvurmaktadirlar. Bu yollardan biri de calisma surelerinin esneklestirilmesidir. Bu calismada, hizmet sektorleri arasinda onemli bir yere sahip olan hastanelerde ki hemsire cizelgeleme problemi icin tam sayili matematiksel bir model olusturulmustur. Olusturulan modelde, klasik cizelgeleme modellerinin aksine hemsirelerin ise baslama saatlerine esneklik getirilmistir. Modelin baslica amaci, hemsirelerin kendi tercihlerine gore haftalik cizelgelerinin olusturulmasidir. Olusturulan model, gercek veriler kullanilarak bir universite hastanesinin genel cerrahi bolumunde denenmistir. Modelin, %99,6 oraninda hemsire tercihlerini yerine getirdigi gorulmektedir. Anahtar Kelimeler: Esnek Calisma Saatleri, Hemsire Cizelgelemesi, Matematiksel Model, Tam Sayili Programlama.

Does Corporate Reputation Improve Stock Performance in an Emerging Economy? Evidence From Turkey

Abstract: Corporate reputation has long been recognized as a valuable asset, and several studies have investigated the relationship between reputation and various performance measures. However, researchers have reached conflicting results for companies in developed countries, indicating positive, conditionally positive and even no relationship. This study finds that a portfolio of highly admired firms in an emerging economy earns about 10 percent less than that market's overall portfolio annually and discusses possible explanations for this finding.

Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection

Abstract: We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level prediction for the time series of each sensor reading and captures trends on multivariate time series data produced by multi-sensor detector. We compare the performance of the rTPNN model with that of each of the Linear Regression (LR), Nonlinear Perceptron (NP), Multi-Layer Perceptron (MLP), Kendall- $\tau $ combined with MLP, Probabilistic Bayesian Neural Network (PBNN), Long-Short Term Memory (LSTM), and Support Vector Machine (SVM) on a publicly available fire data set. Our results show that rTPNN model significantly outperforms all of the other models (with 96% accuracy) while it is the only model that achieves high True Positive and True Negative rates (both above 92%) at the same time. rTPNN also triggers an alarm in only 11 s from the start of the fire, where this duration is 22 s for the second-best model. Moreover, we present that the execution time of rTPNN is acceptable for real-time applications.

Exergoeconomic Environmental Optimization of Piston-Prop Aircraft Engines

Abstract: In this study, exergy, exergoeconomic, exergoenvironmental analyses, and exergoeconomic environmental optimization are applied to a four-cylinder, spark ignition, naturally aspirated and air-cooled piston-prop aircraft engine in the cruise phase of flight for the first time to the best of the authors` knowledge. Here, three piston-prop aircraft engine parameters (altitude, air–fuel ratio (AF), and rated power setting (PS)) are selected for optimization purposes. All exergy, exergoeconomic, and exergoenvironmental values are calculated first. These values are then optimized to find the best results of all analyses. The best altitude, AF ratio, and PS values are finally found while the maximum exergy efficiency, the minimum product specific environmental impact, and the minimum average unit fuel exergy cost are obtained. The best results of optimization indicated that the maximum exergy efficiency varied between 19.54% and 19.80%, the minimum unit fuel exergy cost ranged from 126.30 $/GJ to 127.23 $/GJ, and...