ABSTRACT
COVÍD-19 salgını pek çok alanı etkilediği gibi, eğitim sistemlerini de dünya çapında etkilemiştir. Ülkemizde, COVÍD-19 sebebi ile tüm üniversitelerin uzaktan eğitim sistemine geçmesi, böylesi bir kriz döneminde yönetici/öğreticilerin örgütlerine karşı sadakatlerini çok yönlü analiz etme imkânı doğurmuştur. Bu çalışmada kriz yönetiminin üç alt boyutu olan, kriz öncesi, kriz esnası ve kriz sonrası faaliyetlerin birbiri üzerine ve çalışanların örgütsel sadakat algıları üzerine olan etkisi araştırılmıştır. Bu amaçla, kamu ve vakıf üniversitelerinde görev yapan yönetici ve öğretim elemanlarından oluşan 810 katılımcıdan anket yolu ile elde edilen verilerden yararlanılmıştır. Araştırma sonucunda kriz yönetiminin üç alt boyutunun birbirleri ve örgütsel sadakat üzerine anlamlı etkilerinin bulunduğu tespit edilmiştir.Alternate : The COVID-19 outbreak affected many areas, as well as education systems worldwide. In our country, the transition of all universities to the distance education system due to COVID-19 has created the opportunity to analyse the administrators/academician loyalty in this system in many ways. This study investigated the effects of three sub-dimensions of crisis management, pre-crisis, crisis and post-crisis activities, on each other and employees' perceptions of organizational loyalty. For this purpose, the data was obtained through questionnaires from 810 participants consisting of administrators and academicians working in public and foundation universities. As a result of the research, it was determined that the three sub-dimensions of crisis management have significant effects on each other and organizational loyalty.
ABSTRACT
Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.