Introduction: Secondary Bacterial Infections (SBIs) of the respiratory system are one of the biggest medical concerns in patients undergoing hospitalization with a diagnosis of COVID-19. This study aims to provide relevant data for the initiation of appropriate empirical treatment after examining the etiology and antimicrobial resistance of SBIs in COVID-19 patients under care in the Intensive Care Units (ICUs) in the largest pandemic hospital of our country. Methodology: Between March 16, 2020 and December 31, 2021, 56,993 COVID patients were hospitalized, of which 7684 were admitted to ICUs. A total of 1513 patients diagnosed with SBIs have been included in this study. During the course of the study, demographic data, clinical course, etiology and antimicrobial resistance data of all patients were collected. Results: The most common causative agents of SBIs were inferred as Acinetobacter baumanii (35.1%), Staphylococcus aureus (15.2%), Klebsiella pneumoniae (12.3%) and Pseudomonas aeruginosa (10.4%). The isolation rates of carbapenem-resistant and colistin-resistant A. baumannii, K. pneumoniae and P. aeruginosa were 83.7%; 42.7%, 79.2%, and 5.6%, 42.7%, 1.7%, respectively. Acinetobacter pittii clustering was seen in one of the ICUs in the hospital. Multidrug resistant 92 (5.4%) Corynebacterium striatum isolates were also found as a causative agent with increasing frequency during the study period. Conclusions: SBI of the respiratory system is one of the major complications in patients hospitalized with COVID-19. The antimicrobial resistance rates of the isolated bacteria are generally high, which indicates that more accurate use of antibacterial agents is necessary for SBIs in patients hospitalized with COVID-19 diagnosis.

The traditional gravity model of international trade has been through many changes in order to develop and answer new research questions. Taking this development into account this paper investigates a more enhanced panel data approach by extending the classic approach by allowing for both indiviual and time effects to be apparent in order to capture country specific and time effects with a multidimensional panel data model for APEC countries. By using a three dimensional panel gravity model with a least squares dummy variable approach we were able to identify countries with stronger propensities to import and export.

Kaos Teorisi, doğrusal olmayan dinamik sistemlerin davranışlarını tanımlar ve ekonomi alanında pek çok verinin modellenmesinde kullanılır. Kaos teori, sistemin doğrusal olmayan ve deterministik bir süreç olduğu varsayımlarına dayanır. Doğrusal modeller, ekonometrik sistemleri karmaşıklıklarını ortaya çıkarmakta yetersiz kalmaktadır. Bu çalışmanın amacı, Bitcoin günlük fiyatlarının zamana bağlı doğrusal olmayan dinamik bir sistem tarafından üretilip üretilmediğini araştırmak ve sistemin uzun vadede geleceğe yönelik tahmin yeteneğini araştırmak ve bir tahminleme modeli oluşturmaktır. Birçok ekonomik veri serisinin kaotik davranış gösterdiği bilinmektedir. Bu çalışmada, Bitcoin fiyatlarının kaotik yapısı incelenmiş ve regresyon yöntemi kullanılarak tahmin modeli kurulmuştur. Diğer bir ifadeyle amaç, Bitcoin fiyatlarının getirilerinin kaotik bir davranış gösterip göstermediğini ortaya koyarak elde edilen gömme (embedding) boyutuna bağlı olarak regresyon yöntemini kullanarak tahmin modeli oluşturmaktır. Çalışmada, 2021 Şubat – 2021 Kasım döneminde günlük kapanış fiyatı ( $ ) veri olarak kullanılmıştır. (URL-1,2021)

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