An Intelligent Time and Performance Efficient Algorithm for Aircraft Design Optimization

Die Optimierung des Flugzeugentwurfs erfordert die Beherrschung der komplexen Zusammenhänge mehrerer Disziplinen. Trotz seiner Abhängigkeit von einer Vielzahl unabhängiger Variablen zeichnet sich dieses komplexe Entwurfsproblem durch starke indirekte Verbindungen und eine daraus resultierende geringe Anzahl lokaler Minima aus. Kürzlich entwickelte intelligente Methoden, die auf selbstlernenden Algorithmen basieren, ermutigten die Suche nach einer diesem Bereich zugeordneten neuen Methode. Tatsächlich wird der in dieser Arbeit entwickelte Hybrid-Algorithmus (Cavus) auf zwei Hauptdesignfälle im Luft- und Raumfahrtbereich angewendet: Flugzeugentwurf- und Flugbahnoptimierung. Der implementierte neue Ansatz ist in der Lage, die Anzahl der Versuchspunkte ohne große Kompromisse zu reduzieren. Die Trendanalyse zeigt, dass der Cavus-Algorithmus für die komplexen Designprobleme, mit einer proportionalen Anzahl von Prüfpunkten konservativer ist, um die erfolgreichen Muster zu finden. Aircraft Design Optimization requires mastering of the complex interrelationships of multiple disciplines. Despite its dependency on a diverse number of independent variables, this complex design problem has favourable nature as having strong indirect links and as a result a low number of local minimums. Recently developed intelligent methods that are based on self-learning algorithms encouraged finding a new method dedicated to this area. Indeed, the hybrid (Cavus) algorithm developed in this thesis is applied two main design cases in aerospace area: aircraft design optimization and trajectory optimization. The implemented new approach is capable of reducing the number of trial points without much compromise. The trend analysis shows that, for the complex design problems the Cavus algorithm is more conservative with a proportional number of trial points in finding the successful patterns

Investigation of Bacterial and Viral Etiology in Community Acquired Central Nervous System Infections with Molecular Methods

WOS: 000408311400008PubMed ID: 28929964In this multicenter prospective cohort study, it was aimed to evaluate the bacterial and viral etiology in community-acquired central nervous system infections by standart bacteriological culture and multiplex polymerase chain reaction (PCR) methods. Patients hospitalized with central nervous system infections between April 2012 and February 2014 were enrolled in the study. Demographic and clinical information of the patients were collected prospectively. Cerebrospinal fluid (CSF) samples of the patients were examined by standart bacteriological culture methods, bacterial multiplex PCR (Seeplex meningitis-B ACE Detection (Streptococcus pneumoniae, Neisseria meningitidis, Haemophilus influenzae, Listeria monocytogenes, Group B streptococci) and viral multiplex PCR (Seeplex meningitis-V1 ACE Detection kits herpes simplex virus-1 (HSV1), herpes simplex virus-2 (HSV2), varicella zoster virus (VZV), cytomegalovirus (CMV), Epstein Barr virus (EBV) and human herpes virus 6 (HHV6)) (Seeplex meningitis-V2 ACE Detection kit (enteroviruses)). Patients were classified as purulent meningitis, aseptic meningitis and encephalitis according to their clinical, CSF (leukocyte level, predominant cell type, protein and glucose (blood/CSF) levels) and cranial imaging results. Patients who were infected with a pathogen other than the detection of the kit or diagnosed as chronic meningitis and other diseases during the follow up, were excluded from the study. A total of 79 patients (28 female, 51 male, aged 42.1 +/- 18.5) fulfilled the study inclusion criteria. A total of 46 patients were classified in purulent meningitis group whereas 33 were in aseptic meningitis/encephalitis group. Pathogens were detected by multiplex PCR in 41 patients. CSF cultures were positive in 10 (21.7%) patients (nine S.pneumoniae, one H.influenzae) and PCR were positive for 27 (58.6%) patients in purulent meningitis group. In this group one type of bacteria were detected in 18 patients (14 S.pneumoniae, two N.meningitidis, one H.influenzae, one L.monocytogenes). Besides, it is noteworthy that multiple pathogens were detected such as bacteria-virus combination in eight patients and two different bacteria in one patient. In the aseptic meningitis/encephalitis group, pathogens were detected in 14 out of 33 patients; single type of viruses in 11 patients (seven enterovirus, two HSV1, one HSV2, one VZV) and two different viruses were determined in three patients. These data suggest that multiplex PCR methods may increase the isolation rate of pathogens in central nervous system infections. Existence of mixed pathogen growth is remarkable in our study. Further studies are needed for the clinical relevance of this result