Hakkari Üniversitesi Akademik Veri Yönetim Sistemi
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Türkçe Öğretmenlerinin Kademeli Sorumluluk Devri Modeli’nin Uygulanabilirliğine İlişkin Görüş ve Önerileri
No full textBIBLIOMETRIC ANALYSIS OF CULTURE AND ART LITERATURE: TRENDS AND FINDINGS OF ACADEMIC PUBLICATIONS WITH SCOPUS DATA
No full textVisual and Comparative Analysis of Stone Surface Deterioration Observed in Gaziantep Stone Fountains
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Vibration Control of Passenger Aircraft Active Landing Gear Using Neural Network-Based Fuzzy Inference System
No full textRunway surface roughness is recognized as a principal cause of passenger aircraft vibration during taxiing, adversely affecting ride comfort, safety, and even human health. Effective mitigation of such vibrations is therefore essential for improving passenger experience and operational reliability. Previous studies have investigated passive, semi-active, and intelligent controllers such as PID, H∞, and ANFIS; however, the comprehensive application of a robust adaptive neuro-fuzzy inference system (RANFIS) to active landing-gear control has not yet been addressed. The novelty of this work lies in combining robustness with adaptive learning of fuzzy rules and neural network parameters, thereby filling this critical gap in the literature. To investigate this, a six-degrees-of-freedom aircraft dynamic model was developed, and three controllers were comparatively evaluated: model-based neural network (MBNN), adaptive neuro-fuzzy inference system (ANFIS), and the proposed RANFIS. Performance was assessed in terms of rise time, settling time, peak value, and steady-state error under stochastic runway excitations. Simulation results show that while MBNN and ANFIS provide satisfactory control, RANFIS achieved superior performance, reducing vibration peaks to ≤0.3–1.0 cm, shortening settling times to <1.5 s, and decreasing steady-state errors to <0.05 cm. These findings confirm that RANFIS offers a more effective solution for enhancing comfort, safety, and structural durability in next-generation active landing-gear systems.</p
Prevalence and Molecular Characterization of Moniezia Species in Ruminants Based on ITS1-5.8S rRNA from Van Province, Turkey
No full textThis study aimed to calculate the occurence of Moniezia species in cattle, sheep, and goats in Van province and to identify these species using morphological and molecular methods (ITS1-5.8S rRNA gene region analysis). Additionally, the study aimed to identify the genetic differences between Moniezia expansa and Moniezia benedeni. During the summer of 2022, intestinal contents were collected from 150 ruminants (50 cattle, 50 sheep, and 50 goats) slaughtered in slaughterhouses in Van province. The parasites were examined using Aceto-Carmine staining, and species identification was based on interproglottidal glands. Examination of the intestinal contents revealed that 2 out of 50 cattle (4%), 14 out of 50 sheep (28%), and 9 out of 50 goats (18%) were infected with Moniezia. Morphological and molecular analyses showed that the cattle samples were identified as Moniezia benedeni, goats samples as Moniezia expansa and those from sheep as 11 Moniezia expansa and 3 Moniezia benedeni. Following DNA extraction, the ITS1-5.8S rRNA gene region was amplified using PCR and subjected to sequence analysis. The relationship between species was examined by phylogenetic tree. This study confirms the prevalence of Moniezia spp. in Van/Türkiye by using the ITS1-5.8S rRNA gene
