Bartın Üniversitesi Araştırma ve Akademik Performans Sistemi
DSpace@Bartın, Bartın Üniversitesi’nin bilimsel araştırma ve akademik performansını izleme, analiz etme ve raporlama süreçlerini tek çatı altında buluşturan bütünleşik bilgi sistemidir.
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Öğe Türü: Araştırmacı , Mursaleen, MuhammadProf.Bartın Üniversitesi, Fen Fakültesi,Matematik Bölümü, Analiz Ve Fonksiyonlar Teorisi Anabilim DalıÖğe Türü: Araştırmacı , Okajima, JunnosukeDoç. Dr.Bartın Üniversitesi, Mühendislik, Mimarlık Ve Tasarım Fakültesi, Makine Mühendisliği Bölümü, Enerji Anabilim DalıÖğe Türü: Yayın , Error estimation for shifted q-szász-kantorovich operators and their numerical properties(WILEY, 2026) Nasiruzzaman, Md.; Alshaban, Esmail; Alamrani, Fahad Maqbul; Alatawi, Adel; Mursaleen, Muhammad; Mursaleen, Muhammad; Fen Fakültesi, Matematik BölümüThis paper introduces a novel class of q-Sz & aacute;sz-Mirakjan-Kantorovich operators defined with shifted sequences. Utilizing the basic principles of q-calculus, we design the King-type fundamental construction of q-Sz & aacute;sz-Mirakjan-Kantorovich operators and derive their moments and central moments. The approximation properties are rigorously analyzed within the space of continuous functions. We establish the rate of convergence by employing the modulus of continuity and the integral modulus of continuity. Furthermore, we provide graphical illustrations to demonstrate the convergence behavior and efficiency of the proposed operators. Our results extend and generalize several existing findings in the field of quantum approximation theory.Öğe Türü: Yayın , Ectoparasite infestations in wild mammals from the lake Van basin, Eastern Türkiye, with notes on new host records(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Yılmaz, Ali Bilgin; Afşar, Milad; Yasul, Muhammed; Afshar, Mahsa Torkamanian; Ayan, Adnan; Ayan, Özge Oktay; Göz, Yaşar; Aslan, Loğman; Yasul, Muhammed; Bartın Sağlık Hizmetleri Meslek Yüksekokulu, Sağlık Bakım Hizmetleri BölümüEctoparasites cause skin lesions, anemia, and mortality in wild animals and act as vectors of zoonotic pathogens. This study reports ectoparasites detected during an opportunistic survey on wild mammals admitted to the Van Yuzuncu Yil University Wildlife Protection and Rehabilitation Center in eastern T & uuml;rkiye during 2024-2025. Due to the opportunistic nature of sampling, the findings primarily reflect parasite burdens in injured or debilitated individuals and may not be representative of healthy free-ranging populations. Of the 28 animals examined, 20 (71.4%) were infested. A total of 449 ectoparasites were collected: 370 fleas (82.4%), 58 ticks (12.9%), and 21 lice (4.7%). Identified species included four fleas (Paraceras melis, Chaetopsylla globiceps, Pulex irritans, Archaeopsylla erinacei), four ticks (Ixodes kaiseri, Rhipicephalus turanicus, Haemaphysalis parva, Haemaphysalis erinacei), and one louse (Trichodectes melis). This study provides new host-ectoparasite association records for T & uuml;rkiye: I. kaiseri and R. turanicus on beech marten (Martes foina); I. kaiseri and C. globiceps on Eurasian badger (Meles meles); and P. irritans on Eurasian lynx (Lynx lynx) and brown bear (Ursus arctos). Bipartite network analysis revealed moderate specialization (H2' = 0.52) and marked variation in host specificity, with T. melis, A. erinacei, and P. melis showing high host specificity (d' > 0.85), whereas Chaetopsylla globiceps and Pulex irritans acted as generalists (d' < 0.30). Some identified ectoparasites are known or suspected vectors of zoonotic pathogens. These findings provide baseline data for future research on wildlife health and disease transmission risks in the region.Öğe Türü: Yayın , Drought forecasting across multiple temporal scales using CMIP6 projections and hybrid deep learning models for Central Anatolia, Türkiye(Elsevier B.V., 2026) Çıtakoğlu, Hatice; Minarecioğlu, Necmiye; Aslanbay, Yüksel Gül; Kartal, Veysi; Gemici, Ercan; Gemici, Ercan; Mühendislik Mimarlık ve Tasarım Fakültesi, İnşaat Mühendisliği BölümüThis study proposed a multi-temporal drought forecasting framework that integrates CMIP6 climate projections with hybrid deep learning models to enhance forecasting accuracy under climate change. For the Kayseri, Nevşehir, and Kırşehir stations located in the Central Anatolia Region of Türkiye, climate uncertainty was reduced by adopting a multi-model ensemble mean derived from 19 CMIP6 global climate models. Subsequently, monthly precipitation outputs under three socio-economic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) were used to compute the Standardized Precipitation Index at 1-, 3-, and 6-month time scales (SPI-1, SPI-3, and SPI-6). The SPI time series were decomposed using the Tunable Q-factor Wavelet Transform (TQWT), Maximal Overlap Discrete Wavelet Transform (MODWT), Variational Mode Decomposition (VMD), and Intrinsic Time-Scale Decomposition (ITD). The resulting components were combined with Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) architectures to develop sixteen hybrid models. Model training was conducted using a chronological data partitioning strategy, while future projections were exclusively employed for performance evaluation. The results indicate that the TQWT-GRU model consistently outperforms other approaches at short-term (1-month) and long-term (6-month) time scales, whereas the MODWT-RNN model exhibits superior performance at the medium-term (3-month) scale across all stations and scenarios. For these optimal models, the coefficients of determination (R2) and Nash–Sutcliffe efficiency (NSE) values predominantly exceed 0.99, while the mean absolute error (MAE) and root mean square error (RMSE) remain within a low-error range of approximately 0.01–0.05. As a result, the findings underscore the necessity of scale- and region-specific hybrid model selection in drought forecasting and provide a robust framework for climate risk assessment and sustainable water resources management.
