İstinye Üniversitesi Kurumsal Akademik Arşivi
DSpace@İstinye, İstinye Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector
(Springer Science and Business Media Deutschland GmbH, 2024) Aad, G; Aakvaag, E; Abbott, B; Abeling, K.; Beddall, Andrew John; Çetin, Serkant Ali; Öztürk, Sertaç; Şimşek, Sinem; Uysal, Zekeriya
A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at s = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, tt¯, and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion. © The Author(s) 2024.
Addressing barriers to big data implementation in sustainable smart cities: Improved zero-sum grey game and grey best-worst method
(Elsevier B.V., 2024) Razavian, Behnam; Hamed, S.Masoud; Fayyaz, Maryam; Ghasemi, Peiman; Özkul, Seçkin; Tirkolaee, Erfan Babaee
The optimization of sustainable smart cities is an essential endeavor in modern urban development, aiming to enhance the quality of life for citizens while minimizing environmental impacts. Big data plays a critical role in achieving these goals by enabling the collection, analysis, and utilization of vast amounts of information to make informed decisions. However, implementing big data in smart cities faces significant barriers, including data-sharing challenges, technical limitations, and organizational non-cooperation. Addressing these barriers is crucial for the successful deployment of smart city initiatives. We propose a novel approach to tackle these challenges using the Improved Zero-Sum Grey Game (IZSGG) theory and the Grey Best-Worst Method (G-BWM). This method comprehensively analyzes the risks and uncertainties associated with big data implementation in smart cities. By modeling the interactions between different stakeholders and their competing interests, IZSGG theory provides a framework to identify optimal strategies for data management. The G-BWM further refines these strategies by evaluating and prioritizing the various factors influencing big data utilization. Our findings reveal that the worst-case scenario for a smart city involves the simultaneous occurrence of several risks, all of which have positive values, indicating their potential to significantly disrupt smart city operations. The specific risks identified include: the sharing of data and information, the collection and recording of data, technical limitations and challenges associated with technology, the non-cooperation of organizations, and issues related to the interpretation of complex information. The technical barrier is the most significant with a weight of w(T)=0.6152, indicating its critical role compared to other barriers. Within this category, the sub-barrier of technical and technological constraints is particularly critical, with a weight of 0.39267375. © 2024 The Authors
Nanoarchitectonics and properties of sol-gel-derived bioactive glasses containing maghemite@ZnO core-shell nanoparticles
(Springer heidelberg, 2024) Deliormanlı, Aylin M.; ALMisned, Ghada; Tekin, Hüseyin Ozan
This study comprehensively examined the structural, magnetic, hemocompatibility, and bioactivity properties of magnetic bioactive glass particles embedded with zinc oxide-coated superparamagnetic maghemite (gamma-Fe2O3@ZnO) nanoparticles. Bioactive glass particles with varying concentrations of maghemite (2, 5, 10, and 20 wt%) were synthesized using the sol-gel method. The particles ranged in size from 6.83 mu m to 14.5 mu m, with size decreasing as maghemite content increased. The saturation magnetization values were 1.31 emu/g and 2.74 emu/g for the lowest and highest maghemite concentrations, respectively, indicating superparamagnetic behavior. Hydroxyapatite formation on the glass surfaces diminished with increased maghemite content, but hemocompatibility tests showed no significant hemolytic activity at a concentration of 0.5 mg/ml. The inclusion of gamma-Fe2O3@ZnO nanoparticles significantly enhanced the gamma radiation attenuation properties of the bioactive glasses, particularly at higher maghemite concentrations. In conclusion, gamma-Fe2O3@ZnO-enriched bioactive glasses exhibit promising potential for biomedical applications, offering a balance between magnetic functionality, bioactivity, and radiation shielding. Future research will focus on optimizing nanoparticle concentrations and surface modifications to enhance their multifunctionality.
DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation
(PeerJ Inc., 2024) Topal, Ahmet; Tunga, Burcu; Tirkolaee, Erfan Babaee
Plant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images. To do so, we propose a novel approach as the preprocessing step for the classification in which enhanced multivariance product representation (EMPR) is used to decompose the considered image into components, a new image is constructed using some of those components, and the contrast of the new image is enhanced by applying high-dimensional model representation(HDMR)to highlight the diseased parts of the leaves. Popular convolutional neural network (CNN) architectures, including AlexNet, VGG16, and ResNet50, are evaluated. Results show that VGG16 achieves the highest classification accuracy of approximately 96%, while all models perform well in predicting disease severity levels, with accuracies exceeding 85%. Notably, the ResNet50 model achieves accuracy levels surpassing 90%. This research contributes to the advancement of automated crop health management systems. © 2024 Topal et al.
Defective kinase activity of IKKα leads to combined immunodeficiency and disruption of immune tolerance in humans
(Nature Research, 2024) Çıldır, Gökhan; Aba, Ümran; Pehlivan, Damla; Tvorogov, Denis; Warnock, Nicholas I.; İpşir, Canberk; Arık, Elif; Kok, Chung Hoow; Bozkurt, Ceren; Tekeoğlu, Sidem; İnal, Gaye; Cesur, Mahmut; Küçükosmanoğlu, Ercan; Karahan, İbrahim; Savaş, Berna; Balcı, Deniz; Yaman, Ayhan; Demirbaş, Nazlı Deveci; Tezcan, İlhan; Haskoloğlu, Şule; Doğu, Figen; İkincioğulları, Aydan; Keskin, Özlem; Tümes, Damon J.; Erman, Baran
IKKα is a multifunctional serine/threonine kinase that controls various biological processes, either dependent on or independent of its kinase activity. However, the importance of the kinase function of IKKα in human physiology remains unknown since no biallelic variants disrupting its kinase activity have been reported. In this study, we present a homozygous germline missense variant in the kinase domain of IKKα, which is present in three children from two Turkish families. This variant, referred to as IKKαG167R, is in the activation segment of the kinase domain and affects the conserved (DF/LG) motif responsible for coordinating magnesium atoms for ATP binding. As a result, IKKαG167R abolishes the kinase activity of IKKα, leading to impaired activation of the non-canonical NF-κB pathway. Patients carrying IKKαG167R exhibit a range of immune system abnormalities, including the absence of secondary lymphoid organs, hypogammaglobulinemia and limited diversity of T and B cell receptors with evidence of autoreactivity. Overall, our findings indicate that, unlike a nonsense IKKα variant that results in early embryonic lethality in humans, the deficiency of IKKα‘s kinase activity is compatible with human life. However, it significantly disrupts the homeostasis of the immune system, underscoring the essential and non-redundant kinase function of IKKα in humans. © The Author(s) 2024.
