Faculty of Engineering
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Conference ObjectPublication Open Access Analysis of X(4140) like states and their radial excitations in QCD(Sissa Medialab Srl, 2017) Türkan, Arzu; Dağ, Hüseyin; Natural and Mathematical Sciences; TÜRKAN, Arzu; DAĞ, HüseyinIn this work, we investigated the X(4140) and like states and their radial excitations by using molecular and diquark-antidiquark currents which couple to scalar, axial vector and tensor states via QCD sum rules. In operator product expansion, we considered quark, gluon and mixed vacuum condansates up to dimension eight. For the ground states coupling to these currents, we found that masses are almost degenerate with X(4140). For the excited states, we found that scalar and tensor currents are coupling to D∗ sD∗ s threshold. However for the axial vector currents, the mass of the first excited state is compatible with X(4274). Thus we conclude that, X(4274) might be the first radial excitation of X(4140).ArticlePublication Open Access Comparative characterization of indoor VLC and MMW communications via ray tracing simulations(IEEE, 2023) Hosseinabadi, Fahimeh Aghaei; Eldeeb, H. B.; Bariah, L.; Muhaidat, S.; Uysal, Murat; Electrical & Electronics Engineering; UYSAL, Murat; Hosseinabadi, Fahimeh AghaeiThe demand for ultra-high-speed indoor wireless connectivity is ever-increasing, which poses unique challenges for the next generation wireless communication system design. This has prompted the exploration of higher frequency bands including millimeter wave (MMW) and visible light bands in addition to the conventional sub-6 GHz band. This paper provides a comprehensive comparison of the propagation channels of these frequency bands under the same indoor environment and scenarios. We adopt ray tracing techniques for site-specific channel modeling, which enables the consideration of the three-dimensional models of the indoor environment and objects inside. It allows us to take into account different frequencies, i.e., 2.4 GHz, 6 GHz, 28 GHz, 60 GHz, 100 GHz, and visible light band as well as different transmitter types, i.e., omnidirectional/directional antennas for radio frequency systems and indoor luminaries for visible light communications (VLC). For different frequencies under consideration, we obtain channel impulse responses (CIRs) and present the channel path losses for various user trajectories in indoor environments. Furthermore, we propose closed-form expressions for the cumulative distribution functions (CDFs) of received power levels for all frequency bands under consideration. Our results demonstrate that VLC channels exhibit lower path loss than that in MMW bands but higher than that of 2.4 GHz band. In addition, it is observed that VLC systems exhibit more sensitivity to shadowing and blockage effects. Our findings further indicate that the characteristics of the propagation channel are greatly influenced by the antenna type. For instance, using omnidirectional and rectangular patch antennas results in lower path loss compared to horn antennas, and this difference becomes more significant as the transmission distance decreases.ArticlePublication Open Access Depression screening from voice samples of patients affected by parkinson’s disease(S. Karger AG, 2019-05-01) Özkanca, Yasin Serdar; Öztürk, M. G.; Ekmekci, Merve Nur; Atkins, D. C.; Demiroğlu, Cenk; Ghomi, R. H.; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Özkanca, Yasin Serdar; Ekmekci, Merve NurDepression is a common mental health problem leading to significant disability worldwide. It is not only common but also commonly co-occurs with other mental and neurological illnesses. Parkinson's disease (PD) gives rise to symptoms directly impairing a person's ability to function. Early diagnosis and detection of depression can aid in treatment, but diagnosis typically requires an interview with a health provider or a structured diagnostic questionnaire. Thus, unobtrusive measures to monitor depression symptoms in daily life could have great utility in screening depression for clinical treatment. Vocal biomarkers of depression are a potentially effective method of assessing depression symptoms in daily life, which is the focus of the current research. We have a database of 921 unique PD patients and their self-assessment of whether they felt depressed or not. Voice recordings from these patients were used to extract paralinguistic features, which served as inputs to machine learning and deep learning techniques to predict depression. The results are presented here, and the limitations are discussed given the nature of the recordings which lack language content. Our models achieved accuracies as high as 0.77 in classifying depressed and nondepressed subjects accurately using their voice features and PD severity. We found depression and severity of PD had a correlation coefficient of 0.3936, providing a valuable feature when predicting depression from voice. Our results indicate a clear correlation between feeling depressed and PD severity. Voice may be an effective digital biomarker to screen for depression among PD patients.ArticlePublication Open Access Formation of nano-sized compounds during friction stir welding of Cu–Zn alloys: effect of tool composition(Elsevier, 2020-12) Heidarzadeh, A.; Radi, Amin; Yapıcı, Güney Güven; Mechanical Engineering; YAPICI, Güney Güven; Radi, AminFor the first time, the origin of tool composition effect on microstructure and mechanical properties of the friction stir welded joints has been disclosed. For this aim, nanoindentation, orientation image microscopy, and transmission electron microscopy were employed to analyze the microstructure and mechanical properties in the case of copperzinc alloy joints welded by different tool compositions. The results showed that the nanosized intermetallic compounds were formed in the stir zone when using a hot-work steel tool, which increased the strength of the joint. The outcomes of this work can be used to modify the friction stir welded joints of various metals and alloys.ArticlePublication Open Access A machine learning approach to deal with ambiguity in the humanitarian decision-making(Wiley, 2023-09) Grass, E.; Ortmann, J.; Koyuncu, Burcu Balçık; Rei, W.; Industrial Engineering; KOYUNCU, Burcu BalçıkOne of the major challenges for humanitarian organizations in response planning is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in postdisaster settings may involve missing elements and inconsistencies, which can hamper effective humanitarian decision-making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision-makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision-making. To the best of our knowledge, the integration of ambiguous information into decision-making by combining a cluster machine learning method with stochastic optimization has not been done before. We illustrate the proposed approach on a realistic case study that focuses on locating shelters to serve internally displaced people (IDP) in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates in making shelter location decisions. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively. With the newly proposed methodology, the decision-maker is able to analyze the degree of ambiguity in the data and the degree of consensus between different data sources to ultimately make better decisions for delivering humanitarian aid.Conference ObjectPublication Open Access Model-based runtime monitoring of smart city systems(Elsevier, 2018) İnçki, Koray; Arı, İsmail; Computer Science; ARI, Ismail; İnçki, KorayThe pace of proliferation for smart systems in city wide applications is unmatched. The introduction of Internet of Things (IoT), an enabler of smart city phenomenon, has incubated a productive environment for such innovations. Smart things equipped with IoT capabilities, allow for developing smart city applications at such large scale that each application can be represented as a system of systems (SoS). Nevertheless, the complexity of engineering such SoS has been a major challenge in developing and maintaining smart city applications. One of the engineering challenges that industry face today is the verification of a SoS smart city application at runtime. We introduce utilization of a model-based runtime monitoring approach for providing reliable service. We propose to use message sequence charts for representing a smart city application, later allow the practitioners to express expected behavior of an application in terms of complex-event processing patterns. We demonstrate the fidelity of our approach on a sample smart parking system. Our approach is one of its kind in enabling a non-intrusive monitoring of IoT behavior at runtime (online).EditorialPublication Open Access Moving from symptom management to upstream plastics prevention: The fallacy of plastic cleanup technology(Elsevier, 2023-11-17) Bergmann, M.; Arp, H. P. H.; Carney Almroth, B.; Cowger, W.; Eriksen, M.; Dey, T.; Gündoğdu, S.; Helm, R. R.; Krieger, A.; Syberg, K.; Tekman, Mine Banu; Thompson, R. C.; Villarrubia-Gómez, P.; Warrier, A. K.; Farrelly, T.; Natural and Mathematical Sciences; TEKMAN, Mine BanuPlastic removal technologies can temporarily mitigate plastic accumulation at local scales, but evidence-based criteria are needed in policies to ensure that they are feasible and that ecological benefits outweigh the costs. To reduce plastic pollution efficiently and economically, policy should prioritize regulating and reducing upstream production rather than downstream pollution cleanup.