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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, Hüseyin
In 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).
Open Access
Big data–enabled sign prediction for Borsa Istanbul intraday equity prices
(Elsevier, 2023-12) Kılıç, A.; Güloğlu, B.; Yalçın, Atakan; Üstündağ, A.; International Finance; YALÇIN, Atakan
This paper employs a big data source, the Borsa Istanbul's “data analytics” information, to predict 5-min up, down, and steady signs drawn from closing price changes. Seven machine learning algorithms are compared with 2018 data for the entire year. Success levels for each method are reported for 26 liquid stocks in terms of macro-averaged F-measures. For the 5-min lagged data, nine equities are found to be statistically predictable. For lagged data over longer periods, equities remain predictable, decreasing gradually to zero as the markets absorb the data over time. Furthermore, economic gains for the nine equities are analyzed with algorithms where short selling is allowed or not allowed depending on these predictions. Four equities are found to yield more economic gains via machine learning–supported trading strategies than the equities' own price performances. Under the “efficient market hypothesis,” the results imply a lack of “semistrong-form efficiency.”
Open Access
The causality analysis of air transport and socio-economics factors: the case of OECD countries
(Elsevier, 2017) Küçükönal, Hatice; Sedefoğlu, Gülşah; Professional Flight Program; Aviation Management; Kazda, A.; Smojver, I.; KÜÇÜKÖNAL, Hatice; SEDEFOĞLU, Gülşah
Air transport is one of the most important industries in the world with its rapid growth, and direct and indirect contribution to world economy. In other words, GDP, tourism and employment are the key factors causing that growth in air transport and an increase in those factors boost the demand for air transport. However, uncertainty in economy, rising unemployment and increased terrorist attacks towards tourism would be a big threat to the growth of air transport in the future. To understand the importance of the mentioned factors, we first aim to apply an econometric approach which is panel Granger causality analysis. To achieve that, data from World Bank data set for OECD countries between the year of 2000 and 2013 is used in this study. We apply Pesaran CDLM test and Friedman’s test which are preferred when the number of units (N) is higher than the time (T) to test cross-sectional dependence and we then perform Granger causality analysis in order to see whether there is a causal relationship (unidirectional or bidirectional) or not among air transport, tourism, economic growth and employment. Econometric results indicate that there is a unidirectional short run causal relationship between economic growth, tourism, employment and air transport and that those factors play an important role in the growth of air transport. In this paper, we also aim to discuss the future challenges for air transport within the frame of econometric results and statistical analysis.
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 Aghaei
The 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.
Open Access
Deep transformer-based asset price and direction prediction
(IEEE, 2024) Gezici, Abdul Haluk Batur; Sefer, Emre; Computer Science; SEFER, Emre
The field of algorithmic trading, driven by deep learning methodologies, has garnered substantial attention in recent times. Within this domain, transformers, convolutional neural networks, and patch embedding-based techniques have emerged as popular choices within the computer vision community. Here, inspired by the latest cutting-edge computer vision methodologies and the existing work showing the capability of image-like conversion for time-series datasets, we apply more advanced transformer-based and patch-based approaches for predicting asset prices and directional price movements. The employed transformer models include Vision Transformer (ViT), Data Efficient Image Transformers (DeiT), and Swin. We use ConvMixer for a patch embedding-based convolutional neural network architecture without a transformer. Our tested transformer-based and patch-based methodologies aim to predict asset prices and directional movements using historical price data by leveraging the inherent image-like properties within the historical time-series dataset. Before the implementation of attention-based architectures, the historical time series price dataset is transformed into two-dimensional images. This transformation is facilitated through the incorporation of various common technical financial indicators, each contributing to the data for a fixed number of consecutive days. Consequently, a diverse set of two-dimensional images is constructed, reflecting various dimensions of the dataset. Subsequently, the original images depicting market valleys and peaks are annotated with labels such as Hold, Buy, or Sell. According to the experiments, trained attention-based models consistently outperform the baseline convolutional architectures, particularly when applied to a subset of frequently traded Exchange-Traded Funds (ETFs). This better performance of attention-based architectures, especially ViT, is evident in terms of both accuracy and other financial evaluation metrics, particularly during extended testing and holding periods. These findings underscore the potential of transformer-based approaches to enhance predictive capabilities in asset price and directional forecasting. Our code and processed datasets are available at https://github.com/seferlab/price_transformer.
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 Nur
Depression 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.
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Exploring publicness as social practice: An analysis on social support within an emerging economy
(Wiley) Bilbil, Ebru Tekin; Zihnioğlu, O.; Fırtın, C. E.; Bracci, E.; Hotel Management; BİLBİL, Ebru Tekin
By utilizing the concepts of field, habitus, and capital inherited from Bourdieu, this study explores publicness as a social practice. In doing this, the paper problematizes publicness concerning accountability and public value and empirically explores the organization of social support delivery in Istanbul. We posit our research question: In what manners does publicness open up a space for collaboration and convergence in relation to accountability? The data gathering and analysis follow a qualitative methodology. We found different forms of publicness under three different conditionalities: (1) publicness as political authority based on hierarchization and centralization; (2) publicness as competing positions produced by diverse actors and their diverse positions taken beyond hierarchical relations; (3) publicness as social inclusion and diversity that is all-embracing by employing more inclusive practices. Publicness relationally unfolds public value with and among formal rules, voluntary practices, and networks. By delving into constitutive elements of practice—symbolic capital and habitus—engaging in the field struggles of redefining and owning publicness, the paper goes beyond the conventional dichotomy of normative versus empirical conceptualizations of publicness and instead differentiates among distinct forms of publicness in different conditionalities and contributes to the literature by bridging publicness and accountability habitus.
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, Amin
For 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.
Open Access
Fragile transitions from education to employment Youth, gender and migrant status in the EU
(Taylor & Francis, 2019) Çelik, Ç.; Gökşen, F.; Filiztekin, Orhan Alpay; Öker, İ.; Smith, M.; Economics; FİLİZTEKİN, Orhan Alpay
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How to be a good guest: American ethnographers in Turkey in the long 1968
(Wiley, 2024-03) Sipahi, Ali; Humanities and Social Sciences; SİPAHİ, Ali
The article uncovers a forgotten chapter in the history of anthropology by revealing the experiences of American ethnographers in Turkey between 1967 and 1969. Using original archival documents and oral history interviews, it focuses on the trials of Professor Lloyd A. Fallers as well as doctoral students Michael Meeker, Peter Benedict, and June Starr in navigating Turkish bureaucracy and global politics. Conceptually, the article evaluates the case of anthropologists in Cold War Turkey from the perspective of hospitality studies with a particular focus on guest-to-guest relationships. Adopting the guests’ points of view shows us that hospitality assemblages are forged by other-oriented thinking and behaviour, which involves misunderstandings, empathy, and projection. The article conceives the hospitality relationship as an encounter among perceptions of hospitality.
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 Balçık
One 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.
Open Access
Model-based runtime monitoring of smart city systems
(Elsevier, 2018) İnçki, Koray; Arı, İsmail; Computer Science; ARI, Ismail; İnçki, Koray
The 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).
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 Banu
Plastic 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.
Open Access
Product market competition and the value of diversification
(Elsevier, 2023-12) Şahin, Cansu İskenderoğlu; International Finance; ŞAHİN, Cansu Iskenderoğlu
I examine how industry concentration affects the value of diversification. I find that con- glomerates that operate mainly in concentrated industries (concentrated conglomerates) have higher diversification values. Using tariff reductions as competitive shocks, I show that concentrated conglomerates experience significant decline in their valuations and respond aggressively to threats in less-competitive industries.
Open Access
Quality of Colombian early childhood education: An exploratory study of teacher-child interactions
(Future Academy, 2019) Escalante, E.; Acar, İbrahim Hakkı; Suárez, S.; Raikes, H.; Psychology; ACAR, Ibrahim Hakkı
Quality of adult-child relationships could be influence by children's individual characteristics such as temperament. The examination of the association between temperament and teacher-child relationship has been limited within Latino population. Does regulatory temperament moderate the association between reactive temperament and teacher-child relationships in the Colombian early childhood education environment? Global research studies highlight the importance of quality of relationships to promote quality of early childhood education (ECE). Colombian ECE national evaluation reported association among teachers' interactions and child outcomes. From the ecological perspective, the present study aimed to examine how child temperament contribute to the teacher-child relationships in Colombian ECE environments. The sample included 316 children (58.3% Girls) and their teachers. Data were collected using Student-Teacher Relationship Scale (STRS) and Child Behavior Questionnaire (CBQ). First, a confirmatory factor analysis of STRS for the Colombian sample was conducted. Second, regression analysis was conducted to determine the strength of the relationship between the variables. Results from regression analyses showed that children's negative affectivity predicted teacher-child closeness (b = .06, beta = .15, t = 2.46, p = .01). In addition, children's temperamental surgency predicted teacher-child conflict (b= .08, beta= .12, t = 1.97, p = .04). Results from the current study highlights the importance of children's temperament in their relationships with teachers in the Colombian early childhood education environment. Intervention programs targeting improvement of teachers-child relationships could consider child temperament as children establish different patterns of relationships with teachers depending on their temperament. (C) 2019 Published by Future Academy www.
Open Access
The strategic use of narratives and governance of the COVID-19 pandemic in major autocratisers in Europe
(Taylor & Francis, 2024) Soyaltin-Colella, D.; Sert, Deniz Şenol; International Relations; SERT, Deniz
By the end of 2022, scholars had published heavily on authoritarian consolidation at the time of COVID-19 and explored how governments adopted measures weakening democratic checks and balances yet strengthened their regimes during the COVID crisis. Yet, we do not know much about how political leaders narrated the pandemic in their domestic and foreign policy choices in a way that reinforces their power. By focusing on the major autocratisers in Europe (Hungary, Poland, Turkey, and Serbia) whose democracy scores have fallen the most over the last 10 years, we reveal a set of influential narratives identified in the discourses of state leaders and government representatives which were constructed around the governance of the COVID-19 pandemic. These narratives were utilized by political leaders to legitimize their repressive policies geared towards controlling the society, and to contest the European Union (EU) in particular and the liberal democratic order in general.
Open Access
Temperament and behaviour problems in children: A multilevel analysis of cross-cultural differences
(Wiley, 2023-07-04) Campagna, A. X.; Acar, İbrahim Hakkı; Psychology; ACAR, Ibrahim Hakkı
Early temperament attributes have been linked to emerging behaviour problems and significant long-term consequences; however, these relations are rarely examined cross-culturally. The present study addresses this gap, employing multilevel modelling to explain within- and between-culture variances with respect to temperament predicting a spectrum of behaviour problems across 14 nations from the Joint Effort Toddler Temperament Consortium (JETTC). A total of 865 children between 17 and 40 months, with approximately equal age distribution across this developmental period and about equivalent representation of genders, were recruited from 14 nations. Greater negative emotionality was associated with more internalizing problems, whereas higher surgency and effortful control predicted fewer internalizing difficulties. Controlling for age and gender, temperament explained significant within- and between-culture variances in internalizing and externalizing problems (at the broad-band and fine-grained levels), as well as sleep problems. For internalizing difficulties, temperament accounted for more between-culture variance. In contrast, for externalizing difficulties, temperament accounted more for how individuals within the same culture differed from their same-culture counterparts. The within-culture findings suggest universal patterns of temperament-problem relations, informing cultural adaptation of interventions; between-culture findings enhance understanding of the implications of the cultural niche for normative behaviour and adjustment.

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