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Lyapunov-based model predictive control for stable operation of a 9-level crossover switches cell inverter in grid connection mode
No full textThis study proposes the application of a Lyapunov-based Model Predictive Control (L-MPC) approach to a 9-level Crossover Switches Cell (CSC9) converter operating in grid connection mode. The proposed method utilizes the structure of the classical finite-control-set MPC (FCS-MPC) technique while integrating a cost function that requires no tuning. By deriving the cost function based on Lyapunov theory, the system stability is ensured. Notably, the suggested approach offers several advantages over traditional MPC controllers. Firstly, it eliminates the need for gain tuning, thereby simplifying the implementation process. Secondly, the proposed controller prioritizes stability as a key design aspect. The presented simulation results prove that the proposed controller effectively regulates the voltage of the DC capacitor around its desired value and feed a smooth sinusoidal current to the grid with low total harmonic distortion (THD) while operating at a unity power factor
COVID-19 salgını döneminde ebeveynler arası ilişki niteliği ve ebeveyn duygu düzenlemesinin çocuğun duygu düzenlemesi üzerindeki rolünün incelenmesi
No full textThe COVID-19 pandemic has been a stressful situation with a variety of repercussions on family systems such as economic changes, fear of disease, or social isolation. In a time when so many various stressful events arise, it is crucial to investigate how parents and children regulate their emotions since it might be more challenging for parents and children to manage tough emotions like stress and anxiety during this time. In the family, each part dependent on each other as systems as well as they are independent. In this regard, the interparental relationship is just as crucial to children's ability to control emotions as their parents. Due to the potential for both dependent and independent influences on each member of the family, it is critical to study emotion regulation during COVID-19 within the family systems of the parents, children, and interparental relationship. The aim of the current study is to investigate the relationship among parent emotion regulation, interparental relationship quality, and child emotion regulation in the COVID-19 pandemic. Also, this study attempted to examine the indirect role of maternal emotion regulation in the association between interparental relationship quality and the emotion regulation of children. Also, child age, child gender and family SES were used as control variables. The data was obtained from mothers. The sample consisted of 204 mothers who have 2- to -6 years old children (M: 3.84, SD: 1.34; 55.2% boys, 44.8% girls). The data collection tools were as follows: the Dyadic Adjustment Scale (DAS; Spanier, 1976), the Emotion Regulation Questionnaire (ERQ; Gross & John, 2003), the Emotion Regulation Checklist (ERC; Shields & Cicchetti, 1999), and the COVID-19 Perceived Stress Scale (Acar et al., 2020). Two separate hierarchical regression analyses were conducted for child emotion regulation and child lability. Results showed that mothers' expressive suppression and interparental relationship consensus predicted the child lability. Also, emotion regulation was predicted by mothers' expressive suppression, but none of the interparental relationship quality dimensions. Moreover, the mediation analysis revealed that of interparental relationship quality did not predict child emotion regulation through maternal emotion regulation. This study highlights the importance of the influences of interparental relationship and mothers' emotion regulation on the child's emotion regulation.COVID-19 salgını, aile sistemi üzerinde ekonomik değişiklikler, hastalık korkusu, sosyal izolasyon, gibi çeşitli yan etkileri olan stresli bir süreç olmuştur. Çeşitli birçok stresli olayların ortaya çıktığı bu dönemde, çocukların ve ailelerin stres ve endişe gibi ağır duyguları düzenlemesinin zor olmasından dolayı, ailelerin ve çocukların duygularını nasıl düzenledikleri ayrıca önem arz etmektedir. Ailedeki her sistem birbirine bağımlı olduğu gibi aynı zamanda birbirinden bağımsızdır. Bu bağlamda, ebeveynler arası ilişki niteliği de çocuğun duygu regülasyonu üzerinde ebeveynin kendisi kadar önemli olabilir. Her bir aile üyesi birbiri ile hem bağlı hem de birbirinden bağımsızdır. Birbirlerine olan bu potansiyel etkilerinden dolayı, COVID-19 salgını boyunca aile sistemindeki ebeveynlerin ve çocukların duygu düzenlemesini ve ebeveynler arasındaki ilişkinin niteliğini çalışmak oldukça kritiktir. Bu çalışmanın amacı, COVID-19 salgınında ebeveynlerin duygu regülasyonu, ebeveynler arası ilişki kalitesi ve çocuk duygu regülasyonu arasındaki ilişkiyi incelemektir. Ayrıca, ebeveynler arası ilişki kalitesi ve çocukların duygu regülasyonunun arasındaki ilişkide annenin duygu düzenlemesinin dolaylı rolünü incelemeye çalışmaktadır. Ayrıca çocuk yaşı, cinsiyeti ve aile sosyoekonomik düzeyi kontrol değişkenleri olarak kullanılmıştır. Veriler annelerden alınmıştır. Örneklem 2-6 yaş arası çocuklara (ortalama yaş: 3.84, SS: 1.34; 55.2% oğlan çocukları, 44.8% kız çocukları) sahip 204 anneden alınmıştır. Veri toplama araçları şu şekildedir: Çift Uyum Ölçeği (ÇUÖ; Spanier, 1976), Duygu Düzenleme Ölçeği (DDÖ; Gross & John, 2003), Duygu Düzenleme Becerileri Ölçeği (DDBÖ; Shields & Cicchetti, 1999), ve Algılanan COVID-19 Stresi Ölçeği (Acar et al., 2020). Çocukların duygu düzenlemesi ve duygusal değişkenlik/olumsuzluk alt ölçekleri için iki ayrı hiyerarşik regresyon analizi yürütülmüştür. Sonuçlar, annenin duygu ifadesinin bastırılması ve ebeveynler arası ilişkinin niteliğinin çocuğun duygusal değişkenliğini istatistiksel olarak anlamlı bir şekilde yordadığını göstermektedir. Ayrıca, annenin duygu ifadesinin bastırılması çocuğun duygu regülasyonunu anlamlı olarak yordamaktadır; ancak, ebeveynler arası ilişkilerin niteliğinin hiçbir alt boyutunun çocuk duygu regülasyonunu yormadığı görülmüştür. Üstelik, aracı değişken analizinin ortaya çıkardığı üzere ebeveynler arası ilişkinin niteliği, çocuğun duygu regülasyonunu annenin duygu regülasyonu üzerinden yordamamaktadır. Bu çalışma ebeveynler arası ilişkinin ve annelerin duygu regülasyonunun çocuğun duygu regülasyonu üzerindeki etkilerinin önemini vurgular niteliktedir
Statistical characterization of FSO-based airborne backhaul links
No full textBackhaul solutions with large capacity and global coverage are of great importance for 6G wireless communication networks. With its ultra-large bandwidth and immunity to electromagnetic interference, free-space optical (FSO) communication has stood out as a major connectivity solution for UAV-to-UAV and UAV-to-ground links. While the performance of horizontal terrestrial FSO links is mainly limited by atmospheric-turbulence-induced fading, geometrical and atmospheric losses dominate the link budget of an airborne link between a high-altitude fixed-wing UAV and a ground station. Since the fixed-wing UAV continuously moves, the transmission distance between it and the ground station changes, causing both atmospheric and geometric losses to change, which results in the fading effect. In this paper, we statistically characterize this aggregate channel coefficient. First, we derive a probability density function for the channel coefficient; then, we obtain an expression for the instantaneous received signal-to-noise ratio (SNR). We validate our derived expressions through numerical calculations.TÜBİTAK ; European Cooperation in Science and Technology (COST
Experimental and numerical modal characterization for additively manufactured triply periodic minimal surface lattice structures: Comparison between free-size and homogenization-based optimization methods
No full textHomogenization-based topology optimization (HMTO) is one of the most extensively used grading methods to generate functionally graded lattice structures (FGLs). However, it requires a precharacterization of the lattices, which is time-consuming. As a remedy, free-size optimization-based graded lattice generation (FOGLG) is explored as an alternative method to generate the FGLs. This article builds on the authors’ previous work in which the HMTO and FOGLG approaches are studied to improve the dynamic characteristic of a design by using a single lattice type, namely, double gyroid (DG) structure. To show applicability of the proposed methods, different lattice types including diamond (D), gyroid (G), and I-WP are employed to create FGLs herein. The frequency response analysis is performed, and the results from HMTO and FOGLG are compared in terms of their accuracy and efficiency. The optimized designs are then reconstructed by relative density mapping (RDM) and enhanced relative density mapping (ERDM) methods. The fabricated test samples made of cobalt–chromium using the direct metal laser melting (DMLM) technique are then experimentally validated using a laser vibrometer. The results reveal that HMTO and FOGLG can be used on the lattice types with a variety of configurations and relative densities.TÜBİTA
Integrating cognitive presence strategies: A professional development training for K-12 teachers
No full textFor K-12 teachers to improve effective teaching skills, cognitive presence (CP) integration into teaching and learning process is of utmost value. CP strategy training can serve as a facilitating component in supporting K-12 teachers’ instructional capacity. This study presents findings of a teacher professional development training aiming CP strategy implementation at K-12 level. Following a mixed-method methodology, the present research was carried out with 53 teachers from four different campuses and grade levels, who were guided to implement CP strategies in their teaching context. The data sources were CP-integrated lesson plans, trainers’ feedback on these lesson plans, teacher responses on a questionnaire. The data collection methods were utilizing an end-of-the-training questionnaire directed to teachers, lesson plan evaluation through a CP rubric, content analysis of trainer feedback on lesson plans and revised lesson plans. Results unveiled that this professional development training designed and implemented for K-12 teachers led to significantly positive changes in teachers’ CP strategy integration into lesson plans regardless of levels, subjects or topics. This study could also provide important contributions to designing teacher professional development training for researchers, practitioners and teacher trainers, particularly in CP dimension.Publisher versio
A comparative study on the high-temperature forming and constitutive modeling of Ti-6Al-4V
No full textTi-6Al-4V alloy is often preferred for high-performance components such as aerospace components due to its superior material properties and thermal resistance. In order to produce these components in the desired geometry, it is very important to determine the high-temperature thermomechanical properties of Ti-6Al-4V. In order to define these properties, uniaxial tensile tests at strain rates of 0.001, 0.01 and 0.1 s−1 at 700, 750 and 800 °C were applied in this study. In tests performed at a strain rate of 0.001 s−1 at 800 °C, an elongation at break above 0.8 representing a dominant ductile behavior is observed. It is clearly demonstrated that the initial 17.32% β phase reaches 31.02% at 800 °C, and the α grain size increases with temperature. Existence of dimples and voids in the fracture surfaces are an indicator of increased ductility behavior. In addition to the Modified Johnson–Cook model, which is widely used for modeling flow stress, the use of the extended Ludwik equation is suggested in this study. According to the correlation coefficient (R), it is claimed that the Extended Ludwik model is a more suitable approach for modeling the mechanical behavior for the studied forming temperature range.Ozyegin University ; Turkish Aerospace Industries ; Türk Havacılık ve Uzay Sanayii ; TÜBİTA
Sources of ai innovation: More than a U.S.-China rivalry
No full textMany experts frame the debates around AI technology as a great power rivalry between the U.S. and China. Indeed, by most measures, the United States and China lead the world in AI innovation. Yet focusing solely on the United States and China elides global AI adoption dynamics and yields an incomplete picture about how and why countries acquire certain emerging technologies. While the U.S. and China undoubtedly matter when it comes to fostering AI innovation, cultivating AI talent, generating technology exports to emerging markets, and advancing AI global standard-setting, a diverse range of countries also exert significant influence on AI acquisition and adoption trends
A large neighborhood search algorithm and lower bounds for the variable-sized bin packing problem with conflicts
No full textIn this paper, we study the Variable-Sized Bin Packing Problem with Conflicts (VSBPPC). In VSBPPC, a set of items each with a certain size has to be packed into bins of various types. Bin types differ in terms of their capacity and cost, and certain pairs of items cannot be packed into the same bin due to conflicts. The goal is to pack the items into the bins such that the total cost of the used bins is minimized. VSBPPC generalizes both the Variable-Sized Bin Packing Problem (VSBPP) and Bin Packing Problem with Conflicts (BPPC). We propose new lower bounds and develop a large neighborhood search algorithm for the problem. In the proposed solution approach, we destroy the solution by unpacking some of the bins and then repair the solution by a greedy method considering the unit cost of packing each item followed by a local search procedure. In the local search phase, we improve the repaired solution by (i) transferring items from its current bin to another bin, and (ii) swapping the items between bins. We evaluate the performance of the proposed solution approach not only against a lower bound but also against the benchmark algorithms from the literature. The proposed solution approach outperforms the benchmark algorithms with at least a margin of 4.39% on average. Moreover, the solutions obtained by the proposed approach have an average optimality gap of 2.77% with respect to the lower bound
A new look at Sokolluzade Hasan Paşa’s illustrated universal history
No full textThis article revisits a universal history written in the late sixteenth century for the governor of Baghdad Sokolluzade Hasan Paşa (d. 1602) in light of a new- ly-discovered source that provides the missing concluding section of this universal history. This concluding section (Bibliothèque nationale de France Supplément turc 1322), which was announced in the index but not completed in the extant presen- tation copies (Topkapı Palace Museum Library H. 1369 and H. 1230), reinforces the idea of Sokolluzade Hasan Paşa’s imperial claims at the same time as it highlights the Baghdadi tenor of the work, as it was written in Baghdad by an author who be- longed to the governor’s household. However, the Paris manuscript presents no mere conclusion or continuation of a universal history. It is rather akin to a compilation (mecmuʿa) that juxtaposes sections from this universal history with sections from Za- kariya al-Qazwini’s (d. 1283) ʿAjāʾib al-Makhlūqāt wa Gharāʾib al-Mawjūdāt, thus recontextualizing this late-sixteenth-century universal history.Publisher versio
Uluslararası tahvil piyasalarında vade yapısının ve sabit getirili menkul kıymet getirilerinin makine öğrenimi teknikleri kullanılarak tahmin edilmesi
No full textIn this study, I focus on predicting bond risk premia in Turkish Eurobonds market using machine learning methods. Machine learning uses statistical learning techniques to gather useful structures of a data set without being explicitly programmed. In recent years machine learning has become a very popular topic and shown very good results in a wide variety of fields, but there is a lack of research in the field of term structure modeling. In order to predict Turkish Eurobond returns, I implemented several machine learning models such as OLS, PCA, Ridge, Lasso, Elastic net and neural networks. The raw data set I used comprises of Turkey Government Eurobond yields between 2005 and 2020, inclusive. Both monthly and yearly returns are estimated separately. Zero-coupon rates and forward rates are calculated from the raw data and used as left-hand site elements for machine learning predictions. Macroeconomic variables are also added to forward rates as factors. I compared the out-of-sample performance of the models and I found that Penalized linear regression yields the best results for excess bond return prediction, providing nearly 10% out-of-sample R2. Neural networks are the second-best performer yielding around 3-4% out-of-sample R2. Plus, adding macroeconomic variables to the models slightly improved the results by 2-3%. Also, yearly returns estimation performed better than monthly returns for OLS, Ridge, Lasso and Elastic net regressions, but not for neural networks.Bu tez çalışması, Türkiye Eurobond piyasası getiri eğrisini ve farklı vadelerdeki bono getirilerini makine öğrenmesi yöntemleri kullanılarak modellenmesine odaklanacaktır. Makine öğrenimi, detaylıca programlanmadan bir veri kümesinin yararlı yapılarını toplamak için istatistiksel öğrenme tekniklerini kullanır. Son yıllarda makine öğrenimi çok daha popüler bir konu haline geldi ve çeşitli alanlarda çok iyi sonuçlar gösterdi, ancak getiri eğrisini modelleme alanında araştırma eksikliği olduğu dikkatimizi çekmekte. Faiz oranlarının vade yapısını modellemek için yapay sinir ağları, derin öğrenme yöntemleri, doğrusal modeller, cezalı doğrusal regresyonlar ve boyutluluk azaltma teknikleri gibi makine öğrenmesi tekniklerini kullandık. Ham data, 2005 ve 2020 yılları arasındaki Turkiye Eurobondları faizlerinden oluşturulmuştur. Aylık ve yıllık getiriler ayrı ayrı tahmin edilmiştir. Ham data kullanılarak önce kuponsuz tahvil getiri eğrisi, sonraısında da ileri vadeli getiriler hesaplanmış ve modellerin bağımlı değişkenleri olarak dahil edilmiştir. Bu değişkenlere makro ekonomik veriler de eklenerek ayrıca tahmin yapılmıştır. Modellerin örneklem dışı performanslarını kıyaslarak incelendiğinde cezalı doğrusal regresyonların yaklaşık 10% R2OOS sağlayarak en başarılı modeller olduğu görülmüştür. Sinir ağları modelleri 3-4% R2OOS ile en başarılı ikinci performansı sergilemiştir. Makro verilerin modellere değişken olarak eklenmesi sonuçları 2-3% iyileştirmiştir. Ayıca, doğrusal ve cezalı doğrusal modeller için yıllık getiri tahminleri aylık getiri tahminlerine göre daha iyi sonuç vermiştir