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Medeni Hukuka Giriş ve Başlangıç Hükümleri Pratik Çalışmalı Konu Anlatımı
No full textGenişletilmiş 2. baskı[No Abstract Available
Search for Flavor-Changing Neutral-Current Couplings Between the Top Quark and the Z Boson With Proton-Proton Collisions at √s=13 Tev With the Atlas Detector
No full textA search for flavor-changing neutral-current couplings between a top quark, an up or charm quark, and a Formula Presented boson is presented, using proton-proton collision data at Formula Presented collected by the ATLAS detector at the Large Hadron Collider. The analyzed data set corresponds to an integrated luminosity of Formula Presented. The search targets both single-top-quark events produced as Formula Presented (with Formula Presented, Formula Presented) and top-quark-pair events, with one top quark decaying through the Formula Presented channel. The analysis considers events with three leptons (electrons or muons), a Formula Presented-tagged jet, possible additional jets, and missing transverse momentum. The data are found to be consistent with the background-only hypothesis and 95% confidence-level limits on the Formula Presented branching ratios, assuming only tensor operators of the Standard Model effective field theory framework contribute to the Formula Presented vertices. These are Formula Presented (Formula Presented) for Formula Presented (Formula Presented) for a left-handed Formula Presented coupling, and Formula Presented (Formula Presented) in the case of a right-handed coupling. These results are interpreted as 95% CL upper limits on the strength of the corresponding couplings, yielding limits for Formula Presented and Formula Presented (Formula Presented and Formula Presented) of 0.15 (0.16), and limits for Formula Presented and Formula Presented (Formula Presented and Formula Presented) of 0.22 (0.21), assuming a new-physics energy scale Formula Presented of 1 TeV. © 2023 CERN, for the ATLAS Collaboration.IN2P3-CNRS; CC-IN2P3; 2014-2021; SCI/013; U.S. Department of Energy, USDOE; Alexander von Humboldt-Stiftung, AvH; Alabama Space Grant Consortium, ASGC; Brookhaven National Laboratory, BNL; Canarie; Karlsruhe Institute of Technology, KIT; H2020 Marie Skłodowska-Curie Actions, MSCA; Multiple Sclerosis Scientific Research Foundation, MSSRF; CERN; Compute Canada: 21/SCI/017; Göran Gustafssons Stiftelser; Natural Sciences and Engineering Research Council of Canada, NSERC; National Research Council Canada, NRC; Canada Foundation for Innovation, CFI; Science and Technology Facilities Council, STFC; Leverhulme Trust; European Research Council, ERC; European Cooperation in Science and Technology, COST; Australian Research Council, ARC; National Stroke Foundation, NSF; Neurosurgical Research Foundation, NRF; Helmholtz-Gemeinschaft, HGF; Minerva Foundation; Deutsche Forschungsgemeinschaft, DFG; Agence Nationale de la Recherche, ANR; Japan Society for the Promotion of Science, KAKEN; Ministry of Education, Culture, Sports, Science and Technology, MEXT; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Danmarks Grundforskningsfond, DNRF; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; National Natural Science Foundation of China, NSFC; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; Fundação para a Ciência e a Tecnologia, FCT; Bundesministerium für Bildung und Forschung, BMBF; Chinese Academy of Sciences, CAS; Austrian Science Fund, FWF; Generalitat de Catalunya; Ministry of Science and Technology of the People's Republic of China, MOST; Agencia Nacional de Promoción Científica y Tecnológica, ANPCyT; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO; Bundesministerium für Wissenschaft, Forschung und Wirtschaft, BMWFW; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science; Israel Science Foundation, ISF; Instituto Nazionale di Fisica Nucleare, INFN; Narodowe Centrum Nauki, NCN; Javna Agencija za Raziskovalno Dejavnost RS, ARRS; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; Ministerio de Ciencia e Innovación, MICINN; Centre National pour la Recherche Scientifique et Technique, CNRST; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; British Columbia Knowledge Development Fund, BCKDF; European Regional Development Fund, ERDF; Defence Science Institute, DSI; Narodowa Agencja Wymiany Akademickiej, NAWA; Institutul de Fizică Atomică, IFA; Agencia Nacional de Investigación y Desarrollo, ANID; Royal Society of South Australia, RSSA; Irish Rugby Football Union, IRF
Effects of Simultaneous Versus Post Exposure Epigallocatechin-3 Treatment on Aluminum Induced Neurotoxicity in Rat Hippocampus: a Multi-Approach Study
No full textChronic aluminium(Al) exposure can affect the antioxidant and glutaminergic systems through N-methyl-Daspartate receptors (NMDAR). This study was aimed to investigate the neurotoxic effect of Al through different mechanisms in rat hippocampus and to evaluate the protective role of epigallocatechin gallate (EGCG), a wellknown antioxidant, with simultaneous administration of Al,as well as post-treatment after Al exposure.For this purpose, aluminum chloride(AlCl3) was administered simultaneously with two different EGCG doses for 8 weeks as the first part of the study.In the second part of the study, after 4 weeks of AlCl3 pre-administration, two different EGCG doses were also administered during four additional weeks as post-treatment.Al administration led to oxidative stress and increased acetylcholinesterase levels.NMDAR subunit mRNA expressions were downregulated by Al, which was apparent in NMDAR1/2B subunits.Simultaneous EGCG treatment has shown a better neuroprotective effect in terms of these mechanisms and represents novel approach for the prevention of neurodegenerative diseases likely to be induced by Al.Scientific and Technological Research Council of Turkiye (TUBITAK) [115S533]This study was supported by the The Scientific and Technological Research Council of Turkiye (TUBITAK) with the number of 115S533
A Planar, Nems-Based Terahertz Phase Shifter Using Subwavelength Confinement Waveguides
No full text13th International Conference on Metamaterials, Photonic Crystals and Plasmonics, META 2023 -- 18 July 2023 through 21 July 2023 -- 300609In this paper, we present the Nano-Electro-Mechanical Systems (NEMS)-Based phase shifter using the Terahertz Subwavelength Confinement Waveguides (TSCW). The 90° phase difference is obtained from the OFF- and ON-states of the NEMS-based phase shifter at 0.29 THz. The measurement results of the 90° phase shifter show -4.79 dB worst-case insertion loss for the TSCW with 630 um total length, which indicates the performances of 7.60 dB/mm and 18.8 °/dB. © 2023, META Conference. All rights reserved.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 119E489, 2210
Forgive and Forget? Honor-Oriented Individuals Are Less Forgiving of Transgressing Peers
No full textIndividual differences research on masculine honor has heavily focused on men's aggressive responses to insults by male strangers, but much less is known whether honor-oriented individuals (men and women) are less forgiving - express more avoidant and vengeful, but less benevolent motivations - towards peers following in-sults, and their underlying concerns in being less forgiving. Using 200 British participants (dignity group) in Study 1, and 146 British (dignity group) and 178 Turkish (honor group) participants in Study 2, we examined whether (1) masculine honor-oriented individuals are less forgiving of peers after insults, and (2) this association is indirectly explained by concern with avoiding loss of social respect or maintaining personal integrity. Results showed that masculine honor-oriented individuals were less forgiving of insulting peers, which was indirectly explained by concern with losing respect, but not keeping personal integrity. We also report that the presence of a third-party audience did not have an effect on the observed pattern of relationships. These findings expand our understanding of why, despite the many benefits of forgiveness, some individuals may be less willing to forgive people who have hurt them
Perceptions of Plain Packaging and Health Warnings Among University Students in Turkey: a Survey-Based Experiment
No full textBackgroundCigarette pack design plays a crucial role in attracting customers, especially when other marketing methods are limited by policy. University students who engage in casual smoking take the risk of developing an addiction. The objective of this study was to assess the effects of plain packaging (PP) and graphic health warnings (GHWs) on cigarette packages on three outcome variables (negative affect, avoidant responses, and intentions to quit) among ever-smoker university students in Ankara, Turkey, where youth smoking prevalence is high.MethodsAn online survey-based experiment was used to collect data. The respondents were randomly assigned to one of the five conditions that contained images of cigarette packs with specific design elements. Regression analyses (n = 623) were used to compare across conditions and to estimate the effects of combined warnings (versus text-only warnings), stronger GHWs (versus old GHWs), and PP (versus branded packages) on the outcome variables, accounting for potential confounders.ResultsStronger GHWs generated more negative affect (0.31 points out of 5, p = 0.010) and avoidant responses (0.42 points out of 5, p = 0.002) than old warnings (when brand logos were visible). Plain packages generated more negative affect (0.48 points out of 5, p 0.001) and avoidant responses (0.46 points out of 5, p = 0.001) than branded packages (with old warnings). Disentangling the effects of PP and new GHWs revealed that neither had individual differential effect on intentions to quit within 6 months.ConclusionsAlthough no differential effect of PP or harsher GHWs was found on intentions to quit when respondents were exposed to images on screen, both design elements were found to be effective in generating negative affect and avoidant responses. More work is needed to design effective tobacco control measures among youth during critical years of tertiary education
Elektrikli Araç Hızlı Şarj İstasyonlarında Öncelikli Servis için Derin Pekiştirmeli Öğrenme Tabanlı Kaynak Yönetim Modeli ve Uzun-kısa Süreli Bellek ile Ortalama Bekleme Süresi Tahmini
No full textToday, electric vehicles are rapidly replacing internal combustion engine vehicles. Moreover, electric vehicles have much less potential to contribute to global warming. However, despite this positive impact of electric vehicles, they require much longer charging times, up to 30 minutes at fast charging stations. With the limited number and capacity of fast charging stations, the long charging times increase the queue length and waiting time of vehicles at the stations. Charging stations with priority service can be a solution to reduce the waiting time of a certain portion of the vehicles. In addition, drivers may want to know the average time they will have to wait for charging when they arrive at the station. There are two aims in this thesis. The first objective is to develop a charging station management model with priority service based on Deep Q-Learning, the second objective is to predict the average delay time of electric vehicles using the Long-Short Term Memory method. There are various studies on fast charging service in the literature. There are studies on dynamic pricing of charging, demand balancing, charge station location selection and capacity determination, restricting the number of vehicles accepted for charging to reduce waiting times. In this study, vehicles arriving at the electric charging station are categorized into two vehicle classes as low and high priority. The aim of the express (priority service) charging station management model is to try to keep the ratio of the average delay times of the vehicle classes at a target level. The station determines and announces this target relative delay time ratio. In order to achieve its target rate, the priority class charging from the idle resource is dynamically changed in real time. For this dynamic and real time control, a resource management mechanism based on Deep Q-Learning, a deep reinforcement learning method, is developed. The performance of the method at a single station is tested by simulation under various conditions. In addition, the performance of the developed method is compared with Fescioglu-Unver et al. (2021) and Kakillioglu et al. (2022) the studies about the same subject in the literature. When the results are analyzed, the developed method tracks the target delay rate successfully also the station settles quickly against the impact of instantaneous vehicle impulses. In the literature, there is no study on waiting time prediction in the field of electric vehicles and charging stations. Waiting time estimation studies have mostly been conducted on waiting times in emergency services. In this thesis, Long-Short Term Memory (LSTM) method is used to provide the predicted average waiting time information for both high and low priority vehicles using the data collected from the station real-time. Additionally, the prediction accuracy of the LSTM method was compared by Artificial Neural Network method in the study. As a result, it is found that LSTM has superior performance.Günümüzde elektrikli araçlar içten yanmalı motorlu araçların yerini hızla almaktadır. Kıyaslandığında da elektrikli araçların küresel ısınmaya çok daha az etki etme potansiyeli vardır. Ancak bu olumlu etkisine karşın, elektrikli araçların hızlı şarj istasyonlarında 30 dakika olmak üzere uzun şarj dolum süresine ihtiyacı vardır. Hızlı şarj istasyonlarının sayı ve kapasitelerinin sınırlı olmasıyla birlikte uzun şarj süreleri, istasyonlarda araçların kuyruk uzunluğunu ve bekleme süresini artırmaktadır. Araçların belli bir bölümünün bekleme süresini azaltmak için öncelikli servis veren şarj istasyonları oluşturulabilir. Bunun yanında araç kullanıcıları istasyona varış yaptıklarında şarj olmak için bekleyecekleri ortalama süre hakkında öngörü sahibi olmak isteyebilir. Bu çalışmada iki amaç bulunmaktadır. İlk amaç Derin Q-Öğrenme tabanlı öncelikli servis veren bir şarj istasyonu yönetim modeli geliştirilmesi, ikincisi ise araç kullanıcılarına ortalama bekleme süresi bilgisinin Uzun-Kısa Süreli Bellek yöntemi kullanılarak tahmin edilmesi. Literatür incelendiğinde hızlı şarj hizmetini ele alan çeşitli çalışmalar vardır. Fiyatlandırma, talep dengeleme, istasyon konumlandırma ve kapasite belirleme, bekleme sürelerini azaltmak için şarja kabul edilen araç sayısının kısıtlanması gibi konularda çalışmalar bulunmaktadır. Bu çalışmada elektrikli şarj istasyonuna gelen araçlar düşük ve yüksek öncelikli iki araç sınıfına ayrılmaktadır. Ekspres (öncelikli) şarj istasyonu yönetim modelinin amacı, bu araç sınıflarının ortalama bekleme sürelerinin oranının belirli bir seviyede tutmaktır. İstasyon, hedeflediği görece bekleme süresi oranını belirler ve ilan eder ve hedefini tutturmak için boşalan kaynaktan hizmet alacak öncelik sınıfını gerçek zamanda dinamik olarak değiştirir. Bu değişiklik için Derin Q-Öğrenme tabanlı kaynak kontrol mekanizması geliştirilmiştir ve performansı çeşitli şartlar altında test edilmiştir. Ayrıca geliştirilen yöntemin performansı literatürde bu alandaki Fescioglu-Unver ve diğ. (2021) ve Kakillioglu ve diğ. (2022) çalışmalarıyla kıyaslanmıştır. Sonuçlar incelendiğinde geliştirilen yöntemin hedeflenen bekleme oranını gerçekleştirdiği ve istasyona gelecek ani araç yüklenmeleri karşısında hızlı bir şekilde toparlanabildiği gözlenmiştir. Literatürde elektrikli araçlar ve şarj istasyonları alanında ise bekleme süresi tahmini yapan bir çalışmaya rastlanmamıştır. Bu çalışmada, istasyondan gerçek zamanda veri toplanarak takip eden 5 dakika içinde gelecek araçlar için tahmini bekleme süresi ilan edilmektedir. Tahmin için Uzun-Kısa Süreli Bellek (LSTM) yöntemi kullanılmaktadır. Buna ek olarak, LSTM yönteminin tahmin kalitesi yapay sinir ağı yöntemi de çalışmada uygulanarak karşılaştırılmış ve daha iyi performans gösterdiği görülmüştür
Overview of the Clef-2023 Checkthat! Lab: Task 2 on Subjectivity in News Articles Notebook for the Checkthat! Lab at Clef 2023
No full text24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023 -- 18 September 2023 through 21 September 2023 -- 193170We describe the outcome of the 2023 edition of the CheckThat!Lab at CLEF. We focus on subjectivity (Task 2), which has been proposed for the first time. It aims at fostering the technology for the identification of subjective text fragments in news articles. For that, we produced corpora consisting of 9,530 manually-annotated sentences, covering six languages —Arabic, Dutch, English, German, Italian, and Turkish. Task 2 attracted 12 teams, which submitted a total of 40 final runs covering all languages. The most successful approaches addressed the task using state-of-the-art multilingual transformer models, which were fine-tuned on language-specific data. Teams also experimented with a rich set of other neural architectures, including foundation models, zero-shot classifiers, and standard transformers, mainly coupled with data augmentation and multilingual training strategies to address class imbalance. We publicly release all the datasets and evaluation scripts, with the purpose of promoting further research on this topic. © 2023 Copyright for this paper by its authors.PE00000013, PNRR-M4C2-Investimento 1.3; Qatar Foundation, QF; Qatar National Research Fund, QNRF; Bundesministerium für Bildung und Forschung, BMBF: 01FP20031J; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 120E514; Università di Bologna, UNIBO: 2021-15854, DOT1303118, NPRP 14C-0916-210015, NPRP13S-0206-20028
Mechanical Characterization of Lattice Materials via Additive Manufacturing and Digital Image Correlation Technique
No full textThe 3rd BEYOND 2023: Computational Science, Mathematical Modeling and Engineering Conference TOBB University of Economics and Technology, Ankara-Turkey, 19-20 October 2023The advent of additive manufacturing has sparked a revolution in lattice material research, offering promising applications across diverse industries. However, assessing the mechanical properties of these intricate structures has posed a significant challenge. Ambiguities in plastic material testing standards and the financial hurdles tied to specialized testing equipment have hindered progress. This study presents a simplified, cost-effective methodology employing readily available tools and computational techniques. Our approach utilizes a Formlabs Form 3 Stereolithography (SLA) 3D printer with Formlabs Tough 1500 resin, coupled with the Instron-5944 2kN universal testing system[1]. We meticulously fabricated compression specimens adhering to ASTM standard D695-15[2], then produced hexagonal lattice configurations subjected to compression testing. Accurate displacement and strain measurements were achieved using digital image correlation (DIC) techniques, bolstered by open-source Ncorr software and the Point Track algorithm[3, 4]. One challenge in lattice experiments is creating a reliable speckle pattern on the lattice surface for precise DIC measurements, which we addressed using tattoo papers[5]. To validate our findings, we conducted finite element (FE) simulations of the compression experiments. These simulations closely matched our experimental results, highlighting the reliability of our methodology. In conclusion, our user-friendly approach simplifies lattice material characterization and deepens our understanding of their mechanical behavior. By integrating additive manufacturing, DIC techniques, and FE simulations, our methodology offers an accessible path for advancing lattice material research. It empowers researchers to explore these materials’ mechanical properties without the financial constraints of expensive experimental setups, fostering progress in this field and expanding its industrial applications