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Visuo-Haptic Exploration with Relocated Haptic Feedback: Impact of Virtual and Augmented Reality
No full textRendering haptic feedback on the wrist is an effective solution for freeing users' hands during virtual interactions while still providing haptic feedback, mostly focusing on Virtual Reality (VR) environments. Unfortunately, whether these solutions can be extended to Augmented Reality (AR) interactions has not been investigated before. In this paper, we investigate the perceptual differences between VR and AR environments with a user study experiment based on a stiffness discrimination task. Our findings revealed similar task accuracy and sensitivity in VR and AR but different exploration behaviors (i.e. higher interaction time in AR) and user experience (i.e. higher enjoyment and less mental fatigue in AR). These differences may be explained by the increased cognitive load and the environmental immersion in AR, where users navigate both virtual and real elements simultaneously. This suggests that visuo-haptic psychophysical findings obtained in VR can be extended to AR, even though user experience and exploration behavior might differ.TUBITAK 2232-B International Fellow-ship for Early Stage Researchers Program [121C147]This work is funded by TUBITAK 2232-B International Fellow-Ship for Early Stage Researchers Program, number 121C147Science Citation Index Expande
Evolutionary Algorithms for Optimizing Engineering Problems
No full textÜç boyutlu yumuşak büyüyen robotik manipülatörlerin tasarımını gerçekleştirmeyi amaçlayan çok amaçlı sürekli evrimsel bir çerçeve öneriyoruz. Ulaşılabilirlik, malzeme kullanımı, eğrilik düzgünlüğü ve çarpışmasız alanı tek bir optimizasyon problemine dahil ederek, ağırlık ayarlamasını ve karmaşık Pareto ön yüzü hesaplamalarını ortadan kaldırmak için Sıralama Bölme (Rank Partitioning) ve hayatta kalma stratejilerinden faydalanıyoruz. Dört algoritmayı, Genetik Algoritma (GA), Parçacık Sürüsü Optimizasyonu (PSO), Diferansiyel Evrim (DE) ve Büyük Patlama–Büyük Çöküş (BB–BC), aynı koşullar altında, farklı hedefler ve engeller ile karşılaştırdık. Sonuçlar, bu mühendislik problemi için en iyi algoritmaların sırasıyla GA ve PSO olduğunu, ardından DE'nin geldiğini göstermektedir; ayrıca BB–BC'nin ortalama olarak PSO ve DE'ye kıyasla daha tutarlı olduğunu iddia ediyoruz. Yaklaşımımız, sürekli robot tasarımı için ölçeklenebilir bir çözümdür ve karmaşık, yapısal olmayan alanlar için en uygun algoritmanın seçilmesine yardımcı olur.The 3D design of soft growing robotic manipulators is a demanding task due to theirbuilt-in compliance and the need to optimize several, typically conflicting, objec-tives. This thesis presents a novel multi-objective continuous evolutionary methodfor 3D design of such manipulators. Our approach integrates key factors such asreachability, material efficiency, curvature smoothness, and collision avoidance into asingle optimization problem. By leveraging Rank Partitioning and effective survivalmethods, our approach avoids manual weight tuning and expensive Pareto-front cal-culations. We performed an extensive comparative study of four evolutionary algo-rithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), DifferentialEvolution (DE), and Big Bang-Big Crunch (BB-BC), under the same conditionson different targets and obstacles. Results show that GA and PSO always providesuperior performance for this design problem, while BB-BC is illustrated as beingvery consistent relative to PSO and DE. Scalable solution significantly advancescontinuum robot design since it enables the selection of the optimal algorithm forthis engineering problem
A Large-Scale Replication of Eimer (1996): Can the N2pc Be Elicited by an Isolated Target
No full textConstant, Martin/0000-0001-9574-0674Science Citation Index Expanded - Social Science Citation Inde
Differentiating Functional Connectivity Patterns in Adhd and Autism Among the Young People: a Machine Learning Solution
No full textObjective: ADHD and autism are complex and frequently co-occurring neurodevelopmental conditions with shared etiological and pathophysiological elements. In this paper, we attempt to differentiate these conditions among the young people in terms of intrinsic patterns of brain connectivity revealed during resting state using machine learning approaches. We had two key objectives: (a) to determine the extent to which ADHD and autism could be effectively distinguished via machine learning from one another on this basis and (b) to identify the brain networks differentially implicated in the two conditions.Method: Data from two publicly available resting-state functional magnetic resonance imaging (fMRI) resources-Autism Brain Imaging Data Exchange (ABIDE) and the ADHD-200 Consortium-were analyzed. A total of 330 participants (65 females and 265 males; mean age = 11.6 years), comprising equal subgroups of 110 participants each for ADHD, autism, and healthy controls (HC), were selected from the data sets ensuring data quality and the exclusion of comorbidities. We identified region-to-region connectivity values, which were subsequently employed as inputs to the linear discriminant analysis algorithm.Results: Machine learning models provided strong differentiation between connectivity patterns in participants with ADHD and autism-with the highest accuracy of 85%. Predominantly frontoparietal network alterations in connectivity discriminate ADHD individuals from autism and neurotypical group. Networks contributing to discrimination of autistic individuals from neurotypical group were more heterogeneous. These included language, salience, and frontoparietal networks.Conclusion: These results contribute to our understanding of the distinct neural signatures underlying ADHD and autism in terms of intrinsic patterns of brain connectivity. The high level of discriminability between ADHD and autism, highlights the potential role of brain based metrics in supporting differential diagnostics.Science Citation Index Expanded - Social Science Citation Inde
Remembering the Anthropocene: Memorials Beyond the Human
No full text[No Abstract Available]Social Science Citation Index - Arts and Humanities Citation Inde
Tolerance of Uncontrollability and Psychopathology: the Role of Maladaptive Emotion Regulation
No full textBackground: Tolerance of uncontrollability (TOU) can be defined as the ability to endure the discomfort resulting from uncontrollable events. In the absence of perceived control, people utilize strategies that regulate and enhance the inner perception of control. Repetitive negative thinking (RNT) and thought suppression (TS) are some of the strategies that give illusory control in the short term. However, many studies revealed that these strategies increase distress in the long term. The present study aimed to explore the relationship between TOU, RNT, TS, depression, and anxiety. It was anticipated that TOU would be associated with both depression and anxiety, and RNT and TS would mediate these relationships. Method: A sample of 448 participants aged between 17 and 56 (80.4% females) completed measures assessing TOU, RNT, TS, depression, and anxiety. Results: Findings showed that higher levels of TOU were associated with low levels of depression and anxiety, however when anxiety was controlled, the correlation with depression disappeared. While RNT was the mediator in both outcomes, TS only mediated the relationship between TOU and anxiety. Conclusions: The current study emphasizes that TOU can be a concept specific to anxiety and highlights one of the mechanisms behind TOU and psychological distress. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Social Science Citation Inde
Economic Uncertainty and Climate Change Exposure
No full textDanisman, Gamze Ozturk/0000-0003-3684-6692; Bilyay Erdogan, Seda/0000-0001-6701-4448This paper explores how economic uncertainty affects firms' climate change exposure. We use an extensive sample from 24 countries from 2002 to 2021. Employing a novel measure of firm-level climate change exposure developed by Sautner et al. (2023b), we empirically demonstrate that prior to the Paris Agreement in 2015, economic uncertainty leads to a decrease in climate change disclosures. However, after the Paris Agreement, our findings reveal a positive association between economic uncertainty and climate change exposure. The positive disclosure effect is primarily driven by higher climate-related opportunities and regulatory exposures. Our findings are robust when we employ alternative definitions for economic uncertainty, alternative samples, additional firm-level and country-level control variables, and alternative methodologies. We find that institutional and foreign ownership positively moderates the association between economic uncertainty and climate change exposure after the Paris Agreement. Further analysis investigates the moderating impact of country-level environmental performance indicators. We present novel empirical evidence suggesting that firms operating in countries with less climate vulnerability, higher readiness, more stringent environmental policies, superior climate protection performance, and higher environmental litigation risk tend to have higher climate change exposure in uncertain times.Science Citation Index Expande
Deǧişken Sicaklikta Elektrikli Araç Batarya Kapasitesinin Belirlenmesi
No full textThis paper presents an innovative test method for evaluating the effects of sudden temperature changes on battery capacity. Unlike existing test methods, this method aims to measure the discharge capacity of the battery during abrupt temperature variations using a single test approach. In the test conducted in this study, a fully charged battery was subjected to partial discharge after sudden temperature changes, and then the temperature was returned to the reference value, allowing the discharge to continue until the minimum voltage was reached. In this way, the effect of temperature fluctuations on the usable capacity was observed. Using the same test method, the battery was discharged to the minimum voltage at low temperatures. After the temperature was returned to the reference value, the discharge process continued, and as a result, it was observed that sudden changes in temperature had no effect on capacity. These findings provide a new methodological contribution that can be applied in the battery industry and battery modeling techniques. © 2024 IEEE
Extended Hybridizable Discontinuous Galerkin (x-Hdg) Method for Linear Convection-Diffusion Equations on Unfitted Domains
No full textGURKAN, Ceren/0000-0002-1240-5801; Ahmad, Haroon/0000-0002-3986-8013In this work, we propose a novel strategy for the numerical solution of linear convection diffusion equation (CDE) over unfitted domains. In the proposed numerical scheme, strategies from high order Hybridized Discontinuous Galerkin method and eXtended Finite Element method are combined with the level set definition of the boundaries. The proposed scheme and hence, is named as eXtended Hybridizable Discontinuous Galerkin (XHDG) method. In this regard, the Hybridizable Discontinuous Galerkin (HDG) method is eXtended to the unfitted domains; i.e., the computational mesh does not need to fit to the domain boundary; instead, the boundary is defined by a level set function and cuts through the background mesh arbitrarily. The original unknown structure of HDG and its hybrid nature ensuring the local conservation of fluxes is kept, while developing a modified bilinear form for the elements cut by the boundary. At every cut element, an auxiliary nodal trace variable on the boundary is introduced, which is eliminated afterwards while imposing the boundary conditions. Both stationary and time dependent CDEs are studied over a range of flow regimes from diffusion to convection dominated; using high order (p <= 4) XHDG through benchmark numerical examples over arbitrary unfitted domains. Results proved that XHDG inherits optimal (p + 1) and super (p + 2) convergence properties of HDG while removing the fitting mesh restriction.The Scientific and Technological Research Council of Turkiye (TUBITAK); [121M947]This work is supported by The Scientific and Technological Research Council of Turkiye (TUBITAK) , Career Development Program (CAREER) , project no: 121M947