Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article Citation - WoS: 2Comparing Hand-Based and Controller-Based Interactions in Virtual Reality Learning: Effects on Presence and Interaction Performance(PeerJ Inc, 2025) Saran, MuratVirtual reality (VR) holds significant promise for enhancing science education by providing immersive and interactive learning experiences. However, the optimal interaction modality within educational VR environments remains an open question. This study investigates the impact of hand-based vs. controller-based interaction on sixth-grade students' sense of presence and interaction performance in a VR science laboratory simulation. Fifty-four sixth-grade students were randomly assigned to either a hand-based interaction group or a controller-based interaction group. Participants completed three interactive science experiments (solar system, electrical circuits, and force/energy) within a virtual laboratory environment designed to mimic their school's physical lab. Presence was assessed using a validated Turkish adaptation of the Presence Questionnaire (PQ), while interaction performance was evaluated using a structured observation form completed by a school teacher. Independent samples t-tests and Mann-Whitney U tests were used to compare the presence and performance scores between the groups. Supplementary analyses explored the effects of gender and prior VR experience. Contrary to expectations, no significant differences were found in either presence (t(49.4) = -0.01, p = 0.992) or interaction performance (t(52) = -1.30, p = 0.199) between the hand-based and controller-based interaction groups. Both interaction modalities yielded comparable levels of self-reported presence and observed performance. However, an unexpected finding emerged regarding performance. A supplementary analysis revealed a significant main effect of gender on performance scores (F(1, 50) = 4.844, p = 0.032), independent of interaction type. Specifically, males demonstrated significantly higher performance than females. This study suggests that, for sixth-grade students engaging in these specific VR science simulations, hand-based and controller-based interactions are equally effective in terms of fostering presence and supporting interaction performance. These findings have practical implications for the design and implementation of VR learning environments, particularly in resource-constrained settings where the reduced maintenance and hygiene concerns associated with hand-based interaction may be advantageous.Article Citation - WoS: 6Citation - Scopus: 6Modeling the Transmission Dynamics of Middle Eastern Respiratory Syndrome Coronavirus with the Impact of Media Coverage(Elsevier, 2021) Fatima, BiBi; Alqudah, Manar A.; Zaman, Gul; Jarad, Fahd; Abdeljawad, ThabetMiddle East respiratory syndrome coronavirus has been persistent in the Middle East region since 2012. In this paper, we propose a deterministic mathematical model to investigate the effect of media coverage on the transmission and control of Middle Eastern respiratory syndrome coronavirus disease. In order to do this we develop model formulation. Basic reproduction number R-0 will be calculated from the model to assess the transmissibility of the (MERS-CoV). We discuss the existence of backward bifurcation for some range of parameters. We also show stability of the model to figure out the stability condition and impact of media coverage. We show a special case of the model for which the endemic equilibrium is globally asymptotically stable. Finally all the theoretical results will be verified with the help of numerical simulation for easy understanding.Article Citation - WoS: 7Citation - Scopus: 8Life Cycle Assessment of Geopolymer Materials Utilizing Construction and Demolition Waste(Academic Press Inc Elsevier Science, 2025) Unsal, Zeynep; Ekinci, Mehmet Ozkan; Ilcan, Huseyin; Sahin, Oguzhan; Selcuk, Seda; Sahmaran, MustafaThis study assessed the environmental impacts of construction and demolition waste (CDW)-based geopolymers. For analysis, the cradle-to-gate system boundary was established. Two different geopolymer mixtures were evaluated: one composed entirely of CDW-based precursors-(CDW100), and another incorporating supplementary cementitious materials-(SCMs) as a 20 % replacement of CDW-based precursors-(CDW80SCM20). Raw materials were sourced from a diverse range of demolition waste. NaOH and Ca(OH)2 were employed as activators. Additionally, a cementitious mixture with comparable strength was included in the analysis as a benchmark for comparison with the geopolymers. The results of the impact analyses revealed that CDW80SCM20 had a greater environmental impact across various categories compared to CDW100. The relatively higher environmental impacts of the CDW80SCM20 mixture are largely attributed to the transport-related environmental burdens associated with the inclusion of SCMs. The largest differences were for land occupation and global warming, at 30.8 % and 16.9 %, respectively. Moreover, the results indicated that the environmental impacts of the CDW-based mortars were significantly lower than those of the cementitious system, with the exception of aquatic eutrophication and ozone layer depletion. The increase in ozone layer depletion is mainly associated with the production of NaOH via the chlor-alkali process, which contributes to emissions affecting stratospheric ozone. The advantages of geopolymers in terms of environmental impact made it possible to reduce the effects of global warming by 48.1 %, aquatic acidification by 22.1 %, land occupation by 45.2 %, and nonrenewable energy consumption by 1.83 %. However, aquatic eutrophication and ozone layer depletion were found to be higher compared to cementitious mortar.Article Citation - WoS: 15Citation - Scopus: 18The Role of Positive Relationship Events in Romantic Attachment Avoidance(Amer Psychological Assoc, 2023) Bayraktaroglu, Deniz; Gunaydin, Gul; Selcuk, Emre; Besken, Miri; Karakitapoglu-Aygun, ZahideMotivated by the Attachment Security Enhancement Model (Arriaga et al., 2018), the present research investigated the associations between positive relationship experiences and romantic attachment avoidance in three dyadic studies that combined multiple methods, including daily diaries, laboratory observations, and longitudinal follow-ups. Frequency of daily positive relationship events (but not external positive events) during a 21-day diary period predicted declines in romantic attachment avoidance (but not anxiety) from pre- to post-diary in fledgling couples (Study 1) and newlyweds (Study 2). Video-recorded discussions of fledgling couples' shared positive experiences revealed that behaviors validating the relationship (but not simply showing conversational interest) predicted lagged declines in romantic attachment avoidance (but not anxiety) over 1 month (Study 3). The associations were mediated by positive affect during the diary period in Studies 1 and 2, and by changes in positive affect from pre- to post-discussion in Study 3. Positive relationship experiences did not significantly interact with time in predicting romantic avoidance over a 1-year follow-up with quarterly assessments of attachment orientations in Study 1, over an 8-month follow-up with monthly assessments in Study 2, or over a 2-month follow-up with monthly assessments in Study 3. Altogether, these studies provide one of the most comprehensive tests of how positive relationship experiences in nondistressing contexts are linked to romantic attachment.Article Citation - WoS: 11Citation - Scopus: 17Convolutional Neural Network-Based Deep Learning for Landslide Susceptibility Mapping in the Bakhtegan Watershed(Nature Portfolio, 2025) Feng, Li; Zhang, Maosheng; Mao, Yimin; Liu, Hao; Yang, Chuanbo; Dong, Ying; Nanehkaran, Yaser A.Landslides pose a significant threat to infrastructure, ecosystems, and human safety, necessitating accurate and efficient susceptibility assessment methods. Traditional models often struggle to capture the complex spatial dependencies and interactions between geological and environmental factors. To address this gap, this study employs a deep learning approach, utilizing a convolutional neural network (CNN) for high-precision landslide susceptibility mapping in the Bakhtegan watershed, southwestern Iran. A comprehensive landslide inventory was compiled using 235 documented landslide locations, validated through remote sensing and field surveys. An equal number of non-landslide locations were systematically selected to ensure balanced model training. Fifteen key conditioning factors-including topographical, geological, hydrological, and climatological variables-were incorporated into the model. While traditional statistical methods often fail to extract spatial hierarchies, the CNN model effectively processes multi-dimensional geospatial data, learning intricate patterns influencing slope instability. The CNN model outperformed other classification approaches, achieving an accuracy of 95.76% and a precision of 95.11%. Additionally, error metrics confirmed its reliability, with a mean absolute error (MAE) of 0.11864, mean squared error (MSE) of 0.18796, and root mean squared error (RMSE) of 0.18632. The results indicate that the northern and northeastern regions of the Bakhtegan watershed are highly susceptible to landslides, highlighting areas where proactive mitigation strategies are crucial. This study demonstrates that deep learning, particularly CNNs, offers a powerful and scalable solution for landslide susceptibility assessment. The findings provide valuable insights for urban planners, engineers, and policymakers to implement effective risk reduction strategies and enhance resilience in landslide-prone regions.Article Citation - WoS: 10Citation - Scopus: 11Low-Diameter Topic-Based Pub/Sub Overlay Network Construction With Minimum Maximum Node Degree(Peerj inc, 2021) Yumusak, Semih; Layazali, Sina; Oztoprak, Kasim; Hassanpour, RezaIn the construction of effective and scalable overlay networks, publish/subscribe (pub/sub) network designers prefer to keep the diameter and maximum node degree of the network low. However, existing algorithms are not capable of simultaneously decreasing the maximum node degree and the network diameter. To address this issue in an overlay network with various topics, we present herein a heuristic algorithm, called the constant-diameter minimum-maximum degree (CD-MAX), which decreases the maximum node degree and maintains the diameter of the overlay network at two as the highest. The proposed algorithm based on the greedy merge algorithm selects the node with the minimum number of neighbors. The output of the CD-MAX algorithm is enhanced by applying a refinement stage through the CD-MAX-Ref algorithm, which further improves the maximum node degrees. The numerical results of the algorithm simulation indicate that the CD-MAX and CD-MAX-Ref algorithms improve the maximum node-degree by up to 64% and run up to four times faster than similar algorithms.Article Citation - WoS: 2Citation - Scopus: 6Fast Binary Logistic Regression(Peerj inc, 2025) Saran, Nurdan Ayse; Nar, FatihThis study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus approximation, which enables reformulation of logistic regression parameter estimation into matrix-vector form. We also adopt the L-f-norm penalty, which allows using fractional norms, including the L-2-norm, L-1-norm, and L-0-norm, to regularize the model parameters. We put L-f-norm formulation in matrix-vector form, providing flexibility to include or exclude penalization of the intercept term when applying regularization. Furthermore, to address the common problem of collinear features, we apply singular value decomposition (SVD), resulting in a low-rank representation commonly used to reduce computational complexity while preserving essential features and mitigating noise. Moreover, our approach incorporates a randomized SVD alongside a newly developed SVD with row reduction (SVD-RR) method, which aims to manage datasets with many rows and features efficiently. This computational efficiency is crucial in developing a generalized model that requires repeated training over various parameters to balance bias and variance. We also demonstrate the effectiveness of our fast binary logistic regression (FBLR) method on various datasets from the OpenML repository in addition to synthetic datasets.Article Stability Analysis and Solutions of Fractional Boundary Value Problem on the Cyclopentasilane Graph(Cell Press, 2024) Wang, Guotao; Yuan, Hualei; Baleanu, DumitruThe study is being applied to a model involving silane and on cyclopentasilane graph. We consider a graph with labeled vertices by 0 or 1 inspired by the molecular structure of cyclopentasilane. In this paper, we first study the existence of solutions to fractional conformable boundary value problem on the cyclopentasilane graph by applying Scheafer and Krasnoselskii fixed point theorems. Furthermore, we investigate different kinds of Ulam stability such as Ulam-Hyers stable, generalized Ulam-Hyers stable, Ulam-Hyers-Rassias stable and generalized Ulam-HyersRassias stable for the given problem. Finally, we give an example to support our important results.Article Citation - WoS: 1Citation - Scopus: 2Initial Validation of the Turkish Version of the Defense Mechanisms Rating Scales-Self(Frontiers Media Sa, 2024) Yilmaz, Meltem; Tas, Berke; Celik, Deniz; Perry, J. Christopher; Tanzilli, Annalisa; Di Giuseppe, Mariagrazia; Lingiardi, VittorioThe Defense Mechanisms Rating Scales-Self Report-30 (DMRS-SR-30) was recently developed to add a self-report alternative to the assessment of defenses, reflecting their generally accepted hierarchical organization. In this study, we aimed to examine psychometric properties and factor structure of the Turkish language version of the DMRS-SR-30. The sample consisted of 1.002 participants who filled out a survey comprising the DMRS-SR-30, the Brief Symptom Inventory, and the Inventory of Personality Organization through Qualtrics. Confirmatory Factor Analysis indicated a three-factor structure (CFI = 0.89, RMSEA = 0.05) that confirms the DMRS theoretical frame with a relatively acceptable fit. Defensive categories and total scale scores showed good to excellent reliability (alpha values ranging from 0.64 to 0.89). Correlations between defenses, symptoms, and personality functioning demonstrated good convergent and discriminant validity. The individuals with clinically significant BSI scores (T-score >= 63) differed on the DMRS-SR-30 scores from the individuals in the non-clinical range. The Turkish version of the DMRS-SR-30 is a reliable and valid instrument to self-assess the hierarchy of defense mechanisms and overall defensive functioning. Moreover, the current study supports the validity of the tripartite model of defenses in a language and culture different from the origins of the DMRS and DMRS-SR-30.Article Citation - WoS: 41Citation - Scopus: 44Psychological Well-Being in Europe After the Outbreak of War in Ukraine(Nature Portfolio, 2024) Scharbert, Julian; Humberg, Sarah; Kroencke, Lara; Reiter, Thomas; Sakel, Sophia; ter Horst, Julian; Back, Mitja D.The Russian invasion of Ukraine on February 24, 2022, has had devastating effects on the Ukrainian population and the global economy, environment, and political order. However, little is known about the psychological states surrounding the outbreak of war, particularly the mental well-being of individuals outside Ukraine. Here, we present a longitudinal experience-sampling study of a convenience sample from 17 European countries (total participants = 1,341, total assessments = 44,894, countries with >100 participants = 5) that allows us to track well-being levels across countries during the weeks surrounding the outbreak of war. Our data show a significant decline in well-being on the day of the Russian invasion. Recovery over the following weeks was associated with an individual's personality but was not statistically significantly associated with their age, gender, subjective social status, and political orientation. In general, well-being was lower on days when the war was more salient on social media. Our results demonstrate the need to consider the psychological implications of the Russo-Ukrainian war next to its humanitarian, economic, and ecological consequences.