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Hata Ruminasyonu Ölçeği: Türkçe Versiyonunun Psikometrik Özellikleri
No full textHata Ruminasyonu Ölçeği (HRÖ), algılanan hatalara karşı yoğun, tekrarlayıcı olumsuz düşünce şeklinde verilen bir tepki olan hata ruminasyonu (HR) eğilimini değerlendirmek için geliştirilmiştir. Bu çalışma, HRÖ'nün Türkçe versiyonunun psikometrik niteliklerini 2 çalışmada araştırmayı amaç- lamıştır. Yaşları 18 ile 56 arasında değişen 214 katılımcıdan (118 kadın) (Ort. = 33.45, SS = 11.82) HR, tekrarlayan olumsuz düşünce, mükemmeliyetçilik, erteleme, depresyon ve anksiyete ölçümleri yoluyla veri toplanmıştır. Bulgular orijinal faktör yapısını doğrulamış ve yeterli güvenilirlik, ya- kınsak ve artımsal geçerliliğe işaret etmiştir. İkinci çalışmada, HRÖ'nün ölçüt geçerliliği deneysel bir tasarım kullanılarak test edilmiştir. Katılımcılardan (yaşları 18 ile 28 arasında değişen 127 kişi) bir dizi hata veya düzenli bir olay yaptıklarını hayal etmeleri istenmiştir. Sonuçlar HRÖ'nün ölçüt geçerliliğini desteklemiştir. Sonuç olarak, HRÖ Türk bireylerde HR'yi değerlendirmek için kulla- nılabilir
Depth3DSketch: Freehand Sketching Out of Arm's Reach in Virtual Reality
No full textAmini, Mohammaeza/0009-0009-5711-2162Due to the increasing availability and popularity of virtual reality (VR) systems, 3D sketching applications have also boomed. Most of these applications focus on peripersonal sketching, e.g., within arm's reach. Yet, sketching in larger scenes requires users to walk around the virtual environment while sketching or to change the sketch scale repeatedly. This paper presents Depth3DSketch, a 3D sketching technique that allows users to sketch objects up to 2.5 m away with a freehand sketching technique. Users can select the sketching depth with three interaction methods: using the joystick on a single controller, the intersection from two controllers, or the intersection from the controller ray and the user's gaze. We compared these interaction methods in a user study. Results show that users preferred the joystick to select visual depth, but there was no difference in user accuracy or sketching time between the three methods
A Nano-Design of a Quantum-Based Arithmetic and Logic Unit for Enhancing the Efficiency of the Future Iot Applications
No full textHeidari, Arash/0000-0003-4279-8551; Misra (Senior Ieee Member), Neeraj Kumar/0000-0002-7907-0276The Internet of Things (IoT) is an infrastructure of interconnected devices that gather, monitor, analyze, and distribute data. IoT is an inevitable technology for smart city infrastructure to ensure seamless communication across multiple nodes. IoT, with its ubiquitous application in every sector, ranging from health-care to transportation, energy, education, and agriculture, comes with serious challenges as well. Among the most significant ones is security since the majority of IoT devices do not encrypt normal data transmissions, making it easier for the network to breach and leak data. Traditional technologies such as CMOS and VLSI have the added disadvantage of consuming high energy, further creating avenues for security threats for IoT systems. To counter such problems, we require a new solution to replace traditional technologies with a secure IoT. In contrast to traditional solutions, quantum-based approaches offer promising solutions by significantly reducing the energy footprint of IoT systems. Quantum-dot Cellular Automata (QCA) is one such approach and is an advanced nano-technology that exploits quantum principles to achieve complex computations with the advantages of high speed, less occupied area, and low power consumption. By reducing the energy requirements to a minimum, QCA technology makes IoT devices secure. This paper presents a QCA-based Arithmetic Logic Unit (ALU) as a solution to IoT security problems. The proposed ALU includes more than 12 logical and arithmetic operations and is designed using majority gates, XOR gates, multiplexers, and full adders. The proposed architecture, simulated in QCADesigner 2.0.3, achieves an improvement of 60.45% and 66.66% in cell count and total occupied area, respectively, compared to the best of the existing designs, proving to be effective and efficient.Science Citation Index Expande
Alienation and Hedonic Values in Mass Tourism
No full textThis study aims to reveal (1) the alienation of tourists, (2) whether locals experience alienation or not, and (3) the kind of relationship between alienation, hedonic consumption and hedonic wellbeing. The study employed a qualitative research method on two samples of locals and domestic tourists. Study findings demonstrate that locals' deprivation of hedonic consumption and alienation may negatively affect their hedonic wellbeing. Tourists may experience alienation because they hardly meet their hedonic consumption needs. The study contributes to the gap in the current tourism literature dealing with alienation, which tourists and residents can experience. The study also develops an understanding of the approaches to the subjects of tourist motivations, attitudes of locals, and impacts of tourism.Social Science Citation Inde
A Novel Multiscale Graph Signal Processing and Network Dynamics Approach to Vibration Analysis for Stone Size Discrimination via Nonlinear Manifold Embeddings and a Convolutional Self-Attention Model
No full textBaykas, Tuncer/0000-0001-9535-2102; Mirza, Fuat Kaan/0000-0002-7664-0632Understanding nonlinear dynamics is critical for analyzing the hidden complexities of vibrational behavior in real-world systems. This study introduces a graph-theoretic approach to analyze the complex nonlinear temporal patterns in vibrational signals, utilizing the Tri-Axial Vibro-Dynamic Stone Classification dataset. This dataset captures high-resolution acceleration signals from controlled stone-crushing experiments, providing a unique opportunity to investigate temporal dynamics associated with distinct stone sizes. A 12-level Maximal Overlap Discrete Wavelet Transform is employed to perform multiscale signal decomposition, enabling the construction of transition graphs that encode transient and stable structural characteristics. Conceptually, transition graphs are analyzed as dynamic networks to uncover the interactions and temporal patterns embedded within vibrational signals. These networks are studied using a comprehensive suite of complexity metrics derived from information theory, graph theory, network science, and dynamical systems analysis. Metrics such as Shannon and Von Neumann's entropy evaluate signal dynamics' stochasticity and information retention. At the same time, the spectral radius measures the network's stability and structural robustness. Lyapunov exponents and fractal dimensions, informed by chaos theory and fractal geometry, further capture the degree of nonlinearity and temporal complexity. Complementing these dynamic measures, static network metrics-including the clustering coefficient, modularity, and the static Kuramoto index-offer critical discernment into the network's community structures, synchronization phenomena, and connectivity efficiency. Manifold learning techniques address the high-dimensional feature space derived from complexity metrics, with UMAP outperforming ISOMAP, Spectral Embedding, and PCA in preserving critical data structures. The reduced features are input into a convolutional self-attention model, combining localized feature extraction with long-term sequence modeling, achieving 100% classification accuracy across stone-size categories. This study presents a comprehensive framework for vibrational signal analysis, integrating multiscale graph-based representations, nonlinear dynamics quantification, and UMAP-based dimensionality reduction with a convolutional self-attention classifier. The proposed approach supports accurate classification and contributes to the development of data-driven tools for automated diagnostics and predictive maintenance in industrial and engineering contexts.Science Citation Index Expande
Energy-Efficient Secure Design for iOS and an Aided CF-MIMO Network
No full textIntelligent Omni-Surface (IOS) has attracted considerable attention for its advantages of high energy efficiency, which are similar to those of Reconfigurable Intelligent Surface (RIS), while also being able to overcome the limited scope of RIS services. In this paper, we provide a security energy efficiency (SEE) maximization design for IOS and artificial noise (AN) assisted cell-free massive MIMO (CF-mMIMO) networks, via jointly optimizing the transmission beamforming and AN covariance matrix of the AP, the reflection and transmission phase-shift matrices of the IOS, and the reflection-transmission power ratio of the IOS. To handle the formulated problem with non-convexity and high complexity, we first decouple it into two sub-problems. Then, we design low-complexity algorithms for each sub-problem i.e., an AP transmission beamforming and AN noise covariance matrix joint optimization algorithm based on the SSNCG-ALM, and an IOS reflection and transmission phase-shift matrix joint optimization algorithm based on the RPM-TR. Finally, a SEE maximization iterative algorithm based on block coordinate descent and successive convex approximation is established. The simulation results demonstrate that the proposed design significantly enhances the SEE of CF-mMIMO networks.National Key Research and Development Program of China [2023YFE0206600]; NSFC [62471341]; Fundamental Research Funds for the Central Universities [2042025kf0039]; Science and Technology Major Project of Tibetan Autonomous Region of China [XZ202201ZD0006G04]This work was supported in part by the National Key Research and Development Program of China under Grant 2023YFE0206600, in part by NSFC under Grant 62471341, in part by the Fundamental Research Funds for the Central Universities under Grant 2042025kf0039, and in part by the Science and Technology Major Project of Tibetan Autonomous Region of China under Grant XZ202201ZD0006G04
Sihirli Reçete Mi, Kara Kutu Mu: Siber Krizlere Karşı Esnek-Dayanıklılık Anlatısının İncelenmesi
No full textGelişen dijital teknolojiler ve buna eşlik eden sosyo-ekonomik dönüşüm siber güvenlik krizlerini çoklu krizler döneminin bir parçası haline getirmiştir. Bununla beraber, siber tehdit aktörlerinin ve saldırı yöntemlerinin dinamik doğası, riskleri modellemenin ve saldırıların etkisini tahmin etmenin zorlukları, siber güvenlikte belirsizliği ve güvenliksizliği adeta bir norm haline getirmiştir. Bu çerçevede, siber esnek-dayanıklılık son on yılda siber güvenlik alanında en geçerli paradigmalardan birine dönüşmüş ve krizlerde hayatta kalabilme ve adaptasyon yeteneklerini vurgulayan bir çözüm olarak öne çıkmıştır. Öte yandan, siber esnek-dayanıklılık tek bir çözüm, teknoloji ya da uygulama değildir; esasen sosyo-teknik çözümlerin bir denge içinde uygulanmasını gerektiren çok katmanlı bir yaklaşımdır. Bu çerçevede, araştırmamızda siber esnek-dayanıklılık kavramını siber güvenlik kavramından ayırarak detaylı olarak anlamayı ve bu kavramı özellikle siber güvenlik krizlerinin farklı evreleri için insan-süreç-teknoloji yaklaşımı bağlamında ele almayı hedefliyoruz. İlaveten çoklu krizler çağında siber esnek-dayanıklılığın yaklaşımının derinleştirilebilmesi için nasıl daha erişilebilir, esnek, çevik ve kapsayıcı bir siber güvenlik yaklaşımı geliştirebiliriz sorusunu masaya yatırıyoruz
Impacts of the Changes in Agriproduction on Rural Heritage in the Case of Müşküle, Iznik, Turkey
No full textPurposeAgriculture is both a constituent and an integral part of rural culture. Therefore, agricultural planning is essential to the conservation of rural heritage. However, this relationship has received limited scholarly attention. Focusing on the rural settlement of M & uuml;sk & uuml;le in Turkey, this research paper reveals the vital role of agricultural planning in sustaining rural architectural heritage.Design/methodology/approachWe examine the settlement's history of agricultural production in relation to national agricultural policies and practices from the early 20th century to the present, analyzing how these shifts have affected the built heritage. The research is a combination of literature review, fieldwork and face-to-face interviews. Aerial images, on-site architectural surveys and interviews were used to identify the features of the built environment. These were followed by in-depth thematic analysis.FindingsWe find that the lack of agricultural planning has led to economic decline among rural households, resulting in the neglect of architectural heritage, abandonment of traditional dwellings and increased rural outmigration. As specialized agricultural products shape the character of rural architecture, changes in production can lead to the removal of heritage-valued building elements, degradation of traditional architectural features and loss of traditional knowledge.Originality/valueThis paper demonstrates the strong link between rural production (i.e. agriproduction) and architectural heritage. It shows how agriproduction shapes rural fabric, plan typologies and building elements and underscores the decisive role of agricultural planning in rural heritage conservation.TUBITAK [119K333, ARDEB 1001, 2020-2023]This paper is based on research conducted as part of Damla Pilevne's master's thesis, Conservation of Rural Heritage: The Case of İznik, Muşçukule Rural Settlement. The thesis was supported by the TÜBİTAK-funded research project "In the Context of Urban-Rural Continuity, Web-GIS Based İntegrated Site Management Model for Historic Cities: The Case of İznik" (ARDEB 1001 Grant No. 119K333, 2020-2023), led by Professor Dr. Yonca Erkan. The transcription, translation and visualization of the Temettuat Records used in this study were carried out by Dr. Nazlı Songülen within the scope of the same project
Detection of Early School Drop Out in Vocational and Technical High Schools in Turkey
No full textThis study investigates the factors contributing to early school dropout in vocational and technical high schools in Turkey, utilizing machine learning techniques to analyze a dataset of personal, socio-economic, familial, and academic variables. The data was collected via a detailed survey administered to students at one of the largest Vocational and Technical High School in Istanbul, capturing 35 features (factors) relevant to dropout rates. Various classifiers, including Decision Trees and Random Forest, were employed to identify at-risk students with high accuracy. The Decision Tree model, enhanced by the Synthetic Minority Over-sampling Technique (SMOTE), demonstrated the best results for identifying potential dropouts, indicating its effectiveness in educational settings where early intervention is critical. By feature importance analysis this research reveals that parental education levels, family structure, and financial hardships are significant predictors of dropout likelihood. Despite the study's limitations, such as a small dataset and some features with zero-filled columns, the results underscore the importance of data-driven approaches in developing targeted interventions to reduce dropout rates. This research not only enhances the understanding of dropout phenomena in Turkish vocational education but also provides practical insights for policymakers and educators to improve student retention through early and informed interventions. The findings highlight the potential of machine learning to enhance educational support systems, ensuring that every student can succeed
Kuvvetli Yer Hareketi Deprem Kayıtlarında P-Dalgası Tespiti İçin Varyasyonel Otomatik Kodlayıcılar
No full textEarthquake early warning systems rely on accurate detection of Primary waves before the destructive Secondary waves arrive. However, identifying P-wave onsets in strongmotion accelerograms is challenging due to high noise, limited labeled data, and complex waveforms. This paper proposes a Variational Autoencoder framework for self-supervised P-wave detection in strong-motion data. A Convolutional VAE is trained to reconstruct P-wave segments while rejecting noise and non-P-wave inputs. We employ a sliding window method, combining reconstruction loss and normalized cross-correlation, to locate P-wave arrivals. Experimental results on 1, 2, and 3 second segments show robust performance with area-under-the-curve up to 0.97, demonstrating improved accuracy for longer segments and reduced computational cost for shorter segments