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Yayın Quantum Zeno repeaters(Nature Research, 2022-09-12) Bayrakçı, Veysel; Özaydın, FatihQuantum repeaters pave the way for long-distance quantum communications and quantum Internet, and the idea of quantum repeaters is based on entanglement swapping which requires the implementation of controlled quantum gates. Frequently measuring a quantum system affects its dynamics which is known as the quantum Zeno effect (QZE). Beyond slowing down its evolution, QZE can be used to control the dynamics of a quantum system by introducing a carefully designed set of operations between measurements. Here, we propose an entanglement swapping protocol based on QZE, which achieves almost unit fidelity. Implementation of our protocol requires only simple frequent threshold measurements and single particle rotations. We extend the proposed entanglement swapping protocol to a series of repeater stations for constructing quantum Zeno repeaters which also achieve almost unit fidelity regardless of the number of repeaters. Requiring no controlled gates, our proposal reduces the quantum circuit complexity of quantum repeaters. Our work has potential to contribute to long distance quantum communications and quantum computing via quantum Zeno effect.Yayın Analysis of the benefits, challenges and risks for the integrated use of BIM, RFID and WSN: a mixed method research(Emerald Group Publishing Ltd, 2023-07-11) Seyis, Senem; Sönmez, Alperen MertPurpose The purpose of this study is to identify, classify and prioritize the benefits, challenges and risks for the integrated use of building information modeling (BIM), radio frequency identification (RFID) and wireless sensor network (WSN) in the architecture, engineering, construction and operation (AECO) industry. Design/methodology/approach This study relies on the mixed method approach which consists of systematic literature review, semistructured interviews and Delphi technique. A systematic literature review was performed and face-to-face semistructured interviews with seven subject matter experts (SMEs) were conducted for identification and classification purposes. Delphi method was applied in two structured rounds with eleven SMEs for prioritization purpose. These three research techniques were chosen to reach the most accurate data by combining different perspectives on the subject matter. Data gathered by these three methods was triangulated to increase the validity and reliability of this research. Findings Thirteen benefits, ten challenges and four risks for the integrated use of BIM, RFID and WSN were identified. The results could aid the practitioners and researchers comprehend the pros and cons of this integration by representing SMEs' valuable insights and perspectives about the current and future status, trends, limitations and requirements of the AECO industry. The identified risks and challenges show the requirements for future studies while the benefits demonstrate the capabilities and the potential contributions of this hybrid integration to the AECO industry. Originality/value The integration of BIM, RFID and WSN is still not commonly implemented in the AECO industry. Some studies focused on this topic; however, none of them reveals the benefits, risks and challenges for integrating BIM, RFID and WSN in a holistic manner. This research makes a significant contribution to the AECO literature and industry by uncovering the benefits, challenges and risks for the integrated use of BIM, RFID and WSN that could increase industry applications.Yayın Examining psychological resilience and posttraumatic growth following terrorist attacks in Turkey(American Psychological Association, 2021-06) İkizer, Gözde; Özel, Ebru PelinActs of terrorism, being highly prevalent across the world, disrupt community and social functioning and can lead to negative psychological reactions in individuals. However, positive outcomes can also be evoked after adverse experiences. The current study aimed to explore two salutogenic or positive outcomes—resilience and posttraumatic growth (PTG)—following exposure to terrorist attacks. The sample included 331 university students who were exposed to a terrorist attack in Turkey during the last 18 months prior to data collection. Participants responded to the Connor-Davidson Resilience Scale, the Posttraumatic Growth Inventory, and a participant information form. The relationship between resilience and PTG was examined through correlation analysis and regression analyses with linear and quadratic components. Resilience and PTG were positively correlated. Tendency toward spirituality was the only resilience domain that was significantly correlated with all domains of growth. Total score of resilience was significantly associated with scores on all subscales of the Posttraumatic Growth Inventory except appreciation of life. Results indicated that only linear relationships existed between domains of resilience and PTG in the study sample. The positive and linear association between resilience and PTG suggests that resilience may be an important tool for facilitating growth. After terrorist attacks, mental health care planning should adopt a patient-centered approach that acknowledges the possibility of positive outcomes following traumatic events and focuses on the impact as well as recovery phases in traumatized individuals.Yayın Exploring the impact of Flash technique on test anxiety among adolescents(SAGE Publications Ltd, 2025-07) Çitil Akyol, Canan; İnci İzmir, Sevim BerrinThis study aims to investigate the specific effects of Flash Technique (FT) on adolescents with test anxiety. This follow-up study consists of 38 adolescents, 14–17 years of age (M = 15.39, SD = 1.13). Pre-post assessments were conducted using the Test Anxiety Inventory (TAI), Scale of Attitudes Negatively Affecting the Performance I/Test (POET), and Beck Anxiety Inventory (BAI) at baseline, at the end of the 4thand 12thweeks of therapy. The FT was applied for 12 weeks, with one weekly session as an intervention. As a result of the therapy process, the baseline means of total BAI scores decreased from 25.26 to 2.18; the baseline means of TAI decreased from 149.79 to 39.13, and the baseline mean of POET decreased from 298.47 to 73.84 at the end of the 12th week of therapy. Also, the baseline means of SUD scores decreased from 9.42 to zero at the end of the 12th week of treatment. All the adolescents showed complete improvement after the 12th week of the FT. The study findings showed that the test anxiety symptoms significantly decreased with the treatment of the FT. FT can be an effective intervention for test anxiety in adolescents.Yayın Adaptive locally connected recurrent unit (ALCRU)(Springer Science and Business Media Deutschland GmbH, 2025-07-03) Özçelik, Şuayb Talha; Tek, Faik BorayResearch has shown that adaptive locally connected neurons outperform their fully connected (dense) counterparts, motivating this study on the development of the Adaptive Locally Connected Recurrent Unit (ALCRU). ALCRU modifies the Simple Recurrent Neuron Model (SimpleRNN) by incorporating spatial coordinate spaces for input and hidden state vectors, facilitating the learning of parametric local receptive fields. These modifications add four trainable parameters per neuron, resulting in a minor increase in computational complexity. ALCRU is implemented using standard frameworks and trained with back-propagation-based optimizers. We evaluate the performance of ALCRU using diverse benchmark datasets, including IMDb for sentiment analysis, AdditionRNN for sequence modelling, and the Weather dataset for time-series forecasting. Results show that ALCRU achieves accuracy and loss metrics comparable to GRU and LSTM while consistently outperforming SimpleRNN. In particular, experiments with longer sequence lengths on AdditionRNN and increased input dimensions on IMDb highlight ALCRU’s superior scalability and efficiency in processing complex data sequences. In terms of computational efficiency, ALCRU demonstrates a considerable speed advantage over gated models like LSTM and GRU, though it is slower than SimpleRNN. These findings suggest that adaptive local connectivity enhances both the accuracy and efficiency of recurrent neural networks, offering a promising alternative to standard architectures.Yayın ANN activation function estimators for homomorphic encrypted inference(Institute of Electrical and Electronics Engineers Inc., 2025-06-13) Harb, Mhd Raja Abou; Çeliktaş, BarışHomomorphic Encryption (HE) enables secure computations on encrypted data, facilitating machine learning inference in sensitive environments such as healthcare and finance. However, efficiently handling non-linear activation functions, specifically Sigmoid and Tanh, remains a significant computational challenge for encrypted inference using Artificial Neural Networks (ANNs). This study introduces a lightweight, ANN-based estimator designed to accurately approximate activation functions under homomorphic encryption. Unlike traditional polynomial and piecewise linear approximations, the proposed ANN estimators achieve superior accuracy with lower computational overhead associated with bootstrapping or high-degree polynomial techniques. These estimators are trained on plaintext data and seamlessly integrated into encrypted inference pipelines, significantly outperforming conventional methods. Experimental evaluations demonstrate notable improvements, with ANN estimators enhancing accuracy by approximately 2% for Sigmoid and up to 73% for Tanh functions, improving F1-scores by approximately 2% for Sigmoid and up to 88% for Tanh, and markedly reducing Mean Square Error (MSE) by up to 96% compared to polynomial approximations. The ANN estimator achieves an accuracy of 97.70% and an AUC of 0.9997 when integrated into a CNN architecture on the MNIST dataset, and an accuracy of 85.25% with an AUC of 0.9459 on the UCI Heart Disease dataset during ciphertext inference. These results underscore the estimator’s practical effectiveness and computational feasibility, making it suitable for secure and efficient ANN inference in encrypted environments.Yayın Relationships among organizational-level maturities in artificial intelligence, cybersecurity, and digital transformation: a survey-based analysis(Institute of Electrical and Electronics Engineers Inc., 2025-05-19) Kubilay, Burak; Çeliktaş, BarışThe rapid development of digital technology across industries has highlighted the growing need for enhanced competencies in Artificial Intelligence (AI), Cyber security (CS), and Digital Transformation (DT). While there is extensive research on each of these domains in isolation, few studies have investigated their relationship and joint impact on organizational maturity. This study aims to address this gap by analyzing the relationships among the maturity levels of AI, CS, and DT at the organizational level using Structural Equation Modeling (SEM) and descriptive statistical methods. A mixed-methods design combines quantitative survey data with synthetic modeling techniques to assess organizational preparedness. The findings demonstrate significant bidirectional correlations among AI, CS, and DT, with technology and finance being more advanced than government and education. The research highlights the necessity of an integrated AI-CS strategy and provides actionable recommendations to increase investments in these domains. In contrast to the preceding fragmented evaluations, the current research establishes a comprehensive, empirically grounded framework that acts as a strategic reference point for digital resilience. Follow-up studies will involve collecting real-world industry data in support of empirical validation and predictive ability in measuring AI and CS maturity. This research adds to the existing literature by filling the gaps among fragmented digital maturity models and providing a consistent empirical base for organizations to thrive in an evolving technological environment.Yayın Grammar or crammer? the role of morphology in distinguishing orthographically similar but semantically unrelated words(Institute of Electrical and Electronics Engineers Inc., 2025) Ercan, Gökhan; Yıldız, Olcay TanerWe show that n-gram-based distributional models fail to distinguish unrelated words due to the noise in semantic spaces. This issue remains hidden in conventional benchmarks but becomes more pronounced when orthographic similarity is high. To highlight this problem, we introduce OSimUnr, a dataset of nearly one million English and Turkish word-pairs that are orthographically similar but semantically unrelated (e.g., grammar - crammer). These pairs are generated through a graph-based WordNet approach and morphological resources. We define two evaluation tasks - unrelatedness identification and relatedness classification - to test semantic models. Our experiments reveal that FastText, with default n-gram segmentation, performs poorly (below 5% accuracy) in identifying unrelated words. However, morphological segmentation overcomes this issue, boosting accuracy to 68% (English) and 71% (Turkish) without compromising performance on standard benchmarks (RareWords, MTurk771, MEN, AnlamVer). Furthermore, our results suggest that even state-of-the-art LLMs, including Llama 3.3 and GPT-4o-mini, may exhibit noise in their semantic spaces, particularly in highly synthetic languages such as Turkish. To ensure dataset quality, we leverage WordNet, MorphoLex, and NLTK, covering fully derivational morphology supporting atomic roots (e.g., '-co_here+ance+y' for 'coherency'), with 405 affixes in Turkish and 467 in English.Yayın Intelligent health monitoring in 6G networks: machine learning-enhanced VLC-based medical body sensor networks(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-23) Antaki, Bilal; Dalloul, Ahmed Hany; Miramirkhani, FarshadRecent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions.Yayın TURSpider: a Turkish Text-to-SQL dataset and LLM-based study(Institute of Electrical and Electronics Engineers Inc., 2024-11-25) Kanburoğlu, Ali Buğra; Tek, Faik BorayThis paper introduces TURSpider, a novel Turkish Text-to-SQL dataset developed through human translation of the widely used Spider dataset, aimed at addressing the current lack of complex, cross-domain SQL datasets for the Turkish language. TURSpider incorporates a wide range of query difficulties, including nested queries, to create a comprehensive benchmark for Turkish Text-to-SQL tasks. The dataset enables cross-language comparison and significantly enhances the training and evaluation of large language models (LLMs) in generating SQL queries from Turkish natural language inputs. We fine-tuned several Turkish-supported LLMs on TURSpider and evaluated their performance in comparison to state-of-the-art models like GPT-3.5 Turbo and GPT-4. Our results show that fine-tuned Turkish LLMs demonstrate competitive performance, with one model even surpassing GPT-based models on execution accuracy. We also apply the Chain-of-Feedback (CoF) methodology to further improve model performance, demonstrating its effectiveness across multiple LLMs. This work provides a valuable resource for Turkish NLP and addresses specific challenges in developing accurate Text-to-SQL models for low-resource languages.
