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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 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 Automating cyber risk assessment with public LLMs: an expert-validated framework and comparative analysis(Institute of Electrical and Electronics Engineers Inc., 2026-03-26) Ünal, Nezih Mahmut; Çeliktaş, BarışTraditional cyber risk assessment methodologies face a critical dilemma: they are either quantitative yet static and context-agnostic (e.g., CVSS), or context-aware yet highly labor-intensive and subjective (e.g., NIST SP 800-30). Consequently, organizations struggle to scale risk assessment to match the pace of evolving threats. This paper presents an automated, context-aware risk assessment framework that leverages the reasoning capabilities of publicly available Large Language Models (LLMs) to operationalize expert knowledge. Rather than positioning the LLM as the final decision-maker, the framework decouples semantic interpretation from risk scoring authority through a transparent, deterministic Dynamic Metric Engine. Unlike complex closed box machine learning models, our approach anchors the AI's reasoning to this expert-validated metric schema, with weights derived using the Rank Order Centroid (ROC) method from a survey of 101 cybersecurity professionals. We evaluated the framework through a comparative study involving 15 diverse real-world vulnerability scenarios (C1-C15) and three supplementary sensitivity stress tests (C16-C18). The validation scenarios were independently assessed by a cohort of ten senior human experts and two state-of-the-art LLM agents (GPT-4o and Gemini 2.0 Flash). The results show that the LLM-driven agents achieve scoring consistency closely aligned with the human median (Pearson r ranging from 0.9390 to 0.9717, Spearman ρ from 0.8472 to 0.9276) against a highly reliable expert baseline (Cronbach's α =0.996), while reducing the assessment cycle time by more than 100× (averaging under 4 seconds per case vs. a human average of 6 minutes). Furthermore, a dedicated context sensitivity analysis (C13-C15) indicates that the framework adapts risk scores based on organizational context (e.g., SME vs. Critical Infrastructure) for identical technical vulnerabilities. Importantly, the system is designed not merely to replicate expert intuition, but to enforce bounded, policy-consistent risk evaluation under predefined governance constraints. Overall, these findings suggest that commercially available LLMs, when constrained by expert-validated metric schemas, can support reproducible, transparent, and real-time risk assessments.
