Volume 8.
Issue 07.

The integration of low-voltage (LV) infrastructure — encompassing structured cabling systems (SCS), lighting control networks, building management system (BMS) cable routes, and low-current security and communications pathways — into modern commercial and institutional buildings gives rise to a series of structural, mechanical, and spatial interactions with load-bearing and enclosing elements that the civil and building services engineering literature has not yet systematically examined. This paper presents a systematic review of peer-reviewed publications, technical standards, and engineering guidance documents published between 2003 and 2026, addressing four objectives: (1) the mechanical behaviour and load characteristics of LV cable support systems; (2) the effects of LV system integration on reinforced concrete slabs, structural steel frames, suspended ceiling substructures, and partition wall assemblies; (3) spatial coordination challenges between LV infrastructure, HVAC systems, and the structural frame; and (4) the role of Building Information Modelling (BIM) in detecting and resolving LV–structural conflicts. Following a structured search across Scopus, Web of Science, IEEE Xplore, and ScienceDirect, 45 primary sources were identified for synthesis after systematic screening. The review finds that the seismic structural behaviour of cable tray systems is relatively well characterised in the existing literature, whereas the static distributed load impact of combined LV infrastructure on suspended ceiling substructures and the management of LV riser penetrations through load-bearing elements remain insufficiently addressed. Existing standards — IEC 61537:2023, NEMA VE 1, and EN 50174 — govern individual system performance but provide no cross-discipline coordination guidance. BIM-based clash detection studies consistently report that LV systems are modelled at insufficient levels of detail for structural conflict identification. The review concludes with a consolidated research gap map and a prioritised research agenda at the intersection of structural engineering and building services electrical engineering.

The calculation of the Risk Assessment Matrix (RAM) is fundamental to recognizing diverse possible incidents, managing risks, and promoting safety in various high-risk sectors. However, the traditional methods of calculating a RAM value rely on manual evaluation and expert opinion which is often time-consuming and introduces room for variability. This research sets forth a new methodology to improve risk assessment using Large Language Models (LLMs) to “read” incidents, conduct historical analysis, and compute RAM values. The approach refers to the application of LLMs for the understanding and interpretation of complex multi-structured texts for predicting possible consequences, estimating their effects, and their probabilities using historical data. Concerning computation and LLM assessment of risks, the proposed framework was more efficient, accurate, and reliable than conventional models. It was noted that this method is practically useful for enhancing risk management practices in construction and other high-risk industrial environments.

Digital Very LargeScale Integration (VLSI) design has become increasingly complex due to the rapid growth of semiconductor technology, system-on-chip (SoC) architectures, hardware security requirements, and low-power computing applications. Simulation tools play a significant role in validating hardware functionality, timing behavior, power optimization, and security verification before fabrication. Among the widely used simulators in VLSI design environments are Cadence Verilog-XL, Cadence NCSIM, and Mentor Graphics ModelSim. These simulators provide different levels of performance, simulation speed, debugging capability, memory efficiency, and support for modern verification methodologies. This paper presents a comparative performance analysis of Cadence Verilog-XL, NCSIM, and ModelSim for digital VLSI simulation. The analysis is based on simulation execution time, memory utilization, waveform generation, debugging capability, hardware security validation support, scalability, and compatibility with contemporary VLSI workflows. The paper also discusses the role of Electronic Design Automation (EDA) tools in secure hardware composition and verification. Experimental observations indicate that NCSIM provides superior runtime efficiency and scalability for large-scale digital circuits, while ModelSim offers strong debugging support and educational usability. Verilog-XL remains useful for legacy verification environments despite limitations in performance and modern feature support. The study highlights the importance of selecting appropriate simulation tools according to design complexity, verification requirements, and hardware security constraints in contemporary VLSI systems.

Modern enterprise information systems generate large volumes of operational logs that contain valuable insights into system performance, application behavior, security events, and infrastructure health. Traditional monitoring approaches primarily rely on threshold-based alerts and manual analysis, limiting their effectiveness in predicting operational incidents before service disruption occurs. This paper proposes an intelligent enterprise log analytics framework that combines Splunk-based log management with machine learning techniques for predictive incident detection and operational intelligence. The framework integrates centralized log collection, feature extraction, anomaly detection, predictive classification, and visualization dashboards to identify emerging operational risks across enterprise applications. Machine learning models analyze historical log patterns to detect abnormal system behavior and estimate incident likelihood, while Splunk dashboards provide real-time operational visibility for system administrators. Experimental evaluation using representative enterprise log datasets demonstrates improvements in early anomaly detection, incident prediction accuracy, and operational awareness compared with conventional rule-based monitoring techniques. The proposed framework enables proactive infrastructure management, reduces mean time to detection, supports informed operational decision-making, and contributes to improved enterprise service reliability. The study demonstrates the growing importance of combining machine learning with enterprise log analytics platforms to achieve predictive operational intelligence in large-scale enterprise environments.

The increasing complexity of cyber threats has exposed the limitations of traditional signature-based intrusion detection systems, creating a need for intelligent and adaptive cybersecurity solutions. This study proposes an artificial intelligence-driven cybersecurity framework for enterprise threat detection using the CICIDS2017 benchmark dataset. The framework incorporates data preprocessing, feature engineering, and supervised machine learning to classify network traffic as benign or malicious. Seven machine learning algorithms, including Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Extra Trees, LightGBM, and XGBoost, were evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that ensemble learning models outperform conventional classifiers, with XGBoost achieving the highest performance, recording 99.42% accuracy, 99.39% precision, 99.31% recall, 99.35% F1-score, and an AUC-ROC of 0.999. LightGBM also demonstrated excellent performance with lower computational time. The findings suggest that the proposed XGBoost-based framework provides an accurate, scalable, and efficient solution for real-time enterprise threat detection and can be effectively integrated into modern cybersecurity infrastructures to enhance organizational cyber resilience.