In classical power systems, frequency measurements are transferred via a specialised communication channel, resulting in time delay. The time delay plays a major role in a power system, which can reduce the dynamic performance of the load–frequency control (LFC) system and can destabilise the system. The research to date has tended to focus on developing a new algorithm to determine the delay margin (DM) rather than looking into a hybrid algorithm which includes a nature-inspired metaheuristic optimisation technique. This paper introduces a novel method for computing the DM based on grey wolf optimisation (GWO), specifically for the constant time delay. In the proposed method, GWO is employed to optimise the minimum error of the spectral radius and to determine the best design variable of the crossing frequency. With the help of the proposed method, the sweeping range is no longer required, which improves the accuracy of the result. To evaluate the proposed method, a two-area network power system is considered as a case study. Furthermore, the effect of the PI controller gains on the DM is taken into account. The proposed method efficacy is demonstrated by comparing it with the most recently published methods. The results demonstrate that the proposed method is remarkably better than the existing methods found in the literature, where the smallest percentage inaccuracy using the simulation-based DM based on GWO is found to be 0.000%.

Despite the rapid adoption of artificial intelligence (AI) on a global scale, a comprehensive framework that maps its end-to-end value chain is missing. The presented study employed a multi-layered framework to analyze the value creation and delivery mechanism of the five core layers of an AI value chain, including (1) hardware, (2) data management, (3) foundational AI, (4) advanced AI capabilities, and (5) AI delivery. Using a qualitative–descriptive approach with a multi-faceted thematic analysis and a SWOT-based bottleneck analysis of each core layer, the study maps a sequential value flow from a globally dependent hardware foundation to the deployment of AI services. The analysis reveals that international knowledge flows shape the ecosystem, while the “last-mile” integration challenge is not merely a technical issue; instead, it highlights a significant socio-technical disconnect between technological advancements and the preparedness of the workforce. This study provides a holistic framework that frames the AI value chain as a socio-technical system, offering critical insights for stakeholders. The findings emphasize that unlocking AI’s full potential requires strategic investment in the managerial competencies and digital skills that constitute human–capital readiness.

Parking space shortages are attributed to an increased density of vehicle presence in the urban context, necessitating the implementation of effective parking management strategies, especially in areas where facility expansion is constrained by limited land availability. Many parking facilities remain operationally inefficient and underutilized due to manual VP methods and having little access to parking resource utilization data. This study develops a DT-based SPMS integrating machine vision, data modeling, and DT technology to automate facility management operations. The system uses YOLOv7 for vehicle and License Plate Detection (LPD), and Deep Text Recognition–Scene Text Recognition (DTR-STR) for license plate recognition (LPR). The findings indicate an 89.89% accuracy for VP- and LPR-based occupancy tracking tasks, and 94.86% for vehicle detection or VD-based occupancy tracking. The system in the built environment comprises three features: (1) automated VP at parking entry and exit points, (2) occupancy monitoring through LPR, (3) Object Detection (OD) for occupancy tracking. The 3D BIM DT model in Autodesk Revit processes inference data from machine vision models to visualize parking activity.

Parking space shortages are attributed to an increased density of vehicle presence in the urban context, necessitating the implementation of effective parking management strategies, especially in areas where facility expansion is constrained by limited land availability. Many parking facilities remain operationally inefficient and underutilized due to manual vehicle profiling methods and having little access to parking resource utilization data. This study develops a digital twin-based smart parking management system integrating machine vision, data modeling, and digital twin technology to automate facility management operations. The system uses YOLOv7 for vehicle and license plate detection, and Deep Text Recognition-Scene Text Recognition (DTR-STR) for license plate recognition (LPR). Findings indicate an 89.89% accuracy for vehicle profiling and LPR-based occupancy tracking tasks, and 94.86% for vehicle detection-based occupancy tracking. The system in the built environment comprises of three features: (1) automated vehicle profiling at parking entry and exit points, (2) occupancy monitoring through LPR, and (3) object detection for occupancy tracking. The 3D BIM digital twin model in Autodesk Revit processes inference data from machine vision models to visualize parking activity. Smart parking automation offers a viable solution for business stakeholders interested in operations optimization through manual labor reduction, improving efficiency, and minimizing congestion.

Management of floating macroplastic pollution in urban river system is gaining importance due to the significant impact it has on the marine environment and the economic activity of the area. Studies have shown that most of the plastic waste emitted into the urban rivers system, are believed to be land-based plastic. Urban areas are one of the main sources of plastic pollution because these areas are densely populated and are often the center of commercial activity. Despite this, data on what affects the transport, distribution and fate of floating plastic waste in urban river systems are scarce. This lack of information and data can curtail the evolution of effective mitigation measures to collect and remove floating plastic waste. In freshwater system it is understood that surface river flow velocity is the main environmental parameter affecting the speed, distribution and the distance floating plastic moves. To understand how river surface velocity influence these processes in the Brunei River estuary, passive floater experiments was carried out periodically over a period of six months. The specific aim of this experiment is to 1) measure longitudinal variation of river surface velocity, 2) investigate the spatial variation of macroplastic, 3) understand macroplastic fate and hotspot formation. The information and data collected from the experiments can be used to give a first-order prediction of hotspots in the Brunei River estuary.