Traditional electrical power grids are transitioning from centralised operation with unidirectional energy and information flows (from the generation domain to customers) to smart grids with decentralised mode of operation and bidirectional flows. This reversal of traditional power flow direction is due to the connections of active loads such as distributed energy resources (DERs) and renewable energy sources close to the distribution network. Through advanced and sophisticated information and communication technologies (ICTs), efficient DER management and various applications for reliable and secure power delivery are enabled. However, before the adoption of any ICT solution in the grid, several challenges remain, which include interoperability, security and privacy concerns, and the ever-increasing demands to support various services and applications. Although the information within the grid is becoming more visible because of bidirectional communication flow, this only applies to transmission networks and not active distribution networks, which house numerous smart grid applications. There is also little research that supports the automatic operation of active distribution networks. Hence, this article explores and reviews active distribution network communication technologies, as well as the applications and communication standards. This review paper also highlights issues and challenges with active distribution networks and opportunities and research trends in the distribution domain from an ICT perspective.

Traditional communication technologies for distribution substations will soon have difficulties meeting the needs of emerging applications. This is due to exponential population growth and future energy demands, which necessitate a reliable and fast response from the grid. Thus, this research aims to aid decision-makers in selecting the best communication technologies for an electrical distribution substation. In this study, the Analytical Hierarchy Process (AHP) is used to shortlist, rank, and decide the most suitable communication technologies for an electrical substation based on its geographical location. The main criteria used for the selection are technical criteria, economic criteria, infrastructure, and service standards. The weight obtained from the pairwise comparison for each criterion is based on the literature review and expert opinions. The results comprise rankings of communication technologies tailored to different demographic settings, including urban, suburban, and rural areas. These rankings are generated through a comprehensive pairwise comparison process, building upon the earlier comparative analysis. Additionally, a structured framework has been developed to select the most appropriate communication technology for electrical substations in urban, suburban, and rural areas.

In the future, as populations grow and more end-user applications become available, the current traditional electrical distribution substation will not be able to fully accommodate new applications that may arise. Consequently, there will be numerous difficulties, including network congestion, latency, jitter, and, in the worst-case scenario, network failure, among other things. Thus, the purpose of this study is to assist decision makers in selecting the most appropriate communication technologies for an electrical distribution substation through an examination of the criteria’s in-fluence on the selection process. In this study, nine technical criteria were selected and processed using machine learning (ML) software, RapidMiner, to find the most optimal technical criteria. Several ML techniques were studied, and Naïve Bayes was chosen, as it showed the highest performance among the rest. From this study, the criteria were ranked in order of importance from most important to least important based on the average value obtained from the output. Seven technical criteria were identified as being important and should be evaluated in order to determine the most appropriate communication technology solution for electrical distribution substation as a result of this study.

Resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as machine learning (ML) and deep learning, must be applied to make the model customisable. Moreover, improvisation allows the prediction system to generate the most accurate outcomes and valuable insights from data whilst enabling effective decisions. In this study, we first provide a literature study on the applications of ML and a summary of the hyperparameters influencing the prediction capabilities of the ML models for the communication system. We demonstrate the behaviour of four ML models: k nearest neighbour, classification and regression trees, random forest and support vector machine. Then, we observe and elaborate on the suitable hyperparameter values for each model based on the accuracy in prediction performance. Based on our observation, the optimal hyperparameter setting for ML models is essential because it directly impacts the model’s performance. Therefore, understanding how the ML models are expected to respond to the system utilised is critical.

This study investigates the determinants of translators' willingness to adopt crowdsourcing translation platforms through a sequential explanatory mixed-methods design grounded in pragmatism. In Phase 1, a stratified random sample of 300+ practicing translators completed an online questionnaire derived from the Unified Theory of Acceptance and Use of Technology 2 and augmented with trust and risk constructs. Partial least squares structural equation modelling revealed that performance expectancy, effort expectancy, community support, and trust significantly predict behavioral intention, whereas perceived risk exerts a negative moderating effect. In Phase 2, semi-structured interviews with 15 purposively selected survey respondents unpacked these statistical relationships, showing how cultural values, volunteer fatigue, and platform recognition shape sustained engagement and offering actionable guidance for platform designers, agencies, and policymakers seeking to enhance the sustainability and inclusivity of crowdsourced translation ecosystems.