Timeseries forecasting is applied to many areas of smart factories, including machine health monitoring (MHM), predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust production processes based on different system conditions, optimise production throughput, and minimise energy consumption. However, making accurate data-driven machine speed forecasts is challenging. Given the complex nature of industrial manufacturing processes, predictive models that are robust to noise and can capture the temporal and spatial distributions of the input timeseries are prerequisites for accurate forecasting. Motivated by recent deep learning studies in smart manufacturing, this paper proposes an end-to-end model for multi-step-ahead machine speed prediction. The model, known as 2D-Convolutional LSTM Autoencoder (2DConvLSTMAE), comprises a deep convolutional LSTM (ConvLSTM) encoder-decoder architecture. Extensive empirical analyses using real-world data obtained from a metal packaging plant in the United Kingdom demonstrate the value of the proposed method when compared to state-of-the-art deep learning models.

Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networks – specifically twitter – can improve urban traffic parameter prediction by complementing traffic data with data representing events capable of disrupting regular traffic patterns reported in social media posts. This paper proposes a deep learning urban traffic prediction model that combines information extracted from tweet messages with traffic and weather information. The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom. The findings from extensive empirical analysis using real-world data demonstrate the effectiveness of the approach in improving prediction accuracy when compared to other classical/statistical and machine learning (ML) state-of-the-art models. The improvement in predictive accuracy can lead to reduced frustration for road users, cost savings for businesses, and less harm to the environment.

Traffic parameter forecasting is critical to effective traffic management but is a challenging task due to the stochasticity of traffic flow characteristics, especially in urban road networks. Traffic networks can be affected by external factors, such as weather, events, accidents, and road construction works. The impact of these factors can affect traffic flow parameters by influencing travel time, density, occupancy, and operating speed. Although deep neural networks (DNNs) have recently shown promising signs in traffic prediction using big data, there still exists the issue of maximizing the use of the model capabilities by using big data sources. This paper proposes an improved urban traffic speed prediction approach involving input-level data fusion and deep learning. Motivated by deep learning prediction methods, we propose a Long Short-Term Memory Neural Network (LSTM-NN) for traffic speed prediction that combines traffic and weather datasets on an urban road network in Greater Manchester, United Kingdom. The experimental results substantiate the value of the approach when compared to the use of traffic-only data sources for traffic speed prediction.

This study explores how socio-cultural dynamics influence student engagement with Generative AI technology in Nigerian higher education, using activity theory as theoretical underpinning. By examining the roles of community norms, technological accessibility, and educational objectives, the research identifies critical factors that impact the adoption and utilisation of GenAI. We employ quantitative analysis to analyse 899 survey responses from students across seventeen (17) Nigerian universities to derive interesting insights. Findings reveal that the ease of use of GenAI tools and their alignment with educational goals enhance student engagement. Conversely, regular need for technical support negatively affect engagement, suggesting underlying technological issues. These insights provide actionable recommendations for educators, administrators, and policymakers, emphasising the importance of user-friendly GenAI tools, comprehensive training programs, and robust support systems. This study contributes to the understanding of technology adoption in culturally diverse educational settings and offers strategies to improve educational practices and outcomes both in Nigerian higher education and also potentially in other African (developing countries), where similar socio-cultural dynamics might influence technology integration and educational advancements.

Smart factories are intelligent, fully-connected and flexible systems that can continuously monitor and analyse data streams from interconnected systems to make decisions and dynamically adapt to new circumstances. The implementation of smart factories represents a leap forward compared to traditional automation. It is underpinned by the deployment of cyberphysical systems that, through the application of Artificial Intelligence, integrate predictive capabilities and foster rapid decision-making. Deep Learning (DL) is a key enabler for the development of smart factories. However, the implementation of DL in smart factories is hindered by its reliance on large amounts of data and extreme computational demand. To address this challenge, Transfer Learning (TL) has been proposed to promote the efficient training of models by enabling the reuse of previously trained models. In this paper, by means of a specific example in aluminium can manufacturing, an empirical study is presented, which demonstrates the potential of TL to achieve fast deployment of scalable and reusable predictive models for Cyber Manufacturing Systems. Through extensive experiments, the value of TL is demonstrated to achieve better generalisation and model performance, especially with limited datasets. This research provides a pragmatic approach towards predictive model building for cyber twins, paving the way towards the realisation of smart factories.