The Industrial Internet of Things (IIoT) facilitates enhanced automation, predictive maintenance, real-time monitoring, and data analytics across various sectors, including manufacturing, energy, transportation, agriculture, and supply chain management, thereby improving productivity, efficiency, and operational safety. However, as IIoT networks continue to expand, it is imperative to secure them against increasingly sophisticated cyber threats. Deep Learning (DL) techniques have been extensively utilized for intrusion detection within IIoT systems. Nevertheless, addressing the class imbalance problem remains a significant challenge. The underrepresentation of certain attack types in training data frequently results in the development of DL models that struggle to accurately detect these categories of malicious activities. This limitation represents considerable risks to the security of IIoT networks, as undetected attacks and false alarms may lead to severe operational disruptions. In this paper, we propose a multi-stage deep learning (MSDL) method specifically designed to enhance intrusion detection within IIoT networks by addressing the class imbalance issue. We assessed the effectiveness of our approach utilizing two highly imbalanced datasets: X-IIoTID and WUSTL-IIoT. Our experimental findings indicate that the proposed MSDL method surpasses the baseline DL models as well as state-of-the-art oversampling and undersampling techniques. Specifically, the MSDL method exhibits significant improvements in recognizing minority-class attacks that are frequently misclassified. Consequently, the implementation of the MSDL for intrusion detection is anticipated to strengthen the overall security and resilience of IIoT systems, providing stronger protection against a diverse array of cyber threats in industrial applications.

Digitalization has been increasingly recognized for its role in addressing numerous societal and environmental challenges. However, the rapid surge in data production and the widespread adoption of cloud computing has resulted in an explosion of redundant, obsolete, and trivial (ROT) data within organizations’ data estates. This issue adversely affects energy consumption and carbon footprint, leading to inefficiencies and a higher environmental impact. Thus, this opinion paper aims to discuss the challenges and potential solutions related to the environmental impact of file storage on the cloud, aiming to address the research gap in “digital sustainability” and the Green IT literature. The key findings reveal that technological issues dominate cloud computing and sustainability research. Key challenges in achieving sustainable practices include the widespread lack of awareness about the environmental impacts of digital activities, the complexity of implementing accurate carbon accounting systems compliant with existing regulatory frameworks, and the role of public–private partnerships in developing novel solutions in emerging areas such as 6G technology.

Artificial Intelligence (AI) is reshaping industries and workforce demands globally. To ensure that individuals are prepared for an increasingly AI-driven world, it is crucial to develop robust AI literacy and address persistent gender biases in STEM fields. This paper presents a comparative study of AI literacy and gender bias among 192 participants from the United Kingdom and Indonesia. Using a survey-based approach, the study examines participants’ familiarity with AI concepts, confidence in utilizing AI tools, and engagement in ethical discussions related to AI. The findings reveal that while overall AI literacy levels are similar across both countries, UK respondents demonstrate significantly higher familiarity with programming and AI technologies, likely reflecting differences in educational frameworks and digital infrastructure. Moreover, despite widespread use of AI, discussions on its ethical implications remain limited in both countries. The study also highlights persistent gender biases that affect women’s participation and progression in AI and STEM fields; differences in perceptions of gender bias in recruitment, leadership promotion, and support for women suggest that, although progress is being made, significant barriers still exist. The study uncovers nuanced cultural variations in the perception of gender bias: UK participants exhibit greater confidence in gender inclusivity within recruitment and leadership roles, whereas Indonesian respondents report a higher prevalence of targeted initiatives to support women in technology. Overall, this research deepens our understanding of how AI literacy varies across diverse cultural and technological landscapes and offers valuable strategic guidance for tailoring interventions to overcome specific barriers, ultimately supporting innovative developments for women in STEM and women in AI in particular.

The generation of active power in renewable energy is dependent on several factors. These variables are related to the areas of weather, physical structure, control, and load behavior. Estimating the future value of the active power to be generated is difficult due to their unpredictable character. However, because of the higher precision required of the estimation, this problem becomes more complex if we examine a short-term temporal prediction. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms to perform the Short-term estimate. The environment, the operation, and the generated (normal or faulty) signal are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been realized to conduct an experiment. In addition, the LSTM and the GRU are compared to see how well they perform in this system. The proposed method's end findings outperform the current state-of-the-art.