The rapid advancement of artificial intelligence (AI) has brought significant benefits across various domains, yet it has also led to increased energy consumption and environmental impact. This paper positions Green AI as a crucial direction for future research and development. It proposes a comprehensive framework for understanding, implementing, and advancing sustainable AI practices. We provide an overview of Green AI, highlighting its significance and current state regarding AI’s energy consumption and environmental impact. The paper explores sustainable AI techniques, such as model optimization methods, and the development of efficient algorithms. Additionally, we review energy-efficient hardware alternatives like tensor processing units (TPUs) and field-programmable gate arrays (FPGAs), and discuss strategies for designing and operating energy-efficient data centers. Case studies in natural language processing (NLP) and Computer Vision illustrate successful implementations of Green AI practices. Through these efforts, we aim to balance the performance and resource efficiency of AI technologies, aligning them with global sustainability goals.

The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the device that is suitable for use. Thus far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. This paper summarizes the network architectures of some of the most popular IoT wireless communications protocols. It also presents a comparative analysis of some of the critical features, including power consumption, coverage, data rate, security, cost, and quality of service (QoS). This comparative study shows that low-power wide area network (LPWAN)-based IoT protocols (LoRa, Sigfox, NB-IoT, LTE-M) are more suitable for future industrial applications because of their energy efficiency, high coverage, and cost efficiency. In addition, the study also presents an Industrial Internet of Things (IIoT) application perspective on the suitability of LPWAN protocols in a particular scenario and addresses some open issues that need to be researched. Thus, this study can assist in deciding the most suitable IoT communication protocol for an industrial and production field.

The Industrial Internet of Things (IIoT) integrates cyber-physical systems, edge-cloud computing, Artificial Intelligence (AI), and advanced connectivity to enable intelligent, data-driven operations across manufacturing, energy, transportation, and healthcare. The convergence of Operational Technology (OT) and Information Technology (IT), together with pervasive AI-driven automation, complicates meeting the joint requirements of performance, adaptability, and security (PAS). This survey adopts an explicitly AI-centric view of PAS and reviews IIoT research spanning deep reinforcement learning (DRL), multi-agent reinforcement learning (MARL), digital twins (DTs), federated and collaborative learning, graph neural networks (GNNs), blockchain-assisted analytics, and lightweight and post-quantum cryptography. The reviewed approaches are analyzed across device, edge, fog, cloud, and application layers under industrial constraints such as latency, reliability, resource limits, safety requirements, and regulatory compliance. A PAS-centric taxonomy is introduced that links technique families to deployment layers and application domains, and recurring trade-offs among PAS dimensions are summarized. Open challenges are identified in multi-objective optimization, semantic interoperability, lifecycle validation, and governance, with directions outlined toward secure and sustainable Industry 5.0 IIoT ecosystems.

The Quran is a globally accessed sacred scripture from which many seek guidance. It has been asserted that the Quran explores comparable themes dispersed across its chapters, addressing multiple contexts. Prior studies have found that approximately 75% of its chapters contain recurring textual elements. However, apparent repetitions in the Quran have not been studied for potential interlinks such as creating an index of recurring text patterns or concepts to support contextual information retrieval. This study aims to advance the analysis of textual pattern repetitions in the Quran by devising a methodology to group and interlink recurring patterns, thereby exploring their relationship with the structure and presentation of the text. A consistent ratio of approximately 0.6 was identified between the number of repeated n-grams and the subsequent (n+1) grams, revealing a consistently converging “ring structure” among repeating text patterns. Findings show that repeating patterns enable the retrieval of 5WH ontological information on a basic repeating pattern. The proposed unsupervised information retrieval approach is independent of expert input and therefore offers greater sustainability.