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This research is driven by the imperative to improve the milk pasteurization process's efficiency and safety within the Industry 5.0 framework. It introduces the artificial intelligence of things (AIoT) into the pasteurization system and focuses on deploying the C4.5 algorithm, anticipated to render precise real-time decisions by analyzing critical temperature and duration data essential for the low-temperature long-time (LTLT) pasteurization technique. Test results indicate that the C4.5 model is highly effective, demonstrating nearly perfect metrics with a precision of .78, a recall of .77, and an F1-score of .73, while achieving an overall algorithm accuracy of .76. In terms of heating control, the double jacket method tested on a 300-L system proved the control design's capability to quickly reach and maintain desired temperatures, hitting the set-point within 70 min. However, it was observed that systems with larger capacities took longer to reach the set-point, suggesting a potential for further optimization. The findings affirm that integrating AIoT into control systems can significantly enhance process efficiency and effectiveness, consistent with the goal of incorporating sophisticated technological solutions in the food sector. Practical Applications The article “Enhancing Low-Temperature Long-Time Milk Pasteurization Process with a C4.5 Algorithm-Driven AIoT System for Real-Time Decision-Making” explores an AIoT system's potential to revolutionize the dairy industry's pasteurization methods. This system promises enhanced efficiency by optimizing energy and time, thereby reducing costs. It ensures superior quality control through continuous monitoring, maintaining nutritional integrity and safety. The high automation level minimizes human error and operational demands, while predictive maintenance forecasts preresource increase. Moreover, it guarantees product traceability and compliance with food safety regulations. The generated data offers insights for further supply chain optimizations. This integration of AI and IoT thus presents significant advancements in process management, quality assurance, and scalable operations for the dairy industry. [ABSTRACT FROM AUTHOR]

ChoiWith the rapid development of the Internet of Things and artificial intelligence (AI), the requirement for sensing technologies for smart bearings has increased dramatically. The general bearing sensors can only recognize the basic information from temperature or vibration, far from satisfying the self-diagnosis and self-maintenance. Recently, self-powered sensing technologies based on triboelectric nanogenerators have paved a new route for fabricating smart bearings. In this study, the triboelectric principle is applied to a commercial metal-polymer plain bearing (MPPB) bearing, which can achieve self-sensing, self-diagnosis, and self-maintenance. The geometrical structure of the triboelectric MPPB (T-MPPB) is designed to balance the output efficiency and external load, and the super durability and load capability are verified. Besides, the mechanism behind the output change trend under boundary and hydrostatic fluid lubrication is revealed for the first time. Furthermore, the deep learning algorithm can classify the lubrication states with highly accurate performance. The proposed T-MPPB has the potential to achieve self-maintenance with the lubricating pump according to the lubrication condition classified by the AI. This research not only establishes the feasibility of designing self-powered smart MPPB but also demonstrates a way for identifying lubrication states, thus achieving self-diagnosis and self-maintenance ability by self-powered sensors. [ABSTRACT FROM AUTHOR]

Edge computing technology solves the shortcomings of high latency, mobility and location awareness in remote cloud computing, but it also brings many security challenges to the AIoT. In view of the open and heterogeneous characteristics of edge network, a more secure edge computing intrusion detection method is studied in this paper. The main idea is to combine edge computing with AIoT to realize edge smart interconnection. This method optimizes the weight value in machine learning by increasing the screening and mitigation of cloud server training samples, so as to provide efficient and accurate edge intrusion detection behavior. The experimental results show that the proposed method can more effectively improve the detection accuracy and reduce the detection time. [ABSTRACT FROM AUTHOR]

The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi‐agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor‐based data. Multi‐agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state‐of‐the‐art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions. [ABSTRACT FROM AUTHOR]

Wearable dosimeters form an essential part of the personnel protection scheme in work spaces such as defence, nuclear establishments, and medical fields with radiation hazard. In the current scenario where use of high energy ionizing radiation sources (nuclear sources) is indispensable, the need of radiation detectors is mandatory for measuring the dose received by the person exposed in his work space and also measure the dose for patients where nuclear radiation is used for treatment. Wearable dosimeters need to have some special requirements apart from being accurate, precise, and robust. These need to be light in weight and should cause no harm to the human body while it is touching the body or apparels. This paper comprises an introduction to dosimeters, key characteristics, important techniques used for detection of neutrons and X‐rays and also mentions about a few potential companies involved in developing wearable dosimeters in these application areas. The techniques have been collated, described, compared and the need to detect neutrons and X‐rays has been expressed keeping in mind the demand from the defence and medical disciplines. This review also contains artificial intelligence and internet of things which form the basis of trending and upcoming wearable dosimeters. [ABSTRACT FROM AUTHOR]