This paper presents a comprehensive evaluation of various YOLO architectures for smoke and wildfire detection, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, and YOLO-NAS. The study aims to assess their effectiveness in early detection of wildfires using the Foggia dataset, comprising 8,974 images specifically designed for this purpose. Performance evaluation employs metrics such as Recall, Precision, F1-score, and mean Average Precision to provide a holistic assessment of the models’ performance. The study follows a rigorous methodology involving fixed epochs, continuous performance tracking, and unbiased testing. Results show that YOLOv5, YOLOv7, and YOLOv8 exhibit a balanced performance across all metrics in both validation and testing. YOLOv6 performs slightly lower in recall during validation but achieves a good balance on testing. YOLO-NAS variants excel in recall, making them suitable for critical applications. However, precision performance is lower for YOLO-NAS models. Visual results demonstrate that the top-performing models accurately identify most instances in the test set. However, they struggle with distant scenes and poor lighting conditions, occasionally detecting false positives. In favorable conditions, the models perform well in identifying relevant instances. We conclude that no single model excels in all aspects of smoke and wildfire detection. The choice of model depends on specific application requirements, considering accuracy, recall, and inference time. This research contributes to the field of computer vision in smoke and wildfire detection, providing a foundation for improving detection systems and mitigating the impact of wildfires. Researchers can build upon these findings to propose modifications and enhance the effectiveness of wildfire detection systems.

Despite their great improvements, reliability and availability of power electronic devices remain always a focus. In safety-critical equipment, where the occurrence of faults can generate catastrophic losses, health monitoring of most critical components is absolutely needed to avoid and prevent breakdowns. In this paper, a noninvasive health monitoring method is proposed. It is based on fuzzy logic and the neural network to estimate and predict the equivalent series resistance (ESR) and the capacitance (C) of capacitors and supercapacitors. This method, based on the neo-fuzzy neuron model, performs a real-time processing (time series prediction) of the measured device impedance and the degradation data provided by accelerated ageing tests. To prove the efficiency of the proposed method, two experiments were performed. The first one was dedicated to the estimation of the ESR and C for a set of 8 polymer film capacitors, while the second one was dedicated to the prediction of the ESR and C for a set of 18 supercapacitors. The obtained results showed that combining fuzzy logic and the neural network is an accurate approach for the health monitoring of capacitors and supercapacitors.

The fashion industry often falls short of sustainability goals, but contemporary technological advancements offer a wide range of tools to address this issue. Artificial Intelligence (AI) has emerged as a particularly promising ally in promoting sustainability in fashion. This literature review explores how AI can contribute to the fashion industry’s sustainability, highlighting its potential benefits and limitations. Following PRISMA guidelines, we conducted a review of scientific documents, focusing on the period from 2010 to 2022. After a meticulous selection process, we analyzed 37 scholarly articles to distill their key insights and contributions. Our findings demonstrate that AI has diverse applications in different aspects of the fashion industry, enhancing sustainability efforts in supply chain management, creative design, sales and promotion, waste control, and data analysis. While AI offers significant potential, it is important to acknowledge limitations, such as the volume of data required and associated implementation costs. The reviewed literature aligns with the multifaceted nature of sustainability, emphasizing responsible resource management, accessible services, and efficient customer satisfaction, both now and in the future. In conclusion, despite some reservations, AI stands as a crucial partner in guiding the fashion industry toward a more sustainable future.

This paper provides a thorough analysis and comparison of the YOLOv5 and YOLOv8 models for wildfire and smoke detection, using the Foggia dataset for evaluation. The study examines the small (s), medium (m), and large (l) variants of each architecture and employs various metrics, including recall, precision, F1-Score, and mAP@50, to assess performance. Additional considerations such as training and inference times, along with the number of epochs required for optimal recall, are also evaluated to gauge the models’ real-world efficiency and effectiveness. Quantitatively, YOLOv5 models generally outperform YOLOv8, with the YOLOv5s variant achieving the highest scores across all metrics. However, visual assessments reveal that YOLOv8 models exhibit similar, and in some cases superior, capabilities, particularly in detecting dark and dense smoke. Training times favor YOLOv5 models, contributing to their efficiency, and their shorter inference times offer advantages for real-time applications. While the “best model” variants confirm YOLOv5’s numerical dominance, YOLOv8’s “best models” also display competitive performance. Future research will explore model evaluation on diverse datasets and hyperparameter optimization to further enhance performance, adaptability, and applicability in various real-world object detection scenarios.

Summary: Artificial intelligence (AI) has been used for more than 2 decades as a development tool for solutions in several areas of the exploration and production (E&P) industry: virtual sensing, production control and optimization, forecasting, and simulation, among many others. Nevertheless, AI applications have not been consolidated as standard solutions in the industry, and most common applications of AI still are case studies and pilot projects. In this work, an analysis of a survey conducted on a broad group of professionals related to several E&P operations and service companies is presented. This survey captures the level of AI knowledge in the industry, the most common application areas, and the expectations of the users from AI-based solutions. It also includes a literature review of technical papers related to AI applications and trends in the market and in research and development. The survey helped to verify that (a) data mining and neural networks are by far the most popular AI technologies used in the industry; (b) approximately 50% of respondents declared they were somehow engaged in applying workflow automation, automatic process control, rule-based case reasoning, data mining, proxy models, and virtual environments; (c) production is the area most affected by the applications of AI technologies; (d) the perceived level of available literature and public knowledge of AI technologies is generally low; and (e) although availability of information is generally low, it is not perceived equally among different roles. This work aims to be a guide for personnel responsible for production and asset management on how AI-based applications can add more value and improve their decision making. The results of the survey offer a guideline on which tools to consider for each particular oil and gas challenge. It also illustrates how AI techniques will play an important role in future developments of information-technology (IT) solutions in the E&P industry.