Airport pavement condition assessment plays a critical role in ensuring operational safety, surface functionality, and long-term infrastructure sustainability. Traditional visual inspection methods, although widely used, are increasingly challenged by limitations in accuracy, subjectivity, and scalability. In response, the field has seen a growing adoption of automated and intelligent inspection technologies, incorporating tools such as unmanned aerial vehicles (UAVs), Laser Crack Measurement Systems (LCMS), and machine learning algorithms. This systematic review aims to identify, categorize, and analyze the main technological approaches applied to functional pavement inspections, with a particular focus on surface distress detection. The study examines data collection techniques, processing methods, and validation procedures used in assessing both flexible and rigid airport pavements. Special emphasis is placed on the precision, applicability, and robustness of automated systems in comparison to traditional approaches. The reviewed literature reveals a consistent trend toward greater accuracy and efficiency in systems that integrate deep learning, photogrammetry, and predictive modeling. However, the absence of standardized validation protocols and statistically robust datasets continues to hinder comparability and broader implementation. By mapping existing technologies, identifying methodological gaps, and proposing strategic research directions, this review provides a comprehensive foundation for the development of scalable, data-driven airport pavement management systems.

When it comes to ride a motorcycle the drivers-centered road safety is quintessential; every year a remarkable number of accidents directly related to sleepiness and fatigue occur. With the objective of maximizing the security on a motorcycle, the reported system aims to prevent sleepiness related accidents and to attenuate the effects of a crash. The system was developed as the less intrusive as it could be, with sensors that allow the capture of reaction times to stimuli-response and collect acceleration values. To obviate the lack of data related to sleepiness during motorcycle riding, a machine learning system was developed, based on Artificial Immune Systems. This way, resourcing to a minimum amount of user input, a custom system is synthesized for each user, allowing to assess the sleepiness level of each subject differently.

The deployment of fifth-generation wireless communications (5G) networks brought a significant difference in the data rate and throughput to the wireless systems. It ensures ultra-low latency and high reliability. In particular, Network Slicing (NS), one of the enablers for the 5G phase-II and beyond, has opened enormous opportunities for the Communications Service Provider (CSPs). NS allows CSPs to create independent virtual networks in the same physical network to guarantee high service levels. This paper provides an overview of the advances in NS from the perspective of the business opportunities and associated standardization activities. Standardization is critical in research as it intends to maintain interoperability among multi-vendor scenarios in telcos. We emphasize highlighting the technical facets of slicing within the business implementation and industry standardization process. Additionally, we address the application of Artificial Intelligence (AI) and Machine Learning (ML) to NS-enabled future networks deployments. A set of use cases and the underlying specific requirements challenges are discussed as well. Finally, future research directions are addressed in detail.

This paper presents an optimization approach for Micro-cogeneration systems with internal combustion engines integrated into residential grids, addressing power demand failures caused by intermittent renewable energy sources. The proposed method leverages machine learning techniques, control strategies, and grid data to improve system flexibility and efficiency in meeting electricity and domestic hot water demands. Historical residential grid data were analysed to develop a machine learning-based demand prediction model for electricity and hot water. Thermal energy storage was integrated into the Micro-cogeneration system to enhance flexibility. An optimization model was created, considering efficiency, emissions, and cost while adapting to real-time demand changes. A control strategy was designed for the flexible operation of the Micro-cogeneration system, addressing excess thermal energy storage and resource allocation. The proposed solution’s effectiveness was validated through simulations, with results demonstrating the Micro-cogeneration system’s ability to efficiently address high electricity and hot water demand periods while mitigating power demand failures from renewable energy sources. The research presents a novel approach with the potential to significantly improve grid resilience, energy efficiency, and renewable energy integration in residential grids, contributing to more sustainable and reliable energy systems.

In this study, we focused on analyzing customer-generated data on Facebook to explore how textual content on a social web can provide valuable information for decision support. To accomplish this goal, we used several techniques that included social network analysis (SNA), natural language processing (NLP), data mining (DM), and machine learning (ML), integrating them with artificial intelligence approaches. Our analysis aimed to harness the information generated during the Volkswagen pollutant emissions situation in a case study that was conducted using the textual content from 10,642 posts, that represented the interactions of 25,877 users over a span of twenty-two weeks. The results demonstrated that monitoring online social networks (OSNs) can significantly enhance decision-making processes and might help to mitigate potential damages to brands/businesses. By leveraging the proposed methodological approach, a set of orientations for decision-making was extracted, providing valuable guidance for brand management and reputation protection. Overall, this study highlights the importance of analyzing textual content on OSNs and leveraging advanced computational techniques to improve decision support.

Wild flowers and plants play an important role in protecting biodiversity and providing various ecosystem services. However, some of them are endangered or threatened and are entitled to preservation and protection. This study represents a first step to develop a computer vision system and a supporting mobile app for detecting and monitoring the development stages of wild flowers and plants, aiming to contribute to their preservation. It first introduces the related concepts. Then, surveys related work and categorizes existing solutions presenting their key features, strengths, and limitations. The most promising solutions and techniques are identified. Insights on open issues and research directions in the topic are also provided. This paper paves the way to a wider adoption of recent results in computer vision techniques in this field and for the proposal of a mobile application that uses YOLO convolutional neural networks to detect the stages of development of wild flowers and plants.

This paper aims to explore the use of different deep learning techniques, specifically convolutional neural networks (CNNs), for dietary assessment through image food recognition and compare their performance to the human visual system (HVS). Currently, there are three main techniques for using CNNs in this task: training a network from scratch; using an off-the-shelf pre-trained network; and performing unsupervised pre-training with supervised adjustments. In this study, the authors evaluate the performance of three CNN models with varying numbers of parameters (5,000 to 160 million) based on dataset size and spatial image context. The authors also consider human knowledge and classification to compare the performance of the CNNs to the HVS. They find that while the CNNs make errors across different food classes, the HVS tends to make semantic errors with specific food classes. As a result, the HVS shows more consistency in its answers. Overall, the findings suggest that the HVS is more accurate when the dataset is diverse, while the CNN performs better when the dataset is focused on a particular niche. In conclusion, this study provides empirical evidence that machine learning can be more efficient than the HVS in certain tasks but also highlights the strengths and limitations of both approaches. The authors suggest that combining CNNs with other classification techniques, such as bagof-words, may be a promising approach for improving the accuracy of dietary assessment through image food recognition.