Forests are crucial for preserving biodiversity and regulating the global climate. However, they are increasingly at risk from destructive wildfires that threaten the environment and human communities. Accurate prediction models are essential to minimize the impact of forest fires. This study presents a new hybrid model that combines the Apriori association rule mining algorithm with the binary golden ratio optimization method (BGROM) to improve the accuracy of forest fire prediction. The BGROM, based on the golden ratio observed in plant and animal growth and formulated by the renowned mathematician Fibonacci. It is used to select the candidate features, which are then used by the Apriori algorithm to generate classification rules to predict the risk of wildfires. Integrating the Apriori algorithm with BGROM improves the accuracy of forest fire prediction and enhances our understanding of the complex interactions and patterns that influence wildfire behavior. This innovative approach holds great promise for advancing the development of effective forest fire prevention and management strategies. Experimental results show that the proposed model outperforms existing prediction methods, offering a more reliable tool for early forest fire detection and risk management.

The present work reports the impacts on urban mobility and air quality in Lisbon, Portugal, of the imposed restrictions to curb the transmission of SARS-CoV-2 virus, which causes COVID-19 disease. We performed a data-driven approach over Lisbon Smart cities data, collected from several sources, such as traffic and pollution. During the first Portuguese emergency period (18-03-2020 to 03-05-2020) the sharp reductions in anthropogenic activities, most importantly road traffic, resulted in generally reduced criteria air pollutant concentration compared to an homologous baseline from 2013–2019 measured in the six air quality monitoring stations throughout the city. The most negatively impacted air pollutants were NO2, with a reduction of 54.35% in traffic stations and 28.62% in background stations. Google mobility indicator for local commerce was found to be the main anthropogenic activity indicator for Lisbon, with a moderate and positive correlation with NO 2 concentration ( r =+0.54). A regressor ML pipeline was trained to predict NO 2 concentration with the available anthropogenic activity, weather, and air pollutant inputs from March/2020 to March/2021, achieving R 2 = 0.925 on the test set.

Accurate assessment of electromechanical system status is essential for the safe and efficient management of airport energy stations, where pointer-meter readings serve as key operational indicators. To address pointer-meter detection under complex lighting and cluttered backgrounds, this study proposes YOLO-METER, a vision-based detection model tailored for airport energy stations. The model integrates a Triple Attention Mechanism (TAM) in the backbone to suppress background interference and employs a weighted bidirectional feature pyramid network (BiFPN) in the neck for efficient multi-scale feature fusion. Furthermore, an Improved Sparrow Search Algorithm (ISSA) is used to optimize 12 hyperparameters, substantially improving convergence and detection performance. An inspection-robot platform was built, and on-site images were collected to construct a dedicated pointer-meter detection dataset. Experimental results show that YOLO-METER achieves mAP@0.5 of 97.6%, Precision of 96.46%, and 224.8 FPS, outperforming multiple YOLO variants. These results indicate that YOLO-METER provides an effective and efficient solution for real-time pointer-meter detection, supporting autonomous inspection in airport energy stations.

Collaborative robots (cobots) have revolutionized industrial automation by enabling seamless human–robot interaction in shared workspaces. These advanced robots are designed to work alongside humans, enhancing productivity, efficiency, and safety through sophisticated sensing and control capabilities. This review provides a comprehensive analysis of collaborative robotics, while introducing the Internet of Cobots (IoC) as a pivotal enabler of next-generation manufacturing systems. Through IoC capabilities, cobots are transforming industrial environments by implementing advanced safety protocols, enabling dynamic task allocation, and achieving seamless integration with human workers over 5G networks. This paper synthesizes the key technological advancements, starting from fundamental sensing, control, and actuation architectures to sophisticated artificial intelligence and machine learning algorithms that enable human-like adaptability. Special attention is given to the industrial safety norms that enhance workplace safety of the IoC through real-time data sharing of job processing, human proximity, and potential hazards, while building trust in the decision-making of human–robot collaboration. This review highlights emerging trends within the Industry 5.0 paradigm, where IoC-enabled cobots drive the human-centric and personalized manufacturing processes. By providing a comprehensive overview of networked cobots, their transformative impact, and regulatory frameworks, this review offers a valuable insight into the future of intelligent, adaptable, and human-centred industrial automation.

The concept of Digital Twins (DT) has experienced a remarkable surge in popularity over the past few years. A DT is a computer system designed to monitor, simulate and predict various aspects of a specific physical object. In other words, it is like an enhanced digital counterpart of a real object. Human Digital Twins (HDT) have emerged as an evolution of this concept, where the physical object twinned is a human being. Nevertheless, the inherent complexity of human beings turns the creation of their digital representation into a challenging endeavour. In this study, a systematic literature review was conducted that aimed at clarifying the HDT concept and the different aspects a proper HDT should consider. To shed some light on how the different facets of a HDT are addressed in the literature, we delved into its fields of application, the human dimensions a HDT considers, the information handled, its underlying technological frameworks, what quality assessment processes are being applied to HDT, and lastly, those ethical and legal concerns related to HDT. As a result of this systematic literature review, a HDT research agenda is presented to fill the gaps and shortcomings identified in the literature reviewed and highlight some challenges that should be addressed in the near future.

In this paper, we propose a federated deep reinforcement learning framework, named FL-DDQN, designed to enhance irrigation management in smart farming environments. The proposed framework addresses key challenges posed by fragmented data collected from small-scale farming devices, as well as the complexities associated with varying climate and soil patterns. Specifically, our framework optimizes the selection of clients in the federated learning process through deep reinforcement learning. The evaluation of the proposed framework is conducted across different multifarm configurations, showcasing its scalability and adaptability. Furthermore, the FL-DDQN framework facilitates collaborative training of a weather and climate forecasting model using a hybrid convolutional neural network (CNN)–long short-term memory architecture. This model is enhanced by incorporating regulation and normalization techniques, which help mitigate the effects of distribution shifts and pattern changes in dynamic farming environments. The results demonstrate high accuracy, achieving a low mean absolute error for soil moisture (0.0118), temperature (1.0200), and relative humidity (4.3958). Additionally, we integrate autoencoders based on CNNs to detect anomalies in irrigation system sensor readings by evaluating the reconstruction error. The proposed framework achieves a significantly lower reconstruction error compared to recent state-of-the-art methods, confirming its robustness in anomaly detection.

The neighbor-based method has become a powerful tool for addressing the outlier detection problem, which aims to assess the abnormality of a sample based on its compactness relative to neighboring samples. However, most existing methods primarily focus on designing various processes to identify outliers, while the contributions of different types of neighbors to the detection process have not been adequately explored. To address this gap, this article investigates the role of neighbors in existing outlier detection algorithms and introduces a taxonomy that utilizes three key components: information, neighbor, and methodology, to define hybrid methods. This taxonomy provides a framework that can inspire the development of novel neighbor-based outlier detection algorithms by combining different components from each level. Extensive comparative experiments on both synthetic and real-world datasets, including performance evaluations and case studies, demonstrate that reverse K-nearest neighbor-based methods perform well and that dynamic selection methods are particularly effective in high-dimensional spaces. Furthermore, the results confirm that strategically selecting components from this taxonomy can lead to the development of algorithms that outperform existing methods.

This paper presents an Internet of Things-based system designed to measure classroom acoustic parameters and estimate speech intelligibility in real time. The system consists of multiple wireless sensor nodes equipped with microphones that capture acoustic data and transmit them to a central server for processing. Parameters such as reverberation time (RT60) and the Speech Intelligibility Index were calculated to assess acoustic quality. Experiments were conducted in a university lecture hall under different sound source configurations to evaluate the system's performance. Results indicate that the proposed IoT-based approach effectively characterizes acoustic conditions and provides a cost-efficient alternative to traditional measurement tools, with potential applications in classroom design, audio optimization, and smart learning environments.

Smart farming systems, while enhancing agricultural productivity, are increasingly vulnerable to cyber threats due to their reliance on interconnected devices and networks. However, existing Intrusion Detection Systems (IDS) often suffer from high computational costs and suboptimal detection accuracy due to irrelevant features. To address this challenge, this study proposes a novel hybrid filter-based feature selection method designed to optimize feature selection for artificial neural network (ANN)-based IDS in smart farming environments. Unlike conventional methods that rely solely on chi-square, mutual information, or mean absolute deviation, the proposed method combines these techniques to leverage their complementary strengths. Furthermore, a comprehensive smart farming system was established to collect extensive data, creating a dedicated dataset named Smart-Farm-IDS for binary classification, distinguishing between normal operations and anomalies. Both shallow and deep ANN models were employed to detect these anomalies, with their performances compared in detail. Experimental results demonstrate that the proposed hybrid feature selection method enhances detection accuracy while reducing computational overhead compared to existing methods. This study offers a robust approach for improving the security and resilience of smart farming systems, providing a foundation for more secure agricultural operations.