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A vast amount of mobile apps have been developed during the past few months in an attempt to \"flatten the curve\" of the increasing number of COVID-19 cases. This systematic review aims to shed light into studies found in the scientific literature that have used and evaluated mobile apps for the prevention, management, treatment, or follow-up of COVID-19. We searched the bibliographic databases Global Literature on Coronavirus Disease, PubMed, and Scopus to identify papers focusing on mobile apps for COVID-19 that show evidence of their real-life use and have been developed involving clinical professionals in their design or validation. Mobile apps have been implemented for training, information sharing, risk assessment, self-management of symptoms, contact tracing, home monitoring, and decision making, rapidly offering effective and usable tools for managing the COVID-19 pandemic. Mobile apps are considered to be a valuable tool for citizens, health professionals, and decision makers in facing critical challenges imposed by the pandemic, such as reducing the burden on hospitals, providing access to credible information, tracking the symptoms and mental health of individuals, and discovering new predictors.

The scientific fields of additive manufacturing and especially the extrusion-based technologies have gained immense attention in numerous commercial and research areas in the past decades. However, monitoring the manufacturing procedure and detecting errors during the process remain a technological challenge in the field. Generally, defect detection and dimensional accuracy inspection of the produced component is applied after the manufacturing has been completed and is accomplished via on-site manual monitoring. Hereupon, these approaches could affect the manufacturing production cost via the increase of feedstock material, waste parts, manpower, and machine rates. To overcome these issues, the present paper introduces a vision-based method to scan, filter, segment, and correlate in real-time the physical printed part with the digital 3D model as well as to evaluate the performance of the additive manufacturing process. More specifically, high-resolution point cloud data of the printed part are automatically captured, filtered, segmented, reconstructed, and compared with the corresponding digital 3D model in various stages of the procedure. Finally, the effectiveness of the suggested automatic monitoring and error detection methodology is experimentally validated.

Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

Photogrammetry is the process of creating three-dimensional (3D) reconstructions from two-dimensional images. In this paper, a novel method is introduced that significantly enhances 3D reconstruction by improving image quality through a combination of RGB stretching, Contrast Limited Adaptive Histogram Equalization (CLAHE), and colormaps. This approach effectively increases the number of keypoints and matches between images, resulting in more accurate and detailed 3D reconstructions. Additionally, a heuristic methodology is proposed to identify the optimal colormaps for each dataset based on keypoint matches between image pairs. This approach saves valuable time by recommending the most effective colormaps, eliminating the need to test each individually. A new dataset comprising two real-world image collections from underwater cultural heritage sites is presented to validate the algorithm, characterized by challenging environments such as low visibility and varying lighting conditions. The limitations of existing techniques are addressed by this method, providing a robust solution for enhancing image quality in demanding underwater scenarios. Experimental results show that image enhancement can lead to a 7.91% and 11.4% improvement, and the enhancement fusion with colormaps improves reconstruction accuracy by 10.82% and 64.11%. These findings render the proposed methodology a promising tool for 3D reconstruction in challenging environments, like underwater.

Metal additive manufacturing (AM) has been recently acknowledged as a method to produce metal parts with complex geometries and unique characteristics. In general, metal AM components are used in applications where enhanced mechanical properties are required; thus it is essential to optimize the parameters that affect the metal AM processes. The immense advantages of metal AM technologies in the fields of production and manufacturing placed these technologies in the forefront of scientific sectors relevant to engineering, biomedical science and electronics. In recent years, many studies have been published analyzing the parameters which influence metal AM techniques to achieve advanced overall quality and optimized mechanical behavior of the fabricated products. Existing research has individually studied the influence of various factors on metal AM methods such as feedstock material properties, build orientation, printing conditions, infill patterns and post-processing procedures. The present study aims to review the research carried out until today identifying and classifying all parameters affecting metal AM, based on their impact on the quality of the final product. This survey intents to establish a process map of the categories which influence metal AM components. Additional recommendations, research gaps and directions towards the improvement of metal AM are also provided.

The transition to a decarbonized energy sector, driven by the integration of Renewable Energy Sources (RESs), smart building technology, and the rise of Electric Vehicles (EVs), has highlighted the need for optimized energy system planning. Increasing EV adoption creates additional challenges for charging infrastructure and grid demand, while proactive and informed decisions by residential EV users can help mitigate such challenges. Our work develops a smart residential charging framework that assists residents in making informed decisions about optimal EV charging. The framework integrates a machine-learning-based forecasting engine that consists of two components: a stacking and voting meta-ensemble regressor for predicting EV charging load and a bidirectional LSTM for forecasting national net energy exchange using real-world data from local road traffic, residential charging sessions, and grid net energy exchange flow. The combined forecasting outputs are passed through a data-driven weighting mechanism to generate probabilistic recommendations that identify optimal charging periods, aiming to alleviate grid stress and ensure efficient operation of local charging infrastructure. The framework’s modular design ensures adaptability to local charging infrastructure within or nearby building complexes, making it a versatile tool for enhancing energy efficiency in residential settings.

This paper proposes a generic algorithm for industries with degrading and/or failing equipment with significant consequences. Based on the specifications and the real-time status of the production line, the algorithm provides decision support to machinery operators and manufacturers about the appropriate lifetime extension strategies to apply, the optimal time-frame for the implementation of each and the relevant machine components. The relevant recommendations of the algorithm are selected by comparing smartly chosen alternatives after simulation-based life cycle evaluation of Key Performance Indicators (KPIs), considering the short-term and long-term impact of decisions on these economic and environmental KPIs. This algorithm requires various inputs, some of which may be calculated by third-party algorithms, so it may be viewed as the ultimate algorithm of an overall Decision Support Framework (DSF). Thus, it is called “DSF Core”. The algorithm was applied successfully to three heterogeneous industrial pilots. The results indicate that compared to the lightest possible corrective strategy application policy, following the optimal preventive strategy application policy proposed by this algorithm can reduce the KPI penalties due to stops (i.e., failures and strategies) and production inefficiency by 30–40%.

This study explores the use of ensemble machine learning models to develop wildfire susceptibility maps (WFSMs) in Greece, focusing on their application as regressors. We provide a continuous assessment of wildfire risk, enhancing the interpretability and accuracy of predictions. Two key metrics were developed: Ensemble Mean and Ensemble Max. This dual-metric approach improves predictive robustness and provides critical insights for wildfire management strategies. The ensemble mode effectively handles complex, high-dimensional data, addressing challenges such as over fitting and data heterogeneity. Utilizing advanced techniques like XGBoost, GBM, LightGBM, and CatBoost regressors, our research demonstrates the potential of these methods to enhance wildfire risk estimation. The Ensemble Mean model classified 50% of the land as low risk and 21% as high risk, while the Ensemble Max model identified 38% as low risk and 33% as high risk. Notably, 83% of wildfires between 2000 and 2024 occurred in areas marked as high-risk by both models. The findings reveal that a significant proportion of wildfires occurred in areas identified as high risk by both ensemble models, underscoring their effectiveness. This approach offers significant potential to mitigate wildfires’ environmental, economic, and social impacts, enhance climate resilience, and strengthen preparedness for future wildfire events.

Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.

Indoor localization systems have already wide applications mainly for providing localized information and directions. The majority of them focus on commercial applications providing information such us advertisements, guidance and asset tracking. Medical oriented localization systems are uncommon. Given the fact that an individual’s indoor movements can be indicative of his/her clinical status, in this paper we present a low-cost indoor localization system with room-level accuracy used to assess the frailty of older people. We focused on designing a system with easy installation and low cost to be used by non technical staff. The system was installed in older people houses in order to collect data about their indoor localization habits. The collected data were examined in combination with their frailty status, showing a correlation between them. The indoor localization system is based on the processing of Received Signal Strength Indicator (RSSI) measurements by a tracking device, from Bluetooth Beacons, using a fingerprint-based procedure. The system has been tested in realistic settings achieving accuracy above 93% in room estimation. The proposed system was used in 271 houses collecting data for 1–7-day sessions. The evaluation of the collected data using ten-fold cross-validation showed an accuracy of 83% in the classification of a monitored person regarding his/her frailty status (Frail, Pre-frail, Non-frail).