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The paper presents View VULMA, a data set specifically designed for training machine-learning tools for elaborating fast vulnerability analysis of existing buildings. Such tools require supervised training via an extensive set of building imagery, for which several typological parameters should be defined, with a proper label assigned to each sample on a per-parameter basis. Thus, it is clear how defining an adequate training data set plays a key role, and several aspects should be considered, such as data availability, preprocessing, augmentation and balancing according to the selected labels. In this paper, we highlight all these issues, describing the pursued strategies to elaborate a reliable data set. In particular, a detailed description of both requirements (e.g., scale and resolution of images, evaluation parameters and data heterogeneity) and the steps followed to define View VULMA are provided, starting from the data assessment (which allowed to reduce the initial sample of about 20.000 images to a subset of about 3.000 pictures), to achieve the goal of training a transfer-learning-based automated tool for fast estimation of the vulnerability of existing buildings from single pictures.

Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras. In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions. Two different classifiers were trained to predict the plant treatment-random forest and multilayer perceptron neural networks-finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyll fluorescence, to provide more insights and possible explanations about the classification outputs. Interestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.

Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.

The paper presents an analytical-mechanical based procedure to estimate the seismic overall fragility of existing reinforced concrete building portfolios in town compartments, as reduced areas of a municipality. The proposed methodology is based on two main concepts: (a) to consider all typological parameters characterizing the entire set of buildings located in a certain urban area and their variability through an analytical procedure; (b) to employ a mechanical approach by means of ideal numerical models to estimate the safety level of the focused sample of buildings. Hence, the methodology allows to compute seismic overall fragility curves, obtained by using laws of total variance and expectation and weighing factors proportional to the probability of having a certain configuration of typological parameters with determined values. To test the proposed procedure, some town compartments of the municipality of Bisceglie, Puglia, Southern Italy, were investigated by firstly identifying the most recurrent typological features exploiting multisource data, after by elaborating an extensive campaign of modelling and analysis on different ideal buildings (herein named realizations) and finally by computing fragility curves for each realization and for the set of ideal buildings. The results show overall fragilities curves for the investigated town compartments, which are obtained in a different way from the existing procedures, by avoiding an a-priori selection of one or more index buildings to represent the specific building portfolio and the definition of a specific building taxonomy.

Detecting defects in welds used in critical or non-critical industrial applications is of intense interest. Several non-destructive inspection methods are available, each allowing the preservation of the integrity of the sample under analysis. However, visual-based inspection methods are the most well-assessed, which usually require human experts to inspect each sample, looking for shallow defects. This process often requires time and effort by the human operator, therefore not allowing to perform real-time defect identification, which may result in unexpected (and undesired) production costs. In recent years, several methods have been proposed to automatically deal with visual-based inspection, mainly through convolutional neural networks. However, while effective, these models require a lot of data and computational power to be trained, which is also time-consuming. This paper proposes a high-throughput data gathering and processing method using laser profilometry, along with an automatic defect detection method based on lightweight machine learning algorithms. Six different machine and deep learning approaches are compared, including SVMs, decision forests, and neural networks, achieving a top-1 accuracy of 99.79% for defect identification and 99.71% for defect categorization. Thanks to its effectiveness and the high data throughput achievable by data gathering, the whole method can be implemented in real production lines to minimize costs and perform real-time monitoring and defects assessment.

Autonomous navigation has gained increasing attention with the rise of self‐driving robots in applications ranging from logistics to space exploration. The robot's capabilities, context, and objectives significantly influence the design and selection of efficient navigation methods. However, traditional navigation methods, reliant on rule‐based algorithms and deterministic reasoning, struggle with adaptability and robustness in complex scenarios. Artificial Intelligence (AI) offers a solution, enhancing navigation through techniques like deep learning (DL), semantic understanding, and real‐time anomaly detection. This review explores AI's role in overcoming the limitations of classical approaches, focusing on adaptability, safety, and collaboration in navigation tasks. Analyzing diverse techniques and their integration with sensors highlights the potential of AI to enable reliable, efficient, and secure operation in real‐world environments, guiding future research in intelligent robotics.

Python Library for simulating unManNed vehiclEs(PLANE) is an open source software module, written in Python, that focuses on Unmanned Aerial Vehicles (UAVs), on their movements and on the mechanics of flight, thus devoting particular attention to the equations that describe drones' movement. In the context of the Internet of Drones (IoD), the module can be widely used for the study of the mutual control of position/coordination in scenarios in which drones may find obstacles, as it happens in densely populated urban scenarios. Emphasis is put on ease of use, performance evaluation, documentation, and Application Programming Interface (API) consistency. The software tool has minimal dependencies and is distributed under MIT License. Source code, binaries, and documentation can be downloaded from GitHub.

Multimodal industrial anomaly detection benefits from integrating RGB appearance with 3D surface geometry, yet existing \\emph{unsupervised} approaches commonly rely on memory banks, teacher-student architectures, or fragile fusion schemes, limiting robustness under noisy depth, weak texture, or missing modalities. This paper introduces \\textbf{CMDR-IAD}, a lightweight and modality-flexible unsupervised framework for reliable anomaly detection in 2D+3D multimodal as well as single-modality (2D-only or 3D-only) settings. \\textbf{CMDR-IAD} combines bidirectional 2D\\(\\leftrightarrow\\)3D cross-modal mapping to model appearance-geometry consistency with dual-branch reconstruction that independently captures normal texture and geometric structure. A two-part fusion strategy integrates these cues: a reliability-gated mapping anomaly highlights spatially consistent texture-geometry discrepancies, while a confidence-weighted reconstruction anomaly adaptively balances appearance and geometric deviations, yielding stable and precise anomaly localization even in depth-sparse or low-texture regions. On the MVTec 3D-AD benchmark, CMDR-IAD achieves state-of-the-art performance while operating without memory banks, reaching 97.3\\% image-level AUROC (I-AUROC), 99.6\\% pixel-level AUROC (P-AUROC), and 97.6\\% AUPRO. On a real-world polyurethane cutting dataset, the 3D-only variant attains 92.6\\% I-AUROC and 92.5\\% P-AUROC, demonstrating strong effectiveness under practical industrial conditions. These results highlight the framework's robustness, modality flexibility, and the effectiveness of the proposed fusion strategies for industrial visual inspection. Our source code is available at https://github.com/ECGAI-Research/CMDR-IAD/