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The paradigm for robot usage has changed in the last few years, from a scenario in which robots work isolated to a scenario where robots collaborate with human beings, exploiting and combining the best abilities of robots and humans. The development and acceptance of collaborative robots is highly dependent on reliable and intuitive human-robot interaction (HRI) in the factory floor. This paper proposes a gesture-based HRI framework in which a robot assists a human co-worker delivering tools and parts, and holding objects to/for an assembly operation. Wearable sensors, inertial measurement units (IMUs), are used to capture the human upper body gestures. Captured data are segmented in static and dynamic blocks recurring to an unsupervised sliding window approach. Static and dynamic data blocks feed an artificial neural network (ANN) for static, dynamic, and composed gesture classification. For the HRI interface, we propose a parameterization robotic task manager (PRTM), in which according to the system speech and visual feedback, the co-worker selects/validates robot options using gestures. Experiments in an assembly operation demonstrated the efficiency of the proposed solution.

Near infrared spectroscopy (NIRS) and mid-infrared spectroscopy (MIRS) in combination with chemometric analysis were applied to discriminate the geographical origin of grapevine leaves belonging to the variety “Touriga Nacional” during different vegetative stages. Leaves were collected from plants of two different wine regions in Portugal (Dão and Douro) over the grapes maturation period. A sampling plan was designed in order to obtain the most variability within the vineyards taking into account variables such as: solar exposition, land inclination, altitude and soil properties, essentially. Principal component analysis (PCA) was used to extract relevant information from the spectral data and presented visible cluster trends. Results, both with NIRS and MIRS, demonstrate that it is possible to discriminate between the two geographical origins with an outstanding accuracy. Spectral patterns of grapevine leaves show significant differences during grape maturation period, with a special emphasis between the months of June and September. Additionally, the quantification of total chlorophyll and total polyphenol content from leaves spectra was attempted by both techniques. For this purpose, partial least squares (PLS) regression was employed. PLS models based on NIRS and MIRS, both demonstrate a statistically significant correlation for the total chlorophyll (R 2 P  = 0.92 and R 2 P  = 0.76, respectively). However, the PLS model for the total polyphenols, may only be considered as a screening method, because significant prediction errors, independently of resourcing on NIRS, MIRS or both techniques simultaneously, were obtained.

The long-term preservation of heritage structures relies on effective Structural Health Monitoring (SHM) systems, where sensor placement is key to ensuring early damage detection and guiding conservation efforts. Optimal Sensor Placement (OSP) methods offer a systematic framework to identify efficient sensor configurations, yet their application in historical buildings remains limited. Typically, OSP is driven by numerical models; however, in the context of heritage structures, these models are often affected by substantial uncertainties due to irregular geometries, heterogeneous materials, and unknown boundary conditions. In this scenario, data-driven approaches become particularly attractive as they eliminate the need for potentially unreliable models by relying directly on experimentally identified dynamic properties. This study investigates how the choice of input data influences OSP outcomes, using the Church of Santa Ana in Seville, Spain, as a representative case. Three data sources are considered: an uncalibrated numerical model, a calibrated model, and a data-driven set of modal parameters. Several OSP methods are implemented and systematically compared. The results underscore the decisive impact of the input data on the optimisation process. Although calibrated models may improve certain modal parameters, they do not necessarily translate into better sensor configurations. This highlights the potential of data-driven strategies to enhance the robustness and applicability of SHM systems in the complex and uncertain context of heritage buildings.

An innovative pilot installation and eddy current testing (ECT) inspection system for laser-brazed joints is presented. The proposed system detects both surface and sub-surface welding defects operating autonomously and integrated with a robotized arm. Customized eddy current probes were designed and experimentally validated detecting pore defects with 0.13 mm diameter and sub-surface defects buried 1 mm deep. The integration of the system and the manufacturing process towards an Industry 4.0 quality control paradigm is also discussed.

Wine aroma is one of the most frequently used and explored quality indicators. Typically, its assessment involves estimating the volatile composition of wine or highly trained assessors conducting sensory analysis. However, current methodologies rely on slow, expensive and complicated analytical procedures. Additionally, sensory evaluation is inherently subjective in nature. Therefore, the aim of this work is to verify the feasibility of using FTIR spectroscopy as a fast and easy methodology for the early detection of some of the most common off-odors in wines. FTIR spectroscopy was combined with partial least squares (PLS) regression for the simultaneous measurement of isoamyl alcohol, isobutanol, 1-hexanol, butyric acid, isobutyric acid, decanoic acid, ethyl acetate, furfural and acetoin. The precision and accuracy of developed calibration models (R2P > 0.90, range error ratio > 12.1 and RPD > 3.1) proved the ability of the proposed methodology to quantify the aforementioned compounds.

The structural health monitoring (SHM) of buildings provides relevant data for the evaluation of the structural behavior over time, the efficiency of maintenance, strengthening, and post-earthquake conditions. This paper presents the design and implementation of a continuous SHM system based on dynamic properties, base accelerations, crack widths, out-of-plane rotations, and environmental data for the retrofitted church of Kuñotambo, a 17th century adobe structure, located in the Peruvian Andes. The system produces continuous hourly records. The organization, data collection, and processing of the SHM system follows different approaches and stages, concluding with the assessment of the structural and environmental conditions over time compared to predefined thresholds. The SHM system was implemented in May 2022 and is part of the Seismic Retrofitting Project of the Getty Conservation Institute. The initial results from the first twelve months of monitoring revealed seasonal fluctuations in crack widths, out-of-plane rotations, and natural frequencies, influenced by hygrothermal cycles, and an apparent positive trend, but more data are needed to justify the nature of these actions. This study emphasizes the necessity for extended data collection to establish robust correlations and refine monitoring strategies, aiming to enhance the longevity and safety of historic adobe structures under seismic risk.

Automobile laser brazing remains a complex process whose results are affected by several process variables that may result in nonacceptable welds. A multisensory customized inspection system is proposed, with two distinct non-destructive techniques: the potential drop method and eddy current testing. New probes were designed, simulated, produced, and experimentally validated in automobile’s laser-brazed weld beads with artificially introduced defects. The numerical simulations allowed the development of a new four-point probe configuration in a non-conventional orthogonal shape demonstrating a superior performance in both simulation and experimental validation. The dedicated inspection system allowed the detection of porosities, cracks, and lack of bonding defects, demonstrating the redundancy and complementarity these two techniques provide.

The growing concern about the protection of built heritage and the sustainability of urban areas has driven the reoccupation of existing masonry buildings, which, in the great majority of the cases, were not designed or constructed to withstand significant seismic forces. This fact, associated with territorial occupation often concentrated in areas with high seismic hazard, makes it essential to look at these buildings from the point of view of the assessment of their seismic vulnerability and retrofitting needs. However, to be effective and efficient, such an assessment must be founded on a solid knowledge of the existing methods and tools, as well as on the criteria that should underlie the selection of the most appropriate to use in each context and situation. Aimed at contributing to systematise that knowledge, this paper presents a comprehensive review of the most relevant vulnerability assessment methods applicable at different scales, as well as the most significant traditional and innovative seismic retrofitting solutions for existing masonry buildings.

As ageing structures and infrastructures become a global concern, structural health monitoring (SHM) is seen as a crucial tool for their cost-effective maintenance. Promising results obtained for modern and conventional constructions suggested the application of SHM to historical masonry buildings as well. However, this presents peculiar shortcomings and open challenges. One of the most relevant aspects that deserve more research is the optimisation of the sensor placement to tackle well-known issues in ambient vibration testing for such buildings. The present paper focuses on the application of optimal sensor placement (OSP) strategies for dynamic identification in historical masonry buildings. While OSP techniques have been extensively studied in various structural contexts, their application in historical masonry buildings remains relatively limited. This paper discusses the challenges and opportunities of OSP in this specific context, analysing and discussing real-world examples, as well as a numerical benchmark application to illustrate its complexities. This article aims to shed light on the progress and issues associated with OSP in masonry historical buildings, providing a detailed problem formulation, identifying ongoing challenges and presenting promising solutions for future improvements.

Structural health monitoring (SHM) is essential for preserving historical and modern infrastructure by tracking dynamic properties such as frequencies and mode shapes. Changes in these properties can indicate structural damage, but environmental factors like temperature can also cause similar variations, complicating damage detection. This study investigates from an experimental point of view the effect of temperature on the dynamic behaviour of masonry structures, focusing on a masonry wall subjected to thermal load variations within operational conditions. The experimental setup involved a masonry wall specimen tested at the Structural Laboratory of the University of Minho, Portugal. The mock-up was subjected to various boundary conditions and loading scenarios. The results showed that the natural frequencies of the masonry wall can be significantly influenced by temperature changes, variations strictly related to the boundary conditions and the stress acting on the mock-up. In contrast, mode shapes seem not to be affected by temperature variations. This study provides valuable insights into the temperature-induced variations in the dynamic properties of masonry structures, emphasising the need to consider environmental effects in SHM applications. By filtering out these environmental influences, more accurate damage detection and proactive maintenance strategies can be developed, enhancing the safety and longevity of both historical and modern structures.