Enhancing Maintenance Operations in Industry 5.0: A Conceptual User Interface Design for Task Assignment

The Fifth Industrial Revolution, or Industry 5.0, fosters an innovative, resilient, competitive, and society-centered industry. This era emphasizes enhanced human-machine interactions, enabling individuals to manifest their creativity through personalized products and services. As smart factories evolve, the demand for flexibility and adaptability necessitates increased cognitive efforts, particularly in maintenance tasks critical to the flexibility of production systems. Despite the potential of emerging technologies like Augmented Reality and Artificial Intelligence to aid operators, the complexity of tasks combined with the novelty of such technologies can overwhelm workers, thereby impacting workplace well-being. To tackle these challenges, the DESDEMONA project, funded by the European Union through PRIN as part of NextGenerationEU, is developing a Decision Support System (DSS). This system aims to provide real-time suggestions for assigning the most suitable operators for maintenance tasks characterized by high cognitive demands. The DSS considers three primary factors: the operator’s profile (including skills and age), their emotional state, and the availability of smart devices. This manuscript details the project’s initial results, presenting a simplified mathematical model capable of ranking the optimal list of operators. To demonstrate the effectiveness of the DSS, it is compared, through a simulation approach, with a simulated maintenance supervisor. This comparison highlights the system’s ability to identify, from the k-permutations of N operators, the number of optimal tuples that best fit the operational needs

Optical synchronous signal demodulation-based quartz-enhanced photoacoustic spectroscopy for remote, multi-point methane detection in complex environments

We present a novel optical synchronized signal demodulation (OSSD) method applied in quartz-enhanced photoacoustic spectroscopy (QEPAS) for remote gas sensing. Using 1 % of the laser source as an optical synchronization signal, kilometer-scale remote gas detection was achieved, overcoming the challenges of long-distance real-time detection in complex environments with conventional QEPAS. A time-sharing OSSD-QEPAS system for sewer methane detection was subsequently developed. The system’s modulation depth was optimized, and the catalytic effect of water vapor on photoacoustic signals was validated, resulting in a CH4 sensor achieving a detection limit of 445 ppb with a 300-ms averaging time, and an excellent linear dynamic range with a R2 = 0.999. To demonstrate the stability, robustness, and accuracy of the OSSD-QEPAS system, continuous methane measurements covering a 14-hour period at two different sewer locations on campus were performed

GNCnn: A QuPath extension for glomerulosclerosis and glomerulonephritis characterization based on deep learning

The digitalization of traditional glass slide microscopy into whole slide images has opened up new opportunities for pathology, such as the application of artificial intelligence techniques. Specialized software is necessary to visualize and analyze these images. One of these applications is QuPath, a popular bioimage analysis tool. This study proposes GNCnn, the first open-source QuPath extension specifically designed for nephropathology. It integrates deep learning models to provide nephropathologists with an accessible, automatic detector and classifier of glomeruli, the basic filtering units of the kidneys. The aim is to offer nephropathologists a freely available application to measure and analyze glomeruli to identify conditions such as glomerulosclerosis and glomerulonephritis. GNCnn offers a user-friendly interface that enables nephropathologists to detect glomeruli with high accuracy (Dice coefficient of 0.807) and categorize them as either sclerotic or non-sclerotic, achieving a balanced accuracy of 98.46%. Furthermore, it facilitates the classification of non-sclerotic glomeruli into 12 commonly diagnosed types of glomerulonephritis, with a top-3 balanced accuracy of 84.41%. GNCnn provides real-time updates of results, which are available at both the glomerulus and slide levels. This allows users to complete a typical analysis task without leaving the main application, QuPath. This tool is the first to integrate the entire workflow for the assessment of glomerulonephritis directly into the nephropathologists' workspace, accelerating and supporting their diagnosis

Sliding Viscoelastic Contacts: The Role of Adhesion, Boundary Conditions, and Finite Geometry

In this study, we investigate the tangential sliding of a rigid Hertzian indenter on a viscoelastic substrate, a problem of practical interest due to the crucial role that sliding contacts play in various applications involving soft materials. A finite element model is developed, where the substrate is modelled using a standard linear viscoelastic model with one relaxation time, and adhesion is incorporated using a Lennard–Jones potential law. We propose an innovative approach to model tangential sliding without imposing any lateral displacement, thereby enhancing the numerical efficiency. Our goal is to investigate the roles of adhesive regimes, boundary conditions (displacement and force-controlled conditions), and finite thickness of the substrate. Results indicate significant differences in the system’s behaviour depending on the boundary conditions and adhesion regime. In the short-range adhesion regime, the contact length L initially increases with sliding speed before decreasing, showing a maximum at intermediate speeds. This behaviour is consistent with experimental observations in rubber-like materials and is a result of the transition from small-scale to large-scale viscous dissipation regimes. For long-range adhesion, this behaviour disappears and L decreases monotonically with sliding speed. The viscoelastic friction coefficient μ exhibits a bell-shaped curve with its maximum value influenced by the applied load, both in long-range and short-range adhesion. However, under displacement control, μ can be unbounded near a specific sliding speed, correlating with the normal force crossing zero. Finally, a transition towards a long-range adhesive behaviour is observed when reducing the thickness t of the viscoelastic layer, which is assumed to be bonded to a rigid foundation. Moreover, the friction coefficient reduces when t tends to zero. These findings provide insights into the viscoelastic and adhesive interactions during sliding, highlighting the critical influence of boundary conditions on contact mechanics

A novel mode shape identification approach for structures having planes with rigid-like behavior

The identification of mode shapes of structures through Operational Modal Analysis (OMA) often requires the application of data merging techniques to compensate for the lack of information on mode shapes scaling factors, which is inherent in OMA. In this paper, we propose a novel mode shape identification approach for structures having planes with rigid- like behavior, such as steel or reinforced concrete buildings with rigid floors. The approach is based on a theoretical model that generalizes the mechanical features of the structures under considerations. We show that the mode shapes of the model can be reconstructed starting from two components, i.e., modal centers of rotation and modal rotations; modal rotations depend on scaling factors of mode shapes, while modal centers of rotation turn out to be invariant with respect to mode shape scaling. Afterwards, we develop a method for identifying modal centers of rotation and modal rotations from experimental data, and then for reconstructing mode shapes. Numerical experiments have been performed to assess the capability of the approach with respect to a structural specimen having known modal properties. Compared with classic merging techniques, our approach enables a significant simplification of the experimental setup and a deeper analysis of mode shapes

Safety-Aware Deep-RL for Automated Insulin Delivery: Toward Inclusive Diabetes Care

We present a safety-aware Deep Reinforcement Learning framework for personalized automated insulin delivery. Validated on realistic in silico simulations, it improves glucose control, reduces hypoglycemia risk, lowers insulin dosage, and promotes inclusive access to effective diabetes care

KGUF: Simple Knowledge-Aware Graph-Based Recommender with User-Based Semantic Features Filtering

The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filtering (GCF). Following the same GNNs wave, recommender systems exploiting Knowledge Graphs (KGs) have also been successfully empowered by the GCF rationale to combine the representational power of GNNs with the semantics conveyed by KGs, giving rise to Knowledge-aware Graph Collaborative Filtering (KGCF), which use KGs to mine hidden user intents. Nevertheless, empirical evidence suggests that computing and combining user-level intent might not always be necessary, as simpler approaches can yield comparable or superior results while keeping explicit semantic features. Under this perspective, user historical preferences become essential to refine the KG and retain the most discriminating features, thus leading to concise item representation. Driven by the assumptions above, we propose KGUF, a KGCF model that learns latent representations of semantic features in the KG to better define the item profile. By leveraging user profiles through decision trees, KGUF effectively retains only those features relevant to users. Results on three datasets justify KGUF ’s rationale, as our approach is able to reach performance comparable or superior to SOTA methods while maintaining a simpler formalization

Biological, Biochemical and Elemental Traits of Clavelina oblonga, an Invasive Tunicate in the Adriatic Sea

Clavelina oblonga is an invasive tropical tunicate recently introduced into the Adriatic Sea as a consequence of globalization and climate change. Mussel aquaculture sites provide an ideal environment for this colonial ascidian, where it has recently become the dominant fouling species. This study represents the first investigation of its biological and physical characteristics, as well as its proximal, fatty acid, macroelement, trace element, and toxic metal composition. The entire-tissue chemical composition of C. oblonga resulted in 95.44% moisture. Its composite structure revealed several strong peaks, attributed to O-H, C-H, C-N, and C=O stretching, along with cellulose components overlapping with proteins and carbohydrates. The major fatty acids were palmitic, stearic, and docosahexaenoic acid, followed by docosanoic, elaidic, linoleic, and myristic acid. The saturated fatty acids, polyunsaturated fatty acids, and monounsaturated fatty acids comprised 51.37, 26.96, and 15.41% of the total fatty acids, respectively. Among the analysed trace and macroelements, aluminium and sodium were predominant. C. oblonga exhibited different concentrations of toxic metals, such as arsenic and lead, compared to fouled mussels in the Istria region. It appears that the tunicate has adapted to the environmental conditions of the Adriatic, reaching its maximum spread and biomass in mid-autumn. There is a strong possibility that C. oblonga could colonize and establish itself permanently in the Adriatic. This would have a strong negative impact on shellfish farming, the structure of the ecosystem, plankton biomass, and the distribution of other marine species. However, it also represents a biomass resource with high potential of utilization in different industries