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

Investigating the Role of Stabilizers in Enhancing the Electrocatalytic Activity of PdAu Nanoalloy Catalysts for Methanol Oxidation in Fuel Cells through an Oxidative Addition Mechanism

The formation of nanoalloys through borohydride reduction is an effective method for creating nanocatalysts. Surfactants and stabilizers are crucial for enhancing the stability, dispersion, and electrocatalytic activity of catalysts used in the methanol oxidation reaction (MOR) in fuel cells. This study investigates the effects of various capping agents and stabilizers─reduced graphene oxide, aminoclay, sodium dodecyl sulfate (SDS), cetyltrimethylammonium bromide, polyvinylpyrrolidone, and poly(vinyl alcohol)─on the morphology and electrocatalytic activity of PdAu nanoalloy thin films for methanol oxidation. In electrochemical tests, the PdAu/SDS catalyst demonstrated impressive activity of approximately 700.48 mA mg-1, significantly surpassing conventional Pt/C catalysts. Scanning electron microscopy (SEM), energy-dispersive analysis of X-ray, and elemental mapping analyses after accelerated durability tests revealed no significant structural changes or metal leaching following 200 MOR cycles. Our analysis aimed to clarify their functions while considering various stabilizers (cationic, anionic, nonionic) and proposed a new mechanism. The incorporation of SDS notably enhances the catalytic properties by increasing the electron density on the PdAu surface and facilitating the oxidative-addition of O-H bonds from methanol. Our proposed mechanism includes: methanol adsorption, O-H bonds activation (oxidative-addition), C-H bond activation, nucleophilic attack on a coordinated formyl group, and decarboxylation. As a result, the PdAu/SDS composite establishes itself as the most effective catalyst for methanol fuel cells, with anionic stabilizers outperforming nonionic and cationic surfactants

Three Albanian cultural centers in comparison under an acoustic perspective

After the World War II, Albania was governed by a dictatorship that lasted for about 50 years. The cultural life restarted to be one of the main centers of the society community. Many buildings styles reflected the influence of governors as leaders of countries, in combination with the spread of armed concrete used as the main material for new constructions, given its flexibility compared to brickwork that was not yet developed as it is nowadays. In Albania, many cultural centers were constructed for the local community where citizens can have access to libraries, coffee shops, and auditoria. These latest ones represent the places where live shows are performed, along with international conferences. This paper deals with the assessment of the acoustic response gathered within three auditoria as part of cultural centers in Albania. The acoustic measurements were carried out in accordance with ISO 3382-1. The acoustic response recorded inside the three case studies indicate a good listening condition for both speech and music performance. This outcome has been found in all three auditoria, despite the room volume between each other is different

Finite deformations induce friction hysteresis in normal wavy contacts

Since Hertz's pioneering work in 1882, contact mechanics has traditionally been grounded in linear elasticity, assuming small strains and displacements. However, recent experiments clearly highlighted linear elasticity limitations in accurately predicting the contact behavior of rubbers and elastomers, particularly during frictional slip, which is governed by geometric and material nonlinearity. In this study, we investigate the basic scenario involving normal approach-retraction contact cycles between a wavy rigid indenter and a flat, deformable substrate. Both frictionless and frictional interfacial conditions are examined, considering finite strains, displacements, and nonlinear rheology. We developed a finite element model for this purpose and compared our numerical results with Westergaard's linear theory. Our findings show that, even in frictionless conditions, the contact response is significantly influenced by geometric and material nonlinearity, particularly for wavy indenters with high aspect ratios, where normal-tangential stresses and displacements coupling emerges. More importantly, interfacial friction in nonlinear elasticity leads to contact hysteresis (i.e., frictional energy dissipation) during normal loading–unloading cycles. This behavior cannot be explained in a linear framework; therefore, most of the experiments reporting hysteresis are typically explained invoking other interfacial phenomena (e.g., adhesion, plasticity, or viscoelasticity). Here we present an additional suitable explanation relying on finite strains/displacements with detailed peculiarities, such as vanishing pull-off force. Moreover, we also report an increase of hysteretic losses as for confined systems, stemming from the enhanced normal-tangential nonlinear coupling