Artificial neural network for tilting pad journal bearing characterization

Tilting pad journal bearings (TPJBs) are modeled with Reynold-based models or computational fluid dynamics (CFD) approach. In both cases, the estimation of the dynamic coefficients of the oil-film forces and the static characteristic, can be computationally expensive and time consuming. Artificial Intelligence (AI) is assuming a key role in engineering but is rarely applied in fluid film bearing analysis. A properly trained Deep Learning (DL) model can perform very fast predictions of TPJB behavior with accuracy comparable to more time-consuming models. In this case, the main drawback is the time required to build the training dataset. In this work, an Artificial Neural Network (ANN) is trained to predict the dynamic stiffness and damping coefficients along with the main static quantities of TPJBs, such as minimum oil-film thickness and inlet flowrate. At first, a design of experiment is performed to build an appropriate training dataset. Secondly, a Reynolds-based thermo-hydrodynamic (THD) model is used to populate the training dataset and an appropriate test dataset. Then, a feedforward ANN is trained with Levenberg–Marquardt backpropagation and its architecture is optimized to increase accuracy. Finally, the accuracy of the ANN is tested using the test dataset and experimental data. The time and computational effort required by the ANN regression are much less than those required by the THD model. Therefore, the trained ANN is an effective and efficient tool for the characterization of TPJBs

Opening the black box: a primer for anti-discrimination

The pervasive adoption of Artificial Intelligence (AI) models in the modern information society, requires counterbalancing the growing decision power demanded to AI models with risk assessment methodologies. In this paper, we consider the risk of discriminatory decisions and review approaches for discovering discrimination and for designing fair AI models. We highlight the tight relations between discrimination discovery and explainable AI, with the latter being a more general approach for understanding the behavior of black boxes

GLocalX - From Local to Global Explanations of Black Box AI Models

Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications

Phenotypic and genotypic resistance to colistin in E. coli isolated from wild boar (Sus scrofa) hunted in Italy

The One Health approach is not only focused on diseases and zoonosis control but also on antimicrobial resistance. As concern this important issue, the problem of plasmid-mediated colistin resistance recently emerged. Few studies reported data about colistin resistance and mcr genes in bacteria from wildlife. In this manuscript, 168 Escherichia coli isolated from hunted wild boar were tested; colistin resistance was evaluated by MIC microdilution method, and the presence of mcr-1 and mcr-2 genes was evaluated by PCR. Overall, 27.9% of isolates resulted resistant to colistin, and most of them showed a MIC value > 256 μg/mL. A percentage of 44.6% of tested E. coli scored positive for one or both genes. In details, 13.6% of isolated harbored mcr-1 and mcr-2 in combination; most of them exhibiting the highest MIC values. Interestingly, 19.6% of mcr-positive E. coli resulted phenotypically susceptible to colistin. Wild boar could be considered a potential reservoir of colistin-resistant bacteria. In the light of the possible contacts with domestic animals and humans, this wild species could play an important role in the diffusion of colistin resistance. Thus, the monitoring programs on wildlife should include this aspect

Evaluation of jennies' colostrum: IgG concentrations and absorption in the donkey foals. A preliminary study

Immunoglobulin type G (IgG) concentration both in jennies' colostrum and in serum of donkey foals are mostly unknown in the first 24 h after delivery. The aims of the present study were to evaluate the IgG concentrations of colostrum during the first 24 h of lactation of Amiata jennies, the absorption of colostrum and the weekly body weight gain of the donkey foals. IgG concentrations were assessed in the jennies' colostrum and in the serum of donkey foals. Colostrum was collected in 9 jennies ready after delivery, and at 6, 12, 24 h after foaling from both halves. Serum was collected at the same sampling times from 9 donkey foals. Donkey foals were weighted at birth and then weekly until the 28th days of life. Temporal changes of IgG concentrations in dam's colostrum and in donkey foal serum were analyzed by a linear regression model and a general linear model, respectively. Results showed that colostrum IgG concentration were similar between the left and the right half. Colostrum IgG concentrations decreased continuously throughout the time in all jennies by 0.0244 Log10 mg/mL per hour. Serum IgG concentrations in donkey foals at birth was significantly lower compared to other times. No correlation was found between the colostrum IgG concentrations and the average weekly body weight gain of the donkey foal. The pattern of colostrum IgG levels in jennies and serum IgG concentration in donkey foals seem to be similar to what reported for equine. However, the donkey foals seem to be less agammaglobulinemic at birth compared to the horse foal. The pattern and both serum and colostrum concentrations evaluated in the Amiata donkeys were slightly different from results reported in other donkey breeds, underlying the importance of setting references specific to breed

How to Build a Patient-Specific Hybrid Simulator for Orthopaedic Open Surgery: Benefits and Limits of Mixed-Reality Using the Microsoft HoloLens

Orthopaedic simulators are popular in innovative surgical training programs, where trainees gain procedural experience in a safe and controlled environment. Recent studies suggest that an ideal simulator should combine haptic, visual, and audio technology to create an immersive training environment. This article explores the potentialities of mixed-reality using the HoloLens to develop a hybrid training system for orthopaedic open surgery. Hip arthroplasty, one of the most common orthopaedic procedures, was chosen as a benchmark to evaluate the proposed system. Patient-specific anatomical 3D models were extracted from a patient computed tomography to implement the virtual content and to fabricate the physical components of the simulator. Rapid prototyping was used to create synthetic bones. The Vuforia SDK was utilized to register virtual and physical contents. The Unity3D game engine was employed to develop the software allowing interactions with the virtual content using head movements, gestures, and voice commands. Quantitative tests were performed to estimate the accuracy of the system by evaluating the perceived position of augmented reality targets. Mean and maximum errors matched the requirements of the target application. Qualitative tests were carried out to evaluate workload and usability of the HoloLens for our orthopaedic simulator, considering visual and audio perception and interaction and ergonomics issues. The perceived overall workload was low, and the self-assessed performance was considered satisfactory. Visual and audio perception and gesture and voice interactions obtained a positive feedback. Postural discomfort and visual fatigue obtained a nonnegative evaluation for a simulation session of 40 minutes. These results encourage using mixed-reality to implement a hybrid simulator for orthopaedic open surgery. An optimal design of the simulation tasks and equipment setup is required to minimize the user discomfort. Future works will include Face Validity, Content Validity, and Construct Validity to complete the assessment of the hip arthroplasty simulator

How to Build a Patient-Specific Hybrid Simulator for Orthopaedic Open Surgery: Benefits and Limits of Mixed-Reality Using the Microsoft HoloLens

Orthopaedic simulators are popular in innovative surgical training programs, where trainees gain procedural experience in a safe and controlled environment. Recent studies suggest that an ideal simulator should combine haptic, visual, and audio technology to create an immersive training environment. This article explores the potentialities of mixed-reality using the HoloLens to develop a hybrid training system for orthopaedic open surgery. Hip arthroplasty, one of the most common orthopaedic procedures, was chosen as a benchmark to evaluate the proposed system. Patient-specific anatomical 3D models were extracted from a patient computed tomography to implement the virtual content and to fabricate the physical components of the simulator. Rapid prototyping was used to create synthetic bones. The Vuforia SDK was utilized to register virtual and physical contents. The Unity3D game engine was employed to develop the software allowing interactions with the virtual content using head movements, gestures, and voice commands. Quantitative tests were performed to estimate the accuracy of the system by evaluating the perceived position of augmented reality targets. Mean and maximum errors matched the requirements of the target application. Qualitative tests were carried out to evaluate workload and usability of the HoloLens for our orthopaedic simulator, considering visual and audio perception and interaction and ergonomics issues. The perceived overall workload was low, and the self-assessed performance was considered satisfactory. Visual and audio perception and gesture and voice interactions obtained a positive feedback. Postural discomfort and visual fatigue obtained a nonnegative evaluation for a simulation session of 40 minutes. These results encourage using mixed-reality to implement a hybrid simulator for orthopaedic open surgery. An optimal design of the simulation tasks and equipment setup is required to minimize the user discomfort. Future works will include Face Validity, Content Validity, and Construct Validity to complete the assessment of the hip arthroplasty simulator

Integrative Taxonomy of Armeria Taxa (Plumbaginaceae) Endemic to Sardinia and Corsica

Sardinia and Corsica are two Mediterranean islands where the genus Armeria is represented by 11 taxa, 10 out of which are endemic. An integrative approach, using molecular phylogeny, karyology, and seed and plant morphometry was used to resolve the complex taxonomy and systematics in this group. We found that several taxa are no longer supported by newly produced data. Accordingly, we describe a new taxonomic hypothesis that only considers five species: Armeria leucocephala and A. soleirolii, endemic to Corsica, and A. morisii, A. sardoa, and A. sulcitana, endemic to Sardinia

A novel background reduction strategy for high level triggers and processing in gamma-ray Cherenkov detectors

Gamma ray astronomy is now at the leading edge for studies related both to fundamental physics and astrophysics. The sensitivity of gamma detectors is limited by the huge amount of background, constituted by hadronic cosmic rays (typically two to three orders of magnitude more than the signal) and by the accidental background in the detectors. By using the information on the temporal evolution of the Cherenkov light, the background can be reduced. We will present here the results obtained within the MAGIC experiment using a new technique for the reduction of the background. Particle showers produced by gamma rays show a different temporal distribution with respect to showers produced by hadrons; the background due to accidental counts shows no dependence on time. Such novel strategy can increase the sensitivity of present instruments.Comment: 4 pages, 3 figures, Proc. of the 9th Int. Syposium "Frontiers of Fundamental and Computational Physics" (FFP9), (AIP, Melville, New York, 2008, in press

Presence and characterization of zoonotic bacterial pathogens in wild boar hunting dogs (Canis lupus familiaris) in tuscany (italy)

Domestic dogs (Canis lupus familiaris) used for wild boar (Sus scrofa) hunting may represent incidental hosts for several zoonotic pathogens. This investigation aimed to evaluate the presence of anti-Leptospira antibodies and the occurrence, antimicrobial resistance, and virulence of Salmonella spp., Yersinia enterocolitica, and Listeria monocytogenes in sera and rectal swabs collected from 42 domestic hunting dogs in the Tuscany region (Italy). Regarding Leptospira, 31 out of 42 serum samples (73.8%) were positive and serogroup Pomona was the most detected (71.4%) at titers between 1:100 and 1:400. Four Salmonella isolates (9.52%) were obtained, all belonging to serotype Infantis; two of them showed antimicrobial resistance to streptomycin, while pipB and sopE presence was assessed in all but one isolate. Concerning Yersinia enterocolitica, seven isolates (16.7%) were obtained, six belonging to biotype 1 and one to biotype 4. Resistance to amoxicillin–clavulanic acid, cephalothin, and ampicillin was detected. Biotype 4 presented three of the virulence genes searched (ystA, ystB, inv), while isolates of biotype 1 showed only one gene. No Listeria monocytogenes was isolated from dog rectal swabs. The results suggest that hunting dogs are exposed to different bacterial zoonotic agents, potentially linked to their work activity, and highlight the possible health risks for humans