Proprioceptive Robot Collision Detection through Gaussian Process Regression

This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the currents and the joint configurations are needed. The algorithm extends the standard Gaussian Process models adopted in learning the robot inverse dynamics, using a more rich set of input locations and an ad-hoc kernel structure to model the complex and non-linear behaviors due to frictions in quasi-static configurations. Tests performed on a Universal Robots UR10 show the effectiveness of the proposed algorithm to detect when a collision has occurred.Comment: Published at ACC 201

correction on the stability of manganese tris β diketonate complexes as redox mediators in dsscs

Correction for 'On the stability of manganese tris(β-diketonate) complexes as redox mediators in DSSCs' by Stefano Carli et al., Phys. Chem. Chem. Phys., 2016, 18, 5949–5956

Artificial Neural Network (ANN) modeling of the pulsed heat load during ITER CS magnet operation

Artificial Neural Networks (ANNs) are applied to the development of a simplified transient model of the ITER Central Solenoid (CS), aiming at predicting the evolution of the pulsed heat load from the CS to the LHe bath during plasma operation. The ANNs are trained using the thermal–hydraulic evolution in the CS, computed with the 4C code, due to AC losses. The capability of the ANN model to predict the heat load to the LHe bath is successfully demonstrated in the case of different transients, among which a nominal plasma operating scenario. The gain in speed of the simplified model with respect to the 4C code results is by order of magnitudes, with a small loss of accuracy

Clinicopathological findings and risk factors associated with Cytauxzoon spp. infection in cats : A case-control study (2008-2021)

In Europe, Cytauxzoon spp. infection was documented in domestic and wild felids. Cats often develop a subclinical infection, while fatal disease is rare. Currently, information on the epidemiology, risk factors and clinicopathological findings of Cytauxzoon spp. infection remains limited and obtained by a single subject or small groups of cats. The objective of this case-control study was to evaluate clinicopathological findings and to describe risk factors associated with Cytauxzoon spp. infection in domestic cats. Infected cats (n = 39) and non-infected (n = 190) cats were selected from the database of the referral San Marco Veterinary Laboratory between 2008 and 2021. Demographic information, a preset questionnaire considering lifestyle, environment, and clinical status, and a CBC performed contextually with the PCR analysis were recorded for all cats. Data on the biochemical profile and serum protein electrophoresis were also evaluated when available. Compared to the control group, infection was more likely to occur in stray cats (24/39, 61.5%, P < 0.001), living totally/partially outdoors (36/39, 92.3%, P < 0.001), in an urban context (37/39, 94.9%, P = 0.002), taken or recently adopted from colonies (34/35, 97.1, P < 0.001), with irregular or absent parasite preventive treatments (39/39, 100%, p = 0.005), without fleas (28/35, 80%, P = 0.047) and without clinical signs (22/39, 56.4%, p = 0.026) at the time of medical evaluation. Anemia was not associated with infection, but in cats without clinical signs, the percentage of anemic-infected cats (7/22, 31.8%, P = 0.009) was higher compared to non-infected cats (5/65, 7.7%). Furthermore, a decrease in total iron serum concentration approximating the lowest reference interval [median values (IQR): 79 μg/dL (52.25) vs. 50.5 μg/dL (34), P = 0.007] was likely in infected cats. No other laboratory findings were associated with infection. Interestingly, a partial/total outdoor lifestyle was a risk factor for infection (OR: 8.58, 95% CI: 2.90-37.0, P < 0.001). In conclusion, the present study revealed that Cytauxzoon spp. infection manifests itself prevalently as a subclinical infection, based on physical examination and laboratory findings, in domestic European cats. However, subclinical infected cats were more likely to be anemic compared to non-infected

Design and optimization of Artificial Neural Networks for the modelling of superconducting magnets operation in tokamak fusion reactors

In superconducting tokamaks, the cryoplant provides the helium needed to cool different clients, among which by far the most important one is the superconducting magnet system. The evaluation of the transient heat load from the magnets to the cryoplant is fundamental for the design of the latter and the assessment of suitable strategies to smooth the heat load pulses, induced by the intrinsically pulsed plasma scenarios characteristic of today's tokamaks, is crucial for both suitable sizing and stable operation of the cryoplant. For that evaluation, accurate but expensive system-level models, as implemented in e.g. the validated state-of-the-art 4C code, were developed in the past, including both the magnets and the respective external cryogenic cooling circuits. Here we show how these models can be successfully substituted with cheaper ones, where the magnets are described by suitably trained Artificial Neural Networks (ANNs) for the evaluation of the heat load to the cryoplant. First, two simplified thermal-hydraulic models for an ITER Toroidal Field (TF) magnet and for the ITER Central Solenoid (CS) are developed, based on ANNs, and a detailed analysis of the chosen networks' topology and parameters is presented and discussed. The ANNs are then inserted into the 4C model of the ITER TF and CS cooling circuits, which also includes active controls to achieve a smoothing of the variation of the heat load to the cryoplant. The training of the ANNs is achieved using the results of full 4C simulations (including detailed models of the magnets) for conventional sigmoid-like waveforms of the drivers and the predictive capabilities of the ANN-based models in the case of actual ITER operating scenarios are demonstrated by comparison with the results of full 4C runs, both with and without active smoothing, in terms of both accuracy and computational time. Exploiting the low computational effort requested by the ANN-based models, a demonstrative optimization study has been finally carried out, with the aim of choosing among different smoothing strategies for the standard ITER plasma operation

Artificial Neural Networks: a viable tool to design heat load smoothing strategies for the ITER Toroidal Field coils

In superconducting tokamaks, cryoplants provide the helium needed to cool the superconducting magnet systems. The evaluation of the heat load from the magnets to the cryoplant is fundamental for the design of the latter and the assessment of suitable strategies to smooth the heat load pulses induced by the pulsed plasma scenarios is crucial for the operation. Here, a simplified thermal-hydraulic model of an ITER Toroidal Field (TF) magnet, based on Artificial Neural Networks (ANNs), is developed and inserted into a detailed model of the ITER TF winding and casing cooling circuits based on the state-of-the-art 4C code, which also includes active controls. The low computational effort requested by such a model allows performing a fast parametric study, to identify the best smoothing strategy during standard plasma operation. The ANNs are trained using 4C simulations, and the predictive capabilities of the simplified model are assessed against 4C simulations, both with and without active smoothing, in terms of accuracy and computational time

The dynamic association between Frailty, CD4 and CD4/CD8 ratio in people aging with HIV

To investigate the association between current CD4+ T-cell count and CD4/CD8+ ratio with severity of frailty among people aging with HIV

Early treatment with hydroxychloroquine prevents the development of endothelial dysfunction in a murine model of systemic lupus erythematosus

INTRODUCTION: Accelerated atherosclerosis is one of the major causes of morbidity in patients with systemic lupus erythematosus (SLE). Endothelial dysfunction (ED) is considered an early marker of atherosclerosis. It is a reversible alteration, thus representing an attractive target for prevention strategies against cardiovascular disease. Studies have shown that ED occurs in patients with SLE even in the absence of severe, active disease. Hydroxychloroquine (HCQ) is widely used in SLE to control disease activity, but its use is also associated with an improvement in long-term prognosis. Beyond the beneficial effect in well-established disease, our hypothesis is that treatment with HCQ might have a beneficial impact on ED prevention in SLE. The aim of this study was to assess the impact of early treatment with HCQ on ED in a murine model of SLE. METHODS: Twelve-week-old NZB/W F1 (NZ) and C57BL/6 J mice (controls) were allocated to receive HCQ or vehicle for 6, 12, or 18 weeks. Proteinuria and anti-double-stranded DNA autoantibodies were determined. ED was assessed in mesenteric arteries (pressurized myography). Nitric oxide (NO) availability and reactive oxygen species (ROS) production were evaluated. Vascular ROS production was measured with dihydroethidium (DHE) fluorescent dye. RESULTS: Starting from 18 weeks of age, NZ mice showed a progressive reduction in NO availability, which was normalized by ascorbic acid and apocynin in the up to 24-week-old group, and partly ameliorated in older animals. HCQ administration normalized the NO availability in the up to 24-week-old group, with a partial amelioration in the 30-week-old group. DHE analysis revealed a progressive increment of vascular ROS generation among NZ groups, which was prevented by apocynin. Similarly, in the NZ HCQ-treated group, vascular ROS production was abrogated. CONCLUSIONS: The ED that characterizes this mouse model of SLE is caused by the nicotinamide adenine dinucleotide phosphate oxidase-driven ROS excess. Very early treatment with HCQ is able to exert vascular protection via an antioxidant effect

ground source heat pump systems in historical buildings two italian case studies

Abstract Reducing the energy demand of buildings has become one of the key points of the European Union. The issue related to the air conditioning of old and historical buildings is nowadays one of the most important field of operation for the primary energy saving and, at the same time, for the reduction of the CO 2 emission. The recent development of heat pump able to rise the supply of high temperature at the condenser side makes this technology suitable for the application also in historical buildings that are characterized by low thermal insulation and high thermal capacitance. In this context, the ground source heat pump systems can be used for both heating and cooling. The aim of this work is to analyze the thermal behavior of two historical buildings located in Italy, in Venice and Florence respectively. Detailed computer simulations of the buildings have been carried out by means of a transient calculation tool TRNSYS. Energy simulations of GSHP systems have been performed and a comparison with a common plant system using a gas boiler for heating and air-to-water chiller for cooling has been carried out