Stoke Prevention in Diabetes

Diabetes and ischemic stroke are common disorders that often arise together. Diabetics are at 1.5 to three times the risk of stroke compared with the general population and the associated mortality and morbidity is greater than in those without this underlying condition. Importantly, the relation between disturbed glucose metabolism and cerebrovascular disease is not restricted to acute ischemic stroke. Diabetes is also associated with more insidious ischaemic damage to the brain, mainly manifesting as small-vessel disease and increased risk of cognitive decline and dementia. This paper shows the epidemiologic relationships of stroke in type 2 diabetes and suggest that rigorous assessment and treatment of associated risk factors can substantially reduce the risk of stroke in patients with diabetes

GNSS-Based Navigation for Lunar Missions

Numerous applications, not only Earth-based, but also space-based, have strengthened the interest of the international scientific community in using Global Navigation Satellite Systems (GNSSs) as navigation systems for space missions that require good accuracy and low operating costs. Indeed, already used in Low Earth Orbits (LEOs), GNSS based-navigation GNSS-based navigation systems can maximize the autonomy of a spacecraft while reducing the costs of ground operations, allowing for budget-limited missions of micro- and nanosatellites. This is why GNSS is also very attractive for applications in higher Earth orbits up to the Moon, such as in Moon Transfer Orbits (MTOs). However, while GNSS receivers have already been exploited with success for LEOs, their use in higher Earth orbits above the GNSS constellation is extremely challenging, particularly on the way from the Earth to the Moon, characterized by weaker signals with wider gain variability, larger dynamic ranges resulting in higher Doppler and Doppler rates, critically lower satellite signal availability, and poorer satellite-to-user geometry. In this context, the first research objective and achievement of this PhD research is a feasibility study of GNSS as an autonomous navigation system to reach the Moon, and the determination of the requirements for the design of a code-based GNSS receiver for such a mission. The most efficient combinations of signals transmitted by the GPS, Galileo, and combined GPS-Galileo constellations have been identified by analyzing the theoretical achievable signal acquisition and tracking sensitivities, the resultant constellation availability, the pseudorange error factors, and the geometry error factor and accordingly the achievable navigation performance The results show that GNSS signals can be tracked up to Moon altitude, but not with the current GNSS receiver technology for terrestrial use. The second research objective and achievement is the design and implementation of a GNSS receiver proof-of-concept capable of providing GNSS observations onboard a space vehicle orbiting up to Moon altitude. This research work describes the hardware architecture, the high-sensitivity acquisition and tracking modules and the standalone single-epoch navigation performance of the developed GPS L1 C/A hard-ware receiver, named the WeakHEO receiver. Although they can still be collected, GNSS observations at Moon altitude, if not filtered, but simply used to compute a single-epoch least-squares solution, lead to a very coarse navigation accuracy, on the order of 1 to 10 km, depending on the number and type of signals successfully processed. Therefore, the third and main research objective and achievement is the design and implementation of a GNSS-based orbital filter (OF) determination unit, based on an extended Kalman filter (EKF) and an orbital forces model, able to significantly improve the achievable navigation performance and also to aid acquisition and tracking modules of the GNSS receiver. Simulation results of the OF performance when processing simulated GPS and Galileo observations, but also real GPS L1 C/A observations provided by the WeakHEO receiver (when connected in a hardware in the loop configuration to a full constellation GNSS radio frequency signal simulator), show a positioning accuracy at Moon altitude of a few hundred meters

Fast Uncertainty Estimation for Deep Learning Based Optical Flow

We present a novel approach to reduce the processing time required to derive the estimation uncertainty map in deep learning-based optical flow determination methods. Without uncertainty aware reasoning, the optical flow model, especially when it is used for mission critical fields such as robotics and aerospace, can cause catastrophic failures. Although several approaches such as the ones based on Bayesian neural networks have been proposed to handle this issue, they are computationally expensive. Thus, to speed up the processing time, our approach applies a generative model, which is trained by input images and an uncertainty map derived through a Bayesian approach. By using synthetically generated images of spacecraft, we demonstrate that the trained generative model can produce the uncertainty map 100∼700 times faster than the conventional uncertainty estimation method used for training the generative model itself. We also show that the quality of uncertainty map derived by the generative model is close to that of the original uncertainty map. By applying the proposed approach, the deep learning model operated in real-time can avoid disastrous failures by considering the uncertainty as well as achieving better performance removing uncertain portions of the prediction result

Fast Uncertainty Estimation for Deep Learning Based Optical Flow

We present a novel approach to reduce the processing time required to derive the estimation uncertainty map in deep learning-based optical flow determination methods. Without uncertainty aware reasoning, the optical flow model, especially when it is used for mission critical fields such as robotics and aerospace, can cause catastrophic failures. Although several approaches such as the ones based on Bayesian neural networks have been proposed to handle this issue, they are computationally expensive. Thus, to speed up the processing time, our approach applies a generative model, which is trained by input images and an uncertainty map derived through a Bayesian approach. By using synthetically generated images of spacecraft, we demonstrate that the trained generative model can produce the uncertainty map 100∼700 times faster than the conventional uncertainty estimation method used for training the generative model itself. We also show that the quality of uncertainty map derived by the generative model is close to that of the original uncertainty map. By applying the proposed approach, the deep learning model operated in real-time can avoid disastrous failures by considering the uncertainty as well as achieving better performance removing uncertain portions of the prediction result

Using ground motion prediction equations to monitor variations in quality factor due to induced seismicity: a feasibility study

Sub-surface operations for energy production such as gas storage, fluid reinjection or hydraulic fracking may modify the physical properties of the rocks, in particular the seismic velocity and the anelastic attenuation. The aim of the present study is to investigate, through a synthetic test, the possibility of using empirical ground-motion prediction equations (GMPEs) to observe the variations in the reservoir. In the synthetic test, we reproduce the expected seismic activity (in terms of rate, focal mechanisms, stress drop and the b value of the Gutenberg-Richter) and the variation of medium properties in terms of the quality factor Q induced by a fluid injection experiment. In practice, peak-ground velocity data of the simulated earthquakes during the field operations are used to update the coefficients of a reference GMPE in order to test whether the coefficients are able to capture the medium properties variation. The results of the test show that the coefficients of the GMPE vary during the simulated field operations revealing their sensitivity to the variation of the anelastic attenuation. The proposed approach is suggested as a promising tool that, if confirmed by real data analysis, could be used for monitoring and interpreting induced seismicity in addition to more conventional techniques

Monocular-Based Pose Determination of Uncooperative Space Objects

Vision-based methods to determine the relative pose of an uncooperative orbiting object are investigated in applications to spacecraft proximity operations, such as on-orbit servicing, spacecraft formation flying, and small bodies exploration. Depending on whether the object is known or unknown, a shape model of the orbiting target object may have to be constructed autonomously in real-time by making use of only optical measurements. The Simultaneous Estimation of Pose and Shape (SEPS) algorithm that does not require a priori knowledge of the pose and shape of the target is presented. This makes use of a novel measurement equation and filter that can efficiently use optical flow information along with a star tracker to estimate the target's angular rotational and translational relative velocity as well as its center of gravity. Depending on the mission constraints, SEPS can be augmented by a more accurate offline, on-board 3D reconstruction of the target shape, which allows for the estimation of the pose as a known target. The use of Structure from Motion (SfM) for this purpose is discussed. A model-based approach for pose estimation of known targets is also presented. The architecture and implementation of both the proposed approaches are elucidated and their performance metrics are evaluated through numerical simulations by using a dataset of images that are synthetically generated according to a chaser/target relative motion in Geosynchronous Orbit (GEO)

Report on Non-fatal events cardio-cerebro-vascular to ten years in a Southern Italy cohort

Background: Data relating to non-fatal cardiovascular events are poor but these data are essential to organize targeted interventions on the territory and to understand their effectiveness. Methods: We calculated the rates of morbidity from cardiovascular events covering the period 1998/99 - 2008/09, in a cohort of 1200 persons (600 men and 600 women) aged 25 to 74 years. Data were standardized using the European standard population. Results: The incidence of events to ten years of nonfatal myocardial infarction was 2,2% in men and of 1,8% in women. PCI interventions to ten year have been 3,3% in men and 3,4% in women, the interventions of aorto-coronary bypass have been 2,4% and 0,5% for men and women respectively. While all major cardiovascular events have been more frequent in men, in women there was a higher incidence of stroke (1,6% vs 0,9%). Conclusion: Although by comparison with other European countries Italy is among the countries considered at low-risk of coronary heart disease, in Campania cardiovascular diseases reach higher rates than the rest of the country. Our results are in keeping with the literature data and confirm that cardiovascular diseases are a major public health problem. Local analysis are useful in providing additional information for planning prevention interventions targeted to its own territory

Monocular-Based Pose Determination of Uncooperative Known and Unknown Space Objects

In order to support spacecraft proximity operations, such as on-orbit servicing and spacecraft formation flying, several vision-based techniques exist to determine the relative pose of an uncooperative orbiting object with respect to the spacecraft. Depending on whether the object is known or unknown, a shape model of the orbiting target object may have to be constructed autonomously by making use of only optical measurements. In this paper, we investigate two vision-based approaches for pose estimation of uncooperative orbiting targets: one that is general and versatile such that it does not require a priori knowledge of any information of the target, and the other one that requires knowledge of the target's shape geometry. The former uses an estimation algorithm of translational and rotational dynamics to sequentially perform simultaneous pose determination and 3D shape reconstruction of the unknown target, while the latter relies on a known 3D model of the target's geometry to provide a point-by-point pose solution. The architecture and implementation of both methods are presented and their achievable performance is evaluated through numerical simulations. In addition, a computer vision processing strategy for feature detection and matching and the Structure from Motion (SfM) algorithm for on-board 3D reconstruction are also discussed and validated by using a dataset of images that are synthetically generated according to a chaser/target relative motion in Geosynchronous Orbit (GEO)

Fuzzy Group Decision Making for Influence-Aware Recommendations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Group Recommender Systems are special kinds of Recommender Systems aimed at suggesting items to groups rather than individuals taking into account, at the same time, the preferences of all (or the majority of) members. Most existing models build recommendations for a group by aggregating the preferences for their members without taking into account social aspects like user personality and interpersonal trust, which are capable of affecting the item selection process during interactions. To consider such important factors, we propose in this paper a novel approach to group recommendations based on fuzzy influence-aware models for Group Decision Making. The proposed model calculates the influence strength between group members from the available information on their interpersonal trust and personality traits (possibly estimated from social networks). The estimated influence network is then used to complete and evolve the preferences of group members, initially calculated with standard recommendation algorithms, toward a shared set of group recommendations, simulating in this way the effects of influence on opinion change during social interactions. The proposed model has been experimented and compared with related works

Gastrointestinal Bioaccessibility and Colonic Fermentation of Fucoxanthin from the Extract of the Microalga Nitzschia laevis

The extract of microalga Nitzschia laevis (NLE) is considered a source of dietary fucoxanthin, a carotenoid possessing a variety of health benefits. In the present study, the bioaccessibility and deacetylation of fucoxanthin were studied by simulated in vitro gastrointestinal digestion and colonic batch fermentation. In the gastric phase, higher fucoxanthin loss was observed at pH 3 compared to pH 4 and 5. Lipases are crucial for the deacetylation of fucoxanthin into fucoxanthinol. Fucoxanthinol production decreased significantly in the order: pure fucoxanthin (25.3%) > NLE (21.3%) > fucoxanthin-containing emulsion (11.74%). More than 32.7% of fucoxanthin and fucoxanthinol was bioaccessible after gastrointestinal digestion of NLE. During colon fermentation of NLE, a higher loss of fucoxanthin and changes of short-chain fatty acid production were observed but no fucoxanthinol was detected. Altogether, we provided novel insights on the fucoxanthin fate along the human digestion tract and showed the potential of NLE as a promising source of fucoxanthin.</p