Università degli Studi eCampus

About: Università degli Studi eCampus is a education organization based out in Novedrate, Italy. It is known for research contribution in the topics: Anxiety & Planck. The organization has 124 authors who have published 538 publications receiving 21483 citations. The organization is also known as: Universita degli Studi eCampus.

BDNF Genotype and Baseline Serum Levels in Relation to Electroconvulsive Therapy Effectiveness in Treatment-Resistant Depressed Patients.

Abstract: Objectives Electroconvulsive therapy (ECT) represents one of the most effective therapies for treatment-resistant depression (TRD). The brain-derived neurotrophic factor (BDNF) is a neurotrophin implicated in major depressive disorder and in the effects of different therapeutic approaches, including ECT. Both BDNF peripheral levels and Val66Met polymorphism have been suggested as biomarkers of treatment effectiveness. The objective of this study was to test the potential of serum BDNF levels and Val66Met polymorphism in predicting ECT outcome in TRD patients. Methods Seventy-four TRD patients scheduled to undergo ECT were included in the study. Illness severity was assessed through the Montgomery and Asberg Depression Rating Scale before beginning ECT (T0), the day after the end of ECT (T1), and 1 month after the end of ECT (T2). At T1, patients were classified as responders/nonresponders and remitters/nonremitters, whereas at T2, they were classified as sustained responders/nonresponders and sustained remitters/nonremitters. Serum concentrations of BDNF were measured at T0, and the BDNF Val66Met polymorphism was genotyped. Results No difference in BDNF concentrations was observed in responders versus nonresponders, in remitters versus nonremitters, in sustained responders versus sustained nonresponders, and in sustained remitters versus sustained nonremitters. No association of Val66Met polymorphism was detected with both the response and the remission status. Conclusions Baseline serum BDNF levels and the BDNF Val66Met polymorphism showed no clinical utility in predicting ECT outcome in TRD patients.

Application of DIC to Static and Dynamic Testing of Agglomerated Cork Material

Abstract: In this work, experimental compression tests have been performed on parallelepiped specimens cut from an agglomerated cork slab. The tests have been performed both using a quasi-static testing machine and a polymeric Split Hopkinson Bar, in order to assess the sensitivity of the material to the strain rate. A standard and a high-speed digital camera have been used to collect frames of the samples during the tests. 2D DIC analyses have been conducted on the pictures of lateral faces of the specimens in order to evaluate the actual strain distributions, which showed a significant heterogeneity within each sample. Moreover, the DIC analyses on the dynamic tests have been used for evaluating the local accelerations and to compute the inertia stresses. The latter may affect the global response that can be measured by following the standard Hopkinson bar procedures, and are responsible for the fluctuations in the force histories observed in the tests at highest strain rates.

SLAM-based autonomous wheelchair navigation system for AAL scenarios

Abstract: Commercial electric-power wheelchairs have become much cheaper in the recent years, likewise the availability of reduced size sensors to an affordable price has made their integration easier in this kind of vehicle. This paper presents the development of a smart navigation system applied to an electric wheelchair. The developed work falls within the Ambient Assisted Living field (AAL), which includes all the technologies whose aim is to improve the quality of life of people in the home environment, especially for the elderly and physically impaired. In particular, the present work is focused on the development of technological support aimed at improving the daily life of that population segment who has motor difficulties, and is forced to use personal mobility support systems such as a wheelchair. This system is able to localize the wheelchair while it is moving in an indoor environment. The system exploits a low cost hardware and an integrated open source software, which permit a cheap integration with already available electric wheelchairs.

Details on the Design of a Lockable Spherical Joint for Robotic Applications

Abstract: The paper proposes the mechanical design of a lockable spherical joint, which is designed to be manually or automatically configured in different kinematic solutions. The device is conceived for being used as a conventional spherical joint or converted in a universal joint, or still downgraded to a revolute pair. Therefore different configurations can be chosen according to user needs. In particular, two of the three axes of revolution, arranged in the typical roll-pitch-roll sequence of robot spherical wrists, can be locked alternately in order to provide two differently arranged universal joints. It can be demonstrated that such behavior allows to activate different mobilities of a class of reconfigurable parallel kinematics manipulators and for this task the device has been dimensioned. The transition between such mobilities occurs exploiting the concept of over-constrained kinematics, which is realized by the lockable joint during the switching phase in order to avoid an instantaneous mobility of the robot.

A novel LDA-based approach for motor bearing fault detection

Abstract: Early detection of abnormalities for electrical motors is a key point to reduce economic losses caused by unscheduled maintenance and shutdown time. In this context, health monitoring and fault diagnosis are crucial tasks to be performed. We introduce a novel Linear Discriminant Analysis (LDA) based algorithm to deal with fault data dimension reduction and fault detection issues. In particular the algorithm, namely Δ-LDA, is designed to overcome the problem of a between-class scatter matrix trace very close to zero. Indeed, if the information of the expected value is not sufficient to discriminate the classes, we propose the use of the difference of covariance matrices. A performance comparison with other conventional methods, e.g. principal component analysis and classical LDA, is proposed. In particular experimental results show that the proposed algorithm improves the classification accuracy if the classes are overlapped, and gives comparable results in the remaining scenarios.