169,729 research outputs found
GNSS Spoofing Attack Detection By IMU Measurements Through A Neural Network
No full textSpoofing attacks against global navigation satellite system (GNSS) receivers are a serious threat to secure navigation, also in autonomous driving. Cars typically include, beyond the GNSS receiver, also an inertial measurement unit (IMU), whose data can be used to detect GNSS spoofing attacks. We consider a specific spoofing attack, with the spoofed trajectory that gradually diverges from the true trajectory, and we propose a spoofing detection method based on machine learning. First, a feature vector is designed, collecting the difference of two estimates of the device velocity, obtained from the GNSS receiver and the IMU. Then, a neural network (NN) is trained over a set of true and spoofed trajectories to detect the attack. We compare the proposed solution with an approximated Neyman-Pearson test and a literature reference direct comparison method, confirming the low error probabilities of our novel solution
The Use of a Variable Representing Compliance Improves Accuracy of Estimation of the Effect of Treatment Allocation Regardless of Discontinuation in Trials with Incomplete Follow-up
No full textAbstract–In Clinical Trials, not all randomized patients follow the course of treatment they are allocated to. The potential impact of such deviations is increasingly recognized, and it has been one of the reasons for a redefinition of the targets of estimation (“Estimands”) in the ICH E9 draft Addendum. Among others, the effect of treatment assignment, regardless of the adherence, appears an Estimand of practical interest, in line with the intention-to-treat principle. This study aims at evaluating the performance of different estimation techniques in trials with incomplete post-discontinuation follow-up when a “treatment-policy” strategy is implemented. To achieve that, we have (i) modeled and visualized as directed acyclic diagram a reasonable data-generating model; (ii) investigated which set of variables allows identification and estimation of such effect; (iii) simulated 10,000 trials in Major Depressive Disorder, with varying real treatment effects, proportions of patients discontinuing the treatment, and incomplete follow-up. Our results suggest that, at least in a “Missing at Random” setting, all studied estimation methods increase their performance when a variable representing compliance is used. This effect is more pronounced the higher the proportion of post-discontinuation follow-up is
Sindromi dolorose oro-facciali: proposte per una nosografia differenziale. (Nota III) Casistica clinica
No full textHamilton scale and MADRS are interchangeable in meta-analyses but can disagree at trial level
No full textBackground and Objective: Major depressive disorder is a multidimensional disease, in which demonstrating the efficacy of treatments is difficult. The Hamilton Rating Scale for Depression (HRSD) and the Montgomery–Asberg Depression Rating Scale (MADRS) cover different domains but are used interchangeably as primary measures of the outcome in trials and—with standardized measures—in meta-analyses. We aimed at understanding (i) whether the choice of the outcome measurement tool can influence the outcome of a trial, and if so, (ii) whether one systematically outperforms the other, and (iii) whether using standardized measures of the effect in meta-analysis is justified. Methods: Short-term randomized trials in patients with major depressive disorder that used both the scales were systematically searched and the results were collected. To quantify the differences in the results—both in terms of the standardized mean difference (SMD) and odds ratio (OR) for response—and their range, data were analyzed and plotted with the Bland–Altman method. Results: 161 comparisons from 80 studies were included, involving a total of 18,189 patients. Neither of the two scales appears systematically more sensitive to the treatment effect than the other in terms of SMDs (P-value = 0.06, 95% CI −0.044 to 0.001) or ORs (P-value = 0.15, 95% CI −0.25 to 0.04). However, the variability of differences between the HRSD and MADRS largely depends on the number of patients included in the comparison. Conclusion: No systematic differences between the two scales were found supporting the use of standardized measures in meta-analyses. However, the same trial may give very different results with either scale, especially in small trials. Further research is needed to understand the causes of this variability
Sindrome dolorose oro-facciali:proposte per una nosografia differenziale. (Nota ") Criteri di classificazione
No full textSindrome dolorose oro-facciali:proposte per una nosografia differenziale (NotaI). Basi neurologiche e tecniche d'esame
No full textSindromi dolorose oro-facciali: proposte per una nosografia differenziale. (Nota I) Basi neurologiche e tecniche d'esame
No full textSindromi dolorose oro-facciali: proposte per una nosografia differenziale. (Nota II) Criteri di classificazione
No full textSindromi dolorose oro-facciali: proposte per una nosografia differenziale (Nota 3). Casistica clinica
No full textGoing Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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