5 research outputs found
Resilience of Romanian Institutions during the Global Gag Rule: 20012007
Get PDFThere is a wealth of literature about the negative effects of the executive order called the Global Gag Rule, officially known as the Mexico City Policy, and its various manifestations. Despite this, there is a gap in the research about how institutions in affected countries respond to the Global Gag Rule’s family planning restrictions. This qualitative study seeks to provide some insight into how Romanian institutions responded during President George W. Bush’s reinstatement of the policy by using a model of social resilience. Through a literature review of three subjects (social resilience, the Global Gag Rule, and Romania’s family planning history) and interviews with key experts and Romanian reproductive rights advocates, the author identified the ways in which civil society effectively and ineffectively organized itself to respond to family planning restrictions. NGOs were critical in service provision and also maintained informal relationships with the government, but they often neglected structural issues and grassroots activism. In the future, civiccivil society partnerships should grow in order to more holistically provide reproductive healthcare services, and NGOs should look outside their sphere to organizations who can amplify their lobbying efforts. This study is particularly relevant given the reinstatement and expansion of the Global Gag Rule by the Trump administration this past January
Impact of Input Sequence Types on Healthcare Intrusion Prediction Models
No full textPrediction models are vital for sensing zero-day and even n-day cyberattacks, particularly in healthcare infrastructure. Most existing research focuses on developing classifiers also known as IDS to enhance detection and accuracy. However, predictive intrusion models for healthcare remain underexplored, with limited studies investigating the comparative performance of univariate and multivariate inputs against single-step and multi-step outputs in time series models. This study aims to address these gaps by evaluating the accuracy and error performance of selected predictive models across various input and output configurations. The methodology involves transforming input data sequences into univariate 1∗ n and multivariate m ∗ n formats, establishing single-step and multi-step splitting functions, and evaluating these configurations using the benchmark CIRA-CIC-DoHBrw-2020 dataset. Algorithms including Bidirectional LSTM, Stacked LSTM, Vanilla LSTM, Transformer Encoder-Decoder, Vector Output LSTM (GRU core), and CNN were applied, with results visualized to assess performance. The findings reveal that the Multivariate LSTM model, when trained on a sequence of multivariate inputs, demonstrates superior predictive performance, achieving low MAE error rates of 0.4% for single-step predictions and 0.1% for multi-step predictions. Additionally, GRU and Transformer models exhibit heightened sensitivity to specific input sequence configurations. In conclusion, our study demonstrates that Transformer Encoder-Decoder based prediction models exhibit exceptional prediction performance. This effectiveness is attributed to their ability to capture contextual and critical information from input sequences. These findings provide valuable insights for designing advanced intrusion prediction models, paving the way for improved prediction capabilities in future systems.
Author Keywords
Intrusion prediction model
intrusion detection system (IDS)
multivariate
univariate
data visualization
machine learning in cybersecurity
intrusion prediction in healthcareEffat Universit
Gonadal Function in Men with Cushing Syndrome
No full textCushing syndrome (CS) is scarcely observed in males. Because of this rarity, the real prevalence of gonadal dysfunction in men with hypercortisolism is unknown. Our aim was to analyze gonadal abnormalities in 37 males with CS (median age=28.9±11years) comparatively to age matched healthy men (n=10). For the homogeneity of the study men over 50, children, patients taking medications and those with pituitary deficits were systematically excluded. For the remaining group, we took into account medical history, clinical examination, and hormonal assessment, by radio immunoassay, for testosterone (T), prolactin (PRL), follicle stimulating hormone (FSH), and luteinizing stimulating hormone (LH).Results: 21% consulted for impotency and/or gynecomastia. When questioned, 65.7% complained about decreased libido and erectile dysfunction. Except for 3, body hair growth and repartition, and testicular volume were normal. Gynecomastia was observed in 18.9%.Testosterone was equal to 2.79±1.62ng/ml vs 6.69±3.87ng/ml (p<0.0005). Low testosterone (<3ng/ml) was observed in 67.5%. PRL =9.8 ± 4.2ng/ml vs 4.9 ± 2.6ng/ml (p<0.01). FSH = 3.87 ± 1.9mu/ml vs 3.75 ± 2.25mU/ml (p<0.30). LH = 2.7 ± 2.2mU/ml vs 3.66 ± 0.86 (p<0.30). We have not found any correlation between cortisol and T, PRL or LH, but there was a positive and significant one with FSH (r=0.57, p<0.005).Conclusion: CS causes a franc hypogonadism in 65%. According to FSH and LH results glucocorticoids excess acts probably at hypothalamic pituitary level, but an increase in testosterone degradation and/or inhibition of testis receptors cannot be ruled out. So men with hypogonadism and/or gynecomastia should be systematically checked for CS
Self-correcting Bayesian target tracking
Get PDFThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorAbstract
Visual tracking, a building block for many applications, has challenges such as occlusions,illumination changes, background clutter and variable motion dynamics that may degrade the
tracking performance and are likely to cause failures. In this thesis, we propose Track-Evaluate-Correct framework (self-correlation) for existing trackers in order to achieve a robust tracking.
For a tracker in the framework, we embed an evaluation block to check the status of tracking quality and a correction block to avoid upcoming failures or to recover from failures. We present a generic representation and formulation of the self-correcting tracking for Bayesian trackers using a Dynamic Bayesian Network (DBN). The self-correcting tracking is done similarly to a selfaware
system where parameters are tuned in the model or different models are fused or selected in a piece-wise way in order to deal with tracking challenges and failures. In the DBN model
representation, the parameter tuning, fusion and model selection are done based on evaluation and correction variables that correspond to the evaluation and correction, respectively. The inferences
of variables in the DBN model are used to explain the operation of self-correcting tracking. The specific contributions under the generic self-correcting framework are correlation-based selfcorrecting
tracking for an extended object with model points and tracker-level fusion as described below.
For improving the probabilistic tracking of extended object with a set of model points, we use Track-Evaluate-Correct framework in order to achieve self-correcting tracking. The framework
combines the tracker with an on-line performance measure and a correction technique. We correlate model point trajectories to improve on-line the accuracy of a failed or an uncertain tracker. A model point tracker gets assistance from neighbouring trackers whenever degradation in its
performance is detected using the on-line performance measure. The correction of the model point state is based on the correlation information from the states of other trackers. Partial Least
Square regression is used to model the correlation of point tracker states from short windowed trajectories adaptively. Experimental results on data obtained from optical motion capture systems show the improvement in tracking performance of the proposed framework compared to the
baseline tracker and other state-of-the-art trackers. The proposed framework allows appropriate re-initialisation of local trackers to recover from failures that are caused by clutter and missed
detections in the motion capture data.
Finally, we propose a tracker-level fusion framework to obtain self-correcting tracking. The
fusion framework combines trackers addressing different tracking challenges to improve the
overall performance. As a novelty of the proposed framework, we include an online performance measure to identify the track quality level of each tracker to guide the fusion. The trackers
in the framework assist each other based on appropriate mixing of the prior states. Moreover, the track quality level is used to update the target appearance model. We demonstrate the framework
with two Bayesian trackers on video sequences with various challenges and show its robustness
compared to the independent use of the trackers used in the framework, and also compared to
other state-of-the-art trackers. The appropriate online performance measure based appearance
model update and prior mixing on trackers allows the proposed framework to deal with tracking
challenges
Oxycodone or Higher Dose of Levodopa for the Treatment of Parkinsonian Central Pain: OXYDOPA Trial
No full textInternational audienceBackground: Among the different types of pain related to Parkinson's disease (PD), parkinsonian central pain (PCP) is the most disabling. Objectives We investigated the analgesic efficacy of two therapeutic strategies (opioid with oxycodone‐ prolonged‐release (PR) and higher dose of levodopa/benserazide) compared with placebo in patients with PCP.Methods: OXYDOPA was a randomized, double‐blind, double‐dummy, placebo‐controlled, multicenter parallel‐group trial run at 15 centers within the French NS‐Park network. PD patients with PCP (≥30 on the Visual Analogue Scale [VAS]) were randomly assigned to receive oxycodone‐PR (up to 40 mg/day), levodopa/benserazide (up to 200 mg/day) or matching placebo three times a day (tid) for 8 weeks at a stable dose, in add‐on to their current dopaminergic therapy. The primary endpoint was the change in average pain intensity over the previous week rated on VAS from baseline to week‐10 based on modified intention‐to‐treat analyses.Results: Between May 2016 and August 2020, 66 patients were randomized to oxycodone‐PR (n = 23), levodopa/benserazide (n = 20) or placebo (n = 23). The mean change in pain intensity was −17 ± 18.5 on oxycodone‐PR, −8.3 ± 11.1 on levodopa/benserazide, and −14.3 ± 18.9 in the placebo groups. The absolute difference versus placebo was −1.54 (97.5% confidence interval [CI], −17.0 to 13.90; P = 0.8) on oxycodone‐PR and +7.79 (97.5% CI, −4.99 to 20.58; P = 0.2) on levodopa/benserazide. Similar proportions of patients in each group experienced all‐cause adverse events. Those leading to study discontinuation were most frequently observed with oxycodone‐PR (39%) than levodopa/benserazide (5%) or placebo (15%).Conclusions: The present trial failed to demonstrate the superiority of oxycodone‐PR or a higher dose of levodopa in patients with PCP, while oxycodone‐PR was poorly tolerated. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
