Power-law carrier dynamics in semiconductor nanocrystals at nanosecond time scales

We report the observation of power law dynamics on nanosecond to microsecond time scales in the fluorescence decay from semiconductor nanocrystals, and draw a comparison between this behavior and power-law fluorescence blinking from single nanocrystals. The link is supported by comparison of blinking and lifetime data measured simultaneously from the same nanocrystal. Our results reveal that the power law coefficient changes little over the nine decades in time from 10 ns to 10 s, in contrast with the predictions of some diffusion based models of power law behavior.Comment: 3 pages, 2 figures, compressed for submission to Applied Physics Letter

Hysterectomy, endometrial ablation, and levonorgestrel releasing intrauterine system (Mirena) for treatment of heavy menstrual bleeding : cost effectiveness analysis

Peer reviewedPublisher PD

Defect turbulence in inclined layer convection

We report experimental results on the defect turbulent state of undulation chaos in inclined layer convection of a fluid withPrandtl number $\approx 1$. By measuring defect density and undulation wavenumber, we find that the onset of undulation chaos coincides with the theoretically predicted onset for stable, stationary undulations. At stronger driving, we observe a competition between ordered undulations and undulation chaos, suggesting bistability between a fixed-point attractor and spatiotemporal chaos. In the defect turbulent regime, we measured the defect creation, annihilation, entering, leaving, and rates. We show that entering and leaving rates through boundaries must be considered in order to describe the observed statistics. We derive a universal probability distribution function which agrees with the experimental findings.Comment: 4 pages, 5 figure

Live Demonstration: An IoT Wearable Device for Real-time Blood Glucose Prediction with Edge AI

Blood glucose (BG) prediction is crucial to the successful management of type 1 diabetes (T1D) by allowing for proactive medical interventions and treatment. We present an IoT-enabled wearable device for real-time BG prediction based on continuous glucose monitoring (CGM) and a novel attention-based recurrent neural network (RNN). The complete hardware contains a system on a chip (SoC) that enables BLE connectivity and executes the embedded RNN with edge inference. This device can provide 24-hour predictive glucose alerts, i.e., hypoglycemia, to improve BG control and prevent or mitigate potential complications. Meanwhile, it can be connected to desktop computers and smartphones for the visualization of BG trajectories, data storage, and model update

IoMT-Enabled Real-time Blood Glucose Prediction with Deep Learning and Edge Computing

Blood glucose (BG) prediction is essential to the success of glycemic control in type 1 diabetes (T1D) management. Empowered by the recent development of the Internet of Medical Things (IoMT), continuous glucose monitoring (CGM) and deep learning technologies have been demonstrated to achieve the state of the art in BG prediction. However, it is challenging to implement such algorithms in actual clinical settings to provide persistent decision support due to the high demand for computational resources, while smartphone-based implementations are limited by short battery life and require users to carry the device. In this work, we propose a new deep learning model using an attention-based evidential recurrent neural network and design an IoMT-enabled wearable device to implement the embedded model, which comprises a low-cost and low-power system on a chip to perform Bluetooth connectivity and edge computing for real-time BG prediction and predictive hypoglycemia detection. In addition, we developed a smartphone app to visualize BG trajectories and predictions, and desktop and cloud platforms to backup data and fine-tune models. The embedded model was evaluated on three clinical datasets including 47 T1D subjects. The proposed model achieved superior performance of root mean square error (RMSE), mean absolute error, and glucose-specific RMSE, and obtained the best accuracy for hypoglycemia detection when compared with a group of machine learning baseline methods. Moreover, we performed hardware-in-the-loop in silico trials with 10 virtual T1D adults to test the whole IoMT system with predictive low-glucose management, which significantly reduced hypoglycemia and improved BG control

Hysterectomy, endometrial destruction, and levonorgestrel releasing intrauterine system (Mirena) for heavy menstrual bleeding : systematic review and meta-analysis of data from individual patients

Peer reviewedPublisher PD

Statistical properties of stock order books: empirical results and models

We investigate several statistical properties of the order book of three liquid stocks of the Paris Bourse. The results are to a large degree independent of the stock studied. The most interesting features concern (i) the statistics of incoming limit order prices, which follows a power-law around the current price with a diverging mean; and (ii) the humped shape of the average order book, which can be quantitatively reproduced using a `zero intelligence' numerical model, and qualitatively predicted using a simple approximation.Comment: Revised version, 10 pages, 4 .eps figures. to appear in Quantitative Financ

Cloaked Facebook pages: Exploring fake Islamist propaganda in social media

This research analyses cloaked Facebook pages that are created to spread political propaganda by cloaking a user profile and imitating the identity of a political opponent in order to spark hateful and aggressive reactions. This inquiry is pursued through a multi-sited online ethnographic case study of Danish Facebook pages disguised as radical Islamist pages, which provoked racist and anti-Muslim reactions as well as negative sentiments towards refugees and immigrants in Denmark in general. Drawing on Jessie Daniels’ critical insights into cloaked websites, this research furthermore analyses the epistemological, methodological and conceptual challenges of online propaganda. It enhances our understanding of disinformation and propaganda in an increasingly interactive social media environment and contributes to a critical inquiry into social media and subversive politics

Phase transition in the modified fiber bundle model

We extend the standard fiber bundle model (FBM) with the local load sharing in such a way that the conservation of the total load is relaxed when an isolated fiber is broken. In this modified FBM in one dimension (1D), it is revealed that the model exhibits a well-defined phase transition at a finite nonzero value of the load, which is in contrast to the standard 1D FBM. The modified FBM defined in the Watts-Strogatz network is also investigated, and found is the existences of two distinct transitions: one discontinuous and the other continuous. The effects of the long-range shortcuts are also discussed.Comment: 7 pages, to appear in Europhys. Let