Influence of intrinsic decoherence on nonclassical properties of the output of a Bose-Einstein condensate

We investigate nonclassical properties of the output of a Bose-Einstein condensate in Milburn's model of intrinsic decoherence. It is shown that the squeezing property of the atom laser is suppressed due to decoherence. Nevertheless, if some very special conditions were satisfied, the squeezing properties of atom laser could be robust against the decoherence.Comment: 17 pages, 5 figures, Late

Quantum dense coding in multiparticle entangled states via local measurements

In this paper, we study quantum dense coding between two arbitrarily fixed particles in a (N+2)-particle maximally-entangled states through introducing an auxiliary qubit and carrying out local measurements. It is shown that the transmitted classical information amount through such an entangled quantum channel usually is less than two classical bits. However, the information amount may reach two classical bits of information, and the classical information capacity is independent of the number of the entangled particles in the initial entangled state under certain conditions. The results offer deeper insights to quantum dense coding via quantum channels of multi-particle entangled states.Comment: 3 pages, no figur

New Insights into Traffic Dynamics: A Weighted Probabilistic Cellular Automaton Model

From the macroscopic viewpoint for describing the acceleration behavior of drivers, this letter presents a weighted probabilistic cellular automaton model (the WP model, for short) by introducing a kind of random acceleration probabilistic distribution function. The fundamental diagrams, the spatio-temporal pattern are analyzed in detail. It is shown that the presented model leads to the results consistent with the empirical data rather well, nonlinear velocity-density relationship exists in lower density region, and a new kind of traffic phenomenon called neo-synchronized flow is resulted. Furthermore, we give the criterion for distinguishing the high-speed and low-speed neo-synchronized flows and clarify the mechanism of this kind of traffic phenomena. In addition, the result that the time evolution of distribution of headways is displayed as a normal distribution further validates the reasonability of the neo-synchronized flow. These findings suggest that the diversity and randomicity of drivers and vehicles has indeed remarkable effect on traffic dynamics.Comment: 12 pages, 5 figures, submitted to Europhysics Letter

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

CLEO Spectroscopy Results

Recent contributions of the CLEO experiment to hadron spectroscopy are presented.Comment: 6 pages, 4 figures, presented at Beauty 2005, Assisi, Italy, 20--24 June 2005 References further update

Blood Glucose Prediction in Type 1 Diabetes Using Deep Learning on the Edge

Real-time blood glucose (BG) prediction can enhance decision support systems for insulin dosing such as bolus calculators and closed-loop systems for insulin delivery. Deep learning has been proven to achieve state-of-the-art performance in BG prediction. However, it is usually seen as a very computationally expensive approach, hence difficult to implement in wearable medical devices such as transmitters in continuous glucose monitoring (CGM) systems. In this work, we introduce a novel deep learning framework to predict BG levels with the edge inference on a microcontroller unit embedded in a low- power system. By using glucose measurements from a CGM sensor and a recurrent neural network that builds on long-short term memory, the personalized models achieves state-of-the-art performance on a clinical data set obtained from 12 subjects with T1D. In particular, the proposed method achieves an average root mean square error of 19.10 ± 2.04 for a 30-minute prediction horizon (PH) and 32.61 ± 3.45 for a 60-minute PH with high clinical accuracy. Notably, the framework has been optimized to achieve a minimum use of hardware resources (34KB FLASH and 1KB SRAM) as well as an execution time of 22 ms for low power operations (8 μW). The presented system has the potential to be implemented in wearable medical devices for diabetes care (CGM and insulin pumps) and to be integrated within an Internet of Things platform

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

Effect of berberine on insulin resistance in women with polycystic ovary syndrome: study protocol for a randomized multicenter controlled trial.

BACKGROUND: Insulin resistance and hyperinsulinemia play a key role in the pathogenesis of polycystic ovary syndrome (PCOS), which is characterized by hyperandrogenism, ovulatory dysfunction, and presence of polycystic ovaries on pelvic scanning. Insulin resistance is significantly associated with the long-term risks of metabolic syndrome and cardiovascular disease. Berberine has effects on insulin resistance but its use in women with PCOS has not been fully investigated. In this paper, we present a research design evaluating the effects of berberine on insulin resistance in women with PCOS. METHODS/DESIGN: This is a multicenter, randomized, placebo-controlled and double-blind trial. A total of 120 patients will be enrolled in this study and will be randomized into two groups. Berberine or placebo will be taken orally for 12 weeks. The primary outcome is the whole body insulin action assessed with the hyperinsulinemic-euglycemic clamp. DISCUSSION: We postulate that women with PCOS will have improved insulin resistance following berberine administration. TRIAL REGISTRATION: This study is registered at ClinicalTrials.gov, NCT01138930.published_or_final_versio

Search for Bc(ns)B_c(ns) via the Bc(ns)→Bc(ms)π+π−B_c(ns)\to B_c(ms)\pi^+\pi^- transition at LHCb and Z0Z_0 factory

It is interesting to study the characteristics of the whole family of $B_c$ which contains two different heavy flavors. LHC and the proposed $Z^0$ factory provide an opportunity because a large database on the $B_c$ family will be achieved. $B_c$ and its excited states can be identified via their decay modes. As suggested by experimentalists, $B_c^*(ns)\to B_c+\gamma$ is not easy to be clearly measured, instead, the trajectories of $\pi^+$ and $\pi^-$ occurring in the decay of $B_c(ns)\to B_c(ms)+\pi^+\pi^-$ ($n>m$) can be unambiguously identified, thus the measurement seems easier and more reliable, therefore this mode is more favorable at early running stage of LHCb and the proposed $Z^0$ factory. In this work, we calculate the rate of $B_c(ns)\to B_c(ms)+\pi^+\pi^-$ in terms of the QCD multipole-expansion and the numerical results indicate that the experimental measurements with the luminosity of LHC and $Z^0$ factory are feasible.Comment: 12 pages, 1 figures and 4 tables, acceptted by SCIENCE CHINA Physics, Mechanics & Astronomy (Science in China Series G

Comparison of FDTD Algorithms for Subcellular Modeling of Slots in Shielding Enclosures

Subcellular modeling of thin slots in the finite-difference time-domain (FDTD) method is investigated. Two subcellular algorithms for modeling thin slots with the FDTD method are compared for application to shielding end osures in electromagnetic compatibility (EMC). The stability of the algorithms is investigated, and comparisons between the two methods for slots in planes, and slots in loaded cavities are made. Results for scattering from a finite-length slot in an infinite plane employing one of the algorithms are shown to agree well with published experimental results, and power delivered to an enclosure with a slot agree well with results measured for this study