Effect of radiative and nonradiative energy transfer processes of light-emitting diodes combined with quantum dots for visible light communication.

Though light-emitting diodes (LEDs) combined with various color conversion techniques have been widely explored for VLC (visible light communication), E-O (electro-optical) frequency responses of devices with quantum dots (QDs) embedded within the nanoholes have rarely been addressed. Here we propose LEDs with embedded photonic crystal (PhC) nanohole patterns and green light QDs for studying small-signal E-O frequency bandwidths and large signal on-off keying E-O responses. We observe that the E-O modulation quality of PhC LEDs with QDs is better than a conventional LED with QDs when the overall blue mixed with green light output signal is considered. However, the optical response of only QD converted green light shows a contradictory result. The slower E-O conversion response is attributed to multi-path green light generation from both radiative and nonradiative energy transfer processes for QDs coated on the PhC LEDs.

Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates.

Hawkes processes are used in statistical modeling for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial-temporal covariates. We model the conditional intensity of new COVID-19 cases and deaths in the U.S. at the county level, estimating the dynamic reproduction number of the virus within an EM algorithm through a regression on Google mobility indices and demographic covariates in the maximization step. We validate the approach on both short-term and long-term forecasting tasks, showing that the Hawkes process outperforms several models currently used to track the pandemic, including an ensemble approach and an SEIR-variant. We also investigate which covariates and mobility indices are most important for building forecasts of COVID-19 in the U.S.

Beyond GHz optical frequency up-converted modulation of LEDs with integrated acousto-optic transducer.

Traditional visible light communication (VLC) via light-emitting diodes (LEDs) employs the on-off keying (OOK) modulation scheme. Even though optical frequency modulation has many advantages, it is hardly used for LED VLC because a high carrier frequency cannot be applied to the LED cavity due to the resistance-capacitance limit. Here, by monolithically integrating an LED with an integrated digital transducer, we experimentally demonstrate the intermixing of gigahertz surface acoustic waves and electrical data signals in the LED cavity at room temperature. An optical transmitter was realized by in situ frequency up-conversion of the data signals from an LED, which has the advantages of improving transmission performance by up-shifting the data spectrum away from low-frequency noise. Our proposed integrated acousto-optic transducer opens a new developing scheme on the frequency up-mixed data encoding of an LED beyond its inherent modulation bandwidth for future VLC.

Pattern Discovery from High-Order Drug-Drug Interaction Relations.

Drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) represent a significant public health problem in the USA. The research presented in this manuscript tackles the problems of representing, quantifying, discovering, and visualizing patterns from high-order DDIs in a purely data-driven fashion within a unified graph-based framework and via unified convolution-based algorithms. We formulate the problem based on the notions of nondirectional DDI relations (DDI-nd's) and directional DDI relations (DDI-d's), and correspondingly developed weighted complete graphs and hyper-graphlets for their representation, respectively. We also develop a convolutional scheme and its stochastic algorithm SD 2 ID 2 S to discover DDI-based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns of high-order DDIs.

PRASA: an integrated web server that analyzes protein interaction types.

This work presents the Protein Association Analyzer (PRASA) (http://zoro.ee.ncku.edu.tw/prasa/) that predicts protein interactions as well as interaction types. Protein interactions are essential to most biological functions. The existence of diverse interaction types, such as physically contacted or functionally related interactions, makes protein interactions complex. Different interaction types are distinct and should not be confused. However, most existing tools focus on a specific interaction type or mix different interaction types. This work collected 7234058 associations with experimentally verified interaction types from five databases and compiled individual probabilistic models for different interaction types. The PRASA result page shows predicted associations and their related references by interaction type. Experimental results demonstrate the performance difference when distinguishing between different interaction types. The PRASA provides a centralized and organized platform for easy browsing, downloading and comparing of interaction types, which helps reveal insights into the complex roles that proteins play in organisms.