Charm-strange baryon strong decays in a chiral quark model

The strong decays of charm-strange baryons up to N=2 shell are studied in a chiral quark model. The theoretical predictions for the well determined charm-strange baryons, $\Xi_c^*(2645)$, $\Xi_c(2790)$ and $\Xi_c(2815)$, are in good agreement with the experimental data. This model is also extended to analyze the strong decays of the other newly observed charm-strange baryons $\Xi_c(2930)$, $\Xi_c(2980)$, $\Xi_c(3055)$, $\Xi_c(3080)$ and $\Xi_c(3123)$. Our predictions are given as follows. (i) $\Xi_c(2930)$ might be the first $P$-wave excitation of $\Xi_c'$ with $J^P=1/2^-$, favors the $|\Xi_c'\ ^2P_\lambda 1/2^->$or$|\Xi_c'\ ^4P_\lambda 1/2^->$state. (ii)$\Xi_c(2980)$might correspond to two overlapping$P$-wave states$|\Xi_c'\ ^2P_\rho 1/2^->$and$|\Xi_c'\ ^2P_\rho 3/2^->$, respectively. The$\Xi_c(2980)$observed in the$\Lambda_c^+\bar{K}\pi$final state is most likely to be the$|\Xi_c'\ ^2P_\rho 1/2^->$state, while the narrower resonance with a mass$m\simeq 2.97$GeV observed in the$\Xi_c^*(2645)\pi$channel favors to be assigned to the$|\Xi_c'\ ^2P_\rho 3/2^->$state. (iii)$\Xi_c(3080)$favors to be classified as the$|\Xi_c\ S_{\rho\rho} 1/2^+>$state, i.e., the first radial excitation (2S) of$\Xi_c$. (iv)$\Xi_c(3055)$is most likely to be the first$D$-wave excitation of$\Xi_c$with$J^P=3/2^+$, favors the$|\Xi_c\ ^2D_{\lambda\lambda} 3/2^+>$state. (v)$\Xi_c(3123)$might be assigned to the$|\Xi_c'\ ^4D_{\lambda\lambda} 3/2^+>$,$|\Xi_c'\ ^4D_{\lambda\lambda} 5/2^+>$, or$|\Xi_c\ ^2D_{\rho\rho} 5/2^+>$state. As a by-product, we calculate the strong decays of the bottom baryons$\Sigma_b^{\pm}$,$\Sigma_b^{*\pm}$and$\Xi_b^*$, which are in good agreement with the recent observations as well.Comment: 15 pages, 9 figure

WearETE: A scalable wearable e-textile triboelectric energy harvesting system for human motion scavenging

In this paper, we report the design, experimental validation and application of a scalable, wearable e-textile triboelectric energy harvesting (WearETE) system for scavenging energy from activities of daily living. The WearETE system features ultra-low-cost material and manufacturing methods, high accessibility, and high feasibility for powering wearable sensors and electronics. The foam and e-textile are used as the two active tribomaterials for energy harvester design with the consideration of flexibility and wearability. A calibration platform is also developed to quantify the input mechanical power and power efficiency. The performance of the WearETE system for human motion scavenging is validated and calibrated through experiments. The results show that the wearable triboelectric energy harvester can generate over 70 V output voltage which is capable of powering over 52 LEDs simultaneously with a 9 × 9 cm2 area. A larger version is able to lighten 190 LEDs during contact-separation process. The WearETE system can generate a maximum power of 4.8113 mW from hand clapping movements under the frequency of 4 Hz. The average power efficiency can be up to 24.94%. The output power harvested by the WearETE system during slow walking is 7.5248 µW. The results show the possibility of powering wearable electronics during human motion

Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model

In this paper we consider the problem of computing an $\epsilon$-optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through a generative sampling model that given any state-action pair samples from the transition function in $O(1)$ time. Given such a DMDP with states $S$, actions $A$, discount factor $\gamma\in(0,1)$, and rewards in range $[0, 1]$ we provide an algorithm which computes an $\epsilon$-optimal policy with probability $1 - \delta$ where \emph{both} the time spent and number of sample taken are upper bounded by $$O\left[\frac{|S||A|}{(1-\gamma)^3 \epsilon^2} \log \left(\frac{|S||A|}{(1-\gamma)\delta \epsilon} \right) \log\left(\frac{1}{(1-\gamma)\epsilon}\right)\right] ~.$$ For fixed values of $\epsilon$, this improves upon the previous best known bounds by a factor of $(1 - \gamma)^{-1}$ and matches the sample complexity lower bounds proved in Azar et al. (2013) up to logarithmic factors. We also extend our method to computing $\epsilon$-optimal policies for finite-horizon MDP with a generative model and provide a nearly matching sample complexity lower bound.Comment: 31 pages. Accepted to NeurIPS, 201