Thermalization Effect in semiconductor Si, and metallic silicide NiSi2, CoSi2 by using Non-Adiabatic Molecular Dynamics Approach

Recently, cold source transistor (CSFET) with steep-slope subthreshold swing (SS) < 60 mV/decade has been proposed to overcome Boltzmann tyranny in its ballistic regime. However the scattering, especially by inelastic scattering may lead serious SS degradation through cold carrier thermalization. In this study, the electronic excitation/relaxation dynamic process is investigated theoretically by virtue of the state-of-the-art nonadiabatic molecular dynamics (NAMD) method, i.e., the mixed quantum-classical NAMD. The mixed quantum-classical NAMD considers both carrier decoherence and detailed balance to calculate the cold carrier thermalization and transfer processes in semiconductor Si, and metallic silicide (NiSi2 and CoSi2). The dependence of the thermalization factor, relaxation time, scattering time and scattering rate on energy level are obtained. The thermalization of carrier gradually increases from low energy to high energy. Partially thermalization from the ground state to reach the thermionic current window is realized with sub-100 $fs$ time scale. Fully thermalization to entail energy region depends on the barrier height sensitively, i.e., the scattering rate decreases exponentially as the energy of the out-scattering state increase. The scattering rate of NiSi2 and CoSi2 is 2 orders of magnitude higher than that of Si, arising from their higher density of states than that in Silicon This study can shed light on the material design for low power tunneling FET as well as the emerging CSFET.Comment: 14 pages, 17 figre

PO-246 Effects of Different Methods of Precooling on Sub-maximal Intensity Exercise in Heat and High Humidity

Objective This study aimed to investigate the influence of using different precooling measures on the capacity of competition and the exercise performance in hot and humidity environment. The most effectual means of precooling will be recommended to help coaches and athletes to improve the ability and performance in training and matches Methods Ten male football (Rugby) players who came from the rugby team totally completed four experimental conditions in hot/humid conditions (38℃, 50% humidity). Initially, a 30-min precooling period consisting of either nothing to control (CONT, C); wearing cooling vest (4℃, V); ingesting of ice beverage (2.3 ml /kg of 4℃, I); or the mix method of combination of V and I (V+I, M). Following this, sub-maximal exercise (80% VO2max) of treadmill test occurred, until athletes exhausted Results The running distance of M and V and I have a significant increase (P≤0.05) than CONT. The peak oxygen uptake of exhaustion was no significant difference between each other. After exercise, the change rate of heart rate ratio of M compared with CONT has a very significant decrease (P≤0.01). The core temperature of M and CONT has a significant increase (P≤0.05) in comparison. The surface temperature of I and M and V comparison with CONT has a very significant increase (P≤0.01). When participants exhaust, the RPE of M in comparison with CONT had significantly lower (P≤0.05). The RPB and the rating of thermal sensation of each condition were no significant difference. After exercise, the blood lactic concentration of each ones was no significant difference Conclusions In hot and humidity condition, precooling has a promoting effect on the sub-maximal exercise. Precooling measures could improve the exercise performance and maintain the stability of functional status and physiology, especially the mix method. &nbsp

Precise Measurements of Branching Fractions for Ds+D_s^+ Meson Decays to Two Pseudoscalar Mesons

We measure the branching fractions for seven $D_{s}^{+}$ two-body decays to pseudo-scalar mesons, by analyzing data collected at $\sqrt{s}=4.178\sim4.226$ GeV with the BESIII detector at the BEPCII collider. The branching fractions are determined to be $\mathcal{B}(D_s^+\to K^+\eta^{\prime})=(2.68\pm0.17\pm0.17\pm0.08)\times10^{-3}$, $\mathcal{B}(D_s^+\to\eta^{\prime}\pi^+)=(37.8\pm0.4\pm2.1\pm1.2)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+\eta)=(1.62\pm0.10\pm0.03\pm0.05)\times10^{-3}$, $\mathcal{B}(D_s^+\to\eta\pi^+)=(17.41\pm0.18\pm0.27\pm0.54)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+K_S^0)=(15.02\pm0.10\pm0.27\pm0.47)\times10^{-3}$, $\mathcal{B}(D_s^+\to K_S^0\pi^+)=(1.109\pm0.034\pm0.023\pm0.035)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+\pi^0)=(0.748\pm0.049\pm0.018\pm0.023)\times10^{-3}$, where the first uncertainties are statistical, the second are systematic, and the third are from external input branching fraction of the normalization mode $D_s^+\to K^+K^-\pi^+$. Precision of our measurements is significantly improved compared with that of the current world average values

A hybrid feature selection algorithm combining information gain and grouping particle swarm optimization for cancer diagnosis.

BackgroundCancer diagnosis based on machine learning has become a popular application direction. Support vector machine (SVM), as a classical machine learning algorithm, has been widely used in cancer diagnosis because of its advantages in high-dimensional and small sample data. However, due to the high-dimensional feature space and high feature redundancy of gene expression data, SVM faces the problem of poor classification effect when dealing with such data.MethodsBased on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). The algorithm firstly calculates the information gain values of the features and ranks them in descending order according to the value. Then, ranked features are grouped according to the information index, so that the features in the group are close, and the features outside the group are sparse. Finally, grouped features are searched using grouping PSO and evaluated according to in-group and out-group.ResultsExperimental results show that the average accuracy (ACC) of the SVM on the feature subset selected by the IG-GPSO is 98.50%, which is significantly better than the traditional feature selection algorithm. Compared with KNN, the classification effect of the feature subset selected by the IG-GPSO is still optimal. In addition, the results of multiple comparison tests show that the feature selection effect of the IG-GPSO is significantly better than that of traditional feature selection algorithms.ConclusionThe feature subset selected by IG-GPSO not only has the best classification effect, but also has the least feature scale (FS). More importantly, the IG-GPSO significantly improves the ACC of SVM in cancer diagnostic

Low-Energy-Loss Polymer Solar Cells with 14.52% Efficiency Enabled by Wide-Band-Gap Copolymers

Summary: Two wide band-gap copolymers poly[4,8-bis(5-(2-butylhexylthio)thiophen-2-yl)benzo[1,2-b:4,5-b′]dithiophene-2,6-diyl-alt-TZNT] (PBDTS-TZNT) and poly[4,8-bis(4-fluoro-5-(2-butylhexylthio)thiophen-2-yl)benzo[1,2-b:4,5-b′]dithiophene-2,6-diyl-alt-TZNT] (PBDTSF-TZNT) based on naphtho[1,2-c:5,6-c]bis(2-octyl-[1,2,3]triazole) (TZNT) and benzo[1,2-b:4,5-b']dithiophene (BDT) with different conjugated side chains have been developed for efficient nonfullerene polymer solar cells (NF-PSCs). The rigid planar backbone of BDT and TZNT units imparted high crystallinity and good molecular stacking property to these copolymers. Using 3,9-bis(2-methylene-(3-(1,1-dicyanomethylene)-indanone)-5,5,11,11-tetrakis(4-hexylphenyl)-dithieno[2,3-d:2′,3′-d′]-s-indaceno[1,2-b:5,6-b′]-dithiophene (ITIC) as the acceptor, PBDTSF-TZNT devices showed a high Voc of 0.98 V with an Eloss of 0.61 eV. On selecting 3,9-bis(2-methylene-(5,6-difluoro-(3-(1,1-dicyanomethylene)-indanone)-5,5,11,11-tetrakis(4-hexylphenyl)-dithieno[2,3-d:2′,3′-d′]-s-indaceno[1,2-b:5,6-b’]-dithiophene (IT-4F) instead of ITIC, the devices maintained the high Voc of 0.93 V with an even lower Eloss of 0.59 eV. The combination of the above-mentioned low Eloss, broadened absorption, better matched energy level, improved crystallinity, and fine-tuned morphology promoted the power conversion efficiency (PCE) of PBDTSF-TZNT:IT-4F devices from 12.16% to 13.25%. Homo-tandem devices based on PBDTSF-TZNT:IT-4F subcells further enhanced the light-harvesting ability and boosted the PCE of 14.52%, which is the best value for homo-tandem NF-PSCs at present. : Chemical Synthesis; Energy Materials; Devices Subject Areas: Chemical Synthesis, Energy Materials, Device

Leak detection and location of flanged pipes: An integrated approach of principle component analysis and guided wave mode

International audienc

CGUFS: A clustering-guided unsupervised feature selection algorithm for gene expression data

(Aim) Gene expression data is typically high dimensional with a limited number of samples and contain many features that are unrelated to the disease of interest. Existing unsupervised feature selection algorithms primarily focus on the significance of features in maintaining the data structure while not taking into account the redundancy among features. Determining the appropriate number of significant features is another challenge. (Method) In this paper, we propose a clustering-guided unsupervised feature selection (CGUFS) algorithm for gene expression data that addresses these problems. Our proposed algorithm introduces three improvements over existing algorithms. For the problem that existing clustering algorithms require artificially specifying the number of clusters, we propose an adaptive k-value strategy to assign appropriate pseudo-labels to each sample by iteratively updating a change function. For the problem that existing algorithms fail to consider the redundancy among features, we propose a feature grouping strategy to group highly redundant features. For the problem that the existing algorithms cannot filter the redundant features, we propose an adaptive filtering strategy to determine the feature combinations to be retained by calculating the potentially effective features and potentially redundant features of each feature group. (Result) Experimental results show that the average accuracy (ACC) and matthews correlation coefficient (MCC) indexes of the C4.5 classifier on the optimal features selected by the CGUFS algorithm reach 74.37% and 63.84%, respectively, significantly superior to the existing algorithms. (Conclusion) Similarly, the average ACC and MCC indexes of the Adaboost classifier on the optimal features selected by the CGUFS algorithm are significantly superior to the existing algorithms. In addition, statistical experiment results show significant differences between the CGUFS algorithm and the existing algorithms

Enhanced Photoassisted Li‐O2 Battery with Ce‐UiO‐66 Metal‐Organic Framework Based Photocathodes

Abstract Li‐O2 batteries have attracted extensive attention because of their theoretical specific energy equivalent to gasoline, but the poor electrical conductivity of Li2O2 leads to high overpotential, which limits their further development and application. Herein, this work introduces Ce‐UiO‐66, a metal‐organic framework material, as the photocatalyst to reduce the overpotential in the discharging and charging processes. With the simple in situ growth of Ce‐UiO‐66 on carbon cloth as an integrated photocathode, the photoassisted Li‐O2 batteries display decent discharge and charge voltages of 3.1 and 3.6 V, and a lifespan of 160 cycles. Moreover, theoretical calculations have been carried out to understand the band structure, spectroscopy, and photocatalytic property of Ce‐UiO‐66. The findings will encourage an enormous variety of novel MOFs‐based photocathodes for solar energy utilization systems