Analysis of hydrogen induced failure by hydrogen injection methods in micro- alloyed steels

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Incorporating Real-Time Rando m Time Effects in Neural Networks: A Temporal Summation Mechanism

Implementing random time effects in neural networks has been a challenge for neural network researchers. In this paper, we propose a neurophysiologically inspired temporal summation mechanism to reflect real-time random dynamic processing in neural networks. According to the physiology of neuronal firing, a presynaptic neuron sends out a burst of random spikes to a postsynaptic neuron. In the postsynaptic neuron, spikes arriving at different points in time are summed until the postsynaptic membrane potential exceeds a threshold, thus initiating postsynaptic firing. This temporal summation process can be used as a metric for deriving time predictions in neural networks. To demonstrate potential applications of temporal summation, we have employed a feedforward, two-layer network featuring a Hebbian learning rule to perform simulations using the semantic priming experimental paradigm. W e are able to successfully reproduce not only the basic patterns of observed response time data (e.g., positively skewed response time distributions and speed-accuracy trade-offs) but also the semantic priming effect and the time-course of priming as a function of stimulus-onset-asynchrony. These results suggest that the proposed temporal summation mechanism may be a promising candidate for incorporating real-time, random time effects into neural network modeling of human cognition

Competition between structural distortion and magnetic moment formation in fullerene C20_{20}

We investigated the effect of on-site Coulomb interactions on the structural and magnetic ground state of the fullerene C$_{20}$ based on density-functional-theory calculations within the local density approximation plus on-site Coulomb corrections (LDA+$U$). The total energies of the high symmetry ($I_{h}$) and distorted ($D_{3d}$) structures of C$_{20}$ were calculated for different spin configurations. The ground state configurations were found to depend on the forms of exchange-correlation potentials and the on-site Coulomb interaction parameter $U$, reflecting the subtle nature of the competition between Jahn-Teller distortion and magnetic instability in fullerene C$_{20}$. While the non-magnetic state of the distorted $D_{3d}$ structure is robust for small $U$, a magnetic ground state of the undistorted $I_{h}$ structure emerges for $U$ larger than 4 eV when the LDA exchange-correlation potential is employed.Comment: 4 figures, 1 tabl

Structural and optical properties of ZnO nanorods by electrochemical growth using multi-walled carbon nanotube-composed seed layers

We reported the enhancement of the structural and optical properties of electrochemically synthesized zinc oxide [ZnO] nanorod arrays [NRAs] using the multi-walled carbon nanotube [MWCNT]-composed seed layers, which were formed by spin-coating the aqueous seed solution containing MWCNTs on the indium tin oxide-coated glass substrate. The MWCNT-composed seed layer served as the efficient nucleation surface as well as the film with better electrical conductivity, thus leading to a more uniform high-density ZnO NRAs with an improved crystal quality during the electrochemical deposition process. For ZnO NRAs grown on the seed layer containing MWCNTs (2 wt.%), the photoluminescence peak intensity of the near-band-edge emission at a wavelength of approximately 375 nm was enhanced by 2.8 times compared with that of the ZnO nanorods grown without the seed layer due to the high crystallinity of ZnO NRAs and the surface plasmon-meditated emission enhancement by MWCNTs. The effect of the MWCNT-composed seed layer on the surface wettability was also investigated

Restructuring TCAD System: Teaching Traditional TCAD New Tricks

Traditional TCAD simulation has succeeded in predicting and optimizing the device performance; however, it still faces a massive challenge - a high computational cost. There have been many attempts to replace TCAD with deep learning, but it has not yet been completely replaced. This paper presents a novel algorithm restructuring the traditional TCAD system. The proposed algorithm predicts three-dimensional (3-D) TCAD simulation in real-time while capturing a variance, enables deep learning and TCAD to complement each other, and fully resolves convergence errors.Comment: In Proceedings of 2021 IEEE International Electron Devices Meeting (IEDM

Quality indicators in esophagogastroduodenoscopy

Esophagogastroduodenoscopy (EGD) has been used to diagnose a wide variety of upper gastrointestinal diseases. In particular, EGD is used to screen high-risk subjects of gastric cancer. Quality control of EGD is important because the diagnostic rate is examiner-dependent. However, there is still no representative quality indicator that can be uniformly applied in EGD. There has been growing awareness of the importance of quality control in improving EGD performance. Therefore, we aimed to review the available and emerging quality indicators for diagnostic EGD