Convergence of proximal solutions for evolution inclusions with time-dependent maximal monotone operators

Mathematical Programming - This article studies the solutions of time-dependent differential inclusions which is motivated by their utility in optimization algorithms and the modeling of physical...     

Electronic structures of CO adsorbed Pt-skin surface on Pt–Co and Pt–Ni alloys

By using angle resolved photoemission spectroscopy, we investigate the electronic structures of Pt-skin layer of Pt–Co and Pt–Ni alloys with CO molecules on the surface. Measured Fermi surface maps and band dispersions reflect the signatures of chemical bonding between Pt-skin layer and CO molecules. Furthermore, the degree of chemical bonding strength of CO molecules, estimated from the energy shift of the participating bands, is found to be reduced on both Pt bimetallic alloys. Our results show how the surface band structure of Pt bimetallic alloys is modified with molecular orbitals of CO molecules on the surface, revealing the important role of the electronic structure in the determination of chemical properties of bimetallic alloys.     

Low-Temperature Physical Mechanism of Inelastic Strain of Metallic Glass

Russian Physics Journal - The paper deals with the low-temperature physical mechanism of inelastic strain of metallic glass, which is the quantum atomic tunneling in a double well potential or...     

Diffusion Model of a Non-Integer Order PIγ Controller with TCP/UDP Streams

In this article, a way to employ the diffusion approximation to model interplay between TCP and UDP flows is presented. In order to control traffic congestion, an environment of IP routers applying AQM (Active Queue Management) algorithms has been introduced. Furthermore, the impact of the fractional controller PIγ and its parameters on the transport protocols is investigated. The controller has been elaborated in accordance with the control theory. The TCP and UDP flows are transmitted simultaneously and are mutually independent. Only the TCP is controlled by the AQM algorithm. Our diffusion model allows a single TCP or UDP flow to start or end at any time, which distinguishes it from those previously described in the literature.     

A multigrid method for the ground state solution of Bose–Einstein condensates based on Newton iteration

BIT Numerical Mathematics - In this paper, a new kind of multigrid method is proposed for the ground state solution of Bose–Einstein condensates based on Newton iteration scheme. Instead of...     

Quantized-states-based adaptive control against unknown slippage effects of uncertain mobile robots with input and state quantization

An adaptive tracking design strategy based on quantized state feedback is developed for uncertain nonholonomic mobile robots with unknown wheel slippage effects. All state variables and control torques are assumed to be quantized by the state and input quantizers, respectively, in a network control environment. Thus, the quantized state feedback information is only available for the tracking control design. An approximation-based adaptive controller using quantized states is recursively designed to ensure the robust adaptive tracking against unknown wheel slippage effects where the quantized-states-based adaptive mechanism is derived to compensate for unknown wheel slippage effects, system nonlinearities, and quantization errors. The boundedness of the quantization errors and estimated parameters in the closed-loop system is analyzed by presenting some theoretical lemmas. Based on these lemmas, we prove the uniform ultimate boundedness of closed-loop signals and the convergence of the trajectory tracking error in the presence of wheel slippage effects. Simulations verify the effectiveness of the resulting tracking scheme.     

Energy band modulation of GaAs/Al0.26Ga0.74As quantum well in 3D self-assembled nanomembranes

In this study, we investigate the modulation of energy band in 3D self-assembled nanomembranes containing GaAs/Al_0.26Ga_0.74As quantum wells (QWs). Photoluminescence (PL) characterizations demonstrate that the self-assembled structures have different optical transition properties and the modulation of the energy band is thus realized. Detailed spectral analyses disclose that the small strain change in structures with different curvatures cannot cause remarkable change in energy bands in Al_0.26Ga_0.74As layer. On the other hand, the optical transitions of GaAs QW layer is influenced by the strain evolution in term of light emission intensity. We also find the first order Stark effect in rolled-up nanomembrane with diameter of 150 μm, which is closely connected with the coupling effect between the deformation potential and the piezoelectric potential. Our work may pave a way for the fabrication of high performance rolled-QW infrared photo-detectors.     

First principles investigations of Fe,Co, Ni in model honeycomb carbon networks

The behaviors of ferromagnetic transition metals of the first period: Fe, Co and Ni are examined within density functional theory calculations in two dimensional carbon extended networks using model structure LiC₆. Around geometry optimized structures, the energy-volume equations of states considering non magnetic and spin polarized configurations established ferromagnetic ground states with magnetizations –reduced with respect to the metals’– of 2 μ_B for FeC₆ and 1 μ_B for CoC₆ while no magnetic solution could be identified for NiC₆. In the D_6h point group of the P6/mmm space group l_m decomposition of the d states results with increasing energy into doublet state E_1g with d(x²-y²) and d(xy); singlet state A_1g d(z²) and doublet state E_2g d(xz) and d(yz) lying on E_F and responsible of the onset of magnetic moments. This was mirrored via molecular orbital approach with a construct of Fe embedded between two extended carbon networks thus validating the model structure proposed for TC₆ compounds. The 100% polarization in one spin channel allows proposing potential uses in spintronics applications.     

Star-Shaped Ruthenium Complexes as Prototypes of Molecular Gears

The design and synthesis of two families of molecular-gear prototypes is reported, with the aim of assembling them into trains of gears on a surface and ultimately achieving controlled intermolecular gearing motion. These piano-stool ruthenium complexes incorporate a hydrotris(indazolyl)borate moiety as tripodal rotation axle and a pentaarylcyclopentadienyl ligand as star-shaped cogwheel, equipped with five teeth ranging from pseudo-1D aryl groups to large planar 2D paddles. A divergent synthetic approach was followed, starting from a pentakis(p-bromophenyl)cyclopentadienyl ruthenium(II) complex as key precursor or from its iodinated counterpart, obtained by copper-catalyzed aromatic Br/I exchange. Subsequent fivefold cross-coupling reactions with various partners allowed high structural diversity to be reached and yielded molecular-gear prototypes with aryl-, carbazole-, BODIPY- and porphyrin-derived teeth of increasing size and length.     

Transfer learning in deep neural network based under-sampled MR image reconstruction

In Magnetic Resonance Imaging (MRI), the success of deep learning-based under-sampled MR image reconstruction depends on: (i) size of the training dataset, (ii) generalization capabilities of the trained neural network. Whenever there is a mismatch between the training and testing data, there is a need to retrain the neural network from scratch with thousands of MR images obtained using the same protocol. This may not be possible in MRI as it is costly and time consuming to acquire data. In this research, a transfer learning approach i.e. end-to-end fine tuning is proposed for U-Net to address the data scarcity and generalization problems of deep learning-based MR image reconstruction. First the generalization capabilities of a pre-trained U-Net (initially trained on the human brain images of 1.5 T scanner) are assessed for: (a) MR images acquired from MRI scanners of different magnetic field strengths, (b) MR images of different anatomies and (c) MR images under-sampled by different acceleration factors. Later, end-to-end fine tuning of the pre-trained U-Net is proposed for the reconstruction of the above-mentioned MR images (i.e. (a), (b) and (c)). The results show successful reconstructions obtained from the proposed method as reflected by the Structural SIMilarity index, Root Mean Square Error, Peak Signal-to-Noise Ratio and central line profile of the reconstructed images.