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Sleep along with gastrointestinal disruptions in autism range disorder in kids.
In this work we present a new procedure to compute optical spectra including excitonic effects and approximated quasiparticle corrections with reduced computational effort. The excitonic effects on optical spectra are included by solving the Bethe-Salpeter equation, considering quasiparticle eigenenergies and respective wavefunctions obtained within DFT-1/2 method. The electron-hole ladder diagrams are approximated by the screened exchange. To prove the capability of the procedure, we compare the calculated imaginary part of the dielectric functions of Si, Ge, GaAs, GaP, GaSb, InAs, InP, and InSb with experimental data. The energy position of the absorption peaks are correctly described. The good agreement with experimental results together with the very significant reduction of computational effort makes the procedure suitable on the investigation of optical spectra of more complex systems.Surface diffusion is known to be of prime importance in the growth of semiconductor nanowires. In this work, we used ZnMgO layers as markers to analyze the growth mechanisms and kinetics during the deposition of ZnMgO/ZnO multilayered shells by molecular beam epitaxy on previously grown ZnO nanowire cores (so called core-shell heterostructures). GM6001 Specifically, the influence of the O2 flow sent into the plasma cell on the adatom surface mobility was investigated. By carefully measuring the growth rate on the lateral facets as well as on the top of the nanowires, it is concluded that the surface diffusion length of adatoms, within the used MBE growth conditions, is very low. Such poor surface mobility explains why so few works can be found related to the spontaneous growth (without catalyst) of ZnO nanowires by MBE, contrary to other deposition techniques.The averted cumulative lifetime attributable risk (LAR), the residual dose and highest ground deposition of137Cs complying with a reference dose level of 20 mSv y-1to an individual returning after one year to an area contaminated by nuclear power plant (NPP) fallout were evaluated by applying an existing exposure model designed to compute age- and gender-dependent time-integrated LAR. The model was applied to four types of nuclear fallout scenarios, partly based on data from the Chernobyl and Fukushima releases and from theoretical source terms from Swedish NPPs. For rapid decontamination measures that achieve a 50% relative reduction in external dose rate within 1 y, compliance with the reference level 20 mSv y-1can be attained for an initial137Cs ground deposition of up to 2 MBq m-2with relaxed food restrictions. This compliance can be attained at even higher ground deposition (up to 3.5 MBq m-2) if using the strict food restrictions employed in Japan after 2011. Considering longer than 1 year return times it was also found that the benefit of implementing decontamination decreases rapidly with time (2-3 y half-time), especially if the fallout has a high initial134Cs to137Cs activity ratio and if the ecological half-time of the external dose rate is short ( less then 5 y). Depending on fallout scenario the averted cumulative LAR for newborn girls by decontamination that is achieved after 5 y is only between 6% and 11% of that obtained by evacuation alone during the same time, indicating a rather limited radiological benefit of decontamination if delayed more than a few years. We conclude that decision makers and emergency response planners need to consider that protracted decontamination measures may have limited radiological benefit compared with evacuation in terms of averted future cancer cases, albeit it may have other societal benefits.Herein, we reveal extraordinary enhancements in the photoresponsivities of tin sulfide (SnxSy) grown on SiO2/Si wafers through post-phase transformations induced by electron beam irradiation (EBI) and crystallization. Amorphous SnxSy thin films were formed by room-temperature sputtering, and as-deposited films were subsequently transformed into hexagonal SnS2 and orthorhombic SnS phases by EBI at 600 and 800 V respectively, for only one minute. The use of a low-energy electron beam was sufficient to fabricate a SnxSy photodetector, with no additional heating required. Less than 10 nm thick SnxSy films with well-defined layer structures and stable surface morphologies were obtained through EBI at 600 and 800 V. The resulting phase-controlled SnS thin-film photodetector prepared using 800 V-EBI exhibited a 40 000-fold increase in photoresponsivity; when illuminated by a 450 nm light source, the active SnS-layer-containing photodetector demonstrated a photoresponsivity of 33.2 mA W-1.Near-stoichiometric and under-stoichiometric Cr2Al x C (x = 0.9 and 0.75) amorphous compositions were deposited onto a silicon substrate at 330 K in a layer-by-layer fashion using magnetron sputtering from elemental targets. The film thickness was found to be 0.9 µm and 1.2 µm for the near- and under-stoichiometric compositions respectively. A transmission electron microscope (TEM) heating holder was used to heat thin sample lamellae prepared using focused ion beam milling. Near-stoichiometric Cr2AlC thin films consisted of nano MAX phase after crystallization at 873 K. Under-stoichiometric Cr2Al x C (x = 0.75) thin films contained MAX phase along with nanocrystalline chromium aluminides after crystallization at 973 K. Irradiations with 320 keV xenon ions was performed at 623 K using a TEM with an in-situ ion irradiation (MIAMI) facility. Nanocrystalline films of near-stoichiometric Cr2AlC irradiated up to 83 displacements per atom (dpa) showed no observable changes. Also, irradiation of under-stoichiometric nanocrystalline thin films up to 138 dpa did not show any observable amorphization, and recrystallization was observed. This radiation resistance of near- and under-stoichiometric thin films is attributed to the known self-healing property of Cr2Al x C compositions further enhanced by nanocrystallinity.In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks and cascaded neural networks have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by decoupling the regularization of the solution from ensuring consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained neural network which is used in a Tikhonov regularization framework. By doing so, more complex and sophisticated network architectures can be used for the removal of the artefacts or noise than it is usually the case in iterative networks.
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