NtGCM User's Manual: 1.1 (High Pressure High Temperature Laser based) Nanotube Growth Chamber Monitor

This manual describes the installation and use of NtGCM software. NtGCM is software designed for monitoring the growth of nanotubes in a high temperature and high pressure chamber using a laser*. NtGCM software monitors a dozen dierent parameters that are important to understanding the growth of the nanomaterials including the laser input power, the temperature at eight separate locations inside and outside the growth chamber, as well as the pressure and ow rate of the gaseous media that control the environment in the chamber. The measurements are all made in real time. The program features a robust user account management layer and a rich data display manager that allows plotted data, displayed units and other parameters to be changed on the y for the operator's convenience

Deep Learning-Based Signal Detection for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D- OFDM is a subcarrier index modulation scheme which conveys data bits via both dual-mode 3D constellation symbols and indices of active subcarriers. Thus, this scheme obtains better error performance than the existing IM schemes when using the conventional maximum likelihood (ML) detector, which, however, suffers from high computational complexity, especially when the system parameters increase. In order to address this fundamental issue, we propose the usage of a deep neural network (DNN) at the receiver to jointly and reliably detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading channels in a data-driven manner. Simulation results demonstrate that our proposed DNN detector achieves near-optimal performance at significantly lower runtime complexity compared to the ML detector

Anammox treatment performances using polyethylene sponge as a biomass carrier

Nitrogen removal using a polyethylene (PE) sponge biomass carrier was evaluated in a fixed-bed reactor for nitrogen removal by the anammox process. The fixed-bed reactor was operated continuously for 240 days. Average T-N removal efficiencies of each period increased from 38 % to 67 %, 72 %, 74 % to 75 % with stepwise increases in volumetric T-N loading rates. A T-N removal rate of 2.8 kg N/m3/day was obtained after 240 days of operation. After 3 months, anammox biomass fully covered the surface of the PE sponge carrier and the color of the material changed from white to red. Following 5 months of operation, biomass proliferated on the surface of the material and a dark-red color was observed. These results shown that anammox process using PE sponge materials as biomass carriers in the fixed-bed reactor will be suitable for NH4-N removal from wastewater containing high NH4-N. However, it is necessary to investigate whether PE sponge material can operate under high organic carbon concentrations in anammox process, because these wastewaters always contain high concentration of organic matter

Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called TransD3D-IM, which employs the Transformer framework for signal detection in the Dual-mode index modulation-aided three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the data bits are conveyed using dual-mode 3D constellation symbols and active subcarrier indices. As a result, this method exhibits significantly higher transmission reliability than current IM-based models with traditional maximum likelihood (ML) detection. Nevertheless, the ML detector suffers from high computational complexity, particularly when the parameters of the system are large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is also not impressive enough. To overcome this limitation, our proposal applies a deep neural network at the receiver, utilizing the Transformer framework for signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation results demonstrate that our detector attains to approach performance compared to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness than the existing deep learning-based detector while considerably reducing runtime complexity in comparison with the benchmarks

Deep Neural Network-Based Detector for Single-Carrier Index Modulation NOMA

In this paper, a deep neural network (DNN)-based detector for an uplink single-carrier index modulation nonorthogonal multiple access (SC-IM-NOMA) system is proposed, where SC-IM-NOMA allows users to use the same set of subcarriers for transmitting their data modulated by the sub-carrier index modulation technique. More particularly, users of SC-IMNOMA simultaneously transmit their SC-IM data at different power levels which are then exploited by their receivers to perform successive interference cancellation (SIC) multi-user detection. The existing detectors designed for SC-IM-NOMA, such as the joint maximum-likelihood (JML) detector and the maximum likelihood SIC-based (ML-SIC) detector, suffer from high computational complexity. To address this issue, we propose a DNN-based detector whose structure relies on the model-based SIC for jointly detecting both M-ary symbols and index bits of all users after trained with sufficient simulated data. The simulation results demonstrate that the proposed DNN-based detector attains near-optimal error performance and significantly reduced runtime complexity in comparison with the existing hand-crafted detectors

ANAMMOX TREATMENT PERFORMANCE USING MALT CERAMICS AS A BIOMASS CARRIER

Joint Research on Environmental Science and Technology for the Eart

Magneto-transport properties of monolayer borophene in perpendicular magnetic field: influence of electron-phonon interaction

The magneto-transport properties of a borophene monolayer in a perpendicular magnetic field B are studied via calculating the conductivity tensor and resistance under electron-optical phonon interaction by using the linear response theory. Numerical results are obtained and discussed for some specific parameters. The magnetic field-dependent longitudinal conductivity shows the magneto-phonon resonance effect that describes the transition of electrons between Landau levels by absorbing/emitting an optical phonon. The Hall conductivity increases first and then decreases with the magnetic field strength. Also, the longitudinal resistance increases significantly with increasing temperature, which shows the metal behaviour of the material. Practically, the observed magneto-phonon resonance can be applied to experimentally determine some material parameters, such as the distance between Landau levels and the optical phonon energy

Thermal Properties of Hybrid Carbon Nanotube/Carbon Fiber Polymer

Carbon fiber reinforced polymer (CFRP) composites possess many advantages for aircraft structures over conventional aluminum alloys: light weight, higher strength- and stiffness-to-weight ratio, and low life-cycle maintenance costs. However, the relatively low thermal and electrical conductivities of CFRP composites are deficient in providing structural safety under certain operational conditions such as lightning strikes. One possible solution to these issues is to interleave carbon nanotube (CNT) sheets between conventional carbon fiber (CF) composite layers. However, the thermal and electrical properties of the orthotropic hybrid CNT/CF composites have not been fully understood. In this study, hybrid CNT/CF polymer composites were fabricated by interleaving layers of CNT sheets with Hexcel (Registered Trademark) IM7/8852 prepreg. The CNT sheets were infused with a 5% solution of a compatible epoxy resin prior to composite fabrication. Orthotropic thermal and electrical conductivities of the hybrid polymer composites were evaluated. The interleaved CNT sheets improved the in-plane thermal conductivity of the hybrid composite laminates by about 400% and the electrical conductivity by about 3 orders of magnitude