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In this paper, we investigate the energy harvesting (EH) technique and accordingly design transceivers for a $K$ link multiple-input multiple-output interference channel. Each link consists of two full-duplex (FD) Internet of Things (IoT) nodes exchanging information simultaneously in a bi-directional communication channel. All the nodes suffer from interference, in particular strong self-interference and inter-node interference, due to operating in FD mode and simultaneous transmission at each link, respectively. Further, we divide the received signal at each node into two parts. While one part of the signal is used for information decoding, the other part is used for EH. We jointly design the transmit and receive beamforming vectors and receiver power splitting ratios by minimizing the total transmission power of the system, subject to both signal-to-interference-plus-noise ratio and EH threshold constraints. Furthermore, the case of multiple-input single-output interference channel is also included for the sake of comparison. We also revisit the above problems for the case when the available channel state information (CSI) at the transmitters is imperfect, where the errors of the CSI are assumed to be norm bounded. Simulation results show that the EH technique can harvest enough energy to support power consumption limited IoT devices by aiding in recharging their respective batteries.

The accelerator-driven sub-critical system is driven by external neutron sources, which are generated by the spallation reaction and maintain the stable operation of the sub-critical core, while the external neutron source increases the complexity of reactor neutron kinetic processes. This article focuses on simulation analysis of neutron space–time kinetics with a dynamic analysis code, which is developed based on the improved quasi-static approximation and Monte Carlo neutron transport method. The amplitude function and shape functions are solved to achieve the dynamics simulation process. Then, the transient responses of beam interruptions and reactivity insertions are calculated and analyzed for an experimental facility. To further verify the correctness and reliability of dynamic behavior, the simulation results are compared with experimental values, and the results show that the normalized neutron fluxes varying with time are in good agreement with the corresponding values. It can be concluded that the improved quasi-static coupled probability theory method can be used to solve the neutron space–time kinetic problem of the experimental facility, and the results are reliable.

In this study, the hybrid power system of the diesel, as the main power source, and new energy, as the auxiliary energy is developed. An optimal energy management strategy was proposed for the hybrid ship, aiming to minimize consumption of the fuel. The model of the hybrid power system was established in the MATLAB/SIMULINK simulation environment. The optimal energy management strategy was verified by simulation according to the typical drive cycle of ship compared to original energy management strategy. The simulation results show that the proposed optimization can improve fuel economy while the state of charge of the battery is maintained at reasonable range.

Ultrafast laser-annealing technique for the fabrication of large-grain perovskite films and efficient perovskite solar cells at room temperature.

The pyrolysis of waste disposable paper cups (WDPCs) was investigated using a thermogravimetric analyser coupled with a Fourier transform infrared spectrometer. The activation energies of the pyrolysis reactions were obtained by the Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS) methods respectively. The kinetic model was determined by the master plots method. Thermogravimetric results showed that the highest weight loss rate occurred from 345 to 365 °C as the heating rate was increased from 10 to 30 °C min−1, indicating the pyrolysis of cellulosic material in the WDPC. The weight loss between 400 and 500 °C can be attributed to the decomposition of polyethylene. By analysing the FTIR spectra, it was found that the absorbance of all the evolved gaseous products had peaks at 360 °C due to the decomposition of cellulose fibres and the cracking of polyethylene at 485 °C led to the emergence of a second hydrocarbon peak. Ketones were the most abundant condensable organic products and CO2 was the dominating gaseous product, which can also be produced via secondary cracking of the small molecule organics above 400 °C. Kinetic analysis revealed that the average activation energy of the pyrolysis of the WDPC was 153.75 kJ mol−1 from the FWO method and 151.43 kJ mol−1 from the KAS method. The reaction mechanism can be described by the R3 model.

Microbial fuel cells (MFCs) can potentially be utilized for power generation, but their low power density and low energy storage capabilities remain major bottlenecks for their large-scale development. In this research, a simplistic nitrogen-doped hierarchically porous carbon material (HPC-A) was developed through a one-step carbonization and activation process and was successfully hot-pressed on the carbon cloth (CC) substrate. This process fabricates capacitive bioanodes (HPC-A-CC) that can enhance electricity generation and storage in MFCs. The as-prepared HPC-A-CC anode delivered a power density of 2043.6 mW·m-2 and a cumulative total charge (Qm) of 426.4 ± 13.4C·m-2 at each cycle, which was 2.1 and 34.8 times higher than that of the plain CC anode, respectively. This was a result of the hierarchical and interconnected porous structure, improved hydrophilic surface, and increased number of active centers which host the bacteria for enhanced electron transfer. Electrochemical measurements indicated the superior electrochemical activity and capacitive behavior of the HPC-A-CC anode. Furthermore, biofilm analysis revealed that the HPC-A-CC biofilm exhibited higher cell viability and a more uniform spatial distribution. These findings not only demonstrate the potential of HPC-A-CC for power enhancement in MFCs but also provide a feasible solution to the problem of power generation and demand mismatch in MFC applications.

Abstract Knocking combustion for spark-ignition engine is related to auto-ignition of a portion of the unburned fuel-air mixture. In this investigation, the detailed chemical kinetic mechanism was engaged to numerically study the auto-ignition characteristics of hydrogen-enriched methane under engine-relevant conditions. Compared with the experimental data, the USC Mech 2.0 mechanism obtained the closest agreement, and it was adopted in the sensitivity analysis, the rate of production (ROP) analysis and the reaction pathway analysis. Results showed that at high temperature and high pressure, the ignition delay times (IDs) of CH4/H2 fuel blends reduce significantly (by two orders of magnitude at most) with rising hydrogen fraction, but the decline rate is not so obvious (within 37.3%) at low temperature. The sensitivity analysis indicated that at high temperature the reaction (R1) and reactions (R2, R3) promote each other while at low temperature only the reaction (R3) unilaterally facilitates the reaction (R12). The ROP analysis implied that the decrease of IDs of methane by hydrogen addition is realized through increasing the H, O, and OH radical production. Interestingly, the IDs for CH4/H2 fuel blends show different trends at different temperature, which decrease (by 47.5% at most) at low temperature but increase (by 132.7% at most) at high temperature as the equivalence ratio rises. The sensitivity analysis showed that the ignition kinetics for CH4/H2 fuel blends more depend on the CH4 concentration at low temperature but oxygen concentration at high temperature. This investigation not only supplements the fundamental combustion studies of CH4/H2 fuel blends in elevated pressures, but also reveals the influence mechanism of hydrogen addition and equivalence ratio on the IDs of CH4/H2 fuel blends at high pressure. More importantly, it may offer fundamental insights for the control of knocking combustion of spark-ignition (SI) engine.

To meet power quality requirements, it is necessary to classify and identify the power quality of the power grid connected with renewable energy generation. S-transform (ST) is an effective method to analyze power quality in time and frequency domains. ST is widely used to detect and classify various kinds of non-stationary power quality disturbances. However, the long taper and scaling criteria of the Gaussian window in standard ST (SST) will lead to poor time domain resolution at low frequency and poor frequency resolution at high frequency. To solve the discrete side effects, it is necessary to select the optimal window function to locate the time frequency accurately. This paper proposes a modified ST (MST) method. In this method, an improved window function of energy concentration in time-frequency distribution is introduced to optimize the shape of each window function. This method determines the parameters of Gaussian window to maximize the product of energy concentration in a time-frequency domain within a given time and frequency interval, so as to improve the energy concentration. The result shows that compared with the SST with Gaussian window, ST based on the optimally concentrated window proposed in this paper has better energy concentration in time-frequency distribution.