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Carbon capture and storage (CCS) and carbon capture and utilization (CCU) are two kinds of strategies to reduce the CO2 concentration in the atmosphere, which is emitted from the burning of fossil fuels and leads to the greenhouse effect. With the unique properties of ionic liquids (ILs), such as low vapor pressures, tunable structures, high solubilities, and high thermal and chemical stabilities, they could be used as solvents and catalysts for CO2 capture and conversion into value-added chemicals. In this critical review, we mainly focus our attention on the tuning IL-based catalysts for CO2 conversion into quinazoline-2,4(1H,3H)-diones from o-aminobenzonitriles during this decade (2012~2022). Due to the importance of basicity and nucleophilicity of catalysts, kinds of ILs with basic anions such as [OH], carboxylates, aprotic heterocyclic anions, etc., for conversion CO2 and o-aminobenzonitriles into quinazoline-2,4(1H,3H)-diones via different catalytic mechanisms, including amino preferential activation, CO2 preferential activation, and simultaneous amino and CO2 activation, are investigated systematically. Finally, future directions and prospects for CO2 conversion by IL-based catalysts are outlined. This review is benefit for academic researchers to obtain an overall understanding of the synthesis of quinazoline-2,4(1H,3H)-diones from CO2 and o-aminobenzonitriles by IL-based catalysts. This work will also open a door to develop novel IL-based catalysts for the conversion of other acid gases such as SO2 and H2S.

Abstract Recently, sparse arrays have received considerable attention as they provide larger array aperture and increased degrees‐of‐freedom (DOFs) compared to uniform linear arrays. These features are essential to enhance the direction‐of‐arrival estimation performance. However, most of the existing sparse arrays are mainly designed for circular sources and realize limited increment in DOFs for non‐circular sources. In this letter, a new sparse array configuration for non‐circular sources is presented, which significantly increases the achievable DOFs and improves the direction‐of‐arrival estimation performance. The proposed geometry comprises two effectively configured uniform linear arrays that exploit the characteristics of non‐circular sources and extend the array aperture. For a given number of sensors, its virtual array is advantageously a hole‐free uniform linear array. Moreover, the precise sensor locations, achievable DOFs, and optimal distribution of physical sensors are determined analytically by closed‐form expressions. Owing to these benefits, the proposed array efficiently resolve multiple sources in under‐determined conditions and achieves better direction‐of‐arrival estimation performance than its counterpart structures. Simulation results validate the superiority of the proposed configuration.

The study of the aerodynamics of a flight vehicle under Martian air conditions is experimentally difficult due to its characteristics such as low air density and temperature, making the vehicle operate at an ultra-low Reynolds number, which in turn introduces a complex flow field. In this paper, to find a proper computational fluid dynamics (CFD) method with which to better understand the aerodynamics of rotor-type aircraft under Martian air conditions, the aerodynamic performance of a rotor with rectangular blades under ultra-low density Martian air conditions is studied. A simulation scheme using a large eddy simulation solver and sliding meshing technology is established, and the method is verified based on experimental results from a Mars Air Simulator (MAS). In addition, the influence of the test bench and chamber is investigated through flow field analysis. The results show that the established method can predict lift in a very accurate manner, but that the torque prediction is not so promising. The study also determines that the fixture and the chamber wall of an MAS has little influence on the prediction of aerodynamic performance due to the quickly decreasing of flow speed and dissipation of vortexes. The test bench has about 5% influence on lift prediction, possibly due to the ground effect of the bench. In addition, simulation under actual Martian air conditions shows that the results agree well with the MAS experiment’s results, indicating that the temperature difference has little influence on the lift performance, and therefore that the MAS is a good tool for the lift prediction of Martian helicopters.

The heating and impact of arc and droplet acting on the weld pool lead to the transfer of mass, heat, and momentum, which affects the dynamic behavior of the weld pool and the microstructure in the P-GMAW process. In this paper, an image processing program is used to extract the dynamic behavior characteristics of the droplet transition and the weld pool in high-speed photography. The influence of the current waveform on the arc pressure and the impact of the droplet is quantitatively analyzed with different parameters. The dynamic behavior of the weld pool and the microstructure under different current waveform conditions are further studied. The internal relation of current waveform parameters to weld pool behavior and weld microstructure was expounded. The results show that the droplet impact is positively correlated with the pulse peak current. The rectangular wave pulse has a more significant droplet impact than the exponential wave with the same waveform parameters. The impact of droplet transition on the weld pool enhances the convective intensity of the weld pool. It slows down the cooling rate of the solidified weld microstructure below the tail of the weld pool, increasing the grain size of the weld microstructure.

Multichannel communication has great potential in environments with unknown interference patterns. However, existing link quality metrics and models are generally established and verified under a single-channel scenario, which does not consider the impacts of radio interference and channel change. Therefore, it is hard to directly judge whether these metrics and models are still valid under a multichannel scenario. This paper empirically analyzes the applicability of popular link quality metrics and models in multiple channels with different interference levels. Results show that the link quality estimation (LQE) capability of traditional metrics will be affected by the interference level of the channel, which makes the conclusions obtained under a single-channel scenario no longer valid. Meanwhile, traditional LQE models are basically not adaptive to radio interference and channel change. They are only valid for channels with similar interference under which they are modeled. If these models are directly used under a multichannel scenario, the link quality will be overestimated inevitably. In other words, traditional LQE metrics and models cannot be directly used in the multichannel scenario. It is necessary to deeply analyze the statistical characteristics of popular link quality metrics in multiple typical channels and design channel and interference adaptive metrics and models to support effective multichannel communication.

In this work, we designed and implemented a wearable body temperature monitoring device, which was constructed by a graphene-enhanced polydimethylsiloxane patch and a temperature measurement chip. The body temperature patch adopts a completely flexible solution in combination with near field communication component, which provides the advantages of passive wireless, overall flexibility, and being comfortable to wear. The whole device can be bent and stretched in conformal contact with skin. In order to improve the temperature conduction ability of the patch and make the patch data more accurate, we adopted graphene nanoplates to improve the thermal conductivity of polydimethylsiloxane patch with a significant thermal conductivity increase of 23.8%. With the combination of hollow sandwich structure and small dimension. it will reduce the uncomfortable situation of wearing the device for extended periods and can be served to monitor the human body temperature for a long time. Ultimately, this device is combined with a reading software for analyzing and processing on a smart mobile terminal. The real-time and past temperature range can be a pre-warning; meanwhile, the historical data can be traced and analyzed. Therefore, this device can be utilized in multiple human body temperature measurement scenarios and complex public health situations.

Salinization is considered a threat to agricultural soil and decreases crop yield worldwide. Nitrification and denitrification are the core processes of soil N-cycle. However, the response of nitrifiers and denitrifiers to salinity in agricultural soils remains ambiguous. The study aimed to explore the effect of salinity on nitrifiers and denitrifiers communities in agricultural soils along a naturally occurring salinity gradient. The effects of salinity on the abundance, composition, and interactions of nitrifiers and denitrifiers in surface soils were investigated. The abundance of nitrifiers significantly decreased in response to the increase in salinity. Ammonia-oxidizing archaea (AOA) were more susceptible to salinity elevation than ammonia-oxidizing bacteria (AOB). Nitrospira and Nitrobacter showed a similar trend to the salinity gradient, but the relative abundance of Nitrobacter was increased in the saline soils. High salinity decreased the abundance of napA and nirK, but had no significant effect on other marker genes for denitrification. Besides electrical conductivity, total sulfur (TS)+available potassium (AK) and TN+TS+C/N+total phosphorus (TP)+AK significantly explained the variation in denitrifier and nitrifier communities. We also found that high salinity decreased the connections between different N functional genes. These results implied the alteration of the nitrogen cycling community by high salinity mainly through decreasing AOA, NOB, and some denitrifiers with nitrate or nitrite reduction potentials and weakening the connectivity between nitrogen cycling drivers.

Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations.

In order to achieve high-precision and robust measurement for a single-track absolute encoder, an improved subdivision algorithm based on machine vision technology is proposed. First, the composite subdivision algorithm combining RANdom SAmple Consensus and least square estimation is introduced. Second, the proposed algorithm is proved to be high-precision and effective to remove fault error from signal noise and spot by simulation. Finally, real test results show that the algorithm output angle stably achieves precision within 2.5 arc-seconds and accuracy up to 1.6 arc-seconds. Research results provide a high-precision and robust subdivision algorithm that is improved by modern machine vision technology of RANdom SAmple Consensus and it can be applied in the field of absolute angle sensor in the future.

Unmanned aerial vehicles (UAVs) play a key role in modern surveillance-related missions. A major task for performing these missions is to find the precise location of a moving target in real-time, for which the main challenge is to estimate the target position to high precision using the noisy measurements from the airborne sensors. In this paper, we present a closed-form on-line simultaneous target localization and UAV trajectory optimization method based on the visual platform, which can effectively minimize the localization uncertainty to the target. The proposed method can be elucidated explicitly using two phases, of which, in the target localization phase, the expended information filtering (EIF) is exploited, which can express the predicted Fisher information matrix (FIM) of the target explicitly and iteratively, and in the UAV trajectory optimization phase, the property of the predicted FIM is exploited to establish the UAV waypoint optimization objective by taking account of the UAV motion limit. Compared with existing methods of the same class, the proposed method not only estimates the next target position more correctly, but also takes the error of the target motion into consideration, thus improving the effectiveness of the optimized UAV trajectory. Both simulations and field experiments were conducted, which show that the proposed method outperformed the existing methods.

In this paper, we propose a rateless, three-stage, two-way multiply-and-forward (MF) relaying system over the Rayleigh flat fading channel, where two source nodes communicate with each other through a relay node and all nodes work on half-duplex and time-division mode. We thoroughly analyze the signals during all three stages in the proposed MF system and derive the closed-form symbol error rate (SER) expressions for an uncoded MF-two-way relay network (MF-TWRN). Furthermore, we provide the equivalent point-to-point fading channel model, which is employed to carry out the asymptotic performance analysis. We finally put forth an optimization model for the MF-TWRN with fountain codes. Simulation results show that our optimized degree distribution can provide outstanding performance for the MF-TWRN compared to those in the literature.

We designed a location-context-semantics-based conditional random field (LCS-CRF) framework for the semantic classification of airborne laser scanning (ALS) point clouds. For ALS datasets of high spatial resolution but with severe noise pollutions, more contexture and semantics cues, besides location information, can be exploited to surmount the decrease of discrimination of features for classification. This paper mainly focuses on the semantic classification of ALS data using mixed location-context-semantics cues, which are integrated into a higher-order CRF framework by modeling the probabilistic potentials. The location cues modeled by the unary potentials can provide basic information for discriminating the various classes. The pairwise potentials consider the spatial contextual information by establishing the neighboring interactions between points to favor spatial smoothing. The semantics cues are explicitly encoded in the higher-order potentials. The higher-order potential operates at the clusters level with similar geometric and radiometric properties, guaranteeing the classification accuracy based on semantic rules. To demonstrate the performance of our approach, two standard benchmark datasets were utilized. Experiments show that our method achieves superior classification results with an overall accuracy of 83.1% on the Vaihingen Dataset and an overall accuracy of 94.3% on the Graphics and Media Lab (GML) Dataset A compared with other classification algorithms in the literature.