•Review of graphene-assisted construction strategies of both metal and non-metal based electrocatalysts.•Discussion of CO2RR electrocatalytic performances of various graphene − metal ...composites.•Overview of enhancement approaches of the graphene-based non-metal CO2RR catalytic activities.•Challenges and perspectives for the future development of graphene-based CO2RR electrocatalysts.
The electrochemical conversion of the greenhouse gas, carbon dioxide (CO2), to energy fuels and value-added chemicals presents one of the most valuable approaches to harvest pollutants and produce renewable energy. However, the stable molecular structure of CO2 and the sluggish reaction kinetics make CO2 reduction reaction (CO2RR) formidably challenging to achieve reaction rate and selectivity practical in industry. Graphene and its derivatives have been considered a group of intriguing materials to develop advanced CO2RR electrocatalysts due to their large specific surface area, remarkable electron transfer ability, superior stability, and easy tunability of the structure and surface properties. Herein, we comprehensively discuss the state-of-the-art electrocatalysts constructed with graphene and derivatives for active and selective CO2RR within the recent five years, mainly including the electrocatalysts with both metal-based (e.g., noble, non-noble, or combined thereof) and non-metal (e.g., doped, modified, defected, or composited) catalytic sites. To present the versatile, high-performance metal-based CO2RR electrocatalysts constructed with graphene, we further subdivide them according to the sizes, oxidation states, metal species synergies, dimensionalities, and versatility. Finally, we provide the challenges and perspectives in this emerging area of utilising CO2 to produce various carbon-based fuels and chemicals via graphene chemistry.
•A frequency support methodology has been proposed, incorporating dynamic-droop control from WTGs, to utilize their kinetic energy to provide enhanced system frequency control.•A consecutive power ...dispatch scheme was proposed to effectively coordinate the responses from different WTGs, with the primary aim of mitigating secondary frequency dips.•To evaluate the efficiency of the proposed method, three simulation scenarios are applied and performed in test power system using DIgSILENT PowerFactory software. Results confirm the effectiveness of the proposed scheme.
With the rapid increase of wind energy integrated into power systems, wind turbine generators (WTGs) are required to provide frequency support to maintain the system frequency stability. However, the frequency regulation is achieved by employing temporary energy reserves from WTGs at the initial stage of a disturbance. Therefore, a second frequency dip (SFD) may occur, if no other energy reserve is available to compensate the power deficiency as WTGs have to recover their operating points and rotor speeds back to the initial operating points. To deal with this problem, this paper proposes a consecutive power dispatch scheme to reduce the SFD and prevent WTGs from over-deceleration. All WTGs are divided into two groups with in a wind farm: Group 1 (G1) WTGs operating at maximum power point tracking (MPPT), Group 2 (G2) WTGs operating at deloading power. If a frequency contingency occurs, the proposed scheme aims to release an amount of kinetic energy (KE) stored in the rotating masses of G1 WTGs to improve the frequency nadir (FN). Following this, energy reserves are released from G2 WTGs to compensate the power shortage during the period when G1 WTGs rotor speeds have to be recovered. The simulation results show that the scheme causes a small SFD while improving the first FN and preventing the rotor from over-decelerations in various wind conditions, contingency sizes, and wind penetration levels.
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•Self-assembled lysozyme coated enamel absorbed more polyphemusin I.•Coated enamel combined with polyphemusin I showed better antibiofilm and antibiofouling activity.•Coated enamel ...combined with polyphemusin I regenerated more crystals.•Coated enamel combined with polyphemusin I had significantly shorter lesion depth.
Dental caries is a major public health problem. Streptococcus mutans (S. mutans) are the main etiologic pathogens in dental caries and cause tooth hard tissue demineralization. To prevent dental caries, it is important to inhibit S. mutans and induce tooth remineralization. In this study, we combined a self-assembled lysozyme with polyphemusin I (PI) to form a multi-functional membrane for inhibiting S. mutans and inducing remineralization. The normal enamel coated with lysozyme nanofilm could adsorb significant more PI. After combining with PI, the coated enamel could kill more S. mutans and had the superior ability in inhibiting biofilm formation. In addition, the coated enamel combined with PI could prevent the loss of calcium and phosphate ions. In the pH cycling, the coated enamel regenerated more crystals than normal enamel did. The coated enamel combined with PI had a significant lower lesion depth (72.45 ± 4.07 μm) compared to normal enamel combined with PI (93.30 ± 7.64 μm) after S. mutans demineralization. Moreover, the lysozyme nanofilm and PI had good biocompatibility. Accordingly, the formed multi-functional membrane could be a promising strategy for preventing dental caries.
Specific emitter identification (SEI) is an emerging device authentication technology, which depends on the inherent hardware characteristics of wireless devices. By analysing the received signal, ...the hardware characteristics of a specific emitter can be extracted at the receiver and used for device authentication and association. Most of the existing SEI schemes focus on the identification under closed sets. In view of the explosive growth of the number of IoT devices, based on generative adversarial networks, this study proposes a method that considers the identification of unknown emitters. The reconstruction network to reconstruct the signals of the known class and fully train the feature space of the signals of the known class is designed. In the discriminator, two channels are specifically designed to perform anomaly detection for unknown signals and end‐to‐end closed‐set classification for known signals. In order to better reject unknown signals and accept known signals, Receiver operating characteristic curve and Youden index are used to determine the optimal threshold for anomaly detection. Under the given optimal threshold conditions, the identified threshold points have larger True Positive Rate. In addition, the time‐frequency feature combination vector is designed to reveal the essential characteristics of emitters. The experimental results on the real‐world datasets collected from universal software radio peripherals in short‐range communication scenario show that compared with the existing open‐set recognition methods such as C2AE, Openmax and SoftMax, the average recognition accuracy of the proposed framework improved by 0.08, 0.15, and 0.2 respectively, and the Marco‐F1 score improved by 0.05, 0.1, and 0.16, respectively, which proves the superiority of the proposed framework. In addition to identifying unknown and known Universal software radio peripherals in this article, some potential and widespread applications of the proposed framework include access user security authentication in IoT, such as Hack RF and Blue tooth devices. In addition, as a relatively general signal processing framework, the model can be used for air safety management and maritime ship identity authentication in civil aspects. In the military aspect, it can be used in electronic support and various signal reconnaissance scenarios, such as automatic signal modulation recognition in open set scenarios, individual identity determination of radar emitter and unknown working state identification of transmitters, which proves reference for subsequent research on open‐set signal recognition.
In this paper, we present a novel Open Set Recognition framework for specific emitter identification based on the multichannel reconstructive discriminant network (MRDN) and receiver operating characteristic (ROC) curve. The reconstruction network is introduced to characterise the feature space of signals from known emitter, and the quality of signal reconstruction is evaluated in the discriminator. To improve the complementarity of features, two branches are used to adaptively process time series and dimensionality‐reduced RFF respectively in the discriminator separately.
The existing recognition algorithms of space-time block code (STBC) for multi-antenna (MA) orthogonal frequency-division multiplexing (OFDM) systems use feature extraction and hypothesis testing to ...identify the signal types in a complex com-munication environment. However,owing to the restrictions on the prior information and channel conditions,these existing algo-rithms cannot perform well under strong interference and non-cooperative communication conditions. To overcome these defects,this study introduces deep learning into the STBC-OFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum (FOLMS) and attention-guided multi-scale dilated convolution network (AMDCNet). The fourth-order lag moment vectors of the received signals are calculated,and vectors are stitched to form two-dimensional FOLMS,which is used as the input of the deep learning-based model. Then,the multi-scale dilated convolution is used to extract the details of images at different scales,and a convolutional block attention module (CBAM) is introduced to construct the attention-guided multi-scale dilated convolution module (AMDCM) to make the network be more focused on the target area and obtian the multi-scale guided features. Finally,the concatenate fusion,residual block and fully-connected lay-ers are applied to acquire the STBC-OFDM signal types. Simula-tion experiments show that the average recognition probability of the proposed method at ?12 dB is higher than 98％. Com-pared with the existing algorithms,the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances. In addi-tion,the proposed deep learning-based model can directly iden-tify the pre-processed FOLMS samples without a priori informa-tion on channel and noise,which is more suitable for non-coope-rative communication systems than the existing algorithms.
Wet dust removal systems used to control dust in the polishing or grinding process of Mg alloy products are frequently associated with potential hydrogen explosion caused by magnesium-water reaction. ...For purpose of avoiding hydrogen explosion risks, we try to use a combination of chitosan (CS) and sodium phosphate (SP) to inhibit the hydrogen evolution reaction between magnesium alloy waste dust and water. The hydrogen evolution curves and chemical kinetics modeling for ten different mixing ratios demonstrate that 0.4% wt CS + 0.1% wt SP yields the best inhibition efficiency with hydrogen generation rate of almost zero. SEM and EDS analyses indicate that this composite inhibitor can create a uniform, smooth, tight protective film over the surface of the alloy dust particles. FTIR and XRD analysis of the chemical composition of the surface film show that this protective film contains CS and SP chemically adsorbed on the surface of ZK60 but no detectable Mg(OH)2, suggesting that magnesium-water reaction was totally blocked. Our new method offers a thorough solution to hydrogen explosion by inhibiting the hydrogen generation of magnesium alloy waste dust in a wet dust removal system.
Tertiary lymphoid structures (TLS) are clusters of immune cells that resemble and function similarly to secondary lymphoid organs (SLOs). While TLS is generally associated with an anti-tumour immune ...response in most cancer types, it has also been observed to act as a pro-tumour immune response. The heterogeneity of TLS function is largely determined by the composition of tumour-infiltrating lymphocytes (TILs) and the balance of cell subsets within the tumour-associated TLS (TA-TLS). TA-TLS of varying maturity, density, and location may have opposing effects on tumour immunity. Higher maturity and/or higher density TLS are often associated with favorable clinical outcomes and immunotherapeutic response, mainly due to crosstalk between different proportions of immune cell subpopulations in TA-TLS. Therefore, TLS can be used as a marker to predict the efficacy of immunotherapy in immune checkpoint blockade (ICB). Developing efficient imaging and induction methods to study TA-TLS is crucial for enhancing anti-tumour immunity. The integration of imaging techniques with biological materials, including nanoprobes and hydrogels, alongside artificial intelligence (AI), enables non-invasive in vivo visualization of TLS. In this review, we explore the dynamic interactions among T and B cell subpopulations of varying phenotypes that contribute to the structural and functional diversity of TLS, examining both existing and emerging techniques for TLS imaging and induction, focusing on cancer immunotherapies and biomaterials. We also highlight novel therapeutic approaches of TLS that are being explored with the aim of increasing ICB treatment efficacy and predicting prognosis.
Genomic alterations constitute crucial elements of colorectal cancer (CRC). However, a comprehensive understanding of CRC genomic alterations from a global perspective is lacking. In this study, a ...total of 2,778 patients in 15 public datasets were enrolled. Tissues and clinical information of 30 patients were also collected. We successfully identified two distinct mutation signature clusters (MSC) featured by massive mutations and dominant somatic copy number alterations (SCNA), respectively. MSC-1 was associated with defective DNA mismatch repair, exhibiting more frequent mutations such as
, and
. The mutational co-occurrences of
-
and
-
as well as the methylation silence event of MLH-1 were only found in MSC-1. MSC-2 was linked to the carcinogenic process of age and tobacco chewing habit, exhibiting dominant SCNA such as
(8q24.21) and
(10q23.31) deletion as well as
(6p21.1) and
(17q12) amplification. MSC-1 displayed higher immunogenicity and immune infiltration. MSC-2 had better prognosis and significant stromal activation. Based on the two subtypes, we identified and validated the expression relationship of
and
as a robust biomarker for prognosis and distant metastasis of CRC in 15 independent cohorts and qRT-PCR data from 30 samples. These results advance precise treatment and clinical management in CRC.
Automatic signal recognition (ASR) is becoming increasingly important in spectrum identification and cognitive radio, but most existing space-time block code (STBC) recognition algorithms are ...traditional ones and do not account for the complementarities between different features. To overcome these deficiencies, a multi-delay features fusion scheme for ASR of STBC using a convolutional neural network (CNN) is proposed in this study. The proposed scheme tries to fuse different time-delay features of received STBC signals to obtain more discriminating features. The dilated convolution of different dilation rates is applied to realize automated multi-delay feature extraction. Then, two fusion methods, i.e., max-correlation (MC) fusion and multi-delay average (MDA) fusion, are proposed to combine the features of different time delays, and a residual block is applied to achieve better representation of signals. Finally, the simulation results demonstrate the superior performance of the proposed method. It is notable that the recognition accuracy can reach 97.4% with a signal-to-noise ratio (SNR) of −5 dB. In addition, the proposed multi-delay features fusion CNN (MDFCNN) scheme does not need a priori information, i.e., modulation type, channel coefficients, and noise power, which is well suited to non-collaborative communication.
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