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Detection of the liquefied natural gas (LNG) leakage attracts increasing attention for preventing environments and governments from severe pollution and economic loss. Existing frameworks take advantage of stationary surveillance thermal cameras to detect the LNG leakage, which comprises background subtraction and leakage classification. However, these methods are limited in rural areas due to the lack of sensitivity and accuracy. In this article, a generalized framework, i.e., tensor-based leakage detection (TBLD), is proposed to detect LNG leakage in the rural area from surveillance thermal cameras. First, the proposed TBLD takes advantage of tensor factorization to fuse thermal image and corresponding gradient maps for improving sensitivity. Additionally, a finite-state-machine is designed to maintain leakage foreground along with the video streaming. The experiments demonstrate the robust performance of TBLD in the background subtraction stage. Second, multiple classification techniques are explored in the leakage classification stage. The results suggest that the TBLD can accurately detect the LNG leakage by applying 50 layers of residual networks (ResNet50). Finally, compared with contemporary frameworks, the TBLD has consistently improved performance concerning the different distances of LNG leakage. The experimental results demonstrate the effectiveness of the proposed TBLD, which also shows the great potential of TBLD in future industrial applications.

Condition assessment is an essential process to comprehend the condition of the water pipelines and facilitate the maintenance as well as the renewal plans. Nowadays, varied in-pipe inspection platforms equipped with closed-circuit cameras are employed to capture the internal condition of the water pipelines. However, the automated platform often faces the challenge to negotiate with the installed valves during the inspection. To ensure continuous inspection, the platform needs identify the valve automatically and activate the control mechanism to pass through it. Thus, the valves need to be detected to facilitate the negotiation and ensure that the control mechanism can take an action in time. This paper focuses on real-time valve detection using Jetson TX2™ and a lightweight algorithm, namely YOLOv3-tiny. The performance of the implementation is compared with state-of-the-art real-time detection models. The experimental results demonstrate that YOLOv3-tiny has a high detection speed in frame per second for valve detection and outperforms the state-of-the-art real-time algorithms. Hence, the deployment YOLOv3-tiny into the embedded system will aid the automated platform to accomplish the uninterrupted inspection and enhance the capability for the condition assessment of the water pipelines.

To provide a scientific basis for the resource utilization of chromium slag, this article studies the release law of hexavalent chromium in the aged calcium-free chromium slag. XRD (X-ray diffractometer) and MLA (Mineral Liberation Analyzer) were used to analyze the composition of the chromium slag; using sulfuric acid-nitric acid as the leaching solution, the release law of hexavalent chromium in chromium slag and the leaching kinetics were studied. The results show that main components of the chromium slag are magnesioferrite, chromite, hematite, hydrargillite, and spinel; chromium is mainly present in chromite and magnesioferrite; the leaching rate of hexavalent chromium increases with the increase of temperature or the decrease of pH. The analysis of leaching kinetics shows the leaching rate is controlled by the internal diffusion reaction, and the apparent activation energy is 11.93 kJ·mol–1. The chromium slag is aged in high temperature seasons, which is conducive to the precipitation of hexavalent chromium in the chromium slag, can increase the yield of chromate in the roasting kiln, and is conducive to resource utilization; chromium slag should be stored in order to prevent acid rain erosion which leads to environmental pollution risk (e.g. drinking water).

Low-efficiency charge transfer is a critical factor to limit the photocatalytic H2 evolution activity of semiconductor photocatalysts. The interface design is a promising approach to achieve high charge-transfer efficiency for photocatalysts. Herein, a new 2D/2D atomic double-layer WS2/Nb2O5 shell/core photocatalyst (DLWS/Nb2O5) is designed. The atom-resolved HAADF-STEM results unravel the presence of an unusual 2D/2D shell/core interface in DLWS/Nb2O5. Taking advantage of the advanced femtosecond-resolved ultrafast TAS spectra, the average lifetime of charge carriers for DLWS/Nb2O5 (180.97 ps) is considerably shortened as compared to that of Nb2O5 (230.50 ps), strongly indicating that the 2D/2D shell/core interface enables DLWS/Nb2O5 to achieve ultrafast charge transfer from Nb2O5 to atomic double-layer WS2, thus yielding a high photocatalytic H2 evolution rate of 237.6 μmol/h, up to 10.8 times higher than that of pure Nb2O5 nanosheet. This study will open a new window for the development of high-efficient photocatalytic systems through the interface design.

Titanium nitride (TiN) is widely used in electrode materials in fast charging/discharging supercapacitors (SCs) due to its outstanding conductivity. However, the low capacitance of the TiN electrode limits its further application in the SCs. Therefore, the reasonable design of the TiN electrode with high electrochemical and mechanical properties is still a challenge. In this paper, the silicon nanowires/titanium nitride electrode (Si NWs/TiN) is prepared by depositing TiN onto the etched Si nanowires by direct current magnetron sputtering. The Si NWs are prepared by etching silicon in 4.8 M HF/0.02 M AgNO3 aqueous solution for different times (5 min, 15 min, 30 min, 60 min). The mechanism of the effect of etched silicon substrate morphology on the electrochemical performance of Si NWs/TiN electrode was studied. As the etching time increases, the differences of the TiN surface structure, lattice defects and surface chemical composition will change the capacitance performance and charge storage mechanism of the Si NWs/TiN electrode. The prepared Si30 NWs/TiN electrode exhibits an outstanding specific capacitance as high as 113.55 F g−1 at a scan rate of 5 mV s−1 with 0.5 M H2SO4 solution as electrolyte. The specific capacitance of the Si30 NWs/TiN electrode is as high as 7.5 times that of the electrode without etching at 100 mV s−1. The Si30 NWs/TiN electrode has an excellent cyclic stability performance, which the electrode has a decay rate of 12.4% after 2000 cycles. This indicates that the electrode has reliable stability. The electrode of the supercapacitor prepared by this method can open up a new way to expand the specific surface area of other transition metal nitride.

Improving both surface and space utilization of active carbon is crucial for compact supercapacitors. Herein, two activated carbons are prepared through homogeneous activation (HAC) and traditional outside-in activation (OIAC). Compared with OIAC with deep and branched porous structure, HAC shows an interconnected porous structure, leading to higher density, better gravimetric and volumetric capacitances, and improved rate performance. The results demonstrate that interconnected porous structure by homogeneous activation promotes high surface and space utilization as well as robust ion transport kinetics of activated carbon for capacitive energy storage.

The labeled monitoring data collected from the electromechanical system is limited in the real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory accurate diagnosis results. To deal with this problem, an intelligent fault diagnosis method for electromechanical system based on a new semisupervised graph convolution deep belief network algorithm is proposed in this article. Specifically, the labeled and unlabeled samples are first employed to design a new adaptive local graph learning method for constructing the graph neighbor relationship. Meanwhile, the labeled samples are applied to describe the discriminative structure information of data via the latest circle loss. Finally, the local and discriminative objective functions are reconstructed under the semisupervised learning framework. The experimental results from the motor-bearing system demonstrate that the method can achieve 98.66 $\%$ accuracy with only 10 $\%$ of training labeled data, which indicates that it is a promising semisupervised intelligent fault diagnosis method.

The radial basis reproducing kernel particle method (RRKPM) has been constructed by introducing the radial basis function (RBF) into the reproducing kernel particle method (RKPM). In this article, the RRKPM is extended to the elastic dynamic analysis of the functionally graded material (FGM), and the elastic dynamics governing equation of the FGM based on the RRKPM is obtained. Then, the influence of the penalty factor, the scaling parameter and the shaped parameter are discussed. Finally, numerical examples of the FGM verify that the RRKPM has a smaller calculation error than the RKPM, and prove the correctness of the RRKPM for solving the elastic dynamics problems of the FGM.

As an emerging technique for supervised learning, broad learning system (BLS) has been proved to have many advantages such as fast learning speed, good generalization, etc. However, it is difficult for BLS to perform well on the dataset which only contains few labeled samples. Because the focus of semi-supervised learning is utilizing the information between labeled and unlabeled samples while the original BLS apparently ignores this significant information. In BLS, the mapped features are obtained by sparse autoencoder which is an unsupervised learning method and the enhancement nodes are formed based on the mapped features. But sparse autoencoder will definitely lose the information of labeled instances. Therefore, we propose a novel semi-supervised BLS (S2-BLS) in this paper. In S2-BLS, inspired by the effectiveness of extreme learning machine based autoencoder (ELM-AE), we first propose semi-supervised ELM-AE (SS-ELM-AE) to obtain the mapped features. Then, the discriminative projecting weights between the ground truths and the transformed features (including the mapping features and the enhancement nodes) are calculated directly. Finally, we adopt some public datasets to verify the learning ability of S2-BLS. And the experimental results illustrate that S2-BLS has an obvious advantage to learn labeled and unlabeled data, comparing with related algorithms.

Model predictive control (MPC) has been considered as a promising alternative for the control of nonlinear systems. However, this controller suffers from a challenge that it is difficult to deal with the complex nonlinear systems with incomplete datasets. To solve this problem, a novel MPC, by utilizing knowledge-data-driven model (KDDM), is designed and analyzed in this article. In comparison with the existing literatures, this knowledge-data-driven MPC (KDD-MPC) contains these following contributions. First, a systematic strategy is developed to reduce the online computational burden of KDD-MPC. Therefore, this KDD-MPC can own fast action to achieve favorable control performance. Second, the proposed KDDM intends to not only make full use of limited state information from the current model but also effectively leverage the knowledge from the reference model in the learning process. Therefore, it is more efficient for the complex nonlinear systems with insufficient data. Third, a novel transfer learning mechanism is designed to determine the optimal control sequence of KDD-MPC with strong adaptability. Therefore, it is suitable to achieve the desired control performance for engineering implementations. Finally, the benchmark problem and industrial application are provided to demonstrate the attractiveness and effectiveness of KDD-MPC.

Vascularization is a critical step following implantation of an engineered tissue construct in order to maintain its viability. The ability to spatially pattern or direct vascularization could be therapeutically beneficial for anastomosis and vessel in-growth. However, acellular and cell-based strategies to stimulate vascularization typically do not afford this control. We have developed an ultrasound-based method of spatially- controlling regenerative processes using acellular, composite hydrogels termed acoustically-responsive scaffolds (ARSs). An ARS consists of a fibrin matrix doped with a phase-shift double emulsion (PSDE). A therapeutic payload, which is initially contained within the PSDE, is released by an ultrasound-mediated process called acoustic droplet vaporization (ADV). During ADV, the perfluorocarbon (PFC) phase within the PSDE is vaporized into a gas bubble. In this study, we generated ex situ four different spatial patterns of ADV within ARSs containing basic fibroblast growth factor (bFGF), which were subcutaneously implanted in mice. The PFC species within the PSDE significantly affected the morphology of the ARS, based on the stability of the gas bubble generated by ADV, which impacted host cell migration. Irrespective of PFC, significantly greater cell proliferation (i.e., up to 2.9-fold) and angiogenesis (i.e., up to 3.7-fold) were observed adjacent to +ADV regions of the ARSs compared to -ADV regions. The morphology of the PSDE, macrophage infiltration, and perfusion in the implant region were also quantified. These results demonstrate that spatially-defined patterns of ADV within an ARS can elicit spatially-defined patterns of angiogenesis. Overall, these finding can be applied to improve strategies for spatially-controlling vascularization. Statement of significance Vascularization is a critical step following implantation of an engineered tissue. The ability to spatially pattern or direct vascularization could be therapeutically beneficial for inosculation and vessel in-growth. However, acellular and cell-based strategies to stimulate vascularization typically do not afford this control. We have developed an ultrasound-based method of spatially-controlling angiogenesis using acellular, composite hydrogels termed acoustically-responsive scaffolds (ARSs). An ARS consists of a fibrin matrix doped with a phase-shift double emulsion (PSDE). An ultrasound-mediated process called acoustic droplet vaporization (ADV) was used to release basic fibroblast growth factor (bFGF), which was initially contained within the PSDE. We demonstrate that spatially-defined patterns of ADV within an ARS can elicit spatially-defined patterns of angiogenesis in vivo. Overall, these finding can improve strategies for spatially-controlling vascularization.

Thickness and specific surface area of the film electrode are critical parameters for supercapacitors. The relationship between the thickness and the specific surface area of the film directly affects the capacitance and electrochemical stability performance of super supercapacitors, which virtually affects the contact chance of ion in the electrolyte on the surface of electrode and the ion transport path of electrode. In this paper, the CrN thin films with a thickness of 200–3500 nm are prepared using direct current magnetron sputtering. Atomic force microscopy (AFM) technique is introduced to investigate the relationship between thickness and the specific surface area of the CrN films. The electrochemical performances of CrN electrode with the nanoporousper structure is analyzed in different electrolytes H2SO4, Na2SO4 and NaCl aquous solutions. The specific surface area of the film increases linearly with the film thickness increases. The areal capacitance is also linearly related to the specific surface area. The spurtted CrN film with a thickness of 3370 nm has a specific surface of up to 43.59 cm2 per cm2 footprint area. Its areal and volume capacitances reache to 53.92 mF cm−2 and 650 F cm−3 at 5 mV s−1, respectively. In addition, the areal capacitance of CrN film electrode with 655 nm possesses reaches to 40.53 mF cm−2 for 0.5 M H2SO4 solution, 32.69 mF cm−2 for 0.5 M Na2SO4 solution and 9.17 mF cm−2 for NaCl solution at a scan rate of 5 mV s−1. Furthermore, the CrN film electrode exhibits excellent capacitance retention of 95.3%, 93.8% and 89.9% in H2SO4, Na2SO4 and NaCl electrolytes, respectively, after 2000 cycles. Therefore, the sputtered CrN thin film is an potential electrode material for electrochemical supercapacitors.

Carbon nitride, as one of the metal-free photocatalysts, has aroused wide attention due to its low cost, easy preparation, and excellent optical response. However, challenges of the high recombination rate of electron-hole pair hindered their potential applications. Here, boron-doped carbon nitride nanotubes were designed and prepared by a simple hydrothermal and calcination route. Compared with the bulk carbon nitride, the control strategy forms the ordered nanotube structure, which greatly improved their specific surface area, exposed more active sites, and enhanced the graphitization degree. The transient fluorescence lifetime of tubular carbon nitride is twice as long as that of pure carbon nitride. Furthermore, boron doping carbon nitride nanotubes exhibited a 1.5-fold increase in a lifetime over tubular carbon nitride, which acts a synergistic role with nanotube architecture to further increases the carrier concentration and hinder the recombination of photogenerated electron-hole. Under the irradiation of visible light, the amount of hydrogen evolution of the optimum photocatalyst has achieved 22.1 mmol g−1 h−1, which was 64 times that of the bulk carbon nitride and exhibited excellent stability. This work provides a promising strategy for the development of non-metallic doped carbon nitride nanotube photocatalysts for hydrogen evolution. The project was funded by National Natural Science Foundation of China (Nos. 21901154, 21671129), the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT17R71).

Recently, new states of matter like superconducting or topological quantum states were found in transition metal dichalcogenides (TMDs) and manifested themselves in a series of exotic physical behaviors. Such phenomena have been demonstrated to exist in a series of transition metal tellurides including MoTe2, WTe2, and alloyed MoxW1-xTe2. However, the behaviors in the alloy system have been rarely addressed due to their difficulty in obtaining atomic layers with controlled composition, albeit the alloy offers a great platform to tune the quantum states. Here, we report a facile CVD method to synthesize the MoxW1-xTe2 with controllable thickness and chemical composition ratios. The atomic structure of a monolayer MoxW1-xTe2 alloy was experimentally confirmed by scanning transmission electron microscopy. Importantly, two different transport behaviors including superconducting and Weyl semimetal states were observed in Mo-rich Mo0.8W0.2Te2 and W-rich Mo0.2W0.8Te2 samples, respectively. Our results show that the electrical properties of MoxW1-xTe2 can be tuned by controlling the chemical composition, demonstrating our controllable CVD growth method is an efficient strategy to manipulate the physical properties of TMDCs. Meanwhile, it provides a perspective on further comprehension and sheds light on the design of devices with topological multicomponent TMDC materials.

This work presents a high efficiency, compact 80–110-GHz InP power amplifier (PA) based on stacked common base (CB) topology and a counterpart design using conventional stacked common emitter (CE) topology. The comparison between the two exhibits that the former delivers higher power gain, superior-gain compression behavior (linearity), and higher back-off efficiency in $W$ -band in 250-nm InP heterojunction bipolar transistor (HBT). At 90 GHz, the stacked CB/CE PA achieves 11.8 dB/6dB gain, 33%/34% peak PAE, 16.8%/14.5% PAE at 6-dB back-off, and Psat of 18.7 dBm/19.6 dBm. Modulation test exhibits an EVM of 2.38% at 11.8-dBm average power supporting 3 Gbps 64-QAM for the stacked CB PA. The stacked CB PA with 17.9–18.9-dBm Psat across 80–110 GHz demonstrates one of the highest efficiency, broadband and linear PAs in $W$ -band using InP technology.

Producing highly efficient bifunctional catalyst for the generation of hydrogen and oxygen through overall water splitting is an emerging direction in electrocatalysis. Herein, a dandelion-like hierarchical NiMoP2-Ni2P (nanowire/nanoparticle) heterostructure was synthesized for efficient electrochemical water splitting. The NiMoP2-Ni2P heterostructures grown on carbon cloth as a freestanding integrated electrode exhibited excellent oxygen evolution reaction (OER) activity and hydrogen evolution reaction (HER) activities with low overpotentials (258 mV and 53 mV to reach 10 mA cm−2 for the OER and HER, respectively), and small Tafel slope (45 mV dec-1 and 58 mV dec-1 for the OER and HER, respectively). Moreover, the NiMoP2-Ni2P heterostructure can act as both anode and cathode catalysts for overall water splitting with low overall potential of 1.48 V at 10 mA cm−2. Density functional theory (DFT) combined with structural probes suggests that the amorphous heterogeneous interfaces play an essential role in enhanced catalytic performance.

Two-dimensional (2D) ferromagnets with out-of-plane (OOP) magnetic anisotropy are potential candidates for realizing the next-generation memory devices with ultra-low power consumption and high storage density. However, a scalable approach to synthesize 2D magnets with OOP anisotropy directly on the complimentary metal-oxide semiconductor (CMOS) compatible substrates has not yet been mainly explored, which hinders the practical application of 2D magnets. This work demonstrates a cascaded space confined chemical vapor deposition (CS-CVD) technique to synthesize 2D FexGeTe2 ferromagnets. The weight fraction of iron (Fe) in the precursor controls the phase purity of the as-grown FexGeTe2. As a result, high-quality Fe3GeTe2 and Fe5GeTe2 flakes have been grown selectively using the CS-CVD technique. Curie temperature (TC) of the as-grown FexGeTe2 can be up to ∼ 280 K, nearly room temperature. The thickness and temperature-dependent magnetic studies on the Fe5GeTe2 reveal a 2D Ising to 3D XY behavior. Also, Terahertz spectroscopy experiments on Fe5GeTe2 display the highest conductivity among other FexGeTe2 2D magnets. The results of this work indicate a scalable pathway for the direct growth and integration of 2D ternary magnets on CMOS-based substrates to develop spintronic memory devices.

A wideband antenna pair used for 5G mobile phones is proposed in this paper. Both elements of the antenna pair consisting of an open slot and a coupled feeding line can cover the band of 3.3–6 GHz,...

This letter presents a simultaneous broadband and back-off efficient mm-wave linear Doherty PA utilizing quadrature hybrid and a Doherty bandwidth enhancement technique exploiting quasi non-Foster impedance synthesis at the quadrature isolation port. We show analytically the design methodology of the non-Foster synthesis with ON-chip impedance tuner that allows broadband back-off efficiency, without suffering from nonlinearity due to the low-leakage power of the isolated port. Across 44–64 GHz, the proposed SiGe PA achieves collector efficiency of 26%–34.7% at peak power and 14%–23.2% at 6 dB back-off with 18.5–21.5 dBm Psat and peak gain of 18.5 dB. It supports 6 Gbps 64 QAM signal with −26.5 dB/−28.3 dBc EVM/ACLR at average Pout/PAE of 14.6 dBm/14.6% at 54 GHz. To the best of the authors’ knowledge, this work achieves highest bandwidth among silicon-based mm-wave power amplifiers (PAs) where higher than class-B back-off efficiency is achieved.

Designing low-cost preparation of high-activity electrocatalysts with excellent stability is the route one must take to fully realize large-scale application implementation of zinc-air batteries. 3D nitrogen-doped nanocarbons with transition metals or their derivatives encapsulated in show promising potential in the field of non-precious metal oxygen electrocatalysis. Herein, we report a simple, economical, and large-scale production method to construct worm-like porous nitrogen-doped carbon with in situ-grown carbon nanotubes and uniformly embedded Fe/Fe3C nanoparticles. It not only has high conductivity owing to the nitrogen-doped nature but also has ample active sites and electrolyte diffusion channels benefitting from the uniformly distributed heterostructural Fe/Fe3C nanoparticles and discrete hierarchically porous structures. When used as catalyst materials for a zinc-air battery, an energy density of 719.1 Wh kg-1 and a peak power density of 101.3 mW cm-2 at a 50 mA cm-2 discharge current density is achieved. Additionally, throughout charging and discharging for 200 cycles at a current density of 20 mA cm-2, the charge/discharge voltage gap is nearly constant.

Since the large-scale application of fully autonomous vehicles is difficult to be commercialized in the short term, human-vehicle shared control (HVSC) is a promising technique. To implement the control authority allocation and observe the driver characteristic, it is essential to develop an efficient HVSC dynamic model with the driver’s neuromuscular characteristic (NMS). To further our previous research, a simplified HVSC dynamic model is proposed in this paper. This model simplifies the non-critical NMS parameters such as muscle spindle feedback, which has no significant feedback effect while retaining essential NMS characteristics such as stretch reflection and intrinsic properties. The model consists of a model predictive controller (MPC) coupled with a driver NMS model and a 2 DOF vehicle model. The stability is proved by Lyapunov stability theory. Moreover, a field experiment was conducted for validation of the model. The V-Box is utilized to measure the vehicle’s state signals, such as steering wheel angle and pedal stroke. Subsequently, the adaptive genetic algorithm (AGA) is employed to identify the model parameters based on the experimental results. The comparison between the experiment and the model output shows that the proposed model can accurately represent the driver’s NMS characteristics and vehicle dynamic parameters. This paper will serve as a theoretical basis for the control authority allocation for L3 class autonomous vehicles.

With the increasing demand of consumers for diversified network services, more and more network service providers are competing fiercely in providing network composite services in order to meet the market demand. Network composition service is the most basic service. According to certain rules, it synthesizes new services and then realizes new functions. At present, the pricing of service composition by network service providers in the market is not scientific, meanwhile random and temporary pricing is still relatively common. In order to scientifically guide network service providers to price composite service scientifically and effectively, improve the profit of service providers, and meet the maximum service demand of consumers. Based on the theory of market demand price, this paper applies the pricing model in supply chain management to the pricing link of composite service network. From the perspective of network service providers, a two-objective competitive pricing model is constructed, and the simulation example analysis and sensitivity test are carried out. The simulation results show that the composite service provided by network service providers satisfy the general market demand price theory. In order to help service providers make scientific decisions on price adjustment of network services, this paper also makes sensitivity analysis of market demand on price changes. The research shows that the composite service provided by network service providers satisfy the general market demand price theory, while adjusting the price can only meet the general market demand price change theory if the original price is less than half of the original price.

Person re-identification is a challenging research issue in computer vision and has a broad application prospect in intelligent security. In recent years, with the emergence of large-scale person datasets and the rapid development of deep learning, many outstanding results have been achieved in person re-identification researches, which mainly involves two critical technologies: feature extraction and distance metric. Among them, feature extraction has been well summarized in the current literature of person re-identification, but there is no systematic analysis of the distance metric method in the current review literature. However, effective and reliable distance metric is crucial to improve the accuracy of person re-identification. Therefore, it is necessary to systematically review and summarize the metric learning methods in person re-identification, so as to provide some references for the researchers of metric learning. In this paper, we make a comprehensive analysis of metric learning methods in the past five years, which can be summarized into three aspects: distance metric method, metric learning algorithm, and re-ranking for the metric results. Then, we compare the performance of some representative metric learning methods and discuss them in-depth. Finally, we make a prospect for the future research direction of metric learning in person re-identification.

Silver catalysts play an irreplaceable role in the electroreduction of CO2 to CO. Here, we report the structure of bromide anion-adsorbed coral-like porous microspheres of silver (Ag BACPSs). They ...

To achieve excellent treatment performance of complex and time-varying characteristics, the operation of wastewater treatment process (WWTP) has been considered as a dynamic multiobjective control problem. In this paper, an optimal controller, based on a dynamic multiobjective particle swarm optimization (DMOPSO) algorithm, is developed to deal with the dynamic multiple conflicting criteria [i.e., effluent quality (EQ), operation cost, and operation stability]. The novelties and advantages of this proposed DMOPSO-based optimal controller (DMOPSO-OC) include the following two aspects. First, an integrated optimization framework, where the multiple objectives not only conflict with each other but also change over time, is able to catch more characteristics of WWTP than the existing works. Second, a DMOPSO algorithm, with an adaptive global best selection mechanism, is designed to solve the multiobjective optimization problem (MOP) for the proposed optimal controller, thus leading to a significant improvement of optimal synthesis for performance. Finally, the proposed DMOPSO-OC is tested in the benchmark simulation model No. 1 (BSM1) and implemented in a real WWTP to evaluate its effectiveness. The experimental results demonstrate that this proposed DMOPSO-OC can achieve a significant improvement in optimal control performance and obey the requirement of multiple conflicting criteria.

Public transport plays an essential role in helping people escape from the congested traffic in large and crowded cities. Evaluating customer satisfaction is the key to the continuous quality improvement of public transport services. This work proposes a new public transport customer satisfaction evaluation approach based on an extended thermodynamic method with q-rung orthopair fuzzy sets. First, we use the q-ROFSs to handle the ambiguity and uncertainty of customer satisfaction evaluation information for public transport. Then, we extend the thermodynamic method to determine the customer satisfaction levels of public transport lines. Moreover, the stepwise weight assessment ratio analysis method is utilized to specify the weights of evaluation criteria as it is simple and time-saving. The effectiveness of the proposed approach is illustrated with a customer satisfaction evaluation of the rail transit network in Shanghai, China. Results showed that Line 7 has the highest customer satisfaction and Line 2 has the lowest customer satisfaction among the considered rail transit lines. Besides, nine evaluation criteria need to be optimized to improve the customer satisfaction level of Line 2 and the most critical one is train crowding.

The existing literatures, concentrating on the statistical analysis of transient response of antennas, radiated by high-altitude electromagnetic pulse, always choose an area-based method to calcula...

The pyrolysis of metal-organic frameworks (MOFs) is an ingenious way to synthesize carbon-based materials with unique morphology for various applications including electrocatalysis. In this work, we reported a facile morphology regulation strategy for the synthesis of a spherical superstructure of MOF nanosheets. The use of metal hydroxide nanosheets on Zn particles as precursors/templates allowed MOFs with general polyhedron shape to form nanosheets and assemble into a spherical superstructure in the ligand solution. Further, a hollow spherical superstructure of carbon nanosheets decorated with metal-based nanoparticles was fabricated through the pyrolysis of MOF nanosheet superstructures at 950 °C, where the substrate/template Zn particle cores were evaporated away. The obtained composites possess carbon-based superstructures with abundant mesopores and metal-based nanoparticles with rich alloy/oxide interfaces. These features endow this MOF-derived carbon-based material with outstanding bifunctional activity for oxygen reduction/evolution reactions and great performances in Zn-air batteries.

The evaluation of cell's weatherability is of practical interest. To further improve the soluble lead flow battery's weatherability, physiochemical properties of electrolytes containing fluoborate, perchlorate, methanesulfonate and trifluoromethanesulfonate are investigated from −60 to 50 °C. Activities of CF3SO3H and HClO4 are poor in trifluoromethanesulfonate and perchlorate solutions due to common anion effect. The solubility of lead salt can be improved by increasing temperature, but worsened by increasing acid's content. With the temperature increasing, the conductivity is enhanced, and the viscosity is lowered for four solutions. The same results have been found by increasing acid's content except for CF3SO3H. The high energy efficiency can be achieved for cells over −40–0 °C using fluoborate and perchlorate solutions, 73.2% at −40 °C and 78.1% at −30 °C respectively. Over the temperature range of 20–50 °C, the cells with methanesulfonate and trifluoromethanesulfonate solutions have good performance, 77.4% and 73.7% at 50 °C respectively.

Graphene film is promising candidate as free-standing electrodes for potassium-ion batteries (KIBs) owing to its intrinsic nature of mechanical strength and high electrical conductivity. However, its performance is usually restricted by the tightly stacked structure and sluggish insertion/deinsertion K storage mechanism. Herein, a redox-active engineered holey reduced graphene oxide (HRGO) film anode was prepared by using the carboxylic acid functionalized polystyrene (PS-COOH) spheres as the template. The holey ion diffusion network channels and the oxygen functional groups can be optimized during the PS-COOH spheres decomposition process, which largely promote the enhancement of electrochemical performance because the oxygen functional groups can serve as the surface-redox sites increasing surface-driven reactions and holey channels provide more ion-accessible area for K-ion storage. Moreover, the reduction degree of graphene oxide also be simply tuned by changing the annealing temperature, which can improve the K+ bulk intercalation reaction. As a result, the optimized HRGO-900 (HRGO sample obtained at 900 °C) films exhibits a superior areal capacity (0.80 mAh cm−2 at 0.1 mA cm−2). The electrode design and construction strategies can be effectively applied in other 2D materials, which exhibits practical applications in energy storage devices.

The effects of water content on physicochemical properties such as density, dynamic viscosity, and electrical conductivity of HHexen(Tf2N), composed of monoprotic N-hexylethylenediaminium as cation and bis(trifluoromethylsulfonyl) imide as anion, were studied in the temperature range T = 303.15–353.15 K. HHexen(Tf2N) was dissolved a small amount of water (up to 6.53 wt%, corresponding to w0 = [H2O]/[PIL] = 1.65) as it is a hydrophobic protic ionic liquid (PIL). The physicochemical properties of HHexen(Tf2N) were significantly influenced by its water content. The temperature dependence of the physicochemical properties at different water contents (0.50 wt%, 0.95%, 1.93%, 3.02%, 4.03%, 5.14% and 5.95%, corresponding to w0 = 0.12, 0.23, 0.47, 0.74, 1.0, 1.28 and 1.50, respectively) were studied. With an increase in the water content, the density and dynamic viscosity decreases while the electric conductivity increases in a given temperature range. The Walden lines of HHexen(Tf2N) show a good ionic character as the curves are all located in the range of ΔW = 0.5 ± 0.2 (ΔW = log101/η −log10 Λ) at different water contents. The ionicity shows an increasing trend with an increase of water content; this is evident when the water content rises to approximately 4.0% (w0 = 1.0). Additionally, HHexen(Tf2N) was used to extract Co(II) ions from an aqueous solution. Inductive coupled plasma emission spectrometry results showed that more than 99.5 wt% of Co(II) ions moved from the 100 mmol·kg−1 aqueous layer to the HHexen(Tf2N) layer (of equal volume). After extraction of the Co(II) ions, the PIL layer contained 7.90% of water (w0 = 2.0).

A local Bundle Adjustment (BA) on a sliding window of keyframes has been widely used in visual SLAM and proved to be very effective in lowering the drift. But in lidar SLAM, BA method is hardly used because the sparse feature points (e.g., edge and plane) make the exact point matching impossible. In this paper, we formulate the lidar BA as minimizing the distance from a feature point to its matched edge or plane. Unlike the visual SLAM (and prior plane adjustment method in lidar SLAM) where the feature has to be co-determined along with the pose, we show that the feature can be analytically solved and removed from the BA, the resultant BA is only dependent on the scan poses. This greatly reduces the optimization scale and allows large-scale dense plane and edge features to be used. To speedup the optimization, we derive the analytical derivatives of the cost function, up to second order, in closed form. Moreover, we propose a novel adaptive voxelization method to search feature correspondence efficiently. The proposed formulations are incorporated into a LOAM back-end for map refinement. Results show that, although as a back-end, the local BA can be solved very efficiently, even in real-time at 10Hz when optimizing 20 scans of point-cloud. The local BA also considerably lowers the LOAM drift. Our implementation of the BA optimization and LOAM are open-sourced to benefit the community., 8 pages, 9 figures, conference

The growth and shear properties of intermetallic compound (IMC) in Cu/In-48Sn/Cu solder joints at different bonding times have been studied, and the growth kinetics of interfacial Cu3(In,Sn) phase at different bonding temperatures investigated. The results show that the IMC of the Cu/In-48Sn/Cu joint is composed of Cu6(In,Sn)5 and Cu3(In,Sn) but transforms to Cu3(In,Sn) after 120 min of bonding at 260°C and pressure of 3 MPa. The morphology of the Cu6(In,Sn)5 layer changed from flat to scallop, and the growth rate of Cu3(In,Sn) located directly below the convex part of the Cu6(In,Sn)5 sector was significantly higher than that of Cu3(In,Sn) located directly below the concave part of the Cu6(In,Sn)5 sector. The shear strength of the solder joints first increased then slowly decreased, reaching 21.8 MPa when increasing the bonding time to 60 min. The shear strength of the solder joints was improved by the formation of Cu6(In,Sn)5 and Cu3(In,Sn), and Cu3(In,Sn) phase played a greater role in improving the shear strength of the solder joints than did Cu6(In,Sn)5 phase. The growth kinetics index n of Cu3(In,Sn) was 0.478 to 0.495, and the growth mechanism of Cu3(In,Sn) was controlled by diffusion at bonding temperatures of 250°C to 280°C.

Excitons in monolayer transition metal dichalcogenides (TMDs) provide a paradigm of composite Boson in a two-dimensional system. This Letter reports a photoluminescence and reflectance study of excitons in monolayer molybdenum diselenide (MoSe2) with electrostatic gating. We observe the repulsive and attractive Fermi polaron modes of the band edge exciton, its excited state, and the spin-off excitons, which the simple three-particle trion model is insufficient to explain. The contrasting energy shift between the exciton and charge-bound excitons (repulsive and attractive polaron modes) and the remarkably different gate dependence of the polaron energy splitting between the ground state and the excited state excitons unambiguously support the Fermi polaron picture for excitons in monolayer TMDs.

Clinical diagnosis of Parkingson’s disease (PD) requires the physician to assess the patient’s gait and other symptoms. A dual-branch model is proposed in this paper as an objective diagnostic tool to diagnose PD automatically. In this research, the joint features and independent features of left and right gait are fused innovatively. Convolutional neural network (CNN) and long short-term memory network (LSTM) are used to extract the spatial and temporal characteristics of sensors respectively. After the independent features extracted from the branches are collapsed, LSTM is used to incorporate the joint features between the left and right gait. Compared with other methods, the proposed model can learn the correlation between the two feet and extract higher discriminative features to effectively improve the accuracy of Parkinson detection. The model shows the state-of-the-art performance for the public dataset, with the accuracy, sensitivity, and specificity being 99.22%, 100%, and 98.04%, respectively. A simple, fast, and objective method proposed in this paper was believed to improve diagnostic performance.

A novel extrusion−shearing (ES) composite process was designed to fabricate fine-grained, high strength and tough magnesium alloy. The structural parameters of an ES die were optimized by conducting an orthogonal simulation experiment using finite element software Deform-3D, and Mg−3Zn−0.6Ca−0.6Zr (ZXK310) alloy was processed using the ES die. The results show that the optimized structural parameters of ES die are extrusion angle (α) of 90°, extrusion section height (h) of 15 mm and inner fillet radius (r) of 10 mm. After ES at an extrusion temperature and a die temperature of 350 °C, ZXK310 alloy exhibited good ES forming ability, and obvious dynamic recrystallization occurred in the forming area. The grain size decreased from 1.42 μm of extrusion area to 0.85 μm of the forming area. Owing to the pinning of second phase and formation of ultrafine grains, the tensile strength, yield strength and elongation of alloy reached 362 MPa, 289 MPa and 21.7%, respectively.

This article suggests that the correlation information, hidden in spatial configuration and temporal dynamic of frequencies, is an important indication for bearing health condition. To consider this information, we extend graph-modeling strategy, and introduce a bearing fault detection and diagnosis technique based on temporal-spatio graph. First, short-time periodogram is extracted from vibration signal, and, then, modeled by a temporal-spatio graph. In fault detection phase, the spectrum of temporal channel graph is used to map short-time periodogram to acquire the so-called graph-mapped spectrum (GMS). The principal frequency in resulting GMS is found highly related with the health condition of monitored bearing. Thus, any change of health condition can be detected by checking this principal frequency over time. Once a fault is detected, the spatio channel graph is fed to K-nearest neighbor classifier, coupled with a specific graph distance metric, for fault type identification. Comprehensive experiments on two benchmarking datasets along with theoretical interpretation demonstrate the superiority of proposed method over state of the arts. The proposed temporal-spatio graph provides a significant extension of existing spectrum analysis for fault detection and diagnosis.

Composite materials have been widely used in the aerospace industry and are critical for safe operations. However, the delamination, caused by cyclic loads and corrosive service environment, poses a serious threat to the structural integrity of composite laminates. The acoustic emission technique has been adopted to assess the structural integrity by characterizing damage location, type, and size. This article proposes and compares data-driven prognostic methods to quantify the delamination area efficiently and accurately. To address the problem of insufficient inspection data, the prediction model adopts the path length across the delamination area as the target value. The delamination area can then be estimated with the predicted path length based on formulated geometric relationships. This solution will augment the model training datasets, and consequently, avoid the overfitting problem during the training process. Experimental results on composite coupons demonstrate that the proposed ensemble learning-based model outperforms other state-of-the-art methods in terms of prediction accuracy and efficiency.

In the current study, via using a mixed ligand approach, a new three-dimensional metal–organic framework, [Zn2(L)2(bpp)(H2O)2]n (bpp = 1,3-di(4-pyridyl)propane and H2L = 2,5-thiophenedicarboxylic acid) was synthesized with a high yield under the condition of solvothermal reaction, and its structure was characterized through the diffraction of single crystal X-ray along with EA. Luminescence studies revealed that complex 1 can be utilized as the highly sensitive fluorescent sensor for sensing Fe3+ in aqueous solution. The apoptosis assay was used to assess the death of breast cancer cells after the compound treatment, and the relative expression of the genes related to ROS was also detected. The results for the simulation of molecular docking provided the novel design strategy of the anti-cancer drug and provided deeper insights into the anti-cancer activities.

In the process of multi-crystalline silicon etched by the Metal Assisted Chemical Etching (MACE) method, due to its too small and uneven microstructures, heavy doping often occurs, which usually requires mixed acid (HNO3+HF) treatment, but this will ultimately affect the efficiency of crystalline silicon solar cells. In this article, phenol-formaldehyde (PF) resin, a main component of the positive photoresist, and water were dissolved in ethanol to form a PF resin-water-ethanol solution (PWEs) toward wet etching of multi-crystalline Si wafer, aim to prepared the texture structure with uniform distribution and reasonable size. A liquid film of PWEs was prepared on the Si wafer using dip-coating method. After the ethanol volatilizing, a mask of the resin was formed on the wafer through phase separation between the resin and water. A net like or a particle array mask was formed on the wafer through controlling the water/resin ratio, pulling speed and drying speed of the liquid film. With appropriate parameters above, a desirable particle array mask of PF resin was prepared. The size of particles and their spaces in this array were about $1{\mu }\text{m}$ and $2~{\mu }\text{m}$ respectively. With assistance of this particle array as a mask, a better textured surface of the Si wafer was prepared through MACE combined with etching of HNO3-HF mixed solution. The minimum reflectance of the wafer with the textured surface was as low as 14.2 %.

The perovskite solar cell has emerged rapidly in the field of photovoltaics as it combines the merits of low cost, high efficiency, and excellent mechanical flexibility for versatile applications. However, there are significant concerns regarding its operational stability and mechanical robustness. Most of the previously reported approaches to address these concerns entail separate engineering of perovskite and charge-transporting layers. Herein we present a holistic design of perovskite and charge-transporting layers by synthesizing an interpenetrating perovskite/electron-transporting-layer interface. This interface is reaction-formed between a tin dioxide layer containing excess organic halide and a perovskite layer containing excess lead halide. Perovskite solar cells with such interfaces deliver efficiencies up to 22.2% and 20.1% for rigid and flexible versions, respectively. Long-term (1000 h) operational stability is demonstrated and the flexible devices show high endurance against mechanical-bending (2500 cycles) fatigue. Mechanistic insights into the relationship between the interpenetrating interface structure and performance enhancement are provided based on comprehensive, advanced, microscopic characterizations. This study highlights interface integrity as an important factor for designing efficient, operationally-stable, and mechanically-robust solar cells., Operational stability and mechanical robustness remain as engineering bottlenecks in perovskite solar cells technology. Here, Dong et al. introduce an interpenetrating perovskite at the electron-transporting-layer interface that enables a 1000-hour stable operation and high endurance against bending fatigue over 2500 cycles.

In-service bridges, under long-term service operational environment, are usually accompanied by different damage types. Traditional method for the measure point arrangement of in-service bridge SHM is usually based on engineering experience. A large number of SHM sensors are usually arranged on the structure, followed by a high engineering cost and a heavy maintenance task. These sensors will also produce large amounts of data, creating a challenge for operators requiring to deal with data processing in an effective manner. This study serves as a part of the series of studies on the measure point arrangement strategy of in-service bridge SHM. In this study, the SHM sensor measure point arrangement of in-service continuous girder bridge (a common structural style of high way bridge in China) is proposed. Two-span continuous beam, three-span continuous beam, and four-span continuous beam are taken as examples. Detailed comparison and verification are also performed with consideration of numerical simulation and previous studies. Different traffic speeds and different bridge spans are considered. The effect of different damage locations and different damage degrees are analyzed in detail. This study shows that a general similar trend can be observed for the structural robustness of in-service continuous girder bridge. The elements with smaller structural robustness of this kind of bridge are basically located around the middle cross section of side spans (first span and last span), followed by the middle span. Moreover, the numerical value of structural robustness of different elements in a continuous girder bridge is significantly different from each other, due to the complexity of the joint effect of different traffic speeds and damage locations. Therefore, the measure point should be generally arranged at the side span firstly, followed by the middle span. With consideration of the specific traffic speed and damage location in engineering application, a detailed analysis is also proposed for the further optimization of SHM sensor measure point arrangement. Once the elements are arranged in order of the numerical value of structural robustness, the SHM sensor measure point arrangement of this kind of bridge can be more targeted, and the number of sensors can also be greatly reduced.

The effect of thermo-mechanical treatment on the microstructural evolution of low carbon micro-alloyed high strength steel was studied by combining prestrain with tempering (PST) in this paper. It was found that the prestrain causes the dislocation to plug up around the grain boundary and carbide, resulting in carbide boundary fragmentation. Moreover, it breaks the thermo-dynamic equilibrium between the matrix and carbide, induces the dissolution of carbon in the high energy state, and then changes the distribution of carbon in the matrix. In the subsequent tempering process, the precipitation regularity of carbide was changed, which promoted the precipitation carbide at low temperature. The influence of carbide precipitation on dislocation can be divided into two stages: the first stage was precipitation induced creep, which promoted stress relaxation; the second stage was precipitation pinning dislocation, which improved material strength and inhibited stress relaxation.

Mg–Zn–Ca alloy has a good development prospect in biomedical alloy. However, its development is restricted by large hot tearing susceptibility. Herein, the influence of Ca addition on the hot tearing susceptibility (HTS) of Mg-4Zn-xCa-0.3Zr (0.5, 1, 1.5, and 2 wt%) alloys has been studied by using thermal analysis and numerical simulation software, Procast. The “Clyne–Davies” hot tearing prediction model was improved, and Cracking Susceptibility Coefficient (CSC) was recalculated to highlight the physical significance of the model. The hot tearing zone was observed via optical microscopy and scanning electron microscopy, and the phase composition was analyzed with X-ray diffraction. The results of numerical simulations suggest that the Hot Tearing Indicator (HTI) tended to decrease with increase in Ca addition. However, the experimental results reveal that with the increase in Ca addition, the hot tearing susceptibility of the alloys first decreased with Ca addition up to 1.5 wt% and then displayed a slight increase with the addition up to 2.0 wt%. The SEM images and experimental results reveal that the thickness and ability to resist solidification shrinkage stress of the liquid membrane, quantity of the second phases, and feeding ability were all enhanced with the increase in Ca addition, thereby reducing the HTS of Mg-4Zn-XCa-0.3Zr alloys. When the Ca addition reached 2.0 wt%, the brittle calcium-containing phase forms at the grain boundary, which reduces the high-temperature strength of the matrix and the ability to resist the solidification shrinkage stress of the alloy, resulting in an increase in HTS. Therefore, when the Ca addition was 2.0 wt%, the simulation results were different from the experimental results.

By performing first-principles calculations, we find that the predominant spin exchange of a hexagonal CoO2 layer is in proximity to the ferromagnetic-to-antiferromagnetic transition point. Its mag...

Image fusion is an important task for computer vision as a diverse range of applications are benefiting from the fusion operation. The existing image fusion methods are largely implemented at the pixel level, which may introduce artifacts and/or inconsistencies, while the computational complexity is relatively high. In this article, we propose a symmetric encoder–decoder with residual block (SEDRFuse) network to fuse infrared and visible images for night vision applications. At the training stage, the SEDRFuse network is trained to create a fixed feature extractor. At the fusing stage, the trained extractor is utilized to extract the intermediate and compensation features, which are generated by the residual block and the first two convolutional layers from the input source images, respectively. Two attention maps, which are derived from the intermediate features, are then multiplied by the intermediate features for fusion. The salient compensation features obtained through elementwise selection are passed to the corresponding deconvolutional layers for processing. Finally, the fused intermediate features and the selected compensation features are decoded to reconstruct the fused image. Experimental results demonstrate that the proposed fusion solution, i.e., SEDRFuse, outperforms the state-of-the-art fusion methods in terms of both subjective and objective evaluations.

Indium phosphide (InP) heterojunction bipolar transistors (HBTs) with $f_{t}/f_{\max}$ of 350/675 GHz are studied and explored for a linear, high efficiency and broadband power amplifiers (PAs) at mm-wave frequencies. Unlike the conventional transmission-like-based design, this article presents a compact, broadband transformer-based power combining and impedance matching using the waveform engineering approach. We present, for the first time, mm-wave (40–60 GHz) InP topologies incorporating the following: 1) on-chip transformer for broadband, efficient and compact impedance matching and power combining; 2) synthesis of optimal second-harmonic impedance through transformer center tap to achieve high-efficiency differential PA operation; and 3) biasing techniques to reduce AM–PM distortion for linearity enhancement. This work reports a transformer-based push–pull InP PA in $0.25~\mu \text{m}$ technology across 42–62 GHz demonstrating a peak power added efficiency (PAE) of 39.5% and peak $P_{\text{sat}}$ of 20.6 dBm. The PA supports 4 GHz bandwidth at 52 GHz with an EVM of −22.9 dB and an adjacent channel leakage ratio (ACLR) of −32 dBc for an 8 Gb/s QPSK signal at 13.3 dBm average output power. This work presents one of the highest efficiency with wide bandwidth and highest linearity mm-wave PAs in integrated technology.

The intelligent fault diagnosis powered deep learning (DL) is widely applied in various practical industries, but the conventional intelligent fault diagnosis methods cannot fully juggle the manifold structure information with multiple-order similarity from the massive unlabeled industrial data. Thus, a new Multiple-Order Graphical Deep Extreme Learning Machine (MGDELM) algorithm for unsupervised fault diagnosis (UFD) of rolling bearing is proposed in this study. Specifically, the developed MGDELM algorithm mainly contains two parts: 1) one is unsupervised multiple-order feature extraction, the first-order proximity with Cauchy graph embedded is applied to extract the local structural information, and the second-order proximity is simultaneously employed for mining global structural information and 2) the other used is the unsupervised Fuzzy-C-Mean (FCM) into fault clustering built on the extracted multiple-order graph embedding features. Empirically, two cases of rolling bearing failure data validate the effectiveness of the proposed algorithm and fault diagnosis method. By jointly optimizing the multiple-order objective function, the proposed MGDELM algorithm can synchronously extract local and global structural information from the raw industrial data. This study also provides a novel promising approach for UFD.

Nowadays, the measuring and monitoring data collected from rotating machinery exists a high-dimensional and massive status in quo, but the acquisition of the valuable labeled information is difficult. To address this challenge, an intelligent fault diagnosis method of rotating machinery based on semisupervised deep sparse auto-encoder (SSDSAE) with local and nonlocal information is presented in this article. Primarily, the collected vibration spectrum signals are fed into the constructed SSDSAE algorithm to implement fault feature extraction, and after that the extracted sparse discriminant features are fed into the back-propagation (BP) classifier for fault diagnosis. Accordingly, the unlabeled samples in SSDSAE are used to define local and nonlocal structural information for the original DSAE, and then the topological regularization terms are obtained by minimizing intraclass compactness (locality) and maximizing interclass separability (nonlocality). Moreover, the weighted cross-entropy (WCE) techniques in SSDSAE are used to improve the generalization performance of the fault diagnosis model for labeled data. Eventually, the availability of the proposed method is validated with data from experimental bearing fault and industrial rotor fault, respectively.

In order to find a new type of antifouling coating with higher biological activity and more environmental protection, heterocyclic compounds and benzisothiazolinone were introduced into acrylic resin to prepare a new type of antifouling resin. In this study, a series of grafted acrylic resins simultaneously containing benzoisothiazolinone and heterocyclic monomers were prepared by the copolymerization of an allyl monomer with methyl methacrylate (MMA) and butyl acrylate (BA). Inhibitory activities of the copolymers against marine fouling organisms were also investigated. Results revealed that the copolymers exhibit a clear synergistic inhibitory effect on the growth of three seaweeds: Chlorella, Isochrysis galbana and Chaetoceros curvisetus, respectively, and three bacteria, Staphylococcus aureus, Vibrio coralliilyticus and Vibrio parahaemolyticus, respectively. In addition, the copolymers exhibited excellent inhibition against barnacle larvae. Marine field tests indicated that the resins exhibit outstanding antifouling potency against marine fouling organisms. Moreover, the introduction of the heterocyclic group led to the significantly enhanced antifouling activities of the resins; the addition of the heterocyclic unit in copolymers led to better inhibition than that observed in the case of the resin copolymerized with only the benzoisothiazolinone active monomer.