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Viral covert mortality disease (VCMD) in cultured shrimp is caused by covert mortality nodavirus (CMNV). The infected shrimp mainly die successively in the deep-water areas at the bottom of farming ponds, which is difficult to observe; as such, the term "covert mortality disease" was coined by local farmers. The disease is known as running mortality syndrome by Southeast Asian farmers because a small number of diseased shrimp die daily in the infected ponds, and more continue to die until harvest time. The infected shrimp mostly show symptoms such as carapace softening, hepatopancreas color fading, atrophy, empty stomach, slow growth, etc. In the acute infection stage, the infected shrimp show opaque abdominal muscles. The prevalence of VCMD has caused severe economic losses in the shrimp farming industry over the past decade. CMNV can infect many major cultured shrimp species, including Penaeus vannamei, P. japonicus, P. chinensis, Macrobrachium rosenbergii, and Exopalaemon carinicauda. Moreover, the virus infects the co-inhabiting organisms in shrimp ponds, which makes it difficult to remove from shrimp farming areas due to its wide host range among invertebrates and its transport by many vectors. To elucidate the prevalence of CMNV in the main cultured shrimp species in China in recent years, an epidemiological investigation of CMNV, including clinical sample collection from shrimp and other organisms inhabiting the shrimp ponds, was carried out in the main shrimp culture areas of China from 2021 to 2022, and the collected samples were systematically analyzed using molecular biology, histopathology, and transmission electron microscopy approaches. During this period, 1, 299 samples were collected from Tianjin, Shandong, Jiangsu, Hainan, Hubei, and Xinjiang and included P. vannamei, M. rosenbergii, P. japonicus, P. chinensis, Procambarus clarkia, and other organisms living in shrimp culture ponds. The collected samples were analyzed using CMNV TaqMan probe real-time quantitative reverse transcription PCR (TaqMan RT-qPCR). The results showed that CMNV could be detected in the main cultured shrimp species, including P. vannamei, M. rosenbergii, and P. japonicus. A percentage of the samples collected from Shandong, Jiangsu, Hainan, Xinjiang, Guangxi, and Tianjin were observed to be CMNV-positive in the TaqMan RT-qPCR assays. In addition to cultured shrimp, CMNV was detected in organisms living in the shrimp ponds, such as Artemia collected in 2021 and Perinereis aibuhitensis collected in 2022. The CMNV-positive rate of samples collected in 2021 was 10.04% (69/687), and that of samples collected in 2022 was 11.44% (70/612). Epidemiological investigation showed that CMNV infection in outdoor pond culture usually caused a certain degree of cumulative death, whereas CMNV infection in indoor culture mainly caused the softening and reduced growth of infected shrimp and did not cause a large number of infected shrimp to die. High CMNV loads were detected in samples of Artemia used as fresh bait for shrimp and in samples of P. aibuhitensis living in shrimp pond soils. Accordingly, a high CMNV infection rate was observed in cultured P. vannamei feeding on CMNV-positive Artemia and in that farmed in ponds containing CMNV-positive P. aibuhitensis. The results indicated that CMNV vectors such as Artemia and P. aibuhitensis may pose a high risk of CMNV transition, threatening the health of the shrimp in the culture system. Recent studies on the pathogenesis of CMNV infection indicated that the virus mainly infected epithelial tissues, nerve tissues, and connective tissues in the hepatopancreas, appendages, mid-gut, gills, eye stalks, and nerves of shrimp. Therefore, histopathological and in situ hybridization analyses were performed on samples identified as CMNV-positive using TaqMan RT-qPCR in this study. Eosinophilic virus inclusion bodies were observed in the hepatopancreas and foremidgut tissues, and pathological lesions were observed in the appendicular nerves of diseased Penaeus shrimp. Moreover, obvious purple hybridization signals from the CMNV RNA probe were seen in tissues with lesions, which further confirmed the positive detection results for CMNV in shrimp samples obtained using TaqMan RT-qPCR in this study. The results of this study showed that, compared with previous years, there was still a high CMNV-positive detection rate in many areas of crustacean culture in 2022, and the epidemic risk of this virus was still high. Multiple CMNV-positive results were obtained in the co-inhabiting and feed organisms in crustacean farming ponds. These results remind fishery authorities and stakeholders in the prawn breeding industry to further strengthen the monitoring and early detection of CMNV to prevent and control the spread of the virus in seedlings, reduce the risk of epidemic hazards, and promote the sustainable and healthy development of the crustacean aquaculture industry.

The performance of the anode varies from the impurity ions in the copper electrowinning process. This work focused on the variation of the electrochemical behavior of the Pb-0.06%Ca-1.2%Sn anode as the Fe ions (Fe3+ and/or Fe2+) existed in the electrolyte by electrochemical characterization. Copper electrodeposition experiments were conducted under a current density of 300 A/m2, with the Fe ion concentration in the electrolyte controlled within the range of 0 to 20 g/L and the Cu ion concentration maintained at 45 g/L at a temperature of 45 °C. The variation in the corrosion resistance, catalytic activity, and structural composition of the anode film layer was analyzed in-depth according to the presence of Fe ions. The results show that the structure of PbO2 on the surface of the film was changed as Fe ions doped into the anode film, and the oxygen evolution activity of the anode was also improved. However, the corrosion resistance decreased with increasing Fe3+ concentration. Furthermore, the addition of 2 g/L Fe2+ in the electrolyte containing 2 g/L Fe3+ led to an elevation in the corrosion resistance of the anode to some extent and further increased the oxygen evolution activity.

Most visual simultaneous localization and mapping (SLAM) systems are based on the assumption of a static environment in autonomous vehicles. However, when dynamic objects, particularly vehicles, occupy a large portion of the image, the localization accuracy of the system decreases significantly. To mitigate this challenge, this paper unveils DOT-SLAM, a novel stereo visual SLAM system that integrates dynamic object tracking through graph optimization. By integrating dynamic object pose estimation into the SLAM system, the system can effectively utilize both foreground and background points for ego vehicle localization and obtain a static feature points map. To rectify the inaccuracies in depth estimation from stereo disparity directly on the foreground points of dynamic objects due to their self-similarity characteristics, a coarse-to-fine depth estimation method based on camera–road plane geometry is presented. This method uses rough depth to guide fine stereo matching, thereby obtaining the 3 dimensions (3D)spatial positions of feature points on dynamic objects. Subsequently, by establishing constraints on the dynamic object’s pose using the road plane and non-holonomic constraints (NHCs) of the vehicle, reducing the initial pose uncertainty of dynamic objects leads to more accurate dynamic object initialization. Finally, by considering foreground points, background points, the local road plane, the ego vehicle pose, and dynamic object poses as optimization nodes, through the establishment and joint optimization of a nonlinear model based on graph optimization, accurate six degrees of freedom (DoFs) pose estimations are obtained for both the ego vehicle and dynamic objects. Experimental validation on the KITTI-360 dataset demonstrates that DOT-SLAM effectively utilizes features from the background and dynamic objects in the environment, resulting in more accurate vehicle trajectory estimation and a static environment map. Results obtained from a real-world dataset test reinforce the effectiveness.

The metal–organic framework (MOF) materials with significant steadiness and a large specific surface area have been popular with supercapacitor material in recent years. However, its application in supercapacitors is restricted due to the low specific capacitance and poor conductivity. Herein, sulfur compounds with a high theoretical specific capacitance and highly conductive titanium nitride (TiN) were introduced into Co-based metal–organic frameworks/nickel foam (Co-MOF/NF) through a two-step hydrothermal technique (nickel sulfide/titanium nitride@ Co-based metal–organic frameworks/nickel foam). In detail, the fabricated nickel sulfide/titanium nitride@Co-based metal–organic frameworks/nickel foam (Ni3S2/TiN@Co-MOF/NF) electrode material exhibits a markedly high specific capacitance (2648.8 F g−1) at 1 A g−1, compared with that (770 F g−1) of the precursor Co-MOF/NF. And its mass specific capacitance is retained 88.3% (8 A g−1) after 5000 cycles. Furthermore, a non-symmetrical supercapacitor (ASC) composed of Ni3S2/TiN@Co-MOF/NF and AC exhibits excellent power density (801.8 W kg−1) and energy density (97.8 W h kg−1). Therefore, Ni3S2/TiN@Co-MOF/NF with excellent electrochemical properties and stability provides new ideas for the development of excellent supercapacitor electrode materials.

Facing the significant challenge of 3D object detection in complex weather conditions and road environments, existing algorithms based on single-frame point cloud data struggle to achieve desirable results. These methods typically focus on spatial relationships within a single frame, overlooking the semantic correlations and spatiotemporal continuity between consecutive frames. This leads to discontinuities and abrupt changes in the detection outcomes. To address this issue, this paper proposes a multi-frame 3D object detection algorithm based on a deformable spatiotemporal Transformer. Specifically, a deformable cross-scale Transformer module is devised, incorporating a multi-scale offset mechanism that non-uniformly samples features at different scales, enhancing the spatial information aggregation capability of the output features. Simultaneously, to address the issue of feature misalignment during multi-frame feature fusion, a deformable cross-frame Transformer module is proposed. This module incorporates independently learnable offset parameters for different frame features, enabling the model to adaptively correlate dynamic features across multiple frames and improve the temporal information utilization of the model. A proposal-aware sampling algorithm is introduced to significantly increase the foreground point recall, further optimizing the efficiency of feature extraction. The obtained multi-scale and multi-frame voxel features are subjected to an adaptive fusion weight extraction module, referred to as the proposed mixed voxel set extraction module. This module allows the model to adaptively obtain mixed features containing both spatial and temporal information. The effectiveness of the proposed algorithm is validated on the KITTI, nuScenes, and self-collected urban datasets. The proposed algorithm achieves an average precision improvement of 2.1% over the latest multi-frame-based algorithms.

The electrolytic refining of crude tin is generally carried out in the fluorosilicic acid (H2SiF6) system with the assistance of bone glue and β-naphthol. However, the high saturated vapor pressure and low stability of H2SiF6 present environmental concerns and do not align with sustainable development goals. In this paper, the electrochemical behavior of tin on a glassy carbon (GC) electrode was studied in a relatively green and environmentally friendly methanesulfonic acid (MSA) system. Bone glue, sodium lignosulfonate, and β-naphthol were used as additives to make the deposit morphology smooth and to ensure grain refinement. The electrochemical reduction process of Sn2+ in an MSA system is a quasi-reversible process controlled by diffusion. The apparent activation energy Ea = 14.189 kJ/mol for the ion diffusion of Sn2+ was further calculated. The results of chronoamperometry showed that the electrocrystallization of Sn2+ on the GC electrode gradually tended to three-dimensional instantaneous nucleation with the increase in applied potential. The morphology and phase of the deposits were analyzed using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The results showed that the deposits were uniform and dense pure tin. This work elucidates the electrochemical behavior of tin in a methanesulfonic acid system.

Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six DoF motion in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.

The environment perception algorithm in autonomous driving is trained in the source domain, leading to domain drift and reduced detection accuracy in the target domain due to shifts in background feature distribution. To address this issue, a domain adaptive object detection algorithm based on feature uncertainty is proposed, which can improve the detection performance of object detection algorithms in unlabeled data. Firstly, a local alignment module based on channel information is proposed, which can obtain the model’s uncertainty about different domain data based on the feature channels obtained through the feature extraction network, achieving adaptive dynamic local alignment. Secondly, an instance-level alignment module guided by local feature uncertainty is proposed, which can obtain the corresponding instance-level uncertainty through ROI mapping. To improve the domain invariance of bounding box regression, a multi-class, multi-regression instance-level uncertainty alignment module is proposed, which can achieve spatial decoupling of classification and regression tasks, further improving the model’s domain adaptive ability. Finally, the effectiveness of the proposed algorithm is validated on Cityscapes, KITTI, and real vehicle data.

Abstract The multi‐terminal hybrid line commutated converter (LCC)/modular multilevel converter (MMC)‐based high voltage dc (HVDC) has been applied to solve the simultaneous commutation failure problem in East China. Here, the propagation of the travelling wave is characterised by transfer functions and the characteristic of the fault current travelling wave in the multi‐terminal hybrid LCC/MMC HVDC system is analysed. Then a directional pilot protection for the multi‐terminal hybrid LCC/MMC HVDC system is proposed. The proposed method is implemented to the designed HCM5000 dc control and protection device and verified in the Kun–Liu–Long three‐terminal hybrid LCC/MMC HVDC real‐time digital simulator (RTDS) test system. The test results show that the proposed method meets the engineering requirement of multi‐terminal hybrid LCC/MMC HVDC system for line protection.

The nanoscale size and controllable memristance of Memristor (MR) have shown evident superiorities for structuring new integrated circuits with different functions. With regards to the reported MR based filters, their cut-off frequencies could be hardly hot-line controlled on purposes. In this paper, by using the boundary values of memristance, a method for hot-line adjusting cut-off frequencies is newly proposed by inputting a preset DC voltage together with the to-be-filtered signal, without the requirement of extra memristance write circuits, which is hence beneficial for integrated implementation. The floating MR emulator is firstly presented and the saturation operation of its memristance is interpreted. A family of MR based low-pass filters with controllable cut-off frequency are proposed by combining the connection nodes of a memristive circuit network Z with the op amp. Two circuits of the low-pass filter family are chosen for demonstration, and the cut-off frequency and passband gain are calculated. Square and sinusoidal voltage signals are used to test the performance of the proposed low-pass filter family. Both the simulation and experimental results show that the proposed filters can achieve cut-off frequency adjustment with good filtering performance.

The LLC-type resonant dual-active-bridge (LLC-DAB) DC-DC converter with a high voltage gain, high power density, and low backflow power has attracted increasing attention in recent years. However, its soft-switching and backflow power problems are still not solved, so the improvements to these problems are studied in this paper. Based on the dual phase shift (DPS) modulation method, the operating characteristics are analyzed, and a soft-switching and no backflow power modulation curve is established based on the voltage-current time-domain characteristics. On this basis, a soft-switching and no backflow power optimized control method based on DPS modulation is proposed to achieve soft-switching operation and eliminate backflow power. Due to the complex time-domain characteristics of the resonant tank voltage and current, the relationship between the phase shift ratios is fitted and optimized with this method based on the soft-switching and no backflow power characteristic curve, and the optimized results of the phase shift ratio under different operating conditions are obtained. The simulation results indicate that the soft-switching operation of the LLC-DAB converter can be achieved with the optimized control method proposed in this paper, and the backflow power is effectively eliminated.

Abstract The development of high‐efficiency, low‐cost, and long‐term stability catalysts for water electrolysis is significant for the sustainable production of hydrogen. The aim of this study is to reduce the overpotential and thus energy consumption. Toward this aim, transition metal sulfide catalyst (marked as S5‐40 doped Ni4Mo) is successfully prepared by a fast one‐step electrodeposition process on a copper foam (CF) substrate. The S20 doped Ni4Mo/CF catalyst displays an exceedingly low overpotential of 140 and −128 mV at 10 mA cm−2 for (oxygen evolution reaction) and (hydrogen evolution reaction), respectively. The S20 doped Ni4Mo/CF can be powered by a commercial AA alkaline battery (1.5 V) in 1.0 m KOH. It shows a lower cell voltage of 1.595 V at 10 mA cm−2 for overall water splitting (OWS). Up to 60 h of stability testing, compared with the S20 doped Ni4Mo/CF catalyst before electrolysis, their performance is improved by 15.5% and 41.1% at 2 and −0.7 V versus reversible hydrogen electrode, respectively. This study offers an avenue to fabricate transition metal sulfide bi‐functional catalysts for OWS and provides deep insights into the relationships among the S‐doping Ni4Mo at the 1.0 m KOH electrolyte physical characterization and electrocatalytic activity.

3D object detection methods based on camera and LiDAR fusion are susceptible to environmental noise. Due to the mismatch of physical characteristics of the two sensors, the feature vectors encoded by the feature layer are in different feature spaces. This leads to the problem of feature information deviation, which has an impact on detection performance. To address this problem, a point-guided feature abstract method is presented to fuse the camera and LiDAR at first. The extracted image features and point cloud features are aggregated to keypoints for enhancing information redundancy. Second, the proposed multimodal feature attention (MFA) mechanism is used to achieve adaptive fusion of point cloud features and image features with information from multiple feature spaces. Finally, a projection-based farthest point sampling (P-FPS) is proposed to downsample the raw point cloud, which can project more keypoints onto the close object and improve the sampling rate of the point-guided image features. The 3D bounding boxes of the object is obtained by the region of interest (ROI) pooling layer and the fully connected layer. The proposed 3D object detection algorithm is evaluated on three different datasets, and the proposed algorithm achieved better detection performance and robustness when the image and point cloud data contain rain noise. The test results on a physical test platform further validate the effectiveness of the algorithm.

In order to overcome the defect of low output voltage of the renewable energy-based generators, such as the photovoltaic array and fuel cells, a family of high step-up integrated dc/dc converters with dual coupled inductors is proposed based on combination of the modified quadratic boost converters and the voltage-multiplier modules (VMM). The high voltage gain can be achieved by adjusting the duty cycle and turn ratios of the coupled inductor of VMM. In particular, quite low voltage stress on the power switch can be obtained since the switch is no longer directly excited by the high output voltage, which is useful for enlarging the power efficiency. In addition, the two inductors in MQBC are integrated by using one magnetic core to decrease the size of the magnetic components. Very small ripples of the input current can be realized by ripple suppress abilities of the coupled inductors. Two experimental prototypes with rated-power of 200W are selectively constructed for comparative analysis, and experimental results verify the correctness and practicability of the proposed converters.

The pulse train (PT) controlled switching dc/dc converters possess the benefits of simple structure, fast dynamic response, and good robustness. However, PT controlled converters also give the drawbacks of undesirable low-frequency oscillation phenomenon as operated in continuous conduction mode. In this paper, a single-input dual-output buck converter based on the coupled inductor is newly proposed with the ability to suppress the low-frequency oscillation. The inductor of the conventional buck converter is replaced by the primary side of the coupled inductor, and the secondary side of the coupled inductor connected in series with a diode is paralleled to a capacitor as an equivalent power source for the extra load. By rational allocation of inductance coupling coefficient and the equivalent power supply voltage, low-frequency oscillation of output voltage can be suppressed and the fast response speed is achieved. A hardware prototype of 48 V/16 V converter is built and tested to validate the practicability of the proposed converter.

In order to improve the performance of switched reluctance motor (SRM) systems for photovoltaic (PV) pumps, this paper introduces a new converter topology for SRM with controllable multiple power sources. Only simple switching components need to be added at the front end of the asymmetric half-bridge converter in this topology, which enables the control of multiple power sources. The new PV pump system has four operating modes, which are the PV panel driven mode, battery bank driven mode, dual-source driven mode, and battery charging mode. By adjusting the state of the front-end converter switch, the voltage tracking of PV panel can be achieved, providing a stable bus voltage for the SRM system. By controlling the battery bypass switch, the bus voltage of SRM system can be increased, thereby increasing the system power level. Simulations and experiments based on a four-phase 8/6 SRM demonstrate the effect of the novel converter proposed in this paper.

The Switched Reluctance Generator (SRG) is suitable for wind power generation due to its good reliability and robustness. However, The SRG system adopting the conventional control algorithm with Pulse Width Modulation (PWM) method has a drawback, low response speed. The pulse train (PT) control has been widely used in dc/dc power converters operating in the discontinuous conduction mode due to its advantages of simple implementation and fast response. In this paper, for the first time, the PT control method is modified and adopted for controlling the output voltage of SRG system in order to achieve fast response. The capacitor current on the output side is sampled and combined with the output voltage to select the pulse trains and the low frequency oscillation cased by PT can be suppressed by tuning the feedback coefficient of the capacitor current. Also, good performance can be guaranteed with a wide range of voltage regulations, fast response, and no overshoot. The experimental platform of an 8/6 SRG system is built, and the experimental results show that the PT control can be used for SRG system with good practicability.

To develop hydrogen energy production and address the issues of global warming, inexpensive, effective, and long-lasting transition metal-based electrocatalysts for the synthesis of hydrogen are crucial. Herein, a porous electrocatalyst NiMo/Ni/NF was successfully constructed by a two-step electrodeposition process, and was used in the hydrogen evolution reaction (HER) of electrocatalytic water decomposition. NiMo nanoparticles were coated on porous Ni/NF grown on nickel foam (NF), leading to a resilient porous structure with enhanced conductivity for efficient charge transfer, as well as distinctive threedimensional channels for quick electrolyte diffusion and gas release. Notably, the low overpotential (42 mV) and fast kinetics (Tafel slope of 44 mV dec-1) at a current density of 10 mA cm-2 in 1.0 M KOH solution demonstrate the excellent HER activity of the electrode, which was superior to that of recently reported non-noble metal-based catalysts. Additionally, NiMo/Ni/NF showed extraordinary catalytic durability in stability tests at a current density of 10 mA cm-2 for 70 h. The porous structure catalyst and the electrodeposition-electrocatalysis technique examined in this study offer new approaches for the advancement of the electrocatalysis field because of these benefits. [ABSTRACT FROM AUTHOR]

As a non-noble metal electrocatalyst for the oxygen evolution reaction (OER), the binary NiFe layer double hydroxide (LDH) is expected to replace Ru-based and Ir-based anode materials for water decomposition. To attain threshold current density, nevertheless, a somewhat significant overpotential is still needed. In this work, layered double hydroxides of NiFe LDH are doped with V to form the terpolymer NiFeV LDH, which greatly increases the intrinsic activity of NiFe LDH and improves OER performance. This process is a straightforward and quick one-step electrodeposition process. Notably, NiFeV/NF has a low overpotential (218 mV at 10 mA cm−2) and faster kinetics (Tafel slope of 31 mV dec−1) as well as excellent durability and stability in 1 M KOH solution. In addition, the OER performance of the catalyst prepared in this work is better than that of a non-valuable metal catalyst that was recently reported. The V-doped NiFe LDH layered double hydroxides and the investigation of electrodeposition electrocatalytic methods in this work offer a fresh opportunity for the advancement of electrochemical technology. [ABSTRACT FROM AUTHOR]