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Virtual power plants (VPPs) are encountering multiple challenges due to market uncertainties and power network instability. In this paper, a novel probabilistic prediction-based multi-objective optimization framework for VPP is proposed to maximize operating profit while minimizing pollutant emissions and voltage deviations in the distribution network, which considers the uncertainties of wind power and electricity price. In this framework, the VPP that participates in the energy and ancillary service markets firstly aggregates the wind farms, the electric vehicle charging stations (EVCS), and the combined cooling, heating, and power subsystems to improve the utilization efficiency and operational flexibility of multiple energy sources. Then, a new Pareto optimizer, called multi-objective hybrid sand cat swarm optimization and strength firefly algorithm, is proposed to tackle the multi-objective optimization model of VPP. The proposed hybrid algorithm utilizes the advantages of sand cat swarm optimization and strength firefly algorithm mechanisms to facilitate local exploitation and global exploration. Finally, a new deep reinforcement learning probabilistic prediction approach based on quantile regression deep deterministic policy gradient is modeled to evaluate the uncertainties. The proposed models and methods have been thoroughly discussed on a modified distributed network. It is calculated that compared with the VPP without EVCS, the operating profit of the proposed VPP increases by 18.69%, and the emissions and voltage deviation of the proposed VPP are reduced by 3.42% and 10.44%, respectively. Experimental results also prove that the performance of the proposed Pareto optimizer and probabilistic prediction approach is superior to other benchmark techniques.

Abstract Sign language is an important way to provide expression information to people with hearing and speaking disabilities. Therefore, sign language recognition has always been a very important research topic. However, many sign language recognition systems currently require complex deep models and rely on expensive sensors, which limits the application scenarios of sign language recognition. To address this issue, based on computer vision, this study proposed a lightweight, dual-path background erasing deep convolutional neural network (DPCNN) model for sign language recognition. The DPCNN consists of two paths. One path is used to learn the overall features, while the other path learns the background features. The background features are gradually subtracted from the overall features to obtain an effective representation of hand features. Then, these features are flatten into a one-dimensional layer, and pass through a fully connected layer with an output unit of 128. Finally, use a fully connected layer with an output unit of 24 as the output layer. Based on the ASL Finger Spelling dataset, the total accuracy and Macro-F1 scores of the proposed method is 99.52% and 0.997, respectively. More importantly, the proposed method can be applied to small terminals, thereby improving the application scenarios of sign language recognition. Through experimental comparison, the dual path background erasure network model proposed in this paper has better generalization ability.

Abstract Broad bean (Vicia faba L.) has received attention from the food and animal feed industries as a sustainable, inexpensive, and versatile plant‐based protein source. However, long‐term consumption of broad bean, especially unprocessed ones, can lead to hepatic lipid accumulation. To investigate how broad bean affected hepatic lipid metabolism and its underlying mechanisms, we conducted a multi‐omics analysis of crisp grass carp (Ctenopharyngodon idellus C. et V) model with hepatic steatosis after feeding with single broad bean for 120 days. The glycolysis inhibition, increased de novo lipogenesis, abnormalities in lipid metabolism, and activation of oxidative stress were the key processes involved in broad bean‐induced hepatic lipid accumulation. Disturbances in intestinal flora caused by damage to the intestinal barrier and increased lipopolysaccharide caused by broad bean promoted hepatic lipid accumulation. Reduction of antinutritional factors and balanced diet offer potential strategies to mitigate the liver damage caused by broad bean consumption. Overall, our findings provided a theoretical basis for the wide application of broad bean as animal feeds.

Metadevices have emerged as a new element or system in recent years, from optics to mechanical science, showing superior performance and powerful application potential. In this study, a mechanical metadevice that capable of low-frequency vibration isolation, which is called metamaterial springs or metasprings, is proposed. Meanwhile, a modular design method is reported to obtain the customizable quasi-zero stiffness characteristic of the designed metaspring. As proof-of-concept, we demonstrate, both in simulations and experiments, the quasi-zero stiffness characteristics of the proposed metasprings using 3D-printed experimental specimens. Moreover, the low-frequency vibration isolation properties of the proposed metasprings is demonstrated both in vibration tests and automotive vibration tests. This work provides a new mechanical metadevice, that is, metasprings for low-frequency vibration isolation, as well as a modular design method for designing metasprings, which may revolutionize vibration isolation devices in the field of low-frequency vibration isolation.

In nature, gliding birds frequently execute intricate flight maneuvers such as aerial somersaults, perched landings, and swift descents, enabling them to navigate obstacles or hunt prey. It is evident that birds rely on different wing–tail configurations to accomplish a wide range of aerial maneuvers. For traditional fixed‐wing unmanned aerial vehicles (UAVs), pitch control primarily comes from the tail's elevators, while adjusting flight lift and drag involves deploying wing flaps. Although these designs ensure reliable flight, they compromise the drones’ maneuverability to maintain longitudinal stability. Therefore, the study introduces a biomimetic morphing wing UAV, and presents a pitch control strategy that simultaneously engages morphing wings, ailerons, and tail elevators. The pull‐up maneuver tests indicate that the proposed control method results in a pitch rate that is approximately 2.5 times greater than when using only the elevator control. A closed‐loop control system for the drone is also established. The closed‐loop flight experiment, which tracks a 45° pitch angle, demonstrates the effectiveness of the proposed coupled control method in adjusting the flight attitude. In addition, during cruising, the UAV employs three configurations, straight wing, forward‐swept wing, and back‐swept wing, to cater to different mission objectives and augment its flight capabilities.

In recent years, there has been increasing interest in studying gut microbiome-derived hydrolases in relation to oral drug metabolism, particularly focusing on natural product drugs. Despite the significance of natural product drugs in the field of oral medications, there is a lack of research on the regulatory interplay between gut microbiome-derived hydrolases and these drugs. This review delves into the interaction between intestinal microbiome-derived hydrolases and natural product drugs metabolism from three key perspectives. Firstly, it examines the impact of glycoside hydrolases, amide hydrolases, carboxylesterase, bile salt hydrolases, and epoxide hydrolase on the structure of natural products. Secondly, it explores how natural product drugs influence microbiome-derived hydrolases. Lastly, it analyzes the impact of interactions between hydrolases and natural products on disease development and the challenges in developing microbial-derived enzymes. The overarching goal of this review is to lay a solid theoretical foundation for the advancement of research and development in new natural product drugs and personalized treatment.

The actuator controller is the key component of the active vibration control system. With the rapid development of microelectronics and power electronics in recent years, the control type actuator controller technology based on high-frequency pulse width modulation (PWM) pulse signal has become a research hotspot. At present, the actuator controller generally adopts the classic PID as the control strategy, but this classic PID control strategy is more suitable for controlling fixed loads and has poor real-time performance for dynamic load control.In response to this issue, this article improves the design of the control algorithm and proposes an actuator controller based on frequency feedforward parameter compensation PID control strategy. This strategy improves the real-time and dynamic load response capabilities of the control, with good current waveform following and low distortion. It can effectively improve and enhance the control effect, providing new ideas for the design of subsequent actuator controllers.

This study introduces a novel multi-channel predictive deconvolution method enhanced by Shearlet-based sparse regularization, aimed at improving the accuracy and stability of subsurface seismic imaging, particularly in offshore wind farm site assessments. Traditional multi-channel predictive deconvolution techniques often struggle with noise interference, limiting their effectiveness. By integrating Shearlet transform into the multi-channel predictive framework, our approach leverages its directional and multiscale properties to enhance sparsity and directionality in seismic data representation. Tests on both synthetic and field data demonstrate that our method not only provides more accurate seismic images but also shows significant resilience to noise, compared to conventional methods. These findings suggest that the proposed technique can substantially improve geological feature identification and has great potential for enhancing the efficiency of seabed surveys in marine renewable energy development.

Seismic interferometry is a key technology in geophysical exploration, having achieved significant developments in constructing virtual seismic responses, overcoming the limitation of traditional exploration. However, non-physical reflections in virtual-shot gathers pose challenges for data processing and interpretation. This study focuses on deghosting in virtual-shot gather data processing. We propose a novel method that integrates sparse and nuclear norm constraint inversion for multi-shot simultaneous deghosting. Initially, a pseudo 3D data cube is created to enhance computational efficiency and lay the foundation for subsequent continuity regularization. Subsequently, an inversion framework is constructed to improve deghosting precision and stability by combining sparse and nuclear norm constraint inversion. Both synthetic and field examples demonstrate the superiority of our method, offering a new paradigm for virtual-shot gather data processing, and representing a major advancement in overcoming the inherent limitations of seismic interferometry.

Objective: Previous reports showed that mouse embryonic fibroblasts (MEFs) could support pluripotent stem cell selfrenewaland maintain their pluripotency. The goal of this study was to reveal whether the decellularized extracellularmatrix derived from MEFs (MEF-ECM) is beneficial to promote the proliferation of inner ear-derived cells.Materials and Methods: In this experimental study, we prepared a cell-free MEF-ECM through decellularization.Scanning electron microscope (SEM) and immunofluorescent staining were conducted for phenotype characterization.Organs of Corti were dissected from postnatal day 2 and the inner ear-derived cells were obtained. The identificationof inner ear-derived cells was conducted by using reverse transcription-polymerase chain reaction (RT-PCR). Cellcounting kit-8 (CCK-8) was used to evaluate the proliferation capability of inner ear-derived cells cultured on the MEFECMand tissue culture plate (TCP).Results: The MEF-ECM was clearly observed after decellularization via SEM, and the immunofluorescence stainingresults revealed that MEF-ECM was composed of three proteins, including collagen I, fibronectin and laminin. Mostimportantly, the results of CCK-8 showed that compared with TCP, MEF-ECM could effectively facilitate the proliferationof inner ear-derived cells.Conclusion: The discovery of the potential of MEF-ECM in promoting inner ear-derived cell proliferation indicatesthat the decellularized matrix microenvironment may play a vital role in keeping proliferation ability of these cells. Ourfindings indicate that the use of MEF-ECM may serve as a novel approach for expanding inner ear-derived cells andpotentially facilitating the clinical application of inner ear-derived cells for hearing loss in the future.

IntroductionA large majority of US organizations profess a commitment to diversity, but their definitions of diversity can vary greatly. While previous research demonstrates a shift in diversity definitions to include fewer protected demographic groups and more non-demographic characteristics, the present research examines whether this shift might be a motivated process among dominant group members related to anti-egalitarian and colorblind belief systems.MethodsUsing quantitative and qualitative methods, we explored potential underlying ideologies that may be associated with White Americans’ shifting definitions of diversity. White Americans (N = 498) were asked how they define diversity, as well as who should be included in a range of diversity initiatives.ResultsWhite participants’ higher anti-egalitarian belief was associated with stronger colorblind ideology endorsement, which was then associated with shifting their definition of diversity to include fewer disadvantaged demographic groups, more advantaged demographic groups, and non-demographic groups, as well as employing a colorblind inclusion rhetoric.DiscussionInstead of only “broadening” diversity to include more characteristics than diversity’s original focus, White Americans higher in anti-egalitarian and colorblind motives exhibited a simultaneous “narrowing” of diversity to include fewer protected demographic characteristics. Taken together, these findings have implications for dominant group members’ definition of diversity and the subtle ways in which colorblind ideology may be enacted.

Soil salinization is one of the main problems faced by modern agricultural production, especially cucumber production. This study screened the salt–alkali-tolerant cultivar ‘D1909’ and the salt–alkali-sensitive cultivar ‘D1604’ from 32 different cucumber ecological types on the basis of morphological indicators and salt–alkali indices combined with relevant physiological and biochemical indices. By performing a transcriptome metabolome analysis, the key gene CsSRG1, which is responsive to salt–alkali stress in cucumber, was screened, and its function was verified. The role of CsSRG1 in reducing salt–alkali stress in cucumber was clarified, and the mechanism of salt–alkali tolerance in cucumber was preliminarily explored. This study provides germplasm resources for cucumber salt–alkali tolerance breeding and a theoretical basis for the effective use of saline alkali soil to achieve high quality and high yield in other crops.

In the wild, gliding birds dodge obstacles or predators by folding and twisting their wings swiftly to perform a rapid roll. Accordingly, the authors strive to explore the feasibility of improving the roll rate of drones through this bird‐inspired morphing method, by using the asymmetric sweepback of wings to simulate the contraction of birds’ wings and the deflection of the aileron to imitate wing torsion. Moreover, the effects of wing morphing on the centroid, inertia matrix, and aerodynamic characteristics of the drone are explored herein, and a nonlinear dynamic model is established. Furthermore, a novel cooperative strategy that combines wing morphing with aileron deflection for roll control is introduced, and a flight controller based on this cooperative strategy is developed. Finally, the superiority of the cooperative strategy and the accuracy of the dynamic modeling have been validated in the outdoor flights of the morphing wing drone.

Faba bean water extract (FBW) and vitamin K3 (VK3) have been demonstrated to improve the muscle textural quality of fish. To better apply these two feed additives in commercial aquaculture setting, four experimental diets (control, commercial feed group; 15% FBW, 15% faba bean water extract group; 2.5% VK3, 2.5% vitamin K3 group; combined group, 15% faba bean water extract + 2.5% vitamin K3 group) were formulated to explore their combined effects of FBW and VK3 on the growth, health status, and muscle textural quality of grass carp. The growth performance, textural quality, intestinal characteristics, and oxidative and immune responses were analyzed on days 40, 80 and 120. The results showed that supplementation with higher doses of FBW and VK3 have no influence on growth-related parameters and immune parameters of grass carp. Notably, compared with the control, fish in the combined group had the highest textural qualities (hardness, chewiness and adhesiveness), followed by those in 15% FBW and 2.5% VK3 groups (P < 0.05). Also, FBW and VK3, to some extent, may lower antioxidative ability of grass carp, as illustrated by lower levels of GSH and CAT in 15% FBW, 2.5% VK3, and combined groups on day 120 (P < 0.05). In addition, enhanced lipase activity was observed in the 15% FBW group. Taken together, the combined supplementation of FBW and VK3 was demonstrated to be a more advanced option than their individual supplementation in a commercial setting owing to the resulting combined effects on both the textural quality and health status of grass carp.

As the application of lightweight fiber-reinforced composite structures reaches an unprecedented scale in industry, design technology for composite structures becomes crucial for enhancing performance, improving productivity, and reducing cost. In recent years, the rapid development of intelligent technology, such as big data, deep learning, and machine learning, has promoted the development of design technology. However, the current situation and intellectualization of the design technology is not well summarized. This paper reviews the advance in design technologies for fiber-reinforced composite structures, including prediction and optimization methods for composite properties. Then, their intellectualization development is overviewed. Finally, the development trend of intelligent design technologies and intelligent composite structures are discussed. This work can provide a reference for researchers in the related field.

Crisp grass carp (CGC), the most representative improved varieties of grass carp (GC), features higher muscle hardness after feeding faba bean (Vicia faba L.) for 90–120 days. DNA methylation, a most widely studied epigenetic modification, plays an essential role in muscle development. Previous studies have identified numerous differentially expressed genes (DEGs) between CGC and GC. However, it remains unknown if the expression levels of these DEGs are influenced by DNA methylation. In the present study, we performed a comprehensive analysis of DNA methylation profiles between CGC and GC, and identified important candidate genes related to muscle development coupled with the transcriptome sequencing data. A total of 9,318 differentially methylated genes (DMGs) corresponding to 155,760 differentially methylated regions (DMRs) were identified between the two groups under the CG context in promoter regions. Combined with the transcriptome sequencing data, 14 key genes related to muscle development were identified, eight of which (gsk3b, wnt8a, wnt11, axin2, stat1, stat2, jak2, hsp90) were involved in muscle fiber hyperplasia, six of which (tgf-β1, col1a1, col1a2, col1a3, col4a1, col18a1) were associated with collagen synthesis in crisp grass carp. The difference of methylation levels in the key genes might lead to the expression difference, further resulting in the increase of muscle hardness in crisp grass carp. Overall, this study can help further understand how faba bean modulates muscle development by the epigenetic modifications, providing novel insights into the texture quality improvement in other aquaculture fish species by nutritional programming.

The current morphing technologies are mostly regarded as auxiliary tools, providing additional control torques to enhance the flight maneuverability of unmanned aerial vehicles (UAVs), and they cannot exist independently of the traditional control surfaces. In this paper, we propose a tandem-wing micro aerial vehicle (MAV) with multiple variable-sweep wings, which can reduce the additional inertia forces and moments and weaken the dynamic coupling between longitudinal and lateral motion while the MAV morphs symmetrically for pitch control or asymmetrically for roll control, thereby flying without the traditional aileron and elevator. First, load experiments were conducted on the MAV to verify the structural strength of the multiple variable sweep wings, and the control moments caused by the morphing of the MAV were presented through numerical simulations. Then, the effects caused by symmetric and asymmetric morphing were investigated via dynamic response simulations based on the Kane dynamic model of the MAV, and the generated additional inertia forces and moments were also analyzed during morphing. Finally, dynamic response experiments and open-loop flight experiments were conducted. The experimental results demonstrated that the morphing mode in this study could weaken the coupling between the longitudinal and lateral dynamics and that it was feasible for attitude control without the traditional aileron and elevator while flying.

Diatomic site catalysts utilize two adjacent atomic metal species for their complementary functionalities and synergistic actions. Here, the authors report the orbital coupling of hetero-diatomic nickel-iron site boosts CO2 reduction reaction and oxygen evolution reaction.

Recent advances in single-cell sequencing assays for the transposase-accessibility chromatin (scATAC-seq) technique have provided cell-specific chromatin accessibility landscapes of cis-regulatory elements, providing deeper insights into cellular states and dynamics. However, few research efforts have been dedicated to modeling the relationship between regulatory grammars and single-cell chromatin accessibility and incorporating different analysis scenarios of scATAC-seq data into the general framework. To this end, we propose a unified deep learning framework based on the ProdDep Transformer Encoder, dubbed PROTRAIT, for scATAC-seq data analysis. Specifically motivated by the deep language model, PROTRAIT leverages the ProdDep Transformer Encoder to capture the syntax of transcription factor (TF)-DNA binding motifs from scATAC-seq peaks for predicting single-cell chromatin accessibility and learning single-cell embedding. Based on cell embedding, PROTRAIT annotates cell types using the Louvain algorithm. Furthermore, according to the identified likely noises of raw scATAC-seq data, PROTRAIT denoises these values based on predated chromatin accessibility. In addition, PROTRAIT employs differential accessibility analysis to infer TF activity at single-cell and single-nucleotide resolution. Extensive experiments based on the Buenrostro2018 dataset validate the effeteness of PROTRAIT for chromatin accessibility prediction, cell type annotation, and scATAC-seq data denoising, therein outperforming current approaches in terms of different evaluation metrics. Besides, we confirm the consistency between the inferred TF activity and the literature review. We also demonstrate the scalability of PROTRAIT to analyze datasets containing over one million cells.

Highly fluorescent carbon nanodots (CNDs) have broad application prospects in optoelectronics, energy, biological imaging, and other fields because of their good solubility in water, adjustable photoluminescence (PL), low toxicity, good biocompatibility, and stable chemical properties. In this paper, polyethyleneimine@CNDs (PEI@CNDs) with unique excitation- and concentration-dependent PL properties were synthesized by a one-pot hydrothermal approach. The morphology, structure, surface chemistry, photophysical properties, and stability of the PEI@CNDs were well probed. The PEI@CNDs solution at low concentration displayed blue PL with a quantum yield of 50.6%. As the concentrations of the PEI@CNDs increase, the PL colors changed from blue, cyan, and green, to greenish-yellow. At low concentration, the excitation-independent and excitation-dependent PL property is mainly caused by carbon core and surface state emission. However, at higher concentration, the quenched blue emission and enhanced green emission were found. This is mainly attributed to the aggregate-related inner filter effect, electron transfer, and surface states. Mixing 10.0 mg/mL of PEI@CNDs with polyvinyl alcohol can be used to construct composite films, which were combined with the blue light-emitting diode to construct white light-emitting diodes with white and warm white emissions.

Modular multilevel resonant converter is an promising candidate for high voltage applications since it has advantageous features, such as high efficiency, high voltage capability and easy fault-tolerant operation. However, the inequality of arm inductance in practice will lead to imbalance between the upper and lower arm voltages, which will induce large ripples in the circulating current and a dc bias on the voltage generated by modular circuits. To compensate for the voltage imbalance, effects of arm duty cycle changes on arm voltages are discussed. An arm voltage balancing control method is proposed: adjust arm duty cycle according to arm voltage deviation in every switching cycle. Simulation and experimental results are presented to validate the theoretical analysis and the proposed control method.

Texture is one of the key quality attributes used in the fresh and processed fish industry to assess product quality and consumer acceptability. To improve the textural quality of tilapia, we formulated the expanded pellet diet (EPD) and pellet diet (PD), both containing faba bean (Vicia faba, FB) water extract, a previously reported potential aquafeed additive to increase flesh texture. The common diet was used as a control. After Nile tilapia (Oreochromis niloticus) were fed three kinds of experimental diet for 120 days, muscle textural quality, growth parameters, oxidative response and immune parameters were analyzed. The results showed that there was no significant difference in the growth parameters between the three groups (P > 0.05). The highest measure of textural quality (hardness and chewiness) was found for the PD group, followed by the EPD and the control (P

This article aims to address problems in the current clustering process of low-energy adaptive clustering hierarchy (LEACH) in the wireless sensor networks, such as strong randomness and local optimum in the path optimization. This article proposes an optimal combined weighting (OCW) and improved ant colony optimization (IACO) algorithm for the LEACH protocol optimization. First, cluster head nodes are updated via a dynamic replacement mechanism of the whole network cluster head nodes to reduce the network energy consumption. In order to improve the quality of the selected cluster head nodes, this article proposes the OCW method to dynamically change the weight according to the importance of the cluster head node in different regions, in accordance with the three impact factors of the node residual energy, density, and distance between the node and the sink node in different regions. Second, the network is partitioned and the transmission path among the clusters can be optimized by the transfer probability in IACO with combined local and global pheromone update mechanism. The efficacy of the proposed LEACH protocol optimization method has been verified with MATLAB simulation experiments.

Abstract Electrochemical reduction of molecular O2 to hydrogen peroxide (H2O2) offers a promising solution for water purification and environmental remediation. Here, we design a hierarchical free‐standing single‐Co‐atom (with Co–N4 coordination) electrode for oxygen reduction reaction (ORR) via a two‐electron pathway to make H2O2 in acidic media. The current density of the single‐Co‐atom electrode reached 51 mA/cm2 at 0.1 V vs reversible hydrogen electrode, lasting for more than 10 hours of continuous operation with H2O2 selectivity greater than 80%. Toward practical application, the single‐Co‐atom electrode was directly used to assemble an electrochemical cell to produce H2O2 at a rate of 676 mol/kgcat/h with a cell voltage of about 1.6 V.

Differential power processing (DPP) architectures have attracted lots of research interests in recent years to deal with mismatch problem in residential photovoltaic (PV) systems. However, conventional DPP structures may face hardware cost and efficiency challenge when the PV string is long. This paper proposes an improved DPP structure with a virtual port connected in series with the submodule string. Compared with conventional DPP solutions, the proposed structure not only reduces current stress but also has high system efficiency when the number of PV panels increase. Besides, installation requirement is simplified. Furthermore, a single loop control method is proposed with only one current sensor to reduce hardware cost. Distributed maximum power point tracking (dMPPT) in submodule level is realized. Finally, a 300W prototype is built and experimental results verify the proposed structure.

Lightweight fiber-reinforced composite structures have been applied in aerospace for decades. Their mechanical properties are crucial for the safety of aircraft and mainly depend on manufacturing technologies such as autoclave, resin transfer molding and automated layup technology. In recent years, the rapid development of intelligent technology such as big data, deep learning, and machine learning has encouraged the development of manufacturing technologies to become low-cost, automatic, and intelligent. However, the current situation and intellectualization of manufacturing technologies is not well summarized. This paper reviews the advances in manufacturing technologies for fiber-reinforced composite structures, including autoclave, out of autoclave, resin transfer molding technologies, automated layup technology and additive manufacturing technology. Then, these technologies are compared in advantages and disadvantages, and their intellectualization development and challenges are also discussed. Finally, the development trend of intelligent manufacturing technologies and intelligent composite structures are discussed. This work can provide a reference for researchers in the related filed.

The existence of multiple reflections brings difficulty to seismic data processing and interpretation in seismic reflection exploration. Parabolic Radon transform is widely used in multiple attenuation because it is easily implemented, highly robust and efficient. However, finite seismic acquisition aperture of seismic data causes energy diffusion in the Radon domain, which leads to multiple residuals. In this paper, we propose a sparse parabolic Radon transform with the nonconvex Lq1-Lq2(0

Accurate picking of seismic first arrivals is very important for first arrival travel time tomography, but the first arrivals appearing at far offsets are often more difficult to pick accurately due to the low signal-to-noise ratio (SNR). The conventional supervirtual refraction interferometry (SVI) method can improve the SNR of first arrivals to a certain extent; however, it is not suitable for seismic data that interfered by strong noise. In order to better process the first arrivals at far offsets with serious noise interference, we propose a modified method, in which SVI implemented in the Radon domain (RDSVI) due to the cross-correlation in the Radon domain have a better effect. According to the kinematic characteristics of first arrival refractions, SVI is performed in the linear Radon domain. Both synthetic data and field data demonstrate the proposed method can enhance the effective signal and attenuate the strong noise simultaneously, so as to significantly improve the SNR of the first arrival data. Meanwhile, the RDSVI method is tested on the first arrival data with missing traces, which proves that this method can overcome the influence of abnormal traces and is suitable for the reconstruction of sparsely sampled seismic data.

Mature fruit abscission (MFA) is an important trait in terms of both harvest and yield. MFA can affect the production and economic value of melon fruit. An F3 population segregating for a single gene and derived from a cross between line M2-10, which shows no mature fruit abscission (None MFA), and the MFA line ZT00091 was used to map candidate genes. Specific length amplified fragment (SLAF) sequencing, in conjunction with bulked-segregant analysis (BSA), was used to map loci governing the natural fruit abscission of plants composing the F3-57 family. A candidate locus, mfa10.1, located on chromosome 10 between genomic positions 73,229 and 818,251, was obtained. An insertion-deletion (InDel) marker and 46 recombinant individuals were used to narrow the candidate region to within 35 kb at the genomic position of 650,203 to 685,250; this region included six candidate genes. qRT–PCR gene expression and gene sequence data showed that the CmARM14 gene, which encodes a RING-type E3 ubiquitin transferase (MELO3C012406), was a candidate for melon MFA. Subcellular localization observations revealed that the CmARM14 fusion protein was localized to the golgi apparatus. Taken together, these results provide a molecular basis for melon breeding.

Turbojet engines have a slow response and require mechanical assistance to perform vertical take-offs and landings. In this paper, we propose a vertical take-off and landing (VTOL) aircraft that contains one-dimensional thrust vectoring nozzle modules to solve the problem of dependence on the response of the turbojet. The turbojet engine is placed horizontally in the module, the rotational speeds are fixed, and the force along the turbojet axis remains constant as the nozzle rotates from the horizontal to the vertical position. The aircraft is composed of four modules, whose position and attitude control are realized by using the thrust difference caused by the different angles of the four vector nozzles. The modules no longer rely on the response speed of the turbojet engine, and controlling the angles of four nozzles makes it easier to operate the aircraft. The horizontal placement of the turbojet can reduce the thickness of the aircraft fuselage and decrease air resistance. The process of moving the nozzle from a horizontal to a vertical position prevents the engine from blowing directly on the ground and, thus, reduces the ground effect.

Normal moveout (NMO)-based velocity analysis can provide macro velocity models for prestack data processing and seismic attribute inversion. Datasets with an increasing size require conventional velocity analysis to be transformed to a more automatic mode. The sensitivity to multiple reflections limits the wide application of automatic velocity analysis. Thus, we propose an automatic velocity analysis method for seismic data-containing multiples to overcome the limit of multiple interference. The core idea of the proposed algorithm is to utilize a multi-attribute analysis system to transform the multiple attenuation problem to a multiple identification problem. To solve the identification problem, we introduce the local similarity to attribute the predicted multiples and build a quantitative attribute called multiple similarity. Considering robustness and accuracy, we select two supplementary attributes based on velocity and amplitude difference, i.e., velocity variation with depth and amplitude level. Then we utilize the technique for order preference by similarity to ideal solution (TOPSIS) to balance weights for different attributes in automatic velocity analysis. An RGB system is adopted for multi-attributes fusion in velocity spectra for visualization and quality control. Using both synthetic and field examples to evaluate the effectiveness of the proposed method for data-containing multiples, the results demonstrate the excellent performance in the accuracy of the extracted velocity model.

Dual active bridge (DAB) dc-dc converters have several attractive features including auto-adjust bidirectional power flow, wide voltage gain range, and zero voltage switching (ZVS)-on capability. Various applications based on DAB including solid-state transformer in power grids and traction, energy storage system, and electric vehicle on-board chargers have been proposed by academics. Nevertheless, industrial applications based on DABs are still seldom mainly due to the circulating current and loss of ZVS problems under certain load and voltage gain conditions. There is an intensive research effort underway to solve these problems recently. This paper presents the state-of-the-art view in the cause of circulating current and loss of ZVS and surveys the solutions to these problems.

Morphing aircraft can alter their aerodynamic configuration to obtain multitask adaptability and improve flight performance. In this paper, we apply the variable sweep concept on a tandem-wing micro aerial vehicle (MAV) for multitask adaptability, the two canards of which can undergo backward sweep and the two wings can undergo forward sweep. The variable sweep morphing mode can not only weaken the additional inertia forces and moments caused by morphing, but can also maintain the longitudinal dynamic balance without elevator changes, which generates trim drag. What is more, it was demonstrated that sweep morphing can exert a great effect on the aerodynamic characteristics during the transition process, which are functionalized with the sweep inputs. The effect of addition forces and moments during the transition process was analyzed by dynamic response, and the longitudinal stability of the MAV were evaluated based on a linear parameter varying (LPV) model. Due to the dramatic effects of sweep morphing on the longitudinal stability, a gain scheduled transition controller based on a convex hull algorithm is proposed to guarantee the transition stability and improve the robustness, and a linear quadratic regulator (LQR) is used to guarantee the stability of the boundary point with the consideration of input saturation. Finally, the superior performance of the proposed controller was demonstrated by a theoretical simulation based on a nonlinear model.

Gesture recognition based on wearable devices is one of the vital components of human–computer interaction systems. Compared with skeleton-based recognition in computer vision, gesture recognition using wearable sensors has attracted wide attention for its robustness and convenience. Recently, many studies have proposed deep learning methods based on surface electromyography (sEMG) signals for gesture classification; however, most of the existing datasets are built for surface EMG signals, and there is a lack of datasets for multi-category gestures. Due to model limitations and inadequate classification data, the recognition accuracy of these methods cannot satisfy multi-gesture interaction scenarios. In this paper, a multi-category dataset containing 20 gestures is recorded with the help of a wearable device that can acquire surface electromyographic and inertial (IMU) signals. Various two-stream deep learning models are established and improved further. The basic convolutional neural network (CNN), recurrent neural network (RNN), and Transformer models are experimented on with our dataset as the classifier. The CNN and the RNN models’ test accuracy is over 95%; however, the Transformer model has a lower test accuracy of 71.68%. After further improvements, the CNN model is introduced into the residual network and augmented to the CNN-Res model, achieving 98.24% accuracy; moreover, it has the shortest training and testing time. Then, after combining the RNN model and the CNN-Res model, the long short term memory (LSTM)-Res model and gate recurrent unit (GRU)-Res model achieve the highest classification accuracy of 99.67% and 99.49%, respectively. Finally, the fusion of the Transformer model and the CNN model enables the Transformer-CNN model to be constructed. Such improvement dramatically boosts the performance of the Transformer module, increasing the recognition accuracy from 71.86% to 98.96%.

To investigate the effects of the pixel sizes and the electrode structures on the performance of Ge-based terahertz (THz) photoconductive detectors, vertical structure Ge:Ga detectors with different structure parameters were fabricated. The characteristics of the detectors were investigated at 4.2 K, including the spectral response, blackbody response (Rbb), dark current density-voltage characters, and noise equivalent power (NEP). The detector with the pixel radius of 400 μm and the top electrode of the ring structure showed the best performance. The spectral response band of this detector was about 20–180 μm. The Rbb of this detector reached as high as 0.92 A/W, and the NEP reached 5.4 × 10−13 W/Hz at 0.5 V. Compared with the detector with a pixel radius of 1000 μm and the top electrode of the spot structure, the Rbb increased nearly six times, and the NEP decreased nearly 12 times. This is due to the fact that the optimized parameters increased the equivalent electric field of the detector. This work provides a route for future research into large-scale array Ge-based THz detectors.

This study analyses a double Y shaft 30° winding for six-phase fault-tolerant permanent magnet synchronous motors and introduces a novel modular 18-slot stator topology used in high-performance electric vehicles with unequal teeth width and unequal slot size. The new topology adopts double-layer winding in the same phase and single-layer winding in the adjacent phase. Additionally, two new pole/slot combinations for the six-phase motor, 18-slot/10-pole and 18-slot/14-pole, are proposed by analysing the air-gap flux harmonics that are produced by the stator winding. In addition, the optimal design of the 18-slot/10-pole motor is determined by using the average torque and the torque ripple as indexes. The motor can run continuously in the range of 0–4 times the rated speed with a one-time short-circuit current, a high isolation capability in the different phases and a high efficiency. The final parameters and performance of the 18-slot/10-pole motor are provided.

Now, it is time to set up reliable water electrolysis stacks with active and robust electrocatalysts to produce green hydrogen. Compared with catalytic kinetics, much less attention has been paid to catalyst stability, and the weak understanding of the catalyst deactivation mechanism restricts the design of robust electrocatalysts. Herein, we discuss the issues of catalysts’ stability evaluation and characterization, and the degradation mechanism. The systematic understanding of the degradation mechanism would help us to formulate principles for the design of stable catalysts. Particularly, we found that the dissolution rate for different 3d transition metals differed greatly: Fe dissolves 114 and 84 times faster than Co and Ni. Based on this trend, we designed Fe@Ni and FeNi@Ni core-shell structures to achieve excellent stability in a 1 A cm−2 current density, as well as good catalytic activity at the same time.

Digital rock images may capture more detailed pore structure than the traditional laboratory methods. No explicit function can correlate permeability accurately for flow within the pore space. This has motivated researchers to predict permeability through the application of numerical techniques, e.g., using the finite difference method (FDM). However, in order to get better permeability calculation results, the grid refinement was needed for the traditional FDM and the accuracy of the traditional method decreased in pores with elongated cross sections. The goal of this study is to develop an improved FDM (IFDM) to calculate the permeabilities of digital rock images with complex pore space. An elliptical pore approximation method is invoked to describe the complex pore space. The permeabilities of four types of idealized porous media are calculated by IFDM. The calculated results are in sound agreement with the analytical solutions or semi-empirical solutions. What’s more, the permeabilities of the digital rock images after grid coarsening are calculated by IFDM in three orthogonal directions. These results are compared with the previously validated lattice-Boltzmann method (LBM), which indicates that the predicted permeabilities calculated by IFDM usually agree with permeabilities calculated by LBM. We conclude that the presented IFDM is suitable for complex pore space.

Flavor and nutritional value are important qualities of freshwater fish products, but the key factors affecting these quality parameters remain unclear. In this study, four typical aquaculture modes, including the commercial feed treatment (control), faba bean treatment (FBT), grass powder treatment (GPT), and waving water treatment with commercial feed (WWT), were used to explore the regulatory effect of water quality and feed (eaten and uneaten) on the flesh flavor and nutrition in grass carp (Ctenopharyngodon idella), a freshwater fish of the largest global production. During the culture period (90 days), water quality parameters of the four modes were measured every 15 days, and the flavor quality was evaluated by volatile flavor compounds detection and electronic nose analyzer. Flesh crude protein, crude fat, free fatty acid and free amino acid profiles were also determined. The results showed that, in the late period, the FBT mode had the poorest water quality with highest concentrations of nitrite and nitrate, while the GPT mode has the best water quality among the four modes. Most flesh flavor compounds found in the flesh of the control, GPT and WWT modes were pleasant. In the FBT mode with the poorest water quality, on the other hand, we found lower flavor quality (higher contribution of fishy compounds), higher water content, and lower contents of crude protein, crude fat, free fatty acids and free amino acids, compared to the other three modes. Correlation analyses showed that nitrite and nitrate are probably key water quality factors affecting the flavor quality and nutritional values besides eaten feed and uneaten feed factors. This study can serve as an important reference for ecological regulation and feeding administration of flesh quality in freshwater aquaculture fish.