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The wheel-legged robot combines the functions of wheeled vehicles and legged robots: high speed and high passability. However, the limited performance of existing joint actuators has always been the bottleneck in the actual applications of large wheel-legged robots. This paper proposed a highly integrated electro-hydrostatic actuator (EHA) to enable high-dynamic performance in giant wheel-legged robots (>200 kg). A prototype with a high force-to-weight ratio was developed by integrating a micropump, a miniature spring accumulator, and a micro-symmetrical cylinder. The prototype achieves a large output force of more than 9400 N and a high force-to-weight ratio of more than 2518 N/kg. Compared with existing EHA-based robots, it has a higher force-to-weight ratio and can bear larger loads. A detailed EHA model was presented, and controllers were designed based on sliding mode control and PID methods to control the output position and force of the piston. The model’s accuracy is improved by identifying uncertain parameters such as friction and leakage coefficient. Finally, both simulations and experiments were carried out. The results verified the fast response of force control (step response within 50 ms, the force tracking control frequency about 6.7 Hz) and the developed EHA’s good potential for future large wheel-legged robots.

In the traditional offline context, the mental health industry suffers from poor local demand and a lack of trustworthy supply. Emerging digital platforms can solve this problem to a large extent by building network effects. How to build network effects is a primary issue for mental health platforms. This article explores this problem by investigating two cases from China. Platform firms can design mechanisms to increase user scale, multiply user roles, facilitate user engagement, and enhance user trust on platforms. The pioneer platform and the later entrant can implement different strategies for building network effects.

Methylammonium (MA)-free perovskites such as CsxFA1-xPbX3 (CsFA) show excellent thermal stability, favoring their use in perov-skite solar cells (PSCs). However, the power conversion efficiency (PCE) of CsFA lags behind that of MA-containing formulations due to the difficulty in controlling the film morphology. Herein, we report innovative morphology engineering using solution deposition of a mixture of CsBr, PbI2, and PbCl2 followed by vacuum evaporation of formamidinium iodide (FAI) to fabricate CsFA films. The introduction of a chloride precursor leads to larger grain sizes, forming a high-quality CsFA film under annealing. This enables the realization of robust CsFA-based PSCs with the highest reported PCE up to 24.1% (certified 23.9%) for 0.1 cm2 and 22.8% for 1 cm2 devices. Moreover, the unencapsulated evaporation device shows excellent stability with negligible efficiency decline after 20,000 h of storage in dry air or 1,000 h of exposure to 50% humidity, which is much better than the device prepared using the solution procedure.

Age-related Macular Degeneration (AMD) is the major cause of elders’ vision loss, early screening and treatment are the most efficient way to reduce the rate of blindness. AI-based methods based on ophthalmic images play great potential for AMD diagnosis. However, low levels of accuracy, robustness, and explainability are challenges for AI approaches applied in clinics. Thus, this study proposed a multi-type of data source fusion method and a multi-model fusion approach for AMD detection. Typical unsupervised (Hierarchical Clustering and K-Means), typical supervised (SVM, VGG-16, and ResNet) methods, and proposed methods (multi-source data fusion-based method and multi-model fusion-based approach) are compared based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular color fundus photography (CFP) and Ultra-Wide field Fundus (UWF) images. Data preprocessing and enhancements of each type of data are discussed. A feature extraction based on unsupervised ML models, feature combination and normalization, and multi-layer perception (MLP) algorithm are involved in the proposed multi-source data fusion-based method. Supervised ML and DL models and a voting mechanism are involved in the multi-model fusion-based approach. Findings show that the proposed methods present a high performance of accuracy and robustness. A real-world UWF database is involved from Shenzhen Aier Hospital. Practical and theoretical contributions are delivered. A reference value for medical diagnosis based on multiple digital images is contributed.

Operating experience has shown that vibration can cause pressure fluctuations and initiate bubbles at the oil gap, which seriously threatens the insulation properties of oil-immersed power equipment. The main aim of this study was to determine the characteristics and mechanism of bubble initiation at the transformer oil gap under vibration in this paper. The results show the existence of the vibration-dependent bubbling phenomenon at the oil gap. Interestingly, bubbles initiate during the process of gap expansion, and the evolution of bubble morphology was further analyzed. The threshold characteristic of the bubble initiation process was found, and the relevant influencing factors were experimentally obtained. In the range considered in this study, higher gas content, higher temperature, and lower moisture content are conducive to bubble initiation at the oil gap under vibration, while vibrational frequency does not affect the threshold characteristic of bubble initiation process. The vibration-dependent bubbling phenomenon can be attributed to the vibration-induced cavitation process caused by the decrease in pressure at the oil gap. The influence mechanism of the above factors on the threshold characteristics of bubble initiation was further discussed. This study contributes to providing a reference for risk assessment of abnormal vibration of oil-immersed power equipment.

Binarized neural networks (BNNs) and batch normalization (BN) have already become typical techniques in artificial intelligence today. Unfortunately, the massive accumulation and multiplication in BNN models bring challenges to field-programmable gate array (FPGA) implementations, because complex arithmetics in BN consume too much computing resources. To relax FPGA resource limitations and speed up the computing process, we propose a BNN accelerator architecture based on consolidation compressed tree scheme by combining both XNOR and accumulation operation of the low bit into a systematic one. During the compression process, we adopt 0-padding (not ±1) to achieve no-accuracy-loss from software modeling to hardware implementation. Moreover, we introduce shift-addition-BN free binarization technique to shorten the delay path and optimize on-chip storage. To sum up, we drastically cut down the hardware consumption while maintaining great speed performance with the same model complexity as the previous design. We evaluate our accelerator on MNIST and CIFAR-10 dataset and implement the whole system on the ARTIX-7 100T FPGA with speed performance of 2052.65 GOP/s and area efficiency of 70.15 GOPS/KLUT.

This paper takes the airport terminal area as the main research content and combines genetic algorithm with airport terminal area analysis theory to analyze and study the traffic scheduling in the airport terminal area. Based on the study of traditional traffic scheduling techniques and key techniques of genetic algorithms, this paper participates in the actual project of genetic algorithm-based traffic scheduling, analyzes the requirements of the project, focuses on the design and implementation of the traffic scheduling algorithm module in the genetic algorithm-based traffic scheduling system, and conducts further research on the pathfinding by constraints submodule. In this paper, the flight approach and departure sequencing problem and runway allocation problem are the main research objects. The dynamic optimal scheduling model of flight approach and departure is established by considering the interests and demands of airlines and airports, and a new scheduling algorithm is proposed. In this paper, a brief introduction to the airport terminal area is given, and the feasibility of the approach/departure optimal scheduling is introduced from the perspective of airlines with a long-range parallel two-runway airport as the research background. Secondly, through the analysis of the flight approach and departure process and the study of the approach and departure cooperative optimization strategy, a single-runway flight approach and departure traffic scheduling model under the joint sequencing strategy is established with the optimization objective of minimizing the total flight delay time, and the model is solved by using the sliding time window algorithm. Then, based on the single-runway scheduling model, a multirunway multiobjective flight optimal scheduling model is established with the objectives of minimizing total delay time, increasing runway throughput per unit time and fairness of flight delay time allocation, and a dynamic algorithm (STW-GA) combining sliding time window algorithm and dual-structured chromosome genetic algorithm is proposed to solve the model.

Composite laminates used in aircraft structures are easily subjected to multiple impacts during their service period. The detailed analysis of impact responses could reflect the stiffness of composite structure. Nevertheless, the impact response of laminates with a large width-thickness ratio is quite different from that of ordinary laminates. The deflection could reach several times the thickness during the impact. The present paper performed an experimental study using drop weight equipment to investigate the changes in dynamic mechanical behavior of carbon fiber reinforced polymer laminates during multiple low energy impacts. Three kinds of impact energy levels of 5, 10, and 15 J were applied for the single impact event. The specimens impacted at 5 and 10 J were then subjected to subsequent impacts in order to explore the differences between single and multiple strikes with the same total impact energy. The global mechanical responses during each impact procedure were recorded and analyzed by the changes of impact force, contact time, peak energy moment, energy absorption, and displacement after every single impact. Although the first strike had introduced damages into the plates, the peak force and maximum displacement increased unexpectedly rather than decreased during the second strike. Meanwhile, the damage was evaluated using ultrasonic C-scan and visual inspection. The results indicated that the multiple impacts did not produce as much damage as the first strike. Short matrix cracks were observed at the edge of the dent except for the single impact with 5 J energy.

As one of the most important crops in the world, rice (Oryza sativa) is a model plant for metabolome research. Although many studies have focused on the analysis of specific tissues, the changes in metabolite abundance across the entire life cycle have not yet been determined. In this study, combining both targeted and nontargeted metabolite profiling methods, a total of 825 annotated metabolites were quantified in rice samples from different tissues covering the entire life cycle. The contents of metabolites in different tissues of rice were significantly different, with various metabolites accumulating in the plumule and radicle during seed germination. Combining these data with transcriptome data obtained from the same time period, we constructed the Rice Metabolic Regulation Network. The metabolites and co-expressed genes were further divided into 12 clusters according to their accumulation patterns, with members within each cluster displaying a uniform and clear pattern of abundance across development. Using this dataset, we established a comprehensive metabolic profile of the rice life cycle and used two independent strategies to identify novel transcription factors—namely the use of known regulatory genes as bait to screen for new networks underlying lignin metabolism and the unbiased identification of new glycerophospholipid metabolism regulators on the basis of tissue specificity. This study thus demonstrates how guilt-by-association analysis of metabolome and transcriptome data spanning the entire life cycle in cereal crops provides novel resources and tools to aid in understanding the mechanisms underlying important agronomic traits.

The silicon coated Carbon nanotubes (CNTs) nanocomposite (CNTs@Si) with a shell structure was successfully synthesized by a simple chemical vapor deposition (CVD) method. In this work, the CNTs@Si is not only introduced as a structural material providing oxidation performance, but also as an extremely effective electromagnetic wave (EMW) absorption nanocomposite. Dielectric characteristics EMW absorption properties within the frequency range of 2-18 GHz of CNTs@Si were studied, and the oxidation resistance of CNTs@Si was characterized. Due to the dense space conductive network formed by CNTs, the EMW absorbing properties of CNTs@Si nanocomposite features excellent electromagnetic wave absorption capacity at a filling amount of 1%. The maximum reflection loss (RL) reaches -61.57 dB at the thickness of 1.8 mm, and a wide effective absorption bandwidth (EAB, RL -10 dB) of 2.88 GHz is achieved. The obtained CNTs@Si core-shell nanocomposites exhibit excellent antioxidant performance and absorbing performance due to silicon bridging. Efficient electromagnetic wave absorption and excellent oxidation resistance of CNTs@Si can be regarded as a brand-new competitive candidate for EMW absorption materials in harsh environment.

The benefits and risks of inhibiting the proliferation and migration of vascular smooth muscle cells (VSMCs) in atherosclerosis (AS) remain a subject of debate. In this study, we investigated the effect of ferulic acid (FA) on the proliferation and migration of VSMCs induced by platelet-derived growth factor (PDGF) and the associated mechanism and used ApoE-/- mice to study whether the effect of FA on VSMC proliferation and migration is beneficial in alleviating AS plaques. It was found that FA not only reduced blood lipid levels but also promoted the production of nitric oxide (NO) by MOVAS cells through the endothelial nitric oxide synthase (eNOS) pathway, inhibited the migration and proliferation of VSMCs induced by PDGF, promoted the expression of p21 in VSMCs, and exerted a therapeutic effect against AS.

Highly efficient graphene-like carbon nanosheet stabilized Pd, Rh and Ru nanohybrids have been developed as robust catalysts for H2 generation from the hydrolysis of organosilane, C–C coupling and reduction of 4-nitrophenol for the first time.

NASICON-type Li[Formula: see text]Al[Formula: see text]Ge[Formula: see text]P3O[Formula: see text](LAGP) is one of the most promising inorganic solid-state electrolytes (SSEs). However, its practical applications have been hindered due to the high grain boundary resistance and poor sintering performance. In this work, we introduce a novel LiBF4 sintering aid into LAGP to promote the growth of grains, decrease grain boundary resistance, and supplement the loss of lithium. The experimental results show that the ionic conductivity of LAGP-0.5 wt% LiBF4 has the highest ionic conductivity (3.21 × 10[Formula: see text]S/cm), grain boundary impedance decreases from 162.2 [Formula: see text] to 35.2 [Formula: see text], the relative density increases from 93.6% to 96.5%, and no impurity phase is observed in LAGP–0.5 wt% LiBF4. At the same ionic conductivity, the sintering temperature of LAGP without LiBF4 and with a small amount of LiBF4 decreases from 800[Formula: see text]C to 700[Formula: see text]C. The Li/LAGP-LiBF4/LiFePO4 cell presents excellent cyclic stability (capacity retention of the discharge capacity is still 117.5 mAhg[Formula: see text] after 100 cycles) and a high initial discharge capacity of 164.2 mAhg[Formula: see text] at 0.2 C.

In a model of interconnected conflicts on a network among multiple contestants, we compare the equilibrium effort profiles and payoffs under both two scenarios: uniform effort (UE) in which each contestant is restricted to exert the same effort across all the battles she participates, and discriminatory effort (DE) in which such a restriction is lifted. When the contest technology in each battle is of Tullock form, a surprising neutrality result holds within the class of semi-symmetric conflict network structures: both the aggregate actions and equilibrium payoffs under two regimes are the same. We also show that, in some sense, the Tullock form is necessary for such a neutrality result. Moving beyond the Tullock family, we further demonstrate how the curvature of contest technology shapes the welfare and effort effects. Connection to the literature on price discrimination is also discussed., Comment: 30 pages

Background & aims Ischemia reperfusion (I/R) injury is an inevitable complication of liver transplantation and compromises its prognosis. Glycosyltransferases have been recognized as promising targets for disease therapy, but their roles remain largely unknown in hepatic I/R injury. Here, we aim to demonstrate the exact function and molecular mechanism of a glycosyltransferase, N-Acetylgalactosaminyltransferase-4 (GALNT4), in hepatic I/R injury. Approach & results Via an RNA-seq data-based correlation analysis, we found a close correlation between GALNT4 expression and hepatic I/R-related molecular events in murine model. The mRNA and protein expression of GALNT4 were markedly upregulated upon reperfusion surgery in both clinical samples from subjects underwent liver transplantation and mouse model. We found that GALNT4 deficiency significantly exacerbated I/R-induced liver damage, inflammation and cell death, whereas GALNT4 overexpression led to the opposite phenotypes. Our in-depth mechanistic exploration clarified that GALNT4 directly binds to the apoptosis signal-regulating kinase1 (ASK1) to inhibit its N-terminal dimerization and subsequent phosphorylation, leading to a robust inactivation of the downstream c-Jun N-terminal kinase (JNK) / p38 and nuclear factor kappa B (NF-κB) signalling. Intriguingly, the inhibitory capacity of GALNT4 on ASK1 activation is independent of its glycosyltransferase activity. Conclusions GALNT4 represents a promising therapeutic target for liver I/R injury and improve liver surgery prognosis by inactivating ASK1-JNK/p38 signalling pathway.

Background & aims Although the prevalence of nonalcoholic fatty liver disease (NAFLD) has risen dramatically to 25% of the adult population worldwide, there are as yet no approved pharmacological interventions for the disease due to uncertainty about the underlying molecular mechanisms. It is known that mitochondrial dysfunction is an important factor in the development of NAFLD. Mitochondrial antiviral signaling protein (MAVS) is a critical signaling adaptor for host defenses against viral infection. However, the role of MAVS in mitochondrial metabolism during NAFLD progression remains largely unknown. Approach & results Based on expression analysis, we identified a marked downregulation of MAVS in hepatocytes during NAFLD progression. By employing MAVS global knockout and hepatocyte-specific MAVS knockout mice, we found that MAVS is protective against diet-induced NAFLD. MAVS deficiency induces extensive mitochondrial dysfunction during NAFLD pathogenesis which was confirmed as impaired mitochondrial respiratory capacity and membrane potential. Metabolomics data also showed the extensive metabolic disorders after MAVS deletion. Mechanistically, MAVS interacts with the N-terminal stretch of voltage-dependent anion channel 2 (VDAC2), which is required for the ability of MAVS to influence mitochondrial function and hepatic steatosis. Conclusions In hepatocytes, MAVS plays an important role in protecting against NAFLD by helping to regulate healthy mitochondrial function. These findings provide new insights regarding the metabolic importance of conventional immune regulators and support the possibility that targeting MAVS may represent a new avenue for treating NAFLD.

Purpose Human-like musculoskeletal robots can fulfill flexible movement and manipulation with the help of multi joints and actuators. However, in general, sophisticated structures, accurate sensors and well-designed control are all necessary for a musculoskeletal robot to achieve high-precision movement. How to realize the reliable and accurate movement of the robot under the condition of limited sensing and control accuracy is still a bottleneck problem. This paper aims to improve the movement performance of musculoskeletal system by bio-inspired method. Design/methodology/approach Inspired by two kinds of natural constraints, the convergent force field found in neuroscience and attractive region in the environment found in information science, the authors proposed a structure transforming optimization algorithm for constructing constraint force field in musculoskeletal robots. Due to the characteristics of rigid-flexible coupling and variable structures, a constraint force field can be constructed in the task space of the musculoskeletal robot by optimizing the arrangement of muscles. Findings With the help of the constraint force field, the robot can complete precise and robust movement with constant control signals, which brings in the possibility to reduce the requirement of sensing feedback during the motion control of the robot. Experiments are conducted on a musculoskeletal model to evaluate the performance of the proposed method in movement accuracy, noise robustness and structure sensitivity. Originality/value A novel concept, constraint force field, is proposed to realize high-precision movements of musculoskeletal robots. It provides a new theoretical basis for improving the performance of robotic manipulation such as assembly and grasping under the condition that the accuracy of control and sensory are limited.

Na0.5Bi0.5TiO3-based ceramic specimens have been extensively investigated as ferroelectric materials. After being doped with CaTiO3, the resulting Na0.5Bi0.5TiO3-based ceramics exhibit relaxor characteristics, and improved energy storage density and efficiency. Based on these above results, CeO2 was further employed to modify the polarization of the 0.85Na0.5Bi0.5TiO3-0.15CaTiO3 matrix ceramic to achieve better energy storage performance. The effective energy storage density was enhanced from 1.93 to 2.53 J/cm3 by using the appropriate doping concentration of CeO2. Grain refinement effect can effectively enhance the electric-field strength from approximately 190 to 230 kV/cm. In particular, when doped with 2% CeO2, the energy storage efficiency of the sample was maintained at approximately 90% at 30 °C-150 °C and at approximately 80% in the frequency range of 0.2–200 Hz. This combination has very excellent temperature stability and frequency stability, making it a promising candidate for energy storage applications.

Against the social background of the gradual transformation of the social pension mode and the arrival of the ageing age, the tool design of fitness walkers for the elderly was studied. In this paper, the physiological and psychological status of the elderly with mild or moderate stroke or unable to exercise due to the decline of physical function was analysed. In addition, the corresponding elderly fitness walker product market research was studied. Aiming at these problems, this paper proposes a set of elderly fitness walker frames based on the ergonomic semiotic approach of the product architecture design (SAPAD) model. First, the development and structure of artificial engineering is introduced in detail. Then the framework of walking aid for the elderly based on the SAPAD model is proposed, and the SAPAS model and walking aid for the elderly are introduced, respectively, along with the framework of simulation design and application. The experimental results show that the framework of elderly fitness walker based on the ergonomic SAPAD model can be implemented. It can be applied to the elderly’s fitness assistance. A fitness walker can be suitable for the elderly who want to continue to exercise; it also hopes to care for the elderly who need to exercise from the perspective of the product.

The explosive boiling characteristics of water on the copper surface are studied based on molecular dynamics simulation. The effects of the size, number and shape of nanostructures on the heat tran...

Multi-radar data fusion techniques are important for self-driving vehicles to better perceive the environment. In the process of obtaining targets location and constructing environmental models, multi-radar data fusion can be regarded as a homogeneous time-series data fusion problem which is commonly solved by weighted fusion methods. However, when most sensors data are biased, especially when the data are all larger or smaller than the accurate data at the same time, the weights of sensors defined by sensor consistency and sensor stability can not reflect the real reliability of sensors. Thus, in this study, the correlation change between different observed parameters of one sensor, which is calculated based on the shape-based distance, together with the sensor consistency and sensor stability is leveraged to judge the reliability of the sensors and calculate the weights of the sensors. An experimental study is carried out and the Nuscenes dataset is used where a multi-radar system consists one LIDAR and two millimeter wave radars. In the situation that most sensors data are biased, the relative error and the root-mean-square error of the method proposed in this study are significantly lower than that of the arithmetic averaging method, the trimmed mean method and the weighted fusion method which only considers the sensor consistency and stability. Besides, under other situations, the fusion accuracy is also improved.

Age-related Macular Degeneration (AMD) is one of the most causes for elders’ vision loss, early screening and treatment are the most efficient way to reduce the rate of blindness. AI-based methods based on ophthalmic images play a gat potential for AMD diagnosis. However, the difficulty of computing device obtaining, multiple evidence of image sources, time-wasting, and low level of explanation are challenges for AI models applicated in clinics. Thus, this study proposed a fusion learning method for AMD detection. Three steps are involved, which are image feature extraction, feature matrix fusion, and MLP-based AMD classification. Unsupervised (Hierarchical Clustering, SVM, and ResNet-K Means), supervised (VGG-16 and ResNet) methods and the proposed method are compared based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular fundus photography (RCFP) and Ultra-Widefield Fundus (UWF), respectively and comprehensively. Findings show that the proposed method presents a high performance for integrated ophthalmic image diagnosis, it is timesaving (0.09s per image) with high precision (0.95), sensitivity (0.93), specificity (0.92) and AUC (0.94). Thus, this study concluded that the proposed method is a solution to AMD automatic quick detecting based on multiple data sources. A real-world UWF database is involved from Shenzhen Aier Hospital. Practical and theoretical contributions are delivered. A reference value for medical diagnosis based on multiple digital images is contributed.

Immobilized metal affinity chromatography (IMAC) ensures the specific purification of proteins containing histidine tags through high affinity with transition metal chelators, which has various applications in biological protein separation. Most chromatographic separations currently use a fixed bed. In this form, internal flow pressure drops very sharply, accompanied by uneven solution flow, pore blockages, etc., all of which greatly reduce separation efficiency. Therefore, this study uses hollow fiber membranes (HFMs) with micron-scale inner diameters as a base, thus reducing operating pressure and significantly enhancing mass transmission. Batch adsorption experiments were performed using flat plate membranes to obtain the reaction's thermodynamic and kinetic model parameters for use in a dynamic column breakthrough simulation. The numerical simulation was based on a single HFM model and established a mathematical model for computational fluid dynamics (CFD) in ANSYS Fluent software. Model accuracy was validated by combining the simulation with experiments. The effects of different module and process parameters on the breakthrough curve were investigated by varying parameters such as flow rate, initial feed concentration, and HFM inner diameter. Design parameters and operating conditions contributing to module utilization were subsequently obtained.

Purpose As older adults are increasingly active in virtual communities (VCs), these platforms for knowledge exchange present opportunities for companies to use elder human capital. The purpose of this study is to understand the antecedent factors that motivate older adults’ knowledge contribution and knowledge seeking (KS) behaviors in VCs. Design/methodology/approach Rooted in socio-emotional selectivity and social cognitive theories, this study included five key variables and developed models for older adults’ knowledge contributing (KC)/KS behaviors. This paper tested the hypotheses using data from a sample of 204 older adults in 3 VCs in China. Findings The results provide support for most of the hypotheses and show that while other members’ participation (MP) acts as a substitute for meaning in life and attitude toward aging, it acts as a complement for outcome expectations (OE) focused on others and OE focused on oneself in their impacts on KC/KS activities. Practical implications The study provides practical insights for developing elder human resources via VCs to avoid knowledge loss. Originality/value This study described older adults’ unique characteristics when modeling their information and communication technologies-related behaviors and built two models to explain their KC/KS behaviors. It confirmed that the same factor has different levels of impact on older adults’ KC/KS behaviors in VCs. In addition, it confirmed and reinforced the complementary and substitutive effects of other MP as an environmental factor on these behaviors.

As one of the most promising networks, the air-ocean integrated networks (AOINs) composed of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) meet a variety of requests from different maritime missions with the characteristics of seamless, high-rate, and reliable transmission. However, due to the limited storage capacities of UAVs and uncertain navigation paths of USVs, it is challenging to accomplish the ocean observation mission. In this paper, a UAV and USV cooperative content offloading scheme in AOINs with Q-learning and game theory is proposed. Specifically, the state evaluation mechanism for both UAV and USV is first designed to make cooperation strategies. Afterward, the interaction between UAV and USV is modeled as the bargaining game, where the Nash equilibrium as the optimal transaction price is obtained by the backward induction method. To realize the maximization of revenue, we devise a Q-learning based algorithm to make path planning for each USV to offload contents as many as possible under the limited energy. Finally, the effectiveness and efficiency of the proposed scheme is conducted by extensive simulations.