Block synchronization is an essential component of blockchain systems. Traditionally, blockchain systems tend to send all the transactions from one node to another for synchronization. However, such a method may lead to an extremely high network bandwidth overhead and significant transmission latency. It is crucial to speed up such a block synchronization process and save bandwidth consumption. A feasible solution is to reduce the amount of data transmission in the block synchronization process between any pair of peers. However, existing methods based on the Bloom filter or its variants still suffer from multiple roundtrips of communications and significant synchronization delay. In this paper, we propose a novel protocol named Gauze for fast block synchronization. It utilizes the Cuckoo filter (CF) to discern the transactions in the receiver’s mempool and the block to verify, providing an efficient solution to the problem of set reconciliation in the P2P (Peer-to-Peer Network) network. By up to two rounds of exchanging and querying the CFs, the sending node can acknowledge whether the transactions in a block are contained by the receiver’s mempool or not. Based on this message, the sender only needs to transfer the missed transactions to the receiver, which speeds up the block synchronization and saves precious bandwidth resources. The evaluation results show that Gauze outperforms existing methods in terms of the average processing latency (about 10× lower than Graphene) and the total synchronization space cost (about 10× lower than Compact Blocks) in different scenarios.
By taking a newly developed 5-DOF (degree of freedom) hybrid robot as an exemplar, this paper presents a novel and effective approach for non-singular tool trajectory generation. Based upon singularity analysis via inverse kinematics, an algorithm for the C-axis path optimization is developed using the weighted S-curve. Incorporated singular domain detection with the C-axis angle correction, the algorithm can easily be programmed and embedded into the CNC (computer numerical control) system as a postprocessing module and works in real-time. Then, an offline feedrate scheduling method is proposed by considering the drive and geometric accuracy constraints, allowing the rapid yet smooth movement in the neighborhood of singularity to be achieved. Additionally , it is found that the orientation accuracy can be improved by setting adequate cone angle and feedrate value. The results of both simulations and experiments on a prototype machine show that the C-axis can follow the non-singular tool trajectory well with relatively high rotary speed to pass through the singular domain, thereby verifying the effectiveness of the proposed approach.
Robotic friction stir welding (RFSW) usually comes with a huge upsetting force, and the stiffness of the welding system distributes unevenly over the position, which leads to a large deviation of the plunge depth of the tool at the end of the robot. The conventional constant distance tracking control suffers from the problem of unsmooth compensation leading to the vibration of the robot and thus degrading the weld quality. For this problem, a constant plunge depth control based on online trajectory generation for RFSW is studied, which can generate an accurate welding trajectory according to the rough initial reference path and smoothly compensate for the plunge deviation. Initially, three laser-ranging sensors are utilized to measure the pose deviation of the tool in real-time and generate the ideal welding trajectory according to the projection vector method. Then, a deformation compensation model is established to realize the real-time prediction of the correct value. To ensure the smoothness and rapidity of the dynamic tracking process of displacement deviation, we adopt an online tra-jectory generator as the core of optimization control to meet the process constraints such as speed, acceleration, and jerk during the compensation process. Finally, simulation and experiment are carried out. The results show that the proposed method can effectively reduce the vibration caused by compensation during the welding process and reduce flash, which can improve the welding quality.
Assembly in a confined space, such as the cabin of an aircraft or train, demands the assembling device to be with compact structure, satisfactory kinematics and excellent load carrying capability. A six degree-of-freedom (DoF) parallel robot is proposed and designed for such assembling tasks in this paper. Specially, internal structure changes introduced by topology optimization are considered and the multi-objective optimization reaching large load-to-mass ratio is implemented. First, external dimensions of the base and the ratio of the remaining volume to the complete volume are the inputs. Once a set of input variables are given, the topology optimization will be performed by FEA software to form the structure of the base. The stiffness and mass of the base, being the outputs, are obtained numerically by software. Then, the meta models are established by the response surface model (RSM) method. On this basis, stiffness and mass models of the robot are built by the semi-analytical method. The optimal design is implemented by Pareto-based multi-objective optimization. Different arrangements of the objectives are compared. The results show that kinematic indices on the Pareto fronts are all at a satisfactory level. The optimization having payload-to-mass ratio as objective leads to the optimum with higher stiffness along z-axis and smaller mass. The design 6-DoF parallel assembly robot can carry up to 42.06 kg loads while the mass is only 12.002 kg.
Hydrogel patch-based stem cell transplantation and microenvironment-regulating drug delivery strategy is promising for the treatment of myocardial infarction (MI). However, the low retention of cells and drugs limits their therapeutic efficacies. Here, we propose a prefixed sponge carpet strategy, that is, aldehyde-dextran sponge (ODS) loading anti-oxidative/autophagy-regulating molecular capsules of 2-hydroxy-β[email protected] (HP-β[email protected]) is first bonded to the rat's heart via capillary removal of interfacial water from the tissue surface, and the subsequent Schiff base reaction between the aldehyde groups on ODS and amino groups on myocardium tissue. Then, an aqueous biocompatible hydrazided hyaluronic acid (HHA) solution encapsulating mesenchymal stem cells (MSCs) is impregnated into the anchored carpet to form [email protected]@HP-β[email protected] hydrogel in situ via click reaction, thus prolonging the in vivo retention time of therapeutic drug and cells. Importantly, the HHA added to outer surface consumes the remaining aldehydes to contribute to nonsticky top surface, avoiding adhesion to other tissues. The embedded HP-β[email protected] molecular capsules with antioxidant and autophagy regulation bioactivities can considerably improve cardiac microenvironment, reduce cardiomyocyte apoptosis, and enhance the survival of transplanted MSCs, thereby promoting cardiac repair by facilitating angiogenesis and reducing cardiac fibrosis.
The incorporation of vasculature is known to be effective in tissue or organ functional regeneration. However, a vague understanding of the interaction between epidermal appendages and their vascular niches is a foremost obstacle to obtaining sweat gland (SG)-specific vasculature units. Here, we map their precise anatomical connections and report that the interplay between SG cells (SGCs) and the surrounding vascular niche is key for glandular development and homeostasis maintenance. To replicate this interplay in vitro, we used three-dimensional (3D) bioprinting to generate reproducible SGC spheroids from differentiated adipose-derived mesenchymal stem cells (ADSCs). With dermal microvascular endothelial cells (DMECs), sacrificial templates made from poly (ε-caprolactone) (PCL) were fabricated to pattern the vascular niche. This interplay model promoted physiologically relevant vascularized glandular morphogenesis in vitro and in vivo. We identified a reciprocal regulatory mechanism for promoting SGs regeneration via contact-independent cell communication and direct cell-cell interactions between SGs and the vasculature. We envision the successful use of our approach for vascularized organ regeneration in the near future.
The regeneration of alveolar bone after tooth extraction is critical for the placement of dental implants. Developing a rigid porous scaffold with defect shape adaptability is of great importance but challenging for alveolar bone regeneration. Herein, we design and synthesize a biocompatible poly(l-glutamic acid)-g-poly(ε-caprolactone) (PLGA-g-PCL) porous shape memory (SM) polymer. The PLGA-g-PCL is then copolymerized with acryloyl chloride grafted poly(ω-pentadecalactone) (PPDLDA) having a higher phase transition temperature than shape recovery temperature to maintain stiffness after shape recovery to resist chewing force. The hybrid polydopamine/silver/hydroxyapatite (PDA/Ag/HA) is coated to the surface of (PLGA-g-PCL)-PPDL scaffold to afford the anti-bacterial activity. The porous SM scaffold can be deformed into a compact size and administered into the socket cavity in a minimally invasive mode, and recover its original shape with a high stiffness at body temperature, fitting well in the socket defect. The SM scaffold exhibits robust antibacterial activity against Staphylococcus aureus (S. aureus). The porous microstructure and cytocompatibility of PLGA allow for the ingrowth and proliferation of stem cells, thus facilitating osteogenic differentiation. The micro-CT and histological analyses demonstrate that the scaffold boosts efficient new bone regeneration in the socket of rabbit mandibular first premolar. This porous shape memory self-adaptive stiffened polymer opens up a new avenue for alveolar bone regeneration.
Existence and uniqueness are established for McKean-Vlasov SDEs driven by Lévy processes. By using an approximation technique and coupling by change of measures, Harnack inequalities are investigated for McKean-Vlasov SDEs driven by subordinate Brownian motions.
In this study, an instrument for solid phase extraction of heavy metals was developed. Compared to conventional separation equipment, it has advantages of small size, less reagent consumption, time saving, easy operation, and good reproducibility. After analyzing the partition coefficient and adsorption capacity of the exchange resin, the Chelex-100 resin achieved a strong adsorption of lead at pH of 6.0. Compared to the matrix modifier method, solid phase extraction eliminated the interference of salts and the results were more accurate and reliable. The accuracy and stability of this instrument were verified by inter-laboratory testing of the quality control samples of soy sauce (CFAPA-QC1728-4), and the results from six laboratories were within the standard range with relative standard deviation (RSD) of 2.2%–2.8%. Recovery of three spiked samples was within 80.0%–100.0% and RSD was of 2.8%–9.3%, suggesting that this instrument can be used to separate and enrich lead in high-salt foods.
Biogas is a renewable biomass energy source mainly composed of CH4 and CO2. Dry reforming is a promising technology for the high-value utilization of biogas. Some impurity gases in biogas can not be completely removed after pretreatment, which may affect the performance of dry reforming. In this study, the influence of typical impurities H2S and NH3 on dry reforming was studied using Ni/MgO catalyst. The results showed that low concentration of H2S in biogas could cause serious deactivation of catalyst. Characterization results including EDS, XPS and TOF-SIMS confirmed the adsorption of sulfur on the catalyst surface, which was the cause of catalyst poisoning. We used air calcination method to regenerate the sulfur-poisoned catalysts and found that the regeneration temperature higher than 500 °C could help catalyst recover the original activity. NH3 in the concentration range of 50–10000 ppm showed a slight inhibitory effect on biogas dry reforming. The decline rate of biogas conversion efficiency increased with the increase of NH3 concentration. This was related to the reduction of oxygen activity on catalyst surface caused by NH3. The synergetic effect of H2S and NH3 in biogas was investigated. The results showed that biogas conversion decreased faster under the coexistence of H2S and NH3 than under the effect of H2S alone, so as the surface oxygen activity of catalyst. Air calcination regeneration could also recover the activity of the deactivated catalyst under the synergetic effect of H2S and NH3.
For multiple distributed generation units (DG unit) parallel system, excessive fault current has adverse effects on the safe and stable operation of the utility grid. As a result, a fault current limitation control of multiple DG units is developed in this paper to limit the fault current of related fault branch and ride through the associated fault conditions. The injection fault current amplitude and phase angle of each DG unit for grid-side converter are controlled through the point of common coupling (PCC) voltage support and fault current limitation to limit the total current amplitude at the corresponding fault location. The validity of the proposed control strategy has been verified by simulation and hardware-in-loop (HIL) experiment results.
Extreme events cause blackouts, resulting in the distribution network being unable to obtain power from the upstream transmission system. In this situation, local distributed resources can be fully utilized to improve system resilience. A distribution service restoration model is proposed in this paper, where three-phase unbalance condition and renewable sources uncertainty have been considered. The network reconfiguration strategy based on traditional tie switch operation is adopted to isolate faults. To cope with uncertainty, the chance-constrained method is introduced to enable renewable sources to participate in restoration, improving load recovery level. The proposed distribution service restoration model is essentially a mixed-integer nonlinear programming problem (MINLP) with probability constraints. To this end, linearization method is introduced to convert original MINLP into a deterministic mixed-integer linear programming problem. Simulation tests based on IEEE 33-bus network is carried out to demonstrate the feasibility of proposed distribution service restoration model.
To relieve the impact of the power grid outages on the residential sector, residential building energy management under grid outage events has been becoming the research hotspot in both the academic and industry. Considering the coordination of electric vehicles (EVs) and household load flexibility, this paper proposes a two-stage energy management approach for residential community under the planned outages. In the optimal scheduling stage, after receiving the information of the planned outage from the grid, the EVs’ charging–discharging power and community load curve reshaping schemes are optimal determined by residential community energy management system (CEMS), aiming at to minimize the total amount of unserved load of the whole community over the planned outage horizon. In the power allocation stage, with the formulated power allocation model, the determined residential community load curve from the above stage is then allocated to each house. The numerical test is finally conducted and the results validated the effectiveness and feasibility of the proposed approach.
With the continuous expansion of renewable energy in the power grid, a massive number of operating states have been added to the power system. The heavy computational task brings new challenges to the efficiency of reliability assessment. In this regard, a stacked-denoising-auto-encoders-based (SDAE) model is proposed to replace the time-consuming optimal power flow (OPF) algorithm in the reliability assessment of power system operation. The system state and the optimal load curtailment are used as training samples, and the corruption process of SDAE is improved to make it more suitable for power systems. Moreover, unsupervised pretraining and supervised fine-tuning are used to get the optimal coding parameters which can establish the nonlinear mapping relationship between the system states and the minimum load curtailment. Case studies are performed on the RTS-79 system considering renewable energy. Results indicate that the proposed method can greatly improve the efficiency of reliability assessment for power system operation.
The knowledge contained in academic literature is interesting to mine. Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets, and metrics, are extracted and used as artificial intelligence (AI) markers for AI literature. These entities can be used to trace the research process described in the bodies of papers, which opens up new perspectives for seeking and mining more valuable academic information. Firstly, the named entity recognition model is used to extract AI markers from large-scale AI literature. A multi-stage self-paced learning strategy (MSPL) is proposed to address the negative influence of hard and noisy samples on the model training. Secondly, original papers are traced for AI markers. Statistical and propagation analyses are performed based on the tracing results. Finally, the co-occurrences of AI markers are used to achieve clustering. The evolution within method clusters is explored. The above-mentioned mining based on AI markers yields many significant findings. For example, the propagation rate of the datasets gradually increases. The methods proposed by China in recent years have an increasing influence on other countries.
Under the global zero-carbon campaign (United Nations Climate Change, 2021), more stochastic renewable generations are being integrated into the system. This trend significantly expands the state space and increases the computational burden for the model-driven reliability assessment. Data-driven approaches are developed to improve efficiency based on artificial intelligence. However, the requirement of large-scale samples limits it in applications. To address that, this paper adopts a self-supervised stage to avoid the high cost of labeling, while ensuring the efficiency and accuracy of reliability assessment. The training process of this method is split into two stages. In the first stage, feature reconstruction and unsupervised learning are used to provide the initial network parameters. Thereafter, the second learning stage can be trained in a task-agnostic way with fewer labels. The results of case study demonstrate the effectiveness of the proposed approach.
With the development of 3D technology and the increase in 3D models, 2D image-based 3D model retrieval tasks have drawn increased attention from scholars. Previous works align cross-domain features via adversarial domain alignment and semantic alignment. However, the extracted features of previous methods are disturbed by the residual domain-specific features, and the lack of labels for 3D models makes the semantic alignment challenging. Therefore, we propose disentangled feature learning associated with enhanced semantic alignment to address these problems. On one hand, the disentangled feature learning enables decoupling the twisted raw features into the isolated domain-invariant and domain-specific features, and the domain-specific features will be dropped while performing adversarial domain alignment and semantic alignment to acquire domain-invariant features. On the other hand, we mine the semantic consistency by compacting each 3D model sample and its nearest neighbors to further enhance semantic alignment for unlabeled 3D model domain. We give comprehensive experiments on two public datasets, and the results demonstrate the superiority of the proposed method. Especially on MI3DOR-2 dataset, our method outperforms the current state-of-the-art methods with gains of 2.88% for the strictest retrieval metric NN.
The existing Nonlinear Dynamic Vibration Absorbers (NLDVA) have the disadvantages of complex structure, high cost, high installation space requirements, and difficulty in miniaturization. And most of the NLDVAs have not been applied to reality. To address the above issues, a novel Triple-magnet Magnetic Suspension Dynamic Vibration Absorber (TMSDVA), only composed of triple cylindrical permanent magnets and an acrylic tube, is proposed in this paper. Firstly, the equivalent dynamics model of a TMSDVA cantilever system is established. A comparison of the simulation and experimental results serves to indicate that the suspended magnet will be "locked" when the instantaneous total energy of the system is less than a certain threshold. According to that, we modify the dynamics model. Next, the vibration reduction mechanism of the TMSDVA is revealed from the perspective of transient dynamics and energy. The TMSDVA can resonate with primary structures with different natural frequencies, so that the vibration energy can be targeted transferred to the TMSDVA and dissipated by the damping. Whether the acrylic tube has holes affects the damping of the TMSDVA, then the energy transfer between the primary structure and the TMSDVA and the energy dissipation rate of the TMSDVA, and ultimately the vibration reduction effect of the TMSDVA. By comparing the vibration reduction effects of the TMSDVA with holes and the TMSDVA without holes, we find increasing the damping coefficient appropriately can improve the vibration reduction effect of the TMSDVA, reduce the sensitivity of the vibration reduction effect to the excitation amplitude, and further enhance the robustness of the TMSDVA. Taken together, the TMSDVA has potential application value in the gravity direction vibration reduction of engineering structures.
The addition of accelerator reduces the curing reaction temperature, changes the curing reaction process, affects the morphology of the crosslinking structure, which would lead to the difference in the dielectric properties of the epoxy resin. In this study, the curing behaviors and dielectric properties of diglycidyl ether of bisphenol A (DGEBA)/methyl hexahydrophthalic anhydride (MHHPA) were investigated with respect to the contents of the curing accelerator, Tris-(dimethylaminomethyl) phenol (DMP-30). The curing behavior of epoxy resin was studied by differential scanning calorimetry (DSC), and it was found that the system conforms to the Sestak-Berggren kinetic model (SB ( m,n )). The accelerator content has a saturation effect on the dielectric properties of the epoxy resin curing sample, and excessive content can lead to the increase of dielectric constant and loss and decrease of breakdown strength. The cured sample with 0.5% accelerator addition has the best dielectric properties and the highest breakdown strength. The analysis concludes that the optimum accelerator content allows for a more rational curing crosslinking network.
Arsenic (As) mobilisation assists in remediating As-contaminated soils but might increase ecological and health risks. In this study, risks of applying two mobilising agents were assessed, i.e. an emerging reducing-chelating composite agent [dithionite (Na2S2O4)–EDTA] and a classical low-molecular-weight organic acid (LMWOA) [citric acid (C6H8O7)]. Results showed that both agents induced sharp increase in leachability-based ecological risk of As. Interestingly, the two agents had opposite performances regarding health risks. Na2S2O4–EDTA significantly increased As relative bioavailability (RBA) to 1.83 times that in controls based on in vivo mouse model, and As bioaccessibility to 1.96, 1.65 and 1.20 times in gastric, small intestinal and colon phases based on in vitro PBET-SHIME model. Besides, it caused significant increase of highly toxic As(Ⅲ) in colon fluid. In contrast, C6H8O7 significantly reduced RBA and bioaccessibility of soil As in colon by 44.44% and 14.65%, respectively. Importantly, C6H8O7 restrained bioaccessible As(V) reduction and promoted bioaccessible As(Ⅲ) methylation, further reducing health risk. The phenomena could mainly be attributed to excessive metal components release from soil by C6H8O7 and gut microbiota metabolism of C6H8O7. In summary, C6H8O7 and similar LMWOAs are recommended. The study contributes to mobilising agent selection and development and provides a reference for managing remediation sites.
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