The accuracy of high-precision optical processing robots is influenced by various factors, including static error factors and dynamic error factors. These factors pose significant challenges to the deterministic processing of precision optics. This article proposes a pose residual prediction model for optical processing hybrid robots based on deep spatio-temporal graph convolutional neural networks. In this study, we establish a geometric error model for hybrid robots and calibrate the geometric error parameters using an extended Kalman filter to obtain the pose residuals component. To address the complex spatio-temporal interactions between multiple sensor variables in joint space during robot motion, we introduce the non-Euclidean spatio-temporal graph convolutional neural network. This model effectively extracts advanced spatio-temporal interaction features based on a spatio-temporal attention mechanism. Finally, the performance of the proposed method in pose residual prediction was validated through real experiments, and the results demonstrated its advantages over other state-of-the-art methods.

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Silicon carbide (SiC) devices have shown definite advantages over Si counterparts in high-temperature, high-voltage, and high-frequency applications. To fully exploit the potentiality of SiC devices in high temperatures, die-attach materials that can withstand high temperatures for a long time are required in the power electronics packaging. In this article, the high-temperature die-attach materials, such as high-temperature solders and transient liquid-phase bonding materials, were reviewed first. Then, metallic (mainly Ag and Cu) nanoparticles (NPs) sintering technologies were thoroughly overviewed. The metallic NPs sintering materials, metallic NPs sintering process, and interface and reliability were analyzed, respectively. Finally, the challenges and outlook of promising Cu NPs sintering technology were discussed.

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Towards realizing the future quantum internet, a pivotal milestone entails the transition from two-node proof-of-principle experiments conducted in laboratories to comprehensive multi-node set-ups on large scales. Here we report the creation of memory–memory entanglement in a multi-node quantum network over a metropolitan area. We use three independent memory nodes, each of which is equipped with an atomic ensemble quantum memory3 that has telecom conversion, together with a photonic server where detection of a single photon heralds the success of entanglement generation. The memory nodes are maximally separated apart for 12.5 kilometres. We actively stabilize the phase variance owing to fibre links and control lasers. We demonstrate concurrent entanglement generation between any two memory nodes. The memory lifetime is longer than the round-trip communication time. Our work provides a metropolitan-scale testbed for the evaluation and exploration of multi-node quantum network protocols and starts a stage of quantum internet research.@en

Based on the efficient resource utilization of scrap tires, pyrolysis carbon black (PCB), and pyrolysis oil of waste tire (TPO), scrap tires' products were treated using dry styrene-butadiene-styrene (SBS) polymer modification of asphalt. The products of scrap tires, PCB and TPO, were handled using dry SBS polymer modification of asphalt based on the effective resource use of scrap tires. The consequences of scrap tires, PCB and TPO, were taken using dry SBS polymer modification of asphalt based on the effective resource use of scrap tires. PCB and TPO composite effect seriously degraded. Based on this, the impact of dry SBS polymer modification on the functionality of PCB-TPO-modified asphalt and the mechanism of modification was examined. According to the investigation, the SBS polymer was evenly distributed and fully developed in the asphalt mixture, which significantly enhanced the qualities of asphalt and the asphalt mixture and performed a positive role in the internal structure of the asphalt mixture.

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Pipe materials appear to play an important role in the development of biofilms in drinking water distribution systems. However, there is controversy as to whether pipe materials shape the composition and diversity of bacterial communities in biofilms. To investigate the long-term effects of pipe materials on biofilms, triplicate samples of mature biofilms on unplasticized polyvinyl chloride (PVC-U), grey cast iron and asbestos cement (pipe age > 40 years) were obtained from three areas of an unchlorinated drinking water distribution system in the Netherlands. Illumina sequencing was performed and 773 OTUs (730 OTUs-814 OTUs) were detected within the biofilms on the three pipe materials, all of which were dominated by Proteobacteria (36.2%-46.1%). Both the alpha and beta diversity results showed that the bacterial communities of the biofilms formed on different pipe materials were highly similar. The neutral community model revealed that the assembly of the biofilm communities was governed by environmental selection rather than neutral processes. Among the 142 shared OTUs between the water and biofilm samples, there were 25 enriched OTUs (e.g., OTU7, assigned as Nitrospira spp.), which accounted for 62.6% of the total sequences, while 16 OTUs were disadvantaged (e.g., OTU14 and OTU40, assigned as Hyphomicrobiaceae), accounting for 2.2% of the sequences. Based on the findings, we propose and discuss a harmonisation process by which biofilms with significant differences due to the pipe material harmonize over time resulting in biofilms with similar bacterial communities. Our findings provide valuable insights into long-term biofilm development, bridging an essential gap in our current understanding of the influence of pipe materials on biofilm communities. These findings also highlight the importance of long-term studies and point to a potentially masked harmonizing process during biofilm development over years/decades.

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