Interfacial biocatalytic performance of nanofiber-supported β-galactosidase for production of galacto-oligosaccharides

Molecular distribution, structural conformation and catalytic activity at the interface between enzyme and its immobilising support are vital in the enzymatic reactions for producing bioproducts. In this study, a nanobiocatalyst assembly, β-galactosidase immobilized on chemically modified electrospun polystyrene nanofibers (PSNF), was synthesized for converting lactose into galacto-oligosaccharides (GOS). Characterization results using scanning electron microscopy (SEM) and fluorescence analysis of fluorescein isothiocyanat (FITC) labelled β-galactosidase revealed homogenous enzyme immobilization, thin layer structural conformation and biochemical functionalities of the nanobiocatalyst assembly. The β-galactosidase/PSNF assembly displayed enhanced enzyme catalytic performance at a residence time of around 1 min in a disc-stacked column reactor. A GOS yield of 41% and a lactose conversion of 88% was achieved at the initial lactose concentration of 300 g/L at this residence time. This system provided a controllable contact time of products and substrates on the nanofiber surface and could be used for products which are sensitive to the duration of nanobiocatalysis

RflyMAD: A Dataset for Multicopter Fault Detection and Health Assessment

This paper presents an open-source dataset RflyMAD, a Multicopter Abnomal Dataset developed by Reliable Flight Control (Rfly) Group aiming to promote the development of research fields like fault detection and isolation (FDI) or health assessment (HA). The entire 114 GB dataset includes 11 types of faults under 6 flight statuses which are adapted from ADS-33 file to cover more occasions in which the multicopters have different mobility levels when faults occur. In the total 5629 flight cases, the fault time is up to 3283 minutes, and there are 2566 cases for software-in-the-loop (SIL) simulation, 2566 cases for hardware-in-the-loop (HIL) simulation and 497 cases for real flight. As it contains simulation data based on RflySim and real flight data, it is possible to improve the quantity while increasing the data quality. In each case, there are ULog, Telemetry log, Flight information and processed files for researchers to use and check. The RflyMAD dataset could be used as a benchmark for fault diagnosis methods and the support relationship between simulation data and real flight is verified through transfer learning methods. More methods as a baseline will be presented in the future, and RflyMAD will be updated with more data and types. In addition, the dataset and related toolkit can be accessed through https://rfly-openha.github.io/documents/4_resources/dataset.html

GPPF: A General Perception Pre-training Framework via Sparsely Activated Multi-Task Learning

Pre-training over mixtured multi-task, multi-domain, and multi-modal data remains an open challenge in vision perception pre-training. In this paper, we propose GPPF, a General Perception Pre-training Framework, that pre-trains a task-level dynamic network, which is composed by knowledge "legos" in each layers, on labeled multi-task and multi-domain datasets. By inspecting humans' innate ability to learn in complex environment, we recognize and transfer three critical elements to deep networks: (1) simultaneous exposure to diverse cross-task and cross-domain information in each batch. (2) partitioned knowledge storage in separate lego units driven by knowledge sharing. (3) sparse activation of a subset of lego units for both pre-training and downstream tasks. Noteworthy, the joint training of disparate vision tasks is non-trivial due to their differences in input shapes, loss functions, output formats, data distributions, etc. Therefore, we innovatively develop a plug-and-play multi-task training algorithm, which supports Single Iteration Multiple Tasks (SIMT) concurrently training. SIMT lays the foundation of pre-training with large-scale multi-task multi-domain datasets and is proved essential for stable training in our GPPF experiments. Excitingly, the exhaustive experiments show that, our GPPF-R50 model achieves significant improvements of 2.5-5.8 over a strong baseline of the 8 pre-training tasks in GPPF-15M and harvests a range of SOTAs over the 22 downstream tasks with similar computation budgets. We also validate the generalization ability of GPPF to SOTA vision transformers with consistent improvements. These solid experimental results fully prove the effective knowledge learning, storing, sharing, and transfer provided by our novel GPPF framework.Comment: 22 page

Holographic Storage of Biphoton Entanglement

Coherent and reversible storage of multi-photon entanglement with a multimode quantum memory is essential for scalable all-optical quantum information processing. Although single photon has been successfully stored in different quantum systems, storage of multi-photon entanglement remains challenging because of the critical requirement for coherent control of photonic entanglement source, multimode quantum memory, and quantum interface between them. Here we demonstrate a coherent and reversible storage of biphoton Bell-type entanglement with a holographic multimode atomic-ensemble-based quantum memory. The retrieved biphoton entanglement violates Bell's inequality for 1 microsecond storage time and a memory-process fidelity of 98% is demonstrated by quantum state tomography.Comment: 5 pages, 4 figures, accepted by Phys. Rev. Let