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Additional file 1: Scheme S1. Synthesis of Probe HBAE. Figure S1. UV-Vis spectrum and of fluorescent spectrum of HBAE in DMSO. Figure S2. Particle size of HBAE by dynamic light scattering. Average size=133.4 nm. Figure S3. TEM image of HBAE at pH=7.4. Figure S4. Fluorescence intensity of HBAE (10 μM) at 650 nm versus the different cells membranes protein. a. U87-MG cell membranes protein, b. N2a cell membranes protein, c. Bend.3 cell membranes protein, d.U251 cell membranes protein. The excitation wavelength was 560 nm. Figure S5. Fluorescence intensity of HBAE (10 μM) at 650 nm versus the different cells membranes protein. a. U87-MG cell membranes protein, b. N2a cell membranes protein, c. Bend.3 cell membranes protein, d. HEK293 cell membranes protein. Figure S6. CCK8 of the cell viability of HBAE. (Each sample was tested using three replicates, and the results are reported as the mean ± standard deviation). Figure S7. Western blot images of BACE1 in 4 cell lines. Figure S8. Supplementary information establishment of blood-brain barrier in vitro. Figure S9. Gene banding of AD and wild-type mice model. 1, 2, 3 and 6 belong to AD model mice, and 4, 5, 7 and 8 belong to wild-type mice. Figure S10. Test of hemolysis activity of formulations. (A) Photos of hemolysis after incubation with different formulations. HBAE of different concentrations are 12.5, 25, 50, 100, 200, 400 μ g/mL, respectively. Figure S11. H&E staining of heart, liver, spleen, lung, kidney tissues of C57 BL/6 mice after HBAE treatment, scale bar = 200 μm. Figure S12. Analysis of serum levels of ALT, AST, BUN and Cr in tumor-bearing mice after HBAE treatment. Data were mean ± SD (n = 5). Figure S13. The H&E staining of the AD mice brain after HBAE treatment. Figure S14. 1H NMR (500 MHz, DMSO-d6) of HBAE. Figure S15. 13C NMR (125 MHz, DMSO-d6) of HBAE. Figure S16. MS spectra of HBAE.

This paper proposes a novel technique and algorithm for the simultaneous sensing of three-phase currents based on multiple air-core coils. The basic principle for the current measurement in one conductor using one and two air-core coils is derived, and a means of self-calibration is determined. In order to measure the three-phase currents in parallel overhead lines, a novel smart current sensor has been developed, consisting of four air-core coils located in close proximity to the conductors. Algorithms are derived, simulations and extensive experimental verification is conducted, and a first prototype is fabricated and tested. Measurement errors introduced by variations in sensor orientation and the effect of adjacent conductors are both analyzed. The proposed technique and algorithm promise a novel smart current sensor for three-phase currents, simultaneously sensing the overhead conductors, which are low cost, small in size, easy to deploy extensively, do not require clamping around the utility conductor, and have the capability to self-calibrate. Integrated with the wireless-sensor network technique, these smart sensors can be widely applied in dynamic state estimation, monitoring, and optimal operation of the distribution network, and will become an integral part of energy-management systems of future power distribution networks.

On the basis of deducing the equations about fault distance and electric parameters on the feeder of the distribution network after fault, a novel online fault locating algorithm for multiple branch complex distribution networks has been proposed in this paper. It firstly gets all nodes voltages and the line currents through pre-fault power flow calculation, and sets up the branch equivalent models of distribution network. When the fault occurs in the network, according to the real-time data of the voltage and current value on the feeder, the recursive power flow calculation is executed, searching within each sections until the fault point is found. This new fault locating algorithm considers the characteristics of the distribution network,such as multiple branches, three-phase unbalance, fault resistance uncertainty and the load fluctuations, etc. Simulations and tests on a small distribution network verifies that, the proposed algorithm has good robustness and high precision, realizing online fault location for the complex distribution network, and makes the distribution network online self-healing control possible.

In power system emergency control, the controlled islanding is the last measure when it faces the severe cascading contingencies. Its objective is to maintain the stability of the sub partitions and reduce the total loss of load of the whole system to the minimum. A dynamic partitioning and controlled islanding strategy in power system emergency control has been investigated in this paper. Firstly, according to slow coherency grouping algorithm, generator with similar oscillation mode will separate into different optimal groups, and aggregate these generator groups and corresponding buses into some virtual coherent nodes. On the basis of the Laplace partitioning strategy, a novel weight index has been proposed to measure the “distance” of two adjacent nodes and improve the Laplace partitioning strategy. Then a novel heuristic neighborhood searching partitioning strategy has been raised to search the possible cutsets with the smaller net generation-load imbalance. Finally, a heuristic load-shedding approach has been applied on all these possible cut-sets to calculate the amount of load-shedding of each partition. The applications on 39-bus New England test system and the IEEE118 system validate the effectiveness of the improvement of controlled islanding strategy based on slow coherency and KWP theory proposed in this paper.