Seamless integration of a nickel-based metal-organic framework with three-dimensional substrates for nonenzymatic glucose sensing.

The effective integration of nanomaterials with underlying current collectors is a key factor affecting the performance of nonenzymatic glucose sensors, where an inappropriate integration structure often leads to poor electron transport and instability. In this work, a seamless integrated electrode was constructed by the in situ immobilizing of a nickel-based metal-organic framework (Ni-MOF) on a three-dimensional (3D) conductive nickel foam (NF) for highly sensitive and durable glucose sensing. Facilitated by a rapid microwave-assisted reaction, a robust interfacial interaction between the Ni-MOF and the substrate was established through in situ conversion from nickel oxide (NiO). The fabricated Ni-MOF/NF electrode exhibits an excellent limit of detection (LOD) of 2.65 µM and an impressive sensitivity (14.31 mA cm-2 mM-1) within the linear range (4-576 µM), which is significantly boosted compared with that of an electrode prepared by a typical drop-casting method (3.56 mA cm-2 mM-1 in 4-1836 µM). Characterization and electrochemical tests reveal that this integrated structure on the one hand contributes to fast electron transport and thus has enhanced sensitivity and on the other hand leads to exceptional durability with its structural integrity maintained under bending, shaking, and ultrasonication. Moreover, this seamless integration method was also employed to immobilize the Ni-MOF converted from the pre-chemically deposited NiO layer on another type of substrate, 3D carbon paper (CP), demonstrating the versatility of this facile strategy in creating diverse electrochemical electrodes for applications beyond glucose sensing.

Raman amplification at 2.2 µm in silicon core fibers with prospects for extended mid-infrared source generation.

Raman scattering provides a convenient mechanism to generate or amplify light at wavelengths where gain is not otherwise available. When combined with recent advancements in high-power fiber lasers that operate at wavelengths ~2 µm, great opportunities exist for Raman systems that extend operation further into the mid-infrared regime for applications such as gas sensing, spectroscopy, and biomedical analyses. Here, a thulium-doped fiber laser is used to demonstrate Raman emission and amplification from a highly nonlinear silicon core fiber (SCF) platform at wavelengths beyond 2 µm. The SCF has been tapered to obtain a micrometer-sized core diameter (~1.6 µm) over a length of 6 cm, with losses as low as 0.2 dB cm-1. A maximum on-off peak gain of 30.4 dB was obtained using 10 W of peak pump power at 1.99 µm, with simulations indicating that the gain could be increased to up to ~50 dB by extending the SCF length. Simulations also show that by exploiting the large Raman gain and extended mid-infrared transparency of the SCF, cascaded Raman processes could yield tunable systems with practical output powers across the 2-5 µm range.

Resting-State Functional MRI Adaptation with Attention Graph Convolution Network for Brain Disorder Identification.

Multi-site resting-state functional magnetic resonance imaging (rs-fMRI) data can facilitate learning-based approaches to train reliable models on more data. However, significant data heterogeneity between imaging sites, caused by different scanners or protocols, can negatively impact the generalization ability of learned models. In addition, previous studies have shown that graph convolution neural networks (GCNs) are effective in mining fMRI biomarkers. However, they generally ignore the potentially different contributions of brain regions- of-interest (ROIs) to automated disease diagnosis/prognosis. In this work, we propose a multi-site rs-fMRI adaptation framework with attention GCN (A2GCN) for brain disorder identification. Specifically, the proposed A2GCN consists of three major components: (1) a node representation learning module based on GCN to extract rs-fMRI features from functional connectivity networks, (2) a node attention mechanism module to capture the contributions of ROIs, and (3) a domain adaptation module to alleviate the differences in data distribution between sites through the constraint of mean absolute error and covariance. The A2GCN not only reduces data heterogeneity across sites, but also improves the interpretability of the learning algorithm by exploring important ROIs. Experimental results on the public ABIDE database demonstrate that our method achieves remarkable performance in fMRI-based recognition of autism spectrum disorders.

All-fibre heterogeneously-integrated frequency comb generation using silicon core fibre.

Originally developed for metrology, optical frequency combs are becoming increasingly pervasive in a wider range of research topics including optical communications, spectroscopy, and radio or microwave signal processing. However, application demands in these fields can be more challenging as they require compact sources with a high tolerance to temperature variations that are capable of delivering flat comb spectra, high power per tone, narrow linewidth and high optical signal-to-noise ratio. This work reports the generation of a flat, high power frequency comb in the telecom band using a 17 mm fully-integrated silicon core fibre as a parametric mixer. Our all-fibre, cavity-free source combines the material benefits of planar waveguide structures with the advantageous properties of fibre platforms to achieve a 30 nm bandwidth comb source containing 143 tones with <3 kHz linewidth, 12 dB flatness, and >30 dB OSNR over the entire spectral region.

Raman enhanced four-wave mixing in silicon core fibers.

A strong Raman enhancement to the four-wave mixing (FWM) conversion efficiency is obtained in a silicon core fiber (SCF) when pumped with a continuous-wave (CW) source in the telecom band. By tapering the SCFs to alter the core diameter and length, the role of phase-matching on the conversion enhancement is investigated, with a maximum Raman enhancement of ∼15 dB obtained for an SCF with a zero dispersion wavelength close to the pump. Simulations show that by optimizing the tapered waist diameter to overlap the FWM phase-matching with the peak Raman gain, it is possible to obtain large Raman enhanced FWM conversion efficiencies of up to ∼2 dB using modest CW pump powers over wavelengths covering the extended telecom bands.

Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints.

Individual identification based on brain functional network (BFN) has attracted a lot of research interest in recent years, since it provides a novel biometric for identity authentication, as well as a feasible way of exploring the brain at an individual level. Previous studies have shown that an individual can be identified by its BFN fingerprint estimated from functional magnetic resonance imaging, electroencephalogram, or magnetoencephalography data. Functional near-infrared spectroscopy (fNIRS) is an emerging imaging technique that, by measuring the changes in blood oxygen concentration, can respond to cerebral activities; in this paper, we investigate whether fNIRS-based BFN could be used as a "fingerprint" to identify individuals. In particular, Pearson's correlation is first used to calculate BFN based on the preprocessed fNIRS signals, and then the nearest neighbor scheme is used to match the estimated BFNs between different individuals. Through the experiments on an open-access fNIRS dataset, we have two main findings: (1) under the cases of cross-task (i.e., resting, right-handed, left-handed finger tapping, and foot tapping), the BFN fingerprints generally work well for the individual identification, and, more interestingly, (2) the accuracy under cross-task is well above the accuracy under cross-view (i.e., oxyhemoglobin and de-oxyhemoglobin). These findings indicate that fNIRS-based BFN fingerprint is a potential biometric for identifying individual.

Design of Insulation Tape Tension Control System of Transformer Winding Machine Based on Fuzzy PID.

With the rapid development of science and technology as well as the comprehensive societal progress, the demand for electricity in all walks of life is also increasing. As is known to all, the mechanical structure and tension control of a transformer winding machine is the key to improving the quality of coil winding, due to coil winding being generally considered the core technology of transformer manufacturing. Aiming at the synchronous winding control problem of the conductor and insulating layer of the transformer winding machine, this paper presents a mechanical structure and tension control scheme of a new type of transformer winding machine. Based on the dynamic analysis and modeling of the mechanical structure of the winding machine, the speed control of the main speed roller by the fuzzy PID control rate is implemented initially. Combined with the actual demand of the project, the feasibility and effectiveness of the control target with different tension are verified by the simulation experiment and further compared with the traditional PID control method. The simulation results show that the proposed fuzzy PID control rate can realize the automatic and efficient winding of the transformer winding machine, showing that it is superior to the traditional PID control rate in overcoming the disturbance and controlling effect.

Effects of mis-specified time-correlated model error in the (ensemble) Kalman Smoother.

Data assimilation is often performed under the perfect model assumption. Although there is an increasing amount of research accounting for model errors in data assimilation, the impact of an incorrect specification of the model errors on the data assimilation results has not been thoroughly assessed. We investigate the effect that an inaccurate time correlation in the model error description can have on data assimilation results, deriving analytical results using a Kalman Smoother for a one-dimensional system. The analytical results are evaluated numerically to generate useful illustrations. For a higher-dimensional system, we use an ensemble Kalman Smoother. Strong dependence on observation density is found. For a single observation at the end of the window, the posterior variance is a concave function of the guessed decorrelation time-scale used in the data assimilation process. This is due to an increasing prior variance with that time-scale, combined with a decreasing tendency from larger observation influence. With an increasing number of observations, the posterior variance decreases with increasing guessed decorrelation time-scale because the prior variance effect becomes less important. On the other hand, the posterior mean-square error has a convex shape as a function of the guessed time-scale with a minimum where the guessed time-scale is equal to the real decorrelation time-scale. With more observations, the impact of the difference between two decorrelation time-scales on the posterior mean-square error reduces. Furthermore, we show that the correct model error decorrelation time-scale can be estimated over several time windows using state augmentation in the ensemble Kalman Smoother. Since model errors are significant and significantly time correlated in real geophysical systems such as the atmosphere, this contribution opens up a next step in improving prediction of these systems.

Sensor Modeling and Calibration Method Based on Extinction Ratio Error for Camera-Based Polarization Navigation Sensor.

The performance of camera-based polarization sensors largely depends on the estimated model parameters obtained through calibration. Limited by manufacturing processes, the low extinction ratio and inconsistency of the polarizer can reduce the measurement accuracy of the sensor. To account for the challenges, one extinction ratio coefficient was introduced into the calibration model to unify the light intensity of two orthogonal channels. Since the introduced extinction ratio coefficient is associated with degree of polarization (DOP), a new calibration method considering both azimuth of polarization (AOP) error and DOP error for the bionic camera-based polarization sensor was proposed to improve the accuracy of the calibration model parameter estimation. To evaluate the performance of the proposed camera-based polarization calibration model using the new calibration method, both indoor and outdoor calibration experiments were carried out. It was found that the new calibration method for the proposed calibration model could achieve desirable performance in terms of stability and robustness of the calculated AOP and DOP values.

Low-loss silicon core fibre platform for mid-infrared nonlinear photonics.

Broadband mid-infrared light sources are highly desired for wide-ranging applications that span free-space communications to spectroscopy. In recent years, silicon has attracted great interest as a platform for nonlinear optical wavelength conversion in this region, owing to its low losses (linear and nonlinear) and high stability. However, most research in this area has made use of small core waveguides fabricated from silicon-on-insulator platforms, which suffer from high absorption losses of the use of silica cladding, limiting their ability to generate light beyond 3 µm. Here, we design and demonstrate a compact silicon core, silica-clad waveguide platform that has low losses across the entire silicon transparency window. The waveguides are fabricated from a silicon core fibre that is tapered to engineer mode properties to ensure efficient nonlinear propagation in the core with minimal interaction of the mid-infrared light with the cladding. These waveguides exhibit many of the benefits of fibre platforms, such as a high coupling efficiency and power handling capability, allowing for the generation of mid-infrared supercontinuum spectra with high brightness and coherence spanning almost two octaves (1.6-5.3 µm).

Tapered silicon core fibers with nano-spikes for optical coupling via spliced silica fibers.

Reported here is the fabrication of tapered silicon core fibers possessing a nano-spike input that facilitates their seamless splicing to conventional single mode fibers. A proof-of-concept 30 µm cladding diameter fiber-based device is demonstrated with nano-spike coupling and propagation losses below 4 dB and 2 dB/cm, respectively. Finite-element-method-based simulations show that the nano-spike coupling losses could be reduced to below 1 dB by decreasing the cladding diameters down to 10 µm. Such efficient and robust integration of the silicon core fibers with standard fiber devices will help to overcome significant barriers for all-fiber nonlinear photonics and optoelectronics.