A universal three-dimensional hydrogel electrode for electrochemical detection of SARS-CoV-2 nucleocapsid protein and hydrogen peroxide.

Coronavirus disease 2019 (COVID-19) is a highly contagious illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in a global health crisis. The primary diagnostic method for COVID-19 is quantitative reverse transcription PCR, which is time-consuming and requires expensive instrumentation. Here, we developed an electrochemical biosensor for detecting SARS-CoV-2 biomarkers using a 3D porous polyacrylamide/polyaniline hydrogel (PPG) electrode prepared by UV photopolymerization and in situ polymerization.

MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records.

To address the problem of poor entity recognition performance caused by the lack of Chinese annotation in clinical electronic medical records, this paper proposes a multi-medical entity recognition method F-MNER using a fusion technique combining BART, Bi-LSTM, and CRF. First, after cleaning, encoding, and segmenting the electronic medical records, the obtained semantic representations are dynamically fused using a bidirectional autoregressive transformer (BART) model. Then, sequential information is captured using a bidirectional long short-term memory (Bi-LSTM) network.

Genetic algorithm assisted bridge fiber design and fabrication for few-mode multi-core fiber Fan-in/Fan-out device.

We present a rapid and precise method to design the multiple step-index bridge fiber for ultra-low insertion loss few-mode multi-core fiber Fan-in/Fan-out device. The genetic algorithm is applied to optimize the structural parameters to support multi-mode operation. Based on the proposed intelligent iteration platform, core-based multiplex/demultiplex optimization can be achieved with less than 1.

24 km High-Performance Raman Distributed Temperature Sensing Using Low Water Peak Fiber and Optimized Denoising Neural Network.

Raman distributed optical fiber temperature sensing (RDTS) has been extensively studied for decades because it enables accurate temperature measurements over long distances. The signal-to-noise ratio (SNR) is the main factor limiting the sensing distance and temperature accuracy of RDTS. We manufacture a low water peak optical fiber (LWPF) with low transmission loss to improve the SNR for long-distance application.

Digital in-service relative time delay estimation for SDM self-homodyne coherent systems.

For space division multiplexing self-homodyne coherent systems, we propose a novel digital in-service relative time delay (RTD) estimation method without any additional optoelectronic device. Taking advantage of the frequency-domain periodicity of the colored frequency modulation noise, we manage to find the peak with location reflecting the RTD in its autocorrelation function (ACF). The peak to average ratio is further enhanced by leveraging a low-pass differential finite impulse response filter for robust identification.

Single-ended self-calibration high-accuracy Raman distributed temperature sensing based on multi-core fiber.

Raman distributed temperature sensing (RDTS) obtains the temperature information by measuring the intensities of Raman scattering lights. The anti-stokes only RDTS can avoid the error caused by wavelength-dependent loss and dispersion. However, to eliminate temperature-independent intensity variations, single-wavelength demodulation generally adopts the double-ended detection scheme.

Remarks on Computational Method for Identifying Acid and Alkaline Enzymes.

The catalytic efficiency of the enzyme is thousands of times higher than that of ordinary catalysts. Thus, they are widely used in industrial and medical fields. However, enzymes with protein structure can be destroyed and inactivated in high temperature, over acid or over alkali environment.

Online Fault Diagnosis for Biochemical Process Based on FCM and SVM.

Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis.