Via the photodegradation of dissolved iron (dFe) complexes in the euphotic zone, released free Fe(III) is the most important source of bioavailable iron for eukaryotic phytoplankton. There is an urgent need to establish bioavailability-based dissolved iron speciation (BDIS) methods. Herein, an intelligent system with dFe pretreatment and a colorimetric sensor is developed for real-time monitoring of newly generated Fe(III) ions. According to the photodegradation kinetics of dFe, including kinetic constant and photogenerated time of free Fe(III) ions, 3 sources, 6 kinds, and 12 species of dFe are determined by our photocatalytic-assisted colorimetric sensor and deep learning model within 20.0 min. The algal dFe-uptake for 4 days can be predicted by BDIS with correlation coefficient 0.85, which could be explained by the hard and soft acids and bases theory (HSAB) and density functional theory (DFT). These results successfully demonstrate the proof-of-concept for photodegradation kinetics-based speciation and bioavailability assessments of dissolved metals.
The rapid emergence of deep learning, e.g., deep convolutional
neural networks (DCNNs) as one-click image analysis with super-resolution,
has already revolutionized colorimetric determination. But it is severely
limited by its data-hungry nature, which is overcome by combining
the generative adversarial network (GAN), i.e., few-shot learning
(FSL). Using the same amount of real sample data, i.e., 414 and 447
samples as training and test sets, respectively, the accuracy could
be increased from 51.26 to 85.00% because 13,500 antagonistic samples
are created and used by GAN as the training set. Meanwhile, the generated
image quality with GAN is better than that with the commonly used
convolution self-encoder method. The simple and rapid on-site determination
of Cr(VI) with 1,5-diphenylcarbazide (DPC)-based test paper is a favorite
for environment monitoring but is limited by unstable DPC, poor sensitivity,
and narrow linear range. The chromogenic agent of DPC is protected
by the blending of polyacrylonitrile (PAN) and then loaded onto thin
chromatographic silica gel (SG) as a Cr(VI) colorimetric sensor (DPC/PAN/SG);
its stability could be prolonged from 18 h to more than 30 days, and
its repeatable reproducibility is realized via facile electrospinning.
By replacing the traditional Ed method with DCNN, the detection limit
is greatly improved from 1.571 mg/L to 50.00 μg/L, and the detection
range is prolonged from 1.571–8.000 to 0.0500–20.00
mg/L. The complete test time is shortened to 3 min. Even without time-consuming
and easily stained enrichment processing, its detection limit of Cr(VI)
in the drinking water can meet on-site detection requirements by USEPA,
WHO, and China.
Diagnosing cardiac arrhythmia through interpreting electrocardiogram (ECG) recordings is a challenging and time-consuming task, frequently resulting in inconsistent outcomes and misdiagnosis due to signal noise, interference, and comorbidities. To overcome these challenges, machine learning algorithms have been explored as potential solutions, with promising initial results. However, their lack of generalizability and explainability has hindered their widespread use in clinical settings. This study focuses on evaluating and reproducing a popular Deep Neuron Network (DNN) model proposed by Ribeiro et al. The performance of the model in classifying ECG recordings was found to be influenced by the characteristics of the training dataset, which was composed of different ECG recordings. Although the model exhibited strong generalizability with an F1 score of 0.87 when tested on the CPSC dataset, its performance was inconsistent when applied to the Shaoxing and Ningbo Hospital ECG dataset. To enhance the model's interpretability and performance, an attention layer was incorporated into the network, which improved its focus and resulted in an F1 score of 0.87 from 0.83 trained on the same dataset.
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