Degradation of the 3 pesticides (acephate, omethoate, and dimethyl dichloroviny phosphate [DDVP]) by electrolyzed water was investigated. These pesticides were commonly used as broad-spectrum insecticides in pest control and high-residual levels had been detected in vegetables. Our research showed that the electrolyzed oxidizing (EO) water (pH 2.3, available chlorine concentration:70 ppm, oxidation-reduction potential [ORP]: 1170 mV) and the electrolyzed reducing (ER) water (pH 11.6, ORP: -860 mV) can reduce the pesticide residues effectively. Pesticide residues on fresh spinach after 30 min of immersion in electrolyzed water reduced acephate by 74% (EO) and 86% (ER), omethoate by 62% (EO) and 75% (ER), DDVP by 59% (EO) and 46% (ER), respectively. The efficacy of using EO water or ER water was found to be better than that of using tap water or detergent (both were reduced by more than 25%). Besides spinach, the cabbage and leek polluted by DDVP were also investigated and the degradation efficacies were similar to the spinach. Moreover, we found that the residual level of pesticide residue decreased with prolonged immersion time. Using EO or ER water to wash the vegetables did not affect the contents of Vitamin C, which inferred that the applications of EO or ER water to wash the vegetables would not result in loss of nutrition.

The customary model, that is, bsin = m, for the measurement of the sizes of slots and thin wires by optical diffraction has been used widely for a long time. In this design note, the model is analysed theoretically and experimentally and improved to the new model, btan = m. Two calibrating slots and thin wires (thin cylinders) are measured by means of optical diffraction. The results show that the new model btan = m is more suitable for describing optical diffraction phenomena and more accurate for measuring a thin wire's diameter.

Linear coherent noise attenuation is a troublesome problem in a variety of seismic exploration areas. Traditional methods often use the differences in frequency, wavenumber, or amplitude to separate the useful signal and coherent noise. However, the application of traditional methods is limited or even invalid when the aforementioned differences between useful signal and coherent noise are too small to be distinguished. For this reason, we have managed to develop a new algorithm from the differences in the shape of seismic waves, and thus, introduce mathematical morphological filtering (MMF) into coherent noise attenuation. The morphological operation is calculated in the trace direction of a rotating coordinate system. This rotating coordinate system is along the direction of the trajectory of coherent noise to make the energy of the coherent noise distributed along the horizontal direction. The MMF approach is more effective than mean and median filters in rejecting abnormal values and causes fewer artifacts compared with [Formula: see text]-[Formula: see text] filtering. Our technique requires that coherent noise can be picked successfully. Application of our technique on synthetic and field seismic data demonstrates its successful performance.

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Transformer-based supervised pre-training achieves great performance in person re-identification (ReID). However, due to the domain gap between ImageNet and ReID datasets, it usually needs a larger pre-training dataset (e.g. ImageNet-21K) to boost the performance because of the strong data fitting ability of the transformer. To address this challenge, this work targets to mitigate the gap between the pre-training and ReID datasets from the perspective of data and model structure, respectively. We first investigate self-supervised learning (SSL) methods with Vision Transformer (ViT) pretrained on unlabelled person images (the LUPerson dataset), and empirically find it significantly surpasses ImageNet supervised pre-training models on ReID tasks. To further reduce the domain gap and accelerate the pre-training, the Catastrophic Forgetting Score (CFS) is proposed to evaluate the gap between pre-training and fine-tuning data. Based on CFS, a subset is selected via sampling relevant data close to the down-stream ReID data and filtering irrelevant data from the pre-training dataset. For the model structure, a ReID-specific module named IBN-based convolution stem (ICS) is proposed to bridge the domain gap by learning more invariant features. Extensive experiments have been conducted to fine-tune the pre-training models under supervised learning, unsupervised domain adaptation (UDA), and unsupervised learning (USL) settings. We successfully downscale the LUPerson dataset to 50% with no performance degradation. Finally, we achieve state-of-the-art performance on Market-1501 and MSMT17. For example, our ViT-S/16 achieves 91.3%/89.9%/89.6% mAP accuracy on Market1501 for supervised/UDA/USL ReID. Codes and models will be released to https://github.com/michuanhaohao/ TransReID-SSL.

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