Enhanced Removal of Doxycycline by Simultaneous Potassium Ferrate(VI) and Montmorillonite: Reaction Mechanism and Synergistic Effect

Doxycycline (DOX), a typical antibiotic, is harmful to aquatic ecosystems and human health. This study presents DOX removal by potassium ferrate (Fe(VI)) and montmorillonite and investigates the effect of Fe(VI) dosage, reaction time, initial pH value, montmorillonite dosage, adsorption pH, time and temperature on DOX removal. The results show that DOX removal increases when increasing the Fe(VI) dosage, with the optimal condition for DOX removal (~97%) by Fe(VI) observed under a molar ratio ([Fe(VI)]:[DOX]) of 30:1 at pH 7. The reaction of DOX with Fe(VI) obeyed second-order kinetics with a rate constant of 10.7 ± 0.45 M−1 s−1 at pH 7. The limited promotion (~4%) of DOX adsorption by montmorillonite was observed when the temperature increased and the pH decreased. Moreover, the synergetic effect of Fe(VI) and montmorillonite on DOX removal was obtained when comparing the various types of dosing sequences (Fe(VI) oxidation first and then adsorption; adsorption first and then Fe(VI) oxidation; simultaneous oxidation and adsorption). The best synergistic effect of DOX removal (97%) was observed under the simultaneous addition of Fe(VI) and montmorillonite, maintaining the Fe(VI) dosage (from 30:1 to 5:1). Five intermediates were detected during DOX degradation, and a plausible DOX degradation pathway was proposed

Progressive damage and fracture behavior of brittle rock under multi-axial prestress constraint and cyclic impact load coupling

Abstract To explore the progressive damage and fracture mechanics characteristics of brittle rock materials under combined dynamic-static loading. Taking account of the coupling effect of the constraint states of uniaxial stress (σ 1 ≥ σ 2 = σ 3 = 0), biaxial stress (σ 1 ≥ σ 2 > σ 3 = 0) and true triaxial stress (σ 1 ≥ σ 2 ≥ σ 3 ≠ 0) and impact load, the strain rate effect and prestress constraint effect of dynamic mechanical characteristics of sandstone are studied. The progressive damage evolution law of sandstone under the coupling of true triaxial stress constraint and cyclic impact load is discussed. The results show that with the increase of axial stress σ 1, the dynamic compressive strength and peak strain gradually decrease, and the strain rate gradually increases, resulting in crushing failure under high strain rate. When the axial stress is fixed, the lateral stress constraint reduces the damage degree of sandstone and improves the dynamic compressive strength. With the increase of strain rate, the sample changes from slight splitting failure to inclined shear failure mode. Under the true triaxial stress constraint, the intermediate principal stress σ 2 obviously enhances the dynamic compressive strength of sandstone. Under the constraints of triaxial stress, biaxial stress and uniaxial stress, the enhancement effect of dynamic compressive strength and the deformation resistance of sandstone are weakened in turn. Under the coupling of true triaxial stress constraint and high strain rate, sandstone samples show obvious progressive damage evolution effect under repeated impacts, and eventually inclined shear failure occurs, resulting in complete loss of bearing capacity

Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion

The objective of detection in remote sensing images is to determine the location and category of all targets in these images. The anchor based methods are the most prevalent deep learning based methods, and still have some problems that need to be addressed. First, the existing metric (i.e., intersection over union (IoU)) could not measure the distance between two bounding boxes when they are nonoverlapping. Second, the exsiting bounding box regression loss could not directly optimize the metric in the training process. Third, the existing methods which adopt a hierarchical deep network only choose a single level feature layer for the feature extraction of region proposals, meaning they do not take full use of the advantage of multi-level features. To resolve the above problems, a novel object detection method for remote sensing images based on improved bounding box regression and multi-level features fusion is proposed in this paper. First, a new metric named generalized IoU is applied, which can quantify the distance between two bounding boxes, regardless of whether they are overlapping or not. Second, a novel bounding box regression loss is proposed, which can not only optimize the new metric (i.e., generalized IoU) directly but also overcome the problem that existing bounding box regression loss based on the new metric cannot adaptively change the gradient based on the metric value. Finally, a multi-level features fusion module is proposed and incorporated into the existing hierarchical deep network, which can make full use of the multi-level features for each region proposal. The quantitative comparisons between the proposed method and baseline method on the large scale dataset DIOR demonstrate that incorporating the proposed bounding box regression loss, multi-level features fusion module, and a combination of both into the baseline method can obtain an absolute gain of 0.7%, 1.4%, and 2.2% or so in terms of mAP, respectively. Comparing this with the state-of-the-art methods demonstrates that the proposed method has achieved a state-of-the-art performance. The curves of average precision with different thresholds show that the advantage of the proposed method is more evident when the threshold of generalized IoU (or IoU) is relatively high, which means that the proposed method can improve the precision of object localization. Similar conclusions can be obtained on a NWPU VHR-10 dataset