Progress in the Elimination of Organic Contaminants in Wastewater by Activation Persulfate over Iron-Based Metal-Organic Frameworks.

The release of organic contaminants has grown to be a major environmental concern and a threat to the ecology of water bodies. Persulfate-based Advanced Oxidation Technology (PAOT) is effective at eliminating hazardous pollutants and has an extensive spectrum of applications. Iron-based metal-organic frameworks (Fe-MOFs) and their derivatives have exhibited great advantages in activating persulfate for wastewater treatment. In this article, we provide a comprehensive review of recent research progress on the significant potential of Fe-MOFs for removing antibiotics, organic dyes, phenols, and other contaminants from aqueous environments. Firstly, multiple approaches for preparing Fe-MOFs, including the MIL and ZIF series were introduced. Subsequently, removal performance of pollutants such as antibiotics of sulfonamides and tetracyclines (TC), organic dyes of rhodamine B (RhB) and acid orange 7 (AO7), phenols of phenol and bisphenol A (BPA) by various Fe-MOFs was compared. Finally, different degradation mechanisms, encompassing free radical degradation pathways and non-free radical degradation pathways were elucidated. This review explores the synthesis methods of Fe-MOFs and their application in removing organic pollutants from water bodies, providing insights for further refining the preparation of Fe-MOFs.

Progress and Prospect of Ion Imprinting Technology in Targeted Extraction of Lithium.

Ion Imprinting Technology (IIT) is an innovative technique that produces Ion-Imprinted polymers (IIPs) capable of selectively extracting ions. IIPs exhibit strong specificity, excellent stability, and high practicality. Due to their superior characteristics, the application of IIPs for lithium resource extraction has garnered significant attention. This paper discusses the following aspects based on existing conventional processes for lithium extraction and the latest research progress in lithium IIPs: (1) a detailed exposition of existing lithium extraction processes, including comparisons and summaries; (2) classification, comparison, and summarization of the latest lithium IIPs based on different material types and methods; (3) summarization of the applications of various lithium IIPs, along with a brief description of future directions in the development of lithium IIP applications. Finally, the prospects for targeted recovery of lithium resources using lithium IIPs are presented.

CLEP: Contrastive Learning for Epileptic Seizure Prediction Using a Spatio-Temporal-Spectral Network.

Seizure prediction of epileptic preictal period through electroencephalogram (EEG) signals is important for clinical epilepsy diagnosis. However, recent deep learning-based methods commonly employ intra-subject training strategy and need sufficient data, which are laborious and time-consuming for a practical system and pose a great challenge for seizure predicting. Besides, multi-domain characterizations, including spatio-temporal-spectral dependencies in an epileptic brain are generally neglected or not considered simultaneously in current approaches, and this insufficiency commonly leads to suboptimal seizure prediction performance. To tackle the above issues, in this paper, we propose Contrastive Learning for Epileptic seizure Prediction (CLEP) using a Spatio-Temporal-Spectral Network (STS-Net). Specifically, the CLEP learns intrinsic epileptic EEG patterns across subjects by contrastive learning. The STS-Net extracts multi-scale temporal and spectral representations under different rhythms from raw EEG signals. Then, a novel triple attention layer (TAL) is employed to construct inter-dimensional interaction among multi-domain features. Moreover, a spatio dynamic graph convolution network (sdGCN) is proposed to dynamically model the spatial relationships between electrodes and aggregate spatial information. The proposed CLEP-STS-Net achieves a sensitivity of 96.7% and a false prediction rate of 0.072/h on the CHB-MIT scalp EEG database. We also validate the proposed method on clinical intracranial EEG (iEEG) database from our Xuanwu Hospital of Capital Medical University, and the predicting system yielded a sensitivity of 95%, a false prediction rate of 0.087/h. The experimental results outperform the state-of-the-art studies which validate the efficacy of our method. Our code is available at https://github.com/LianghuiGuo/CLEP-STS-Net.

Observation and research of deep underground multi-physical fields-Huainan -848 m deep experiment.

Compared with the surface, the deep environment has the advantages of allowing "super-quiet and ultra-clean"-geophysical field observation with low vibration noise and little electromagnetic interference, which are conducive to therealization of long-term and high-precision observation of multi-physical fields, thus enabling the solution of a series of geoscience problems. In the Panyidong Coal Mine, where there are extensive underground tunnels at the depth of 848 m belowsea level, we carried out the first deep-underground geophysical observations, including radioactivity, gravity, magnetic, magne-totelluric, background vibration and six-component seismic observations. We concluded from these measurements that (1) the background of deep subsurface gravity noise in the long-period frequency band less than 2 Hz is nearly two orders ofmagnitude weaker than that in the surface observation environment; (2) the underground electric field is obviously weaker thanthe surface electric field, and the relatively high frequency of the underground field, greater than 1 Hz, is more than two orders of magnitude weaker than that of the surface electric field; the east-west magnetic field underground is approximately the same asthat at the surface; the relatively high-frequency north-south magnetic field underground, below 10 Hz, is at least one order ofmagnitude lower than that at the surface, showing that the underground has a clean electromagnetic environment; (3) in additionto the high-frequency and single-frequency noises introduced by underground human activities, the deep underground spacehas a sig-nificantly lower background vibration noise than the surface, which is very beneficial to the detection of weakearthquake and gravity signals; and (4) the underground roadway support system built with ferromagnetic material interferesthe geomagnetic field. We also found that for deep observation in the "ultra-quiet and ultra-clean" environment, the existinggeophysical equipment and observation technology have problems of poor adaptability and insufficient precision as well asdata cleaning problems, such as the effective separation of the signal and noise of deep observation data. It is also urgent tointerpret and comprehensively utilize these high-precision multi-physics observation data. Electronic Supplementary Material: Supplementary material is available in the online version of this article at 10.1007/s11430-022-9998-2.

A graphical approach for mixed ratio optimisation in the binary mixed amine solution.

Chemical absorption method plays an important role in the process of CO2 separation. One major problem for chemical absorption is huge energy consumption, which is affected by the performance of absorbents. Developing a type of absorbent with high absorption capacity and low regenerative energy consumption is a research topic that attracts attention. The combination of two or more amines is one way to develop new solvents. However, the change of amine liquid ratio can cause a series of complex nonlinear changes in absorption capacity, absorption heat, the heat of vaporisation and sensible heat. It is of interest to visualise the amine solution mixing ratio optimisation to help reduce the energy consumption and increase the absorption capacity. Derivative analysis of standardised vs variables diagram (DSVD), a kind of graphical method based on maximum benefit and minimum consumption, is proposed to determine the optimal mixing ratio of binary amine solution. This novel approach helps to visualise what kind of amines are not suitable for compounding, what kind of amines have the best compounding ratio, and how to determine the optimal compounding ratio. The optimal mixing ratio of the Methyldiethanolamine (MDEA) - Piperazine (PZ) system and MDEA - Monoethanolamine (MEA) were optimised by this method. The optimal ratio of MDEA - PZ and MDEA - MEA are 0.6 (PZ: MDEA = 0.6:0.4, wt.%) and 0.8 (MEA: MDEA = 0.8:0.2, wt.%).

A Temporal-Spectral-Based Squeeze-and- Excitation Feature Fusion Network for Motor Imagery EEG Decoding.

Motor imagery (MI) electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with the outside world via external devices. Recent deep learning methods, which fail to fully explore both deep-temporal characterizations in EEGs itself and multi-spectral information in different rhythms, generally ignore the temporal or spectral dependencies in MI-EEG. Also, the lack of effective feature fusion probably leads to redundant or irrelative information and thus fails to achieve the most discriminative features, resulting in the limited MI-EEG decoding performance. To address these issues, in this paper, a MI-EEG decoding framework is proposed, which uses a novel temporal-spectral-based squeeze-and-excitation feature fusion network (TS-SEFFNet). First, the deep-temporal convolution block (DT-Conv block) implements convolutions in a cascade architecture, which extracts high-dimension temporal representations from raw EEG signals. Second, the multi-spectral convolution block (MS-Conv block) is then conducted in parallel using multi-level wavelet convolutions to capture discriminative spectral features from corresponding clinical subbands. Finally, the proposed squeeze-and-excitation feature fusion block (SE-Feature-Fusion block) maps the deep-temporal and multi-spectral features into comprehensive fused feature maps, which highlights channel-wise feature responses by constructing interdependencies among different domain features. Competitive experimental results on two public datasets demonstrate that our method is able to achieve promising decoding performance compared with the state-of-the-art methods.

[Shengjing tablets for semen non-liquefaction: a clinical study of 100 cases].

OBJECTIVE: To investigate the efficacy and action mechanism of Shengjing Tablets in the treatment liquefaction. METHODS: We randomly assigned 150 patients with semen non-liquefaction to receive Shengjing Tablets group, n = 100) and vitamin E capsules (control group, n = 50) for 2 courses of 45 days each, followed by observation liquefaction time and other semen parameters. RESULTS: After the first course, 68 of the patients in the treatment group 20 responded and 12 failed to respond; and after the second course, 84 were cured, 9 responded and 7 failed to respond, effective rate of 93.0%. In comparison, only 8 of the controls were cured, 8 responded and 34 failed to respond after medication. There were statistically significant differences between the two groups (P < 0.01). Meanwhile, the treatment showed obvious improvement in sperm motility and concentration. CONCLUSION: Shengjing Tablets may shorten the time liquefaction, and can be used as a safe and effective therapy for semen non-liquefaction.