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Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million m3 ${\mathrm{m}}^{3}$ on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over 95% $95\%$. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal‐to‐noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards. Plain Language Summary: Steep mountains and hills produce dangerous rockfalls and similar phenomena such as landslides and debris flows. A major collapse is typically preceded by a series of rockfalls over days or months. It is therefore crucial to reliably detect these events and recognize the warning signs of an impending major collapse. When rocks bounce on the ground they release seismic waves, which generate vibrations that propagate long distances. Such vibrations stretch and compress fiber optic cables within the ground enough so they can be measured with a novel technique called Distributed Acoustic Sensing (DAS). Here we show how to identify such DAS signals using machine learning algorithms to detect precursory rockfall activity and a major collapse on a slope in Switzerland. We compare our detections with radar measurements, which are highly reliable but also come at a greater cost for installation. Since we can apply DAS to unused fiber within the ground, our approach may pave the way for the next generation of natural hazard warning. Key Points: A semi‐supervised neural network is developed for rock‐slope failure monitoring with Distributed Acoustic Sensing at Brienz, SwitzerlandOur model achieves over 95% precision for rock slope failures detected by a Doppler radar system over 45 daysThe sustained detection of slope failures before the major collapse highlights the potential of our approach for early warning [ABSTRACT FROM AUTHOR]

Intending to remove toxic graphene oxide (GO) from wastewater, LDH (layered double hydroxide) was employed to recover GO by adsorption method. The adsorption performance and the mechanism of LDH for GO have been systematically studied by diverse characterization technologies and methods. The relevant effects of solution pH (2-9), absorbent dosage (5-25mg) and the concentration of GO (20-160mg/L) were investigated in detail. The main driving force of GO condensation on LDH may be electrostatic interaction and hydrogen bonding, SEM, TEM, AFM, FT-IR and XRD analysis further confirmed this. XPS test shows that the adsorption process is carried out through C−O and O−C=O. We have got a high removal rate of 92% and an adsorption capacity of 1472 mg/g under an optimized conditions (pH = 3.0, equilibrium time = 6.0 h, dosage = 10mg, C0 = 160 mg/L). The analyses implied that LDH will be a very promising candidate for recovery of GO from wastewater.

The Chinese government recently proposed ammonia (NH3) emissions reductions (but without a specific national target) as a strategic option to mitigate PM2.5 pollution. We combined a meta-analysis of nationwide measurements and air quality modelling to identify efficiency gains by striking a balance between controlling NH3 and acid gas (SO2 and NOx) emissions. We found that PM2.5 concentrations decreased from 2000 to 2019, but annual mean PM2.5 concentrations still exceeded 35 µg m-3 at 74% of 1498 monitoring sites in 2015-2019. Secondary inorganic aerosols (SIA) were the dominant contributor to ambient PM2.5 concentrations. While sulfate concentrations significantly decreased over the time period, no significant change was observed for nitrate and ammonium concentrations. Model simulations indicate that the effectiveness of a 50% NH3 emission reduction for controlling SIA concentrations decreased from 2010 to 2017 in four megacity clusters of eastern China, simulated for the month of January under fixed meteorological conditions (2010). Although the effectiveness further declined in 2020 for simulations including the natural experiment of substantial reductions in acid gas emissions during the CoVID-19 pandemic, the resulting reductions in SIA concentrations were on average 20.8% lower than that in 2017. In addition, the reduction of SIA concentrations in 2017 was greater for 50% acid gas reductions than for the 50% NH3 emissions reduction. Our findings indicate that persistent secondary inorganic aerosol pollution in China is limited by acid gases emissions, while an additional control on NH3 emissions would become more important as reductions of SO2 and NOx emissions progress. [ABSTRACT FROM AUTHOR]

Müller glia, as prominent glial cells within the retina, plays a significant role in maintaining retinal homeostasis in both healthy and diseased states. In lower vertebrates like zebrafish, these cells assume responsibility for spontaneous retinal regeneration, wherein endogenous Müller glia undergo proliferation, transform into Müller glia-derived progenitor cells, and subsequently regenerate the entire retina with restored functionality. Conversely, Müller glia in the mouse and human retina exhibit limited neural reprogramming. Müller glia reprogramming is thus a promising strategy for treating neurodegenerative ocular disorders. Müller glia reprogramming in mice has been accomplished with remarkable success, through various technologies. Advancements in molecular, genetic, epigenetic, morphological, and physiological evaluations have made it easier to document and investigate the Müller glia programming process in mice. Nevertheless, there remain issues that hinder improving reprogramming efficiency and maturity. Thus, understanding the reprogramming mechanism is crucial toward exploring factors that will improve Müller glia reprogramming efficiency, and for developing novel Müller glia reprogramming strategies. This review describes recent progress in relatively successful Müller glia reprogramming strategies. It also provides a basis for developing new Müller glia reprogramming strategies in mice, including epigenetic remodeling, metabolic modulation, immune regulation, chemical small-molecules regulation, extracellular matrix remodeling, and cell-cell fusion, to achieve Müller glia reprogramming in mice., (Copyright © 2025 Copyright: © 2025 Neural Regeneration Research.)