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Microplastics are commonly recognized as environmental and biotic contaminants. The prevalent presence of microplastics in aquatic settings raises concerns about plastic pollution. Therefore, it is critical to develop methods that can eliminate these microplastics with low cost and high effectiveness. This review concisely provides an overview of various methods and technologies for removing microplastics from wastewater and marine environments. Dynamic membranes and membrane bioreactors are effective in removing microplastics from wastewater. Chemical methods such as coagulation and sedimentation, electrocoagulation, and sol-gel reactions can also be used for microplastic removal. Biological methods such as the use of microorganisms and fungi are also effective for microplastic degradation. Advanced filtration technologies like a combination of membrane bioreactor and activated sludge method show high microplastic removal efficiency. 

Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task. But an initialization contains relatively little information about the source task. Instead, we show that we can learn highly informative posteriors from the source task, through supervised or self-supervised approaches, which then serve as the basis for priors that modify the whole loss surface on the downstream task. This simple modular approach enables significant performance gains and more data-efficient learning on a variety of downstream classification and segmentation tasks, serving as a drop-in replacement for standard pre-training strategies. These highly informative priors also can be saved for future use, similar to pre-trained weights, and stand in contrast to the zero-mean isotropic uninformative priors that are typically used in Bayesian deep learning. 

Critical mineral deposits form through an interplay of magmatic-hydrothermal processes in carbonatites and (per)alkaline systems during their emplacement in the Earth’s crust. Hydrothermal aqueous fluids can lead to the mobilization, transport, and deposition of the rare earth elements (REE) coupled to development of alteration zones at the deposit scale [1]. However, unraveling the underlying processes that affect the solubility of REE in these geologic fluids is a challenge in high temperature and pressure fluids [2]. A holistic approach is key to understand the controls of fluid-rock interaction in mobilizing REE in critical mineral deposits. Through a joint effort, we formed a new U.S. geoscience critical minerals experimental–thermodynamic research hub between New Mexico Tech, Los Alamos National Laboratory and Indiana University. The goal of this project is to conduct frontiers research on the behavior of critical elements in supercritical aqueous fluids by integration of a wide array of high temperature solubility experiments complemented by spectroscopic measurements and molecular dynamic simulations. Here we present current advances to simulate a significant vein paragenesis of barite + fluorite +calcite +bastnäsite-(Ce) observed in many critical mineral deposits. A case study will be presented from the Gallinas Mountains REE-fluorite hydrothermal breccia deposit in New Mexico. Using the GEMS code package [3] and the MINES thermodynamic database (https://geoinfo.nmt.edu/mines-tdb), we highlight our current capabilities and limitations to simulate the behavior of REE in these hydrothermal fluids and minerals. A thermodynamic model is presented to simulate the partitioning of REE between calcite- and fluorite-fluid based on recent and ongoing experimental and thermodynamic work on the synthesis of REE doped minerals [4] and REE speciation in acidic and alkaline fluids. We further show how to integrate multiple experimental datasets and develop new thermodynamic models based on the new research efforts from the research hub and future directions to improve our prediction capabilities of REE complexation in supercritical fluids. [1] Gysi et al. (2016), Econ. Geol. 111, 1241-1276; [2] Migdisov et al. (2016), Chemical Geology 439, 13-42. [3] Kulik et al. (2013), Comput Geosci 17, 1–24. [4] Perry and Gysi (2020), Geochim. Cosmochim. Acta 286, 177-197. 

A fully-integrated mixed reality game system called multiphysics enriched mixed reality for integrated geotechnical education (MERGE) is developed to improve student education in the context of geotechnical engineering. This work allows students to learn the design of geothermal pile in a more inclusive way while playing a game and gain an "integrated geotechnical learning experience". Several mini games are designed for students to enhance the geotechnical knowledge. Players can earn points and update their appearance by playing these mini games, which stimulates their interests in geotechnical engineering. By providing students with visualization, collaboration, and simulation tools, we hope to promote the understanding of geotechnical experiments. Based on the laboratory results, numerical experiments are conducted to help students understand the geotechnical application. The leveraging mixed reality technology offers an opportunity for students to access advanced equipment in geotechnical experiments. The main contribution of this work is a discussion of the educational technology and processes behind implementing a mixed reality educational game. We provide developmental insights and educational background to inform researchers who seek to develop similar games. 

A search for pair production of squarks or gluinos decaying via sleptons or weak bosons is reported. The search targets a final state with exactly two leptons with same-sign electric charge or at least three leptons without any charge requirement. The analysed data set corresponds to an integrated luminosity of 139 fb⁻¹of proton-proton collisions collected at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Multiple signal regions are defined, targeting several SUSY simplified models yielding the desired final states. A single control region is used to constrain the normalisation of theWZ+ jets background. No significant excess of events over the Standard Model expectation is observed. The results are interpreted in the context of several supersymmetric models featuring R-parity conservation or R-parity violation, yielding exclusion limits surpassing those from previous searches. In models considering gluino (squark) pair production, gluino (squark) masses up to 2.2 (1.7) TeV are excluded at 95% confidence level.

 

A search for supersymmetry targeting the direct production of winos and higgsinos is conducted in final states with either two leptons (eorμ) with the same electric charge, or three leptons. The analysis uses 139 fb⁻¹ofppcollision data at$$ \sqrt{s} $$$\sqrt{s}$= 13 TeV collected with the ATLAS detector during Run 2 of the Large Hadron Collider. No significant excess over the Standard Model expectation is observed. Simplified and complete models with and withoutR-parity conservation are considered. In topologies with intermediate states including eitherWhorWZpairs, wino masses up to 525 GeV and 250 GeV are excluded, respectively, for a bino of vanishing mass. Higgsino masses smaller than 440 GeV are excluded in a naturalR-parity-violating model with bilinear terms. Upper limits on the production cross section of generic events beyond the Standard Model as low as 40 ab are obtained in signal regions optimised for these models and also for anR-parity-violating scenario with baryon-number-violating higgsino decays into top quarks and jets. The analysis significantly improves sensitivity to supersymmetric models and other processes beyond the Standard Model that may contribute to the considered final states.