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A review of coagulation explaining its definition, mechanism, coagulant types, and optimization models;RSM, and ANN

Current Research in Green and Sustainable Chemistry 2023年 6卷

作者： El-taweel, Reem M. Mohamed, Nora Alrefaey, Khlood A. Husien, Sh Abdel-Aziz, A.B. Salim, Alyaa I. Mostafa, Nagwan G. Said, Lobna A. Fahim, Irene Samy Radwan, Ahmed G. School of Engineering and Applied Sciences Nile University Giza Egypt Industrial Engineering Program SESC Research Center Nile University Giza Egypt October High Institute for Engineering Technology Giza Egypt Environmental and Sanitary Engineering Division Public Works Department Faculty of Engineering Cairo University Giza Egypt Nanoelectronics Integrated Systems Center (NISC) Nile University Giza Egypt Engineering Mathematics and Physics Dept. Faculty of Engineering Cairo University Giza 12613 Egypt

The textile business is one of the most hazardous industries since it produces several chemicals, such as dyes, which are released into water streams with ef-fluents. For the survival of the planet's life and the advancement of humanity, water is a crucial resource. One of the anthropogenic activities that pollute and consume water is the textile industry. Thus, the purpose of the current effort is to Apply coagulation as a Physico-chemical and biological treatment strat-egy with different techniques and mechanisms to treat the effluent streams of textile industries. The discharge of these effluents has a negative impact on the environment, marine life, and human health. Therefore, the treatment of these effluents before discharging is an important matter to reduce their adverse ef-fect. Many physico-chemical and biological treatment strategies for contaminants removal from polluted wastewater have been proposed. Coagulation is thought to be one of the most promising physico-chemical strategies for removing con-taminants and colouring pollutants from contaminated water. Coagulation is accompanied by a floculation process to aid precipitation, as well as the collection of the created sludge following the treatment phase. Different commercial, and natural coagulants have been applied as a coagulants in the process of coagulation. Additionally, many factors such as;pH, coagulant dose, pollu-tants concentration are optimized to obtain high coagulants removal capacity. This review will discuss the coagulation process, coagulant types and aids in addition to the factors affecting the coagulation process. Additionally, a brief comparison between the coagulation process, and the other processes;princi-ple, advantages, disadvantages, and their efficiency were discussed throgh the review. Furthermore, it discusses the models and optimization techniques used for the coagulation process including response surface methodology (RSM), ar-tificial neural network (ANN), and several

关键词： Coagulants Coagulation Mathematical models optimization Textile Textile effluents

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Partially constrained internal linear combination: A method for low-noise CMB foreground mitigation

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Physical Review D 2021年第10期103卷 103510-103510页

作者： Y. Sultan Abylkairov Omar Darwish J. Colin Hill Blake D. Sherwin Physics Department Nazarbayev University Nur-Sultan 010000 Kazakhstan Energetic Cosmos Laboratory Nazarbayev University Nur-Sultan 010000 Kazakhstan Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge CB3 0WA United Kingdom Department of Physics Columbia University New York New York 10027 USA Center for Computational Astrophysics Flatiron Institute New York New York 10010 USA Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 OHA United Kingdom

Internal linear combination (ILC) methods are some of the most widely used multifrequency cleaning techniques employed in cosmic microwave background (CMB) data analysis. These methods reduce foregrounds by minimizing the total variance in the coadded map (subject to a signal-preservation constraint), although often significant foreground residuals or biases remain. A modification to the ILC method is the constrained ILC, which explicitly nulls certain foreground components; however, this foreground nulling often comes at a high price for ground-based CMB datasets, with the map noise increasing significantly on small scales. In this paper we explore a new method, the partially constrained ILC, which allows us to optimize the tradeoff between foreground bias and variance in ILC methods. In particular, this method allows us to minimize the variance subject to an inequality constraint requiring that the constrained foregrounds are reduced by at least a fixed factor, which can be chosen based on the foreground sensitivity of the intended application. We test our method on simulated sky maps for a Simons Observatory–like experiment; we find that for cleaning thermal Sunyaev-Zel’dovich contamination at ℓ∈[3000,4800], if a small thermal Sunyaev-Zel’dovich residual of 20% of the standard ILC residual can be tolerated, the variance of the CMB temperature map is reduced by at least 50% over the constrained ILC value. We also demonstrate an application of this method to reduce noise in CMB lensing reconstruction.

关键词： Cosmic microwave background Cosmology

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Local large temperature difference and ultra-wideband photothermoelectric response of the silver nanostructure film/carbon nanotube film heterostructure

arXiv

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arXiv 2021年

作者： Lv, Bocheng Wu, Weidong Xie, Yan Zhu, Jia-Lin Cao, Yang Ma, Wanyun Yang, Ning Chu, Weidong Wei, Jinquan Sun, Jia-Lin State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Department of Engineering Physics Tsinghua University Beijing100084 China School of Instrumentation Science and Opto-Electronics Engineering Beijing Information Science & Technology University Beijing100192 China Institute of Applied Physics and Computational Mathematics Beijing100088 China Key Lab for Advanced Materials Processing Technology of Education Ministry School of Materials Science and Engineering Tsinghua University Beijing100084 China

Photothermoelectric materials have important applications in many fields. Here, we joined a silver nanostructure film (AgNSF) and a carbon nanotube film (CNTF) by van der Waals force to form a AgNSF/CNTF heterojunction, which shows excellent photothermal and photoelectric conversion properties. The local temperature difference (Δτ) and the output photovoltage increase rapidly when the heterojunction is irradiated by lasers with wavelengths ranging from ultraviolet to terahertz. The maximum of the Δτreaches 205.9 K, which is significantly higher than that of other photothermoelectric materials reported in literatures. The photothermal and photoelectric responsivity depend on the wavelength of lasers, which are 175~601 K W-1 and 9.35~40.4 mV W-1, respectively. We demonstrate that light absorption of the carbon nanotube is enhanced by local surface plasmons, and the output photovoltage is dominated by Seebeck effect. The AgNSF/CNTF heterostructure can be used as high-efficiency sensitive photothermal materials or as ultra-wideband fast-response photoelectric *** Codes 78 © 2021, CC BY.

关键词： Heterojunctions

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Covariant transverse-traceless projection for secondary gravitational waves

arXiv

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arXiv 2021年

作者： Ota, Atsuhisa Macpherson, Hayley J. Coulton, William R. HKUST Jockey club Institute for Advanced Study The Hong Kong University of Science and Technology Clearwater Bay Hong Kong Department of Physics Astronomy Ohio University AthensOH45701 United States Department of Applied Mathematics and Theoretical Physics CambridgeCB3 0WA United Kingdom Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States

Second-order tensor modes induced by nonlinear gravity are a key component of the cosmological background of gravitational waves. A detection of this background would allow us to probe the primordial power spectrum at otherwise inaccessible scales. Usually, the energy density of these gravitational waves is studied within perturbation theory in a particular gauge - a connection between our physical spacetime and a fictitious background. It is a widely recognized issue that the second-order, scalar-induced gravitational waves are gauge dependent. This issue arises because they are not well-defined as tensors in the physical spacetime at second-order and are thus unphysical. In this paper, we propose the covariant transverse-traceless projection of the extrinsic curvature to study cosmological gravitational waves on a spatial hypersurface. We define a new energy density which is based purely on spacetime tensors, independent of perturbation theory, and thus is gauge invariant by definition. We show that, in the context of second-order perturbation theory, this new energy density contains only propagating modes in the constant-time hypersurface in the Newtonian gauge. We further show that we can recover the same gravitational waves after a transformation to the synchronous gauge, so long as we correctly identify the Newtonian hypersurface. Copyright © 2021, The Authors. All rights reserved.

关键词： Gages

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Active Crowds

arXiv

引用

arXiv 2021年

作者： Bruna, Maria Burger, Martin Pietschmann, Jan-Frederik Wolfram, Marie-Therese Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department Mathematik Friedrich-Alexander Universität Erlangen-Nürnberg Cauerstrasse 11 Erlangen91058 Germany Fakultät für Mathematik Technische Universität Chemnitz Reichenhainer Straße 41 Chemnitz Germany University of Warwick Mathematics Institute CoventryCV4 7AL United Kingdom Radon Institute for Computational and Applied Mathematics Altenbergerstr. 69 Linz4040 Austria

This chapter focuses on the mathematical modelling of active particles (or agents) in crowded environments. We discuss several microscopic models found in literature and the derivation of the respective macroscopic partial differential equations for the particle density. The macroscopic models share common features, such as cross diffusion or degenerate mobilities. We then take the diversity of macroscopic models to a uniform structure and work out potential similarities and differences. Moreover, we discuss boundary effects and possible applications in life and social sciences. This is complemented by numerical simulations that highlight the effects of different boundary conditions. Copyright © 2021, The Authors. All rights reserved.

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The Phonon Quasiparticle Approach for Anharmonic Properties of Solids

arXiv

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arXiv 2021年

作者： Zhang, Zhen Zhang, Dong-Bo Sun, Tao Wentzcovitch, Renata M. Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States College of Nuclear Science and Technology Beijing Normal University Beijing100875 China Beijing Computational Science Research Center Beijing100193 China Key Laboratory of Computational Geodynamics Chinese Academy of Sciences Beijing100049 China Department of Earth and Environmental Sciences Columbia University New YorkNY10027 United States Lamont-Doherty Earth Observatory Columbia University PalisadesNY10964 United States

Knowledge of lattice anharmonicity is essential to elucidate distinctive thermal properties in crystalline solids. Yet, accurate ab initio investigations of lattice anharmonicity encounter difficulties owing to the cumbersome computations. Here we introduce the phonon quasiparticle approach and review its application to various materials. This method efficiently and reliably addresses lattice anharmonicity by combining ab initio molecular dynamics and lattice dynamics calculations. Thus, in principle, it accounts for full anharmonic effects and overcomes finite-size effects typical of ab initio molecular dynamics. The validity and effectiveness of the current approach are demonstrated in the computation of thermodynamic and heat transport properties of weakly and strongly anharmonic systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Phonons

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Hopping-Transport Mechanism for Reconfigurable Logic in Disordered Dopant Networks

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Physical Review applied 2022年第6期17卷 064025-064025页

作者： Henri Tertilt Jesse Bakker Marlon Becker Bram de Wilde Indrek Klanberg Bernard J. Geurts Wilfred G. van der Wiel Andreas Heuer Peter A. Bobbert Institut für physikalische Chemie Westfälische Wilhelms-Universität Münster Münster Germany NanoElectronics Group University of Twente Enschede Netherlands Multiscale Modeling and Simulation Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) University of Twente Enschede Netherlands Center for Computational Energy Research (CCER) Eindhoven University of Technology Netherlands Fachbereich Physik Westfälische Wilhelms-Universität Münster Münster Germany Department of Applied Physics Eindhoven University of Technology Netherlands

We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic in dopant network processing units. Kinetic Monte Carlo simulations of the hopping process show temperature-dependent current-voltage characteristics and artificially evolved basic Boolean logic gates in very good agreement with experiment. The simulations provide unique insights into the local electrostatic potential and current flow in the dopant network, showing subtle changes induced by control voltages that set the conditions for the logic operation. These insights will be crucial in the systematic further development of this burgeoning technology for unconventional computing. The establishment of the principles underlying the logic functionality of these devices encourages the exploration and utilization of the same principles in other materials and device geometries.

关键词： Doping effects Hopping transport Neuromorphic computing Devices for digital logic, storage & processing Disordered systems Doped semiconductors Genetic algorithm Monte Carlo methods

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A Nodal Immersed Finite Element-Finite Difference Method

arXiv

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arXiv 2021年

作者： Wells, David R. Vadala-Roth, Ben Lee, Jae H. Griffith, Boyce E. Department of Mathematics University of North Carolina Chapel HillNC United States Westborough MA United States Department of Mechanical Engineering Institute for Computational Medicine Johns Hopkins University BaltimoreMD United States Department of Mathematics Applied Physical Sciences and Biomedical Engineering University of North Carolina Chapel HillNC United States Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC United States Computational Medicine Program University of North Carolina Chapel HillNC United States McAllister Heart Institute University of North Carolina Chapel HillNC United States Center for Drug Evaluation and Research U.S. Food and Drug Administration Silver SpringMD United States

The immersed finite element-finite difference (IFED) method is a computational approach to modeling interactions between a fluid and an immersed structure. The IFED method uses a finite element (FE) method to approximate the stresses, forces, and structural deformations on a structural mesh and a finite difference (FD) method to approximate the momentum and enforce the incompressibility of the entire fluid-structure system on a Cartesian grid. The fundamental approach used by this method follows the immersed boundary framework for modeling fluid-structure interaction (FSI), in which a force spreading operator prolongs structural forces to a Cartesian grid, and a velocity interpolation operator restricts a velocity field defined on that grid back onto the structural mesh. With an FE structural mechanics framework, force spreading first requires that the force itself be projected onto the finite element space. Similarly, velocity interpolation requires projecting velocity data onto the FE basis functions. Consequently, evaluating either coupling operator requires solving a matrix equation at every time step. Mass lumping, in which the projection matrices are replaced by diagonal approximations, has the potential to accelerate this method considerably. This paper provides both numerical and computational analyses of the effects of this replacement for evaluating the force projection and for the IFED coupling operators. Constructing the coupling operators also requires determining the locations on the structure mesh where the forces and velocities are sampled. Here we show that sampling the forces and velocities at the nodes of the structural mesh is equivalent to using lumped mass matrices in the IFED coupling operators. A key theoretical result of our analysis is that if both of these approaches are used together, the IFED method permits the use of lumped mass matrices derived from nodal quadrature rules for any standard interpolatory element. This is different from s

关键词： Fluid structure interaction

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Earthmover-based manifold learning for analyzing molecular conformation spaces

arXiv

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arXiv 2019年

作者： Zelesko, Nathan Moscovich, Amit Kileel, Joe Singer, Amit Department of Mathematics Brown University Program in Applied and Computational Mathematics Princeton University Department of Mathematics Princeton University

In this paper, we propose a novel approach for manifold learning that combines the Earthmover's distance (EMD) with the diffusion maps method for dimensionality reduction. We demonstrate the potential benefits of this approach for learning shape spaces of proteins and other flexible macromolecules using a simulated dataset of 3-D density maps that mimic the non-uniform rotary motion of ATP synthase. Our results show that EMD-based diffusion maps require far fewer samples to recover the intrinsic geometry than the standard diffusion maps algorithm that is based on the Euclidean distance. To reduce the computational burden of calculating the EMD for all volume pairs, we employ a wavelet-based approximation to the EMD which reduces the computation of the pairwise EMDs to a computation of pairwise weighted-`1 distances between wavelet coefficient vectors. Copyright © 2019, The Authors. All rights reserved.

关键词： Electron microscopes

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