检索结果-内蒙古大学图书馆

Finding shortest and nearly shortest path nodes in large substantially incomplete networks

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

The effect of lard concentration and ozonation on changes in polarization angle of olive oil

学校读者我要写书评

暂无评论

Journal of Physics: Conference Series 2021年第1期1943卷

作者： F A Saputra M Azam F Arianto K F Sofjan H Sugito I Alkian Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia 50275 Study Program of Automation Engineering Technology Vocational School Diponegoro University

This research aims to analyze the effect of ozone exposure time and lard mixture on the polarization angle of transmission of olive oil. The study uses a transmission polarization method with a laser (λ = 532 nm) as a light source. The research sample in the form of olive oil mixed with lard at a concentration of 10, 20, 40, and 80%, with an ozone exposure time of 0-30 minutes. The sample is subjected to external electric fields with variations from 0-4.5 x 102 kV/m to increase the change's polarization angle. The results showed that the change in polarization angle would change linearly with the increase in lard concentration with olive oil. Olive oil with a low concentration of lard (10%) experienced an increase in the change in polarization angle with a value of 0.31 ° due to the effect of 90 minutes of ozone exposure. In general, the results of the study showed that increased concentrations of lard, ozone exposure time, electric field strengths caused an increase in changes in the polarization angle in a mixture of olive oil and lard.

关键词：

Many-core algorithms for high-dimensional gradients on phylogenetic trees

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Gangavarapu, Karthik Ji, Xiang Baele, Guy Fourment, Mathieu Lemey, Philippe Matsen, Frederick A. Suchard, Marc A. Department of Biomathematics David Geffen School of Medicine at UCLA University of California Los Angeles United States Department of Mathematics School of Science & Engineering Tulane University New Orleans United States Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium Australian Institute for Microbiology and Infection University of Technology Sydney UltimoNSW Australia Public Health Sciences Division Fred Hutchinson Cancer Research Center SeattleWA United States Department of Statistics University of Washington Seattle United States Department of Genome Sciences University of Washington Seattle United States Howard Hughes Medical Institute Fred Hutchinson Cancer Research Center SeattleWA United States Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles United States Department of Human Genetics David Geffen School of Medicine at UCLA University of California Los Angeles United States Departments of Biostatistics Biomathematics and Human Genetics University of California Los Angeles 695 Charles E. Young Dr. South Los AngelesCA90095-7088 United States

Advancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences N. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes O(N2) operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in O(N), enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as Markov-modulated and codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations. We benchmark these GPU algorithms on three computing systems using three evolutionary inference examples exploring complete genomes from 997 dengue viruses, 62 carnivore mitochondria and 49 yeasts, and observe a greater than 128-fold speedup over the CPU implementation for codon-based models and greater than 8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable. We provide an implementation of our GP

关键词： Phylogenetic

CLASSIFICATION OF RANK TWO WEAK FANO BUNDLES ON DEL PEZZO THREEFOLDS OF DEGREE FOUR

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Fukuoka, Takeru Hara, Wahei Ishikawa, Daizo Graduate School of Mathematical Sciences The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo153-8914 Japan The Mathematics and Statistics Building University of Glasgow University Place GlasgowG12 8QQ United Kingdom Department of Mathematics School of Science and Engineering Waseda University Ohkubo 3-4-1 Shinjuku Tokyo169-8555 Japan

We classify rank two vector bundles on a given del Pezzo threefold of degree four whose projectivizations are weak Fano into seven cases. We also give an example for each of these seven *** Codes 14J30, 14J45, 14J60 C... 详细信息

关键词： Classification (of information)

Regularity theory for time-fractional advection-diffusion-reaction equations

学校读者我要写书评

暂无评论

arXiv 2019年

作者： McLean, William Mustapha, Kassem Ali, Raed Knio, Omar M. School of Mathematics and Statistics University of New South Wales Sydney2052 Australia Department of Mathematics and Statistics KFUPM Dhahran31261 Saudi Arabia Computer Electrical Mathermatical Sciences and Engineering Division KAUST Thuwal23955 Saudi Arabia

We investigate the behavior of the time derivatives of the solution to a linear time-fractional, advection-diffusion-reaction equation, allowing space- and time-dependent coefficients as well as initial data that may have low regularity. Our focus is on proving estimates that are needed for the error analysis of numerical methods. The nonlocal nature of the fractional derivative creates substantial difficulties compared with the case of a classical parabolic PDE. In our analysis, we rely on novel energy methods in combination with a fractional Gronwall inequality and certain properties of fractional integrals.26A33, 35B45, 35B65, 35D30, 35K57, 35Q84, 35R11 Copyright © 2019, The Authors. All rights reserved.

关键词： Numerical methods

Anomalous nonlinear dynamics behavior of fractional viscoelastic structures

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Suzuki, Jorge Varghaei, Pegah Kharazmi, Ehsan Zayernouri, Mohsen Department of Mechanical Engineering Michigan State University MI48824 United States Michigan State University MI48824 United States Department of Statistics and Probability Michigan State University MI48824 United States Division of Applied Mathematics Brown University RI02912 United States

Fractional models and their parameters are sensitive to changes in the intrinsic micro-structures of anomalous materials. We investigate how such physics-informed models propagate the evolving anomalous rheology to the nonlinear dynamics of mechanical systems. In particular, we analyze the vibration of a fractional, geometrically nonlinear viscoelastic cantilever beam, under base excitation and free vibration, where the viscoelastic response is general through a distributed-order fractional model. We employ Hamilton’s principle to obtain the corresponding equation of motion with the choice of specific material distribution functions that recover a fractional Kelvin-Voigt viscoelastic model of order α. Through spectral decomposition in space, the resulting time-fractional partial differential equation reduces to a nonlinear time-fractional ordinary differential equation, in which the linear counterpart is numerically integrated by employing a direct L1-difference scheme. We further develop a semi-analytical scheme to solve the nonlinear system through a method of multiple scales, which yields a cubic algebraic equation in terms of the frequency. Our numerical results suggest a set of α-dependent anomalous dynamic qualities, such as far-from-equilibrium power-law amplitude decay rates, super-sensitivity of amplitude response at free vibration, and bifurcation in steady-state amplitude at primary *** Codes 34A08 Copyright © 2020, The Authors. All rights reserved.

关键词： Viscoelasticity

Highly sensitive salinity sensing using annular one-dimensional photonic crystals

学校读者我要写书评

暂无评论

Scientific reports 2025年第1期15卷 18337页

作者： Hassan Sayed Ashour M Ahmed Ali Hajjiah M A Abdelkawy Arafa H Aly TH-PPM Group Physics Department Faculty of Sciences Beni-Suef University Beni Suef 62514 Egypt. hassansayed@science.bsu.edu.eg. Physics Department College of Science Imam Mohammad Ibn Saud Islamic University (IMSIU) 11623 Riyadh Saudi Arabia. Electrical Engineering Department College of Engineering and Petroleum Kuwait University Kuwait City Kuwait. Department of Mathematics and Statistics College of Science Imam Mohammad Ibn Saud Islamic University (IMSIU) 11432 Riyadh Saudi Arabia. TH-PPM Group Physics Department Faculty of Sciences Beni-Suef University Beni Suef 62514 Egypt.

This study explores annular one-dimensional (1D) photonic crystals (PCs) in detail as potentially useful instruments for applications involving salinity detection. The distinctive configuration of annular 1D PCs facilitates the containment and manipulation of light within a concise and unified framework, facilitating the creation of compact and portable sensing apparatus suitable for on-site applications and real-time monitoring. Unlike conventional planar and texturing-based PC sensors, the annular configuration enhances light confinement and defect mode engineering, leading to superior sensing performance. The study describes the simulation process that uses the COMSOL Multiphysics technique and the Finite Element Method (FEM) to create annular 1D PCs, underscoring the criticality of precise management of layer thickness and uniformity. Wherein, the structure of a 1D- annular PC is created as [Formula: see text], since A represents Silicon dioxide ([Formula: see text] and B signifies Titanium dioxide ([Formula: see text]) with material thicknesses set at 850 nm for each. D represents the central defect layer from saline water, which equals 3400 nm, and N equals 5. Hence, we achieve an exceptional sensitivity of 1910.6 nm/RIU, surpassing most reported 1D-PC salinity sensors. Also, the materials used in our design ([Formula: see text] are highly chemically and mechanically stable which resistant to etching in the saline water. Furthermore, we discuss the feasibility of fabricating the proposed sensor using advanced nanofabrication techniques, ensuring its practical implementation in environmental and biomedical monitoring applications.

关键词： And salinity sensor Annular one-dimensional (1D) photonic crystals (PCs) COMSOL multiphysics Photonic band gap

Health burden and inequities of urban environmental stressors in Sofia, Bulgaria

学校读者我要写书评

暂无评论

Environmental Research 2025年第Pt 1期279卷 121782页

作者： Khomenko, Sasha Burov, Angel Dzhambov, Angel M. de Hoogh, Kees Helbich, Marco Mijling, Bas Hlebarov, Ivaylo Popov, Ivaylo Dimitrova, Donka Dimitrova, Reneta Markevych, Iana Germanova, Nevena Brezov, Danail Iungman, Tamara Montana, Federica Chen, Xuan Gehring, Ulrike Khreis, Haneen Mueller, Natalie Zapata-Diomedi, Belen Zhang, Jiawei Nieuwenhuijsen, Mark Barcelona Spain Barcelona Spain Madrid Spain Health and Quality of Life in a Green and Sustainable Environment Research Group Strategic Research and Innovation Program for the Development of MU – Plovdiv Medical University of Plovdiv Plovdiv Bulgaria Environmental Health Division Research Institute at Medical University of Plovdiv Medical University of Plovdiv Plovdiv Bulgaria Department of Urban Planning Faculty of Architecture University of Architecture Civil Engineering and Geodesy Sofia Bulgaria Swiss Tropical and Public Health Institute Allschwil Switzerland University of Basel Basel Switzerland Department of Human Geography and Spatial Planning Faculty of Geosciences Utrecht University Utrecht Netherlands De Bilt Netherlands Clean Air Team Environmental Association Za Zemiata/FoE Bulgaria Department of Health Management and Health Economics Faculty of Public Health Medical University of Plovdiv Plovdiv Bulgaria Department of Meteorology and Geophysics Faculty of Physics Sofia University "St. Kliment Ohridski" Bulgaria National Institute of Geophysics Geodesy and Geography Bulgarian Academy of Sciences Bulgaria Institute of Psychology Jagiellonian University Krakow Poland Department of Spatial and Strategic Planning of Sofia Municipality Sofiaplan Bulgaria Department of Mathematics Faculty of Transportation Engineering University of Architecture Civil Engineering and Geodesy Bulgaria Utrecht University Utrecht Netherlands MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge United Kingdom Texas A&M Transportation Institute Texas A&M University System College StationTX United States Department of Public Health University of Copenhagen Copenhagen Denmark

Background: The number of studies on the health impacts of urban environmental stressors has been growing. However, research for South-Eastern Europe remains limited. We conducted a baseline Health Impact Assessment for Sofia, Bulgaria, focusing on air pollution, green space, road-traffic noise, and urban heat island (UHI) exposure aiming to promote discussions on sustainable, health-centric urban and transport planning policies. Methods: The analysis was conducted at the neighbourhood level (n = 4969). The study population included 1,168,382 inhabitants, considering adults and children. Data were retrieved from Europe-wide and local exposure models, local censuses and surveys. We used comparative risk assessment methodology, comparing baseline with optimal scenarios for health, such as meeting World Health Organization (WHO) air quality and road-traffic noise guidelines, green space recommendations, and no UHI effect. We also examined exposure and health impact distributions by area-level socioeconomic status (SES). We approximated differences in baseline disease rates by SES using data from local surveys. The results were stratified by SES quartiles and analysed spatially using global and local bivariate Moran's I statistics, the latter to identify priority areas for intervention based on SES, environmental exposures, and health outcomes. Results: All Sofia residents lived in neighbourhoods where particulate matter with diameter ≤2.5 μm (PM2.5) and nitrogen dioxide (NO2) levels exceeded the WHO air quality guidelines, with mean noise levels of 62.2 dB(A) Lday, 77 % lacking sufficient availability of green space, and an average summer UHI of 2.5 °C. The largest mortality burden was from PM2.5 exposure (1939 annual deaths (95 % CI: 1349–2571)), followed by NO2 (1172 annual deaths (444-2027)), road-traffic noise (902 annual deaths (556-1311)), insufficient green space (217 annual deaths (169–262)), and UHI (95 summer deaths (58–130)). PM2.5 contributed to 17–21 % of

关键词： Air quality

Climate, food and humans predict communities of mammals in the United States

学校读者我要写书评

暂无评论

Diversity and Distributions 2024年第9期30卷 e13900-e13900页

作者： Kays, Roland Snider, Matthew H. Hess, George Cove, Michael V. Jensen, Alex Shamon, Hila McShea, William J. Rooney, Brigit Allen, Maximilian L. Pekins, Charles E. Wilmers, Christopher C. Pendergast, Mary E. Green, Austin M. Suraci, Justin Leslie, Matthew S. Nasrallah, Sophie Farkas, Dan Jordan, Mark Grigione, Melissa LaScaleia, Michael C. Davis, Miranda L. Hansen, Chris Millspaugh, Josh Lewis, Jesse S. Havrda, Michael Long, Robert Remine, Kathryn R. Jaspers, Kodi J. Lafferty, Diana J. R. Hubbard, Tru Studds, Colin E. Barthelmess, Erika L. Andy, Katherine Romero, Andrea Hawkins, Melissa T. R. Lombardi, Jason V. Sergeyev, Maksim Fisher-Reid, M. Caitlin Rentz, Michael S. Nagy, Christopher Davenport, Jon M. Rega-Brodsky, Christine C. Appel, Cara L. Lesmeister, Damon B. Giery, Sean T. Whittier, Christopher A. Alston, Jesse M. Sutherland, Chris Rota, Christopher Murphy, Thomas Lee, Thomas E. Mortelliti, Alessio Bergman, Dylan L. Compton, Justin A. Gerber, Brian D. Burr, Jess Rezendes, Kylie DeGregorio, Brett A. Wehr, Nathaniel H. Benson, John F. Jachowski, David S. Gray, Morgan Beyer, Dean E. Belant, Jerrold L. Horan, Robert V. Lonsinger, Robert C. Kuhn, Kellie M. Hasstedt, Steven C. M. Zimova, Marketa Moore, Sophie M. Herrera, Daniel J. Fritts, Sarah Edelman, Andrew J. Flaherty, Elizabeth A. Petroelje, Tyler R. Neiswenter, Sean A. Risch, Derek R. Iannarilli, Fabiola van der Merwe, Marius Maher, Sean P. Farris, Zach J. Webb, Stephen L. Mason, David S. Lashley, Marcus A. Wilson, Andrew M. Vanek, John P. Wehr, Samuel R. Conner, L. Mike Beasley, James C. Bontrager, Helen L. Baruzzi, Carolina Ellis-Felege, Susan N. Proctor, Mike D. Schipper, Jan Weiss, Katherine C. B. Darracq, Andrea K. Barr, Evan G. Alexander, Peter D. Şekercioğlu, Çağan H. Bogan, Daniel A. Schalk, Christopher M. Fantle-Lepczyk, Jean E. Lepczyk, Christopher A. LaPoint, Scott Whipple, Laura S. Rowe, Helen Ivy Mullen, Kayleigh Bird, Tori Zorn, Adam Brandt, LaRoy Lathrop, Richard G. McCain, Craig Crupi, Anthony P. Clark, James Parsons, Arielle Department of Forestry and Environmental Resources North Carolina State University Raleigh NC United States North Carolina Museum of Natural Sciences Raleigh NC United States Department of Forestry and Environmental Conservation Clemson University Clemson SC United States Smithsonian's National Zoo and Conservation Biology Institute Front Royal VA United States Illinois Natural History Survey Prairie Research Institute University of Illinois Champaign IL United States Fort Hood Natural Resources Management Branch United States Army Garrison Fort Hood TX United States Environmental Studies Department Center for Integrated Spatial Research University of California Santa Cruz Santa Cruz CA United States Science Research Initiative University of Utah Salt Lake City UT United States Sageland Collaborative Salt Lake City UT United States Conservation Science Partners Inc. Truckee CA United States National Climate Adaptation Science Center United States Geological Survey Reston VA United States Biology Department Swarthmore College Swarthmore PA United States Entomology Department Cornell University Ithaca NY United States Information Technology Department Pace University Pleasantville NY United States Seattle University Department of Biology Seattle WA United States Department of Biology Pace University Pleasantville NY United States Department of Ecology and Evolutionary Biology Storrs CT United States Wildlife Biology Program Department of Ecosystem and Conservation Sciences University of Montana Missoula MT United States Department of Forest and Environmental Biology SUNY-ESF Syracuse NY United States Arizona State University College of Integrative Sciences and Arts Mesa AZ United States University of Washington School of Environmental and Forest Sciences Seattle WA United States Woodland Park Zoo Seattle WA United States Department of Biology Wildlife Ecology and Conservation Science Lab Northern Michigan University Marquette M

Aim: The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. Location: Conterminous United States. Time Period: 2010–2021. Major Taxa Studied: Twenty-five species of mammals. Methods: We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. Results: Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main Conclusions: Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities o

关键词： climate macroecology mammal communities masting species distribution models