Research on Multi-Agent Simulation of Epidemic News Spread Characteristics

The spread of news about an epidemic can easily lead to a social panic. In order to devise measures to control such a panic, it is necessary to consider characteristics of the spread of epidemic news, based on mechanisms at the individual level. In this paper, first, some features of multi-agent simulation are reviewed. Then a multi-agent simulation model of epidemic news spread (ENS) is designed and realized. Based on simulation experiments and sensitivity analyses, the influence of social relationships, the degree of trust in news of the epidemic, the epidemic spread intensity and the network structure of the epidemic news spread are studied. The research results include: (1) As the number of social relationships increases, the rate of spread of epidemic news rapidly rises, and the ratio of people who have heard the news directly decreases. The result is that the \'radiation effect\' of the epidemic news spread will be enhanced when the number of social relationships increases. (2) With the increase of the degree of trust in the news, the rate of spread of the news will also rapidly increase, but variation in the ratio of the people who have heard the news directly is not significant. This means that the \'radiation effect\' of the spread of the news does not change much more in relation to the degree of trust in the epidemic news. (3) The ratio of the people who have heard the news directly increases when the infection range increases (i.e. the spread intensity of epidemic increases), and vice versa. But the variation of the speed of the epidemic news spread is not significant. (4) When the network structure is assumed to be a small world network, the spread speed will be slower than that in a random network with the same average vertex degree and the forgetting speed will be faster than that in a random network with the same average vertex degree.Multi-Agent Simulation, News Spread, Small World Network , Epidemic

A Dynamic Intelligent Decision Approach to Dependency Modeling of Project Tasks in Complex Engineering System Optimization

Complex engineering system optimization usually involves multiple projects or tasks. On the one hand, dependency modeling among projects or tasks highlights structures in systems and their environments which can help to understand the implications of connectivity on different aspects of system performance and also assist in designing, optimizing, and maintaining complex systems. On the other hand, multiple projects or tasks are either happening at the same time or scheduled into a sequence in order to use common resources. In this paper, we propose a dynamic intelligent decision approach to dependency modeling of project tasks in complex engineering system optimization. The approach takes this decision process as a two-stage decision-making problem. In the first stage, a task clustering approach based on modularization is proposed so as to find out a suitable decomposition scheme for a large-scale project. In the second stage, according to the decomposition result, a discrete artificial bee colony (ABC) algorithm inspired by the intelligent foraging behavior of honeybees is developed for the resource constrained multiproject scheduling problem. Finally, a certain case from an engineering design of a chemical processing system is utilized to help to understand the proposed approach

An Improved Approach to Automatic Sketching of Mechanism Based

Abstract: An improved approach to automatic sketching of planar kinematic chain from its adjacent matrix is proposed in this paper. The basic loops of the graph, which correspond to the adjacent matrix of kinematic chain, are derived by means of breadth-first spanning tree, then these loops are standardized and configured according to some steps. Finally, the graph of kinematic chain is automatically sketched in terms of the configured loops. In addition, the comparison between the proposed approach and existing ones is made. Through some application examples, its effectiveness as well as completeness is verified, the corresponding computer prototype system is also implemented

A Resilience Approach to Symbiosis Networks of Ecoindustrial Parks Based on Cascading Failure Model

Evaluation and improvement of resilience in ecoindustrial parks have been the pressing issues to be addressed in the study of safety. In this paper, eco-industrial systems are extracted as symbiosis networks by using social network analysis first. We then construct a novel cascading failure model and propose an evaluation method of node importance according to the features of symbiosis networks of eco-industrial parks. Based on the cascading model, an effective new method, that is, the critical threshold, is put forward to quantitatively assess the resilience of symbiosis networks of eco-industrial parks. Some theoretical analysis is furthermore provided to the critical threshold. Finally, we take Jinjie eco-industrial system in Shanxi Province of China as a case to investigate its resilience. The key potential nodes are identified by using our model. We also find the respective relation among the resilience of symbiosis networks and the parameters in our cascading model. Theoretical analysis results and numerical simulations both show the optimal value of the tunable load parameter with which the strongest resilience level against cascading failures can be attained in symbiosis networks of eco-industrial parks

Paeonol Ameliorates Glucose and Lipid Metabolism in Experimental Diabetes by Activating Akt

Our previous study proved that paeonol (Pae) could lower blood glucose levels of diabetic mice. There are also a few reports of its potential use for diabetes treatment. However, the role of Pae in regulating glucose and lipid metabolism in diabetes remains largely unknown. Considering the critical role of serine/threonine kinase B (Akt) in glucose and lipid metabolism, we explored whether Pae could improve glucose and lipid metabolism disorders via Akt. Here, we found that Pae attenuated fasting blood glucose, glycosylated serum protein, serum cholesterol and triglyceride (TG), hepatic glycogen, cholesterol and TG in diabetic mice. Moreover, Pae enhanced glucokinase (GCK) and low-density lipoprotein receptor (LDLR) protein expressions, and increased the phosphorylation of Akt. In insulin-resistant HepG2 cells, Pae increased glucose uptake and decreased lipid accumulation. What’s more, Pae elevated LDLR and GCK expressions as well as Akt phosphorylation, which was consistent with the in vivo results. Knockdown and inhibition experiments of Akt revealed that Pae regulated LDLR and GCK expressions through activation of Akt. Finally, molecular docking assay indicated the steady hydrogen bond was formed between Pae and Akt2. Experiments above suggested that Pae ameliorated glucose and lipid metabolism disorders and the underlying mechanism was closely related to the activation of Akt

P-MaNGA : full spectral fitting and stellar population maps from prototype observations

MC acknowledges support from a Royal Society University Research Fellowship.MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is a 6-yearSDSS-IV survey that will obtain resolved spectroscopy from 3600 Å to10300 Å for a representative sample of over 10,000 nearby galaxies.In this paper, we derive spatially resolved stellar population properties and radial gradients by performing full spectral fitting of observed galaxy spectra from P-MaNGA, a prototype of the MaNGA instrument. These data include spectra for eighteen galaxies, covering a large range of morphological type. We derive age, metallicity, dust and stellar mass maps, and their radial gradients, using high spectral-resolution stellar population models, and assess the impact of varying the stellar library input to the models. We introduce a method to determine dust extinction which is able to give smooth stellar mass maps even in cases of high and spatially non-uniform dust attenuation.With the spectral fitting we produce detailed maps of stellar population properties which allow us to identify galactic features among this diverse sample such as spiral structure, smooth radial profiles with little azimuthal structure in spheroidal galaxies, and spatially distinct galaxy sub-components. In agreement with the literature, we find the gradients for galaxies identified as early-type to be on average flat in age, and negative (- 0.15 dex / Re ) in metallicity,whereas the gradients for late-type galaxies are on average negative in age (- 0.39 dex / Re ) and flat in metallicity. We demonstrate howdifferent levels of data quality change the precision with which radialgradients can be measured. We show how this analysis, extended to thelarge numbers of MaNGA galaxies, will have the potential to shed lighton galaxy structure and evolution.PostprintPeer reviewe

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

SDSS-IV/MaNGA: Spectrophotometric Calibration Technique

Mapping Nearby Galaxies at Apache Point Observatory (MaNGA), one of three core programs in the Sloan Digital Sky Survey-IV, is an integral-field spectroscopic survey of roughly 10,000 nearby galaxies. It employs dithered observations using 17 hexagonal bundles of 2'' fibers to obtain resolved spectroscopy over a wide wavelength range of 3600–10300 Å. To map the internal variations within each galaxy, we need to perform accurate spectral surface photometry, which is to calibrate the specific intensity at every spatial location sampled by each individual aperture element of the integral field unit. The calibration must correct only for the flux loss due to atmospheric throughput and the instrument response, but not for losses due to the finite geometry of the fiber aperture. This requires the use of standard star measurements to strictly separate these two flux loss factors (throughput versus geometry), a difficult challenge with standard single-fiber spectroscopy techniques due to various practical limitations. Therefore, we developed a technique for spectral surface photometry using multiple small fiber-bundles targeting standard stars simultaneously with galaxy observations. We discuss the principles of our approach and how they compare to previous efforts, and we demonstrate the precision and accuracy achieved. MaNGA's relative calibration between the wavelengths of Hα and Hβ has an rms of 1.7%, while that between [N ii] λ6583 and [O ii] λ3727 has an rms of 4.7%. Using extinction-corrected star formation rates and gas-phase metallicities as an illustration, this level of precision guarantees that flux calibration errors will be sub-dominant when estimating these quantities. The absolute calibration is better than 5% for more than 89% of MaNGA's wavelength range

Overview of the SDSS-IV MaNGA survey: mapping nearby galaxies at Apache Point Observatory

We present an overview of a new integral field spectroscopic survey called MaNGA (Mapping Nearby Galaxies at Apache Point Observatory), one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV) that began on 2014 July 1. MaNGA will investigate the internal kinematic structure and composition of gas and stars in an unprecedented sample of 10,000 nearby galaxies. We summarize essential characteristics of the instrument and survey design in the context of MaNGA's key science goals and present prototype observations to demonstrate MaNGA's scientific potential. MaNGA employs dithered observations with 17 fiber-bundle integral field units that vary in diameter from 12'' (19 fibers) to 32'' (127 fibers). Two dual-channel spectrographs provide simultaneous wavelength coverage over 3600-10300 Å at R ~ 2000. With a typical integration time of 3 hr, MaNGA reaches a target r-band signal-to-noise ratio of 4-8 (Å–1 per 2'' fiber) at 23 AB mag arcsec–2, which is typical for the outskirts of MaNGA galaxies. Targets are selected with M * 109 M ☉ using SDSS-I redshifts and i-band luminosity to achieve uniform radial coverage in terms of the effective radius, an approximately flat distribution in stellar mass, and a sample spanning a wide range of environments. Analysis of our prototype observations demonstrates MaNGA's ability to probe gas ionization, shed light on recent star formation and quenching, enable dynamical modeling, decompose constituent components, and map the composition of stellar populations. MaNGA's spatially resolved spectra will enable an unprecedented study of the astrophysics of nearby galaxies in the coming 6 yr