1,721,458 research outputs found
Phase transitions in sexual populations subject to stabilising selection
No full textIn this paper we show that a simple model of an evolving sexual population, which dates back to some of the earliest work in theoretical population genetics, exhibits an unexpected and previously unobserved phase transition between ordered and disordered states. This behavior is not present in populations evolving asexually without recombination and is thus important in any comparison of sexual and asexual populations. In order to calculate the details of the phase transition, we use techniques from statistical physics. We introduce the correlation of the population as the order parameter of the system and use maximum entropy inference to find the state of the population at any time
Petroleum Engineering, Richard Thrasher, Chris Rendeiro, Rudy Rogers, Alex Vadie, David Sawyer
Full text linkPetroleum Engineering faculty members shown from left to right: Richard Thrasher, Chris Rendeiro, Rudy Rogers, Alex Vadie, and David Sawyerhttps://scholarsjunction.msstate.edu/ua-photo-collection/5262/thumbnail.jp
AudioMoth Hardware
No full textHardware design files for AudioMoth. Data files are are hosted on circuithub.com, with a closed copy archived within the university.
AudioMoth is a low-cost, full-spectrum acoustic logger, based on the Silicon Labs Gecko processor range. Just like its namesake, AudioMoth can listen for sound from audible frequencies, well into ultrasonic frequencies.
The research was funded through:
EPSRC Studentship (1658469) to A. Hill</span
Modelling Driver Interdependent Behaviour in Agent-Based Traffic Simulations for Disaster Management
No full textAccurate modelling of driver behaviour in evacuations is vitally important in creating realistic training environments for disaster management. However, few current models have satisfactorily incorporated the variety of factors that affect driver behaviour. In particular, the interdependence of driver behaviours is often seen in real-world evacuations, but is not represented in current state-of-the art traffic simulators. To address this shortcoming, we present an agent-based behaviour model based on the social forces model of crowds. Our model uses utility-based path trees to represent the forces which affect a driver's decisions. We demonstrate, by using a metric of route similarity, that our model is able to reproduce the real-life evacuation behaviour whereby drivers follow the routes taken by others. The model is compared to the two most commonly used route choice algorithms, that of quickest route and real-time re-routing, on three road networks: an artificial "ladder" network, and those of Lousiana, USA and Southampton, UK. When our route choice forces model is used our measure of route similarity increases by 21%-93%. Furthermore, a qualitative comparison demonstrates that the model can reproduce patterns of behaviour observed in the 2005 evacuation of the New Orleans area during Hurricane Katrina
Intelligent Agents for the Smart Grid
No full textMeeting the challenge of cutting global greenhouse gas emissions by 50% by 2050, and ensuring energy security in the face of dwindling oil and gas reserves, requires a radical change in the way energy (and particularly electricity) is generated, distributed and consumed. Central to delivering this change, is the vision of a smart electrical distribution network (the Smart Grid) within which micro-generation and storage capabilities are ubiquitous, where intelligent sensing devices allow users to make informed choices about the control of devices in their home, and where producers and consumers are connected via a series of dynamically negotiated supply contracts. In this article, we describe why we believe intelligent agents are essential to delivering on the vision of a Smart Grid, and describe some results applying agents to the problem of coordinating micro-storage within a model of the smart grid
SnapperGPS: Collection of GNSS Signal Snapshots
No full textThis data collection contains digital global navigation satellite system (GNSS) signal snapshots and is accompanied by a repository with utilities to simplify working with the files, which you can find at https://github.com/JonasBchrt/snapshot-gnss-data.
We recorded the data in 2020 and 2021 using three of our SnapperGPS low-cost receivers, whose core components are an Echo 27 GPS L1 antenna and an SE4150L integrated GPS receiver circuit. Like most civilian low-cost GPS receivers, SnapperGPS operates in the L1 band with a centre frequency of 1.57542 GHz. However, Galileo's E1 signal, BeiDou's B1C signal, GPS' novel L1C signal, and SBAS' L1 signal have the identical centre frequency. So, we captured those signals, too. A SnapperGPS receiver down-mixes the incoming signal to a nominal intermediate frequency of 4.092 MHz, samples the resulting near-baseband signal at 4.092 MHz and digitises it with an amplitude resolution of one bit per sample. It considers only the in-phase component and discards the quadrature component.
The data collection consists of four static and seven dynamic tests under various conditions with 3700 GNSS signal snapshots in total.
We captured the 225 static snapshots on a hill top, on a bridge, in a courtyard, and in a park in 5-30 s intervals and the 3475 dynamic ones while cycling in either urban or rural environments and using 10 s intervals.
We obtained ground truth locations or tracks either by using an Ordnance Survey trig point, by employing satellite imagery from Google Maps or Google Earth, or with a Moto C smartphone with built-in GPS and A-GPS receiver.
While the trig point provides a ground-truth position with centimetre-level accuracy, the positions obtained from satellite imagery or with the Moto C are up to 5 m wrong with outliers up to 10 m.
The eleven datasets are stored in one folder per set named "A"-"K". Each snapshot is in a single binary ".bin" file with a name derived from the timestamp. One byte of the file holds the amplitude values of eight signal samples, i.e., the first byte holds the first eight samples. A zero bit represents a signal amplitude of +1 and a one bit a signal amplitude of -1. The order of the bits is 'little', i.e., reversed. For example, the byte 0b01100000 corresponds to the signal chunk [1 1 1 1 1 -1 -1 1]. In addition to the raw GNSS signal snapshots, you can find more data in a single "meta.json" file in each folder. The JSON struct in this file provides approximate latitude and longitude of the ground truth location of a static test in decimal degrees, an estimate of the true intermediate frequency in Hertz (the actual value differs from the nominal 4.092 MHz due to imprecisions of the hardware), all the file names of the binary files, the UTC timestamps of all files, and optionally temperature and pressure measurements from an on-board BMP280 sensor in degrees Celsius and pascal, respectively. Furthermore, a ".gpx" or ".kml" file holds the ground truth track for a dynamic test as nodes of a polyline. (Folder "I" contains two files that represent the first and the second part of the track, respectively.) Finally, each folder incorporates the broadcasted satellite navigation data from the respective day as RINEX 3.04 ".rnx" file downloaded from NASA's archive (https://cddis.nasa.gov/archive/gnss/data/daily/). The RINEX files allow to calculate, e.g., satellite orbits and clock corrections for all GNSS.
The datasets:
"A": 181 snapshots, static, hill top, ground truth from trig point, no temperatures & pressures
"B": 14 snapshots, static, bridge, ground truth from Google Maps, no temperatures & pressures
"C": 6 snapshots, static, courtyard, ground truth from Google Maps, no temperatures & pressures
"D": 24 snapshots, static, park, ground truth from Google Maps, incl. temperatures & pressures
"E": 380 snapshots, dynamic, urban, ground truth from Google Earth, incl. temperatures & pressures
"F": 339 snapshots, dynamic, urban, ground truth from Google Earth, incl. temperatures & pressures
"G": 693 snapshots, dynamic, urban/rural, ground truth from Google Earth, incl. temperatures & pressures
"H": 628 snapshots, dynamic, urban, ground truth from Moto C, incl. temperatures & pressures
"I": 1023 snapshots, dynamic, urban/rural, ground truth from Google Earth / Moto C, incl. temperatures & pressures
"J": 346 snapshots, dynamic, urban/rural, ground truth from Moto C, incl. temperatures & pressures
"K": 66 snapshots, dynamic, urban, ground truth from Moto C, incl. temperatures & pressure
Modelling Genetic Algorithms and Evolving Populations
No full textA formalism for modelling the dynamics of genetic algorithms using methods from statistical physics, originally due to Pr¨ugel-Bennett and Shapiro, is extended to ranking selection, a form of selection commonly used in the genetic algorithm community. The extension allows a reduction in the number of macroscopic variables required to model the mean behaviour of the genetic algorithm. This reduction allows a more qualitative understanding of the dynamics to be developed without sacrificing quantitative accuracy. The work is extended beyond modelling the dynamics of the genetic algorithm. A caricature of an optimisation problem with many local minima is considered — the basin with a barrier problem. The first passage time — the time required to escape the local minima to the global minimum — is calculated and insights gained as to how the genetic algorithm is searching the landscape. The interaction of the various genetic algorithm operators and how these interactions give rise to optimal parameters values is studied
Listening to the forest and its curators: lessons learnt from a bioacoustic smartphone application deployment
No full textOur natural environment is complex and sensitive, and is home to a number of species on the verge of extinction. Surveying is one approach to their preservation, and can be supported by technology. This paper presents the deployment of a smartphone-based citizen science biodiversity application. Our findings from interviews with members of the biodiversity community revealed a tension between the technology and their established working practices. From our experience, we present a series of general guidelines for those designing citizen science apps
Full Citation
Moran, Stuart, Pantidi, Nadia, Rodden, Tom, Chamberlain, Alan, Griffiths, Chloe, Zilli, Davide, Merrett, Geoff V. and Rogers, Alex (2014) Listening to the forest and its curators: lessons learnt from a bioacoustic smartphone application deployment. In, ACM CHI Conference on Human Factors in Computing Systems, Toronto, CA, 26 Apr - 01 May 2014. (doi:10.1145/2556288.255702)
Provenance of Decisions in Emergency Response Environments
No full textMitigating the devastating ramifications of major disasters requires emergency workers to respond in a maximally efficient way. Information systems can improve their efficiency by organizing their efforts and automating many of their decisions. However, absence of documenting how decisions were made by the system prevents decisions from being reviewed to check the reasons for their making or their compliance with policies. We apply the concept of provenance to decision making in emergency response situations and use the Open Provenance Model to express provenance produced in RoboCup Rescue Simulation. We produce provenance DAGs using a novel OPM profile that conceptualizes decisions in the context of emergency response. Finally, we traverse the OPM DAGs to answer some provenance questions about those decisions
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