A hybrid case-based model for forecasting

An investigation is described into the application of artificial intelligence to forecasting in the domain of oceanography. A hybrid approach to forecasting the thermal structure of the water ahead of a moving vessel is presented which combines the ability of a case-based reasoning system for identifying previously encountered similar situations and the generalizing ability of an artificial neural network to guide the adaptation stage of the case-based reasoning mechanism. The system has been successfully tested in real time in the Atlantic Ocean; the results obtained are presented and compared with those derived from other forecasting methods

Adaptation of Cases for Case Based Forecasting with Neural Network Support

A novel approach to the combination of a case based reasoning system and an artificial neural network is presented in which the neural network is integrated within the case based reasoning cycle so that its generalizing ability may be harnessed to provide improved case adaptation performance. The ensuing hybrid system has been applied to the task of oceanographic forecasting in a real-time environment and has produced very promising results. After presenting classifications of hybrid artificial intelligence problem-solving methods, the particular combination of case based reasoning and neural networks, as a problem-solving strategy, is discussed in greater depth. The hybrid artificial intelligence forecasting model is then explained and the experimental results obtained from trials at sea are outlined

Historical Fire Detection of Tropical Forest from NDVI Time-series Data: Case Study on Jambi, Indonesia

In addition to forest encroachment, forest fire is a serious problem in Indonesia. Attempts at managing its widespread and frequent occurrence has led to intensive use of remote sensing data. Coarse resolution images have been employed to derive hot spots as an indicator of forest fire. However, most efforts to verify the hot spot data and to verify fire accidents have been restricted to the use of medium or high resolution data. At present, it is difficult to verify solely upon those data due to severe cloud cover and low revisit time. In this paper, we present a method to validate forest fire using NDVI time series data. With the freely available NDVI data from SPOT VEGETATION, we successfully detected changes in time series data which were associated with fire accidents

Hybrid Instance-bsed system for predicting ocean temperatures

An instance-based problem solving model is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. The situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation it has been found that an instance-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The instance-based reasoning system incorporates a radial basis function artificial neural network for the instance adaptation. The results obtained from experiments, in which the system operated in real time in the oceanographic environment, are presented

"We are the heroes because we are ready to die for this country": Participants' decision-making and grounded ethics in an Ebola vaccine clinical trial.

The 2014-2016 Ebola epidemic presented a challenging setting in which to carry out clinical trials. This paper reports findings from social science research carried out in Kambia, Northern Sierra Leone during first year of an Ebola vaccine trial (August 2015-July 2016). The social science team collected data through ethnographic observation, 42 in depth interviews; 4 life narratives; 200 exit interviews; 31 key informant interviews; and 8 focus group discussions with trial participants and community members not enrolled in the trial. Whilst research often focuses on why people refuse vaccination, we instead explore participant motivations for volunteering for the study, in spite of prevailing anxieties, rumours and mistrust during and after the Ebola outbreak. In so doing the paper contributes to on-going debates about research ethics and community engagement in resource poor contexts, offering reflections from an emergency and post-epidemic setting. We analyse participants' perceptions of the risks and benefits of participations, highlighting the importance of a contextual approach. We focus on four types of motivation: altruism; curiosity and hope; health-seeking; and notions of exchange, and argue for the role of social science in developing grounded research ethics and community engagement strategies that can take into account context and local realities

The Grizzly, December 7, 1979

Faculty Approves Combined Majors • C&C Offered Next Semester • College Applies To Phi Beta Kappa • Vandalism Strikes Beardwood • Letters to the Editor • Roving Reporter: Final exams • Timely Displays Of Popular American Feeling • Music News: The Year In Music • Ursinus To Present Messiah • Alumnus Donates Sculpture • Friends Of The Library Hold Fall Meeting • College Institutes Math/Econ Program • Swimmers Rebound To Notch Initial Win • Penalty Flicks Hurt Hockey • Volleyball Club Shows Progresshttps://digitalcommons.ursinus.edu/grizzlynews/1030/thumbnail.jp

The structure of a resuscitation-promoting factor domain from Mycobacterium tuberculosis shows homology to lysozymes

Resuscitation-promoting factor (RPF) proteins reactivate stationary-phase cultures of (G+C)-rich Gram-positive bacteria including the causative agent of tuberculosis, Mycobacterium tuberculosis. We report the solution structure of the RPF domain from M. tuberculosis Rv1009 (RpfB) solved by heteronuclear multidimensional NMR. Structural homology with various glycoside hydrolases suggested that RpfB cleaved oligosaccharides. Biochemical studies indicate that a conserved active site glutamate is important for resuscitation activity. These data, as well as the presence of a clear binding pocket for a large molecule, indicate that oligosaccharide cleavage is probably the signal for revival from dormancy

The Grizzly, November 30, 1979

College Costs To Skyrocket • Committee Explores Campus Problems • Audiovisual Services Relocated in Myrin • USGA Notes • Letters to the Editor • Roving Reporter: Iranian Crisis • Portrait of a Professor: Dr. John Wickersham • JDB - Full of Surprises • Vonnegut\u27s \u27Jailbird\u27: Life of Walter Starbuck • Pilgrim To Speak On Economy • Cagers Open 79-80 Campaign With Victory • Gridders Win Finale With Shut-Out • Garner Races To 68th In Nation • Sports Profile: Rick Morris • Pool Records Sethttps://digitalcommons.ursinus.edu/grizzlynews/1029/thumbnail.jp

Utilizing "Omic" technologies to identify and prioritize novel sources of resistance to the oomycete pathogen <i>Phytophthora infestans</i> in potato germplasm collections

The biggest threat to potato production world-wide is late blight, caused by the oomycete pathogen Phytophthora infestans. A screen of 126 wild diploid Solanum accessions from the Commonwealth Potato Collection (CPC) with P. infestans isolates belonging to the genotype 13-A2 identified resistances in the species S. bulbocastanum, S. capsicibaccatum, S. microdontum, S. mochiquense, S. okadae, S. pinnatisectum, S. polyadenium, S. tarijense and S. verrucosum. Effector-omics, allele mining and diagnostic RenSeq (dRenSeq) were utilized to investigate the nature of resistances in S. okadae accessions. dRenSeq in resistant S. okadae accessions 7129, 7625, 3762 and a bulk of 20 resistant progeny confirmed the presence of full-length Rpi-vnt1.1 under stringent mapping conditions and corroborated allele mining results in the accessions 7129 and 7625 as well as Avr-vnt1 recognition in transient expression assays. In contrast, susceptible S. okadae accession 3761 and a bulk of 20 susceptible progeny lacked sequence homology in the 5’ end compared to the functional Rpi-vnt1.1 gene. Further evaluation of S. okadae accessions with late blight isolates that have a broad spectrum of virulence demonstrated that, although S. okadae accessions 7129, 7625 and 7629 contain functional Rpi-vnt1.1, they also carry a novel resistance gene. We provide evidence that existing germplasm collection are important sources of novel resistances and that ‘omic’ technologies such as dRenSeq-based genomics and effector-omics are efficacious tools to rapidly explore the diversity within these collections

Integrating BDI agents with Agent-based simulation platforms

Agent-Based Models (ABMs) is increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the "brains" of an agent can be modelled in the BDI system in the usual way, while the "body" exists in the ABM system. The architecture is exible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community