6,218 research outputs found
Remote Sensing/gis Integration for Site Planning and Resource Management
The development of an interactive/batch gridded information system (array of cells georeferenced to USGS quad sheets) and interfacing application programs (e.g., hydrologic models) is discussed. This system allows non-programer users to request any data set(s) stored in the data base by inputing any random polygon's (watershed, political zone) boundary points. The data base information contained within this polygon can be used to produce maps, statistics, and define model parameters for the area. Present/proposed conditions for the area may be compared by inputing future usage (land cover, soils, slope, etc.). This system, known as the Hydrologic Analysis Program (HAP), is especially effective in the real time analysis of proposed land cover changes on runoff hydrographs and graphics/statistics resource inventories of random study area/watersheds
Site participation in the small community experiment
The Small Community Solar Thermal Experiment, planned to test a small, developmental solar thermal power plant in a small community application, is assessed. The baseline plan is to install a field of parabolic dishes with distributed generation to provide 1 MWe of experimental power. Participation by the site proposer is an integral element of the experiment; the proposer will provide a ten-acre site, a connection to the electrical distributional system serving the small community, and various services. In addition to the primary participant, site study efforts may be pursued at as many as five alternative sites
Exponential-family Random Network Models
Random graphs, where the connections between nodes are considered random
variables, have wide applicability in the social sciences. Exponential-family
Random Graph Models (ERGM) have shown themselves to be a useful class of models
for representing com- plex social phenomena. We generalize ERGM by also
modeling nodal attributes as random variates, thus creating a random model of
the full network, which we call Exponential-family Random Network Models
(ERNM). We demonstrate how this framework allows a new formu- lation for
logistic regression in network data. We develop likelihood-based inference for
the model and an MCMC algorithm to implement it. This new model formulation is
used to analyze a peer social network from the National Lon- gitudinal Study of
Adolescent Health. We model the relationship between substance use and
friendship relations, and show how the results differ from the standard use of
logistic regression on network data
Simulated effects of climate change on the production pattern of winter cauliflower in the UK
Parameterizing by the Number of Numbers
The usefulness of parameterized algorithmics has often depended on what
Niedermeier has called, "the art of problem parameterization". In this paper we
introduce and explore a novel but general form of parameterization: the number
of numbers. Several classic numerical problems, such as Subset Sum, Partition,
3-Partition, Numerical 3-Dimensional Matching, and Numerical Matching with
Target Sums, have multisets of integers as input. We initiate the study of
parameterizing these problems by the number of distinct integers in the input.
We rely on an FPT result for ILPF to show that all the above-mentioned problems
are fixed-parameter tractable when parameterized in this way. In various
applied settings, problem inputs often consist in part of multisets of integers
or multisets of weighted objects (such as edges in a graph, or jobs to be
scheduled). Such number-of-numbers parameterized problems often reduce to
subproblems about transition systems of various kinds, parameterized by the
size of the system description. We consider several core problems of this kind
relevant to number-of-numbers parameterization. Our main hardness result
considers the problem: given a non-deterministic Mealy machine M (a finite
state automaton outputting a letter on each transition), an input word x, and a
census requirement c for the output word specifying how many times each letter
of the output alphabet should be written, decide whether there exists a
computation of M reading x that outputs a word y that meets the requirement c.
We show that this problem is hard for W[1]. If the question is whether there
exists an input word x such that a computation of M on x outputs a word that
meets c, the problem becomes fixed-parameter tractable
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