3,352 research outputs found
A RECREATION OPTIMIZATION MODEL BASED ON THE TRAVEL COST METHOD
A recreation allocation model is developed which efficiently selects recreation areas and degree of development from an array of proposed and existing sites. The model does this by maximizing the difference between gross recreation benefits and travel, investment, management, and site-opportunity costs. The model presented uses the Travel Cost Method for estimating recreation benefits within an operations research framework. The model is applied to selection of potential wilderness areas in Colorado. This example is then extended to show the model's capability in budget analysis and in planning to meet recreation targets.Resource /Energy Economics and Policy,
POTENTIAL PITFALLS IN RENEWABLE RESOURCE DECISION MAKING THAT UTILIZES CONVEX COMBINATIONS OF DISCRETE ALTERNATIVES
Decision makers in renewable resource planning are often unable to specify their objective function a priori, and are presented with a discrete set of alternatives reflecting a range of options that are actually much more continuous. It is common for the decision maker to be interested in some other alternative than those originally developed. An iterative process thus often takes place between decision maker an analyst as they search for a satisfactory alternative. This paper analyzes the economic tenability of simply interpolating (taking convex combinations of) initial alternatives to generate new alternatives in this process. It is shown that convex combinations of outputs will be producible (feasible) with the interpolated input levels, under very common conditions. In fact, the cost estimate resulting from interpolating the cost of two (or more) alternatives will generally be an overestimate. The magnitude of this overestimate is investigated in a test case. It is concluded that this cost overestimate can be rather large, and is not systematically predictable. Only when the output sets in the original alternatives are very similar are the interpolated cost estimates fairly accurate.Resource /Energy Economics and Policy,
Prediction error identification of linear dynamic networks with rank-reduced noise
Dynamic networks are interconnected dynamic systems with measured node
signals and dynamic modules reflecting the links between the nodes. We address
the problem of \red{identifying a dynamic network with known topology, on the
basis of measured signals}, for the situation of additive process noise on the
node signals that is spatially correlated and that is allowed to have a
spectral density that is singular. A prediction error approach is followed in
which all node signals in the network are jointly predicted. The resulting
joint-direct identification method, generalizes the classical direct method for
closed-loop identification to handle situations of mutually correlated noise on
inputs and outputs. When applied to general dynamic networks with rank-reduced
noise, it appears that the natural identification criterion becomes a weighted
LS criterion that is subject to a constraint. This constrained criterion is
shown to lead to maximum likelihood estimates of the dynamic network and
therefore to minimum variance properties, reaching the Cramer-Rao lower bound
in the case of Gaussian noise.Comment: 17 pages, 5 figures, revision submitted for publication in
Automatica, 4 April 201
Local module identification in dynamic networks with correlated noise: the full input case
The identification of local modules in dynamic networks with known topology
has recently been addressed by formulating conditions for arriving at
consistent estimates of the module dynamics, typically under the assumption of
having disturbances that are uncorrelated over the different nodes. The
conditions typically reflect the selection of a set of node signals that are
taken as predictor inputs in a MISO identification setup. In this paper an
extension is made to arrive at an identification setup for the situation that
process noises on the different node signals can be correlated with each other.
In this situation the local module may need to be embedded in a MIMO
identification setup for arriving at a consistent estimate with maximum
likelihood properties. This requires the proper treatment of confounding
variables. The result is an algorithm that, based on the given network topology
and disturbance correlation structure, selects an appropriate set of node
signals as predictor inputs and outputs in a MISO or MIMO identification setup.
As a first step in the analysis, we restrict attention to the (slightly
conservative) situation where the selected output node signals are predicted
based on all of their in-neighbor node signals in the network.Comment: Extended version of paper submitted to the 58th IEEE Conf. Decision
and Control, Nice, 201
Start-up inertia as an origin for heterogeneous flow
For quite some time non-monotonic flow curve was thought to be a requirement
for shear banded flows in complex fluids. Thus, in simple yield stress fluids
shear banding was considered to be absent. Recent spatially resolved
rheological experiments have found simple yield stress fluids to exhibit shear
banded flow profiles. One proposed mechanism for the initiation of such
transient shear banding process has been a small stress heterogeneity rising
from the experimental device geometry. Here, using Computational Fluid Dynamics
methods, we show that transient shear banding can be initialized even under
homogeneous stress conditions by the fluid start-up inertia, and that such
mechanism indeed is present in realistic experimental conditions
Π‘ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ Π°ΡΠΏΠ΅ΠΊΡΡ ΠΏΠ°ΡΠΎΠ³Π΅Π½Π΅Π·Π° ΠΈ Π»Π΅ΡΠ΅Π½ΠΈΡ ΡΠ½Π΄ΠΎΠΊΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π±Π΅ΡΠΏΠ»ΠΎΠ΄ΠΈΡ
ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΎ ΠΏΠ°ΡΠΎΠ³Π΅Π½Π΅Π·Π΅, ΠΏΡΠΈΡΠΈΠ½Π°Ρ
, ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΎΡΠΌΠ°Ρ
, Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΈ Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΡΠ½Π΄ΠΎΠΊΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π±Π΅ΡΠΏΠ»ΠΎΠ΄ΠΈΡ. ΠΠΏΠΈΡΠ°Π½Ρ ΠΏΡΠΈΡΠΈΠ½Ρ Π²ΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ ΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΠ½Π΄ΡΠΎΠΌΠ° ΠΏΠΎΠ»ΠΈΠΊΠΈΡΡΠΎΠ·Π½ΡΡ
ΡΠΈΡΠ½ΠΈΠΊΠΎΠ² ΠΈ ΡΡ
Π΅ΠΌΡ ΡΡΠΈΠΌΡΠ»ΡΡΠΈΠΈ ΠΎΠ²ΡΠ»ΡΡΠΈΠΈ.ΠΠ°Π²Π΅Π΄Π΅Π½ΠΎ Π»ΡΡΠ΅ΡΠ°ΡΡΡΠ½Ρ Π΄Π°Π½Ρ ΠΏΡΠΎ ΠΏΠ°ΡΠΎΠ³Π΅Π½Π΅Π·, ΠΏΡΠΈΡΠΈΠ½ΠΈ, ΠΊΠ»ΡΠ½ΡΡΠ½Ρ ΡΠΎΡΠΌΠΈ, Π΄ΡΠ°Π³Π½ΠΎΡΡΠΈΠΊΡ ΠΉ Π»ΡΠΊΡΠ²Π°Π½Π½Ρ Π΅Π½Π΄ΠΎΠΊΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π±Π΅Π·ΠΏΠ»ΡΠ΄Π΄Ρ. ΠΠΏΠΈΡΠ°Π½ΠΎ ΠΏΡΠΈΡΠΈΠ½ΠΈ Π²ΠΈΠ½ΠΈΠΊΠ½Π΅Π½Π½Ρ ΠΉ ΡΠΎΠ·Π²ΠΈΡΠΊΡ ΡΠΈΠ½Π΄ΡΠΎΠΌΡ ΠΏΠΎΠ»ΡΠΊΡΡΡΠΎΠ·Π½ΠΈΡ
ΡΡΡΠ½ΠΈΠΊΡΠ² Ρ ΡΡ
Π΅ΠΌΠΈ ΡΡΠΈΠΌΡΠ»ΡΡΡΡ ΠΎΠ²ΡΠ»ΡΡΡΡ.The literature data about the pathogenesis, causes, clinical forms, diagnosis, and treatment for endocrine infertility are reported. The causes and development of polycystic ovary syndrome as well as the schemes of ovulation stimulation are described
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