282,164 research outputs found
Maximum precision-lifetime curve for joint sensor selection and data routing in sensor networks
In many classes of monitoring applications employing battery-limited sensor networks, periodic sampling of an area with a given precision level is required. For such applications, we provide mathematical programming formulations for deriving the optimal trade-off curve between network lifetime and data precision, and design a practical heuristic for near-optimal operation. The properties of our models and the effectiveness of our heuristic are demonstrated by computational experiments
Formulation of a Mathematical Model for the Allocation of Colorado river Waters in Utah
A Mathematical model for the allocation of Utah’s water resources is formulated in the linear programming format. The availability of water from various sources is considered with the demands for water in each of the nine hydrologic study areas of Utah. The applications of mathematical models of this type are studied and the merits of the linear programming approach are discussed
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Network problems & algorythms
Special structure linear programming problems have received considerable attention during the last two decades and among them network problems are of particular importance and have found numerous applications in manage- ment science and technology.
The mathematical models of the shortest route, maximal flow, and pure minimum cost flow problems are presented and various interrelationships among them are investigated. Finally three algorithms due to Dijkstra and Ford and Fulkerson which deal with the solution of the above three network problems are discussed
Pesticide externalities from the US agricultural sector -- The impact of internalization, reduced pesticide application rates, and climate change
Pesticides used in agricultural production affect environmental quality and human health. These external costs can amplify due to climate change because pest pressure and optimal pesticide application rates vary with weather and climate conditions. This study uses mathematical programming to examine alternative assumptions about regulations of external costs from pesticide applications in US agriculture. We use two climate projections given by the Canadian and Hadley climate models. The impacts of the internalization of the pesticide externality and climate change are assessed both independently and jointly. We find that, without external cost regulation, climate change benefits from increased agricultural production in the US may be more than offset by increased environmental costs. The internalization of the pesticide externalities increase farmers’ production costs but increase farmers’ income because of price adjustments and associated welfare shifts from consumers to producers. Our results also show that full internalizations of external pesticide costs substantially reduces preferred pesticide applications rates for corn and soybeans as climate change.climate change impacts, pesticide externalities, farm management adaptation, agricultural sector model, welfare maximization, environmental policy analysis, mathematical programming, United States
Subtropical Real Root Finding
We describe a new incomplete but terminating method for real root finding for
large multivariate polynomials. We take an abstract view of the polynomial as
the set of exponent vectors associated with sign information on the
coefficients. Then we employ linear programming to heuristically find roots.
There is a specialized variant for roots with exclusively positive coordinates,
which is of considerable interest for applications in chemistry and systems
biology. An implementation of our method combining the computer algebra system
Reduce with the linear programming solver Gurobi has been successfully applied
to input data originating from established mathematical models used in these
areas. We have solved several hundred problems with up to more than 800000
monomials in up to 10 variables with degrees up to 12. Our method has failed
due to its incompleteness in less than 8 percent of the cases
Compartmental Modeling for the Neophyte: An Application of Berkeley Madonna
Compartmental modeling serves as a necessary framework in many fields, especially biomathematics and ecology. This article introduces readers to a user-friendly approach to constructing compartmental models and solving the resulting systems of differential equations to simulate real-world applications. The platform used is Berkeley Madonna, a software package that has an intuitive graphical interface which empowers users—even those with limited mathematical and programming backgrounds—to focus on modeling concepts rather than mathematical or programming intricacies. This makes Berkeley Madonna an ideal platform for students, educators, and researchers
Concrete Swelling in Existing Dams
Several chemical reactions are able to produce swelling of concrete for decades after its initial curing, a problem that affects a considerable number of concrete dams around the world. Principia has had several contracts to study this problem in recent years, which have required reviewing the state-of-the-art, adopting appropriate mathematical descriptions, programming them into user routines in Abaqus, determining model parameters on the basis of some parts of the dams’ monitored histories, ensuring reliability using some other parts, and finally predicting the future evolution of the dams and their safety margins. The paper describes some of the above experience, including the programming of sophisticated non-isotropic swelling models, that must be compatible with cracking and other nonlinearities involved in concrete behaviour. The applications concentrate on two specific cases, an archgravity dam and a double-curvature arch dam, both with a long history of concrete swelling and which, interestingly, entailed different degrees of success in the modelling effort
Design and development of financial applications using ontology-based multi-agent systems
Researchers in the field of finance now use increasingly sophisticated mathematical models that require intelligent software on high performance computing systems. Agent models to date that are designed for financial markets have their knowledge specified through low level programming that require technical expertise in software, not normally available with finance professionals. Hence there is a need for system development methodologies where domain experts and researchers and can specify the behaviour of the agent applications without any knowledge of the underlying agent software. This paper proposes an approach to achieve the above objectives through the use of ontologies that drive the behaviours of agents. This approach contributes towards the building of semantically aware intelligent services, where ontologies are used rather than low level programming to dictate the characteristics of the agent applications. This approach is expected to allow more extensive usage of multi-agent systems in financial business applications
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