5,749 research outputs found
A linear programming based heuristic framework for min-max regret combinatorial optimization problems with interval costs
This work deals with a class of problems under interval data uncertainty,
namely interval robust-hard problems, composed of interval data min-max regret
generalizations of classical NP-hard combinatorial problems modeled as 0-1
integer linear programming problems. These problems are more challenging than
other interval data min-max regret problems, as solely computing the cost of
any feasible solution requires solving an instance of an NP-hard problem. The
state-of-the-art exact algorithms in the literature are based on the generation
of a possibly exponential number of cuts. As each cut separation involves the
resolution of an NP-hard classical optimization problem, the size of the
instances that can be solved efficiently is relatively small. To smooth this
issue, we present a modeling technique for interval robust-hard problems in the
context of a heuristic framework. The heuristic obtains feasible solutions by
exploring dual information of a linearly relaxed model associated with the
classical optimization problem counterpart. Computational experiments for
interval data min-max regret versions of the restricted shortest path problem
and the set covering problem show that our heuristic is able to find optimal or
near-optimal solutions and also improves the primal bounds obtained by a
state-of-the-art exact algorithm and a 2-approximation procedure for interval
data min-max regret problems
IncidĂȘncia do Pineapple Mealybug wilt Associated VĂrus, PMWAV no Banco Ativo de Germoplasma de abacaxi in vitro da Embrapa Mandioca e Fruticultura.
O abacaxi (Ananas comosus var. comosus) Ă© uma das frutas tropicais mais apreciadas no mundo. O Brasil, como um dos centros de origem e diversidade genĂ©tica, tem se preocupado com a conservação de germoplasma desta importante fruteira. O abacaxizeiro por ser de propagação vegetativa, possui a vantagem da multiplicação clonal do material de plantio, entretanto, esta prĂĄtica favorece a disseminação de doenças como as viroses. O PMWaV (Pineapple mealybug wilt-associated virus) Ă© um vĂrus que infecta o abacaxi causando a doença denominada popularmente de ?Mucha do abacaxi?. O vĂrus transmitido pela cochonilha Dysmicoccus brevipes, e atualmente, acredita-se que a doença seja causada por um complexo viral, denominados PMWaV-1, PMWaV-2 e PMWaV-3, que se diferenciam pela sequĂȘncia e organização do genoma. O PMWV pertence a famĂlia Closteroviridae, gĂȘnero Ampelovirus, possui partĂcula alongada flexuosa e genoma de RNA fita simples com aproximadamente 14Kb. AlĂ©m dos danos diretos na produção da planta, a contaminação dos acessos do Banco Ativo de Germoplasma -BAG Ă© um fator preocupante. Diante disto, o objetivo deste trabalho foi a avaliar a incidĂȘncia do PMWaV-1,2,3 nos acesso do BAG in vitro da Embrapa Mandioca e Fruticultura.PDF. 094
Analytical results for long time behavior in anomalous diffusion
We investigate through a Generalized Langevin formalism the phenomenon of
anomalous diffusion for asymptotic times, and we generalized the concept of the
diffusion exponent. A method is proposed to obtain the diffusion coefficient
analytically through the introduction of a time scaling factor . We
obtain as well an exact expression for for all kinds of diffusion.
Moreover, we show that is a universal parameter determined by the
diffusion exponent. The results are then compared with numerical calculations
and very good agreement is observed. The method is general and may be applied
to many types of stochastic problem
The agro-industrial system regional sustainable development, a coherent strategy
The agro-industrial system represents annually circa 3,6 x 109 ⏠in the formation of the Centro
Region of Portugal gross income and that accounts for 39% of the Portuguese overall return for
this sector. Given this dynamics it is of utmost importance to perform a consistent strategy to
promote the sustainable growth of this regional system income.
Therefore, the CERNAS/IPC research unit has developed an integrated approach bringing
together several regional actors under a networking logic that links the industrial needs with the
academia R&D capabilities, and of capacity building and entrepreneurship (2011-2013).
This strategy is rooted in the InovCluster, where CERNAS leads two anchor projects, the
in_AGRI and the ECODEEP, and collaborates with a third one, the AGRITRAINING. The
in_AGRI aims the upgrade of the system value chains by bridging the academia with the
industry in a series of workshops, supported with a knowledge transfer platform and a network
of research facilities, and ECODEEP will develop eco-efficiency tools, based on a LCA
approach to enhance the overall sustainability by improving practises and find new solutions
within an industrial ecology framework. The AGRITRAINING surveys the training needs of the
system, looking forward to complement the actual capacity building achieved by the Master
courses in Food Engineering and Environmental Management. In addition, an advanced training
in Environmental Entrepreneurship is being implemented, and an Innovation Management for
SMEâs program is being designed, promoting a cultural change towards the sustainable welfare
of our present and future generations.InAGRI â Proj. n.Âș 3494 (Mais Centro/PORC);
EcoDeep â Proj. n.Âș 18643 (SIAC/COMPETE/POFC); InovEnergyâ Proj. n.Âș 18642 (SIAC/COMPETE/POFC);
Agritraining â Proj. n.Âș 8310 (SIAC/COMPETE/POFC); GovCluster âProj. n.Âș 8063 (SIAC/COMPETE/POFC
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