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

Incidência do Pineapple Mealybug wilt Associated VÍrus, PMWAV no Banco Ativo de Germoplasma de abacaxi in vitro da Embrapa Mandioca e Fruticultura.

O abacaxi (Ananas comosus var. comosus) é uma das frutas tropicais mais apreciadas no mundo. O Brasil, como um dos centros de origem e diversidade genética, 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 prática favorece a disseminação de doenças como as viroses. O PMWaV (Pineapple mealybug wilt-associated virus) é um vírus que infecta o abacaxi causando a doença denominada popularmente de ?Mucha do abacaxi?. O vírus 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 sequência e organização do genoma. O PMWV pertence a família Closteroviridae, gênero Ampelovirus, possui partícula alongada flexuosa e genoma de RNA fita simples com aproximadamente 14Kb. Além dos danos diretos na produção da planta, a contaminação dos acessos do Banco Ativo de Germoplasma -BAG é um fator preocupante. Diante disto, o objetivo deste trabalho foi a avaliar a incidência 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 $\lambda$. We obtain as well an exact expression for $\lambda$ for all kinds of diffusion. Moreover, we show that $\lambda$ 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 SME’s program is being designed, promoting a cultural change towards the sustainable welfare of our present and future generations.InAGRI – Proj. n.º 3494 (Mais Centro/PORC); EcoDeep – Proj. n.º 18643 (SIAC/COMPETE/POFC); InovEnergy– Proj. n.º 18642 (SIAC/COMPETE/POFC); Agritraining – Proj. n.º 8310 (SIAC/COMPETE/POFC); GovCluster –Proj. n.º 8063 (SIAC/COMPETE/POFC