Two-stage stochastic minimum s − t cut problems: Formulations, complexity and decomposition algorithms

We introduce the two‐stage stochastic minimum s − t cut problem. Based on a classical linear 0‐1 programming model for the deterministic minimum s − t cut problem, we provide a mathematical programming formulation for the proposed stochastic extension. We show that its constraint matrix loses the total unimodularity property, however, preserves it if the considered graph is a tree. This fact turns out to be not surprising as we prove that the considered problem is NP-hard in general, but admits a linear time solution algorithm when the graph is a tree. We exploit the special structure of the problem and propose a tailored Benders decomposition algorithm. We evaluate the computational efficiency of this algorithm by solving the Benders dual subproblems as max-flow problems. For many tested instances, we outperform a standard Benders decomposition by two orders of magnitude with the Benders decomposition exploiting the max-flow structure of the subproblems

Combinatorial content of CCL3L and CCL4L gene copy numbers influence HIV-AIDS susceptibility in Ukrainian children

Transmission risk is greatest when mother and offspring both have low CCL3L or CCL4L gene doses. The impact on HIV-AIDS susceptibility of the chemokine gene-rich locus on 17q12 is dependent on the balance between the doses of genes conferring protective (CCL3La and CCL4La) versus detrimental (CCL4Lb) effects. Hence, the combinatorial genomic content of distinct genes within a copy number variable region may determine disease susceptibility

Extending results from agricultural fields with intensively monitored data to surrounding areas for water quality management

A 45% reduction in riverine total nitrogen flux from the 1980–1996 time period is needed to meet water quality goals in the Mississippi Basin and Gulf of Mexico. This paper addresses the goal of reducing nitrogen in the Mississippi River through three objectives. First, the paper outlines an approach to the site-specific quantification of management effects on nitrogen loading from tile drained agriculture using a simulation model and expert review. Second, information about the net returns to farmers is integrated with the nitrogen loading information to assess the incentives to adopt alternative management systems. Third, the results are presented in a decision support framework that compares the rankings of management systems based on observed and simulated values for net returns and nitrogen loading. The specific question addressed is how information about the physical and biological processes at Iowa State University’s Northeast Research Farm near Nashua, Iowa, could be applied over a large area to help farmers select management systems to reduce nitrogen loading in tile drained areas. Previous research has documented the parameterization and calibration of the RZWQM model at Nashua to simulate 35 management system effects on corn and soybean yields and N loading in tileflow from 1990 to 2003. As most management systems were studied for a 6 year period and in some cases weather had substantial impacts, a set of 30 alternative management systems were also simulated using a common 1974–2003 input climate dataset. To integrate an understanding of the economics of N management, we calculated net returns for all management systems using the DevTreks social budgeting tool. We ranked the 35 observed systems in the Facilitator decision support tool using N loading and net returns and found that rankings from simulated results were very similar to those from the observed results from both an onsite and offsite perspective. We analyzed the effects of tillage, crop rotation, cover crops, and N application method, timing, and amount for the 30 long term simulations on net returns and N loading. The primary contribution of this paper is an approach to creating a quality assured database of management effects on nitrogen loading and net returns for tile drained agriculture in the Mississippi Basin. Such a database would systematically extend data from intensively monitored agricultural fields to the larger area those fields represent.This article is from Agricultural Systems 106 (2012): 59–71, doi:10.1016/j.agsy.2011.10.010.</p