A polynomial algorithm for special case of the one-machine scheduling problem with time-lags

The standard one-machine scheduling problem consists in schedulinga set of jobs in one machine which can handle only one job at atime, minimizing the maximum lateness. Each job is available forprocessing at its release date, requires a known processing timeand after finishing the processing, it is delivery after a certaintime. There also can exists precedence constraints between pairsof jobs, requiring that the first jobs must be completed beforethe second job can start. An extension of this problem consistsin assigning a time interval between the processing of the jobsassociated with the precedence constrains, known by finish-starttime-lags. In presence of this constraints, the problem is NP-hardeven if preemption is allowed. In this work, we consider a specialcase of the one-machine preemption scheduling problem with time-lags, where the time-lags have a chain form, and propose apolynomial algorithm to solve it. The algorithm consist in apolynomial number of calls of the preemption version of the LongestTail Heuristic. One of the applicability of the method is to obtainlower bounds for NP-hard one-machine and job-shop schedulingproblems. We present some computational results of thisapplication, followed by some conclusions.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.

Beyond Smith's rule: An optimal dynamic index, rule for single machine stochastic scheduling with convex holding costs

Most research on single machine scheduling has assumedthe linearity of job holding costs, which is arguablynot appropriate in some applications. This motivates ourstudy of a model for scheduling $n$ classes of stochasticjobs on a single machine, with the objective of minimizingthe total expected holding cost (discounted or undiscounted). We allow general holding cost rates that are separable,nondecreasing and convex on the number of jobs in eachclass. We formulate the problem as a linear program overa certain greedoid polytope, and establish that it issolved optimally by a dynamic (priority) index rule,whichextends the classical Smith's rule (1956) for the linearcase. Unlike Smith's indices, defined for each class, ournew indices are defined for each extended class, consistingof a class and a number of jobs in that class, and yieldan optimal dynamic index rule: work at each time on a jobwhose current extended class has larger index. We furthershow that the indices possess a decomposition property,as they are computed separately for each class, andinterpret them in economic terms as marginal expected cost rate reductions per unit of expected processing time.We establish the results by deploying a methodology recentlyintroduced by us [J. Niño-Mora (1999). "Restless bandits,partial conservation laws, and indexability. "Forthcomingin Advances in Applied Probability Vol. 33 No. 1, 2001],based on the satisfaction by performance measures of partialconservation laws (PCL) (which extend the generalizedconservation laws of Bertsimas and Niño-Mora (1996)):PCL provide a polyhedral framework for establishing theoptimality of index policies with special structure inscheduling problems under admissible objectives, which weapply to the model of concern.

Functionality of the plastron in adults of Neochetina eichhorniae Warner (Coleoptera, Curculionidae): aspects of the integument coating and submersion laboratory experiments

The plastron theory was tested in adults of Neochetina eichhorniae Warner, 1970, through the analysis of the structure that coats these insects' integument and also through submersion laboratorial experiments. The tegument processes were recognized in three types: agglutinated scales with large perforations, plumose scales of varied sizes and shapes, and hairs. The experiments were carried out on 264 adult individuals which were kept submerged at different time intervals (n = 11) and in two types of treatment, natural non-aerated water and previously boiled water, with four repetitions for each treatment. The tests showed a maximum mortality after 24 hours of immersion in the previously boiled water treatment. The survival of the adults was negative and significantly correlated with the types of treatment employed and within the different time intervals. The values of oxygen dissolved in water (mg/l) differed significantly within the types of treatment employed. They were positively correlated with the survival of the adults in the two types of treatment, although more markedly in the treatment with previously boiled water. The mortality of adults after 24 hours of submersion in the treatment with previously boiled water may be associated with the physical-chemical conditions of the non-tested water in this study, such as low surface tension and concentration of solutes. These results suggest plastron functionality in the adults of this species.

Effect of fertilizer-N application and seed coating with rhizobial inoculants on soybean yield in eastern Paraguay

Nitrogen removal in soybean grains at harvest may exceed biological N2 fixation, particularly if grain yields are as high as typically achieved on "Terra Rossa" soils of Eastern Paraguay. Applying N fertilizer or coating seeds with rhizobial inoculants that enhance nodulation may represent a way of balancing the N budget. However, the effects of such treatments appear to be highly site-specific. The objective of this study was to examine the effects of N application (N) and rhizobial inoculation (I) on nodulation, N accumulation and soybean yields in Eastern Paraguay. Field experiments were conducted in two consecutive soybean seasons. Dry conditions in the first year delayed sowing and reduced plant number m-2 and pod number plant-1. Grain yields were generally below 2 t ha-1 but the +N+I treatment increased yields by about 75%. In the second year favorable conditions resulted in yields of around 4 t ha-1 and the treatments had no effect. Nitrogen accumulation was higher in the first year and could therefore not explain the observed yield differences between years and treatment combinations. The positive effect of the +N+I treatment in year one was associated with a more rapid root growth which could have reduced susceptibility to intermittent drought stress. Nodule biomass decreased between flowering and pod setting stages in the +I treatment whereas further increases in nodule biomass in the -I treatment may have led to competition for assimilates between nodules and developing pods. Based on these preliminary results we conclude that N application and seed inoculation can offer short-term benefits in unfavorable years without negative effects on yield in favorable years.

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Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.