Generating datasets for the project portfolio selection and scheduling problem

The article presents two variants of the project portfolio selection and scheduling problem (PPSSP). The primary objective of the PPSSP is to maximise the total portfolio value through the selection and scheduling of a subset of projects subject to various operational constraints. This article describes two recently-proposed, generalised models of the PPSSP [1,2] and proposes a set of synthetically generated problem instances for each. These datasets can be used by researchers to compare the performance of heuristic and meta-heuristic solution strategies. In addition, the Python program used to generate the problem instances is supplied, allowing researchers to generate new problem instances

Solving a novel multi-divisional project portfolio selection and scheduling problem

A common problem faced by organizations is how to select and schedule an optimal portfolio of projects subject to various constraints, such as a limited budget. This problem is known as the project portfolio selection and scheduling problem (PPSSP). Despite the widespread nature of this problem, no existing model adequately addresses a sufficient set of characteristics that arise in real-world problems. One contribution of this article is the proposal of a novel, practical class of PPSSP that consists of multiple groups of projects, proposed by different sections of a major organization. The proposed problem can be considered as a generalized PPSSP given that many specific PPSSPs reported in the literature can be generated by relaxing certain constraints. As this is a novel formulation, existing algorithms cannot ensure high-quality solutions to this problem. Thus, a further contribution of this article is the design of three hybrid meta-heuristic algorithms based on a custom-purpose heuristic and local search operator. A case problem, inspired by future force design (FFD) in the Australian Defence Force (ADF), is presented to exemplify the applicability of this model to a real-world problem. Results indicate that the obtained solutions are of acceptable quality for implementation

Wanted dead or alive : high diversity of macroinvertebrates associated with living and ’dead’ Posidonia oceanica matte

The Mediterranean endemic seagrass Posidonia oceanica forms beds characterised by a dense leaf canopy and a thick root-rhizome ‘matte’. Death of P. oceanica shoots leads to exposure of the underlying matte, which can persist for many years, and is termed ‘dead’ matte. Traditionally, dead matte has been regarded as a degraded habitat. To test whether this assumption was true, the motile macroinvertebrates of adjacent living (with shoots) and dead (without shoots) matte of P. oceanica were sampled in four different plots located at the same depth (5–6 m) in Mellieha Bay, Malta (central Mediterranean). The total number of species and abundance were significantly higher (ANOVA; P<0.05 and P<0.01, respectively) in the dead matte than in living P. oceanica matte, despite the presence of the foliar canopy in the latter. Multivariate analysis (MDS) clearly showed two main groups of assemblages, corresponding to the two matte types. The amphipods Leptocheirus guttatus and Maera grossimana, and the polychaete Nereis rava contributed most to the dissimilarity between the two different matte types. Several unique properties of the dead matte contributing to the unexpected higher number of species and abundance of motile macroinvertebrates associated with this habitat are discussed. The findings have important implications for the conservation of bare P. oceanica matte, which has been generally viewed as a habitat of low ecological value.peer-reviewe

Dynamic modeling of mean-reverting spreads for statistical arbitrage

Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is a noisy realization of some hidden states. Real-time estimation of the unobserved spread process can reveal temporary market inefficiencies which can then be exploited to generate excess returns. Building on previous work, we embrace the state-space framework for modeling spread processes and extend this methodology along three different directions. First, we introduce time-dependency in the model parameters, which allows for quick adaptation to changes in the data generating process. Second, we provide an on-line estimation algorithm that can be constantly run in real-time. Being computationally fast, the algorithm is particularly suitable for building aggressive trading strategies based on high-frequency data and may be used as a monitoring device for mean-reversion. Finally, our framework naturally provides informative uncertainty measures of all the estimated parameters. Experimental results based on Monte Carlo simulations and historical equity data are discussed, including a co-integration relationship involving two exchange-traded funds.Comment: 34 pages, 6 figures. Submitte

Gaussian-valued particle swarm optimization

This paper examines the position update equation of the particle swarm optimization (PSO) algorithm, leading to the proposal of a simplified position update based upon a Gaussian distribution. The proposed algorithm, Gaussian-valued particle swarm optimization (GVPSO), generates probabilistic positions by retaining key elements of the canonical update procedure while also removing the need to specify values for the traditional PSO control parameters. Experimental results across a set of 60 benchmark problems indicate that GVPSO outperforms both the standard PSO and the bare bones particle swarm optimization (BBPSO) algorithm, which also employs a Gaussian distribution to generate particle positions.The National Research Foundation (NRF) of South Africa (Grant Number 46712) and the Natural Sciences and Engineering Research Council of Canada (NSERC).http://link.springer.combookseries/5582019-10-03hj2018Computer Scienc

Iron, silicate, and light co-limitation of three Southern Ocean diatom species

The effect of combined iron, silicate, and light co-limitation was investigated in the three diatom species Actinocyclus sp. Ehrenberg, Chaetoceros dichaeta Ehrenberg, and Chaetoceros debilis Cleve, isolated from the Southern Ocean (SO). Growth of all species was co-limited by iron and silicate, reflected in a significant increase in the number of cell divisions compared to the control. Lowest relative Si uptake and drastic frustule malformation was found under iron and silicate co-limitation in C. dichaeta, while Si limitation in general caused cell elongation in both Chaetoceros species. Higher light intensities similar to SO surface conditions showed a negative impact on growth of C. dichaeta and Actinocyclus sp. and no effect on C. debilis. This is in contrast to the assumed light limitation of SO diatoms due to deep wind driven mixing. Our results suggest that growth and species composition of Southern Ocean diatoms is influenced by a sensitive interaction of the abiotic factors, iron, silicate, and light

What traits are carried on mobile genetic elements, and why?

Although similar to any other organism, prokaryotes can transfer genes vertically from mother cell to daughter cell, they can also exchange certain genes horizontally. Genes can move within and between genomes at fast rates because of mobile genetic elements (MGEs). Although mobile elements are fundamentally self-interested entities, and thus replicate for their own gain, they frequently carry genes beneficial for their hosts and/or the neighbours of their hosts. Many genes that are carried by mobile elements code for traits that are expressed outside of the cell. Such traits are involved in bacterial sociality, such as the production of public goods, which benefit a cell's neighbours, or the production of bacteriocins, which harm a cell's neighbours. In this study we review the patterns that are emerging in the types of genes carried by mobile elements, and discuss the evolutionary and ecological conditions under which mobile elements evolve to carry their peculiar mix of parasitic, beneficial and cooperative genes

Potential health impacts of heavy metals on HIV-infected population in USA.

Noninfectious comorbidities such as cardiovascular diseases have become increasingly prevalent and occur earlier in life in persons with HIV infection. Despite the emerging body of literature linking environmental exposures to chronic disease outcomes in the general population, the impacts of environmental exposures have received little attention in HIV-infected population. The aim of this study is to investigate whether individuals living with HIV have elevated prevalence of heavy metals compared to non-HIV infected individuals in United States. We used the National Health and Nutrition Examination Survey (NHANES) 2003-2010 to compare exposures to heavy metals including cadmium, lead, and total mercury in HIV infected and non-HIV infected subjects. In this cross-sectional study, we found that HIV-infected individuals had higher concentrations of all heavy metals than the non-HIV infected group. In a multivariate linear regression model, HIV status was significantly associated with increased blood cadmium (p=0.03) after adjusting for age, sex, race, education, poverty income ratio, and smoking. However, HIV status was not statistically associated with lead or mercury levels after adjusting for the same covariates. Our findings suggest that HIV-infected patients might be significantly more exposed to cadmium compared to non-HIV infected individuals which could contribute to higher prevalence of chronic diseases among HIV-infected subjects. Further research is warranted to identify sources of exposure and to understand more about specific health outcomes

The Aquatic Symbiosis Genomics Project: probing the evolution of symbiosis across the Tree of Life

We present the Aquatic Symbiosis Genomics Project, a global collaboration to generate high quality genome sequences for a wide range of eukaryotes and their microbial symbionts. Launched under the Symbiosis in Aquatic Systems Initiative of the Gordon and Betty Moore Foundation, the ASG Project brings together researchers from across the globe who hope to use these reference genomes to augment and extend their analyses of the dynamics, mechanisms and environmental importance of symbioses. Applying large-scale, high-throughput sequencing and assembly technologies, the ASG collaboration will assemble and annotate the genomes of 500 symbiotic organisms – both the “hosts” and the microbial symbionts with which they associate. These data will be released openly to benefit all who work on symbioses, from conservation geneticists to those interested in the origin of the eukaryotic cell. The Aquatic Symbiosis Genomics Project is a worldwide effort to find the genome sequences of a variety of organisms and their microbial partners living in water. Supported by the Gordon and Betty Moore Foundation, this project involves scientists from around the world. The genome sequences will help scientists to better understand how these organisms interact with each other and their environment. The project will use advanced technology to map out the genes of 500 pairs of host organisms and their microbial symbionts. This information will be freely available, helping everyone from researchers studying species conservation to those exploring the beginnings of complex cell life