Study Life Cycle Research Articles

Sustainable locating of petroleum refinery projects

The Petroleum Refinery Industry (PRI) is notorious for its significant contribution to environmental degradation. Operations of refineries in local neighborhoods often lead to adverse impacts on the surrounding community and environment. However, PRI projects also yield positive effects on local, national, and international economies. Mitigating the negative consequences of refinery operations necessitates a scientific and quantitative approach to selecting refinery locations. This process should consider a multitude of parameters and alternatives, encompassing social and environmental resiliency, transportation factors, and product markets throughout the refinery life cycle, in an integrated manner. Quantifying the impact of these parameters on the triple bottom line is essential for informed decision-making.In this study, we employ a previously developed integrated sustainability assessment framework in conjunction with a Multiple Attribute Decision Making (MADM) based scenario model to evaluate the impact of various location alternatives on the three pillars of sustainability. Despite the complexities inherent in refinery location decisions and the unpredictability of long-term and short-term parameters, this study's life-cycle approach offers a valuable tool for locating petroleum refineries.

Neogen: A tool to predict genetic effective population size (Ne ) for species with generational overlap and to assist empirical Ne study design.

Molecular genetic estimates of population effective size (Ne ) lose accuracy and precision when insufficient numbers of samples or loci are used. Ideally, researchers would like to forecast the necessary power when planning their project. neogen (genetic Ne for Overlapping Generations) enables estimates of precision and accuracy in advance of empirical investigation and allows exploration of the power available in different user-specified age-structured sampling schemes. neogen provides a population simulation and genetic power analysis framework that simulates the demographics, genetic composition, and Ne , from species-specific life history, mortality, population size, and genetic priors. neogen guides the user to establish a tractable sampling regime and to determine the numbers of samples and microsatellite or SNP loci required for accurate and precise genetic Ne estimates when sampling a natural population. neogen is useful at multiple stages of a study's life cycle: when budgeting, as sampling and locus development progresses, and for corroboration when empirical Ne estimates are available. The underlying model is applicable to a wide variety of iteroparous species with overlapping generations (e.g., mammals, birds, reptiles, long-lived fishes). In this paper, we describe the neogen model, detail the workflow for the point-and-click software, and explain the graphical results. We demonstrate the use of neogen with empirical Australian east coast zebra shark (Stegostoma fasciatum) data. For researchers wishing to make accurate and precise genetic Ne estimates for overlapping generations species, neogen facilitates planning for sample and locus acquisition, and with existing empirical genetic Ne estimates neogen can corroborate population demographic and life history properties.