Optimal design of water pipeline networks for the development of shale gas resources

One of the major concerns in shale gas production is water management. Millions of gallons of water are injected to fracture each well and a significant amount returns to the surface as flowback. Operators are increasingly reusing flowback to reduce freshwater consumption and impaired water disposal. Because of this, networks of water pipelines in U.S. shales are growing fast. This work is aimed at addressing the optimal planning of shale gas operations in multiple wellpads together with the design of water distribution networks (WDN). We propose a multiperiod mixed-integer linear programming model to solve the challenging stay-or-mobilize trade-off. The proposed model permits to schedule operations at a detailed level, accounting for the WDN required to maximize the reuse of impaired water. We present illustrative examples involving up to 20 pads, 4 frac-crews, and 100 wells developed over 1 year, showing that the design of the WDN can be effectively optimized.     

Sodium phthalamates as corrosion inhibitors for carbon steel in aqueous hydrochloric acid solution

Three compounds of N-alkyl-sodium phthalamates were synthesized and tested as corrosion inhibitors for carbon steel in 0.5 M aqueous hydrochloric acid. Tests showed that inhibitor efficiencies were related to aliphatic chain length and dependent on concentration. N-1-n-tetradecyl-sodium phthalamate displayed moderate efficiency against uniform corrosion, 42–86% at 25 °C and 25–60% at 40 °C. Tests indicated that compounds behave as mixed type inhibitors where molecular adsorption on steel followed Langmuir isotherm, whereas thermodynamic suggested that a physisorption process occurred. XPS analysis confirmed film formation on surface, where Fe⁺² complexes and Fe⁺² chelates with phthalamates prevented steel from further corrosion.     

Systematic Review on Tumor Microenvironment in Glial Neoplasm: From Understanding Pathogenesis to Future Therapeutic Perspectives

Despite the multidisciplinary management in the treatment of glioblastomas, the average survival of GBM patients is still 15 months. In recent years, molecular biomarkers have gained more and more importance both in the diagnosis and therapy of glial tumors. At the same time, it has become clear that non neoplastic cells, which constitute about 30% of glioma mass, dramatically influence tumor growth, spread, and recurrence. This is the main reason why, in recent years, scientific research has been focused on understanding the function and the composition of tumor microenvironment and its role in gliomagenesis and recurrence. The aim of this review is to summarize the most recent discovery about resident microglia, tumor-associated macrophages, lymphocytes, and the role of extracellular vesicles and their bijective interaction with glioma cells. Moreover, we reported the most recent updates about new therapeutic strategies targeting immune system receptors and soluble factors. Understanding how glioma cells interact with non-neoplastic cells in tumor microenvironment is an essential step to comprehend mechanisms at the base of disease progression and to find new therapeutic strategies for GBM patients. However, no significant results have yet been obtained in studies targeting single molecules/pathways; considering the complex microenvironment, it is likely that only by using multiple therapeutic agents acting on multiple molecular targets can significant results be achieved.     

Downstream environmental effects of dam operations: Changes in habitat quality for native fish species

Hydropeaking dam operation and water extractions for irrigation have been broadly stated as alterations to natural flow regimes, which have also been noticed in the Biobío Watershed, in Central Chile, since 1996. In the Biobío River, most of native fish species are endemic and very little is known about them. Their ecological and social values have never been estimated, and there is lack of information about their habitat preferences. Furthermore, changes on fish habitat availability due to natural and/or man‐made causes have not been evaluated. In this study, eight native fish species, in a representative reach of the Biobío River, were studied and their preferred habitats were surveyed and characterized. A hydrodynamic model was built and linked to the fish habitat simulation model CASiMiR. Fuzzy rules and fuzzy sets were developed for describing habitat preference of the native fish species. CASiMiR was then used to simulate how physical habitat conditions vary due to flow control (i.e. upstream dams). Results show how overall habitat quality, expressed as weighted usable area (WUA) and hydraulic habitat suitability (HHS), changes and fluctuates due to the dam operation and how the daily hydropeaking is influencing quantity, quality and location of different habitats. The study suggests that the analysed fish are highly susceptible to flow control, as dams are currently operated, and fish habitat improvement suggestions are proposed. Copyright © 2010 John Wiley & Sons, Ltd.     

Age and productivity as determinants of firm survival over the industry life cycle

This paper contributes to fill the gap between the literature on the determinants of firm survival and the empirical works on the industry life cycle (ILC). Using a representative sample of Spanish firms with 10 or more employees over the period 1993–2009, the role played by firm age and productivity in firm survival is empirically analysed across three stages of the life cycle of forty-seven 3-digit manufacturing sectors. In the ‘early’ stage of the ILC, firm age is negatively correlated with hazard rates while firm productivity is not. Firm productivity is associated with lower hazard in the ‘mature’ stage of the ILC, when competition is primarily efficiency-driven, while firm age does not play a significant role for firm survival. In the ‘intermediate’ stage, both age and productivity play a role in reducing firms’ hazard rates.     

An optimization method for learning statistical classifiers in structural reliability

Monte Carlo simulation is a general and robust method for structural reliability analysis, affected by the serious efficiency problem consisting in the need of computing the limit state function a very large number of times. In order to reduce this computational effort the use of several kinds of solver surrogates has been proposed in the recent past. Proposals include the Response Surface Method (RSM), Neural Networks (NN), Support Vector Machines (SVM) and several other methods developed in the burgeoning field of Statistical Learning (SL). Many of these techniques can be employed either for function approximation (regression approach) or for pattern recognition (classification approach). This paper concerns the use of these devices for discriminating samples into safe and failure classes using the classification approach, because it constitutes the core of Monte Carlo simulation as applied to reliability analysis as such. Due to the flexibility of most SL methods, a critical step in their use is the generation of the learning population, as it affects the generalization capacity of the surrogate. To this end it is first demonstrated that the optimal population from the information viewpoint lies around in the vicinity of the limit state function. Next, an optimization method assuring a small as well as highly informative learning population is proposed on this basis. It consists in generating a small initial quasi-random population using Sobol sequence for triggering a Particle Swarm Optimization (PSO) performed over an iteration-dependent cost function defined in terms of the limit state function. The method is evaluated using SVM classifiers, but it can be readily applied also to other statistical classification techniques because the distinctive feature of the SVM, i.e. the margin band, is not actively used in the algorithm. The results show that the method yields results for the probability of failure that are in very close agreement with Monte Carlo simulation performed on the original limit state function and requiring a small number of learning samples.