Antiproliferative Activity Of Synthetic Fatty Acid Amides From Renewable Resources.

In the work, the in vitro antiproliferative activity of a series of synthetic fatty acid amides were investigated in seven cancer cell lines. The study revealed that most of the compounds showed antiproliferative activity against tested tumor cell lines, mainly on human glioma cells (U251) and human ovarian cancer cells with a multiple drug-resistant phenotype (NCI-ADR/RES). In addition, the fatty methyl benzylamide derived from ricinoleic acid (with the fatty acid obtained from castor oil, a renewable resource) showed a high selectivity with potent growth inhibition and cell death for the glioma cell line-the most aggressive CNS cancer.

A genetic algorithm for project scheduling with multi-modes and renewable resources

This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.

Polímeros a partir de fuentes renovables. Polymers from renewable resources.

Las poliolefinas (polietileno y polipropileno) y el poliestireno se obtienen por polimerización de monómeros derivados del petróleo. La utilización creciente del petróleo incrementa la emisión a la atmósfera de gases que provocan el recalentamiento global. Por otra parte, la escasez de reservas de petróleo provocó en los últimos años un incremento en el precio del crudo y en el de sus derivados. Por tal motivo, esto pone de manifiesto el interés actual por reemplazar al petróleo y al gas natural por materias primas renovables. El ácido poliláctico (APL) y el poli(3-hidroxibutirato) (PHB) son poliésteres de origen bacteriano que poseen propiedades termoplásticas y elastómeras similares a los plásticos derivados del petróleo, pero son biodegradables y se producen a partir de sustratos renovables. Sin embargo, su costo es aún demasiado elevado. Una de las estrategias utilizadas para abaratarlos es la utilización de sustratos de costo bajo o nulo (residuos agroindustriales y permeado de lactosuero). Por lo tanto, el principal objetivo de este proyecto es sintetizar plásticos biodegradables alternativos a los polímeros sintéticos ya existentes a partir de recursos renovables de bajo costo. En particular, se pretende utilizar permeado de lactosuero proveniente de distintas industrias de San Francisco y su zona. San Francisco se encuentra estratégicamente ubicada dentro de una de las principales cuencas lecheras de este país. Los trabajos a desarrollar serán teórico y experimentales, y se relacionan con la síntesis y caracterización de los productos y el modelado de dichos procesos. Desde el punto de vista experimental se pretende: a) sintetizar el bio-monómero (ácico láctico) y los polímeros (APL y PHB) ; b) caracterizar el bio-monómero y los polímeros mediante el empleo de técnicas volumétricas, espectroscópicas y cromatográficas; y c) medir propiedades finales (fundamentalmente mecánicas) y establecer las relaciones estructuras-propiedades. Desde el punto de vista teórico se modelarán los procesos de síntesis (bio-monómero) y polimerización. Los modelos se utilizarán para la predicción de características físicas y moleculares de los productos finales, para la simulación y la optimización de procesos, y para complementar técnicas de caracterización. Este proyecto se enmarca dentro de la Química Verde o Sustentable con lo cual se pretende incentivar el desarrollo de productos más saludables y químicamente adaptados al medio ambiente que reemplacen a los polímeros sintéticos existentes sin la pérdida de sus propiedades finales. De este modo, se espera que los resultados contribuyan al conocimiento científico y tecnológico y resulten de interés regional e internacional.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Extinction, pollution, and the sustainable use of exhaustible and renewable resources

Single ownership of natural resources is conunon in many developing countries and socialist economies. The sole owner is usually the .state or society at large, and governments are responsible for either distributing exploitation rights or engaging in exploitation through their own corporations. • Under this circumstance, the notion of externality may not fully explain pollution problems existent in these nations. This paper studies the case where a single agent owns both exhaustible and renewable resources, and attempts to maximize its welfare. The resources are either perfect or imperfect substitutes. Initially, exhaustible resource extraction does not affect the renewable resource, and sustainable growth is attainable. A lactor of pollution flowing from the extraction of the nc.nrenewable resource into the growth of the renewable resource is introduced. The continuous exploitation of the exhaustible resource leads to the " optimal " extinction of the renewable resource, and sustainable growth is no longer reached. Regulation from a supra governmental agency such as an multinational institution may prove to be of utmost importance, if sustainability is to be achieved. The paper is divided into five sections. Section two provides a brief survey of the relevant literature. Section three presents the model without pollution. This factor is introduced in section four. The final section discusses some possible approaches for attaining sustainable growth, and contains the concluding remarks .

Rhamnolipids as biosurfactants from renewable resources: Concepts for next-generation rhamnolipid production

Several microorganisms are known to produce a wide variety of surface-active substances, which are referred to as biosurfactants. Interesting examples for biosurfactants are rhamnolipids, glycolipids mainly known from Pseudomonas aeruginosa produced during cultivation on different substrates like vegetable oils, sugars, glycerol or hydrocarbons. However, besides costs for downstream processing of rhamnolipids, relatively high raw-material prices and low productivities currently inhibit potential economical production of rhamnolipids on an industrial scale. This review focuses on cost-effective and sustainable production of rhamnolipids by introducing new possibilities and strategies regarding renewable substrates. Additionally, past and recent production strategies using alternative substrates such as agro-industrial byproducts or wastes are summarized. Requirements and concepts for next-generation rhamnolipid producing strains are discussed and potential targets for strain-engineering are presented. The discussion of potential new strategies is supported by an analysis of the metabolism of different Pseudomonas species. According to calculations of theoretical substrate-to-product conversion yields and current world-market price analysis, different renewable substrates are compared and discussed from an economical point of view. A next-generation rhamnolipid producing strain, as proposed within this review, may be engineered towards reduced formation of byproducts, increased metabolic spectrum, broadened substrate spectrum and controlled regulation for the induction of rhamnolipid synthesis. (C) 2012 Elsevier Ltd. All rights reserved.

Reversible polymerization of novel monomers bearing furan and plant oil moieties: a double click exploitation of renewable resources

Monomers based on plant oil derivatives bearing furan heterocycles appended through thiol-ene click chemistry were prepared and, subsequently, polymerized via a second type of click reaction, i. e. the Diels-Alder (DA) polycondensation between furan and maleimide complementary moieties. Two basic approaches were considered for these DA polymerizations, namely (i) the use of monomers with two terminal furan rings in conjunction with bismaleimides (AA + BB systems) and (ii) the use of a protected AB monomer incorporating both furan and maleimide end groups. This study clearly showed that both strategies were successful, albeit with different outcomes, in terms of the nature of the ensuing products. The application of the retro-DA reaction to these polymers confirmed their thermoreversible character, i. e. the clean-cut return to their respective starting monomers, opening the way to original macromolecular materials with interesting applications, like mendability and recyclability.

New eco-friendly polyesters from renewable resources

Nowadays the development of sustainable polymers, with convenient properties to substitute the traditional petroleum-based materials, is one of the major issues for material science. The utilization of renewable resources as feedstock for biopolyesters is a challenging target.The research work described in the present thesis is strictly connected to these urgent necessities and is focused mainly in finding new biopolymers, in particular biopolyesters, which are obtainable from biomass and characterized by a wide range of properties, in order to potentially substitute polyolefins and aromatic polyesters (for example, poly(ethylene terephthalate))

Structure-property relations in polymers from renewable resources

The present research project focuses its attention on the study of structure-property relations in polymers from renewable sources (bio-based polymers) such as polymers microbially produced, i.e. polyhydrohyalkanoates (PHAs) or chemically synthesized using monomers from renewable sources, i.e. polyammide 11 (PA11). By means of a broad spectrum of experimental techniques, the influence of different modifications on bio-based polymers such as blending with other components, copolymerization with different co-monomers and introduction of branching to yield complex architectures have been investigated. The present work on PHAs focused on the study of the dependence of polymer properties on both the fermentation process conditions (e.g. bacterial strain and carbon substrate used) and the method adopted to recover PHAs from cells. Furthermore, a solvent-free method using an enzyme and chemicals in an aqueous medium, was developed in order to recover PHAs from cells. Such a method allowed to recover PHA granules in their amorphous state, i.e. in native form useful for specific applications (e.g. paper coating). In addition, a commercial PHA was used as polymeric matrix to develop biodegradable and bio-based composites for food packaging applications. Biodegradable, non-toxic, food contact plasticizers and low cost, widely available lignocellulosic fibers (wheat straw fibers) were incorporated in such a polymeric matrix, in order to decrease PHA brittleness and the polymer cost, respectively. As concerns the study of polyamide 11, both the rheological and the solid-state behavior of PA11 star samples with different arm number and length was studied. Introduction of arms in a polymer molecule allows to modulate melt viscosity behavior which is advantageous for industrial applications. Also, several important solid-state properties, in particular mechanical properties, are affected by the presence of branching. Given the importance of using ‘green’ synthetic strategies in polymer chemistry, novel poly(-amino esters), synthesized via enzymatic-catalyzed polymerization, have also been investigated in this work.

The renewable resources extension act /

Mode of access: Internet.

Wood waste disposal in Illinois / Illinois Department of Energy and Natural Resources, Office of Solid Waste and Renewable Resources.

"ILENR/RR-91/04."

A recommended renewable resources program : 1980 update.

Issued Sept. 1980.

A five-year national plan for renewable resources extension programs.

Issued June 1980.

River of No Return Wilderness proposals : hearing before the Subcommittee on Parks, Recreation, and Renewable Resources of the Committee on Energy and Natural Resources, United States Senate, Ninety-sixth Congress, first session.

Hearings on S.95-97.

Message passing for distributed optimisation of power allocation with renewable resources

The increase in renewable energy generators introduced into the electricity grid is putting pressure on its stability and management as predictions of renewable energy sources cannot be accurate or fully controlled. This, with the additional pressure of fluctuations in demand, presents a problem more complex than the current methods of controlling electricity distribution were designed for. A global approximate and distributed optimisation method for power allocation that accommodates uncertainties and volatility is suggested and analysed. It is based on a probabilistic method known as message passing [1], which has deep links to statistical physics methodology. This principled method of optimisation is based on local calculations and inherently accommodates uncertainties; it is of modest computational complexity and provides good approximate solutions.We consider uncertainty and fluctuations drawn from a Gaussian distribution and incorporate them into the message-passing algorithm. We see the effect that increasing uncertainty has on the transmission cost and how the placement of volatile nodes within a grid, such as renewable generators or consumers, effects it.