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Multiple factors, such as the vineyard environment and winemaking practices, are known to affect the development of vines as well as the final composition of grapes. Water stress promotes the synthesis of phenols and is associated with grape quality as long as it does not inhibit production. To identify the key parameters for managing water stress and grape quality, multivariate statistical analysis is essential. Classification and regression trees are methods for constructing prediction models from data, especially when data are complex and when constructing a single global model is difficult and models are challenging to interpret. The models were obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. The partitioning can be represented graphically as a decision tree. This approach permitted the most decisive variables for predicting the most vulnerable vineyards and wine quality parameters associated with water stress. In Priorat AOC, Carignan grapevines had the highest water potential and abscisic acid concentration in the early growth plant stages and permitted vineyards to be classified by mesoclimate. This information is useful for identifying which measurements could most easily differentiate between early and late‐ripening vineyards. LWP and Ts during an early physiological stage (pea size) permitted warm and cold areas to be differentiated.

Sustainability has become a challenge of relevant importance for organizational management. Companies should not only achieve appropriate levels of economic efficiency, but also of environmental and social efficiency. In this sense, the inclusion of sustainability principles in the scheduling of flexible job shop systems (FJS) has focused on the evaluation of energy consumption and key economic indicators (makespan, total weighted tardiness …). An industrial case (Medellin, Colombia) from the metal-mechanic sector is used to illustrate the simultaneous evaluation of the three sustainability dimensions: economic, environmental and social. The makespan is the indicator associated with the economic performance; CO2 equivalent emissions, water consumption, metal waste, and chrome waste are the indicators to evaluate the environmental performance; and weight manipulated at workstations, noise, ambient temperature, and vibrations are the indicators associated with the social performance. The measurement, normalization, and weighted sum of the previous indicators, allow for the estimation of the performance of each sustainability dimension. The NSGA-II and NSGA-III methods are used in order to carry out the multi-objective evaluation process, based on the values estimated for the three general objectives (i.e., environmental, social, and economic). Consequently, in the industrial case analyzed, the applied methods identify the corresponding structure of the Pareto optimal fronts. The post-optimal analysis of the solutions shows that the solutions can be directly associated with market demand tendencies, and their implementation can be detected in advance. Solutions that balance economic, environmental, and social efficiency, could be potentially selected according to the market trend, reaching a win-win scenario for all actors involved.

Liquid biofuels can facilitate the transition towards a more sustainable transportation sector by curbing carbon emissions while maintaining most of the current vehicle fleet. Today, a myriad of alternatives are available to produce biofuels, where different decisions for the fuel type, blend, conversion process and carbon source will affect the final cost and environmental impact of the product. In this contribution, we analyze the performance of 72 different biofuels routes based on 12 indicators that cover the three sustainability dimensions: economic, environmental and social. The proposed multi-criteria approach combines Data Envelopment Analysis with Life Cycle Assessment to evaluate biofuels from a cradle-to-wheel perspective, that is, considering the production chain spanning from biomass production to the combustion of the biofuel in the engine. Results reveal that there are 35 biofuels routes performing better than the rest, with renewable diesel being a better option than ethanol-based blends or biodiesel, and waste biomass preferred over cellulosic biomass or bio-oils. The selection of the carbon source proofed to be the most important decision, highlighting the need to consider regional aspects related to soil and climate before promoting a certain biofuel. Overall, our results can help to derive effective policies for the adoption of biofuels attaining the best performance at minimum cost and environmental risks.

Assessing the footprint of anthropogenic activities has become particularly challenging in today's globalized markets, where a large number of economic transactions taking place across the globe need to be disentangled before the most critical sectors are identified. In this work, we present an approach to quantify the eco-efficiency of economic activities that combines multi-regional environmentally-extended input-output tables and data envelopment analysis. We employ this method to assess the European Union manufacturing sectors in terms of three inputs -global warming potential, potential acidifying equivalent and tropospheric ozone forming potential- and one output (economic wealth), following both production and consumption-based accounting approaches. Our approach classifies economic sectors as efficient and inefficient, and for the latter it provides improvement targets that if attained would make them efficient. We find that there is a significant mismatch at the sectorial level between both accounting schemes, each providing complementary information for policy-making. In the production-based case, targets allow identifying sectors and pollutants requiring more stringent regulations and/or higher investments in cleaner technologies. Conversely, the consumption-based accounting allows identifying the ultimate sources of impact, an insight that can assist in the selection of alternative “cleaner” suppliers via eco-labelling of products/services and proper taxation schemes.

A collection of 338 grapevine accessions from 24 countries planted on the estates of Bodegues Sumarroca (AOC Penedes) was genotyped with 20 microsatellite markers. A total of 294 different molecular profiles were obtained; 28 of them are presented for the first time. Twenty-six accession names are considered new synonyms and are commonly used in several regions.

A wide range of impact assessment methodologies are available for quantifying the life cycle environmental impact of anthropogenic activities. The calculation of these metrics requires typically large amounts of data that are hard to collect in practice. To shed light on the extent to which these input data can be reduced (while yet obtaining accurate impact assessment values), this work applies a multivariate statistical analysis to the ecoinvent database. Numerical results show that many life cycle impact assessment (LCIA) metrics are highly correlated, but despite this high level of correlation no single indicator is capable of predicting the others with accuracy via univariate linear regression. Our findings open new avenues for the development of advanced streamlined LCIA methods based on multiple data regression that could exploit this high level of correlation and potentially lead to significant savings in time and resources associated with LCA studies.

Understanding how anthropogenic impacts are generated at a global scale is a major challenge to face. This work studies the environmental impact patterns of the wealthiest nations using environmentally extended multi-regional input–output tables. A multivariate statistical analysis is performed on data covering 69 environmental indicators (classified into 5 main categories: energy, emissions, material, water and land) and 41 countries. This analysis shows that damages in different categories (and also within the same one) are highly correlated and that the wealthiest countries display very similar environmental impact patterns. These findings might help to develop more effective environmental regulations that will focus on controlling a reduced number of key indicators. In addition, the analysis of pollution patterns at a global scale will help to establish unified environmental regulations in countries with similar patterns.

Tetrakis (hydroxymethyl) phosponium chloride ionic liquid was evaluated to recover carbohydrates, especially cellulose, from municipal wastewater treatment plant (WWTP) primary sludge (96 % water content). Different process operative conditions were evaluated: temperature (35, 60 and 80 ℃), time (6, 13 and 20 h) and sludge weight:ionic liquid volume ratio (1 g TS of SLUDGE: 7, 12 and 17 mL). A three levels, three factors central design was applied to optimize the procedure of recovery of the cellulose. Precipitated solids were analysed to quantify the content of cellulose, hemicellulose, lignin, proteins and ashes. The ionic liquid showed good performances in the recovery of cellulose. The average amount of carbohydrates recovered using the ionic liquid method was comparable with the amount of carbohydrates measured by the Dubois method in the raw sludge. All cellulose was recovered in the precipitated solid, but also part of the protein and ashes. According to the statistical tests, two interactions were found: between temperature and sludge:IL ratio for recovery of hemicellulose; between temperature and time for recovery of proteins. Experimental design was not useful to find optimized conditions of the process. Experiments realised with central values (60 °C; 13 h; 1:12 mL) gave very good results, allowing the recovery of high quantities of cellulose with the best removal of ashes but maintaining high quantities of proteins in the precipitate. For these reasons central values can be considered optimal reaction conditions. The design of novel ionic liquids will be necessary to fully purify the cellulose in a single dissolution step.

Assessing the sustainability level of the power sector can aid the transition towards a sustainable energy system. In this contribution, we propose a novel approach to compare and optimize electricity mixes according to sustainability criteria and apply it to the 28 EU members. Our method combines life cycle assessment, data envelopment analysis (DEA) and rigorous mathematical programming tools in three main steps. Firstly, DEA is applied to assess the efficiency level of electricity mixes of EU member countries considering the three dimensions of sustainability. Then, electricity portfolios of inefficient countries are optimized by solving a model that seeks to attain the targets provided by DEA while simultaneously considering the technical aspects governing electricity generation. This model, which constitutes the cornerstone of our approach, complements standard DEA by ensuring realistic and meaningful targets. In the third step, we evaluate the electricity portfolios optimized previously by running DEA again. Our results suggest that energy policies in the EU should favor the deployment of hydropower, wind and solar, while simultaneously displacing non-renewable sources and bioenergy. The deployment of carbon capture and storage is also a potential alternative that could be incentivized via policies worded in terms of carbon intensity targets.

The presented work analyses the feasibility of biodiesel production by transesterification of vegetable oil extracted from Cynara Cardunculus . The performance assessment of a plant with a capacity of 5000 t/year biodiesel production is carried out based on its rigorous simulation in AspenHysys®. A modular automated evaluation tool programmed in Matlab® retrieves the inventory of the energy and material inputs/outputs, and the environmental releases from the simulation results. The simulation performs a combined acid-catalyzed pretreatment and alkali-catalyzed transesterification based on the cardoon oil characterization and the kinetic data extracted from published experimental studies. The performance of the plant is optimized considering four potential alternatives. The Eco-indicator 99 methodology is used for the impact assessment, achieving similar results to other vegetable sources of biodiesel. With respect to the economic study, a profitability analysis yields better results than the reported in previous works for oils of other agricultural crops, identifying two critical factors: the biodiesel sale price and the plant capacity.

In this work a real sour stripping plant of a petrochemical complex simulated in AspenHysys® is improved using an automated tool programmed in Matlab®. The new developed procedure provides the performance evaluation and the comparison between different alternatives of the plant through the calculation and aggregation of environmental and economic indicators. The results obtained give insights into the plant environmental behavior, being very useful to find the suitable modifications in operation and topology of the plant. Moreover, the use of this tool allows the implementation of optimization algorithms and heat integration strategies, with the aim of minimizing resources consumption, pollutants releases, energy requirements and total costs. Specifically, the procedure helps to significantly improve the performance of the studied process: the operating costs are reduced near 8%, while the amount of released CO2-eq is 7% lower, and the process consumes 66% less water.

Since the environmental awareness increases and regulations become more restrictive, chemical industries are forced to adopt measures for minimizing the environmental impact and to include these techniques in the process design. This work proposes a methodology where environment and human health considerations are coupled with the process design of new and existing plants. With this aim, a new assessment tool for the environmental evaluation of chemical processes is presented. It includes the development of a new environmental indicator (Material Balance Environmental Index, MBEI) based on the toxicities of the chemicals involved and the materials flows between the process and environment. Moreover, a total index is computed following three levels of aggregation using the geometric mean of the ratios of several environmental impact categories. The environmental evaluation tool is tested using two case studies (formaldehyde and styrene production), where data are obtained from rigorous process simulation v...

IkB kinase β (IKK-2) is a serine-threonine protein kinase that is critically involved in activating the transcription factor NF-κβ in response to various inflammatory stimuli (hence, the interest in synthesizing molecules that can inhibit IKK-2). Some pyridine derivates have been shown to be among the most potent IKK-2 inhibitors known at present. This study reports how pyridine derivates have been used to develop a 3D-QSAR model for rationalizing results from previous structure-activity relationship studies on IKK-2 inhibition by these molecules; and predicting the IC50 of other pyridine-derivate IKK-2 inhibitors whose activity is unknown. Therefore, 3D-QSAR models were built with PHASE and a training set of 21 molecules with IC50 values that span five orders of magnitude (ranging from 0.004 to more than 20 μM) and in which each order of magnitude had from two to seven molecules. The resulting models were validated with a test set made up of 16 molecules of known IC50. Only 3 out of 1245 3D-QSAR models had statistical parameter values in the following ranges: a) R2, Q2 and r-Pearson value≥0.8000; b) RMSE/SD in the [0.90–1.05] interval; c) Q2/R2≈1; and d) P

Resampling schemes, and especially the bootstrap method, were proposed as a subclass of perturbation methods to ensure statistical confidentiality in statistical databases. Later, a method based on bootstrapping was presented to achieve the more specific task of anonymising contingency tables. In this paper, we argue that the latter proposal is either inefficient from a computational point of view or insecure due to a high disclosure risk. For illustration, we show that this bootstrap-based procedure for contingency tables can be emulated and outperformed by a cell-oriented random perturbation method, whose complexity can be theoretically quantified. For a given disclosure risk, our cell-oriented perturbation method is more efficient. For a given computational complexity, our cell-oriented method exhibits a lower disclosure risk. More generally, it can be concluded that the very principle of resampling precludes the design of contingency table anonymisation schemes simultaneously providing security, computational efficiency, and data quality.

Multi-objective optimization (MOO) has recently emerged as a useful technique in environmental engineering. One major limitation of this approach is that its computational burden grows rapidly with the number of environmental objectives, which causes difficulties regarding the computation and visualization of the Pareto solutions. In this work we present several theoretical and algorithmic developments for grouping environmental objectives into clusters on the basis of which the multi-objective optimization can be performed, thereby facilitating the computation and analysis of the Pareto solutions. Our method is based on a novel mixed-integer linear program (MILP) that identifies in a systematic manner groups of objectives that behave similarly. We test the capabilities of our approach using several examples, in which we compare it against other well-known clustering methods. Fil: Oliva, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina. Universitat Rovira I Virgili; España Fil: Guillén Gosálbez, G.. Universitat Rovira I Virgili; España Fil: Mateo Sanz, J.m.. Universitat Rovira I Virgili; España Fil: Jiménez Esteller, L.. Universitat Rovira I Virgili; España

Microalgae oil has been identified as a reliable resource for biodiesel production due to its high lipid productivity and potential cultivation in non-fertile locations. However, high scale production of microalgae based biodiesel depends on the optimization of the entire process to be economically feasible. The selected strain, medium, harvesting methods, etc., sorely affects the ash content in the dry biomass which have a direct effect in the lipid content. Moreover, the suitable lipids for biodiesel production, some of the neutral/saponifiable, are only a fraction of the total ones (around 30% dry base biomass in the best case). The present work uses computational tools for the modeling of different scenarios of the harvesting, oil extraction and transesterification. This rigorous modeling approach detects process bottlenecks that could have led to an overestimation of the potentiality of the microalgae lipids as a resource for the biodiesel production.

10.1016/j.biortech.2013.07.145 Microalgae-based biodiesel has several benefits over other resources such as less land use, potential cultivation in non-fertile locations, faster growth and especially a high lipid-to-biodiesel yield. Nevertheless, the environmental and economic behavior for high scale production depends on several variables that must be addressed in the scale-up procedure. In this sense, rigorous modeling and multicriteria evaluation are performed in order to achieve optimal topology for third generation biodiesel production. Different scenarios and the most promising technologies tested at pilot scale are assessed. Besides, the sensitivity analysis allows the detection of key operating variables and assumptions that have a direct effect on the lipid content. The deviation of these variables may lead to an erroneous estimation of the scale-up performance of the technology reviewed in the microalgae-based biodiesel process. The modeling and evaluation of different scenarios of the harvesting, oil extraction and transesterification help to identify greener and cheaper alternatives.

The aim of this work is to show the improvements achieved in terms of sustainability, environment and economic profit in a specific process design problem. The procedure combines rigorous simulation in Hysys® with an environmental evaluation tool programmed in Matlab®. This tool determines categorized impacts using the simulation results and a final global index through an aggregation method based on the geometric mean of the ratios of impacts with respect to the base case. This Hysys®-Matlab® connection allows us the automation in the modification of simulation variables, in the calculation of the environmental impacts, and in the implementation of optimization tools. The procedure is tested using a case study (styrene production), where data obtained from rigorous simulation validated with industrial data.

Flavonoids are functional constituents of many fruits and vegetables. Procyanidins are flavonoids with an oligomeric structure, and it has been shown that they can improve the pathological oxidative state of a diabetic situation. To evaluate whether procyanidins can modulate inflammation, an event strongly associated with obesity, diabetes and insulin resistance states, we used human adipocytes (SGBS) and macrophage-like (THP-1) cell lines and administered an extract of grape-seed procyanidins (GSPE). THP-1 and SGBS cells pre-treated with GSPE showed a reduction of IL-6 and MCP-1 expression after an inflammatory stimulus. GSPE stimuli alone modulate adipokine (APM1 and LEP) and cytokine (IL-6 and MCP-1) gene expression. GSPE partially inhibited NF-kappaB translocation to the nucleus in both cell lines. These preliminary findings demonstrate that GSPE reduces the expression of IL-6 and MCP-1 and enhances the production of the anti-inflammatory adipokine adiponectin suggesting that may have a beneficial effect on low-grade inflammatory diseases such obesity and type 2 diabetes.

Microaggregation is a clustering problem with cardinality constraints that originated in the area of statistical disclosure control for micro data. This article presents a method for multivariate microaggregation based on genetic algorithms (GA). The adaptations required to characterize the multivariate microaggregation problem are explained and justified. Extensive experimentation has been carried out with the aim of finding the best values for the most relevant parameters of the modified GA: the population size and the crossover and mutation rates. The experimental results demonstrate that our method finds the optimal solution to the problem in almost all experiments when working with small data sets. Thus, for small data sets the proposed method performs better than known polynomial heuristics and can be combined with these for larger data sets. Moreover, a sensitivity analysis of parameter values is reported which shows the influence of the parameters and their best values

Web Search Engines store and analyze queries made by their users in order to build their profiles and offer them personalized search results (i.e., results are ranked according to each user’s preferences). Even though this service is positive for the users, their profiles may contain sensitive information that can threaten their privacy. Literature in this field stresses the existence of a trade-off between the level of privacy achieved and the quality of service received. Current proposals try to maximize this trade-off, yet we argue that there is still room for improvement. In this paper, we present a new approach which improves the trade-off by grouping users who share similar profiles. Moreover, our new proposal is based on a hybrid P2P architecture that outperforms current schemes in terms of scalability. The proposed system has been tested in terms of performance and privacy achieved, using real data from the AOL dataset. Simulation results show that: (i) the system protects users’ privacy when they behave honestly, and penalizes selfish users; (ii) it supports a large number of users; (iii) the runtime required to group users is affordable; and (iv) groups are dynamic and their topology is unpredictable, which we argue that is positive from the privacy point of view. This work was partly supported by the Spanish Ministry of Science and Innovation (through Projects eAEGIS TSI2007-65406-C03-01, CO-PRIVACY TIN2011-27076- C03-01, ARES-CONSOLIDER INGENIO 2010 CSD2007-00004, Audit Transparency Voting Process IPT-430000-2010-31 and Ballot Next IPT-2012-0603-430000) and by the Government of Catalonia (under Grant 2009 SGR 1135). Francesc Solsona and Damià Castellà are funded by the MEyC under Contract TIN2011-28689-C02-02. Francesc Solsona is a member of the research group 2009 SGR145, funded by Government of Catalonia.