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The petrochemical sector will play a crucial role in developing low-carbon transition technologies, but the industry also contributes a significant proportion of greenhouse gas emissions. Momentum is building to help reduce the carbon footprint of this hard-to-abate sector, particularly through replacing fossil carbon feedstocks with carbon from biomass, captured CO2, and other recycled resources, but the broader implications of these so-called “solutions” remain unclear. Here, we assess the overall sustainability of such “renewable carbon pathways” by quantifying their life-cycle environmental footprints with respect to the previously defined nine planetary boundaries. We show that although a shift toward renewable carbon pathways could indeed reduce CO2 emissions by 25% to over 100%, the scenario with the lowest carbon footprint could exceed the biodiversity planetary boundary by at least 30%. Our work highlights the potential pitfalls of overlooking global environmental guardrails beyond greenhouse gas emissions reduction and identifies new avenues for quantifying the environmental footprint of decarbonization solutions for hard-to-abate sectors., One Earth, 4 (4), ISSN:2590-3322

© 2020 The Author(s)., [Purpose]: The DIANA study aimed to evaluate how often antimicrobial de-escalation (ADE) of empirical treatment is performed in the intensive care unit (ICU) and to estimate the effect of ADE on clinical cure on day 7 following treatment initiation., [Methods]: Adult ICU patients receiving empirical antimicrobial therapy for bacterial infection were studied in a prospective observational study from October 2016 until May 2018. ADE was defined as (1) discontinuation of an antimicrobial in case of empirical combination therapy or (2) replacement of an antimicrobial with the intention to narrow the antimicrobial spectrum, within the first 3 days of therapy. Inverse probability (IP) weighting was used to account for time-varying confounding when estimating the effect of ADE on clinical cure., [Results]: Overall, 1495 patients from 152 ICUs in 28 countries were studied. Combination therapy was prescribed in 50%, and carbapenems were prescribed in 26% of patients. Empirical therapy underwent ADE, no change and change other than ADE within the first 3 days in 16%, 63% and 22%, respectively. Unadjusted mortality at day 28 was 15.8% in the ADE cohort and 19.4% in patients with no change [p = 0.27; RR 0.83 (95% CI 0.60–1.14)]. The IP-weighted relative risk estimate for clinical cure comparing ADE with no-ADE patients (no change or change other than ADE) was 1.37 (95% CI 1.14–1.64)., [Conclusion]: ADE was infrequently applied in critically ill-infected patients. The observational effect estimate on clinical cure suggested no deleterious impact of ADE compared to no-ADE. However, residual confounding is likely.

Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 Intensive Care Units(ICU) in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results: The database included a total of 2,022 patients (mean age 64[IQR5-71] years, 1423(70.4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A(mild) phenotype (537;26.7%) included older age (65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.Conclusion: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

Applied Energy, 298, ISSN:0306-2619, ISSN:1872-9118

Additional file 1. Supplementary online content.

The global agenda is undoubtedly determined by the success of achieving the Sustainable Development Goals (SDGs). Both public and private institutions show great efforts towards the full integration of the SDGs in their own agendas. Ultimately, national governments are responsible for the effective budget allocation for sustainable development. The lack of open, discussed and widely accepted general guidelines related to how to link national public spending (based on the classification of the functions of government) to the achievement of the SDGs is reported in the literature. Thus, the aim of this paper is to propose an initial mapping between them, as well as to assess, through data envelopment analysis (DEA), the national public spending efficiency where government expenditure is consumed (inputs) to attain a certain progress in indicators specific to all 17 SDGs (outputs). On the one hand, results were analyzed for each SDG by income groups, unraveling inefficient spending strategies, thus identifying potential weaknesses that should be overcome before some countries can achieve the same level of progress on SDGs as the best performing nations. On the other hand, it was demonstrated that low income and high income countries deliver higher average public spending efficiency. Countries of these two groups are more often deemed efficient, being displayed alongside the efficient frontiers of the DEA. This finding highlights that low middle-income and upper middle-income countries exhibit the major room for improvement in public spending. One of the authors (J. Cristóbal) acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities through the "Beatriz Galindo" grant (BEAGAL18/00035)

The global food system inextricably connects human health and environmental integrity. It holds the transformative capability to significantly reduce levels of environmental degradation, caused by current food production practices, and alleviate the ‘triple burden’ of malnutrition, existing due to food consumption patterns. System-wide transitions are therefore paramount to tackling environmental and nutritional challenges that are exacerbated by a rapidly growing population. This work presents a novel application of Data Envelopment Analysis (DEA) to study the sustainability of food supply patterns around the world and appraise the potential to lower environmental pressure without compromising the supply of calories and nutritional quality. By relating environmental impacts to caloric availability and nutritional adequacy, DEA computes a relative performance score for 139 countries and identifies only 18 countries with per capita food supplies that are ‘efficient’ in transforming five environmental inputs (land use, greenhouse gas emissions, acidification potential, eutrophication potential and freshwater withdrawals) into calories and nutrition. The widespread extent of ‘inefficiency’ stresses that the significant opportunity and need to reduce environmental impacts from food is truly global and extensive. Results of this analysis also provide quantitative information on the varying degrees of potential to improve the ways in which each nation's population is fed and therefore offers country-specific insight for decision-makers into the integration of environmental and nutritional outcomes for sustainable development., Science of The Total Environment, 755 (Part 1), ISSN:0048-9697, ISSN:1879-1026

In a plant, different utilities may be produced from different sources whose costs vary singularly. Therefore, evaluating the financial risk associated with heat exchanger networks (HEN) with multiple utilities is essential. This work aims to address the HEN synthesis under uncertainty by presenting several optimization models for managing financial risk. To this end, three different risk metrics are employed: variability index, downside risk, and risk area ratio. The resulting models are based on an enhanced version of the stage-wise superstructure, which incorporates the potential to place utilities at all HEN stages. The study considers fixed utility sources as well as the possibility to vary those yearly. Furthermore, we propose a novel “pseudointersection” calculation scheme for the risk area ratio metric, which proves efficient in assessing how risks can be reduced at minimal opportunity loss. The proposed models are able to efficiently identify solutions appealing to different investor profiles.

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.

This paper is devoted to the study of the global well-posedness for a non-local-in-time Navier-Stokes equation. Our results recover in particular other existing well-posedness results for the Navier-Stokes equations and their time-fractional version. We show the appropriate manner to apply Kato's strategy and this context, with initial conditions in the divergence-free Lebesgue space \begin{document}$ L^\sigma_d(\mathbb{R}^d) $\end{document}. Temporal decay at \begin{document}$ 0 $\end{document} and \begin{document}$ \infty $\end{document} are obtained for the solution and its gradient.

The current trend towards improving energy efficiency in industry calls for advanced decision-support tools for quantifying the level of efficiency of industrial facilities. This work applies Data Envelopment Analysis (DEA) to assess the performance of a set of 34 Air Separation Units (ASUs) producing industrial gases via air distillation. We identify the best ASUs according to energy efficiency and productivity criteria and define improvement targets for the units found inefficient. Furthermore, we analyze the temporal evolution of the efficiency scores using the Malmquist Productivity Index (MPI), which is calculated from real data of a gas company operating ASUs around the world. Our results provide insight on how to improve the efficiency of existing plants by identifying sources of inefficiency and reference facilities that could be used for benchmarking.

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.

The difficulties in climate change negotiations together with the recent withdrawal of the U.S. from the Paris Agreement call for new cooperative mechanisms to enable a resilient international response. In this study we propose an approach to aid such negotiations based on quantifying the benefits of interregional cooperation and distributing them among the participants in a fair manner. Our approach is underpinned by advanced optimisation techniques that automate the screening of millions of alternatives for differing levels of cooperation, ultimately identifying the most cost-effective solutions for meeting emission targets. We apply this approach to the Clean Power Plan, a related act in the U.S. aiming at curbing carbon emissions from electricity generation, but also being withdrawn. We find that, with only half of the states cooperating, the cost of electricity generation could be reduced by US$41 billion per year, while simultaneously cutting carbon emissions by 68% below 2012 levels. These win–win scenarios are attained by sharing the emission targets and trading electricity among the states, which allows exploiting regional advantages. Fair sharing of dividends may be used as a key driver to spur cooperation since the global action to mitigate climate change becomes beneficial for all participants. Even if global cooperation remains elusive, it is worth trying since the mere cooperation of a few states leads to significant benefits for both the U.S. economy and the climate. These findings call on the U.S. to reconsider its withdrawal but also boost individual states to take initiative even in the absence of federal action., Energy & Environmental Science, 11 (3), ISSN:1754-5692, ISSN:1754-5706

Although various Heat Exchanger Network (HEN) synthesis methods have been proposed in the literature, fundamental study for addressing uncertainties arisen from market fluctuations using stochastic variables and parameters is scarce. Such feature certainly adds difficulties to a problem already not straightforward to solve. In that manner, this work adapts a meta-heuristic approach to be able to efficiently perform such task. Uncertainties are assumed from variations in costs of commodities related to production of utilities. Several forecast scenarios are generated via Monte Carlo Simulation in order to obtain discretized distributions for the uncertain variables. Five financial risk metrics are applied for risks management. Each metric is formulated as secondary function to expected total annual costs (ETAC) in a multi-objective optimization (MOO) model with two objective functions. A benchmark case study is adapted in order to demonstrate the method reliability. The approach is able to achieve results that fit for different types of investors ( e.g. , risk-averse, risk-taker), handling uncertainty by efficiently performing trade-offs in heat exchange areas and utilities requirement.

The combined use of data envelopment analysis (DEA) and life cycle assessment (LCA) has recently emerged as a suitable technique for assessing the environmental efficiency of products. The standard approach DEA + LCA requires the input/output data to be perfectly known in advance. In practice, however, the environmental impact calculations are typically affected by a high degree of uncertainty stemming from lack of data and/or inaccurate measurements. This contribution introduces a methodology that combines DEA, LCA and stochastic modelling to evaluate the environmental efficiency of products under uncertainty. The capabilities of this approach are illustrated through its application to the assessment of eleven technologies for electricity generation. We show that the efficiency scores in the nominal and the stochastic cases can differ significantly, to the point that a technology can be deemed efficient or inefficient depending on the values of the uncertain parameters. These results support the need to incorporate uncertainty modeling into the DEA + LCA framework in order to further assess the validity of the deterministic calculations.

Cryogenic air separation to produce nitrogen, oxygen and argon with high quality requirements is an energy-intensive industrial process that requires large quantities of electricity. The complexity in operating these networks stems from the volatile conditions, namely electricity prices and products demands, which vary every hour, creating a clear need for computer-aided tools to attain economic and energy savings. In this article, we present a multiperiod mixed-integer linear programming (MILP) model to determine the optimal production schedule of an industrial cryogenic air separation process so as to maximize the net profit by minimizing energy consumption (which is the main contributor to the operating costs). The capabilities of the model are demonstrated by means of its application to an existing industrial process, where significant improvements are attained through the implementation of the MILP.

The concept of circular economy has gained wide interest in industry as a powerful strategy to reduce impacts while remaining economically competitive. Here, we explore the benefits of this approach in the chemical industry, quantifying explicitly the economic and environmental benefits of ethylene recovery from polyethylene plastics using process modeling coupled with Life Cycle Assessment (LCA). Our results show that the recovered ethylene represents a win-win alternative, offering a highly competitive cost of 0.386 €/kg vs 0.835 €/ kg with the standard naphtha-based route. Furthermore, substantial reductions of as much as 87%, 89%, and 164% could also be attained (compared to the naphtha-based ethylene) in impacts on human health, ecosystem quality, and resources scarcity, respectively. Overall, this work aims to promote the adoption of circular economy strategies in the chemical sector, while highlighting the role of process modeling, LCA, and systems thinking in quantifying the associated benefits to build strong cases for policymaking.

Journal of Cleaner Production, 270, ISSN:0959-6526

The development of sustainable products and processes has recently gained wide interest in the chemical industry. Here, we review the application of the life cycle optimization framework to chemical systems, where the key idea is to combine life cycle assessment (LCA) with multiobjective optimization (MOO) so as to identify solutions attaining simultaneously economic and environmental benefits. We introduce the fundamentals of this method and highlight its capabilities in three examples at various spatial and temporal scales, i.e., the sustainable design of hydrogen supply chains and process flowsheets and the environmental assessment of chemicals. We argue that the combined use of MOO and LCA will play a key role in the transition to a more sustainable chemical industry, yet we identify methodological and practical issues that still need to be resolved for its widespread use in industry.

Thermal insulation based on biomaterials with low energy embodied is a promising alternative to reduce the energy demand of the actual building stock and help meeting the climatic 2050 EU goals. Despite their appealing properties, these materials are not widely available in the insulation market for a variety of reasons such as a more laborious implementation compared to commercial alternatives. To overcome this limitation and change this trend, this work introduces a systematic method to generate efficient combinations of biomaterials with reduced cost and environmental impacts that could be manufactured as commercial sandwich or solid panels and fiber mats.

Transitioning towards a more sustainable society calls for systematic tools to assess the sustainability performance of urban systems. To perform this task effectively, this work introduces a novel method based on the combined use of Data Envelopment Analysis (DEA) and the hypervolume indicator. In essence, DEA is applied to (i) distinguish between efficient and inefficient urban systems through the identification of best practices; and to (ii) establish improvement targets for the inefficient urban systems that, if attained, would make them efficient. Meanwhile, the hypervolume indicator is employed in conjunction with DEA to evaluate how urban systems evolve with time. The capabilities of this approach are illustrated through its application to the sustainability assessment of London boroughs between 2012–2014. Results reveal that most boroughs tend to perform well in terms of the indicators selected, with 20–25 of the 32 boroughs found efficient depending on the year. Regarding the temporal assessment, a global improvement in sustainability performance was found, with a strong relationship between the boroughs’ performances and their locations. The method proposed opens new pathways of social and environmental research for the application of advanced multi-criteria decision-support tools in the assessment and optimisation of urban systems.

University rankings have become an important tool to compare academic institutions within and across countries. Yet, they rely on aggregated scores based on subjective weights which render them sensitive to experts’ preferences and not fully transparent to final users. To overcome this limitation, we apply Data Envelopment Analysis (DEA) to evaluate UK universities in the field of chemical engineering as a case study, using data retrieved from two national rankings. DEA is a non-parametric approach developed for the multi-criteria assessment of entities that avoids the use of subjective weightings and aggregated scores; this is accomplished by calculating an efficiency index, on the basis of which universities can be classified as either ‘efficient’ or ‘inefficient’. Our analysis shows that the Higher Education Institutions (HEI) occupying the highest positions in the chemical engineering rankings might not be the most efficient ones, and vice versa, which highlights the need to complement the use of rankings with other analytical tools. Overall, DEA provides further insight into the assessment of HEIs, allowing institutions to better understand their weaknesses and strengths, while pinpointing sources of inefficiencies where improvement efforts must be directed.

Time to clearance of pathogens is probably critical to outcome in septic shock. Current guidelines recommend intervention for source control within 12 hours after diagnosis. We aimed to determine the epidemiology of source control in the management of sepsis and to analyze the impact of timing to source control on mortality.Prospective observational analysis of the Antibiotic Intervention in Severe Sepsis study, a Spanish national multicenter educational intervention to improve antibiotherapy in sepsis.Ninety-nine medical-surgical ICUs in Spain.We enrolled 3,663 patients with severe sepsis or septic shock during three 4-month periods between 2011 and 2013.Source control and hospital mortality.A total of 1,173 patients (32%) underwent source control, predominantly for abdominal, urinary, and soft-tissue infections. Compared with patients who did not require source control, patients who underwent source control were older, with a greater prevalence of shock, major organ dysfunction, bacteremia, inflammatory markers, and lactic acidemia. In addition, compliance with the resuscitation bundle was worse in those undergoing source control. In patients who underwent source control, crude ICU mortality was lower (21.2% vs 25.1%; p = 0.010); after adjustment for confounding factors, hospital mortality was also lower (odds ratio, 0.809 [95% CI, 0.658-0.994]; p = 0.044). In this observational database analysis, source control after 12 hours was not associated with higher mortality (27.6% vs 26.8%; p = 0.789).Despite greater severity and worse compliance with resuscitation bundles, mortality was lower in septic patients who underwent source control than in those who did not. The time to source control could not be linked to survival in this observational database.

Processes for recycling or reusing material resources have naturally appeared along with the history of technology development when need, resource scarcity, opportunities, and enabling technologies have combined conveniently with insight, intuition, and fortune. The current pressure for saving resources by efficiently integrating production and consumption processes requires the development of systematic approaches exploring opportunities and determining the best alternatives. While recent research has addressed the problem of directly connecting sources and sinks, we turn our attention to doing so with improved efficiency and at a lower cost. This work addresses the event of a systematic framework for the synthesis and optimization of the industrial process networks required by an economic system aimed at adding value to waste and minimizing the continuous need for non-renewable resources from a circular economy perspective. The systematic framework presented relies upon a novel model formulated as a superstructure that includes the transformation technologies needed for the revaluation of waste, which, in turn, raises resource-sharing opportunities otherwise ignored. The model is flexible enough to address primary uncertainty sources and manage different objectives (e.g., economic and environmental. A case study on hydrocarbons recovery from waste plastic illustrates the capabilities of the model, which proves to be a useful tool for the identification of the most promising routes for waste transformation and the less efficient processes where substantial improvement is required to increase their competitive edge. The methodology is valuable for the multiple actors involving the waste revaluation sector, from private companies to policymakers.

Reducing inequality is essential for sustainable development, yet our understanding of its many dimensions and driving forces is still limited. Here we study the global distribution of 25 environmental burdens encompassing natural resources (water, materials and land use) and air emissions, all related to activities underpinning human welfare. We find large disparities in inequality levels across burdens and a general, yet slow, decline in inequality in the period 1995–2009, explained mostly by the faster economic growth of emerging economies. Acknowledging that allocation issues may hamper greater equality, we propose a framework for an optimal allocation of quotas for environmental burdens respecting a maximum allowable inequality limit while ensuring a safe operation within the Earth’s ecological capacity. Our results shed light on the global distribution of environmental burdens and provide a roadmap for achieving a greater environmental equality using systems optimisation. It is hoped that this work will trigger further discussion on the need to address environmental inequality, currently missing in the Sustainable Development Goals, and open up new research avenues on the use of whole-systems approaches in solving global sustainability problems.

Current energy systems models focus on cost minimization with a bound on some greenhouse gas emissions. This limited environmental scope can lead to mixes that are not consistent with our sustainable development. To circumvent this limitation, we here make use of the concept of monetization and life cycle assessment to quantify the indirect costs of electricity generation in the design of energy systems. Applying our approach to the United States, we found that the indirect costs of electricity generation could be reduced by as much as 63% by meeting the Paris Agreement. Consequently, the total opportunity cost (i.e., direct and indirect costs) of withdrawing from the Paris Agreement and continuing with the current mix would be as high as 1103 ± 206 billion USD2013 in 2030 (i.e., 6% of the United States gross domestic product in 2017). By optimizing the direct and indirect costs of electricity generation concurrently, we found an optimal ecological solution that attains total economic savings compared to the Paris Agreement mix of as much as 373 ± 164 billion USD2013 in 2030. Our work highlights the need to extend the environmental policies that regulate energy systems beyond the direct greenhouse emissions to consider other critical environmental criteria.

Methanol from captured CO2 provides a more sustainable alternative to gasoline due to its low carbon footprint, yet it requires a large amount of renewable energy that could be used instead to decarbonise the electricity mix. A techno-economic and environmental analysis of methanol produced from captured CO2 and renewable energy is conducted to shed light on the transportation-power nexus. The investigated scenarios differ in how the carbon-free energy source – electricity from wind – is used to either decarbonise the electricity mix or produce green methanol to displace conventional fossil fuels. The assessment is carried out for six countries that differ in the composition of their electricity mix and gasoline consumption. The results of this holistic assessment show that, at present, decarbonising the electricity grid using renewable energy sources and carbon capture and storage would offer greater environmental benefits, without incurring large changes in direct economic costs, compared to producing methanol from the hydrogenation of captured CO2. Though this insight might change in the future if the carbon intensity of the mix is reduced and green methanol becomes cheaper, it highlights the need to consider the transportation-power nexus in assessing alternative fuels and thereby prevent shifting of emissions from one sector to another over their life cycle.

Correction for ‘Powering sustainable development within planetary boundaries’ by Ibrahim M. Algunaibet et al., Energy Environ. Sci., 2019, 12, 1890–1900., Energy & Environmental Science, 12 (12), ISSN:1754-5692, ISSN:1754-5706

The new European Union’s Common Agricultural Policy will cover the period from 2015 to 2020. Significant novelties in the payment scheme have been introduced which may potentially encourage farmers to implement changes at the farm level by meeting certain environmental requirements in return for support payments. The mandatory requirements, commonly known as ‘greening rules’, consist of crop diversification, maintenance of permanent grassland and establishment of an ecological focus area. This paper presents a decision-support tool based on a multi-stage linear programming model that identifies optimal cropping plan decisions under the new Common Agricultural Policy. The capabilities of our tool are illustrated through its application to the Spanish agricultural regions. Our method identifies the optimal cropping plan (i.e., crops to be grown and their acreage each year during the reform horizon) that maximizes the farmer’s net return in each region. Furthermore, the model can also be used to calculate the minimum subsidy value that would make the implementation of greening rules economically appealing, thereby promoting the widespread adoption of more sustainable agricultural practises.

Mucormycosis is usually an acute angioinvasive infections, which leads to non-suppurative necrosis and significant tissue damage. It represents 1.6% of all the invasive fungal infections and predominates in immunosuppressed patients with risk factors. Incidence has been significantly increased even in immunocompetent patients. Due to finding a case of disseminated mucormycosis caused by Rhizomucor pusillus in a young immunocompetent patient, a systematic review was carried out of reported cases in PubMed of mucormycosis in immunocompetent adults according to the main anatomic locations, and especially in disseminated cases. A review of the main risk factors and pathogenicity, clinical manifestations, techniques of early diagnosis, current treatment options, and prognosis is presented. Taxonomy and classification of the genus Mucor has also been reviewed.

Policies are encouraging to reduce emissions in all sectors, due to the high energy demand during the life cycle of the processes. Constructions are not an exception and policies are encouraging to reduce energy demand in buildings. Although new constructions are reducing their energy demand according to new directives, without interventions in the actual building stock, the European goals proposed for 2050 cannot be achieved. Insulation is known to achieve an important reduction in the energy demand in the operation phase of the buildings. Despite that, this implementation carries out an initial investment in both, cost and impact, which suggest that using low embodied materials, such us bio-based materials, could improve this solution. Despite their good thermal properties, these new materials are not arriving at the actual market for insulators. In order to improve this scalability, an evaluation of the efficiency of different materials is carried out, with a following combination of the efficient materials in order to generate new hybrid solutions. These hybrid materials cope to simplify the applicability of bio-based materials, generating sandwich panels or combined mats to generate materials similar to the commercialized ones which have an increasing market nowadays. The methodology presented combines Data Envelopment Analysis (DEA) and Building modelling simulation. Using thermal simulations of EnergyPlus and the evaluation of efficiency and further projections of DEA. Seven bio-based materials and polyurethane for comparison have been studied, with a thickness that varies from 1 cm and 26 cm in increments of 5 cm. DEA analysis is used to quantify the efficiency score of the different thicknesses of the materials. The non-efficient materials are projected to this frontier, achieving a combination of materials. Those combinations have been analysed in order to model only the feasible solutions, which have proved to achieve efficiency in all the combinations proposed.

Resumen Antecedentes Las mucormicosis son infecciones poco frecuentes en pacientes inmunocompetentes, y se han descrito muy pocos casos de mucormicosis asociadas a aspergilosis en pacientes no hematologicos. Caso clinico Un varon de 17 anos, inmunocompetente y sin factores de riesgo previamente conocidos, ingreso en el hospital tras presentar crisis convulsivas 11 dias despues de sufrir un accidente de moto. Presento un curso clinico torpido por infeccion fungica invasiva mixta, con afectacion pulmonar por Aspergillus niger y mucormicosis diseminada por Rhizomucor pusillus (diagnostico histopatologico y microbiologico en varios lugares no contiguos). Fue tratado con anfotericina B liposomal durante 7 semanas (dosis total acumulada > 10 g) y preciso multiples reintervenciones quirurgicas. El paciente sobrevivio y fue dado de alta de UCI tras 5 meses de evolucion y multiples complicaciones. Conclusiones El tratamiento con anfotericina B liposomal y el manejo quirurgico agresivo consiguio la erradicacion de la infeccion fungica invasiva mixta, pero alertamos de la necesidad de mantener un mayor grado de sospecha clinica y de realizar tecnicas de diagnostico precoz de infecciones fungicas invasivas en pacientes no inmunodeprimidos para evitar la diseminacion de la enfermedad y el mal pronostico asociado.

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.

Mathematical programming models for heat exchanger networks (HEN) synthesis generally do not take into account the fluctuations in the costs of commodities related to the plant operation. This work proposes to include such features in the HEN synthesis model by assuming utility production costs as uncertain and considering them as stochastic variables. These are discretized via Monte Carlo Simulation (MCS) with the Geometric Brownian Motion (GBM) model, which creates an appropriate number of scenarios throughout plant lifetime based on natural gas and electricity historical price data. The downside risk, which is a target based metric, is used for financial risks management. Multi-objective optimization (MOO) can be performed to better address trade-offs between solutions’ expected total annual costs (ETAC) and the chosen metric. A meta-heuristic two-level approach is adapted to handle such MOO. The developed scheme is based on the e-constraint method. Two costs targets are tested with downside risk. The number of scenarios with costs higher than the targets is reduced when compared with single ETAC optimization. The developed meta-heuristic solution method was able to efficiently perform MOO in a model with rather large number of scenarios, and is a good option for applying risk metrics including uncertainties in HEN synthesis.

Cryogenic air separation technology is used to produce large amounts of technical gases with high purity levels. It is an industrial energy-intensive technology consuming high volumes of electricity. The complexity in operating air separation processes increases since the volatile conditions (i.e., electricity prices and product demand) can hourly change. Thus, the use of computer-aided tools to obtain energy and economic savings becomes crucial. In this paper, we state a multiperiod and multiproduct mixed-integer linear programming (MILP) model to determine the optimal production schedule of an industrial cryogenic air separation process. The MILP model contributes to minimize the energy consumption in the network whereas the net profit is maximized. The model capabilities are developed and validated using an existing air separation plant.