Modeling,simulation and intensification of hydroprocessing of micro-algae oil to produce renewable aviation fuel

Micro-algae are photosynthetic organisms, which represent a promissory renewable raw material for biofuels production, since they can be cultivated in non-fertile lands, avoiding the competition with food crops for land use. From micro-algae, oil can be obtained oil that can be converted to biodiesel, green diesel and biojet fuel. In particular, the renewable aviation fuel is one of the less explored biofuels; nevertheless, for the aviation sector, this is the best alternative to reduce CO₂ emissions, allowing its sustainable development. In order to produce hydrocarbons in the boiling point range of jet fuel, we need to transform the micro-algae oil. A number of research projects report the use of micro-algae oil for the production of biojet fuel through the hydrotreating process. However, the application of process intensification strategies for the hydroprocessing of micro-algae oil has not been reported. Therefore, in this work we propose the modeling, simulation and intensification of the hydrotreating process to produce biojet fuel, considering micro-algae oil as raw material. The hydroprocessing of micro-algae oil is modeled in Aspen Plus processes simulator, based on data from an experimental study recently reported. The produced renewable hydrocarbons are purified through conventional and intensified distillation sequences; thereby, conventional and intensified hydrotreating processes are defined and evaluated in terms of total annual costs, CO₂ emissions and biojet fuel price. Simulation results show that the implementation of intensification strategies leads to the production of biojet fuel with reduced carbon dioxide emissions, 34% less, and a competitive price per liter, 78% cheaper than fossil jet fuel price.     

Perceptions of the school social context across the transition to middle school: Heightened sensitivity among Latino students?

The current study was designed to examine the ways in which perceived behavioral norms among grade mates and school social climate vary across the transition to middle school. The main goals of the study were to test whether Latino students may be more sensitive to the school social climate than White students and whether perceived behavioral norms might help explain this association. Cross-sectional analyses comparing Latino and White students (N = 383) revealed that perceptions of school social climate and behavioral norms became more negative across the middle school transition. Multiple regression analysis showed that school climate perceptions predicted self-reported academic compliance and rule breaking only for Latino students. Mediational analysis revealed that the association between perceived climate and self-reported rule breaking was partially accounted for by perceptions of behavioral norms for rule breaking only among Latino students. Taken together, the results suggest that compared with White students, Latino students are more sensitive to school social climate as it relates to their school conduct. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Interchanging lexical resources on the Semantic Web

Lexica and terminology databases play a vital role in many NLP applications, but currently most such resources are published in application-specific formats, or with custom access interfaces, leading to the problem that much of this data is in “data silos” and hence difficult to access. The Semantic Web and in particular the Linked Data initiative provide effective solutions to this problem, as well as possibilities for data reuse by inter-lexicon linking, and incorporation of data categories by dereferencable URIs. The Semantic Web focuses on the use of ontologies to describe semantics on the Web, but currently there is no standard for providing complex lexical information for such ontologies and for describing the relationship between the lexicon and the ontology. We present our model, lemon, which aims to address these gaps while building on existing work, in particular the Lexical Markup Framework, the ISOcat Data Category Registry, SKOS (Simple Knowledge Organization System) and the LexInfo and LIR ontology-lexicon models.     

Development of an optimized multi-biomarker panel for the detection of lung cancer based on principal component analysis and artificial neural network modeling

Lung cancer is a public health priority worldwide due to the high mortality rate and the costs involved. Early detection of lung cancer is important for increasing the survival rate, however, frequently its diagnosis is not made opportunely, since detection methods are not sufficiently sensitive and specific. In recent years serum biomarkers have been proposed as a method that might enhance diagnostic capabilities and complement imaging studies. However, when used alone they show low sensitivity and specificity because lung cancer is a heterogeneous disease. Recent reports have shown that simultaneous analysis of biomarkers has the potential to separate lung cancer patients from control subjects. However, it has become clear that a universal biomarker panel does not exist, and optimized panels need to be developed and validated in each population before their application in a clinical setting. In this study, we selected 14 biomarkers from literature, whose diagnostic or prognostic value had been previously demonstrated for lung cancer, and evaluated them in sera from 63 patients with lung cancer and 87 non-cancer controls (58 Chronic Obstructive Pulmonary Disease (COPD) patients and 29 current smokers). Principal component analysis and artificial neural network modeling allowed us to find a reduced biomarker panel composed of Cyfra 21.1, CEA, CA125 and CRP. This panel was able to correctly classify 135 out of 150 subjects, showing a correct classification rate for lung cancer patients of 88.9%, 93.3% and 90% in training, validation and testing phases, respectively. Thus, sensitivity was increased 18.31% (sensitivity 94.5% at specificity 80%) with respect to the best single marker Cyfra 21.1. This optimized panel represents a potential tool for assisting lung cancer diagnosis, therefore it merits further consideration.     

Paving the way to collaborative context-aware mobile applications: a case study on preventing worsening of allergy symptoms

Multimedia Tools and Applications - In recent years, the evolution of smartphones and their software applications has grown exponentially; together with the advance of the Internet of Things and...     

Event-driven temporal models for explanations - ETeMoX: explaining reinforcement learning

Software and Systems Modeling - Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a...     

Ensuring the quasi-termination of needed narrowing computations

We present a characterization of first-order functional programs which are quasi-terminating with respect to the symbolic execution mechanism of needed narrowing, i.e., computations in these programs consist of a sequence of finitely many different function calls (up to variable renaming). Quasi-terminating programs are particularly useful for program analysis and transformation, since in this context quasi-termination often amounts to full termination.     

Prevalence and predictors of smoking behavior among Vietnamese men living in California.

Vietnamese populations in Vietnam and the United States have a high prevalence of smoking. The associations among behavioral risk factors, acculturation, and smoking among the Vietnamese population living in the United States are not well documented. The present study aimed to identify the factors associated with smoking behavior among Vietnamese men living in Santa Clara County, California. A cross-sectional random-digit-dialed telephone survey was conducted. The sampling frame consisted of 27 Vietnamese surnames from the Santa Clara County telephone directory. A total of 660 adult respondents were interviewed to collect information on general health status, alcohol and tobacco use, HIV/AIDS, sexual behavior, injury control, hypertension, cholesterol screening, and acculturation. Of the 660 adults interviewed, 364 (55.2%) were male and 296 (44.8%) were female. Among males, 31.9% were current smokers, and among females, only one woman reported smoking. Univariate analyses revealed that having less than a college education, having poor English language skills, using Vietnamese at home and with friends, being less acculturated, not having a routine physical or blood cholesterol check, and being a binge drinker were significantly associated with an increased likelihood of smoking. Multivariate analysis revealed two independently associated factors: Respondents who were more acculturated were less likely to smoke (OR = 0.38, 95% CI = 0.18-0.83), and those not having cholesterol checked were more likely to smoke (OR = 2.48, 95% CI = 1.30-4.71). Acculturation level was inversely associated with smoking among Vietnamese adult men in Santa Clara County. Other health risk behaviors coexisted with smoking behavior and should be considered in prevention programs.     

A headspace solid-phase microextraction method of use in monitoring hexanal and pentane during storage: Application to liquid infant foods and powdered infant formulas

The determination of two secondary lipid oxidation compounds (hexanal and pentane) in liquid infant foods using a headspace solid-phase microextraction gas chromatographic (HS-SPME-GC) method has been developed and validated.     

Production of a high‐protein meal and fermentable sugars from defatted soybean meal,a co‐product of the soybean oil industry

The goal of this research was to produce a high‐protein meal by treating defatted soybean meal, a by‐product of soybean oil production, with dilute acid. Treatments were a mild hydrolysis at 80 °C with sulphuric acid at concentrations ranging from 0.5% to 2.0% (w/v) and times varying from 1 to 16 h that were arranged according to a central composite rotatable experimental design. The end products were an enhanced‐protein meal and a carbohydrate concentrate of fermentable and nonfermentable sugars. The highest protein content rise, from 48% to 58%, was for treatments with concentrations of acid ranging between 1.2% and 1.7% and times between 1.0 and 2.6 h. The maximum yield of fermentable sugar was 21.0% d.b. at 2.0% H₂SO₄ and treatments of at least 6 h. The conditions that provide a highest protein and sugar contents were the treatments with concentrations of sulphuric acid ranging from 0.9 to 1.9% H₂SO₄ for 1–4 h.