922 resultados para INTEGRATED BIOLOGICAL POND SYSTEM
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In this study, a novel hybrid thermochemical-biological refinery integrated with power-to-x approach was developed for obtaining biopolymers (namely polyhydroxyalkanoates, PHA). Within this concept, a trilogy process schema comprising of, (i) thermochemical conversion via integrated pyrolysis-gasification technologies, (ii) anaerobic fermentation of the bioavailable products obtained through either thermochemistry or water-electrolysis for volatile fatty acids (VFA) production, (iii) and VFA-to-PHA bioconversion via an original microaerophilic-aerobic process was developed. During the first stage of proposed biorefinery where lignocellulosic (wooden) biomass was converted into, theoretically fermentable products (i.e. bioavailables) which were defined as syngas and water-soluble fraction of pyrolytic liquid (WS); biochar as a biocatalyst material; and a dense-oil as a liquid fuel. Within integrated pyrolysis - gasification process, biomass was efficiently converted into fermentable intermediates representing up to 66% of biomass chemical energy content in chemical oxygen demand (COD) basis. In the secondary stage, namely anaerobic fermentation for obtaining VFA rich streams, three different downstream process were investigated. First fermentation test was acidogenic bioconversion of WS materials obtained through pyrolysis of biomass within an original biochar-packed bioreactor, it was sustained up to 0.6 gCOD/L-day volumetric productivity (VP). Second, C1 rich syngas materials as the gaseous fraction of pyrolysis-gasification stage, was fermented within a novel char-based biofilm sparger reactor (CBSR), where up to 9.8 gCOD/L-day VP was detected. Third was homoacetogenic bioconversion within the innovative power-to-x pathway for obtaining commodities via renewable energy sources. More specifically, water-electrolysis derived H2 and CO2 as a primary greenhouse gas was successfully bio-utilized by anaerobic mixed cultures into VFA within CBSR system (VP: 18.2 gCOD/L-day). In the last stage of the developed biorefinery schema, VFA is converted into biopolymers within a new continuous microaerophilic-aerobic microplant, where up to 60% of PHA containing sludges was obtained.
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Mercury (Hg) pollution is one of the most serious environmental problems. Due to public concern prompted by the symptoms displayed by people who consumed contaminated fish in Minamata, Japan in 1956, Hg pollution has since been kept under constant surveillance. However, despite considerable accumulation of knowledge on the noxious effects of ingested or inhaled Hg, especially for humans, there is virtually nothing known about the genotoxic effects of Hg. Because increased mitotic crossing over is assumed to be the first step leading to carcinogenesis, we used a sensitive short-term test (homozygotization index) to look for DNA alterations induced by Hg fumes. In one Aspergillus nidulans diploid strain (UT448//UT184), the effects of the Hg fumes appeared scattered all over the DNA, causing 3.05 times more recombination frequencies than the mean for other strains. Another diploid (Dp II- I//UT184) was little affected by Hg. This led us to hypothesize that a genetic factor present in the UT184 master strain genome, close to the nicB8 genetic marker, is responsible for this behavior. These findings corroborate our previous findings that the homozygotization index can be used as a bioassay for rapid and efficient assessment of ecotoxicological hazards.
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Background: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term patholog to mean a homolog of a human disease-related gene encoding a product ( transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. Results: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity ( 70 - 85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool ( FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic ( 53%), hereditary ( 24%), immunological ( 5%), cardio-vascular (4%), or other (14%), disorders. Conclusions: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.
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The fabrication of heavy-duty printer heads involves a great deal of grinding work. Previously in the printer manufacturing industry, four grinding procedures were manually conducted in four grinding machines, respectively. The productivity of the whole grinding process was low due to the long loading time. Also, the machine floor space occupation was large because of the four separate grinding machines. The manual operation also caused inconsistent quality. This paper reports the system and process development of a highly integrated and automated high-speed grinding system for printer heads. The developed system, which is believed to be the first of its kind, not only produces printer heads of consistently good quality, but also significantly reduces the cycle time and machine floor space occupation.
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This paper proposes a template for modelling complex datasets that integrates traditional statistical modelling approaches with more recent advances in statistics and modelling through an exploratory framework. Our approach builds on the well-known and long standing traditional idea of 'good practice in statistics' by establishing a comprehensive framework for modelling that focuses on exploration, prediction, interpretation and reliability assessment, a relatively new idea that allows individual assessment of predictions. The integrated framework we present comprises two stages. The first involves the use of exploratory methods to help visually understand the data and identify a parsimonious set of explanatory variables. The second encompasses a two step modelling process, where the use of non-parametric methods such as decision trees and generalized additive models are promoted to identify important variables and their modelling relationship with the response before a final predictive model is considered. We focus on fitting the predictive model using parametric, non-parametric and Bayesian approaches. This paper is motivated by a medical problem where interest focuses on developing a risk stratification system for morbidity of 1,710 cardiac patients given a suite of demographic, clinical and preoperative variables. Although the methods we use are applied specifically to this case study, these methods can be applied across any field, irrespective of the type of response.
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In the previous two papers in this three-part series, we have examined visual pigments, ocular media transmission, and colors of the coral reef fish of Hawaii. This paper first details aspects of the light field and background colors at the microhabitat level on Hawaiian reefs and does so from the perspective and scale of fish living on the reef. Second, information from all three papers is combined in an attempt to examine trends in the visual ecology of reef inhabitants. Our goal is to begin to see fish the way they appear to other fish. Observations resulting from the combination of results in all three papers include the following. Yellow and blue colors on their own are strikingly well matched to backgrounds on the reef such as coral and bodies of horizontally viewed water. These colors, therefore, depending on context, may be important in camouflage as well as conspicuousness. The spectral characteristics of fish colors are correlated to the known spectral sensitivities in reef fish single cones and are tuned for maximum signal reliability when viewed against known backgrounds. The optimal positions of spectral sensitivity in a modeled dichromatic visual system are generally close to the sensitivities known for reef fish. Models also predict that both UV-sensitive and red-sensitive cone types are advantageous for a variety of tasks. UV-sensitive cones are known in some reef fish, red-sensitive cones have yet to be found. Labroid colors, which appear green or blue to us, may he matched to the far-red component of chlorophyll reflectance for camouflage. Red cave/hole dwelling reef fish are relatively poorly matched to the background they are often viewed against but this may be visually irrelevant. The model predicts that the task of distinguishing green algae from coral is optimized with a relatively long wavelength visual pigment pair. Herbivorous grazers whose visual pigments are known possess the longest sensitivities so far found. Labroid complex colors are highly contrasting complementary colors close up but combine, because of the spatial addition, which results from low visual resolution, at distance, to match background water colors remarkably well. Therefore, they are effective for simultaneous communication and camouflage.
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This paper describes the development and the implementation of a multi-agent system for integrated diagnosis of power transformers. The system is divided in layers which contain a number of agents performing different functions. The social ability and cooperation between the agents lead to the final diagnosis and to other relevant conclusions through integrating various monitoring technologies, diagnostic methods and data sources, such as the dissolved gas analysis.
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The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .
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This paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.
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OBJECTIVE To analyze the access and utilization profile of biological medications for psoriasis provided by the judicial system in Brazil.METHODSThis is a cross-sectional study. We interviewed a total of 203 patients with psoriasis who were on biological medications obtained by the judicial system of the State of Sao Paulo, from 2004 to 2010. Sociodemographics, medical, and political-administrative characteristics were complemented with data obtained from dispensation orders that included biological medications to treat psoriasis and the legal actions involved. The data was analyzed using an electronic data base and shown as simple variable frequencies. The prescriptions contained in the lawsuits were analyzed according to legal provisions.RESULTS A total of 190 lawsuits requesting several biological drugs (adalimumab, efalizumab, etanercept, and infliximab) were analyzed. Patients obtained these medications as a result of injunctions (59.5%) or without having ever demanded biological medication from any health institution (86.2%), i.e., public or private health services. They used the prerogative of free legal aid (72.6%), even though they were represented by private lawyers (91.1%) and treated in private facilities (69.5%). Most of the patients used a biological medication for more than 13 months (66.0%), and some patients were undergoing treatment with this medication when interviewed (44.9%). Approximately one third of the patients discontinued treatment due to worsening of their illness (26.6%), adverse drug reactions (20.5%), lack of efficacy, or because the doctor discontinued this medication (13.8%). None of the analyzed medical prescriptions matched the legal prescribing requirements. Clinical monitoring results showed that 70.3% of the patients had not undergone laboratory examinations (blood work, liver and kidney function tests) for treatment control purposes.CONCLUSIONS The plaintiffs resorted to legal action to get access to biological medications because they were either unaware or had difficulty in accessing them through institutional public health system procedures. Access by means of legal action facilitated long-term use of this type of medication through irregular prescriptions and led to a high rate of adverse drug reactions as well as inappropriate clinical monitoring.
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The forthcoming smart grids are comprised of integrated microgrids operating in grid-connected and isolated mode with local generation, storage and demand response (DR) programs. The proposed model is based on three successive complementary steps for power transaction in the market environment. The first step is characterized as a microgrid’s internal market; the second concerns negotiations between distinct interconnected microgrids; and finally, the third refers to the actual electricity market. The proposed approach is modeled and tested using a MAS framework directed to the study of the smart grids environment, including the simulation of electricity markets. This is achieved through the integration of the proposed approach with the MASGriP (Multi-Agent Smart Grid Platform) system.
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Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering
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J. Am. Chem. Soc., 2009, 131 (23), pp 7990–7998 DOI: 10.1021/ja809448r
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In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.
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Poly(dimethylsiloxane) (PDMS) is an organosilicon polymer widely used in the fabrication of microfluidic systems to integrate biochips. In this study, we propose the use of an adapted PDMS mould for the creation of a miniaturized, reusable, reference electrode for in-chip electrochemical measurements. Through its integrated microfluidic system it is possible to replenish internal buffer solutions, unclog critical junctions and treat the electrode’s surface, assuring a long term reuse of the same device. Planar Ag/AgCl reference electrodes were microfabricated over a passivated p-type Silicon Wafer. The PDMS mould, containing an integrated microfluidic system, was fabricated based on patterned SU-8 mould, which includes a lateral horizontal inlet access point. Surface oxidation was used for irreversible permanent bondage between flat surfaces. The final result was planar Ag/AgCl reference electrode with integrated microfluidic that allows for electrochemical analysis in biochips