323 research outputs found
Climate Assessment of Vegetable Oil and Biodiesel from Camelina Grown as an Intermediate Crop in Cereal-Based Crop Rotations in Cold Climate Regions
The oilseed crop winter camelina (Camelina sativa) is attracting increasing interest for biofuel production. This study assessed the climate impacts of growing camelina as an intermediate crop in northern Europe (Sweden) for the production of vegetable oil and biofuel. Climate impacts were analyzed using life cycle assessment (LCA), while impacts on biodiversity and eutrophication were discussed. Three functional units were considered: 1 ha of land use, 1 kg of oil, and 1 MJ biofuel (hydrogenated vegetable oil, HVO). The results showed that dry matter yield over the whole crop rotation was higher in the camelina crop rotation, despite the lower yield of peas due to relay cropping with camelina. In the whole camelina crop rotation, fat production more than doubled, protein and fiber production marginally increased, and the production of carbohydrates decreased. Higher climate impacts related to field operations and fertilizer use in the camelina crop rotation, with associated N2O emissions, were compensated for by increased soil carbon accumulation due to the increased return of organic matter from the additional crop in the rotation. The total climate impact was around 0.5 kg CO2 eq/kg camelina oil when macronutrient allocation was used. The global warming potential was 15 g CO2 eq/MJ HVO, or 27 g CO2 eq/MJ HVO when soil organic carbon effects were not included, representing an 84% and 71% reduction, respectively, compared with fossil fuels
Self-Supervised Encoder for Fault Prediction in Electrochemical Cells
Predicting faults before they occur helps to avoid potential safety hazards.
Furthermore, planning the required maintenance actions in advance reduces
operation costs. In this article, the focus is on electrochemical cells. In
order to predict a cell's fault, the typical approach is to estimate the
expected voltage that a healthy cell would present and compare it with the
cell's measured voltage in real-time. This approach is possible because, when a
fault is about to happen, the cell's measured voltage differs from the one
expected for the same operating conditions. However, estimating the expected
voltage is challenging, as the voltage of a healthy cell is also affected by
its degradation -- an unknown parameter. Expert-defined parametric models are
currently used for this estimation task. Instead, we propose the use of a
neural network model based on an encoder-decoder architecture. The network
receives the operating conditions as input. The encoder's task is to find a
faithful representation of the cell's degradation and to pass it to the
decoder, which in turn predicts the expected cell's voltage. As no labeled
degradation data is given to the network, we consider our approach to be a
self-supervised encoder. Results show that we were able to predict the voltage
of multiple cells while diminishing the prediction error that was obtained by
the parametric models by 53%. This improvement enabled our network to predict a
fault 31 hours before it happened, a 64% increase in reaction time compared to
the parametric model. Moreover, the output of the encoder can be plotted,
adding interpretability to the neural network model
Multiscale simulation models of Xe bubble formation in irradiated Mo
Multiscale simulation models for Xe bubble
nucleation and growth in irradiated Mo were developed
that consist Ab-initio calculations of the interatomic
potentials for the Mo and Xe-Mo systems, atomistic MD
simulations of the kinetic rate coefficients of radiation
defects, and nucleation mechanisms of Xe bubbles in Mo.
Simulations of various Xe concentrations, temperatures
and pressures were carried out. A critical concentration of
Xe atoms was determined at which the nucleation occurs
spontaneously
Is Fragmentation a Threat to the Success of the Internet of Things?
The current revolution in collaborating distributed things is seen as the
first phase of IoT to develop various services. Such collaboration is
threatened by the fragmentation found in the industry nowadays as it brings
challenges stemming from the difficulty to integrate diverse technologies in
system. Diverse networking technologies induce interoperability issues, hence,
limiting the possibility of reusing the data to develop new services. Different
aspects of handling data collection must be available to provide
interoperability to the diverse objects interacting; however, such approaches
are challenged as they bring substantial performance impairments in settings
with the increasing number of collaborating devices/technologies.Comment: 16 pages, 2 figures, Internet of Things Journal
(http://ieee-iotj.org
Hepatoprotective effect of basil (Ocimum basilicum L.) on CCl4-induced liver fibrosis in rats
The hepatoprotective effect of basil (Ocimum basilicum) extract against liver fibrosis-induced by carbon tetrachloride (CCl4) was studied in rats. Rats were allocated into five groups: Group I (control group); Group II [CCl4 group; rats were injected subcutaneously with CCl4 (1 ml/kg b.w.) twice weekly for 4 weeks (phenobarbital, 350 mg/L, was added to the drinking water throughout the experiment)]; Group III received daily oral doses of basil extract of 200 mg/kg b.w. along with CCl4 and phenobarbital for 6 weeks; Groups IV and V rats were treated with phenobarbital and CCl4 for 6 weeks then treated daily with oral dose of 200 mg/kg b.w basil extract, or by 300 mg/kg b.w dimethyl diphenyl bicarboxylate (DDB), respectively for 6 weeks. Basil-treatment significantly reduced the liver content of hydroxyproline and significantly increased the activity of hyaluronidase (HAase). The hepatic activity of superoxide dismutase (SOD) was stimulated while the lipid peroxidation was significantly reduced by the effect of basil extract. Treatment with CCl4 significantly increased the activities of transaminases [aspartate aminotransferase (AST), alanine aminotransferase (ALT)], and alkaline phosphatase (ALP). These activities were significantly decreased by basil extract. The higher levels of serum urea and creatinine in CCl4 group were significantly guarded by the protection of basil.Key words: Carbon tetrachloride, liver fibrosis, antioxidant, Ocimum basilicum, dimethyl diphenyl bicarboxylate
Recent advances in the theory and practice of logical analysis of data
Logical Analysis of Data (LAD) is a data analysis methodology introduced by Peter L. Hammer in 1986. LAD distinguishes itself from other classification and machine learning methods by the fact that it analyzes a significant subset of combinations of variables to describe the positive or negative nature of an observation and uses combinatorial techniques to extract models defined in terms of patterns. In recent years, the methodology has tremendously advanced through numerous theoretical developments and practical applications. In the present paper, we review the methodology and its recent advances, describe novel applications in engineering, finance, health care, and algorithmic techniques for some stochastic optimization problems, and provide a comparative description of LAD with well-known classification methods
Role of Interactive infographics in the Interior design of contemporary museums
This research is an interdisciplinary study exploring the role of infographics and interactive displays in the interior design of museums, showing the impact of new technologies on the process of delivering information and exploring newfangled ways to deliver the interactive experience in developing and promoting new learning techniques. With an intensive focus on the different types of infographics and interactive displays through the exposure to diverse applications and models supporting the creative design ideas, considering it a space for engagement, discovery, learning and playing. Finally, the study emphasized the importance of linking the various disciplines in the design process leading to a successful and integrated design to contribute in sharing knowledge across the world
Data Preparation in Machine Learning for Condition-based Maintenance
ABSTRACT: Using Machine Learning (ML) prediction to achieve a successful, cost-effective, Condition-Based Maintenance (CBM) strategy has become very attractive in the context of Industry 4.0. In other fields, it is well known that in order to benefit from the prediction capability of ML algorithms, the data preparation phase must be well conducted. Thus, the objective of this paper is to investigate the effect of data preparation on the ML prediction accuracy of Gas Turbines (GTs) performance decay. First a data cleaning technique for robust Linear Regression imputation is proposed based on the Mixed Integer Linear Programming. Then, experiments are conducted to compare the effect of commonly used data cleaning, normalization and reduction techniques on the ML prediction accuracy. Results revealed that the best prediction accuracy of GTs decay, found with the k-Nearest Neighbors ML algorithm, considerately deteriorate when changing the data preparation steps and/or techniques. This study has shown that, for effective CBM application in industry, there is a need to develop a systematic methodology for design and selection of adequate data preparation steps and techniques with the proposed ML algorithms
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