Refine
Year of publication
Document Type
- Article (536) (remove)
Language
- English (536) (remove)
Is part of the Bibliography
- yes (536) (remove)
Keywords
- Answer set programming (10)
- answer set programming (8)
- Answer Set Programming (6)
- Machine learning (3)
- formal languages (3)
- monitoring (3)
- Analytical models (2)
- Automata systems (2)
- E-learning (2)
- Equilibrium logic (2)
Institute
- Institut für Informatik und Computational Science (536) (remove)
Supervised machine learning to assess methane emissions of a dairy building with natural ventilation
(2020)
A reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach.
Spotlocator is a game wherein people have to guess the spots of where photos were taken. The photos of a defined area for each game are from panoramio.com. They are published at http://spotlocator. drupalgardens.com with an ID. Everyone can guess the photo spots by sending a special tweet via Twitter that contains the hashtag #spotlocator, the guessed coordinates and the ID of the photo. An evaluation is published for all tweets. The players are informed about the distance to the real photo spots and the positions are shown on a map.
Using the timing flexibility of modern automatic test equipment (ATE) test response data can be compacted without the need for additional X-masking logic. In this article the test response is compacted by several multiple input shift registers without feedback (NF-MISR). The shift registers are running on a k-times higher clock frequency than the test clock. For each test clock cycle only one out of the k outputs of each shift register is evaluated by the ATE. The impact of consecutive X values within the scan chains is reduced by a periodic permutation of the NF-MISR inputs. As a result, no additional external control signals or test set dependent control logic is required. The benefits of the proposed method are shown by the example of an implementation on a Verigy ATE. Experiments on three industrial circuits demonstrate the effectiveness of the proposed approach in comparison to a commercial DFT solution.
Secondary activation of the endothelin system is thought to be involved in toxic liver injury. This study tested the hypothesis that dual endothelin-converting enzyme / neutral endopeptidase blockade might: be able to attenuate acute toxic liver injury.
Male Sprague-Dawley rats were implanted with subcutaneous minipumps to deliver the novel compound SLV338 (10 mg/kg*d) or vehicle. Four days later they received two intraperitoneal injections of D-galactosamine (1.3 g/kg each) or vehicle at an interval of 12 hours. The animals were sacrificed 48 hours after the first injection.
Injection of D-galactosamine resulted in very severe liver injury, reflected by strongly elevated plasma liver enzymes, hepatic necrosis and inflammation, and a mortality rate of 42.9 %. SLV338 treatment did not show any significant effect on the extent of acute liver injury as judged from plasma parameters, hepatic histology and mortality. Plasma measurements of SLV338 confirmed adequate drug delivery. Plasma concentrations of big endothelin-1 and endothelin-1 were significantly elevated in animals with liver injury (5-fold and 62-fold, respectively). Plasma endothelin-1 was significantly correlated with several markers of liver injury. SLV338 completely prevented the rise of plasma big endothelin-1 (p<0.05) and markedly attenuated the rise of endothelin-1 (p = 0.055).
In conclusion, dual endothelin-converting enzyme / neutral endopeptidase blockade by SLV338 did not significantly attenuate D-galactosamine-induced acute liver injury, although it largely prevented the activation of the endothelin system. An evaluation of SLV338 in a less severe model of liver injury would be of interest, since very severe intoxication might not be relevantly amenable to pharmacological interventions.