1,829 research outputs found

    Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases

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    Analyzing huge amounts of spatial data plays an important role in many emerging analysis and decision-making domains such as healthcare, urban planning, agriculture and so on. For extracting meaningful knowledge from geographical data, the relationships between spatial data objects need to be analyzed. An important class of such relationships are topological relations like the connectedness or overlap between regions. While real-world geographical regions such as lakes or forests do not have exact boundaries and are fuzzy, most of the existing analysis methods neglect this inherent feature of topological relations. In this paper, we propose a method for handling the topological relations in spatial databases based on fuzzy region connection calculus (RCC). The proposed method is implemented in PostGIS spatial database and evaluated in analyzing the relationship of diseases as an important application domain. We also used our fuzzy RCC implementation for fuzzification of the skyline operator in spatial databases. The results of the evaluation show that our method provides a more realistic view of spatial relationships and gives more flexibility to the data analyst to extract meaningful and accurate results in comparison with the existing methods.Comment: ICEE201

    Three embedded techniques for finite element heat flow problem with embedded discontinuities

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s00466-017-1382-7The present paper explores the solution of a heat conduction problem considering discontinuities embedded within the mesh and aligned at arbitrary angles with respect to the mesh edges. Three alternative approaches are proposed as solutions to the problem. The difference between these approaches compared to alternatives, such as the eXtended Finite Element Method (X-FEM), is that the current proposal attempts to preserve the global matrix graph in order to improve performance. The first two alternatives comprise an enrichment of the Finite Element (FE) space obtained through the addition of some new local degrees of freedom to allow capturing discontinuities within the element. The new degrees of freedom are statically condensed prior to assembly, so that the graph of the final system is not changed. The third approach is based on the use of modified FE-shape functions that substitute the standard ones on the cut elements. The imposition of both Neumann and Dirichlet boundary conditions is considered at the embedded interface. The results of all the proposed methods are then compared with a reference solution obtained using the standard FE on a mesh containing the actual discontinuity.Peer ReviewedPostprint (author's final draft

    Sources of unbounded priority inversions in real-time systems and a comparative study of possible solutions

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    In the design of real-time systems, tasks are often assigned priorities. Preemptive priority driven schedulers are used to schedule tasks to meet the timing requirements. Priority inversion is the term used to describe the situation when a higher priority task's execution is delayed by lower priority tasks. Priority inversion can occur when there is contention for resources among tasks of different priorities. The duration of priority inversion could be long enough to cause tasks to miss their dead lines. Priority inversion cannot be completely eliminated. However, it is important to identify sources of priority inversion and minimize the duration of priority inversion. In this paper, a comprehensive review of the problem of and solutions to unbounded priority inversion is presented


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    Organic farming often has to deal with a scarcity of readily available nutrients, and this is in contrast to chemical farming which relies on soluble fertilisers. The present study was conducted to ascertain the effect of different combinations of organic manures, rice residues and biofertilisers in organic farming of wheat. The field experiments were carried out on the research farm of Indian Agricultural Research Institute (IARI), New Delhi in 2006-07 and 2007-08. Treatments consisted of a control (no fertiliser) and six fertiliser treatments, namely, farmyard manure (FYM), vermicompost (VC), FYM + rice residue (RR), VC + RR, FYM + RR + biofertilisers (B), and VC + RR + B. FYM and VC were applied on nitrogen basis (60 kg ha-1), whereas RR was applied at 6 t ha-1. For biofertilisers, Azotobacter, cellulolytic culture (CC) and phosphate solubilising bacteria (PSB) were used. The combinations of FYM + RR + B and VC + RR + B resulted in the highest increased growth and yield attributing characters of wheat and increased grain yield of wheat over the control by 81% and 89% (Year 1 & Year 2), and net return by 82% and 73%. These combinations were significantly superior to all other combinations for all the growth and yield parameters, yield, net profit and grain quality of wheat. The results of this study show that VC + RR + B was the most productive treatment, while FYM + RR + B was the most economical treatment with respect to increasing net profit. This was because of the higher price of vermicompost compared with FYM. Both of these combinations resulted in improved grain quality and nutrient uptake by grain. The present study thus indicates that a combination of FYM + RR + biofertilisers or VC + RR + biofertilisers hold promise for organic wheat farming

    Residual influence of organic materials, crop residues, and biofertilizers on performance of succeeding mung bean in an organic rice-based cropping system

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    The present investigation was undertaken to assess the residual influence of organic materials and biofertilizers applied to rice and wheat on yield, nutrient status, and economics of succeeding mung bean in an organic cropping system. The field experiments were carried out on the research farm of IARI, New Delhi during crop cycles of 2006 to 2007 and 2007 to 2008 to study the effects of residual organic manures, crop residues, and biofertilizers applied to rice and wheat on the performance of succeeding mung bean. The experiment was laid out in a randomized block design with three replications. Treatments consisted of six combinations of different residual organic materials, and biofertilizers included residual farmyard manure (FYM) and vermicompost (VC) applied on nitrogen basis at 60 kg ha-1 to each rice and wheat crops, FYM + wheat and rice residues at 6 t ha-1 and mung bean residue at 3 t ha-1 in succeeding crops (CR), VC + CR, FYM + CR + biofertilizers (B), VC + CR + B, and control (no fertilizer applied). For biofertilizers, cellulolytic culture, phosphate-solubilizing bacteria and Rhizobium applied in mung bean. Results Incorporation of crop residue significantly increased the grain yield of mung bean over residual of FYM and VC by 25.5% and 26.5%, respectively. The combinations of FYM + CR + B and VC + RR + B resulted in the highest increase growth and yield attributing characters of mung bean and increased grain yield of mung bean over the control by 47% and net return by 27%. Conclusions The present study thus indicate that a combination of FYM + CR + B and VC + CR + B were economical for the nutrient need of mung bean in organic farming of rice-based cropping system

    On the limits of measuring the bulge and disk properties of local and high-redshift massive galaxies

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    A considerable fraction of the massive quiescent galaxies at \emph{z} ≈\approx 2, which are known to be much more compact than galaxies of comparable mass today, appear to have a disk. How well can we measure the bulge and disk properties of these systems? We simulate two-component model galaxies in order to systematically quantify the effects of non-homology in structures and the methods employed. We employ empirical scaling relations to produce realistic-looking local galaxies with a uniform and wide range of bulge-to-total ratios (B/TB/T), and then rescale them to mimic the signal-to-noise ratios and sizes of observed galaxies at \emph{z} ≈\approx 2. This provides the most complete set of simulations to date for which we can examine the robustness of two-component decomposition of compact disk galaxies at different B/TB/T. We confirm that the size of these massive, compact galaxies can be measured robustly using a single S\'{e}rsic fit. We can measure B/TB/T accurately without imposing any constraints on the light profile shape of the bulge, but, due to the small angular sizes of bulges at high redshift, their detailed properties can only be recovered for galaxies with B/TB/T \gax\ 0.2. The disk component, by contrast, can be measured with little difficulty

    Subconjunctival Mitomycin C Injection into Pterygium Decreases Its Size and Reduces Associated Complications

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    Purpose: To evaluate the safety and efficacy of subconjunctival injection of low dose mitomycin C (MMC) in the management of pterygium

    Neural Network Approaches to Medical Toponym Recognition

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    Toponym identification, or place name recognition, within epidemiology articles is a crucial task for phylogeographers, as it allows them to analyze the development, spread, and migration of viruses. Although, public databases, such as GenBank (Benson et al., November 2012), contain the geographical information, this information is typically restricted to country and state levels. In order to identify more fine-grained localization information, epidemiologists need to read relevant scientific articles and manually extract place name mentions. In this thesis, we investigate the use of various neural network architectures and language representations to automatically segment and label toponyms within biomedical texts. We demonstrate how our language model based toponym recognizer relying on transformer architecture can achieve state-of-the-art performance. This model uses pre-trained BERT as the backbone and fine tunes on two domains of datasets (general articles and medical articles) in order to measure the generalizability of the approach and cross-domain transfer learning. Using BERT as the backbone of the model, resulted in a large highly parameterized model (340M parameters). In order to obtain a light model architecture we experimented with parameter pruning techniques, specifically we experimented with Lottery Ticket Hypothesis (Frankle and Carbin, May 2019) (LTH), however as indicated by Frankle and Carbin (May 2019), their pruning technique does not scale well to highly parametrized models and loses stability. We proposed a novel technique to augment LTH in order to increase the scalability and stability of this technique to highly parametrized models such as BERT and tested our technique on toponym identification task. The evaluation of the model was performed using a collection of 105 epidemiology articles from PubMed Central (Weissenbacher et al., June 2015). Our proposed model significantly improves the state-of-the-art model by achieving an F-measure of 90.85% compared to 89.13%
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