Search Results (10)

Direct numerical simulations (DNSs) of the T106A low-pressure turbine were conducted for various turbulence intensities and length scales to investigate their effects on flow behaviour and transition. A source-term formulation of the synthetic eddy method (SEM) was implemented in the Nektar++ spectral/hp element framework to introduce anisotropic turbulence into the flow field. A single sponge layer was imposed, which covers the inflow and outflow regions just downstream and upstream of the inflow and outflow boundaries, respectively, to avoid acoustic wave reflections on the boundary conditions. Additionally, in the T106A model, mixed polynomial orders were utilized, as Nektar++ allows different polynomial orders for adjacent elements. A lower polynomial order was employed in the outflow region to further assist the sponge layer by coarsening the mesh and diffusing the turbulence near the outflow boundary. Thus, this study contributes to the development of a more robust and efficient model for high-fidelity simulations of turbine blades by enhancing stability and producing a more accurate flow field. The main findings are compared with experimental and DNS data, showing good agreement and providing new insights into the influence of turbulence length scales on flow separation, transition, wake behaviour, and loss profiles. Full article

Automatic term extraction aims at extracting terminological units from specialized corpora to assist terminographers in developing glossaries, thesauri, and termbases. Unfortunately, traditional methods often overlook the complex relation between terminologies of different subject fields that co-occur in a single specialized corpus. This study illustrates Domain Concept Relatedness, a novel term extraction technique meant to isolate the terminology of a given subject field. We test our technique against the term extraction tool of Sketch Engine and the contrastive approach by applying them to the extraction of waste management terms from a new Italian corpus about waste management legislation. The results show that Domain Concept Relatedness effectively extracts multi-word terms belonging to a given subject field but still fails to extract single-word terms. Full article

This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, representing posterior information. The sensitivity matrix is then decomposed into distinct work areas based on these clusters. The elimination of smooth edge effects is achieved through differentiation of the images from the decomposed sensitivity matrix and further post-processing reliant on image features. The algorithm ensures low computational complexity and avoids introducing extra parameters. Numerical simulations and experimental data verification highlight the effectiveness of SMDR. The proposed SMDR algorithm demonstrates higher accuracy and robustness compared to the typical Tikhonov regularization and the iterative penalty term-based regularization method (with an improvement of up to 0.1156 in correlation coefficient). Moreover, SMDR achieves a harmonious balance between image fidelity and sparsity, effectively addressing practical application requirements. Full article

Non-axiomatic logic (NAL) is a term-based, non-monotonic, multi-valued logic with evidence-based formal semantics. All those characteristics position NAL as an excellent candidate for modeling natural language expressions and supporting artificial agents while performing knowledge discovery and commonsense reasoning tasks. In this article, we propose a set of rules for the automatic translation of natural language (NL) text into the formal language of non-axiomatic logic (NAL). Several free available tools are used to support a previous linguistic analysis, and a common sense ontology is used to populate a background knowledge base that helps to delimit the scope and the semantics of logical formulas translated. Experimentation shows our set to be the most comprehensive NL-to-NAL translation rule set known so far. Furthermore, we included an extensive set of examples to show how our proposed set of rules can be used for translating a wide range of English statements with varying grammatical structures. Full article

Medical retrieval systems have become significantly important in clinical settings. However, commercial retrieval systems that heavily rely on term-based indexing face challenges when handling continuous medical data, such as electroencephalography data, primarily due to the high cost associated with utilizing neurologist analyses. With the increasing affordability of data recording systems, it becomes increasingly crucial to address these challenges. Traditional procedures for annotating, classifying, and interpreting medical data are costly, time consuming, and demand specialized knowledge. While cross-modal retrieval systems have been proposed to address these challenges, most concentrate on images and text, sidelining time-series medical data like electroencephalography data. As the interpretation of electroencephalography signals, which document brain activity, requires a neurologist’s expertise, this process is often the most expensive component. Therefore, a retrieval system capable of using text to identify relevant signals, eliminating the need for expert analysis, is desirable. Our research proposes a solution to facilitate the creation of indexing systems employing electroencephalography signals for report generation in situations where reports are pending a neurologist review. We introduce a method incorporating a convolutional-neural-network-based encoder from DeepSleepNet, which extracts features from electroencephalography signals, coupled with a transformer which learns the signal’s auto-correlation and the relationship between the signal and the corresponding report. Experimental evaluation using real-world data revealed our approach surpasses baseline methods. These findings suggest potential advancements in medical data retrieval and a decrease in reliance on expert knowledge for electroencephalography signal analysis. As such, our research represents a significant stride towards making electroencephalography data more comprehensible and utilizable in clinical environments. Full article

Brain structure segmentation on magnetic resonance (MR) images is important for various clinical applications. It has been automatically performed by using fully convolutional networks. However, it suffers from the class imbalance problem. To address this problem, we investigated how loss weighting strategies work for brain structure segmentation tasks with different class imbalance situations on MR images. In this study, we adopted segmentation tasks of the cerebrum, cerebellum, brainstem, and blood vessels from MR cisternography and angiography images as the target segmentation tasks. We used a U-net architecture with cross-entropy and Dice loss functions as a baseline and evaluated the effect of the following loss weighting strategies: inverse frequency weighting, median inverse frequency weighting, focal weighting, distance map-based weighting, and distance penalty term-based weighting. In the experiments, the Dice loss function with focal weighting showed the best performance and had a high average Dice score of 92.8% in the binary-class segmentation tasks, while the cross-entropy loss functions with distance map-based weighting achieved the Dice score of up to 93.1% in the multi-class segmentation tasks. The results suggested that the distance map-based and the focal weightings could boost the performance of cross-entropy and Dice loss functions in class imbalanced segmentation tasks, respectively. Full article

Oxidation of methionine to methionine sulfoxide is a type of posttranslational modification reversed by methionine sulfoxide reductases (Msrs), which present an exceptionally high number of gene copies in plants. The side-form general antioxidant function-specific role of each Msr isoform has not been fully studied. Thirty homologous genes of Msr type A (MsrA) and type B (MsrB) that originate from the genomes of Arabidopsis thaliana, Populus trichocarpa, and Oryza sativa were analyzed in silico. From 109 to 201 transcription factors and responsive elements were predicted for each gene. Among the species, 220 and 190 common transcription factors and responsive elements were detected for the MsrA and MsrB isoforms, respectively. In a comparison of 14 MsrA and 16 MsrB genes, 424 transcription factors and responsive elements were reported in both types of genes, with almost ten times fewer unique elements. The transcription factors mainly comprised plant growth and development regulators, transcription factors important in stress responses with significant overrepresentation of the myeloblastosis viral oncogene homolog (MYB) and no apical meristem, Arabidopsis transcription activation factor and cup-shaped cotyledon (NAC) families and responsive elements sensitive to ethylene, jasmonate, sugar, and prolamine. Gene Ontology term-based functional classification revealed that cellular, metabolic, and developmental process terms and the response to stimulus term dominated in the biological process category. Available experimental transcriptomic and proteomic data, in combination with a set of predictions, gave coherent results validating this research. Thus, new manners Msr gene expression regulation, as well as new putative roles of Msrs, are proposed. Full article

The field of systemology does not yet have a standardised terminology; there are multiple glossaries and diverse perspectives even about the meanings of fundamental terms. This situation undermines researchers’ and practitioners’ ability to communicate clearly both within and outside their own specialist communities. Our perspective is that different vocabularies can in principle be reconciled by seeking more generalised definitions that reduce, in specialised contexts, to the nuanced meaning intended in those contexts. To this end, this paper lays the groundwork for a community effort to develop an ‘Ontology of Systemology’. In particular we argue that the standard methods for ontology development can be enhanced by drawing on systems thinking principles, and show via four examples how these can be applied for both domain-specific and upper ontologies. We then use this insight to derive a systemic and systematic framework for selecting and organising the terminology of systemology. The outcome of this paper is therefore twofold: We show the value in applying a systems perspective to ontology development in any discipline, and we provide a starting outline for an Ontology of Systemology. We suggest that both outcomes could help to make systems concepts more accessible to other lines of inquiry. Full article

Infrared moving target tracking plays a fundamental role in many burgeoning research areas of Smart City. Challenges in developing a suitable tracker for infrared images are particularly caused by pose variation, occlusion, and noise. In order to overcome these adverse interferences, a total variation regularization term-based low-rank and sparse matrix representation (TV-LRSMR) model is designed in order to exploit a robust infrared moving target tracker in this paper. First of all, the observation matrix that is derived from the infrared sequence is decomposed into a low-rank target matrix and a sparse occlusion matrix. For the purpose of preventing the noise pixel from being separated into the occlusion term, a total variation regularization term is proposed to further constrain the occlusion matrix. Then an alternating algorithm combing principal component analysis and accelerated proximal gradient methods is employed to separately optimize the two matrices. For long-term tracking, the presented algorithm is implemented using a Bayesien state inference under the particle filtering framework along with a dynamic model update mechanism. Both qualitative and quantitative experiments that were examined on real infrared video sequences verify that our algorithm outperforms other state-of-the-art methods in terms of precision rate and success rate. Full article

To deal with the complexities associated with the rapid growth in a merged concept lattice, a formal method based on an entropy-based weighted concept lattice (EWCL) is proposed as a mechanism for merging multi-source geographic ontologies (geo-ontologies). First, formal concept analysis (FCA) is used to formalize different term-based representations in relation to the geographic domain, and to construct a merged formal context. Second, a weighted concept lattice (WCL) is applied to reduce the merged concept lattice, based on information entropy and a deviance analysis. The entropy of the attribute set is exploited to acquire the intent weight value, and the standard deviation contributes to computing the intent importance deviance value, according to the user preferences and interests. Some nodes of the merged concept lattice are then removed if their intent weights are lower than the intent importance thresholds specified by the user. Finally, experiments were conducted by combining fundamental geographic information data and spatial data in the hydraulic engineering domain from China. The results indicate that the proposed method is feasible and valid for reducing the complexities associated with the merging of geo-ontologies. Although there are still some problems in the application, the manuscript offers a new approach for the merging of geo-ontologies. Full article