Efficient Use of Linguistic and Situational Contexts for Enhanced Understanding of the Content of English Screen Subtitles

Research in the production of powerful screen subtitles receives more attention from those who are interested in movies business for the benefit of viewers worldwide. Arguably viewers hardly benefit from screen subtitles due to the inconsistency between scenes and the pragmatic meaning of subtitles. Specifically, the study aims to examine the extent to which understanding pragmatic meaning of screen subtitles largely depends on understanding linguistic and situational contexts elements. The force of context is assumed to have powerful effect interpretation of the source text. Both descriptive and experimental methods were adopted. These included a test and paper-and-pencil-questionnaires where participants provided their impressions about the effect of context in eliminating pragmatic meaning of screen subtitles. Participants were experienced viewers of subtitled films. Results showed that linguistic forms and contextual cues together form a powerful element in understanding the pragmatic meaning of screen subtitles. Results also revealed that communicative translation fits the screen translation giving more attention to the effect of context. The association of context and communicative translation makes subtitles globally more economical and intelligible. Context forms a central pragmatic element for film language to be intelligible. Keywords—Interpretation, context-dependent-interpretation, International context, scenes, linguistic context DOI: 10.7176/JLLL/92-03 Publication date:October 31st 2022

Complex information networks – detecting community structure in bipartite networks

The last decade has witnessed great expansion in research and study of complex networks. A complex network is a large-scale network that reflects the interactions between objects or components of complicated systems. These components, known as clusters, communities or modules, perform together in order to provide one or more functions of the system. A vast number of systems, from the brain to ecosystems, power grids and the Internet, criminal relationships and financial transactions, can all be described as large complex networks. For most complex networks, the complexity arises from the fact that the structure is highly irregular, complex and dynamically evolving in time; and that the observed patterns of interactions highly influence the behaviour of the entire system. One of the topological properties that can expose the hierarchical structure of complex networks is community structure. Community detection is a common problem in complex networks that consists in general of finding groups of densely connected nodes with few connections to nodes outside of a group. The lack of consensus on a definition for a community leads to extensive studies on community structure of complex networks in order to provide improved community detection methods. Community structure is a common and important topological characteristic of many real world complex networks. In particular, identifying communities in bipartite networks is an important task in many scientific domains. In a bipartite network, the node set consists of two disjoint sets of nodes, primary set (P) and secondary set (S), such that links between nodes may occur only if the nodes belong to different sets. There are really two approaches to identifying clusters in a bipartite network: the first, and more common, is when our real interest is in community structure within the primary node set P; and the second is when our real interest is in bipartite communities within the whole network. Thus, in this research we investigate and study the state-of-the-art of community detection algorithms, in particular, those to identify the communities in bipartite networks in order to provide us with a more complete understanding of the relationship between communities. The practical aim is to derive a coarse-grain description of the network topology that will aid understanding of its hierarchical structure. The research of the thesis consists of four main phases. First, one of the best algorithms for community detection in classical networks, Infomap, has not been adapted to the big and important class of bipartite networks. This research gap is one focus of the thesis. We integrate the weighted projection method for bipartite networks based on common neighbors similarity into Infomap, to acquire a weighted one mode network that can be clustered by this random walks technique. We apply this method to a number of real world bipartite networks, to detect significant community structure. We measure the performance of our approach based on the ground truth. This requires deep knowledge of the formation of relations within and between clusters in these real world networks. Although such investigation is excessively time consuming, and impractical or impossible in large networks, the result is much more accurate and more meaningful and gives us confidence that this method can be usefully applied to large networks where ground truth is not known. Second, several possible edge additions are conducted to test how random walks based algorithm, Infomap, performs when the minimal modification is made to convert a bipartite network to a nearly bipartite (but unipartite) network. The experiments on small bipartite networks obtain encouraging results. Third, we shift focus from community detection based on random walks to community detection based on the strongest communities possible in a bipartite network, which are bicliques. We develop a novel algorithm to identify overlapping communities at the base level of hierarchy in bipartite networks. We combine existing techniques (bicliques, cliques, structural equivalence) into a novel method to solve this new research problem. We classify the output communities into 5 categories based on community strength. From this base level, we apply the Jaccard index as a threshold in order to reduce the redundancy of overlapping communities, to obtain higher levels of the hierarchy. We compare results from our overlapping approach with other concurrent approaches not only directly to the ground truth, but also using a widely accepted scale for evaluating the quality of partitions, Normalized Mutual Information (NMI). In the last phase of the thesis, a large financial bipartite network collected during 6 months fieldwork is analysed and tested in order to reveal its hierarchical structure. We apply all methods presented in Chapter 3, Chapter 4 and Chapter 5. The main contribution of this thesis is an improved method to detect the hierarchical and overlapping community structure in bipartite complex networks based on structural equivalence of nodes. More generally, it aims to derive a coarse-grain depiction of real large-scale networks through structural properties of their identified communities as well as their performance with respect to the known ground truth

Finding maximal bicliques in bipartite networks using node similarity

In real world complex networks, communities are usually both overlapping and hierarchical. A very important class of complex networks is the bipartite networks. Maximal bicliques are the strongest possible structural communities within them. Here we consider overlapping communities in bipartite networks and propose a method that detects an order-limited number of overlapping maximal bicliques covering the network. We formalise a measure of relative community strength by which communities can be categorised, compared and ranked. There are very few real bipartite datasets for which any external ground truth about overlapping communities is known. Here we test three such datasets. We categorise and rank the maximal biclique communities found by our algorithm according to our measure of strength. Deeper analysis of these bicliques shows they accord with ground truth and give useful additional insight. Based on this we suggest our algorithm can find true communities at the first level of a hierarchy. We add a heuristic merging stage to the maximal biclique algorithm to produce a second level hierarchy with fewer communities and obtain positive results when compared with other overlapping community detection algorithms for bipartite networks

Full-thickness bilateral rotator cuff tears as a result of a bench-pressing accident: case report and literature review of treatment of bilateral rotator cuff tears

Rotator cuff injuries are frequent pathologies presenting to orthopedic surgeons. These injuries especially occur among older adults due to intrinsic or extrinsic degeneration. They can however present in young athletes, but as result of different etiologies. Overhead athletes may incur rotator cuff injuries due to repetitive trauma. Bilateral simultaneous traumatic shoulder dislocations have been reported in the literature following acute trauma or weight-lifting activity, but bilateral traumatic rotator cuff tears following bench pressing is an unusual presentation in a young individual. To our knowledge, there has been no previous report describing this injury. This article presents a case of a young male athlete who had bilateral rotator cuff tears after a barbell bench press. Both shoulders were treated operatively in a sequential manner, three months apart, and the patient regained excellent functional status 24 months postoperatively

Estimation of the performance limits of a concentrator solar cell coupled with a micro heat sink based on a finite element simulation

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordConcentrated photovoltaic (CPV) technology makes use of cheap optical elements to amplify the irradiance and focus it on small-sized solar cells enabling the extraction of higher amounts of electricity. However, increasing the solar concentration raises the temperature of the PV cell which can deter its performance and can also cause its failure. To combat this issue both active and passive cooling mechanisms are utilized for different types of CPV systems. In this study, we determine the limits of passive cooling systems and establish when an active cooling system is needed based on the recommended operating temperature of the solar cell. We investigate the temperature characteristics of the solar cells bonded to three different substrate materials under different solar concentrations. Results showed that cell temperature is linearly dependent on the concentration ratio and ambient temperature independent of the substrate material. Further, the integration of a micro-finned heatsink results in higher heat dissipation by 25.32%, 23.13%, and 22.24% in comparison with a flat plate heatsink for Direct Bonded Copper (DBC), Insulated Metal Substrate (IMS), and Silicon Wafer (Si wafer) substrates respectively. The low thermal resistance of the IMS substrate compared to the DBC and the Si wafer substrates result in the best thermal performance in terms of maintaining the cell temperature < 80 °C and allowing a wider range of high concentration ratio.Saudi Arabia Culture Burea

Gas flow visualisation in low aspect ratio packed beds by three-dimensional modelling and near-infrared tomography

Nonuniform local flow inside randomly porous media of gas-solid packed beds of low aspect ratios ranging from 1.5 to 5 was investigated by three-dimensional modelling and near-infrared tomography. These beds are known to demonstrate heterogeneous mixing and uneven distributions of mass and heat. The effects of the confining wall on flow dynamics were found nonlinear, particularly for aspect ratios lower than 3. High velocities were mainly observed in regions near the wall of aspect ratio value of 1.5 and those of values higher than 3, owing to high local porosities in these zones. Mass dispersion characterised by both experimental near-infrared imaging and by particle tracking showed discrepancies with literature models, particularly for aspect ratios lower than 3. Uncertainties were more significant with the radial dispersion due bed size limits. Beyond this value, the wall affected more the axial dispersion, confirming the nonlinear impact of the wall on global hydrodynamic

Increasing Plant Tolerance to Drought Stress by Inoculation with Arbuscular Mycorrhizal Fungi

Abstract: The present study was aimed to evaluate the effects of Glomus mosseae in three levels of soil infestation (300, 600 and 900 spores pot -1 ) to improve tolerance of maize plants (Zea mays L.) for drought stress conditions with bearing in mind determine some plant growth parameters (PGP) and biochemical [plant height, stem length, root length, plant fresh wt., shoot dry wt., root dry wt., root/shoot ratio, plant chlorophyll content, soluble protein, proline in leaves and Phosphorus (P) uptake] in the presence or absence of G. mosseae. The result shown that the drought treatment causing decrease in values of almost PGP, except plant root dry weight, which was increased when comparing with well irrigation treatment. The plants treated by G. mossea were recorded a significant (P˂0.05) increase in all PGP comparing with untreated plants in both normal irrigation and drought stress. The highest PGP values were recorded when plant inoculated by 900 spores pot -1 . The water deficit treatment was caused a significant decrease in plant soluble protein by rate 29.34% comparing with plants that well irrigate by normal way. While the G. mossea treatments were caused increase in plant soluble protein by rate 13.33, 22.18 and 29.27% in the normal irrigation treatment, and by rate 24.89, 36.25 and45.17% in the drought treatment comparing with plant in soil free from mycorrhizae. On contrast the proline content in plant leaves was increased in drought treatment by rate 22% comparing with plant in well irrigation. The treatments with G. mossea causing decreased in plant proline by rate 28.88, 38.05 and 43.19% in the drought treatment respectively with three levels of soil infestation. The drought treatment caused decrease in plant P uptake by rate 72.09% comparing with well irrigation treatment. The inculcation by G. mosseae caused increased in plant P uptake by rate 42.66, 76.11 and 79.32% in normal irrigation treatments and 88.34, 93.58 and 94.91% in drought stress comparing with plant free mycorrhizal in both water treatments

A founder CEP120 mutation in Jeune asphyxiating thoracic dystrophy expands the role of centriolar proteins in skeletal ciliopathies.

Jeune asphyxiating thoracic dystrophy (JATD) is a skeletal dysplasia characterized by a small thoracic cage and a range of skeletal and extra-skeletal anomalies. JATD is genetically heterogeneous with at least nine genes identified, all encoding ciliary proteins, hence the classification of JATD as a skeletal ciliopathy. Consistent with the observation that the heterogeneous molecular basis of JATD has not been fully determined yet, we have identified two consanguineous Saudi families segregating JATD who share a single identical ancestral homozygous haplotype among the affected members. Whole-exome sequencing revealed a single novel variant within the disease haplotype in CEP120, which encodes a core centriolar protein. Subsequent targeted sequencing of CEP120 in Saudi and European JATD cohorts identified two additional families with the same missense mutation. Combining the four families in linkage analysis confirmed a significant genome-wide linkage signal at the CEP120 locus. This missense change alters a highly conserved amino acid within CEP120 (p.Ala199Pro). In addition, we show marked reduction of cilia and abnormal number of centrioles in fibroblasts from one affected individual. Inhibition of the CEP120 ortholog in zebrafish produced pleiotropic phenotypes characteristic of cilia defects including abnormal body curvature, hydrocephalus, otolith defects and abnormal renal, head and craniofacial development. We also demonstrate that in CEP120 morphants, cilia are shortened in the neural tube and disorganized in the pronephros. These results are consistent with aberrant CEP120 being implicated in the pathogenesis of JATD and expand the role of centriolar proteins in skeletal ciliopathies